A UAV flight route planning method and system for power operation and maintenance

Abstract

The present invention discloses a method and system for planning the flight route of an unmanned aerial vehicle (UAV) for power operation and maintenance, and relates to the field of path planning technology. The method comprises: collecting data within the flight area to be planned; obtaining a three-dimensional point cloud map of the global environment; obtaining a flight route planning area; determining the final flight path based on the origin and destination of the UAV; performing obstacle avoidance planning and power grid inspection during the flight process on the final flight path; monitoring the flight status of the UAV in real time and recording flight data, and performing adjustments and optimizations. The present invention implements a complete path planning and obstacle avoidance inspection process from data acquisition to map generation, then to path planning and obstacle avoidance planning, and finally to power grid inspection and feedback optimization. The above contents realize a complete path planning and obstacle avoidance inspection process, systematically and intelligently plan the flight route of the UAV in power operation and maintenance, enable the UAV to complete the inspection task quickly and accurately and avoid invalid flight, improve inspection efficiency and safety, and ensure the reliable operation of power facilities.

Description

Technical Field

[0001] The present invention belongs to the field of path planning technology, and in particular relates to a method and system for planning the flight route of an unmanned aerial vehicle (UAV) for power operation and maintenance. Background Art

[0002] Traditional power grid inspections rely on manpower, resulting in low efficiency, high risk, and limited coverage. With the development of drone technology, the use of drones for power grid inspections has become possible, enabling more efficient and safer inspections. However, precise flight planning to ensure coverage of critical points and maximize the drone's endurance remains a pressing challenge. Summary of the Invention

[0003] The purpose of the present invention is to provide a method and system for UAV flight route planning for power operation and maintenance, which can be achieved through the following technical solutions:

[0004] In a first aspect, an embodiment of the present application provides a method for planning a flight route of a drone for power operation and maintenance, comprising the following steps:

[0005] Collect geographic data, meteorological data and power facility data within the planned flight area;

[0006] Pre-acquiring a three-dimensional point cloud map of the global environment of the flight area to be planned;

[0007] Processing the three-dimensional point cloud map based on the geographic data and the power facility data to obtain inaccessible areas, passable areas, and dangerous areas of the three-dimensional point cloud map in three-dimensional space;

[0008] Excluding inaccessible areas within the flight area to be planned, and combining accessible areas and dangerous areas into a flight route planning area;

[0009] Based on geographic data and meteorological data, a multi-layer planning map is generated within the flight route planning area; each layer of map information includes a traffic type and a traffic cost multiplier for each traffic node in the layer; the traffic types include normal traffic type, slowed traffic type, and obstructed traffic type;

[0010] Determine multiple basic flight paths based on the origin and destination of the UAV and select a final flight path from among them;

[0011] Performing obstacle avoidance planning for the UAV's flight process on the final flight path based on the geographic data;

[0012] performing a power grid inspection on the final flight path based on the power facility data;

[0013] Monitor the flight status of the drone in real time as it flies along the planned path, record the flight data generated by each flight mission, and adjust and optimize the drone's flight mission and flight path based on the flight data.

[0014] Preferably, the geographic data includes terrain, obstacles, road distribution and land type distribution; the meteorological data includes temperature, humidity, precipitation and wind speed; and the power facility data includes tower location information, power line distribution and power equipment location.

[0015] Preferably, determining the final flight path includes the following:

[0016] determining one or more first flight paths from a plurality of basic flight paths according to a first preset path rule;

[0017] If there is only one first flight path, determining the first flight path as the final flight path; if there are multiple first flight paths, determining one or more second flight paths from the multiple first flight paths according to a second preset path rule;

[0018] If there is only one second flight path, this second flight path is determined as the final flight path; if there are multiple second flight paths, the final flight path is determined from the multiple second flight paths according to the path planning algorithm and the adaptive differential target evolution algorithm, specifically:

[0019] Obtaining an optimal flight path according to the path planning algorithm;

[0020] Acquiring a flight path according to the adaptive differential target evolution algorithm;

[0021] A final flight path is determined based on the optimal flight path and the track flight path.

