A wind farm accessory lossless transportation unmanned aerial vehicle scheduling method and device

Abstract

The application provides a wind farm accessory lossless transportation unmanned aerial vehicle scheduling method and device, wherein the method comprises the following steps: receiving a transportation demand of a wind farm accessory; generating an optimal route through a multi-objective optimization algorithm, and distributing the optimal route to a target unmanned aerial vehicle; dynamically adjusting a flight attitude of the target unmanned aerial vehicle or replanning an alternative route in a flight process of the target unmanned aerial vehicle transporting the accessory according to the optimal route; after the target unmanned aerial vehicle arrives at a destination team, docking and unloading the accessory through a cabin parking platform, recording transportation whole-process data and archiving, and generating a transportation statistical report. Through planning an optimal route based on a transportation demand and dynamically adjusting a target unmanned aerial vehicle in a flight process, efficient transportation of a wind farm accessory is realized, operation and maintenance costs are reduced, and unmanned aerial vehicle transportation efficiency is improved.

Description

Technical Field

[0001] This application relates to the field of drone scheduling technology, and in particular to a drone scheduling method and apparatus for non-destructive transportation of wind farm components. Background Technology

[0002] Wind farms, as a crucial component of clean energy, are widely used in remote areas such as mountains, grasslands, and offshore. Their operation and maintenance heavily rely on the timeliness and safety of spare parts transportation. Among related technologies, a preliminary technical system for wind farm spare parts transportation has been established through the collaborative operation of drones, intelligent dispatch platforms, and protection modules. Specifically, this system covers the entire process from demand initiation and route planning to transportation execution, including key aspects such as multi-rotor drone transport, GPS / BeiDou positioning and navigation, and basic obstacle avoidance, aiming to improve spare parts delivery efficiency.

[0003] However, existing drone transportation methods directly adopt general logistics and distribution solutions without systematically adapting to the complex terrain, extreme weather, and protection needs of precision components in wind farms. This may result in components being damaged during transportation due to vibration, collision, or moisture, or delays in emergency repairs due to the lack of dynamic route adjustment and multi-drone collaborative scheduling capabilities, thereby affecting the operation and maintenance efficiency and equipment safety of wind farms and increasing overall operating costs. Summary of the Invention

[0004] This application aims to at least partially address one of the technical problems in the related art.

[0005] Therefore, this application proposes a method, apparatus, electronic device, and storage medium.

[0006] One embodiment of this application proposes a drone scheduling method for the non-destructive transportation of wind farm components, including: S1, receive the transportation request for wind farm parts, parse the parts parameters, destination turbine coordinates and emergency level in the transportation request, and verify the inventory status of the corresponding parts in the ground supply station; S2, based on the accessory parameters, the destination aircraft coordinates and the emergency level, combined with real-time meteorological data, geographic information data and UAV status data, an optimal route is generated through a multi-objective optimization algorithm, and the optimal route is assigned to the target UAV; S3, during the flight of the target UAV transporting the accessory according to the optimal route, the environmental parameters of the accessory and the flight parameters of the target UAV are collected in real time. When the environmental parameters or the flight parameters exceed the safety threshold, the flight attitude of the target UAV is dynamically adjusted or an alternative route is replanned. S4. After the target UAV arrives at the destination aircraft group, it docks with the aircraft docking platform and unloads the accessories, records and archives the entire transportation process data, and generates a transportation statistical report.

[0007] Optionally, S1 includes: The wind farm operation and maintenance management system automatically identifies fault types, matches the required parts, generates transportation requirements, and pushes them to the intelligent dispatch platform. The transportation demand is analyzed, and the real-time inventory of spare parts is checked in conjunction with the spare parts storage cabinets at the ground supply station. If the inventory is sufficient, the task list is automatically entered and the spare parts protection requirements and special transportation specifications are marked. If the inventory is insufficient, a replenishment reminder is pushed.

[0008] Optionally, S2 includes: Based on the weight of the accessories, transportation distance, and urgency level in the mission requirements, as well as the real-time status of all drones, at least one target drone is assigned. When multiple drones have simultaneous requirements, a regional segmentation and mission merging mode is adopted. Missions in the same region are merged and transported in batches by the nearest drone. The system retrieves geographic information system maps, real-time meteorological data, unit operation and maintenance data, airspace control data, and personnel location data. Through an improved multi-objective genetic algorithm, it dynamically optimizes the flight path according to task priority to generate the optimal flight path. Simultaneously, it completes flight path pre-simulation and marks obstacle avoidance points, resupply points, and estimated take-off and landing times. For long-distance missions, it automatically plans mid-course charging or battery swapping nodes.

[0009] Optionally, the step of dynamically optimizing the route according to task priority using an improved multi-objective genetic algorithm to generate the optimal route includes: The optimization objectives are to minimize transportation time, drone energy consumption, flight safety, risk of damage during parts transportation, probability of route conflict, and number of recharging and replenishment trips. The objective weights are dynamically adjusted according to the mission urgency level, and an improved multi-objective genetic algorithm is used to optimize the route to generate the optimal route.

[0010] Optionally, S3 includes: The built-in temperature and humidity sensor and triaxial vibration sensor collect environmental parameters in real time during the transportation of parts. The positioning module and flight control system collect flight speed, altitude, attitude and battery parameters in real time and synchronize them to the intelligent dispatch platform. When environmental parameters exceed a preset first threshold, or flight parameters fall below a preset second threshold, an alarm is triggered and the flight attitude is adjusted, the flight speed is reduced and the aircraft avoids turbulent areas, or the scheduling platform algorithm replans alternative routes in real time and sends adjustment instructions to the target UAV.

[0011] Optionally, the method further includes: Before the target drone returns, the optimal return route is replanned based on real-time weather and the drone's remaining battery power, prioritizing the avoidance of areas with severe environmental conditions. After the target drone arrives at the resupply station's take-off and landing point, it automatically docks with the charging device and begins charging. The dispatch platform simultaneously receives data from the entire flight, performs health checks on the power system, obstacle avoidance module, and communication module, and generates equipment status reports. If potential faults are detected, maintenance reminders are pushed to maintenance personnel. After charging is complete, the target drone enters standby mode.

[0012] Optionally, S4 includes: Record requirement information, task allocation records, flight path, flight parameters, parts status data, loading and unloading confirmation information, and abnormal handling process, archive them by task number, and store them in encrypted form; Regularly generate transportation statistics reports, covering data on the number of transportations, average duration, parts damage rate, drone energy consumption, and scheduling efficiency. Analyze the process through algorithms to optimize space and output operation and maintenance optimization suggestions.

[0013] Another embodiment of this application proposes a drone dispatching device for non-destructive transportation of wind farm components, including: The demand parsing module is used to receive transportation demands for wind farm components, parse the component parameters, destination turbine coordinates and emergency level in the transportation demands, and verify the inventory status of the corresponding components in the ground supply station. The route optimization module is used to generate the optimal route based on the accessory parameters, the destination aircraft coordinates and the emergency level, combined with real-time meteorological data, geographic information data and UAV status data, through a multi-objective optimization algorithm, and to allocate the optimal route to the target UAV. The dynamic adjustment module is used to collect environmental parameters of the accessory and flight parameters of the target UAV in real time during the flight of the target UAV transporting the accessory according to the optimal route. When the environmental parameters or the flight parameters exceed the safety threshold, the module dynamically adjusts the flight attitude of the target UAV or replans alternative routes. The recording module is used to dock with and unload the accessories through the cabin docking platform after the target UAV arrives at the destination aircraft group, record and archive the data of the entire transportation process, and generate transportation statistical reports.

[0014] Another embodiment of this application provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, it implements the method described in the foregoing aspect.

[0015] Another embodiment of this application proposes a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method described in the foregoing aspect.

[0016] Another embodiment of this application proposes a chip including processing circuitry configured to perform the method described in one aspect above.

[0017] Another embodiment of this application proposes a computer program product that, when executed by a processor, implements the method described in the foregoing aspect.

[0018] The UAV scheduling method, device, electronic equipment, chip, and storage medium for non-destructive transportation of wind farm components proposed in this application can achieve the following beneficial effects: By planning the optimal route based on transportation demand and dynamically adjusting it during the flight of the target drone, efficient transportation of wind farm components was achieved, reducing operation and maintenance costs and improving drone transportation efficiency.

[0019] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0020] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 A flowchart illustrating a method for non-destructive transportation of wind farm components provided in this application embodiment; Figure 2 This is a schematic diagram of the structure of a drone dispatching device for non-destructive transportation of wind farm accessories provided in an embodiment of this application. Detailed Implementation

[0021] The embodiments of this application are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0022] The following description, with reference to the accompanying drawings, outlines a drone scheduling method, apparatus, electronic device, chip, and storage medium for non-destructive transportation of wind farm components according to embodiments of this application.

