A method and system for processing the inspection path planning of a substation drone based on a 3D model

Abstract

The present invention discloses a method and system for processing an unmanned aerial vehicle (UAV) inspection path planning based on a three-dimensional model, which includes: obtaining three-dimensional environmental data within a substation; based on the three-dimensional environmental data, establishing an obstacle model through an obstacle point cloud recognition algorithm, and generating an obstacle distribution map according to the obstacle model; the obstacles include power lines and equipment within the substation; constructing an electromagnetic interference field distribution map, which is generated by combining the electromagnetic radiation height of the equipment within the substation and the equipment layout position information; based on the obstacle distribution map and the electromagnetic interference field distribution map, using a global path planning algorithm to generate an initial UAV inspection path; the above method can effectively cope with possible physical obstacles and electromagnetic interference in the environment, and provides a flight path planning guarantee with high safety and high reliability for UAV inspections.

Description

Technical Field

[0001] The present invention relates to the field of UAV path planning data processing, and particularly to a method and system for processing UAV inspection path planning in a substation based on a three-dimensional model. Background Art

[0002] During the inspection process of a substation, it is necessary to conduct UAV path inspection and path planning over densely populated electrical equipment; however, due to the limited internal space and numerous obstacles in the substation, it is required that the UAV has high-precision positioning capabilities (such as RTK GPS technology) and obstacle avoidance functions to achieve accurate flight path planning and autonomous flight.

[0003] However, this is not the most dangerous. Specifically, researchers have found that it is most necessary to pay attention to path planning by determining target obstacles in combination with the laser point cloud analysis of obstacles within a safe distance and analyzing their electromagnetic interference; there are a large number of power equipment and strong electric fields in the substation, which may interfere with the electronic equipment of the UAV. Therefore, it is necessary to perform high-precision navigation and recognition of the laser point cloud and electromagnetic interference analysis to implement effective measures to reduce the impact of electromagnetic interference on flight control. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for processing UAV inspection path planning in a substation based on a three-dimensional model, which solves the above-mentioned technical problems pointed out in the prior art.

[0005] The present invention proposes a method for processing UAV inspection path planning in a substation based on a three-dimensional model, including the following operating steps:

[0006] Obtain the three-dimensional environmental data in the substation, where the three-dimensional environmental data includes the point cloud data of the power lines obtained by lidar measurement and the three-dimensional point cloud data of the equipment in the substation;

[0007] Based on the three-dimensional environmental data, establish an obstacle model through an obstacle point cloud recognition algorithm, and generate an obstacle distribution map according to the obstacle model; the obstacles include power lines and equipment in the substation;

[0008] Construct an electromagnetic interference field distribution map, which is generated by combining the electromagnetic radiation height of the equipment in the substation and the equipment layout position information;

[0009] Based on the obstacle distribution map and the electromagnetic interference field distribution map, use a global path planning algorithm to generate an initial UAV inspection path;

[0010] During the flight of the UAV, dynamically update the obstacle distribution map and the electromagnetic interference field distribution map based on real-time sensor data, and use a local path optimization algorithm to adjust the flight path.

[0011] Preferably, as an implementable solution; in step S1, obtaining the three-dimensional environmental data inside the substation includes:

[0012] Scanning the internal environment of the substation using a lidar device installed on a drone to collect point cloud data;

[0013] Preprocessing the collected point cloud data, including point cloud denoising, ground segmentation, and point cloud coordinate transformation;

[0014] Registering the three-dimensional point cloud data of the equipment inside the substation and the point cloud data of the power lines after preprocessing with the three-dimensional BIM model of the equipment inside the substation to generate an obstacle model to be completed.

[0015] Preferably, as an implementable solution; in step S1, based on the three-dimensional environmental data, establishing an obstacle model through an obstacle point cloud recognition algorithm, including:

[0016] Extracting the obstacle point cloud through a point cloud segmentation algorithm; the point cloud segmentation algorithm includes a segmentation method based on region growing;

[0017] Using a machine learning algorithm to classify the extracted obstacle point cloud, classifying the obstacles into static obstacles and dynamic obstacles, and recording it as the classification result of the obstacles;

[0018] Based on the classification result of the obstacles, generating an obstacle distribution map and assigning a danger weight value to each obstacle; the danger weight value is determined by calling a preset database according to the classification result of the obstacles; the database is used to store the danger weight values corresponding to the classification types of each obstacle;

[0019] The obstacle model is a three-dimensional model including an obstacle distribution map and marking the classification type of each obstacle.