[0022] Preferably, the first preset path rule includes the following content:

[0023] The flight path corresponds to a flight altitude greater than the highest altitude or the tallest building within the flight area;

[0024] There is no flight mission identical to the current one in the same flight area;

[0025] The aircraft's noise decibel level is lower than the decibel requirement within the flight area;

[0026] The number of flight paths in the danger zone is less than the number of flight paths in the safe zone.

[0027] Preferably, the second preset path rule includes the following content:

[0028] The meteorological environment of the flight path meets the operational requirements for safe operation of the aircraft;

[0029] The mission saturation of the aircraft within the flight path is lower than the mission saturation threshold;

[0030] The number of aircraft in the flight path is less than the saturation threshold.

[0031] Preferably, obtaining the optimal flight path according to the path planning algorithm includes the following:

[0032] S1, put the origin into the intention node set;

[0033] S2, select the node with the smallest total cost value in the intention node set as the current parent node, and move the current parent node to the visited node set and the planned node set;

[0034] S3, based on the travel type and travel cost multiplier of each node, calculate the total cost of the nodes in the neighboring nodes of the current parent node that are not in the visited node set and are not of the pass-blocking type; add the nodes in the neighboring nodes of the current parent node that are not in the visited node set and are not of the pass-blocking type to the intended node set;

[0035] The total cost of a node is the sum of the cost from the origin to the current node via the parent node and the estimated cost from the current node to the target node.

[0036] S4, if the target node is included in the neighboring nodes of the current parent node, the target node is moved to the visited node set and the planned node set; in the planned node set, the optimal flight path is found based on the parent-child relationship between the nodes;

[0037] S5, otherwise, return to step S2.

[0038] Preferably, obtaining the flight path according to the adaptive differential target evolution algorithm includes the following:

[0039] S101, determine the planning space, origin and destination, initialize the population using a heuristic initialization algorithm, and complete population preprocessing to transform relative polar coordinate individuals into absolute value coordinate individuals;

[0040] S102, discretizing the navigation traces of the individuals in the population according to the navigation speed and sampling time step;

[0041] S103, using the discretized navigation track points of the individuals in the initial population to perform track constraint verification and target value calculation, and calculating the sum of the constraint values ​​of all constraints of each individual;

[0042] S104, performing genetic crossover and mutation operations on the parent population according to the adaptive difference operator to form a subpopulation;

[0043] S105, discretizing the navigation traces of the offspring individuals in the subpopulation, bringing the discretized navigation traces into the problem model, and calculating the sum of the individual constraints, the objective function value, and the constraint values ​​of all constraints;

[0044] S106, the parent and offspring form a mixed group, and an improved fast non-dominated sort is performed on the mixed group to determine the layer to which each individual in the mixed group is distributed;

[0045] S107, performing an improved aggregation distance and exclusion mechanism on the individuals in each layer of the mixed group, and selecting individuals of the same population size from the mixed group according to the dominance relationship and individual crowding degree to form a new parent population;

[0046] S108, judging whether the evolution process is finished, if it reaches the maximum iteration number, go to S109; otherwise go to S104;

[0047] S109, selecting an individual from the non-dominated layer of the population, the track represented by the individual is the desired track flight path.

[0048] Preferably, the final flight path is determined as follows:

[0049] The optimal flight path is compared with the track flight path. If the two coincide, one of them is selected as the final flight path. Otherwise, curve fitting is performed on the two to determine the final flight path.

[0050] Preferably, obstacle avoidance planning is performed, including the following:

[0051] Obtaining multiple sets of obstacle information based on the geographic data, each set of obstacle information corresponding to one obstacle; wherein the set of obstacle information includes the type of obstacle, the distance between the UAV and the obstacle, and the location of the obstacle;

[0052] Determine the threat probability of the corresponding obstacle based on multiple sets of obstacle information;

[0053] Obtain obstacle avoidance strategies based on multiple sets of obstacle information and the threat probabilities corresponding to the obstacles;

[0054] Control the drone to adjust its flight state according to the obstacle avoidance strategy to avoid obstacles and return to the original flight route;

[0055] The original flight path represents the final flight path set for the drone before obstacle avoidance.

