Dynamic planning method and system for photovoltaic multi-unmanned aerial vehicle cooperative inspection route

Abstract

The invention discloses a dynamic planning method and system for a photovoltaic multi-unmanned aerial vehicle cooperative inspection route, and belongs to the technical field of photovoltaic power station inspection, and the method comprises the steps: carrying out the three-dimensional modeling, dividing a no-fly area and a flight area, and building a multi-unmanned aerial vehicle ad hoc network communication network; analyzing parameters of the intruding aircraft based on a prediction algorithm, and predicting a future path of the intruding aircraft; the ground control center receives and shares flight state information of each unmanned aerial vehicle in real time, allocates inspection layers of different heights according to parameters of the unmanned aerial vehicles, introduces an avoidance mechanism to adjust flight parameters, generates a global optimal route based on a dynamic planning algorithm of heuristic search, and performs routing inspection on the unmanned aerial vehicles. And the safe distance model converts unmanned aerial vehicle flight limitation, environmental factors and anti-collision constraints into a cost function, and dynamically adjusts an inspection route. According to the invention, the limitation of the unmanned aerial vehicle in a complex environment can be effectively improved, the adaptability of the photovoltaic power station unmanned aerial vehicle to environment change in patrol inspection is improved, and the safety of the whole patrol inspection process is ensured while the patrol inspection efficiency is ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of photovoltaic power station inspection, and particularly relates to a dynamic planning method and system for a collaborative inspection route of multiple photovoltaic drones. Background Art

[0002] With the rapid development of the photovoltaic power generation industry, the scale and quantity of photovoltaic power stations are increasing continuously. Efficient and accurate inspection of photovoltaic power stations is the key to ensuring their stable operation and improving power generation efficiency. The previous manual inspection method has problems such as low efficiency, high cost, and limited detection range, and it is difficult to meet the inspection requirements of large-scale photovoltaic power stations.

[0003] As an improvement, using drones to inspect photovoltaic power stations is an emerging method. Due to its high flexibility, fast speed, and strong accessibility, the inspection drone has gradually become an important means for photovoltaic power station inspection. With the increasing demand for efficiency, the on-site application has gradually developed from the independent operation of a single drone to the collaborative operation of multiple drones, and then the inspection work of collaborative operation is carried out.

[0004] However, at present, most of the existing drone inspection route planning methods are static planning, and do not fully consider the environmental changes during the inspection process. When multiple drones are simultaneously carrying out inspection operations on-site or there are external aircraft intruding and interfering, during the inspection process, each drone may collide with the inspection route because it does not know the position information of other drones, and it is difficult to ensure the relevant safety. At the same time, each drone also lacks a coordinated cooperation mechanism for jointly carrying out a unified inspection task. Therefore, to sum up, in the current drone inspection of photovoltaic power stations, it cannot effectively cope with the environmental changes during the inspection process; and when multiple drones carry out inspections simultaneously or there are external aircraft intruding and interfering, it may be interfered and collided, and the safety of the operation needs to be further improved. Summary of the Invention

[0005] The present invention provides a dynamic planning method and system for a collaborative inspection route of multiple photovoltaic drones, aiming to solve the problems that in the current drone inspection of photovoltaic power stations, it cannot effectively cope with the environmental changes during the inspection process; and when multiple drones carry out inspections simultaneously or there are external aircraft intruding and interfering, it may be interfered and collided, and the safety of the operation needs to be further improved.

[0006] To achieve the above object, the present invention adopts the following technical solutions: The present invention provides a dynamic planning method for a collaborative inspection route of multiple photovoltaic drones, including the following steps: S1. Perform three-dimensional modeling on the photovoltaic inspection area through lidar, and divide the no-fly zone and the flyable area; establish an ad-hoc communication network between multiple drones to realize real-time data sharing between the drones and the ground control center; S2. Monitor external intruding aircraft in the inspection area in real time through the detection module, and predict the travel route of the external intruding aircraft; S3. Receive and share the flight status information of each UAV in real time through the ground control center; S4. Allocate inspection layers at different heights according to UAV parameters, and further divide the operation height within the same inspection layer; Detect the intersection of flight paths between UAVs in real time. When the detected distance between UAVs approaches the preset safety threshold, trigger the avoidance mechanism and adjust the flight parameters; S5. Generate a dynamic inspection route for each UAV based on the dynamic programming algorithm of heuristic search, combined with the safety distance model and the real-time task allocation result; S6. Dynamically adjust the inspection route according to the real-time updated environmental data and UAV status.

