Ultra-resolution reconstruction method for panoramic monitoring image of protection system of extra-high voltage converter station

Abstract

The method for super-resolution reconstruction of panoramic monitoring image of protection system of extra-high voltage converter station belongs to the technical field of power equipment detection, and solves the problems of unclearness and low resolution of the existing panoramic monitoring image, which cannot meet the demand of panoramic monitoring of inspectors; by adopting multi-scale convolution blocks to construct low-order and high-order features of images of various scales in the deep multi-scale residual network model, the phenomenon of incomplete extraction of image details is avoided, the residual learning mechanism is adopted in the network model to retain low-order rough features, reduce the training difficulty, promote the reuse of features, and thus improve the reconstruction capability of the image; the reconstructed image has better structural similarity and peak signal-to-noise ratio performance; the standard data set and the panoramic monitoring image data set of the extra-high voltage converter station are used in sequence for image super-resolution reconstruction and target recognition experiment, and the experimental results show that the high-resolution image reconstructed by the method can meet the demand of panoramic monitoring of inspectors.

Description

Technical Field

[0001] This invention belongs to the field of power equipment testing technology, and relates to a method for super-resolution reconstruction of panoramic monitoring images of ultra-high voltage converter station protection systems. Background Technology

[0002] Traditional image enhancement and reconstruction methods typically enhance image contrast to highlight target objects, including histogram equalization, logarithmic transform, sharpening, wavelet transform, and Retinex at different scales. These methods are computationally inefficient and highly portable, but their enhancement effects are limited as general-purpose algorithms, and the processed images often fail to meet the needs of panoramic surveillance in specific scenarios. Image enhancement and reconstruction is a classic research topic in computer vision, and Single Image Super Resolution (SISR) is a crucial component. SISR utilizes a set of low-quality, low-resolution images to generate a single high-quality, high-resolution image, acquiring a region of interest with higher spatial resolution. This allows for focused analysis of the target object, transforming the image from detection level to recognition level, or even further to fine-resolution level, thereby improving the recognition capability and accuracy of panoramic surveillance images for converter stations.

[0003] Currently, SISR algorithms can be broadly categorized into three types: interpolation-based, reconstruction-based, and deep learning-based. Interpolation algorithms offer low computational cost and high real-time performance, but lack external information features, leading to the loss of high-frequency features after image degradation, resulting in images with noticeable blurring and ringing effects. Compared to interpolation algorithms, reconstruction-based algorithms show more significant improvements, but as the reconstruction magnification increases, high-frequency features become blurred. Deep learning-based methods have become mainstream in recent years, utilizing the mapping relationship between observed low-resolution (LR) images and original high-resolution (HR) images, along with a large number of training samples, to learn more high-frequency details in HR images. However, reconstructed images still suffer from detail distortion and high computational complexity. Convolutional neural networks (CNNs) are widely used in visual analysis due to their powerful image feature learning capabilities. In recent years, SISR algorithms based on CNNs have been proposed and have achieved significant performance improvements. The paper "Image Super-Resolution Using Deep Convolutional Networks" (C. Dong, IEEE Transactions on Pattern Analysis and Machine Intelligence, published in 2016) proposed a CNN model called SRCNN, which replaces dictionary modeling with automatic adjustment of hidden layer parameters, learning the nonlinear mapping relationship from low-resolution input to high-resolution output, improving reconstruction accuracy and reducing computation time. However, SRCNN also has some shortcomings. For example, bicubic interpolation can cause blurred and jagged edges in the image, and with the number of model parameters remaining constant, a larger super-resolution factor indicates a larger input resolution, resulting in higher computational cost. The paper "Accelerating the Super-Resolution Convolutional Neural Network" (Chao D, European Conference on Computer Vision, published in 2016) proposed an improved algorithm, FSRCNN, to address the slow training of SRCNN. It uses deconvolution for upsampling and 1×1 convolutions for dimensionality reduction, reducing the computational cost of the model and accelerating training. The core of ResNet is to add a skip connection between the output of the convolutional layer and the input of the previous convolutional layer to solve the gradient vanishing problem. H(x) represents the underlying mapping fitted by several stacked convolutional layers, where the input of the first convolutional layer is x, and x is connected to the output of the last convolutional layer. The stacked layers only need to learn the mapping F(x) = H(x) - x. If F(x) is zero, the residual unit can fit the identity mapping.

