Digital low-voltage distribution network rural area network structure transaction detection method and system, and medium

Abstract

The invention discloses a digitized low-voltage distribution network rural area network structure transaction detection method and system and a medium. The method comprises the following steps: acquiring a historical network structure image and a current network structure image of a digitized low-voltage distribution network rural area; converting the two images into grayscale images; performing color threshold analysis and texture analysis in sequence, and extracting a final historical network structure grayscale image and a final current network structure grayscale image; respectively extracting topological feature vectors of the final historical network structure grayscale image and the final current network structure grayscale image; calculating a dynamic time warping distance between the topological feature vectors, comparing the dynamic time warping distance with a threshold value, and judging whether a historical network structure is consistent with a current network structure or not; and if not, superposing and comparing the historical network structure and the current network structure, and visually displaying the change result of the network structure of the court. The method has the advantages that the detection method is simple, manual intervention is not needed, time and labor are saved, and the accuracy is high.

Description

Technical Field

[0001] The present invention relates to the field of power grid digitization, and in particular to a method, system and medium for detecting abnormal changes in network structure of a rural area of a digital low-voltage distribution network. Background Art

[0002] The modernization of rural power grids is driving the expansion of smart grids into rural areas. Rural distribution networks cover a wide area, with sparsely distributed users, long lines with numerous branches, and geographical factors (such as mountainous areas and farmland), resulting in a complex network structure. The structure of rural distribution networks is dynamic and prone to change due to factors such as line additions and subtractions, transformer capacity expansions, user connections and exits, and the integration of distributed photovoltaic and energy storage devices. Failure to promptly monitor changes in the rural distribution network structure will reduce the efficiency of distribution network operations and maintenance, making it more difficult to analyze issues such as three-phase imbalance, overloads, and line losses, making it difficult to ensure safe and reliable power supply to users in rural areas.

[0003] Currently, the main methods for detecting structural anomalies in rural power grids include manual inspections and drone inspections. Manual inspections require regular staff visits to rural power grids to inspect each line, record anomaly data, and report these anomalies to the distribution network management platform for comparison and correction. This method suffers from data lag, difficulty locating anomalies, and is time-consuming and labor-intensive. While drone inspections have improved the automation level of detecting structural anomalies in rural power grids and reduced the workload to a certain extent, they still require on-site personnel to operate and identify anomalies through drone image comparison, making it difficult to achieve automatic diagnosis of anomalies. Furthermore, the method is complex, time-consuming, and labor-intensive, and its accuracy is low. Summary of the Invention

[0004] In view of the above-mentioned deficiencies in the prior art, the purpose of the present invention is to provide a method, system and medium for detecting network structure anomalies in rural areas of a digital low-voltage distribution network, so as to realize the detection of network structure anomalies in rural areas of a digital low-voltage distribution network without the need for human intervention, saving time and effort and with high accuracy.

[0005] In a first aspect, a method for detecting abnormal changes in the network structure of a digital low-voltage distribution network in a rural area is provided, comprising the following steps:

[0006] S1: Obtain historical and current network structure images of rural substations in the digital low-voltage distribution network;

[0007] S2: Convert the historical network structure image and the current network structure image into grayscale images;

[0008] S3: Perform color threshold analysis and texture analysis in sequence to extract the final historical network structure grayscale image and the final current network structure grayscale image;

[0009] S4: extract the topological feature vectors of the final historical network structure grayscale image and the final current network structure grayscale image respectively;

[0010] S5: Calculate the dynamic time warping distance between topological feature vectors, compare the dynamic time warping distance with its threshold, and determine whether the historical network structure is consistent with the current network structure;

[0011] S6: Superimpose and compare the historical network structure and the current network structure to intuitively display the changes in the substation network structure.

[0012] According to the first aspect, in some possible implementations, the historical network structure image and the current network structure image both contain information on substation transformers, users, and lines. The substation transformers, lines, and users are connected in sequence. The lines include three-phase four-wire transmission lines and single-phase lines, which are distinguished by thick and thin lines.

