Unmanned aerial vehicle flight path planning method and system for electric power operation and maintenance

Abstract

The invention discloses an unmanned aerial vehicle flight path planning method and system for electric power operation and maintenance, and relates to the technical field of path planning. The method comprises the steps of collecting data in a to-be-planned flight area; acquiring a three-dimensional point cloud map of the global environment; obtaining a flight path planning area; determining a final flight path based on the starting place and the destination of the unmanned aerial vehicle; obstacle avoidance planning and power grid inspection are carried out in the flight process on the final flight path; and the flight state of the unmanned aerial vehicle is monitored in real time, and flight data are recorded, adjusted and optimized. According to the invention, from data acquisition, map generation, path planning and obstacle avoidance planning, and finally power grid inspection and feedback optimization, a complete path planning and obstacle avoidance inspection process is realized through the above contents, and the flight path of the unmanned aerial vehicle in power operation and maintenance is systematically and intelligently planned. The unmanned aerial vehicle can quickly and accurately complete the inspection task, invalid flight is avoided, the inspection efficiency and safety are improved, and reliable operation of electric power facilities is ensured.

Description

Technical Field

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

[0002] Traditional power grid inspection relies on manpower, which has problems such as low efficiency, high risk, and limited coverage. With the development of drone technology, it has become possible to use drones for power grid inspection, which can complete inspection tasks more efficiently and safely. However, how to accurately plan the flight route of drones to ensure that all key points are covered and maximize the endurance of drones is an urgent problem that needs to be solved. 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: 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: Collect geographical data, meteorological data and power facility data within the flight area to be planned; Pre-acquire a three-dimensional point cloud map of the global environment of the flight area to be planned; Based on the geographic data and the power facility data, the three-dimensional point cloud map is processed to obtain an inaccessible area, a passable area, and a dangerous area of ​​the three-dimensional point cloud map in three-dimensional space; Excluding the inaccessible area in the flight area to be planned, and combining the accessible area and the dangerous area into a flight route planning area; Based on geographic data and meteorological data, a multi-layer planning map is generated in 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; 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 flight process of the UAV 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 UAV in real time as it flies along the planned path, record the flight data generated by each flight mission, and adjust and optimize the flight mission and flight path of the UAV based on the flight data.

[0004] Preferably, the geographic data includes terrain, obstacles, road distribution and land type distribution; the meteorological data includes temperature and humidity, precipitation, and wind speed; and the power facility data includes pole tower location information, power line distribution and power equipment location. Preferably, 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, 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 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.

[0005] Preferably, the first preset path rule includes the following content: The flight altitude corresponding to the flight path is greater than the highest altitude or the highest building in the flight area; There is no flight mission identical to the current flight mission in the same flight area; The aircraft's noise decibel level is lower than the decibel requirement in the flight area; The number of flight paths in the danger zone is less than the number of flight paths in the safe zone. Preferably, the second preset path rule includes the following content: 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.

[0006] Preferably, obtaining the optimal flight path according to the path planning algorithm includes the following contents: 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, according to 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 the nodes that hinder the passage; add the nodes in the neighboring nodes of the current parent node that are not in the visited node set and are not the nodes that hinder the passage to the intended node set; The total cost of a node is the sum of the cost of the path from the origin to the current node via the parent node and the estimated cost of the path 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 according to the parent-child relationship between the nodes; S5, otherwise, return to step S2. Preferably, obtaining the flight path according to the adaptive differential target evolution algorithm includes the following contents: S101, determine the planning space, the origin and the destination, initialize the population using a heuristic initialization algorithm, and complete the population preprocessing of transforming relative polar coordinate individuals into absolute value coordinate individuals; S102, discretizing the navigation traces of the population individuals according to the navigation speed and the sampling time step; S103, using the discretized navigation track points of the individuals of the initial population to perform track constraint verification and target value calculation, and calculating the sum of constraint values ​​of all constraints of each individual; S104, performing genetic crossover and mutation operations on the parent population according to an adaptive difference operator to form a sub-population; S105, completing discretization of the navigation traces of the offspring individuals in the subpopulation, bringing the discretized navigation traces into the problem model, and completing calculation of the sum of the constraint values ​​of the individual constraints, the objective function value, and all the constraints; S106, the parent generation and the child generation 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 to form a new parent population according to the dominance relationship and individual crowding degree; S108, judging whether the evolution process is finished, if the maximum number of iterations is reached, proceeding to S109; otherwise, proceeding 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.