[0004] With the development of power grids, the scale of grid interconnection is constantly expanding, and electrical connections within the grid are becoming increasingly close. This has led to a growing prominence of the safety and stability issues facing large power grids, significantly increasing the technical difficulty and safety risks associated with operation and management. The safe and reliable operation of ultra-high-voltage (UHV) converter stations plays an undeniably crucial role in the safe and stable operation of the power grid. Therefore, manual inspections are necessary during the daily operation and maintenance of UHV systems to troubleshoot equipment faults and ensure system safety and stability. However, this manual inspection mode is labor-intensive, and its performance is easily affected by the experience and sense of responsibility of the personnel. To improve the efficiency of UHV converter station operation and maintenance management, panoramic monitoring systems are widely deployed within UHV converter stations to monitor the operating status of equipment at all stages.

[0005] The following status signal parameters, applicable to the core links of UHVDC protection, need to be monitored by the protection device of the UHV converter station: A. Monitoring of the outlet pressure plate status; B. Temperature measurement of terminal blocks inside the cabinet; C. Monitoring of the front panel of secondary equipment inside the cabinet; D. Operating temperature of secondary equipment inside the cabinet; E. Operating voltage of secondary equipment inside the cabinet; F. Fiber optic light intensity monitoring; G. Cable insulation detection; H. Outlet circuit detection; I. Auxiliary contact position; J. Cable status detection; K. Detection of environmental parameters, such as temperature and humidity; L. Corrosion status of wiring terminals. However, the operating environment of the monitoring system and the long-term use of the equipment inevitably lead to vibration and shaking, as well as interference such as dust accumulation and cobwebs on the lens, resulting in blurred video images and inaccurate panoramic monitoring data acquisition. Therefore, there is an urgent need for a super-resolution reconstruction method for panoramic monitoring images of the UHV converter station protection system, so that the reconstructed high-resolution image can meet the panoramic monitoring needs of inspection personnel. Summary of the Invention

[0006] The purpose of this invention is to design a super-resolution reconstruction method for panoramic monitoring images of UHV converter station protection systems, in order to solve the problems of blurry and low resolution in current panoramic monitoring images of UHV converter station protection systems, which cannot meet the panoramic monitoring needs of inspection personnel.

[0007] The present invention solves the above-mentioned technical problems through the following technical solutions:

[0008] A method for super-resolution reconstruction of panoramic monitoring images of ultra-high voltage converter station protection systems includes the following steps:

[0009] S1. Establish a deep multi-scale residual network model on the edge side;

[0010] The deep multi-scale residual network model includes: an input convolutional layer, an output convolutional layer, and k multi-scale convolutional blocks. The input convolutional layer acts as an encoder to extract the original low-level features of the low-resolution image. The output convolutional layer is used to fuse multi-scale detail features to reconstruct a high-resolution image. Skip connections are established between the input and output convolutional layers to create an identity mapping from the low-resolution image to the high-resolution image for global residual learning. The k multi-scale convolutional blocks are stacked sequentially to obtain the network model depth. The original low-level features are connected to the k multi-scale convolutional blocks via k corresponding paths, and local residual learning enhances the network model's ability to learn complex features.

[0011] S2. Input sample dataset and train the deep multi-scale residual network model;

[0012] S3. Test the peak signal-to-noise ratio and structural similarity index of the trained deep multi-scale residual network model using a standard dataset.