[0023] Figure 1 This is a flowchart illustrating a method for scheduling unmanned aerial vehicles (UAVs) for non-destructive transportation of wind farm components, as provided in an embodiment of this application.

[0024] As one implementation, the drone scheduling method for non-destructive transportation of wind farm components in this application embodiment can be configured in a drone scheduling device for non-destructive transportation of wind farm components. The drone scheduling device for non-destructive transportation of wind farm components can be applied to any electronic device so that the electronic device can perform the drone scheduling function for non-destructive transportation of wind farm components.

[0025] Among them, electronic devices can be any device with computing capabilities, such as mobile terminals, which can be hardware devices with various operating systems, touch screens and / or displays, such as mobile phones, tablets, personal digital assistants, wearable devices, etc.

[0026] As another implementation, the drone scheduling method for non-destructive transportation of wind farm accessories in this application embodiment can also be executed by a chip with processing capabilities. The chip includes an image signal processing chip (ISP), a central processing unit (CPU), an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a field-programmable gate array (FPGA), a system on a chip (SOC), a reduced instruction set computer (RISC), etc., which will not be listed here.

[0027] It should be noted that all data collection operations related to users in this application are conducted with the user's authorization and in strict compliance with relevant laws and regulations such as privacy and security.

[0028] like Figure 1 As shown, the method may include the following steps: S1, receive the transportation request for wind farm parts, parse the parts parameters, destination turbine coordinates and emergency level in the transportation request, and verify the inventory status of the corresponding parts in the ground supply station; In this embodiment, when receiving a transportation request for wind farm components, the system first obtains the transportation request automatically triggered by the wind farm operation and maintenance management system or manually submitted by the operation and maintenance personnel through a multi-channel access method. The request includes at least the parameter information of the components to be transported, the precise coordinates of the destination turbine, and the urgency level of the task.

[0029] Subsequently, the received transportation requests are intelligently analyzed, automatically identifying and extracting key information such as parts parameters, destination coordinates, and urgency level. Based on this information, a verification process is performed against real-time inventory data from parts storage cabinets at ground supply stations to confirm the adequacy of the required parts inventory. If inventory is sufficient, the transportation request is added to the scheduling task list; if inventory is insufficient, a replenishment reminder is immediately generated and fed back to relevant systems or personnel, thereby avoiding ineffective scheduling.

[0030] As one implementation method, transportation demand can be generated automatically through identification and matching after a wind turbine fault alarm, or it can be manually entered through a mobile APP or computer web terminal. It also supports quickly obtaining part parameters by scanning the RFID tag of the parts. The intelligent analysis process is completed within a preset time and is linked to the parts storage cabinet of the ground supply station for inventory verification. Tasks are automatically classified according to urgency level and transportation area to generate a visual scheduling task ledger.

[0031] Through the above steps, we can achieve accurate reception and rapid analysis of transportation demands, and ensure effective verification of spare parts inventory status before scheduling. This avoids ineffective scheduling due to insufficient inventory, improves the execution efficiency and resource utilization of transportation tasks, and provides an accurate and reliable data foundation for subsequent route planning and task allocation.

[0032] S2, based on the accessory parameters, the destination aircraft coordinates and the emergency level, combined with real-time meteorological data, geographic information data and UAV status data, an optimal route is generated through a multi-objective optimization algorithm, and the optimal route is assigned to the target UAV; In this embodiment, based on the accessory parameters, the destination aircraft coordinates, and the emergency level, combined with real-time meteorological data, geographic information data, and UAV status data, an optimal flight path is generated through a multi-objective optimization algorithm, and the optimal flight path is assigned to the target UAV. Specifically, this step aims to achieve intelligent allocation of transportation tasks and dynamic optimization of flight paths. Its core lies in seeking a balance among multiple conflicting optimization objectives based on multi-dimensional input information through an algorithm model, thereby planning a flight path with optimal overall performance for each transportation task.

[0033] The accessory parameters reflect the weight, dimensions, and special protection requirements of the items to be transported; the destination aircraft coordinates determine the final location of the transport; and the emergency level characterizes the time sensitivity of the mission. Based on this, the system further integrates real-time meteorological data to assess the impact of environmental conditions such as wind field and visibility on flight safety and energy consumption, combines geographic information data to identify terrain, obstacles, and no-fly zones, and refers to UAV status data to understand the real-time operating conditions of each available UAV, including battery power, payload capacity, and current location.

[0034] Through a multi-objective optimization algorithm, the system can simultaneously consider multiple objectives such as transportation time, energy consumption, safety factor, and damage risk, and dynamically adjust the weights of each objective according to the urgency level of the mission, thereby searching the solution space for the optimal route that satisfies the constraints. This route includes not only the spatial path but also parameters such as flight speed and altitude. Subsequently, based on mission requirements and the real-time status of each UAV, the system automatically assigns the generated optimal route to the target UAV most suitable for performing the mission, ensuring the rationality and efficiency of the scheduling decision.

[0035] As one implementation method, the multi-objective optimization algorithm can adopt an improved multi-objective genetic algorithm. This algorithm can simultaneously optimize six core objectives, including the shortest transportation time, the lowest UAV energy consumption, the highest flight safety factor, the lowest risk of damage during parts transportation, the lowest probability of route conflict, and the fewest charging and resupply times. It can also dynamically adjust the objective weights according to the urgency level of the mission. For example, emergency missions prioritize transportation time and safety factor, while routine missions prioritize energy consumption and safety factor, thereby generating the optimal route plan that is suitable for different scenarios.

[0036] This step achieves intelligent route planning and task allocation through multi-objective optimization algorithms, significantly improving the scientific nature and adaptability of transportation scheduling. It can balance transportation efficiency, flight safety, and energy economy in complex and ever-changing wind farm environments, laying a reliable foundation for subsequent automated transportation.

[0037] S3, during the flight of the target UAV transporting the accessory according to the optimal route, the environmental parameters of the accessory and the flight parameters of the target UAV are collected in real time. When the environmental parameters or the flight parameters exceed the safety threshold, the flight attitude of the target UAV is dynamically adjusted or an alternative route is replanned. In this embodiment, during the flight of the target UAV transporting the parts according to the optimal route, multiple types of sensors deployed on the parts protection module and the UAV carrying unit collect environmental parameters reflecting the transportation status of the parts and flight parameters reflecting the operating status of the UAV in real time.

[0038] The environmental parameters include at least key indicators affecting the integrity of the accessory, such as vibration acceleration, temperature, and humidity of the space where the accessory is located. The flight parameters include at least core data affecting flight safety and mission execution, such as the UAV's flight speed, altitude, attitude, and remaining battery power. The collected parameter data is synchronized in real time to the intelligent scheduling platform via a wireless communication link, and the platform's built-in monitoring module continuously compares each parameter with preset safety thresholds.

[0039] When any of the aforementioned environmental parameters or flight parameters exceeds its corresponding safety threshold—for example, vibration acceleration exceeding a preset upper limit, humidity exceeding the allowable range, or abnormal deviation in flight attitude—the intelligent scheduling platform immediately triggers an adaptive adjustment mechanism. This mechanism generates corresponding adjustment instructions based on the type and degree of deviation of the exceeded parameter. These instructions include, but are not limited to, reducing flight speed, changing flight altitude, adjusting flight attitude to avoid turbulent areas or severe weather zones, and, if necessary, replanning and issuing alternative flight routes based on current real-time data to guide the UAV to bypass risk areas or dock at a nearby safe location.

[0040] As one implementation method, when the vibration acceleration of the accessory exceeds 5g or the humidity exceeds 85%, the system can automatically reduce the flight speed and adjust the flight path to avoid turbulent areas, while triggering an audible and visual alarm inside the box; when communication is interrupted or positioning fails, it can automatically switch to a backup communication link or visual positioning mode to ensure the continuity of command transmission and flight control.

[0041] This step, by constructing a real-time status perception and dynamic response mechanism during flight, enables proactive identification and immediate intervention of transportation risks, significantly improving the safety and reliability of parts transportation, effectively avoiding parts damage or flight accidents caused by sudden environmental changes or equipment malfunctions, and ensuring the smooth completion of transportation tasks.

[0042] S4. After the target UAV arrives at the destination aircraft group, it docks with the aircraft docking platform and unloads the accessories, records and archives the entire transportation process data, and generates a transportation statistical report.

