Power distribution network facility panoramic intelligent acceptance system based on unmanned aerial vehicle and ai image recognition

Abstract

The present application relates to the technical field of power distribution network acceptance, and more particularly to a panoramic intelligent acceptance system for power distribution network facilities based on unmanned aerial vehicle and AI image recognition. The system comprises a front-end lightweight algorithm integration module, an intelligent cruise and data acquisition module, a defect intelligent identification module, and an acceptance data management module. An algorithm integration unit compatible with the structure of the unmanned aerial vehicle remote controller can be developed, and lightweight target recognition, binocular distance measurement, and defect recognition algorithms can be deployed to generate an interactive adaptation protocol. Based on the interactive adaptation protocol, the unmanned aerial vehicle collects panoramic image data of the towers and line facilities. The characteristics of the power distribution network line are obtained, and the key work site is positioned and the defect characteristics are matched to generate a defect detection result. Based on the defect detection result, an acceptance report is generated and standardized, and an acceptance ledger data is generated. The cruise trajectory, collection parameters, and algorithm execution logs are recorded synchronously to realize acceptance full-process data closed-loop management. The present application can improve the acceptance efficiency of power distribution network facilities.

Description

Technical Field

[0001] This invention relates to the field of power distribution network acceptance technology, and in particular to a panoramic intelligent acceptance system for power distribution network facilities based on drones and AI image recognition. Background Technology

[0002] In recent years, with the rapid advancement of power distribution network construction towards intelligence and large-scale operation, higher demands have been placed on the efficiency, accuracy, and coverage integrity of power distribution network facilities (pole towers, lines, corridor environment, etc.). Traditional power distribution network facility acceptance relies heavily on manual on-site inspections, which are not only labor-intensive and risky, but also have blind spots, making it difficult to fully capture details of pole towers, defects in construction techniques, and potential hazards in the corridor environment. At the same time, manual acceptance is highly subjective, lacks standardized criteria, and is prone to missed or misjudged defects. Furthermore, acceptance data is difficult to archive and trace systematically, thus reducing the accuracy and efficiency of power distribution network facility acceptance. Summary of the Invention

[0003] Therefore, it is necessary for the present invention to provide a panoramic intelligent acceptance system for power distribution network facilities based on drones and AI image recognition, in order to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, a panoramic intelligent acceptance system for power distribution network facilities based on drones and AI image recognition includes the following modules: The front-end lightweight algorithm integration module is used to develop algorithm integration units adapted to the structure of UAV remote controllers, deploy lightweight target recognition, binocular ranging and defect recognition algorithms, generate an interactive adaptation protocol between the algorithm and the UAV; based on the interactive adaptation protocol, the UAV is controlled to perform adaptive route planning along the power distribution network line and collect panoramic image data of the towers and line facilities. The intelligent cruise and data acquisition module is used to acquire the characteristics of the power distribution network lines, and to locate key work areas based on panoramic image data combined with the characteristics of the power distribution network lines, generating cruise key area marking data; based on the cruise key area marking data, the drone is controlled to perform autonomous cruise, and to collect multi-dimensional image data of tower information, construction process details and channel environment; The intelligent defect identification module is used to input multi-dimensional image data into the defect identification algorithm, match defect features with power distribution network construction specifications, and generate defect detection results; based on the defect detection results, it generates an acceptance report containing defect location, type, and severity. The acceptance data management module is used to standardize and organize panoramic image data, multi-dimensional image data, defect detection results and acceptance reports to generate acceptance ledger data; it also records the cruise trajectory, collection parameters and algorithm execution logs simultaneously to achieve closed-loop management of data throughout the acceptance process.

[0005] The beneficial effects of this invention are: The panoramic intelligent acceptance system for power distribution facilities based on UAVs and AI image recognition proposed in this invention consists of a front-end lightweight algorithm integration module, an intelligent cruise and data acquisition module, a defect intelligent identification module, and an acceptance data management module. Compared with the prior art, the beneficial effects of this application are that by developing an algorithm integration unit adapted to the structure of the UAV remote controller, lightweight target recognition, binocular ranging, and defect identification algorithms are centrally deployed, avoiding the functional limitations of a single algorithm. At the same time, the generated interactive adaptation protocol ensures smooth collaboration between the algorithm and the UAV without additional human intervention. Based on the protocol, the UAV is controlled to perform adaptive flight path planning, which can flexibly adjust the flight path according to the actual route of the power distribution line and the distribution of towers. Compared with the disadvantages of manual inspection being limited by terrain and altitude, UAVs can easily reach high-altitude, remote, and other areas that are difficult for humans to reach. The panoramic image data collected completely covers the entire view of the towers and line facilities, completely eliminating the visual blind spots of manual inspection. This automated acquisition mode not only greatly reduces the labor intensity of inspection personnel and avoids the safety risks of high-altitude and field operations, but also ensures the integrity of the acceptance coverage through full-range image acquisition. Secondly, after acquiring the characteristics of the power distribution network lines, the system combines panoramic image data to locate key operational areas (such as tower connections, line joints, insulators, etc.). The resulting marker data for key patrol areas makes the drone's autonomous patrol more targeted, avoiding resource waste caused by indiscriminate flight. Based on the marker data, the drone performs autonomous patrols, focusing on collecting multi-dimensional image data of tower information, construction process details, and the corridor environment. Compared to the limitations of manual inspections, which rely solely on visual observation and handheld devices, the drone can capture close-up, multi-angle images of the target area, clearly recording whether the construction process meets specifications and whether there are hidden dangers such as trees obstructing the corridor environment. This avoids the omission of details caused by limited energy and a single perspective during manual inspections. Then, by inputting multidimensional image data into a defect identification algorithm, the algorithm strictly matches defect features according to power distribution network construction specifications and follows unified judgment standards. This avoids inconsistencies in standards caused by experience differences and subjective judgments in manual acceptance. The generated defect detection results clearly present the specific situation of the defects. Based on this, the resulting acceptance report includes key information such as defect location, type, and severity. Compared with verbal feedback and simple records in manual acceptance, the report content is more systematic, comprehensive, and traceable. This reduces the possibility of missed or misjudged defects, makes the acceptance results more standardized, provides solid technical support for the acceptance conclusion, and avoids information distortion caused by communication errors in manual acceptance. Finally, by standardizing and organizing the panoramic image data, multidimensional image data, defect detection results, and acceptance reports, a unified format of acceptance ledger data is generated. This avoids the drawbacks of messy data formats and scattered storage in manual acceptance, facilitating subsequent querying, statistics, and analysis.Synchronously recording the cruise trajectory, collected parameters, and algorithm execution logs, the system fully restores key information throughout the entire acceptance process. Compared to manual acceptance, which can only retain some paper records or scattered images, it achieves full traceability of acceptance data and solves the problem of traditional acceptance data being difficult to reuse, allowing acceptance data to play a long-term value and further improving the accuracy and efficiency of power distribution network facility acceptance work. Attached Figure Description

[0006] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a schematic diagram of the module of the panoramic intelligent acceptance system for power distribution facilities based on drones and AI image recognition of the present invention. Detailed Implementation

[0007] The technical system of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0008] To achieve the above objectives, please refer to Figure 1 This invention provides a panoramic intelligent acceptance system for power distribution network facilities based on drones and AI image recognition. The system includes the following modules: The front-end lightweight algorithm integration module is used to develop algorithm integration units adapted to the structure of UAV remote controllers, deploy lightweight target recognition, binocular ranging and defect recognition algorithms, generate an interactive adaptation protocol between the algorithm and the UAV; based on the interactive adaptation protocol, the UAV is controlled to perform adaptive route planning along the power distribution network line and collect panoramic image data of the towers and line facilities. In this embodiment of the invention, an algorithm integration unit adapted to the structure of a UAV remote controller is developed. Based on the remote controller's quad-core ARM architecture (1.5GHz computing power, 3GB DDR4 memory), a hardware adaptation module with USB 3.2 (10Gbps) and UART (230400bps) interfaces is designed. Lightweight algorithms are deployed: the target recognition algorithm adopts the MobileNetV3 architecture, with weighted quantization (32-bit to 8-bit) and pruning (removing branches with contribution <0.008), compressing the parameters to 2.1 million, achieving an inference speed of 70ms / frame; the binocular ranging algorithm is based on SGBM matching, with 1.5 million parameters and a measurement error of ±0.1m; the defect recognition algorithm is based on the MobileNetV2 architecture, with 1.8 million parameters and a recognition accuracy of 92%. An interactive adaptation protocol is generated: the binary format is "frame header 0xAB67 + function identifier + data body + CRC32 check + frame tail 0xCD89", where the function identifier includes flight path control (0x01) and image acquisition (0x02). If the command response times out by 80ms, it is retransmitted, up to a maximum of 4 times. Based on the protocol, the UAV plans its flight path along the 10kV East Ring Line (8km, 32 towers): the route coordinates (117.2000° / 38.5000° to 117.5000° / 38.6000°) are obtained through GPS + Beidou dual-mode positioning (accuracy ±0.3m). The flight altitude is 20m and the speed is 5m / s in the plains section, and the altitude is 22m and the speed is 3m / s in the mountainous section. 4K panoramic images are collected (3 frames / second, a total of 1820 TIFF images).

[0009] The intelligent cruise and data acquisition module is used to acquire the characteristics of the power distribution network lines, and to locate key work areas based on panoramic image data combined with the characteristics of the power distribution network lines, generating cruise key area marking data; based on the cruise key area marking data, the drone is controlled to perform autonomous cruise, and to collect multi-dimensional image data of tower information, construction process details and channel environment; In this embodiment of the invention, by acquiring the characteristics of the distribution network line, the line is divided into 8 sub-regions (1 per 1km), including 22 straight towers, 6 tension towers, and 4 corner towers. The corridor covers 3km of mountainous areas, 3km of plains, and 2km of villages. In key parts, the insulator strings are 12-18m above the ground, and the fittings are located at both ends of the crossarm. Based on panoramic imagery, the key parts are located: the target recognition algorithm extracts the pixel coordinates (150,1800)-(850,1800) of the towers through Canny edge detection (threshold 120-230), locates the insulator strings (1800,500)-(1900,780) through HSV color segmentation (Hue0-20, Saturation30-80), and locks the fittings (1800,780)-(1900,820) through SIFT feature matching, generating labeled data containing part name, pixel range, and priority. Controlling the drone for autonomous navigation: In sub-area 5 (tension towers 16-18), the flight speed is 2m / s, the hovering time is 45 seconds per tower, the image equipment is focused at 4.5m, the exposure compensation is +0.5EV, and data such as tower height (28m), bolt threads (2-3 threads), and distance of grounding device from the building (8m) are collected, generating 1175 multi-dimensional images (920 4K detail images and 340 2K panoramic images). Each image is associated with the collection time (20241202151000) and GPS coordinates.

[0010] The intelligent defect identification module is used to input multi-dimensional image data into the defect identification algorithm, match defect features with power distribution network construction specifications, and generate defect detection results; based on the defect detection results, it generates an acceptance report containing defect location, type, and severity. In this embodiment of the invention, by inputting a multi-dimensional image into the defect recognition algorithm, a target mask image (1 pixel for the inspection object, 0 pixels for the background) is first generated through a semantic segmentation model (U-Net++ architecture, 2.2 million parameters) to separate areas such as insulators and hardware. The algorithm's convolutional layer extracts shallow texture (crack edges) and deep semantic (damage morphology) features to generate a 256-dimensional feature vector. Defects are matched in conjunction with construction specifications: insulator cracks ≥3mm, bolt loosening ≥20°, and hardware corrosion ≥15% are considered defects. The feature library is compared using cosine similarity calculation (threshold 0.8). A crack (5mm) in the insulator of tower 17 and corrosion (18%) in the hardware of tower 18 are identified, generating a detection result containing defect ID (DQ2024120205), location (117.4250° / 38.5550°), type, size, and moderate severity level. Generate an acceptance report: PDF format (A4 paper size), including line information, defect statistics (62 defects, 38 moderate and 24 minor), details table (including rectification suggestions: moderate defects should be replaced within 7 days), embedded defect screenshots and original image links, and stored in a specified path.

[0011] The acceptance data management module is used to standardize and organize panoramic image data, multi-dimensional image data, defect detection results and acceptance reports to generate acceptance ledger data; it also records the cruise trajectory, collection parameters and algorithm execution logs simultaneously to achieve closed-loop management of data throughout the acceptance process.

[0012] In this embodiment of the invention, acceptance data is standardized and organized. The panoramic image is named "20241202-sub-region-serial number.TIFF" and stored in the path " / acceptance data / image / 20241202 / ". The metadata includes resolution (3840×2160) and file size (15MB). The multidimensional image is named "sub-region-location-serial number.TIFF" and supplemented with sharpness (135) and contrast (205). The defect results are organized into a structured table containing ID, location, and type. The report is named "10kV East Ring Line-20241202.pdf". An acceptance ledger is generated: each entry contains a unique identifier (SY2024120205001), a classification label, an associated ID, and core information, totaling 3072 entries, which are stored in a distributed database (HBase). Synchronously record all process information: cruise trajectory (2880 coordinates, 10-second intervals), acquisition parameters (speed 2-5 m / s, exposure +0.5 EV), and algorithm log (call time 202412021510, time taken 65 ms). Establish an update mechanism (data synchronized daily at 20:00, real-time receipt of rectification feedback), store logs in a dedicated database, configure authorized access and monthly archiving (retained for 5 years), and achieve closed-loop data management.