[0013] S4. Input the panoramic monitoring image of the UHV converter station into the trained deep multi-scale residual network model to complete the super-resolution reconstruction and recognition.

[0014] The super-resolution reconstruction method for panoramic monitoring images of UHV converter station protection systems of this invention avoids incomplete image detail extraction by constructing low-order and high-order features of the image at multiple scales using multi-scale convolutional blocks in a deep multi-scale residual network model. A residual learning mechanism is employed in the network model to preserve low-order coarse features, reducing training difficulty and promoting feature reuse, thereby improving image reconstruction capabilities. The reconstructed image exhibits better structural similarity and peak signal-to-noise ratio performance. Experiments on image super-resolution reconstruction and target recognition were conducted using a standard dataset and a UHV converter station panoramic monitoring image dataset. The results show that the high-resolution images reconstructed by the method of this invention can meet the panoramic monitoring needs of inspection personnel.

[0015] Furthermore, both the input and output convolutional layers use convolutional kernels with a stride of 1, and the input convolutional layer uses ReLU activation.

[0016] Furthermore, the multi-scale convolutional block extracts multi-level detailed features from the input image using convolutional kernels of four scales: 3×3, 3×2, 2×3, and 2×2. Then, the feature maps of the four scales are concatenated pairwise along a specified dimension through a cross-connection mechanism and fed into a 3×3 convolutional layer for feature mapping, generating a new feature map of the same size as the input and feeding it into the next multi-scale convolutional block.

[0017] Furthermore, the local residual learning is defined as follows:

[0018] H k =G k (H k-1 )+F (1)

[0019] Among them, G k H is the feature map learned by the k-th multi-scale convolutional block. k This is the output of the k-th multi-scale convolutional block. is the output of the (k-1)th multi-scale convolutional block, and F is the original low-order feature extracted by the input convolutional layer.

[0020] Furthermore, the mappings of the k multi-scale convolutional blocks learned from the global and local residuals are represented as follows:

[0021]

[0022] Where F0() is the mapping that the input convolutional layer needs to learn, F -1 () represents the mapping that the output convolutional layer needs to learn, where I HR I LR These represent high-resolution and low-resolution images, respectively. G is the feature map learned by the (k-1)th multi-scale convolutional block. k R is the feature map learned from the first multi-scale convolutional block, and R() is the mapping operation.

[0023] Furthermore, the loss function of the aforementioned deep multi-scale residual network model is:

[0024]

[0025] Where represents the parameters of the deep multi-scale residual network, and the loss function is minimized using the Adam optimizer; X (i) For sample dataset The i-th sub-image in Y (i) For the corresponding label, N is a positive integer.

[0026] Furthermore, the panoramic monitoring image of the ultra-high voltage converter station includes images of secondary equipment, hard pressure plates, and terminal corrosion.

[0027] Furthermore, the standard datasets include three basic datasets: Set5, Set14, and Urban100.

[0028] Furthermore, the formula for calculating the peak signal-to-noise ratio is as follows:

[0029]

[0030] Where MSE is the mean square error between the original image and the processed image, and MAX is the mean square error between the original image and the processed image. I This represents the maximum value of the image color.

[0031] Furthermore, the formula for calculating the structural similarity index is as follows:

[0032]

[0033]

[0034]

[0035] SSIM(X,Y)=L(X,Y)*C(X,Y)*S(X,Y) (8)

[0036] Among them, u X u Y σ X and σ Y Let σ represent the mean and standard deviation of images X and Y, respectively. XY This represents the covariance of images X and Y. C1, C2, and C3 are constants, typically taken as C1 = (K1 * L). 2 C2 = (K2 * L) 2 C3 = C2 / 2, K1 = 0.01, K2 = 0.03, and L is the range of pixel values.

