Unmanned aerial vehicle function expansion cabinet control system and method for facilitating power transmission line patrol

Abstract

This invention relates to the field of high-end equipment manufacturing technology, and in particular provides a control system and method for a UAV function expansion cabinet that facilitates power transmission line inspection. The system includes an equipment pre-setting subsystem for sensing line environment image data collected by high-definition cameras and thermal imagers, transmitting the line environment image data to a defect database; a control system for the UAV nest embedded in the tower generates equipment pre-setting instructions, moving the corresponding equipment to the hoisting interface for standby; a multi-UAV collaborative dispatch subsystem compares the coordinates of risk points with a pre-entered set of tower coordinates to determine the UAV nest relay network topology within the line section where the risk point is located; and generates collaborative dispatch instructions; a nest status reset subsystem compares real-time temperature data with a preset work completion temperature threshold, generating a work completion status marker when the temperature threshold is reached. This invention achieves full-process automation from risk perception, decision-making and dispatching, collaborative operation to status reset.

Description

Technical Field

[0001] This invention relates to the field of high-end equipment manufacturing technology, and in particular to a control system and method for an unmanned aerial vehicle (UAV) function expansion cabinet that facilitates power transmission line inspection. Background Technology

[0002] As a crucial component of the power system, the safe and stable operation of transmission lines directly impacts the normal functioning of the national economy. Due to their wide distribution, complex terrain, and long-term exposure to the natural environment, transmission lines are susceptible to external disturbances such as bird nests, floating debris, and icing. Failure to detect and address these risks promptly can lead to line tripping, equipment damage, or even large-scale power outages. Traditional manual inspection methods suffer from inefficiency, delayed response, and high operational risks, making them unsuitable for meeting the demands of modern power grids for intelligent and precise operation and maintenance.

[0003] In recent years, drone inspection technology has been widely used in the field of power transmission line inspection. Equipped with high-definition cameras, thermal imagers, and other sensors, drones can efficiently collect image data of the transmission line corridors and towers, effectively improving the coverage and data collection efficiency of inspections. However, current drone inspection systems primarily focus on the detection phase, i.e., identifying line defects or potential hazards through image data, while the automation and intelligence of the handling phase remain relatively low. Once a risk is identified, how to quickly dispatch suitable equipment, plan the optimal flight path, coordinate multiple drones working together, and automatically assess the effectiveness of the operation and reset the system status to form a closed-loop management system from detection to handling remains a major challenge for the technology.

[0004] Currently, existing technologies related to UAV inspection of power transmission lines mainly focus on the following aspects: 1. Risk detection technology based on image recognition: Existing technologies use algorithms such as deep learning to analyze images of power lines collected by UAVs to identify defects such as bird nests, floating objects, and insulator damage. They typically include a preset feature library or defect library, comparing the collected images with feature thresholds in the library to determine if a risk exists. For example, by comparing the shape, size, and texture of target objects, bird nests and icing can be classified and labeled. 2. UAV nest scheduling technology: Existing technologies propose deploying UAV nests, or UAV airports, on power transmission line towers for UAV parking, charging, and task scheduling. The nests typically have environmental control functions, such as constant temperature, dehumidification, and fast charging. When a task needs to be performed, the control system selects one or more nests according to the task requirements and assigns a UAV equipped with the corresponding sensors to take off and proceed to the target point for inspection. 3. Temperature monitoring-based operation judgment technology: When using drones equipped with laser bird deterrents, laser obstacle clearing devices and other equipment for disposal operations, existing technologies use thermal imagers to monitor the temperature changes of the target area in real time; when the temperature of the target area reaches a preset critical value, such as the temperature for deterring birds or melting, the system judges that the operation is completed and instructs the drone to stop the operation or return to base.

[0005] Most existing technologies treat risk detection and operational handling as two separate stages. After the image recognition system outputs risk information, human intervention is usually required to assess the severity of the risk, such as the risk level, and to decide which operational equipment to deploy, such as laser bird deterrents or de-icing robots. This human intervention not only reduces response speed but also increases the subjectivity and uncertainty of decision-making, making it difficult to achieve rapid and accurate automated response. When scheduling multiple drone nests, current technologies often only consider the straight-line distance between a single nest and the risk point, ignoring terrain obstacles in the actual flight path, such as mountains, buildings, and passage constraints, requiring flight along power lines. Furthermore, when planning collaborative operations, there is a lack of effective methods for uniformly quantifying energy consumption based on the geometric characteristics of the flight path and horizontal and vertical distances. This fails to comprehensively consider factors such as the energy consumption differences of drones in different flight attitudes, the real-time battery status of the nests, and charging efficiency, such as ambient temperature and battery life. Consequently, the selected starting nest or relay path may not be globally optimal, posing a risk of mission interruption due to inaccurate battery estimation. Summary of the Invention

[0006] To achieve the above objectives, the present invention adopts the following technical solution: In one aspect, the present invention provides a UAV function expansion cabinet control system for facilitating power transmission line inspection, comprising: The pre-positioning subsystem for operational equipment is used to transmit line environment image data collected by high-definition cameras and thermal imagers to a defect database. The defect database classifies and labels the line environment image data according to three preset feature thresholds: bird nests, floating objects, and icing. The risk information after classification and labeling is compared with the pre-stored risk level judgment conditions to obtain the matching risk level and the corresponding operational equipment type. The real-time status data of the tower-embedded nest is retrieved, and the operational equipment type is matched with the real-time status data to filter out the tower-embedded nest number that is currently loaded with the corresponding operational equipment and has sufficient power for flight. The control system of the tower-embedded nest generates a pre-positioning command to move the corresponding operational equipment to the hoisting interface for standby. The multi-aircraft collaborative dispatch subsystem is used to extract the coordinates of risk points from risk information, compare the risk point coordinates with the pre-entered set of tower coordinates, and determine the relay network topology of the nests within the line section where the risk point is located; calculate the first flight path based on the risk point coordinates and the main nest coordinates, calculate the second flight path based on the risk point coordinates and the coordinates of each sub-nest, add the first flight path and each second flight path to obtain the total flight mileage corresponding to each nest, and select the nest with the minimum total flight mileage as the starting nest; the subsequent nests on the path from the starting nest to the risk point are sequentially determined as relay nests; generate a collaborative dispatch instruction containing the numbers of the starting nest and each relay nest, the takeoff order of each nest and the corresponding hoisting operation equipment type, and send it to the control system of the corresponding nest; The nest status reset subsystem is used by the thermal imager to collect real-time temperature data of risk points, compare the real-time temperature data with the preset operation completion temperature threshold, and generate an operation completion status mark when the temperature threshold is reached. After obtaining the operation completion status mark, a return command is sent to the UAV performing the operation, and the UAV returns to the departure nest. The cabin entry detection device of the departure nest collects the equipment interface status data after the UAV enters the cabin, compares the equipment interface status data with the equipment loading list recorded before departure, generates status update commands for each equipment slot, causes the nest control system to update the internal inventory records and reset the nest status to standby mode. The operation completion status mark and the nest reset status are synchronized to the defect database, and the record corresponding to the risk point is marked as handled.

[0007] Another aspect of the present invention provides a control method for a drone function expansion cabinet that facilitates power transmission line inspection, comprising the following steps: The system collects line environment image data from high-definition cameras and thermal imagers, and transmits the line environment image data to a defect database. The defect database classifies and labels the line environment image data according to three preset feature thresholds: bird nests, floating objects, and icing. The risk information after classification and labeling is compared with the pre-stored risk level judgment conditions to obtain the matching risk level and the corresponding type of work equipment. The system retrieves the real-time status data of the tower-embedded nests, matches the work equipment type with the real-time status data, and filters out the tower-embedded nest numbers that are currently loaded with the corresponding work equipment and have sufficient power for flight. The control system of the tower-embedded nests generates preset equipment instructions to move the corresponding work equipment to the hoisting interface for standby. The coordinates of risk points are extracted from the risk information and compared with the pre-entered set of tower coordinates to determine the relay network topology of the racks within the line section where the risk point is located. The first flight path is calculated based on the risk point coordinates and the main rack coordinates. The second flight path is calculated based on the risk point coordinates and the coordinates of each sub-rack. The first flight path and each second flight path are added together to obtain the total flight mileage corresponding to each rack. The rack with the minimum total flight mileage is selected as the starting rack. Subsequent racks on the path from the starting rack to the risk point are sequentially determined as relay racks. A collaborative scheduling instruction containing the numbers of the starting rack and each relay rack, the takeoff order of each rack, and the corresponding hoisting equipment type is generated and sent to the control system of the corresponding rack. The thermal imager collects real-time temperature data of the risk points and compares the real-time temperature data with the preset operation completion temperature threshold. When the temperature threshold is reached, an operation completion status marker is generated. After obtaining the operation completion status marker, a return command is sent to the UAV performing the operation, and the UAV returns to the departure nest. The cabin entry detection device of the departure nest collects the equipment interface status data after the UAV enters the cabin and compares the equipment interface status data with the equipment loading list recorded before departure. It generates status update commands for each equipment slot, so that the nest control system updates the internal inventory records and resets the nest status to standby mode. The operation completion status marker and the nest reset status are synchronized to the defect database, and the record corresponding to the risk point is marked as handled.