[0020] Preferably, as an implementable solution; constructing an electromagnetic interference field distribution map, including:

[0021] Obtaining the electromagnetic radiation characteristic information and equipment layout position information of each piece of equipment inside the substation, and based on the electromagnetic radiation characteristic information of the equipment inside the substation, calculating the electromagnetic field distribution (especially the electromagnetic radiation height) of the electromagnetic field generated by the equipment inside the current substation;

[0022] Performing grid processing on the calculated electromagnetic field distribution in the space inside the substation, so that the space inside the substation is divided into several grid units, and each grid unit contains an electromagnetic interference intensity value (obtained by direct matching in the database) and an electromagnetic radiation height;

[0023] Overlay the grid-based electromagnetic field distribution with the current obstacles in the obstacle distribution map and the current device layout position information to generate a comprehensive electromagnetic interference field distribution map; the electromagnetic interference field distribution map includes device layout position information, electromagnetic radiation height, and influence height; the influence height is the sum of the highest point height of the current device where the device layout position information is located and the height affecting electromagnetic radiation under electromagnetic field interference.

[0024] Preferably, as an implementable solution; generate an initial inspection path based on the comprehensive electromagnetic interference field distribution map, including:

[0025] In the comprehensive electromagnetic interference field distribution map, search for the global optimal path from the starting point to the target point with reference to the risk weight and influence height;

[0026] Smooth the generated global optimal path, and use Bezier curve to optimize the continuous flyability of the path to obtain the smoothed path;

[0027] Take the smoothed path as the initial inspection path of the UAV.

[0028] Preferably, as an implementable solution; in the comprehensive electromagnetic interference field distribution map, search for the global optimal path from the starting point to the target point with reference to the risk weight and influence height, specifically including:

[0029] Search for the global optimal path in the comprehensive electromagnetic interference field distribution map. When executing, use the dynamic weighted cost function combined with the obstacle risk weight, electromagnetic interference intensity, and flight time for path search; at the same time, set the constraint conditions of the comprehensive cost function;

[0030] Define the comprehensive cost function as:

[0031] ;

[0032] Where: is the cumulative sum of the obstacle d risk weights of the path segment from node i to node j, ; ( is the risk weight of the kth grid cell);

[0033] , take the maximum value of the electromagnetic interference intensity in the path segment, is the interference intensity of the kth grid cell;

[0034] are the node coordinates, v is the UAV cruise speed;

[0035] is the heuristic function, take the Euclidean distance from node j to the end point;

[0036] For dynamically adjusting the weight coefficient;

[0037] The constraint condition of the comprehensive cost function is that the height of the path node satisfies , where is the influence height of node j (equipment height + radiation influence height);

[0038] Preferably, as an implementable solution; the generated global optimal path is smoothed, and the continuous flyability of the path is optimized by using a Bezier curve to obtain the smoothed path, which specifically includes:

[0039] Obtain the generated sequence of path points , and use a piecewise cubic Bezier curve for smoothing to ensure that the path is continuously flyable and avoid the electromagnetic interference area;

[0040] Determine the generation of control points: for each path point , according to its influence height generate a vertical safety offset:

[0041] z{k}=

[0042] Where: is the influence height corresponding to the path point ; represents the actual flight height of the UAV at the path point ; is the offset adjustment amount, which is a constant;

[0043] Execute the curve fitting of the control points: input the offset point set into the Bezier curve generator, and the objective function is:

[0044] ; is the Bezier curve, is the smoothing factor;

[0045] Generate an initial anti-interference inspection path; superimpose and verify the smoothed path with the electromagnetic interference field distribution map; during the superimposed verification, if the ( is a preset threshold), then trigger the local replanning processing operation, and finally output a path that meets the following conditions:

[0046] The first anti-interference optimization condition; ;

[0047] The second anti-interference optimization condition: , the total hazard weight value is lower than the critical threshold;

[0048] The third anti-interference optimization condition: .

[0049] Preferably, as an implementable solution; the first anti-interference optimization condition is a high constraint condition; the second anti-interference optimization condition is an accumulated hazard weight constraint; the third anti-interference optimization condition is an electromagnetic interference intensity constraint.

[0050] The present invention provides an electronic device, including a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a method for processing the inspection path planning of a substation drone based on a three-dimensional model.

[0051] The present invention provides a computer storage medium storing a computer program, which when executed by a computer, implements a method for processing the inspection path planning of a substation drone based on a three-dimensional model.