[0037] The advantages of this invention are:

[0038] The super-resolution reconstruction method for panoramic monitoring images of UHV converter station protection systems of this invention avoids incomplete image detail extraction by constructing low-order and high-order features of the image at multiple scales using multi-scale convolutional blocks in a deep multi-scale residual network model. A residual learning mechanism is employed in the network model to preserve low-order coarse features, reducing training difficulty and promoting feature reuse, thereby improving image reconstruction capabilities. The reconstructed image exhibits better structural similarity and peak signal-to-noise ratio performance. Experiments on image super-resolution reconstruction and target recognition were conducted using a standard dataset and a UHV converter station panoramic monitoring image dataset. The results show that the high-resolution images reconstructed by the method of this invention can meet the panoramic monitoring needs of inspection personnel. Attached Figure Description

[0039] Figure 1 This is an architecture diagram of the deep multi-scale residual network model of the super-resolution reconstruction method according to an embodiment of the present invention;

[0040] Figure 2This is a structural diagram of the multi-scale convolutional block in the super-resolution reconstruction method of this invention.

[0041] Figure 3 These are PSNR performance curves of the super-resolution reconstruction method of this invention at different network model depths;

[0042] Figure 4 , Figure 5 , Figure 6 These are comparison images showing the reconstruction effects of the super-resolution reconstruction method of this invention with other algorithms on secondary equipment monitoring images, hard pressure plate images, and terminal corrosion images. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0044] The technical solution of the present invention will be further described below with reference to the accompanying drawings and specific embodiments:

[0045] Example 1

[0046] like Figure 1 As shown, the method for super-resolution reconstruction of panoramic monitoring images of the UHV converter station protection system includes the following steps:

[0047] 1. Establish a deep multi-scale residual network model on the edge side.

[0048] 1.1 Deep Multi-scale Residual Network (DMRN)

[0049] Figure 1 This network employs a deep multi-scale residual network architecture, consisting of convolutional layers, k multi-scale convolutional blocks (MC blocks), and skip connections. Stacking the k MC blocks achieves greater depth, while the convolutional operations are improved by employing small kernels of different scales to extract and fuse detailed features at different scales in the image. This enhances the network's ability to reconstruct the microscopic texture and macroscopic geometric features of the input panoramic surveillance image, resulting in more realistic HR images. Residual structures are incorporated during network training to enable feature reuse, reduce network redundancy, accelerate convergence, and address the vanishing gradient problem.

[0050] 1.2 Multi-scale convolutional blocks

[0051] DMRN uses a multi-scale convolutional block architecture to perform super-resolution tasks. Convolutional layers with different scales form a multi-scale convolutional block, which can generate and combine detailed features at different levels.

[0052] Figure 2 This is a structural diagram of a single multi-scale convolutional block, where x represents the input of the multi-scale convolutional block and y is the output of the convolutional block. Convolutional blocks of different scales can extract details at different frequencies. In each multi-scale convolutional block, the input image is processed using convolutional kernels of four scales: 3x3, 3x2, 2x3, and 2x2 to extract multi-level detail features. Then, the feature maps of the four scales are concatenated pairwise along a specified dimension using a cross-connection mechanism, and then fed into a 3x3 convolutional layer for feature mapping, generating a new feature map of the same size as the input, which is then fed into the next multi-scale convolutional block. Multi-scale convolutional blocks better preserve the edge information of the image and increase the detail information of the reconstructed high-resolution image.

[0053] 1.3 Residual Learning Mechanism

[0054] The DMRN network architecture introduces global residual learning and local residual learning mechanisms for network training. Due to the similarity between low-resolution and high-resolution images, DMRN establishes an identity mapping from low-resolution to high-resolution images through skip connections between input and output to perform global residual learning.

[0055] There are two reasons for using local residual learning: First, the details needed in high-resolution reconstruction are the sum of high-frequency features and low-order features. Figure 1 The first convolutional layer in the algorithm acts as an encoder, extracting the original low-order features of the low-resolution image. Local residual learning can preserve these low-order features. Second, there are multiple paths between low-order features and multi-scale convolutional blocks. Through local residual learning, the network's ability to learn more complex features can be enhanced.

