Intelligent planning method and system for inspection route of power equipment

Abstract

The invention relates to the field of path planning, in particular to an intelligent routing inspection path planning method and system for power equipment, and the method comprises the steps: dividing a power equipment environment into grids based on three-dimensional point cloud data, and planning an initial routing inspection path; and after the real-time environment data is matched with the grids, obstacles are identified through connected domain analysis, and grid collision risk values are calculated. And dynamically adjusting the trajectory time sequence length of the DWA algorithm by using a local peak grid, and predicting multiple trajectories. And according to the collision risk value weighted average of the track grid sequence, obtaining the risk comprehensive degree, mapping the risk difference value of adjacent moments through a negative exponential function, generating a dynamic feedback coefficient, and correcting a DWA evaluation function. Finally, the track corresponding to the maximum dynamic avoidance evaluation function is selected, the inspection route is updated, intelligent path planning is achieved, and by introducing the dynamic feedback coefficient and collision risk evaluation, the applicability and reliability of the algorithm in different scenes are improved.

Description

Technical Field

[0001] The present application relates to the field of path planning, and specifically to a method and system for intelligently planning inspection routes for power equipment. Background Art

[0002] Power equipment is a key infrastructure for energy transmission and distribution in modern society, and is used in various links such as power generation, transmission, transformation and distribution. The stable operation of these equipment is crucial to ensure the reliability and safety of the power system. Power equipment usually includes complex and high-value facilities such as high-voltage transmission lines, substations, transformers, switchgear, cables, etc. They are distributed over a wide geographical area and face the dual challenges of natural environment and human factors. As the scale of the power system continues to expand and the technical complexity increases, the management and maintenance of power equipment has become more critical.

[0003] Inspection of power equipment is an important part of ensuring its normal operation. Traditional inspection methods usually rely on manual labor, which is inefficient and easily affected by human factors. With the development of technology, intelligent inspection technology has gradually emerged, and intelligent planning of inspection routes is the core component of intelligent inspection systems. At present, the operating environment of power equipment is complex and changeable, and there are many dynamic obstacles. These obstacles not only interfere with the normal operation of inspection equipment, but also may cause collision accidents, affecting the continuity of inspection work and the service life of equipment.

[0004] At present, the DWA algorithm is usually used to plan local inspection routes for power equipment. However, the traditional DWA algorithm uses a fixed evaluation function to evaluate the advantages and disadvantages of different trajectories. As a result, in the complex and changeable power equipment inspection environment, it cannot timely reflect the changes of dynamic obstacles in the environment, which may cause the planned path to have a collision risk and affect the safety of the inspection equipment. At the same time, it is impossible to effectively use real-time environmental information for path optimization, which may lead to unreasonable path planning and increase inspection time and resource consumption. Summary of the invention

[0005] In order to solve the problem that the traditional DWA algorithm uses a fixed evaluation function and cannot adapt to the changes of dynamic obstacles in the inspection of power equipment, resulting in collision risks in path planning and increasing inspection time and resource consumption, the present application provides an intelligent inspection route planning method and system for power equipment.

[0006] According to the first aspect of the present application, a method for intelligent planning of inspection routes for power equipment is provided, comprising: obtaining environmental data of the area where the power equipment is located according to three-dimensional point cloud data, dividing the environmental data into grid data, and using the grid data to plan the initial inspection route of the power equipment; obtaining real-time environmental data of the area to be inspected for the power equipment, matching the real-time environmental data with the grid data to obtain dynamic data and perform connected domain analysis, identifying obstacle data, and obtaining a collision risk value of the grid according to the time difference and position relationship between the obstacle data and the grid and the dynamic characteristics of the obstacle data; obtaining a local peak grid of the collision risk value, and using the local peak grid to calculate the DWA. The time series length of each trajectory in the method is dynamically adjusted to obtain the dynamic time series length of the adjusted multiple trajectories, and the multiple trajectories are predicted; the grid sequence and collision risk value passed by each trajectory are obtained, and the weighted average value is obtained according to the position order of the grids in the trajectory to obtain the comprehensive risk degree, and the difference in the comprehensive risk degree of adjacent moments is mapped with a negative exponential function to obtain the dynamic feedback coefficient and correct the evaluation function of the DWA algorithm to obtain the dynamic avoidance evaluation function; based on the dynamic avoidance evaluation function, the trajectory corresponding to the value of the maximum dynamic avoidance evaluation function is selected as the result of local path planning, and the inspection route of the inspection equipment is updated according to the result of local path planning, so as to complete the intelligent planning of the inspection route of power equipment.

