Outdoor distribution box inspection system and method based on path planning

Abstract

The invention discloses an outdoor distribution box inspection system and method based on path planning. The system comprises a master control terminal, an inspection unmanned aerial vehicle, an unmanned aerial vehicle landing base station and a distributed RTK positioning module. Wherein the main control terminal integrates an image processing unit and a dynamic path planning engine, and interacts with the patrol unmanned aerial vehicle in real time through a wireless communication protocol; the patrol unmanned aerial vehicle carries a dual-mode visual system and comprises a vertically downward path planning camera and an acquisition camera for direction adjustment of a holder, and the holder dynamically adjusts the orientation of the acquisition camera based on real-time position feedback of the RTK module to ensure that the optical axis of the holder is always aligned with the RTK module at the top of the nearest distribution box.

Description

Technical Field

[0001] This application relates to the technical field of power operation and maintenance, and particularly to an outdoor distribution box inspection system and method based on path planning. Background Art

[0002] Outdoor power distribution cabinets need to be inspected at regular intervals, and it is necessary to ensure the integrity of the outer shell structure of the power distribution cabinets to avoid accidental problems caused by exposed wires. Moreover, the conventional inspection method is to conduct inspections manually or set up special monitoring probes at the locations of the power distribution cabinets. Although the manual inspection method can quickly maintain the power distribution cabinets, due to the large number of power distribution cabinets, the inspection work progresses slowly. The method of setting up special monitoring probes has a high cost and also requires manual image identification.

[0003] Therefore, neither the conventional manual inspection nor the method of setting up special monitoring probes for monitoring can quickly learn about the damage problems of the power distribution cabinets. Therefore, a more effective outdoor distribution box inspection system is urgently needed. Summary of the Invention

[0004] This application provides an outdoor distribution box inspection system and method based on path planning to solve the above problems.

[0005] On the one hand, this application provides an outdoor distribution box inspection system based on path planning. The system includes: a main control terminal, an inspection drone, a drone landing base station, and a distributed RTK positioning module; wherein, the main control terminal integrates an image processing unit and a dynamic path planning engine, and interacts with the inspection drone in real time through a wireless communication protocol; the inspection drone is equipped with a bimodal vision system, including a path planning camera facing vertically downward and a collection camera with a pan-tilt for orientation adjustment. The pan-tilt dynamically adjusts the orientation of the collection camera based on the real-time position feedback of the RTK module to ensure that its optical axis is always aligned with the RTK module at the top of the nearest distribution box; the path planning engine uses a multi-objective optimization algorithm to synchronously calculate the shortest flight path and the optimal image acquisition point, specifically including: generating a circumferential path reference circle based on the three-dimensional space coordinates of the distribution box and a preset safety radius; collecting ground images in real time through the path planning camera, and combining a convolutional neural network to identify the distribution of obstacles; using the intersection point of the extension line of the diagonal of the distribution box and the reference circle as a candidate positioning point, and generating an inspection sequence by combining the linear distance weight and the obstacle avoidance factor; the image processing unit implements a hierarchical analysis strategy, including abnormal area detection based on gray level mutation, multi-frame image feature fusion, and adaptive threshold calibration, to realize real-time marking and data compression transmission of the surface defects of the distribution box.

[0006] In an implementation manner of the present application, the image processing unit includes: a precision image registration module for correcting the tilted image acquired by the acquisition camera into a front view projection through feature point matching; a multispectral analysis subunit for integrating visible light and infrared imaging data to cross-verify the surface temperature distribution and physical deformation of the distribution box; a dynamic mask generator for constructing a standard template of the distribution box based on historical inspection data and generating the contour of the abnormal area in real time through pixel-level differential calculation.

[0007] In an implementation manner of the present application, the path planning engine specifically performs the following optimization process: establishing a bi-objective function with the goals of minimizing energy consumption and maximizing coverage rate, and the constraint conditions include the drone endurance limit, the safe flight altitude range, and the image acquisition resolution threshold; using an improved ant colony algorithm to solve the optimal path, where the pheromone update rule introduces a path curvature penalty factor and an obstacle density weighting coefficient; fusing RTK positioning data and visual SLAM in real time to construct a three-dimensional environmental map and dynamically correcting the positioning deviation in path planning; in the transition flight stage between adjacent distribution boxes, enabling the Bezier curve interpolation algorithm to generate a smooth obstacle avoidance trajectory to ensure the stability of the gimbal attitude to maintain the image acquisition quality.