[0056] In a second aspect, an embodiment of the present application provides a drone flight route planning system for power operation and maintenance, which is applied to the drone flight route planning method described above, including:

[0057] Data acquisition module, used to collect geographic data, meteorological data and power facility data within the planned flight area;

[0058] A map generation module is used to pre-acquire a three-dimensional point cloud map of the global environment of the flight area to be planned;

[0059] Processing the three-dimensional point cloud map based on the geographic data and the power facility data to obtain inaccessible areas, passable areas, and dangerous areas of the three-dimensional point cloud map in three-dimensional space;

[0060] Excluding inaccessible areas within the flight area to be planned, and combining accessible areas and dangerous areas into a flight route planning area;

[0061] Based on geographic data and meteorological data, a multi-layer planning map is generated within the flight route planning area; each layer of map information includes a traffic type and a traffic cost multiplier for each traffic node in the layer; the traffic types include normal traffic type, slowed traffic type, and obstructed traffic type;

[0062] A path planning module is used to determine multiple basic flight paths based on the origin and destination of the UAV and select the final flight path from them;

[0063] an obstacle avoidance planning module, configured to perform obstacle avoidance planning for the UAV during its flight along the final flight path based on the geographic data;

[0064] a power grid inspection module, configured to perform power grid inspection on the final flight path based on the power facility data;

[0065] The feedback optimization module is used to monitor the flight status of the UAV in real time when it flies along the planned path, record the flight data generated by each flight mission, and adjust and optimize the UAV's flight mission and flight path based on the flight data.

[0066] The beneficial effects of the present invention are:

[0067] (1) The present invention implements a complete path planning and obstacle avoidance inspection process from data acquisition to map generation, then to path planning and obstacle avoidance planning, and finally to power grid inspection and feedback optimization. Through the above content, the flight route of the UAV in power operation and maintenance is planned systematically and intelligently, so that the UAV can complete the inspection task quickly and accurately and avoid invalid flight, thereby improving inspection efficiency and safety and ensuring the reliable operation of power facilities.

[0068] (2) The present invention first collects geographic data, meteorological data and power facility data within the flight area to be planned; then obtains a three-dimensional point cloud map of the global environment of the flight area to be planned in advance; then, based on the geographic data and power facility data, processes the three-dimensional point cloud map to obtain the inaccessible area, passable area and dangerous area of ​​the three-dimensional point cloud map in three-dimensional space; then, excludes the inaccessible area within the flight area to be planned, and combines the passable area and dangerous area into a flight route planning area; then, based on the geographic data and meteorological data, generates a multi-layer planning map within the flight route planning area; each layer of map information includes the traffic type and traffic cost multiplier of each traffic node in this layer of map; the traffic type includes normal traffic type, slowed traffic type and obstructed traffic type; then, path planning is performed: multiple basic flight paths are determined based on the origin and destination of the UAV; a route is determined from the multiple basic flight paths according to the first preset path rule. One or more first flight paths; if there is only one first flight path, the first flight path is determined as the final flight path; if there are multiple first flight paths, one or more second flight paths are determined from the multiple first flight paths according to the second preset path rule; if there is only one second flight path, the second flight path is determined as the final flight path; if there are multiple second flight paths, the final flight path is determined from the multiple second flight paths according to the path planning algorithm; then obstacle avoidance planning is performed: obstacle avoidance planning is performed on the flight process of the UAV on the final flight path based on the geographic data; then power grid inspection is performed: power grid inspection is performed on the final flight path based on the power facility data; finally, feedback optimization is performed: the flight status of the UAV is monitored in real time when it flies according to the planned path, the flight data generated by each flight mission is recorded, and the flight mission and flight path of the UAV are adjusted and optimized based on the flight data. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] For better understanding and implementation, the technical solution of the present application is described in detail below with reference to the accompanying drawings.

[0070] Figure 1 A flowchart of the steps of a method for planning a UAV flight route for power operation and maintenance provided in an embodiment of the present application;

[0071] Figure 2 A flowchart of the steps for obtaining the optimal flight path provided in an embodiment of the present application;

[0072] Figure 3 A flowchart of the steps for obtaining a flight path provided in an embodiment of the present application;

[0073] Figure 4 A flowchart of the steps for performing obstacle avoidance planning provided in an embodiment of the present application;

[0074] Figure 5 A schematic structural diagram of a UAV flight route planning system for power operation and maintenance provided in an embodiment of the present application. DETAILED DESCRIPTION

[0075] To further illustrate the technical means and effectiveness of the present invention in achieving its intended purpose, exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all possible embodiments consistent with the present application. Rather, they are merely examples of methods and systems consistent with certain aspects of the present application, as detailed in the appended claims.