[0007] Preferably, dividing the environmental data into grid data includes: A grid system is constructed in three-dimensional space based on a preset side length. The grid system is used as a division unit. The environmental data is divided into equally spaced grids using the division units to obtain a number of grid units in the area to be planned.

[0008] By building a grid system based on preset side lengths in three-dimensional space, the environmental data is divided into equally spaced units as division units, and the complex environmental data is structured into several orderly grid units. This not only simplifies the data processing process and improves the calculation efficiency, but also clearly distinguishes the obstacle area from the passable area, providing an accurate basis for initial path planning.

[0009] Preferably, the initial path includes: In the divided grid data, the grids belonging to static obstacles are marked as obstacle coordinates, and the corresponding grids of the power equipment to be inspected in the grid data are marked as inspection coordinate points; Determine the starting point and end point coordinates of the inspection equipment, use the path planning algorithm to obtain a feasible path from the starting point to the end point, and use it as the initial path for power equipment inspection.

[0010] Preferably, the obtaining of dynamic data includes: The real-time environment data is matched with the grid data using a point cloud registration algorithm to obtain the registered real-time environment data; the registered real-time point cloud data is XOR-ed with the grid data to obtain dynamic data.

[0011] Preferably, the obstacle identification data includes: The connected domain analysis is performed on the dynamic data to obtain the connected domain of the point cloud data corresponding to each dynamic object. The dynamic data whose connected domain volume of the point cloud data is less than the preset volume threshold is eliminated. The remaining connected domains correspond to dynamic obstacles, and the data of each connected domain is used as the data of the corresponding obstacle.

[0012] By analyzing the connected domains of dynamic data, the point cloud data area corresponding to each dynamic object can be accurately identified, and the connected domains with too small volume can be eliminated to filter out noise and invalid information. The remaining connected domains correspond to real dynamic obstacles, and the dynamic obstacle data that needs attention can be accurately extracted from complex environments.

[0013] Preferably, obtaining the collision risk value of the grid includes: Perform connected domain analysis on the point cloud data of each obstacle to obtain the geometric center of a single obstacle, and use the historical coordinates of the geometric center at a preset number of moments before the obstacle as the historical running trajectory of the obstacle, perform trajectory prediction on the historical running trajectory of the obstacle, and obtain the trajectory prediction result of the obstacle; The volume data of multiple time points in the historical running trajectory of the obstacle are obtained to form a volume sequence. The volume sequence is first-order differentiated to obtain the volume change at adjacent time points, and the mean of the first-order difference results is calculated to obtain the average volume change of the obstacle. The exponential function is used to map the product of the average volume change and the hyperparameter to obtain the volume risk coefficient. Calculate the number of inspection equipment arriving at the The time point of the grid and the Obstacles reach the The time difference between the time points of the grids is multiplied by the hyperparameter to adjust the weight; the absolute value or square of the adjusted time difference is mapped using an exponential function to obtain the time risk coefficient.

[0014] Get the inspection equipment and obstacles to reach the first At the time point of each grid, compare which one arrives first, the inspection equipment or the obstacle. grids to determine the objects that arrive first and the objects that arrive later; In response to the inspection equipment arriving at the The time point of the grid is less than or equal to the time when the obstacle reaches the If the time point of a grid is less than 1, the inspection equipment will reach the object first, otherwise the obstacle will reach the object first. Calculate the first arriving object to arrive at When there are grids, the position coordinates of the late arriving object are calculated and the position coordinates of the late arriving object are compared with the first The Euclidean distance of the center points of the grids is calculated, and the exponential function is used to map the product of the Euclidean distance and the hyperparameter to obtain the distance risk coefficient.

