Light | deep learning empowered optical metrology

Writing | Zuo Chao Qian Jiaming

In March 2016, DeepMind, a Google-owned artificial intelligence (AI) company, defeated Go world champion Lee Sedol 4:1 with its AlphaGo artificial intelligence system, triggering a new wave of artificial intelligence – deep learning technology. Since then, people have witnessed the rapid rise and wide application of deep learning technology – it has solved many problems and challenges in computer vision, computational imaging, and computer-aided diagnostics with unprecedented performance. At the same time, Google, Facebook, Microsoft, Apple, and Amazon, the five tech giants without exception, are investing more and more resources to seize the artificial intelligence market, and even transforming into artificial intelligence-driven companies as a whole. They have begun to "ignite" the "art" of data mining and developed easy-to-use open-source deep learning frameworks. These deep learning frameworks enable us to use pre-built and optimized component sets to build complex, large-scale deep learning models in a clearer, concise, and user-friendly way without having to delve into the details of the underlying algorithms. Domestic "BAT" also regards deep learning technology as a key strategic direction, and actively deploys the field of artificial intelligence with its own advantages. Deep learning has rapidly left the halls of academia and is beginning to reshape industry.

Optical metrology, on the other hand, is a type of measurement science and technology that uses optical signals as the standard/information carrier. It is an ancient discipline, because the development of physics has been driven by optical metrology from the very beginning. But in turn, optical metrology has also undergone major changes with the invention of lasers, charge-coupled devices (CCDs), and computers. It has now developed into a broad interdisciplinary field and is closely related to disciplines such as photometry, optical engineering, computer vision, and computational imaging. Given the great success of deep learning in these related fields, researchers in optical metrology cannot suppress their curiosity and have begun to actively participate in this rapidly developing and emerging field. Unlike traditional methods based on "physics a priori", "data-driven" deep learning technology offers new possibilities for solving many challenging problems in the field of optical metrology, and shows great application potential.

In this context, in March 202,Nanjing University of Science and TechnologywithNanyang Technological University, SingaporeThe research team published in the top international optical journal "Lighting: Science & Applications"A joint statement entitled"Deep learning in optical metrology: a review"The first author of the review article is Nanjing University of Science and TechnologyZuo ChaoProfessor, PhD student at Nanjing University of Science and TechnologyQian JiamingCo-first author, Nanjing University of Science and TechnologyZuo Chao,Chen QianProfessor, Nanyang Technological University, SingaporeChandler SpearProfessor is the co-corresponding author of the paper, and Nanjing University of Science and Technology is the first unit of the paper.

This paper systematically summarizes the classical techniques and image processing algorithms in optical metrology, briefly describes the development history, network structure and technical advantages of deep learning, and comprehensively reviews its specific applications in various optical metrology tasks (such as fringe denoising, phase demodulation and phase unwrapping). By comparing the similarities and differences in principle and thought between deep learning methods and traditional image processing algorithms, the unique advantages of deep learning in solving "problem reconstruction" and "actual performance" in various optical metrology tasks are demonstrated. Finally, the paper points out the challenges faced by deep learning technology in the field of optical metrology, and looks forward to its potential future development direction.

Traditional optical metrology

Image generation model and image processing algorithm

Optical metrology technology cleverly uses the basic properties of light (such as amplitude, phase, wavelength, direction, frequency, speed, polarization and coherence, etc.) as the information carrier of the measured object to realize the acquisition of various characteristic data of the measured object (such as distance, displacement, size, morphology, roughness, strain and stress, etc.). Optical metrology has been increasingly widely used in CAD /CAE, reverse engineering, online detection, quality control, medical diagnosis, cultural relics protection, human-interaction machine and other fields due to its advantages of non-contact, high speed, high sensitivity, high resolution and high accuracy.In optical metrology, the most common information carriers are "streaks" and "speckles."For example, the images processed by most interferometry methods (classical interference, photoelasticity, digital speckle, digital holography, etc.) are interference fringes formed by the coherent superposition of object light and reference light, and the measured physical quantity is modulated in the phase information of the interference fringes. In addition, the fringe pattern can also be generated in a non-interferometric way, such as fringe projection profilometry (FPP) directly projecting the fringe pattern of structured light to the surface of the measured object to measure the three-dimensional surface shape of the object. In digital image correlation (DIC), the captured image is the speckle pattern before and after the deformation of the sample surface, from which the total field displacement and deformation distribution of the measured object can be obtained. Combining DIC with stereo vision or photogrammetry, the depth information of the measured scene can also be obtained based on multi-view speckle images. Figure 1 summarizes the image generation process of these techniques and their corresponding mathematical models.

Figure 1 The image generation process and corresponding mathematical model in traditional optical metrology technology

Traditional optical metrology is inseparable from image processing technologyImage processing of fringe/speckleIt can be understood as a process of inverting the required physical quantity to be measured from the captured original intensity image. Usually, this process is not "Instead, it consists of three logically hierarchical image processing steps – pre-processing, analysis, and post-processing.Each step involves a series of image processing algorithms, which are layered on top of each other to form a "pipeline" structure [Figure 2], where each algorithm corresponds to a "map"Operation, which converts the matrix input of an image/similar image into the output of the corresponding dimension (or resampling)."

(1) PretreatmentImage preprocessing improves image quality by suppressing or minimizing unnecessary interference signals (such as noise, aliasing, distortion, etc.). Representative image preprocessing algorithms in optical metrology include image denoising, image enhancement, color channel separation, and image registration and correction.

(2) Analysis: Image analysis is the core step of image processing algorithms, which is used to extract important information carriers related to the physical quantities to be measured from the input image. In phase measurement technology, the main task of image analysis is to reconstruct phase information from fringe images. The basic algorithms include phase demodulation and phase unfolding. For stereo matching technology, image analysis refers to determining the displacement vector between points corresponding to the speckle image (the speckle pattern before and after the deformation of the sample surface/the multi-view speckle image), which generally includes two steps of subset matching and sub-pixel optimization.

(3) Post-processing:The purpose of image post-processing is to further optimize the measured phase data or speckle displacement fields and eventually convert them into physical quantities to be measured. Common post-processing algorithms in optical metrology include noise removal, error compensation, digital refocus, and parameter conversion. Figure 3 provides an overview of the image processing hierarchy of optical metrology and various image processing algorithms distributed in different layers.

A typical image processing process for optical metrology (e.g. fringe projection profiling) can be divided into three main steps: preprocessing (e.g. denoising, image enhancement), analysis (e.g. phase demodulation, phase unwrapping), and post-processing (e.g. phase-depth mapping).

Figure 3 Overview of the optical metrology image processing hierarchy and various image processing algorithms distributed in different layers

Deep learning technology

Principle, development and convolutional neural networks

Deep learning is an important branch in the field of machine learning. It builds neural structures that simulate the information processing of the human brainArtificial neural networks (ANN), enabling machines to perform bottom-up feature extraction from large amounts of historical data, thus enabling intelligent decision-making on future/unknown samples. ANN originated from a simplified mathematical model of biological nerve cells established by McCulloch and Pitts in 1943 2 ?? [Fig. 4a]. In 1958, Rosenblatt et al 2 ??, inspired by the biological nerve cell model, first proposed a machine that could simulate human perceptual abilities – a single-layer perceptron. As shown in Fig. 4b, a single-layer perceptron consists of a single nerve cell. The nerve cell maps the input to the output through a non-linear activation function with bias (b) and weight (w) as parameters. The proposal of perceptrons has aroused the interest of a large number of researchers in ANNs, which is a milestone in the development of neural networks. However, the limitation that single-layer perceptrons can only handle linear classification problems has caused the development of neural networks to stagnate for nearly 20 years. In the 1980s, the proposal of backpropagation (BP) algorithm made it possible to train multi-layer neural networks efficiently. It continuously adjusts the weights between nerve cells based on the chain rule to reduce the output error of multi-layer networks, effectively solving the problem of nonlinear classification and learning, triggering a boom in "shallow learning" 2 2. In 1989, LeCun et al. 2 3 proposed the idea of convolutional neural networks (CNNs) inspired by the structure of mammalian visual cortex, which laid the foundation for deep learning for modern computer vision and image processing. Subsequently, as the number of layers of neural networks increased, the problem of layer disappearance/explosion of BP algorithm became increasingly prominent, which caused the development of ANN to stagnate in the mid-1990s. In 2006, Hinton et al. proposed a deep belief network (DBN) training method to deal with the problem of layer disappearance; at the same time, with the development of computer hardware performance, GPU acceleration technology, and the emergence of a large number of labeled datasets, neural networks entered the third development climax, from the "shallow learning" stage to the "deep learning" stage. In 2012, AlexNet based on CNN architecture won the ImageNet image recognition competition in one fell swoop, making CNN one of the mainstream frameworks for deep learning after more than 20 years of silence. At the same time, some new deep learning network architectures and training methods (such as ReLU 2 and Dropout 2)It was proposed to further solve the problem of layer disappearance, which promoted the explosive growth of deep learning. In 2016, AlphaGo, an artificial intelligence system developed by Google’s AI company DeepMind, defeated Lee Sedol, the world champion of Go, which aroused widespread attention to deep learning technology among all mankind 2. Figure 4 shows the development process of artificial neural networks and deep learning technologies and the structural diagram of typical neural networks.

Figure 4 The development process of deep learning and artificial neural networks and the structural diagram of typical neural networks

Figure 5 Typical CNN structure for image classification tasks  

A) A typical CNN consists of an input layer, a convolutional layer, a fully connected layer, and an output layer b) a convolutional operation c) a pooling operation

The single-layer perceptron described above is the simplest ANN structure and consists of only a single nerve cell [Fig. 4b]. Deep neural networks (DNNs) are formed by connecting multiple layers of nerve cells to each other, with nerve cells between adjacent layers typically stacked in a fully connected form [Fig. 4g]. During network training, the nerve cell multiplies the corresponding input by a weight coefficient and adds it to the bias value, outputting it to the next layer through a non-linear activation function, while network losses are computed and backpropagated to update network parameters. Unlike conventional fully connected layers, CNNs use convolutional layers to perform feature extraction on the input data 2 [Fig. 5a]. In each layer, the input image is convoluted with a set of convolutional filters and added biases to generate a new output image [Fig. 5b]. Pooling layers in CNNs take advantage of the local correlation principle of the image to subsample the image, reducing the amount of data processing while preserving useful information [Fig. 5c]. These features make CNNs widely used in tasks of computer vision, such as object detection and motion tracking. Traditional CNN architectures are mostly oriented towards "classification" tasks, discarding spatial information at the output and producing an output in the form of a "vector". However, for image processing tasks in optical metrology techniques, neural networks must be able to produce an output with the same (or even higher) full resolution as the input. For this purpose, a fully convolutional network architecture without a fully connected layer should be used. Such a network architecture accepts input of any size, is trained with regression loss, and produces pixel-level matrix output. Networks with such characteristics are called "fully convolutional network architectures" CNNs, and their network architectures mainly include the following three categories:

(1) SRCNN:Dong et al. 3 2 skip the pooling layer in the traditional CNN structure and use a simple stacking of several convolutional layers to preserve the input dimension at the output [Fig. 6a]. SRCNN constructed using this idea has become one of the mainstream network frameworks for image super-resolution tasks.

(2) FCN:A fully convolutional network (FCN) is a network framework for semantic segmentation tasks proposed by Long et al. As shown in Figure 6b, FCN uses the convolutional layer of a traditional CNN [Fig. 5] as the network coding module and replaces the fully connected layer with a deconvolutional layer as the decoding module. The deconvolutional layer is able to upsample the feature map of the last convolutional layer so that it recovers to an output of the same size as the input image. In addition, FCN combines coarse high-level features with detailed low-level features through a skip structure, allowing the network to better recover detailed information while preserving pixel-level output.

(3) U-Net:Ronneberger et al. made improvements to FCN and proposed U-Net network 3. As shown in Figure 6c, the basic structure of U-Net includes a compressed path and an extended path. The compressed path acts as the encoder of the network, using four convolutional blocks (each convolutional block is composed of three convolutional layers and a pooling layer) to downsample the input image and obtain the compressed feature representation; the extended path acts as the network decoder using the upsampling method of transposed convolution to output the prediction result of the same size as the input. U-Net uses jump connection to perform feature fusion on the compressed path and the extended path, so that the network can freely choose between shallow features and deep features, which is more advantageous for semantic segmentation tasks.

The above-mentioned fully convolutional network structure CNN can convert input images of any size into pixel-level matrix output, which is completely consistent with the input and output characteristics of the "mapping" operation corresponding to the image processing algorithm in the optical metrology task, so it can be very convenient to "deep learning replacement" for traditional image processing tasks, which laid the foundation for the rapid rise of deep learning in the field of optical metrology.

Fig.6 Three representative fully convolutional network architectures of CNNs capable of generating pixel-level image output for image processing tasks

A) SRCNN b) FCN c) U-Net.

Optical metrology in deep learning

Changes in thinking and methodology

In optical metrology, the mapping between the original fringe/speckle image and the measured physical quantity can be described as a combination of forward physical model and measurement noise from parameter space to image space, which can explain the generation process of almost all original images in optical metrology. However, extracting the physical quantity to be measured from the original image is a typical "inverse problem". Solving such inverse problems faces many challenges, such as: unknown or imprecise forward physical model, error accumulation and local optimal solution, and pathology of inverse problems. In the field of computer vision and computational imaging, the classic method for solving inverse problems is to define the solution space by introducing the prior of the measured object as a regularization means to make it well-conditioned [Figure 7]. In the field of optical metrology, the idea of solving the inverse problem is quite different. The fundamental reason is that optical metrology is usually carried out in a "highly controllable" environment, so it is more inclined to "actively adjust" the image acquisition process through a series of "active strategies", such as lighting modulation, object regulation, multiple exposures, etc., which can reshape the original "sick inverse problem" into a "well-conditioned and sufficiently stable regression problem". For example, demodulating the absolute phase from a single fringe image: the inverse problem is ill-conditioned due to the lack of sufficient information in the forward physical model to solve the corresponding inverse problem uniquely and stably. For researchers in optical metrology, the solution to this problem is very simple: we can make multiple measurements, and by acquiring additional multi-frequency phase-shifted fringe images, the absolute phase acquisition problem evolves into a good-state regression problem. We can easily recover the absolute phase information of the measured object from these fringe images by multi-step phase-shifting and time-phase unwrapping [Figure 8].

