Dynamic optimization system and method for intelligent operation and maintenance path of photovoltaic base station

Abstract

The invention relates to a dynamic optimization system and method for an intelligent operation and maintenance path of a photovoltaic base station. Real-time data are accessed and processed by using a data integration platform; the electronic map building module is used for calling the existing map service, marking on the map and dynamically updating the detailed information of the base station in real time to generate an electronic map; acquiring system work order data and work order task priority data in real time by using a work order acquisition module; selecting a corresponding path planning strategy by using a path planning optimization module, and for a single task, calculating an optimal path by using an A * algorithm; for multiple tasks, a priority weighted summation method is used for sorting, and then a path is planned; for new tasks, dynamically adjusting the task sequence and performing path fine adjustment; and finally, displaying the operation and maintenance path in real time by using a visual navigation module and navigating. Through multi-source data fusion and an intelligent algorithm, operation and maintenance path planning is optimized, task scheduling and path planning are dynamically adjusted according to real-time conditions, and it is ensured that tasks are efficiently completed.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent operation and maintenance of photovoltaic base stations, and in particular to a system and method for dynamically optimizing intelligent operation and maintenance paths of photovoltaic base stations. Background Art

[0002] As the automation and intelligence of communication systems continue to improve, the unattended mode of stacked optical base stations is gradually realized. In this mode, although the internal structure of the base station is relatively simple, due to the large number of base stations and their wide distribution, how to efficiently perform operation and maintenance over a large range is still a challenge. At the same time, operation and maintenance tasks need to be dynamically adjusted according to the real-time environment (such as traffic conditions, weather changes, etc.), which is difficult to achieve in existing technologies. The deficiencies of the aforementioned existing technologies are specifically manifested as follows:

[0003] (1) Low O&M efficiency: Due to the large number of base stations and their wide distribution, it is difficult for O&M personnel to cover multiple base stations in a short period of time and reach faulty equipment in a timely manner.

[0004] (2) Insufficient response to dynamic environments: Existing path planning technologies are mostly static planning and cannot respond to changes in the environment such as traffic and weather in real time.

[0005] (3) Unreasonable resource scheduling: In the traditional operation and maintenance process, resource scheduling lacks flexibility, resulting in resource waste or uneven task burden.

[0006] (4) Inaccurate positioning and navigation: Existing navigation technology cannot dynamically adjust based on real-time information, making it difficult for operation and maintenance personnel to quickly find faulty equipment.

[0007] Therefore, there is an urgent need for an intelligent and automated path planning system to solve the operation and maintenance efficiency issues of stacked optical base stations. Summary of the invention

[0008] The present invention aims to solve the problems of low efficiency, inaccurate path planning, insufficient response to emergencies, etc. in the current operation and maintenance of stacked optical communication base stations, and provides an intelligent operation and maintenance path planning method based on the distribution map of stacked optical base stations. Through multi-source data fusion and intelligent algorithms, the path planning of operation and maintenance personnel is optimized, and task scheduling and path planning are dynamically adjusted according to real-time conditions to ensure efficient completion of tasks.

[0009] The technical solution adopted by the present invention is as follows:

[0010] A photovoltaic base station intelligent operation and maintenance path dynamic optimization system comprises a data integration platform, an electronic map construction module, a work order acquisition module, a path planning optimization module and a visual navigation module; the data integration platform is responsible for accessing and processing real-time data from different data sources, including detailed information of base stations; the electronic map construction module generates an electronic map by calling an existing online map service, marking and dynamically updating detailed information of base stations on a map in real time; the work order acquisition module is used to acquire system work order data and work order task priority data in real time; the path planning optimization module is used to select a corresponding path planning strategy according to the task type, and for a single task, the A* algorithm is used to calculate the optimal path; for multiple tasks, the priority weighted sum method is used to sort and then plan the path; when a new task is added, the system dynamically adjusts the task order and performs path fine-tuning; the visual navigation module is used to display the operation and maintenance path in real time through an electronic map, and integrates a navigation function to help operation and maintenance personnel complete tasks more intuitively and efficiently.

[0011] The electronic map construction module includes an integrated map service interface unit, a base station location marking unit and a map dynamic update unit. The integrated map service interface unit directly uses ready-made map resources and obtains map data in real time by connecting with the API of the existing online map service; the base station location marking unit obtains detailed information of the base station through the data integration platform and automatically marks the information on the map; the map dynamic update unit is used to update the markings on the map in real time.

