Inspection robot path planning method based on transformer substation environment

Abstract

The invention provides an inspection robot path planning method based on a transformer substation environment, and belongs to the technical field of transformer substation inspection, and the method comprises the steps: constructing a transformer substation weighted map, obtaining a plurality of optimal planning paths through employing an iteration method, and selecting the optimal planning path with the highest pheromone concentration as the optimal inspection path of an inspection robot. Comprising the following steps: calculating heuristic estimation cost of each node to obtain an initial planning path, introducing an inspection importance weight factor to calculate heuristic information between the nodes, and evaluating path fitness to obtain an optimal initial planning path in a current iteration process; calculating a pheromone increment between the nodes to update a pheromone concentration between the nodes; and calculating a selection probability based on an ant colony algorithm, and selecting a next node according to the selection probability until a planned path is constructed as an optimal planned path of the iteration. The problem that an existing automatic inspection system is insufficient in path planning in a complex environment is solved.

Description

Technical Field

[0001] The invention relates to a path planning method for an inspection robot based on a substation environment, and belongs to the technical field of substation inspection. Background Art

[0002] In modern power systems, substations, as key facilities, undertake important tasks of power conversion and distribution. Their normal operation directly affects the safety and stability of the entire power network. With the continuous growth of power load, the number and scale of substations are also continuing to expand, facing increasingly complex equipment maintenance and safety monitoring challenges. The previous manual inspection method is not only inefficient because it relies on manpower for equipment inspection, but also poses a great safety risk in substations with complex environments and high risks. Therefore, a new type of intelligent inspection system is urgently needed to improve inspection efficiency, reduce labor costs, and ensure the safety of power facilities.

[0003] Traditional inspection methods involve manual inspections, including regular inspections and equipment status assessments, which can perform certain monitoring tasks when the technical means are relatively backward. However, with the increase in the types of equipment and the advancement of technology, the limitations of manual inspections have gradually emerged: first, the intensity of manual inspections is high, which may cause fatigue to the staff, resulting in false detection or missed detection; second, the interior of the substation is often full of obstacles and complex corridor layouts, and it is difficult to avoid potential safety hazards during manual inspections. In addition, manual inspections are not flexible enough and cannot respond quickly to the actual status of the equipment.

[0004] To solve these problems, the rise of automated inspection robots has provided a new solution for substation inspections. Inspection robots can not only autonomously navigate and avoid obstacles in complex environments, but can also monitor equipment status in real time by integrating advanced sensors and monitoring equipment. However, existing automated inspection systems have problems with insufficient path planning in complex environments. Summary of the invention

[0005] The purpose of the present invention is to provide a path planning method for an inspection robot based on a substation environment, which effectively avoids obstacles in a complex environment while taking into account the priority of the inspection task and the shortest distance of the path, thereby ensuring that the robot completes the task efficiently and safely during the inspection process, and solving the problem of insufficient path planning of existing automatic inspection systems in complex environments.

[0006] In order to solve the above technical problems, the present invention is implemented by adopting the following technical solutions.

[0007] The present invention provides a path planning method for an inspection robot based on a substation environment, comprising:

[0008] The devices inside the substation are regarded as nodes, the accessible paths between the devices are regarded as edges, the pheromone concentration between the nodes is initialized, and a weighted map of the substation is constructed;

[0009] According to the weighted map of the substation, an iterative method is used to obtain multiple optimal planning paths, and the optimal planning path with the highest pheromone concentration is selected as the optimal inspection path for the inspection robot;

[0010] Among them, the iterative method includes:

[0011] Calculate the heuristic estimated cost of each node and obtain the initial planning path;

[0012] Based on the initial planned path, the inspection importance weight factor is introduced to calculate the heuristic information between the nodes, and the path fitness is evaluated according to the heuristic information to obtain the optimal initial planned path in the current iteration process;

[0013] According to the path fitness and the pheromone concentration between nodes, the pheromone increment between nodes is calculated and the pheromone concentration between nodes is updated;

[0014] Based on the ant colony algorithm, the selection probability is calculated according to the pheromone concentration and heuristic information between the nodes, and the next node is selected according to the selection probability until a planning path is constructed as the optimal planning path for this iteration.