[0008] In an implementation manner of the present application, the data compression and transmission is specifically as follows: performing wavelet transform and region of interest coding on the original image, compressing the background area to 10% of the original size while retaining the lossless data of the abnormal area; constructing a distributed edge computing node to perform differential data transmission between the drone and the landing base station, and only uploading the differential data packets that deviate from the reference model; using blockchain technology to perform timestamp encryption and distributed storage on the inspection records to ensure the traceability of data tampering; in the case of communication interruption, enabling local cache queue management to preferentially transmit key data frames according to the abnormal level.

[0009] In an implementation manner of the present application, the drone landing base station includes: a multi-band charging matrix supporting adaptive wireless charging of the drone at any azimuth angle; a meteorological monitoring array for real-time collecting wind speed, precipitation, and electromagnetic interference data and feeding them back to the main control terminal; an autonomous cleaning mechanism for removing dust and ice and snow on the drone takeoff and landing platform through the coordinated action of a robotic arm and high-pressure air flow; an emergency recovery device for implementing forced landing protection by ejecting a safety net when detecting abnormal drone battery or communication loss.

[0010] On the other hand, the present application also provides an outdoor distribution box inspection method based on path planning, which applies the aforementioned outdoor distribution box inspection system based on path planning. The method includes the following steps: Step S1: Based on the space grid scanning signal emitted by the UAV landing base station, a three-dimensional topological map including the GPS coordinates and altitude of all distribution boxes is established. The Delaunay triangulation algorithm is used to generate an initial inspection network, and the Euclidean distance and visibility parameters between each node are calculated; among them, the UAV camera is adjusted through the pan-tilt head. Step S2: Based on the greedy strategy, the distribution box closest to the landing base station is selected as the starting node, and the nearest neighbor algorithm is iteratively applied to generate the global inspection sequence. When a new distribution box or a faulty node is detected, the local path replanning module is triggered to maintain the continuity of the overall inspection sequence. Step S3: When hovering at each inspection positioning point, visible light images, infrared thermal maps, and laser point cloud data are synchronously acquired. The multi-source data is fused into a structured inspection report by using the timestamp alignment technology, and the spatial coordinates and severity levels of the abnormal areas are automatically marked.

[0011] In an implementation manner of the present application, the selection process of the inspection positioning point specifically includes: establishing a polar coordinate system on the vertical projection plane of the RTK module on the top of the distribution box, and generating radial scan lines at intervals of the first preset angle (5°); obtaining ground images in the directions of each scan line through the path planning camera, and segmenting the feasible area by using the U-Net network; preferentially selecting positioning points that meet the following conditions: the included angle with the connection line of adjacent distribution boxes is greater than the second preset angle (45°) to obtain multi-angle coverage; the variance of the ground flatness is less than the set threshold to ensure hovering stability; the height of the surrounding obstacles is lower than the safety flight margin of the UAV; for conflicting candidate points, a fuzzy comprehensive evaluation model is introduced to calculate the priority weight, and the final selected position is dynamically adjusted.

[0012] In an implementation manner of the present application, the automatic marking of the spatial coordinates and severity levels of the abnormal area specifically includes: when the image processing unit detects a surface defect, a three-level response mechanism is automatically triggered; among them, the three-level response mechanism is the primary response, the intermediate response, and the high-level response; the primary response is to increase the combination of the shooting angle and the lighting condition at the current position to obtain multi-modal verification data; the intermediate response is to call the additional positioning point generation module to add 45° oblique viewing points on the front and side of the distribution box; the high-level response is to start the emergency communication link and transmit the abnormal image with augmented reality marking to the operation and maintenance terminal in real time; insert the dynamic path planning engine into the return detection task, and automatically navigate to the abnormal point for re-inspection after completing the current inspection cycle.

[0013] In an implementation manner of the present application, the control method of the pan-tilt is specifically as follows: establish an attitude solution model based on quaternions, and calculate in real time the azimuth angle of the RTK module relative to the UAV body; use a PID controller to adjust the pitch and deflection angles of the pan-tilt, so that the included angle between the optical axis of the acquisition camera and the connection line of the RTK module is less than the third preset angle (2°); in a strong wind disturbance environment, enable the visual servo compensation mechanism, and correct the movement trajectory of the pan-tilt through the displacement feedback of feature points between consecutive frames; when a mechanical limit conflict of the pan-tilt is detected, automatically switch to the wide-angle lens mode and trigger path replanning to obtain a compliant shooting pose.

