Energy relay based resident uav task relay and route dynamic planning method

Abstract

This invention discloses a method for mission relay and dynamic route planning for stationary UAVs based on energy relay, belonging to the field of UAV control technology. It includes a smart relay station receiving macro-level mission commands and dynamically dividing them into multiple route segments, generating a mission beacon broadcast containing route and priority information; stationary UAVs listening to the beacons and autonomously deciding whether to relay based on local bidding rules and their own status; the UAV deciding to relay becomes a mission UAV and executes the corresponding flight segment. The mission UAV monitors its battery level in real time, and when it triggers a low battery threshold and meets safety conditions, it generates and broadcasts an incomplete mission beacon, simultaneously selecting a target station to return to based on real-time charging queuing information broadcast by each smart relay station; this incomplete beacon triggers other stationary UAVs to initiate a new round of autonomous relay decision-making. This invention employs a dynamic mission decomposition and release mechanism, combined with autonomous bidding decision-making, enabling dynamic relay and dynamic route planning for long-endurance UAV missions.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) control technology, and in particular to a method for relaying and dynamically planning flight routes for stationary UAV missions based on energy relay. Background Technology

[0002] Unmanned aerial vehicles (UAVs), or drones for short, are aircraft with a high degree of autonomous flight capabilities and are widely used in many fields such as aerial surveillance, logistics transportation, power line inspection, and geographic surveying. With the continuous expansion of application scenarios and the increasing complexity of tasks, utilizing swarms of multiple UAVs to collaboratively execute missions has become an important direction in current technological development.

[0003] In related technologies, Chinese invention patent CN109582034B discloses a multi-task route planning method, device, and electronic device, including: creating multiple main task routes for a UAV; analyzing the multiple main task routes to obtain flight environment information corresponding to each main task route; configuring switching route information between the multiple main task routes based on the flight environment information, so that the UAV executes multiple route tasks according to the multiple main task routes and the switching route information; if it is determined that the real-time environment information corresponding to the main task route currently being flown by the UAV does not match the preset standard environment information, the UAV is switched from the current main task route to another main task route to be flown, and continues to execute other route tasks.

[0004] However, the aforementioned existing technical solutions have the following technical drawbacks. Limited by the endurance of a single UAV, it cannot independently complete long-distance, long-endurance continuous tasks; its trajectory planning and task scheduling are highly dependent on the central server, which places high demands on server performance and poses a risk of single point of failure, resulting in insufficient system robustness; lacking closed-loop intelligent energy scheduling, the UAV cannot make optimal choices based on global information when charging, which can easily lead to site congestion, increase waiting time, and reduce overall efficiency. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides a method for relaying and dynamically planning flight routes for long-endurance UAV missions based on energy relay. By employing a dynamic task decomposition and release mechanism, combined with autonomous bidding decision-making, it can achieve dynamic relaying and dynamic flight route planning for long-endurance UAV missions.

[0006] The above objectives can be achieved through the following approach:

[0007] A method for relaying and dynamically planning flight routes for stationary drones based on energy relay systems includes: intelligent relay stations receiving macro-level mission instructions and dynamically dividing them into multiple flight segments, generating mission beacon broadcasts containing flight route and priority information; stationary drones listening to the beacons and autonomously deciding whether to relay based on local bidding rules and their own status; the drone deciding to relay becomes a mission drone and executes the corresponding flight segment. The mission drone monitors its battery level in real time, and when it triggers a low battery threshold and meets safety conditions, it generates and broadcasts an incomplete mission beacon, simultaneously selecting a target station to return to based on real-time charging queuing information broadcast by each intelligent relay station; this incomplete beacon triggers other stationary drones to initiate a new round of autonomous relay decisions.

[0008] Optionally, the autonomous relay decision-making based on preset local bidding rules and the stationary drone's own status information, to obtain the decision result, includes: obtaining the stationary drone's own status information including current battery level and current location, and obtaining the task priority in the task beacon; calculating the estimated arrival time to the task handover point based on the current location and the flight path information in the task beacon; calculating the stationary drone's comprehensive score for the task beacon based on the current battery level, the estimated arrival time, and the task priority; and comparing the comprehensive score with a preset decision threshold for balancing response enthusiasm and resource consumption to generate a decision result.