[0015] Furthermore, it also includes that the inspection robot performs automatic inspection according to the optimal inspection path of the inspection robot, obtains inspection results according to the path data of the automatic inspection of the inspection robot, adjusts the internal environment information of the substation according to the inspection results, and updates the weighted map of the substation, wherein the inspection results include equipment failures and obstacles encountered.

[0016] Furthermore, the heuristic estimated cost of each node is calculated to obtain the initial planning path, including:

[0017] The safety penalty weight factor is introduced to improve the heuristic function, and the improved heuristic function is used to calculate the heuristic estimated cost of each intermediate node between the current node and the target node;

[0018] According to the heuristic estimated cost, an intermediate node is selected from the current node to the target node as the initial planning path.

[0019] Furthermore, the improved heuristic function is expressed as:

[0020] ;

[0021] In the formula, represents the first The heuristic estimated cost of the intermediate nodes, It represents the first value calculated by the original heuristic function of the A* algorithm. The heuristic estimated cost of the intermediate nodes, represents the safety penalty weight factor, Indicates The distance between the intermediate node and the safe area.

[0022] Furthermore, based on the initial planned path, the inspection importance weight factor is introduced to calculate the heuristic information between the nodes, and the path fitness is evaluated according to the heuristic information to obtain the optimal initial planned path in the current iteration process; wherein the heuristic information between the nodes is expressed as:

[0023] ;

[0024] In the formula, Indicates the first Nodes and The heuristic information between nodes, Indicates The inspection importance weight factor of each node, Indicates The coordinates of the nodes and The coordinates of the nodes The Euclidean distance between .

[0025] Furthermore, according to the path fitness and the pheromone concentration between the nodes, the pheromone increment between the nodes is calculated, and the pheromone concentration between the nodes is updated, including:

[0026] Calculate the pheromone volatilization rate according to the fitness of the current initial planning path and the fitness of the optimal initial planning path in the current iteration process;

[0027] According to the fitness of the optimal initial planning path in the current iteration and the pheromone concentration between nodes, the pheromone increment is obtained;

[0028] The pheromone concentration between nodes is updated according to the pheromone volatilization rate and the pheromone increment.

[0029] Further, the pheromone volatilization rate is expressed as:

[0030] ;

[0031] In the formula, Indicates The pheromone volatilization rate of the iteration process is represents the exponential function, represents the adjustment parameters, Represents the fitness of the optimal initial planning path in the current iteration process, Indicates The fitness of the initial planning path of the iterative process.

[0032] Furthermore, according to the pheromone volatilization rate and the pheromone increment, the pheromone concentration between nodes is updated, wherein the updating formula of the pheromone concentration is expressed as:

[0033] ;

[0034] In the formula, Indicates the updated Nodes and The pheromone concentration between nodes, Indicates The pheromone volatilization rate of the iteration process is Indicates the number before the update Nodes and The pheromone concentration between nodes is updated after Nodes and The pheromone concentration between nodes, Indicates that from The node moves to The pheromone increment of each node.

[0035] Furthermore, the calculation expression of the selection probability is:

[0036] ;

[0037] In the formula, Indicates that from The node chooses to move to The probability of a node, Indicates Nodes and The pheromone concentration between nodes, Indicates nodes and neighbor nodes The pheromone concentration between Represents the pheromone importance factor, which is used to adjust the influence of pheromone in path selection. Indicates Nodes and The heuristic information between nodes, Indicates nodes and neighbor nodes The heuristic information between Represents the heuristic information importance factor, which is used to adjust the influence of heuristic information in path selection. Indicates The set of all neighbor nodes of a node.