[0007] In some embodiments, in S1, the 3D modeling specifically includes: identifying poles, cables, and obstacles in the photovoltaic area through lidar to generate a spatial model including the boundary of the no-fly zone.

[0008] In some embodiments, in S1, the ad hoc communication network includes a Mesh network, and the Mesh network is used for two-way real-time communication between the UAV and the ground control center.

[0009] In some embodiments, in S2, the flight status information of the UAV includes: position, speed, and flight direction.

[0010] In some embodiments, in S2, predicting the travel route of the external intruding aircraft includes: calculating the future flight path and occupied space of the external intruding aircraft according to its speed, direction, and historical trajectory.

[0011] In some embodiments, in S4, the UAV parameters include UAV model, maximum flight height, and sensor type.

[0012] In some embodiments, in S4, the avoidance mechanism includes: adjusting the flight parameters of the UAV according to the preset priority, and the UAV with a higher priority maintains its original route; and / or adjusting the flight path of the UAV according to the preset bypass direction, including bypassing in the clockwise or counterclockwise direction.

[0013] In some embodiments, in S5, the dynamic programming algorithm of heuristic search includes Algorithm, The algorithm optimizes the path search through a dynamic weight factor to generate a globally optimal route under the condition of meeting the safety distance constraint.

[0014] Further, in S6, the construction of the safety distance model includes: converting the flight restrictions of the UAVs, environmental factors, and anti-collision constraint conditions between UAVs into a cost function, and dynamically optimizing the route interval through the distance between UAVs; The dynamic adjustment includes: when the UAV receives updated environmental data or task assignment results, regenerating the inspection route of the UAV.

[0015] The present invention also provides a dynamic planning system for a photovoltaic multi-UAV collaborative inspection route. The system includes a modeling and communication module, an aircraft monitoring module, a flight status module, an inspection layer allocation and avoidance module, a dynamic route planning module, and a route dynamic adjustment module; wherein: The modeling and communication module: is used to perform three-dimensional modeling on the photovoltaic inspection area through lidar, divide the no-fly zone and the flyable area; establish an ad hoc communication network between multiple UAVs to realize real-time data sharing between the UAVs and the ground control center; The aircraft monitoring module: is used to monitor external intruding aircraft in the inspection area in real time through a detection module and predict the travel route of the external intruding aircraft; The flight status module: is used to receive and share the flight status information of each UAV in real time through the ground control center; The inspection layer allocation and avoidance module: is used to allocate inspection layers at different heights according to UAV parameters, and further divide the operation layer height within the same inspection layer; detect the intersection of flight paths between UAVs in real time, and when it is detected that the distance between UAVs is close to the preset safety threshold, trigger an avoidance mechanism and adjust the flight parameters; The dynamic route planning module: is used to generate a dynamic inspection route for each UAV based on a dynamic programming algorithm based on heuristic search, in combination with the safety distance model and the real-time task assignment result; The route dynamic adjustment module: is used to dynamically adjust the inspection route according to the real-time updated environmental data and UAV status.

[0016] Compared with the prior art, the dynamic planning method and system for a photovoltaic multi-UAV collaborative inspection route of the present invention have the following beneficial effects: A dynamic programming method for the collaborative inspection route of multiple photovoltaic drones. The method conducts three-dimensional modeling of the photovoltaic inspection area through lidar, divides no-fly zones and flyable areas, establishes a multi-drone ad-hoc communication network, and realizes real-time data sharing between the drones and the ground control center. Based on a prediction algorithm, it analyzes the speed, direction, and historical trajectory of intruding aircraft to predict their future paths. The ground control center of the present invention receives and shares the flight status information of each drone in real time, including position, speed, and flight direction, assigns inspection layers at different heights according to the drone parameters, further subdivides the operation height within the same layer, and real-time detects the crossing of flight paths between drones. When the distance approaches a preset safety threshold, it triggers an avoidance mechanism and adjusts the flight parameters. Based on a dynamic programming algorithm with heuristic search, combined with a safety distance model and the results of real-time task allocation, it generates a globally optimal route. The safety distance model converts the flight restrictions of drones, environmental factors, and anti-collision constraints into a cost function, and dynamically adjusts the inspection route according to the real-time updated environmental data and drone status. Through environmental modeling, real-time communication, dynamic prediction and avoidance, intelligent planning, and adaptive adjustment, the present invention realizes the efficiency and safety of multi-drone collaborative inspection. Through the collaborative optimization of multiple technologies and dynamic response capabilities, it can effectively improve the limitations of existing static planning methods in complex environments and enhance the adaptability of the drone inspection process in photovoltaic power plants to environmental changes; when multiple drones conduct inspections simultaneously or there are external aircraft intrusions and interferences, it reduces and is expected to avoid drones being interfered with and collided, improving the safety of operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The accompanying drawings in the specification are used to provide a further understanding of the present invention, and constitute a part of the present invention. The schematic embodiments of the present invention and their descriptions are used to explain the present invention, and do not constitute an improper limitation of the present invention.