Figure 7 In computer vision (e.g. image deblurring), the inverse problem is ill-conditioned because the forward physical model mapped from the parameter space to the image space is not ideal. A typical solution is to reformulate the original ill-conditioned problem as a well-conditioned optimization problem by adding some prior assumptions (smoothing) that aid regularization

Fig. 8 Optical metrology transforms a ill-conditioned inverse problem into a well-conditioned regression problem by actively controlling the image acquisition process. For example, in fringe projection profilometry, by acquiring additional phase-shifted fringe images of different frequencies, the absolute phase can be easily obtained by multi-frequency phase-shift method and temporal phase expansion method

However, when we step out of the laboratory and into the complex environment of the real world, the situation can be very different. The above active strategies often impose strict restrictions on the measurement conditions and the object being measured, such as:Stable measurement system, minimal environmental disturbance, static rigid objects, etcHowever, for many challenging applications, such as harsh operating environments and fast-moving objects, the above active strategy may become a "Luxury"Even impractical requirements. In this case, traditional optical metrology methods will face serious physical and technical limitations, such as limited data volume and uncertainty of forward models.How to extract high-precision absolute (unambiguous) phase information from minimal (preferably single-frame) fringe patterns remains one of the most challenging problems in optical metrology today.Therefore, we look forward to innovations and breakthroughs in the principles and methods of optical metrology, which are of great significance for its future development.

As a "data-driven" technology that has emerged in recent years, deep learning has received more and more attention in the field of optical metrology and has achieved fruitful results in recent years. Different from traditional physical model-driven methods,The deep learning method creates a set of training datasets composed of real target parameters and corresponding original measurement data, establishes their mapping relationships using ANN, and learns network parameters from the training dataset to solve the inverse problem in optical metrology[Figure 9]. Compared to traditional optical metrology techniques, deep learning moves active strategies from the actual measurement phase to the network training phase, gaining three unprecedented advantages:

1) From "model-driven" to "data-driven"Deep learning overturns the traditional "physical model-driven" approach and opens up a new paradigm based on "data-driven". Reconstruction algorithms (inverse mappings) can learn from experimental data without prior knowledge of physical models. If the training dataset is collected based on active strategies in a real experimental environment (including measurement systems, sample types, measurement environments, etc.), and the amount of data is sufficient (diversity), then the trained model should be able to reflect the real situation more accurately and comprehensively, so it usually produces more accurate reconstruction results than traditional physical model-based methods.

(2) From "divide and conquer" to "end-to-end learning":Deep learning allows for "end-to-end" learning of structures in which neural networks can learn a direct mapping relationship between raw image data and the desired sample parameters in one step, as shown in Figure 10, compared to traditional optical metrology methods of independently solving sequences of tasks. The "end-to-end" learning method has the advantage of synergy compared to "step-by-step divide-and-conquer" schemes: it is able to share information (features) between parts of the network performing different tasks, contributing to better overall performance compared to solving each task independently.

(3) From "solving linear inverse problems" to "directly learning pseudo-inverse maps": Deep learning uses complex neural networks and nonlinear activation functions to extract high-dimensional features of sample data, and directly learns a nonlinear pseudo-inverse mapping model ("reconstruction algorithm") that can fully describe the entire measurement process (from the original image to the physical quantity to be measured). For regularization functions or specified priors than traditional methods, the prior information learned by deep learning is statistically tailored to real experimental data, which in principle provides stronger and more reasonable regularization for solving inverse problems. Therefore, it bypasses the obstacles of solving nonlinear ill-conditioned inverse problems and can directly establish the pseudo-inverse mapping relationship between the input and the desired output.

Fig. 9 Optical metrology based on deep learning  

A) In deep learning-based optical metrology, the mapping of image space to parameter space is learned from a dataset by building a deep neural network b) The process of obtaining a training dataset through experimentation or simulation.

Figure 10 Comparison of deep learning and traditional algorithms in the field of fringe projection

A) The basic principle of fringe projection profiling is 3D reconstruction based on optical triangulation (left). Its steps generally include fringe projection, phase recovery, phase unwrapping, and phase-height mapping b) Deep learning-based fringe projection profiling is driven by a large amount of training data, and the trained network model can directly predict the encoded depth information from a single frame of fringes

Application of deep learning in optical metrology

A complete revolution in image processing algorithms

Due to the above advantages, deep learning has received more and more attention in optical metrology, bringing a subversive change to the concept of optical metrology technology. Deep learning abandons the strict reliance on traditional "forward physical models" and "reverse reconstruction algorithms", and reshapes the basic tasks of digital image processing in almost all optical metrology technologies in a "sample data-driven" way. Breaking the functional/performance boundaries of traditional optical metrology technologies, mining more essential information of scenes from very little raw image data, significantly improving information acquisition capabilities, and opening a new door for optical metrology technology.Figure 11 reviews typical research efforts using deep learning techniques in the field of optical metrology. Below are specific application cases of deep learning in optical metrology according to the image processing level of traditional optical metrology techniques.

Figure 11 Deep learning in optical metrology: Since deep learning has brought significant conceptual changes to optical metrology, the implementation of almost all tasks in optical metrology has been revolutionized by deep learning

(1) Image preprocessing:Early work on applying deep learning to optical metrology focused on image preprocessing tasks such as image denoising and image enhancement. Yan et al. constructed a CNN composed of 20 convolutional layers to achieve fringe image denoising [Fig. 12a]. Since noise-free ideal fringe images are difficult to obtain experimentally, they simulated a large number of fringe images with Gaussian noise added (network input) and corresponding noise-free data (true value) as training datasets for neural networks. Figures 12d-12e show the denoising results of traditional denoising methods – windowed Fourier transform (WFT 3) and deep learning methods. It can be seen from the results that the deep learning-based method overcomes the edge artifacts of traditional WFT and exhibits better denoising performance. Shi et al. proposed a deep learning-based method for fringe information enhancement [Fig. 13a]. They used the fringe images captured in real scenes and the corresponding quality-enhanced images (acquired by subtracting two fringe images with a phase shift of π) as a dataset to train neural networks to achieve a direct mapping between the fringe images to the quality-enhanced fringe information. Fig. 13b-Fig. 13d shows the results of the 3D reconstruction of the moving hand by the traditional Fourier transform (FT) 3 and deep learning methods. From this, it can be seen that the deep learning method is significantly better than the traditional method in imaging quality.

Figure 12 The denoising method of fringe image based on deep learning and the denoising results of different methods.

A) The process of fringe denoising using depth learning: the fringe image with noise is used as the input of neural networks to directly predict the denoised image b) the input noise image c) the true phase distribution d) the denoising result of deep learning e) the denoising result of WFT 3

Fig. 13 Fringe information enhancement method based on deep learning and 3D reconstruction results under different methods.

A) using depth learning for fringe information addition process: the original fringe image and the acquired quality enhancement image are used to train DNN to learn the mapping between the input fringe image and the output quality enhancement fringe information b) input fringe image c) conventional FT method 38 3D reconstruction results d) 3D reconstruction results of deep learning method

(2) Image analysis:Image analysis is the most core image processing link in optical metrology technology, so most deep learning techniques applied to optical metrology are for processing tasks related to image analysis. For phase measurement technology, deep learning has been widely explored in phase demodulation and phase unwrapping. Zuo et al. applied deep learning technology to fringe analysis for the first time, and effectively improved the three-dimensional measurement accuracy of FPP. The idea of this method is to use only one fringe image as input, and use CNN to simulate the phase demodulation process of the traditional phase shift method. As shown in Figure 14a, two convolutional neural networks (CNN1 and CNN 2) are constructed, where CNN1 is responsible for processing the fringe image from the input (IExtract background information (ACNN 2 then uses the extracted background image and the sinusoidal portion of the desired phase of the original input image generation.M) and the cosine part (D); Finally, the output sine-cosine result is substituted into the arctangent function to calculate the final phase distribution. Compared with the traditional single-frame phase demodulation methods (FT 3 and WFT 3), the deep learning-based method can extract phase information more accurately, especially for the surface of objects with rich details, and the phase accuracy can be improved by more than 50%. Only one input fringe image is used, but the overall measurement effect is close to the 12-step phase shift method [Fig. 14b]. This technology has been successfully applied to high-speed 3D imaging, achieving high-precision 3D surface shape measurement up to 20000Hz [Fig. 14c]. Zuo et al. further generalized deep learning from phase demodulation to phase unwrapping, and proposed a deep learning-based geometric phase unwrapping method for single-frame 3D topography measurement. As shown in Figure 15a, the stereo fringe image pairs and reference plane information captured under the multi-view geometric system are fed into the CNN to determine the fringe order. Figures 15b-15e show the 3D reconstruction results obtained by the traditional geometric phase unwrapping method and the deep learning method. These results show that the deep learning-based method can achieve phase unwrapping of dense fringe images in a larger measurement volume and more robustly under the premise of projecting only a single frame of fringe images.

Fig. 14 Fringe analysis method based on deep learning and three-dimensional reconstruction results under different methods 3 a) Fringe analysis method flow based on deep learning: First, the background image A is predicted from the single frame fringe image I by CNN1; then CNN2 is used to realize the fringe pattern I, The mapping between the background image A and the sinusoidal part M and the cosine part D that generate the desired phase; finally, the phase information can be wrapped with high accuracy through the tangent function b) Comparison of three-dimensional reconstruction of different phase demodulation methods (FT 3, WFT 3, deep learning-based method and 12-step phase shift method 3 3) c) Deep reconstruction results of a high-speed rotating table fan using depth learning method

Fig. 15 Geometric phase unwrapping method based on deep learning and 3D reconstruction results under different methods < unk > a) Flow of geometric phase unwrapping method assisted by deep learning: CNN1 predicts the wrapping phase information from the stereo fringe image pair, CNN2 predicts the fringe order from the stereo fringe image pair and reference information. The absolute phase can be recovered by the predicted wrapping phase and fringe order, and then 3D reconstruction is performed b) 3D reconstruction results obtained by combining phase shift method, three-camera geometric phase expansion technique, and adaptive depth constraint method, c) 3D reconstruction results obtained by combining phase shift method, two-camera geometric phase expansion technique, d) 3D reconstruction results obtained by geometric constraint method based on reference surface, e) 3D reconstruction results obtained by deep learning method

Deep learning is also widely used for stereo matching and achieves better performance than traditional subset matching and sub-pixel optimization methods. Zbontar and LeCun ?? propose a deep learning method for stereo image disparity estimation [Fig. 16]. They constructed a Siamese-type CNN to solve the matching cost calculation problem by learning similarity metrics from two image blocks. The output of the CNN is used to initialize the stereo matching cost, and then to achieve disparity map estimation by refining the initial cost through cross-based cost aggregation and semi-global matching. Fig. 16d-Fig. 16h are disparity images obtained by traditional Census transformation and deep learning methods. From this, it can be seen that the deep learning-based method achieves lower error rates and better prediction results. Pang et al. propose a cascaded CNN architecture for sub-pixel matching. As shown in Figure 17a, the initial disparity estimation is first predicted from the input stereo image pair by DispFulNet with upsampling module, and then the multi-scale residual signal is generated by the hourglass-structured DispResNet, which synthesizes the output of the two networks and finally obtains the disparity map with sub-pixel accuracy. Figures 17d-17g show the disparity map and error distribution predicted by DispfulNet and DispResNet. It can be seen from the experimental results that the quality of the disparity map has been significantly improved after the optimization of DispResNet in the second stage.

Figure 16 The disparity estimation results of the subset matching method based on deep learning and the disparity estimation results of different methods ?? a) The algorithm flow of disparity map estimation using depth learning: Siamese CNN is constructed to learn similarity metrics from two image blocks to solve the matching cost calculation problem, and finally realizes the disparity estimation through a series of post-processing b-c) The input stereo image d) true value e, g) Census and the disparity estimation results obtained by CNN

Figure 17 a) Sub-pixel matching method based on deep learning: First, the initial disparity estimation is predicted from the input stereo image pair through DispFulNet, and then the multi-scale residual signal is generated through the hourglass structure DispResNet, and the final output of the two networks is obtained. The disparity map with sub-pixel accuracy b) the left viewing angle of the input stereo image c) true value d-g) the disparity map and error distribution predicted by DispfulNet and DispResNet

(3) Post-processing: Deep learning also plays an important role in the post-processing phase of optical metrology (phase denoising, error compensation, digital refocus, phase-height mapping, etc.). As shown in Figure 18a, Montresor et al. input the sine and cosine components of the noise phase image into the CNN to predict the noise-removed high-quality phase image, and the predicted phase is fed back to the CNN for iterative refining to achieve better denoising effect. Figures 18b-18e show the phase denoising results of the traditional WFT 3 method and the deep learning method. Experimental results show that the CNN can achieve lower denoising performance than the WFT peak-valley phase error.

Figure 18 Phase denoising method based on deep learning and phase denoising results of different methods a) The process of phase denoising using depth learning: the sine and cosine components of the noise phase image are input to the CNN to predict the high-quality phase image with noise removal, and the predicted phase is fed back to the CNN again for iterative refining to achieve better denoising effect b) input noise phase image c) denoising result of WTF 3 d) denoising result of deep learning e) Comparison of WTF and deep learning method denoising results

Li et al. proposed a phase-height mapping method for fringe projection profilometry based on shallow BP neural networks. As shown in Figure 19a, the camera image coordinates and the corresponding projector image horizontal coordinates are used as network inputs to predict the three-dimensional information of the measured object. To obtain training data, the dot calibration plate is fixed on a high-precision displacement table and stripe images of the calibration plate are captured at different depth positions. By extracting the sub-pixel centers of the calibration plate dots, and using the absolute phase, the matching points of the camera and projector images corresponding to each marker center are calculated. Figures 19c and 19d show the error distribution of the three-dimensional surface shape results of the stepped standard parts obtained by the traditional phase height conversion method < unk > < unk > and the neural networks method. The results show that the neural networks-based method can learn more accurate phase height models from a large amount of data.