[0038] Furthermore, after obtaining the optimal inspection path, the method further includes removing redundant points in the optimal inspection path by using an RDP algorithm to smooth and optimize the optimal inspection path:

[0039] (1) Connecting the initial node and the terminal node of the optimal inspection path to form a straight line segment as a reference segment, and adding the initial node and the terminal node to the optimal inspection node set;

[0040] (2) Calculate the vertical distance from each node in the optimal inspection path to the reference section, determine the maximum vertical distance and the corresponding node with the maximum vertical distance;

[0041] (3) Compare the maximum vertical distance with the preset threshold:

[0042] If the maximum vertical distance is less than or equal to the preset threshold, all nodes in the optimal inspection node set are connected as the final optimal inspection path;

[0043] If the maximum vertical distance is greater than a preset threshold, the node with the maximum vertical distance is added as a split node to the optimal inspection node set. For all sub-paths split by the split node, steps (2) to (3) are repeated until the maximum vertical distances of all nodes in all sub-paths are less than or equal to the preset threshold. All nodes in the optimal inspection node set are connected as the final optimal inspection path.

[0044] Compared with the prior art, the present invention has the following beneficial effects:

[0045] 1. The present invention realizes the intelligent planning of substation inspection paths by combining the A* algorithm with the ant colony algorithm. First, the A* algorithm is used to quickly calculate the initial planning path, which provides a good starting point for the subsequent ant colony algorithm. Then, by introducing the inspection importance weight factor and dynamically updating the pheromone concentration according to the path fitness and pheromone increment, the planning path can fully consider the importance of each device in the substation and the inspection priority. Finally, the optimal planning path with the highest pheromone concentration is selected as the optimal inspection path for the inspection robot, ensuring that the inspection robot can perform inspections along the most efficient and accurate route, thereby greatly improving the efficiency and accuracy of the inspection;

[0046] 2. The present invention obtains the internal environmental information of the substation, constructs the network topology of the internal environment of the substation, and initializes the pheromone concentration between nodes, thereby obtaining a weighted map that can reflect the actual layout and traffic conditions of the substation. On this basis, through an iterative optimization process, the path selection is continuously adjusted according to the heuristic estimated cost, heuristic information and pheromone concentration, so that the planned optimal inspection path can adapt well to the complex environment of the substation. In addition, since this method adopts a strategy of dynamically updating the pheromone concentration, it can flexibly respond to changes in the equipment layout or traffic conditions in the substation, ensuring the real-time and effectiveness of the inspection path. This environmental adaptability and flexibility make this method more widely applicable and more reliable in practical applications;

[0047] 3. The present invention not only realizes the intelligent planning of inspection robot paths based on the substation environment, but also can dynamically adjust the internal environmental information of the substation according to the inspection results, including equipment failures and obstacles encountered, after the inspection robot completes the automatic inspection. This dynamic adjustment mechanism enables the substation weighted map to reflect the actual situation of the substation in real time, including key information such as equipment status and path passability. By continuously updating the substation weighted map, the inspection robot can use more accurate environmental information during the next inspection, thereby further optimizing the inspection path and improving inspection efficiency and accuracy. This combination of dynamic optimization and intelligent inspection makes the operation and maintenance management of the substation more intelligent and efficient;

[0048] 4. The present invention also introduces a safety penalty weight factor in the path planning process, and calculates the heuristic estimated cost between nodes by improving the heuristic function, thereby obtaining a safer initial planning path, so that the inspection robot can fully consider safety factors when planning the path and avoid selecting potential dangerous areas. In addition, in the path selection process, the present invention further ensures the reliability and optimality of the inspection path by dynamically updating the pheromone concentration between nodes and calculating the selection probability, thereby improving the safety and reliability of the inspection process and reducing the risk of equipment damage or casualties caused by improper path planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 The figure is a flow chart of a path planning method for an inspection robot based on a substation environment provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0050] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations on the technical solution of the present invention. The embodiments of the present invention and the technical features in the embodiments may be combined with each other unless there is a conflict.

[0051] The term "and / or" is only a description of the association relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " generally indicates that the related objects are in an "or" relationship.

[0052] Example 1

[0053] like Figure 1 As shown, this embodiment introduces a path planning method for an inspection robot based on a substation environment, including:

[0054] Step 1: Take the devices inside the substation as nodes, the accessible paths between the devices as edges, initialize the pheromone concentration between the nodes, and construct a weighted map of the substation.

