Distribution network line inspection path planning method based on oil-electricity hybrid unmanned aerial vehicle

Abstract

The invention provides a distribution network line routing inspection path planning method based on a gas-electric hybrid unmanned aerial vehicle, which combines geographic information system data and power equipment information to construct a three-dimensional geographic information model, and determines a routing inspection sequence through a scientific and reasonable region division and scoring mechanism. Meanwhile, detailed path evaluation is carried out on the key inspection points and reachable inspection sub-paths of each sub-region, an optimal inspection sub-path is selected by adopting a comprehensive scoring model, and finally an optimal inspection total path passing through all the key inspection points is generated, so that the overall inspection time is greatly shortened, and the inspection efficiency is improved. According to the method, position coordinates and residual energy consumption information of the unmanned aerial vehicle are monitored in real time, so that continuity and safety of inspection work are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles, and in particular to a method for planning inspection paths for distribution network lines based on an oil-electric hybrid unmanned aerial vehicle. Background Art

[0002] With the continuous development and expansion of the power system, distribution lines are becoming increasingly complex and widely distributed. Traditional distribution line inspection methods mainly rely on manual inspection and manned helicopter inspection. Manual inspection is inefficient and labor-intensive, and it is difficult to carry out comprehensive and in-depth inspections in some complex terrains (such as mountainous areas, swamps, etc.) or in severe weather conditions. Although manned helicopter inspections can cover a large area and are relatively efficient, they are costly, have strict requirements on the flight environment, and there are certain risks when approaching power equipment for detailed inspections. For example, the airflow generated by the helicopter rotor may interfere with the power equipment, or safety accidents may occur due to operational errors.

[0003] In recent years, drone technology has developed rapidly and has been widely used in many fields. Hybrid drones combine the advantages of fuel engines and electric engines, with long flight time and relatively large load capacity, providing a new solution for distribution network line inspection. However, to achieve efficient, safe and accurate distribution network line inspection, reasonable path planning has become a key technical problem. If the path planning is unreasonable, the drone may waste energy, take too long to inspect, miss key parts during the inspection, or cause safety accidents due to the flight path being close to dangerous areas. Summary of the invention

[0004] In view of the shortcomings of the prior art described above, the purpose of the present invention is to provide a method for patrol route planning of distribution network lines based on a hybrid oil-electric UAV, so as to solve the problem of reasonable planning of distribution network lines of UAVs in the prior art.

[0005] To achieve the above objectives and other related objectives, the present invention provides the following technical solutions:

[0006] A method for distribution network line inspection path planning based on a hybrid UAV includes the following steps:

[0007] S1, obtaining geographic information data and power equipment data of the distribution network line, establishing a three-dimensional geographic information model of the distribution network line, dividing the entire distribution network line inspection area into multiple sub-areas in the three-dimensional geographic information model, scoring the sub-areas and determining the inspection order of the sub-areas based on the geographical location relationship;

[0008] S2, determine the key inspection points of each sub-area and the boundary points between adjacent sub-areas, search for reachable inspection sub-paths connecting each key inspection point from the previous boundary point to the next boundary point, perform path evaluation on each reachable inspection sub-path, and determine the optimal inspection sub-path;

[0009] S3, according to the inspection order of the sub-areas, sequentially connect and optimize the optimal inspection sub-paths of each sub-area to obtain the optimal inspection total path passing through the key inspection point;

[0010] S4, the UAV inspects according to the optimal inspection route. After completing a key inspection point task, it collects the location coordinates and remaining energy consumption information in real time. Based on the collected information, it determines whether the UAV should continue inspection or replenish energy.

[0011] To implement the above technical solution, first, by acquiring the geographic information data of the distribution network line and the power equipment data, a three-dimensional geographic information model is constructed. In this model, the entire distribution network line inspection area is reasonably divided into multiple sub-areas to facilitate more detailed and efficient inspection planning. For each sub-area, a regional score will be performed. At the same time, the geographical location relationship between the sub-areas, such as proximity and convenience of connection, is considered to determine a reasonable inspection sequence so that the inspection process can efficiently transition from one sub-area to another. In each sub-area, key inspection points are determined based on factors such as the importance of the power equipment and the probability of failure. These points are the parts that must be focused on during the inspection process. At the same time, the boundary points between adjacent sub-areas are clarified. These boundary points are the key nodes for connecting the paths between the sub-areas. With the boundary points as the starting and ending points, a search is performed to search for reachable inspection sub-paths that can connect various key inspection points. The search process involves consideration of various terrains, obstacles and other factors in the geographic information model to ensure the feasibility of the path. For each reachable inspection sub-path searched, a comprehensive path evaluation will be performed based on multiple indicators, and the optimal inspection sub-path will be screened out through the path; according to the inspection order of the sub-areas determined in S1, the optimal inspection sub-paths of each sub-area will be connected in sequence. In the connection process, there may be some optimization problems at the connection point, such as the smoothness of the path connection, the optimization of the steering angle at the connection point, etc. By optimizing the connected path, the optimal inspection total path passing through all key inspection points is finally obtained. The optimal inspection total path can maximize the inspection efficiency, reduce energy consumption and risks while meeting the inspection requirements; the drone starts the inspection operation according to the planned optimal inspection total path. After completing the task of a key inspection point, the sensor on the drone collects its own position coordinate information in real time, and obtains the remaining energy consumption information at the same time, and inputs this information into the pre-set judgment mechanism. The mechanism makes a comprehensive judgment based on the remaining energy consumption and the distance to the next key inspection point or energy replenishment point, the flight energy consumption estimate and other factors, whether to continue the inspection or replenish energy.