[0013] Furthermore, the front-end lightweight algorithm integration module includes the following functions: S101: Based on the hardware structure parameters of the drone remote controller, including processor computing power, memory storage capacity, interface transmission rate and power supply and battery life specifications, design the hardware adaptation interface of the algorithm integration unit, clarify the CPU resource allocation ratio, memory usage threshold, data transmission bandwidth standard and power consumption control range during algorithm operation, and generate a hardware adaptation solution. In this embodiment of the invention, the hardware structure parameters of the drone remote controller adapted to the network acceptance scenario are obtained as follows: the processor is a quad-core ARM architecture with a computing power of 1.5GHz, a single-core floating-point operation speed of 600MFLOPS, and a total floating-point operation power of F... total =4 cores × 600 MFLOPS / core = 2400 MFLOPS, meeting the real-time running requirements of lightweight front-end algorithms; 3GB DDR4 memory storage capacity (M total =3GB), peak read / write speed of 1800Mbps, supports multi-algorithm concurrent storage; interface includes USB 3.2 (rated transfer rate B total =10Gbps), UART (rated transmission rate B) total=230400bps) and HDMI 2.0 (rated transmission rate B total =18Gbps), adapted for high-speed image transmission and command interaction; power supply specifications are 14.8V DC input, battery capacity C=7.3Ah (corresponding to power capacity 14.8V×7.3Ah=108.04Wh), and continuous working rated power consumption P nom =18W, calculated according to the endurance guarantee formula, the theoretical endurance time T≥(108.04Wh×0.85) / 15W≈6.12 hours, matching the endurance requirements of long distribution network acceptance. CPU resource allocation quantification formula: single algorithm CPU resource usage ratio α i =(This algorithm requires F floating-point operations) i / Total floating-point performance of the processor F total ) × adaptation redundancy coefficient k1 (k1 takes a value of 1.1~1.3 to cope with computational fluctuations); total resource allocation of all algorithms Σα i ≤90%, with 10%~15% system redundancy reserved. 2. Memory usage threshold quantization formula: Maximum memory usage threshold M for a single algorithm. imax = (Algorithm base memory requirement M) base +Data cache increment M) × safety factor k2 (k2 ranges from 1.2 to 1.5 to avoid memory overflow); total memory usage threshold M totalmax ≤ Total memory capacity (M) total ×70% (30% memory reserved for system scheduling and handling of sudden data bursts). 3. Standard quantification formula for data transmission bandwidth: Lower limit of bandwidth usage for a specific interface B imin = The peak bit rate of the data transmitted by this interface R peak × Stability coefficient k3 (k3 ranges from 0.8 to 0.9 to ensure transmission stability); Maximum bandwidth usage B imax ≤ Interface rated transmission rate B total ×80% (20% bandwidth reserved for concurrent interaction of multiple devices). 4. Power consumption control range quantization formula: Average power consumption P during algorithm operation average ≤ Power supply and rated power consumption P nom ×85%; Peak power consumption P peak ≤ Power supply and rated power consumption P nom ×95%; Endurance guarantee formula: Actual endurance time T≥ (Battery capacity C×Discharge efficiency η) / Algorithm average power consumption P average(η ranges from 0.8 to 0.9, representing the typical discharge efficiency of a lithium battery). Based on the above parameters and the corresponding quantization formula, the hardware adaptation interface of the algorithm integration unit is designed. The specific parameter derivation is as follows: 1. CPU resource allocation: Target recognition algorithm floating-point operation requirement F1 = 840 MFLOPS, α1 = (840 MFLOPS / 2400 MFLOPS) × 1.2 ≈ 35% (supporting the identification of key work parts); Binocular ranging algorithm floating-point operation requirement F2 = 720 MFLOPS, α2 = (720 MFLOPS / 2400 MFLOPS) × 1.25 ≈ 30% (ensuring distance measurement accuracy); Defect recognition algorithm floating-point operation requirement F3 = 600 MFLOPS, α3 = (600 MFLOPS / 2400 MFLOPS) × 1.25 ≈ 25% (achieving rapid defect diagnosis); The system reserves 10% (to cope with sudden operations in complex channels), satisfying Σα i =90%≤90% requirement. 2. Memory usage threshold: Basic memory requirement M for a single algorithm. base Both are 400MB, and the data cache increment M is 100MB, according to the formula M imax = (400MB + 100MB) × 1.2 = 600MB, limiting the maximum memory usage of a single algorithm to 600MB; total memory usage threshold M totalmax =3GB × 70% ≈ 2.1GB, actually set to 2GB (reserving redundancy to further mitigate risks), to avoid memory overflow affecting acceptance continuity. 3. Data transmission bandwidth standard: USB 3.2 transmission panoramic image peak bitrate R peak =7.5Gbps, according to formula B imin =7.5Gbps × 0.8 = 6Gbps, the bandwidth usage is set to ≥6Gbps when transmitting panoramic images; the peak code rate of the UART transmission route adjustment command is R. peak =100kbps, according to formula B imax =230400bps×80%≈184.32kbps, actual bandwidth usage ≤80kbps (more bandwidth redundancy is reserved, approximately 1-80 / 184.32=56.6%). 4. Power consumption control range: according to formula P average ≤18W×85%=15.3W, the average power consumption during algorithm operation is set to ≤15W; according to formula P peak ≤18W×95%=17.1W, setting peak power consumption ≤17W; power consumption is adjusted in real time through a dynamic voltage adjustment module, combined with a battery life guarantee formula to ensure actual battery life ≥6 hours, achieving a balance between battery life and performance. Finally, a hardware adaptation solution is generated, clearly defining interface pin definitions, resource allocation details, and power consumption control parameters to ensure stable operation of core algorithms such as target recognition, binocular ranging, and defect recognition on the remote controller, supporting the implementation of autonomous cruise and intelligent acceptance functions.

[0014] S102: Prune the lightweight target recognition, binocular ranging and defect recognition algorithms by removing calculation branches whose contribution is lower than a set threshold, merging duplicate feature extraction layers, and compressing algorithm parameters using weight quantization and activation value quantization techniques to generate a lightweight algorithm model. In this embodiment of the invention, to meet the real-time requirements of long-line distribution network acceptance testing, pruning is performed on the original target recognition algorithm (based on CNN architecture, containing 16 convolutional layers and 5 fully connected layers), binocular ranging algorithm (based on SGBM matching, containing 10 feature extraction layers and 4 matching layers), and defect recognition algorithm (based on MobileNet architecture, containing 20 convolutional layers and 3 classification layers): A contribution threshold of 0.008 is set. By calculating the feature contribution value of each calculation branch, three convolutional branches in the target recognition algorithm with contributions below the threshold are removed. Four duplicate feature extraction layers in the defect recognition algorithm (output feature similarity 96%) are merged. Redundant matching branches in the binocular ranging algorithm are deleted. Weight quantization technology is used to convert algorithm parameters from 32-bit floating-point numbers to 8-bit integers, with quantization error controlled within 2.5%. Activation value quantization technology is used to map the activation function output value to the 0-255 integer range, achieving a compression ratio of 4:1. Simultaneously, in conjunction with the requirements for distribution network defect identification, the feature extraction layer of the defect identification algorithm was optimized to enhance the feature response of high-frequency defects such as insulator damage and bolt loosening. After processing, the number of parameters in the target identification algorithm was reduced from 12 million to 2.2 million, the number in the binocular ranging algorithm was reduced from 8 million to 1.5 million, and the number in the defect identification algorithm was reduced from 15 million to 3 million. The overall algorithm model size was compressed from 180MB to 35MB, and the single-frame image processing time was shortened from 400ms to 60ms, generating a lightweight algorithm model that meets the computing and storage requirements of the UAV remote controller and supports the efficient operation of front-end visual recognition and intelligent defect identification functions.

[0015] S103: Based on hardware adaptation solutions and lightweight algorithm models, formulate binary data interaction format, command response timing mechanism, data verification rules and transmission encryption specifications between algorithms and UAVs, clarify the mapping relationship between algorithm output commands and UAV execution actions, and generate interaction adaptation protocols. In this embodiment of the invention, based on a hardware adaptation scheme and a lightweight algorithm model, and combined with the command interaction and data transmission requirements of power distribution network project acceptance, an interaction adaptation protocol between the algorithm and the UAV is formulated: the binary data interaction format adopts the structure of "frame header (2 bytes) + function identifier (1 byte) + data type (1 byte) + data length (2 bytes) + data body (N bytes) + checksum (2 bytes) + frame tail (2 bytes)", the frame header is fixed at 0xAB67, and the function identifier is divided into flight path control (0x01), image acquisition (0x02), defect identification (0x03), and status feedback (0x04), ensuring the tracking of project acceptance task execution. Data interaction accuracy is ensured; the command response timing mechanism is set so that after the algorithm sends a control command, the UAV must return a response signal within 80ms. If no response is received within the time limit, a second retransmission will be initiated, with a maximum of 4 retransmissions, to avoid command loss during long-distance acceptance testing; the data verification rule adopts CRC32 verification, and the verification range covers the function identifier to the data body. If the verification fails, the data frame is discarded and retransmission is triggered to ensure the integrity of image data and defect data; the transmission encryption specification adopts the AES-256 encryption algorithm. The key is pre-stored in the secure storage module of the remote controller and the UAV through the hardware interface. The encryption process is completed before data transmission, and decryption is performed immediately after reception to protect the security of acceptance data. The protocol clearly defines the mapping relationship between algorithm output commands and UAV actions: when the target recognition algorithm outputs the command "Tower positioning completed," the UAV executes "hover + adjust gimbal angle to preset shooting position"; when the binocular ranging algorithm outputs the command "Distance less than safety threshold," the UAV executes "lateral offset + elevation"; when the defect recognition algorithm outputs the command "Moderate defect detected," the UAV executes "hover + surround shooting (360°, one shot every 20°) + record position coordinates"; when the algorithm outputs the command "Flight deviation," the UAV executes "adjust trajectory according to adaptive planning." The protocol also specifies data transmission bandwidth allocation: image data occupies 75% of the USB 3.2 bandwidth, and control commands occupy 85% of the UART bandwidth. A complete interactive adaptation protocol is generated to ensure efficient, secure, and accurate data transmission between the algorithm and the UAV, supporting standardized image data collection during adaptive flight planning and acceptance processes.

[0016] S104: Based on the interactive adaptation protocol, the UAV is controlled to perform adaptive route planning along the power distribution network line and collect panoramic image data of the towers and line facilities.

[0017] In this embodiment of the invention, based on an interactive adaptation protocol and considering the long length of power distribution network lines and the complex channel environment, the UAV is controlled to perform adaptive route planning along the power distribution network lines. The starting point of the route planning is set at the coordinates of the starting tower of the power distribution network line (longitude 117.2°, latitude 38.5°), and the ending point is the coordinates of the ending tower of the line (longitude 117.5°, latitude 38.6°). The total length of the line is 8km, containing 32 towers, covering different channel environments such as mountains, plains, and villages. After takeoff, the UAV obtains its own position in real time through GPS + Beidou dual-mode positioning (positioning accuracy ±0.3m). Combining the tower position and channel boundary feature data output by the lightweight target recognition algorithm, the flight trajectory is dynamically adjusted. The flight altitude is maintained at 8m above the top of the tower and 6m horizontally away from the tower. The flight speed is adjusted according to the scenario: 5m / s for plains sections, 3m / s for mountain sections, and 2.5m / s for densely populated village sections. During flight, following the command mapping relationship in the interaction protocol, when the target recognition algorithm identifies a tower, the drone hovers and collects panoramic image data through the gimbal camera. The acquisition resolution is set to 4K (3840×2160), and the acquisition angle covers a 360° panoramic view of the tower (one image is taken every 45°, for a total of 8 images per tower). When passing line connection nodes and fittings, the binocular ranging algorithm measures the distance in real time. When the distance is less than 6m, the camera automatically zooms (2-6x zoom) to collect detailed images. When a defect is detected, the drone performs surround shooting and location recording according to the protocol. A total of 256 panoramic images of the tower, 1280 detailed images of node fittings, and 64 close-up images of defects are collected throughout the process. The image data is named in the standardized format of "Acceptance Date-Line Name-Tower Number-Shooting Location" and transmitted in real time to the remote controller via USB 3.2 interface. The lightweight algorithm model then performs subsequent defect identification and analysis, realizing autonomous acceptance of distribution network lines and standardized acquisition of image data, improving acceptance efficiency and quality.

[0018] Furthermore, the process of controlling the UAV based on the interactive adaptation protocol to perform adaptive flight path planning along the power distribution network lines and collecting panoramic image data of the towers and line facilities includes: Based on the binary data interaction format in the interactive adaptation protocol, the distribution network line routing feature data output by the lightweight algorithm model is parsed, including the coordinate sequence of the line centerline, the routing deflection angle and curvature change data, as well as the geographic environment contour data, including the terrain undulation contour, obstacle distribution contour and channel boundary contour. The parsed data is then denoised to generate a basic dataset for route planning. In this embodiment of the invention, the 8km distribution network route characteristic data output by the lightweight algorithm model is parsed based on the binary format of "frame header 0xAB67 + function identifier 0x01 (route control) + data type 0x05 (line characteristics) + data length + data body + CRC32 check + frame tail 0xCD89" in the interactive adaptation protocol: the line centerline coordinate sequence is (117.2000° / 38.5000°, 117.2006 ...6°, 117.2006° / 38.5006°, 117.2006° 0.5000°, ..., 117.5000° / 38.6000°), a total of 1334 coordinate points (interval 6m); the orientation deflection angle is calculated once every 30m, with 0° (due south) for segments 1-50, 3° (east) for segments 51-100, and -2° (west) for segments 101-150, for a total of 267 angle values; the curvature change data is recorded once every 150m, with a curvature radius of 800-1000m in mountainous areas and 1200-1500m in plains. The geographic environment contour data includes: topographic relief contour (elevation 40-75m, one elevation value sampled every 100m, elevation difference 35m in mountainous areas), obstacle distribution contour (tree location 117.3200° / 38.5200°, height 18m; house location 117.4100° / 38.5500°, height 10m; utility pole location 117.3600° / 38.5300°, height 15m), and passage boundary contour (east boundary 117.2000°+0.0003°, west boundary 117.2000°-0.0003°). The analytical data underwent noise reduction: the coordinate sequence was filtered using Kalman filtering (state equation Xk=1.02Xk-1+0.01Bu, observation equation Zk=1.01Xk+0.005Vk) to correct for a drift error of ±0.0001°; the angle data was filtered using a 5-point moving average to eliminate abrupt changes of ±1°; and the elevation data was filtered using the 3σ criterion to remove outliers such as 85m and 25m (mean 55m, standard deviation 10m). The resulting dataset generates a basic dataset for route planning, with coordinate errors ≤0.00005°, angle errors ≤0.3°, and elevation errors ≤0.8m, providing accurate data for route calculations in complex corridors.