[0055] The present invention collects environmental information inside the substation through sensors, cameras or other data collection means. The environmental information inside the substation includes the specific location and type of equipment and the accessible paths between equipment, etc., providing basic data for building network topology and path planning.

[0056] Based on the acquired environmental information, the present invention abstracts the internal environment of the substation into a network topology structure, wherein the devices are nodes of the network topology structure, and the accessible paths between the devices are edges of the network topology structure. At the same time, the pheromone concentration between the nodes is initialized. The pheromone concentration is a key parameter in the ant colony algorithm and is used to simulate the accumulation of pheromones in the biological foraging process. Through the above process, the present invention obtains a weighted map that can reflect the internal environmental structure and traffic conditions of the substation, providing a model basis for path planning.

[0057] Step 2: Based on the weighted map of the substation, an iterative method is used to obtain multiple optimal planning paths, and the optimal planning path with the highest pheromone concentration is selected as the optimal inspection path for the inspection robot:

[0058] Among them, the iterative method includes:

[0059] According to the substation weighted map, the heuristic estimated cost of each node is calculated based on the A* algorithm to obtain an initial planning path.

[0060] The A* algorithm is a heuristic search algorithm that uses a heuristic function to estimate the cost from the current node to the target node. The present invention improves the heuristic function by introducing a safety penalty weight factor to make the planned path safer. This step can quickly generate an initial planned path from the starting point to the end point.

[0061] Based on the initial planned path, the inspection importance weight factor is introduced to calculate the heuristic information between the nodes, and the path fitness is evaluated according to the heuristic information to obtain the optimal initial planned path in the current iteration process.

[0062] In the ant colony algorithm, heuristic information is used to guide ants to select the next node. The present invention introduces the inspection importance weight factor to enable the heuristic information to reflect the inspection priority and importance of the equipment, so that the path planning is more in line with the actual inspection needs of the substation, and the inspection efficiency and accuracy are improved.

[0063] According to the path fitness and the pheromone concentration between nodes, the pheromone increment between nodes is calculated and the pheromone concentration between nodes is updated.

[0064] The present invention dynamically updates the pheromone concentration according to the fitness of the path, that is, the quality of the path and the pheromone increment between nodes, so that the path planning process can gradually converge to the optimal solution, thereby improving the stability and accuracy of the path planning.

[0065] Based on the ant colony algorithm, the selection probability is calculated according to the pheromone concentration and heuristic information between the nodes, and the next node is selected according to the selection probability until a planning path is constructed as the optimal planning path for this iteration.

[0066] The ant colony algorithm finds the optimal path by simulating the pheromone accumulation and selection behavior of organisms in the foraging process. Based on the ant colony algorithm, the present invention calculates the selection probability according to the pheromone concentration and heuristic information between nodes, and selects the next node accordingly. By simulating the foraging behavior of organisms, a complete path from the starting point to the end point is gradually constructed.

[0067] In the ant colony algorithm, the higher the pheromone concentration, the better the path. Therefore, after the iteration, the optimal planning path with the highest pheromone concentration is selected as the optimal inspection path of the inspection robot, and an optimal path that meets the inspection requirements of the substation is obtained, which provides guidance for the automatic inspection of the inspection robot, realizes the intelligent inspection of substation equipment, improves the inspection efficiency and accuracy of substation equipment, and reduces the cost and risk of manual inspection.

[0068] Example 2

[0069] Based on the same inventive concept as Example 1, this example introduces a path planning method for an inspection robot based on a substation environment, comprising the following steps:

[0070] Step 1: Take the devices inside the substation as nodes, the accessible paths between the devices as edges, initialize the pheromone concentration between nodes, and construct a weighted map of the substation.

[0071] Step 2: According to the weighted map of the substation, an iterative method is used to obtain multiple optimal planning paths, and the optimal planning path with the highest pheromone concentration is selected as the optimal inspection path of the inspection robot.