[0012] In one embodiment of the present invention, the S1 includes:

[0013] S11, obtaining geographic information data of the distribution network line from the geographic information system, including line direction, tower location, topography and obstacle distribution;

[0014] S12, collecting data of power equipment on the distribution network, including the location, type and status of the power equipment;

[0015] S13, associating the power equipment data with the geographic information data to obtain associated information, and constructing a three-dimensional geographic information model of the distribution network line according to the associated information;

[0016] S14, dividing the entire distribution network line inspection area into multiple sub-areas in the three-dimensional geographic information model according to the distribution of distribution network lines, the density of power equipment, and the endurance of the drone;

[0017] S15, taking the importance of distribution network lines, the criticality of power equipment, the historical failure frequency and the sensitivity of the surrounding environment as scoring indicators, conducts regional scoring for each sub-area, and comprehensively considers the geographical location relationship between each sub-area to determine the inspection order of the sub-area.

[0018] To implement the above technical solution, we first use the powerful data source of geographic information system to accurately obtain various geographic information data of distribution network lines. Line direction data can clarify the route trajectory of power transmission, tower location information is crucial for determining key nodes and building line framework, topographic data such as the distribution of mountains, rivers, plains, etc. affect the flight path planning of drones, and obstacle distribution data helps to plan avoidance strategies in advance. For areas with dense lines and many equipment, smaller sub-areas may be divided to ensure the inspection details, while in areas with relatively sparse equipment and strong drone endurance, larger sub-areas can be divided to reduce the number of regional switching. In the three-dimensional geographic information model, according to these comprehensive considerations, the entire inspection area is scientifically and reasonably divided into multiple sub-areas, each of which has the characteristics of being relatively independent and closely connected to the whole; multiple key scoring indicators are selected to evaluate the sub-areas, and then the optimal inspection order is determined based on the geographical location relationship between the sub-areas, such as adjacency and connection convenience, so that the drone can traverse each sub-area in the most reasonable order, reducing unnecessary flight path return and detours. By comprehensively collecting geographic information data and power equipment data and effectively associating them to build a three-dimensional model, an extremely accurate data foundation is provided for the entire inspection route planning. Decisions such as regional division, scoring, and inspection sequence determination based on this model are more scientific and reasonable, avoiding route planning errors caused by missing or inaccurate data, and improving the reliability and effectiveness of the inspection plan.

[0019] In one embodiment of the present invention, the method for determining the inspection order of the sub-areas includes:

[0020] S151, setting an original scoring model to obtain an original score according to the importance of the distribution network lines in the sub-region, the criticality of the power equipment, the historical fault frequency, and the sensitivity of the surrounding environment;

[0021] S152, obtaining a starting score based on the shortest distance between the starting point of the drone and each sub-area and the original score, and selecting the sub-area with the highest starting score as the starting area;

[0022] S153, calculate the center distance from the current sub-area where the drone is located to the remaining sub-areas, calculate the corrected scores of the remaining sub-areas based on the original scores and the center distances, select the sub-area with the highest corrected score as the next flight area, and repeat step S153 until all sub-areas have been visited, thereby determining the inspection order of the sub-areas.

[0023] To implement the above technical solution, the original score of the sub-area is determined by using a pre-set original score model. This score preliminarily reflects the relative position of each sub-area in terms of inspection priority. After determining the original score, the starting point of the drone is considered, and the shortest distance between the starting point of the drone and each sub-area is calculated. This distance factor is combined with the original score to obtain the starting score, and then the sub-area with the highest starting score is selected from all sub-areas as the starting area. The purpose of this is to consider factors such as the importance of the area while taking into account that the drone can quickly reach the first inspection key area with a better path after taking off, thereby reducing the waste of resources and time consumption of the initial flight. After the starting area is determined, the drone enters the inspection process. During the inspection process, when the drone is in the current sub-area, the center distance to the remaining sub-areas is calculated. The calculation of this center distance helps to evaluate the flight cost of the drone to other sub-areas, and then the original score calculated previously and the center distance are combined to obtain the revised score of the remaining sub-areas, and the sub-area with the highest revised score is selected as the next flight area. After that, the drone flies to the area and repeats this calculation and selection process until all sub-areas have been visited, thereby determining the complete sub-area inspection order.

[0024] In one embodiment of the present invention, the original scoring model is as follows:

[0025]

[0026] in, For the The raw scores of the sub-regions, The importance of distribution network lines, The criticality of the power equipment, is the historical fault frequency, is the sensitivity value of the surrounding environment, , , , , is the corresponding weight coefficient, and ;

[0027] The initial score calculation method is as follows:

[0028]

[0029] in, For the The starting score of each sub-region, For the The raw scores of the sub-regions, is the distance influence factor, The starting point of the drone The shortest distance between sub-regions.

[0030] Implement the above technical solution, and determine the original score Finally, in order to select a more suitable starting area for drone inspection, the starting score was introduced. Calculation method of starting score In the original score Based on the distance factor The starting point of the drone The shortest distance between sub-regions The product of When the value is large, it means that the distance factor has a more significant impact on the starting score, and the drone is more inclined to prioritize the sub-area that is closer to the starting point and has a relatively high original score as the starting area; on the contrary, when When the value is small, the original score plays a stronger leading role, but the distance factor will still affect the selection of the starting area to a certain extent. Through such calculation, the importance of the area itself and the convenience of the drone to reach the area are comprehensively considered, so as to determine the sub-area with the highest starting score as the starting area, so that the drone can quickly enter the key inspection area with a better path when starting the inspection, and improve the inspection efficiency in the initial stage.