[0014] In an implementation manner of the present application, the method further includes: establishing a historical inspection database, statistically analyzing the correlation between the abnormal occurrence frequency of each distribution box and the environment; applying a random forest model to predict high-incidence fault areas, and inserting preventive detection nodes into the regular inspection path; implementing energy-aware path optimization, automatically switching to the low-power hover mode and requesting manual intervention when the remaining battery power is less than 20%; regularly performing full-system virtual simulation through the digital twin platform to verify the path planning robustness and fault recovery ability in extreme scenarios.

[0015] An outdoor distribution box inspection system and method based on path planning provided by the present application have the following

[0016] Beneficial effects:

[0017] (1) By using an inspection UAV, it is possible to replace manual inspection, and the above inspection method can obtain the shell condition of the current distribution box with the least number of photos taken.

[0018] (2) After a problem occurs, it is possible to screen images through visual recognition and facilitate the manual confirmation link.

[0019] (3) By setting additional positioning points, it is possible to perform shooting operations on the two key observation surfaces of the distribution cabinet and facilitate problem-solving.

[0020] (4) By optimizing the UAV inspection route through path planning and setting a pan-tilt control optimization method, the accuracy of the inspection is further improved. Description of the Drawings

[0021] The drawings described herein are used to provide a further understanding of the present application, and constitute a part of the present application. The schematic embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation to the present application. In the drawings:

[0022] Figure 1 It is a block diagram of an outdoor distribution box inspection system based on path planning provided by an embodiment of the present application;

[0023] Figure 2 Schematic diagram of the inspection system provided by the embodiment of the present application;

[0024] Figure 3 Schematic diagram of the power distribution cabinet and related positioning point structures provided by the embodiment of the present application;

[0025] Figure 4 Flowchart of a method for inspecting outdoor distribution boxes based on path planning provided by the embodiment of the present application;

[0026] Reference numerals: 1, UAV landing base station; 2, inspection UAV; 3, power distribution cabinet; 4, RTK module; 5, inspection positioning point; 6, obstacle; 7, additional positioning point. Detailed implementation manners

[0027] To make the objectives, technical solutions, and advantages of the present application clearer, the technical solutions of the present application will be clearly and completely described below in conjunction with the specific embodiments of the present application and the corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts shall fall within the protection scope of the present application.

[0028] The embodiment of the present application provides an outdoor distribution box inspection system and method based on path planning. The technical solutions proposed in the embodiments of the present application will be described in detail below with reference to the drawings.

[0029] Figure 1 Block diagram of the composition of an outdoor distribution box inspection system based on path planning provided by the embodiment of the present application. As Figure 1As shown in the figure, the system includes: a main control terminal, an inspection UAV, a UAV landing base station, and a distributed RTK positioning module. Among them, the main control terminal is provided with an image processing module and a dynamic path planning module; and, the image processing module includes: an image acquisition unit, which can acquire the pictures collected by the two camera units of the inspection UAV 2, and the two camera units are respectively used for different operations; an image positioning and grayscale processing unit, which can position the distribution box area in the pictures collected by the acquisition camera unit and perform grayscale processing. When the inspection UAV 2 is at a fixed height and fixed position to take pictures, the area position of the distribution box in the picture is fixed. Only grayscale processing is required. After setting a preset threshold, it can be judged whether its shell is damaged or the cabinet door is lost, etc. Under normal circumstances, there is a threshold for the grayscale value of the grayscale image of the picture taken at the position of the distribution box. When the grayscale values of adjacent pixel points in the area jump relatively large, it can prove that there is a problem; an image screening unit, which can identify the abnormalities in the image, mark and screen them. The abnormalities in the image are that after the area of the distribution box in the picture is grayscale processed, its grayscale value exceeds the preset threshold or the grayscale values of adjacent pixel points jump. At this time, this image needs to be marked and confirmed by the staff, so it needs to be screened. Figure 2 Schematic diagram of the inspection system provided by the embodiment of the present application; Figure 3 Schematic diagram of the distribution cabinet and related positioning point structures provided by the embodiment of the present application.