[0009] Optionally, when the power monitoring triggers a preset low power threshold and the safe return-to-home condition is met, the step of the mission drone generating an incomplete mission beacon and broadcasting it includes: obtaining the remaining power of the mission drone and obtaining the location information of all surrounding smart relay stations; calculating a set of smart relay stations that can be safely reached based on the remaining power and the location information, as a selectable set of smart relay stations; determining that the return-to-home condition is met when the number of smart relay stations included in the selectable set of smart relay stations meets a preset redundancy threshold for ensuring redundant alternative landings; obtaining the flight mission progress information of the current flight segment executed by the mission drone and encapsulating the flight mission progress information into the incomplete mission beacon; and controlling the mission drone to continuously broadcast the incomplete mission beacon during the return journey.

[0010] Optionally, selecting a target smart relay station for the mission drone based on the charging queuing information includes: obtaining the charging queuing information of each smart relay station in the set of optional smart relay stations; obtaining the estimated flight time from the current position of the mission drone to each smart relay station; summing the estimated flight time corresponding to each smart relay station with the estimated waiting time obtained from its charging queuing information to obtain the total time; sorting the total time by value and selecting the smart relay station with the smallest total time as the target smart relay station.

[0011] Optionally, the method further includes: the stationary drone is equipped with a power selection module for selecting and switching between external power supply and onboard battery power supply; when the stationary drone is in standby mode, it is connected to a ground power source through an external power supply interface, and the power selection module selects the ground power source to power the drone system; when the stationary drone receives a mission beacon command, the power selection module automatically performs a power supply switch, switching from external power supply to onboard battery power supply, and the external power supply interface automatically disconnects during takeoff.

[0012] Optionally, when the decision result is a relay, controlling the stationary UAV to send a confirmation signal to the smart relay station broadcasting the mission beacon and converting into a mission UAV to execute the flight mission corresponding to the mission beacon includes: the stationary UAV sending a confirmation signal containing its own number, current status, and relay commitment to the smart relay station broadcasting the mission beacon; after receiving the confirmation signal, the smart relay station marks that the mission of the mission segment has been taken over, and the stationary UAV is simultaneously converted into a mission UAV; the mission UAV parses the route information and mission priority in the mission beacon and completes the flight mission of the route segment path planning according to the starting coordinates, ending coordinates, flight altitude, flight speed, and mission duration.

[0013] Optionally, the step of enabling other stationary drones in a stationary state to listen to the unfinished task beacon and trigger a new round of autonomous relay decision-making includes: the stationary drones continuously listening to and receiving the unfinished beacon, parsing and obtaining the route information, task priority, and task progress information of the remaining route segment; the stationary drones combining their own status information and the local bidding rules to autonomously score the unfinished beacon and judge the feasibility of relaying; generating a new round of decision results based on the feasibility judgment results, and the drone that decides to relay sends a confirmation to the corresponding intelligent relay station and takes over the task.

[0014] Optionally, the step of dynamically dividing the macro-task instruction into multiple flight path segments and generating a task beacon containing flight path information and task priority for each flight path segment includes: parsing the macro-task instruction to obtain the target area, task duration, task type, task urgency, and total flight path; based on the distribution location of intelligent relay stations within the area, communication coverage, and the typical range of the stationing UAV, dividing the total flight path into several continuous and non-overlapping flight path segments, with the endpoint of each flight path segment located within the communication coverage of the corresponding intelligent relay station; assigning a corresponding task priority to each flight path segment according to the task duration, task type, task urgency, and importance of the target area; encapsulating the start coordinates, end coordinates, flight altitude, flight speed, and task duration of each flight path segment into flight path information, and combining the flight path information with the assigned task priority to generate a task beacon.

[0015] Optionally, the method further includes: when a smart relay station is detected to have malfunctioned, stopping the mission beacon broadcast and charging queuing information broadcast of that smart relay station; causing the resident drones originally located within the communication range of that smart relay station to switch to listening to the mission beacons broadcast by other normal smart relay stations and participating in the autonomous relay decision-making of their missions; controlling the mission drones originally scheduled to return to that smart relay station to recalculate and select a new target smart relay station based on the charging queuing information received from other normal smart relay stations.