[0019] Furthermore, considering the characteristics of the distribution network line corridor environment, including terrain complexity, vegetation density, building distribution, and meteorological conditions, along with tower distribution density data, safety distance thresholds for route planning are set, including horizontal safety distances to the tower body, vertical safety distances to the line conductors, and obstacle avoidance safety distances. Shooting angle parameters are defined, including horizontal shooting angle ranges, vertical shooting angle ranges, and circumferential shooting angle intervals. Data acquisition frequency standards are determined, including image shooting frame rate, location information sampling frequency, and status parameter recording frequency, and route constraint conditions are generated. In this embodiment of the invention, the characteristics of the 8km distribution network line corridor environment are combined: the complexity of the terrain (3km in mountainous areas, slope 8-18°; 3km in plains, slope 2-5°; 2km in villages, slope 1-3°), the density of vegetation (8-15m tree spacing in mountainous areas, 15-25m in plains, and 20-30m in villages), the distribution of buildings (1-2 two- to three-story houses every 500m in villages, with the shortest distance from the line being 12m), the meteorological conditions (wind speed 2.5m / s and light intensity 4500lux at the time of acceptance), and the density of pole distribution (32 poles / 8km, spacing 250m, 22 straight poles, 6 tension poles, and 4 corner poles). Set flight path safety distance thresholds: Horizontal safety distance from the main tower body ≥ 6m (to avoid collision with crossarms), vertical safety distance from the power line conductor ≥ 4m (the lowest point of the conductor sag is ≥ 4m from the drone; in mountainous areas with large sag, the threshold is increased by 1m), and obstacle avoidance safety distance ≥ 3m (trees 18m high, drone flight altitude 22m, vertical distance 4m; houses 10m high, flight altitude 13m, vertical distance 3m; power poles 15m high, flight altitude 19m, vertical distance 4m). Define shooting angle parameters: Horizontal shooting angle range -50° (west) to 50° (east) (covering hardware on both sides of the tower), vertical shooting angle range -35° (downward, shooting insulator strings) to 15° (upward, shooting the top of the tower), and circumferential shooting angle interval of 25° (one shot every 25°, 14 shots per tower, ensuring coverage of blind spots on corner towers). Establish the following data acquisition frequency standards: image capture frame rate of 3 frames / second (4K resolution, ensuring detailed acquisition), location information sampling frequency of 2Hz (GPS + BeiDou dual-mode, improving positioning continuity), and status parameter recording frequency of 1Hz (battery power, flight speed, gimbal angle, real-time monitoring of equipment status). Organize these parameters into a flight path constraint document, clarifying parameter adjustment rules for each scenario to ensure a balance between safety and acceptance quality.

[0020] Furthermore, the basic dataset of route planning and route constraints are input into the adaptive route planning algorithm. The initial route is calculated using the A-star path search algorithm, and the path nodes are optimized and adjusted using a genetic algorithm to generate an initial route plan. This initial route plan includes the coordinate sequence of route nodes, the flight speed between each node, and the turning angle. In this embodiment of the invention, the basic dataset of route planning and the constraints are input into the adaptive route planning algorithm: First, the initial path is calculated using the A-star path search algorithm, with the starting point (117.2000° / 38.5000°, altitude 20m) as the starting point and the ending point (117.5000° / 38.6000°, altitude 20m) as the target point. The cost function is f(n)=1.2g(n)+0.8h(n) (g(n) is the actual distance from the starting point to point n, and h(n) is the straight-line distance from point n to the ending point, with weighted emphasis on safe paths). 160 path nodes (interval 50m) are obtained through the search, with node coordinates such as (117.2050° / 38.5000°, 20m) and (117.2100° / 38.5000°, 20m). The node altitude in mountainous sections is adjusted to 22m. The path nodes were then optimized using a genetic algorithm: the fitness function was set as "100% safe distance compliance rate, ≥98% shooting angle coverage, and shortest flight distance," with a population size of 60, 40 iterations, a crossover probability of 0.85, and a mutation probability of 0.06. After optimization, the 78th node (originally 117.3900° / 38.5400°, 20m) was adjusted to 117.3900° / 38.5402°, 20m (to avoid houses); the 125th node (originally 117.4500° / 38.5700°, 20m) was adjusted to 117.4500° / 38.5700°, 22m (to increase altitude in mountainous areas). The flight speed between nodes was set as follows: 6m / s in plains, 3.5m / s in mountains, and 3m / s in villages, with a turning angle ≤8° / s (to avoid sharp turns affecting shooting). An initial flight path plan was generated, including coordinates of 160 nodes, speed and turning angle, with a safety distance compliance rate of 100%, a shooting coverage rate of 99%, and a total flight distance of 8.3km.

[0021] Furthermore, the binocular ranging algorithm is used to detect obstacle distance data in the initial flight path in real time, including the straight-line distance between the obstacle and the UAV, the relative height difference and the horizontal offset distance. Combined with the outline size data and morphological feature data of the obstacle, the conflict path segments in the initial flight path are dynamically adjusted, the detour path nodes are replanned, and the final adaptive flight path is generated. In this embodiment of the invention, a binocular ranging algorithm (based on lightweight SGBM matching, with a processing speed of 50ms / frame) is used to detect obstacle distances in the initial flight path in real time: At node 117.3200° / 38.5200° (mountainous section), a tree (coordinates 117.3200° / 38.5200°) is detected with a straight-line distance of 5.5m (less than the safe distance of 6m) from the drone, a relative height difference of 4m (drone 22m, tree 18m), and a horizontal offset distance of 0.2m; the tree's outline dimensions are a diameter of 4m and a height of 18m, with a conical shape (branch and leaf extension range of 2m). At node 117.3600° / 38.5300°, a utility pole is detected with a straight-line distance of 5.8m from the drone (close to the threshold) and a relative height difference of 4m, with no conflict. At node 117.4100° / 38.5500° (village section), a house is detected with a straight-line distance of 7m from the drone (meets the standard) and a relative height difference of 3m, with no conflict. Dynamic adjustments were made to the conflict path segment (117.3150°-117.3250° / 38.5200°) where trees were located: two new detour nodes were added: Node 1 (117.3200° / 38.5203°, 22m) and Node 2 (117.3225° / 38.5203°, 22m), increasing the detour distance by 0.08km to ensure a horizontal distance of ≥6.2m from the trees. After adjustment, the path smoothness was recalculated, with a turning angle ≤7° / s and a flight speed maintained at 3.5m / s. The entire flight path was repeatedly tested, correcting a total of 3 conflicts (2 trees, 1 low utility pole), generating a final adaptive flight path containing 165 nodes. The path smoothness met the requirements for shooting stability.

[0022] Furthermore, the drone is controlled to fly along the final adaptive route. Using the onboard image acquisition equipment, it captures images of the overall structure, key connection parts, and vulnerable points of the tower and line facilities from multiple angles according to the set shooting angle parameters and acquisition frequency standards. Simultaneously, it records the position coordinate data, angle parameter data, light intensity data, equipment exposure parameters, and shooting timestamps for each shot. The raw images are then converted and preliminarily screened to remove invalid images caused by equipment shaking, excessively strong or weak lighting, and retain clear and identifiable target images to generate panoramic image data containing multi-dimensional perspectives.

[0023] In this embodiment of the invention, by controlling the UAV to fly along the final adaptive route, the onboard image acquisition device operates according to the set parameters: horizontal -50° to 50°, vertical -35° to 15°, taking one picture every 25° around the tower; frame rate 3 frames / second, 4K resolution; position sampling 2Hz, status recording 1Hz. During flight, at straight tower 10 (node ​​50: 117.3500° / 38.5200°), the drone hovered for 45 seconds, taking 7 overall images from horizontal angles of -40° (west side fittings), -15° (west side insulator), 15° (east side insulator), and 40° (east side fittings), and vertical angles of -30° (lower part of the insulator string), -10° (middle part of the insulator string), and 10° (tower crossarm). At tension tower 18 (node ​​90: 117.4200° / 38.5500°), a 360° surround shot was performed, taking 14 shots every 25°, focusing on the connection between the tension clamp and the conductor. At the conductor joint in the village section (node ​​130: 117.4600° / 38.5800°), 4 detailed images were taken from horizontal angles of 0° and vertical angles of -25°, magnifying the crimped joint. Synchronous recording of shooting data: Position 117.3500° / 38.5200°, Angle -30° (vertical), Illumination 4800 lux, Exposure 1 / 600s, Timestamp 20241202151000. The original images underwent format conversion (JPEG to TIFF, lossless compression). Initial screening used a sharpness assessment (grayscale gradient value ≥110 for sharpness), removing 8 invalid images due to shaking (gradient 95), excessively strong lighting (grayscale >245), or excessively weak lighting (grayscale <30). 1820 sharp target images were retained, generating panoramic image data containing horizontal, vertical, and surround multi-dimensional perspectives. The image resolution is 3840×2160, with a 99% recognition rate for key parts of the tower, providing high-quality material for subsequent defect identification.

[0024] Furthermore, the intelligent cruise and data acquisition module includes the following functions: Acquire panoramic image data, and use a lightweight target recognition algorithm to extract features from key operating parts corresponding to the main tower, line segment, connecting hardware, insulator string, and grounding device in the panoramic image data. Extract the contour edge features of each key operating part, identify the coordinates of feature points, determine the pixel coordinate range, contour morphology parameters and feature point distribution information of each key operating part in the image, and generate patrol key area marking data. In this embodiment of the invention, the previously generated panoramic image data (1820 4K TIFF images, including straight towers, tension towers, conductor joints and other scenes) is obtained and input into a lightweight target recognition algorithm (based on the MobileNetV3 architecture, containing 16 convolutional layers, 4 pooling layers, 2.1 million parameters, and an inference speed of 70ms / frame). Feature extraction was performed on key operational components: For the main tower body, Canny edge detection (threshold 120-230) was used to extract the contour, identifying the tower base pixel coordinates (150, 1800)-(850, 1800) and the tower top (450, 300)-(550, 300). The contour morphology parameters were rectangularity 0.93, aspect ratio 0.35, and feature point distribution interval 50 pixels. For the line segment, Hough linear transformation was used to extract the edges, with pixel coordinates ranging from (1200, 800) to (2600, 800). The contour morphology parameter was straightness 0.98, and the feature points were conductor joints (1800, 800) and (2200, 800). For the connecting hardware (suspension clamps), SIFT feature matching was used, as shown in the image... The pixel coordinates are (1800, 780)-(1900, 820), the aspect ratio is 2.8, and the feature points are 3 points in total, including the two ends and the middle of the clamp; the insulator string is extracted by HSV color segmentation (Hue0-20, Saturation30-80, Value50-200), the pixel coordinates are (1800, 500)-(1900, 780), the outline shape parameters are string length 280 pixels and diameter 30 pixels, and the feature points are 8 points in total, including the two ends of the insulator and the shed node; the grounding device is extracted by LBP texture, the pixel coordinates are (300, 1600)-(400, 1800), the outline shape parameter is irregularity 0.4, and the feature points are 3 points in total, including the two ends of the grounding electrode and the connection point. Recording data in the format of "image number-location name-pixel coordinate range-contour parameter-feature point coordinates" generates key area marking data for cruise operations, marking a total of 8,900 key areas with a feature extraction accuracy of 97%, providing accurate location information for sub-region division.

[0025] Furthermore, based on the marked data of key cruise areas and the coordinates of the path nodes of the final adaptive route, cruise sub-areas are divided according to the importance, distribution location and acceptance priority of key operational parts. Each cruise sub-area clearly includes a list of key operational parts. Cruise flight speed, hovering time, image capture resolution parameters, focus distance parameters and exposure compensation parameters are set for each cruise sub-area to generate a sub-area cruise plan. In this embodiment of the invention, based on the cruise key area marking data and combined with the coordinates of 165 path nodes of the final adaptive route (e.g., node 50 corresponding to straight tower 10: 117.3500° / 38.5200°, node 90 corresponding to tension tower 18: 117.4200° / 38.5500°), the critical operation parts are prioritized according to their importance: Level 1 (insulator strings, conductor joints, affecting insulation and current carrying safety, priority 100), Level 2 (connecting hardware, grounding devices, affecting structural stability). The 8km line is divided into 8 patrol sub-areas (1 per 1km) based on its distribution location. Sub-area 1 covers nodes 1-21 (117.2000°-117.2200°), including towers 1-4, 4 sets of insulator strings, and 2 conductor joints. Sub-area 2 covers nodes 22-42 (117.2200°-117.3200°), including towers 5-8, 4 sets of insulator strings, and 3 conductor joints. The list of key operating parts for each sub-area is defined: Sub-area 5 (covering nodes 83-103, 117.4000°-117.4500°) lists "tension towers 16-18, 6 sets of insulator strings, 6 tension clamps, 4 conductor joints, and 3 sets of grounding devices". The sub-area cruise parameters are set as follows: Level 1: Flight speed 2 m / s, hovering time 45 seconds, shooting resolution 4K (3840×2160), focusing distance 4.5 m, exposure compensation +0.5 EV; Level 2: Flight speed 3 m / s, hovering time 30 seconds, resolution 4K, focusing distance 6 m, exposure compensation 0 EV; Level 3: Flight speed 4 m / s, hovering time 15 seconds, resolution 2K (2560×1440), focusing distance 8 m, exposure compensation -0.3 EV. Specific parameters for sub-area 5: Tension clamp shooting speed 2 m / s, hovering time 45 seconds, 4K resolution, focusing distance 4.5 m, +0.5 EV; Tower body speed 4 m / s, hovering time 15 seconds, 2K resolution, focusing distance 8 m, -0.3 EV. This sub-area cruise plan ensures more detailed data collection for high-priority areas.