[0052] Compared with the prior art, the embodiments of the present invention have at least the following technical advantages:

[0053] The present invention provides a method for processing the inspection path planning of a substation drone based on a three-dimensional model. Starting from point cloud data, it uses a lidar device to scan the equipment and power lines in the substation to obtain three-dimensional environmental data of the substation. The three-dimensional environmental data includes point cloud data of power lines obtained by lidar measurement and three-dimensional point cloud data of equipment in the substation. Based on the three-dimensional environmental data, an obstacle model is established through an obstacle point cloud recognition algorithm, and an obstacle distribution map is generated according to the obstacle model. The obstacles include power lines and equipment in the substation. An electromagnetic interference field distribution map is constructed, and the electromagnetic interference field distribution map is generated by combining the electromagnetic radiation height of equipment in the substation and the equipment layout position information. S4. Based on the obstacle distribution map and the electromagnetic interference field distribution map, a global path planning algorithm is used to generate an initial inspection path for the drone.

[0054] Analyzing the above technical solution, it can be seen that the above method for processing the inspection path planning of a substation drone based on a three-dimensional model can accurately reconstruct the actual situation in the substation by obtaining three-dimensional environmental data. By using obstacle point cloud recognition and electromagnetic interference field modeling, the environmental risks are refined into two dimensions (physical and electromagnetic), providing a sufficient basis for path planning. On the basis of global path planning, real-time update and local path optimization during flight are added to ensure that when the actual scenario changes, the flight path can still be dynamically adjusted to reduce risks.

[0055] Generally speaking, the above-mentioned substation UAV inspection path planning and processing method based on a 3D model realizes a complete UAV inspection path planning process from environmental data collection, obstacle and electromagnetic interference field modeling, global path planning to dynamic adjustment during flight. Considering physical obstacles and electromagnetic interference comprehensively, it ensures the safe and efficient execution of inspection tasks by UAVs within the substation. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0057] Figure 1 FIG. is a schematic diagram of the main process of the substation UAV inspection path planning and processing method based on a 3D model;

[0058] Figure 2 FIG. is a schematic diagram of a specific process for obtaining 3D environmental data within a substation in the substation UAV inspection path planning and processing method based on a 3D model;

[0059] Figure 3 FIG. is a schematic diagram of a specific process for establishing an obstacle model through an obstacle point cloud recognition algorithm based on the 3D environmental data in the substation UAV inspection path planning and processing method based on a 3D model;

[0060] Figure 4 FIG. is for constructing the distribution of the electromagnetic interference field in the substation UAV inspection path planning and processing method based on a 3D model Figure 1 Specific process schematic diagram;

[0061] Figure 5 FIG. is a schematic diagram of a specific process for generating an initial inspection path in the substation UAV inspection path planning and processing method based on a 3D model;

[0062] Figure 6 FIG. is a schematic diagram of the principle structure of an electronic device. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0063] The following will clearly and completely describe the technical solutions of the present invention with reference to the drawings. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments of the present invention belong to the scope of protection of the present invention.

[0064] The present invention will be further described in detail below through specific embodiments in conjunction with the accompanying drawings. Embodiment 1

[0065] As Figure 1 shown, the present invention provides a method for processing the path planning of an unmanned aerial vehicle (UAV) for substation inspection based on a three-dimensional model, including the following operating steps:

[0066] S1. Obtain the three-dimensional environmental data in the substation, where the three-dimensional environmental data includes the point cloud data of the power line obtained by lidar measurement and the three-dimensional point cloud data of the equipment in the substation;

[0067] S2. Based on the three-dimensional environmental data, establish an obstacle model through an obstacle point cloud recognition algorithm, and generate an obstacle distribution map according to the obstacle model; the obstacles include power lines and equipment in the substation;

[0068] S3. Construct an electromagnetic interference field distribution map, which is generated by combining the electromagnetic radiation height of the equipment in the substation and the equipment layout position information;

[0069] S4. Based on the obstacle distribution map and the electromagnetic interference field distribution map, use a global path planning algorithm to generate an initial UAV inspection path;

[0070] S5. During the flight of the UAV, dynamically update the obstacle distribution map and the electromagnetic interference field distribution map based on real-time sensor data, and use a local path optimization algorithm to adjust the flight path (that is, receive and transmit data in real time, follow the changes in the environment and equipment in the sensor data detected by the sensor, dynamically start the update program, and finally use the local path optimization algorithm to adjust the flight path, that is, determine the path of the current change position according to the changed positions of the environment and equipment for S4 operation and update processing).