[0016] Based on the same inventive concept, this invention also provides a system for relaying and dynamically planning flight routes for stationary UAVs based on energy relays. The system includes: a macro-level task instruction receiving and distributing module, used to acquire macro-level task instructions issued by a task scheduling center and send the macro-level task instructions to intelligent relay stations within the region; a task dynamic segmentation and beacon generation module, used to dynamically segment the macro-level task instructions into multiple flight route segments using the intelligent relay stations, and generate a task beacon containing flight route information and task priority for each flight route segment; a task beacon broadcasting module, used to broadcast the task beacon within the communication range of the intelligent relay stations; a UAV autonomous bidding decision module, used for stationary UAVs in stationary state to listen to the task beacons and make autonomous relay decisions based on preset local bidding rules and the stationary UAV's own state information, obtaining a decision result; and a task confirmation and state transition control module, used to control the UAV when the decision result is a relay. The system controls the stationed UAV to send an acknowledgment signal to the smart relay station broadcasting the mission beacon, and then converts into a mission UAV to execute the flight mission corresponding to the mission beacon's flight path. A real-time battery monitoring module monitors the remaining battery power of the mission UAV in real time during the flight mission and obtains the battery monitoring results. An incomplete mission beacon generation module generates and broadcasts an incomplete mission beacon when the battery monitoring triggers a preset low battery threshold and the safe return-to-home conditions are met. A smart charging relay station selection module acquires charging queuing information broadcast by multiple smart relay stations, including real-time queue length and estimated waiting time, and selects a target smart relay station for the mission UAV based on the charging queuing information, controlling the mission UAV to return to the target smart relay station. A mission relay re-triggering module enables other stationed UAVs in stationary state to listen to the incomplete mission beacon and trigger a new round of autonomous relay decision-making.

[0017] Compared with the prior art, the present invention has the following advantages:

[0018] This invention overcomes the limitations of single-unit endurance by using dynamic task segmentation and distributed relay, enabling drone swarms to collaboratively complete long-distance, long-endurance macroscopic tasks and ensuring the spatiotemporal continuity of the mission.

[0019] This invention adopts a decentralized architecture based on local autonomous decision-making and bidding, which reduces the dependence on the central server, avoids single points of failure, and improves system response speed and robustness.

[0020] This invention establishes a closed-loop intelligent energy scheduling process, enabling drones to select the best return point for charging based on real-time queuing information from each relay station, thus avoiding congestion and shortening the waiting time for energy replenishment.

[0021] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 This is a flowchart illustrating the method for relay and dynamic route planning of stationary UAV missions based on energy relay, according to an embodiment of the present invention.

[0024] Figure 2 This is a surface diagram of the autonomous bidding comprehensive score in an embodiment of the present invention.

[0025] Figure 3 This is a heatmap of the comprehensive scoring of autonomous relay decision-making in an embodiment of the present invention.

[0026] Figure 4 This is a schematic diagram of the structure of the stationary UAV mission relay and route dynamic planning system based on energy relay, according to an embodiment of the present invention. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] Reference Figure 1 One embodiment of the present invention proposes a method for relaying and dynamically planning flight routes for stationary UAV missions based on energy relay. It adopts a dynamic task decomposition and release mechanism, combined with autonomous bidding decision-making, which can realize dynamic relaying and dynamic flight route planning for long-endurance UAV missions.

[0029] The method described in this embodiment specifically includes:

[0030] S1. Obtain the macro task instruction issued by the task scheduling center and send the macro task instruction to the intelligent relay station in the area;

[0031] Specifically, the mission scheduling center, as the highest management level of the system, first generates standardized macro-level mission instructions based on preset flight operation objectives. This instruction is a structured data packet containing a geofence over the target area, the total mission duration, the mission type, and a total flight path consisting of a series of continuous waypoints. The mission scheduling center uses fiber optic networks or wireless communication links to distribute this instruction in real time to various intelligent relay stations distributed throughout the operation area. As ground infrastructure with local computing power, the intelligent relay stations immediately initiate a parsing process upon receiving the instruction. Their internal mission processing modules extract the mission urgency and target area importance weights from the instruction and calculate the initial mission priority using preset weighted logic.