[0030] In addition to the above-mentioned acquisition and processing of images, in order to move, in the above system, the dynamic path planning module includes:

[0031] An inspection positioning point determination unit, which can determine the inspection point positioning point according to the intersection of the extension line of the diagonal of the distribution cabinet 3 and the central circle formed with the RTK module of the distribution cabinet 3 as the center and a preset radius. It should be noted that the fewer the number of images acquired, the less the workload. Therefore, by taking pictures at the position of the diagonal extension line, the information of the two surfaces of the distribution box can be directly obtained. Therefore, through the above method, it can be ensured that the pictures taken at the inspection positioning point 5 can include both surfaces at the same time, and the shell condition of the distribution box can be reflected by two pictures without accident;

[0032] Then, after determining the shooting position, in order to quickly complete the shooting operation, in the above system, the dynamic path planning module includes: a path selection unit, which can select the corresponding next inspection positioning point 5 according to the images collected by the path planning camera unit and the straight-line distance between the inspection positioning point 5 and the RTK module 4 of the next distribution cabinet 3;

[0033] The images collected by the path planning camera unit can be grayscale processed. After grayscale processing, according to different grayscale value information, it can reflect whether there are obstacles, which is convenient for avoidance. Under normal circumstances, it is set in the greenway. In different seasons, just select the corresponding grayscale value for confirmation. The purpose is to avoid obstacles and ensure that shooting operations can be carried out at the inspection and positioning point 5, and ensure that there are no obstacles between the inspection and positioning point 5 and the distribution box. Under normal circumstances, if the greenway can be directly photographed and the grayscale value falls within the threshold, it can be judged that there are no obstacles directly above the greenway;

[0034] After that, it is necessary to minimize the movement path as much as possible because the inspection UAV 2 needs to carry out shooting operations at two inspection and positioning points 5. The distance from the inspection UAV 2 to the first inspection and positioning point 5 is constant. Therefore, it is necessary to determine that the distance between the second inspection and positioning point 5 and the next distribution box to be inspected is the smallest.

[0035] Moreover, in the above structure, two camera units are provided below the inspection UAV 2. One of them is a path planning camera unit, which is set vertically downward and can be used for path planning. The other is a collection camera unit, which is set on the cloud platform. The cloud platform is cooperatively set with the RTK module 4, that is, the collection camera unit can always face the nearest RTK module 4 to obtain the image information of the distribution box and complete the inspection operation.

[0036] Through the above structure, it can be realized that the inspection UAV 2 is used to replace manual inspection, which is faster and can avoid the problem of cost increase caused by setting a large number of special monitoring probes.

[0037] The above is an outdoor distribution box inspection system based on path planning provided by an embodiment of the present application. Based on the same inventive concept, an embodiment of the present application also provides an outdoor distribution box inspection method based on path planning. Figure 4 This is a composition diagram of an outdoor distribution box inspection system based on path planning provided by an embodiment of the present application. As Figure 4 shown, the method mainly includes:

[0038] Step S1: Based on the space grid scanning signal emitted by the UAV landing base station, establish a three-dimensional topological map containing the GPS coordinates and altitude of all distribution boxes, generate an initial inspection network using the Delaunay triangulation algorithm, and calculate the Euclidean distance and visibility parameters between each node; among them, the UAV camera is adjusted through the cloud platform.

[0039] Step S2: Based on the greedy strategy, select the distribution box closest to the landing base station as the starting node, iteratively apply the nearest neighbor algorithm to generate the global inspection order. When a new distribution box or a faulty node is detected, trigger the local path replanning module to maintain the continuity of the overall inspection sequence.

[0040] Step S3: When hovering at each inspection positioning point, synchronously obtain visible light images, infrared thermal maps, and laser point cloud data. Use timestamp alignment technology to fuse multi-source data into a structured inspection report, and automatically mark the spatial coordinates and severity levels of abnormal areas.

[0041] Formulation of inspection path:

[0042] (1) Starting from the 3RTK module 4 of the current power distribution cabinet, plan a path and move to an inspection positioning point 5 of the current power distribution cabinet 3 as the end point;

[0043] The specific implementation method of the above method is that in step (1), the method of selecting the inspection positioning point 5 is:

[0044] Obtain the image of the path planning camera unit when the inspection UAV 2 is directly above the 3RTK module 4 of the current power distribution cabinet, and perform grayscale processing on the image. When the grayscale area of the grayscale image within the central circle area is within the preset threshold, it indicates that there is no obstacle, and the determination of the above grayscale value needs to be determined according to the location of the distribution box. Whether it is on the cement floor or within the green belt, the preset grayscale threshold also needs to change. And if it exceeds the threshold, it can be judged that there is an obstacle at this position. When selecting the position of the diagonal inspection positioning point 5, it needs to be avoided, that is, as Figure 2 shown in the power distribution cabinet at the upper right corner position;

[0045] And when all inspection positioning points 5 are located in the normal grayscale value area, select the inspection positioning point 5 at the diagonal position of the inspection positioning point 5 closest to the next power distribution cabinet 3 as the end point.

