Multi-terminal ai sensing data and beidou positioning data integrated control system

Abstract

The application discloses a multi-terminal AI sensing data and Beidou positioning data integrated fusion control system and relates to the technical field of data fusion.The application first performs coordinate matching on power distribution network inspection information, which is convenient for unified management of multi-terminal data, then analyzes multi-source heterogeneous data collected by an intelligent individual inspection terminal, a vehicle-mounted inspection terminal and a UAV device used during inspection operation, and matches relative positions, image characteristic elements, coordinate positions, image positions and image data of Beidou positioning data of the vehicle-mounted inspection terminal, Beidou positioning data of the intelligent individual inspection terminal and Beidou positioning data of the UAV device, and finally performs a supplementary sampling operation.The application breaks the data barrier under different power distribution network inspection methods, forms a unified data format, deepens data value mining, restores a real and objective environment of an operation site in combination with positioning information, and further improves the informationization development of power distribution network inspection data.

Description

Technical Field

[0001] This invention relates to the field of data fusion technology, specifically to an integrated control system based on multi-terminal AI sensing data and BeiDou positioning data. Background Technology

[0002] As a crucial link in power transmission, the power distribution network is characterized by its wide distribution of lines, large number of devices, and complex operating environment. The quality of its inspection directly affects the safe and stable operation of the power grid. Currently, power distribution network inspection has gradually formed a multi-device collaborative operation mode, including vehicle-mounted inspection, intelligent individual inspection, and drone inspection. Various inspection terminals can obtain spatial location information through BeiDou positioning and rely on AI visual perception to automatically identify targets such as line defects and equipment anomalies, thus improving inspection efficiency and intelligence to a certain extent.

[0003] There are still some shortcomings in the existing technology when conducting multi-device collaborative inspection of power distribution networks. Specifically, these shortcomings are reflected in the following aspects: (1) Although the current power distribution network inspection business is equipped with intelligent single-soldier inspection terminals, vehicle-mounted inspection terminals and UAV equipment, the self-coordinate systems of each terminal equipment are different and the Beidou positioning accuracy is different, which makes it difficult to manage the power distribution network inspection data collected by each device and makes it difficult to effectively match and manage them in a unified manner.

[0004] (2) In the existing technology, the image data collected by vehicle-mounted inspection terminals, intelligent individual inspection terminals and UAV equipment have different formats and data standards, different postures when collecting data, different accuracy of positioning data, coordinate frames, coordinate data expression methods, etc., and different AI recognition capabilities lead to different AI perception results, making data fusion difficult.

[0005] (3) Existing technologies rely on data and location information identified by vehicle-mounted inspection terminals as the core, without linking with UAV equipment and intelligent individual inspection terminals to collect additional points of interest, which will result in a lack of accuracy in the collected data. Summary of the Invention

[0006] The purpose of this invention is to provide an integrated control system based on multi-terminal AI sensing data and BeiDou positioning data to address the aforementioned technical shortcomings.

[0007] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: The present invention provides an integrated fusion control system based on multi-terminal AI perception data and Beidou positioning data, including: a coordinate system analysis module: used to obtain power distribution network information and power distribution network inspection information from the data center, and then use geographic information technology and Beidou positioning technology to perform coordinate matching on the power distribution network inspection information to obtain a unified coordinate system.

[0008] Coordinate matching module: Used to receive multi-source heterogeneous data through data communication, and then perform standardized data processing and cleaning, and unify the positioning coordinates of inspection equipment, including intelligent individual inspection terminals, vehicle-mounted inspection terminals and drone equipment.

[0009] The data matching module is used to perform relative position matching calculations on the BeiDou positioning data of the vehicle-mounted inspection terminal, the intelligent individual soldier inspection terminal, and the UAV equipment within a unified coordinate system. Then, based on the feature elements extracted by the AI ​​perception capability of the vehicle-mounted inspection terminal and the calculated position information, image feature element matching and coordinate position matching calculations are performed with the feature elements extracted by the AI ​​perception capability of the UAV equipment. Finally, the position and image data of the images collected by the intelligent individual soldier inspection terminal are matched and calculated.