[0071] The present invention provides a method for processing the path planning of an unmanned aerial vehicle (UAV) for substation inspection based on a three-dimensional model. Starting from the point cloud data, it uses a lidar device to scan the equipment and power lines in the substation to obtain the three-dimensional environmental data in the substation. The three-dimensional environmental data includes the point cloud data of the power line obtained by lidar measurement and the three-dimensional point cloud data of the equipment in the substation; based on the three-dimensional environmental data, establish an obstacle model through an obstacle point cloud recognition algorithm, and generate an obstacle distribution map according to the obstacle model; the obstacles include power lines and equipment in the substation;

[0072] Construct an electromagnetic interference field distribution map, which is generated by combining the electromagnetic radiation height of the equipment in the substation and the equipment layout position information; S4. Based on the obstacle distribution map and the electromagnetic interference field distribution map, use a global path planning algorithm to generate an initial UAV inspection path;

[0073] Analysis of the above technical solutions shows that the above-mentioned substation drone inspection path planning and processing method based on a three-dimensional model can accurately reconstruct the actual situation in the substation by acquiring three-dimensional environmental data. By using obstacle point cloud recognition and electromagnetic interference field modeling, environmental risks are refined into two dimensions (physical and electromagnetic), providing sufficient basis for path planning. On the basis of global path planning, real-time updates and local path optimization are added during the flight process to ensure that when the actual scene changes, the flight path can still be dynamically adjusted to reduce risks.

[0074] In general, the above-mentioned substation UAV inspection path planning and processing method based on the three-dimensional model realizes the complete UAV inspection path planning process from environmental data collection, obstacle and electromagnetic interference field modeling, global path planning to dynamic adjustment during flight. Comprehensive consideration of physical obstacles and electromagnetic interference ensures that UAVs can perform inspection tasks safely and efficiently in substations.

[0075] like Figure 2 As shown, in the above embodiment, the step S1 of acquiring the three-dimensional environmental data in the substation includes:

[0076] S11. Use the laser radar device installed on the drone to scan the internal environment of the substation and collect point cloud data;

[0077] S12, preprocessing the collected point cloud data, including point cloud denoising, ground segmentation and point cloud coordinate conversion;

[0078] S13, aligning the pre-processed three-dimensional point cloud data of the equipment in the substation, the point cloud data of the power line and the three-dimensional BIM model of the equipment in the substation to generate an obstacle model to be completed.

[0079] It should be noted that point cloud registration is the process of aligning multiple 3D point cloud data sets acquired from different perspectives or times into the same coordinate system. After the obstacle model to be completed is processed by S21-S23, the obstacle point cloud and hazard weights can be added to finally generate an obstacle model including an obstacle distribution map.

[0080] The technical solution of the above steps S11-S13 uses the lidar equipment on the drone to obtain the original point cloud data inside the substation; the pre-processed point cloud data is aligned with the BIM model of the equipment, which can effectively compensate for the possible deviations from a single data source and construct an accurate three-dimensional environmental model.

[0081] See also Figure 3, in the above embodiment, in step S1, based on the three-dimensional environmental data, an obstacle model is established through an obstacle point cloud recognition algorithm, including:

[0082] S21. Extract the obstacle point cloud through a point cloud segmentation algorithm (such as a segmentation method based on region growing); the point cloud segmentation algorithm includes a segmentation method based on region growing;

[0083] S22. Classify the extracted obstacle point cloud using a machine learning algorithm, and classify the obstacles into static obstacles and dynamic obstacles, which is recorded as the classification result of the obstacles;

[0084] S23. Based on the classification result of the obstacles, generate an obstacle distribution map and assign a hazard weight value to each obstacle; the hazard weight value is determined by calling a preset database according to the classification result of the obstacles; the database is used to store the hazard weight values corresponding to the classification types of each obstacle;

[0085] The obstacle model is a three-dimensional model including an obstacle distribution map and marking the classification type of each obstacle.

[0086] In the above technical solution, it uses a point cloud segmentation algorithm based on region growing to extract obstacles in the environment; through a machine learning method, the obstacles are classified into static and dynamic ones, so as to clarify the different risks that may be caused by each; at the same time, it calls the hazard weight value according to the preset database, so that the risk of each obstacle can be quantified, and finally an obstacle distribution map is generated based on this, providing a technical basis for subsequent global path search.