[0043] In this embodiment, after the target UAV arrives at the destination aircraft, it precisely docks with and unloads the accessories via the cabin docking platform. The entire transportation process data is recorded and archived, generating a transportation statistical report. Specifically, this step aims to achieve closed-loop management of the UAV transportation mission. Its core is to ensure the precise and safe unloading of accessories at the destination aircraft and to systematically collect and archive the data generated throughout the transportation process, forming a traceable and analyzable transportation record. To achieve precise docking, the cabin docking platform can be configured with positioning calibration and fixing functions. When the UAV approaches the destination aircraft, the platform can precisely dock with the UAV through mechanisms such as visual recognition, magnetic fixation, or mechanical locking, thus providing a stable operating platform for the safe unloading of accessories.

[0044] After unloading, the system automatically records all process data, including but not limited to task number, drone identification, parts information, flight path, flight parameters, environmental parameters, loading and unloading confirmation information, and anomaly handling procedures. This data is then linked and archived according to preset rules to form a structured transportation file. Based on this, the system further performs statistical analysis on the archived data, generating transportation statistical reports covering key indicators such as the number of transport trips, average transport time, parts damage rate, and energy consumption, providing data support for subsequent operation and maintenance decisions and scheduling optimization.

[0045] As one implementation method, before the drone arrives, the dispatch platform can issue a command to the docking platform in the cabin to activate its positioning calibration components. After the drone accurately docks through visual recognition, the magnetic suction device automatically locks and fixes it. After the maintenance personnel unload the parts, they upload confirmation information through a mobile terminal. The dispatch platform automatically records the abnormal situation and initiates traceability analysis. The entire transportation process data is archived according to the task number, with a storage period of no less than five years, and transportation statistical reports are generated regularly to analyze the potential for process optimization.

[0046] This step, through precise docking and full-process data archiving, significantly improves the operational safety and data management standardization of transportation terminals, ensures the stability and reliability of parts unloading, and provides a solid data foundation for continuous optimization of transportation processes and refined operation and maintenance management.

[0047] Optionally, S1 includes: The wind farm operation and maintenance management system automatically identifies fault types, matches the required parts, generates transportation requirements, and pushes them to the intelligent dispatch platform. The transportation demand is analyzed, and the real-time inventory of spare parts is checked in conjunction with the spare parts storage cabinets at the ground supply station. If the inventory is sufficient, the task list is automatically entered and the spare parts protection requirements and special transportation specifications are marked. If the inventory is insufficient, a replenishment reminder is pushed.

[0048] In this embodiment, the triggering of transportation demand includes two modes: automatic triggering and manual triggering.

[0049] In automatic trigger mode, when a wind turbine malfunctions and issues an alarm, the wind farm operation and maintenance management system automatically identifies the type of malfunction, such as sensor failure or gear assembly damage. Based on the malfunction type, it matches the model and quantity of the corresponding spare parts, then generates a transportation request that includes spare parts parameters, destination turbine coordinates, and urgency level, and pushes it to the intelligent dispatch platform. At the same time, it simultaneously marks the urgency level of the task, such as emergency repair, routine maintenance, or spare parts replenishment.

[0050] In manual trigger mode, maintenance personnel submit requests via a mobile app using voice input or QR code scanning, or via a web-based computer, entering parts information, destination aircraft number, urgency level, and transportation time limit. QR code scanning supports reading the parts' RFID tags to obtain accurate parameters. Upon receiving a transportation request, the intelligent dispatch platform parses the request information within 10 seconds, including parts parameters, destination aircraft coordinates, urgency level, and transportation time limit requirements.

[0051] After parsing, the platform synchronously links with the ground supply station's spare parts storage cabinets to verify the real-time inventory status of the corresponding spare parts. If the inventory is sufficient, the task is automatically entered into the task list, and its protection requirements and special transportation specifications are marked according to the spare parts type, such as the need for low-speed and stable flight for precision spare parts. If the inventory is insufficient, a replenishment reminder is immediately pushed to the operation and maintenance management system and supply station staff, while also providing feedback to the request initiator to avoid invalid scheduling. Through the above process, a complete closed loop is achieved from request triggering, intelligent parsing to inventory verification, ensuring the accuracy and timeliness of scheduling tasks.

[0052] This specific implementation method significantly improves the efficiency and accuracy of transportation demand response through a demand triggering mechanism with both automatic and manual modes, combined with rapid intelligent analysis and inventory linkage verification. It avoids ineffective scheduling due to insufficient inventory and provides a reliable data foundation for subsequent task allocation and route planning.

[0053] Optionally, S2 includes: Based on the weight of the accessories, transportation distance, and urgency level in the mission requirements, as well as the real-time status of all drones, at least one target drone is assigned. When multiple drones have simultaneous requirements, a regional segmentation and mission merging mode is adopted. Missions in the same region are merged and transported in batches by the nearest drone. The system retrieves geographic information system maps, real-time meteorological data, unit operation and maintenance data, airspace control data, and personnel location data. Through an improved multi-objective genetic algorithm, it dynamically optimizes the flight path according to task priority to generate the optimal flight path. Simultaneously, it completes flight path pre-simulation and marks obstacle avoidance points, resupply points, and estimated take-off and landing times. For long-distance missions, it automatically plans mid-course charging or battery swapping nodes.

[0054] Optionally, the step of dynamically optimizing the route according to task priority using an improved multi-objective genetic algorithm to generate the optimal route includes: The optimization objectives are to minimize transportation time, drone energy consumption, flight safety, risk of damage during parts transportation, probability of route conflict, and number of recharging and replenishment trips. The objective weights are dynamically adjusted according to the mission urgency level, and an improved multi-objective genetic algorithm is used to optimize the route to generate the optimal route.

[0055] In this embodiment, the intelligent scheduling platform receives the weight of spare parts, transportation distance, and urgency level from the task requirements, while simultaneously acquiring real-time status data of all drones, including current battery level, remaining payload, current location, and health status. Based on these inputs, the platform's built-in intelligent algorithm automatically calculates and allocates the optimal drone for the task. Specifically, the algorithm prioritizes the matching degree between the task requirements and the drone's status, selecting drones with sufficient battery power, adequate payload capacity, and closest proximity to the task's starting point. When multiple drones have simultaneous requirements, the platform adopts a regional segmentation and task merging mode, combining multiple transportation tasks within the same geographical area and having the nearest drone transport them in batches, thereby reducing the number of drone takeoffs and landings and improving overall scheduling efficiency. The allocation results are simultaneously pushed to the control module of the corresponding drone and ground personnel, and dispatchers can manually fine-tune the process according to the actual situation.

[0056] Subsequently, after identifying the target drone, the platform performs flight path planning. The platform retrieves Geographic Information System (GIS) maps, real-time meteorological data (such as wind speed, wind direction, and visibility), wind farm turbine operation and maintenance data (including turbine operating status, real-time personnel location, and temporary no-fly zones), airspace control information, and personnel location data to construct a multi-dimensional flight path planning database. Based on this, an improved multi-objective genetic algorithm is used to generate the optimal flight path within 30 seconds. This algorithm simultaneously optimizes six core objectives: shortest transportation time, lowest drone energy consumption, highest flight safety, lowest risk of damage during parts transportation, lowest probability of flight path conflicts, and fewest recharging / replenishment attempts. The weights of each objective are dynamically adjusted according to the urgency level of the task. For emergency repair tasks (priority level 1), the weights are ranked as follows: transportation time > safety > damage risk, prioritizing timeliness; for routine maintenance tasks (priority level 2), the weights are ranked as follows: energy consumption > safety > transportation time, prioritizing energy reduction.

[0057] After the algorithm outputs the optimal route, the platform simultaneously completes a route pre-simulation, marking key nodes in the route trajectory on a visual dashboard, including obstacle avoidance points, resupply points, and estimated takeoff and landing times. Dispatchers can preview and fine-tune the route. For long-distance transport missions (one-way exceeding half of the drone's range), the platform automatically plans intermediate charging or battery swapping nodes to ensure transport continuity. The final generated optimal route is then sent to the target drone's flight control system, guiding it to execute the transport mission along the planned path.

[0058] Through the aforementioned task allocation and route planning mechanism, this embodiment achieves intelligent matching of UAV resources and dynamic optimization of routes. Its beneficial effects are as follows: Firstly, by automatically allocating the optimal UAV through intelligent algorithms and combining regional segmentation with task merging, scheduling efficiency in multi-task scenarios is significantly improved, reducing UAV idleness and repeated takeoffs and landings. Secondly, by employing an improved multi-objective genetic algorithm and dynamically adjusting weights, timeliness, energy consumption, and safety can be balanced under different urgency levels. Simultaneously, through route rehearsal and charging node planning, the risks and probability of interruptions during transportation are effectively reduced, ensuring the efficiency and reliability of wind farm component transportation.