[0010] Automated inspection control module: Used to determine whether data supplementation is needed for drone equipment or intelligent individual inspection terminals, and to perform the corresponding supplementation operation.

[0011] The beneficial effects of the present invention are as follows: (1) The present invention uses geographic information technology and Beidou positioning technology to store and match the collected distribution network inspection information, sort out and build a topological relationship suitable for distribution network inspection business management, facilitate the unification and control of multi-terminal data, and solve the current situation of data management difficulties caused by different collection methods in distribution network inspection business.

[0012] This invention breaks down data barriers between different power distribution network inspection methods by integrating multi-terminal AI sensing data from vehicle-mounted inspection terminals, drone equipment, and intelligent individual inspection terminals with BeiDou positioning data, forming a unified data format, deepening data value mining, and restoring the real objective environment of the work site by combining it with positioning information, thereby further improving the informatization development of power distribution network inspection data.

[0013] (3) This invention centers on vehicle-mounted inspection equipment and integrates intelligent single-soldier inspection terminals and drone equipment for supplementary data collection during inspection operations. This breaks through the limitations of inspection scenarios and single-method data collection. Through integrated control and data fusion, it realizes air-ground integration of inspection operations, enabling the viewing and analysis of multi-dimensional distribution network interest elements, thereby improving the quality of distribution network inspection operations and the objectivity and authenticity of data. At the same time, it achieves unified data processing, unified analysis, and unified fusion at the edge. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 This is a schematic diagram of the system structure connection of the present invention. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Reference Figure 1 As shown, the present invention provides an integrated control system based on multi-terminal AI perception data and BeiDou positioning data. It first includes a coordinate system analysis module: used to obtain power distribution network information and power distribution network inspection information from the data center, and then use geographic information technology and BeiDou positioning technology to perform coordinate matching on the power distribution network inspection information to obtain a unified coordinate system.

[0018] In a specific example, the distribution network information includes basic topology data of the power grid, operation data of the power grid, equipment ledger data and fault data of the power grid; the distribution network inspection information includes inspection information from vehicle-mounted inspection terminals, intelligent individual inspection terminals and drone equipment.

[0019] It should be noted that the basic topology data of the distribution network includes line routes, tower locations, transformer distribution, and switchgear parameters; equipment ledger data includes equipment models, installation times, maintenance records, and rated parameters; power grid operation data includes line voltage, current, load data, and equipment operating temperature; and power grid fault data includes historical fault locations, fault types, and handling records.

[0020] The inspection information of vehicle-mounted inspection terminals, intelligent individual inspection terminals, and drone equipment specifically includes equipment appearance images (such as real-time photos and videos of poles, lines, and equipment), equipment anomaly detection data (AI-identified defect types and defect locations), BeiDou positioning coordinates, inspection record data (inspection time, inspection personnel, and inspection route), and on-site environmental data (weather and terrain-related information of the inspection area).

[0021] In a specific example, geographic information technology and BeiDou positioning technology are used to perform coordinate matching on power distribution network inspection information to obtain a unified coordinate system. The specific process is as follows: S1. Obtain the geographic base map of the inspection area from the data center. After preprocessing the geographic base map of the inspection area, power distribution network data and power distribution network inspection information, input them into the GIS platform and use the CGCS2000 coordinate system as the global unified coordinate reference.

[0022] S2. In the GIS platform, the R-tree indexing algorithm is used to generate a unique spatial ID for each ledger device. The spatial ID is bound to the device ID of each ledger device, thereby establishing the association between space and ledger device.

[0023] S3. Using any coordinate in the CGCS2000 coordinate system as the center and a preset distance as the radius, retrieve all distribution network information and distribution network inspection information within the radius in the GIS platform, match the distribution network information, distribution network inspection information and the corresponding coordinates one by one, and store them in the data center.

[0024] It should be noted that preprocessing includes removing invalid data and filling in missing data.

[0025] It should be noted that the inspection data after being bound to coordinates is stored in a unified standard format of "equipment ID + GIS coordinates + timestamp + equipment status + multimedia attachments" to achieve multi-terminal data synchronization between web, mobile and monitoring centers. The inspection trajectory, inspected equipment, defect locations and uninspected areas are visualized on the GIS map, and the matched inspection results are automatically backfilled into the power distribution network database to update the equipment operating status, forming a closed-loop management process.