Fig. 19 a) Learning-based phase-depth mapping method: camera image coordinates and the horizontal coordinates of the corresponding projector image are used as network inputs to predict the three-dimensional information of the measured object b) The three-dimensional results of the step-shaped standard obtained by the learning-based method c, d) Error distribution of the three-dimensional surface shape results of the step-shaped standard obtained by the traditional phase height conversion method ?? and neural networks method e, f) Input phase images and output three-dimensional information of complex workpieces

Challenges and opportunities of deep learning in optical metrology

At present, deep learning has gradually "penetrated" into the discipline of computational imaging and optical measurement, and has shown amazing performance and strong application potential in fringe analysis, phase recovery, phase unfolding, etc. However, deep learning still faces many challenges in the field of optical metrology:

(1) As a data-driven technology, the performance of deep learning network output largely depends on a large number of labeled training data. The data collection process of most optical metrology experiments is complicated and time-consuming, and often the ideal true value cannot be obtained accurately and reliably after data collection [Figure 20].

Fig. 20 The challenge of deep learning in optical metrology – the high cost of acquiring and labeling training data. Taking fringe projection profilometry as an example, the multi-frequency time phase unwrapping method is used to obtain high-quality training data at the cost of projecting a large number of fringe images. However, in practice, hardware errors, ambient light interference, calibration errors and other factors make it difficult to obtain the ideal true value through traditional algorithms

(2) So far, there is still no theory that clearly explains what structure of neural networks is most suitable for specific imaging needs [Figure 21]?

(3) The success of deep learning usually depends on the "common" features learned and extracted from the training examples as prior information. Therefore, when artificial neural networks are faced with "rare examples", it is very easy to give a wrong prediction without realizing it.

(4) Unlike the traditional "transparent" deduction process based on physical model methods, most current deep learning-based decision-making processes are generally regarded as "black boxes" driven by training data. In optical metrology, interpretability is often crucial, as it ensures traceability of errors.

(5) Since information is not "created out of nothing", the results obtained by deep learning cannot always be accurate and reliable. This is often fatal for many application fields of optical measurement, such as reverse engineering, automatic control, defect detection, etc. In these cases, the accuracy, reliability, repeatability and traceability of the measurement results are the primary considerations.

Figure 21 The challenge of deep learning in optical metrology – empiricism in model design and algorithm selection. Taking phase extraction in fringe projection profilometry as an example, the same task can be achieved by different neural networks models with different strategies: The fringe image can be directly mapped to the corresponding phase map via DNN1; The numerator and denominator terms of the tangent function used to calculate the phase information can also be output from the fringe image and the corresponding background image via DNN2; The numerator and denominator can be predicted directly from the fringe image using a more powerful DNN

Although the above challenges have not been fully addressed, with the further development of computer science and artificial intelligence technology, it can be expected that deep learning will play an increasingly prominent role in optical metrology in the future through the following three aspects:

(1) The application of emerging technologies (such as adversarial learning, transfer learning, automated machine learning, etc.) to the field of optical metrology can promote the wide acceptance and recognition of deep learning in the field of optical metrology.

(2) Combining Bayesian statistics with deep neural networks to estimate and quantify the uncertainty of the estimate results, based on which it is possible to evaluate when neural networks produce unreliable predictions. This gives researchers another possible choice between "blind trust" and "blanket negation", namely "selective" adoption.

(3) The synergy between prior knowledge of image generation and physical models and data-driven models learned from experimental data can bring more expertise in optical metrology into deep learning frameworks, providing more efficient and "physically sound" solutions to specific optical metrology problems [Figure 22].

Figure 22 Introducing a physical model into deep learning can provide a more "reasonable" solution to a specific optical metrology problem. A) Directly predict the wrapped phase from the fringe image based on the end-to-end network structure (DNN1) b) It is difficult for the end-to-end strategy to accurately reproduce the 2π phase truncation, resulting in the loss function of the network not converging during training c) Incorporate the physical model of the traditional phase shift method into deep learning to predict the molecular and denominator terms of the tangent function used to calculate the phase information from the fringe image 39 d) The loss function of the deep learning network combined with the physical model can be stably converged during training

Summary and Outlook

There is no doubt that deep learning technology offers powerful and promising new solutions to many challenging problems in the field of optical metrology, and promotes the transformation of optical metrology from "physics and knowledge-based modeling" to "data-driven learning" paradigm. A large number of published literature results show that methods based on deep learning for specific problems can provide better performance than traditional knowledge-based or physical model methods, especially for many optical metrology tasks where physical models are complex and the amount of information available is limited.

But it has to be admitted that deep learning technology is still in the early stage of development in the field of optical measurement. A considerable number of researchers in this field are rigorous and rational. They are skeptical of the "black box" deep learning solutions that lack explainability at this stage, and are hesitant to see their applications in industrial testing and biomedicine. Should we accept deep learning as our "killer" solution to the problem, or reject such a "black box" solution? This is a highly controversial issue in the current optical metrology community.

From a positive perspective, the emergence of deep learning has brought new "vitality" to the "traditional" field of optical metrology. Its "comprehensive penetration" in the field of optical metrology also shows us the possibility of artificial intelligence technology bringing huge changes to the field of optical metrology. On the contrary, we should not overestimate the power of deep learning and regard it as a "master key" to solve every challenge encountered in the future development of optical metrology. In practice, we should rationally evaluate whether the large amount of data resources, computing resources, and time costs required to use deep learning for specific tasks are worth it. Especially for many applications that are not so "rigorous", when traditional physical model-based and "active policy" techniques can achieve better results with lower complexity and higher interpretability, we have the courage to say "no" to deep learning!

Will deep learning take over the role of traditional technology in optical metrology and play a disruptive role in the next few years? Obviously, no one can predict the future, but we can participate in it. Whether you are a "veteran" in the field of optical metrology who loves traditional technology, or a "newbie" who has not been involved in the field for a long time, we encourage you to take this "ride" – go and try deep learning boldly! Because it is really simple and often works!

Note: This article comes with a deep learning sample program for single-frame fringe analysis (Supplemental Material File #1) and its detailed step guide (Supplementary Information) to facilitate readers’ learning and understanding. For more details related to this article, please click https://www.nature.com/articles/s41377-022-00714-x Come and read the body of the 54-page paper.

Paper information

Zuo, C., Qian, J., Feng, S. et al. Deep learning in optical metrology: a review. Light Sci Appl 11, 39 (2022). 

https://doi.org/10.1038/s41377-022-00714-x

The first author of this article is Professor Zuo Chao of Nanjing University of Science and Technology, and PhD student Qian Jiaming of Nanjing University of Science and Technology is co-author. Co-authors include Associate Professor Feng Shijie of Nanjing University of Science and Technology, PhD student Yin Wei of Nanjing University of Science and Technology, PhD student Li Yixuan of Nanjing University of Science and Technology, PhD student Fan Pengfei of Queen Mary University of London, UK, Associate Professor Han Jing of Nanjing University of Science and Technology, Professor Qian Kemao of Nanyang University of Technology in Singapore, and Professor Chen Qian of Nanjing University of Science and Technology.

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Boyue L’s global debut: to inherit, but also to innovate

August 5, Beilun, Ningbo.

At Geely Boyue "where the dream began", the FX11 made its world debut and was given the official name Boyue L. As Gan Jia, CEO of Geely Automobile Group, put it: "Innovation is the gene that flows in Geely’s blood." Geely has positioned the Boyue L as a "smart SUV innovator".

As an innovator, Boyue L aims to create a landmark work of smart SUVs in China. The "L" stands for Larger (bigger), Luxury (flagship), Legend (return of the king). How does Geely perform a comprehensive innovation this time?

The Boyue family: innovators in the automotive industry

In 2016, Ningbo Beilun and Boyue went public, and a "hello, Boyue" ushered in a new era of intelligent connected SUVs.

To this day, people are still talking about this "most beautiful Chinese SUV". Whether it is design or quality, Boyue is the benchmark of independent compact high-end SUVs. Boyue is the beginning of a new era of SUV Strategy and Development for Geely, and it is the key product that helps Geely become the global leader of Chinese brands.

"Innovate technology and popularize the public" is the mission of the times for each generation of Boyue to innovate the industry, and the global market has also given the Boyue family a warm response: in six years, the global cumulative sales of the Boyue family have exceeded 1.40 million vehicles. It is worth mentioning that, as the first SUV of a Chinese brand to achieve full export of products, technologies and management, Boyue has achieved localized production in Belarus, Malaysia and other places, and has sold well in 30 countries and regions. The market share of many places is the first, setting a benchmark for the globalization of Chinese brands.

On August 5th, Ningbo Beilun, the city where the first generation of Boyue was listed in 2016, was the origin of Boyue’s take-off. Boyue L started from here to innovate and start again, with five smart technologies and ten hard core standards, once again revolutionizing the smart SUV standard.

Innovation Comes: Defining a New Standard for Smart SUVs Again

Bigger, flagship, and the return of the king, Boyue L bears multiple missions.

According to the data, Boyue L, as the flagship strategic product of "Intelligent Geely 2025" scientific and technological achievements and "Comprehensive Architecture Vehicle", inherits the five global genes of CMA architecture "safety, health, intelligence, performance and energy saving", and revolutionizes the five world-class intelligent technologies of intelligent architecture, intelligent hybrid, intelligent driving, intelligent space and intelligent safety.


Taking the intelligent architecture as an example, in the words of Fan Junyi, general manager of Geely Automobile Sales Company, making a car is no longer as simple as a color TV + refrigerator + sofa. Therefore, the comprehensive innovation of the CMA architecture has endowed Boyue L with the mechanical quality of a luxury brand.

We speak with numbers: Boyue L’s 100-kilometer braking distance is 35.8 meters, and the Elk’s test speed is 78 kilometers per hour. The results comparable to racing cars are the "talent" brought by the CMA architecture.

In addition, in terms of intelligent driving, Boyue L has achieved "leapfrog". The new car is equipped with Geely’s first NOA lane-level autonomous driving pilot system. According to reports, this system can achieve full coverage of more than 100 high-speed driving scenarios under the support of centimeter-level high-precision positioning, human-machine co-driving VR navigation and all-lane dynamic monitoring technology. It is expected that the price range of Boyue L is between 140,000 and 180,000, and it is indeed a disruptive innovation.

The Boyue L did not stop there. While realizing the innovation and popularization of the five core areas, it established its core leadership with the first Geely Galaxy OSAir version, 13.2-inch central control vertical screen, 10.25-inch digital LCD instrument, L2-level intelligent driver assistance system, 540 ° God’s Eye transparent chassis, mobile APP remote control, aerospace-grade 7-series aluminum alloy collision beam, 178LX digital rhythm LED headlights, racing-grade braking system and integrated boron steel thermoformed door ring "Top Ten Hardcore Standard".

Redefining the entry criteria for the smart SUV market – Boyue L is serious.

Chinese Aesthetics: Blending and Transforming of Traditional Culture

How to inherit Chinese aesthetic expression in the digital age? How to integrate intelligent technology, futuristic sense and oriental aesthetics to interpret the spirit of integration and innovation in traditional Chinese culture?


As the first production car based on the "Vision Starburst" concept car, Boyue L adopts the new "Digital Symphony Technology Aesthetics" design concept, retaining the dynamic and cool design style of the concept car and a strong sense of digital technology. It not only inherits the "Technology Geely 4.0" product design style, but also integrates intelligent technology, futuristic sense and digital concept.


The Boyue L adopts mainstream designs such as a two-tone body and a suspended roof. The body size is 4670 × 1900 × 1705mm, and it has a leading 2777mm ultra-long wheelbase. Combined with a short front and rear overhang design, it forms a leading 59.5% axle length ratio in the same class, achieving a win-win situation of stable control and ample practical space.

What stands out is the sharp and slender arrow design of Boyue L. The digital arrow LED penetrating taillight is composed of 290 LED light-emitting units, and the ultra-red LED with a wavelength of 633nm. The streamer light language presented in the welcome/farewell scene echoes the headlight group. The technology is full of digital sense and is very tense. I believe it will be welcomed by the younger generation who pursue individuality.

In terms of power, the Boyue L fuel version will be equipped with 1.5T and 2.0T turbocharged engines, respectively, and a 7-speed dual-clutch transmission.

The hybrid version of the model is also divided into two versions: gasoline-electric hybrid and plug-in hybrid, equipped with Geely Raytheon Zhiqing Hi · X hybrid system. The comprehensive battery life of full fuel and full power can reach 1300 kilometers, and the fuel consumption of 100 kilometers is as low as 4.2 liters.

Another key point: Boyue L is intimately equipped with an antiviral door handle with a sterilization rate of more than 99%, which can inhibit the growth of bacteria and viruses for a long time. It can be described as intimate and practical at the moment.

Write at the end:

Gan Jiayue, chief executive of Geely Automobile Group, said at the press conference: "Innovation is the gene that flows in Geely’s blood. Boyue L brings the latest, most popular and best smart technologies to users, injecting the vitality of continuous evolution into smart cars."

This can’t help but remind people of the press conference, the song "Blooming Life" brought by Mr. Wang Feng:

I want a life in full bloom, like flying in the vast sky, like walking through the boundless wilderness, with the power to break free from everything.

Evolution brings vitality, which is innovation.

Hello, innovator Boyue L!

What is the torque of Mercedes e300?

https://car2.autoimg.cn/cardfs/product/g30/M03/67/C2/800x600_0_autohomecar__autohomecar__ChxknGVkexGAA0UpAFbJFx_useY705.jpg

The maximum torque of Mercedes e300 is 370Nm. Mercedes-Benz e300 is a luxury car, and its biggest highlight is two large LCD screens on the center console. The car is powered by a 2.0T turbine engine with a maximum power of 245 HP and a peak torque of 370 Nm. In addition, the appearance design of Mercedes-Benz e300 is also very dynamic. The front face is the same as the imported version, the split high beam and low beam lights are sharp, and the LED light group under the front of the car is more fashionable.

