Power grid spatial information analysis system based on sub-meter image and vector fusion

Abstract

The present invention relates to the technical field of power grid monitoring and analysis, and is a power grid spatial information analysis system based on the fusion of sub-meter images and vectors, specifically comprising: a line node operation data acquisition module, a line channel loss measurement module, a sub-meter image acquisition module, a surface state assessment module, a satellite image assessment module, a vector map marking module and an analysis result visualization module; the present invention solves the problem in the prior art that the real-time high-precision analysis of the power grid through sub-meter images leads to poor timeliness of the power grid analysis results due to the huge real-time data processing volume, and at the same time, the real-time acquisition of sub-meter images consumes huge manpower, material and financial resources.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid monitoring and analysis, and is a power grid spatial information analysis system based on sub-meter-level image and vector fusion. Background Art

[0002] Power grid spatial information analysis refers to enhancing the real-time response capability of the power grid by optimizing planning and improving operational efficiency. This analysis can ensure the stable operation and environmental sustainability of the power grid. Sub-meter images refer to images with a resolution of 1 meter or higher. This type of image can provide very detailed surface information and has higher clarity and details than ordinary satellite images. And precisely because sub-meter images have higher clarity and details, if you want to achieve all-weather uninterrupted sub-meter image capture of the wide-area power grid status, not only will the huge amount of real-time data collection increase the load on the data processor, but it will also lead to poor timeliness of the final power grid analysis results. At the same time, the high acquisition cost of sub-meter images will also consume huge manpower, material and financial resources during the long data collection process.

[0003] In the existing disclosed invention technology, for example, the patent with application publication number CN114036453A discloses a method for calculating the stress of the overhead line ground wire using point cloud data, specifically involving a method for calculating the stress of the overhead line ground wire using point cloud data, and the specific steps for implementing the method include: step S1: converting the point cloud data of the overhead line ground wire into two-dimensional coordinate data; step S2: performing quadratic function fitting on the two-dimensional coordinate data of the overhead line ground wire; step S3: calculating the horizontal stress at the lowest point of the sag of the overhead line ground wire; step S4: calculating various stress states of the overhead line ground wire.

[0004] The point cloud data in the above patent will contain a large amount of spatial data. Converting it into two-dimensional coordinates and performing fitting and calculation will require high-performance computing resources and a long processing time, which will result in poor timeliness of the power grid analysis results. Summary of the invention

[0005] The technical problem to be solved by the present invention is that in the prior art, real-time and high-precision analysis of the power grid is performed through sub-meter-level images, but the timeliness of the power grid analysis results is poor due to the huge amount of real-time data processing. At the same time, real-time collection of sub-meter-level images consumes huge manpower, material and financial resources. A power grid spatial information analysis system based on sub-meter-level images and vector fusion is proposed.

[0006] In order to achieve the above objectives, the power grid spatial information analysis system based on sub-meter image and vector fusion of the present invention includes the following modules:

[0007] Line node operation data acquisition module, line channel loss calculation module, sub-meter image acquisition module, surface status assessment module, satellite image assessment module, vector map marking module and analysis result visualization module;

[0008] The line node operation data acquisition module is used to collect and record real-time operation status data of overhead transmission lines; the line channel loss measurement module is used to calculate and evaluate the channel loss rate of overhead transmission lines; the sub-meter image acquisition module is used to obtain high-resolution images with sub-meter accuracy; the surface state assessment module is used to assess the surface state and obtain the surface evolution risk value; the satellite image assessment module is used to analyze and process environmental water images acquired from satellites; the vector map marking module is used to divide the buffer area into vector maps; the analysis result visualization module is used to visualize the analysis data on the power grid information display platform.

[0009] Specifically, the line node operation data acquisition module includes: a support tower counting unit, a current sensor unit, a voltage sensor unit and a mechanical sensor unit;

[0010] Among them, the supporting tower counting unit is used to record the number N of supporting towers in each overhead transmission bus line, and the overhead transmission line includes: overhead transmission sub-lines from power plants to substations and overhead transmission sub-lines from substations to distribution stations; the current sensor unit is used to collect input current data and output current data of the overhead transmission sub-lines above the supporting towers; the voltage sensor unit is used to collect input voltage data and output voltage data of the overhead transmission sub-lines above the supporting towers; the mechanical sensor unit is used to collect input side conductor stress data and output side conductor stress data of the overhead transmission sub-lines above the supporting towers.