[0008] This invention dynamically constructs a drone nest relay network by matching risk point coordinates with tower coordinate sets, and selects the optimal departure nest and relay sequence based on a flight mileage optimization algorithm, achieving optimal planning and intensive resource scheduling for multi-UAV collaborative operation paths. During operations, real-time temperature monitoring triggers decision-making. After the UAV returns, it automatically updates the nest inventory record and resets its standby status through cabin inspection and equipment status comparison, while simultaneously synchronizing the handling results to the defect database, forming a closed loop for risk handling. This achieves full-process automation from risk perception, decision-making and scheduling, collaborative operation to status reset, improving the response speed, accuracy, and resource utilization efficiency of patrol operations, while reducing the need for manual intervention and maintenance costs. Attached Figure Description

[0009] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a block diagram of the UAV function expansion cabinet control system for facilitating power transmission line inspection, provided in Embodiment 1 of the present invention. Figure 2 This is a schematic diagram of the control system for the UAV function expansion cabinet that facilitates power transmission line inspection, as provided in Embodiment 1 of the present invention. Figure 3 This is a block diagram of the pre-set subsystem for work equipment provided in Embodiment 2 of the present invention; Figure 4 This is a block diagram of the multi-machine collaborative dispatch subsystem provided in Embodiment 5 of the present invention; Figure 5 This is a block diagram of the nest state reset subsystem provided in Embodiment 11 of the present invention; Figure 6 This is a flowchart of the control method for the UAV function expansion cabinet that facilitates power transmission line inspection, as provided in Embodiment 12 of the present invention. Figure 7 A block diagram of the electronic device provided by the present invention; Figure 8 A block diagram of a computer-readable storage medium provided for this invention. Detailed Implementation

[0010] The technical solutions of the present invention will now be described with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0011] Hereinafter, the terms "first," "second," etc., are used for descriptive convenience only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0012] In this invention, unless otherwise explicitly specified and limited, the term "connection" should be interpreted broadly. For example, "connection" can be a fixed mechanical connection, a detachable mechanical connection, or an integral part; or, "connection" can be a direct connection or an indirect connection through an intermediate medium. Furthermore, unless otherwise explicitly specified and limited, the term "coupling" should be interpreted broadly. For example, "coupling" can be a direct electrical connection, such as physical contact and electrical conduction between two components; it can also be understood as an electrical connection between different components in a circuit structure through physical lines capable of transmitting electrical signals, such as copper foil or wires on a printed circuit board (PCB), to transmit electrical signals; or, "coupling" can be an indirect electrical connection between two components through an intermediate medium; or, "coupling" can be an electrical connection between two components in a non-contact manner, such as an electrical connection between two components using capacitive coupling to transmit electrical signals.

[0013] In this embodiment of the invention, directional terms such as "up," "down," "left," and "right" may be defined relative to the orientation of the components shown in the accompanying drawings. It should be understood that these directional terms can be relative concepts, used for relative description and clarification, and can change accordingly depending on the orientation of the components in the accompanying drawings.

[0014] Example 1: As Figure 1 As shown, this embodiment of the invention provides a UAV function expansion cabinet control system for convenient power transmission line inspection, comprising: The pre-positioning subsystem for operational equipment is used to transmit line environment image data collected by high-definition cameras and thermal imagers to a defect database. The defect database classifies and labels the line environment image data according to three preset feature thresholds: bird nests, floating objects, and icing. The risk information after classification and labeling is compared with the pre-stored risk level judgment conditions to obtain the matching risk level and the corresponding operational equipment type. The real-time status data of the tower-embedded nest is retrieved, and the operational equipment type is matched with the real-time status data to filter out the tower-embedded nest number that is currently loaded with the corresponding operational equipment and has sufficient power for flight. The control system of the tower-embedded nest generates a pre-positioning command to move the corresponding operational equipment to the hoisting interface for standby. The multi-aircraft collaborative dispatch subsystem is used to extract the coordinates of risk points from risk information, compare the risk point coordinates with the pre-entered set of tower coordinates, and determine the relay network topology of the nests within the line section where the risk point is located; calculate the first flight path based on the risk point coordinates and the main nest coordinates, calculate the second flight path based on the risk point coordinates and the coordinates of each sub-nest, add the first flight path and each second flight path to obtain the total flight mileage corresponding to each nest, and select the nest with the minimum total flight mileage as the starting nest; the subsequent nests on the path from the starting nest to the risk point are sequentially determined as relay nests; generate a collaborative dispatch instruction containing the numbers of the starting nest and each relay nest, the takeoff order of each nest and the corresponding hoisting operation equipment type, and send it to the control system of the corresponding nest; The nest status reset subsystem is used by the thermal imager to collect real-time temperature data of risk points, compare the real-time temperature data with the preset operation completion temperature threshold, and generate an operation completion status mark when the temperature threshold is reached. After obtaining the operation completion status mark, a return command is sent to the UAV performing the operation, and the UAV returns to the departure nest. The cabin entry detection device of the departure nest collects the equipment interface status data after the UAV enters the cabin, compares the equipment interface status data with the equipment loading list recorded before departure, generates status update commands for each equipment slot, causes the nest control system to update the internal inventory records and reset the nest status to standby mode. The operation completion status mark and the nest reset status are synchronized to the defect database, and the record corresponding to the risk point is marked as handled.

[0015] The line environment image data refers to the raw images and thermal images generated by high-definition cameras and thermal imagers in the perception layer after collecting data on the transmission line corridor, towers, and surrounding environment. This data includes various physical information such as the surface condition of line equipment, traces of bird activity within the line corridor, locations of foreign objects, and the extent of icing coverage. Feature thresholds refer to the preset image feature judgment boundary values ​​in the defect database for three types of risks: bird nests, floating objects, and icing. These boundary values ​​are derived from the quantification of the morphology, size, temperature distribution, and texture features of different types of risks in the images, and are used to classify the collected line environment image data into the corresponding risk type. Risk information refers to the structured data output after the line environment image data has been classified and labeled by the defect database according to the feature thresholds. This data includes the risk type, risk location coordinates, risk level, and corresponding image segments; it serves as the direct basis for subsequent equipment selection and scheduling. The equipment type refers to the category of equipment used to perform specific tasks, output after comparing the pre-stored risk level judgment conditions and risk information in the decision-making layer. Equipment categories include low-power laser bird deterrent devices, high-power laser fusing devices, de-icing robots, and fire extinguishing bomb delivery devices. Each type of equipment is pre-configured in different equipment slots within the tower-embedded drone nest. The tower-embedded drone nest is a closed equipment compartment embedded inside the tower structure, measuring 1.8m × 1.2m × 0.8m, with a load-bearing capacity of 30kg. The compartment integrates constant temperature control and dehumidification functions, has a 30-minute fast charging capability, and is divided into multiple equipment slots for storing different operational equipment. It also has a hoisting interface for drone docking and retrieval. Risk point coordinates refer to the geospatial coordinate data of the risk occurrence location contained in the risk information. This data is collected using the BeiDou system and expressed as longitude, latitude, and elevation values. It is used to compare the spatial location with the pre-entered tower coordinate set to determine the line section where the risk point is located and the corresponding drone nest relay network. Temperature threshold refers to the preset temperature boundary value for determining the completion status of an operation in the application layer's inspection management platform. This temperature boundary value is set based on the critical temperature required for the target surface to reach the point of repulsion or melting during low-power laser bird deterrence or high-power laser melting operations, and is used for comparison with real-time temperature data collected by the thermal imager. Equipment interface status data refers to the binary identifier data collected by the in-flight detection device after the UAV returns to the nest and enters the cabin. This data, collected by photoelectric sensors and mechanical contacts, indicates the equipment interface position, equipment fixation status, remaining material quantity, or robot return status. It is used for item-by-item comparison with the equipment loading list recorded before departure. The equipment loading list refers to the data set recorded by the nest control system before the UAV is dispatched for operation, including the slot number, equipment type, equipment serial number, and initial material quantity of the equipment carried by the UAV's hoisting interface during the mission. The list is generated and stored in the temporary memory of the nest control system when the UAV leaves the cabin.

[0016] The principles described in the above embodiments are referenced in the appendix. Figure 2 This embodiment forms a complete closed-loop management system for UAV patrol of power transmission lines. Through the organic linkage of data collection, intelligent analysis, equipment scheduling, multi-UAV collaboration, and status reset, it achieves rapid identification, accurate response, and efficient handling of line risks. Based on image data and threshold comparison with a defect database, the system automatically completes risk classification and level determination, and selects suitable operating equipment and available UAVs based on the real-time status of UAV nests, ensuring the accuracy and timeliness of the task preparation phase. By matching the coordinates of risk points with the coordinate set of the towers, a UAV nest relay network is dynamically constructed, and the optimal starting UAV nest and relay sequence are selected based on a flight mileage optimization algorithm, realizing optimal planning and resource-intensive scheduling of multi-UAV collaborative operation paths. During the operation, real-time temperature monitoring triggers the judgment process. After the UAV returns, it automatically updates the UAV nest inventory record and resets the standby status through cabin inspection and equipment status comparison, while simultaneously synchronizing the handling results to the defect database, forming a closed loop for risk handling. The overall system automates the entire process from risk perception, decision-making and scheduling, collaborative operations to state reset, improving the response speed, accuracy and resource utilization efficiency of patrol operations, while reducing the need for manual intervention and operation and maintenance costs.

[0017] Example 2: As Figure 3 As shown, based on Embodiment 1, the work equipment pre-setting subsystem provided in this embodiment of the invention includes: The risk grading component extracts four structured fields from risk information: risk type identifier, risk location coordinates, target pixel percentage in risk image segments, and extreme temperature values ​​in the target area. These four structured fields are then matched item by item with pre-stored risk level determination criteria, including risk type weighting coefficients, spatial location priority coefficients, target size thresholds, and temperature anomaly thresholds. Each matching item yields a matching score, which is then summed and compared with a pre-stored risk level threshold range to obtain the risk level value. The equipment selection component receives the risk level value and retrieves a pre-stored work equipment type association library. This library contains the mapping relationship between risk level values ​​and work equipment types, the correspondence between work equipment types and work power levels, and the constraint relationship between work power levels and single work duration. Based on the risk level value, the component searches the work equipment type association library to obtain the work equipment type that matches the risk level value, the corresponding work power level, and the single work duration under the work power level. The component then combines the work equipment type, work power level, and single work duration into an equipment selection command, which is output to the work equipment preset subsystem. The task priority sorting component receives equipment selection instructions, extracts the task equipment type from the instructions, and extracts risk point coordinates from the risk information. It then sorts the task equipment types and risk point coordinates accordingly. Next, it retrieves the rack coordinates from the real-time status data of each tower-embedded rack, calculates the spatial distance between the rack coordinates and the risk point coordinates, and sorts these distances from nearest to farthest, resulting in a rack spatial distance sequence. This sequence is then associated with the task equipment type. Racks equipped with the corresponding task equipment type are selected from the spatial distance sequence, and a rack deployment priority sequence is generated based on the spatial distance from nearest to farthest. Finally, the rack deployment priority sequence and the equipment selection instructions are merged into a preset scheduling instruction, which is then sent to the control system of the tower-embedded racks.