[0087] See Figure 4 , in the above embodiment, in step S3, constructing an electromagnetic interference field distribution map includes:

[0088] S31. Obtain the electromagnetic radiation characteristic information and equipment layout position information of the equipment in each substation, and based on the electromagnetic radiation characteristic information of the equipment in the substation, calculate the electromagnetic field distribution (especially the electromagnetic radiation height) of the electromagnetic field generated by the equipment in the current substation;

[0089] S32. Perform grid processing on the calculated electromagnetic field distribution in the space of the substation, so that the space in the substation is divided into several grid units, and each grid unit includes an electromagnetic interference intensity value (obtained by direct matching in the database) and an electromagnetic radiation height;

[0090] S33. Superimpose the gridded electromagnetic field distribution on the current obstacles in the obstacle distribution map and the current device layout position information to generate a comprehensive electromagnetic interference field distribution map; the electromagnetic interference field distribution map includes device layout position information, electromagnetic radiation height, and influence height; the influence height is the sum of the highest point height of the current device where the device layout position information is located and the electromagnetic radiation height affected by the electromagnetic field interference.

[0091] In the above technical solution, through grid processing, the continuous electromagnetic field distribution is changed into discrete grid cells, and each cell contains specific interference values; the gridded interference data is superimposed on the obstacle distribution and device information, thereby constructing a comprehensive map reflecting the actual interference situation, providing key reference information for globally searching for the optimal path. The superposition of this map with the obstacle distribution map enables path planning to not only consider physical obstacles but also effectively avoid potential risk areas caused by electromagnetic interference.

[0092] See Figure 5 , in the above embodiment, in step S4, generating an initial inspection path based on the comprehensive electromagnetic interference field distribution map includes:

[0093] S41. In the comprehensive electromagnetic interference field distribution map, search for the global optimal path from the starting point to the target point with reference to the risk weight value and influence height;

[0094] S42. Smooth the generated global optimal path, and use a Bezier curve to optimize the continuous flyability of the path to obtain a smoothed path;

[0095] S43. Use the smoothed path as the initial inspection path of the unmanned aerial vehicle.

[0096] In the above technical solution, a Bezier curve is used to smooth the initial path, making the path turn more naturally and the curve smoother, meeting the physical requirements of continuous flight of the unmanned aerial vehicle, thereby minimizing the potential safety hazards caused by path discontinuity.

[0097] In the global optimization process, comprehensively consider the obstacle risk weight value and the influence height of electromagnetic interference, so that the searched path optimizes the flight time and distance as much as possible on the basis of the minimum risk.

[0098] In the above embodiment, in the comprehensive electromagnetic interference field distribution map, searching for the global optimal path from the starting point to the target point with reference to the risk weight value and influence height specifically includes:

[0099] S411. Search for the global optimal path in the comprehensive electromagnetic interference field distribution map. When executing, use a dynamic weighted cost function combined with the obstacle risk weight value, electromagnetic interference intensity, and flight time for path search; at the same time, set the constraint conditions of the comprehensive cost function;

[0100] Define the comprehensive cost function as follows:

[0101] ;

[0102] Where: is the cumulative sum of the obstacle d risk weights for the path segment from node i to node j, ; ( is the risk weight of the k-th grid cell);

[0103] , take the maximum value of the electromagnetic interference intensity in the path segment, is the interference intensity of the k-th grid cell;

[0104] are the node coordinates, and v is the cruising speed of the UAV;

[0105] is the heuristic function, taking the Euclidean distance from node j to the end point;

[0106] is the dynamically adjusted weight coefficient;

[0107] The constraint condition of the comprehensive cost function is: the height of the path nodes satisfies , where is the influence height of node j (equipment height + radiation influence height);

[0108] For each path point, by calculating the vertical safety offset, ensure that the path meets the equipment influence height requirements in terms of height; use Bezier curves for smooth fitting, and then superimpose and verify the generated path with the comprehensive electromagnetic interference field distribution. If it is detected that the local risk exceeds the preset threshold, trigger local replanning and use multi-dimensional anti-interference optimization conditions to correct the path again.

[0109] In the above technical solution, use the dynamic weighted cost function to quantify the risks and interference intensities of obstacles in the path, ensuring that risk points can be fully considered during the search process; at the same time, introduce a heuristic function (such as the Euclidean distance) from the current node to the end point to ensure that the path search takes into account both distance and risk; at the same time, set the height constraint of the path nodes to prevent risks caused by flying too low or too high, realizing double guarantees of flight height and safety, and ensuring that the UAV flight path can avoid high-risk areas while meeting the requirements of flight energy consumption and time.