[0046] (2) Starting from the current inspection positioning point 5, plan a path and move to the inspection positioning point 5 at its diagonal position as the end point;

[0047] And the way to achieve path planning is that in step (2), the moving path of the inspection UAV 2 is an arc with the RTK module 4 as the center, and it is set to avoid obstacles. The above obstacles are the ranges exceeding the grayscale threshold within the central circle area.

[0048] (3) Starting from the current inspection positioning point 5, plan a path and move to the RTK module 4 of the next power distribution cabinet 3 as the end point;

[0049] When performing the final step (3), since there is no need to perform an image acquisition operation, in order to avoid interference from the obstacle 6, when executing step (3), the flight altitude of the inspection drone 2 is higher than that when performing other steps. That is, by raising the altitude of the inspection drone 2, obstacle avoidance can be achieved. Moreover, when the inspection drone 2 reaches the next power distribution cabinet position after raising its altitude, it is also convenient to obtain images through the path planning camera unit.

[0050] It should be noted that in the above steps, an image is acquired using the acquisition camera unit every time a patrol positioning point 5 is passed. In this way, the acquisition of the image can be completed. And in step (1), the altitude of the inspection drone 2 needs to be lowered to facilitate the shooting operation.

[0051] Complete the inspection of all power distribution cabinets 3, plan the path with the drone landing base 1 as the end point and move until landing and completing the operation. At this time, the staff can observe and confirm the power distribution cabinet housing according to the screened acquired images. When a problem occurs, send staff to handle it.

[0052] As an implementation method, in some cases, the information on the front of the power distribution cabinet cannot be clearly obtained from the images taken from the side. When its cabinet door is damaged, in order to facilitate subsequent maintenance, it is necessary to obtain accurate front images and use them to confirm and solve the problem. Therefore, in order to observe the specific situation of the power distribution cabinet 3 after an accident is confirmed, it also includes the step of determining the additional positioning point 7:

[0053] Set a preset radius and use the center line parallel to the width direction of the power distribution cabinet 3 as a reference to determine two intersection points as the additional positioning points 7. The additional positioning points 7 are located on the front sides of the power distribution box, and the images taken are relatively clear.

[0054] Moreover, when there is no problem with the housing, the above operations are not required. Therefore, the conditions and execution methods for triggering an accident are as follows: In step (2), obtain the image of the acquisition camera unit at the patrol positioning point 5 and perform gray-scale processing on the image. If the gray-scale value of the position where the power distribution cabinet 3 is located exceeds the preset threshold, mark the image and use the acquisition camera unit to obtain an image at the next additional positioning point 7 passed by the moving path of the inspection drone 2, that is, complete additional image acquisition. When it is normal, it is not required.

[0055] In the above steps, when the image is marked but the moving path of the inspection drone 2 does not pass through the next additional positioning point 7, in step (2), switch the end point to the position where the additional positioning point 7 is located to ensure that the additional image can be obtained.

[0056] The above method can be used to obtain the required images in the shortest way, which is different from the existing methods. It also ensures that the number of images taken is the minimum, reduces the difficulty of manual identification, and combines visual recognition to screen images to ensure that the inspection work is completed quickly.

[0057] The following specifically shows an application scenario example of the outdoor distribution box inspection system and method based on path planning provided by the present application:

[0058] Scene background: There are 120 outdoor distribution boxes distributed in an industrial park in a high-tech development zone, which are responsible for supplying power to production equipment and lighting systems in the park. Since the park covers a large area (about 5 square kilometers), the distribution boxes are scattered and some are located in green belts or building gaps. Traditional manual inspections are inefficient and difficult to cover details in complex environments. To this end, the park introduced the drone inspection system based on path planning described in this application to improve inspection efficiency and accuracy.

[0059] The hardware configuration of the system includes: (1) The patrol drone model uses Matrice 300RTK, and the camera configuration uses Zenmuse P1 (vertically downward, 20 million pixels, supporting real-time image transmission) path planning camera. The acquisition camera uses Zenmuse H20T (integrated visible light, infrared thermal imaging and laser ranging modules, supporting 360° gimbal adjustment). The processor uses NVIDIA Jetson AGX Xavier (embedded AI computing platform, computing power 32TOPS), and the positioning module uses the built-in Qianxun Position RTK high-precision positioning system (positioning accuracy ±1cm). (2) The drone landing base station uses SkyGrid A3 intelligent base station, which has the following functions: multi-band wireless charging (supports 6.78MHz / 13.56MHz adaptive switching); meteorological monitoring (built-in wind speed, precipitation and electromagnetic interference sensors); autonomous cleaning (equipped with high-pressure airflow nozzles and robotic arms to remove debris from the landing and take-off platform). (3) The model of the main control terminal uses the Atlas 800AI server (equipped with the Ascend 910 chip and supporting distributed edge computing).

