Line planning method and system

Abstract

The invention discloses a line planning method and system, and relates to the technical field of electric power line inspection. According to the line planning method, a plurality of feasible inspection paths are generated by obtaining a list of tasks to be inspected and three-dimensional coordinates of an electric tower and an inspection part of the electric tower, combining regional environment data, constructing a semantic knowledge graph and a spatial topological structure chart, determining an optimal path through evaluation, screening and analysis, and transmitting the optimal path to an unmanned aerial vehicle to execute an inspection task. According to the method, the flight state is monitored in real time, the abnormal condition is found in time, and the residual path is dynamically replanned in the inspection process, so that the smooth execution of the inspection task is ensured. The flight state and the environment interference of the unmanned aerial vehicle are monitored in real time, and the dynamic path is replanned according to the actual condition; according to the method, the flexibility and emergency response capacity of the inspection task are remarkably improved, the efficiency and safety of the inspection task are effectively improved by dynamically adjusting the path, and the adaptive capacity of the unmanned aerial vehicle in a complex environment is enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of power line inspection, and in particular to a line planning method and system. Background Art

[0002] With the development and advancement of intelligent power systems, power line inspections are becoming increasingly important. Traditional manual inspection methods face challenges such as low efficiency, high risk, and high costs. This is especially true when inspecting at high altitudes or in remote areas. Manual inspections pose safety risks and are prone to missed or false detections. Therefore, the use of drones for line inspections is becoming an efficient and safe solution. Drones offer advantages such as high flexibility, ease of operation, and strong adaptability. They can quickly reach towers or line inspection points and conduct inspections using high-definition cameras, infrared sensors, and other equipment, significantly improving inspection efficiency and safety.

[0003] However, in practical applications, line inspections often involve complex environmental factors and diverse tower layouts. Planning reasonable inspection routes in dynamic environments to ensure smooth inspection progress has become a key challenge facing drone inspection technology. Traditional path planning methods typically only consider static map information and ignore real-time changes during the inspection process, such as aircraft status, environmental interference, and weather changes. These factors can render inspection routes unsuitable or increase safety risks.

[0004] Prior art, such as the invention patent application with publication number CN116301032A, discloses a method for planning high-voltage transmission line inspections using drones. This method addresses the issues of low inspection efficiency and high labor costs associated with manually designing inspection waypoints and trajectories when inspecting high-voltage transmission lines in high-altitude areas, remote mountainous areas, deserts, or lakes. The present invention preprocesses collected image information, performs Gaussian kernel filtering for noise reduction, and employs a random sampling consensus algorithm to curve fit the high-voltage line to obtain a high-voltage line inspection plan. A quadrotor flight controller designed with backstepping sliding mode control provides navigation information to the drone according to the high-voltage line inspection plan, achieving accurate identification of the high-voltage line, enhancing the robustness and adaptability of the drone, and significantly improving inspection accuracy. The present invention is suitable for drone inspections.

[0005] Based on the above solution, it is found that the limitations of the existing technology include at least the following problems. First, the traditional line inspection path planning method lacks a real-time adjustment mechanism for the inspection path during execution, especially when encountering abnormal inspection status. The existing method relies more on pre-set static inspection paths. These paths are usually planned based on environmental data, but these environmental data are only static and it is difficult to fully reflect the complex or emergency situations that may be encountered during the inspection process. For example, when the drone encounters certain unknown obstacles or the status of the tower changes during the inspection process, the existing path planning method is difficult to make timely adjustments, causing the drone to deviate from the planned route, resulting in delays in mission progress, repeated inspections, and even unnecessary waste of resources. Summary of the Invention

[0006] In view of the deficiencies of the prior art, the present invention provides a route planning method and system, which solves the problem of the prior art lacking the ability to adjust the route in real time and adapt to dynamic environments.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: a line planning method, comprising the following steps: obtaining a list of tasks to be inspected, including the three-dimensional coordinates of several towers, the three-dimensional coordinates of several inspection locations corresponding to each tower, and the spatial coordinate information of the transmission line path segments between connected towers; obtaining regional inspection environment data within a set area of ​​each tower inspection location of each tower, and combining with the list of tasks to be inspected, establishing a semantic knowledge graph of the inspection area and a corresponding spatial topological structure graph, and analyzing and generating several feasible inspection paths, wherein the regional inspection environment data includes regional wind speed value, regional temperature value, regional air pressure value, and regional obstacle density value; evaluating, screening and analyzing several feasible inspection paths, determining the optimal inspection path, and transmitting it to the drone end; in the process of the drone performing the inspection task according to the optimal inspection path, evaluating the inspection status in real time, and replanning the remaining inspection paths when there is an abnormality in the inspection status.

[0008] Furthermore, the specific steps for establishing the semantic knowledge graph of the inspection area are as follows: read the three-dimensional coordinates of each tower in the list of inspection tasks and the three-dimensional coordinates of each inspection location corresponding to each tower, and establish a mapping relationship between the towers and the inspection locations; assign a type identifier to each inspection location of each tower, and use it as a semantic node in the semantic knowledge graph of the inspection area; read the spatial coordinate information of the transmission line path segments between the connected towers, and use them as independent entity nodes to establish connection semantic relationships with the connected towers; read the regional inspection environment data within the set area of ​​each inspection location corresponding to each tower, and use them as additional semantic information of each inspection location node in the semantic knowledge graph of the inspection area; perform structured modeling on the semantic relationships between all tower nodes, inspection location nodes, transmission line path segment nodes and additional environmental semantic information nodes to generate a semantic knowledge graph of the inspection area.

[0009] Furthermore, the specific steps for establishing the spatial topological structure diagram of the inspection area are as follows: based on the three-dimensional coordinates of each tower and each inspection location in the inspection task list, all spatial target points are extracted and used as a node set in the spatial topological structure diagram; the spatial coordinate information of the transmission line path segment between the connected towers is read, and according to the connection relationship between the towers, the path segment is decomposed into feasible track segments composed of several spatial control points, and used as the path segment elements in the spatial topological structure diagram; the spatial connectivity between each node is judged, and the node pairs that meet the track feasibility are screened out; spatial edges are established between the feasible node pairs, and spatial constraint attributes are marked for each edge; all nodes, edges and path segment structures are organized into a graph structure form to obtain the spatial topological structure diagram of the inspection area.

[0010] Furthermore, the specific steps for analyzing and generating several feasible inspection paths are as follows: extract the transmission line path segments between the connected towers from the spatial topology map of the inspection area, and generate feasible candidate inspection paths for each segment; supplement the inspection location information in the candidate inspection paths based on the semantic map of the inspection area; and generate several feasible inspection paths based on the feasible candidate inspection paths, starting from the starting tower node.

[0011] Furthermore, several feasible inspection paths are evaluated, screened and analyzed, and the specific steps for determining the optimal inspection path are as follows: feature analysis is performed on each feasible inspection path to obtain a path feature set for each feasible inspection path, wherein the path feature set includes the total length of the path, the flight altitude difference of the path, the curvature of the path, the path environment score, the path climbing angle, and the path complexity; based on the path feature set of each feasible inspection path, the path scoring index of each feasible inspection path is analyzed; the path scoring index of each feasible inspection path is compared and analyzed, and the feasible inspection path corresponding to the minimum path scoring index is selected as the optimal inspection path.