Supervising the 6-board demon was tortured by the soul.

K diagram 002988_0

  On Thursday (November 16th), the three A-share indexes weakened collectively, with a total turnover of 850.7 billion yuan in the two cities, shrinking by 139.3 billion yuan from the previous trading day. As of the close,Down 0.71%,Down 1.23%,It fell by 1.85%.

  In terms of plates,, photoresist, etc. are among the top gainers; BC, optical modules, etc. were among the top losers.

01

Many foreign investors expressed optimism.

  On Thursday, the market failed to hit the 3080 resistance level, which was an important support level in the early stage. After falling below it, it formed an important counter-pressure level, and it was also a 60-day moving average. It was not easy to stand up effectively at once.

  UbsChina’s stock strategyLei Meng said that since the third quarter, China’s national debt, yield to maturity, has rebounded, and the commodity price index has also rebounded, indicating that they may be pricing China’s macroeconomic rebound.

  "The positive impact brought by the continuous efforts of policies will continue to be reflected, and A-share earnings are expected to continue to rebound in the next six months, thus driving the A-share market to usher in repair." Lei Meng said.

  It is believed that although the A-share market has rebounded obviously in the past few weeks, this is only a repair of the previous oversold, and the subsequent A-shares are still worthy of optimism. In the investment direction, the fund said: "We are optimistic about the technology sector that really benefits from the rapid development of the AI industry, the high-end manufacturing sector with a high level of prosperity and continuous overweight policies, andMedicine and other sectors with steady growth rate. "

02

The 6-board demon suddenly fell.

  In the case that the market wind was suppressed on Thursday, the old hot.I began to show fatigue. I walked out of 6 days and 6 boards before.Conceptual bibcockOn Thursday, it suddenly fell below the limit, with more than 20,000 orders closed, and the turnover exceeded 600 million yuan, with a cumulative decline of over 15% in the past two trading days.

  On October 26th, on the interactive question-and-answer platform, it was indicated that some pallet products in the company’s automobile lightweight business passed.Enterprises indirectly supply series products to the world. At present, its proportion in the company’s automobile lightweight business is not high, so investors should pay attention to investment risks.

  Since then, the company’s share price began to change, and it closed up 7.6% on October 31. Since then, although it has been mixed, the bottom has been rising. On November 7, a word board suddenly appeared, and after six trading days, it went out of six consecutive boards, with a cumulative increase of over 77%.

03

"soul torture" in Shenzhen Stock Exchange

  The stock price change has also attracted regulatory attention. On November 14th, Haomei New Materials received a letter of concern from Shenzhen Stock Exchange, which focused on automobile lightweight business.

  Shenzhen Stock Exchange requires the company to explain the business development of automobile lightweight business, and at the same time explain whether the company has direct cooperation with BMW, Huawei and other companies, whether it is a supplier of BMW, Huawei and other companies or only a supplier of related enterprises.

  On November 15th, Haomei New Materials stated in the reply letter that the company’s current automobile lightweight business mainly includes pallets, anti-collision beams and sub-frames.Materials and components of components, shock-absorbing brackets, power brackets and other products, among which battery tray and anti-collision beam materials and components are the most important products.

  In the above reply letter, Haomei New Materials stated that most of the lightweight materials and components provided by the company need to be processed and assembled by enterprises and then supplied to the OEM, and the company’s position in the supply chain is the second-tier supplier or the third-tier supplier of the OEM.

  Haomei New Materials also said that BMW, Mercedes-Benz, Honda, Toyota, Guangzhou Automobile Aian mentioned in the investor relations record form,, Huawei Wenjie, Changan and other automakers are not direct customers of the company, and the company does not directly sell related products to the above automakers. The corresponding direct customers of the company are, casma,Such as auto parts enterprises, the related products supplied by the company need to be processed and assembled by the above-mentioned auto parts enterprises before they can be supplied to the automobile factory.

  As of September, 2023, the revenue from the lightweight business of Haomei New Materials Automobile was 840 million yuan, accounting for 19.81%. Automobile lightweight business50,223,400 yuan, with net profit accounting for 40.40%. In contrast, in 2022, the revenue of automobile lightweight business was 933 million yuan, accounting for 17.24%; The net profit of automobile lightweight business was 12,518,900 yuan, accounting for 11.24%.

04

What does the boundary really contribute to geometry?

  For the short-term funds in the market, we are more concerned about the order situation. Haomei New Materials said that for the series (Wujie M5 and M7), the company successively obtained the designated projects of M5 and M7 in September 2021 and March 2022, and the corresponding direct customers were parts enterprises, and the main products provided by the company were battery tray materials. The related materials produced by the company are processed into battery trays by the parts enterprises and delivered to the power battery enterprises, and then the power battery enterprises add batteries to form battery packs and deliver them to the automobile factory.

  Haomei New Materials said that in the above-mentioned supply chain, the company is a third-tier supplier of the car factory.

  At the same time, for the contribution of the order to the performance, Haomei New Materials said that in 2022 and 2023, the shipment amount (including tax) of Haomei New Materials to the series through parts enterprises was: 72.69 million yuan in 2022, accounting for 1.34% of the company’s total sales revenue; From January to September, 2023, the amount of indirect shipments to Wenjie series was 4.79 million yuan, accounting for 1.34% of the company’s total sales revenue in the same period; In October, 2023, the amount of indirect shipments to Wenjie series was 10.99 million yuan, accounting for 1.98% of the company’s total sales revenue in the same period.

  Haomei New Materials specifically pointed out that the increase in the shipment amount in October 2023 compared with January-September 2023 was mainly due to the increase in the company’s shipments through parts companies after mass production of M7 in Wenjie.

05

"Cross-border" optical communication was also inquired.

  In addition, Haomei New Materials previously disclosed that the company plans to cut into the field of optical communication.

  According to the foreign investment disclosed on November 6,, Haomei New Materials plans to make a D round investment in Sols Optoelectronics at US$ 2.6179 per share, with an investment amount of US$ 40 million (about RMB 290 million according to the current RMB exchange rate).After the exercise, it acquired 15,279,400 shares of Sols Optoelectronics, accounting for about 5.79% of the shares of Sols Optoelectronics after the investment was completed. Haomei New Materials said that this investment in Sols Optoelectronics, the world’s leading supplier of optical communication components, will help the company share the opportunities brought about by the rapid development of optical modules.

  This investment was also inquired by Shenzhen Stock Exchange, asking Haomei New Materials to explain whether to invest in Sols Optoelectronics to acquire its relevant equity or subscribe for its convertible bonds, the reasons and necessity of the company’s cross-border acquisition of assets in optical communication-related fields, and whether the company has the feasibility of the above-mentioned cross-border acquisition in terms of staffing, technology research and development, market development, etc., and whether there are speculative concepts and hot spots.

  In this regard, Haomei New Materials replied that the investment in Sols Optoelectronics is due to its optimistic development prospects for the optical module industry. After the investment is completed, the company will not participate in the daily operation and management of Sols Optoelectronics. However, it should be noted that Haomei New Materials has no relevant reserves in personnel allocation, technology research and development, market development, etc. in the field of optical communication.

06

Hao mei Xin Cai

  Fierce game of hot money seats

  Some market participants said that hot money speculation pays attention to a "hazy beauty", which is often referred to as the imagination space. After continuous pulling, there is an impulse to cash in at a high level, and the expectation of overlapping imagination is broken, and short-term funds naturally profit and flee.

  From the data of the Dragon and Tiger List on Thursday, the top five seats of Haomei New Materials Trading were dominated by the hot money business department. The net purchase of the top five seats was 45.73 million yuan, and the net sale of the top five seats was 54.55 million yuan.

  Buy a seat forBeijing Haidian Branch, the seat bought 15.344 million yuan, sold 128,900 yuan, and the net purchase amount was 15.215 million yuan.

  Buy two seats as well-known hot moneyBeijingStreet securities business department, the seat bought 12.653 million yuan, sold 403.8 million yuan, and the net purchase amount was 12.2492 million yuan.

  Sell a seat forThe first business department of Zhongshan South Road, Huangpu District, Shanghai, bought 19,700 yuan, sold 13,551,800 yuan, and the net sales amount was 13,532,100 yuan.

07

Many popular linked stocks fell.

  In addition to Haomei New Materials, there are many recent high-profile bull stocks on Thursday, such asWaiting for the intraday diving limit.

  For example, the chip concept leader who recently walked out of the 7-board board quickly dived after the opening on Thursday, and closed at the end of the day. At the close, the stock reported 5.42 yuan, and the daily limit closed at 160,000 lots.

  The company issued a risk warning on Wednesday night, saying that the current business situation is normal, the internal and external business environment has not changed significantly, and the company’s fundamentals have not changed significantly. Shenzhen Huangting Fund Management Co., Ltd., a wholly-owned subsidiary of the company, holds 27.8145% of the shares of Yifa Power, and the proportion of voting rights in Yifa Power is 85.5629% through concerted action agreement. From January to September, 2023, the intentional power was realized.136,802,700 yuan, accounting for 13.5% of the operating income of listed companies; Realized a net profit of-16,413,000 yuan, accounting for the ownership of listed companies.The net profit ratio is 3.21% (note: the above financial data are unaudited). At present, powerThe income and profit generated by the business account for a low proportion of the company.

  In addition, the company’s net profit after deducting non-recurring gains and losses attributable to shareholders of listed companies in 2020, 2021, 2022 and the third quarter of 2023 are all losses. The company’s financial situation will not change greatly in the short term. Investors are advised to invest rationally and pay attention to the risk of performance loss.

  The concept leader of short drama had gone out of 10 days and 8 boards before, and suddenly fell to a high limit on Thursday, sealing 14 thousand lots, with a turnover of 1.5 billion yuan.

  In the announcement of the change, it is indicated that the company is holdingFor the partial equity of the media, the expected profit of the short drama business of the media (including Hippo Theater) in 2023 will not exceed 3 million, and the overall profit of the media company in 2023 will be pre-lost. The operation of China Radio and Television Media, a non-corporate financial consolidated statement enterprise, and Hippo Theater will not have a significant impact on the company’s operation, nor will it have a significant impact on the company’s financial situation. Investors are advised to pay attention to investment risks.

It is for investors’ reference only and does not constitute investment advice.

Xiaohongshu sees no filter.

You can really see the little red book blogger from your life.

Wen | Yiqing

Edited by Shi Can

Home is a studio and a conference room, and work and life have long been inseparable. This is the change after Jane became a full-time blogger of Little Red Book.

Jane settled her home in Chaoyang District, Beijing. The living room is 30 square meters, which is about the size of three carriages of Beijing Line 5 subway. The dining table area at home is quite satisfactory. When the Hedgehog Commune (ID: ciweigongshe) went to visit, five girls in slippers were gathering around for a meeting, with five laptops on the table. Jane didn’t wear makeup that day, so she tied a ponytail at will and stepped on the carpet barefoot.

There is a huge lighting lamp in the living room, and the light is in contrast to a carved wood rhombic sofa. The sofa has a very high photogenic rate in Jane’s little red book video, and it is found in almost every video.

Jane has changed her position at the moment and is sitting on the sofa with her chin slightly raised and her back straight, much like a female artist sitting in the live broadcast room, waiting for all her fans to go online, and interacting with herself through the little red book account "vibrant girl Jane".

Go and stir the warm water.

Jane used to work in a head e-commerce company, and her job required her to produce content. Therefore, she specially interviewed the behind-the-scenes team from the media account and saw countless KOLs every day.

Soon she learned about the creative process, income and office status of the media through her own advantages. "At that time, I felt that that was the life I wanted."

Later, she went to a video platform to make a short play and a short synthesis. In the past two years, Jane has an "extremely relaxed" working environment because of the appreciation of the leaders. I may work less than an hour every day, and I also enjoy the treatment of "serving tea and handing water and holding an umbrella for you". Usually, this situation is only owned by Party A..

But she hasn’t raised her salary for more than two years. After indulging for a while, she decided to jump out and don’t want to live the life of boiling frogs in warm water.

At that time, it happened that a game company was about to open up a short video section. Jane has a talent for social and emotional content. When she saw the opportunity, she left the video platform and went to the game company.

When she first arrived at the game company, she was ready to roll up her sleeves and do a big job. Jane and the leader reached a consensus on the upcoming work, and they had a good chat with each other. Both of them were ambitious. But as time passed, she gradually realized a serious problem.

"People in our company don’t pay much attention to content, but pay more attention to products and technology. I feel that my role doesn’t seem to be great." She said.

After a pause, she went on to say, "I don’t see anyone more powerful than me giving me some growth and stimulation here." In fact, they didn’t want to understand what they were going to do, so they let me try it indiscriminately. I think this is delaying my time and I haven’t made any contribution to the company. Then we will see each other peacefully. "

At the beginning of 2021, Jane resigned and began to work full-time as a blogger in the social and emotional field. On the day of naked resignation, she told her boss a swear word: "You don’t deserve me in terms of content!"

The rhetoric has been released, and we have to make some achievements. Jane found Lu Bai, the former editor-in-chief of Mi Meng, and asked how to make a short video. After spending 1000 yuan, she only got one sentence-"The explosion is repeated".

Jane spent another week thinking about this sentence. She studied the name, profile and explosion video of the standard account, and then improved it with her own differences.

She said frankly: "I once wanted to be creative and didn’t want to do what others already had." Now it is found that there is no conflict between displaying one’s talents under propositional composition. Because my talent is not enough to support me to send one now. "

When Xiaohongshu’s account reached 1,000 fans, Azhen changed from single-handedly fighting to team fighting, and her best friend and cousin successively "took shares". Since then, the "Jane’s Small Workshop" has been formally formed.

At the time of 30,000 to 40,000 fans, Jane met the first hurdle of rising powder. She thinks there are two reasons. The first one is that the advice given by MCN company signed at that time is ridiculous.