[0012] The path planning optimization module includes a single-task path planning unit, a multi-task optimization and cluster planning unit, and a dynamic cluster adjustment and real-time path updating unit. The single-task path planning unit uses the A* algorithm in combination with the base station location and map information according to the current location and road conditions of the operation and maintenance personnel, and finds the optimal path from the current location to the target base station through heuristic search to ensure that the operation and maintenance personnel can reach the target base station in the shortest time, reducing the operation and maintenance time and cost; the multi-task optimization and cluster planning unit generates an optimal inspection path for it through a multi-task optimization algorithm, and the path can cover multiple base stations and reduce unnecessary repeated trips; the dynamic cluster adjustment and real-time path updating unit can dynamically adjust the path planning to ensure that the operation and maintenance personnel are always in the optimal route, dynamically adjust the path based on real-time information during the path execution process, and continuously optimize future path planning through a feedback mechanism.

[0013] The multi-task optimization algorithm first sorts the tasks by the priority weighted summation method. The task priorities are converted into weights and combined with the path costs to calculate the weighted total path cost. Then, based on the urgency of the work order, the base station location and the current location of the operation and maintenance personnel, the A* algorithm is used to plan the optimal inspection path covering all tasks according to the sorting results, thereby reducing unnecessary repeated trips.

[0014] The dynamic adjustment path planning is as follows: when a new task is added, the system inserts the new task into the task queue and reorders it according to priority; the system adjusts the task order according to the urgency and location of the task, and uses a local optimization algorithm to fine-tune the path to ensure that the new task can be processed in time; a rolling optimization algorithm is used to continuously evaluate the rationality of the current path, and the path is updated in time in an emergency situation and pushed to the terminal device of the operation and maintenance personnel.

[0015] The visual navigation module includes an electronic map visualization unit, a navigation system integration unit, and a path dynamic update and prompt unit. The electronic map visualization unit visualizes the operation and maintenance path on the electronic map; the navigation system integration unit provides real-time navigation guidance for the operation and maintenance personnel based on the planned path; the path dynamic update and prompt unit dynamically updates the new path on the electronic map and promptly notifies the operation and maintenance personnel to make adjustments to avoid delays caused by environmental changes.

[0016] The present invention also claims a method for dynamically optimizing a photovoltaic base station intelligent operation and maintenance path, comprising the following steps:

[0017] Step 1: construct an electronic map by calling an existing online map service, marking and dynamically updating detailed information of base stations on the map in real time to generate an electronic map;

[0018] Step 2: Obtain system work order data and work order task priority data in real time;

[0019] Step 3: Select the corresponding path planning strategy according to the task type. For a single task, use the A* algorithm to calculate the optimal path; for multiple tasks, use the priority weighted sum method to sort and then plan the path; when a new task is added, the system dynamically adjusts the task order and fine-tunes the path;

[0020] Step 4: Display the operation and maintenance path in real time through an electronic map and integrate navigation functions to help operation and maintenance personnel complete tasks more intuitively and efficiently.

[0021] In step 1, by connecting with the API of the existing online map service, the existing map resources are directly used to obtain map data in real time; the detailed information of the base station is obtained through the data integration platform, and the information is automatically marked on the map; based on the changes in the base station information, the annotations on the map are updated in real time to ensure that the map seen by all operation and maintenance personnel always reflects the latest base station layout and status.

[0022] In step 3, for a single task, based on the current location and road conditions of the operation and maintenance personnel, the A* algorithm is used in combination with the base station location and map information to find the optimal path from the current location to the target base station through heuristic search, ensuring that the operation and maintenance personnel can reach the target base station in the shortest time, reducing the operation and maintenance time and cost; for multiple tasks, an optimal inspection path is generated for them through a multi-task optimization algorithm, which can cover multiple base stations and reduce unnecessary repeated trips; for new tasks, the path planning is dynamically adjusted to ensure that the operation and maintenance personnel are always on the optimal route, and the path is dynamically adjusted based on real-time information during the path execution process, and the future path planning is continuously optimized through a feedback mechanism.

[0023] The multi-task optimization algorithm described in step 3 first sorts the tasks by the priority weighted summation method. The task priorities are converted into weights and combined with the path costs to calculate the weighted total path cost. Then, based on the urgency of the work order, the base station location, and the current location of the operation and maintenance personnel, the A* algorithm is used to plan the optimal inspection path covering all tasks according to the sorting results, thereby reducing unnecessary repeated trips.