[0013] According to the first aspect, in some possible implementations, the color threshold analysis step is:

[0014] S301: Obtain the brightness value of each pixel of the historical network structure grayscale image and the current network structure grayscale image;

[0015] S302: Performing a logical comparison between the brightness value of each pixel of the historical network structure grayscale image and the current network structure grayscale image and their respective brightness value thresholds. If the pixel brightness value is less than the brightness value threshold, the comparison result is 1, otherwise it is 0;

[0016] S303: Generate a binary matrix of the brightness “0-1” of the grayscale image of the historical network structure and a binary matrix of the brightness “0-1” of the grayscale image of the current network structure respectively according to S302.

[0017] According to the first aspect, in some possible implementations, the texture analysis step is:

[0018] S311: Calculate the local entropy of the binary matrix of the brightness "0-1" of the historical network structure grayscale image and the binary matrix of the brightness "0-1" of the current network structure grayscale image to obtain the entropy value matrix of the historical network structure grayscale image and the entropy value matrix of the current network structure grayscale image;

[0019] S312: linearly normalizing the values of the historical network structure grayscale image entropy matrix and the current network structure grayscale image entropy matrix to the range of [0, 1] respectively, and generating the corresponding historical network structure standard grayscale image and the current network structure standard grayscale image;

[0020] S313: Perform a logical comparison between each pixel value of the historical network structure standard grayscale image and the current network structure standard grayscale image and the pixel threshold. If the pixel value is greater than the pixel threshold, the comparison result is 1, otherwise it is 0;

[0021] S314: Generate a “0-1” binary matrix of the historical network structure standard grayscale image and a “0-1” binary matrix of the current network structure standard grayscale image according to S313.

[0022] According to the first aspect, in some possible implementations, in step S3, a "logical AND" operation is performed on the result of color threshold analysis of the historical network structure grayscale image and the result of texture analysis to extract the final historical network structure grayscale image; a "logical AND" operation is performed on the result of color threshold analysis of the current network structure grayscale image and the result of texture analysis to extract the final historical network structure grayscale image and the final current network structure grayscale image.

[0023] According to the first aspect, in some possible implementations, the topological feature vector includes line area, line density, average line direction angle, and standard deviation of line direction angle.

[0024] According to the first aspect, in some possible implementations, the step of calculating the circuit area includes:

[0025] S401: using the binary image 4-neighborhood connectivity rule;

[0026] S402: Traverse the network structure grayscale image row by row, and perform the following operations on each foreground pixel: if the left neighbor and the upper neighbor of the current pixel are both background pixels, assign a new label to the current pixel; if the left neighbor or the upper neighbor is a foreground pixel, mark the current pixel with the same label as the connected neighbor; if the labels of the left neighbor and the upper neighbor are different, record the equivalence relationship of the labels; wherein the network structure grayscale image is the final historical network structure grayscale image or the final current network structure grayscale image;

[0027] S403: Use a union-find data structure to merge equivalent labels to ensure that the same connected area has a unique label;

[0028] S404: Calculate the number of foreground pixels in each connected region as the area of each connected region;

[0029] S405: Rank the connected regions from largest to smallest, and take the top K connected regions as the line areas, where K is a preset positive integer.

[0030] According to the first aspect, in some possible implementations, the line density is equal to the sum of all pixel values of the network structure grayscale image divided by the number of pixel points of the network structure grayscale image; wherein the network structure grayscale image is the final historical network structure grayscale image or the final current network structure grayscale image.

[0031] According to the first aspect, in some possible implementations, Hough transform is used to calculate the direction angle of a straight line in line grayscale as the line direction angle.

[0032] According to the first aspect, in some possible implementation methods, the historical network structure and the current network structure are superimposed and compared to intuitively display the results of the network structure changes, specifically including: respectively extracting the contour lines of the final historical network structure grayscale image and the final current network structure grayscale image, and superimposing the two contour lines, and then marking the changed parts with differentiated colors to achieve an intuitive display of the network structure changes in the substation area.