[0015] Preferably, the step of obtaining a peak grid of collision risk values ​​includes: Use a voxel window to slide the point cloud data to cover the entire area to be inspected, where the voxel window is a cube with a preset number of grids, and the sliding step length is usually consistent with the side length of the voxel; In response to the collision risk value of the central grid of the voxel window being greater than or equal to the collision risk values ​​of all grids in the neighborhood of the central grid, the central grid of the voxel window is a local peak point grid, otherwise, it is not a local peak point grid.

[0016] Preferably, the dynamic time series length includes: Calculate the Euclidean distance between the three-dimensional coordinate point of the current inspection equipment and the central grid of each local peak point grid, and take the local peak point grid with the smallest Euclidean distance as the risk grid; Calculate the Euclidean distance between the three-dimensional coordinate point of the current inspection device and the three-dimensional coordinate point of the risk grid, use the negative exponential function to map the Euclidean distance, obtain the spatial distance between the current inspection device and the risk grid, and use the sum of the spatial distance and the collision risk value as the risk indicator; Calculate the cosine similarity of the relative angle value between the current inspection device and the risk grid and the relative angle value between each trajectory and the risk grid; The product of the initial time series length, the risk index and the cosine similarity and the maximum value of the preset adjusted time series length is taken as the time series prediction length value of each trajectory.

[0017] According to a second aspect of the present application, there is also provided an intelligent inspection route planning system for electric power equipment, comprising a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, an intelligent inspection route planning method for electric power equipment according to the first aspect of the present application is implemented.

[0018] The technical solution of the present application has the following beneficial technical effects: by acquiring environmental data in real time and dynamically adjusting the evaluation function, it can timely reflect the changes in dynamic obstacles in the environment, effectively avoid obstacles, reduce collision risks, and ensure the safe operation of inspection equipment; the introduction of dynamic feedback coefficients and collision risk assessment enables the algorithm to adapt to the complex and changeable power equipment inspection environment, flexibly adjust the path planning strategy, and improve the applicability and reliability of the algorithm in different scenarios; by using real-time environmental information and dynamic adjustment mechanisms, a better inspection path can be generated, reducing unnecessary path adjustments and repeated inspections, thereby saving inspection time and resources and improving inspection efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flow chart of a method for intelligently planning inspection routes for electric power equipment according to an embodiment of the present application.

[0020] Figure 2 It is a structural block diagram of an intelligent planning system for inspection routes of electric power equipment according to an embodiment of the present application. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0022] According to the first aspect of the present application, the present application provides a method and system for intelligent planning of inspection routes for power equipment. Figure 1 1 is a flow chart of a method for intelligently planning inspection routes for power equipment according to an embodiment of the present application. Figure 1 As shown, the inspection route intelligent planning method for power equipment includes steps S1 to S5, which are described in detail below.

[0023] Step S1: Acquire environmental data of the area where the power equipment is located based on the three-dimensional point cloud data, divide the environmental data into grid data, and use the grid data to plan an initial path for power equipment inspection.

[0024] It should be noted that the 3D point cloud data acquisition equipment (such as a laser scanner, etc.) is used to scan the area where the power equipment is to be routed to obtain the 3D point cloud data in the area. These data contain the spatial position information of each point in the area.

[0025] A grid system is constructed in three-dimensional space based on a preset side length. The grid system is used as a division unit. The environmental data is divided into equally spaced grids using the division units to obtain a number of grid units in the area to be planned.

[0026] Exemplarily, the preset side length of the cube is 0.05 meters, which can be adjusted according to specific implementation conditions.

[0027] In the divided grid data, the grids belonging to static obstacles are marked as obstacle coordinates, and the corresponding grids of the power equipment to be inspected in the grid data are marked as inspection coordinate points; the specific locations of obstacles and inspection targets in the environment are clarified, providing necessary information for path planning.

[0028] Determine the starting point and end point coordinates of the inspection equipment, use the path planning algorithm to obtain a feasible path from the starting point to the end point, and use it as the initial path for power equipment inspection.

[0029] Existing path planning methods include but are not limited to: A-star algorithm, Dijkstra algorithm and other path planning methods, which can effectively find the optimal path in a grid environment. This technology is well known to those skilled in the art and will not be described in detail.