[0012] Furthermore, the specific formula for calculating the path scoring index of a feasible inspection path is as follows: ; in, is the path scoring index of a feasible inspection path, is the total length of a feasible inspection path, is the flight altitude difference of a feasible inspection path, is the flight altitude difference influence coefficient stored in the database, is the path curvature of a feasible inspection path, is the path curvature influence coefficient stored in the database, Score the path environment of a feasible inspection path, is the path environmental impact coefficient stored in the database, is the path climbing angle of a feasible inspection path, is the path climbing angle influence coefficient stored in the database, is the path complexity of a feasible inspection path, is the path complexity impact coefficient stored in the database.

[0013] Furthermore, in the process of the UAV performing the inspection task according to the optimal inspection path, the specific steps of real-time evaluation of the inspection status are as follows: real-time acquisition of the UAV's real-time flight status data and real-time interference status data in the set area, the real-time flight status data including the remaining battery power, battery temperature, flight speed, flight altitude, flight attitude index, and flight load weight, and the real-time interference status data including the regional GPS signal strength value, regional electromagnetic interference intensity value, regional light intensity value, and regional sound wave interference intensity value; based on the UAV's real-time flight status data and the real-time interference status data in the set area, analyzing the UAV's real-time flight status index and the interference status index in the set area; based on the UAV's real-time flight status index and the interference status index in the set area, analyzing the UAV's real-time inspection status index.

[0014] Furthermore, the specific formula for calculating the real-time inspection status index of the drone is as follows: ; in, is the real-time inspection status index of the drone, is the real-time flight status index of the UAV, is the flight status influence coefficient stored in the database, The interference status index within the set area of ​​the drone, is the interference state influence coefficient stored in the database, is the interaction coefficient stored in the database, is a natural constant.

[0015] Furthermore, when there is an abnormality in the inspection status, the specific steps for re-planning the remaining inspection path are as follows: the real-time inspection status index of the drone is judged and analyzed with the preset inspection status evaluation interval; when the real-time inspection status index is outside the preset inspection status evaluation interval, it is regarded as an abnormal inspection status, and the remaining inspection path is re-planned.

[0016] A line planning system includes: a data acquisition unit for obtaining a list of tasks to be inspected, including the three-dimensional coordinates of several power towers, the three-dimensional coordinates of several inspection locations corresponding to each power tower, and the spatial coordinate information of the transmission line path segments between connected power towers; a path generation unit for obtaining regional inspection environment data within a set area of ​​each tower inspection location of each power tower, and establishing a semantic knowledge graph of the inspection area and a corresponding spatial topological structure graph in combination with the list of tasks to be inspected, and analyzing and generating several feasible inspection paths, wherein the regional inspection environment data includes regional wind speed value, regional temperature value, regional air pressure value, and regional obstacle density value; a screening and analysis unit for evaluating, screening and analyzing several feasible inspection paths, determining the optimal inspection path, and transmitting it to a drone end; an inspection monitoring unit for evaluating the inspection status in real time during the process of the drone performing the inspection task according to the optimal inspection path, and replanning the remaining inspection paths when there is an abnormality in the inspection status.

[0017] The present invention has the following beneficial effects: (1) This route planning method can significantly improve the flexibility and emergency response capability of the inspection task. The traditional inspection path planning method usually adopts static path design and lacks real-time evaluation and dynamic adjustment mechanism during the execution process. This makes it difficult to adjust the inspection path in time when encountering emergencies, such as weather changes, environmental interference or equipment failure, resulting in low inspection efficiency or safety risks for the drone. On the contrary, this method monitors the flight status and environmental interference of the drone in real time and replans the path according to the actual situation, thereby enhancing the emergency response capability of the inspection task. During the inspection process, once the drone encounters an abnormal state or changes in environmental factors, the remaining inspection path can be replanned in real time to ensure the continuity of the task and the safety of the drone. By dynamically evaluating the flight status and environmental interference data of the drone, the path can be adjusted in real time, avoiding the blind execution and potential safety risks in the traditional method. Therefore, the application of this method in the inspection task greatly improves the ability to cope with complex and changing environments and ensures that the inspection task can be completed smoothly.

[0018] (2) This route planning method can intelligently evaluate and select the optimal inspection path by comprehensively considering multi-dimensional path characteristics, such as total path length, flight altitude difference, path curvature, etc. This optimization process can effectively reduce unnecessary flight distance and time, thereby improving inspection efficiency. Traditional inspection path planning methods often ignore the diversity of paths and usually rely on simple distance calculations, while ignoring the complexity and environmental adaptability of paths, resulting in lengthy and inefficient paths during the inspection process. By comprehensively analyzing the multi-dimensional characteristics of each feasible inspection path and combining environmental influencing factors, this method can ensure that the selected inspection path not only meets the actual flight requirements, but also minimizes energy consumption and flight time, thereby improving the overall efficiency of the task. In addition, the optimization of path selection also ensures that the selected path is safer and more reliable by considering environmental factors such as wind speed and obstacle density, avoiding the hidden dangers that may cause inspection interruption or flight abnormality in traditional methods. Therefore, the path optimization process of this method not only improves inspection efficiency, but also effectively guarantees safety.

[0019] (3) This route planning method greatly enhances the intelligence and automation level of the system and reduces the need for human intervention. Traditional inspection route planning and execution usually require manual adjustments based on on-site conditions, which results in high labor costs and operational risks. This method automatically establishes a semantic knowledge graph and a spatial topological structure graph of the inspection area, and combines real-time flight status and environmental data. The system can self-analyze and make path adjustments. When generating feasible paths, it takes into account various environmental factors of the inspection area, and can automatically evaluate the inspection status of the drone based on real-time flight status data and environmental interference, and automatically determine whether the path needs to be adjusted. This not only saves the time and cost of human intervention, but also reduces errors or delays caused by human operations, and improves the reliability of the system and the accuracy of task execution. With the improvement of the intelligence level, the system can complete the inspection task autonomously without human intervention, further improving the automation level and operational efficiency of the inspection task. This makes this method particularly suitable for large-scale and long-term inspection tasks, especially in inspection tasks in remote or dangerous areas, and can ensure efficient and accurate completion of tasks.

[0020] (4) The route planning system collects the three-dimensional coordinates of the tower, inspection location information and environmental data (such as wind speed, temperature, air pressure and obstacle density) in real time through the data acquisition unit. The system can fully understand the spatial distribution and real-time environmental conditions of the inspection area. Combined with the semantic knowledge graph and spatial topology structure graph generated by the path generation unit, the system can dynamically analyze and generate multiple feasible inspection paths. During the inspection process, the screening and analysis unit evaluates the multi-dimensional characteristics of the path, selects the optimal path and transmits it to the drone end. At the same time, the inspection monitoring unit can evaluate the flight status in real time and re-plan the remaining inspection paths in time when anomalies are found, such as insufficient battery power or environmental interference. This ability to adjust the inspection path through real-time data and status monitoring greatly improves the flexibility and reliability of task execution, ensures that the drone can cope with complex flight environments and emergencies, and thus improves inspection efficiency and safety. This intelligent and dynamically adjusted path planning method ensures the smooth completion of the drone inspection task, has strong adaptability, and can greatly improve the inspection quality and overall performance.