[0033] In the second aspect, a system for detecting network structure anomalies in rural areas of a digital low-voltage distribution network is provided, comprising a memory and a processor connected to each other, wherein a computer program or instruction is stored on the memory, and when the processor executes the computer program or instruction, the steps of a method for detecting network structure anomalies in rural areas of a digital low-voltage distribution network as described above are implemented.

[0034] In a third aspect, a computer-readable storage medium is provided, on which a computer program or instruction is stored. When the computer program or instruction is executed by a computer device, the steps of the method for detecting network structure anomalies in rural areas of a digital low-voltage distribution network as described above are implemented.

[0035] The present invention proposes a method, system and medium for detecting abnormal changes in the network structure of a rural substation in a digital low-voltage distribution network. Compared with the existing technology, the method has the following beneficial effects: it includes obtaining the historical network structure and current network structure images of the rural substation in a digital low-voltage distribution network; converting the historical network structure image and the current network structure image into grayscale images; performing color threshold analysis and texture analysis in sequence to extract the final historical network structure grayscale image and the final current network structure grayscale image; extracting the topological feature vectors of the final historical network structure grayscale image and the final current network structure grayscale image respectively; calculating the dynamic time warping distance between the topological feature vectors, comparing the dynamic time warping distance with its threshold, and judging whether the historical network structure and the current network structure are consistent; superimposing and comparing the historical network structure and the current network structure to intuitively display the results of the network structure change. The method has the advantages of simple detection method, easy programming implementation, no need for manual intervention, time-saving and labor-saving, and high accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0037] Figure 1 This is a flow chart of a method for detecting abnormal changes in the network structure of a rural area in a digital low-voltage distribution network provided by an embodiment of the present invention;

[0038] Figure 2 This is a historical network structure image of a rural area of a distribution network provided by an embodiment of the present invention.

[0039] Figure 3 This is an image of the current network structure of a rural area of a distribution network provided by an embodiment of the present invention.

[0040] Figure 4 A grayscale image of the historical network structure diagram of the rural substation area of the distribution network provided in an embodiment of the present invention.

[0041] Figure 5 A grayscale image of the current network structure diagram of the rural area of the distribution network provided by an embodiment of the present invention.

[0042] Figure 6 The historical network structure grayscale image provided by the embodiment of the present invention is a binary image with brightness "0-1".

[0043] Figure 7 The current network structure grayscale image brightness "0-1" binary image provided by the embodiment of the present invention.

[0044] Figure 8 This is the final grayscale image of the historical network structure provided by the embodiment of the present invention.

[0045] Figure 9 This is the final grayscale image of the current network structure provided by the embodiment of the present invention.

[0046] Figure 10 This is a diagram of the network structure anomaly of a rural substation in a digital low-voltage distribution network provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0047] To make the objectives, technical solutions, and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail below. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other implementations obtained by those of ordinary skill in the art without inventive effort are within the scope of protection of the present invention.

[0048] like Figure 1 As shown, an embodiment of the present invention provides a method for detecting network structure anomalies in rural areas of a digital low-voltage distribution network, comprising the following steps:

[0049] S1: Obtain historical and current network structure images of rural substations in the digital low-voltage distribution network.

[0050] In this embodiment, the historical network structure image and the current network structure image both contain information about substation transformers, users, and lines. The substation transformers, lines, and users are connected in sequence. The lines include three-phase four-wire transmission lines and single-phase lines, which are distinguished by lines of different colors and thicknesses. Figure 2 The figure shows the historical network structure image of the rural area of the distribution network in this embodiment. In this embodiment, due to the line structure adjustment, the current network structure image of the rural area of the digital low-voltage distribution network is as follows: Figure 3 As shown in the figure, a three-phase four-wire user and a single-phase user are added inside the dotted box.