[0024] The dynamic adjustment path planning described in step 3 is as follows: when a new task is added, the new task is inserted into the task queue and reordered according to priority; the task order is adjusted according to the urgency and location of the task, and the path is fine-tuned using a local optimization algorithm to ensure that the new task can be processed in a timely manner; a rolling optimization algorithm is used to continuously evaluate the rationality of the current path, and the path is updated in a timely manner in emergency situations and pushed to the terminal device of the operation and maintenance personnel.

[0025] In step 4, the operation and maintenance path is visualized on the electronic map, and real-time navigation guidance is provided to the operation and maintenance personnel; the new path on the electronic map is dynamically updated, and the operation and maintenance personnel are notified in time to make adjustments to avoid delays caused by environmental changes.

[0026] The present invention has the following beneficial effects:

[0027] (1) Electronic map integration and dynamic update: Use services such as Google Maps and Baidu Maps to integrate base station locations, simplify map construction, and dynamically update to ensure accurate information.

[0028] (2) Real-time optimization of single-task paths: Based on the location of operation and maintenance personnel and road conditions, the optimal path is calculated in real time to quickly complete the task and improve efficiency.

[0029] (3) Multi-task path optimization: The system generates multi-task optimal inspection routes through optimization algorithms, reducing repeated trips and reducing operation and maintenance time and costs.

[0030] (4) Dynamic task cluster adjustment: When new tasks are added or completed, the system automatically adjusts clusters and paths to enhance the flexibility and responsiveness of the system.

[0031] (5) Real-time path update and navigation: The path is dynamically updated and the navigation is pushed to the operation and maintenance terminal to ensure efficient and accurate task execution. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 This is a flowchart of the electronic map construction described in an embodiment of the present invention.

[0033] Figure 2 The present invention is a flowchart of a method for dynamically optimizing a photovoltaic base station intelligent operation and maintenance path according to an embodiment of the present invention. DETAILED DESCRIPTION

[0034] In order to enable ordinary technicians in the field to better understand the technical problems to be solved by the present invention, the technical solutions adopted, and the technical effects achieved, the present invention will be clearly and completely described below in conjunction with specific embodiments.

[0035] 1. The technical solution of the embodiment of the present invention includes:

[0036] 1. Electronic map construction technology

[0037] The present invention calls the interface of existing map services (such as Amap, Baidu, Google, etc.) to mark the specific location of the stacked optical base station on the map, generate an electronic map containing base station information, and provide basic data support for operation and maintenance path planning. Figure 1 The process of marking base station locations on a map and generating an electronic map by calling an existing online map service is demonstrated, including:

[0038] Integrated map service interface: The system connects with the API of Amap service, directly uses existing map resources, and obtains map data in real time, including road networks, geographic features and other information.

[0039] Base station location marking: The internal data management system automatically marks the base station's geographic coordinates, number, status and other information on the map. The detailed information of each base station (such as type, operating status, etc.) can be displayed intuitively, making it convenient for operation and maintenance personnel to view and locate.

[0040] Dynamic update: Based on changes in base station status or location information, the annotations on the map are updated in real time to ensure that the map seen by all operation and maintenance personnel always reflects the latest base station layout and status.

[0041] In addition to using online map services, for scenarios where network connections are unstable or base stations are located in remote areas, base station location marking and route planning can be achieved by integrating offline map data. Alternatively, base station locations and road data can be marked on the map based on a self-built GIS platform, thus avoiding reliance on external map services.

[0042] 2. Obtain base station work order data

[0043] The present invention acquires the work order data and work order task priority data of the base station monitoring system in real time, and plans the operation and maintenance route according to the location of the operation and maintenance personnel.

[0044] 3. Path planning and optimization technology

[0045] The path planning and optimization technology of the present invention realizes efficient path planning for the operation and maintenance of optical base stations through intelligent algorithms and real-time data fusion. This technology can not only provide the optimal path for a single base station work order, but also dynamically optimize the inspection route of operation and maintenance personnel in the case of multiple tasks, reduce travel and resource waste, and adapt to real-time changes in tasks. Figure 2 The path planning and optimization process is demonstrated. First, the location and task data of the operation and maintenance personnel are initialized, and then the corresponding path planning strategy is selected according to the task type (single task, multi-task or new task insertion). For single tasks, the A* algorithm is used to calculate the optimal path; for multi-tasks, the priority weighted sum method is used to sort and then plan the path. If new tasks are added, the task order is dynamically adjusted and the path is fine-tuned. During the whole process, the rationality of the path is continuously monitored through the rolling optimization algorithm, and emergencies are responded to in a timely manner and the updated path is pushed to the operation and maintenance personnel.