[0011] Specifically, the line channel loss calculation module is used to implement the following strategies:

[0012] S11: extracting input current data, output current data, input voltage data, and output voltage data collected by the current sensor unit and the voltage sensor unit on each supporting tower, and importing the input current data, output current data, input voltage data, and output voltage data into a line power loss rate calculation strategy to calculate and obtain the line power loss rate. The line power loss rate calculation strategy is specifically as follows:

[0013] ;

[0014] in, It represents the line power loss rate generated when the transmission line passes through the nth supporting tower in the overhead transmission bus line;

[0015] t is a subscript, indicating the tth data collection unit period; T is the total number of data collection unit periods;

[0016] The input current data and input voltage data when the transmission line passes through the nth supporting tower;

[0017] The output current data and output voltage data of the transmission line when it passes through the nth supporting tower;

[0018] They are respectively the target current fluctuation range and the target voltage fluctuation range when the transmission line passes through the supporting tower;

[0019] S12: extracting input side conductor stress data and output side conductor stress data collected by the mechanical sensor unit on each supporting tower, importing the input side conductor stress data and the output side conductor stress data into the line stress loss rate calculation strategy, and calculating and obtaining the line stress loss rate. The line stress loss rate calculation strategy is specifically as follows:

[0020] ;

[0021] in, It represents the line stress loss rate generated when the transmission line passes through the nth supporting tower in the overhead transmission bus line;

[0022] Respectively represent the input side conductor stress data in the tth and t-1th data collection unit periods;

[0023] They represent the stress data of the conductor at the output side of the segment at the t-th and t-1-th data collection units respectively;

[0024] are the target fluctuation ranges of stress on the input side and output side of the supporting tower respectively;

[0025] S13: Based on S11-S12, evaluate the line channel loss rate when the overhead transmission line passes through each supporting tower. The specific evaluation strategy is as follows:

[0026] ;

[0027] in, is the line channel loss rate when the overhead transmission line passes through the nth supporting tower.

[0028] Specifically, the line channel loss calculation module is also used to execute the following strategy:

[0029] S14: Extract the channel loss rate of the overhead transmission line passing through the nth supporting tower , preset line channel loss threshold , and make a judgment on the line loss tolerance;

[0030] S15: When When the channel loss rate of the overhead transmission line at the n-1th supporting tower upstream of the nth supporting tower is returned, , to judge the line loss tolerance;

[0031] S16: If , then the nth supporting tower is determined to be the starting point of the abnormal section of the line tower, and step S17 is continued;

[0032] like , then continue to return to retrieve the line channel loss rate of the n-2th supporting tower , continue to perform line loss tolerance judgment until the line channel loss rate of the nu-th supporting tower in this overhead transmission bus line is When , mark the nuth supporting tower as the starting point of the abnormal section of the line tower, and stop the cycle;

[0033] Wherein, u represents the first difference parameter, and u is a positive integer less than n;

[0034] S17: Synchronously retrieve the line channel loss rate of the overhead transmission line at the n+1th supporting tower downstream of the nth supporting tower , to judge the line loss tolerance;

[0035] S18: If , then the nth supporting tower is judged to be the end point of the abnormal section of the line tower;

[0036] like , then continue to return to retrieve the line channel loss rate of the n+2th supporting tower , continue to perform line loss tolerance judgment until the line channel loss rate of the n+vth supporting tower in this overhead transmission bus line is When , mark the n+vth supporting tower as the end point of the abnormal section of the line tower, and stop the cycle;

[0037] Where v represents the second difference parameter, v is a positive integer and n+v <N;

[0038] S19: Confirm the area scope of the abnormal line tower section according to the starting point and the end point of the abnormal line tower section.

[0039] Specifically, the sub-meter image acquisition module includes:

[0040] Sub-meter image acquisition unit, ground control point selection unit, geometric correction unit, denoising unit and image fusion and splicing unit;

[0041] The sub-meter image acquisition unit acquires sub-meter images through high-resolution satellites; the ground control point selection unit is used to select the four corner vertices of the supporting tower and five points in the center of the tower as ground control points; the geometric correction unit performs geometric correction on the sub-meter images through a block alignment algorithm; the denoising unit simultaneously performs geometric correction on the sub-meter images through wavelet transform; the image fusion and stitching unit fuses the sub-meter images with the lidar data through a band fusion algorithm, and stitches multiple image blocks.

[0042] Specifically, the surface state assessment module is used to run the following assessment strategy:

[0043] S21: In the abnormal section of the line tower, capture the sub-meter level images of the ground where each supporting tower is located, perform local contrast enhancement processing, and use the edge detection algorithm to identify the edges of surface cracks on the processed sub-meter level images;

[0044] S22: extract the number of pixels corresponding to the crack length and the number of pixels corresponding to the crack width in the sub-meter image for each surface crack area in different monitoring periods, and calculate the surface crack evolution coefficient. The calculation strategy is:

[0045] ;

[0046] in, is a subscript, indicating the Surface crack areas, is the total number of surface fracture areas in sub-meter images;

[0047] Respectively represent , First The number of pixels corresponding to the crack length in the surface crack area;

[0048] Respectively represent , First The number of pixels corresponding to the crack width in the surface crack area;

[0049] S23: Eliminate the surface crack area in the sub-meter image, and mark the area that is not eliminated in the sub-meter image as the surface background area, extract the pixel value of each pixel point in each surface background area, and calculate the surface soil quality transformation coefficient according to the pixel value of each pixel point. The calculation strategy is:

[0050] ;

[0051] in, is a subscript, indicating the The surface background area, is the total number of surface background areas in sub-meter images;

[0052] g is a subscript, indicating the The g-th pixel point in the ground background area, G is the g-th pixel point in the ground background area, The total number of pixels in the surface background area;

[0053] is the pixel value of the g-th pixel; To be closest The pixel value of

[0054] For the The maximum and minimum pixel values ​​of the surface background area.