[0051] S2: Convert the historical network structure image and the current network structure image into grayscale images. By converting the two images into grayscale, it is convenient for subsequent analysis and processing, and the amount of analysis data is reduced. Figure 2 and Figure 3 After being converted into grayscale images, Figure 4 and Figure 5 shown.

[0052] S3: Perform color threshold analysis and texture analysis in sequence to extract the final historical network structure grayscale image and the final current network structure grayscale image.

[0053] right Figure 4 and Figure 5 The steps for color threshold analysis of the grayscale image shown are:

[0054] S301: Obtain the brightness value of each pixel of the historical network structure grayscale image and the current network structure grayscale image;

[0055] S302: Performing a logical comparison between the brightness value of each pixel of the historical network structure grayscale image and the current network structure grayscale image and their respective brightness value thresholds. If the pixel brightness value is less than the brightness value threshold, the comparison result is 1, otherwise it is 0;

[0056] S303: Generate a binary matrix of the brightness “0-1” of the grayscale image of the historical network structure and a binary matrix of the brightness “0-1” of the grayscale image of the current network structure respectively according to S302.

[0057] In this embodiment, the brightness value threshold is equal to the product of the average value of the network structure grayscale image (historical network structure grayscale image or current network structure grayscale image) and the coefficient 0.6 (in other embodiments, 0.5 or 0.7 can also be selected according to actual conditions). Figure 4 and Figure 5 The brightness thresholds are 151.1026 and 151.0464 respectively. The generated historical network structure grayscale image brightness "0-1" binary image and the current network structure grayscale image brightness "0-1" binary image are as follows: Figure 6 、 Figure 7 shown.

[0058] right Figure 6 and Figure 7 Continue to carry out texture analysis to enhance line extraction. The specific steps are as follows:

[0059] S311: Calculate the historical network structure grayscale image and the current network structure grayscale image after color threshold analysis (i.e. Figure 6 and Figure 7 ) local entropy, and obtain the grayscale image entropy matrix of the historical network structure and the grayscale image entropy matrix of the current network structure;

[0060] S312: linearly normalizing the values of the historical network structure grayscale image entropy matrix and the current network structure grayscale image entropy matrix to the range of [0, 1] respectively, and generating the corresponding historical network structure standard grayscale image and the current network structure standard grayscale image;

[0061] S313: Perform a logical comparison between each pixel value of the historical network structure standard grayscale image and the current network structure standard grayscale image and a pixel threshold (0.4 in this embodiment, and other embodiments can be selected according to actual needs, such as 0.35 or 0.45). If the pixel value is greater than the pixel threshold, the comparison result is 1, otherwise it is 0;

[0062] S314: Generate a “0-1” binary matrix of the historical network structure standard grayscale image and a “0-1” binary matrix of the current network structure standard grayscale image according to S313.

[0063] Perform a "logical AND" operation on the brightness "0-1" binary matrix of the historical network structure grayscale image and the "0-1" binary matrix of the historical network structure standard grayscale image to extract the final historical network structure grayscale image, such as Figure 8 The current network structure grayscale image brightness "0-1" binary matrix and the current network structure standard grayscale image "0-1" binary matrix are subjected to "logical AND" operation to extract the final current network structure grayscale image, as shown in Figure 9 shown.

[0064] S4: Extract the topological feature vectors of the final historical network structure grayscale image and the final current network structure grayscale image respectively.

[0065] In order to achieve Figure 8 The final grayscale image of the historical network structure and Figure 9 Finally, the comparison of the current network structure grayscale image requires extracting the topological feature vector from the image. In this embodiment, the topological feature vector includes line area, line density, average line direction angle, and standard deviation of line direction angle.