[0046] 3.1 Single-task path planning

[0047] Generate the optimal path for a single base station work order based on the operator's current location and road conditions (such as traffic conditions, road closures, etc.), ensuring that the operator can reach the target base station in the shortest possible time, reducing operation and maintenance time and costs

[0048] The A* algorithm is used to calculate the optimal path from the operator's current location to the target base station. The A* algorithm combines the base station location and map information to find the shortest path through heuristic search to ensure operation and maintenance efficiency.

[0049] 3.2 Multi-task optimization and cluster planning

[0050] When maintenance personnel need to handle work orders for multiple base stations, the system generates an optimal inspection route for them through a multi-task optimization algorithm. This route can cover multiple base stations and reduce unnecessary repeated trips.

[0051] Task priority sorting: First, the tasks are sorted by the priority weighted sum method. The priority of the task is converted into a weight and combined with the path cost (such as time or distance) to calculate the weighted total path cost.

[0052] Multi-task planning: Based on the urgency of the work order, the location of the base station and the current location of the operation and maintenance personnel, the A* algorithm is used to plan the optimal inspection route covering all tasks according to the sorting results, reducing unnecessary repeated trips.

[0053] 3.3 Dynamic Cluster Adjustment and Real-time Path Update

[0054] When new work orders are added or existing tasks are completed, the path planning can be adjusted dynamically to ensure that the operation and maintenance personnel are always on the optimal route. During the path execution process, the system will dynamically adjust the path based on the real-time location of the operation and maintenance personnel, traffic conditions, weather changes and other information, and continuously optimize future path planning through a feedback mechanism.

[0055] Dynamic task adjustment: When new tasks are added, the system inserts the new tasks into the task queue and reorders them according to priority. The system adjusts the task order according to the urgency and location of the tasks, and uses local optimization algorithms to fine-tune the path to ensure that new tasks can be processed in a timely manner.

[0056] Real-time path update: Using a rolling optimization algorithm, the system continuously evaluates the rationality of the current path, and updates the path in a timely manner in emergencies (such as road closures, weather changes), and pushes it to the terminal devices of the operation and maintenance personnel.

[0057] 4. System architecture and implementation details

[0058] The present invention adopts modular design to ensure that the path planning algorithm can be extended to other types of distributed facility operation and maintenance scenarios. The system architecture consists of multiple functional units such as data integration platform and path planning module, ensuring that the system has high concurrent processing capabilities and supports real-time data fusion and large-scale path optimization.

[0059] Data integration platform: responsible for accessing and processing real-time data from different data sources. The system has high concurrent processing capabilities and supports data preprocessing, standardization, and format conversion. Through the combination of edge computing and cloud computing, it ensures that the system can run efficiently in different network environments.

[0060] Modular design: Each functional module of the system is designed independently to support future expansion to other distributed facility operation and maintenance scenarios. For example, the path optimization algorithm can be applied to equipment inspection of new energy stations, urban power grid maintenance and other scenarios.

[0061] 5. Visualization and navigation technology

[0062] The operation and maintenance path is displayed in real time through electronic maps, and the navigation function is integrated to help operation and maintenance personnel complete tasks more intuitively and efficiently.

[0063] Electronic map visualization: The operation and maintenance path is visualized on the electronic map. The operation and maintenance personnel can view the current path, the location information of the target base station and the key nodes along the way (such as checkpoints or supply points) in real time through the mobile terminal. The visualization of the electronic map not only includes static geographic information, but also dynamically updates in combination with real-time data (such as traffic conditions and weather changes).

[0064] Navigation system integration: The system integrates navigation functions. When the operation and maintenance personnel start to perform tasks, the system will provide real-time navigation guidance according to the planned path. The navigation functions include voice navigation, path prompts, turn reminders, etc., so that the operation and maintenance personnel can reach the target base station according to the optimal path even in complex geographical environments.

[0065] Dynamic path updates and prompts: During the execution of operation and maintenance tasks, if the path is dynamically adjusted, the system will update the new path on the electronic map in real time, and promptly notify the operation and maintenance personnel to make adjustments through the navigation system to avoid delays caused by environmental changes.

[0066] 2. Comparison between the present invention and the prior art:

[0067] (1) The existing technology has low operation and maintenance efficiency. Due to the large number of base stations and their wide distribution, it is difficult for operation and maintenance personnel to cover multiple base stations in a short time and reach the faulty equipment in time. The present invention improves the operation and maintenance efficiency and reduces the operation and maintenance time and cost through intelligent path planning technology.