[0007] Preferably, the final flight path is determined as follows: 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. Preferably, obstacle avoidance planning is performed, including the following: Based on the geographic data, a plurality of sets of obstacle information are obtained, wherein one set of obstacle information corresponds 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 according to multiple sets of obstacle information; Obtain obstacle avoidance strategies based on multiple sets of obstacle information and the threat probabilities corresponding to the obstacles; According to the obstacle avoidance strategy, the UAV is controlled to adjust the flight state to avoid obstacles and return to the original flight route; The original flight route represents the final flight path set for the drone before obstacle avoidance.

[0008] In a second aspect, an embodiment of 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 as described above, including: A data acquisition module is used to collect geographic data, meteorological data and power facility data within the flight area to be planned; A map generation module, used for pre-acquiring a three-dimensional point cloud map of the global environment of the flight area to be planned; Based on the geographic data and the power facility data, the three-dimensional point cloud map is processed to obtain an inaccessible area, a passable area, and a dangerous area of ​​the three-dimensional point cloud map in three-dimensional space; Excluding the inaccessible area in the flight area to be planned, and combining the accessible area and the dangerous area into a flight route planning area; Based on geographic data and meteorological data, a multi-layer planning map is generated in 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; A path planning module, which is used to determine multiple basic flight paths based on the origin and destination of the UAV and to select and determine the final flight path from them; An obstacle avoidance planning module, used for performing obstacle avoidance planning for the flight process of the UAV on 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 flight mission and flight path of the UAV based on the flight data.

[0009] The beneficial effects of the present invention are: (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. It systematically and intelligently plans the flight route of drones in power operation and maintenance, allowing drones to complete inspection tasks quickly and accurately and avoid invalid flights, thereby improving inspection efficiency and safety and ensuring the reliable operation of power facilities.

[0010] (2) The present invention first collects geographic data, meteorological data and power facility data within the flight area to be planned; then obtains in advance a three-dimensional point cloud map of the global environment of the flight area to be planned; then, based on the geographic data and the power facility data, processes 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, excludes the inaccessible areas within the flight area to be planned, and combines the passable areas and dangerous areas 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 types include 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 drone; a route is determined from the multiple basic flight paths according to a 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 along 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 according to the flight data. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] For better understanding and implementation, the technical solution of the present application is described in detail below with reference to the accompanying drawings.

[0012] Figure 1 A flowchart of the steps of a method for planning a flight route of a drone for power operation and maintenance provided in an embodiment of the present application; Figure 2 A flowchart of the steps for obtaining the optimal flight path provided in an embodiment of the present application; Figure 3 A flowchart of the steps for obtaining a flight path provided in an embodiment of the present application; Figure 4 A flowchart of the steps for performing obstacle avoidance planning provided in an embodiment of the present application; Figure 5 A structural schematic diagram of a UAV flight route planning system for power operation and maintenance provided in an embodiment of the present application. DETAILED DESCRIPTION

[0013] In order to further explain the technical means and effects taken by the present invention to achieve the predetermined invention purpose, exemplary embodiments will be described in detail here, and examples thereof are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are only examples of methods and systems consistent with some aspects of the present application as detailed in the attached claims.

[0014] The terms used in this application are only for the purpose of describing specific embodiments and are not intended to limit this application. The singular forms of "a", "said" and "the" used in this application and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used in this article refers to any or all possible combinations of one or more associated listed items.