[0076] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The singular forms "a," "an," "the," and "the" used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" as used herein refers to any or all possible combinations of one or more of the associated listed items.

[0077] The following describes in detail the specific implementation methods, features and effects of the present invention in conjunction with the accompanying drawings and preferred embodiments.

[0078] Example 1

[0079] See also Figure 1 The present invention provides a method for planning a flight route of a UAV for power operation and maintenance, comprising the following steps:

[0080] Data acquisition: Collect geographic data, meteorological data and power facility data within the planned flight area;

[0081] Map generation: obtaining in advance a three-dimensional point cloud map of the global environment of the flight area to be planned;

[0082] Processing the three-dimensional point cloud map based on the geographic data and the power facility data to obtain inaccessible areas, passable areas, and dangerous areas of the three-dimensional point cloud map in three-dimensional space;

[0083] Excluding inaccessible areas within the flight area to be planned, and combining accessible areas and dangerous areas into a flight route planning area;

[0084] Based on geographic data and meteorological data, a multi-layer planning map is generated within the flight route planning area; each layer of map information includes a traffic type and a traffic cost multiplier for each traffic node in the layer; the traffic types include normal traffic type, slowed traffic type, and obstructed traffic type;

[0085] Path planning: Determine multiple basic flight paths based on the drone's origin and destination and select the final flight path from among them;

[0086] Obstacle avoidance planning: performing obstacle avoidance planning for the UAV's flight process on the final flight path based on the geographic data;

[0087] Power grid inspection: performing power grid inspection on the final flight path based on the power facility data;

[0088] Feedback optimization: Real-time monitoring of the drone's flight status as it flies along the planned path, recording the flight data generated by each flight mission, and adjusting and optimizing the drone's flight mission and flight path based on the flight data.

[0089] Specifically, since the traditional power grid inspection method that relies on manual inspection will increase labor costs and reduce inspection efficiency, the present application adopts a drone inspection method. However, the drone will be affected by the geographical information, meteorological information and obstacles in the flight area, so the present application plans the flight path of the drone to improve the efficiency, accuracy and safety of the drone inspection, specifically: first, collect the geographical data, meteorological data and power facility data in the flight area to be planned; then obtain in advance the three-dimensional point cloud map of the global environment of the flight area to be planned; then, based on the geographical data and power facility data, process the three-dimensional point cloud map to obtain the inaccessible areas, passable areas and dangerous areas of the three-dimensional point cloud map in three-dimensional space; then exclude the inaccessible areas in the flight area to be planned, and combine the passable areas and dangerous areas into a flight route planning area; then, based on the geographical data and meteorological data, generate a multi-layer planning map in the flight route planning area; each layer of map information includes the pass type and pass cost multiplier of each pass node in this layer of map; the pass type includes normal pass type, mitigation Passage type and obstruction passage type; then path planning is performed: multiple basic flight paths are determined based on the origin and destination of the UAV; one or more first flight paths are determined from the multiple basic flight paths according to the first preset path rule; if there is only one first flight path, the first flight path is determined as the final flight path; if there are multiple first flight paths, one or more second flight paths are determined from the multiple first flight paths according to the second preset path rule; if there is only one second flight path, the second flight path is determined as the final flight path; if there are multiple second flight paths, the final flight path is determined from the multiple second flight paths according to the path planning algorithm; then obstacle avoidance planning is performed: obstacle avoidance planning is performed on the flight process of the UAV on the final flight path based on the geographic data; then power grid inspection is performed: power grid inspection is performed on the final flight path based on the power facility data; finally, feedback optimization is performed: the flight status of the UAV is monitored in real time when it flies according to the planned path, the flight data generated by each flight mission is recorded, and the flight mission and flight path of the UAV are adjusted and optimized based on the flight data.