[0030] It should be pointed out that although the initial path planning provides the basic inspection route, the environment may change during the actual inspection process, and dynamic factors (such as moving obstacles, etc.) may appear. In order to deal with these dynamic factors, local path planning will be performed in the subsequent steps to avoid dynamic factors and ensure that the inspection equipment can complete the inspection task safely and efficiently.

[0031] Step S2: Obtain real-time environmental data of the area to be inspected for power equipment, match the real-time environmental data with the grid data to obtain dynamic data and perform connected domain analysis, identify obstacle data, and obtain the collision risk value of the grid based on the time difference and position relationship between the obstacle data and the grid and the dynamic characteristics of the obstacle data.

[0032] It should be noted that a depth camera is installed in the area where the power equipment is to be inspected, which scans the surrounding environment at a frequency of 10 times per second to obtain three-dimensional point cloud data with depth information; at the same time, the position and attitude information of the inspection equipment is recorded through the inertial navigation unit (IMU) installed on the inspection equipment.

[0033] Get dynamic data, including: The real-time environment data is matched with the grid data using a point cloud registration algorithm to obtain the registered real-time environment data; the registered real-time point cloud data is XOR-ed with the grid data to obtain dynamic data.

[0034] It should be noted that the point cloud registration algorithms include: ICP (Iterative Closest Point) algorithm, GMM (Gaussian Mixture Model) algorithm and CPD (Coherent Point Drift) algorithm, which are well-known technologies in the field and will not be described in detail.

[0035] Among them, the rules of the XOR operation are as follows: if the two input values ​​are the same, the output is 0; if the two input values ​​are different, the output is 1.

[0036] In point cloud data processing, the existence of each point can be considered as a logical value (0 or 1). Through the XOR operation, the difference between two point cloud data sets can be identified.

[0037] Example, point cloud : Represents mesh data (static environment); point cloud : Represents real-time point cloud data (dynamic environment). Each point cloud data is represented as a set, where the position coordinates of each point are an element in the set. For example: point cloud : ; Point Cloud : , divide the point cloud data into grids. Each grid cell can be represented by a binary value: if there is point cloud data in the grid cell, the value is 1; if there is no point cloud data in the grid cell, the value is 0.

[0038] Perform an XOR operation on each grid. If the grid cell is 1 in the point cloud and 0 in the point cloud, or vice versa, the XOR result of the grid cell is 1; if the grid cell is 1 or 0 in both the point cloud and the point cloud, the XOR result of the grid cell is 0. Through the XOR operation, a point cloud data set with table differences can be obtained, in which the point cloud data set contains the parts that change between point clouds, that is, dynamic data.

[0039] Example, point cloud (Static environment): ; Point Cloud (Dynamic environment): ; Divide the two point cloud data into grids and perform XOR operations: grid : There are, If there is, it is 0; grid : There are, If there is, it is 0; grid : There are, If there is no grid, it is 1; : No, If there is, it is 1; the dynamic data of the XOR result is: .

[0040] During the inspection of power equipment, dynamic obstacles (such as moving people and vehicles) can be quickly identified through XOR operations, thereby providing data support for subsequent collision risk assessment and path planning.

[0041] Identify obstacle data, including: The connected domain analysis is performed on the dynamic data to obtain the connected domain of the point cloud data corresponding to each dynamic object. The dynamic data whose connected domain volume of the point cloud data is less than the preset volume threshold is eliminated. The remaining connected domains correspond to dynamic obstacles, and the data of each connected domain is used as the data of the corresponding obstacle.

[0042] Exemplarily, the preset volume threshold is 0.5 cubic meters, and implementers can adjust it according to specific circumstances.

[0043] Get the collision risk value of the mesh, including: The connected domain analysis is performed on the point cloud data of each obstacle to obtain the geometric center of a single obstacle. The historical coordinates of the geometric center at a preset number of moments before the obstacle are used as the historical running trajectory of the obstacle. The trajectory prediction of the obstacle is performed to obtain the trajectory prediction result of the obstacle.