[0072] Among them, the iterative method includes:

[0073] Step 2.1: Calculate the heuristic estimated cost of each node and obtain the initial planning path.

[0074] In this embodiment, according to the weighted map of the substation, the heuristic estimated cost of each node is calculated based on the A* algorithm to obtain the initial planning path, including:

[0075] The safety penalty weight factor is introduced to improve the heuristic function, and the improved heuristic function is used to calculate the heuristic estimated cost of each intermediate node between the current node and the target node;

[0076] According to the heuristic estimated cost, an intermediate node is selected from the current node to the target node as the initial planning path.

[0077] In this embodiment, the improved heuristic function is expressed as:

[0078] ;

[0079] In the formula, represents the first The heuristic estimated cost of the intermediate nodes, It represents the first value calculated by the original heuristic function of the A* algorithm. The heuristic estimated cost of the intermediate nodes, represents the safety penalty weight factor, Indicates The distance between the intermediate node and the safe area.

[0080] Step 2.2: Based on the initial planned path, the inspection importance weight factor is introduced to calculate the heuristic information between the nodes, and the path fitness is evaluated according to the heuristic information to obtain the optimal initial planned path in the current iteration process.

[0081] In this embodiment, the heuristic information between the nodes is expressed as:

[0082] ;

[0083] In the formula, Indicates the first Nodes and The heuristic information between nodes, Indicates The inspection importance weight factor of each node, Indicates The coordinates of the nodes and The coordinates of the nodes The Euclidean distance between .

[0084] Step 3.3: Dynamically update the pheromone concentration between nodes based on the path fitness and the pheromone increment between nodes.

[0085] In this embodiment, according to the path fitness and the pheromone concentration between the nodes, the pheromone increment between the nodes is calculated, and the pheromone concentration between the nodes is updated, including:

[0086] According to the fitness of the current initial planning path and the fitness of the optimal initial planning path in the current iteration process, the pheromone volatilization rate is calculated, and the pheromone volatilization rate is expressed as:

[0087] ;

[0088] In the formula, Indicates The pheromone volatilization rate of the iteration process is represents the exponential function, represents the adjustment parameters, Represents the fitness of the optimal initial planning path in the current iteration process, Indicates The fitness of the initial planning path of the iterative process.

[0089] According to the fitness of the optimal initial planning path in the current iteration and the pheromone concentration between nodes, the pheromone increment is obtained;

[0090] According to the pheromone volatilization rate and the pheromone increment, the pheromone concentration between nodes is updated, wherein the updating formula of the pheromone concentration is expressed as:

[0091] ;

[0092] In the formula, Indicates the updated Nodes and The pheromone concentration between nodes, Indicates The pheromone volatilization rate of the iteration process is Indicates the number before the update Nodes and The pheromone concentration between nodes is updated after Nodes and The pheromone concentration between nodes, Indicates that from The node moves to The pheromone increment of each node.

[0093] Step 3.4: Based on the ant colony algorithm, the selection probability is calculated according to the pheromone concentration and heuristic information between the nodes, and the next node is selected according to the selection probability until a planning path is constructed as the optimal planning path for this iteration.

[0094] In this embodiment, the calculation expression of the selection probability is:

[0095] ;

[0096] In the formula, Indicates that from The node chooses to move to The probability of a node, Indicates Nodes and The pheromone concentration between nodes, Indicates nodes and neighbor nodes The pheromone concentration between Represents the pheromone importance factor, which is used to adjust the influence of pheromone in path selection. Indicates Nodes and The heuristic information between nodes, Indicates nodes and neighbor nodes The heuristic information between Represents the heuristic information importance factor, which is used to adjust the influence of heuristic information in path selection. Indicates The set of all neighbor nodes of a node.

[0097] Step 4: After obtaining the optimal inspection path, the method further includes removing redundant points in the optimal inspection path by using an RDP algorithm to smooth and optimize the optimal inspection path.