[0090] This application implements a complete path planning and obstacle avoidance inspection process from data acquisition to map generation, then to path planning and obstacle avoidance planning, and finally to power grid inspection and feedback optimization. Through the above content, it realizes the complete path planning and obstacle avoidance inspection process, systematically and intelligently plans the flight route of drones in power operation and maintenance, enables drones to complete inspection tasks quickly and accurately and avoid invalid flights, improves inspection efficiency and safety, and ensures the reliable operation of power facilities.

[0091] In an embodiment provided in the present application, the geographic data includes terrain, obstacles, road distribution and land type distribution; the meteorological data includes temperature, humidity, precipitation and wind speed; and the power facility data includes pole tower location information, power line distribution and power equipment location.

[0092] Specifically, the above-mentioned geographic data and meteorological data help to identify the characteristics, obstacles and potential risks of the flight area, ensuring the safety and efficiency of drone flights; and the above-mentioned power facility data can reflect the power distribution situation where drones need to conduct power inspections in the flight area, which helps to plan the flight route of drones based on this data.

[0093] In one embodiment provided in this application, determining the final flight path includes the following:

[0094] determining one or more first flight paths from a plurality of basic flight paths according to a first preset path rule;

[0095] If there is only one first flight path, determining the first flight path as the final flight path; if there are multiple first flight paths, determining one or more second flight paths from the multiple first flight paths according to a second preset path rule;

[0096] If there is only one second flight path, this second flight path is determined as the final flight path; if there are multiple second flight paths, the final flight path is determined from the multiple second flight paths according to the path planning algorithm and the adaptive differential target evolution algorithm, specifically:

[0097] Obtaining an optimal flight path according to the path planning algorithm;

[0098] Acquiring a flight path according to the adaptive differential target evolution algorithm;

[0099] A final flight path is determined based on the optimal flight path and the track flight path.

[0100] In an embodiment provided in this application, the first preset path rule includes the following content:

[0101] The flight path corresponds to a flight altitude greater than the highest altitude or the tallest building within the flight area;

[0102] There is no flight mission identical to the current one in the same flight area;

[0103] The aircraft's noise decibel level is lower than the decibel requirement within the flight area;

[0104] The number of flight paths in the danger zone is less than the number of flight paths in the safe zone.

[0105] In an embodiment provided in this application, the second preset path rule includes the following content:

[0106] The meteorological environment of the flight path meets the operational requirements for safe operation of the aircraft;

[0107] The mission saturation of the aircraft within the flight path is lower than the mission saturation threshold;

[0108] The number of aircraft in the flight path is less than the saturation threshold.

[0109] like Figure 2 As shown, in one embodiment provided in this application, obtaining the optimal flight path according to the path planning algorithm includes the following:

[0110] S1, put the origin into the intention node set;

[0111] S2, select the node with the smallest total cost value in the intention node set as the current parent node, and move the current parent node to the visited node set and the planned node set;

[0112] S3, based on the travel type and travel cost multiplier of each node, calculate the total cost of the nodes in the neighboring nodes of the current parent node that are not in the visited node set and are not of the pass-blocking type; add the nodes in the neighboring nodes of the current parent node that are not in the visited node set and are not of the pass-blocking type to the intended node set;

[0113] The total cost of a node is the sum of the cost from the origin to the current node via the parent node and the estimated cost from the current node to the target node.

[0114] S4, if the target node is included in the neighboring nodes of the current parent node, the target node is moved to the visited node set and the planned node set; in the planned node set, the optimal flight path is found based on the parent-child relationship between the nodes;

[0115] S5, otherwise, return to step S2.