[0059] Optionally, S3 includes: The built-in temperature and humidity sensor and triaxial vibration sensor collect environmental parameters in real time during the transportation of parts. The positioning module and flight control system collect flight speed, altitude, attitude and battery parameters in real time and synchronize them to the intelligent dispatch platform. When environmental parameters exceed a preset first threshold, or flight parameters fall below a preset second threshold, an alarm is triggered and the flight attitude is adjusted, the flight speed is reduced and the aircraft avoids turbulent areas, or the scheduling platform algorithm replans alternative routes in real time and sends adjustment instructions to the target UAV.

[0060] In this embodiment, after the accessories are loaded into the customized buffer box, the two sets of temperature and humidity sensors and one set of triaxial vibration sensors built into the box begin to operate. These sensors are evenly distributed at the front, back, left, right, and center of the box, acquiring environmental parameters during the transportation process in real time at a sampling frequency of 1 time per second. Specifically, this includes humidity data inside the box and the vibration acceleration values ​​experienced by the accessories. Simultaneously, the target drone's own positioning module, which integrates GPS, BeiDou, and visual positioning technologies, as well as the flight control system, collects the drone's flight parameters in real time, including flight speed, altitude, attitude, and remaining battery power. All of the above environmental and flight parameters are synchronized to the intelligent scheduling platform in real time via an enhanced wireless module.

[0061] Once the intelligent dispatch platform receives the data, it compares it with preset safety thresholds. Specifically, when the vibration acceleration in the environmental parameters exceeds 5g, or the humidity exceeds 85%, or when the battery level in the flight parameters is below 30%, the system determines that the current state is abnormal. At this time, the intelligent dispatch platform immediately triggers the miniature audible and visual alarms located at the four corners of the enclosure, emitting an 80dB alarm signal, and simultaneously activates the dynamic adjustment mechanism.

[0062] In one possible implementation, if the anomaly is minor, such as excessive vibration acceleration, the platform will issue a command to the drone to adjust its flight attitude, specifically by reducing flight speed and actively avoiding turbulent areas to minimize impact on the components. If the anomaly is severe, such as low battery or sudden severe weather, the improved multi-objective genetic algorithm built into the scheduling platform will replan alternative routes in real time. This algorithm completes the calculation within one second and issues adjustment commands to the target drone's control module, while simultaneously pushing the adjustment information to the mobile devices of maintenance personnel for manual monitoring. Through this real-time monitoring and rapid response mechanism, the safety of the transportation process and the integrity of the components are ensured.

[0063] This specific implementation method, by introducing specific sensor types, acquisition frequencies, safety threshold parameters, and response actions, achieves refined monitoring and adaptive adjustment of the transportation status of parts, significantly reducing the risk of parts being damaged during transportation due to vibration, moisture, or insufficient drone power, and improving the reliability and intelligence level of the transportation process.

[0064] Optionally, the method further includes: Before the target drone returns, the optimal return route is replanned based on real-time weather and the drone's remaining battery power, prioritizing the avoidance of areas with severe environmental conditions. After the target drone arrives at the resupply station's take-off and landing point, it automatically docks with the charging device and begins charging. The dispatch platform simultaneously receives data from the entire flight, performs health checks on the power system, obstacle avoidance module, and communication module, and generates equipment status reports. If potential faults are detected, maintenance reminders are pushed to maintenance personnel. After charging is complete, the target drone enters standby mode.

[0065] In this embodiment, after the target UAV completes the unloading of accessories and receives confirmation from the destination aircrew, the dispatch platform initiates the return-to-home process. The dispatch platform first acquires real-time weather data and the target UAV's current remaining battery power, and then, combined with geographic information data, replans an optimal return-to-home route. This route planning prioritizes avoiding areas with adverse environmental conditions, such as strong winds, thunderstorms, or low visibility, to ensure safe return. After planning, the dispatch platform sends the return-to-home route to the target UAV's control module, and the target UAV then executes the return flight according to the planned route.

[0066] Once the target drone arrives at the designated take-off and landing point of the ground resupply station, it automatically docks with the charging device inside the station and begins charging. Simultaneously, the dispatch platform receives flight data recorded by the target drone throughout the entire transport mission, including flight parameters, power status, load status, and equipment health status. Based on this data, the dispatch platform performs health checks on the target drone's power system, obstacle avoidance module, and communication module, generating an equipment status report. This report details the operating conditions of each module and automatically diagnoses any potential faults.

[0067] If a potential fault is detected, such as abnormal motor operating parameters, decreased battery life, or communication module signal attenuation, the dispatch platform immediately pushes a maintenance reminder to the maintenance personnel's mobile device, along with fault details and handling suggestions. After charging is complete, the target drone enters standby mode, awaiting the next dispatch instruction. Through the above return-to-home and resupply maintenance process, closed-loop management of drone transportation missions is achieved, ensuring the continuous availability and operational safety of the drones.

[0068] This specific implementation method achieves closed-loop management of drone transportation tasks through automated return route planning, charging docking, health detection, and fault early warning. It significantly improves the automation level and operation and maintenance efficiency of drone scheduling, reduces the risk of transportation delays caused by equipment failure, and ensures the long-term stable operation of the system.

[0069] Optionally, the process described in step S4, where the target UAV arrives at the destination aircraft group, involves precise docking and unloading of parts via the cabin docking platform, recording and archiving the entire transportation process data, and generating a transportation statistical report, is specifically implemented through the following methods: When the target UAV is flying along the optimal route and is about to arrive at the destination aircraft, the dispatch platform automatically sends an activation command to the cabin docking platform control system on the destination aircraft 5 minutes before the estimated arrival time. This command, as input, triggers the positioning calibration components on the cabin docking platform to enter the working state, and at the same time switches the magnetic fixing device to standby mode.

[0070] Subsequently, when the target drone arrives above the docking platform, its onboard visual recognition module activates, guiding the drone to a precise descent and dock by recognizing the pre-set positioning calibration QR code on the docking platform. After docking, the magnetic locking device on the docking platform automatically locks in place, firmly securing the drone to the platform and ensuring a docking deviation of less than or equal to 1 millimeter.

[0071] The control system of the cabin docking platform then sends a "stable docking" status signal to the dispatch platform, indicating that the docking process is complete. After confirming stable docking, maintenance personnel inside the cabin open the buffer box of the component protection module and retrieve the required components. The maintenance personnel then upload a "unloading complete" confirmation message via their mobile app, simultaneously noting the component's condition. If any abnormalities are found in the components during unloading, such as moisture or impact, the maintenance personnel must upload photos and a text description of the abnormality via the mobile app.

[0072] Upon receiving the anomaly information, the dispatch platform automatically records the anomaly data and initiates a source tracing analysis process to determine the cause of the anomaly. After the unloading confirmation information is uploaded, the dispatch platform issues a return-to-home command to the target UAV. The magnetic attachment device on the docking platform then unlocks, the target UAV detaches from its fixed position, and initiates the return-to-home procedure, flying back to the ground resupply station according to the return route replanned by the dispatch platform.

[0073] Through the aforementioned precise docking and safe unloading process, this embodiment achieves high-precision and high-stability docking of drones on wind turbine nacelles, effectively avoiding the risk of component damage or drone overturning due to docking deviations. Simultaneously, the real-time confirmation and anomaly reporting mechanism via a mobile app ensures full traceability of the component transportation status, providing an accurate data foundation for subsequent fault analysis and process optimization, further enhancing the reliability and intelligent management level of wind farm component transportation.

[0074] Optionally, S4 includes: Record requirement information, task allocation records, flight path, flight parameters, parts status data, loading and unloading confirmation information, and abnormal handling process, archive them by task number, and store them in encrypted form; Regularly generate transportation statistics reports, covering data on the number of transportations, average duration, parts damage rate, drone energy consumption, and scheduling efficiency. Analyze the process through algorithms to optimize space and output operation and maintenance optimization suggestions.

[0075] In this embodiment, the dispatching platform automatically records data throughout the entire transportation process. Its input sources include: demand information (including parts parameters, destination aircraft coordinates, emergency level, and inventory verification results), task allocation records (including target UAV number, optimal route parameters, and allocation time), route trajectory (the optimal route and the sequence of coordinate points of the dynamically adjusted alternative routes), flight parameters (the target UAV's real-time uploaded flight speed, altitude, attitude, battery level, and power system operating parameters), parts status data (environmental parameters collected in real-time by the temperature and humidity sensor and the three-axis vibration sensor built into the parts protection module, with a collection frequency of 1 time / second), loading and unloading confirmation information (unloading completion confirmation information uploaded by maintenance personnel at the destination aircraft via a mobile APP, including a mark of the parts' good condition or abnormal photos and descriptions), and abnormal handling process (route adjustment instructions after triggering a safety threshold alarm, flight attitude adjustment records, and emergency response plan execution logs).