[0098] In this embodiment, the RDP algorithm is used to remove redundant points in the optimal inspection path to smooth and optimize the optimal inspection path, including:

[0099] (1) Connecting the initial node and the terminal node of the optimal inspection path to form a straight line segment as a reference segment, and adding the initial node and the terminal node to the optimal inspection node set.

[0100] In this embodiment, the set of optimal inspection node sets is used to store all nodes on the final inspection path.

[0101] (2) Calculate the vertical distance from each node in the optimal inspection path to the reference section, and determine the maximum vertical distance and the corresponding node with the maximum vertical distance.

[0102] This embodiment quantifies the degree of deviation between each node and the reference section by traversing each node in the optimal inspection path and calculating the vertical distance from each node to the reference section, determining the maximum vertical distance and the corresponding node with the maximum vertical distance, and finding the node with the largest deviation.

[0103] (3) Compare the maximum vertical distance with the preset threshold:

[0104] The purpose is to determine whether the path needs to be split according to the degree of deviation of the node. If the maximum vertical distance is less than or equal to the preset threshold, it means that all nodes are close enough to the reference section, and the reference section can be directly used as part of the inspection path, and all nodes that have been added to the optimal inspection node set are connected to form the final path. If the maximum vertical distance is greater than the preset threshold, it means that there is at least one node far away from the reference section, and the node needs to be split.

[0105] In this embodiment, if the maximum vertical distance is less than or equal to a preset threshold, all nodes in the optimal inspection node set are connected as the final optimal inspection path;

[0106] In this embodiment, if the maximum vertical distance is greater than a preset threshold, the node with the maximum vertical distance is added to the optimal inspection node set as a split node, and steps (2) to (3) are repeated for all sub-paths split by the split node until the maximum vertical distances of all nodes in all sub-paths are less than or equal to the preset threshold, and all nodes in the optimal inspection node set are connected as the final optimal inspection path.

[0107] In this embodiment, the node with the maximum vertical distance is used as a split node, so that the original path can be split into two or more sub-paths.

[0108] Step 5: The inspection robot automatically inspects according to the optimal inspection path of the inspection robot, obtains inspection results according to the path data of the automatic inspection of the inspection robot, adjusts the internal environment information of the substation according to the inspection results, and updates the weighted map of the substation.

[0109] In this embodiment, the inspection results include equipment failure and obstacle encounter.

[0110] After the inspection robot performs automatic inspection according to the optimal inspection path, the inspection result is obtained according to the path data of the automatic inspection by the inspection robot, the internal environment information of the substation is adjusted according to the inspection result, and the substation weighted map is updated, wherein the inspection result includes equipment failure and obstacle encounter, the equipment represents the node of the substation weighted map, and the accessible paths between the equipment represent the edges of the substation weighted map.

[0111] In this embodiment, the inspection robot can reflect changes or abnormal conditions discovered during the inspection process, such as equipment status, environmental parameters, new accessible paths or obstacle changes, to the internal environmental information and weighted map of the substation, thereby facilitating the continuous updating and maintenance of the substation weighted map, ensuring that the inspection path of the substation always remains in the optimal state, and improving inspection efficiency and safety.

[0112] Example 3

[0113] Based on the same inventive concept as other embodiments, this embodiment provides a computer-readable storage medium on which computer instructions are stored, characterized in that when the computer instructions are executed by a processor, the steps of the method in the above-mentioned embodiment 1 or 2 are implemented.

[0114] Example 4

[0115] Based on the same inventive concept as other embodiments, this embodiment further provides a computer program product, including computer instructions, characterized in that when the computer instructions are executed by a processor, the steps of the method in the above-mentioned embodiment 1 or 2 are implemented.