[0110] In the above embodiment, smooth processing is performed on the generated global optimal path, and Bezier curves are used to optimize the continuous flyability of the path to obtain the smoothed path, specifically including:

[0111] S421. Obtain the generated sequence of path points , and use a piecewise cubic Bezier curve for smoothing to ensure that the path is continuously flyable and avoid electromagnetic interference areas;

[0112] Determine the generation of control points: For each path point , generate a vertical safety offset according to its influence height :

[0113] z{k}=

[0114] where: is the path point corresponding to its influence height; represents the actual flight height of the UAV at the path node (or position point) ; is the offset adjustment amount, which is a constant;

[0115] Execute the curve fitting of the control points: Input the offset point set into the Bezier curve generator, and the objective function is:

[0116] is the Bezier curve, is the smoothing factor;

[0117] S422. Generate an initial anti-interference inspection path; superimpose and verify the smoothed path with the electromagnetic interference field distribution map; during the superimposed verification, if the current segment of the path ( is a preset threshold), then trigger the local replanning processing operation, and finally output a path that meets the height constraint condition, the cumulative hazard weight constraint, and the electromagnetic interference intensity constraint condition:

[0118] wherein, the first anti-interference optimization condition is the height constraint condition; the second anti-interference optimization condition is the cumulative hazard weight constraint; the third anti-interference optimization condition is the electromagnetic interference intensity constraint;

[0119] The first anti-interference optimization condition; ;

[0120] The second anti-interference optimization condition: , the total hazard weight is lower than the critical threshold;

[0121] The third anti-interference optimization condition: .

[0122] In the height constraint condition, : represents the actual flight height of the UAV at the path node (or position point) ; : is the logical symbol "for all" ", indicating that this constraint applies to every node on the path;

[0123] In the height constraint, the influencing height : is composed of the superposition of the physical height of the device and the influencing height of electromagnetic radiation (see S33), represents the minimum safe flight height of the UAV at position . Ensure that the UAV always flies above the influence range of the electromagnetic interference field to avoid communication interruption or sensor failure caused by electromagnetic interference. At the same time, it can also be dynamically adjusted according to this: if a certain path segment does not meet this condition, the path needs to be adjusted by vertical climbing or horizontal detouring.

[0124] Cumulative hazard weight value constraint ; In the above cumulative hazard weight value constraint condition, the hazard weight of the path segment is the sum of the obstacle hazard weights of all grid cells on it ), reflecting the comprehensive risk of this path segment (such as being close to high-voltage equipment, easy-to-collide areas, etc.). The preset critical threshold : is preset according to the task safety requirements. For example, in the high-voltage area it is relatively low, and can be relaxed in the open area. Dynamically adjust the path through the cost function (S41), and preferentially select the path segment with a lower hazard weight.

[0125] In the electromagnetic interference intensity constraint condition, the electromagnetic interference intensity : takes the maximum value of the electromagnetic interference intensity of all grid cells in the path segment to avoid the UAV getting out of control due to local strong interference. The threshold ( ): is set according to the UAV's anti-electromagnetic interference ability (such as the tolerance threshold of the communication module). ).

[0126] If a certain path segment , trigger local replanning, and avoid the strong interference area by increasing the height or detouring.

[0127] Through the setting of multiple constraints in the above technical solutions, the system can eliminate unsafe options at the initial stage of path planning, ensuring that the final inspection path meets all safety standards at the same time. The height constraint ensures that the UAV always maintains a sufficient vertical safety distance during flight; the cumulative hazard weight value constraint ensures that the obstacle risk of the overall path is within a controllable range; the electromagnetic interference intensity constraint directly avoids high-interference areas and reduces the risk of equipment errors or signal interruption caused by complex electromagnetic environments;

[0128] It is decomposed into three independent conditions, which is convenient for step-by-step detection and layer-by-layer optimization in the path planning and local replanning processes, making the entire planning process more rigorous and secure. Embodiment 2

[0129] Based on the same concept as the above method embodiments, the embodiments of the present invention further provide a three-dimensional model-based substation UAV inspection path planning processing system for implementing the above three-dimensional model-based substation UAV inspection path planning processing method of the present invention. Since the principle of solving problems in this electronic device embodiment is similar to the method, it has at least all the beneficial effects brought by the technical solutions of the above embodiments, and will not be elaborated one by one here.