[0076] The dispatch platform archives all the above data by task number, using encrypted storage with a data retention period of at least 5 years, complying with wind farm operation and maintenance data management standards. The archived data supports multi-dimensional retrieval and traceability by task number, component model, drone number, and time range, and can be exported as Excel or PDF reports. Based on this, the dispatch platform periodically generates transportation statistics reports. These reports are input from the archived full-process data, and the processing involves statistical analysis of core indicators such as transportation frequency, average transportation time, component damage rate, drone energy consumption, and dispatch efficiency using built-in algorithms. Furthermore, the algorithm analyzes process optimization potential, such as identifying optimized flight paths for frequently faulty turbines or improving drone dispatch matching. The algorithm's output serves as operation and maintenance optimization suggestions, which are simultaneously pushed to the wind farm operation and maintenance management system, providing data support for subsequent transportation dispatching and equipment operation and maintenance.

[0077] This specific implementation method achieves full traceability and intelligent analysis of the transportation process through automatic data archiving and multi-dimensional statistical analysis, significantly improving the standardization level and data utilization efficiency of wind farm component transportation management, and providing accurate data support for operation and maintenance decisions.

[0078] In one possible embodiment, a drone intelligent dispatching system for the non-destructive transportation of wind farm components is provided, comprising: Intelligent dispatching platform: used to receive parts transportation requests, combine wind farm geographic information, real-time weather data, and drone status information to plan the optimal route, generate dispatching instructions, and monitor the entire transportation process in real time; Unmanned aerial vehicle (UAV) transport unit: Equipped with a positioning module, a power adjustment module, and a load adaptation mechanism, it receives dispatch instructions to execute transport tasks and has the ability to resist wind, avoid obstacles, and take off and land precisely; Accessory protection module: includes a customized buffer box, a moisture-proof sealing layer, a shockproof fixing structure and a status sensor. The buffer box has adjustable partitions inside, and the status sensor monitors the temperature, humidity and vibration parameters of the accessories in real time during transportation. Site adaptation module: includes a wind turbine nacelle docking platform, a ground supply station and charging device. The docking platform is equipped with a positioning calibration component to enable precise docking of UAVs. Safety monitoring module: Includes real-time video transmission unit, electronic fence module and emergency response module, used to monitor the flight status of drone, trigger alarms and perform emergency operations such as forced landing and return to home in case of abnormal situation.

[0079] The intelligent dispatching platform includes a demand intelligent reception and accurate analysis module, a dynamic route planning and multi-objective optimization module, a multi-UAV collaborative dispatching and cluster management module, a data visualization and full-process traceability management module, and an emergency dispatching and fault handling module. It realizes route planning and task scheduling, deeply integrates the needs of wind farm operation and maintenance scenarios, and achieves closed-loop management of "demand-dispatch-transportation-traceability-analysis". It is suitable for cross-regional dispatching of a single wind farm and differentiated management of emergency and daily transportation.

[0080] The buffer box of the accessory protection module adopts a honeycomb aluminum composite structure, filled with elastic cushioning material. The moisture-proof sealing layer meets the IP67 protection level. The intelligent fixing mechanism automatically clamps the accessories through electric clamps, adapting to wind power accessories of different sizes (including bolts, sensors, small motor components, control cabinet modules, insulator assemblies, etc.), and setting up dedicated protection for different accessories. Standardized loading and condition calibration are achieved through condition detection.

[0081] The drone carrier unit is equipped with millimeter-wave radar and visual obstacle avoidance sensors, which can identify obstacles such as towers and cables in the wind farm. It has a wind resistance level of no less than level 12, an endurance of ≥60 minutes, and a load capacity of 5-75kg.

[0082] The intelligent scheduling platform is integrated with the wind farm operation and maintenance management system, which can automatically synchronize spare parts demand information, equipment fault location and operation and maintenance personnel scheduling information to generate an integrated operation and maintenance plan.

[0083] The electronic fence module of the safety monitoring module presets no-fly zones in the wind farm and dynamically adjusts flight permissions based on real-time wind speed and visibility data. The emergency response module can automatically initiate a return-to-home or docking procedure when the battery is low, accessories are in abnormal condition, or extreme weather occurs.

[0084] 1. Intelligent scheduling platform Core Positioning: As the intelligent central hub for the entire process of wind farm component transportation, the intelligent dispatch platform combines precise demand response, dynamic intelligent dispatch, multi-system collaborative linkage, data visualization management, and full-cycle operation and maintenance support capabilities. It not only realizes route planning and task scheduling, but also deeply integrates with the needs of wind farm operation and maintenance scenarios to achieve closed-loop management of "demand-dispatch-transportation-traceability-analysis". It is adaptable to cross-regional dispatch of a single wind farm and differentiated management of emergency and daily transportation, thereby improving the overall intelligence and practicality of the system.

[0085] Core functions: Module for intelligent reception and accurate analysis of demand: (1) Multi-channel demand access: When the wind turbine alarms, the wind farm operation and maintenance management system automatically identifies the fault type, matches the required parts, generates transportation demand and pushes it to the dispatch platform without manual submission; it has a mobile APP and computer Web terminal manual submission channels, supports voice input and parts scanning input, and parts need to be affixed with exclusive RFID tags. Scanning the code can quickly obtain information such as model, size, weight, and protection requirements, which is suitable for operation and maintenance personnel to perform high-altitude operations, outdoor inspections and other multi-scenario operations. (2) Intelligent demand analysis and classification: The built-in accessory demand matching algorithm automatically analyzes demand information, such as accessory parameters, precise coordinates of the destination unit, emergency level (divided into emergency repair [priority level 1], daily operation and maintenance [priority level 2], spare parts supply [priority level 3]), transportation time limit requirements, etc.; combined with wind farm accessory inventory data, it is linked with the accessory storage cabinet of the ground supply station in real time to automatically check the accessory inventory status. When the inventory is insufficient, it immediately pushes a replenishment reminder to the operation and maintenance management system to avoid ineffective scheduling; tasks are automatically classified according to emergency level, transportation area and accessory type to generate a scheduling task list.

[0086] Dynamic route planning and multi-objective optimization module: (1) Multi-dimensional route planning: By integrating geographic information system (GIS), real-time wind speed, wind direction, visibility and other meteorological data, UAV status data, real-time operation and maintenance data of wind farm (such as unit operation status, real-time location of operators, temporary construction no-fly zone), airspace control information (connecting to the civil aviation low-altitude control system to obtain real-time airspace permissions), and special requirements for parts transportation (such as precision parts need to fly at low speed and smoothly, and overweight parts need to optimize power distribution routes), avoid obstacles and bad weather areas, and build a multi-dimensional route planning database. (2) Algorithm optimization: An improved multi-objective genetic algorithm is adopted to optimize six core objectives simultaneously: shortest transportation time, lowest UAV energy consumption, highest flight safety factor, lowest risk of damage during parts transportation, lowest probability of route conflict, and fewest charging and resupply times. The algorithm can dynamically adjust the target weights according to the urgency level of the task. For example, the priority weight for emergency tasks is: transportation time > safety factor > damage risk; the priority weight for daily tasks is: energy consumption > safety factor > transportation time, generating the optimal route plan. It supports route pre-simulation, and dispatchers can preview the route trajectory and key nodes, such as obstacle avoidance points, take-off and landing points, resupply points, and expected transportation parameters, and can manually fine-tune and optimize them. (3) Dynamic route adaptive adjustment: Various dynamic data (sudden changes in wind speed and direction, sudden obstacles, UAV fault warnings, and abnormal parts status) are synchronized in real time during flight. When the data exceeds the safety threshold, the algorithm replans alternative routes in real time, pushes them to the UAV carrier unit, and simultaneously reminds the maintenance personnel. For long-distance transportation tasks (a single trip exceeds half of the UAV's range), charging / battery swapping nodes at intermediate resupply stations are automatically planned to ensure continuous transportation.

[0087] Multi-UAV collaborative scheduling and cluster management module: (1) Upgraded collaborative scheduling capability: Supports cluster scheduling of 1-20 UAVs, adds dynamic task allocation function, can automatically allocate the best UAV to perform tasks according to the real-time status of UAVs (battery, remaining load, current location, health) and task requirements (parts weight, transportation distance); For scenarios with multiple groups of simultaneous needs, adopts the "regional segmentation + task merging" scheduling mode, multiple tasks in the same area are merged and transported by the same UAV in batches, reducing the number of UAV take-offs and landings and improving scheduling efficiency; Built-in route conflict warning and avoidance mechanism, through real-time dynamic path planning, avoids UAV aerial intersection conflicts, conflict warning response time ≤0.5 seconds, avoidance adjustment time ≤1 second. (2) Status management of UAV cluster: Real-time monitoring of all UAV status data in all dimensions, including flight parameters (flight speed, altitude, attitude, position accuracy), power status (battery power, remaining range, power system operating parameters), load status (component weight, protection module status, sensor data), and equipment health status (operating conditions of motors, propellers, and communication modules, and automatic diagnosis of potential faults); generating a UAV status visualization dashboard, automatically highlighting and alarming abnormal statuses (such as battery power below 30%, power system abnormality, and communication interruption), and marking the fault level and emergency handling suggestions.