[0026] For example, selecting the coordinates (116.39748°, 39.90882°) in the CGCS2000 coordinate system as the center, and setting a preset distance of 10m as the radius, a search operation is initiated in the GIS platform. The search retrieves the distribution network information within this radius, which is a 10kV tower (Equipment ID: GT-1001, Equipment Name: 10kV XX Line Tower No. 12, Voltage Level: 10kV, Line: 10kV XX Line, Corresponding CGCS2000 Coordinates: 116.39752°, 39.90885°). The search also retrieves the recent inspection record in the distribution network information of this tower (Inspection Time: xxxx year xx month xx day). Inspection personnel: XXX, Equipment operating status: normal, Corresponding inspection positioning CGCS2000 coordinates: 116.39750°, 39.90883°; Subsequently, the distribution network information of the 10kV tower is matched one-to-one with the corresponding coordinates (116.39752°, 39.90885°), and the distribution network inspection information of the tower is matched one-to-one with the corresponding inspection positioning coordinates (116.39750°, 39.90883°). At the same time, the association relationship between the distribution network information and the corresponding distribution network inspection information is established. Finally, the above distribution network information, distribution network inspection information, their respective corresponding coordinates, and the relationship between the two are uploaded to the data center for storage.

[0027] Secondly, it includes a coordinate matching module: used to receive multi-source heterogeneous data through data communication, and then perform standardized data processing and cleaning, and unify the positioning coordinates of the inspection equipment, including intelligent individual inspection terminals, vehicle-mounted inspection terminals and drone equipment.

[0028] In one specific instance, the multi-source heterogeneous data includes image data, AI perception data, and BeiDou positioning data.

[0029] In a specific example, the process of receiving multi-source heterogeneous data through data communication and then performing standardized data processing and cleaning is as follows: receiving multi-source heterogeneous data collected by inspection equipment through a preset wireless communication link, labeling the multi-source heterogeneous data according to the device ID + timestamp, and storing the data in the data center after data standardization and cleaning.

[0030] It should be noted that the preset wireless communication links include, for example, 5G, LoRa, or Bluetooth.

[0031] In a specific example, the positioning coordinates of the unified inspection equipment are determined as follows: The raw BeiDou positioning data output by the intelligent individual soldier inspection terminal, vehicle-mounted inspection terminal, and UAV equipment during the same inspection time period are obtained from the data center. Simultaneously, acceleration, angular velocity, and attitude angle data output by the inertial measurement units (IMUs) on the intelligent individual soldier inspection terminal, vehicle-mounted inspection terminal, and UAV equipment are collected. The BeiDou positioning data of the same terminal is tightly coupled with the acceleration, angular velocity, and attitude angle data output by the IMU for combined navigation calculation. Through Kalman filtering fusion calculation, the positioning result and attitude information of the terminal are output, and calculations are performed on each terminal accordingly.

[0032] It should be noted that the tightly coupled integrated navigation calculation includes BeiDou / GNSS single-point positioning solution, inertial dead reckoning, and Kalman filter optimal estimation. The inertial navigation solution results are corrected by observing position and velocity residuals to improve the continuity and accuracy of positioning in obstructed and multipath environments.

[0033] For the positioning deviations of each terminal in each installation location, orientation, and geographic coordinate system after calculation, the positioning data of each terminal are sequentially converted from the geodetic coordinate system to the spatial rectangular coordinate system, the coordinate offset is corrected, the coordinate rotation is transformed, and the attitude angle is aligned by using a preset unified spatial reference coordinate system.

[0034] It should be noted that by correcting coordinate offsets, transforming coordinates, and aligning attitude angles, the inconsistency in positioning caused by differences in terminal type, installation position deviation, carrier attitude change, and inconsistency in coordinate system can be eliminated. Ultimately, this achieves complete unification of the positioning coordinate system and accurate matching of positioning data for intelligent individual inspection terminals, vehicle-mounted inspection terminals, and UAV equipment under the same spatiotemporal reference.

[0035] It should be noted that the preset unified spatial reference coordinate system includes converting the original positioning data of each terminal into the CGCS2000 coordinate system, completing the unified mapping between geodetic latitude and longitude and plane rectangular coordinates.