[0055] Specifically, the surface state assessment module is also used to run the following assessment strategy:

[0056] S24: In the synchronous extraction step S21, the sub-meter level images of the ground where each supporting tower is located are captured, and the sub-meter level images at different angles are extracted to form a stereo image pair, and a ground elevation spatial model is generated by stereo matching technology, wherein the ground elevation spatial model includes: relative height data of 20 data collection points evenly distributed on each data collection circle;

[0057] S25: Calculate the surface concavity coefficient b according to the ground elevation spatial model. The specific calculation strategy of the surface concavity coefficient is:

[0058] ;

[0059] Wherein, z is a subscript, indicating the zth data collection circle, and Z is the total number of data collection circles; y is a subscript, indicating the yth data collection point on the zth data collection circle;

[0060] Indicates the relative height data collected at the yth data collection point on the zth data collection circle;

[0061] They are the baseline height data of the four corner vertices of the supporting tower;

[0062] S26: According to S21-S25, the surface conditions of each supporting tower in the abnormal section of the line tower are evaluated to obtain the ground surface morphological risk value We. The calculation strategy of the ground surface morphological risk value is:

[0063] ;

[0064] in, They are the risk contribution factors of surface crack evolution coefficient and surface soil transformation coefficient respectively.

[0065] Specifically, the satellite image assessment module is used to run the following assessment strategy:

[0066] S31: Acquire environmental water images within 50 kilometers of each supporting tower in the abnormal section of the line tower through synthetic aperture radar satellites, and divide the environmental water images into water area and non-water area through support vector machine;

[0067] S32: Calculate the water area of ​​each water area: , ,in, They respectively represent the total number of pixels in the k-th water body area and the actual ground area corresponding to each pixel in the environmental water body image;

[0068] S33: According to the water area of ​​each water area , calculate the average water area within 50 km of the supporting tower ;

[0069] S34: Calculate and obtain the environmental state switching factor , ,in, are the mean water area in the tth and t-1th data collection unit periods, respectively.

[0070] Specifically, the vector map marking module includes: a buffer level determination unit, a vector map division unit and a vector map grading color marking unit;

[0071] Among them, the buffer level judgment unit is used to judge the buffer level of the surface around each supporting tower in the abnormal section of the line tower; the vector map division unit is used to divide the vector map into major buffer areas and general buffer areas; the vector map graded color marking unit is used to color the buffer areas of different buffer levels.

[0072] Specifically, the buffer level determination unit is used to execute the following determination strategy:

[0073] S41: The ground is represented by the risk value We and the environmental state switching factor Import the buffer level judgment strategy, specifically:

[0074] ;

[0075] Among them, hc is the buffer value;

[0076] S42: Presetting buffer level division thresholds respectively ,

[0077] when When the support tower is located, it is judged that the area where the support tower is located is a slight buffer area;

[0078] when When the supporting tower is located, it is judged that the area where the supporting tower is located is a general buffer area;

[0079] when When the supporting tower is located, it is judged that the area where the supporting tower is located is a major buffer area.

[0080] A storage medium stores instructions, and when a computer reads the instructions, the computer runs the power grid spatial information analysis system based on sub-meter image and vector fusion.

[0081] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor runs the computer program, the above-mentioned power grid spatial information analysis system based on sub-meter image and vector fusion is implemented.

[0082] Compared with the prior art, the technical effects of the present invention are as follows:

[0083] 1. The line node operation data acquisition module in the present invention accurately collects real-time data through supporting tower counting, current sensors, voltage sensors and mechanical sensors. It alleviates the huge data volume problem caused by sub-meter-level images; through real-time monitoring and detailed data acquisition, it prioritizes the screening of abnormal line tower sections in each overhead power transmission bus line, and conducts targeted sub-meter-level image acquisition of supporting towers in abnormal line tower sections, thereby improving the system's response speed and data processing efficiency.

[0084] 2. The sub-meter image acquisition module of the present invention obtains sub-meter images through high-resolution satellites, and combines ground control points and geometric correction to provide an accurate view of the power grid, optimize the data processing process, and improve the accuracy of subsequent assessment of the earth's surface risk value.

[0085] 3. The surface state assessment module in the present invention evaluates the risk of geological changes by calculating surface cracks and soil transformation coefficients, which helps to predict and prevent geological changes that may affect the stability of the power grid and improve the reliability and safety of power grid operation.