[0021] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 The present invention is a flow chart of a route planning method.

[0023] Figure 2 The present invention provides a flowchart of the specific steps for establishing a spatial topological structure diagram of an inspection area in a route planning method.

[0024] Figure 3 This is a block diagram of a route planning system of the present invention. DETAILED DESCRIPTION

[0025] See also Figure 1, an embodiment of the present invention provides a technical solution: a line planning method, comprising the following steps: obtaining a list of tasks to be inspected, including the three-dimensional coordinates of several power towers, the three-dimensional coordinates of several inspection locations corresponding to each power tower, and the spatial coordinate information of the transmission line path segments between the connected power towers; obtaining regional inspection environment data within a set area of ​​each tower inspection location of each power tower, and establishing a semantic knowledge graph of the inspection area and a corresponding spatial topological structure graph in combination with the list of tasks to be inspected, and analyzing and generating several feasible inspection paths, wherein the regional inspection environment data includes regional wind speed value, regional temperature value, regional air pressure value, and regional obstacle density value; evaluating, screening and analyzing several feasible inspection paths, determining the optimal inspection path, and transmitting it to the drone end; in the process of the drone performing the inspection task according to the optimal inspection path, evaluating the inspection status in real time, and replanning the remaining inspection paths when there is an abnormality in the inspection status.

[0026] Among them, the regional wind speed value can be measured and obtained by a wind speed sensor.

[0027] The regional temperature value can be measured and obtained by a temperature sensor.

[0028] The regional air pressure value can be measured and obtained by the air pressure sensor.

[0029] The regional obstacle density value can be obtained by scanning the inspection area using a laser radar (LiDAR) sensor carried by a drone. The LiDAR accurately measures the position and height of obstacles in the surrounding area by emitting laser beams and receiving reflected signals, generating three-dimensional point cloud data, and analyzing the density of the point cloud to calculate the number of obstacles per unit area or volume, thereby obtaining the obstacle density value. Secondly, infrared sensors and high-definition cameras can also assist in detecting obstacles, especially in low light or poor visibility conditions. Image processing technology and computer vision models (such as deep learning) are used to analyze the captured images, identify obstacles and calculate their density. In addition, through the geographic information system (GIS) and existing regional data, the obstacle distribution in areas without sensors can be supplemented or estimated, and the density of obstacles can be further calculated. Finally, these data are combined with the terrain characteristics of a specific area, and the obstacle density value is calculated through a gridding method, ultimately obtaining the obstacle density value of the area.

[0030] Specifically, the steps for establishing a semantic knowledge graph for the inspection area are as follows: read the 3D coordinates of each tower in the inspection task list and the 3D coordinates of each inspection location corresponding to each tower, and establish a mapping relationship between the towers and the inspection locations, which is specifically as follows: Retrieve the inspection task list (usually a task configuration table or JSON structure); Read the unique number of each tower and its corresponding three-dimensional coordinate point (x, y, z); For each tower, extract the set of inspection parts under it, such as the tower top, crossarms, insulators, etc. Each part also corresponds to a unique three-dimensional coordinate; Establish a "tower-inspection location" mapping structure, for example: T1→{P1,P2,P3}, where T1 is the tower number and P1–P3 are the locations of the tower; This mapping relationship serves as the entity relationship basis of the semantic graph and provides structural support for subsequent connections between nodes; Each inspection part of each tower is assigned a type identifier (such as tower top, insulator, crossarm, etc.) and serves as a semantic node in the semantic knowledge graph of the inspection area. Specifically, Based on the inspection task rule library or preset templates, each inspection location is labeled with a structural type label, such as "tower top", "crossarm", "insulator", "lightning protection connection point", etc. These inspection locations are considered as semantic nodes (entities) in the graph, i.e. points on a graph; Each node records the following attribute fields: Node ID: such as P3; Tower: such as T1; Structural type: such as crossarms; Three-dimensional position: such as (x=100.5, y=230.0, z=40.8); Add the node to the semantic graph entity set and make it a basic node element in the graph structure; The spatial coordinate information of the transmission line path segments between the connected towers is read and each segment is treated as an independent entity node to establish a connection semantic relationship with the connected towers. Specifically: Obtain the spatial coordinate information of the transmission line path segment between every two adjacent towers, usually the starting and ending points of the line segment or a set of linear path points; Each line segment (e.g. L1) is treated as an independent node entity in the graph; Record attribute fields for each line segment node: Path segment ID: such as L1; Starting and ending points: such as (T1 coordinate, T2 coordinate); Add semantic relationships to the graph, including but not limited to the following examples: <Line segment L1>–[Connection]→<Tower T1>; <Line segment L1>–[Connection]→<Tower T2>; Form a connection diagram structure between towers and line segments, providing a structural link for path feasibility analysis; Read the regional inspection environment data within the set area of ​​each inspection location corresponding to each tower, and use it as additional semantic information for each inspection location node in the semantic knowledge graph of the inspection area, specifically: For each inspection location, define its set environmental sampling area (e.g., a spherical space with a radius of 10 meters centered on the location coordinates); Collect or import corresponding environmental perception data within the area; Attach these data as attributes to the corresponding inspection part node; The semantic relationships among all tower nodes, inspection location nodes, transmission line path segment nodes, and additional environmental semantic information nodes are structured and modeled to generate a semantic knowledge graph for the inspection area. Specifically, the graph is as follows: Summarize all graph node entities: tower T, location P, line segment L, risk information R; Convert all semantic relationships such as "the tower includes parts", "the parts have additional risks", and "the lines connect to the tower" into triple structures: <t1>–[Include] → <p3> ； <p3>–[Risk Level] →<Strong Wind>; <l1>–[Connect]→ <t2>; Use graph database modeling methods (such as RDF and Neo4j syntax) to build graph data structures; Output graph structure G_task or inspection area semantic knowledge graph for reference by path evaluation module.

[0031] In this implementation plan, by constructing an accurate semantic knowledge graph of the inspection area, it can provide strong data support for subsequent inspection path planning and optimization. First, by using three-dimensional coordinates and structured mapping relationships, the spatial distribution and structural characteristics of the towers and their various inspection locations can be accurately depicted. The type identification and location data of each inspection location are clearly recorded as a graph node, which helps the system to quickly identify and process different types of inspection tasks. Secondly, by modeling the spatial information of the transmission line path segments between connected towers, the system can better understand the connection relationship between towers and the feasibility of the path, providing a basis for path analysis and planning, and further , combined with environmental data such as wind speed and temperature, this information is embedded as additional semantic information into the inspection site nodes, which enhances the system's perception of environmental factors. Ultimately, this structured graph method enables the comprehensive integration of spatial information and environmental information in the inspection area, greatly improving the accuracy and practicality of path planning. At the same time, through the modeling method of the graph database, it is convenient for dynamic updating and expansion, providing convenient query and analysis support for subsequent path evaluation and replanning. Therefore, establishing a semantic knowledge graph for the inspection area not only improves the efficiency of data integration, but also provides an accurate and flexible basis for subsequent decision-making and path planning.