[0018] Among them, the risk type identifier refers to the risk category code contained in the risk information. This code comes from the preset identifier fields corresponding to the three types of risks—bird nests, floating objects, and icing—output by the defect database after classifying and marking the line environmental image data according to feature thresholds. It is used to identify the category to which the current risk belongs during the risk level determination process. The risk location coordinates refer to the geospatial coordinate data of the location where the risk occurs, contained in the risk information. It is acquired using the Beidou system and expressed as longitude, latitude, and elevation values. It is directly extracted from the risk information and used for spatial distance calculation and nest screening. The target pixel ratio in the risk image segment refers to the ratio of the number of pixels occupied by the risk target in the image segment extracted from the risk information to the total number of pixels in the image segment. The ratio is obtained by counting the number of pixels in the target area after binarizing the image segment and is used to characterize the visual salience of the risk target. The extreme temperature value of the target area refers to the highest or lowest temperature value in the risk target area in the risk image segment acquired by the thermal imager. The extreme value is extracted by comparing the pixel temperature values ​​of the target area in the image segment and is used to characterize the temperature characteristics of the risk target. The risk type weighting coefficient refers to the pre-set weighting coefficients for the three risk categories of bird nests, floating objects, and icing in the pre-stored risk level determination conditions. It is used to weight and correct the matching score of the risk type identifier during item-by-item matching, reflecting the different degrees of impact of different risk types on line safety. The spatial location priority coefficient refers to the pre-set priority weighting coefficients for different sections of the line corridor where the risk point is located in the pre-stored risk level determination conditions. This coefficient is pre-set based on the correlation between the risk point's location and key areas such as towers, overpasses, and crossings of important facilities. It is used to weight and correct the matching score of the risk location coordinates during item-by-item matching. The target size threshold refers to the pre-set judgment boundary value for the proportion of target pixels in the risk image segment in the pre-stored risk level determination conditions. This boundary value is used to compare with the proportion of target pixels in the risk image segment to determine whether the size of the risk target reaches the level requiring action. The temperature anomaly threshold refers to the pre-set judgment boundary value for extreme temperature values ​​in the target area in the pre-stored risk level determination conditions. This boundary value is used to compare with extreme temperature values ​​in the target area to determine whether the temperature of the risk target is in an abnormal state. The mapping relationship between risk level values ​​and work equipment types refers to the correspondence rules between risk level values ​​and work equipment types that are pre-established in the work equipment type association library. The rules associate different risk level values ​​with one or more of the following: low-power laser bird deterrent device, high-power laser fusing device, de-icing robot, or fire extinguishing bomb delivery device. This is used to determine the type of work equipment to be called based on the risk level value when selecting equipment.The correspondence between equipment type and power level refers to the pre-established rules in the equipment type association library that correspond to various types of equipment and their executable power levels. These rules are pre-set based on the physical parameters and operational capabilities of each piece of equipment and are used to determine the appropriate power level for the selected equipment type. The constraint relationship between power level and single operation duration refers to the pre-established association rules in the equipment type association library that correspond to each power level and its corresponding longest single operation duration. These rules are pre-set based on the equipment's energy consumption characteristics, heat dissipation capacity, and safe operating time at a specific power level and are used to constrain the upper limit of the single operation duration after the power level is determined.

[0019] In the above embodiments, this embodiment achieves intelligent pre-positioning and scheduling of transmission line risk management equipment through modular collaborative processing. First, the risk grading component extracts multi-dimensional structured fields of risk information and matches and accumulates scores with preset judgment conditions to achieve quantitative assessment of risk levels. It comprehensively considers multiple factors such as risk type, spatial location, target size, and temperature anomalies, avoiding the limitations of single-indicator judgment and improving the accuracy and objectivity of risk level classification. Second, the equipment selection component, based on the risk level value, automatically matches the corresponding operating equipment type, power level, and operation duration by querying a preset association library. It directly links the risk level with equipment performance parameters and operational constraints, ensuring the scientific nature and applicability of equipment selection, providing clear power and duration guidance for subsequent operations, and avoiding low operational efficiency or resource waste caused by improper equipment selection. Finally, the task priority ranking component combines equipment type and risk point coordinates to calculate and rank the spatial distance between each machine nest and the risk point, generating a machine nest deployment priority sequence based on spatial proximity. Under the premise of ensuring equipment type matching, it prioritizes scheduling the available machine nests closest to the risk point, effectively shortening the equipment transportation path and response time, and improving the timeliness of emergency response.

[0020] In summary, this embodiment realizes an automated decision-making chain from risk quantification and classification, precise equipment selection to scheduling priority ranking, enhances the pertinence of equipment pre-positioning and scheduling efficiency, and provides reliable equipment support and execution basis for UAV collaborative operations.

[0021] Example 3: Based on Example 2, the job priority sorting component provided in this embodiment of the invention includes: The sortable nest screening sub-component is used to extract the nest coordinates and current remaining power percentage from the real-time status data of each embedded nest on the tower. After calculating the spatial distance between each nest coordinate and the risk point coordinate, the spatial distance value corresponding to each nest is obtained. The spatial distance value of each nest and the current remaining power percentage are judged item by item according to the correspondence, and nests with the current remaining power percentage greater than or equal to the lowest power percentage corresponding to the spatial distance are selected to obtain the sortable nest set. The sortable nest set contains the nest number, nest coordinates and spatial distance value of each sortable nest. The equipment matching sub-component is used to associate the set of available machine nests with the types of work equipment. It retrieves the current equipment slot loading record of each machine nest in the set of available machine nests from the real-time status data of each embedded machine nest in each tower. The current equipment slot loading record contains the type of work equipment stored in each slot and the availability status indicator of the work equipment. The current equipment slot loading record is compared item by item with the work equipment type in the equipment selection instruction. From the set of available machine nests, machine nests that match the type of work equipment and whose availability status indicator is available are selected from the current equipment slot loading record. The loading matching machine nest set contains the machine nest number, machine nest coordinates and spatial distance value of each loading matching machine nest. The deployment priority sequence generation sub-component is used to extract the duration of a single operation from the equipment selection instruction and the spatial distance value of each nest from the set of matching nests. Based on the pre-set flight speed and hovering energy consumption parameters of the UAV corresponding to each nest, the round-trip flight time required for each nest to perform the task is calculated according to the spatial distance value and conversion relationship of each nest. The round-trip flight time is added to the duration of a single operation to obtain the total task duration of each nest. The total task duration is compared with the flight time corresponding to the current remaining power percentage of each nest, and nests with a total task duration less than or equal to the flight time are selected to obtain a set of nests with qualified endurance. The operation priority sorting component generates a nest deployment priority sequence according to the spatial distance value of each nest in the set of nests with qualified endurance in order from near to far. The nest deployment priority sequence is merged with the equipment selection instruction into a preset scheduling instruction.

[0022] In the above embodiments, this embodiment constructs a hive deployment decision model that balances reliability, availability, and economy through a multi-layered, progressive selection process considering power, equipment, range, and distance. It ensures that the selected hive not only reaches the risk point and has suitable equipment but also safely completes the entire operational process, thereby improving the robustness of scheduling decisions and the success rate of mission execution.

[0023] Example 4: Based on Example 3, the dispatch priority sequence generation sub-component provided in this embodiment of the invention includes: The instruction encapsulation and slot locking module is used to combine the first hive number in the hive deployment priority sequence with the work equipment type, work power level, and single operation duration in the equipment selection instruction to form a preset scheduling instruction body; it retrieves the equipment slot locking status register from the real-time status data of the tower-embedded hive corresponding to the hive number. The equipment slot locking status register records the locking identifier and locking task number of each equipment slot; the generation sub-component extracts the work equipment type from the equipment selection instruction, compares the work equipment type with the current equipment slot loading record of the hive to obtain the matching slot number, writes the slot number into the equipment slot locking status register, and generates a slot locking instruction to be attached to the preset scheduling instruction body to obtain a preset scheduling instruction containing the hive number, work equipment type, work power level, single operation duration, and locked slot number; The relay preparation instruction derivation module is used to extract the sequence of subsequent hive numbers (excluding the first hive) from the hive deployment priority sequence. It compares the types of work equipment in the preset scheduling instructions with the current equipment slot loading records of each subsequent hive, and selects the spare slot numbers in each subsequent hive that are loaded with the same type of work equipment. The subsequent hive numbers and their corresponding spare slot numbers are arranged in the order of the hive deployment priority sequence to generate a relay preparation instruction. The relay preparation instruction includes the relay hive number sequence and the spare slot number corresponding to each relay hive. The deployment priority sequence generation sub-component appends the relay preparation instruction to the end of the preset scheduling instruction to obtain a composite preset scheduling instruction that includes the main work hive instruction and the relay preparation sequence. The instruction framing and transmission module is used to obtain the real-time latency value and the maximum allowable single-frame data length of the current embedded tower nest. It splits the composite preset scheduling instruction into main operation nest instruction frames and relay nest instruction frames according to the nest number, and calculates the data length of each instruction frame. The data length of each instruction frame is compared with the maximum allowable single-frame data length item by item. When the data length of the instruction frame is greater than the maximum allowable single-frame data length, the instruction frame is split into multiple subframes according to the maximum allowable single-frame data length. A reassembly identifier and frame sequence number are added to the header of each subframe. The reassembly identifier contains the nest number corresponding to the instruction frame and the total number of subframes. All instruction frames and the split subframes are arranged in order of priority, with the main operation nest instruction frames taking precedence and the relay nest instruction frames following the dispatch priority sequence, to generate a transmission sequence. Each frame in the transmission sequence is sent sequentially to the control system of the corresponding nest.