[0044] The volume data of multiple time points in the historical running trajectory of the obstacle are obtained to form a volume sequence. The volume sequence is first-order differentiated to obtain the volume changes at adjacent time points. The mean of the first-order difference results is calculated to obtain the average volume change of the obstacle.

[0045] The exponential function is used to map the product of the average volume change and the hyperparameter to obtain the volume risk coefficient.

[0046] Specifically, the volume risk factor satisfies the following relationship: ; In the formula, Indicates In the grid The volume risk factor of each obstacle is represents the hyperparameter, Indicates The first-order difference result of the obstacle volume sequence is: represents the mean function, Represented by natural numbers An exponential function with base .

[0047] That is, the hyperparameters , can be adjusted according to the specific scenario, and the volume change can be mapped to a positively correlated risk value. The larger the volume risk coefficient, the higher the collision risk of the obstacle.

[0048] Calculate the number of inspection equipment arriving at the The time point of the grid and the Obstacles reach the The time difference between the time points of the grids is multiplied by the hyperparameter for weight adjustment; The absolute value or square of the adjusted time difference is mapped using an exponential function to obtain the time risk coefficient.

[0049] Specifically, the time risk coefficient satisfies the following relationship: ;or ; In the formula, Indicates In the grid The time risk factor of each obstacle is represents the hyperparameter, Indicates that the inspection equipment has reached The time point of the grid, Indicates Obstacles reach the The time point of the grid.

[0050] For example, hyperparameters , which can be adjusted according to the specific scenario. The smaller the time difference, the higher the risk.

[0051] Get the inspection equipment and obstacles to reach the first At the time point of each grid, compare which one arrives first, the inspection equipment or the obstacle. grids to determine the objects that arrive first and the objects that arrive later; In response to the inspection equipment arriving at the The time point of the grid is less than or equal to the time when the obstacle reaches the If the time point of a grid is less than 1, the inspection equipment will reach the object first, otherwise the obstacle will reach the object first. Calculate the first arriving object to arrive at When there are grids, the position coordinates of the late arriving object are calculated and the position coordinates of the late arriving object are compared with the first The Euclidean distance of the center points of the grids is calculated, and the exponential function is used to map the product of the Euclidean distance and the hyperparameter to obtain the distance risk coefficient.

[0052] Specifically, the distance risk coefficient satisfies the following relationship: ; In the formula, Indicates In the grid The distance risk factor of obstacles is represents the hyperparameter, Indicates the coordinate point of the late arriving object, Indicates The coordinates of the grid center points, Represented by natural numbers An exponential function with base .

[0053] For example, hyperparameters , which can be adjusted according to the implementation situation. The smaller the distance, the higher the risk.

[0054] Specifically, the collision risk value satisfies the following relationship: ; In the formula, Indicates In the grid The collision risk value of obstacles, Indicates The volume risk factor of each obstacle is Indicates In the grid The time risk factor of each obstacle is Indicates In the grid The risk factor of the distance to the obstacle.

[0055] Perform a point cloud expansion operation on the grid occupied by the initial path, and use the grid with a preset number of expanded grids as the selectable grid; calculate the Euclidean distance between the geometric center of the obstacle and the real-time position of the inspection equipment, and in response to the Euclidean distance being less than or equal to the preset distance, regard the obstacle as an obstacle that may collide.

[0056] Exemplarily, the number of expanded grids is 30, which can be adjusted according to specific scenarios; the preset distance is 10 meters, which can be adjusted according to specific scenarios.

[0057] Step S3: Obtain a local peak grid of the collision risk value, use the local peak grid to dynamically adjust the time series length of each trajectory in the DWA algorithm, obtain the adjusted dynamic time series length of multiple trajectories, and perform multi-trajectory prediction.

[0058] By analyzing the risk factors in the environment, the timing length in the DWA algorithm is dynamically adjusted to ensure that the inspection equipment can complete its tasks safely and efficiently.

[0059] First, obtain the peak grid of collision risk values. The local risk value grid represents the highest risk area in the environment, including: Use a voxel window to slide the point cloud data to cover the entire area to be inspected, where the voxel window is a cube with a preset number of grids, and the sliding step length is usually consistent with the side length of the voxel; Exemplarily, the voxel window is a cube of a 26-neighborhood grid, and one voxel corresponds to one cube in step S1.