[0015] The specific implementation methods, features and effects of the present invention are described in detail below in conjunction with the accompanying drawings and preferred embodiments. Example 1 See also Figure 1 The present application embodiment provides a method for planning a flight route of a UAV for power operation and maintenance, comprising the following steps: Data acquisition: Collect geographic data, meteorological data and power facility data within the planned flight area; Map generation: obtaining in advance a three-dimensional point cloud map of the global environment of the flight area to be planned; Based on the geographic data and the power facility data, the three-dimensional point cloud map is processed to obtain an inaccessible area, a passable area, and a dangerous area of ​​the three-dimensional point cloud map in three-dimensional space; Excluding the inaccessible area in the flight area to be planned, and combining the accessible area and the dangerous area into a flight route planning area; Based on geographic data and meteorological data, a multi-layer planning map is generated in 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; Path planning: Determine multiple basic flight paths based on the origin and destination of the drone and select the final flight path from them; Obstacle avoidance planning: performing obstacle avoidance planning for the flight process of the UAV on the final flight path based on the geographic data; Power grid inspection: performing power grid inspection on the final flight path based on the power facility data; Feedback optimization: Real-time monitoring of the flight status of the UAV as it flies along the planned path, recording the flight data generated by each flight mission, and adjusting and optimizing the UAV's flight mission and flight path based on the flight data.

[0016] 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, and its flight path and flight status. Therefore, 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 pre-acquire a 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 gather 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 traffic type and traffic cost multiplier of each traffic node in this layer of map; the traffic type includes normal traffic type, mitigation traffic type, traffic cost multiplier ... Passage type and obstruction passage type; then perform path planning: determine multiple basic flight paths based on the origin and destination of the UAV; determine one or more first flight paths from the multiple basic flight paths according to the first preset path rule; if there is only one first flight path, determine this first flight path as the final flight path; if there are multiple first flight paths, determine one or more second flight paths from the multiple first flight paths according to the second preset path rule; if there is only one second flight path, determine this second flight path as the final flight path; if there are multiple second flight paths, determine the final flight path from the multiple second flight paths according to the path planning algorithm; then perform obstacle avoidance planning: perform obstacle avoidance planning for the flight process of the UAV on the final flight path based on the geographic data; then perform power grid inspection: perform power grid inspection on the final flight path based on the power facility data; finally perform feedback optimization: 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 flight mission and flight path of the UAV based on the flight data.

[0017] 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. It systematically and intelligently plans the flight routes of drones in power operation and maintenance, allowing drones to complete inspection tasks quickly and accurately and avoid invalid flights, thereby improving inspection efficiency and safety and ensuring the reliable operation of power facilities.

[0018] 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.

[0019] Specifically, the above-mentioned geographic data and meteorological data help to identify the characteristics, obstacles and potential risks of the flight area, and ensure the safety and efficiency of the drone flight; and the above-mentioned power facility data can reflect the power distribution situation where the drone needs to conduct power inspections in the flight area, which helps to plan the flight route of the drone based on this data.

[0020] In an embodiment provided in the present application, determining the final flight path includes the following contents: 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, 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 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.

[0021] In an embodiment provided in the present application, the first preset path rule includes the following content: The flight altitude corresponding to the flight path is greater than the highest altitude or the highest building in the flight area; There is no flight mission identical to the current flight mission in the same flight area; The aircraft's noise decibel level is lower than the decibel requirement in the flight area; The number of flight paths in the danger zone is less than the number of flight paths in the safe zone.

[0022] In an embodiment provided in the present application, the second preset path rule includes the following content: 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.

[0023] like Figure 2 As shown, in one embodiment provided in the present application, obtaining the optimal flight path according to the path planning algorithm includes the following contents: 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, according to 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 the nodes that hinder the passage; add the nodes in the neighboring nodes of the current parent node that are not in the visited node set and are not the nodes that hinder the passage to the intended node set; The total cost of a node is the sum of the cost of the path from the origin to the current node via the parent node and the estimated cost of the path 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 according to the parent-child relationship between the nodes; S5, otherwise, return to step S2.