[0018] Figure 1 It is a schematic flow chart of a dynamic programming method for the collaborative inspection route of multiple photovoltaic drones of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] In order to make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Usually, the components of the embodiments of the present invention described and illustrated herein can be arranged and designed in various different configurations.

[0020] Accordingly, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0021] It should be noted that like reference numerals and letters denote like items in the following drawings. Therefore, once an item is defined in one drawing, it does not require further definition and explanation in subsequent drawings.

[0022] In the description of the embodiments of the present invention, it should be noted that if terms such as "upper", "lower", "horizontal", "inner", etc. indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the inventive product is customarily placed during use, it is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation of the present invention. In addition, terms such as "first", "second", etc. are only used for distinguishing descriptions and cannot be construed as indicating or implying relative importance.

[0023] In addition, if the term "horizontal" appears, it does not mean that the component is required to be absolutely horizontal, but it can be slightly inclined. For example, "horizontal" only means that its direction is more horizontal relative to "vertical", and does not mean that the structure must be completely horizontal, but it can be slightly inclined.

[0024] In the description of the embodiments of the present invention, it should also be noted that unless otherwise clearly defined and limited, if terms such as "set", "installed", "connected", "coupled" are to be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0025] How to establish information interconnection and mutual communication among multiple drones internally through an unmanned aerial vehicle (UAV) ad-hoc network; through a dynamic programming algorithm based on heuristic search and a highly hierarchical inspection operation method to carry out the overall planning of the inspection routes of each UAV, thereby ensuring the inspection efficiency while also guaranteeing the safety during the entire inspection process.

[0026] Based on this, a dynamic programming method for a photovoltaic multi-UAV collaborative inspection route of the present invention includes the following steps: S1. Conduct 3D modeling on the photovoltaic inspection area through lidar, and divide no-fly zones and flyable areas; establish an ad-hoc communication network among multiple drones to achieve real-time data sharing between the drones and the ground control center; S2. Use a detection module to monitor external intruding aircraft in the inspection area in real time and predict the travel route of the external intruding aircraft; S3. Receive and share the flight status information of each drone in real time through the ground control center; S4. Allocate inspection layers at different heights according to the drone parameters, and further divide the operation height within the same inspection layer; detect flight path intersections between drones in real time. When the detected distance between drones approaches the preset safety threshold, trigger an avoidance mechanism and adjust the flight parameters; S5. Generate a dynamic inspection route for each drone based on a dynamic programming algorithm of heuristic search, combined with a safety distance model and the real-time task allocation result; S6. Dynamically adjust the inspection route according to the real-time updated environmental data and drone status.

[0027] The dynamic programming method for the collaborative inspection route of multiple photovoltaic drones of the present invention provides high-precision three-dimensional space data, ensures the accuracy of no-fly zone division, and avoids collisions between drones and obstacles (such as poles and cables). The Mesh ad-hoc communication supports multi-hop routing and two-way real-time communication, ensuring data synchronization and providing a stable communication foundation for the collaborative operation of multiple drones. By fusing real-time data with historical trajectories through the Kalman filter algorithm, the prediction accuracy is high, providing a reliable basis for avoidance decisions. The present invention allocates the inspection layer height according to the drone performance, avoiding resource conflicts. When triggering avoidance, the drone with a higher priority maintains the original route, and other drones fly around in a preset direction, reducing the path adjustment time.