[0088] Data visualization and full-process traceability management module: (1) Multi-dimensional visualization management: Build a multi-dimensional data visualization dashboard, including task scheduling dashboard (real-time display of task progress, number of tasks to be assigned / executed / completed, and timeout warning), drone status dashboard (cluster operation status, anomaly statistics, and battery life distribution), transportation trajectory dashboard (real-time display of each drone's flight trajectory, current location, and estimated arrival time, and support for historical trajectory playback), and data statistics dashboard (daily / monthly transportation frequency, average transportation time, accessory damage rate, drone energy consumption statistics, and scheduling efficiency analysis); the dashboard supports custom configuration and can filter and display core data according to operation and maintenance management needs, adapting to different levels of personnel (schedulers view details, and managers view statistics). (2) Deepening full-process traceability: In addition to the original transportation data records, new records are added for scheduling decision-making process data (route planning algorithm parameters, task allocation logic, reasons for route adjustment), UAV operation and maintenance data (equipment wear and tear, maintenance records, and charging times after each task), and spare parts full life cycle data (transportation times, changes in transportation status, and usage destination); all data are archived in association with task number, and support multi-dimensional retrieval and traceability by task number, spare parts model, UAV number, time range, etc.; the data storage period is ≥5 years, which complies with the wind farm operation and maintenance data management specifications, and can export Excel and PDF format reports for operation and maintenance analysis.

[0089] Emergency dispatch and fault handling module: (1) Optimization of emergency dispatch mechanism: For sudden scenarios (such as emergency repair of major unit failures, emergency resupply before extreme weather, and sudden failure of UAVs), a new emergency dispatch green channel is added to automatically prioritize emergency tasks and suspend or adjust non-emergency tasks; reserve emergency backup UAVs (default 2-3, configurable), which are in standby status during normal times and are activated immediately after an emergency task is triggered to ensure that the emergency response time is ≤5 minutes; for extreme weather (such as winds above level 10, heavy rainfall, and low visibility), automatically assess flight safety, dispatch UAVs on the way to return or dock nearby in advance, and resume transportation after the weather improves. (2) Intelligent fault handling: Built-in intelligent handling plan library for common UAV faults (power system failure, communication interruption, positioning failure, battery abnormality) and transportation abnormalities (parts status exceeding standards, route obstruction, docking platform failure); when an abnormality occurs, the corresponding plan is automatically matched and handling instructions are pushed (such as starting the backup communication module when communication is interrupted, switching visual positioning when positioning fails, and dispatching backup UAVs to relay transportation when UAV fails); at the same time, an abnormality alarm is pushed to the mobile terminal of the operation and maintenance personnel, with fault details, handling progress and manual intervention suggestions to ensure that the fault is resolved quickly and transportation delays are reduced.

[0090] 2. Unmanned Aerial Vehicle (UAV) Carrier Unit Structural Design: It adopts a multi-rotor UAV architecture, with the fuselage made of lightweight high-strength carbon fiber material and equipped with a redundant power system to ensure flight stability in extreme weather conditions; the positioning module integrates GPS, Beidou and visual positioning technologies, with a positioning accuracy of ±0.5 meters; the UAV carrier unit is equipped with millimeter-wave radar and visual obstacle avoidance sensors, which can identify obstacles such as towers and cables in the wind farm.

[0091] Adaptability: The load-bearing mechanism can dynamically adjust the power output according to the weight of the parts, supporting the transportation of parts weighing 5-75kg, meeting the parts needs of daily operation and maintenance of wind farms (such as bolts, washers, sensors, small controllers, etc.).

[0092] 3. Accessory Protection Module Main protective enclosure: The customized buffer box adopts a "hard outside, soft inside" design. The outer shell is made of 5-8mm honeycomb aluminum composite panel, which is impact-resistant and deformation-resistant. The inner wall is fitted with a 3mm thick silicone cushioning layer. The outer moisture-proof sealing layer is made of IP67-rated waterproof and breathable material to prevent rain, snow and moisture from entering, making it suitable for the high humidity and rainy / snowy environment of wind farms. The enclosure adopts reinforced modular splicing, with aluminum alloy reinforcing strips at the corners to prevent deformation of large-sized enclosures. The top is equipped with two sets of quick-release buckles for precise docking with the drone's payload platform, and the bottom is equipped with two sets of anti-slip and wear-resistant pads to improve placement stability. The side is equipped with a double emergency opening door for easy access to accessories.

[0093] Internal Adaptive Protection Structure: (1) The interior is equipped with adjustable partitions, using reinforced honeycomb detachable partitions, supporting independent placement of a single medium-sized accessory or combined loading of multiple small accessories; the bottom of the partition is covered with a 15-20mm high-density EVA buffer pad, and a foldable limit stop is added to strengthen the fixation of large-sized accessories. (2) The intelligent fixing mechanism consists of 6 sets of electric linkage clamps, symmetrically arranged on both sides, with a clamp arm stroke of 0-150mm and a clamping force adjustment range of 10-50N, adapting to accessory sizes; the clamps have a built-in pressure feedback module to calibrate the clamping force in real time, preventing large-sized accessories from shaking. (3) Dedicated protection: For small gear components, wear-resistant protective bushings are added to prevent tooth surface wear during transportation; for control cabinet modules, an anti-static partition is configured, with built-in buffer airbags that can be inflated and expanded to fit the surface of the components, providing all-round shock absorption and protection.

[0094] Status Monitoring: Two built-in temperature and humidity sensors and one triaxial vibration sensor are evenly distributed inside the enclosure (front, back, left, right, and center) to ensure comprehensive environmental parameter monitoring coverage within the large space. The sensor data acquisition frequency is 1 time / second, ensuring monitoring accuracy. Four miniature audible and visual alarms are installed inside the enclosure, distributed at the four corners, with an alarm volume of 80dB, ensuring rapid detection of anomalies by maintenance personnel. The data transmission module is an enhanced wireless module, avoiding signal obstruction by the large enclosure and ensuring real-time data synchronization with the scheduling platform. During operation, environmental parameters during parts transportation are collected in real time and synchronized to the intelligent scheduling platform. If parameters exceed safety thresholds (e.g., vibration acceleration > 5g, humidity > 85%), an alarm is immediately triggered and the flight attitude is adjusted (e.g., reducing flight speed, avoiding turbulent areas).

[0095] Auxiliary protection: (1) Four sets of rubber shock-absorbing supports are installed at the connection between the box and the drone. The shock-absorbing damping is optimized to 0.4-0.6 to absorb the additional vibration brought by the large-size box. The inner corners of the box are equipped with arc-shaped anti-collision strips, and a 20mm shock-absorbing buffer layer is added to the bottom of the box. (2) Temperature and humidity control: Two sets of micro constant temperature dehumidification modules are set and symmetrically arranged on both sides of the box to maintain uniform internal temperature and humidity. (3) Marking: A large electronic label is affixed to the outside of the box with a built-in QR code for easy scanning and traceability. (4) The anti-ultraviolet coating is wear-resistant and corrosion-resistant. (5) Installation and fixing: The top double set of quick-release buckles correspond to the reinforced docking structure of the drone's load-bearing platform. The buckles are set with positioning pins + infrared calibration, and the installation deviation is ≤1mm. After docking, the double set of electromagnetic locks are activated for fixing, and the bottom anti-slip limit blocks are used to prevent displacement and falling off during flight.

[0096] 4. Station Adaptation Module Nacelle docking platform: Installed on the outside of the wind turbine nacelle, it is equipped with a positioning calibration QR code and a magnetic fixing device. After the drone arrives, it is accurately docked through visual recognition, and the magnetic device automatically fixes the drone, ensuring stability during the loading and unloading of parts. Ground supply station: Equipped with charging devices, spare parts storage cabinets and status monitoring equipment, it can realize automatic charging of drones and rapid loading of spare parts, and support 24-hour uninterrupted operation; the storage cabinets have moisture-proof and dust-proof functions to ensure that the spare parts to be transported are in good condition.

[0097] 5. Security monitoring module Real-time monitoring: Equipped with high-definition cameras and infrared thermal imagers, it transmits flight footage and accessory status in real time, allowing maintenance personnel to remotely view the transportation progress; Safety and protection: The electronic fence module is preset with no-fly zones in wind farms (such as transformer areas, densely populated areas, and military no-fly zones), and dynamically adjusts flight permissions based on real-time meteorological data; the emergency response module can automatically initiate a return-to-home procedure or dock at the nearest safe area when the battery power is below 20%, the accessories are in abnormal condition, or extreme weather such as gale-force winds of level 12 or heavy rain occurs, in order to avoid equipment damage and loss of accessories.