[0032] Specifically, if Figure 2 As shown in the figure, the specific steps for establishing the spatial topology structure diagram of the inspection area are as follows: Based on the three-dimensional coordinates of each tower and each inspection location in the inspection task list, all spatial target points are extracted and used as the node set in the spatial topology structure diagram, which is specifically: Traverse all the towers recorded in the inspection task list; Extract the three-dimensional coordinates (x, y, z) of each tower as a "tower node"; The 3D coordinates of the inspection locations under each tower (such as the tower top, crossarm, and insulator) are further extracted as "inspection location nodes." These nodes are abstracted into a set of spatial target points V={v1,v2,...,v n }; This node set constitutes the "node layer" of the spatial topology graph, which is the basis for subsequent graph construction; Read the spatial coordinate information of the transmission line path segment between connected towers, and according to the connection relationship between the towers, decompose the path segment into feasible track segments consisting of several spatial control points, and use them as path segment elements in the spatial topology map. Specifically, it obtains the line segment coordinates between the tower pairs in the power grid design map or path data; Determine whether there is a transmission connection between each pair of towers (e.g., T1–T2); Interpolate or smooth the coordinates of each line segment and extract intermediate control points (e.g., one every 5 meters); Each path segment is represented as a set of continuous spatial control points L1={c1,c2,...,c k }; Use these track control points as "relay path nodes" in the topology graph to connect the starting and target spatial points; The spatial connectivity between each node is judged to select node pairs that meet the feasibility of the trajectory, which are as follows: Traverse any two node pairs in the node set (v i ,v j ), to determine the space flight connectivity between them; Judging criteria include but are not limited to the following examples: Whether there are obstacles (based on DEM or point cloud obstacle information); Whether the maximum ascent / descent angle requirements of the aircraft are met; Whether the minimum turning radius requirements are met; Add the node pairs that pass the judgment into the connected pair set E_possible={(v i ,v j )}; Eliminate all physically unreachable or extremely high-risk path connections; Establish spatial edges between feasible node pairs and mark each edge with spatial constraint attributes such as spatial distance, height difference, start and end point numbers, etc. The specific attributes are as follows: For each node pair that satisfies the connectivity condition (v i ,v j ), establish directed or undirected edges e ij ; Calculate the Euclidean space distance d of the edge ij =√[(x2–x1)²+(y2–y1)²+(z2–z1)²]; Record the starting point number and the ending point number; At the same time, the height difference Δh=zj–zi is calculated for flight strategy optimization; Append these attributes to the edge data structure, for example: { "start": "v3", "end": "v5", "distance": 14.7, "altitude_diff": 3.2 }; All nodes, edges, and path segments are organized into a graph structure to obtain the spatial topology of the inspection area, which is as follows: All the nodes V, edges E, and the path segment control points L generated above are organized into a graph structure G_topo=(V,E); Each node of the graph contains spatial location attributes, and the edges contain flight constraints and path attributes; The graph can also retain the control point sequence of the path segment for use by path planning algorithms (such as A* or improved TSP); The graph structure can be exported into visualization forms (GeoJSON, NetworkX objects, 3D rendering models), or used for subsequent path evaluation and generation.

[0033] In this implementation plan, by constructing a spatial topological structure diagram of the inspection area, more accurate and efficient support can be provided for path planning. First, a set of spatial target points is extracted based on the three-dimensional coordinates of the towers and inspection locations, and used as a node set of the topological diagram, which can clearly represent the spatial distribution between the towers and inspection locations. This structured node division provides a basis for subsequent path analysis, so that the position and relationship of each tower and inspection location can be accurately expressed. Secondly, by reading the spatial coordinate information of the transmission line path segment and decomposing it into control points, the system can generate feasible track segments to help determine the connectivity between the nodes. This process ensures The feasibility of the inspection path is ensured, and by judging conditions such as obstacles, flight angles and turning radius, inappropriate paths are filtered out, ensuring the safety and feasibility of path planning. Furthermore, spatial edges are established between node pairs, and spatial constraint attributes (such as distance and height difference) are annotated for each edge, so that the dynamic limitations of the aircraft and environmental conditions can be taken into account during path planning. Finally, the generated spatial topology map provides a visual form for subsequent path evaluation and planning algorithms (such as A* or improved TSP). This precise topological modeling provides strong data support for inspection path planning, ensuring the rationality, safety and efficiency of path selection.

[0034] Specifically, the specific steps for analyzing and generating several feasible inspection paths are as follows: extract the transmission line path segments between the connected towers in the spatial topology map of the inspection area, and generate feasible candidate inspection paths respectively. Specifically, based on the node and edge information in the spatial topology structure graph (G_topo), traverse all connected tower pairs (such as T1-T2, T2-T3), extract their corresponding transmission line path segments (such as L1, L2) and their spatial control point sequences (such as L1={c1,c2,...,c k Through the spatial connectivity constraints of the path segments (such as obstacles and flight angle restrictions), the line segments between adjacent towers are disassembled into track segments that can be flown by drones. Combined with the aircraft kinematic model (such as maximum turning radius and climb rate), the control points of the path segments are smoothly interpolated to generate a set of basic candidate paths (such as Path_L1 and Path_L2). Each path contains a spatial coordinate sequence, flight altitude and turning point parameters to form a preliminary transmission line coverage path plan; based on the semantic map of the inspection area, the inspection part information in the candidate inspection path is supplemented, which is specifically: sub-), extract its three-dimensional coordinates, type labels and environmental risk attributes, and the key parts that must be inspected (such as insulators and lightning protection points) are divided into Map it to the candidate path and insert inspection waypoints on the path segment of the corresponding tower. For example, add an inspection sub-path that detours to P1 and P2 at the end of the path segment L1 of tower T1 to ensure that the drone can hover for shooting or sensor scanning; starting from the starting tower node, generate several feasible inspection paths based on feasible candidate inspection paths. Specifically, take the starting tower (such as T0) as the root node, use an improved heuristic search algorithm (such as A*, ant colony algorithm), and traverse all towers and part nodes that need to be inspected based on the spatial topology map and semantically enhanced path.