[0036] It should also be noted that the positioning data of each terminal are sequentially processed by converting between the geodetic coordinate system and the spatial rectangular coordinate system, correcting coordinate offsets, performing coordinate rotation transformations, and aligning attitude angles. Among them, the coordinate offset correction is performed by calibrating the installation deviation between the antenna center of each terminal and the carrier reference point, and compensating for the positioning calculation results with three-axis translation to eliminate the system offset error caused by the physical installation position. The coordinate rotation transformation is based on the heading angle, roll angle, and pitch angle of each terminal carrier, and a rotation matrix is ​​constructed to perform spatial rotation transformation on the positioning point and attitude angle to achieve unified alignment of the positioning reference under different carrier attitudes.

[0037] It should also be noted that BeiDou positioning data includes pseudorange, carrier phase, latitude and longitude, elevation, velocity, and positioning status information. During integrated navigation calculations, invalid observation data such as insufficient satellite counts or exceeding DOP limits are preferentially eliminated.

[0038] It also includes a data matching module: used to first match and calculate the relative positions of the BeiDou positioning data of the vehicle-mounted inspection terminal, the BeiDou positioning data of the intelligent individual inspection terminal, and the BeiDou positioning data of the UAV within a unified coordinate system; then, based on the feature elements extracted by the AI ​​perception capability of the vehicle-mounted inspection terminal and the calculated position information, perform image feature element matching and coordinate position matching calculation with the feature elements extracted by the AI ​​perception capability of the UAV; finally, perform matching calculation based on the position and image data of the images collected by the intelligent individual inspection terminal.

[0039] In a specific example, the process of matching and calculating the relative positions of the BeiDou positioning data of the vehicle-mounted inspection terminal, the BeiDou positioning data of the intelligent individual inspection terminal, and the BeiDou positioning data of the UAV equipment is as follows: S1001, extract the coordinates of the vehicle-mounted inspection terminal and the intelligent individual inspection terminal in the same inspection time period and the same inspection area from the data center, then extract the coordinates of the power distribution line segment collected by the vehicle-mounted inspection terminal, and generate the inspection trajectory coordinates of the vehicle-mounted inspection terminal and the intelligent individual inspection terminal. Associate the inspection trajectory coordinates of the intelligent individual inspection terminal with the coordinates of the power distribution line segment collected by the vehicle-mounted inspection terminal, thereby clarifying the specific location node of the intelligent individual inspection terminal on the vehicle-mounted inspection line.

[0040] S1002. Extract continuous coordinates from the data center during the same time period of the UAV equipment inspection process to generate the UAV equipment inspection trajectory. Combine the flight altitude and shooting angle of the UAV equipment, and use a spatial projection algorithm to project the aerial coordinates of the UAV equipment onto the ground plane, converting them into ground coordinates consistent with those of the vehicle-mounted inspection terminal and the intelligent individual soldier inspection terminal. Then, compare the projected ground coordinates of the UAV equipment with the driving trajectory coordinates of the vehicle-mounted inspection terminal and the positioning coordinates of the intelligent individual soldier inspection terminal, calculate the spatial distance between the three, and perform position association between the inspection data collected by the UAV equipment and the inspection data collected by the vehicle-mounted inspection terminal and the intelligent individual soldier inspection terminal.

[0041] In a specific example, the feature elements extracted and the calculated location information based on the AI ​​perception capability of the vehicle-mounted inspection terminal are respectively matched with the feature elements extracted and the calculated location information based on the AI ​​perception capability of the drone device for image feature element matching and coordinate position matching calculation. The specific process is as follows: S2001, AI perception feature elements are extracted from the same inspection information collected by the vehicle-mounted inspection terminal and the drone device through image processing algorithms. After the extracted AI perception feature elements are labeled, they are associated with the coordinates and inspection time of the corresponding collection terminal.

[0042] It should be noted that the AI ​​perception features of the vehicle-mounted inspection terminal include equipment defect features such as damaged sheath of power distribution lines, aging insulators, and foreign objects in conductors, as well as geometric features such as line routing, tower outlines, and equipment spacing; the AI ​​perception features of the drone include the topological features of power distribution lines from a high-altitude perspective, the overall shape features of equipment, the distribution features of large-scale defects, and the topographic features of the inspection area.