[0086] 4. The vector map marking module of the present invention performs vector map division and color marking on the buffer area. By grading and color marking the buffer area, the buffer areas of different risk levels can be clearly displayed, helping power grid analysts to quickly identify and respond to potential power grid problems, thereby improving data availability and decision-making accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. 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 creative labor. Among them:

[0088] Figure 1 It is a structural schematic diagram of the power grid spatial information analysis system based on sub-meter image and vector fusion of the present invention;

[0089] Figure 2 It is a structural schematic diagram of the vector map marking module of the present invention. DETAILED DESCRIPTION

[0090] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0091] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0092] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0093] Embodiment 1:

[0094] like Figure 1 , 2 As shown, the power grid spatial information analysis system based on sub-meter image and vector fusion according to the embodiment of the present invention is as follows: Figure 1 As shown, it includes the following modules:

[0095] Line node operation data acquisition module, line channel loss calculation module, sub-meter image acquisition module, surface status assessment module, satellite image assessment module, vector map marking module and analysis result visualization module;

[0096] The line node operation data acquisition module is used to collect and record real-time operation status data of the overhead transmission line;

[0097] The line node operation data acquisition module includes: a support tower counting unit, a current sensor unit, a voltage sensor unit and a mechanical sensor unit;

[0098] Among them, the supporting tower counting unit is used to record the number N of supporting towers in each overhead transmission bus line, and the overhead transmission line includes: overhead transmission sub-lines from power plants to substations and overhead transmission sub-lines from substations to distribution stations; the current sensor unit is used to collect input current data and output current data of the overhead transmission sub-lines above the supporting towers; the voltage sensor unit is used to collect input voltage data and output voltage data of the overhead transmission sub-lines above the supporting towers; the mechanical sensor unit is used to collect input side conductor stress data and output side conductor stress data of the overhead transmission sub-lines above the supporting towers.

[0099] The line channel loss calculation module is used to calculate and evaluate the channel loss rate of the overhead transmission line;

[0100] The line channel loss calculation module is used to implement the following strategies:

[0101] S11: extracting input current data, output current data, input voltage data, and output voltage data collected by the current sensor unit and the voltage sensor unit on each supporting tower, and importing the input current data, output current data, input voltage data, and output voltage data into a line power loss rate calculation strategy to calculate and obtain the line power loss rate. The line power loss rate calculation strategy is specifically as follows:

[0102] ;

[0103] in, It represents the line power loss rate generated when the transmission line passes through the nth supporting tower in the overhead transmission bus line;

[0104] t is a subscript, indicating the tth data collection unit period; T is the total number of data collection unit periods;

[0105] The input current data and input voltage data when the transmission line passes through the nth supporting tower;

[0106] The output current data and output voltage data of the transmission line when it passes through the nth supporting tower;

[0107] They are respectively the target current fluctuation range and the target voltage fluctuation range when the transmission line passes through the supporting tower;

[0108] S12: extracting input side conductor stress data and output side conductor stress data collected by the mechanical sensor unit on each supporting tower, importing the input side conductor stress data and the output side conductor stress data into the line stress loss rate calculation strategy, and calculating and obtaining the line stress loss rate. The line stress loss rate calculation strategy is specifically as follows:

[0109] ;

[0110] in, It represents the line stress loss rate generated when the transmission line passes through the nth supporting tower in the overhead transmission bus line;

[0111] Respectively represent the input side conductor stress data in the tth and t-1th data collection unit periods;

[0112] They represent the stress data of the conductor at the output side of the segment at the t-th and t-1-th data collection units respectively;

[0113] are the target fluctuation ranges of stress on the input side and output side of the supporting tower respectively;

[0114] S13: Based on S11-S12, evaluate the line channel loss rate when the overhead transmission line passes through each supporting tower. The specific evaluation strategy is as follows:

[0115] ;

[0116] in, is the line channel loss rate when the overhead transmission line passes through the nth supporting tower.

[0117] The line channel loss calculation module is also used to execute the following strategy:

[0118] S14: Extract the channel loss rate of the overhead transmission line passing through the nth supporting tower , preset line channel loss threshold , and make a judgment on the line loss tolerance;

[0119] S15: When When the channel loss rate of the overhead transmission line at the n-1th supporting tower upstream of the nth supporting tower is returned, , to judge the line loss tolerance;

[0120] S16: If , then the nth supporting tower is determined to be the starting point of the abnormal section of the line tower, and step S17 is continued;

[0121] like , then continue to return to retrieve the line channel loss rate of the n-2th supporting tower , continue to perform line loss tolerance judgment until the line channel loss rate of the nu-th supporting tower in this overhead transmission bus line is When , mark the nuth supporting tower as the starting point of the abnormal section of the line tower, and stop the cycle;

[0122] Wherein, u represents the first difference parameter, and u is a positive integer less than n;

[0123] S17: Synchronously retrieve the line channel loss rate of the overhead transmission line at the n+1th supporting tower downstream of the nth supporting tower , to judge the line loss tolerance;

[0124] S18: If , then the nth supporting tower is judged to be the end point of the abnormal section of the line tower;

[0125] like , then continue to return to retrieve the line channel loss rate of the n+2th supporting tower , continue to perform line loss tolerance judgment until the line channel loss rate of the n+vth supporting tower in this overhead transmission bus line is When , mark the n+vth supporting tower as the end point of the abnormal section of the line tower, and stop the cycle;

[0126] Where v represents the second difference parameter, v is a positive integer and n+v <N;

[0127] S19: Confirm the area scope of the abnormal line tower section according to the starting point and the end point of the abnormal line tower section.