[0031] In one embodiment of the present invention, the center distance is calculated as follows:

[0032]

[0033] in, For the The center point of the sub-region to the The distance between the center points of the sub-regions, For the current region, For the Unvisited areas, For the The coordinates of the center point of each sub-region, For the The coordinates of the center points of the unvisited sub-regions.

[0034] The above technical solution is implemented to accurately calculate the straight-line distance between the center points of two sub-areas in the space of the three-dimensional geographic information model, so as to measure the spatial cost of the drone flying from the current sub-area to other unvisited sub-areas. It enables the drone to prioritize the sub-area that is closer to the current position and more important as the next inspection target based on the actual spatial position relationship, effectively reducing the spatial span of flight between different sub-areas, avoiding unreasonable long-distance jumping flights, and further improving the inspection efficiency. It also helps to reduce the flight energy consumption of the drone, extend its single inspection range, and ensure that the entire inspection task can be successfully completed.

[0035] In one embodiment of the present invention, the modified score calculation method is as follows:

[0036]

[0037] in, For the Corrected scores for unvisited areas, For the Raw scores for unvisited areas, is another distance influencing factor.

[0038] The application of the revised scoring calculation method to implement the above technical solution makes the inspection path planning highly dynamic and adaptable. During the entire inspection process, as the drone continuously flies from one sub-area to another, its position is constantly changing, and the distance of each unvisited sub-area relative to the current position is also constantly changing. By combining the original score and the center distance factor calculated based on the current position to generate a revised score, the inspection priority of each unvisited sub-area can be re-evaluated in real time based on the actual position of the drone.

[0039] In one embodiment of the present invention, the method for determining the boundary points in S2 is as follows:

[0040] b1, for adjacent sub-regions and , respectively determine their respective key inspection point sets and , calculate the sub-region Key inspection points With sub-area Key inspection points in The distance between ,and , ;

[0041]

[0042] b2, find The key inspection point pairs corresponding to the minimum distance values ​​in are and ,and , ;

[0043] b3, calculate the adjacent sub-region and The boundary point coordinates

[0044] b4, coordinates of the boundary points Perform feasibility analysis. If it is feasible, determine the coordinate as the boundary point. If it is not feasible, Find the key inspection point pair corresponding to the next smallest distance value and repeat steps b3 and b4 until a feasible boundary point is determined.

[0045] To implement the above technical solution, after performing such distance calculation on all possible key inspection point pairs, the key inspection point pair with the smallest distance value is found. This step aims to determine the two key inspection points that are closest in space. and , by calculating the average value of their coordinates to preliminarily determine the boundary coordinate points of adjacent sub-regions, and obtain the boundary point coordinates After that, it is necessary to conduct a feasibility analysis. This feasibility analysis may involve many factors, such as whether the coordinate position is within a reasonable geographical area (for example, whether it is within the flyable area, whether it avoids obstacles, etc.), whether it meets the actual distribution network line layout and inspection requirements, etc. If the coordinate meets all relevant requirements after analysis, that is, it is feasible, then the coordinate is officially determined as a boundary point; on the contrary, if it is not feasible, it means that the coordinates of the initially calculated boundary point do not conform to the actual situation and cannot be used as a valid boundary point. At this time, it is necessary to find the key inspection point pair corresponding to the second smallest distance value among the distances between all the key inspection point pairs calculated previously, and then repeat steps b3 and b4, that is, recalculate the boundary point coordinates and perform feasibility analysis again, and repeat this until a boundary point that meets the feasibility requirements is determined. In this way, through continuous iterative adjustment, it is ensured that the boundary point finally determined is a valid boundary point that meets the actual inspection scene and geographic space conditions.

[0046] In one embodiment of the present invention, the path evaluation method includes:

[0047] A comprehensive scoring model is set according to the length, safety value, feasibility assessment value and additional factor value of the accessible inspection sub-path to obtain a comprehensive score;

[0048] Sort the comprehensive scores from small to large, and select the sub-path with the lowest comprehensive score as the optimal inspection sub-path;

[0049] The comprehensive scoring model is as follows:

[0050]

[0051] in, For path Ratings, For path Length, For path Safety value, For path The feasible evaluation value of For path The additional factor value of is the path length weight, is the safety weight, is the feasibility weight, is the additional factor weight.

[0052] To implement the above technical solution, the convenience of drone data collection can be considered as an additional factor. For example, when inspecting a key substation equipment, if a certain path allows the drone to observe the equipment 360 degrees, this advantage can be reflected through the additional factor value and the appropriate additional factor weight. After calculating the comprehensive score Score (r) of each accessible inspection sub-path through the comprehensive scoring model, these comprehensive scores are sorted in order from small to large. The reason why the sub-path with the lowest comprehensive score is selected as the optimal inspection sub-path is that under the framework of this comprehensive scoring model, a low score means that the path has better comprehensive performance in terms of length, safety, feasibility and additional factors, which is more in line with the inspection work's expectations for efficiency, safety, feasibility and other additional requirements. This multi-dimensional evaluation method enables the selected optimal inspection sub-path to achieve a relatively good balance in terms of length, safety, feasibility and additional factors, which can not only improve the efficiency of drone inspections, reduce flight time and energy consumption, but also maximize safety during the inspection process.