[0024] like Figure 3 As shown, in one embodiment provided in the present application, obtaining a flight path according to the adaptive differential target evolution algorithm includes the following contents: S101, determine the planning space, the origin and the destination, initialize the population using a heuristic initialization algorithm, and complete the population preprocessing of transforming relative polar coordinate individuals into absolute value coordinate individuals; S102, discretizing the navigation traces of the population individuals according to the navigation speed and the sampling time step; S103, using the discretized navigation track points of the individuals of the initial population to perform track constraint verification and target value calculation, and calculating the sum of constraint values ​​of all constraints of each individual; S104, performing genetic crossover and mutation operations on the parent population according to an adaptive difference operator to form a sub-population; S105, completing discretization of the navigation traces of the offspring individuals in the subpopulation, bringing the discretized navigation traces into the problem model, and completing calculation of the sum of the constraint values ​​of the individual constraints, the objective function value, and all the constraints; S106, the parent generation and the child generation 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 to form a new parent population according to the dominance relationship and individual crowding degree; S108, judging whether the evolution process is finished, if the maximum number of iterations is reached, proceeding to S109; otherwise, proceeding 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.

[0025] In an embodiment provided in the present application, determining the final flight path is specifically as follows: 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.

[0026] Specifically, the present 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 the most suitable path for the current UAV flight and mission execution; if the two do not overlap, the two are merged, and the curve fitting technology is specifically used to fit the two to obtain the final flight path. The present application can avoid deviations and errors in the flight path to the greatest extent through the above method, thereby ensuring the accuracy of the final flight path.

[0027] like Figure 4 As shown, in one embodiment provided in the present application, obstacle avoidance planning is performed, including the following contents: Based on the geographic data, a plurality of sets of obstacle information are obtained, wherein one set of obstacle information corresponds 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 according to multiple sets of obstacle information; Obtain obstacle avoidance strategies based on multiple sets of obstacle information and the threat probabilities corresponding to the obstacles; According to the obstacle avoidance strategy, the UAV is controlled to adjust the flight state to avoid obstacles and return to the original flight route; The original flight route represents the final flight path set for the drone before obstacle avoidance.

[0028] Specifically, this embodiment obtains obstacle information from geographic information and performs threat probability analysis on the obstacle information to obtain different obstacle avoidance strategies according to different obstacle situations, and then uses the obstacle avoidance strategy to comprehensively judge the local route flight plan to adjust the flight state of the drone to flexibly avoid obstacles until safely returning to the set original flight route. The above method improves the obstacle avoidance capability of the drone and improves the flight efficiency and autonomy of the drone.

[0029] Example 2 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 as described above, including: A data acquisition module is used to collect geographic data, meteorological data and power facility data within the flight area to be planned; A map generation module, used for pre-acquiring a three-dimensional point cloud map of the global environment of the flight area to be planned; Based on the geographic data and the power facility data, the three-dimensional point cloud map is processed to obtain an inaccessible area, a passable area, and a dangerous area of ​​the three-dimensional point cloud map in three-dimensional space; Excluding the inaccessible area in the flight area to be planned, and combining the accessible area and the dangerous area into a flight route planning area; Based on geographic data and meteorological data, a multi-layer planning map is generated in 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; A path planning module, which is used to determine multiple basic flight paths based on the origin and destination of the UAV and to select and determine the final flight path from them; An obstacle avoidance planning module, used for performing obstacle avoidance planning for the flight process of the UAV on 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 flight mission and flight path of the UAV based on the flight data.

[0030] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0031] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be 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 in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in 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, which will not be repeated here.