[0116] In summary, the present invention realizes intelligent planning of substation inspection paths by combining the A* algorithm with the ant colony algorithm. First, the A* algorithm is used to quickly calculate the initial planning path, which provides a good starting point for the subsequent ant colony algorithm. Then, by introducing the inspection importance weight factor and dynamically updating the pheromone concentration according to the path fitness and pheromone increment, the planned path can fully consider the importance of each equipment in the substation and the inspection priority. Finally, the optimal planned path with the highest pheromone concentration is selected as the optimal inspection path for the inspection robot, ensuring that the inspection robot can perform inspections along the most efficient and accurate route, thereby greatly improving the efficiency and accuracy of the inspection;

[0117] The present invention obtains the internal environmental information of the substation, constructs the network topology of the internal environment of the substation, and initializes the pheromone concentration between nodes, thereby obtaining a weighted map that can reflect the actual layout and traffic conditions of the substation. On this basis, through an iterative optimization process, the path selection is continuously adjusted according to the heuristic estimated cost, heuristic information and pheromone concentration, so that the planned optimal inspection path can adapt well to the complex environment of the substation. In addition, because the method adopts a strategy of dynamically updating the pheromone concentration, it can flexibly respond to changes in the equipment layout or traffic conditions in the substation, ensuring the real-time and effectiveness of the inspection path. This environmental adaptability and flexibility make the method more widely applicable and more reliable in practical applications;

[0118] The present invention not only realizes the intelligent planning of inspection robot paths based on the substation environment, but also can dynamically adjust the internal environmental information of the substation according to the inspection results, including equipment failures and obstacles encountered, after the inspection robot completes the automatic inspection. This dynamic adjustment mechanism enables the substation weighted map to reflect the actual situation of the substation in real time, including key information such as equipment status and path passability. By continuously updating the substation weighted map, the inspection robot can use more accurate environmental information during the next inspection, thereby further optimizing the inspection path and improving inspection efficiency and accuracy. This combination of dynamic optimization and intelligent inspection makes the operation and maintenance management of the substation more intelligent and efficient;

[0119] The present invention also introduces a safety penalty weight factor in the path planning process, and calculates the heuristic estimated cost between nodes by improving the heuristic function, so as to obtain a safer initial planning path, so that the inspection robot can fully consider the safety factor when planning the path and avoid selecting potential dangerous areas. In addition, in the path selection process, the present invention further ensures the reliability and optimality of the inspection path by dynamically updating the pheromone concentration between nodes and calculating the selection probability, improves the safety and reliability of the inspection process, and reduces the risk of equipment damage or casualties caused by improper path planning. It should be understood by those skilled in the art that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0120] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0121] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0122] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0123] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which all fall within the protection of the present invention.

Claims

1. A patrol robot path planning method based on a substation environment, characterized in that: include: The devices inside the substation are regarded as nodes, the accessible paths between the devices are regarded as edges, the pheromone concentration between the nodes is initialized, and a weighted map of the substation is constructed; According to the weighted map of the substation, an iterative method is used to obtain multiple optimal planning paths, and the optimal planning path with the highest pheromone concentration is selected as the optimal inspection path for the inspection robot; Among them, the iterative method includes: Calculate the heuristic estimated cost of each node and obtain the initial planning path; Based on the initial planned path, the inspection importance weight factor is introduced to calculate the heuristic information between the nodes, and the path fitness is evaluated according to the heuristic information to obtain the optimal initial planned path in the current iteration process; According to the path fitness and the pheromone concentration between nodes, the pheromone increment between nodes is calculated and the pheromone concentration between nodes is updated; Based on the ant colony algorithm, the selection probability is calculated according to the pheromone concentration and heuristic information between the nodes, and the next node is selected according to the selection probability until a planning path is constructed as the optimal planning path for this iteration.

2. The inspection robot path planning method based on the substation environment according to claim 1 is characterized in that: It also includes a patrol robot performing automatic patrol according to the optimal patrol path, obtaining patrol results based on the path data of the automatic patrol of the patrol robot, adjusting the internal environment information of the substation according to the patrol results, and updating the weighted map of the substation, wherein the patrol results include equipment failures and obstacles encountered.

3. The inspection robot path planning method based on the substation environment according to claim 1 is characterized in that: Calculate the heuristic estimated cost of each node and get the initial planning path, including: The safety penalty weight factor is introduced to improve the heuristic function, and the improved heuristic function is used to calculate the heuristic estimated cost of each intermediate node between the current node and the target node; According to the heuristic estimated cost, an intermediate node is selected from the current node to the target node as the initial planning path.