[0098] 1. Request Initiation and Intelligent Analysis Demand Triggering: Two modes are available—① Automatic Triggering: After a wind turbine fault alarm, the operation and maintenance management system automatically identifies the fault type (such as sensor fault, gear component damage), matches the corresponding required parts model and quantity, generates a transportation request, and pushes it to the intelligent dispatch platform, simultaneously marking the task's urgency level; ② Manual Triggering: Operation and maintenance personnel submit requests via mobile APP (voice / QR code input) or computer web terminal, entering parts information, destination turbine number, urgency level (level 1-3), and transportation time limit, supporting QR code scanning to read the parts' RFID tags to obtain accurate parameters.

[0099] Intelligent verification and analysis: After receiving a request, the dispatch platform completes the analysis within 10 seconds and simultaneously links with the ground supply station's spare parts storage cabinet to verify the real-time spare parts inventory. If the inventory is sufficient, the task list is automatically entered, noting the spare parts' protection requirements and special transportation specifications (such as precision spare parts requiring low-speed flight). If the inventory is insufficient, a replenishment reminder is immediately pushed to the operation and maintenance management system and supply station staff, while also providing feedback to the request initiator to avoid invalid dispatch. Tasks are automatically classified according to urgency level and transportation area, generating a visual dispatch task ledger.

[0100] 2. Task allocation and dynamic route planning Multi-drone task allocation: The dispatching platform automatically allocates the optimal drone based on task requirements (parts weight, transportation distance, urgency level) and the real-time status of all drones (battery level, remaining payload, current location, health). When multiple drones have simultaneous needs, a "regional segmentation + task merging" mode is adopted, with tasks in the same region being merged and transported in batches by the nearest drone. The allocation results are simultaneously pushed to the corresponding drone control module and ground staff, and manual adjustments by dispatchers are supported.

[0101] Multi-dimensional route planning: The platform retrieves multi-dimensional data such as GIS maps, real-time weather, crew operation and maintenance status, airspace control, and personnel locations. Through an improved multi-objective genetic algorithm, it dynamically optimizes routes according to task priority (prioritizing timeliness for emergency tasks and energy conservation for routine tasks), generating the optimal route within 30 seconds. Simultaneously, it completes route pre-simulation, marking obstacle avoidance points, resupply points, and estimated take-off and landing times, which dispatchers can preview and fine-tune. For long-distance tasks, it automatically plans intermediate charging / battery swapping nodes to ensure continuous transportation.

[0102] 3. Parts loading and system linkage preparation Loading instruction push: The dispatch platform sends a loading instruction to the intelligent control system of the ground supply station, specifying the drone number, parts information, protection requirements, and estimated takeoff time. The parts storage cabinet of the supply station automatically unlocks the corresponding compartment, and the charging device prepares to recharge the drone before takeoff.

[0103] Standardized loading and status calibration: Staff remove the parts and place them in a customized buffer box. Following the platform prompts, they secure the parts using electric clamps and activate the status sensors. The sensors automatically calibrate, collecting initial temperature, humidity, and vibration parameters, and simultaneously uploading them to the dispatch platform. The platform verifies the secure mounting status of the parts, the operational status of the sensors, and the sealing performance of the buffer box. Once all standards are met, the platform reports "loading qualified," and the drone receives the takeoff command and arrives at the designated takeoff and landing point at the supply station. If any abnormalities are found (such as clamps not being tightened or sensor malfunctions), an alarm is immediately sent, and the subsequent process is only initiated after the issues are resolved and repaired.

[0104] 4. Automated transportation and dynamic route adaptation Precise takeoff and full-process monitoring: The UAV takes off according to the planned route and avoids obstacles during flight using millimeter-wave radar and visual obstacle avoidance sensors. The positioning module (GPS + Beidou + vision) ensures positioning accuracy of ±0.5 meters. The dispatch platform receives real-time flight parameters (speed, altitude, attitude, battery level), accessory status data (temperature, humidity, vibration), and real-time video footage from the safety monitoring module. The visual dashboard displays the trajectory and status simultaneously, and maintenance personnel can view it remotely.

[0105] Dynamic route adjustment: In case of sudden wind speed changes, sudden obstacles, drone malfunction warnings, or parts exceeding the standard, the scheduling platform algorithm will replan alternative routes in real time, issue adjustment instructions to the drone within 1 second, and simultaneously remind maintenance personnel; when communication is interrupted, the backup communication link will be automatically switched to ensure instruction transmission; when parts are abnormal in emergency missions, the flight attitude will be adjusted first (deceleration, stable flight), and if necessary, the nearest docking point will be planned for temporary handling.

[0106] 5. Precise docking and parts unloading Dock preparation and precise docking: 5 minutes before the drone arrives at the destination, the dispatch platform sends an instruction to the cabin docking platform control system to activate the positioning calibration component and put the magnetic fixing device into standby mode; after the drone arrives, it precisely docks with the docking platform through visual recognition, the magnetic device automatically locks, the docking deviation is ≤1mm, and the platform reports "stable docking" to the dispatch center.

[0107] Safe unloading and information confirmation: Maintenance personnel open the buffer box in the cabin according to specifications, take out the parts, check the condition of the parts, and upload "unloading complete" confirmation information through the mobile APP, while simultaneously marking the good condition of the parts; if the parts are abnormal (such as dampness or impact), they immediately upload abnormal photos and descriptions, and the dispatch platform automatically records the abnormal information and initiates the source tracing analysis process; after unloading confirmation, the platform issues a return command, the magnetic attachment device unlocks, and the drone starts the return procedure.

[0108] 6. Drone return and resupply maintenance Return route planning: Before the drone returns, the dispatch platform combines real-time weather and the drone's remaining battery power to replan the optimal return route, prioritizing the avoidance of areas with severe environmental conditions to ensure safe return.

[0109] Return to base for resupply and status monitoring: When the drone arrives at the resupply station's take-off and landing point, it automatically docks with the charging device to begin charging. The dispatch platform simultaneously receives data from the drone's entire flight and performs health checks on the power system, obstacle avoidance module, communication module, etc., generating an equipment status report. If any potential faults are detected (such as abnormal motor operation or reduced battery range), a maintenance reminder is pushed to the maintenance personnel. After charging is completed, the drone enters standby mode, waiting for the next dispatch command.

[0110] 7. End-to-end data archiving and analysis Automatic data archiving: The dispatching platform automatically records data throughout the entire transportation process, including demand information, task allocation records, route trajectories, flight parameters, parts status data, loading and unloading confirmation information, and anomaly handling processes. Data is archived by task number, encrypted, and retained for ≥5 years, supporting multi-dimensional retrieval and traceability.

[0111] Data statistical analysis: The platform regularly generates transportation statistical reports, covering data such as the number of transportations, average duration, parts damage rate, drone energy consumption, and scheduling efficiency; through algorithm analysis of process optimization space (such as route optimization for high-frequency faulty units and directions for improving drone scheduling matching), it outputs operation and maintenance optimization suggestions, which are simultaneously pushed to the wind farm operation and maintenance management system to provide data support for subsequent transportation scheduling and equipment operation and maintenance.

[0112] In one possible embodiment, the operation process of the drone dispatching system for the non-destructive transportation of wind farm components is as follows: Application scenario: A mountain wind farm with 30 5MW wind turbine units installed. The units are scattered, and some units are located in mountainous areas with a slope greater than 30°. The journey is long and the parts are easily damaged during transportation. System configuration: Deploy 10 multi-rotor drones with a payload of 5-75kg, 3 ground supply stations (located in the south, central and north areas of the wind farm respectively), install a docking platform on the nacelle of each wind turbine, and connect the intelligent dispatch platform with the existing operation and maintenance management system of the wind farm; Implementation process: (1) Request initiation: The wind farm operation and maintenance management system automatically identifies the fault type, matches the required parts, generates transportation requests and pushes them to the dispatch platform, or after the operation and maintenance personnel discover a fault in the wind turbine, they manually submit the parts request through the mobile APP or computer web terminal, specifying the parts type, quantity, destination turbine number and urgency; (2) Dispatch planning: After receiving the request, the intelligent dispatch platform synchronously retrieves the wind farm GIS map, real-time meteorological data (wind speed, wind direction, visibility) and drone status (battery power, load capacity), plans the optimal route and generates dispatch instructions; (3) Parts loading: According to the dispatch instructions, the ground staff put the parts into the customized buffer box, fix them with electric clamps, and the status sensor starts and begins monitoring. (4) Automatic transport: After receiving the instruction, the UAV takes off from the ground supply station and flies along the planned route. During the flight, it avoids obstacles through millimeter-wave radar and visual obstacle avoidance sensors and feeds back the flight status and accessory parameters to the dispatch platform in real time. (5) Precise docking: After the UAV arrives at the destination crew, it achieves precise docking through the positioning calibration component of the cabin docking platform and fixes the UAV with a magnetic suction device. (6) Accessory unloading: The maintenance personnel open the buffer box in the cabin, take out the accessories, and confirm the unloading through the APP. The UAV automatically returns to the ground supply station. (7) Full traceability: The intelligent dispatch platform records the data of the entire transportation process (including route trajectory, flight parameters, accessory status, loading and unloading time) to form an operation and maintenance ledger, which is convenient for subsequent traceability and analysis.