They suggested that Jane share some experience in beauty, resume making and interview. After listening to the suggestion, Jane changed the time and frequency of posting emotional content, from one article a day to one article a week, but the transformation was too fast for fans to accept, and Jane could not accept it either. "Resolutely cancel the contract with them (MCN)."

Another reason may be that every blogger who is on the rise has experienced it. Jane mentioned: "At first, I didn’t do a full-fledged job. Everyone only remembered the essence and vitality of the play, but this is not a character thing."

When the first explosion "How I Raided the School Grass" was born, Jane’s "green tea" was established from then on.

The word "green tea" can be said to have its own flow, and different people have different understandings of it, which is bound to bring some controversy. Under this explosion, many people said, "You are just stepping in."

Hearing these questions, Jane immediately responded, "I just want to encourage everyone to bravely pursue the person they like." "Pursuing is not equal to licking the dog."

She said to Hedgehog Commune: "The data of this response is also very good, so these two articles let my people have a memory point. Later, I also sent a message about green tea-style Uighur people, and the feedback was very good. "

Changes happen to Jane all the time. In three months, Xiaohongshu’s account has risen to 100,000 yuan, and the quotation is updated every week. In the last three days, it has risen by another 10,000 yuan. Half a year later, Jane is already a waist blogger with 300,000 fans.

When asked why she chose to be a full-time blogger, Azhen said frankly: "In first-tier cities, even if I earn 30,000 to 40,000 yuan a month, I am selling my time. 996 is not suitable for me. I like to do interesting content and live an unconstrained life. After thinking about it, it seems that the media suits me best and may change my fate. The most important thing is to work for yourself. "

"The Great Mrs. Mercer" Stills: Source Network

Jane is not the only one who refuses to be a frog in warm water.

Brother Yu studied mechanical engineering at the University of Michigan in the United States, and his first job after returning to China was to be a translator in the system.

Brother Yu’s life seems full of coincidences. When she talked about the experience of taking the exam, she smiled: "Actually, I didn’t expect to take the exam at that time. I just prepared with my friends. As a result, I entered the interview by mistake, and I got the opportunity because the interview played better."

Being a little red book blogger is another coincidence of hers.

In October 2017, Yu Ge sent his first note on the account "Da Yu Ge" in Xiaohongshu, talking about Turkey’s travel strategy. She said: "At that time, I was still working as a translator, and it was rare to go abroad for a long vacation. At that time, I didn’t think about being a blogger, just sent it, and then received a lot of attention and collection, and suddenly I felt a sense of accomplishment. Later, I shared my usual life one after another. "

At the end of 2017, Brother Yu sent a note about learning English. This note was selected as a push by Xiaohongshu backstage, and it rose by 7,000 fans a day for two weeks in a row. She bluntly said: "At this time, I felt as if many people were interested in learning English, so I added a few more articles about my IELTS experience."

In 2018, Xiaohongshu made a different growth strategy, and also ushered in a breakthrough growth of 10 million levels for the first time. Brother Yu felt the change of Little Red Book.

She mentioned: "Little Red Book began to be known by more people in 2018, and suddenly many fans poured in, but there were few English bloggers and educational bloggers at that time, so it was easier for me to find this track."

After starting to be a blogger, she found that there are many choices in life.

The stills of the movie "Wind and Rain on Harvard Road"

In 2019, Yu Ge decided to resign. "There is no opportunity to leave the system, that is, it is natural, and it is time to change another life."

Later, the leader said to her, "I felt that I couldn’t keep you at first."

Brother Yu himself admits: "Even if I am a Buddhist sometimes, there are still some restless factors in my personality."

Life in the sea, there is no fixed destination, and the restless DNA in the body makes them unwilling to be stable.

Escape from the fate of life

At 22: 33 on November 17, 2021, Chen Langlang wrote in a circle of friends: "For ordinary people, it is really happy to be here today!"

One minute ago, she just finished reading an article, "I finally have a house and a car. What 40 things I want to show off secretly", specially picked out this sentence in the article, and sent four suns and an expression of scattering flowers.

Chen Langlang drifted north for 8 years, and when he recalled Beijing, everything he saw and felt was gray.

The dry air makes her rhinitis and asthma, and her annual salary of one million is just an illusion. With a gentle poke, the soapy water all over the floor is despair.

"If you want to buy a house, you have to live outside the Sixth Ring Road, Yanjiao, or Huilongguan and Tiantongyuan. The family of four is crowded in a small house of 60 square meters. The hope of foreigners for Beijing is that there is no room for imagination."

2019 is the most difficult year. In that year, Chen Langlang often had a fever. In severe cases, he ran to the emergency room three times a week, but the torture was not just physical.

She mentioned: "Until one day, I suddenly felt that I couldn’t live any longer, and I was extremely depressed and in poor health. I can’t control my emotions, I need to go to the hospital for stability, and I also go to see a doctor because of bipolar disorder. "

Chen Langlang is also an opportunity to become a blogger of Little Red Books. Internally, she needs a window to vent. Externally, she wants to see if people in other circles have the same pain and thoughts.

According to Hedgehog Commune, Chen Langlang’s depression also comes from the anxiety of the whole industry environment. She first planned in an advertising company, and then entered a short video company to do marketing. She did not shy away: "In our business, everyone has had a online celebrity dream."

Every day, I have to buy a large number of bloggers’ accounts. Chen Langlang found that the cost of an advertiser for a blogger is his salary for one year. More than once, she thought that if she can get hot, she can be a full-time blogger.

"Don’t you have to do this stupid job?" Later, Chen Langlang recalled to Hedgehog Commune.

But the reality is that Chen Langlang understands that a group of bloggers have become popular and a group of bloggers have disappeared. online celebrity’s life cycle is too short. She only dared to think about it.

She will constantly remind herself that being a full-time blogger is an escape from real life. She said: "Ten years ago, I read a sentence on Douban, which had a particularly deep influence on me. It said that you can’t write a novel, not because you are at work. If I go full-time, I may soon run out of inspiration. "

Chen Langlang wants to ride the waves and is afraid. Even if he wants to be a full-time blogger so much, he has never left the workplace. The pulling mood made her suffer. In the end, she found a compromise-part-time blogger.

She put her counterattack experience in the little red book account "Ugly Poor Girl Chen Langlang", which inspired many people. A single video received nearly 20,000 likes and 16,000 collections-starting from three academic degrees, she served the largest public relations company in Asia, a short video company, and as a columnist covering vogue, she interviewed countless top stars. This story is full of blood and full of hope.

The movie "The Devil Wears Prada" Stills: The Source Network

Chen Langlang is currently working in a head e-commerce company. In Hangzhou, Zhejiang, she changed to a house four times larger than when she lived in Beijing.

In Beijing, she lives near Houchang Village with a monthly rent of more than 6,000 yuan, with a room facing north and a building area of 50 square meters. It seems that "this community is about 333 meters walk from Xierqi Station" is the biggest selling point of this room.

Now, the houses in Hangzhou are well ventilated, and Chen Langlang’s asthma is no longer committed. Wherever you go, there is a dedicated passage. Whether it is a medical examination or insurance, even taking a taxi will give people the illusion that "people are on top".

There is no shortage of bitterness behind the dignitaries. Chen Langlang said: "I have sent a lot of negative energy things before, but I deleted them later." Because I found that people like’ mourning’ are not of great value in Little Red Book, which is more like a reference book or encyclopedia. "

At first, Chen Langlang was very disgusted with the idea of "being a successful scholar", but now he is teaching everyone how to win the offer of a big factory.

Talking about this change, she said: "This thing is not that I am going to give in to anything, but that my mentality has changed."

"I used to prefer the kind of value output, but I found that it was just a kind of self-expression. You feel very high, but it is not of high use value to others. Most people’s needs are more realistic. They are more concerned about whether I can live a better life and solve the immediate problems. "

It is also very important to live in the present. After all, only by solving the immediate difficulties can there be poems and distant fields.

In Little Red Book, she labeled herself as "Ugly Poor Girl". In her self-introduction, she wrote without hesitation: I love do face and spent 200,000 yuan on it. In the video, I also frankly shared my experience of medical beauty, plastic surgery and braces. I can learn the beauty of appearance with personal experience.

When asked about the relationship between bloggers’ filters and authenticity, she replied: "The problem with filters is not the thickness but the diversity."

"I thought those things were hypocritical at first, but later I found myself very narrow-minded. People are really rich and really haven’t suffered any hardships in life. Ta (editor’s note: Ta refers to a blogger) is just sharing the daily life of Ta. Perhaps everyone has his own life, and there is no such thing as allowing Ta to experience the thickness of life in his life. So you will think that Ta is very shallow and fake. "

In the top note, Chen Langlang wrote: "Fake it till u make it". It seems to be a portrayal of her life in the first 29 years.

For her, sending little red book notes is more like a record of revisiting herself and examining her life.

Identity is the mother proposition.

No one can guarantee that being a blogger will make money, and even after making money, he will be able to support himself.

Many interviewees said that it was to gain recognition and to find resonance. If you can be altruistic, so much the better.

More than one blogger mentioned that working in a big factory requires more than a dozen people to review any copy, and sometimes even "laymen guide the experts". After a long time, it is inevitable to doubt your own ability.

In Xiaohongshu, when you see that your own things are on fire, you will feel that you can do the content well. A sense of identity is really important to the people who make the content.

Every blogger has been scolded more or less, and in repeated injuries, they have learned not to resist or explain. Because they are not afraid of being scolded, afraid of not being recognized.

"Friends" Stills, Tuyuan Network

The blogger "Amy who lives upstairs" (hereinafter referred to as "Amy") is a fitness instructor. Her biggest hobby is eating and drinking. In January 2021, she became a food blogger of Xiaohongshu and began to evaluate various desserts.

Out of professional habits, she will remind everyone to exercise restraint and lose weight. Once she laughed and teased in the video: "After all, boys like thin legs." But I didn’t expect that because of this sentence, she was scolded and flattered in bilibili, and even someone posted a special post in Douban to blacken her.

But none of this will make her sad. Compared with being scolded, she is more afraid of not getting the approval of her fans. As a food blogger, I recommend delicious food to everyone, and I am happier than eating my own food.

But sometimes, the recommended food will be resisted, after all, it is difficult to adjust. May said: "I have eaten the food in this store many times, and I really think it’s delicious and recommend it to everyone. But some people commented that it’s terrible, so don’t be fooled, and said that I was just having a meal, so I would emo."

Squid became a blogger of lipstick color test in the Spring Festival of 2018. That year, she was a freshman and went to school in Canada. She just opened a little red book account "Squid Squid".

The threshold of lipstick color test is very low, and it can be completed with only one mobile phone and one lamp.

As a blogger of color testing, the negative comment that squid is most afraid of seeing is: "You are not allowed to have this color, you have a filter, and all the fakes are fake."

At first, she would patiently explain and send private letters one by one. "Even if the skin and lip color are exactly the same as mine, the color of this lipstick will be different under different light." But now, she doesn’t want to explain, because how to explain it is not clear.

Chen Langlang thinks humiliation is too common. After she issued the "Raiders of the Han Dynasty in Art Gallery", people often scolded her: "You are just dirty. You still feel proud to talk about ten boyfriends, don’t you?" In the message area, if such comments can’t be sent out, just trust her privately.

Chen Langlang said, "There is nothing I can do about it. Some people think that the art gallery is a sacred place. She thinks that I have soiled her white sheets. But some people will be very ugly. At first, I will be sad and even swear back, but now it is relatively calm. "

Talking about this, Chen Langlang suddenly said, "I only slept for three hours last night. The video released yesterday (the secret book of 30-year-old peach blossom trick) is what I really want to share. I haven’t had an article that I want to express in this way for a long time and I have received good traffic feedback, so it will make me particularly excited."

The above videos are classified in the collection of "Women’s Love Growth", which encourages girls to work hard, refuse appearance anxiety and keep a good attitude. The most praised message in the comment area is a sentence extracted from her video by a user: "You are not a commodity to be sold when the uterus is freshest."

Chen Langlang said that her initial intention as a blogger is to hope that she and her fans can get emotional resonance and influence others’ values to some extent.

Xiao Ran, a blogger of Xiaohongshu, is often moved by some fine things, and the two-way need with her fans will make her more motivated to stick to it.

In 2019, Xiao Ran settled in Xiaohongshu, and she wrote "Female Master of University of Sydney" in the introduction column. Xiao Ran said: "In fact, there is still a half sentence behind this, which is called’ bloggers are like this, putting education first’. I wanted to ridicule this before, but later I found that I had to write too much. That sentence was nonsense, so I deleted it. "

As a home blogger, Xiao Ran often shares her home on the account "Xiao Ran is the master".

On the blogger’s personal homepage, you can see who collects notes. Xiao Ran said with a smile: "Before, a fan collected my’ One Habitat’, and the album name she took was’ With His Home’, which touched me at once."

The stills of the movie "Angels Love Beauty"

Xiao Ran recalled: "In 2019, I just came to Beipiao, and I really wanted to add oil to my life, and then I chose Xiaohongshu. At that time, I was just sharing and recording beautiful moments, and I didn’t regard it as a way to increase my income. I just hope that the content I output can affect others’ emotions and awaken their love and yearning for life."

Self in the crevice

Bloggers’ topics are always pulled by traffic and users, and the form is also influenced by the platform. Whether it is graphic, short video or live broadcast, it depends on what the platform promotes during this period.

In May 2021, Jess, the person in charge of the Little Red Book Creation, revealed to the media that the next 10 billion traffic upward plan will be launched, focusing on video creators, live creators and creators of pan-knowledge and pan-entertainment categories to provide directional support. Live broadcast, video and vertical categories have become three key words. In addition, Xiaohongshu also announced the launch of the "Sparkling Star Anchor" program, which intends to provide 3 billion traffic to support the live broadcast anchor.

No matter what stage bloggers are in, they will face value confusion. While striking a balance between self-expression and users’ needs, they are frantically cramming new skills and adding tracks, hoping to do it well for a long time.