[0035] In this implementation, by combining the spatial topology structure diagram and the semantic map, a more accurate and comprehensive inspection path can be generated to ensure that the path planning not only meets the physical limitations of the aircraft but also covers all key inspection locations. First, the transmission line path segments between the connected towers are extracted based on the spatial topology map, and the path smoothing interpolation processing is performed in combination with the aircraft's kinematic model (such as maximum turning radius, climb rate) to ensure that the generated candidate paths are practical and feasible and avoid obstacles or physical limitations during flight. This process effectively improves the accuracy of path planning and flight safety. Secondly, by combining the semantic map information of the inspection area (such as the three-dimensional coordinates of each inspection location of the tower, the type of By integrating data (type labels and environmental risk attributes) into path planning, the system not only considers spatial connectivity, but also ensures that key parts (such as insulators and lightning protection points) can be effectively covered. The insertion of inspection waypoints in the path ensures that the drone can effectively inspect each key part, improving the integrity and accuracy of the inspection task. Finally, based on the spatial topology map and semantic information, improved heuristic search algorithms (such as A* and ant colony algorithms) can generate multiple feasible inspection paths to ensure that the task is completed efficiently according to multiple requirements such as priority and shortest path. This comprehensive path generation method provides a reliable solution for complex inspection tasks and ensures an efficient and safe inspection process.

[0036] Specifically, several feasible inspection paths are evaluated, screened and analyzed, and the specific steps for determining the optimal inspection path are as follows: feature analysis is performed on each feasible inspection path to obtain a path feature set for each feasible inspection path, and de-unit processing is performed. The path feature set includes the total length of the path, the flight altitude difference of the path, the curvature of the path, the path environment score, the path climbing angle, and the path complexity; based on the path feature set of each feasible inspection path, the path scoring index of each feasible inspection path is analyzed; the path scoring index of each feasible inspection path is compared and analyzed, and the feasible inspection path corresponding to the minimum path scoring index is selected as the optimal inspection path.

[0037] The specific steps for obtaining the total path length are as follows: first, accumulate the Euclidean distances of the spatial control point sequences of the transmission lines between all connected towers (such as the sum of the point-by-point distances from the start to the end of line segment L1). Second, for each inspection location of each tower (such as the tower top and crossarm), calculate the three-dimensional straight-line distance from the drone from the end of the line segment to each location and add it to the total length. For example, the distance from the end of the line segment of tower T1 to its inspection location P1 needs to be included in the total path length. Finally, the total path length is the sum of the geometric distances between all line segments and inspection sub-paths, reflecting the overall spatial span of the path.

[0038] The specific steps to obtain the path flight altitude difference are: extract the altitude (z coordinate) of adjacent waypoints in the path in sequence, calculate the absolute altitude difference for each section, for example, the altitude difference from waypoint A (z=50m) to waypoint B (z=55m) is 5m, and the accumulated altitude differences of all sections are the path flight altitude difference.

[0039] The specific steps to obtain the path curvature are as follows: by quantifying the path turning angle and curvature, taking three consecutive waypoints as an example, calculate the angle between adjacent segments (such as the vector angle between waypoints A→B and B→C), and accumulate the absolute value of all angles as the curvature.

[0040] The specific steps to obtain the path environment score are as follows: based on the weighted calculation of the environmental parameters of each inspection location, first, the environmental data of each location (such as wind speed, obstacle density, GPS strength) is normalized, and then a weighted analysis is performed after normalization. Secondly, according to the importance of the location (such as insulators are more important than the tower top), the scores of all locations under the same tower are weighted and summed to obtain the tower-level score. Finally, all tower scores are weighted again according to the inspection priority to obtain the path environment score.

[0041] The specific steps to obtain the path climb angle are as follows: calculate the slope angle for the vertical height difference and horizontal distance d of each track segment, and record the maximum absolute value of the angle in all segments as the path climb angle. For example, if Δh = 10m and d = 100m in a segment, the path climb angle is ≈ 5.7°.

[0042] The specific steps to obtain path complexity are: count the obstacle density (number of obstacles per unit volume) and electromagnetic interference value in the area traversed by the path, normalize them, and then take the weighted sum; secondly, calculate the proportion of the additional detour distance caused by obstacle avoidance to the total path. The final complexity is the weighted result of the two.

[0043] The specific formula for calculating the path scoring index of a feasible inspection path is as follows: ; in, is the path scoring index of a feasible inspection path, is the total length of a feasible inspection path, is the flight altitude difference of a feasible inspection path, is the flight altitude difference influence coefficient stored in the database, is the path curvature of a feasible inspection path, is the path curvature influence coefficient stored in the database, Score the path environment of a feasible inspection path, is the path environmental impact coefficient stored in the database, is the path climbing angle of a feasible inspection path, is the path climbing angle influence coefficient stored in the database, is the path complexity of a feasible inspection path, is the path complexity impact coefficient stored in the database.

[0044] It should be explained that the flight altitude difference influence coefficient stored in the database , path curvature influence coefficient , path environmental impact coefficient , path climbing angle influence coefficient , path complexity influence coefficient The specific steps for obtaining the score are as follows: collect correlation data between different parameters (such as flight height difference, curvature, etc.) and inspection effects (such as efficiency, safety) through historical inspection data or experimental tests; secondly, use statistical regression analysis or machine learning models (such as linear regression, decision tree) to quantify the influence weight of each parameter on the score and determine each influence coefficient.

[0045] This implementation utilizes a systematic path feature analysis and scoring system to comprehensively and accurately evaluate each feasible inspection route, thereby selecting the optimal path. First, through detailed quantitative analysis of multiple dimensions, including total path length, altitude difference, curvature, environmental score, climb angle, and complexity, the system comprehensively assesses the efficiency, safety, and adaptability of the path. These metrics not only consider the path's physical characteristics (such as length, altitude difference, and curvature), but also fully account for environmental factors (such as wind speed, obstacle density, and GPS signal strength), ensuring that the selected path not only meets the dynamic constraints of the aircraft but can also adapt to environmental changes during the inspection process. Furthermore, path complexity analysis, by assessing factors such as obstacle density and electromagnetic interference, helps identify potential risks in advance, further enhancing the reliability of path selection. Finally, by integrating various path features with influence coefficients derived from historical data or experimental testing, a path scoring index accurately reflects the overall quality of the path. This multi-level evaluation approach not only improves the scientific nature of path selection but also ensures the efficient, safe, and smooth execution of inspection tasks, providing reliable path planning support for complex inspection tasks.

[0046] Specifically, the specific steps for real-time evaluation of the inspection status are as follows: real-time acquisition of the real-time flight status data of the UAV and the real-time interference status data within the set area, the real-time flight status data including the remaining battery power, battery temperature, flight speed, flight altitude, flight attitude index, and flight load weight, and the real-time interference status data including the regional GPS signal strength value, regional electromagnetic interference intensity value, regional light intensity value, and regional sound wave interference intensity value; based on the real-time flight status data of the UAV and the real-time interference status data within the set area, analyze the real-time flight status index of the UAV and the interference status index within the set area; based on the real-time flight status index of the UAV and the interference status index within the set area, analyze the real-time inspection status index of the UAV.

[0047] Among them, the remaining battery power is obtained through real-time monitoring of the drone battery management system (BMS).

[0048] The battery temperature is monitored in real time by a built-in temperature sensor, usually located in the battery management system.

[0049] The flight speed is obtained through the air speed sensor and GPS system on the drone.

[0050] The flight altitude is obtained by the drone's barometer (or ground radar) and GPS system.