[0066] The steps of calculating the line area include:

[0067] S401: using the binary image 4-neighborhood connectivity rule;

[0068] S402: Traverse the network structure grayscale image row by row, and perform the following operations on each foreground pixel: if the left neighbor and the upper neighbor of the current pixel are both background pixels, assign a new label to the current pixel; if the left neighbor or the upper neighbor is a foreground pixel, mark the current pixel with the same label as the connected neighbor; if the labels of the left neighbor and the upper neighbor are different, record the equivalence relationship of the labels; wherein the network structure grayscale image is the final historical network structure grayscale image or the final current network structure grayscale image;

[0069] S403: Use a union-find data structure to merge equivalent labels to ensure that the same connected area has a unique label;

[0070] S404: Calculate the number of foreground pixels in each connected region as the area of each connected region;

[0071] S405: Rank the connected regions from largest to smallest, and take the top K connected regions as the line areas, where K is a preset positive integer. In this embodiment, K is 5. In other embodiments, the value of K can be set according to actual needs, such as 4 or 6.

[0072] In this embodiment, Figure 8 The final line area calculation result of the grayscale image of the historical network structure is [90450,912, 398, 116, 41]. Figure 9 The final calculation result of the line area of the current network structure grayscale image is [92943,912, 419, 398, 116]. Figure 8 and Figure 9 The range of network topology changes is not large, so the line area changes are relatively small.

[0073] Among them, the line density is equal to the sum of all pixel values of the network structure grayscale image divided by the number of pixel points of the network structure grayscale image; and the network structure grayscale image is the final historical network structure grayscale image or the final current network structure grayscale image. Figure 8 The line density of the final historical network structure grayscale image is 0.0155. Figure 9 Finally, the line density of the current network structure grayscale image is 0.0160. Figure 9 Due to the increased line length, the line density is higher Figure 8 There has been an increase.

[0074] In this embodiment, Hough transform is used to calculate the direction angle of a straight line in the line grayscale as the line direction angle.

[0075] According to the above eigenvector calculation method, Figure 8 The final grayscale image of the historical network structure and Figure 9 Finally, the topological feature vectors of the grayscale image of the current network structure are [90450, 912, 398, 116, 41, 0.0155, -0.5, 52.11] and [92943, 912, 419, 398, 116, 0.0160, -0.5, 52.11] respectively.

[0076] S5: Calculate the dynamic time warping distance between the topological feature vectors, compare the dynamic time warping distance with its threshold, and determine whether the historical network structure is consistent with the current network structure.

[0077] Calculate the dynamic time warping distance between the above topological feature vectors, compare the dynamic time warping distance with its threshold, and determine whether the historical network structure and the current network structure are consistent. If they are consistent, it means that the substation network structure has not changed. If they are inconsistent, it means that the substation network structure has changed. In actual environments, although the network topology structure of rural substations in low-voltage distribution networks changes frequently, each change is relatively small compared to the overall topology. Therefore, in this embodiment, the threshold value of the dynamic time warping distance is 0.004 times the length of the topological feature vector data (empirical value, other values such as 0.005 can also be selected as needed in other embodiments), the length (dimension) of the topological feature vector is 8, and the threshold value of the dynamic time warping distance is 0.032. Calculate based on this Figure 8 The final grayscale image of the historical network structure and Figure 9 Finally, the distance between the topological feature vectors of the current network structure grayscale image is 1.4627. Since 1.4627 is greater than the set threshold of 0.032, it is determined that the network structure of the rural substation of the digital low-voltage distribution network has changed.

[0078] S6: If the historical network structure is inconsistent with the current network structure, it means that the network structure of the substation has changed. The historical network structure and the current network structure are superimposed and compared to intuitively display the results of the network structure change in the substation.

[0079] In order to intuitively display the change results, in this embodiment, by extracting Figure 8 The final grayscale image of the historical network structure and Figure 9 Finally, the contour line of the grayscale image of the current network structure is obtained, and the two contour lines are superimposed, and the abnormal parts are marked with different colors, such as Figure 10 As shown, the network structure changes of the rural substations in the digital low-voltage distribution network are intuitively displayed to the staff.