[0113] Actual results: During the 3-month trial operation, a total of 240 parts were delivered (including sensors, bolts, insulating components, etc.), with a parts damage rate of 0. The average delivery time was reduced from the traditional 100 minutes to 20 minutes. Emergency fault handling efficiency was improved by 70%, manpower input was reduced by 2 people per shift, maintenance costs were reduced by 30%, and no flight safety accidents occurred.

[0114] This invention solves the core pain point of wind farm component transportation through intelligent scheduling, dedicated protection, and precise adaptation design, providing an efficient, safe, and reliable intelligent solution for wind farm operation and maintenance, and has broad application prospects.

[0115] To achieve the above embodiments, this application also proposes a drone scheduling device for non-destructive transportation of wind farm components.

[0116] Figure 2 This is a schematic diagram of the structure of a drone dispatching device for non-destructive transportation of wind farm accessories provided in an embodiment of this application.

[0117] like Figure 2 As shown, the device may include: The demand parsing module 210 is used to receive the transportation demand of wind farm parts, parse the parts parameters, destination turbine coordinates and emergency level in the transportation demand, and verify the inventory status of the corresponding parts in the ground supply station. The route optimization module 220 is used to generate the optimal route based on the accessory parameters, the destination aircraft coordinates and the emergency level, combined with real-time meteorological data, geographic information data and UAV status data, through a multi-objective optimization algorithm, and to allocate the optimal route to the target UAV. The dynamic adjustment module 230 is used to collect environmental parameters of the accessory and flight parameters of the target UAV in real time during the flight of the target UAV transporting the accessory according to the optimal route. When the environmental parameters or the flight parameters exceed the safety threshold, the module dynamically adjusts the flight attitude of the target UAV or replans alternative routes. The recording module 240 is used to dock with and unload the accessories through the cabin docking platform after the target UAV arrives at the destination aircraft group, record and archive the data of the entire transportation process, and generate transportation statistical reports.

[0118] It should be noted that the foregoing explanation of the method embodiments also applies to the apparatus of this embodiment, and will not be repeated here.

[0119] To implement the above embodiments, this application also proposes a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the method described in the foregoing method embodiments.

[0120] To implement the above embodiments, this application also proposes a computer program product having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method described in the foregoing method embodiments.

[0121] To implement the above embodiments, this application also proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, it implements the method described in the foregoing method embodiments.

[0122] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0123] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0124] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0125] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0126] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0127] Those skilled in the art will understand that all or part of the steps of the methods described in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it includes one or a combination of the steps of the method embodiments.

[0128] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0129] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for scheduling unmanned aerial vehicles (UAVs) for the non-destructive transportation of wind farm components, characterized in that, include: S1, receive the transportation request for wind farm parts, parse the parts parameters, destination turbine coordinates and emergency level in the transportation request, and verify the inventory status of the corresponding parts in the ground supply station; S2, based on the accessory parameters, the destination aircraft coordinates and the emergency level, combined with real-time meteorological data, geographic information data and UAV status data, an optimal route is generated through a multi-objective optimization algorithm, and the optimal route is assigned to the target UAV; S3, during the flight of the target UAV transporting the accessory according to the optimal route, the environmental parameters of the accessory and the flight parameters of the target UAV are collected in real time. When the environmental parameters or the flight parameters exceed the safety threshold, the flight attitude of the target UAV is dynamically adjusted or alternative routes are replanned. S4. After the target UAV arrives at the destination aircraft group, it docks with the aircraft docking platform and unloads the accessories, records and archives the entire transportation process data, and generates a transportation statistical report.

2. The method according to claim 1, characterized in that, S1 includes: The wind farm operation and maintenance management system automatically identifies fault types, matches the required parts, generates transportation requirements, and pushes them to the intelligent dispatch platform. The transportation demand is analyzed, and the real-time inventory of spare parts is checked in conjunction with the spare parts storage cabinets at the ground supply station. If the inventory is sufficient, the task list is automatically entered and the spare parts protection requirements and special transportation specifications are marked. If the inventory is insufficient, a replenishment reminder is pushed.

3. The method according to claim 1, characterized in that, S2 includes: Based on the weight of the accessories, transportation distance, and urgency level in the mission requirements, as well as the real-time status of all drones, at least one target drone is assigned. When multiple drones have simultaneous requirements, a regional segmentation and mission merging mode is adopted. Missions in the same region are merged and transported in batches by the nearest drone. The system retrieves geographic information system maps, real-time meteorological data, unit operation and maintenance data, airspace control data, and personnel location data. Through an improved multi-objective genetic algorithm, it dynamically optimizes the flight path according to task priority to generate the optimal flight path. Simultaneously, it completes flight path pre-simulation and marks obstacle avoidance points, resupply points, and estimated take-off and landing times. For long-distance missions, it automatically plans mid-course charging or battery swapping nodes.

4. The method according to claim 3, characterized in that, The process of dynamically optimizing the route according to task priority using an improved multi-objective genetic algorithm to generate the optimal route includes: The optimization objectives are to minimize transportation time, drone energy consumption, flight safety, risk of damage during parts transportation, probability of route conflict, and number of recharging and replenishment trips. The objective weights are dynamically adjusted according to the mission urgency level, and an improved multi-objective genetic algorithm is used to optimize the route to generate the optimal route.

5. The method according to claim 1, characterized in that, S3 includes: The built-in temperature and humidity sensor and triaxial vibration sensor collect environmental parameters in real time during the transportation of parts. The positioning module and flight control system collect flight speed, altitude, attitude and battery parameters in real time and synchronize them to the intelligent dispatch platform. When environmental parameters exceed a preset first threshold, or flight parameters fall below a preset second threshold, an alarm is triggered and the flight attitude is adjusted, the flight speed is reduced and the aircraft avoids turbulent areas, or the scheduling platform algorithm replans alternative routes in real time and sends adjustment instructions to the target UAV.

6. The method according to claim 1, characterized in that, The method further includes: Before the target drone returns, the optimal return route is replanned based on real-time weather and the drone's remaining battery power, prioritizing the avoidance of areas with severe environmental conditions. After the target drone arrives at the resupply station's take-off and landing point, it automatically docks with the charging device and begins charging. The dispatch platform simultaneously receives data from the entire flight, performs health checks on the power system, obstacle avoidance module, and communication module, and generates equipment status reports. If potential faults are detected, maintenance reminders are pushed to maintenance personnel. After charging is complete, the target drone enters standby mode.

7. The method according to claim 1, characterized in that, S4 includes: Record requirement information, task allocation records, flight path, flight parameters, parts status data, loading and unloading confirmation information, and abnormal handling process, archive them by task number, and store them in encrypted form; Regularly generate transportation statistics reports, covering data on the number of transportations, average duration, parts damage rate, drone energy consumption, and scheduling efficiency. Analyze the process through algorithms to optimize space and output operation and maintenance optimization suggestions.

8. A drone dispatching device for non-destructive transportation of wind farm components, characterized in that, include: The demand parsing module is used to receive transportation demands for wind farm components, parse the component parameters, destination turbine coordinates and emergency level in the transportation demands, and verify the inventory status of the corresponding components in the ground supply station. The route optimization module is used to generate the optimal route based on the accessory parameters, the destination aircraft coordinates and the emergency level, combined with real-time meteorological data, geographic information data and UAV status data, through a multi-objective optimization algorithm, and to allocate the optimal route to the target UAV. The dynamic adjustment module is used to collect environmental parameters of the accessory and flight parameters of the target UAV in real time during the flight of the target UAV transporting the accessory according to the optimal route. When the environmental parameters or the flight parameters exceed the safety threshold, the module dynamically adjusts the flight attitude of the target UAV or replans alternative routes. The recording module is used to dock with and unload the accessories through the cabin docking platform after the target UAV arrives at the destination aircraft group, record and archive the data of the entire transportation process, and generate transportation statistical reports.

9. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, it implements the method as described in any one of claims 1-7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of the preceding claims 1-7.

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