[0051] The flight load weight is calculated by the drone’s load sensor or automatically.

[0052] The regional GPS signal strength is monitored in real time by the GPS receiver inside the drone.

[0053] The electromagnetic interference intensity is obtained by an electromagnetic sensor or an RF (radio frequency) interference detector.

[0054] The regional light intensity is obtained in real time through the onboard light sensor.

[0055] The regional acoustic interference intensity is obtained through an acoustic wave sensor.

[0056] Among them, the flight attitude index is obtained by weighted summation of the UAV's flight angular velocity and acceleration.

[0057] The specific steps for analyzing the real-time flight status index of the drone are as follows: obtain the flight status parameter data of the drone, including the maximum parameter value of the battery temperature, the maximum parameter value of the flight speed, the maximum parameter value of the flight altitude, the maximum parameter index of the flight attitude, and the maximum parameter value of the flight load weight.

[0058] The maximum battery temperature parameter is obtained from the battery technical specifications provided by the drone manufacturer. It refers to the highest temperature the battery can withstand under normal operating conditions.

[0059] The maximum flight speed parameter is obtained based on UAV design specifications and historical flight test data, and represents the maximum safe flight speed of the aircraft under optimal conditions.

[0060] The maximum flight altitude parameter is obtained through the technical specifications provided by the drone manufacturer and reflects the maximum flight altitude of the drone under safe and normal operating conditions.

[0061] The maximum parameter index of flight attitude is obtained by weighted summing the maximum parameter value of flight angular velocity and the maximum parameter value of acceleration of the UAV.

[0062] The maximum flight payload weight parameter refers to the maximum load that the drone can carry. This value is usually provided by the drone manufacturer in the product description and is based on the drone's design capabilities (such as motor power, structural strength, etc.) and the maximum load-bearing capacity of the power system.

[0063] The real-time flight status data of the UAV is combined with the flight status parameter data for comprehensive analysis to obtain the real-time flight status index of the UAV. The calculation formula is as follows: ; in, is the real-time flight status index of the UAV, The remaining battery power of the drone. is the remaining power impact coefficient stored in the database, is the battery temperature of the drone, is the maximum parameter value of the drone’s battery temperature, is the temperature influence coefficient stored in the database, is the flight speed of the drone, is the maximum flight speed parameter of the UAV, is the speed influence coefficient stored in the database, is the flight altitude of the UAV, is the maximum parameter value of the UAV’s flight altitude, is the height influence coefficient stored in the database, is the flight attitude index of the UAV, is the maximum parameter index of the UAV’s flight attitude, is the flight attitude influence coefficient stored in the database, is the flight load weight of the UAV, is the maximum parameter value of the UAV’s flight load weight, It is the load weight influence coefficient stored in the database.

[0064] It should be explained that the remaining power stored in the database affects the coefficient , Temperature influence coefficient , speed influence coefficient , height influence coefficient , Flight attitude influence coefficient , load influence coefficient The specific acquisition steps are as follows: First, the database will record the flight parameters of the UAV in real time, such as the remaining battery power, battery temperature, flight speed, etc. Each parameter has corresponding historical data and maximum value reference in the database. The impact of historical data and current status on the mission is evaluated during calculation. Then, each influence coefficient is obtained by weighted processing and correlation analysis of these data. For example, the remaining battery power is calculated by the ratio of battery capacity to current power, and the flight speed and attitude are standardized based on the maximum value of the aircraft. Finally, these influence coefficients will be used to calculate the flight status index.

[0065] The specific steps of analyzing the interference status index in the set area of ​​the drone are as follows: the real-time interference status data includes the regional GPS signal strength value, the regional electromagnetic interference intensity value, the regional light intensity value, and the regional sound wave interference intensity value.

[0066] Obtain interference status parameter data within the set area of ​​the drone, including the maximum parameter value of the regional GPS signal strength, the maximum parameter value of the regional electromagnetic interference intensity, the maximum parameter value of the regional illumination intensity, and the maximum parameter value of the regional acoustic interference intensity; The maximum parameter value of regional GPS signal strength can be obtained through historical data and regional environmental testing. It usually takes into account the influence of terrain, buildings and weather conditions to obtain the maximum acceptable GPS signal strength in the area.

[0067] The maximum parameter value of the regional electromagnetic interference intensity is determined through multiple environmental tests and electromagnetic compatibility (EMC) tests, reflecting the upper limit of the impact of electromagnetic interference on aircraft under specific environmental conditions.

[0068] The maximum parameter value of regional light intensity is based on the test data of ambient light intensity, especially monitoring and evaluation under conditions of strong sunlight intensity or nighttime conditions to determine the maximum light intensity of the area.

[0069] The maximum parameter value for regional acoustic interference intensity is based on acoustic test data. Considering that high-noise areas may affect flight stability, the maximum value reflects the maximum tolerance of the drone in such an environment.

[0070] The interference state index within the set area of ​​the UAV is combined with the interference state parameter data for comprehensive analysis to obtain the interference state index within the set area of ​​the UAV. The calculation formula is as follows: ; in, The interference status index within the set area of ​​the drone, The GPS signal strength value of the area within the set area of ​​the drone. The maximum GPS signal strength value in the set area of ​​the drone. is the signal strength influence coefficient stored in the database, The electromagnetic interference intensity value of the drone in the set area. The maximum parameter value of the regional electromagnetic interference intensity within the set area of ​​the drone, is the electromagnetic interference influence coefficient stored in the database, The regional light intensity value within the set area of ​​the drone, The maximum parameter value of the regional light intensity within the set area of ​​the drone, is the light intensity influence coefficient stored in the database, The regional sound wave interference intensity value within the set area of ​​the drone, The maximum parameter value of the regional sound wave interference intensity within the set area of ​​the drone, is the acoustic interference influence coefficient stored in the database.

[0071] It should be explained that the signal strength influence coefficient stored in the database , electromagnetic interference coefficient , Light intensity influence coefficient , acoustic interference influence coefficient The specific acquisition steps are as follows: The impact coefficient of each interference source is calculated by comparing and analyzing real-time environmental data (such as GPS signals, electromagnetic interference, light intensity, and acoustic interference) with historical data in a database. First, the real-time data is recorded and normalized against historical maximum and minimum values ​​to ensure that the values ​​of each interference factor are within a standard range. Then, based on this data, the system calculates the specific impact coefficient of each interference source on the inspection task.

[0072] The specific formula for calculating the real-time inspection status index of the drone is as follows: ; in, is the real-time inspection status index of the drone, is the real-time flight status index of the UAV, is the flight status influence coefficient stored in the database, The interference status index within the set area of ​​the drone, is the interference state influence coefficient stored in the database, is the interaction coefficient stored in the database, is a natural constant, and in this embodiment, its value is 2.71.

[0073] What needs to be explained is that the specific expression of the tanh function is: ,in, is a natural constant and can be taken as 2.71 in this embodiment, with a domain of (−∞, +∞) and a range of (−1, +1).