On the night when the song "Mohe Ballroom" became popular, the blogger "Five Crispy Corners" (hereinafter referred to as "Crispy Corners") wrote a handwritten thousand-character essay in Xiaohongshu, and then she wrote in a circle of friends, "You have to listen to songs and write." With a screenshot of the song page.

The content of the thousand-character text was created by a user of Netease Cloud Music, and Kang’s reply to Dequan. "Mohe Ballroom" originated from the past of Zhang Dequan’s old man and his wife Kang’s family. Musician Liu Shuang felt very moved after listening to this story and got this song.

This story not only touched Liu Shuang, but also touched Miaocuijiao. Miaocuijiao became a blogger of Xiaohongshu culture in November 2020.

Cultural bloggers may be the most difficult to define. Writing short poems, writing a circle of friends, recommending a book every day, etc. are all cultural bloggers.

Miao Cuijiao writes golden sentences extracted from books and movies by hand every day, which is popular with the series of "365 love letters". Even Tik Tok has her high imitation number.

"When I saw the bloggers of Little Red Book writing love letters, I realized that I was really angry, so that others imitated me."

Miaocuijiao prefers the emotions carried by paper letters to the communication through the screen, which is also the reason why the series of "love letters" appeared.

She called the "love letter" a candy in a tired life, and it was also the heat in her heart. She smiled and said, "A girl’s heart is a pearl, which is the softness of her heart. I want to record this beauty. If it happens to resonate, it is my pleasure. "

Adults are always tortured beyond recognition by all kinds of things and anxieties in the world. She doesn’t want to become a cold and numb person. "No matter what you see or experience, you must believe in the existence of beautiful feelings, otherwise I feel that it is meaningless to live in this world."

Handwriting is a retro and romantic thing, and Miaocuijiao likes to write. If you want to practice your handwriting well, no matter how impetuous you are, you need to be patient, calm down, persist in doing it, and persist for years. "I may not be able to bring any substantial benefits to others when I am a blogger, but I can give her a kind of persistence. It means a lot to me. "

The stills of the movie "Being Jane Austen" are from the Internet.

But I don’t know when, users like to watch "dry goods" more and more, and "three tricks to teach you ××××" and "one-click get×××" are all popular titles.

Here, right-angled shoulders can be accelerated, beautiful women can be accelerated, graduation thesis can be accelerated, and even bloggers with a monthly income of several hundred thousand can be accelerated. In the life of mutual volume, everyone hopes that one second will be effective in the next.

In the era of quick success and instant benefit, it takes only a moment to plant grass and pull weeds, but Miaocuijiao refuses all "efficient" things. For example, labeling can help people quickly extract highlights and deepen their memories, but she refuses to do so.

"You see I am round, I am round, you see I am square, then I am a square. As long as you can get what you want from me, OK. Labels are all meanings given by others, and I know exactly what kind of person I am. "

When a number of new consumer brands are aiming at Little Red Book, there are still bloggers like Miaocuijiao who think that "money is not the main thing"; 330,000 fans are waist bloggers in Xiaohongshu, but they can be called "top stream" in cultural bloggers.

Despite this, Miao Cuijiao said frankly: "I have no idea about running an account. Although I am affiliated with an MCN organization, I signed it just like I didn’t sign it. They didn’t give me any resources. Instead, I rely on my popularity to sign agreements with other bloggers. I signed it from hundreds of thousands to more than 300 thousand now, and I am doing it myself. "

Miao Cuijiao said: "The fans who help me the most are very lovely and sincere people. I am grateful to meet them. They can give me a lot of energy."

When asked what kind of business Miao Cuijiao wants to take, she has always been very restrained. Miaocuijiao only wants to promote some stationery products. "Who doesn’t like beautiful pens and notebooks!" " She answered.

Fans often leave messages asking Miao Cuijiao: "Can I ask for a link to the stationery?"

She replied: "I bought it at the grocery store, one yuan a copy."

Remarks: The interviewees in this article are all pseudonyms.

Cultivated in spring, melon farmers in Zhangqiu, Jinan are busy raising seedlings

Qilu. com Lightning News February 27 th The spring is chilly and everything is reviving. In the melon shed of the melon boutique garden in Gaoguanzhai Street, Zhangqiu District, Jinan City, the seedlings are covered with golden flowers, and the small melons are struggling to grow, as if they have smelled the sweet smell of harvest.
Walking into the sunlight seedling greenhouse of Shandong Dongfang Jiaozi Seedling Co., Ltd., warm air came to my face, and green pumpkin seedlings and watermelon seedlings grew gratifying. Workers were seizing the opportunity to graft watermelon seedlings, and the shed showed a busy scene.
"20 million melon seedlings in the first phase have all been sold, and melon farmers have already transplanted them years ago. It is expected that the first melons will be available in late March. At present, 40% of Hami melon seedlings have been sold. Now the workers in the nursery are busy grafting watermelon seedlings, which are mainly sold to local and nearby farmers in Inner Mongolia, Henan and Zhangqiu District, all of which are sold by order. " Zhang Linqiao, general manager of Shandong Dongfang Jiaozi Seedling Co., Ltd. introduced.
Gaoguanzhai Street is a national ecological township. The water source of the Yellow River, sandy loam and suitable north temperate climate have made the unique quality of melons, and won the reputation of "the first town of selenium-enriched melons in Shandong" for Gaoguanzhai. As the gold medal of the 21st and 22nd China Green Food Exposition, "Gaoguanzhai Melon" is well-known in the world.
Lightning journalist Angel reports.
Reporting/feedback

"Dry Beach" Becomes "Golden Beach" —— Investigation on Poverty Alleviation Cooperation in Suining Town, Yinchuan City

  In 1996, the cooperation between the east and the west started a magnificent journey, and Fujian and Ningxia formed a helping pair. After more than 20 years of poverty alleviation, as a demonstration window of poverty alleviation cooperation between the two provinces, Suining Village has developed into Suining Town. The "dry beach" in the past has become the "golden beach" today, and 66,000 ecological immigrants who moved from Xihaigu area have lived a good life. The picture shows Yuanlong Village, an immigrant village in Suining Town, Yongning County, Ningxia. Shen Jinxiang/photo

  The cooperation between the east and the west has created a miracle, and the old look of Suining Town in Ningxia has taken on a new look. Since Fujian and Ningxia formed a helping pair, over the past 20 years, through financial support, introducing projects, training skills, helping industries and other measures, a magnificent chapter of cooperation between Fujian and Ningxia in poverty alleviation has been written in the Gobi dry plateau. The picture above shows that in the early stage of the construction of Minning Town, Yongning County, Ningxia, the immigrants reclaimed land on the Gobi Desert (data photo issued by Xinhua News Agency). The picture below shows the photovoltaic vegetable greenhouse in Yuanlong Immigrant Village, Suining Town (photo by Xinhua News Agency reporter Wang Peng).

  Work together to achieve a comprehensive well-off dream, and Fujian-Ningxia counterpart poverty alleviation cooperation has achieved fruitful results. Based on the development of characteristic industries, Suining Town has cultivated a number of characteristic industries, such as photovoltaic, planting and wine, which provides a "golden key" for local people to open the door to wealth. The picture shows the workers in Helanshen Winery, Suining Town, Yongning County, Ningxia, inspecting the oak barrels. Zhan Anwen/photo

  闽宁对口扶贫协作给“穷沟沟”带来山乡巨变,实现“苦瘠甲天下”到“绿水青山”的蝶变。当年十年九旱的西海固地区通过移民搬迁、退耕还林、平整土地、保墒增收,如今生态得到了极大修复,生产生活条件得到了极大改善。图为宁夏西海固地区西吉县震湖乡小流域治理生态修复区。 强继周/摄

  巍巍贺兰山层峦叠嶂,守护着一望无垠的宁夏平原。在山的东麓,坐落着一个现代化生态移民示范镇——闽宁镇。这里,红瓦白墙,绿树成荫,农家小楼鳞次栉比,工厂车间热火朝天,田间地头欢声笑语,一场丰收又将来临。

  时针拨回到1997年。那时这里是银川城外永宁县的一片戈壁滩,“空中不飞鸟,地上不长草,风吹沙砾满地跑”。这年初春,时任福建省委副书记的习近平来到宁夏,调研对口帮扶工作,部署“移民吊庄”工程。面对这片荒滩,他坚定地说:“今日的干沙滩,明日要变成金沙滩。”由此,闽宁镇大踏步赶上了时代的脚步,创造了东西部协作发展的新模式,实现了从“干沙滩”到“金沙滩”的凤凰涅槃。

  2016年7月19日,习近平总书记来到闽宁镇考察,看到在20年对口帮扶下,昔日的“干沙滩”变成了“金沙滩”,老百姓过上了幸福生活,“打心眼里感到高兴”。他与村民代表座谈,深情地说:“闽宁镇探索出了一条康庄大道,我们要把这个宝贵经验向全国推广。”

一、趟出扶贫致富的新路子

  “闽宁村奠基那天,习近平同志代表对口帮扶领导小组发来贺信。我就站在台下听人读那信,听着听着就哭了,虽然那时闽宁村还是一片荒凉,但我知道搬出山沟沟一定会有希望”。20多年过去了,闽宁镇居民谢兴昌当年流泪憧憬的梦想一一变为现实。曾经“胡风怒卷黄如雾”、“穷荒绝漠鸟不飞”的贺兰山下荒滩,如今是绿树成荫、良田万顷、经济繁荣、百姓富裕的“金沙滩”,6.6万易地搬迁移民过上了过去想都不敢想的好日子。

  22 years in a flash. Minning Town was born out of poverty alleviation and prospered from poverty alleviation. At present, the whole town covers an area of 210 square kilometers, and there are 6 villagers’ committees and 86 villagers’ groups under its jurisdiction. The income of residents has increased steadily and it has successfully entered the ranks of key towns in China. From scratch, from poverty to wealth, Minning Town is a microcosm of China’s great project to help the poor. It is the result of decades of hard work led by the party, which shows the creative exploration of poverty alleviation cooperation between the east and the west and the superiority of Socialism with Chinese characteristics system in essence.

  Minning Town is an ecological immigrant town, and all the residents moved from Xihaigu area in southern Ningxia. The mountain valley in Xihaigu area of Ningxia is deep, with long-term drought and water shortage, serious soil erosion and fragile ecological environment. It is known as "the bitterness is the best in the world", and its poverty is unimaginable without visiting its territory. What is poverty? Xihaigu people in the 1980s will tell you: there is no grain in the pot, no firewood at the bottom of the pot, no water in the jar, and no money on you. Experts from the United Nations Food Development Agency once thought that Xihaigu was one of the most unsuitable places for human survival.

  为了改善西海固地区的生态环境和群众生活,20世纪80年代,宁夏按照党中央“三西”建设部署,开始实施西海固地区的生态移民搬迁,动员当地一部分贫困群众,到近水近路的地方建设“吊庄”,另谋生活出路。1990年10月,在国家扶贫开发政策支持下,宁夏组织西海固地区的西吉、海原两县1000多户贫困群众搬迁到贺兰山东麓的永宁县,在戈壁荒滩上建立了玉泉营和玉海经济开发区两处“吊庄”移民点,开始了西海固地区有组织的生态搬迁扶贫。这便是闽宁镇的前身。

  1996年,中央决定实施东西部扶贫协作,福建对口协作帮扶宁夏。同年10月,福建省成立对口帮扶宁夏领导小组,时任福建省委副书记的习近平担任组长;11月,习近平同志在福州主持召开闽宁对口扶贫协作第一次联席会议,拉开了闽宁对口扶贫协作的大幕。

  1997年4月,习近平同志率团到宁夏调研考察,深入宁夏南部贫困山区访贫问苦。习近平同志边调研、边思考、边规划闽宁对口扶贫协作。在他的建议下,同年召开的闽宁两省区第二次联席会议确定,以玉泉营开发区黄羊滩“吊庄”移民点为主体,集中力量共同建设以福建和宁夏两省区简称命名的闽宁村,作为两省区对口扶贫协作的示范窗口。从此,贺兰山东麓这片毫无生机的“干沙滩”开始沸腾起来,并逐步成为接收生态移民、助力贫困群众脱贫致富的“金沙滩”,移民规模不断扩大。

  2001年12月7日,经宁夏回族自治区人民政府批准,在闽宁村的基础上成立了闽宁镇。新成立的闽宁镇,行政隶属由西海固地区的西吉县划归银川市永宁县管辖,解决了易地搬迁移民的属地管理问题,使闽宁镇扶贫开发有了更加稳固的体制机制支撑。2017年6月6日,中共宁夏回族自治区第十二次代表大会提出,落实中央东西部扶贫协作战略部署,深化闽宁对口扶贫协作。闽宁镇这个塞上移民镇,乘着东西部扶贫协作的东风,走上了经济社会协同发展的康庄大道。

二、“金钥匙”打开了致富门

  东西部扶贫协作是我们党的伟大创造。从1996年9月中央确定闽宁对口扶贫协作关系以来,福建和宁夏两省区按照习近平同志倡导的“优势互补、互惠互利、长期协作、共同发展”方针,用“闽宁示范村”模式这把金钥匙,打开了深度贫困地区和贫困群众脱贫致富之门。

  发展特色产业是重要支撑。早在闽宁对口扶贫协作之初,习近平同志就明确指出,扶贫协作要以基本解决贫困人口的温饱问题为重点,以产业协作为基础,加大企业和社会力量扶贫协作的规模和力度。在习近平同志亲自谋划下,闽宁村转变发展思路,一边兴修水利、整理土地、引黄入滩,一边从福建引资引智引项目,培育发展特色产业,帮助群众彻底“拔穷根”。随着闽宁两省区协作不断加深,一批批福建企业和人才到宁夏投资兴业,他们不仅带来了资金和技术,而且带来了沿海地区先进的市场观念和“爱拼才会赢”的精神,给闽宁镇的发展注入了巨大活力。在闽宁对口帮扶下,闽宁镇坚持把当地资源优势和用好市场机制结合起来,先后培育形成了菌草、葡萄、黄牛等特色产业,协作扶贫这颗“金种子”在这片干涸的土地上生根发芽,结出了丰硕的果实。

  Pairing assistance is the dynamic mechanism.At the first joint meeting of Fujian-Ningxia counterpart poverty alleviation cooperation, the "point-to-point, one-to-one" pairing assistance mode was determined. Eight economically developed counties along the coast of Fujian helped eight counties in the southern mountainous area of Ningxia. More than 20 departments of provincial organs established assistance and cooperation relations with relevant departments in Ningxia, and concentrated the financial, material and human resources of the government and all walks of life, making key breakthroughs in developing Ningxia’s rural economy and improving the lives of poor people. Since the 18th National Congress of the Communist Party of China, in accordance with the new requirements of accurate poverty alleviation, the twinning assistance between the two provinces and regions has developed in depth, extending from counties to towns and administrative villages, forming a closer development community. In October 2016, Fujian Zhangzhou Taiwanese Investment Zone signed a twinning and co-construction agreement with villages in Yongning County and Suining Town, and established a new "3+1" counterpart cooperation model of twinning, mutual help and common development at the county, town and village levels. On June 13, 2018, at the 22nd joint meeting of Fujian-Ningxia counterpart poverty alleviation cooperation, 10 departments including Party Committee Organization Department, Industry and Information Department and Education Department of the two provinces and regions signed cooperation agreements respectively. "These paired places and departments have become a strong guarantee for the two places to work together to achieve tangible results." Liang Jiyu, director of the Ningxia Hui Autonomous Region Poverty Alleviation Office, said.