[0068] (2) The existing technologies are not good at dealing with dynamic environments. Most existing path planning technologies are static and cannot deal with changes in traffic, weather and other environments in real time. The present invention solves this problem through multi-source data fusion and dynamic path optimization technology.

[0069] (3) The resource scheduling in the prior art is unreasonable, and the resource scheduling in the operation and maintenance process lacks flexibility, resulting in resource waste or uneven task burden. The present invention optimizes resource allocation through dynamic scheduling of operation and maintenance resources.

[0070] (4) The existing technology is not accurate in positioning and navigation, and cannot be adjusted dynamically in combination with real-time information, which makes it difficult for maintenance personnel to quickly find faulty equipment. The present invention provides accurate navigation function by combining electronic maps and real-time data.

[0071] Compared with the prior art, the present invention uses a weighted summation method of task priorities in a path planning and optimization system to perform weighted summation of the priorities of multiple operation and maintenance tasks, and calculates the total cost based on the weight and path cost to ensure that high-priority tasks are given priority. Through a dynamic task insertion and path reordering mechanism, it is ensured that during the task execution process, new tasks can be dynamically inserted and reordered according to the urgency of the new task and the priority of the existing tasks to optimize the task path. Through a rolling optimization algorithm, the rationality of the current path is continuously monitored, and when an emergency is detected, the path is automatically adjusted and pushed to the terminal device of the operation and maintenance personnel.

[0072] The specific embodiments described above are merely illustrative examples of the present invention, and do not limit the present invention to be implemented only through the aforementioned embodiments. Based on the embodiments listed in the present invention, all other embodiments that can be obtained by ordinary technicians in this field without making creative efforts belong to the protection scope of the present invention.

Claims

1. A photovoltaic base station intelligent operation and maintenance path dynamic optimization system, characterized in that: It includes a data integration platform, an electronic map construction module, a work order acquisition module, a path planning optimization module and a visual navigation module; the data integration platform is responsible for accessing and processing real-time data from different data sources, including detailed information of base stations; the electronic map construction module generates an electronic map by calling existing map services, marking and dynamically updating detailed information of base stations on the map in real time; the work order acquisition module is used to obtain system work order data and work order task priority data in real time; the path planning optimization module is used to select the corresponding path planning strategy according to the task type. For a single task, the A* algorithm is used to calculate the optimal path; for multiple tasks, the priority weighted sum method is used to sort and then plan the path; when a new task is added, the task order is dynamically adjusted and the path is fine-tuned; the visual navigation module is used to display the operation and maintenance path in real time through an electronic map, and integrates the navigation function to help operation and maintenance personnel complete tasks more intuitively and efficiently.

2. The photovoltaic base station intelligent operation and maintenance path dynamic optimization system according to claim 1 is characterized in that: The electronic map construction module includes an integrated map service interface unit, a base station location marking unit and a map dynamic update unit. The integrated map service interface unit directly uses the existing map resources to obtain map data in real time by connecting with the API of the existing map service; The base station location marking unit obtains detailed information of the base station through the data integration platform and automatically marks the information on the map; the map dynamic update unit is used to update the markings on the map in real time.

3. The photovoltaic base station intelligent operation and maintenance path dynamic optimization system according to claim 1 is characterized in that: The path planning optimization module includes a single-task path planning unit, a multi-task optimization and cluster planning unit, and a dynamic cluster adjustment and real-time path updating unit. The single-task path planning unit uses the A* algorithm in combination with the base station location and map information according to the current location and road conditions of the operation and maintenance personnel, and finds the optimal path from the current location to the target base station through heuristic search to ensure that the operation and maintenance personnel can reach the target base station in the shortest time, reducing the operation and maintenance time and cost; the multi-task optimization and cluster planning unit generates an optimal inspection path for it through a multi-task optimization algorithm, and the path can cover multiple base stations and reduce unnecessary repeated trips; the dynamic cluster adjustment and real-time path updating unit can dynamically adjust the path planning to ensure that the operation and maintenance personnel are always in the optimal route, dynamically adjust the path based on real-time information during the path execution process, and continuously optimize future path planning through a feedback mechanism.