[0130] The embodiments of the present invention provide a three-dimensional model-based substation UAV inspection path planning processing system, including:

[0131] An acquisition module that acquires three-dimensional environment data inside the substation. The three-dimensional environment data includes point cloud data of power lines obtained by lidar measurement and three-dimensional point cloud data of equipment inside the substation;

[0132] A modeling module that, based on the three-dimensional environment data, establishes an obstacle model through an obstacle point cloud recognition algorithm and generates an obstacle distribution map according to the obstacle model. The obstacles include power lines and equipment inside the substation;

[0133] A generation processing module that constructs an electromagnetic interference field distribution map, which is generated by combining the electromagnetic radiation height of equipment inside the substation and the equipment layout position information;

[0134] A path output module that, based on the obstacle distribution map and the electromagnetic interference field distribution map, generates an initial UAV inspection path using a global path planning algorithm;

[0135] A path update module that, during the flight of the UAV, dynamically updates the obstacle distribution map and the electromagnetic interference field distribution map based on real-time sensor data and adjusts the flight path using a local path optimization algorithm. Embodiment 3

[0136] The present invention provides an electronic device, as Figure 6 shown. The computing device may include a storage component 31 and a processing component 32. The storage component 31 stores one or more computer instructions, where the one or more computer instructions are called and executed by the processing component 32 to implement a three-dimensional model-based substation UAV inspection path planning processing method provided in Embodiment 1.

[0137] Among them, the processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above methods. Of course, the processing component may also be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components for executing the above methods. Embodiment 4

[0138] The present invention provides a computer storage medium storing a computer program, which when executed by a computer, implements a method for processing an inspection path planning of a substation drone based on a three-dimensional model provided in Embodiment 1.

[0139] In summary, the present invention provides a method for processing an inspection path planning of a substation drone based on a three-dimensional model. Through a full-process technical solution that hierarchically and gradually constructs from data collection, preprocessing, obstacle and interference modeling to global and local path planning based on multi-dimensional evaluation indicators, it finally realizes the fine and safe planning of the inspection path of the drone in the complex environment of the substation. The above method can effectively cope with the possible physical obstacles and electromagnetic interference in the environment, and provides a guarantee for the flight path planning of the drone with high safety and high reliability.

[0140] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; those of ordinary skill in the art can modify the technical solutions recorded in the foregoing embodiments, or equivalently replace some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for processing the inspection path planning of a substation drone based on a 3D model, characterized in that The operation steps are as follows: Obtain the three-dimensional environment data of the substation, where the three-dimensional environment data includes the point cloud data of the power line obtained by lidar measurement and the three-dimensional point cloud data of the equipment in the substation; Based on the three-dimensional environment data, establish an obstacle model through an obstacle point cloud recognition algorithm, and generate an obstacle distribution map according to the obstacle model; The obstacles include power lines and equipment in the substation; Construct an electromagnetic interference field distribution map, which is generated by combining the electromagnetic radiation height of the equipment in the substation and the equipment layout position information; Based on the obstacle distribution map and the electromagnetic interference field distribution map, use a global path planning algorithm to generate an initial UAV inspection path; During the UAV flight, dynamically update the obstacle distribution map and the electromagnetic interference field distribution map based on real-time sensor data, and use a local path optimization algorithm to adjust the flight path; Among them, constructing the electromagnetic interference field distribution map includes: Obtain the electromagnetic radiation characteristic information and equipment layout position information of each piece of equipment in the substation, and based on the electromagnetic radiation characteristic information of the equipment in the substation, calculate the electromagnetic field distribution of the electromagnetic field generated by the equipment in the current substation; Perform grid processing on the calculated electromagnetic field distribution in the space of the substation, so that the space in the substation is divided into several grid units, and each grid unit contains an electromagnetic interference intensity value and an electromagnetic radiation height; Overlay the gridded electromagnetic field distribution with the current obstacles in the obstacle distribution map and the current equipment layout position information to generate a comprehensive electromagnetic interference field distribution map; the electromagnetic interference field distribution map includes equipment layout position information, electromagnetic radiation height and influence height; the influence height is the sum of the highest point height of the current equipment where the equipment layout position information is located and the electromagnetic radiation height affected by the electromagnetic field interference; It also includes generating an initial inspection path based on the comprehensive electromagnetic interference field distribution map, including: in the comprehensive electromagnetic interference field distribution map, search for the global optimal path from the starting point to the target point with reference to the danger weight value and the influence height; Perform smoothing processing on the generated global optimal path, and use a Bezier curve to optimize the continuous flyability of the path to obtain a smoothed path; Use the smoothed path as the initial inspection path of the UAV; Among them, performing smoothing processing on the generated global optimal path, and using a Bezier curve to optimize the continuous flyability of the path to obtain a smoothed path, specifically includes: Obtain the generated sequence of path points , and use piecewise cubic Bezier curves for smoothing to ensure that the path is continuously flyable and avoids electromagnetic interference areas; Determine control point generation: For each path point , generate a vertical safety offset according to its influence height : ； Perform curve fitting of the control points: the offset point set is input to a Bezier curve generator, and the objective function is: ; is a Bessel curve, is a smoothing factor; Generate an anti-interference initial inspection path; superimpose and verify the smoothed path with the electromagnetic interference field distribution map; during the superimposed verification, if the of the current segment path, then trigger a local replanning processing operation, and finally output a path that meets the following conditions: is a preset threshold value; The first anti-interference optimization condition; ; Second anti-interference optimization condition: , the total risk weight is lower than the critical threshold; The third anti-interference optimization condition: .