[0043] It should be noted that the extracted AI-perceptual feature elements are labeled, and the labeling content includes type and location, etc.

[0044] S2002. After preprocessing the labeled AI perception feature elements, based on the coordinate and relative position matching results of the vehicle-mounted inspection terminal and the drone equipment, the AI ​​perception feature elements collected by the vehicle-mounted inspection terminal and the drone equipment are aligned with their corresponding ground coordinates, thereby clarifying the ground coordinates corresponding to all AI perception feature elements.

[0045] It should be noted that preprocessing includes optimizing image clarity through image enhancement algorithms to eliminate image blurring caused by lighting and weather (such as rain and fog), and using image segmentation algorithms to segment the power distribution equipment area and background area in the image to remove irrelevant background interference. Subsequently, the preprocessed image is standardized to unify image resolution, pixel format and color parameters to ensure the comparability of vehicle-mounted and drone images. At the same time, image feature elements (such as equipment edge contours, texture features and pixel features of defect areas) are extracted from the image and associated with AI perception feature elements.

[0046] S2003. Using a feature point matching algorithm, coordinate matching is performed on the same AI sensing feature elements collected by the vehicle-mounted inspection terminal and the drone equipment to ensure that the coordinates of the same AI sensing feature element are consistent in the inspection information of the vehicle-mounted inspection terminal and the drone equipment. At the same time, the coordinates of the AI ​​sensing feature element are compared with the corresponding equipment coordinates in the power grid basic topology data. If the coordinates of the AI ​​sensing feature element fall within the coordinate range of the corresponding equipment and the AI ​​sensing feature element matches the ledger information of the corresponding equipment, then the matching of image feature element and coordinate position is completed; otherwise, the above steps are repeated until the matching of image feature element and coordinate position is completed.

[0047] In a specific example, the matching calculation based on the location and image data of the intelligent individual soldier inspection terminal is performed as follows: S3001, retrieve the continuous images, real-time Beidou positioning coordinates, shooting attitude angle and shooting timestamp of the intelligent individual soldier inspection terminal collected in any time period from the data center, and associate each frame of image with its corresponding positioning coordinates and attitude parameters one by one with the timestamp as the index to construct an individual soldier image-location time series dataset.

[0048] S3002. After preprocessing the individual soldier image-location time series dataset, the pixel coordinates of each continuous image in the individual soldier image-location time series dataset are reverse-mapped to the ground coordinate system through spatial projection and coordinate transformation algorithms, thereby obtaining the corresponding real geographic coordinates.

[0049] It should be noted that the preprocessing includes improving image clarity through image enhancement, dehazing, and noise reduction algorithms, identifying key targets in the image such as power distribution equipment, defects, towers, and conductors using target detection and segmentation algorithms, extracting image features such as equipment outlines, defect features, and texture features, and labeling feature categories and image pixel coordinates.

[0050] S3003. The real geographic coordinates corresponding to the pixels of each continuous image in the individual soldier image-location time series dataset are compared with the relevant matching information of the AI ​​perception feature elements and geographic coordinates that have been matched by the vehicle-mounted inspection terminal and the UAV equipment.

[0051] It should be noted that the specific process of comparing relevant matching information is as follows: comparing whether the equipment type, outline features, and defect features within the same geographical coordinate range are consistent; comparing whether the spatial deviation between the feature target coordinates and the corresponding coordinates of the vehicle and drone is within a preset threshold; and comparing whether the feature target matches the equipment coordinates and attribute information in the power grid topology ledger.

[0052] S3004. If the real geographic coordinates corresponding to the pixels of each consecutive image in the individual soldier image-location time series dataset are consistent with the relevant matching information of the AI ​​perception feature elements and geographic coordinates that have been matched by the vehicle-mounted inspection terminal and the UAV equipment, then the matching is complete. Otherwise, the inconsistent relevant matching information is extracted, and the coordinates are optimized by individual soldier terminal trajectory interpolation, image reprojection correction and feature secondary recognition. S3001-S3003 are repeated until the matching is complete.