[0032] 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. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0033] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solution of the present invention still fall 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 in that: The steps include: Collect geographical data, meteorological data and power facility data within the flight area to be planned; Pre-acquire a three-dimensional point cloud map of the global environment of the flight area to be planned; Based on the geographic data and the power facility data, the three-dimensional point cloud map is processed to obtain an inaccessible area, a passable area, and a dangerous area of ​​the three-dimensional point cloud map in a three-dimensional space; Excluding the inaccessible area in the flight area to be planned, and combining the accessible area and the dangerous area into a flight route planning area; Based on geographic data and meteorological data, a multi-layer planning map is generated in 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; 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 flight process of the UAV 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 UAV in real time as it flies along the planned path, record the flight data generated by each flight mission, and adjust and optimize the flight mission and flight path of the UAV based on the flight data.

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 pole 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: Determine the final flight path, including 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, 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 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.

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

5. The method for planning a UAV flight route for power operation and maintenance according to claim 3, characterized in that: The second preset path rule includes the following content: 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.

6. The method for planning a UAV flight route for power operation and maintenance according to claim 3, 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, according to 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 the nodes that hinder the passage; add the nodes in the neighboring nodes of the current parent node that are not in the visited node set and are not the nodes that hinder the passage to the intended node set; The total cost of a node is the sum of the cost of the path from the origin to the current node via the parent node and the estimated cost of the path 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 according to the parent-child relationship between the nodes; S5, otherwise, return to step S2.

7. The method for planning a UAV flight route for power operation and maintenance according to claim 3, characterized in that: The flight path is obtained according to the adaptive differential target evolution algorithm, including the following contents: S101, determine the planning space, the origin and the destination, initialize the population using a heuristic initialization algorithm, and complete the population preprocessing of transforming relative polar coordinate individuals into absolute value coordinate individuals; S102, discretizing the navigation traces of the population individuals according to the navigation speed and the sampling time step; S103, using the discretized navigation track points of the individuals of the initial population to perform track constraint verification and target value calculation, and calculating the sum of constraint values ​​of all constraints of each individual; S104, performing genetic crossover and mutation operations on the parent population according to an adaptive difference operator to form a sub-population; S105, completing discretization of the navigation traces of the offspring individuals in the subpopulation, bringing the discretized navigation traces into the problem model, and completing calculation of the sum of the constraint values ​​of the individual constraints, the objective function value, and all the constraints; S106, the parent generation and the child generation 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 to form a new parent population according to the dominance relationship and individual crowding degree; S108, judging whether the evolution process is finished, if the maximum number of iterations is reached, proceeding to S109; otherwise, proceeding 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.

8. The method for planning a UAV flight route for power operation and maintenance according to claim 3, characterized in that: Determine the final flight path, specifically: 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.

9. 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: Based on the geographic data, a plurality of sets of obstacle information are obtained, wherein one set of obstacle information corresponds 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 according to multiple sets of obstacle information; Obtain obstacle avoidance strategies based on multiple sets of obstacle information and the threat probabilities corresponding to the obstacles; According to the obstacle avoidance strategy, the UAV is controlled to adjust the flight state to avoid obstacles and return to the original flight route; The original flight route represents the final flight path set for the drone before obstacle avoidance.

10. 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 9, characterized in that: include: A data acquisition module is used to collect geographic data, meteorological data and power facility data within the flight area to be planned; A map generation module, used for pre-acquiring a three-dimensional point cloud map of the global environment of the flight area to be planned; Based on the geographic data and the power facility data, the three-dimensional point cloud map is processed to obtain an inaccessible area, a passable area, and a dangerous area of ​​the three-dimensional point cloud map in a three-dimensional space; Excluding the inaccessible area in the flight area to be planned, and combining the accessible area and the dangerous area into a flight route planning area; Based on geographic data and meteorological data, a multi-layer planning map is generated in 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; A path planning module, which 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, used for performing obstacle avoidance planning for the flight process of the UAV on 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 flight mission and flight path of the UAV based on the flight data.

Images