[0116] like Figure 3 As shown, in one embodiment provided by the present application, obtaining a flight path according to the adaptive differential target evolution algorithm includes the following:

[0117] S101, determine the planning space, origin and destination, initialize the population using a heuristic initialization algorithm, and complete population preprocessing to transform relative polar coordinate individuals into absolute value coordinate individuals;

[0118] S102, discretizing the navigation traces of the individuals in the population according to the navigation speed and sampling time step;

[0119] S103, using the discretized navigation track points of the individuals in the initial population to perform track constraint verification and target value calculation, and calculating the sum of the constraint values ​​of all constraints of each individual;

[0120] S104, performing genetic crossover and mutation operations on the parent population according to the adaptive difference operator to form a subpopulation;

[0121] S105, discretizing the navigation traces of the offspring individuals in the subpopulation, bringing the discretized navigation traces into the problem model, and calculating the sum of the individual constraints, the objective function value, and the constraint values ​​of all constraints;

[0122] S106, the parent and offspring form a mixed group, and an improved fast non-dominated sort is performed on the mixed group to determine the layer to which each individual in the mixed group is distributed;

[0123] S107, performing an improved aggregation distance and exclusion mechanism on the individuals in each layer of the mixed group, and selecting individuals of the same population size from the mixed group according to the dominance relationship and individual crowding degree to form a new parent population;

[0124] S108, judging whether the evolution process is finished, if it reaches the maximum iteration number, go to S109; otherwise go to S104;

[0125] S109, selecting an individual from the non-dominated layer of the population, the track represented by the individual is the desired track flight path.

[0126] In one embodiment provided in this application, determining the final flight path is specifically as follows:

[0127] The optimal flight path is compared with the track flight path. If the two coincide, one of them is selected as the final flight path. Otherwise, curve fitting is performed on the two to determine the final flight path.

[0128] Specifically, this application uses two different technologies to calculate the optimal flight path and the track flight path respectively, and then judges the overlap between the two through path comparison. If the two overlap, one of them is arbitrarily selected as the final flight path. At this time, the selected path is already the path most suitable for the current drone flight and mission execution; if the two do not overlap, they are merged, specifically using curve fitting technology to fit the two to obtain the final flight path. Through the above method, this application can avoid deviations and errors in the flight path to the greatest extent, thereby ensuring the accuracy of the final flight path.

[0129] like Figure 4 As shown, in one embodiment provided in this application, obstacle avoidance planning includes the following:

[0130] Obtaining multiple sets of obstacle information based on the geographic data, each set of obstacle information corresponding to one obstacle; wherein the set of obstacle information includes the type of obstacle, the distance between the UAV and the obstacle, and the location of the obstacle;

[0131] Determine the threat probability of the corresponding obstacle based on multiple sets of obstacle information;

[0132] Obtain obstacle avoidance strategies based on multiple sets of obstacle information and the threat probabilities corresponding to the obstacles;

[0133] Control the drone to adjust its flight state according to the obstacle avoidance strategy to avoid obstacles and return to the original flight route;

[0134] The original flight path represents the final flight path set for the drone before obstacle avoidance.

[0135] Specifically, this embodiment obtains obstacle information from geographic data and performs a threat probability analysis on it to develop different obstacle avoidance strategies based on different obstacle situations. This obstacle avoidance strategy then comprehensively determines the local route flight plan and adjusts the drone's flight state to flexibly avoid obstacles until it safely returns to the set original flight path. This method improves the drone's obstacle avoidance capabilities, and enhances its flight efficiency and autonomy.

[0136] Example 2

[0137] Please refer to Figure 5 The present application provides a UAV flight route planning system for power operation and maintenance, which is applied to the UAV flight route planning method described above, including:

[0138] Data acquisition module, used to collect geographic data, meteorological data and power facility data within the planned flight area;

[0139] A map generation module is used to pre-acquire a three-dimensional point cloud map of the global environment of the flight area to be planned;

[0140] Processing the three-dimensional point cloud map based on the geographic data and the power facility data to obtain inaccessible areas, passable areas, and dangerous areas of the three-dimensional point cloud map in three-dimensional space;

[0141] Excluding inaccessible areas within the flight area to be planned, and combining accessible areas and dangerous areas into a flight route planning area;

[0142] Based on geographic data and meteorological data, a multi-layer planning map is generated within the flight route planning area; each layer of map information includes a traffic type and a traffic cost multiplier for each traffic node in the layer; the traffic types include normal traffic type, slowed traffic type, and obstructed traffic type;

[0143] A path planning module is used to determine multiple basic flight paths based on the origin and destination of the UAV and select the final flight path from them;

[0144] an obstacle avoidance planning module, configured to perform obstacle avoidance planning for the UAV during its flight along the final flight path based on the geographic data;

[0145] a power grid inspection module, configured to perform power grid inspection on the final flight path based on the power facility data;

[0146] The feedback optimization module is used to monitor the flight status of the UAV in real time when it flies along the planned path, record the flight data generated by each flight mission, and adjust and optimize the UAV's flight mission and flight path based on the flight data.