  Selecting cadres on attachment is an effective way.Over the past 20 years, Fujian Province has sent 11 batches of 183 cadres to help the poor areas in Ningxia. They worked one after another, explored the path of poverty alleviation and development according to local conditions, spared no effort to cultivate sustainable industries for the recipient areas, and tried every means to attract enterprises to participate in poverty alleviation. The model of "Suining Demonstration Village" continued to glow with new vitality. A batch of cadres who are on the job relay to help the poor, so that the hand-in-hand connection between Fujian and Ningxia provinces spanning 3,000 kilometers has never stopped, and it has become a powerful force to help Ningxia get rid of poverty. Huang Jiaming is a member of the ninth batch of Fujian cadres who aid Nanjing, 2014&mdash; In 2015, he was appointed deputy secretary of the Party Committee of Suining Town. Under his matchmaking, not only four Fujian enterprises, such as Qingchuan Pipe Industry, settled down in the town, but also six villages, such as jiao mei zhen in Longhai, Zhangzhou, Yuanlong Village in Minning Town and Wuzhai Village in jiao mei zhen, formed a "pair" to help each other and promote cooperation and development. "The dedication of these temporary cadres is admirable. When some cadres come to work, they bring their wives to teach, and their children transfer to Guyuan to go to school. " Ma Zhenjiang, deputy inspector of the Ningxia Hui Autonomous Region Poverty Alleviation Office and director of the Social Poverty Alleviation Office, said.

  Improving people’s livelihood is the foothold.For more than 20 years, Fujian and Ningxia have continuously expanded the field of poverty alleviation cooperation, from a single economic cooperation to cooperation in education, medical care, culture and other fields, and the local people have gained more benefits. Counterpart assistance has also enabled the rapid development of various social undertakings in Suining Town. At present, every village has primary schools, clinics, cultural activity centers and people’s livelihood service halls. In recent years, Minning Town has fully implemented the transformation of shanty towns, and promoted the "three reforms" project of improving water, toilets and kitchens. Immigrants have used solar energy, and more and more residents have used flush toilets. With the improvement of production and living conditions, Minning Town has paid more attention to the construction of ecological environment, continuously carried out five major projects: ecological restoration, sand prevention and control, farmland forest network, town and village greening and environmental improvement, comprehensively implemented efficient and water-saving agriculture, and planted more than 10,000 mu of trees, completely bidding farewell to the history of "gray in sunny days and mud in rainy days".

  Spiritual poverty alleviation is a long-term goal."Rich pockets" are more "rich brains". Minning Town pays more attention to education as an important part of poverty alleviation cooperation, insists on paying equal attention to material poverty alleviation and spiritual poverty alleviation, and makes efforts to "uproot the poor". Zhangzhou Taiwanese Investment Zone invests 200,000 yuan each year in education aid funds to help poor students in Minning Town and its surrounding areas. Fujian Province sent 12 backbone teachers to teach in Minning Town, and Minning Middle School and Fuzhou No.1 Middle School formed a sister school to promote common development through mutual visits and online teaching and research. Especially in the past two years, in order to strive for a "national civilized village and town", Minning Town has started with poverty alleviation, ambition support and wisdom support, and extensively carried out activities such as moral typical tree selection, harmonious construction of ethnic and religious groups, and cultivation of rural cultural teams, which effectively improved the overall quality of residents and made the people’s mental outlook of the town look brand-new.

  Practice has proved that poverty alleviation cooperation and counterpart support between the east and the west is a grand strategy to promote regional coordinated development, coordinated development and common development, a grand layout to strengthen regional cooperation, optimize industrial layout and expand new space for opening up at home and abroad, and a great measure to achieve the goal of helping the rich first and then achieving common prosperity.

三、闽宁镇蝶变

  到2020年全面建成小康社会,是我们党向人民、向历史作出的庄严承诺。全面建成小康社会,最艰巨最繁重的任务在农村,特别是在贫困地区。习近平总书记多次强调,“小康路上一个都不能少”,2016年他在宁夏考察时再次强调,到2020年全面建成小康社会,任何一个地区、任何一个民族都不能落下。

  哪里贫困程度深,哪里就是习近平总书记最牵挂的地方,哪里就有习近平总书记的足迹。习近平总书记曾触景生情地回忆:1997年我来到西海固,被当地的贫困景象所震撼,下决心贯彻党中央决策部署,推动福建和宁夏开展对口帮扶。闽宁对口扶贫协作是从探索解决西海固地区深度贫困与环境恶化这个两难问题起步的。闽宁镇作为西海固地区生态移民安置点的示范窗口,能不能走出一条发展新路,让从西海固地区搬迁来的贫困群众过上好日子,从根本上决定着能否攻克西海固地区深度贫困这个堡垒。在习近平同志精心指导下,闽宁镇先行先试发展特色产业,走上了产业扶贫新路,如同凤凰涅槃,重振羽翅,冲天飞翔。“闽宁示范村”模式的脱贫成效表明,扶贫攻坚找对了路子,扶贫协作扶到了点子上。

  Nowadays, through the butterfly change from "blood transfusion" to "hematopoiesis", Minning Town has gradually explored and established a new industrial development mechanism of "government attracting investment, enterprise leading and social participation", and formed five leading industries of "characteristic planting, characteristic breeding, photovoltaic industry, tourism industry and labor service industry", which have become the source of migrants’ prosperity. By the end of 2018, Suining Town registered 48 trademarks of various agricultural products, and five enterprises were established as leading enterprises in agricultural industrialization in the autonomous region, realizing the integrated development from the initial traditional planting industry to the current primary, secondary and tertiary industries. At present, all kinds of enterprises in Fujian have invested 2.28 billion yuan in Minning Town, and the major industries in the town have formed certain scale advantages and agglomeration effects, which have effectively supported farmers’ income increase and modernization. At the same time, Fujian and Ningxia provinces have jointly built an industrial park for poverty alleviation in Minning town, and six enterprises have settled down with an investment of 300 million yuan, which has effectively promoted the agricultural industrialization of Minning town and lifted farmers out of poverty. "Everyone is full of hope for the future life, which has a good demonstration role for Ningxia to get rid of poverty." Liang Jiyu, director of the Ningxia Hui Autonomous Region Poverty Alleviation Office, said.

  Driven by the development of characteristic industries, the process of poverty alleviation in Suining Town has accelerated, which resonates with rural revitalization. In 2018, the per capita disposable income of Suining Town reached 12,988 yuan, and the per capita disposable income of Funing Village, where Suining Village was located when the foundation stone was laid, reached 21,640 yuan, far higher than the per capita disposable income level of rural residents in China. By the end of 2018, 1,593 households and 6,536 people in the whole town had been lifted out of poverty, and the incidence of poverty dropped to 0.9%. All five poverty-stricken villages met the conditions for listing. In recent years, Minning Town has closely combined precision poverty alleviation with rural revitalization, closely focused on the construction of Minning Cooperative Industrial City and Minning Poverty Alleviation Industrial Park, and created a unique model town, a livable town with rich life and a model town of national unity and progress. In today’s Minning Town, the wide roads are lined with trees, and there are all kinds of infrastructure such as squares, shops, hospitals and schools. Residents enjoy more than 20 kinds of old-age insurance and medical insurance for urban and rural residents, and their lives are happy.

  More importantly, over the past 20 years, driven by the demonstration of Minning Town, more than 110 demonstration villages, more than 20 new villages and 320 relocation and resettlement areas have emerged in Ningxia, and more than 1 million people have been relocated in Xihaigu area. The large-scale relocation of millions of immigrants has greatly eased the contradiction between population and resources in Xihaigu, thus enabling the smooth progress of returning farmland to forests and grasslands, closing hillsides for grazing and ecological restoration in this area. This is another miracle that our party unites and leads the people to create, and it will surely shine in history.

四、“闽宁示范村”模式的奥秘

  20多年来,在习近平同志精心指导下,闽宁两省区守望相助,开创了一条具有典范意义的扶贫协作道路。作为东西部扶贫协作的示范窗口,闽宁镇已成为贫困地区通过对口帮扶走向全面小康的成功典范,在脱贫攻坚征程上,标注了具有重大实践意义的“闽宁示范村”模式。

  “坚持党对一切工作的领导”。闽宁对口扶贫协作是东西部扶贫协作的成功实践,离不开我们党的坚强有力领导,离不开我们党团结带领人民的接续奋斗。两省区坚决贯彻落实党中央的战略举措,坚持从“两个大局”、逐步实现共同富裕的战略高度推进工作,探索建立了以联席会议制度为核心的扶贫协作工作机制,开展了多层次、多形式、宽领域、全方位的扶贫协作,形成了以政府援助、企业合作、社会帮扶、人才支持为主要内容的帮扶体系。两省区党委和政府每年召开对口扶贫协作联席会议,共同研究帮扶事项,共同推进任务落实,共同解决重大问题,20多年从未间断,推动扶贫协作不断向纵深拓展。实践证明,党是我们事业的坚强领导核心,党的坚强领导是东西部扶贫协作取得成功的有力保证。应对前进中的风险挑战,完成艰巨历史使命,根本上要靠党的领导。

  Concentrate on doing great things.Getting rid of poverty is not only a matter for poor areas, but also a matter for the whole society, and it is the common responsibility of the whole party and society. To make good use of the important magic weapon of the socialist system to concentrate on doing great things, it is necessary to mobilize and unite all forces and sing the "chorus" of poverty alleviation. For more than 20 years, the two provinces and regions have insisted on tackling poverty as the top priority and the first livelihood project, actively built a platform for social poverty alleviation, cultivated diverse social poverty alleviation subjects, widely mobilized social organizations and individuals such as party committees and governments at all levels, democratic parties, federations of industry and commerce, workers, youths and women, and chamber of commerce associations to participate in poverty alleviation, and guided various enterprises to invest and start businesses in poverty-stricken areas, thus building a large-scale poverty alleviation pattern with the participation of the whole society, forming a strong driving force of "working together for a well-off society". By giving full play to the role of social forces, Fujian and Ningxia provinces have gone from one-way poverty alleviation to industrial docking, and from economic assistance to in-depth cooperation in many fields, which fully demonstrates the superiority of Socialism with Chinese characteristics’s system to concentrate on doing great things.

  "Right &lsquo; Prescription &rsquo; To unplug &lsquo; Poor roots &rsquo; " .An important reason for the success of Fujian-Ningxia counterpart cooperation in poverty alleviation is that it attaches importance to the role of market mechanism while giving play to the leading role of the government, and has opened the right prescription for poverty control and achieved a breakthrough in the development of characteristic industries. The two provinces firmly grasp the key to enhance the self-development ability of poverty-stricken areas, regard the development of characteristic industries as the fundamental measure to improve the self-development ability of poverty-stricken areas, adhere to the market orientation and industrial cooperation as the basis, vigorously promote the development of superior resources in poverty-stricken areas, drive the poor people out of poverty for a long time, and embark on a "hematopoietic" poverty alleviation path driven by enterprise cooperation, industry poverty alleviation and projects. As the organizer and implementer of poverty alleviation, the government’s role is mainly reflected in developing strategic planning, building a development platform and promoting the implementation of poverty alleviation policies. It is more important for enterprises to combine their own advantages with local characteristics to achieve shared development.

  "Poverty alleviation must first help the ambition".To get rid of poverty, we should not only get rid of material poverty, but also get rid of the poverty of consciousness and thinking. This is an important inspiration from the cooperation between Fujian and Ningxia in poverty alleviation. For more than 20 years, Fujian has not only given material assistance to poverty-stricken areas in Ningxia, but also brought new ideas of reform and development, which has promoted the change of ideological concepts of cadres and masses in Ningxia. The Fujian spirit of "Only if you work hard can you win" inspires Ningxia cadres and masses to further emancipate their minds and work hard. Most of the farmers in Minning Town moved spontaneously from Xihaigu. They can turn the former Gobi desert into today’s characteristic town by their entrepreneurial spirit and perseverance. The history of entrepreneurial struggle in Suining Town explains the same truth: "It is important to get rid of poverty and become rich. As long as you have ambition and confidence, there is no hurdle that you can’t get past."