[0053] In one embodiment of the present invention, S3 includes:

[0054] According to the inspection order of the sub-areas, the optimal inspection sub-paths of each sub-area are connected in sequence to construct a preliminary inspection total path;

[0055] Smoothing the preliminary inspection total path, eliminating the redundant and repeated parts in the path, and obtaining a smooth inspection total path;

[0056] The optimal inspection path is obtained by global optimization based on the length of the smooth inspection path, flight energy consumption, and inspection time, and it is ensured that the optimal inspection path can pass through all key inspection points.

[0057] To implement the above technical solution, the optimal inspection sub-paths of each sub-area are integrated based on the planning order of the sub-areas to form a preliminary inspection total path that covers all sub-areas and passes through key inspection points; when connecting the optimal inspection sub-paths of adjacent sub-areas in the preliminary inspection total path, due to the sub-area boundary point determination method or the local optimality of sub-path planning, there may be abrupt turns at the connection and partial path overlap. Curve fitting technology is used to replace the original abrupt turning path segments with smooth curve segments, so that the turning of the drone during flight is more natural and smooth; after obtaining the smooth inspection total path, the optimal inspection total path obtained by global optimization based on length, flight energy consumption and inspection time can achieve the optimal allocation of resources while meeting the inspection task requirements (i.e. passing through all key inspection points).

[0058] In one embodiment of the present invention, the S4 includes:

[0059] After the drone completes a key inspection point, it collects real-time location coordinates and remaining energy consumption information;

[0060] Calculate the path distance of the drone to the next key inspection point, and evaluate whether the drone has enough energy to reach the next key inspection point and whether it has enough energy to return to the starting point after completing the task at the next key inspection point based on the path distance;

[0061] If the remaining energy consumption is not enough to support the drone to reach the next key inspection point or the next key inspection point mission cannot be safely returned to the starting point after completion, the return path is immediately planned and returned to the starting point to replenish energy.

[0062] To implement the above technical solution, when the drone completes the inspection task of a key inspection point, it will use its own positioning system to collect real-time position coordinates, and calculate the path distance from the current position of the drone to the next key inspection point based on the acquired real-time position coordinates and the pre-planned optimal inspection path. This calculation process will take into account the actual terrain, flight altitude and other factors in the three-dimensional geographic information model to determine the flight distance as accurately as possible. Combined with the energy consumption model of the drone (comprehensively considering the relationship between factors such as flight speed, load, flight environment and energy consumption), the energy consumption required to reach the next key inspection point is estimated based on the path distance. If, after the above energy consumption evaluation, it is found that the remaining energy consumption is not enough to support the drone to successfully reach the next key inspection point, or even if it can reach and complete the task, it cannot safely return to the starting point later, an optimal path to return to the starting point is planned based on the current real-time position coordinates, so that the drone can return to the starting point as soon as possible along the path to replenish energy, ensuring that subsequent inspection tasks can continue to be carried out smoothly with sufficient energy guarantee.

[0063] As described above, the method of the present invention for planning the inspection path of the distribution network line based on the hybrid oil-electric UAV has the following beneficial effects: by comprehensively collecting the geographic information data of the distribution network line and the data of the power equipment, a three-dimensional geographic information model is constructed, and the entire inspection area is divided into multiple sub-areas according to the distribution of the distribution network line, the density of the power equipment and the endurance of the UAV, and the importance of the distribution network line, the criticality of the power equipment, the frequency of historical failures and the sensitivity of the surrounding environment are comprehensively considered to set the scoring model to determine the inspection order of the sub-areas. In the process of path evaluation, the length, safety value, feasible evaluation value and additional factor value of the reachable inspection sub-path are comprehensively considered to set the comprehensive scoring model to select the optimal inspection sub-path, and then the optimal inspection sub-paths of each sub-area are sequentially connected and optimized according to the inspection order of the sub-area, and finally the optimal inspection total path passing through all key inspection points is obtained, which greatly shortens the overall inspection time and improves the efficiency of the inspection operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 It is shown as a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0065] The following describes the embodiments of the present invention through specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0066] See also Figure 1The present invention provides a method for planning the inspection path of a distribution network line based on a hybrid oil-electric UAV, comprising the following steps: S1, obtaining geographic information data and power equipment data of the distribution network line, establishing a three-dimensional geographic information model of the distribution network line, dividing the entire distribution network line inspection area into multiple sub-areas in the three-dimensional geographic information model, performing regional scoring on the sub-areas and determining the inspection order of the sub-areas in combination with the geographical location relationship; S2, determining the key inspection points of each sub-area and the boundary points between adjacent sub-areas, searching for reachable inspection sub-paths connecting each key inspection point from the previous boundary point to the next boundary point, performing path evaluation on each reachable inspection sub-path, and determining the optimal inspection sub-path; S3, according to the inspection order of the sub-areas, connecting and optimizing the optimal inspection sub-paths of each sub-area in sequence to obtain the optimal inspection total path passing through the key inspection points; S4, the UAV inspects according to the optimal inspection total path, collects the position coordinates and the remaining energy consumption information in real time after completing a key inspection point task, and judges whether the UAV should continue the inspection or replenish energy based on the collected information.