[0128] The sub-meter image acquisition module is used to obtain high-resolution images with sub-meter accuracy;

[0129] The sub-meter image acquisition module includes:

[0130] Sub-meter image acquisition unit, ground control point selection unit, geometric correction unit, denoising unit and image fusion and splicing unit;

[0131] The sub-meter image acquisition unit acquires sub-meter images through high-resolution satellites; the ground control point selection unit is used to select the four corner vertices of the supporting tower and five points in the center of the tower as ground control points; the geometric correction unit performs geometric correction on the sub-meter images through a block alignment algorithm; the denoising unit simultaneously performs geometric correction on the sub-meter images through wavelet transform; the image fusion and stitching unit fuses the sub-meter images with the lidar data through a band fusion algorithm, and stitches multiple image blocks.

[0132] The surface state assessment module is used to assess the surface state and obtain the surface state risk value;

[0133] The surface state assessment module is used to run the following assessment strategy:

[0134] S21: In the abnormal section of the line tower, capture the sub-meter level images of the ground where each supporting tower is located, perform local contrast enhancement processing, and use the edge detection algorithm to identify the edges of surface cracks on the processed sub-meter level images;

[0135] S22: extract the number of pixels corresponding to the crack length and the number of pixels corresponding to the crack width in the sub-meter image for each surface crack area in different monitoring periods, and calculate the surface crack evolution coefficient. The calculation strategy is:

[0136] ;

[0137] in, is a subscript, indicating the Surface crack areas, is the total number of surface fracture areas in sub-meter images;

[0138] Respectively represent , First The number of pixels corresponding to the crack length in the surface crack area;

[0139] Respectively represent , First The number of pixels corresponding to the crack width in the surface crack area;

[0140] S23: Eliminate the surface crack area in the sub-meter image, and mark the area that is not eliminated in the sub-meter image as the surface background area, extract the pixel value of each pixel point in each surface background area, and calculate the surface soil quality transformation coefficient according to the pixel value of each pixel point. The calculation strategy is:

[0141] ;

[0142] in, is a subscript, indicating the The surface background area, is the total number of surface background areas in sub-meter images;

[0143] g is a subscript, indicating the The g-th pixel point in the ground background area, G is the g-th pixel point in the ground background area, The total number of pixels in the surface background area;

[0144] is the pixel value of the g-th pixel; To be closest The pixel value of

[0145] For the The maximum and minimum pixel values ​​of the surface background area.

[0146] The surface state assessment module is also used to run the following assessment strategy:

[0147] S24: In the synchronous extraction step S21, the sub-meter level images of the ground where each supporting tower is located are captured, and the sub-meter level images at different angles are extracted to form a stereo image pair, and a ground elevation spatial model is generated by stereo matching technology, wherein the ground elevation spatial model includes: relative height data of 20 data collection points evenly distributed on each data collection circle;

[0148] The data collection circle division strategy is specifically as follows: starting from the center of the tower and extending outward, a circular data collection circle is formed every 1 meter until one of the four corner vertices of the supporting tower is divided into the data collection circle, and the data collection circle division is completed;

[0149] Exemplarily, in this embodiment, according to the above-mentioned data collection circle division strategy of mechanical energy division, the number of data collection circles obtained is 8.

[0150] S25: Calculate the surface concavity coefficient b according to the ground elevation spatial model. The specific calculation strategy of the surface concavity coefficient is:

[0151] ;

[0152] Wherein, z is a subscript, indicating the zth data collection circle, and Z is the total number of data collection circles; y is a subscript, indicating the yth data collection point on the zth data collection circle;

[0153] Indicates the relative height data collected at the yth data collection point on the zth data collection circle;

[0154] They are the baseline height data of the four corner vertices of the supporting tower;

[0155] S26: According to S21-S25, the surface conditions of each supporting tower in the abnormal section of the line tower are evaluated to obtain the ground surface morphological risk value We. The calculation strategy of the ground surface morphological risk value is:

[0156] ;

[0157] in, They are the risk contribution factors of surface crack evolution coefficient and surface soil transformation coefficient respectively.