4. The photovoltaic base station intelligent operation and maintenance path dynamic optimization system according to claim 3 is characterized in that: The multi-task optimization algorithm first sorts the tasks by the priority weighted summation method, the task priorities are converted into weights and combined with the path costs to calculate the weighted total path cost; then based on the urgency of the work order, the base station location and the current location of the operation and maintenance personnel, according to the sorting results, the A* algorithm is used to plan the optimal inspection path covering all tasks, thereby reducing unnecessary repeated trips; The dynamic adjustment path planning is as follows: when a new task is added, the new task is inserted into the task queue and reordered according to priority; the task order is adjusted according to the urgency and location of the task, and the path is fine-tuned using a local optimization algorithm to ensure that the new task can be processed in a timely manner; Use a rolling optimization algorithm to continuously evaluate the rationality of the current path, and update the path in a timely manner in emergency situations and push it to the terminal devices of the operation and maintenance personnel.

5. The photovoltaic base station intelligent operation and maintenance path dynamic optimization system according to claim 1 is characterized in that: The visual navigation module includes an electronic map visualization unit, a navigation system integration unit, and a path dynamic update and prompt unit. The electronic map visualization unit visualizes the operation and maintenance path on the electronic map; the navigation system integration unit provides real-time navigation guidance for the operation and maintenance personnel based on the planned path; the path dynamic update and prompt unit dynamically updates the new path on the electronic map and promptly notifies the operation and maintenance personnel to make adjustments to avoid delays caused by environmental changes.

6. A method for dynamic optimization of intelligent operation and maintenance paths of photovoltaic base stations, characterized in that: The following steps are involved: Step 1: construct an electronic map by calling the existing map service, marking the detailed information of the base station on the map and dynamically updating it in real time to generate an electronic map; Step 2: Obtain system work order data and work order task priority data in real time; Step 3: Select the corresponding path planning strategy according to the task type. For a single task, use the A* algorithm to calculate the optimal path. For multiple tasks, use the priority weighted sum method to sort and then plan the path. When a new task is added, dynamically adjust the task order and fine-tune the path. Step 4: Display the operation and maintenance path in real time through an electronic map and integrate navigation functions to help operation and maintenance personnel complete tasks more intuitively and efficiently.

7. The method for dynamic optimization of photovoltaic base station intelligent operation and maintenance paths according to claim 6 is characterized in that: The step 1 directly uses the existing map resources to obtain map data in real time by connecting with the API of the existing map service; obtains detailed information of the base station through the data integration platform, and automatically marks the information on the map; based on the changes in the base station information, updates the annotations on the map in real time to ensure that the map seen by all operation and maintenance personnel always reflects the latest base station layout and status.

8. The method for dynamic optimization of photovoltaic base station intelligent operation and maintenance paths according to claim 6 is characterized in that: In step 3, for a single task, based on the current location and road conditions of the operation and maintenance personnel, the A* algorithm is used in combination with the base station location and map information to find the optimal path from the current location to the target base station through heuristic search, ensuring that the operation and maintenance personnel can reach the target base station in the shortest time, reducing the operation and maintenance time and cost; for multiple tasks, an optimal inspection path is generated for them through a multi-task optimization algorithm, which can cover multiple base stations and reduce unnecessary repeated trips; for new tasks, the path planning is dynamically adjusted to ensure that the operation and maintenance personnel are always on the optimal route, and the path is dynamically adjusted based on real-time information during the path execution process, and the future path planning is continuously optimized through a feedback mechanism.

9. The method for dynamic optimization of photovoltaic base station intelligent operation and maintenance paths according to claim 8, characterized in that: The multi-task optimization algorithm described in step 3 first sorts the tasks by the priority weighted sum method. The priorities of the tasks are converted into weights and combined with the path costs to calculate the total weighted path cost. Then, based on the urgency of the work order, the location of the base station and the current location of the operation and maintenance personnel, the optimal inspection path covering all tasks is planned by the A* algorithm according to the sorting results to reduce unnecessary repeated trips. The dynamic adjustment path planning described in step 3 is as follows: when a new task is added, the new task is inserted into the task queue and reordered according to priority; the task order is adjusted according to the urgency and location of the task, and the path is fine-tuned using a local optimization algorithm to ensure that the new task can be processed in a timely manner; Use a rolling optimization algorithm to continuously evaluate the rationality of the current path, and update the path in a timely manner in emergency situations and push it to the terminal devices of the operation and maintenance personnel.

10. The method for dynamic optimization of photovoltaic base station intelligent operation and maintenance paths according to claim 6, characterized in that: In step 4, the operation and maintenance path is visualized on the electronic map, and real-time navigation guidance is provided to the operation and maintenance personnel; the new path on the electronic map is dynamically updated, and the operation and maintenance personnel are notified in time to make adjustments to avoid delays caused by environmental changes.

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