[0080] In summary, the embodiment of the present invention provides a method for detecting abnormal changes in the network structure of a rural substation in a digital low-voltage distribution network, which obtains the historical network structure and current network structure images of the rural substation in the digital low-voltage distribution network; converts the historical network structure image and the current network structure image into grayscale images; performs color threshold analysis and texture analysis in sequence to extract the final historical network structure grayscale image and the final current network structure grayscale image; extracts the topological feature vectors of the final historical network structure grayscale image and the final current network structure grayscale image respectively; calculates the dynamic time warping distance between the topological feature vectors, compares the dynamic time warping distance with the threshold, and determines whether the historical network structure and the current network structure are consistent; superimposes and compares the historical network structure and the current network structure to intuitively display the results of the network structure change, which has the advantages of simple detection method, easy programming implementation, no need for manual intervention, time-saving and labor-saving, and high accuracy.

[0081] In addition, an embodiment of the present invention also provides a digital low-voltage distribution network rural substation network structure anomaly detection system, including an interconnected memory and a processor, the memory storing a computer program or instructions, and the processor implementing the steps of a digital low-voltage distribution network rural substation network structure anomaly detection method as described above when executing the computer program or instructions.

[0082] In addition, an embodiment of the present invention also provides a computer-readable storage medium on which a computer program or instruction is stored. When the computer program or instruction is executed by a computer device, the steps of the method for detecting network structure anomalies in rural areas of a digital low-voltage distribution network as described above are implemented.

[0083] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0084] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0085] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0086] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0087] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0088] Although the embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and are not to be construed as limitations on the present invention. A person skilled in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A method for detecting abnormal changes in the network structure of a digital low-voltage distribution network in rural areas, characterized in that: The steps include: S1: Obtain historical and current network structure images of rural substations in the digital low-voltage distribution network; S2: Convert the historical network structure image and the current network structure image into grayscale images; S3: Perform color threshold analysis and texture analysis in sequence to extract the final historical network structure grayscale image and the final current network structure grayscale image; S4: extract the topological feature vectors of the final historical network structure grayscale image and the final current network structure grayscale image respectively; S5: Calculate the dynamic time warping distance between topological feature vectors, compare the dynamic time warping distance with its threshold, and determine whether the historical network structure is consistent with the current network structure; S6: Superimpose and compare the historical network structure and the current network structure to intuitively display the changes in the substation network structure.

2. The method for detecting abnormal changes in network structure of a digital low-voltage distribution network in rural areas according to claim 1 is characterized in that: The historical network structure image and the current network structure image both contain information about transformers, users, and lines in the substation area. The transformers, lines, and users are connected in sequence. The lines include three-phase four-wire transmission lines and single-phase lines, which are distinguished by lines of different thicknesses.

3. The method for detecting abnormal changes in network structure of a digital low-voltage distribution network in rural areas according to claim 1 is characterized in that: The steps of the color threshold analysis are: S301: Obtain the brightness value of each pixel of the historical network structure grayscale image and the current network structure grayscale image; S302: Performing a logical comparison between the brightness value of each pixel of the historical network structure grayscale image and the current network structure grayscale image and their respective brightness value thresholds. If the pixel brightness value is less than the brightness value threshold, the comparison result is 1, otherwise it is 0; S303: Generate a binary matrix of the brightness "0-1" of the grayscale image of the historical network structure and a binary matrix of the brightness "0-1" of the grayscale image of the current network structure respectively according to S302.