[0024] In the above embodiments, this embodiment realizes closed-loop management of the entire process from instruction generation and resource locking to reliable transmission. It avoids resource contention through a slot locking mechanism, builds task redundancy through a relay preparation mechanism, and adapts to communication constraints through a frame-based transmission mechanism, thereby ensuring the accurate execution of scheduling instructions and the stable progress of the task chain.

[0025] Example 5: Figure 4 As shown, based on Embodiment 1, the multi-machine collaborative dispatch subsystem provided in this embodiment of the invention includes: The flight path generation component is used to extract the coordinates of the main nest and each sub-nest from the tower coordinate set, and extract the coordinates of risk points from the risk information; it retrieves pre-stored terrain elevation data and transmission line channel coordinate set, uses the coordinates of risk points and each nest as the path endpoints, generates a polyline path along the transmission line channel coordinate set between the path endpoints, and avoids the obstacle positions in the terrain elevation data; it obtains the first flight path segment and each second flight path segment, each flight path containing the path inflection point coordinate sequence and the horizontal projection length and vertical climb length of each polyline segment; The path unification conversion component is used to retrieve the horizontal flight energy consumption coefficient and vertical climb energy consumption coefficient from the pre-stored UAV performance parameter library. It multiplies the horizontal projection length of each polyline segment in the first flight path with the horizontal flight energy consumption coefficient to obtain the horizontal energy consumption equivalent; it multiplies the vertical climb length of each polyline segment with the vertical climb energy consumption coefficient to obtain the vertical energy consumption equivalent; and it sums the horizontal energy consumption equivalent and vertical energy consumption equivalent of each polyline segment to obtain the total energy consumption equivalent of the first flight path. The same processing is performed on the second flight path of the main nest and each sub-nest to obtain the total energy consumption equivalent of each second flight path. The departure nest selection component retrieves the current remaining power percentage of the main nest and each sub-nest, compares the current remaining power percentage of each nest with the sum of the total energy equivalent of the first and second flight paths, and filters out nests whose total energy equivalent is less than or equal to the flight range corresponding to the current remaining power percentage. From the filtered nests, the nest with the smallest total energy equivalent is selected as the departure nest, and the path inflection point coordinate sequence corresponding to the departure nest is output to the cooperative scheduling command.

[0026] In the above embodiments, the multi-drone collaborative dispatch subsystem of this embodiment achieves the selection of the optimal departure drone nest in a multi-drone collaborative operation scenario through the coordination of three stages: path generation, energy consumption conversion, and drone nest optimization. Its technical effects are mainly reflected in the following aspects: First, the flight path generation component uses the power transmission line channel coordinate set as a benchmark and combines it with terrain elevation data to generate obstacle avoidance flight paths from each drone nest to risk points. These paths follow the spatial range of the power transmission line channel, effectively avoiding terrain obstacles and areas outside the channel, ensuring that the flight paths meet the spatial constraints and safety requirements of the inspection operation. The generated paths include a sequence of inflection points and horizontal and vertical segment lengths, providing structured input for energy consumption calculation. Second, the path unification conversion component introduces horizontal flight energy consumption coefficients and vertical climb energy consumption coefficients to convert the geometric length of each flight path into a unified energy consumption equivalent. This conversion considers the energy consumption differences of drones under different flight attitudes, transforming the path evaluation standard from simple spatial distance to a more quantitative indicator closer to actual energy consumption, improving the accuracy and practicality of subsequent drone nest selection. Finally, the departure nest selection component combines the current remaining power percentage of each nest to verify the flyability of the total energy equivalent of each candidate path. After filtering out nests with sufficient power to cover the total energy consumption of the round trip, the nest with the smallest total energy equivalent is further selected as the departure nest. This mechanism optimizes energy consumption while ensuring the completion of the flight mission, avoiding mission interruption due to insufficient power, minimizing overall energy consumption, and extending the sustainable operation capability of the nest group.

[0027] In summary, this embodiment constructs a collaborative scheduling decision model that takes energy efficiency as its core and considers both flight safety and power feasibility by generating path constraints, uniformly quantifying energy consumption, and optimizing power supply. This ensures that the selected departure aircraft can not only safely reach the risk point and return, but also complete the task with minimal energy consumption, thereby improving the resource utilization efficiency and task economy of multi-aircraft collaborative operations.

[0028] Example 6: Based on Example 5, the path unification conversion component provided in this embodiment of the invention includes: The energy consumption segmented accumulation sub-component is used to sequentially accumulate the horizontal and vertical energy consumption equivalents of each polyline segment in the first flight path according to the order of the polyline segments in the path inflection point coordinate sequence. After accumulating the energy consumption equivalent of each polyline segment, an intermediate accumulated value corresponding to the polyline segment is generated. The intermediate accumulated value is associated with the elevation value of the current polyline segment's end inflection point and stored in a temporary cache area to obtain a segmented accumulation record set. The energy consumption weight correction sub-component is used to retrieve the inflection point elevation values ​​of two adjacent accumulated record entries in the segmented accumulated record set from the temporary cache, calculate the elevation change rate between adjacent inflection points, retrieve the pre-stored elevation change rate correction coefficient, multiply the horizontal energy consumption equivalent and vertical energy consumption equivalent in the accumulated intermediate value of each segment by the correction coefficient matching the corresponding elevation change rate, and then re-accumulate them. The corrected segmented accumulated value replaces the corresponding accumulated intermediate value in the temporary cache. The end-of-flight energy consumption correction sub-component is used to extract the corrected segmented cumulative value of the last cumulative record entry from the temporary buffer as the initial total energy consumption equivalent, retrieve the charging pile output power and the actual battery voltage value output by the drone from the charging interface status data of the engine pod, input the charging pile output power and battery voltage value into the pre-stored charging efficiency compensation coefficient table to obtain the compensation coefficient, and multiply the initial total energy consumption equivalent by the compensation coefficient to obtain the total energy consumption equivalent of the first flight path.

[0029] In the above embodiments, this embodiment constructs a multi-level, adjustable energy consumption calculation model by segmented recording, dynamic correction based on terrain changes, and charging efficiency compensation; it not only reflects the impact of flight path geometry and terrain undulations on energy consumption, but also integrates the actual energy conversion characteristics of the charging process, thereby achieving a more accurate estimate of the total energy consumption of the flight mission and providing a more reliable quantitative basis for subsequent nest selection.

[0030] Example 7: Based on Example 6, the end-point energy consumption correction sub-component provided in this embodiment of the invention includes: The benchmark compensation value extraction module is used to compare the output power of the charging pile with the pre-stored power classification interval table to determine the power level to which the output power belongs; at the same time, it compares the battery voltage value with the pre-stored voltage classification interval table to determine the voltage level to which the battery voltage belongs; it combines the power level and voltage level into an index key value, retrieves the benchmark compensation value corresponding to the index key value in the charging efficiency compensation coefficient table, and outputs the benchmark compensation value to the temporary buffer area to obtain the benchmark compensation value record; The environmental factor correction module is used to retrieve the constant temperature control status register and dehumidification control status register of the engine compartment, extract the current cabin temperature value from the constant temperature control status register, and extract the current cabin humidity value from the dehumidification control status register; compare the current cabin temperature value with the pre-stored temperature compensation coefficient curve to obtain the temperature compensation coefficient, compare the current cabin humidity value with the pre-stored humidity compensation coefficient curve to obtain the humidity compensation coefficient; multiply the baseline compensation value in the baseline compensation value record by the temperature compensation coefficient and then by the humidity compensation coefficient to obtain the environmental correction compensation value; The lifespan decay correction module is used to retrieve the charging cycle count register corresponding to the UAV performing this mission from the engine nest, extract the cumulative charging cycle count value of the UAV from the charging cycle count register, compare the cumulative charging cycle count value with the pre-stored cycle lifespan decay coefficient table, obtain the decay coefficient matching the cumulative charging cycle count value, and multiply the environmental correction compensation value by the decay coefficient to obtain the final compensation coefficient.

[0031] In the above embodiments, this embodiment constructs a hierarchical, multi-factor coupled compensation coefficient calculation model by progressively introducing charging equipment parameters, environmental conditions, and battery life factors. This model not only reflects the basic output characteristics of the charging system but also comprehensively considers the impact of environmental conditions and battery aging on charging efficiency, thus making the final compensation coefficient closer to the energy conversion efficiency in the actual charging process. Based on the total energy consumption equivalent corrected by the compensation coefficient, the energy support level obtainable by the UAV under actual charging conditions can be more accurately characterized, improving the completeness and reliability of flight mission energy consumption assessment.