[0147] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0148] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0149] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0150] The above description is merely a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as a preferred embodiment as above, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to equivalent embodiments using the technical contents disclosed above without departing from the scope of the technical solution of the present invention. However, any simple modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A method for planning a UAV flight route for power operation and maintenance, characterized by: The steps include: Collect geographic data, meteorological data and power facility data within the planned flight area; Pre-acquiring a three-dimensional point cloud map of the global environment of the flight area to be planned; Processing the three-dimensional point cloud map based on the geographic data and the power facility data to obtain inaccessible areas, passable areas, and dangerous areas of the three-dimensional point cloud map in three-dimensional space; Excluding inaccessible areas within the flight area to be planned, and combining accessible areas and dangerous areas into a flight route planning area; Based on geographic data and meteorological data, a multi-layer planning map is generated within the flight route planning area; each layer of map information includes a traffic type and a traffic cost multiplier for each traffic node in the layer; the traffic types include normal traffic type, slowed traffic type, and obstructed traffic type; Determine multiple basic flight paths based on the origin and destination of the UAV and select a final flight path from among them; Performing obstacle avoidance planning for the UAV's flight process on the final flight path based on the geographic data; performing a power grid inspection on the final flight path based on the power facility data; Monitor the flight status of the drone in real time as it flies along the planned path, record the flight data generated by each flight mission, and adjust and optimize the drone's flight mission and flight path based on the flight data; Among them, determining the final flight path includes the following: determining one or more first flight paths from a plurality of basic flight paths according to a first preset path rule; If there is only one first flight path, determining the first flight path as the final flight path; if there are multiple first flight paths, determining one or more second flight paths from the multiple first flight paths according to a second preset path rule; If there is only one second flight path, this second flight path is determined as the final flight path; if there are multiple second flight paths, the final flight path is determined from the multiple second flight paths according to the path planning algorithm and the adaptive differential target evolution algorithm, specifically: Obtaining an optimal flight path according to the path planning algorithm; Acquiring a flight path according to the adaptive differential target evolution algorithm; A final flight path is determined based on the optimal flight path and the track flight path.

2. The method for planning a UAV flight route for power operation and maintenance according to claim 1, characterized in that: The geographic data includes terrain, obstacles, road distribution and land type distribution; the meteorological data includes temperature, humidity, precipitation and wind speed; the power facility data includes tower location information, power line distribution and power equipment location.

3. The method for planning a UAV flight route for power operation and maintenance according to claim 1, characterized in that: The first preset path rule includes the following: The flight path corresponds to a flight altitude greater than the highest altitude or the tallest building within the flight area; There is no flight mission identical to the current one in the same flight area; The aircraft's noise decibel level is lower than the decibel requirement within the flight area; The number of flight paths in the danger zone is less than the number of flight paths in the safe zone.

4. The method for planning a UAV flight route for power operation and maintenance according to claim 1, characterized in that: The second preset path rule includes the following: The meteorological environment of the flight path meets the operational requirements for safe operation of the aircraft; The mission saturation of the aircraft within the flight path is lower than the mission saturation threshold; The number of aircraft in the flight path is less than the saturation threshold.

5. The method for planning a UAV flight route for power operation and maintenance according to claim 1, characterized in that: Obtaining the optimal flight path according to the path planning algorithm includes the following: S1, put the origin into the intention node set; S2, select the node with the smallest total cost value in the intention node set as the current parent node, and move the current parent node to the visited node set and the planned node set; S3, based on the travel type and travel cost multiplier of each node, calculate the total cost of the nodes in the neighboring nodes of the current parent node that are not in the visited node set and are not of the pass-blocking type; add the nodes in the neighboring nodes of the current parent node that are not in the visited node set and are not of the pass-blocking type to the intended node set; The total cost of a node is the sum of the cost from the origin to the current node via the parent node and the estimated cost from the current node to the target node. S4, if the target node is included in the neighboring nodes of the current parent node, the target node is moved to the visited node set and the planned node set; in the planned node set, the optimal flight path is found based on the parent-child relationship between the nodes; S5, otherwise, return to step S2.