[0056] Local residual learning is defined as follows:

[0057] H k =G k (H k-1 )+F (1)

[0058] Among them, G k H is the feature map learned by the k-th multi-scale convolutional block. k is the output of the k-th multi-scale convolutional block, and F is the original low-order feature extracted by the first convolutional layer.

[0059] Let F0 be the mapping that the first convolutional layer (with ReLU) needs to learn.-1 For the mappings that the last convolutional layer (without ReLU) needs to learn, the mappings of the k multi-scale convolutional blocks learned based on the global and local residuals can be expressed as follows:

[0060] I HR =R(I LR ) = I LR +F -1 (G k (G k-1 (…(G 1 (F)+F)…)+F)+F) (2)

[0061] Where F = F0(I LR ) is a primitive, low-level feature.

[0062] 1.4 DMRN Network Details

[0063] Figure 1 The main structure of DMRN differs from ResNet. DMRN removes pooling layers and batch normalization layers. This is because SISR aims to achieve accurate pixel prediction, and removing pooling layers helps preserve more image details. Batch normalization layers, which normalize features, eliminate the network's range flexibility and are detrimental to image reconstruction; therefore, they are also removed. DMRN uses convolutional kernels with a stride of 1 and ReLU activation, thus accepting images of arbitrary size as input. Furthermore, DMRN uses two 5×5 convolutional layers in the first and last layers to extract coarse features and fuse multi-scale detail features to reconstruct the HR image.

[0064] 2. Input sample data and train the deep multi-scale residual network model.

[0065] Eighty hundred monitoring images, each with a resolution of 1600*1200, were collected from the panoramic monitoring system of the UHV converter station. First, the high-resolution images were reduced to one-third of their original resolution using a bicubic interpolation algorithm, and then their dimensions were adjusted to the original image size. From the adjusted images, 24,000 sub-images of size 32×32 were selected with a step size of 32 as the dataset. Where N = 24000, X (i) For the i-th sub-image, Y (i) The corresponding labels are used. 80% of the images are randomly selected as the training set, and the remaining 20% ​​as the test set. Mean squared error (MSE) is used as the loss function for the network.

[0066]

[0067] Where are the parameters of DMRN, and the loss function is minimized using the Adam optimizer.

[0068] 3. The trained deep multi-scale residual network model was tested and analyzed using a standard dataset.

[0069] After the DMRN network was trained, it was first tested using three standard datasets: Set5, Set14, and Urban100. Since human vision is more sensitive to changes in brightness, the images were converted to the YCbCr space, and the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) on the Y channel were used to evaluate the performance of super-resolution reconstruction.

[0070] PSNR is defined as the ratio of the maximum power of a signal to the power of noise, measured in decibels (dB). It is commonly used to evaluate the quality of image compression; a higher value indicates a more realistic image. The formula for calculating PSNR is as follows:

[0071]

[0072] Where MSE is the mean square error between the original image and the processed image, and MAX is the mean square error between the original image and the processed image. I This represents the maximum value of the image color.

[0073] SSIM evaluates the similarity between the original image and the processed image, with values ​​ranging from [0, 1]. A higher value indicates less image distortion. The formula for calculating SSIM is as follows:

[0074]

[0075]

[0076]

[0077] SSIM(X,Y)=L(X,Y)*C(X,Y)*S(X,Y) (8)

[0078] Among them, u X u Y σ X and σ Y Let σ represent the mean and standard deviation of images X and Y, respectively. XY This represents the covariance of images X and Y. C1, C2, and C3 are constants, typically taken as C1 = (K1 * L). 2 C2 = (K2 * L) 2 C3 = C2 / 2, K1 = 0.01, K2 = 0.03, and L is the range of pixel values.