[0067] First, by acquiring the geographic information data of the distribution network line and the power equipment data, a three-dimensional geographic information model is constructed. In this model, the entire distribution network line inspection area is reasonably divided into multiple sub-areas for more detailed and efficient inspection planning. For each sub-area, a regional score will be performed. At the same time, the geographical location relationship between sub-areas, such as proximity and convenience of connection, is considered to determine a reasonable inspection sequence so that the inspection process can efficiently transition from one sub-area to another. In each sub-area, key inspection points are determined based on factors such as the importance of power equipment and the probability of failure. These points are the parts that must be focused on during the inspection process. At the same time, the boundary points between adjacent sub-areas are clarified. These boundary points are the key nodes for connecting the paths between sub-areas. With the boundary points as the starting and ending points, a search is conducted to search for accessible inspection sub-paths that can connect various key inspection points. The search process involves consideration of various terrains, obstacles and other factors in the geographic information model to ensure the feasibility of the path. For each reachable inspection sub-path searched, a comprehensive path evaluation will be performed based on multiple indicators, and the optimal inspection sub-path will be screened out through the path; according to the inspection order of the sub-areas determined in S1, the optimal inspection sub-paths of each sub-area will be connected in sequence. In the connection process, there may be some optimization problems at the connection point, such as the smoothness of the path connection, the optimization of the steering angle at the connection point, etc. By optimizing the connected path, the optimal inspection total path passing through all key inspection points is finally obtained. The optimal inspection total path can maximize the inspection efficiency, reduce energy consumption and risks while meeting the inspection requirements; the drone starts the inspection operation according to the planned optimal inspection total path. After completing the task of a key inspection point, the sensor on the drone collects its own position coordinate information in real time, and obtains the remaining energy consumption information at the same time, and inputs this information into the pre-set judgment mechanism. The mechanism makes a comprehensive judgment based on the remaining energy consumption and the distance to the next key inspection point or energy replenishment point, the flight energy consumption estimate and other factors, whether to continue the inspection or replenish energy.

[0068] By dividing the inspection area into multiple sub-areas and reasonably determining the inspection order, as well as accurately locating key inspection points in the sub-areas and planning the optimal sub-path, blind flight and unnecessary detours of drones during the inspection process can be avoided. Drones can inspect each key part in turn according to an efficient path, greatly shortening the overall inspection time and improving the efficiency of the inspection operation.

[0069] The S1 includes: S11, acquiring geographic information data of the distribution network line from the geographic information system, including the line direction, tower position, topography and obstacle distribution; S12, collecting power equipment data on the distribution network line, including the location, type and status of the power equipment; S13, associating the power equipment data with the geographic information data to obtain associated information, and constructing a three-dimensional geographic information model of the distribution network line according to the associated information; S14, dividing the entire distribution network line inspection area into multiple sub-areas in the three-dimensional geographic information model according to the distribution of the distribution network line, the density of the power equipment and the endurance of the drone; S15, taking the importance of the distribution network line, the criticality of the power equipment, the historical fault frequency and the sensitivity of the surrounding environment as scoring indicators, performing regional scoring on each sub-area, and comprehensively considering the geographical location relationship between each sub-area to determine the inspection order of the sub-area.

[0070] First, we use the powerful data source of geographic information system to accurately obtain various geographic information data of distribution network lines. Line direction data can clarify the route trajectory of power transmission. Tower location information is crucial for determining key nodes and building line frameworks. Topographic data such as the distribution of mountains, rivers, plains, etc. affect the flight path planning of drones, and obstacle distribution data helps to plan avoidance strategies in advance. For areas with dense lines and many devices, smaller sub-areas may be divided to ensure the inspection details, while in areas with relatively sparse equipment and strong drone endurance, larger sub-areas can be divided to reduce the number of area switching. In the three-dimensional geographic information model, according to these comprehensive considerations, the entire inspection area is scientifically and reasonably divided into multiple sub-areas. Each sub-area has the characteristics of being relatively independent and closely connected to the whole; multiple key scoring indicators are selected to evaluate the sub-areas, and then the optimal inspection order is determined based on the geographical location relationship between the sub-areas, such as proximity and connection convenience, so that the drone can traverse each sub-area in the most reasonable order, reducing unnecessary flight path turns and detours. By comprehensively collecting geographic information data and power equipment data and effectively associating them to build a three-dimensional model, an extremely accurate data foundation is provided for the entire inspection route planning. Decisions such as regional division, scoring, and inspection sequence determination based on this model are more scientific and reasonable, avoiding route planning errors caused by missing or inaccurate data, and improving the reliability and effectiveness of the inspection plan.

[0071] The method for determining the inspection order of the sub-areas includes: S151, setting an original scoring model to obtain an original score according to the importance of the distribution network lines in the sub-areas, the criticality of the power equipment, the historical fault frequency and the sensitivity of the surrounding environment; S152, obtaining a starting score according to the shortest distance between the starting point of the drone and each sub-area combined with the original score, and selecting the sub-area with the highest starting score as the starting area; S153, calculating the center distance from the current sub-area where the drone is located to the remaining sub-areas, calculating the revised scores of the remaining sub-areas according to the original score and the center distance, selecting the sub-area with the highest revised score as the next flight area, and repeating step S153 until all sub-areas are visited, thereby determining the inspection order of the sub-areas.