4. The method for detecting abnormal changes in network structure of a digital low-voltage distribution network in rural areas according to claim 3 is characterized in that: The steps of texture analysis are: S311: Calculate the local entropy of the binary matrix of the brightness "0-1" of the historical network structure grayscale image and the binary matrix of the brightness "0-1" of the current network structure grayscale image to obtain the entropy value matrix of the historical network structure grayscale image and the entropy value matrix of the current network structure grayscale image; S312: linearly normalizing the values of the historical network structure grayscale image entropy matrix and the current network structure grayscale image entropy matrix to the range of [0, 1] respectively, and generating the corresponding historical network structure standard grayscale image and the current network structure standard grayscale image; S313: Perform a logical comparison between each pixel value of the historical network structure standard grayscale image and the current network structure standard grayscale image and the pixel threshold. If the pixel value is greater than the pixel threshold, the comparison result is 1, otherwise it is 0; S314: Generate a “0-1” binary matrix of the historical network structure standard grayscale image and a “0-1” binary matrix of the current network structure standard grayscale image according to S313.

5. The method for detecting abnormal changes in network structure of a digital low-voltage distribution network in rural areas according to claim 1 is characterized in that: In step S3, the results of the historical network structure grayscale image obtained by color threshold analysis and the results obtained by texture analysis are subjected to a "logical AND" operation to extract the final historical network structure grayscale image; the results of the current network structure grayscale image obtained by color threshold analysis and the results obtained by texture analysis are subjected to a "logical AND" operation to extract the final historical network structure grayscale image and the final current network structure grayscale image.

6. The method for detecting abnormal changes in network structure of a digital low-voltage distribution network in rural areas according to claim 1 is characterized in that: The topological feature vector includes line area, line density, line direction angle average value, and line direction angle standard deviation.

7. The method for detecting abnormal changes in network structure of a digital low-voltage distribution network in rural areas according to claim 6 is characterized in that: The step of calculating the line area includes: S401: using the binary image 4-neighborhood connectivity rule; S402: Traverse the network structure grayscale image row by row, and perform the following operations on each foreground pixel: if the left neighbor and the upper neighbor of the current pixel are both background pixels, assign a new label to the current pixel; if the left neighbor or the upper neighbor is a foreground pixel, mark the current pixel with the same label as the connected neighbor; if the labels of the left neighbor and the upper neighbor are different, record the equivalence relationship of the labels; wherein the network structure grayscale image is the final historical network structure grayscale image or the final current network structure grayscale image; S403: Use a union-find data structure to merge equivalent labels to ensure that the same connected area has a unique label; S404: Calculate the number of foreground pixels in each connected region as the area of each connected region; S405: Rank the connected regions from largest to smallest, and take the top K connected regions as the line areas, where K is a preset positive integer.

8. The method for detecting abnormal changes in network structure of a digital low-voltage distribution network in rural areas according to claim 6 is characterized in that: The line density is equal to the sum of all pixel values of the network structure grayscale image divided by the number of pixel points of the network structure grayscale image; wherein the network structure grayscale image is the final historical network structure grayscale image or the final current network structure grayscale image.

9. The method for detecting abnormal changes in network structure of a digital low-voltage distribution network in rural areas according to claim 6, characterized in that: Hough transform is used to calculate the direction angle of the straight line in the line grayscale as the line direction angle.

10. The method for detecting abnormal changes in network structure of a digital low-voltage distribution network in rural areas according to claim 1, characterized in that: The historical network structure and the current network structure are superimposed and compared to intuitively display the results of the network structure changes, specifically including: extracting the contour lines of the final historical network structure grayscale image and the final current network structure grayscale image respectively, and superimposing the two contour lines, and then marking the changed parts with differentiated colors to achieve an intuitive display of the network structure changes in the substation area.

11. A digital low-voltage distribution network rural area network structure abnormality detection system, comprising a memory and a processor connected to each other, wherein the memory stores a computer program or instruction, characterized in that: When the processor executes the computer program or instruction, the processor implements the steps of a method for detecting abnormal changes in the network structure of a rural substation in a digital low-voltage distribution network as described in any one of claims 1 to 10.

12. A computer-readable storage medium having a computer program or instruction stored thereon, characterized in that: When the computer program or instruction is executed by a computer device, the steps of a method for detecting abnormal changes in the network structure of a rural substation in a digital low-voltage distribution network as described in any one of claims 1 to 10 are implemented.

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