6. The method for planning a UAV flight route for power operation and maintenance according to claim 1, characterized in that: The flight path is obtained according to the adaptive differential target evolution algorithm, including the following: S101, determine the planning space, origin and destination, initialize the population using a heuristic initialization algorithm, and complete population preprocessing to transform relative polar coordinate individuals into absolute value coordinate individuals; S102, discretizing the navigation traces of the individuals in the population according to the navigation speed and sampling time step; S103, using the discretized navigation track points of the individuals in the initial population to perform track constraint verification and target value calculation, and calculating the sum of the constraint values ​​of all constraints of each individual; S104, performing genetic crossover and mutation operations on the parent population according to the adaptive difference operator to form a subpopulation; S105, discretizing the navigation traces of the offspring individuals in the subpopulation, bringing the discretized navigation traces into the problem model, and calculating the sum of the individual constraints, the objective function value, and the constraint values ​​of all constraints; S106, the parent and offspring form a mixed group, and an improved fast non-dominated sort is performed on the mixed group to determine the layer to which each individual in the mixed group is distributed; S107, performing an improved aggregation distance and exclusion mechanism on the individuals in each layer of the mixed group, and selecting individuals of the same population size from the mixed group according to the dominance relationship and individual crowding degree to form a new parent population; S108, judging whether the evolution process is finished, if it reaches the maximum iteration number, go to S109; otherwise go to S104; S109, selecting an individual from the non-dominated layer of the population, the track represented by the individual is the desired track flight path.

7. The method for planning a UAV flight route for power operation and maintenance according to claim 1, characterized in that: Determine the final flight path, including: The optimal flight path is compared with the track flight path. If the two coincide, one of them is selected as the final flight path. Otherwise, curve fitting is performed on the two to determine the final flight path.

8. The method for planning a UAV flight route for power operation and maintenance according to claim 1, characterized in that: Perform obstacle avoidance planning, including the following: Obtaining multiple sets of obstacle information based on the geographic data, each set of obstacle information corresponding to one obstacle; wherein the set of obstacle information includes the type of obstacle, the distance between the UAV and the obstacle, and the location of the obstacle; Determine the threat probability of the corresponding obstacle based on multiple sets of obstacle information; Obtain obstacle avoidance strategies based on multiple sets of obstacle information and the threat probabilities corresponding to the obstacles; Control the drone to adjust its flight state according to the obstacle avoidance strategy to avoid obstacles and return to the original flight route; The original flight path represents the final flight path set for the drone before obstacle avoidance.

9. A UAV flight route planning system for power operation and maintenance, applied to the UAV flight route planning method according to any one of claims 1 to 8, characterized in that: include: Data acquisition module, used to collect geographic data, meteorological data and power facility data within the planned flight area; A map generation module is used to pre-acquire a three-dimensional point cloud map of the global environment of the flight area to be planned; Processing the three-dimensional point cloud map based on the geographic data and the power facility data to obtain inaccessible areas, passable areas, and dangerous areas of the three-dimensional point cloud map in three-dimensional space; Excluding inaccessible areas within the flight area to be planned, and combining accessible areas and dangerous areas into a flight route planning area; Based on geographic data and meteorological data, a multi-layer planning map is generated within the flight route planning area; each layer of map information includes a traffic type and a traffic cost multiplier for each traffic node in the layer; the traffic types include normal traffic type, slowed traffic type, and obstructed traffic type; A path planning module is used to determine multiple basic flight paths based on the origin and destination of the UAV and select the final flight path from them; an obstacle avoidance planning module, configured to perform obstacle avoidance planning for the UAV during its flight along the final flight path based on the geographic data; a power grid inspection module, configured to perform power grid inspection on the final flight path based on the power facility data; The feedback optimization module is used to monitor the flight status of the UAV in real time when it flies along the planned path, record the flight data generated by each flight mission, and adjust and optimize the UAV's flight mission and flight path based on the flight data.

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