[0032] S2. Using the intelligent relay station, the macro task instruction is dynamically divided into multiple route segments, and a task beacon containing route information and task priority is generated for each route segment.

[0033] Optionally, the step of dynamically dividing the macro-task instruction into multiple route segments and generating a task beacon containing route information and task priority for each route segment includes:

[0034] The macro-level task instructions are analyzed to obtain the target area, task duration, task type, task urgency, and total flight path.

[0035] Based on the distribution location of intelligent relay stations in the region, the communication coverage range, and the typical range of the stationed UAVs, the total route is divided into several continuous and non-overlapping route segments, with the endpoint of each route segment located within the communication coverage range of the corresponding intelligent relay station.

[0036] Based on the mission duration, mission type, mission urgency, and importance of the target area, assign corresponding mission priorities to each flight segment;

[0037] The starting coordinates, ending coordinates, flight altitude, flight speed, and mission duration of each flight segment are encapsulated as flight information, and the flight information is combined with the assigned mission priority to generate a mission beacon.

[0038] Specifically, the macro-level task instructions issued by the superior task scheduling center are standardized and parsed to extract core metadata for route planning and task characterization. When the intelligent relay station receives the macro-level task instructions, its internal task processing module first parses the instructions. These instructions are usually in structured data formats such as XML or JSON. The parsing process extracts the geofence coordinates of the target area, the estimated total task duration, the task type code (e.g., surveillance, inspection, or surveying), the task urgency level, and a series of ordered waypoints defining the total task path, i.e., the total route.

[0039] Based on the constraints of networked physical infrastructure and the performance boundaries of UAVs, the overall macroscopic flight path is logically divided spatially, generating a series of flight segments that can be physically completed by a single UAV relay. This segmentation algorithm is based on three key input parameters: the precise geographical distribution of all smart relay stations within the region, the effective communication coverage of each smart relay station (typically a circular area with a radius of 5 to 10 kilometers), and the typical range of the UAV under standard mission payload. This is an engineering value with redundancy considerations, generally set to 70% to 80% of the maximum theoretical range. The algorithm starts from the beginning of the overall flight path and moves forward along the path to find the optimal segmentation point. The segmentation point must simultaneously meet two conditions: first, the distance from the start of the flight segment to the segmentation point must be less than or equal to the typical range; second, the segmentation point must be within the communication coverage of the next smart relay station.

[0040] After completing the segmentation of the macro-level task, the intelligent relay station performs a comprehensive weighted evaluation based on four core dimensions: task duration, task type, task urgency, and target area importance. This evaluation assigns a quantified task priority to each independent segment. The priority is calculated using a pre-defined scoring model, taking into account the location and scope of the target area, the estimated execution time of the segment, differences in task types such as security patrols, the urgency level (whether the task requires immediate execution), and the importance level (whether the target area is a critical facility or a core control zone). This model is a multi-dimensional weighted comprehensive scoring model. Its core is to convert the quantitative characteristics of the four dimensions—task duration, task type, urgency, and target area importance—into quantifiable priority values ​​ranging from 0 to 100 points, as shown below:

[0041] ,

[0042] The final score is based on the priority of the route segment tasks. To normalize the score for task type, To normalize the score based on urgency, The target region importance score is normalized. These are the dimension weight coefficients, with a weight sum of 1, which can be dynamically adjusted according to the scenario. Task type normalized score. Based on a positive weighting system of task value and control importance, core area security patrol tasks receive 25 points, emergency rescue and troubleshooting tasks receive 20 points, routine security patrol tasks receive 15 points, daily data collection and patrol tasks receive 10 points, and other low-value tasks receive 5 points. Higher scores indicate higher importance for the task type. The target area importance score is then normalized. Based on a positive correlation between the regional control level and the degree of core importance, core control areas receive 25 points, key control areas receive 20 points, general control areas receive 10 points, and non-control areas receive 5 points. Higher scores indicate greater importance of the target area. Urgency level is also normalized. Task urgency is assigned positively: 25 points for Extreme Urgency, 20 points for Urgent, 10 points for Normal, and 5 points for Low. Task duration is also normalized for scoring. This indicates that the shorter the task duration, the higher the execution efficiency and the higher the priority score. The formula is as follows:

[0043] ,

[0044] This indicates the estimated execution time for the flight segment, calculated by the intelligent relay station based on the flight distance and the drone's speed. This indicates the system's preset maximum allowed duration for a single flight segment, which can be configured based on the drone's endurance. Take directly =0. This priority will be encapsulated in the mission beacon and broadcast, serving as an important basis for the autonomous bidding and relay of drones. This ensures that missions of high importance, high urgency, and critical areas can be prioritized and quickly taken over, achieving reasonable allocation and efficient execution of mission resources.

[0045] The geometric and attribute information of each flight segment is encapsulated into a standardized, broadcastable data unit, namely a mission beacon. For each segment generated by the cut-out process, the system packages its starting point's 3D coordinates, ending point's 3D coordinates, suggested flight altitude, recommended cruise speed, and the estimated mission execution time based on the segment length and speed into structured flight information. This flight information is then combined with the mission priority value assigned in the previous step to generate a complete mission beacon data packet. Afterward, the intelligent relay station begins periodically broadcasting this mission beacon within its communication coverage area, awaiting an autonomous response from the resident UAV.

[0046] For example, after receiving the macro-level task instruction, the intelligent relay station parses out the total task duration as 3600 seconds, the task type as power line inspection, the urgency level as 20, and obtains the total flight path. The relay station retrieves the distribution map of surrounding stations, confirms a communication radius of 8 kilometers, and sets a standard range of 25 kilometers. The algorithm searches along the total flight path and determines the split point 22 kilometers from the starting point. This point is within the communication range of relay station No. 2, satisfying the constraint that the split point is within the coverage area and less than the range, thus completing the dynamic split of the first flight segment. For this flight segment, the system identifies that it covers the core power grid and determines the importance of the target area. The value is 25, and the task type is... The urgency level is 15. The value is 20. The estimated duration of this flight segment is known. The longest single segment is 1466 seconds. The time limit is 3000 seconds. The task duration score is calculated according to the formula. Set weights It is 0.3. It is 0.2. It is 0.2. The value is 0.3. Calculated according to the scoring formula. Finally, the system encapsulates the start and end points of the flight segment, altitude of 120 meters, speed of 15 meters per second, and duration of 1466 seconds into flight route information, along with a score of 18.335, to generate a mission beacon and broadcast it.

[0047] S3. Broadcast the mission beacon within the communication range of the intelligent relay station;

[0048] After the intelligent relay station completes dynamic task allocation, it enters the task beacon broadcasting phase. The relay station encapsulates structured data containing flight path coordinates, task type, and priority into protocol packets, which are then periodically broadcast within its communication coverage area via a preset public frequency. To ensure reliable reception of critical beacons, the system can dynamically adjust the transmission power or coding scheme according to the urgency of the task to enhance coverage and anti-interference capabilities. The UAV in its stationary state continuously monitors this frequency; once the signal is captured and demodulated, the task parameters can be obtained, thus completing the information transmission from ground facilities to the air unit.

[0049] S4. The stationary drone in the stationary state listens to the mission beacon and makes an autonomous relay decision based on the preset local bidding rules and the stationary drone's own state information to obtain the decision result.

[0050] Optionally, the autonomous relay decision-making based on preset local bidding rules and the self-state information of the stationary drone, and the resulting decision, include:

[0051] Obtain the self-status information of the stationary drone, including its current battery level and current location, and obtain the task priority from the task beacon;

[0052] Based on the current location and the flight path information in the mission beacon, the estimated arrival time to the mission handover point is calculated.

[0053] Based on the current battery level, the estimated arrival time, and the task priority, a comprehensive score for the stationing drone relative to the task beacon is calculated.

[0054] The comprehensive score is compared with a preset decision threshold used to balance response enthusiasm and resource consumption to generate a decision result.