  Draw a blueprint to the end.Since the cooperation between Fujian and Ningxia in helping the poor, each helper has devoted himself sincerely, made sincere efforts, and made relay efforts, which has stimulated the enthusiasm and motivation of the poor people, inspired the cadres in party member, Ningxia, and formed a powerful spiritual force to get rid of poverty and tackle the tough problems. It is a difficult and complicated process to get rid of poverty and attack hard. To fundamentally change poverty and backwardness, the broad masses of the people need to carry forward the tenacity of "dripping water wears away stones" and the arduous entrepreneurial spirit of silent dedication and make unremitting efforts for a long time. Party member cadres and grass-roots party organizations, in particular, should give full play to the vanguard and exemplary role and the role of fighting bastion, always keep the poor people warm and cold in mind, shoulder the task of tackling poverty, show the spirit and determination of "never breaking the loulan and never returning it", keep the passion and tenacity of "sticking to the green hills and never relaxing", do more things to lay the foundation and manage the long term, truly practice the party’s purpose in tackling poverty, and lead the poor people to a well-off society in an all-round way. (Our reporter Wang Zhaobin)

@all, here is the most detailed tour guide to the scenic spot of the Li Canal Cultural Corridor in history. Please take it away!

  If you are not careful, the summer will be half over, and your limbs are weak in the hot summer. Do you feel bored? Never mind! Let’s get together and go surfing while there’s still time! A lot of time is wasted, let’s take action! Xiaobian this will take you to a deep tour of canal culture!

   First stop:

  Qingjiangpu memory house

  Qingjiangpu Memory Museum has a building area of 1,600 square meters. It takes the development of Pucheng City in Qingjiang River in the past 600 years since Ming and Qing Dynasties as the thread, reappears the memory of the city as the main line, explores the root of Huai ‘an’s urban development through scene reappearance and film and television language expression, and through "City &mdash; Street &mdash; The three sections of "House" staged a freeze-frame urban drama, which made tourists feel the prosperity of Qingjiangpu, which was the thoroughfare of nine provinces in the past, and further demonstrated Huai’ an’s profound cultural heritage as the "Canal Capital".

  It is reported that the exhibition hall has a great sense of history, integration and artistry. It uses situational environmental art space layout, integrates multi-sensory interactive devices, intelligent robots, world-leading virtual reality tour and other high-tech integrated information technology means, breaks the traditional museum mode, leads visitors with scenes, attracts visitors with situations, and moves visitors with emotions, so as to construct an all-round and multi-dimensional full display of Qingjiangpu since its opening 600 years ago.

Huaiyuan tea house

A cup of Huai ‘an tea, a column of fragrant burning.

Close your eyes and sniff is the taste of a thousand years of history.

Leisure and slow products are the mountains and rivers, and they are the exquisite flowing of the canal.

Ancient houses, flowers and birds, and green flowers.

The environment of inheritance seems to go back to the old days.

Use the accumulation of thick ink in every bit of leisure.

Have a cup of green tea and some special snacks in Huai ‘an stopped you?

No, it’s just a corner of the scenic spot. Let’s hurry to the second stop:

  qingjiang pu lou

  Qingjiangpu Building, located in Zhongzhou Island, Qingjiangpu Scenic Area, was built in 2003, with a height of 23 meters. It is an antique building with three bright, three dark and five pavilions, and its cornices are upturned and magnificent. It is an excellent place to climb and enjoy the scenery of Li Canal, and it is also a symbol of Qingjiangpu’s place name.

  Inside the Qingjiangpu Building, Chen Youqing’s Notes on Jiangpu Building, the scene of "Qingjiangpu" misspelled by Ganlong’s southern tour and the interactive poem wall of "Celebrities Chanting Huai ‘an" were exhibited.

  Chenpan Ergong Temple

  Chen Pan Ergong Temple, built in the Ming Dynasty (1436-1449), was first dedicated to the first general officer of water transport and Bo Chen Xuan in Pingjiang, commonly known as Chen Gong Temple. During the reign of Emperor Qianlong of the Qing Dynasty (1736-1795), Pan Jixun, a famous water conservancy expert and Prime Minister of Ming Dynasty, was added to Chen Gong Temple, which was renamed Chen Pan Ergong Temple. Chen Pan’s Ergong Temple showed their life stories and historical achievements in water control, and at the same time met the demand of ancestral temple sacrifice.

  Interactive game

  Two innovative large-scale multimedia exhibitions have been added to Chen Pan Ergong Temple. The first is the exhibition hall of Qingjiang Gate, which consists of 4D dynamic platform, projection system, sound system and centralized control system. Let tourists truly experience the dangerous scene of crossing the Qingjiang Gate in the Ming and Qing Dynasties. The second is the interactive game of "attacking sand with water", which constructs plastic models such as remote dike, continuous dike, lattice dike and moon dike according to the principle of "attacking sand with water". Tourists choose the location and timing of the dam according to the prompts, and the central control system projects the corresponding dam shape on the plastic mold according to the selected parameters. If tourists make mistakes, it will cause the Yellow River to flood, destroy the dam and overflow the coastal villages. Through this "wrong" design, visitors can understand the principle and function of Pan Jixun’s theory of "Harnessing water to attack sand".

  Wu Gong Temple

  Wu Gong Temple was built in 1877, the third year of Guangxu, to commemorate Wu Tang, the governor of grain transportation. Wu Tang (1813 &mdash; In 1876), he was born in Xuyi County (now mingguang city) in the late Qing Dynasty. He was a powerful governor in the history of grain transportation in Qing Dynasty, and a top minister of frontier defense in the late Qing Dynasty. Wu Tang lived in Huai River for more than 10 years from the magistrate of Qinghe County to the governor of grain transportation. He managed water and disaster relief, built a city against twisting, protected the environment and people, built the Dacheng Hall of the Confucian Temple, founded the Chongshi Academy, and settled the field to raise troops. His political voice was outstanding and he became a pivotal figure in the late Qing Dynasty.

  Interactive game

  The interactive game exhibition hall in Shoucheng is set in the early years of Wufang Xianfeng, and the audience helped Qingjiang County to design interactive games to resist the twisting army on the earth embankment. The interactive game hall of guarding the city allows the audience to experience the most authentic and exciting interactive feelings, and constructs a model of the city wall and some architectural models, supplemented by background painting and projection technology. Tourists stand on the city wall, aim at shooting to attack the enemy, and truly experience Wu Tang’s resistance to the attack of the Nian army.

  Doulao Palace

  Doulao Palace was built in the apocalypse of Ming Dynasty. Qingganlong was built in 1738 and 1750, respectively. The Taoist god Doulao, also known as Doulao Yuanjun and Doulao Tianzun, is the mother of the Emperor, the Emperor Ziwei and the Big Dipper, and is a god who is full of maternal love, saves lives and prolongs life. One of the most interesting is the "Divination Hall", which sets up tourist experience exhibitions &mdash; &mdash; Digital divination, tourists ask for signs and measure characters, and the simulated robot makes answers.

  Dear friends, after visiting the exhibition halls in Qingjiangpu Scenic Area, have you got a better understanding of Huai ‘an and the Canal? Do you want to experience the prosperous scenery of Qingjiangpu and the construction achievements of the Li Canal Cultural Corridor at the present stage along the route of Emperor Kangxi and Emperor Qianlong going down to the south of the Yangtze River? Then let’s hurry up to the third stop:

  Enjoy the beautiful scenery by boat.

  Boarding the original boat, weeping willows on both sides of the strait, crossing stone bridges, like shuttling through time tunnel, seems to go back in time.

  Visitors can reserve tea ceremony, guzheng and saxophone performances in advance, accompanied by the music, and the fragrant tea, KTV on the water and whipped eggs on the water are a unique leisure enjoyment.

Such an arrangement

Are you very excited?

Better get moving than just be moved; take action right now

Hurry up and make an appointment!

La Liga: las palmas vs Atletico Madrid! Blood pressure? Why does Atletico Madrid like the home team type?

Let’s have our first chat today.La Liga: las palmas vs Atletico Madrid!

At the beginning of the season, we talked about Atletico Madrid’s away game against Vallecano. At that time, we analyzed why Atletico Madrid likes teams like Vallecano.

Coincidentally, today’s las palmas is also quite similar to Vallecano.Can Atletico continue to satisfy the fans?

This las palmas is just like.Basaer teamSame.

After playing in Barcelona for more than ten years, coach Pimienta has been a youth training echelon coach in Barcelona for twenty years.

After he came to las palmas, he brought in many players from Barcelona’s youth training to play football in Barcelona.

So,The style of this team is very similar to that of Barcelona, and many data are also very close to that of Barcelona.For example, the possession rate, the average number of passes per game, the high-pressure forced data and so on.

They have many advantages of Barcelona, such as high-intensity and effective high-level grabbing.Not only can it disrupt the opponent’s offensive organization, but it can often launch a counterattack on the spot to get a chance to break the door after stealing.

Therefore, it is very powerful for them to play the passing and controlling teams such as Huang Qian and Royal Society, and it will be very difficult for opponents to organize attacks normally.

But Atletico don’t buy it.

We introduced it before when Atletico Madrid played Vallecano.Atletico’s attack is very simple and fast, and it pays attention to the quick delivery of the ball, which will not give the opponent too much time to rush forward.

Moreover, because Atletico Madrid’s lineup is very stable, their ability to kick the ball may even be close to the level of Real Madrid.

Then, if Real Sociedad, Vallecano and other high-ranking teams that are equally fierce are hard to win the ball from Atletico Madrid, it may be difficult for las palmas.

Besides,Las palmas also has many shortcomings of Barcelona, for example, because a large number of people pressed into the frontcourt and forced to grab, which led to being easily beaten by opponents.

In almost every game this season, their opponents can get excellent scoring opportunities by playing behind them. For example, the last goal of almeria was a long pass behind them.

As far as Barcelona can play like this, it is backed by a strong defender. araujo, Comte and Christensen are all first-class in La Liga.

But las palmas doesn’t have a first-class defender. Even when playing Huang Qian, Huang Qian’s 1.95-meter high center Soloff is actually faster than their defender …

The combination of Atletico’s Gleizman and Morata is one of the best combinations in the five major leagues, and Morata is in the best season of his career, so it is hard for las palmas’s defenders to guard against it.

In addition, on the 1st, las palmas went to Manakol Island in the Mediterranean to play in the Cup, with a journey of 4,000 kilometers, which was still a bit of a toss-up.

Although the rotation has been made, many main players have also played, which may be somewhat worn out and may be at a disadvantage in physical fitness.

In the case that there is a big gap between the lineup configuration and competition experience of the two teams, if the weak side has no advantage in technical and tactical style and physical fitness, it will be difficult to play this game.

If Atletico Madrid wins tonight,You can temporarily climb to the top of La Liga.The fighting spirit may be relatively strong.

All things considered, can Atletico satisfy the fans? What do you think? The cocktail party expects them to score three points away from home!

Well, in other competitions today, Xiaojiu will still send you text analysis in the evening. If you have anything unclear, you can ask me, "Look at the ball and see clearly", and we will be there or not!

If you like it, move your fingers and pay attention to it!

Ministry of culture and tourism: the tourism market has entered the fast lane of recovery and development

China Youth Daily, Beijing, December 14th (reporter Xia Jin from Zhongqing.com, Zhongqing.com) At the press conference of "Accelerating the Construction of a Cultural Power and Promoting the High-quality Development of Culture and Tourism" held by the the State Council Press Office today, Du Jiang, Vice Minister of the Ministry of Culture and Tourism, said that since this year, the tourism market has entered the fast lane of recovery and development, and the Ministry of Culture and Tourism has continuously improved the quality of tourism products, innovated the supply of tourism products, strengthened the docking between supply and demand, stimulated consumption potential, and constantly met the diversity of the people.

At the press conference. Zhongqing Daily Zhongqing Net reporter Xia Wei/photo

Du Jiang introduced that in order to strengthen the implementation of policy planning and consolidate the policy guarantee for high-quality supply, the Ministry of Culture and Tourism has promoted the implementation of the Tenth Five-Year Plan for Tourism Development, the Outline for the Development of National Tourism and Leisure (2022-2030), and Several Opinions on Releasing Tourism Consumption Potential to Promote High-quality Development of Tourism. Together with the relevant ministries and commissions, a series of policy documents were issued, such as the Tourism Development Plan for Northeast China, accelerating the integration of urban and rural road passenger transport and tourism, strengthening the collaborative innovation and development of 5G+ smart tourism, and financial support for the high-quality development of rural tourism, so as to coordinate and promote the construction of Beijing-Zhangjiakou sports and cultural tourism belt.

In terms of improving the quality of tourism products and meeting tourism demand with high-quality supply, the Ministry of Culture and Tourism has improved the product series of "urban leisure" and "rural holiday", and promoted the equal development of sightseeing and leisure vacation through brand creation, standard review and pilot demonstration. Du Jiang introduced that at present, there are 14,900 A-level scenic spots, more than 700 national and provincial tourist resorts, 111 national tourist and leisure blocks, 243 national night culture and tourism consumption gathering areas, 142 national industrial tourism demonstration bases, 1,597 national rural tourism key villages and towns, and 8 rural tourism key villages and towns such as Yucun, Zhejiang Province have been identified as "the best tourist villages" by the United Nations World Tourism Organization. At the same time, the pilot work of the national red tourism integration development is also progressing solidly.

In order to innovate the supply of tourism products and create tourism demand with new supply, the Ministry of Culture and Tourism actively develops new products and formats such as camping tourism, ice and snow tourism, sports tourism, marine tourism and tourism performing arts, and has launched 7 national ski resorts, 22 camps of the third batch of national self-driving motor homes 5C and 4C, 13 national sports tourism demonstration bases, the first batch of 24 pilot projects for cultivating new spaces for immersive experience of smart tourism and 40 excellent tourism performing arts projects. The tourism products from all over the country are beaded into chains, and 10 national tourist routes with the theme of the Yangtze River, the top ten go on road trip boutique routes of "China Road", more than 400 national rural tourist boutique routes with the theme of "Four Seasons in the Country", the theme of "Reading Li Bai and Traveling to China" and the theme of tea culture in China have been launched. In addition, the Ministry of Culture and Tourism also launched a new supply and new demand docking activity, and jointly launched the first batch of 46 rich examples of integration of transportation and tourism with the Ministry of Transport and other six departments to promote the development of new products and formats of transportation and tourism.

Source: China Youth Daily client