2. The method for processing the inspection path planning of a substation drone based on a three-dimensional model according to claim 1, characterized in that, Obtain the three-dimensional environment data of the substation, including: Use the lidar equipment installed on the UAV to scan the internal environment of the substation and collect point cloud data; Perform preprocessing on the collected point cloud data, including point cloud denoising, ground segmentation and point cloud coordinate transformation; Register the three-dimensional point cloud data of the equipment in the substation, the point cloud data of the power line after preprocessing with the three-dimensional BIM model of the equipment in the substation to generate a to-be-completed obstacle model.

3. The method for processing the inspection path planning of a substation UAV based on a 3D model according to claim 2, wherein, Based on the three-dimensional environment data, establish an obstacle model through an obstacle point cloud recognition algorithm, including: Extract the obstacle point cloud through a point cloud segmentation algorithm; the point cloud segmentation algorithm includes a segmentation method based on region growing; Use machine learning algorithms to classify the extracted obstacle point clouds, divide the obstacles into static obstacles and dynamic obstacles, and record them as the classification results of the obstacles; Based on the classification results of the obstacles, generate an obstacle distribution map and assign a danger weight value to each obstacle; the danger weight value is determined by calling a preset database according to the classification results of the obstacles; the database is used to store the danger weight values corresponding to the classification types of each obstacle; The obstacle model is a three-dimensional model that includes an obstacle distribution map and marks the classification types of each obstacle.

4. The method for processing the inspection path planning of a substation UAV based on a 3D model according to claim 3, wherein, In the comprehensive electromagnetic interference field distribution map, search for the global optimal path from the starting point to the target point with reference to the danger weight value and the influence height. Specifically, it includes: Search for the global optimal path in the comprehensive electromagnetic interference field distribution map. When executing, use a dynamic weighted cost function combined with the obstacle danger weight value, electromagnetic interference intensity, and flight time for path search; at the same time, set the constraint conditions of the comprehensive cost function; Define the comprehensive cost function as: ； Wherein: is the cumulative sum of the obstacle d danger weights of the path segment from node i to node j, ; is the danger weight of the k-th grid cell; , take the maximum value of the electromagnetic interference intensity in the path segment, which is the interference intensity of the k-th grid cell; is the node coordinate, and v is the cruising speed of the UAV; is the heuristic function, taking the Euclidean distance from node j to the end point; for dynamically adjusting the weight coefficient; The constraint condition of the comprehensive cost function is that the height of the path node satisfies , where is the influence height of node j.

5. The method for processing the path planning of the substation UAV inspection based on the 3D model according to claim 4, wherein, The first anti-interference optimization condition is the height constraint condition; the second anti-interference optimization condition is the cumulative danger weight value constraint; the third anti-interference optimization condition is the electromagnetic interference intensity constraint.

6. An electronic device, characterized in that, It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a method for planning the inspection path of a substation UAV based on a three-dimensional model as described in any one of claims 1-5.

7. A computer storage medium, characterized in that, A computer program is stored, and when the computer program is executed by a computer, it implements a method for planning the inspection path of a substation UAV based on a three-dimensional model as described in any one of claims 1-5.

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