[0060] In response to the collision risk value of the central grid of the voxel window being greater than or equal to the collision risk values ​​of all grids in the neighborhood of the central grid, the central grid of the voxel window is a local peak point grid, otherwise, it is not a local peak point grid.

[0061] That is to say, during the sliding process, the collision risk value of the center grid of the voxel window is compared with the collision risk values ​​of all grids in its neighborhood. If the value of the center grid is the largest, it is identified as the local peak point grid.

[0062] Dynamic timing length, including: Calculate the Euclidean distance between the three-dimensional coordinate point of the current inspection equipment and the central grid of each local peak point grid, and take the local peak point grid with the smallest Euclidean distance as the risk grid; Calculate the Euclidean distance between the three-dimensional coordinate point of the current inspection device and the three-dimensional coordinate point of the risk grid, use the negative exponential function to map the Euclidean distance, obtain the spatial distance between the current inspection device and the risk grid, and use the sum of the spatial distance and the collision risk value as the risk indicator; Calculate the cosine similarity of the relative angle value between the current inspection device and the risk grid and the relative angle value between each trajectory and the risk grid; The product of the initial time series length, the risk index and the cosine similarity and the maximum value of the preset adjusted time series length is taken as the dynamic time series length of each trajectory.

[0063] Specifically, the dynamic timing length satisfies the following relationship: ; In the formula, Indicates The dynamic time length of each trajectory, represents the initial time series length, Indicates the Euclidean distance between the three-dimensional coordinate point of the current inspection equipment and the three-dimensional coordinate point of the risk grid. represents the collision risk value of the risk grid, Indicates the maximum value of the preset adjustment timing length. Indicates the relative angle between the current inspection device and the risk grid during real-time inspection and the The cosine similarity of the relative angle between the trajectory and the risk grid, Indicates that natural numbers are The exponential function of the base.

[0064] In other words, is a comprehensive risk assessment indicator used to measure the overall risk of the inspection equipment when it approaches a risk grid. The value of decreases with the increase of the Euclidean distance, reflecting that the longer the distance, the lower the risk. It is the collision risk value of the risk grid, which directly reflects the possibility of collision in the grid.

[0065] Step S4: Obtain the grid sequence and collision risk value passed by each trajectory, perform weighted average according to the position order of the grids in the trajectory to obtain the comprehensive risk degree, use a negative exponential function to map the difference in the comprehensive risk degree at adjacent moments, obtain the dynamic feedback coefficient and correct the evaluation function of the DWA algorithm to obtain the dynamic avoidance evaluation function.

[0066] Specifically, the dynamic feedback coefficient satisfies the following relationship: ; ; In the formula, Indicates The overall risk level of each trajectory is Indicates the total number of grids that the trajectory passes through, Indicates The weight of the grid, Indicates The collision risk value of the grid, , Respectively represent the comprehensive risk level of the trajectory at the two moments before and after. Represented by natural numbers The exponential function with base , Indicates The dynamic feedback coefficient of the trajectory.

[0067] Specifically, the dynamic avoidance evaluation function satisfies the following relationship: ; In the formula, Indicates The dynamic avoidance evaluation function of the trajectory is: Indicates The dynamic feedback coefficient of the trajectory, Indicates The initial evaluation function of the trajectory.

[0068] It should be noted that the weights of grids close to the starting point of the trajectory are higher because they are closer to the current moment and have a greater impact on the immediate risk. The collision risk value of each grid is multiplied by its weight and summed to obtain the comprehensive risk level of the trajectory.

[0069] The characteristic of the negative exponential function is that when the difference is small, the mapping value is large; when the difference is large, the mapping value is small. The dynamic feedback coefficient obtained in this way can reflect the rate of risk change. The smaller the difference, the slower the risk change and the larger the feedback coefficient; the larger the difference, the more drastic the risk change and the smaller the feedback coefficient.