[0072] The original score of the sub-area is determined by using the pre-set original score model. This score initially reflects the relative position of each sub-area in terms of inspection priority. After determining the original score, the starting point of the drone is considered, and the shortest distance between the starting point of the drone and each sub-area is calculated. This distance factor is combined with the original score to obtain the starting score, and then the sub-area with the highest starting score is selected from all sub-areas as the starting area. The purpose of this is to consider factors such as the importance of the area while taking into account that the drone can quickly reach the first inspection key area with a better path after taking off, reducing the waste of resources and time consumption of the initial flight. After the starting area is determined, the drone enters the inspection process. During the inspection process, when the drone is in the current sub-area, the center distance to the remaining sub-areas is calculated. The calculation of this center distance helps to evaluate the flight cost of the drone to other sub-areas, and then the original score calculated previously and this center distance are combined to obtain the revised score of the remaining sub-areas, and the sub-area with the highest revised score is selected as the next flight area. After that, the drone flies to the area and repeats this calculation and selection process until all sub-areas have been visited, thereby determining the complete sub-area inspection order.

[0073] The original scoring model is as follows:

[0074]

[0075] in, For the The raw scores of the sub-regions, The importance of distribution network lines, The criticality of the power equipment, is the historical fault frequency, is the sensitivity value of the surrounding environment, , , , , is the corresponding weight coefficient, and ;

[0076] The initial score calculation method is as follows:

[0077]

[0078] in, For the The starting score of each sub-region, For the The raw scores of the sub-regions, is the distance influence factor, The starting point of the drone The shortest distance between sub-regions.

[0079] In determining the original score Finally, in order to select a more suitable starting area for drone inspection, the starting score was introduced. Calculation method of starting score In the original score Based on the distance factor The starting point of the drone The shortest distance between sub-regions The product of When the value is large, it means that the distance factor has a more significant impact on the starting score, and the drone is more inclined to prioritize the sub-area that is closer to the starting point and has a relatively high original score as the starting area; on the contrary, when When the value is small, the original score plays a stronger leading role, but the distance factor will still affect the selection of the starting area to a certain extent. Through such calculation, the importance of the area itself and the convenience of the drone to reach the area are comprehensively considered, so as to determine the sub-area with the highest starting score as the starting area, so that the drone can quickly enter the key inspection area with a better path when starting the inspection, and improve the inspection efficiency in the initial stage.

[0080] The center distance is calculated as follows:

[0081]

[0082] in, For the The center point of the sub-region to the The distance between the center points of the sub-regions, For the current region, For the Unvisited areas, For the The coordinates of the center point of each sub-region, For the The coordinates of the center points of the unvisited sub-regions.

[0083] In the space of the three-dimensional geographic information model, the straight-line distance between the center points of two sub-areas is accurately calculated to measure the spatial cost of the drone flying from the current sub-area to other unvisited sub-areas. It allows the drone to prioritize the sub-area that is closer to the current position and more important as the next inspection target based on the actual spatial position relationship, effectively reducing the spatial span of flight between different sub-areas, avoiding unreasonable long-distance jumping flights, and further improving the inspection efficiency. It also helps to reduce the flight energy consumption of the drone, extend its single inspection range, and ensure that the entire inspection task can be successfully completed.

[0084] The modified score calculation method is as follows:

[0085]

[0086] in, For the Corrected scores for unvisited areas, For the Raw scores for unvisited areas, is another distance influencing factor.

[0087] The application of the modified score calculation method makes the inspection path planning highly dynamic and adaptable. During the entire inspection process, as the drone continuously flies from one sub-area to another, its position is constantly changing, and the distance of each unvisited sub-area relative to the current position is also constantly changing. By combining the original score and the center distance factor calculated based on the current position to generate a modified score, the inspection priority of each unvisited sub-area can be re-evaluated in real time based on the actual position of the drone.

[0088] The method for determining the boundary points in S2 is as follows: b1, for adjacent sub-areas and , respectively determine their respective key inspection point sets and , calculate the sub-region Key inspection points With sub-area Key inspection points in The distance between ,and , ;

[0089]

[0090] b2, find The key inspection point pairs corresponding to the minimum distance values ​​in are and ,and , ; b3, calculate the adjacent sub-regions and The boundary point coordinates ; b4, coordinates of boundary points Perform feasibility analysis. If it is feasible, determine the coordinate as the boundary point. If it is not feasible, Find the key inspection point pair corresponding to the next smallest distance value and repeat steps b3 and b4 until a feasible boundary point is determined.

[0091] After calculating the distance of all possible key inspection point pairs, the key inspection point pair with the smallest distance value is found. This step aims to determine the two key inspection points that are closest in space. and , by calculating the average value of their coordinates to preliminarily determine the boundary coordinate points of adjacent sub-regions, and obtain the boundary point coordinates After that, it is necessary to conduct a feasibility analysis. This feasibility analysis may involve many factors, such as whether the coordinate position is within a reasonable geographical area (for example, whether it is within the flyable area, whether it avoids obstacles, etc.), whether it meets the actual distribution network line layout and inspection requirements, etc. If the coordinate meets all relevant requirements after analysis, that is, it is feasible, then the coordinate is officially determined as a boundary point; on the contrary, if it is not feasible, it means that the coordinates of the initially calculated boundary point do not conform to the actual situation and cannot be used as a valid boundary point. At this time, it is necessary to find the key inspection point pair corresponding to the second smallest distance value among the distances between all the key inspection point pairs calculated previously, and then repeat steps b3 and b4, that is, recalculate the boundary point coordinates and perform feasibility analysis again, and repeat this until a boundary point that meets the feasibility requirements is determined. In this way, through continuous iterative adjustment, it is ensured that the boundary point finally determined is a valid boundary point that meets the actual inspection scene and geographic space conditions.