[0158] The satellite image evaluation module is used to analyze and process environmental water body images acquired from satellites;

[0159] The satellite image assessment module is used to run the following assessment strategy:

[0160] S31: Acquire environmental water images within 50 kilometers of each supporting tower in the abnormal section of the line tower through synthetic aperture radar satellites, and divide the environmental water images into water area and non-water area through support vector machine;

[0161] S32: Calculate the water area of ​​each water area: , ,in, They respectively represent the total number of pixels in the k-th water body area and the actual ground area corresponding to each pixel in the environmental water body image;

[0162] S33: According to the water area of ​​each water area , calculate the average water area within 50 km of the supporting tower ;

[0163] S34: Calculate and obtain the environmental state switching factor , ,in, are the mean water area in the tth and t-1th data collection unit periods, respectively.

[0164] The vector map marking module is used to divide the buffer area into vector maps;

[0165] The vector map marking module includes: a buffer level determination unit, a vector map division unit and a vector map grading color marking unit;

[0166] Among them, the buffer level judgment unit is used to judge the buffer level of the surface around each supporting tower in the abnormal section of the line tower; the vector map division unit is used to divide the vector map into major buffer areas and general buffer areas; the vector map graded color marking unit is used to color the buffer areas of different buffer levels.

[0167] The buffer level determination unit is used to execute the following determination strategy:

[0168] S41: The ground is represented by the risk value We and the environmental state switching factor Import the buffer level judgment strategy, specifically:

[0169] ;

[0170] Among them, hc is the buffer value;

[0171] S42: Presetting buffer level division thresholds respectively ,

[0172] when When the support tower is located, it is judged that the area where the support tower is located is a slight buffer area;

[0173] when When the supporting tower is located, it is judged that the area where the supporting tower is located is a general buffer area;

[0174] when When the supporting tower is located, it is judged that the area where the supporting tower is located is a major buffer area.

[0175] The analysis result visualization module is used to visualize the analysis data on the power grid information display platform.

[0176] Embodiment 2:

[0177] This embodiment provides an electronic device, including: a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0178] The processor runs on the above-mentioned power grid spatial information analysis system based on sub-meter image and vector fusion by calling the computer program stored in the memory.

[0179] The electronic device may have relatively large differences due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) and one or more memories, wherein the memory stores at least one computer program, which is loaded by the processor and runs on the above-mentioned power grid spatial information analysis system based on sub-meter image and vector fusion. The electronic device may also include other components for realizing the functions of the device, for example, the electronic device may also have components such as wired or wireless network interfaces and input and output interfaces for data input and output. This embodiment will not be described in detail here.

[0180] Embodiment three:

[0181] This embodiment provides a computer-readable storage medium having a rewritable computer program stored thereon;

[0182] When the computer program runs on a computer device, the above-mentioned power grid spatial information analysis system based on sub-meter image and vector fusion runs on the computer device.

[0183] For example, the computer readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a compact disc (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, etc.

[0184] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0185] It should be understood that determining B based on A does not mean determining B only based on A. B can also be determined based on A and / or other information.

[0186] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When a computer instruction or computer program is loaded or executed on a computer, a process or function according to an embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center through a wired network or / and a wireless network. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0187] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0188] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0189] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of units is only one, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0190] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0191] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0192] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0193] In summary, compared with the prior art, the technical effects of the present invention are as follows:

[0194] 1. The line node operation data acquisition module in the present invention accurately collects real-time data through supporting tower counting, current sensors, voltage sensors and mechanical sensors. It alleviates the huge data volume problem caused by sub-meter-level images; through real-time monitoring and detailed data acquisition, it prioritizes the screening of abnormal line tower sections in each overhead power transmission bus line, and conducts targeted sub-meter-level image acquisition of supporting towers in abnormal line tower sections, thereby improving the system's response speed and data processing efficiency.

[0195] 2. The sub-meter image acquisition module of the present invention obtains sub-meter images through high-resolution satellites, and combines ground control points and geometric correction to provide an accurate view of the power grid, optimize the data processing process, and improve the accuracy of subsequent assessment of the earth's surface risk value.

[0196] 3. The surface state assessment module in the present invention evaluates the risk of geological changes by calculating surface cracks and soil transformation coefficients, which helps to predict and prevent geological changes that may affect the stability of the power grid and improve the reliability and safety of power grid operation.

[0197] 4. The vector map marking module of the present invention performs vector map division and color marking on the buffer area. By grading and color marking the buffer area, the buffer areas of different risk levels can be clearly displayed, helping power grid analysts to quickly identify and respond to potential power grid problems, thereby improving data availability and decision-making accuracy.