[0053] Finally, there is an automated inspection control module: used to determine whether data supplementation is needed for drone equipment or intelligent individual inspection terminals, and to perform the corresponding supplementation operation.

[0054] In a specific instance, the process of determining whether data supplementation is needed for unmanned aerial vehicle (UAV) equipment or intelligent individual soldier inspection terminal, and performing the corresponding supplementation operation, is as follows: when there is a coverage blind spot in the vehicle-mounted inspection terminal, it is determined that data supplementation should be performed; when the confidence level of the collection target is less than 0.8, it is determined that data supplementation should be performed.

[0055] When there is a coverage blind spot in the vehicle-mounted inspection terminal, the inspection route of the current vehicle-mounted inspection terminal is retrieved from the data center by the drone equipment. If an inspection route exists, the data is supplemented by the drone equipment. If no inspection route exists, the data is supplemented by the intelligent individual inspection terminal.

[0056] When the confidence level of the target data collection is less than 0.8, data supplementation is performed through the intelligent individual soldier inspection terminal.

[0057] It should be noted that when supplementing data collection for drone equipment or intelligent individual inspection terminals, the data matching module within the configured edge computing box performs matching operations on the supplemented data, performing association and fusion based on multi-device AI + BeiDou data. Data supplementation for individual inspection terminals requires manual intervention. If the drone equipment has a pre-stored inspection path, spatial coordinate matching can be performed based on the inspection path to achieve automated drone equipment inspection. If the drone equipment does not have an inspection path, manual intervention is required to supplement the data on-site.

[0058] It should also be noted that the individual soldier terminal trajectory interpolation, image reprojection correction and feature secondary recognition, spatial projection and coordinate transformation algorithm, Kalman filtering algorithm and feature point matching algorithm mentioned in this invention are all existing technologies and will not be described in detail here.

[0059] The examples described in this invention are not limited to the specific embodiments listed above. The examples are merely illustrative to facilitate understanding of the invention and do not constitute a limitation on the scope of protection of this invention. Any modifications, equivalent substitutions, etc., made within the spirit and principles of this invention should be included within the scope of protection.

[0060] The above description is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they should all fall within the protection scope of the present invention.

Claims

1. A fusion control system based on multi-terminal AI sensing data and BeiDou positioning data, characterized in that: Includes the following modules: Coordinate system analysis module: used to obtain power distribution network information and power distribution network inspection information from the data center, and then use geographic information technology and Beidou positioning technology to perform coordinate matching on the power distribution network inspection information to obtain a unified coordinate system; Coordinate matching module: Used to receive multi-source heterogeneous data through data communication, then perform standardized data processing and cleaning, and unify the positioning coordinates of inspection equipment, including intelligent individual inspection terminals, vehicle-mounted inspection terminals and drone equipment. The data matching module is used to perform relative position matching calculations on the BeiDou positioning data of the vehicle-mounted inspection terminal, the intelligent individual soldier inspection terminal, and the UAV equipment within a unified coordinate system. Then, based on the feature elements extracted and calculated from the AI ​​perception capabilities of the vehicle-mounted inspection terminal, it performs image feature element matching and coordinate position matching calculations with the feature elements extracted and calculated from the AI ​​perception capabilities of the UAV equipment. Finally, it performs matching calculations based on the position and image data of the images collected by the intelligent individual soldier inspection terminal. Automated inspection control module: Used to determine whether data supplementation is needed for drone equipment or intelligent individual inspection terminals, and to perform the corresponding supplementation operation.

2. The integrated control system based on multi-terminal AI sensing data and BeiDou positioning data according to claim 1, characterized in that, The distribution network information includes basic topology data, operation data, equipment ledger data, and fault data; the distribution network inspection information includes inspection information from vehicle-mounted inspection terminals, intelligent individual inspection terminals, and unmanned aerial vehicle (UAV) equipment.