[0079] The number of multi-scale convolutional blocks determines the depth of DMRN. Here, models with different numbers of multi-scale convolutional blocks (k = {8, 10, 12, 14}) are selected, such as... Figure 3 As shown, the average PSNR and SSIM performance of 50 randomly selected images from the Set5, Set14, and Urban100 test datasets are presented. With the increase in the number of multi-scale convolutional blocks, the PSNR performance of DMRN on Set5, Set14, and Urban100 steadily improves, indicating that the method of this invention achieves the expected goal of "the deeper the better." However, excessively deep networks also lead to increased computational complexity. The performance improvement of k=14 compared to k=12 is limited; therefore, the parameter setting of k=12 was used in subsequent experiments.

[0080] The SSIM and PSNR values ​​for testing on the standard datasets Set5, Set14, and Urban100 are shown in Tables 1-2. The tables also compare these values ​​with other methods, including Bicubic interpolation, SRCNN, and FSRCNN.

[0081] Table 1. Structural similarity indices for the Set5, Set14, and Urban100 datasets.

[0082]

[0083] Table 2 Peak Signal-to-Noise Ratio of Set5, Set14, and Urban100 Datasets

[0084]

[0085] Here, DMRN with k=12 is selected as the comparison model. As shown in the table, the average SSIM values ​​of SRCNN, FSRCNN, and DMRN are 0.7784, 0.7827, and 0.8082, respectively, while the structural similarity of the algorithm of this invention increases by 0.0043 and 0.0298, respectively. The average PSNR values ​​of SRCNN, FSRCNN, and DMRN are 27.50 dB, 27.67 dB, and 28.33 dB, respectively, while the algorithm of this invention improves by 0.17 dB and 0.83 dB, respectively. This result indicates that the algorithm of this invention can establish a nonlinear mapping relationship from LR to HR by fusing low-order and high-order features and using a combination of global and local residuals.

[0086] 4. Input the panoramic monitoring image of the UHV converter station into the trained deep multi-scale residual network model to complete super-resolution reconstruction.

[0087] Figure 4 , Figure 5 , Figure 6Super-resolution reconstruction images of the panoramic monitoring images of the UHV converter station are presented, including images of secondary equipment, hard pressure plates, and terminal corrosion. The method of this embodiment is compared with Bicubic, SRCNN, and FSRNN, and Tables 3 and 4 show the quantitative experimental results. The images before and after reconstruction were input into the YOLOv3 recognition model used in the UHV converter station, and the recognition results are shown in Table 5. The experimental results show that compared with other methods, DMRN has better SSIM and PSNR performance, recovers clearer edges and more details, such as the indicator lights and corresponding blurred text information in the first image, the switch status and text display of the hard pressure plate in the second image, and the terminal corrosion status in the third image, which can better help inspection personnel to conduct panoramic monitoring.

[0088] Table 3. Structural Similarity Index of Monitoring Images of UHV Converter Stations

[0089]

[0090] Table 4 Peak Signal-to-Noise Ratio of Monitoring Images from UHV Converter Stations

[0091]

[0092] Table 5. Image Recognition Results of UHV Converter Station Monitoring

[0093]

[0094] This invention proposes a deep multi-scale residual network (DMRN) to achieve fast super-resolution reconstruction of panoramic monitoring images of UHV converter station protection systems, meeting the panoramic monitoring needs of inspection personnel. In DMRN, multi-scale convolutional blocks are used to construct low-order and high-order features of the image at multiple scales, solving the problem of incomplete image detail extraction. The network employs residual learning to preserve low-order coarse features, reducing training difficulty, promoting feature reuse, and thus improving image reconstruction capabilities. Experimental results show that compared with other methods, DMRN has better SSIM and PSNR performance, recovering clearer edges and more details from standard datasets and UHV panoramic monitoring image sets, improving the quality of high-resolution image reconstruction, and meeting the panoramic monitoring needs of inspection personnel for UHV converter station protection systems.