[0060] The specific inspection results are as follows:

[0061] Step (1): 3D map construction and initial path planning.

[0062] Data collection: The drone landing base station emits spatial grid scanning signals. Combining with the park GIS map, the GPS coordinates of all distribution boxes are obtained (such as distribution box A: 22.7632°N, 113.9415°E, altitude 15.2m). An initial inspection network is generated through the Delaunay triangulation algorithm, and the Euclidean distance and visibility parameters between nodes are calculated. For example, the distance between distribution box A and distribution box B is 320 meters, and the visibility is good; distribution box C is located behind the building and needs to be bypassed.

[0063] Global path generation: The greedy strategy is adopted to select the distribution box (number 001) closest to the base station as the starting point, and the nearest neighbor algorithm is iteratively applied to generate the global inspection order. The estimated total length of the initial path is 18.6 kilometers, and the time consumption is about 45 minutes (including the hovering shooting time).

[0064] Step (2), Dynamic path optimization and obstacle avoidance

[0065] Multi-objective optimization algorithm: Objective function: Minimize energy consumption (battery capacity 27,000 mAh, endurance time 55 minutes) and maximize coverage rate (120 distribution boxes need to be covered). Constraints: Flight height limit (5 - 30 meters), image resolution ≥ 5 cm / pixel. The improved ant colony algorithm is adopted, and a path curvature penalty factor (weight 0.3) and an obstacle density weighting coefficient (weight 0.5) are introduced. After 20 iterations of the algorithm, the optimal path is determined (total length 17.8 kilometers, saving 8% energy consumption).

[0066] Real-time obstacle avoidance strategy: The path planning camera continuously collects ground images (frame rate 30 fps), and uses the YOLOv5 model to identify obstacles (such as trees, vehicles). When an obstacle is detected (such as a temporary storage container near distribution box D), the Bezier curve interpolation algorithm is triggered to generate a smooth obstacle avoidance trajectory (curvature radius ≥ 5 meters) to ensure the stability of the pan-tilt attitude.

[0067] Step (3), Image collection and anomaly detection

[0068] Hovering shooting and multi-modal data fusion: The drone hovers directly above the RTK module on the top of the distribution box (height 10 meters), and the pan-tilt adjusts the acquisition camera to a 45° oblique angle to synchronously obtain: visible light image (resolution 3840×2160); infrared thermal map (temperature difference sensitivity 0.05°C); laser point cloud data (accuracy ±2 cm). After timestamp alignment, the data is fused into a structured report (single processing time consumption < 1 second).

[0069] Anomaly Detection and Response Mechanism: Local temperature anomaly (highest temperature 52°C, exceeding the threshold by 10°C) is detected on the surface of Distribution Box E, triggering an intermediate response: adding a 45° oblique viewing angle observation point to obtain multi-angle infrared images; the path planning engine inserts a return inspection task and automatically navigates to the anomaly point for secondary shooting after completing the current cycle. The image processing unit uses adaptive threshold calibration to mark the anomaly area (such as the missing cabinet door of Distribution Box F, and the area of the gray-scale mutation region ≥ 200 pixels).

[0070] Step (4), Data Transmission and Emergency Handling

[0071] Data Compression and Encryption: The original image is compressed by wavelet transform (the background area is compressed to 10% of the original size), and the differential data packet (average single transmission volume 15MB) is uploaded to the edge computing node through a 5G link. Blockchain technology records the inspection log (such as "2023-10-05 14:23:05, Distribution Box G is abnormal, hash value 0x1a3f...").

[0072] Emergency Scenario Response: When the drone encounters strong wind (wind speed 12m / s) during the inspection of Distribution Box H, the visual servo compensation mechanism is activated, and the pan-tilt deflection angle is corrected by the displacement of feature points in consecutive frames (PID parameters Kp = 0.8, Ki = 0.2, Kd = 0.1) to ensure that the optical axis deviation < 3°. When the battery level is lower than 20%, it switches to the low-power hover mode (power consumption reduced by 40%) and sends an alarm to the operations and maintenance personnel's mobile APP.