[0092] The path evaluation method comprises: setting a comprehensive scoring model according to the length, safety value, feasibility evaluation value and additional factor value of the reachable inspection sub-path to obtain a comprehensive score; sorting the comprehensive scores from small to large, and selecting the sub-path with the lowest comprehensive score as the optimal inspection sub-path;

[0093] The comprehensive scoring model is as follows:

[0094]

[0095] in, For path Ratings, For path Length, For path Safety value, For path The feasible evaluation value of For path The additional factor value of is the path length weight, is the safety weight, is the feasibility weight, is the additional factor weight.

[0096] In the process of path evaluation, the length, safety value, feasibility evaluation value and additional factor value (such as data collection convenience, observation advantage of special equipment, connection with subsequent maintenance work, etc.) of the reachable inspection sub-path are comprehensively considered to set up a comprehensive scoring model. After calculating the comprehensive score Score (r) of each reachable inspection sub-path through the comprehensive scoring model, these comprehensive scores are sorted in order from small to large. The reason why the sub-path with the lowest comprehensive score is selected as the optimal inspection sub-path is that under the framework of this comprehensive scoring model, a low score means that the path has better comprehensive performance in terms of length, safety, feasibility and additional factors, which is more in line with the inspection work's expectations for efficiency, safety, feasibility and other additional requirements. This multi-dimensional evaluation method enables the selected optimal inspection sub-path to achieve a relatively good balance in terms of length, safety, feasibility and additional factors, which can not only improve the efficiency of drone inspection, reduce flight time and energy consumption, but also maximize the safety of the inspection process.

[0097] The S3 includes: according to the inspection order of the sub-areas, connecting the optimal inspection sub-paths of each sub-area in sequence to construct a preliminary inspection total path; smoothing the preliminary inspection total path to eliminate redundant and repeated parts in the path to obtain a smooth inspection total path; performing global optimization based on the length of the smooth inspection total path, flight energy consumption, and inspection time to obtain the optimal inspection total path, and ensuring that the optimal inspection total path can pass through all key inspection points.

[0098] Based on the planning order of the sub-areas, the optimal inspection sub-paths of each sub-area are integrated to form a preliminary inspection total path that covers all sub-areas and passes through key inspection points. When connecting the optimal inspection sub-paths of adjacent sub-areas in the preliminary inspection total path, due to the way the sub-area boundary points are determined or the local optimality of the sub-path planning, there may be abrupt turns at the connection and partial path overlap. Curve fitting technology is used to replace the original abrupt turning path segments with smooth curve segments, making the turning of the UAV more natural and smooth during flight. After obtaining the smooth inspection total path, the optimal inspection total path obtained by global optimization based on length, flight energy consumption and inspection time can achieve the optimal allocation of resources while meeting the inspection task requirements (i.e. passing through all key inspection points).

[0099] The S4 includes: after the drone completes a key inspection point, collecting real-time location coordinates and remaining energy consumption information; calculating the path distance of the drone to the next key inspection point, and evaluating whether the drone's remaining energy consumption can reach the next key inspection point and whether there is enough energy to return to the starting point after completing the next key inspection point task based on the path distance; if the remaining energy consumption is not enough to support the drone to reach the next key inspection point or it cannot safely return to the starting point after completing the next key inspection point task, immediately plan a return path and return to the starting point to replenish energy.

[0100] When the drone completes the inspection task of a key inspection point, it will use its own positioning system to collect real-time location coordinates. Based on the acquired real-time location coordinates and the pre-planned optimal inspection path, the drone will calculate the path distance from the current location to the next key inspection point. This calculation process will take into account the actual terrain, flight altitude and other factors in the three-dimensional geographic information model to determine the flight distance as accurately as possible. Combined with the drone's energy consumption model (comprehensively considering the relationship between factors such as flight speed, load, flight environment and energy consumption), the energy consumption required to reach the next key inspection point is estimated based on the path distance. If, after the above energy consumption evaluation, it is found that the remaining energy consumption is not enough to support the drone to successfully reach the next key inspection point, or even if it can reach and complete the task, it cannot return to the starting point safely afterwards, an optimal path to return to the starting point is planned based on the current real-time location coordinates, so that the drone can return to the starting point as soon as possible along the path to replenish energy, ensuring that subsequent inspection tasks can continue to be carried out smoothly with sufficient energy guarantee.

[0101] The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. All equivalent modifications or changes made by those skilled in the art without departing from the spirit and technical ideas disclosed by the present invention shall still be covered by the claims of the present invention.

Claims

1. A method for inspection path planning of distribution network lines based on hybrid UAV, characterized in that: The following steps are involved: S1, obtaining geographic information data and power equipment data of the distribution network line, establishing a three-dimensional geographic information model of the distribution network line, dividing the entire distribution network line inspection area into multiple sub-areas in the three-dimensional geographic information model, scoring the sub-areas and determining the inspection order of the sub-areas based on the geographical location relationship; S2, determine the key inspection points of each sub-area and the boundary points between adjacent sub-areas, search for reachable inspection sub-paths connecting each key inspection point from the previous boundary point to the next boundary point, perform path evaluation on each reachable inspection sub-path, and determine the optimal inspection sub-path; S3, according to the inspection order of the sub-areas, sequentially connect and optimize the optimal inspection sub-paths of each sub-area to obtain the optimal inspection total path passing through the key inspection point; S4, the UAV inspects according to the optimal inspection route. After completing a key inspection point task, it collects the location coordinates and remaining energy consumption information in real time. Based on the collected information, it determines whether the UAV should continue inspection or replenish energy.