[0032] Example 8: Based on Example 7, the environmental factor correction module provided in this embodiment of the invention includes: The coefficient stability correction submodule is used to retrieve historical temperature values ​​from the constant temperature control status register for the five consecutive sampling periods preceding the current cabin temperature value, and historical humidity values ​​from the dehumidification control status register for the five consecutive sampling periods preceding the current cabin humidity value. It calculates the variance of the historical temperature and humidity values ​​respectively, compares the temperature variance with a pre-stored temperature stability threshold, and if the temperature variance exceeds the threshold, retrieves a pre-stored temperature variance correction coefficient to correct the temperature compensation coefficient. Similarly, it compares the humidity variance with a pre-stored humidity stability threshold, and if the humidity variance exceeds the threshold, retrieves a pre-stored humidity variance correction coefficient to correct the humidity compensation coefficient, thus obtaining the corrected temperature and humidity compensation coefficients. The interaction factor superposition submodule is used to retrieve the pre-stored temperature and humidity interaction coupling coefficient table, combine the corrected temperature compensation coefficient and the corrected humidity compensation coefficient into a temperature and humidity combination key value, retrieve the interaction coupling coefficient corresponding to the temperature and humidity combination key value from the temperature and humidity interaction coupling coefficient table, and multiply the corrected temperature compensation coefficient and the corrected humidity compensation coefficient by the interaction coupling coefficient to obtain the coupled and corrected temperature and humidity comprehensive coefficient. The local compensation submodule is used to retrieve the airflow velocity values ​​collected by the airflow sensor array arranged inside the engine compartment, extract the airflow compensation coefficient that matches the airflow velocity value from the pre-stored airflow velocity compensation coefficient curve, multiply the coupled and corrected temperature and humidity comprehensive coefficient by the airflow compensation coefficient to obtain the final environmental comprehensive compensation coefficient, and multiply the benchmark compensation value in the benchmark compensation value record by the environmental comprehensive compensation coefficient to obtain the environmental correction compensation value.

[0033] The embedded rack housing integrates temperature control and dehumidification functions, maintaining a uniform temperature and humidity distribution within the cabin through an internal circulating fan. Airflow sensor arrays collect airflow velocity values ​​inside the rack, reflecting the operating status of the circulating fan and the intensity of airflow within the cabin. When the airflow velocity increases, the heat exchange efficiency between the cabin air and the drone battery surface improves, allowing heat generated during charging to be carried away more quickly. This results in the battery's actual operating temperature being lower than the ambient temperature recorded in the temperature control register, thus affecting charging efficiency. Simultaneously, the airflow velocity affects the spatial distribution of humidity within the cabin; the evaporation rate in high-velocity areas is higher than in low-velocity areas, causing a difference between the local humidity at the drone's location and the value recorded in the ambient humidity register. Therefore, the airflow velocity value, as a local microenvironment correction factor, is combined with the temperature and humidity compensation coefficients to correct the baseline compensation value, making the final charging efficiency compensation coefficient more accurately reflect the energy conversion efficiency of the drone during actual charging within the rack.

[0034] In the above embodiments, this embodiment constructs a hierarchical, multi-factor coupled compensation coefficient calculation model by introducing charging equipment parameters, environmental conditions, and battery life factors step by step. This model not only reflects the basic output characteristics of the charging system, but also comprehensively considers the impact of environmental conditions and battery aging on charging efficiency, so that the final compensation coefficient is closer to the energy conversion efficiency in the actual charging process. Based on the total energy consumption equivalent corrected by this compensation coefficient, it can more accurately characterize the energy support level that the UAV can obtain under actual charging conditions, and improve the completeness and reliability of flight mission energy consumption assessment.

[0035] Example 9: Based on Example 8, the interaction factor superposition submodule provided in this embodiment of the invention includes: The dynamic quantization level generation unit is used to obtain the corrected temperature compensation coefficient and the corrected humidity compensation coefficient, and simultaneously retrieve the historical temperature compensation coefficient sequence and historical humidity compensation coefficient sequence recorded in the previous three tasks of the hive from the temporary buffer; calculate the difference between the corrected temperature compensation coefficient and the mean of the historical temperature compensation coefficient sequence, divide the difference by the range of the historical temperature compensation coefficient sequence to obtain the temperature dynamic adjustment factor; compare the temperature dynamic adjustment factor with the pre-stored temperature quantization interval boundary value to determine the temperature quantization level to which the corrected temperature compensation coefficient belongs; perform the same processing on the humidity compensation coefficient to obtain the humidity quantization level; The cross-validation correction unit is used to combine the temperature quantization level and the humidity quantization level into a temporary key value, retrieve the pre-stored temperature and humidity mutual exclusion table, which records the flags indicating whether there is a physical mutual exclusion relationship under the combination of temperature quantization level and humidity quantization level; when the mutual exclusion flag corresponding to the temporary key value is present, the unit retrieves the coefficient pair most recently recorded in the historical temperature compensation coefficient sequence and the historical humidity compensation coefficient sequence, calculates the deviation between the coefficient pair and the current corrected coefficient, takes the quantization level of the coefficient with the smaller deviation as the primary level, and recombines the primary level with the quantization level of another coefficient to obtain the corrected temporary key value; The time decay key value encoding unit is used to retrieve the cumulative runtime register of the machine nest, extract the cumulative running hours of the machine nest since its commissioning from the cumulative runtime register, compare the cumulative running hours with the pre-stored time decay coefficient curve to obtain the time decay coefficient, multiply the temperature quantization level and humidity quantization level in the corrected temporary key value by the time decay coefficient and round them to obtain the decayed temperature quantization level and humidity quantization level, and concatenate the decayed temperature quantization level and humidity quantization level into a temperature and humidity combined key value according to the preset encoding rules.

[0036] In the above embodiments, this embodiment generates dynamic quantization levels, combines the corrected temperature and humidity compensation coefficients with historical sequence data, calculates dynamic adjustment factors and maps them to preset quantization intervals to achieve discretized grading of environmental parameters; using a pre-stored temperature and humidity mutual exclusion table, it performs physical consistency verification on the quantization level combinations, and adjusts the primary and secondary levels based on historical data deviations when mutual exclusion is detected, optimizing the physical rationality of the combined key values; the time decay key value encoding introduces the time decay coefficient corresponding to the cumulative running time, and generates combined key values ​​after decay correction of the quantization levels, reflecting the gradual performance change characteristics of the equipment during long-term operation; it improves the adaptability and robustness of environmental compensation parameters, and enhances the system's ability to analyze complex working conditions and its long-term stability through dynamic grading, mutual exclusion verification and time decay mechanisms.

[0037] Example 10: Based on Example 9, the time decay key encoding unit provided in this embodiment of the invention includes: The level bit width compression subunit is used to determine the encoding bit width of temperature quantization level and humidity quantization level according to the cumulative operating hours of the nest. The attenuated temperature quantization level and humidity quantization level are converted into binary according to the corresponding bit width to obtain temperature binary code and humidity binary code. The code bit interleaving subunit is used to retrieve the machine nest number of the machine nest and convert it into a binary sequence. The temperature binary code and humidity binary code are alternately inserted into the corresponding positions of the machine nest number binary sequence according to the bit order to obtain the interleaved binary sequence. The check bit append subunit is used to retrieve the key value record table generated in the last ten tasks of the nest, and compare the interleaved binary sequence with the historical key values ​​in the record table item by item; when there is a duplicate, append the four-bit binary sequence after the millisecond-level timestamp of the current task; when there is no duplicate, append the preset padding bit, and output the final binary sequence as the temperature and humidity combined key value.

[0038] In the above embodiments, this embodiment adaptively adjusts the encoding bit width of the quantization level based on the cumulative runtime to achieve a balance between data expression efficiency and storage resources; it interleaves the temperature and humidity binary code with the nest number to enhance the device uniqueness and parameter fusion characteristics of the key value; the check bit appending subunit detects and avoids the generation of duplicate key values ​​by comparing historical key value records. When duplicates occur, a timestamp binary sequence is introduced to ensure the uniqueness of the key value; otherwise, a preset padding bit is used to maintain structural consistency. This embodiment generates a composite key value with device association, time sensitivity, and uniqueness, improving the system's ability to distinguish data identifiers and the reliability of historical tracing in a multi-nest environment, while optimizing storage and processing efficiency through binary bit operations.

[0039] Example 11: As Figure 5 As shown, based on Embodiment 1, the nest state reset subsystem provided in this embodiment of the invention includes: The operation threshold matching module is used to extract the operation equipment type from the equipment selection instruction, input the operation equipment type into the pre-stored operation temperature threshold library, which stores the driving away critical temperature corresponding to the low-power laser bird deterrent device and the melting critical temperature corresponding to the high-power laser melting device; according to the operation equipment type, the corresponding critical temperature is retrieved as the operation completion temperature threshold, and the temperature threshold is output to the comparison unit. The effective temperature value extraction module is used to retrieve the pixel array data of the risk point area collected by the thermal imager. The pixel array of the risk point area contains the temperature values ​​of multiple pixels. The module compares the temperature value of each pixel in the pixel array with the pre-stored temperature anomaly rejection threshold point by point, and filters out the pixels whose temperature values ​​are within the normal range to form an effective pixel set. The module calculates the arithmetic mean of the temperature values ​​of all pixels in the effective pixel set and uses the arithmetic mean as the real-time effective temperature value. The task completion status generation module continuously inputs real-time effective temperature values ​​into a sliding window register. The sliding window register stores five consecutive samples of real-time effective temperature values ​​at fixed time intervals. The module compares each of the five real-time effective temperature values ​​in the sliding window register with a task completion temperature threshold. When all five real-time effective temperature values ​​are greater than or equal to the task completion temperature threshold, the module calculates the variance of the fluctuation of the five real-time effective temperature values ​​and compares it with a pre-stored temperature stability threshold. When the variance of the fluctuation is less than or equal to the temperature stability threshold, a task completion status marker is generated.