[0073] Actual Effects and Value, (1), Efficiency Improvement: Traditional manual inspection takes 8 hours per time, while the drone system only takes 2 hours, with an efficiency improvement of 300%. (2), Fault Detection Rate: The system detected 7 potential faults (such as overheating, cabinet body damage), and the accuracy rate of manual re-inspection and confirmation is 100%. (3), Cost Savings: The annual operation and maintenance cost is reduced by 60%, avoiding an average daily loss of about 500,000 yuan caused by fault shutdown. Through the application of this system, the industrial park has achieved the intelligentization and unmanned operation of distribution box inspection, providing a reliable guarantee for the efficient operation and maintenance of power facilities.

[0074] Each embodiment in this application is described in a progressive manner. For the same or similar parts between each embodiment, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for the device embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiment.

[0075] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or apparatus comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or apparatus. Without further limitation, an element defined by the phrase "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or apparatus comprising said element.

[0076] The above are only examples of the present application and are not intended to limit the present application. For those skilled in the art, various modifications and changes can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. An outdoor distribution box inspection system based on path planning, characterized in that, The system includes: a master control terminal, an inspection drone, a drone landing base station, and a distributed RTK positioning module; among them, the master control terminal integrates an image processing unit and a dynamic path planning engine, and interacts with the inspection drone in real time through a wireless communication protocol; the inspection drone is equipped with a bimodal vision system, including a path planning camera facing vertically downward and a collection camera with a pan-tilt for orientation adjustment. The pan-tilt dynamically adjusts the orientation of the collection camera based on the real-time position feedback of the RTK module to ensure that its optical axis is always aligned with the RTK module at the top of the nearest distribution box; the path planning engine uses a multi-objective optimization algorithm to synchronously calculate the shortest flight path and the optimal image acquisition point, specifically including: Generating a circumferential path reference circle based on the three-dimensional space coordinates of the distribution box and a preset safety radius; Real-time collecting ground images through the path planning camera, and combining a convolutional neural network to identify the distribution of obstacles; Taking the intersection point of the extension line of the diagonal of the distribution box and the reference circle as a candidate positioning point, and generating an inspection sequence by combining the straight-line distance weight and the obstacle avoidance factor; The image processing unit implements a hierarchical analysis strategy, including abnormal area detection based on gray-scale mutation, multi-frame image feature fusion, and adaptive threshold calibration, to realize real-time marking and data compression transmission of surface defects of the distribution box.

2. The outdoor power distribution box inspection system based on path planning according to claim 1, characterized in that, The image processing unit includes: A high-precision image registration module for correcting the tilted image obtained by the collection camera into a frontal projection through feature point matching; A multi-spectral analysis sub-unit for integrating visible light and infrared imaging data to cross-verify the surface temperature distribution and physical deformation of the distribution box; A dynamic mask generator for constructing a standard template of the distribution box based on historical inspection data and generating the contour of the abnormal area in real time through pixel-level differential calculation.

3. The outdoor distribution box inspection system based on path planning according to claim 1, wherein, The path planning engine specifically executes the following optimization process: Establishing a bi-objective function with the goal of minimizing energy consumption and maximizing coverage rate, and the constraint conditions include the drone endurance limit, the safe flight altitude range, and the image acquisition resolution threshold; Using an improved ant colony algorithm to solve the optimal path, where the pheromone update rule introduces a path curvature penalty factor and an obstacle density weighting coefficient; Real-time fusing RTK positioning data and visual SLAM to construct a three-dimensional environmental map, and dynamically correcting the positioning deviation in path planning; In the transitional flight stage between adjacent distribution boxes, enabling the Bezier curve interpolation algorithm to generate a smooth obstacle avoidance trajectory to ensure the stability of the pan-tilt attitude to maintain the image acquisition quality.

4. The outdoor distribution box inspection system based on path planning according to claim 1, characterized in that, The data compression transmission is specifically: performing wavelet transform and region of interest coding on the original image, retaining the lossless data of the abnormal area while compressing the background area to 10% of the original size; Constructing a distributed edge computing node, implementing differential data transmission between the drone and the landing base station, and only uploading the difference data packets deviating from the reference model; Using blockchain technology to perform timestamp encryption and distributed storage on the inspection records to ensure the traceability of data tampering; In the case of communication interruption, enabling local cache queue management and preferentially transmitting key data frames according to the abnormal level.