3. The integrated control system based on multi-terminal AI sensing data and BeiDou positioning data according to claim 2, characterized in that, The process of using geographic information technology and BeiDou positioning technology to perform coordinate matching on power distribution network inspection information to obtain a unified coordinate system is as follows: S1. Obtain the geographic base map of the inspection area from the data center, and input the geographic base map, power distribution network data and power distribution network inspection information of the inspection area into the GIS platform after preprocessing, using the CGCS2000 coordinate system as the global unified coordinate reference. S2. In the GIS platform, the R-tree indexing algorithm is used to generate a unique spatial ID for each ledger device. The spatial ID is bound to the device ID of each ledger device, thereby establishing the association between space and ledger device. S3. Using any coordinate in the CGCS2000 coordinate system as the center and a preset distance as the radius, retrieve all power distribution network information and power distribution network inspection information within the radius in the GIS platform, match the power distribution network information and power distribution network inspection information with the corresponding coordinates, and store them in the data center.

4. The integrated control system based on multi-terminal AI sensing data and BeiDou positioning data according to claim 3, characterized in that, The multi-source heterogeneous data includes image data, AI perception data, and BeiDou positioning data.

5. The integrated control system based on multi-terminal AI sensing data and BeiDou positioning data according to claim 4, characterized in that, The process of receiving heterogeneous data from multiple sources via data communication, and then performing standardized data processing and cleaning, is as follows: The system receives multi-source heterogeneous data collected by inspection equipment through a preset wireless communication link. At the same time, it labels the multi-source heterogeneous data according to the device ID and timestamp, and stores the data in the data center after data standardization and cleaning.

6. The integrated control system based on multi-terminal AI sensing data and BeiDou positioning data according to claim 5, characterized in that, The positioning coordinates of the unified inspection equipment are determined as follows: The system acquires raw BeiDou positioning data from intelligent individual soldier inspection terminals, vehicle-mounted inspection terminals, and UAVs during the same inspection period from the data center. Simultaneously, it collects acceleration, angular velocity, and attitude angle data output by the inertial measurement units (IMUs) on the intelligent individual soldier inspection terminals, vehicle-mounted inspection terminals, and UAVs. The system performs tightly coupled navigation calculations by combining the BeiDou positioning data from the same terminal with the acceleration, angular velocity, and attitude angle data output by the IMUs. Through Kalman filtering fusion calculation, the system outputs the positioning result and attitude information of the terminal, and performs calculations for each terminal accordingly. For the positioning deviations of each terminal in each installation location, orientation, and geographic coordinate system after calculation, the positioning data of each terminal are sequentially converted from the geodetic coordinate system to the spatial rectangular coordinate system, the coordinate offset is corrected, the coordinate rotation is transformed, and the attitude angle is aligned by using a preset unified spatial reference coordinate system.

7. The integrated control system based on multi-terminal AI sensing data and BeiDou positioning data according to claim 6, characterized in that, The process of matching and calculating the relative positions of the BeiDou positioning data from the vehicle-mounted inspection terminal, the intelligent individual soldier inspection terminal, and the UAV equipment is as follows: S1001. Extract the coordinates of vehicle-mounted inspection terminals and intelligent individual inspection terminals within the same inspection time period and inspection area from the data center. Then, extract the coordinates of the power distribution line segments collected by the vehicle-mounted inspection terminals and generate the inspection trajectory coordinates of the vehicle-mounted inspection terminals and intelligent individual inspection terminals. Associate the inspection trajectory coordinates of the intelligent individual inspection terminals with the coordinates of the power distribution line segments collected by the vehicle-mounted inspection terminals to determine the specific location node of the intelligent individual inspection terminal on the vehicle-mounted inspection line. S1002. Extract continuous coordinates from the data center during the same time period of the UAV equipment inspection process to generate the UAV equipment inspection trajectory. Combine the flight altitude and shooting angle of the UAV equipment, and use a spatial projection algorithm to project the aerial coordinates of the UAV equipment onto the ground plane, converting them into ground coordinates consistent with those of the vehicle-mounted inspection terminal and the intelligent individual soldier inspection terminal. Then, compare the projected ground coordinates of the UAV equipment with the driving trajectory coordinates of the vehicle-mounted inspection terminal and the positioning coordinates of the intelligent individual soldier inspection terminal, calculate the spatial distance between the three, and perform position association between the inspection data collected by the UAV equipment and the inspection data collected by the vehicle-mounted inspection terminal and the intelligent individual soldier inspection terminal.