[0070] The original evaluation function is usually based on factors such as path length and smoothness, while the dynamic feedback coefficient introduces dynamic adjustment of risk assessment. In this way, the dynamic avoidance evaluation function not only considers the geometric characteristics of the trajectory, but also integrates the risk factors in the dynamic environment, making path planning more intelligent and safer.

[0071] The dynamic feedback coefficient adjusts the original evaluation function according to the risk change, making the evaluation result more consistent with the actual situation in the dynamic environment. It effectively combines risk assessment with path planning, improving the adaptability and safety of inspection equipment in complex environments.

[0072] Step S5: Based on the dynamic avoidance evaluation function, select the trajectory corresponding to the maximum dynamic avoidance evaluation function value as the result of local path planning, update the inspection route of the inspection equipment according to the result of local path planning, and complete the intelligent planning of the power equipment inspection route.

[0073] Through the above process, the intelligent planning of the entire power equipment inspection route is completed. It not only takes into account obstacles in the static environment, but also dynamically adapts to changes in the environment, ensuring that the inspection equipment can complete its tasks safely and efficiently in a complex and changing environment.

[0074] At this point, a method for intelligently planning inspection routes for power equipment has been completed.

[0075] The present application also discloses an intelligent inspection route planning system for electric power equipment, such as Figure 2 As shown, the system includes a processor and a memory, and the memory stores computer program instructions. When the computer program instructions are executed by the processor, a part processing detection method according to the first aspect of the present invention is implemented. The system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface, whose settings and functions are known in the art, so they are not described here. The above system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface, whose settings and functions are known in the art, so they are not described here.

[0076] Although this specification has shown and described a plurality of embodiments of the present application, it is obvious to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will think of many changes, modifications and alternatives without departing from the thought and spirit of the present application. It should be understood that in the process of practicing the present application, various alternatives to the embodiments of the present application described herein may be adopted.

[0077] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for intelligent planning of inspection routes for power equipment, characterized in that: include: Obtain environmental data of the area where the power equipment is located based on the three-dimensional point cloud data, divide the environmental data into grid data, and use the grid data to plan the initial path for power equipment inspection; Obtain real-time environmental data of the area to be inspected for power equipment, match the real-time environmental data with the grid data to obtain dynamic data and perform connected domain analysis, identify obstacle data, and obtain the collision risk value of the grid based on the time difference and position relationship between the obstacle data and the grid and the dynamic characteristics of the obstacle data; Obtain the local peak grid of the collision risk value, use the local peak grid to dynamically adjust the time series length of each track in the DWA algorithm, obtain the dynamic time series length of the adjusted multiple tracks, and predict the multiple tracks; Obtain the grid sequence and collision risk value of each trajectory, take the weighted average according to the position order of the grids in the trajectory to get the comprehensive risk degree, use the negative exponential function to map the difference of the comprehensive risk degree at adjacent moments, get the dynamic feedback coefficient and correct the evaluation function of the DWA algorithm to obtain the dynamic avoidance evaluation function; Based on the dynamic avoidance evaluation function, the trajectory corresponding to the maximum dynamic avoidance evaluation function value is selected as the result of local path planning. The inspection route of the inspection equipment is updated according to the result of local path planning to complete the intelligent planning of the inspection route of the power equipment.

2. A method for intelligently planning inspection routes for electric power equipment according to claim 1, characterized in that: The step of dividing the environmental data into grid data includes: A grid system is constructed in three-dimensional space based on a preset side length. The grid system is used as a division unit. The environmental data is divided into equally spaced grids using the division units to obtain a number of grid units in the area to be planned.

3. A method for intelligently planning inspection routes for electric power equipment according to claim 1, characterized in that: The initial path includes: In the divided grid data, the grids belonging to static obstacles are marked as obstacle coordinates, and the corresponding grids of the power equipment to be inspected in the grid data are marked as inspection coordinate points; Determine the starting point and end point coordinates of the inspection equipment, use the path planning algorithm to obtain a feasible path from the starting point to the end point, and use it as the initial path for power equipment inspection.