[0198] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited by the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. A power grid spatial information analysis system based on sub-meter image and vector fusion, characterized by: The system includes the following specific modules: Line node operation data acquisition module, line channel loss calculation module, sub-meter image acquisition module, surface status assessment module, satellite image assessment module, vector map marking module and analysis result visualization module; The line node operation data acquisition module is used to collect and record real-time operation status data of the overhead transmission line; The line node operation data acquisition module includes: a support tower counting unit, a current sensor unit, a voltage sensor unit and a mechanical sensor unit; Wherein, the support tower counting unit is used to record the number N of support towers in each overhead power transmission bus line, and the overhead power transmission line includes: an overhead power transmission subline from a power plant to a substation and an overhead power transmission subline from a substation to a distribution station; the current sensor unit is used to collect input current data and output current data of the overhead power transmission subline above the support tower; the voltage sensor unit is used to collect input voltage data and output voltage data of the overhead power transmission subline above the support tower; the mechanical sensor unit is used to collect input side conductor stress data and output side conductor stress data of the overhead power transmission subline above the support tower; The line channel loss calculation module is used to calculate and evaluate the channel loss rate of the overhead transmission line; The sub-meter image acquisition module is used to obtain high-resolution images with sub-meter accuracy; The sub-meter image acquisition module includes: Sub-meter image acquisition unit, ground control point selection unit, geometric correction unit, denoising unit and image fusion and splicing unit; The sub-meter image acquisition unit acquires sub-meter images through high-resolution satellites; the ground control point selection unit is used to select the four corner vertices of the supporting tower and five points in the center of the tower as ground control points; the geometric correction unit performs geometric correction on the sub-meter image through a block alignment algorithm; the denoising unit performs geometric correction on the sub-meter image at the same time through wavelet transform; the image fusion and splicing unit fuses the sub-meter image with the laser radar data through a band fusion algorithm, and splices multiple image blocks; The surface state assessment module is used to assess the surface state and obtain the surface state risk value; The satellite image evaluation module is used to analyze and process environmental water body images acquired from satellites; The vector map marking module is used to divide the buffer area into vector maps; The vector map marking module includes: a buffer level determination unit, a vector map division unit and a vector map grading color marking unit; The buffer level judgment unit is used to judge the buffer level of the ground surface around each supporting tower in the abnormal section of the line tower; the vector map division unit is used to divide the vector map into major buffer areas and general buffer areas; the vector map graded color marking unit is used to perform color marking on buffer areas of different buffer levels; The analysis result visualization module is used to visualize the analysis data on the power grid information display platform.

2. The power grid spatial information analysis system based on sub-meter image and vector fusion according to claim 1 is characterized in that: The line channel loss calculation module is used to implement the following strategies: S11: extracting input current data, output current data, input voltage data, and output voltage data collected by the current sensor unit and the voltage sensor unit on each supporting tower, and importing the input current data, output current data, input voltage data, and output voltage data into a line power loss rate calculation strategy to calculate and obtain the line power loss rate. The line power loss rate calculation strategy is specifically as follows: ； in, It represents the line power loss rate generated when the transmission line passes through the nth supporting tower in the overhead transmission bus line; t is a subscript, indicating the tth data collection unit period; T is the total number of data collection unit periods; The input current data and input voltage data when the transmission line passes through the nth supporting tower; The output current data and output voltage data of the transmission line when it passes through the nth supporting tower; They are respectively the target current fluctuation range and the target voltage fluctuation range when the transmission line passes through the supporting tower; S12: extracting input side conductor stress data and output side conductor stress data collected by the mechanical sensor unit on each supporting tower, importing the input side conductor stress data and the output side conductor stress data into the line stress loss rate calculation strategy, and calculating and obtaining the line stress loss rate. The line stress loss rate calculation strategy is specifically as follows: ； in, It represents the line stress loss rate generated when the transmission line passes through the nth supporting tower in the overhead transmission bus line; Respectively represent the input side conductor stress data in the tth and t-1th data collection unit periods; They represent the stress data of the conductor at the output side of the segment at the t-th and t-1-th data collection units respectively; are the target fluctuation ranges of stress on the input side and output side of the supporting tower respectively; S13: Based on S11-S12, evaluate the line channel loss rate when the overhead transmission line passes through each supporting tower. The specific evaluation strategy is as follows: ； in, is the line channel loss rate when the overhead transmission line passes through the nth supporting tower.

3. The power grid spatial information analysis system based on sub-meter image and vector fusion according to claim 2 is characterized in that: The line channel loss calculation module is also used to execute the following strategy: S14: Extract the channel loss rate of the overhead transmission line passing through the nth supporting tower , preset line channel loss threshold , and make a judgment on the line loss tolerance; S15: When When the channel loss rate of the overhead transmission line at the n-1th supporting tower upstream of the nth supporting tower is returned, , to judge the line loss tolerance; S16: If , then the nth supporting tower is determined to be the starting point of the abnormal section of the line tower, and step S17 is continued; like , then continue to return to retrieve the line channel loss rate of the n-2th supporting tower , continue to perform line loss tolerance judgment until the line channel loss rate of the nu-th supporting tower in this overhead transmission bus line is When , mark the nuth supporting tower as the starting point of the abnormal section of the line tower, and stop the cycle; Wherein, u represents the first difference parameter, and u is a positive integer less than n; S17: Synchronously retrieve the line channel loss rate of the overhead transmission line at the n+1th supporting tower downstream of the nth supporting tower , to judge the line loss tolerance; S18: If , then the nth supporting tower is judged to be the end point of the abnormal section of the line tower; like , then continue to return to retrieve the line channel loss rate of the n+2th supporting tower , continue to perform line loss tolerance judgment until the line channel loss rate of the n+vth supporting tower in this overhead transmission bus line is When , mark the n+vth supporting tower as the end point of the abnormal section of the line tower, and stop the cycle; Where v represents the second difference parameter, v is a positive integer and n+v <N; S19: Confirm the area scope of the abnormal line tower section according to the starting point and the end point of the abnormal line tower section.