[0028] Moreover, the present invention balances the path length and the safety distance through a dynamic weight factor to generate a globally optimal route. The safety distance model: The cost function comprehensively considers the collision risk, route deviation, and environmental impact to ensure the safety of the route. The drone performance, environmental data, and task requirements are uniformly incorporated into the planning model to achieve flexible adaptation in complex scenarios.

[0029] In some embodiments, the 3D modeling of the present invention identifies poles, cables, and obstacles through lidar to generate a spatial model including the boundaries of no-fly zones. Lidar provides high-precision point cloud data, ensuring the accuracy of no-fly zone and flyable area division, and being able to identify obstacles in complex environments, providing a safe flight space for drones. The present invention adopts a Mesh network, which supports two-way real-time communication between drones and the ground control center. The Mesh network supports multi-hop communication, ensuring communication stability in complex environments.

[0030] In some embodiments, the flight state information of the unmanned aerial vehicle (UAV) of the present invention includes position, speed, and flight direction. Predicting the travel route of an external intruding aircraft includes calculating the future flight path and occupied space based on its speed, direction, and historical trajectory. UAV parameters include UAV model, maximum flight altitude, and sensor type. By sharing the flight state information in real time, the ground control center can comprehensively grasp the dynamics of the UAV, accurately predict the future path of the intruding aircraft based on the historical trajectory and real-time data, provide a time window for the avoidance mechanism, and reduce the collision risk.

[0031] Furthermore, the present invention adjusts the flight path according to the task priority and preset rules to ensure that high-priority tasks are not affected. By adjusting the bypass direction, path conflicts are reduced, and the overall inspection efficiency is improved. The dynamic programming algorithm for heuristic search is the A* algorithm, which can optimize the path search through dynamic weight factors and generate a globally optimal flight route. The construction of the safety distance model includes converting the UAV flight restrictions, environmental factors, and anti-collision constraints into a cost function, and dynamically optimizing the flight route interval through the UAV spacing; the dynamic adjustment includes regenerating the flight route according to the updated data, updating the flight route through real-time data, and ensuring the safety of the inspection.

[0032] The present invention also provides a dynamic programming system for the collaborative inspection flight routes of photovoltaic multi-UAVs. The system includes a modeling and communication module, an aircraft monitoring module, a flight state module, an inspection layer allocation and avoidance module, a dynamic flight route planning module, and a flight route dynamic adjustment module; wherein: Modeling and communication module: used to perform three-dimensional modeling on the photovoltaic inspection area through lidar, divide the no-fly zone and flyable area; establish an ad hoc communication network between multiple UAVs to achieve real-time data sharing between the UAVs and the ground control center; Aircraft monitoring module: used to monitor external intruding aircraft in the inspection area in real time through a detection module and predict the travel route of the external intruding aircraft; Flight state module: used to receive and share the flight state information of each UAV in real time through the ground control center; Inspection layer allocation and avoidance module: used to allocate inspection layers at different heights according to UAV parameters, and further divide the operation layer height within the same inspection layer; detect the intersection of flight paths between UAVs in real time, and when it is detected that the distance between UAVs approaches the preset safety threshold, trigger the avoidance mechanism and adjust the flight parameters; Dynamic flight route planning module: used to generate a dynamic inspection flight route for each UAV based on the dynamic programming algorithm for heuristic search, combined with the safety distance model and the real-time task allocation result; Flight route dynamic adjustment module: used to dynamically adjust the inspection flight route according to the real-time updated environmental data and UAV status.

[0033] The present invention integrates flight monitoring, flight restrictions, inspection layer allocation, environmental factors, anti-collision constraints, and dynamic route planning and adjustment to form a model, improving the practicality and safety of drones in photovoltaic inspection operations.

[0034] The following further details a dynamic planning method and system for a collaborative inspection route of multiple photovoltaic drones according to the present invention through specific embodiments.

[0035] As Figure 1 shown, in this embodiment, the system mainly includes a ground control center, a detection module, a self-organizing network, a route dynamic planning module, etc.

[0036] First, perform three-dimensional modeling on the external environment through lidar, divide no-fly zones for poles, high-rise buildings, and cables, and clarify the regional space where drones can carry out inspection flights.

[0037] Second, establish a stable and reliable communication network among multiple drones. Adopt self-organizing network communication technology (such as Mesh network) to achieve real-time data sharing and information interaction among drones.