[0040] In the above embodiments, the nest status reset subsystem of this embodiment uses a task threshold matching module to dynamically match the critical temperature threshold corresponding to the task type based on the equipment selection instructions, thereby achieving precise adaptation of the task completion standard. The effective temperature value extraction module performs abnormal temperature removal and effective pixel screening on the thermal imaging pixel array, and obtains the real-time effective temperature value reflecting the overall thermal state of the area through arithmetic mean calculation, improving the anti-interference capability of temperature monitoring. The task completion status generation module uses a sliding window register to store the continuously sampled effective temperature values ​​in a time sequence. Through threshold comparison and fluctuation variance analysis, it comprehensively judges whether the temperature continuously meets the standard and is in a stable state, thereby generating a reliable task completion marker. This embodiment constructs a task completion judgment mechanism based on multi-dimensional temperature analysis, enhancing the system's adaptability to different equipment operation processes, the robustness of monitoring data, and the accuracy of status judgment, ensuring the rigor and stability of task completion judgment.

[0041] Example 12: As Figure 6 As shown, based on Embodiments 1-11, the UAV function expansion cabinet control method for facilitating power transmission line inspection provided by this embodiment of the invention includes the following steps: Step S100: The line environment image data collected by the high-definition camera and thermal imager is transmitted to the defect database. The defect database classifies and marks the line environment image data according to three preset feature thresholds: bird nests, floating objects, and icing. The risk information after classification and marking is compared with the pre-stored risk level judgment conditions to obtain the matching risk level and the corresponding type of work equipment. The real-time status data of the tower embedded nest is retrieved, and the type of work equipment is matched with the real-time status data to filter out the tower embedded nest number that is currently loaded with the corresponding work equipment and has sufficient power for flight. The control system of the tower embedded nest generates the equipment preset command to move the corresponding work equipment to the hoisting interface for standby. Step S200: Extract the coordinates of the risk point from the risk information, compare the risk point coordinates with the pre-entered tower coordinate set, and determine the relay network topology of the nests within the line section where the risk point is located; calculate the first flight path based on the risk point coordinates and the main nest coordinates, calculate the second flight path based on the risk point coordinates and the coordinates of each sub-nest, add the first flight path and each second flight path to obtain the total flight mileage corresponding to each nest, and select the nest with the minimum total flight mileage as the starting nest; the subsequent nests on the path from the starting nest to the risk point are sequentially determined as relay nests; generate a collaborative scheduling instruction containing the numbers of the starting nest and each relay nest, the takeoff order of each nest and the corresponding hoisting operation equipment type, and send it to the control system of the corresponding nest; Step S300: The thermal imager collects real-time temperature data of the risk points and compares the real-time temperature data with the preset operation completion temperature threshold. When the temperature threshold is reached, an operation completion status marker is generated. After obtaining the operation completion status marker, a return command is sent to the UAV performing the operation, and the UAV returns to the departure nest. The cabin entry detection device of the departure nest collects the equipment interface status data after the UAV enters the cabin and compares the equipment interface status data with the equipment loading list recorded before departure. It generates status update commands for each equipment slot, so that the nest control system updates the internal inventory records and resets the nest status to standby mode. The operation completion status marker and the nest reset status are synchronized to the defect database, and the record corresponding to the risk point is marked as handled.

[0042] In the above embodiments, this embodiment achieves intelligent collaborative operation of the power transmission line patrol drone system by integrating perception, decision-making, and execution. Its technical effects are mainly reflected in the following aspects: Through real-time interaction between perception data and a defect database, automatic identification and classification of line environmental risks are achieved. The system marks image data based on preset feature thresholds and matches them with risk level conditions, thereby accurately determining the risk type and required equipment; combined with real-time status data of the embedded drone nests on the towers, it dynamically selects nests with appropriate operating equipment and sufficient power, generating preset equipment instructions, shortening emergency response time, and improving the accuracy and efficiency of equipment scheduling. Based on the comparison of risk point coordinates and tower coordinate sets, an optimized nest relay network topology is constructed; by calculating the total flight path mileage, the optimal starting nest and relay sequence are selected to generate collaborative scheduling instructions; the ineffective flight distance of the drones is reduced, energy consumption is reduced, and the coverage of a single mission is expanded through multi-nest relay operations, enhancing the system's ability to handle risk points in distant or complex terrain. By introducing thermal imaging data as a criterion for task completion, closed-loop control of the task process was achieved. By comparing real-time temperature with preset thresholds, a task completion marker was automatically generated and a return-to-home command was triggered. After the UAV returned, the nest verified the equipment status through the cabin entry detection device, updated the inventory records, and reset the standby mode. At the same time, the task completion status and the nest status were synchronized to the defect database to complete the closed-loop management of risk point handling, ensuring the traceability of task records and the real-time synchronization of system status.

[0043] In summary, this embodiment achieves full-process automation from risk identification, equipment scheduling, path planning to operation verification, improving the response speed, operation accuracy and system coordination efficiency of transmission line patrol, while reducing the need for manual intervention and operation and maintenance costs.

[0044] Specific application scenario: Emergency response to icing of power transmission lines in mountainous areas during winter. Location: A mountainous section between towers #45 and #46 of a 220kV high-voltage transmission line in a certain province. This section is located on a mountain ridge at an altitude of 1200 meters. In winter, it is often affected by cold waves, and the line is prone to icing.

[0045] Event Trigger: A Level 2 risk alarm was issued on the large screen of the line maintenance center. The cause was that during routine inspections, the high-definition camera and thermal imager deployed on tower #45 detected obvious white deposits on the surface of the conductors within the #45-#46 span. Thermal imaging data showed that the temperature in this area was significantly lower than the surrounding environment.

[0046] Step 1: Risk Perception and Intelligent Classification 1. Data Acquisition and Classification: Environmental image data of the line collected by UAVs or fixed monitoring equipment, including visible light images and thermal imaging data, are transmitted to the defect database in real time; 2. Defect database labeling: The defect database automatically classifies and labels image data as having icing risk based on preset icing feature thresholds, morphology, texture, and temperature distribution; 3. Structured Risk Information: The system outputs structured risk information, including: Risk type: Icing; Risk point coordinates: 118.XXXX°E, 31.XXXX°N, elevation 1245 meters; Target pixel ratio: The icy area in the image occupies 15% of the total image area; Extreme temperature in the target area: -8.5℃; 4. Risk Level Quantification: The risk grading component extracts the above four fields and matches them one by one with the risk type weight coefficient, icing weight 0.9, spatial location priority coefficient, center of the gap, key area, coefficient 1.2, target size threshold, pixel ratio >10%, and temperature anomaly threshold, <-5℃ in the risk level determination conditions, and accumulates the scores; the final score is compared with the risk level threshold range to output the level 2 risk.

[0047] Step 2: Equipment Selection and Prioritization 1. Equipment Selection: The equipment selection component automatically matches the equipment type based on the secondary risk value in the work equipment type association library. Equipment type: De-icing robot; Operating power setting: Medium, vibration de-icing mode; Duration of a single operation: 8 minutes; 2. Available Cell Screening: The job priority sorting component retrieves real-time status data for all embedded cell nests on the towers within this section, including main cell #45, sub-cell #44, and sub-cell #46. #45 Main Nest: 92% power, 0.8 km from the danger point; #44 Child Nest: 65% power, 1.2 km from the danger point; #46 Child Nest: 88% power, 0.9 km from the danger point; All nests have sufficient power to meet the minimum power requirements for flying to the risk point, and all have entered the assembly of available nests. 3. Equipment matching and battery life verification: The equipment matching sub-component checked the equipment slot loading records of each nest and found that the #4 main nest and the #46 sub-nest were both loaded with de-icing robots and were available, while the #44 sub-nest was not loaded.

[0048] The deployment priority sequence generation sub-component calculates the total mission duration for each nest based on the round-trip flight time plus 8 minutes of operation time, and compares it with the available flight time corresponding to the battery power. All three conditions are met. Finally, the deployment priority sequence is generated by sorting from closest to furthest spatial distance: #45 main nest (closest) > #46 sub-nest (second closest).

[0049] 4. Command generation and resource locking: The instruction encapsulation and slot locking module sets #45 as the main work machine nest and locks its No. 3 slot, which is used to load the de-icing robot, to prevent it from being occupied by other tasks. The relay preparation instruction derivative module generates a relay preparation instruction for sub-nest #46, locking its No. 2 slot, which is equipped with the de-icing robot, as a backup. The instruction framing module splits the composite instruction, main operation instruction, and relay preparation instruction into data frames, which are first sent to the #45 main nest, and then sent to the #46 sub-nest.

[0050] Step 3: Multi-machine collaborative path planning and energy consumption optimization: 1. Flight Path Generation: The multi-aircraft collaborative dispatch subsystem acquires the coordinates of the #45 main nest and the risk point, and retrieves terrain elevation data and the coordinate set of the transmission line corridor. The system avoids the steep slope of the ridge between towers #45 and #46, generates a flight path along the line corridor consisting of multiple turning points, and calculates the horizontal projection length and vertical climb length of each segment. 2. Energy consumption equivalent conversion: The path unification conversion component calls the horizontal flight energy consumption coefficient of 0.8 units / meter and the vertical climb energy consumption coefficient of 1.5 units / meter to convert the geometric length of the path into the total energy consumption equivalent; the energy consumption segment accumulation sub-component accumulates the energy consumption in the order of the broken line segments to form a segment accumulation record; the energy consumption weight correction sub-component dynamically corrects the energy consumption equivalent according to the elevation change rate between adjacent breakpoints, for example, by increasing the energy consumption coefficient in steep climb sections; The end-point energy consumption correction sub-component further introduces charging efficiency compensation. The system reads the charging pile output power of #45 main nest (600W) and the drone battery voltage (48V), and obtains a baseline compensation value of 0.95 from the charging efficiency compensation coefficient table. Then, based on the current temperature of -5℃ and humidity of 85% inside the nest, it obtains a temperature compensation coefficient of 1.05, a low-temperature charging efficiency reduction compensation coefficient, and a humidity compensation coefficient of 0.98 from the compensation coefficient curve. Finally, combined with the lifespan decay coefficient of 0.97 corresponding to 120 charging cycles of the drone battery, the final compensation coefficient is calculated to be approximately 0.95. 3. Optimal Nest Selection: The departure nest selection component compares the corrected total energy consumption equivalent with the flight range corresponding to 92% of the current remaining power of the #45 main nest to confirm sufficient power. At the same time, the path to the #46 sub-nest is calculated in the same way; although the path to the #45 main nest is slightly longer, its power is more sufficient and its total energy consumption equivalent is the smallest, so it is identified as the departure nest.