[0026] Furthermore, based on the sub-area cruise plan, the drone is controlled to enter each cruise sub-area in sequence. The image acquisition device collects data on the size of the tower structure, the tightness of the line connection, the standardized execution data of the construction process, the vegetation impact data in the passage environment, and the building safety distance data according to the set parameters. Simultaneously, the collected multi-dimensional image data is associated and marked with the identification information and collection parameter information of the corresponding sub-area to form a data association table. In this embodiment of the invention, the UAV is controlled to enter sub-region 5 (117.4000°-117.4500°) according to the sub-regional cruise scheme: reaching node 17 of the tension tower (117.4250° / 38.5550°), flying at a speed of 2m / s and hovering for 45 seconds according to the primary location parameters, with the image acquisition device focused at 4.5m and exposure compensation of +0.5EV, acquiring insulator string size data (pixel measurement length 280 pixels, converted to actual length 2.8m, deviation ±0.1m) and tension clamp fastening status data (exposed bolt threads). 2-3 threads, with 2 threads observed per pixel; fly to conductor joint 12 (node ​​95: 117.4350° / 38.5600°), speed 2m / s, hover for 45 seconds, collect construction process data (crimped joint is flat, burr-free, pixel flatness error < 2 pixels); fly to grounding device 15 (node ​​100: 117.4450° / 38.5650°), speed 3m / s, hover for 30 seconds, collect channel environment data (grounding device is 8m horizontally from the building, pixel distance is 800 pixels, converted to actual 8m, meets the standard). Simultaneously associate and mark the collected multi-dimensional image data (insulator string image number 501, tension clamp image 502, conductor joint image 503) with sub-region identifier (sub-region 5) and acquisition parameters (speed 2m / s, focus 4.5m, etc.) to form a data association table. The table structure is "Image Number - Sub-region Identifier - Key Parts - Acquisition Speed ​​- Hover Time - Resolution - Focus Distance - Exposure Compensation - Acquisition Data Type", such as "501 - Sub-region 5 - Insulator String - 2m / s - 45s - 4K - 4.5m - +0.5EV - Size Data", generating a total of 1260 associated records to ensure that the data can be traced back to the specific sub-region and acquisition parameters.

[0027] Furthermore, the collected multidimensional image data is standardized by converting image data of different formats into a preset image format. The image quality assessment algorithm is used to detect the image sharpness, contrast and noise content, remove blurry, overexposed, underexposed and noisy invalid images, retain clear and valid target images, and generate a standardized multidimensional image dataset. In this embodiment of the invention, the acquired multidimensional image data (a total of 1260 images, including TIFF and JPEG formats, 920 4K images and 340 2K images) was standardized: all images were uniformly converted to TIFF format (lossless compression, preserving original pixel information) using an image format conversion tool; 4K images retained 3840×2160 pixels, and 2K images retained 2560×1440 pixels, with a pixel loss rate of <0.1% during the conversion process. Image quality assessment algorithms were used to detect quality: sharpness was calculated using the Laplacian operator gradient value (≥120 for sharpness), contrast was calculated using the standard deviation of grayscale values ​​(≥60 for acceptable), and noise content was calculated using the Gaussian filter residual (≤18 for acceptable). The detection results showed: 42 images had sharpness gradient values ​​of 95-119 (blurry), 28 images had contrast values ​​of 45-59 (low contrast), 15 images had noise values ​​of 19-25 (high noise), and a total of 85 invalid images. After removing invalid images, 1175 clear and valid target images (resolution ≥ 120, contrast ≥ 60, noise ≤ 18) were retained to generate a standardized multidimensional image dataset. The dataset was named according to "sub-region number-image number.TIFF" (e.g., sub-region 5-501.TIFF), and quality assessment parameters were attached during storage (501.TIFF: resolution 135, contrast 68, noise 15). The data pass rate reached 93.2%, meeting the image quality requirements for subsequent defect identification.

[0028] Furthermore, based on the data association table, the standardized multidimensional image dataset is associated and stored with the patrol key area marking data. The pixel coordinate mapping relationship, feature point association relationship and acceptance item correspondence relationship between each image data and the corresponding key operation part are established, and a two-way index between image data and key operation parts is constructed.

[0029] In this embodiment of the invention, a standardized multidimensional image dataset (1175 TIFF images) is associated and stored with the key patrol area marking data based on a data association table: a pixel coordinate mapping relationship is established, such as the pixel coordinates (1800, 500)-(1900, 780) in the marking data corresponding to sub-region 5-501.TIFF (insulator string image), and the conversion ratio between image pixels and actual size is recorded (100 pixels = 1m); a feature point association relationship is established, with the insulator skirt node pixels (1850, 600) and (1850, 680) in the image corresponding to the feature points in the marking data, and the corresponding acceptance item (insulator skirt damage inspection) is marked at this point; an acceptance item correspondence relationship is established, with each image associated with 1-2 acceptance items (501.TIFF is associated with "insulator string length compliance inspection, skirt damage inspection"). A bidirectional index is constructed: the forward index is arranged according to "image number → key parts → acceptance items → marked data" (e.g., 501 → insulator string → length compliance → pixel range 1800-1900 / 500-780); the reverse index is arranged according to "key parts → acceptance items → image number" (e.g., insulator string → damage inspection → 501, 506, 512). The index is stored in a relational database, supporting quick lookup of the corresponding tower location by image number (501 corresponds to tension tower 17, coordinates 117.4250° / 38.5550°), or lookup of related images (502, 508, 515) by "tension clamp tightening inspection", providing efficient data retrieval support for subsequent intelligent acceptance and defect tracing.

[0030] Furthermore, before performing format standardization processing on the acquired multidimensional image data, feature enhancement processing is also performed on the multidimensional image data, including: Extract the basic visual features of each image from the multidimensional image data, including pixel brightness distribution histogram, image contrast value, edge contour sharpness index, target area pixel ratio and color channel distribution data, classify and record them according to image number and feature type, and generate image basic feature dataset. In this embodiment of the invention, the basic visual features of each image in the previously generated standardized multidimensional image dataset (1175 TIFF images, including tension towers, insulator strings, etc.) are extracted: the pixel brightness distribution histogram is generated by statistically analyzing the proportion of pixels at the 0-255 gray level. For example, the histogram of sub-region 5-501.TIFF (insulator string image) shows that the proportion of pixels at the gray level of 80-150 is 65%, 150-220 accounts for 20%, and 0-80 and 220-255 each account for 7.5%; the image contrast value is determined by calculating the difference between the maximum and minimum gray level values. The maximum gray level value of this image is 210, and the minimum gray level value is 60. The contrast ratio is 150; the edge contour sharpness index is calculated by the Sobel operator using the mean gradient of edge pixels. The mean gradient of the insulator skirt edge in this image is 90 (the higher the value, the sharper the contour); the pixel ratio of the target area is calculated by segmenting the target and background pixels. The number of target pixels in the insulator string is 86,400 (1800-1900 pixels × 500-780 pixels), and the total number of pixels is 8,294,400 (3840 × 2160), with a ratio of 1.04%; the color channel distribution data is for the RGB three color channels, and the mean gray level of each channel is calculated separately. The mean R channel is 125, the mean G channel is 118, and the mean B channel is 112. The data is categorized and recorded in the format of "image number-feature type-feature value", such as "sub-region 5-501-brightness histogram-grayscale 80-150 percentage 65%" and "sub-region 5-501-contrast-150". A total of 1175×5=5875 feature data are recorded, generating a basic image feature dataset, which provides a quantitative basis for subsequent weight setting.

[0031] Furthermore, based on the key consideration dimensions of power distribution network facility acceptance, including structural integrity acceptance, connection tightness acceptance, process standardization acceptance, and safety distance acceptance, the importance weights of each basic visual feature under different acceptance dimensions are set, the weight coefficients of each feature are determined by the analytic hierarchy process, and the feature items in the image basic feature dataset are prioritized according to the size of the weight coefficients to generate a feature priority sequence. In this embodiment of the invention, four key dimensions of acceptance of power distribution network facilities are considered: structural integrity (inspecting insulator damage and tower cracks, requiring clear edges and brightness distribution support), connection tightness (inspecting loose bolts and tight clamps, requiring high contrast to distinguish details), process standardization (inspecting conductor crimping and hardware installation, requiring accurate color and target proportion assistance), and safety distance (inspecting the distance between equipment and obstacles, requiring brightness and contrast to ensure measurement accuracy). The importance weights of each basic visual feature are set under different dimensions: Structural integrity dimension: edge sharpness weight 0.4, brightness histogram weight 0.3, contrast weight 0.2, target proportion weight 0.05, color distribution weight 0.05; Connectivity and tightness dimension: contrast weight 0.45, edge sharpness weight 0.3, brightness histogram weight 0.15, target proportion weight 0.05, color distribution weight 0.05; Process standardization dimension: color distribution weight 0.35, contrast weight 0.25, target proportion weight 0.2, edge sharpness weight 0.1, brightness histogram weight 0.1; Safety distance acceptance dimension: brightness histogram weight 0.35, contrast weight 0.3, target proportion weight 0.2, edge sharpness weight 0.1, color distribution weight 0.05. A judgment matrix is ​​constructed using the analytic hierarchy process (AHP), and the weight coefficients of each feature in the overall acceptance system are calculated: contrast weight 0.32, edge sharpness weight 0.28, brightness histogram weight 0.22, target proportion weight 0.1, color distribution weight 0.08. Sort the features by weight coefficient from largest to smallest to generate a feature priority sequence: contrast > edge sharpness > brightness histogram > target proportion > color distribution, and identify high-weight features as the focus of subsequent enhancement.

[0032] Furthermore, a lightweight image enhancement algorithm is used to enhance the high-weight visual features in the feature priority sequence, an adaptive histogram equalization algorithm is used to improve image contrast, a bilateral filtering algorithm is used to enhance edge contour clarity, and a super-resolution reconstruction algorithm is used to supplement blurred detail feature information in the image, thereby generating enhanced image feature data. In this embodiment of the invention, a lightweight image enhancement algorithm is used to enhance high-weight visual features (contrast, edge sharpness) in the feature priority sequence: For contrast, an adaptive histogram equalization algorithm is used to divide the image grayscale level into 16 non-overlapping blocks (each block is 240×135 pixels), and histogram equalization is performed independently on each block, limiting the contrast gain to 2.0 to avoid excessive noise enhancement. After processing the sub-region 5-501.TIFF, the contrast ratio increases from 150 to 205, and the grayscale level 80-200 accounts for 88%, making it easier to distinguish bolt threads and wire clamp edges; For edge sharpness, an adaptive histogram equalization algorithm is used to enhance the image grayscale level in the sequence. A bilateral filtering algorithm, with a spatial domain standard deviation of 6 and a gray-level domain standard deviation of 12, smooths background noise (such as sky streaks) while preserving edge details. After processing, the mean edge gradient of the image increases from 90 to 125, clearly revealing pixel-level details (1-2 pixels wide) of insulator skirt cracks. For low-weight but critical blurred details (such as wire crimping textures), a super-resolution reconstruction algorithm (based on bicubic interpolation) is used to increase the image resolution from 4K (3840×2160) to 8K (7680×4320), supplementing the pixel information of the textures and reducing the measurement accuracy of crimping flatness error from 3 pixels to 1 pixel. Enhanced image feature data is generated after processing, including an enhanced contrast map, edge gradient map, and super-resolution detail map, corresponding one-to-one with the original image sequence number, such as "sub-region 5-501-enhanced edge gradient map-mean gradient 125".

[0033] Furthermore, the enhanced image feature data is fused with the original multidimensional image data. A pixel-level feature overlay algorithm is used to embed the enhanced feature data into the corresponding pixel positions of the original image. The fusion ratio between the enhanced features and the original image is adjusted by a weighted fusion rule to generate feature-enhanced multidimensional image data.

[0034] In this embodiment of the invention, enhanced image feature data is fused with the original multidimensional image data: a pixel-level feature overlay algorithm is used to embed the enhanced edge gradient data into the corresponding pixel position of the original image. For example, in sub-region 5-501.TIFF, the original gray value of the insulator skirt edge pixel (1850, 600) is 130. After overlaying the enhanced gradient value of 35, the new gray value = 130 + 35 × 0.4 = 144 (the gradient value is scaled by a ratio of 0.4 to avoid overexposure). The ratio of the equalized contrast data and the original gray value data is adjusted according to the weighted fusion rule, with the weight of the contrast enhancement part being 0.6 and the weight of the original image being 0.4. For example, if the original pixel gray value is 100, after equalization it becomes 140, and the fused gray value = 100 × 0.4 + 140 × 0.6 = 124. The detail data reconstructed by super-resolution is replaced with the original image pixel by pixel. The blurred wire crimp texture pixels (such as 2200, 800) in the low-resolution image are replaced with clear texture pixels under high resolution, while retaining the original color information. During the fusion process, the image size remains consistent with the original (3840×2160 pixels), and edge feathering is used to avoid obvious stitching marks between the enhanced and original regions. The processed image generates feature-enhanced multidimensional image data, such as sub-region 5-501-enhanced.TIFF. This image has a contrast ratio of 205, a mean edge gradient of 125, and a detail resolution equivalent to 8K. The insulator damage detection rate is improved from 88% to 98%, and the pixel-level judgment error for loose bolts is reduced from ±2 pixels to ±0.5 pixels, meeting the image detail accuracy requirements for intelligent acceptance of power distribution network facilities.

[0035] Furthermore, the defect intelligent identification module includes the following functions: A standardized multidimensional image dataset is obtained. The target region is extracted from each image in the standardized multidimensional image dataset using a semantic segmentation algorithm. Based on a pre-trained semantic segmentation model, the corresponding acceptance objects and background environment of the towers, lines, hardware, insulators, and grounding devices in the images are identified. A binary mask image is generated. The acceptance objects and background environment are separated by multiplying the pixels of the binary mask image with those of the original image to generate the target region image data. In this embodiment of the invention, a previously generated standardized multidimensional image dataset (1175 TIFF images, including acceptance objects such as tension towers, insulator strings, and hardware) is obtained and input into a pre-trained semantic segmentation model (based on the U-Net++ architecture, fine-tuned with 15,000 sets of distribution network facility samples, parameters compressed to 2.2 million, and inference speed of 65ms / frame). The model extracts shallow edge and deep semantic features of the image through an encoder (containing 8 convolutional layers), and performs pixel-level classification through a decoder (containing 8 deconvolutional layers), dividing the image pixels into 6 categories: "towers, lines, hardware, insulators, grounding devices, and background," generating a binary mask image (acceptance object pixel value 1, background pixel value 0). For example, in the mask image of sub-region 5-501.TIFF (insulator string image), the insulator string region has a pixel value of 1 (pixel coordinates 1800-1900×500-780, accounting for 1.04%), while the background (sky, trees) has a pixel value of 0 (accounting for 98.96%). By multiplying the pixel values ​​of the masked image and the original image (original pixel value × mask pixel value), the inspection target is separated from the background: the pixels of the insulator string retain their original grayscale values ​​(e.g., 120-150), while the background pixels become 0 (black), generating the target area image data. After processing 1175 images one by one, the accuracy rate of inspection target extraction reached 97.5%, and the extraction error of small targets such as insulators and fittings was ≤1.5 pixels, effectively eliminating background interference for defect identification.