[0038] Third, the detection module continuously monitors illegal intruding aircraft in the inspection operation area. If a non-networked aircraft is found, track and draw its moving state, and predict its action route and moving space based on its speed and direction.

[0039] Fourth, the control center shares the movement information of each drone with all parties in real time. Each drone sends its own position, speed, flight direction, etc. to other drones and the ground control center through the communication network in real time, ensuring that each party can master the real-time dynamics of drones in the entire operation area.

[0040] Fifth, during inspection operations, divide the inspection collection heights at different heights from the ground. The ground control center allocates inspection collection height layers at different heights from the ground according to drone parameters. Drones with the same type of parameters get the first priority, and further subdivide the inspection operation height layer at the same hierarchical height. The operation height layer should be more than 10 times the navigation control accuracy under the current actual conditions to avoid overlapping problems.

[0041] Sixth, when it is detected that there is an intersection in the moving space between different drones, avoidance is required. When a drone detects that the distance to other drones is close to the preset safety distance threshold, immediately activate the avoidance mechanism. Send an avoidance request to surrounding drones through the communication network, and at the same time, automatically adjust its own flight height, speed, or direction according to the preset rules to avoid collision. For example, according to the priority rule, the drone performing an emergency inspection task gives priority to maintaining its original route, and other drones make way; or according to the preset avoidance direction, such as clockwise or counterclockwise for detouring and avoidance.

[0042] Seventh, when planning the flight routes, fully consider the safety distance between the UAVs, and incorporate collision avoidance as an important constraint condition into the algorithm. Based on real-time data and the task assignment results, use a dynamic programming algorithm based on heuristic search (such as algorithm) to plan the optimal inspection flight routes for each UAV: Construct a safety distance model: In the algorithm, convert the flight restrictions of the UAVs (such as the maximum flight distance, maximum flight altitude, turning radius, etc.), environmental factors (such as the influence of wind speed on the flight path), and constraints such as avoiding collisions between UAVs into a cost function. Taking the distance between UAVs as a key parameter, construct a safety distance model. When the predicted flight routes of two UAVs may cause them to enter an unsafe distance range at a certain moment, the algorithm automatically adjusts the flight routes of one or both UAVs to increase the safety interval between the flight routes.

[0043] Eighth, the inspection flight routes are dynamically adjusted automatically according to the dynamic changes of the UAVs. During the flight, the UAVs receive the updated data sent by the ground control center in real time, including the state changes of other UAVs and the update of environmental data. When the data changes, restart the flight route planning algorithm and dynamically adjust the flight routes according to the latest information to ensure a safe flight distance throughout the operation process and effectively prevent collisions between UAVs.

[0044] Generally speaking, for a dynamic programming method and system for collaborative inspection flight routes of multiple photovoltaic UAVs of the present invention, establish the detection of the azimuth of external UAVs through a detection radar, and establish information intercommunication and mutual connection between internal multiple UAVs based on the UAV ad hoc network; through a dynamic programming algorithm based on heuristic search and a height-stratified inspection operation method, carry out the overall planning of the inspection flight routes of each UAV, thereby ensuring the inspection efficiency while also ensuring the safety during the entire inspection process. The present invention can reduce the safety risks existing in the process of collaborative operation of multiple UAVs, reduce the risks of flight route conflicts to the collaborative inspection operation of UAVs, and achieve safe and efficient inspection operations of multiple UAVs.

[0045] Finally, it should be noted that: The above description is only a preferred embodiment of the present invention, and does not impose any form of limitation on the present invention; any ordinary technical personnel in the industry can smoothly implement the present invention according to the description in the specification and the above description. Slight modifications, decorations, and equivalent changes made using the technical content disclosed above are all equivalent embodiments of the present invention; at the same time, any equivalent changes, modifications, and evolutions made to the above embodiments based on the essential technology of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A dynamic planning method for cooperative inspection routes of photovoltaic multiple UAVs, characterized in that: The steps include: S1. Use laser radar to conduct three-dimensional modeling of the photovoltaic inspection area, divide the no-fly zone and the flyable area; establish a self-organizing network communication network between multiple drones to achieve real-time data sharing between drones and the ground control center; S2. Monitor the external intruding aircraft in the inspection area in real time through the detection module, and predict the route of the external intruding aircraft; S3, receiving and sharing the flight status information of each UAV in real time through the ground control center; S4. Allocate inspection layers of different heights according to drone parameters, and further divide the operating layer height within the same inspection layer; Real-time detection of the intersection of flight paths between drones. When the distance between drones is detected to be close to the preset safety threshold, the avoidance mechanism is triggered and the flight parameters are adjusted. S5. A dynamic programming algorithm based on heuristic search, combined with the safety distance model and real-time task allocation results, generates a dynamic inspection route for each drone; S6. Dynamically adjust the inspection route based on real-time updated environmental data and drone status.