[0051] Step 4: Task execution and effect evaluation: 1. Collaborative Operation: The drone carrying the de-icing robot from the #45 main nest takes off and flies along the planned path to the icing point. The drone hovers, and the de-icing robot is released through the hoisting interface and attached to the guide wire, beginning to vibrate and de-ic at medium power. 2. Real-time temperature monitoring: The operation threshold matching module of the machine nest status reset subsystem retrieves the critical temperature for ice shedding from the operation temperature threshold library according to the operation type of the de-icing robot, such as 0℃ as the operation completion temperature threshold; 3. Effective Temperature Value Extraction: The thermal imager continuously collects pixel array data of the risk point area. The effective temperature value extraction module removes abnormal pixels, such as isolated high temperature points blocked by birds. It calculates the arithmetic mean of the effective pixel set to obtain the real-time effective temperature value, for example, gradually rising from -8℃ to 0.5℃. 4. Job Completion Determination: The job completion status generation module stores five consecutively sampled real-time effective temperature values ​​(0.4℃, 0.5℃, 0.6℃, 0.6℃, 0.5℃) into a sliding window register. If all five values ​​are ≥0℃ and the fluctuation variance is less than the stability threshold, the system determines that the de-icing operation has been successfully completed and generates a job completion status marker.

[0052] Step 5: Return to Home, Reset Status, and Closed-Loop Archiving: 1. Automatic return-to-home: The system sends a return-to-home command to the drone at the #45 main nest, and the drone carrying the de-icing robot returns to the #45 main nest; 2. Entry Inspection: After the UAV enters the cabin, the entry inspection device collects equipment interface status data. The de-icing robot has returned to its position and is in good fixed condition. The data is then compared with the equipment loading list recorded before departure to confirm that there are no errors. 3. Status Reset: The Nest Status Reset Subsystem generates an equipment slot status update command, updates the status of slot #3 of the #45 main nest from locked / out to available / in stock, and resets the nest status to standby mode; 4. Data Synchronization: The system synchronizes the job completion status marker and the machine nest reset status to the defect database. The defect database updates the record status of the risk point from pending to handled to handled, archives the risk information, and triggers the system to restart data collection and monitoring for the next inspection cycle; Thus, from the automatic detection, intelligent classification, precise scheduling, collaborative operation, effect verification to system reset of the ice accumulation risk, the entire emergency response process is completed automatically without human intervention, taking a total of about 12 minutes, while traditional manual handling may take several hours or even longer.

[0053] Figure 7 A block diagram of an exemplary electronic device suitable for implementing embodiments of the present invention is shown.

[0054] Electronic devices may include a central processing unit / microprocessor / main control chip; and a storage medium coupled to the central processing unit / microprocessor / main control chip, wherein computer-executable instructions are stored for performing the steps of various methods of embodiments of the present invention when executed by a processor.

[0055] The central processing unit / microprocessor / main control chip may include, but is not limited to, one or more processors or microprocessors.

[0056] Storage media may include, but are not limited to, random access memory (RAM), read-only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, and computer storage media (such as hard disks, floppy disks, solid-state drives, removable disks, CD-ROMs, DVD-ROMs, Blu-ray discs, etc.).

[0057] In addition, the electronic device may include (but is not limited to) a data bus, an input / output bus / external bus / device bus, a display, and input / output devices (e.g., keyboard, mouse, speaker, etc.).

[0058] The central processing unit / microprocessor / main control chip can communicate with external devices via wired or wireless networks (not shown) through input / output buses / external buses / device buses.

[0059] The storage medium may also store at least one computer-executable instruction for performing the steps of various functions and / or methods in the embodiments described herein when the central processing unit / microprocessor / main control chip is running.

[0060] In one embodiment, the at least one computer-executable instruction may also be compiled into or comprise a software product, wherein one or more computer-executable instructions are executed by a processor to perform the steps of the various functions and / or methods in the embodiments described herein.

[0061] Figure 8 A schematic diagram of a computer-readable storage medium according to an embodiment of the present invention is shown.

[0062] like Figure 8As shown, instructions, such as computer-readable instructions, are stored on a non-transitory computer-readable storage medium. When the computer-readable instructions are executed by a processor, the various methods described above can be performed. The non-transitory computer-readable storage medium includes, but is not limited to, volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory. Non-transitory non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. For example, the non-transitory computer-readable storage medium can be connected to a computing device such as a computer, and then, when the computing device executes the computer-readable instructions stored on the non-transitory computer-readable storage medium, the various methods described above can be performed.

[0063] In the embodiments provided by this invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between systems or units may be electrical, mechanical, or other forms.

[0064] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0065] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0066] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for executing all or part of the steps of the methods of the various embodiments of this invention through a computer device (which may be a personal computer, server, or network device, etc.). The aforementioned storage medium includes: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0067] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A control system for an unmanned aerial vehicle (UAV) functional expansion cabinet that facilitates power transmission line inspection, characterized in that: Include: The pre-set subsystem for operational equipment is used to realize the classification and labeling of line environment image data collected by high-definition cameras and thermal imagers based on three pre-set feature thresholds: bird nests, floating objects, and ice accumulation. The risk information after classification and labeling is compared with the pre-stored risk level judgment conditions. The multi-aircraft collaborative dispatch subsystem is used to extract the coordinates of risk points from risk information, compare the risk point coordinates with the pre-entered set of tower coordinates, and determine the relay network topology of the nests within the line section where the risk point is located; calculate the first flight path based on the risk point coordinates and the main nest coordinates, calculate the second flight path based on the risk point coordinates and the coordinates of each sub-nest, add the first flight path and each second flight path to obtain the total flight mileage corresponding to each nest, and select the nest with the minimum total flight mileage as the starting nest; the subsequent nests on the path from the starting nest to the risk point are sequentially determined as relay nests; generate a collaborative dispatch instruction containing the numbers of the starting nest and each relay nest, the takeoff order of each nest and the corresponding hoisting operation equipment type, and send it to the control system of the corresponding nest; The nest status reset subsystem is used by the thermal imager to collect real-time temperature data of risk points, compare the real-time temperature data with the preset operation completion temperature threshold, and generate an operation completion status mark when the temperature threshold is reached.

2. The UAV function expansion cabinet control system for facilitating power transmission line inspection as described in claim 1, characterized in that, The multi-machine collaborative dispatch subsystem includes: The flight path generation component is used to extract the coordinates of the main nest and each sub-nest from the tower coordinate set, and to extract the coordinates of risk points from the risk information. The coordinates of the risk points and the coordinates of each nest are used as the path endpoints. A polyline path is generated between the path endpoints along the coordinate set of the transmission line channel, avoiding the obstacle positions in the terrain elevation data. The first flight path and each of the second flight paths are obtained; The path unification conversion component is used to multiply the horizontal projection length of each polyline segment in the first flight path by the horizontal flight energy consumption coefficient to obtain the horizontal energy consumption equivalent; multiply the vertical climb length of each polyline segment by the vertical climb energy consumption coefficient to obtain the vertical energy consumption equivalent; and sum the horizontal energy consumption equivalent and vertical energy consumption equivalent of each polyline segment to obtain the total energy consumption equivalent of the first flight path; the same processing is performed on the second flight path of the main nest and each sub-nest to obtain the total energy consumption equivalent of each second flight path; The departure nest selection component retrieves the current remaining power percentage of the main nest and each sub-nest, compares the current remaining power percentage of each nest with the sum of the total energy equivalent of the first and second flight paths, and filters out nests whose total energy equivalent is less than or equal to the flight range corresponding to the current remaining power percentage. From the filtered nests, the nest with the smallest total energy equivalent is selected as the departure nest, and the path inflection point coordinate sequence corresponding to the departure nest is output to the cooperative scheduling command.

3. The UAV function expansion cabinet control system for facilitating power transmission line inspection as described in claim 2, characterized in that, The unified path conversion component includes: The energy consumption segment accumulation sub-component is used to accumulate the horizontal and vertical energy consumption equivalents of each polyline segment in the first flight path according to the order of the polyline segments in the path inflection point coordinate sequence. After accumulating the energy consumption equivalent of each polyline segment, the corresponding accumulated intermediate value of the polyline segment is generated. After associating the accumulated intermediate value with the elevation value of the current polyline segment's end inflection point, store it in a temporary buffer area to obtain a segmented accumulation record set; The energy consumption weight correction sub-component is used to retrieve the inflection point elevation values ​​of two adjacent accumulated record entries in the segmented accumulated record set from the temporary cache, calculate the elevation change rate between adjacent inflection points, retrieve the pre-stored elevation change rate correction coefficient, multiply the horizontal energy consumption equivalent and vertical energy consumption equivalent in the accumulated intermediate value of each segment by the correction coefficient matching the corresponding elevation change rate, and then re-accumulate them. The corrected segmented accumulated value replaces the corresponding accumulated intermediate value in the temporary cache. The end-of-line energy consumption correction sub-component is used to extract the corrected segmented cumulative value of the last cumulative record entry from the temporary buffer as the initial total energy consumption equivalent, and retrieve the charging pile output power and the actual battery voltage value output by the drone from the charging interface status data of the engine nest. The charging pile output power and battery voltage value are input into the pre-stored charging efficiency compensation coefficient table to obtain the compensation coefficient. The initial total energy consumption equivalent is multiplied by the compensation coefficient to obtain the total energy consumption equivalent of the first flight path.