[0036] Furthermore, based on the structural installation standards, process execution requirements, allowable range of dimensional deviations, and defect manifestations in historical defect cases in the power distribution network construction specifications, visual feature parameters of various defects are collected, including the shape features, color features, texture features, size features, and location features of the defect area. The judgment thresholds and classification standards of various defects are clarified, and a defect feature database is established. In this embodiment of the invention, based on the power distribution network construction specifications (structural installation standards: insulator string spacing ≥ 300mm, tower verticality deviation ≤ 1.5‰; process execution requirements: burr-free conductor crimp joints, 2-3 exposed threads on hardware bolts; allowable dimensional deviation range: clamp-to-conductor gap ≤ 2mm, grounding device burial depth ≥ 0.7m) and 2000 historical defect cases, visual characteristic parameters of 12 common defects were collected: insulator damage (shape characteristics: irregular polygon, color characteristics: gray value of the damaged area < 70, texture characteristics: crack texture contrast > 65, size characteristics: crack length ≥ 3mm, location characteristics: umbrella skirt edge) (Issuance); conductor strand breakage (shape characteristics: linear fracture, color characteristics: gray value at breakage > 200, texture characteristics: discontinuous conductor texture, size characteristics: number of broken strands ≥ 2, location characteristics: middle of conductor or joint); hardware corrosion (shape characteristics: flaky coverage, color characteristics: average R channel value of corrosion area < 95, texture characteristics: rough texture density > 0.85, size characteristics: corrosion area ≥ 15%, location characteristics: hardware surface); bolt loosening (shape characteristics: bolt and nut misalignment, color characteristics: no obvious color difference, texture characteristics: thread texture offset, size characteristics: loosening angle ≥ 20°, location characteristics: hardware connection node). Clearly defined defect judgment thresholds: crack length ≥ 3mm is judged as damage, number of broken strands ≥ 2 is judged as broken strand, corrosion area ≥ 15% is judged as corrosion, loosening angle ≥ 20° is judged as loosening; classification standards are based on "defect location + defect morphology" (e.g., insulator damage, hardware corrosion). A defect feature library was established, storing five feature parameters, judgment thresholds, and classification labels for 12 types of defects, with a total of 60 sets of feature data, providing a quantitative basis for defect identification.

[0037] Furthermore, the target area image data is input into a lightweight defect recognition algorithm. The shallow texture features and deep semantic features of the target area are extracted through the convolutional layer of the convolutional neural network to generate a feature map. The feature map is then dimensionality-reduced through a pooling layer. The processed features are then compared with the feature parameters in the defect feature library using cosine similarity calculation to identify the specific location coordinates, morphological features, and defect category of potential defects, generating preliminary defect recognition results. In this embodiment of the invention, the target region image data (such as the insulator target image of sub-region 5-501.TIFF) is input into a lightweight defect recognition algorithm (based on the MobileNetV2 architecture, containing 13 convolutional layers, 3 max pooling layers, and 1.8 million parameters): the first 5 convolutional layers extract shallow texture features (such as the edge texture of cracks on the insulator surface) to generate a 64-channel feature map (size 960×540); the last 8 convolutional layers extract deep semantic features (such as the semantic category of the damaged area) to generate a 128-channel feature map (size 240×135); the dimensionality of the feature map is reduced by a 2×2 max pooling layer to retain key features and reduce computation. The processed feature vector (dimensional 256) is then compared with the feature parameters of "insulator damage" (crack texture contrast 65, grayscale value 70) in the defect feature library using cosine similarity calculation, with a similarity value of 0.93 (threshold 0.8, considered a match). The system identifies the specific location coordinates of potential defects (edge ​​of insulator skirt, pixel coordinates 1880-1910×620-650), defect morphology (irregular crack, 5mm in length), and defect category (insulator damage), generating preliminary defect identification results. Processing 1175 target images, 62 defect images were identified, including 15 images of insulator damage, 12 images of broken conductor strands, 20 images of corroded hardware, and 15 images of loose bolts, achieving a preliminary identification accuracy of 92%.

[0038] Furthermore, the binocular ranging algorithm is used to calculate the three-dimensional coordinates of the defect area in the preliminary defect identification results. The disparity is calculated by using the defect area images captured by the left and right cameras. Combined with the camera intrinsic matrix, extrinsic matrix and baseline distance parameters, the actual three-dimensional spatial coordinates of the defect area, the length, width, depth, positional deviation and relative distance data with the surrounding structure are obtained, and defect quantification data is generated. In this embodiment of the invention, a binocular ranging algorithm is used to calculate the three-dimensional coordinates of the defect area (such as the insulator damage area in sub-region 5-501.TIFF) in the preliminary defect identification results: the baseline distance of the binocular camera is set to 120mm, and the intrinsic parameter matrix (focal length f=8mm, principal point coordinates (1920,1080)) and extrinsic parameter matrix (rotation matrix R=[[1,0,0],[0,1,0],[0,0,1]], translation vector T=[0,0,-120]) are obtained through camera calibration. Left and right camera images of the defect area are captured, and the disparity (the horizontal offset of the defect pixels in the left and right images) is calculated: the left image pixel (1880,620) and the right image pixel (1865,620) of the damaged area have a disparity of 15 pixels. The depth Z is calculated as (8×120) / 15 = 64mm using the formula "Depth Z = (f×B) / d" (B is the baseline distance, d is the parallax). Combining the image pixel to actual size conversion ratio (100 pixels = 1m), the defect dimensions are calculated as follows: length 5mm (50 pixels), width 1.2mm (12 pixels), depth 0.3mm (3 pixels). The positional deviation is: horizontal distance from the damaged area to the center of the insulator string 18mm (1800 pixels); relative distance to surrounding structures: 25mm (250 pixels) from the adjacent insulator skirt. The UAV's position (117.4250° / 38.5550°, altitude 22m) is obtained using GPS+BeiDou dual-mode positioning. Combined with the depth data, the actual three-dimensional spatial coordinates of the defect area are calculated (117.4250° / 38.5550°, altitude 21.936m). Defect quantification data is generated, including three-dimensional spatial coordinates, 6 size and distance parameters, and a total of 10 sets of quantified data, providing a precise quantitative basis for defect level classification.

[0039] Furthermore, by combining the preliminary defect identification results with the defect quantification data, and referring to the defect judgment standards and classification thresholds in the defect feature library, the severity of the defects is classified into levels, and a defect detection result containing the defect location coordinates, defect type name, defect size data, severity level identifier, and original image evidence is generated. Based on the defect detection result, an acceptance report containing the defect location, type, and severity is automatically generated.

[0040] In this embodiment of the invention, by combining the preliminary defect identification results (insulator damage, location 1880-1910×620-650) with defect quantification data (crack length 5mm, depth 0.3mm), and referring to the judgment criteria in the defect feature library (crack length 3-8mm is a moderate defect, ≥8mm is a severe defect), the severity of the defect is classified into levels: 5mm falls within the 3-8mm range and is judged as a moderate defect. A defect detection result is generated, including the defect location coordinates (latitude and longitude 117.4250° / 38.5550°, pixel coordinates 1880-1910×620-650), defect type name (insulator damage), defect size data (length 5mm, width 1.2mm, depth 0.3mm), severity level indicator (moderate), and original image evidence (sub-region 5-501.TIFF and enhanced image). Based on the detection results of 62 defect images, an acceptance report is automatically generated. The report includes basic information about the accepted line (line name: 10kV East Ring Line; length: 8km; number of towers: 32), defect statistics (a total of 62 defects, 38 moderate and 24 minor), a defect details table (including defect location, type, size, level, and image link), and rectification suggestions (moderate defects to be rectified within 7 days, and minor defects within 15 days). The report is in PDF format and includes embedded defect image thumbnails and quantitative data tables.

[0041] Furthermore, the acceptance data management module includes the following functions: Collect panoramic image data, standardized multidimensional image datasets, defect detection results, and acceptance reports. Develop multidimensional data classification rules, categorizing data by data type into image data, image data, detection results, and report documents; by collection time into daily collection data, weekly collection data, and project phase collection data; by acceptance area into line section data, tower number data, and geographical zone data; and by defect level into no-defect data, minor defect data, general defect data, and severe defect data. Label each type of data according to the multidimensional data classification rules to generate a data classification label set. In this embodiment of the invention, by collecting the generated panoramic image data (1820 4K TIFF images), standardized multidimensional image dataset (1175 TIFF images), defect detection results (62 defect records), and acceptance reports (8 sub-region PDF reports), a multidimensional data classification rule is established: Classified by data type, image data includes the original panoramic image file (e.g., sub-region 5-001.TIFF), image data includes the standardized target image (e.g., sub-region 5-501.TIFF), detection results include structured data such as defect location, size, and grade (e.g., insulator damage record table), and report documents include acceptance report PDF files (e.g., 10kV East Ring Line Acceptance Report.pdf); classified by collection time, daily collected data is labeled "2". 0241202-Sub-region 1”, weekly data collection is labeled “202412W1-Eastern Section of Line”, project phase data collection is labeled “2024Q4-10kV Eastern Ring Line Acceptance”; according to the acceptance area, the line section data is labeled “10kV Eastern Ring Line-01 Section (117.2000°-117.3000°)”, the tower number data is labeled “Tower 17 (117.4250° / 38.5550°)”, and the geographical zoning data is labeled “Mountainous Section-Sub-region 5”; according to the defect level, no defect data is labeled “No Defect-Tower 10”, minor defect data is labeled “Minor-Broken Conductor Strand-Tower 8”, general defect data is labeled “General-Metal Corrosion-Tower 15”, and serious defect data is labeled “Serious-Insulator Damage-Tower 17”. According to the rules, various types of data are labeled. For example, the panoramic image "sub-region 5-001.TIFF" is labeled as "image data type + 20241202-sub-region 5 + 10kV East Ring Line-05 section + no defects-tower 17", generating a data classification label set with a total of 3072 data entries, providing a classification basis for data management.

[0042] Furthermore, based on the data classification tag set, a standardized storage structure for acceptance data is designed, clarifying the storage path rules, file naming conventions, and metadata field definitions for image data, the thumbnail generation standards, feature index fields, and associated data fields for image data, the field data types, field length limits, and relationship definitions for detection results, the format templates, chapter structures, and data filling rules for report documents, formulating association index rules between various types of data, and generating data storage specifications. In this embodiment of the invention, a standardized storage structure for acceptance data is designed based on a data classification tag set: the storage path rule for image data is " / acceptance data / line name / acquisition time / image data / " (e.g., " / acceptance data / 10kV East Ring Line / 20241202 / image data / "), and the file naming convention is "acquisition time-sub-area number-image sequence number.TIFF" (e.g., "20241202-5-001.TIFF"). Metadata fields include acquisition device model, GPS coordinates, light intensity, and exposure parameters (each field is 20-50 characters long). The thumbnail generation standard for image data is 640×360 pixels resolution, JPEG format, and the feature index field includes target location, pixel coordinate range, and sharpness value (field type is text + numerical). Related data fields... The system includes the corresponding panoramic image number and defect detection result ID (field length 10-20 characters). The data types for detection result fields are defined as: Defect ID (character, 10 digits), Location Coordinates (floating-point, 6 decimal places), Defect Type (character, 20 digits), Dimension Data (floating-point, 2 decimal places), and Severity Level (character, 10 digits). Field length is limited to 10-50 characters. The association is defined as "Defect ID → Image Number → Acceptance Report Number". The report document format template is PDF (A4 paper size, 2.5cm margins). The chapter structure includes basic route information, defect statistics, defect details, and rectification suggestions (each chapter title is in bold, size 3; content is in Song font, size 4). The data filling rule is "the defect details table automatically extracts detection result data, and rectification suggestions are matched with preset text according to the severity level." Association indexing rules are established: image data and detection results are associated through "GPS coordinates," and image data and acceptance reports are mapped through "acceptance project number," generating data storage specifications to ensure standardized naming and structured management of engineering data.

[0043] Furthermore, the categorized acceptance data is stored in a structured manner according to the data storage specifications. The image data and the corresponding defect detection results are linked by the location coordinate field, and the image data and the corresponding acceptance report are mapped by the acceptance item field. The unstructured data is described in a structured manner to generate acceptance ledger data containing unique data identifiers, classification labels, association identifiers and core information summaries. In this embodiment of the invention, the classified acceptance data is stored in a structured manner according to the data storage specifications: image data (such as "20241202-5-001.TIFF") and the corresponding defect detection results (defect ID: DQ2024120205) are associated with each other through the location coordinate field (117.4250° / 38.5550°), and the mapping relationship of "image file path → defect ID → coordinate value" is recorded in the database; image data (such as "sub-region 5-501.TIFF") and the corresponding acceptance report (report number: BG2024120205) are associated with each other through the acceptance item field ("insulator damage inspection"), and the association record of "image number → report number → acceptance item" is marked; unstructured data (such as the original panoramic image file) is described in a structured manner, and core information such as file size (15MB), resolution (3840×2160), and acquisition time (20241202151000) are recorded. Acceptance ledger data was generated, with each entry containing a unique data identifier (e.g., "SY2024120205001"), a category tag ("Image Data Category + 20241202 - Sub-region 5 + Tower 17"), an association identifier (Defect ID: DQ2024120205), and a core information summary ("Acquired in 20241202, panoramic image of Tower 17, no defects"). A total of 3072 acceptance ledger data entries were generated, with a data association accuracy rate of 99.5%, achieving intelligent management of acceptance ledgers.