2. The method for dynamically planning a photovoltaic multi-UAV collaborative inspection route according to claim 1 is characterized in that: In S1, the three-dimensional modeling specifically includes: identifying poles, cables and obstacles in the photovoltaic area by laser radar, and generating a space model including the boundary of the no-fly zone.

3. The method for dynamically planning a photovoltaic multi-UAV collaborative inspection route according to claim 1 is characterized in that: In S1, the ad hoc communication network includes a Mesh network, and the Mesh network is used for two-way real-time communication between the UAV and the ground control center.

4. The method for dynamically planning a photovoltaic multi-UAV collaborative inspection route according to claim 1 is characterized in that: In S2, the flight status information of the UAV includes: position, speed and flight direction.

5. The method for dynamically planning a photovoltaic multi-UAV collaborative inspection route according to claim 1 is characterized in that: In S2, predicting the travel route of the external intruding aircraft includes: calculating the future flight path and occupied space of the external intruding aircraft according to the speed, direction and historical trajectory of the external intruding aircraft.

6. The method for dynamically planning a photovoltaic multi-UAV collaborative inspection route according to claim 1 is characterized in that: In S4, the drone parameters include the drone model, the maximum flight altitude, and the sensor type.

7. The method for dynamically planning a photovoltaic multi-UAV collaborative inspection route according to claim 1 is characterized in that: In S4, the avoidance mechanism includes: adjusting the flight parameters of the drone according to a preset priority, and the drone with a high priority maintains the original route; and / or adjusting the flight path of the drone according to a preset detour direction, including avoiding in a clockwise or counterclockwise direction.

8. The method for dynamically planning a photovoltaic multi-UAV collaborative inspection route according to claim 1 is characterized in that: In S5, the dynamic programming algorithm of the heuristic search includes Algorithm is used to generate the global optimal route while satisfying the safety distance constraint.

9. The method for dynamically planning a photovoltaic multi-UAV collaborative inspection route according to claim 8 is characterized in that: In said S6, the construction of the safety distance model includes: converting the flight restrictions of the UAVs, environmental factors and anti-collision constraints between the UAVs into a cost function, and dynamically optimizing the route interval according to the distance between the UAVs; Dynamic adjustment includes: when the drone receives updated environmental data or task assignment results, the drone's inspection route is regenerated.

10. A system based on the dynamic planning method of the photovoltaic multi-UAV collaborative inspection route according to any one of claims 1 to 9, characterized in that: The system includes a modeling and communication module, an aircraft monitoring module, a flight status module, an inspection layer allocation and avoidance module, a dynamic route planning module, and a route dynamic adjustment module; wherein: Modeling and communication module: used to perform three-dimensional modeling of the photovoltaic inspection area through laser radar, divide the no-fly zone and the flyable area; establish an ad hoc communication network between multiple drones, and realize real-time data sharing between drones and the ground control center; Aircraft monitoring module: used to monitor external intruding aircraft in the inspection area in real time through the detection module, and predict the travel route of external intruding aircraft; Flight status module: used to receive and share the flight status information of each drone in real time through the ground control center; Inspection layer allocation and avoidance module: used to allocate inspection layers of different heights according to drone parameters, and further divide the operating layer height within the same inspection layer; real-time detection of the intersection of flight paths between drones, and triggering the avoidance mechanism and adjusting flight parameters when it is detected that the distance between drones is close to the preset safety threshold; Dynamic route planning module: It is used to generate dynamic inspection routes for each UAV by combining the dynamic planning algorithm based on heuristic search and the safety distance model with the real-time task allocation results; Route dynamic adjustment module: used to dynamically adjust the inspection route according to real-time updated environmental data and drone status.

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