2. The method for inspection path planning of distribution network lines based on hybrid electric drone according to claim 1 is characterized in that: The S1 includes: S11, obtaining geographic information data of the distribution network line from the geographic information system, including line direction, tower location, topography and obstacle distribution; S12, collecting data of power equipment on the distribution network, including the location, type and status of the power equipment; S13, associating the power equipment data with the geographic information data to obtain associated information, and constructing a three-dimensional geographic information model of the distribution network line according to the associated information; S14, dividing the entire distribution network line inspection area into multiple sub-areas in the three-dimensional geographic information model according to the distribution of distribution network lines, the density of power equipment, and the endurance of the drone; S15, taking the importance of distribution network lines, the criticality of power equipment, the historical failure frequency and the sensitivity of the surrounding environment as scoring indicators, conducts regional scoring for each sub-area, and comprehensively considers the geographical location relationship between each sub-area to determine the inspection order of the sub-area.

3. The method for inspection path planning of distribution network lines based on hybrid UAV according to claim 2 is characterized in that: The method for determining the inspection order of the sub-areas includes: S151, setting an original scoring model to obtain an original score according to the importance of the distribution network lines in the sub-region, the criticality of the power equipment, the historical fault frequency, and the sensitivity of the surrounding environment; S152, obtaining a starting score based on the shortest distance between the starting point of the drone and each sub-area and the original score, and selecting the sub-area with the highest starting score as the starting area; S153, calculate the center distance from the current sub-area where the drone is located to the remaining sub-areas, calculate the corrected scores of the remaining sub-areas based on the original scores and the center distances, select the sub-area with the highest corrected score as the next flight area, and repeat step S153 until all sub-areas have been visited, thereby determining the inspection order of the sub-areas.

4. The method for inspection path planning of distribution network lines based on hybrid UAV according to claim 3 is characterized in that: The original scoring model is as follows: in, For the The raw scores of the sub-regions, The importance of distribution network lines, The criticality of the power equipment, is the historical fault frequency, is the sensitivity value of the surrounding environment, , , , , is the corresponding weight coefficient, and ; The initial score calculation method is as follows: in, For the The starting score of each sub-region, For the The raw scores of the sub-regions, is the distance influence factor, The starting point of the drone The shortest distance between sub-regions.

5. The method for inspection path planning of distribution network lines based on hybrid electric drone according to claim 4 is characterized in that: The center distance is calculated as follows: in, For the The center point of the sub-region to the The distance between the center points of the sub-regions, For the current region, For the Unvisited areas, For the The coordinates of the center point of each sub-region, For the The coordinates of the center points of the unvisited sub-regions.

6. The method for inspection path planning of distribution network lines based on hybrid electric drone according to claim 5 is characterized in that: The modified score calculation method is as follows: in, For the Corrected scores for unvisited areas, For the Raw scores for unvisited areas, is another distance influencing factor.

7. The method for inspection path planning of distribution network lines based on hybrid electric drone according to claim 1 is characterized in that: The method for determining the boundary points in S2 is as follows: b1, for adjacent sub-regions and , respectively determine their respective key inspection point sets and , calculate the sub-region Key inspection points With sub-area Key inspection points in The distance between ,and , ; b2, find The key inspection point pairs corresponding to the minimum distance values ​​in are and ,and , ; b3, calculate the adjacent sub-region and The boundary point coordinates ; b4, coordinates of the boundary points Perform feasibility analysis. If it is feasible, determine the coordinate as the boundary point. If it is not feasible, Find the key inspection point pair corresponding to the next smallest distance value and repeat steps b3 and b4 until a feasible boundary point is determined.

8. The method for inspection path planning of distribution network lines based on hybrid electric drone according to claim 1 is characterized in that: The path evaluation method comprises: A comprehensive scoring model is set according to the length, safety value, feasibility assessment value and additional factor value of the accessible inspection sub-path to obtain a comprehensive score; Sort the comprehensive scores from small to large, and select the sub-path with the lowest comprehensive score as the optimal inspection sub-path; The comprehensive scoring model is as follows: in, For path Ratings, For path Length, For path Safety value, For path The feasible evaluation value of For path The additional factor value of is the path length weight, is the safety weight, is the feasibility weight, is the weight of additional factors.

9. The method for inspection path planning of distribution network lines based on hybrid electric drone according to claim 1 is characterized in that: The S3 includes: According to the inspection order of the sub-areas, the optimal inspection sub-paths of each sub-area are connected in sequence to construct a preliminary inspection total path; Smoothing the preliminary inspection total path, eliminating the redundant and repeated parts in the path, and obtaining a smooth inspection total path; The optimal inspection path is obtained by global optimization based on the length of the smooth inspection path, flight energy consumption, and inspection time, and it is ensured that the optimal inspection path can pass through all key inspection points.

10. The method for inspection path planning of distribution network lines based on hybrid UAV according to claim 1 is characterized in that: The S4 includes: After the drone completes a key inspection point, it collects real-time location coordinates and remaining energy consumption information; Calculate the path distance of the drone to the next key inspection point, and evaluate whether the drone has enough energy to reach the next key inspection point and whether it has enough energy to return to the starting point after completing the task at the next key inspection point based on the path distance; If the remaining energy consumption is not enough to support the drone to reach the next key inspection point or the next key inspection point mission cannot be safely returned to the starting point after completion, the return path is immediately planned and returned to the starting point to replenish energy.

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