[0074] Flight status influence coefficient stored in the database , interference state influence coefficient , interaction coefficient The specific acquisition steps are as follows: the flight status influence coefficient is calculated through the flight status data stored in the database. These data include the battery power, flight speed, and flight altitude of the drone. Usually, they are analyzed based on historical flight data to determine the degree of influence of the current flight status on the execution of the mission. The interference status influence coefficient is obtained by real-time monitoring of interference data of the external environment, such as GPS signal strength, electromagnetic interference intensity, and light intensity. These data are stored in the database and used to evaluate the impact of interference on flight missions. The interaction influence coefficient is a coefficient that comprehensively considers the interaction between the flight status and the interference status. It evaluates the combined impact of the two on the inspection mission by analyzing the joint changes of the flight status and the interference status. It usually requires experimental data from historical missions and real-time data analysis to calculate.

[0075] The following is an example of calculating the real-time inspection status index of a drone, with the following parameters: The real-time flight status index of the drone is approximately: 0.875.

[0076] The flight status influence coefficient stored in the database is approximately: 1.75.

[0077] The interference state index within the set area of ​​the drone is approximately: 0.732.

[0078] The interference state influence coefficient stored in the database is approximately: 1.23.

[0079] The interaction coefficient stored in the database is approximately: 0.92.

[0080] The value of the natural constant is 2.71.

[0081] Substituting the above data into the specific formula for calculating the real-time inspection status index of the drone, we get: The real-time inspection status index of the drone = tanh ((((0.875)^1.75)+((0.732)^1.23)+((0.875×0.732)^0.92)) / (2.71-1))≈0.848.

[0082] In this implementation plan, by real-time evaluation of the UAV's flight status and environmental interference, it can provide dynamic monitoring and timely adjustment capabilities for the smooth execution of inspection tasks. First, the system obtains flight status data (such as remaining battery power, flight speed, flight altitude, etc.) and environmental interference data (such as GPS signal strength, electromagnetic interference, etc.) in real time, and compares and analyzes these data with historical data. It can accurately evaluate the impact of the current flight status and external environment on task execution. This real-time feedback mechanism can effectively predict and respond to problems that may arise during the flight, such as low battery power or signal interference, and take measures in advance to ensure the safety and smooth completion of the task. Secondly, through By introducing a comprehensive analysis of the flight status index and the interference status index, the system can perform weighted processing on multiple influencing factors to ensure that the evaluation results are more accurate and comprehensive. This not only improves the reliability of the inspection task, but also enhances the flexibility and adaptability during the task execution process. Finally, by using the interaction influence coefficient, the system can identify the interaction between the flight status and environmental interference, thereby performing a more intelligent inspection status assessment. Through this mechanism, the system can adjust the inspection path in real time to avoid potential risks and ensure that the task is completed efficiently and safely. Overall, this real-time evaluation method provides powerful dynamic control for the inspection task, improving the intelligence level and execution effect of the task.

[0083] Specifically, when the inspection status is abnormal, the specific steps for replanning the remaining inspection path are as follows: the real-time inspection status index of the drone is judged and analyzed with the preset inspection status evaluation interval; when the real-time inspection status index is outside the preset inspection status evaluation interval, it is considered that the inspection status is abnormal, and the remaining inspection path is replanned, which is specifically as follows: Confirm the exception type: Aircraft health issues: such as low battery, abnormal flight attitude, etc.

[0084] Environmental interference issues: such as GPS signal loss, excessive electromagnetic interference intensity, increased obstacle density, etc.

[0085] According to the type of anomaly, the system can classify the source of the anomaly so as to adopt different response strategies.

[0086] Re-evaluate inspection routes: Re-evaluate the feasibility of the remaining paths based on the current aircraft status (such as battery power, flight speed, etc.) and environmental interference data.

[0087] If the aircraft's state or environmental interference changes, the original inspection path may no longer be suitable. For example, the path is too complex, the aircraft cannot withstand it, or the interference is too strong to make stable flight difficult.

[0088] Path optimization and planning: After reassessment, the system uses a path planning algorithm (such as A* or Dijkstra) to regenerate a feasible inspection path based on the new aircraft status and interference data. The optimal path is selected, taking into account the aircraft's health status and current environmental conditions.

[0089] When rerouting, consider the following factors: Aircraft battery level: Ensure that the planned path does not cause the battery level to run low, affecting mission completion.

[0090] Aircraft flight stability: Avoid areas of high interference that may affect the aircraft's attitude and stability.

[0091] Environmental factors: Avoid areas with high obstacle density, weak GPS signals, and strong electromagnetic interference.

[0092] Path length and time: Choose a shorter or more suitable path to save flight time without affecting mission completion.

[0093] Transmit the new path to the drone: Once the new optimal path is calculated, the new inspection path is transmitted to the drone and the flight strategy is adjusted in real time.

[0094] After receiving the new path, the drone begins to continue its inspection mission along the replanned path.

[0095] Real-time monitoring and continuous evaluation: When the drone executes the new path, it continues to monitor the inspection status index in real time to ensure that no new anomalies occur.

[0096] If an anomaly occurs again, the system will continue to adjust the path based on the new conditions and replan if necessary.

[0097] In this implementation, by real-time monitoring of the drone's inspection status index and comparing it with a preset status assessment interval, the system can instantly detect and identify issues with the aircraft's health status or environmental interference. When an abnormal situation occurs, the system can promptly identify the source of the abnormality (such as low battery or loss of GPS signal) and take different countermeasures based on the type of abnormality. This mechanism ensures that risks in mission execution are effectively controlled and reduces the need for manual intervention. Secondly, the system will re-evaluate the feasibility of the remaining inspection paths and dynamically adjust the path planning based on the current aircraft status and environmental interference data to avoid the original path being difficult to execute due to environmental changes or aircraft limitations. This flexible path optimization and planning allows inspection tasks to choose shorter and safer paths without affecting mission completion, thereby improving inspection efficiency. In addition, through real-time monitoring and continuous evaluation, the system can quickly respond to and adjust to unexpected situations to ensure the smooth completion of the mission. Overall, this dynamic re-planning solution greatly improves the safety, efficiency, and intelligence of the inspection process, ensuring that drones can efficiently and reliably perform inspection tasks in complex and changing environments.

[0098] See also Figure 3 , an embodiment of the present invention provides a technical solution: a route planning system, comprising: A data acquisition unit is used to obtain a list of tasks to be inspected, including the three-dimensional coordinates of several towers, the three-dimensional coordinates of several inspection locations corresponding to each tower, and the spatial coordinate information of the transmission line path segments between connected towers; a path generation unit is used to obtain the regional inspection environment data within the set area of ​​each tower inspection location of each tower, and in combination with the list of tasks to be inspected, establish a semantic knowledge graph of the inspection area and a corresponding spatial topological structure graph, and analyze and generate several feasible inspection paths, wherein the regional inspection environment data includes regional wind speed value, regional temperature value, regional air pressure value, and regional obstacle density value; a screening and analysis unit is used to evaluate, screen and analyze several feasible inspection paths, determine the optimal inspection path, and transmit it to the drone end; an inspection monitoring unit is used to evaluate the inspection status in real time when the drone performs the inspection task according to the optimal inspection path, and re-plan the remaining inspection paths when there is an abnormality in the inspection status.