[0044] Furthermore, the system records the latitude and longitude coordinate sequence of the drone's cruise trajectory, flight speed change data, altitude change data, parameter configuration data, intermediate calculation result data, model call records during algorithm execution, equipment operating parameters, acquisition time series, data quality assessment results, and feature matching data, threshold judgment data, and grade classification basis during the defect identification process, forming a complete acceptance process operation log. In this embodiment of the invention, the entire process of UAV cruise operation is recorded in the log: the latitude and longitude coordinate sequence of the cruise trajectory is (117.2000° / 38.5000°, 117.2006° / 38.5000°, ..., 117.5000° / 38.6000°), recorded once every 10 seconds, for a total of 2880 coordinate points; the flight speed change data is labeled "sub-region 1-3m / s, sub-region 5-2m / s (tower 17 hovering)", and the altitude change data is labeled "plain section 20m, mountain section 22m (tower 17 22m)"; the algorithm execution process parameter configuration data includes the target recognition algorithm threshold (0.8), the defect recognition algorithm learning rate (0.001), and the intermediate calculation results include the feature vector dimension (256). The similarity value is 0.93. The model call record includes "202412021510-call semantic segmentation model, time taken 65ms"; the data acquisition equipment working parameters include camera focal length (8mm) and ISO (100); the acquisition time series includes "20241202151000-shoot panoramic view of tower 17, 20241202151045-shoot insulator details"; the data quality assessment results include sharpness (135), contrast (205), and noise (15); the defect identification process feature matching data includes crack texture contrast (65) and gray value (70); the threshold judgment data includes crack length (5mm≥3mm); the grade classification basis includes "5mm is in the 3-8mm range, judged as a moderate defect". The log is recorded in the format of "timestamp-operation type-data content" and a total of 8640 logs are generated, which fully covers the entire acceptance process and supports the tracking of the execution of the project acceptance task.

[0045] Furthermore, a data update mechanism is established, setting a timed synchronization cycle for newly added acceptance data, and a real-time receiving interface for defect rectification feedback data is built, including a data submission port, format verification rules, and identity authentication mechanism. An active reporting channel for algorithm optimization records is set up, specifying the data type, reporting frequency, and storage path for reporting. Newly added acceptance data, defect rectification feedback data, and algorithm optimization records are synchronized in real time to ensure the timeliness and completeness of acceptance ledger data and achieve closed-loop management of data throughout the entire acceptance process.

[0046] In this embodiment of the invention, a data update mechanism is established to achieve closed-loop data management throughout the entire acceptance process: New acceptance data is scheduled to be synchronized daily at 20:00, automatically synchronizing the image and video data collected that day (such as the data for "20241203-Sub-region 6") to the database, with file integrity verified before synchronization (MD5 value comparison); a real-time interface for receiving defect rectification feedback data is established, with the data submission port using the TCP / IP protocol (port number 8080), and the format verification rule being "rectification data must include defect ID, rectification time, and rectification result (text + image)". The identity authentication mechanism employs a dual verification method: account and password + verification code (the account is the employee ID of the acceptance personnel, and the verification code is valid for 5 minutes). Upon receipt, it automatically associates with the original defect record (e.g., the rectification data of DQ2024120205 is associated with the original insulator damage record). A proactive reporting channel for algorithm optimization records is set up, and the reported data includes model parameter adjustments (e.g., semantic segmentation model threshold 0.8 → 0.75) and inference speed optimization (65ms → 55ms). The reporting frequency is every Friday at 18:00, and the storage path is " / acceptance data / algorithm optimization / 202412W1 / ". New acceptance data is synchronized in real time (daily synchronization success rate 99%), defect rectification feedback data (real-time reception delay <10s), and algorithm optimization records (weekly reporting completeness 100%), ensuring the timeliness (new data synchronization delay <30 minutes) and completeness (data missing rate <0.5%) of the acceptance ledger data. This achieves closed-loop management of the entire process of distribution network acceptance data from collection, identification, reporting to rectification, supporting intelligent defect identification and continuous improvement of acceptance quality.

[0047] Furthermore, the step of structuring and storing the categorized acceptance data according to data storage specifications includes: Based on the field definitions in the data storage specification, the classified acceptance data is processed by field extraction and filling, and unstructured image data and semi-structured report data are converted into structured data format to generate a structured acceptance dataset. In this embodiment of the invention, based on the field definitions in the data storage specification, the classified acceptance data is subjected to field extraction and filling: For unstructured image data (such as "20241202-5-001.TIFF"), the extracted fields include the unique data identifier (SY2024120205001), file path ( / acceptance data / 10kV East Ring Line / 20241202 / image data / ), resolution (3840×2160), file size (15MB), acquisition time (20241202151000), and GPS coordinates (117.425000° / 38.555000°). Target location (panoramic view of tower 17), quality parameters (clarity 135, contrast 205, noise 15), fill into the corresponding fields of the "Image Data Table"; for semi-structured report data (such as "10kV East Ring Line Acceptance Report.pdf"), extract the following fields: report number (BG2024120205), line name (10kV East Ring Line), acceptance period (20241202-20241203), number of sub-areas (8), total number of defects (62), number of defects of each level (38 moderate, 24 minor), summary of rectification suggestions (moderate defects to be rectified within 7 days), fill into the corresponding fields of the "Acceptance Report Table". The extracted fields are organized into a structured format. Each record of image data contains 12 fields, and each record of report data contains 9 fields. At the same time, the corresponding classification labels (such as "Image Data Class + 20241202 - Sub-region 5") and association identifiers (such as Defect ID: DQ2024120205) are associated to generate a structured acceptance dataset, which includes three core tables: "Image Data Table", "Inspection Result Table", and "Acceptance Report Table", with a total of 3072 structured records.

[0048] Furthermore, data validation rules are designed to verify the completeness, consistency, and logical rationality of fields in the structured acceptance dataset, identify missing data, erroneous data, and conflicting data, and generate data validation results. In this embodiment of the invention, the structured acceptance dataset is validated in multiple dimensions by designing data validation rules: the field integrity validation rule is "no null values ​​in required fields", and the required fields include the unique data identifier, collection time, GPS coordinates, and defect ID (in the test result table), and check whether there are null values; the data consistency validation rule is "matching of associated field values", such as the GPS coordinates (117.425000° / 38.555000°) of the "image data table" must be consistent with the GPS coordinates of the corresponding defect ID (DQ2024120205) in the "test result table", and the total number of defects (62) in the "acceptance report table" must be equal to the sum of the number of records in the "test result table"; the logical rationality validation rule is "the value conforms to the business range", such as the sharpness value (135) must be in the range of 0-200, the defect length (5mm) must be in the range of 0-100mm, and the flight altitude (22m) must be in the range of 5-50m. After verification according to the rules, 2 missing data entries (2 entries in the "Image Data Table" with missing GPS coordinates), 3 erroneous data entries (1 entry in the "Detection Result Table" with a defect length recorded as 500mm, and 2 entries in the "Acceptance Report Table" with incorrect sub-area defect count statistics), and 1 conflicting data entry (the GPS coordinate deviation between the same defect ID in the "Image Data Table" and the "Detection Result Table" is 0.001°). Data verification results are generated, recording the location, type, and specific discrepancies of the abnormal data, for a total of 6 abnormal records.

[0049] Furthermore, for abnormal data in the data verification results, a source tracing analysis is conducted by combining the original collected data with the operation log of the entire acceptance process to determine the cause of the abnormality. Data completion, correction or removal are then carried out to generate a purified structured acceptance dataset. In this embodiment of the invention, a source tracing analysis was conducted on six abnormal data points in the data verification results, combined with the original acquisition data and the entire acceptance process operation log: Two image data points lacking GPS coordinates were identified by referring to the operation log "202412021508 - GPS signal briefly interrupted during acquisition of sub-region 5," and the GPS coordinates (117.424800° / 38.554900°, 117.425200° / 38.555100°) were extracted from the EXIF ​​information of the original image file to complete the data; an erroneous data point recording a defect length of 500mm was verified against the original image pixel measurement record (50 pixels). (Converted to 5mm), when confirming the entry, an extra "0" was entered, which was corrected to 5mm; 2 reports with incorrect sub-region defect counts were re-summarized in the "Inspection Result Table" to reflect the corresponding sub-region record counts (sub-region 3 actually had 12 defects, originally recorded as 10; sub-region 6 actually had 8 defects, originally recorded as 9), and the statistical values ​​were corrected; 1 data point with GPS coordinate conflict was compared with the operation log "202412021510-UAV positioning drift at tower 17, subsequent calibration", and the GPS coordinates (117.425000° / 38.555000°) in the "Inspection Result Table" were used as the standard to correct the corresponding field in the "Image Data Table". After processing, 0 abnormal data without traceability were removed, and a cleaned structured acceptance dataset of 3072 records was generated.

[0050] Furthermore, based on the relationships and indexing rules in the data storage specifications, inter-table relationships within the structured acceptance dataset are established, generating a data relationship graph; In this embodiment of the invention, based on the association relationships and indexing rules in the data storage specification, the inter-table relationships within the structured acceptance dataset are established: the "Image Data Table" and the "Inspection Result Table" are linked through the "GPS Coordinates" field, with a foreign key association set as "Image Data Table.GPS Coordinates → Inspection Result Table.GPS Coordinates" to ensure that one image can be associated with multiple defect records (e.g., a panoramic view of tower 17 is associated with two insulator damage records); the "Inspection Result Table" and the "Acceptance Report Table" are linked through the "Sub-region Number" field, with a association set as "Inspection Result Table.Sub-region Number → Acceptance Report Table.Sub-region Number" to achieve a single report summarizing all defects in the corresponding sub-region; the "Image Data Table" and the "Acceptance Report Table" are indirectly linked through the "Acceptance Item Number" field, using the "Inspection Result Table" as an intermediate table to form a chain association of "Image Data → Inspection Result → Acceptance Report". A data relationship graph is generated based on the association relationships. The graph nodes include 3 core tables and key fields such as "unique data identifier", "defect ID", and "sub-region number". The edges are labeled with the association type (foreign key association, summary association) and the associated fields, such as "image data table - foreign key association - GPS coordinates - inspection result table" and "inspection result table - summary association - sub-region number - acceptance report table". The graph contains 3 core nodes, 12 field nodes, and 8 association edges, clearly showing the logical relationships between data and supporting rapid data retrieval and association analysis.

[0051] Furthermore, the purified structured acceptance dataset and data association graph are stored in a distributed database according to storage specifications, and a data backup mechanism is established to ensure the security and recoverability of the acceptance data.

[0052] In this embodiment of the invention, the purified structured acceptance dataset and data association map are stored in a distributed database (using HBase, partitioned by "line name + acquisition time") according to the storage specifications: the "Image Data Table" is stored with the row key "SY + acquisition time + sub-region number + serial number" (e.g., SY2024120205001), and the column family contains "basic information", "quality parameters", and "association identifier"; the "Detection Result Table" has the row key "DQ + acquisition time + defect serial number" (e.g., DQ2024120205), and the column family contains "location information", "defect parameters", and "level information"; the "Acceptance Report Table" has the row key "BG + acquisition time + line name" (e.g., BG20241202 East Ring Line), and the column family contains "line information", "defect statistics", and "rectification suggestions"; the data association map is stored in the "Association Map Table" in JSON format with the row key "MAP + line name + acquisition time". Establish a data backup mechanism: adopt a combination of local backup and off-site backup. Local backup automatically performs a full backup at 22:00 every day and stores it on the spare disk (RAID5 array) of the server where the database is located. Off-site backup performs incremental backup every Sunday at 00:00 and stores it on the off-site disaster recovery center server through encrypted transmission (AES-256). The backup data retention period is 1 year, and recovery tests are performed regularly (on the 1st of each month) to verify the effectiveness of the backup.

[0053] Furthermore, the establishment of a data update mechanism to synchronize newly added acceptance data, defect rectification feedback data, and algorithm optimization records in real time includes: Design a data update triggering mechanism, set a timed synchronization cycle for newly added acceptance data, a real-time receiving interface for defect rectification feedback data, and an active reporting channel for algorithm optimization records, and generate data update triggering rules; In this embodiment of the invention, a data update triggering mechanism is designed to clarify the update rules for three types of data: The timed synchronization cycle for newly added acceptance data (images, pictures, and test results) is set to 20:00 daily, and the synchronization range includes all data collected from 12:00 on the current day to 12:00 on the next day. Data integrity must be checked before synchronization (file MD5 value verification), and the triggering condition is "reaching the set time and the existence of unsynchronized data"; the real-time interface for receiving defect rectification feedback data adopts the TCP / IP protocol (port number 8081), supports POST requests, and the data submission format is JSON, including the fields Defect ID and Rectification Time. The rectification results (text description + image number after rectification) and the employee ID of the person responsible for rectification are required. The trigger condition is "the interface receives request data that conforms to the format and the identity authentication is successful" (authentication method is employee ID + dynamic verification code, the verification code is valid for 5 minutes). The algorithm optimization record active reporting channel is set to every Friday at 18:00. The reported data types include model version number, parameter adjustment content (such as semantic segmentation model threshold 0.8→0.75), inference speed change (65ms→55ms), and optimization test accuracy. The reporting path is " / acceptance data / algorithm optimization / weekly update / ", and the trigger condition is "the set time has arrived and there is an optimization record". The rules are organized into a data update trigger rule document, which clarifies the trigger conditions, time nodes, format requirements and authentication mechanisms for various types of data to ensure the standardization of the update process.

[0054] Furthermore, based on the data update triggering rules, the generation status of newly added acceptance data, the submission status of defect rectification feedback data, and the update dynamics of algorithm optimization records are monitored in real time to generate data update triggering signals; In this embodiment of the invention, a multi-dimensional monitoring system is built based on data update trigger rules: New acceptance data monitoring uses a scheduled task (starting daily at 20:00) to scan the storage directory of the data collection device ( / UAV data / collected today / ) to detect the existence of unsynchronized files (by comparing the "synchronized file list" with the files in the directory). If unsynchronized image data such as "20241203-6-001.TIFF" exists, a "new acceptance data trigger signal" is generated. The signal includes the data type (image data), quantity (15 entries), and storage path. Defect rectification feedback data monitoring uses an interface listening program to monitor port 8081 in real time. When a trigger signal is received... JSON data (e.g., {"Defect ID":"DQ2024120205","Rectification Time":"202412041030","Rectification Result":"Insulator replaced, Image ID SY2024120405001","Rectification Personnel ID":"GY001"}) that has passed authentication generates a "Defect Rectification Data Trigger Signal," which includes the defect ID and submission time. Algorithm optimization record monitoring involves scanning the algorithm optimization storage directory every Friday at 18:00. If "202412W1-Algorithm Optimization Record.json" exists, an "Algorithm Optimization Data Trigger Signal" is generated, containing the model version and a summary of the optimization content. During monitoring, the signal generation time, data identifier, and trigger reason are recorded in real time, generating a data update trigger signal log. A total of 3 trigger signals are generated on the same day (1 for new acceptance data, 1 for defect rectification, and 1 for algorithm optimization), with a signal generation delay of less than 1 second to ensure timely data updates.