4. The inspection robot path planning method based on the substation environment according to claim 3 is characterized in that: The improved heuristic function is expressed as: ； In the formula, represents the first The heuristic estimated cost of the intermediate nodes, It represents the first value calculated by the original heuristic function of the A* algorithm. The heuristic estimated cost of the intermediate nodes, represents the safety penalty weight factor, Indicates The distance between the intermediate node and the safe area.

5. The inspection robot path planning method based on the substation environment according to claim 1 is characterized in that: The heuristic information between the nodes is expressed as: ； In the formula, Indicates the first Nodes and The heuristic information between nodes, Indicates The inspection importance weight factor of each node, Indicates The coordinates of the nodes and The coordinates of the nodes The Euclidean distance between .

6. The inspection robot path planning method based on the substation environment according to claim 1 is characterized in that: According to the path fitness and the pheromone concentration between nodes, the pheromone increment between nodes is calculated and the pheromone concentration between nodes is updated, including: Calculate the pheromone volatilization rate according to the fitness of the current initial planning path and the fitness of the optimal initial planning path in the current iteration process; According to the fitness of the optimal initial planning path in the current iteration and the pheromone concentration between nodes, the pheromone increment is obtained; The pheromone concentration between nodes is updated according to the pheromone volatilization rate and the pheromone increment.

7. The inspection robot path planning method based on the substation environment according to claim 6 is characterized in that: The pheromone volatilization rate is expressed as: ； In the formula, Indicates The pheromone volatilization rate of the iteration process is represents the exponential function, represents the adjustment parameters, Represents the fitness of the optimal initial planning path in the current iteration process, Indicates The fitness of the initial planning path of the iterative process.

8. The inspection robot path planning method based on the substation environment according to claim 7 is characterized in that: According to the pheromone volatilization rate and the pheromone increment, the pheromone concentration between nodes is updated, wherein the updating formula of the pheromone concentration is expressed as: ； In the formula, Indicates the updated Nodes and The pheromone concentration between nodes, Indicates The pheromone volatilization rate of the iteration process is Indicates the number before the update Nodes and The pheromone concentration between nodes is updated after Nodes and The pheromone concentration between nodes, Indicates that from The node moves to The pheromone increment of each node.

9. The inspection robot path planning method based on the substation environment according to claim 1 is characterized in that: The calculation expression of the selection probability is: ； In the formula, Indicates that from The node chooses to move to The probability of a node, Indicates Nodes and The pheromone concentration between nodes, Indicates nodes and neighbor nodes The pheromone concentration between Represents the pheromone importance factor, which is used to adjust the influence of pheromone in path selection. Indicates Nodes and The heuristic information between nodes, Indicates nodes and neighbor nodes The heuristic information between Represents the heuristic information importance factor, which is used to adjust the influence of heuristic information in path selection. Indicates The set of all neighbor nodes of a node.

10. The inspection robot path planning method based on the substation environment according to claim 1 is characterized in that: After obtaining the optimal inspection path, the method further includes removing redundant points in the optimal inspection path by using an RDP algorithm to smooth and optimize the optimal inspection path: (1) Connecting the initial node and the terminal node of the optimal inspection path to form a straight line segment as a reference segment, and adding the initial node and the terminal node to the optimal inspection node set; (2) Calculate the vertical distance from each node in the optimal inspection path to the reference section, determine the maximum vertical distance and the corresponding node with the maximum vertical distance; (3) Compare the maximum vertical distance with the preset threshold: If the maximum vertical distance is less than or equal to the preset threshold, all nodes in the optimal inspection node set are connected as the final optimal inspection path; If the maximum vertical distance is greater than a preset threshold, the node with the maximum vertical distance is added as a split node to the optimal inspection node set. For all sub-paths split by the split node, steps (2) to (3) are repeated until the maximum vertical distances of all nodes in all sub-paths are less than or equal to the preset threshold. All nodes in the optimal inspection node set are connected as the final optimal inspection path.

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