4. The power grid spatial information analysis system based on sub-meter image and vector fusion according to claim 3 is characterized in that: The surface state assessment module is used to run the following assessment strategy: S21: In the abnormal section of the line tower, capture the sub-meter level images of the ground where each supporting tower is located, perform local contrast enhancement processing, and use the edge detection algorithm to identify the edges of surface cracks on the processed sub-meter level images; S22: extract the number of pixels corresponding to the crack length and the number of pixels corresponding to the crack width in the sub-meter image for each surface crack area in different monitoring periods, and calculate the surface crack evolution coefficient. The calculation strategy is: ； in, is a subscript, indicating the Surface crack areas, is the total number of surface fracture areas in sub-meter images; Respectively represent , First The number of pixels corresponding to the crack length in the surface crack area; Respectively represent , First The number of pixels corresponding to the crack width in the surface crack area; S23: Eliminate the surface crack area in the sub-meter image, and mark the area that is not eliminated in the sub-meter image as the surface background area, extract the pixel value of each pixel point in each surface background area, and calculate the surface soil quality transformation coefficient according to the pixel value of each pixel point. The calculation strategy is: ； in, is a subscript, indicating the The surface background area, is the total number of surface background areas in sub-meter images; g is a subscript, indicating the The g-th pixel point in the ground background area, G is the g-th pixel point in the ground background area, The total number of pixels in the surface background area; is the pixel value of the g-th pixel; To be closest The pixel value of For the The maximum and minimum pixel values ​​of the surface background area.

5. The power grid spatial information analysis system based on sub-meter image and vector fusion according to claim 4 is characterized in that: The surface state assessment module is also used to run the following assessment strategy: S24: In the synchronous extraction step S21, the sub-meter level images of the ground where each supporting tower is located are captured, and the sub-meter level images at different angles are extracted to form a stereo image pair, and a ground elevation spatial model is generated by stereo matching technology, wherein the ground elevation spatial model includes: relative height data of 20 data collection points evenly distributed on each data collection circle; S25: Calculate the surface concavity coefficient b according to the ground elevation spatial model. The specific calculation strategy of the surface concavity coefficient is: ； Wherein, z is a subscript, indicating the zth data collection circle, and Z is the total number of data collection circles; y is a subscript, indicating the yth data collection point on the zth data collection circle; Indicates the relative height data collected at the yth data collection point on the zth data collection circle; They are the baseline height data of the four corner vertices of the supporting tower; S26: According to S21-S25, the surface conditions of each supporting tower in the abnormal section of the line tower are evaluated to obtain the ground surface morphological risk value We. The calculation strategy of the ground surface morphological risk value is: ； in, They are the risk contribution factors of surface crack evolution coefficient and surface soil transformation coefficient respectively.

6. The power grid spatial information analysis system based on sub-meter image and vector fusion according to claim 5 is characterized in that: The satellite image assessment module is used to run the following assessment strategy: S31: Acquire environmental water images within 50 kilometers of each supporting tower in the abnormal section of the line tower through synthetic aperture radar satellites, and divide the environmental water images into water area and non-water area through support vector machine; S32: Calculate the water area of ​​each water area: , ,in, They respectively represent the total number of pixels in the k-th water body area and the actual ground area corresponding to each pixel in the environmental water body image; S33: According to the water area of ​​each water area , calculate the average water area within 50 km of the supporting tower ; S34: Calculate and obtain the environmental state switching factor , ,in, are the mean water area in the tth and t-1th data collection unit periods, respectively.

7. The power grid spatial information analysis system based on sub-meter image and vector fusion according to claim 6 is characterized in that: The buffer level determination unit is used to execute the following determination strategy: S41: The ground is represented by the risk value We and the environmental state switching factor Import the buffer level judgment strategy, specifically: ； Among them, hc is the buffer value; S42: Presetting buffer level division thresholds respectively , when When the support tower is located, it is judged that the area where the support tower is located is a slight buffer area; when When the supporting tower is located, it is judged that the area where the supporting tower is located is a general buffer area; when When the supporting tower is located, it is judged that the area where the supporting tower is located is a major buffer area.

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