[0055] Furthermore, when a data update trigger signal is received, the core fields and associated identifiers of the newly added data are extracted, and the newly added data is standardized and verified in accordance with data storage specifications and data verification rules to generate a compliant new dataset; In this embodiment of the invention, when a data update trigger signal is received, standardized processing and verification are performed according to type: New acceptance data (e.g., "20241203-6-001.TIFF") has its core fields extracted (data unique identifier SY2024120306001, acquisition time 20241203142000, GPS coordinates 117.480000° / 38.570000°, target location tower 22 panoramic view, quality parameter clarity 128). These fields are then filled into the "Image Data Table" fields according to the data storage specifications. Field integrity (no empty values ​​in required fields) and logical rationality are verified (clarity 128 is in the 0-200 range). No abnormalities are found. The core fields of the rectification feedback data were extracted (Defect ID DQ2024120205, Rectified Image Number SY2024120405001). The existence of the Defect ID in the "Detection Result Table" and the validity of the rectified image number were verified. One data entry was found to have an incorrect rectification time format ("20241204" should be "202412041030"). An error message was returned, requiring resubmission. After correction, the verification passed. The core fields of the algorithm optimization records (Model Version V2.1, Inference Speed ​​55ms) were extracted. The conformity of parameter adjustments (e.g., "Threshold 0.8 → 0.75") and the accuracy within the 0-100 range were verified. No anomalies were found. The verified data was compiled into a new compliance dataset, containing 15 new acceptance data entries, 2 defect rectification data entries, and 1 algorithm optimization data entry.

[0056] Furthermore, by using data association graphs to locate the associated positions of newly added compliant datasets in existing structured acceptance datasets, data insertion, update, or replacement operations are performed, and acceptance ledger data and operation logs are updated synchronously. In this embodiment of the invention, the associated location of the newly added compliant dataset is located through the data association map: the newly added acceptance data (SY2024120306001) is associated with sub-region 6 of the "Inspection Result Table" through "GPS coordinates 117.480000° / 38.570000°", and a data insertion operation is performed to add one record to the "Image Data Table" and simultaneously update the associated data of "sub-region 6-tower 22" in the acceptance log; the defect rectification data (DQ2024120205) is located with the corresponding record in the "Inspection Result Table" through "Defect ID", and a data update operation is performed to add the fields "Rectification Status: Completed" and "Rectification Time: 202412041030", and simultaneously update the status identifier of the defect in the acceptance log; the algorithm optimization data (V2.1) is associated with the "Algorithm Parameter Table" through "Model Version", and a data replacement operation is performed to replace the parameter record of the original V2.0 version and simultaneously update the algorithm call record in the operation log. During the operation, the consistency of associated fields is verified in real time (such as the GPS coordinates of the newly added image matching the sub-region range). After the update is completed, data update confirmation information is generated, including the amount of updated data (18 records), the number of successful records (18 records), and the associated location, to ensure that the newly added data is accurately integrated into the existing structured acceptance dataset, and the data synchronization update delay of the acceptance ledger is less than 30 seconds.

[0057] Furthermore, the entire data update process is recorded, including the source of the updated data, the update time, the update content, and the operator's information. A data update log is generated and stored in conjunction with the acceptance ledger data to ensure data traceability.

[0058] In this embodiment of the invention, a structured data update log is generated by recording the entire data update process. Each log entry includes the source of the updated data (new acceptance data from drone collection, defect rectification from acceptance personnel submission, and algorithm optimization from the technical team), update time (202412032005, 202412041032, 202412051802), update content (15 new image data entries, 2 updated defect rectification status entries, and 1 replaced algorithm parameter), operator information (new data operator "System Automatic Synchronization", defect rectification operator "GY001", and algorithm optimization operator "JS002"), and update result (success / failure, with the reason recorded for failure). The log format uses "timestamp-update type-data identifier-operation content-operator-result", such as "202412032005-New Acceptance Data-SY2024120306001-Insert Image Data Table-System Automatic Synchronization-Success". Logs and acceptance ledger data are linked and stored using "unique data identifier" and "defect ID". Log records are 100% complete. The update history of a certain data can be quickly traced through the acceptance ledger (such as the synchronization time and operator of SY2024120306001), ensuring that the entire data update process is traceable and supporting the security and reliability of acceptance data management.

[0059] Furthermore, the entire data update process is recorded, including the source of the updated data, the update time, the update content, and the operator information, including: Establish field specifications for data update logs, clearly defining the unique identifier of the update event, the unique identifier of the data, the snapshot of the original data state, the snapshot of the data state after the update, the update trigger method type, the operator's identity identifier, the operator's department, the operator's terminal device identifier, the operator's terminal IP address, the update request initiation time, the update operation execution time, the update operation completion time, and the update time statistics. Define the data type, field length, and format requirements for each field. In this embodiment of the invention, by formulating field specifications for the data update log, 13 core fields and technical requirements are defined: Unique identifier for update event (character type, 20 characters, format "EVT + year month day hour minute second + 6-digit random number", e.g., EVT202412032005123456); Unique identifier for data (character type, 15 characters, associated with acceptance data identifier, e.g., SY2024120306001); Snapshot of original data state (text type, storing core field values ​​before update, e.g., "clarity 128, GPS 117.480000° / 38.570000°"); Snapshot of data state after update (text type, storing core field values ​​after update, e.g., "rectification status completed, rectification time 202412041030"); Update triggering method type (character type, 10 characters, value "timed synchronization / real-time submission / active reporting"); Operator identification (character type, ... 10 digits, such as "System Automatic Synchronization / GY001 / JS002"; Operator's Department (character type, 20 digits, such as "Operations Department / Technical Department / Acceptance Group"); Operating Terminal Device Identifier (character type, 30 digits, such as "UAV Terminal SN20241203001 / PC Terminal SN20241204002"); Operating Terminal IP Address (character type, 15 digits, such as "192.168.1.100 / 10.0.0.5"); Update Request Initiation Time (date and time type, format "YYYYMMDDHHMMSS", such as 20241203200500); Update Operation Execution Time (date and time type, same format as above, such as 20241203200502); Update Operation Completion Time (date and time type, same format as above, such as 20241203200505); Update Time Statistics (numerical type, unit milliseconds, such as 5000). Define non-empty field rules: the first 12 fields are required, and the update time statistics are calculated as (completion time - initiation time) to ensure that the log fields completely cover the entire update chain information.

[0060] Furthermore, during the execution of data update operations, key data at each stage is captured in real time through system hook functions, including update request parameters, data verification results, related node query results, database operation statements, and data submission results. Update-related information is extracted according to field specifications, the complete link information of each data update is recorded, and raw log data is generated. In this embodiment of the invention, during the data update operation, key data at each stage is captured in real time through system hook functions (deployed at key nodes of the data processing module): update request parameter capture (the hook function listens to the "data submission interface" to obtain the request parameters for adding acceptance data, such as "file path / UAV data / 20241203 / 6 / 001.TIFF, data type image data class"); data verification result capture (the hook function is bound to the "verification module" to record verification pass / fail information, such as "SY2024120306001, field integrity verification passed, logical rationality verification passed"); and related node query result capture (the hook function listens to the "related graph query interface"). The system records the results of associating GPS coordinates with sub-region 6, such as "117.480000° / 38.570000°→sub-region 6, association successful"; captures database operation statements (the hook function binds to the "database execution module" to record SQL statements, such as "INSERT INTO image data table (identifier, path, GPS) VALUES('SY2024120306001', ' / ... / 001.TIFF', '117.480000° / 38.570000°')"); captures data submission results (the hook function listens to the "database return interface" and records the execution results, such as "INSERT operation successful, affecting 1 row"). It extracts update-related information according to field specifications, such as the operator's identity "system automatically synchronized", the operator's terminal IP "192.168.1.100", and various time nodes, recording the complete link information for each data update, generating raw log data, with 15 raw data records generated for each update event (covering 5 stages).

[0061] Furthermore, the raw log data is cleaned to remove duplicate records, invalid fields, and format errors. The raw log data is then grouped and organized according to the unique identifier of the update event, missing log field content is supplemented, and standardized log data is generated. In this embodiment of the invention, the original log data is cleaned as follows: duplicate records are removed (duplicates are removed by using "unique identifier of update event + unique identifier of data", and two duplicate records generated by repeated triggering of hook functions are deleted, such as the duplicate verification result record of EVT202412032005123456-SY2024120306001); invalid fields are processed (non-standard fields such as "temporary cache path" are deleted, and a total of 3 invalid fields are removed); format errors are corrected ("update request initiation time 2024-12-03 20:05" is corrected to "20241203200500", which meets the requirements of date and time format); the original log data is grouped according to the unique identifier of update event (such as EVT202412032005123456), and each group contains the original data corresponding to 13 core fields; missing fields are supplemented (such as when "operator's department" is not captured, it is supplemented by associating "acceptance group" information with the operator's identity identifier "GY001"). After cleaning, standardized log data is generated, with each update event corresponding to one standardized record containing complete information of 13 core fields. The field missing rate has decreased from 15% before cleaning to 0%, and the format error rate has decreased from 8% to 0%.

[0062] Furthermore, the standardized log data is structurally transformed according to field specifications, and the information of each field is organized using a preset log data storage format. An association index between the unique identifier of the update event and the unique identifier of the data is established to generate a structured data update log. In this embodiment of the invention, standardized log data is structurally converted according to field specifications: JSON format is used as the preset log storage format to organize the information of each field, such as {"Unique identifier for update event":"EVT202412032005123456","Unique identifier for data":"SY2024120306001","Snapshot of original data status":"Clarity 128, GPS 117.480000° / 38.570000°, No associated rectification information","Snapshot of updated data status":"Clarity 128, GPS 117.480000° / 38.570000°, Associated sub-region 6, Acceptance ledger synchronized","Update trigger method type":"Timed synchronization","Operator identification":} The system automatically synchronizes the following: "Operator's Department": "Operation and Maintenance Department", "Operation Terminal Equipment Identifier": "UAV Terminal SN20241203001", "Operation Terminal IP Address": "192.168.1.100", "Update Request Initiation Time": "20241203200500", "Update Operation Execution Time": "20241203200502", "Update Operation Completion Time": "20241203200505", "Update Time Statistics": 5000; An association index is established between the unique identifier of the update event and the unique identifier of the data, generating a mapping table of "EVT202412032005123456→SY2024120306001", with an index query response time of <100 milliseconds. A structured data update log is generated, with 18 update events corresponding to 18 structured logs. Each log has complete fields and a uniform format, and can be directly used for storage and querying.

[0063] Furthermore, the structured data update logs are linked and bound to the corresponding acceptance ledger data update records through a unique data identifier, and synchronously stored in a dedicated log database. A cross-index is established with the acceptance full-process operation logs, and access control rules for log data are set to grant query and export permissions only to authorized management personnel. At the same time, a log data archiving strategy is configured to partition and compress log data according to time periods to ensure that the entire data update process is traceable and auditable.

[0064] In this embodiment of the invention, structured data update logs are associated and bound with corresponding acceptance ledger data update records using a unique data identifier (e.g., SY2024120306001), and synchronously stored in a dedicated log database (using PostgreSQL, partitioned by "update month and year", e.g., partition 202412); a cross-index is established with the entire acceptance process operation log, and algorithm calls and equipment parameter records in the operation log are associated with the "unique identifier of update event", forming a traceability link of "data update log → operation log → acceptance ledger"; access control rules are set: only authorized personnel (e.g., operations and maintenance department) are authorized. Managers and technical department supervisors can log in to the system using their employee ID and password. Query permissions allow access to all log fields, while export permissions only allow the export of non-sensitive fields (operation terminal IP and device identifier are masked). Permission verification is implemented via the LDAP protocol. Configure log data archiving strategy: at the end of each month, automatically migrate the current month's log data from the online database to archive storage (using a tape library). Before migration, ZIP compression (50% compression rate) is performed. Archived data is retained for 5 years. Regular (quarterly) archived data recovery tests are conducted to ensure readability, achieving full traceability and auditability of the data update process, and supporting compliant management of distribution network acceptance data.

[0065] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A panoramic intelligent acceptance system for power distribution network facilities based on drones and AI image recognition, characterized in that, Includes the following modules: The front-end lightweight algorithm integration module is used to develop algorithm integration units adapted to the structure of UAV remote controllers, deploy lightweight target recognition, binocular ranging and defect recognition algorithms, generate an interactive adaptation protocol between the algorithm and the UAV; based on the interactive adaptation protocol, the UAV is controlled to perform adaptive route planning along the power distribution network line and collect panoramic image data of the towers and line facilities. The intelligent cruise and data acquisition module is used to acquire the characteristics of the power distribution network lines, and to locate key work areas based on panoramic image data combined with the characteristics of the power distribution network lines, generating cruise key area marking data; based on the cruise key area marking data, the drone is controlled to perform autonomous cruise, and to collect multi-dimensional image data of tower information, construction process details and channel environment; The intelligent defect identification module is used to input multi-dimensional image data into the defect identification algorithm, combine it with the power distribution network construction specifications to perform defect feature matching, and generate defect detection results. An acceptance report is generated based on the defect detection results, including the location, type, and severity of the defects. The acceptance data management module is used to standardize and organize panoramic image data, multi-dimensional image data, defect detection results and acceptance reports to generate acceptance ledger data; it also records the cruise trajectory, collection parameters and algorithm execution logs simultaneously to achieve closed-loop management of data throughout the acceptance process.

2. The panoramic intelligent acceptance system for power distribution network facilities based on UAV and AI image recognition as described in claim 1, characterized in that, The front-end lightweight algorithm integration module includes the following functions: Based on the hardware structure parameters of the drone remote controller, including processor computing power, memory storage capacity, interface transmission rate and power supply and battery life specifications, design the hardware adaptation interface of the algorithm integration unit, clarify the CPU resource allocation ratio, memory usage threshold, data transmission bandwidth standard and power consumption control range during algorithm operation, and generate a hardware adaptation solution. The lightweight target recognition, binocular ranging and defect recognition algorithms are pruned by removing computational branches with contributions below a set threshold, merging duplicate feature extraction layers, and compressing algorithm parameters using weight quantization and activation value quantization techniques to generate a lightweight algorithm model. Based on hardware adaptation solutions and lightweight algorithm models, we formulate binary data interaction formats, command response timing mechanisms, data verification rules, and transmission encryption specifications between algorithms and UAVs, clarify the mapping relationship between algorithm output commands and UAV execution actions, and generate an interaction adaptation protocol. Based on the interactive adaptation protocol, the UAV is controlled to perform adaptive route planning along the power distribution network line and collect panoramic image data of the towers and line facilities.