4. A method for intelligently planning inspection routes for electric power equipment according to claim 1, characterized in that: The obtaining of dynamic data comprises: The real-time environment data is matched with the grid data using a point cloud registration algorithm to obtain the registered real-time environment data; the registered real-time point cloud data is XOR-ed with the grid data to obtain dynamic data.

5. A method for intelligent planning of inspection routes for electric power equipment according to claim 1, characterized in that: The obstacle identification data includes: The connected domain analysis is performed on the dynamic data to obtain the connected domain of the point cloud data corresponding to each dynamic object. The dynamic data whose connected domain volume of the point cloud data is less than the preset volume threshold is eliminated. The remaining connected domains correspond to dynamic obstacles, and the data of each connected domain is used as the data of the corresponding obstacle.

6. A method for intelligently planning inspection routes for electric power equipment according to claim 1, characterized in that: The obtaining of the collision risk value of the grid includes: Perform connected domain analysis on the point cloud data of each obstacle to obtain the geometric center of a single obstacle, and use the historical coordinates of the geometric center at a preset number of moments before the obstacle as the historical running trajectory of the obstacle, perform trajectory prediction on the historical running trajectory of the obstacle, and obtain the trajectory prediction result of the obstacle; The volume data of multiple time points in the historical running trajectory of the obstacle are obtained to form a volume sequence. The volume sequence is first-order differentiated to obtain the volume change at adjacent time points, and the mean of the first-order difference results is calculated to obtain the average volume change of the obstacle. The exponential function is used to map the product of the average volume change and the hyperparameter to obtain the volume risk coefficient. Calculate the inspection equipment to arrive at the The time point of the grid and the Obstacles reach the The time difference between the time points of the grids is multiplied by the hyperparameter to adjust the weight; the absolute value or square of the adjusted time difference is mapped using an exponential function to obtain the time risk coefficient; Get the inspection equipment and obstacles to reach the first At the time point of each grid, compare which one arrives first, the inspection equipment or the obstacle. grids to determine the objects that arrive first and the objects that arrive later; In response to the inspection equipment arriving at the The time point of the grid is less than or equal to the time when the obstacle reaches the If the time point of a grid is less than 1, the inspection equipment will reach the object first, otherwise the obstacle will reach the object first. Calculate the first arriving object to arrive at When there are grids, the position coordinates of the late arriving object are calculated. The Euclidean distance of the center points of the grids is calculated, and the exponential function is used to map the product of the Euclidean distance and the hyperparameter to obtain the distance risk coefficient.

7. A method for intelligently planning inspection routes for electric power equipment according to claim 1, characterized in that: The step of obtaining a peak grid of collision risk values ​​includes: Use a voxel window to slide the point cloud data to cover the entire area to be inspected, where the voxel window is a cube with a preset number of grids, and the sliding step length is usually consistent with the side length of the voxel; In response to the collision risk value of the central grid of the voxel window being greater than or equal to the collision risk values ​​of all grids in the neighborhood of the central grid, the central grid of the voxel window is a local peak point grid, otherwise, it is not a local peak point grid.

8. A method for intelligently planning inspection routes for electric power equipment according to claim 1, characterized in that: The dynamic time sequence length includes: Calculate the Euclidean distance between the three-dimensional coordinate point of the current inspection equipment and the central grid of each local peak point grid, and take the local peak point grid with the smallest Euclidean distance as the risk grid; Calculate the Euclidean distance between the three-dimensional coordinate point of the current inspection device and the three-dimensional coordinate point of the risk grid, use the negative exponential function to map the Euclidean distance, obtain the spatial distance between the current inspection device and the risk grid, and use the sum of the spatial distance and the collision risk value as the risk indicator; Calculate the cosine similarity of the relative angle value between the current inspection device and the risk grid and the relative angle value between each trajectory and the risk grid; The product of the initial time series length, the risk index and the cosine similarity and the maximum value of the preset adjusted time series length is taken as the time series prediction length value of each trajectory.

9. An intelligent inspection route planning system for power equipment, characterized in that: include: processor; and a memory storing computer instructions for a flaw detection method for pot forgings based on image recognition, wherein when the computer instructions are executed by the processor, the device executes an intelligent inspection route planning method for electric power equipment according to any one of claims 1-8.

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