[0099] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0100] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications. < / p3>

Claims

1. A route planning method, characterized in that: The following steps are involved: Obtain a list of tasks to be inspected, including the 3D coordinates of several towers, the 3D coordinates of several inspection locations corresponding to each tower, and the spatial coordinate information of the transmission line path segments between connected towers; Obtain regional inspection environment data within the set area of ​​each tower inspection location for each tower, and combine it with the list of tasks to be inspected to establish a semantic knowledge graph of the inspection area and the corresponding spatial topology structure diagram, and analyze and generate several feasible inspection paths. The regional inspection environment data includes regional wind speed value, regional temperature value, regional air pressure value, and regional obstacle density value; Evaluate, screen and analyze several feasible inspection paths, determine the optimal inspection path, and transmit it to the drone end; When the UAV performs the inspection task according to the optimal inspection path, the inspection status is evaluated in real time, and when there is an abnormality in the inspection status, the remaining inspection path is replanned.

2. The route planning method according to claim 1, characterized in that: The specific steps to build a semantic knowledge graph for the inspection area are as follows: Read the 3D coordinates of each tower in the inspection task list and the 3D coordinates of each inspection location corresponding to each tower, and establish a mapping relationship between the towers and the inspection locations; Each inspection location of each tower is assigned a type identifier, which serves as a semantic node in the semantic knowledge graph of the inspection area. Read the spatial coordinate information of the transmission line path segments between connected towers, and establish connection semantic relationships between them as independent entity nodes; Read the regional inspection environment data within the set area of ​​each inspection location corresponding to each tower, and use it as additional semantic information for each inspection location node in the semantic knowledge graph of the inspection area; The semantic relationships between all tower nodes, inspection location nodes, transmission line path segment nodes and additional environmental semantic information nodes are structured and modeled to generate a semantic knowledge graph of the inspection area.

3. The route planning method according to claim 2, characterized in that: The specific steps to establish the spatial topology diagram of the inspection area are as follows: Based on the three-dimensional coordinates of each tower and each inspection location in the inspection task list, all spatial target points are extracted and used as a node set in the spatial topology diagram; Read the spatial coordinate information of the transmission line path segment between the connected towers, and according to the connection relationship between the towers, decompose the path segment into feasible track segments consisting of several spatial control points, and use them as path segment elements in the spatial topology map; Determine the spatial connectivity between each node and select node pairs that meet the feasibility of the trajectory; Establish spatial edges between feasible node pairs and label each edge with spatial constraint attributes; All nodes, edges and path segment structures are organized into a graph structure to obtain the spatial topological structure diagram of the inspection area.

4. The route planning method according to claim 3, characterized in that: The specific steps for analyzing and generating several feasible inspection paths are as follows: Extract the transmission line path segments between connected towers from the spatial topology of the inspection area and generate feasible candidate inspection paths for each segment; Supplement the inspection location information in the candidate inspection path based on the semantic graph of the inspection area; Starting from the starting tower node, several feasible inspection paths are generated based on feasible candidate inspection paths.

5. The route planning method according to claim 1, characterized in that: The specific steps for evaluating, screening and analyzing several feasible inspection paths and determining the optimal inspection path are as follows: Perform feature analysis on each feasible inspection path to obtain a path feature set for each feasible inspection path, including the total path length, path flight altitude difference, path curvature, path environment score, path climb angle, and path complexity; Based on the path feature set of each feasible inspection path, the path scoring index of each feasible inspection path is analyzed; The path scoring index of each feasible inspection path is compared and analyzed, and the feasible inspection path corresponding to the minimum path scoring index is selected as the optimal inspection path.

6. The route planning method according to claim 5, characterized in that: The specific formula for calculating the path scoring index of a feasible inspection path is as follows: ； in, is the path scoring index of a feasible inspection path, is the total length of a feasible inspection path, is the flight altitude difference of a feasible inspection path, is the flight altitude difference influence coefficient stored in the database, is the path curvature of a feasible inspection path, is the path curvature influence coefficient stored in the database, Score the path environment of a feasible inspection path, is the path environmental impact coefficient stored in the database, is the path climbing angle of a feasible inspection path, is the path climbing angle influence coefficient stored in the database, is the path complexity of a feasible inspection path, is the path complexity impact coefficient stored in the database.

7. The route planning method according to claim 1, characterized in that: When the drone is performing an inspection task according to the optimal inspection route, the specific steps for real-time evaluation of the inspection status are as follows: Real-time acquisition of the drone's real-time flight status data and real-time interference status data within a set area. The real-time flight status data includes remaining battery power, battery temperature, flight speed, flight altitude, flight attitude index, and flight load weight. The real-time interference status data includes regional GPS signal strength value, regional electromagnetic interference intensity value, regional light intensity value, and regional acoustic interference intensity value. Based on the real-time flight status data of the UAV and the real-time interference status data in the set area, analyze the real-time flight status index of the UAV and the interference status index in the set area; Based on the real-time flight status index of the UAV and the interference status index in the set area, the real-time inspection status index of the UAV is analyzed.

8. The route planning method according to claim 7, characterized in that: The specific formula for calculating the real-time inspection status index of the drone is as follows: ； in, is the real-time inspection status index of the drone, is the real-time flight status index of the UAV, is the flight status influence coefficient stored in the database, The interference status index within the set area of ​​the drone, is the interference state influence coefficient stored in the database, is the interaction coefficient stored in the database, is a natural constant.

9. The route planning method according to claim 7, characterized in that: When an abnormality occurs in the inspection status, the specific steps for replanning the remaining inspection paths are as follows: Analyze the real-time inspection status index of the drone and the preset inspection status evaluation interval; When the real-time inspection status index is outside the preset inspection status evaluation range, it is considered that the inspection status is abnormal, and the remaining inspection paths are replanned.

10. A route planning system, applying the route planning method according to any one of claims 1 to 9, characterized in that: include: A data acquisition unit is used to obtain a list of tasks to be inspected, including the three-dimensional coordinates of several power towers, the three-dimensional coordinates of several inspection locations corresponding to each power tower, and the spatial coordinate information of the transmission line path segments between connected power towers; A path generation unit is used to obtain regional inspection environment data within a set area of ​​each inspection location of each tower, and establish a semantic knowledge graph of the inspection area and a corresponding spatial topological structure graph in combination with a list of tasks to be inspected, and analyze and generate several feasible inspection paths. The regional inspection environment data includes regional wind speed values, regional temperature values, regional air pressure values, and regional obstacle density values; The screening and analysis unit is used to evaluate, screen and analyze several feasible inspection paths, determine the optimal inspection path, and transmit it to the drone end; The inspection monitoring unit is used to evaluate the inspection status in real time while the UAV is performing the inspection task according to the optimal inspection path, and to re-plan the remaining inspection path when there is an abnormality in the inspection status.

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