3. The panoramic intelligent acceptance system for power distribution network facilities based on UAV and AI image recognition as described in claim 2, characterized in that, The method of controlling the UAV based on the interactive adaptation protocol to perform adaptive flight path planning along the power distribution network line and collecting panoramic image data of the towers and line facilities includes: Based on the binary data interaction format in the interactive adaptation protocol, the distribution network line routing feature data output by the lightweight algorithm model is parsed, including the coordinate sequence of the line centerline, the routing deflection angle and curvature change data, as well as the geographic environment contour data, including the terrain undulation contour, obstacle distribution contour and channel boundary contour. The parsed data is then denoised to generate a basic dataset for route planning. Based on the characteristics of the distribution network line corridor environment, including the complexity of the terrain, the density of vegetation, the distribution of buildings and the influence of meteorological conditions, and the data on the distribution density of poles and towers, safe distance thresholds for route planning are set, including the horizontal safe distance from the main body of the poles and towers, the vertical safe distance from the line conductors and the safe distance for avoiding obstacles. The shooting angle parameters are defined, including the range of horizontal shooting angles, the range of vertical shooting angles and the interval of surrounding shooting angles. The data acquisition frequency standards are determined, including the image shooting frame rate, the sampling frequency of location information and the recording frequency of status parameters, and route constraints are generated. The basic dataset of route planning and route constraints are input into the adaptive route planning algorithm. The initial route is calculated by the A-star path search algorithm. The path nodes are optimized and adjusted by the genetic algorithm to generate an initial route plan. The initial route plan includes the coordinate sequence of route nodes, the flight speed between each node and the turning angle. The binocular ranging algorithm is used to detect obstacle distance data in the initial flight path in real time, including the straight distance between the obstacle and the UAV, the relative height difference and the horizontal offset distance. Combined with the outline size data and morphological feature data of the obstacle, the conflict path segments in the initial flight path are dynamically adjusted, the detour path nodes are replanned, and the final adaptive flight path is generated. The drone is controlled to fly along a final adaptive route. Using its onboard image acquisition equipment, the drone captures images of the overall structure, key connections, and vulnerable points of the towers and power lines from multiple angles, according to the set shooting angle parameters and acquisition frequency standards. Simultaneously, the drone records the position coordinates, angle parameters, light intensity, equipment exposure parameters, and shooting timestamps for each shot. The raw images are then converted and preliminarily screened to remove invalid images caused by equipment shake, excessively strong or weak lighting, and retain clear and identifiable target images, generating panoramic image data containing multi-dimensional perspectives.

4. The panoramic intelligent acceptance system for power distribution network facilities based on UAV and AI image recognition as described in claim 3, characterized in that, The intelligent cruise and data acquisition module includes the following functions: Acquire panoramic image data, and use a lightweight target recognition algorithm to extract features from key operating parts corresponding to the main tower, line segment, connecting hardware, insulator string, and grounding device in the panoramic image data. Extract the contour edge features of each key operating part, identify the coordinates of feature points, determine the pixel coordinate range, contour morphology parameters and feature point distribution information of each key operating part in the image, and generate patrol key area marking data. Based on the key area marking data of the cruise, combined with the path node coordinates of the final adaptive route, the cruise sub-areas are divided according to the importance, distribution location and acceptance priority of the key operation parts. Each cruise sub-area clearly includes a list of key operation parts. The cruise flight speed, hovering time, image capture resolution parameters, focus distance parameters and exposure compensation parameters of each cruise sub-area are set to generate a sub-area cruise plan. According to the sub-area cruise plan, the UAV is controlled to enter each cruise sub-area in sequence. The image acquisition device collects the size data of the tower structure, the tightness data of the line connection, the standardized execution data of the construction process, the vegetation impact data and the building safety distance data in the passage environment according to the set parameters. At the same time, the collected multi-dimensional image data is associated and marked with the identification information and collection parameter information of the corresponding sub-area to form a data association table. The acquired multidimensional image data is format-standardized, converting image data of different formats into a preset image format. The image quality assessment algorithm is used to detect the image sharpness, contrast, and noise content, removing blurry, overexposed, underexposed, and noisy invalid images, retaining clear and valid target images, and generating a standardized multidimensional image dataset. Based on the data association table, the standardized multidimensional image dataset is associated and stored with the patrol key area marking data. The pixel coordinate mapping relationship, feature point association relationship and acceptance item correspondence relationship between each image data and the corresponding key operation location are established, and a two-way index between image data and key operation location is constructed.

5. The panoramic intelligent acceptance system for power distribution network facilities based on UAV and AI image recognition as described in claim 4, characterized in that, Before performing format standardization on the acquired multidimensional image data, feature enhancement processing is also performed on the multidimensional image data, including: Extract the basic visual features of each image from the multidimensional image data, including pixel brightness distribution histogram, image contrast value, edge contour sharpness index, target area pixel ratio and color channel distribution data, classify and record them according to image number and feature type, and generate image basic feature dataset. Based on the key concern dimensions of power distribution network facility acceptance, including structural integrity acceptance, connection tightness acceptance, process standardization acceptance, and safety distance acceptance, the importance weights of each basic visual feature under different acceptance dimensions are set. The weight coefficients of each feature are determined by the analytic hierarchy process. The feature items in the image basic feature dataset are prioritized according to the size of the weight coefficients to generate a feature priority sequence. A lightweight image enhancement algorithm is used to enhance high-weight visual features in the feature priority sequence, an adaptive histogram equalization algorithm is used to improve image contrast, a bilateral filtering algorithm is used to enhance edge contour clarity, and a super-resolution reconstruction algorithm is used to supplement blurred detail feature information in the image, thereby generating enhanced image feature data. The enhanced image feature data is fused with the original multidimensional image data. A pixel-level feature overlay algorithm is used to embed the enhanced feature data into the corresponding pixel positions of the original image. The fusion ratio between the enhanced features and the original image is adjusted by a weighted fusion rule to generate feature-enhanced multidimensional image data.

6. The panoramic intelligent acceptance system for power distribution network facilities based on UAV and AI image recognition according to claim 4, characterized in that, The defect intelligent identification module includes the following functions: A standardized multidimensional image dataset is obtained. The target region is extracted from each image in the standardized multidimensional image dataset using a semantic segmentation algorithm. Based on a pre-trained semantic segmentation model, the corresponding acceptance objects and background environment of the towers, lines, hardware, insulators, and grounding devices in the images are identified. A binary mask image is generated. The acceptance objects and background environment are separated by multiplying the pixels of the binary mask image with those of the original image to generate the target region image data. Based on the structural installation standards, process execution requirements, allowable range of dimensional deviations, and defect manifestations in historical defect cases in power distribution network construction specifications, we collect visual feature parameters of various defects, including the shape, color, texture, size, and location features of defect areas, clarify the judgment thresholds and classification standards for various defects, and establish a defect feature database. The target region image data is input into a lightweight defect recognition algorithm. The shallow texture features and deep semantic features of the target region are extracted through the convolutional layer of the convolutional neural network to generate a feature map. The feature map is then dimensionality-reduced through a pooling layer. The processed features are then compared with the feature parameters in the defect feature library using cosine similarity calculation to identify the specific location coordinates, morphological features, and defect category of potential defects, generating preliminary defect recognition results. The binocular ranging algorithm is used to calculate the three-dimensional coordinates of the defect area in the preliminary defect identification results. The disparity is calculated by using the defect area images captured by the left and right cameras. Combined with the camera intrinsic matrix, extrinsic matrix and baseline distance parameters, the actual three-dimensional spatial coordinates of the defect area, the length, width and depth of the defect, the positional deviation value and the relative distance data with the surrounding structure are obtained, and defect quantification data is generated. Combining preliminary defect identification results with defect quantification data, and referring to the defect judgment standards and classification thresholds in the defect feature library, the severity of defects is classified into levels, generating defect detection results that include defect location coordinates, defect type name, defect size data, severity level identifier, and original image evidence. Based on these defect detection results, an acceptance report containing defect location, type, and severity is automatically generated.

7. The panoramic intelligent acceptance system for power distribution network facilities based on UAV and AI image recognition as described in claim 4, characterized in that, The acceptance data management module includes the following functions: Collect panoramic image data, standardized multidimensional image datasets, defect detection results, and acceptance reports. Develop multidimensional data classification rules, categorizing data by data type into image data, image data, detection results, and report documents; by collection time into daily collection data, weekly collection data, and project phase collection data; by acceptance area into line section data, tower number data, and geographical zone data; and by defect level into no-defect data, minor defect data, general defect data, and severe defect data. Label each type of data according to the multidimensional data classification rules to generate a data classification label set. Based on the data classification tag set, a standardized storage structure for acceptance data is designed, clarifying the storage path rules, file naming conventions and metadata field definitions for image data, thumbnail generation standards, feature index fields and associated data fields for image data, field data types, field length limits and relationship definitions for detection results, format templates, chapter structures and data filling rules for report documents, and establishing association index rules between various types of data to generate data storage specifications. The categorized acceptance data is stored in a structured manner according to the data storage specifications. Image data and corresponding defect detection results are linked by the location coordinate field. Image data and corresponding acceptance reports are mapped by the acceptance item field. Unstructured data is described in a structured manner to generate acceptance ledger data containing unique data identifiers, classification labels, association identifiers and core information summaries. Record the latitude and longitude coordinate sequence of the drone's cruise trajectory, flight speed change data, altitude change data, parameter configuration data, intermediate calculation result data, model call records during algorithm execution, equipment operating parameters, acquisition time series, data quality assessment results, feature matching data, threshold judgment data, and level classification basis during the defect identification process, forming a full-process operation log for acceptance. Establish a data update mechanism, set a timed synchronization cycle for newly added acceptance data, build a real-time receiving interface for defect rectification feedback data, including data submission port, format verification rules and identity authentication mechanism, set up an active reporting channel for algorithm optimization records, clarify the data type, reporting frequency and storage path of the reported data, and synchronize newly added acceptance data, defect rectification feedback data and algorithm optimization records in real time to ensure the timeliness and completeness of acceptance ledger data and realize closed-loop management of data throughout the acceptance process.

8. The panoramic intelligent acceptance system for power distribution network facilities based on UAV and AI image recognition as described in claim 7, characterized in that, The structured storage of the categorized acceptance data according to data storage specifications includes: Based on the field definitions in the data storage specification, the classified acceptance data is processed by field extraction and filling, and unstructured image data and semi-structured report data are converted into structured data format to generate a structured acceptance dataset. Design data validation rules to validate the completeness, consistency, and logical rationality of fields in the structured acceptance dataset, identify missing data, erroneous data, and conflicting data, and generate data validation results; For abnormal data in the data verification results, a source tracing analysis is conducted by combining the original collected data with the operation log of the entire acceptance process to determine the cause of the abnormality. Data completion, correction or removal are then carried out to generate a cleaned structured acceptance dataset. Based on the relationships and indexing rules in the data storage specifications, establish inter-table relationships within the structured acceptance dataset and generate a data relationship graph. The purified structured acceptance dataset and data association graph are stored in a distributed database according to storage specifications. At the same time, a data backup mechanism is established to ensure the security and recoverability of the acceptance data.

9. The panoramic intelligent acceptance system for power distribution network facilities based on UAV and AI image recognition as described in claim 8, characterized in that, The establishment of a data update mechanism to synchronize newly added acceptance data, defect rectification feedback data, and algorithm optimization records in real time includes: Design a data update triggering mechanism, set a timed synchronization cycle for newly added acceptance data, a real-time receiving interface for defect rectification feedback data, and an active reporting channel for algorithm optimization records, and generate data update triggering rules; Based on data update triggering rules, the generation status of newly added acceptance data, the submission status of defect rectification feedback data, and the update dynamics of algorithm optimization records are monitored in real time to generate data update triggering signals. When a data update trigger signal is received, the core fields and associated identifiers of the newly added data are extracted. The newly added data is then standardized and verified in accordance with data storage specifications and data verification rules to generate a compliant new dataset. By locating the associated position of newly added compliant datasets in the existing structured acceptance datasets through data association graphs, data insertion, update or replacement operations are performed, and acceptance ledger data and operation logs are updated synchronously. The entire data update process is recorded, including the source of the updated data, the update time, the update content, and the operator information. A data update log is generated and stored in conjunction with the acceptance ledger data to ensure data traceability.

10. The panoramic intelligent acceptance system for power distribution network facilities based on UAV and AI image recognition as described in claim 9, characterized in that, The entire data update process is recorded, including the source of the updated data, the update time, the update content, and the operator's information. Establish field specifications for data update logs, clearly defining the unique identifier of the update event, the unique identifier of the data, the snapshot of the original data state, the snapshot of the data state after the update, the update trigger method type, the operator's identity identifier, the operator's department, the operator's terminal device identifier, the operator's terminal IP address, the update request initiation time, the update operation execution time, the update operation completion time, and the update time statistics. Define the data type, field length, and format requirements for each field. During the execution of data update operations, key data at each stage is captured in real time through system hook functions, including update request parameters, data verification results, related node query results, database operation statements, and data submission results. Update-related information is extracted according to field specifications, the complete link information of each data update is recorded, and raw log data is generated. The raw log data is cleaned to remove duplicate records, invalid fields, and format errors. The raw log data is then grouped and organized according to the unique identifier of the update event, missing log field content is added, and standardized log data is generated. The standardized log data is structurally transformed according to field specifications, and the information of each field is organized using a preset log data storage format. An association index between the unique identifier of the update event and the unique identifier of the data is established to generate a structured data update log. The structured data update logs are linked and bound to the corresponding acceptance ledger data update records using a unique data identifier, and synchronously stored in a dedicated log database. Cross-indexing is established with the acceptance process operation logs, and access control rules for log data are set to grant query and export permissions only to authorized management personnel. At the same time, log data archiving strategies are configured, and log data is partitioned and compressed according to time periods to ensure that the entire data update process is traceable and auditable.

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