[0095] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for super-resolution reconstruction of panoramic monitoring images of UHV converter station protection systems, characterized in that, Includes the following steps: S1. Establish a deep multi-scale residual network model on the edge side; The deep multi-scale residual network model includes: input convolutional layers, output convolutional layers, k The input convolutional layer acts as an encoder to extract the original low-level features of the low-resolution image; the output convolutional layer is used to fuse multi-scale detail features to reconstruct the high-resolution image; the input and output convolutional layers have skip connections to establish an identity mapping from the low-resolution image to the high-resolution image for global residual learning. k The aforementioned multi-scale convolutional blocks are stacked and connected sequentially to obtain the network model depth; the original low-order features and k The multi-scale convolutional blocks are interconnected via... k Each path corresponds to a connection, and local residual learning enhances the network model's ability to learn complex features; Both the input and output convolutional layers use convolutional kernels with a stride of 1, and the input convolutional layer uses ReLU activation. The multi-scale convolutional block extracts multi-level detailed features from the input image using convolutional kernels of four scales: 3×3, 3×2, 2×3, and 2×2. Then, the feature maps of the four scales are concatenated in pairs along a specified dimension through a cross mechanism and fed into a 3×3 convolutional layer for feature mapping to generate a new feature map of the same size as the input, which is then fed into the next multi-scale convolutional block. The local residual learning is defined as follows: （1） in, For the first k The feature maps learned by multi-scale convolutional blocks For the first k The output of a multi-scale convolutional block -1 For the first k-1 The output of a multi-scale convolutional block F The original low-order features extracted by the input convolutional layer; Global residuals and local residuals learned k The mapping of a multi-scale convolutional block is represented as follows: （2） in, () represents the mapping that the input convolutional layer needs to learn. F -1 () represents the mapping that the output convolutional layer needs to learn, where, , These represent high-resolution and low-resolution images, respectively. -1 For the first k-1 The feature maps learned by multi-scale convolutional blocks The feature map learned from the first multi-scale convolutional block. R () represents a mapping operation; S2. Input sample dataset and train the deep multi-scale residual network model; S3. Test the peak signal-to-noise ratio and structural similarity index of the trained deep multi-scale residual network model using a standard dataset. S4. Input the panoramic monitoring image of the UHV converter station into the trained deep multi-scale residual network model to complete the super-resolution reconstruction.

2. The method for super-resolution reconstruction of panoramic monitoring images of an ultra-high voltage converter station protection system according to claim 1, characterized in that, The loss function of the deep multi-scale residual network model is: （3） in, The parameters of the deep multi-scale residual network are defined, and the loss function is minimized using the Adam optimizer. For sample dataset The first in Sub-images, For the corresponding label, N is a positive integer.

3. The method for super-resolution reconstruction of panoramic monitoring images of an ultra-high voltage converter station protection system according to claim 1, characterized in that, The panoramic monitoring images of the ultra-high voltage converter station include images of secondary equipment, hard pressure plates, and terminal corrosion.

4. The method for super-resolution reconstruction of panoramic monitoring images of an ultra-high voltage converter station protection system according to claim 1, characterized in that, The standard datasets mentioned include three basic datasets: Set5, Set14, and Urban100.

5. The method for super-resolution reconstruction of panoramic monitoring images of an ultra-high voltage converter station protection system according to claim 1, characterized in that, The formula for calculating the peak signal-to-noise ratio is as follows: （4） in, The mean square error between the original image and the processed image is denoted as . This represents the maximum value of the image color.

6. The method for super-resolution reconstruction of panoramic monitoring images of an ultra-high voltage converter station protection system according to claim 1, characterized in that, The formula for calculating the structural similarity index is as follows: （5） （6） （7） （8） in, , , and Representing images respectively and The mean and standard deviation, Representing an image and covariance, , and It is a constant. , , , , , This represents the range of pixel values.

Images