8. The integrated control system based on multi-terminal AI sensing data and BeiDou positioning data according to claim 7, characterized in that, The feature elements extracted and the calculated location information based on the AI ​​perception capability of the vehicle-mounted inspection terminal are respectively matched with the feature elements extracted and the calculated location information based on the AI ​​perception capability of the drone device for image feature element matching and coordinate position matching calculation. The specific process is as follows: S2001. Extract AI perception feature elements from the same inspection information collected by the vehicle-mounted inspection terminal and the drone equipment through image processing algorithms. After labeling the extracted AI perception feature elements, associate them with the coordinates of the corresponding collection terminal and the inspection time. S2002. After preprocessing the labeled AI perception feature elements, based on the coordinate and relative position matching results of the vehicle-mounted inspection terminal and the drone equipment, the AI ​​perception feature elements collected by the vehicle-mounted inspection terminal and the drone equipment are aligned with their corresponding ground coordinates, thereby clarifying the ground coordinates corresponding to all AI perception feature elements. S2003. Using a feature point matching algorithm, coordinate matching is performed on the same AI sensing feature elements collected by the vehicle-mounted inspection terminal and the drone equipment to ensure that the coordinates of the same AI sensing feature element are consistent in the inspection information of the vehicle-mounted inspection terminal and the drone equipment. At the same time, the coordinates of the AI ​​sensing feature element are compared with the corresponding equipment coordinates in the power grid basic topology data. If the coordinates of the AI ​​sensing feature element fall within the coordinate range of the corresponding equipment and the AI ​​sensing feature element matches the ledger information of the corresponding equipment, then the matching of image feature element and coordinate position is completed; otherwise, the above steps are repeated until the matching of image feature element and coordinate position is completed.

9. The integrated control system based on multi-terminal AI sensing data and BeiDou positioning data according to claim 8, characterized in that, The specific process of matching and calculating the location and image data based on the images collected by the intelligent individual soldier inspection terminal is as follows: S3001. Retrieve continuous images, real-time BeiDou positioning coordinates, shooting attitude angle and shooting timestamp collected by the intelligent individual soldier inspection terminal in any time period from the data center. Using the timestamp as an index, associate each frame of image with its corresponding positioning coordinates and attitude parameters to construct an individual soldier image-location time series dataset. S3002. After preprocessing the individual soldier image-location time series dataset, the pixel coordinates of each continuous image in the individual soldier image-location time series dataset are reverse-mapped to the ground coordinate system through spatial projection and coordinate transformation algorithms, thereby obtaining the corresponding real geographic coordinates. S3003. Compare the real geographic coordinates corresponding to the pixels of each continuous image in the individual soldier image-location time series dataset with the relevant matching information of the AI ​​perception feature elements and geographic coordinates that have been matched by the vehicle-mounted inspection terminal and the UAV equipment. S3004. If the real geographic coordinates corresponding to the pixels of each consecutive image in the individual soldier image-location time series dataset are consistent with the relevant matching information of the AI ​​perception feature elements and geographic coordinates that have been matched by the vehicle-mounted inspection terminal and the UAV equipment, then the matching is complete. Otherwise, the inconsistent relevant matching information is extracted, and the coordinates are optimized by individual soldier terminal trajectory interpolation, image reprojection correction and feature secondary recognition. S3001-S3003 are repeated until the matching is complete.

10. The integrated control system based on multi-terminal AI sensing data and BeiDou positioning data according to claim 9, characterized in that, The process of determining whether data supplementation is needed for unmanned aerial vehicle (UAV) equipment or intelligent individual soldier inspection terminals, and performing the corresponding supplementation operation, is as follows: When there is a coverage blind spot in the vehicle-mounted inspection terminal, it is determined to perform supplementary data collection; when the confidence level of the data collection target is less than 0.8, it is determined to perform supplementary data collection. When there is a coverage blind spot in the vehicle-mounted inspection terminal, the inspection route of the current vehicle-mounted inspection terminal is retrieved from the data center by the drone equipment. If an inspection route exists, the data is supplemented by the drone equipment. If no inspection route exists, the data is supplemented by the intelligent single-soldier inspection terminal. When the confidence level of the target data collection is less than 0.8, data supplementation is performed through the intelligent individual soldier inspection terminal.

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