4. The UAV function expansion cabinet control system for facilitating power transmission line inspection as described in claim 3, characterized in that, The end-point energy consumption correction sub-component includes: The benchmark compensation value extraction module is used to compare the output power of the charging pile with the pre-stored power classification interval table to determine the power level to which the output power belongs; at the same time, it compares the battery voltage value with the pre-stored voltage classification interval table to determine the voltage level to which the battery voltage belongs. The power level and voltage level are combined into an index key value. The reference compensation value corresponding to the index key value is retrieved from the charging efficiency compensation coefficient table. The reference compensation value is output to the temporary buffer area to obtain the reference compensation value record. The environmental factor correction module is used to retrieve the constant temperature control status register and dehumidification control status register of the engine compartment, extract the current cabin temperature value from the constant temperature control status register, and extract the current cabin humidity value from the dehumidification control status register; compare the current cabin temperature value with the pre-stored temperature compensation coefficient curve to obtain the temperature compensation coefficient, and compare the current cabin humidity value with the pre-stored humidity compensation coefficient curve to obtain the humidity compensation coefficient. Multiply the baseline compensation value in the baseline compensation value record by the temperature compensation coefficient, and then multiply by the humidity compensation coefficient to obtain the environmental correction compensation value; The lifespan decay correction module is used to retrieve the charging cycle count register corresponding to the UAV performing this mission from the engine nest, extract the cumulative charging cycle count value of the UAV from the charging cycle count register, compare the cumulative charging cycle count value with the pre-stored cycle lifespan decay coefficient table, obtain the decay coefficient matching the cumulative charging cycle count value, and multiply the environmental correction compensation value by the decay coefficient to obtain the final compensation coefficient.

5. The UAV function expansion cabinet control system for facilitating power transmission line inspection as described in claim 4, characterized in that, The environmental factor correction module includes: The coefficient stability correction submodule is used to retrieve the historical temperature values ​​of the five consecutive sampling periods before the current cabin temperature value from the constant temperature control status register, and the historical humidity values ​​of the five consecutive sampling periods before the current cabin humidity value from the dehumidification control status register; calculate the fluctuation variance of the historical temperature value and the fluctuation variance of the historical humidity value respectively, compare the temperature fluctuation variance with the pre-stored temperature stability threshold, and when the temperature fluctuation variance exceeds the temperature stability threshold, retrieve the pre-stored temperature fluctuation correction coefficient to correct the temperature compensation coefficient; The humidity fluctuation variance is compared with the pre-stored humidity stability threshold. When the humidity fluctuation variance exceeds the humidity stability threshold, the pre-stored humidity fluctuation correction coefficient is retrieved to correct the humidity compensation coefficient, thus obtaining the corrected temperature compensation coefficient and humidity compensation coefficient. The interaction factor superposition submodule is used to retrieve the pre-stored temperature and humidity interaction coupling coefficient table, combine the corrected temperature compensation coefficient and the corrected humidity compensation coefficient into a temperature and humidity combination key value, retrieve the interaction coupling coefficient corresponding to the temperature and humidity combination key value from the temperature and humidity interaction coupling coefficient table, and multiply the corrected temperature compensation coefficient and the corrected humidity compensation coefficient by the interaction coupling coefficient to obtain the coupled and corrected temperature and humidity comprehensive coefficient. The local compensation submodule is used to retrieve the airflow velocity values ​​collected by the airflow sensor array arranged inside the engine compartment, extract the airflow compensation coefficient that matches the airflow velocity value from the pre-stored airflow velocity compensation coefficient curve, multiply the coupled and corrected temperature and humidity comprehensive coefficient by the airflow compensation coefficient to obtain the final environmental comprehensive compensation coefficient, and multiply the benchmark compensation value in the benchmark compensation value record by the environmental comprehensive compensation coefficient to obtain the environmental correction compensation value.

6. The UAV function expansion cabinet control system for facilitating power transmission line inspection as described in claim 5, characterized in that, The interaction factor overlay submodule includes: The dynamic quantization level generation unit is used to obtain the corrected temperature compensation coefficient and the corrected humidity compensation coefficient, and at the same time retrieve the historical temperature compensation coefficient sequence and the historical humidity compensation coefficient sequence recorded in the first three tasks of the nest from the temporary buffer; calculate the difference between the corrected temperature compensation coefficient and the mean of the historical temperature compensation coefficient sequence respectively, and divide the difference by the range of the historical temperature compensation coefficient sequence to obtain the temperature dynamic adjustment factor. The temperature dynamic adjustment factor is compared with the pre-stored temperature quantization interval boundary value to determine the temperature quantization level to which the corrected temperature compensation coefficient belongs. The same processing is applied to the humidity compensation coefficient to obtain the humidity quantification level; The cross-validation correction unit is used to combine the temperature quantization level and the humidity quantization level into a temporary key value, and retrieve the pre-stored temperature and humidity mutual exclusion table. The temperature and humidity mutual exclusion table records the identifiers of whether there is a physical mutual exclusion relationship under the combination of temperature quantization level and humidity quantization level. When the mutex flag corresponding to the temporary key value exists, retrieve the coefficient pair most recently recorded in the historical temperature compensation coefficient sequence and the historical humidity compensation coefficient sequence, calculate the deviation between the coefficient pair and the current corrected coefficient, take the quantization level of the coefficient with the smaller deviation as the main level, and recombine the main level with the quantization level of another coefficient to obtain the corrected temporary key value. The time decay key value encoding unit is used to retrieve the cumulative runtime register of the machine nest, extract the cumulative running hours of the machine nest since its commissioning from the cumulative runtime register, compare the cumulative running hours with the pre-stored time decay coefficient curve to obtain the time decay coefficient, multiply the temperature quantization level and humidity quantization level in the corrected temporary key value by the time decay coefficient and round them to obtain the decayed temperature quantization level and humidity quantization level, and concatenate the decayed temperature quantization level and humidity quantization level into a temperature and humidity combined key value according to the preset encoding rules.

7. The UAV function expansion cabinet control system for facilitating power transmission line inspection as described in claim 6, characterized in that, Time decay key-value encoding unit, comprising: The level bit width compression subunit is used to determine the encoding bit width of temperature quantization level and humidity quantization level according to the cumulative operating hours of the nest. The attenuated temperature quantization level and humidity quantization level are converted into binary according to the corresponding bit width to obtain temperature binary code and humidity binary code. The code bit interleaving subunit is used to retrieve the machine nest number of the machine nest and convert it into a binary sequence. The temperature binary code and humidity binary code are alternately inserted into the corresponding positions of the machine nest number binary sequence according to the bit order to obtain the interleaved binary sequence. The check bit append subunit is used to retrieve the key value record table generated in the last ten tasks of the nest, and compare the interleaved binary sequence with the historical key values ​​in the record table item by item; when there is a duplicate, append the four-bit binary sequence after the millisecond-level timestamp of the current task; when there is no duplicate, append the preset padding bit, and output the final binary sequence as the temperature and humidity combined key value.

8. The UAV function expansion cabinet control system for facilitating power transmission line inspection as described in claim 1, characterized in that, The operation equipment pre-setting subsystem is used to retrieve the real-time status data of the tower-embedded nest, match the operation equipment type with the real-time status data, filter out the tower-embedded nest number that is currently loaded with the corresponding operation equipment and whose power meets the flight requirements, and generate equipment pre-setting instructions from the control system of the tower-embedded nest to move the corresponding operation equipment to the hoisting interface to stand by.

9. The UAV function expansion cabinet control system for facilitating power transmission line inspection as described in claim 1, characterized in that, The nest status reset subsystem is used to send a return command to the UAV performing the operation after obtaining the operation completion status marker, and the UAV returns to the departure nest. The cabin entry detection device of the departure nest collects the equipment interface status data after the UAV enters the cabin, compares the equipment interface status data with the equipment loading list recorded before departure, generates status update instructions for each equipment slot, and causes the nest control system to update the internal inventory records and reset the nest status to standby mode. The operation completion status marker and the nest reset status are synchronized to the defect database, and the records corresponding to the risk points are marked as handled.

10. A control method for a UAV function expansion cabinet that facilitates power transmission line inspection, used to implement the UAV function expansion cabinet control system for facilitating power transmission line inspection as described in any one of claims 1 to 9, characterized in that, Includes the following steps: It realizes the perception of high-definition camera and thermal imager to collect line environment image data, and transmits the line environment image data to the defect database; The defect database classifies and marks the line environment image data according to three preset feature thresholds: bird nests, floating objects, and ice accumulation. The risk information after classification and marking is compared with the pre-stored risk level judgment conditions to obtain the matching risk level and the corresponding type of work equipment. Retrieve real-time status data of the embedded nests on the towers, match the type of work equipment with the real-time status data, and filter out the numbers of the embedded nests on the towers that are currently loaded with the corresponding work equipment and whose power meets the flight requirements. The coordinates of risk points are extracted from the risk information, and the coordinates of risk points are compared with the pre-entered set of tower coordinates to determine the topology of the relay network within the line section where the risk point is located. The nest corresponding to the minimum total flight distance is selected as the departure nest; the subsequent nests on the path from the departure nest to the risk point are sequentially determined as relay nests; a coordinated scheduling instruction is generated and sent to the control system of the corresponding nest; The thermal imager collects real-time temperature data at risk points and compares the real-time temperature data with a preset temperature threshold for completing the operation. When the temperature threshold is reached, a status marker for completing the operation is generated. After obtaining the status marker for completing the operation, a return command is sent to the UAV performing the operation, and the UAV returns to the departure nest. The cabin entry detection device in the departure nest collects the equipment interface status data after the UAV enters the cabin and compares the equipment interface status data with the equipment loading list recorded before departure, generating status update commands for each equipment slot.

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