5. The outdoor power distribution box inspection system based on path planning according to claim 1, wherein, The drone landing base station includes: A multi-band charging matrix that supports adaptive wireless charging of the drone at any azimuth angle; Meteorological monitoring array, which collects wind speed, precipitation and electromagnetic interference data in real time and feeds them back to the main control terminal; Autonomous cleaning mechanism, which removes dust, ice and snow on the UAV takeoff and landing platform through the coordinated action of the robotic arm and high-pressure air flow; Emergency recovery device, which ejects a safety net to implement forced landing protection when detecting abnormal UAV battery or communication loss; 6. A method for inspecting outdoor distribution boxes based on path planning, which applies the outdoor distribution box inspection system based on path planning described in any one of claims 1-5, characterized in that, The method includes the following steps: Step S1: Based on the UAV landing base station transmitting spatial grid scanning signals, a three-dimensional topological map containing the GPS coordinates and altitude of all distribution boxes is established. The Delaunay triangulation algorithm is used to generate an initial inspection network, and the Euclidean distance and visibility parameters between each node are calculated; among them, the UAV camera is adjusted through the pan-tilt head; Step S2: Based on the greedy strategy, the distribution box closest to the landing base station is selected as the starting node, and the nearest neighbor algorithm is iteratively applied to generate the global inspection sequence. When a new distribution box or a faulty node is detected, the local path re-planning module is triggered to maintain the continuity of the overall inspection sequence; Step S3: When hovering at each inspection positioning point, visible light images, infrared thermal maps and laser point cloud data are synchronously acquired, and the multi-source data is fused into a structured inspection report using timestamp alignment technology, and the spatial coordinates and severity levels of abnormal areas are automatically marked.

7. The outdoor distribution box inspection method based on path planning according to claim 6, wherein, The selection process of the inspection positioning point specifically includes: A polar coordinate system is established on the vertical projection plane of the RTK module on the top of the distribution box, and radial scanning lines are generated at intervals of the first preset angle; The ground images in the directions of each scanning line are obtained through the path planning camera, and the U-Net network is used to segment the feasible area; The positioning points that meet the following conditions are preferentially selected: The included angle with the connection line of adjacent distribution boxes is greater than the second preset angle to obtain multi-view coverage; The variance of the ground flatness is less than the set threshold to ensure hovering stability; The height of surrounding obstacles is lower than the safety flight margin of the UAV; For conflicting candidate points, a fuzzy comprehensive evaluation model is introduced to calculate the priority weight, and the final selected position is dynamically adjusted.

8. The outdoor distribution box inspection method based on path planning according to claim 6, characterized in that, The automatic marking of the spatial coordinates and severity levels of abnormal areas specifically includes: When the image processing unit detects surface defects, a three-level response mechanism is automatically triggered; among them, the three-level response mechanism is the primary response, the intermediate response and the advanced response; the primary response is to increase the combination of shooting angle and lighting conditions at the current position to obtain multi-modal verification data; the intermediate response is to call the additional positioning point generation module to add 45° oblique view observation points on the front and side of the distribution box; the advanced response is to start the emergency communication link and transmit the abnormal image with augmented reality annotation to the operation and maintenance terminal in real time; The dynamic path planning engine is inserted into the return detection task, and after completing the current inspection cycle, it automatically navigates to the abnormal point for re-inspection.

9. The outdoor distribution box inspection method based on path planning according to claim 6, wherein, The control method of the pan-tilt head is specifically: An attitude solution model based on quaternions is established to calculate the azimuth angle of the RTK module relative to the UAV body in real time; The PID controller is used to adjust the pitch and deflection angles of the pan-tilt head so that the included angle between the optical axis of the acquisition camera and the connection line of the RTK module is less than the third preset angle; In a strong wind disturbance environment, enable the visual servo compensation mechanism to correct the pan-tilt movement trajectory through the displacement feedback of feature points between consecutive frames; When a pan-tilt mechanical limit conflict is detected, automatically switch to the wide-angle lens mode and trigger path replanning to obtain a compliant shooting pose.

10. The outdoor distribution box inspection method based on path planning according to claim 6, characterized in that, The method further includes: Establish a historical inspection database, and statistically analyze the correlation between the abnormal occurrence frequency of each distribution box and the environment; Apply a random forest model to predict high-fault areas and insert preventive detection nodes into the regular inspection path; Implement energy-aware path optimization, automatically switch to the low-power hover mode and request manual intervention when the remaining battery power is below 20%; Regularly conduct full-system virtual simulation through the digital twin platform to verify the path planning robustness and fault recovery ability in extreme scenarios.

Images