[0055] Specifically, the onboard flight control system of the stationary UAV needs to continuously integrate two types of key information. The first type is its own real-time status information, mainly including the current battery level and current location. The current battery level is monitored and provided in real time by the onboard battery management system (BMS). The current location is obtained by fusing the onboard GNSS receiver and inertial measurement unit, providing high-precision latitude and longitude coordinates. The second type of information comes from external sources, namely, by successfully listening to and parsing the mission beacon broadcast by the intelligent relay station through the onboard data link communication module. The mission beacon is a structured data packet from which the stationary UAV extracts the mission priority field, which is crucial for decision-making. The higher the value of the field, the more urgent or important the mission.

[0056] Precisely quantifying the time cost required for a stationary UAV to respond to a mission is a key time-dimensional indicator for assessing its relay feasibility. After obtaining its current position and the flight path information contained in the mission beacon, the UAV immediately calculates its estimated arrival time. The flight path information explicitly defines the three-dimensional coordinates of the mission handover point, typically the starting point of the next flight segment. Based on its current position coordinates and the mission handover point coordinates, the stationary UAV calculates the straight-line spatial distance between the two points using a spherical distance formula or, within a smaller scope, a simplified Euclidean distance. This distance is then divided by a preset average cruising speed, an engineering parameter that comprehensively considers energy consumption and efficiency, to obtain the estimated arrival time.

[0057] A standardized mathematical model integrates three different dimensions of indicators—drone endurance, response time, and mission importance—into a single, quantifiable comprehensive score, which serves as the sole basis for bidding decisions. Furthermore, each indicator needs to be normalized. Subsequently, based on current battery level, estimated arrival time, and mission priority, the comprehensive score of the stationing drone for the mission beacon is calculated. The formula for calculating this comprehensive score can be designed as follows:

[0058] ,

[0059] In this formula, This represents the final overall score. This represents the normalized battery score, the value of which is determined by the current battery level. The higher the battery level, the higher the score. For example, the battery percentage can be directly mapped to the range of 0 to 1. This represents the normalized task priority score, which is obtained by linear mapping directly from the task priority values ​​in the task beacon, ensuring that high-priority tasks receive higher base scores. This represents the normalized time cost score, which is a function of the estimated arrival time. The longer the arrival time, the higher the score, reflecting its negative impact. These are preset weighting coefficients. The sum of these three coefficients is usually set to 1. They reflect the system designer's strategic preferences for the trade-offs between resources, tasks, and efficiency. The comprehensive scoring surface for autonomous bidding is as follows: Figure 2 As shown.

[0060] The calculated quantitative comprehensive score is compared with a preset decision threshold, ultimately outputting a clear and actionable binary decision: "relay" or "no relay." The stationary drone will then use the calculated comprehensive score... With a system-preset decision threshold A comparison is then made. This decision threshold is a key control parameter used to balance the system's responsiveness with the effective consumption of drone resources. If the threshold is set too low, a large number of drones in suboptimal states may participate in the relay, resulting in resource waste; if it is set too high, no drones may respond to the task in certain situations. The typical engineering setting for this threshold is between 0.65 and 0.8. When the overall score is... Greater than or equal to the decision threshold When the overall score is below a certain threshold, the drone's decision is "relay," indicating that it assesses its own condition as highly suitable for performing the task. Conversely, if the overall score is below this threshold, the decision is "no relay," and the drone will continue to remain stationary, listening to other mission beacons. The heatmap of the autonomous relay decision-making overall score is shown below. Figure 3 As shown.

[0061] For example, during standby, the stationary UAV captures the mission beacon broadcast by the smart relay station via its wireless listening module and parses the mission priority for that flight segment. At this time, the onboard battery management system detects a remaining battery percentage of 90%, resulting in a normalized battery score of 0.9. Simultaneously, the Global Navigation Satellite System (GNSS) calculates the UAV's current position coordinates and, combined with the three-dimensional coordinates of the mission handover point in the beacon, calculates the straight-line spatial distance between them to be 2 kilometers. Assuming a preset average cruising speed of 10 meters per second for the UAV to execute the handover response, the estimated arrival time to the mission handover point is calculated to be 200 seconds. For comprehensive scoring, the system performs reverse normalization on the estimated arrival time within a preset range of 0 to 1000 seconds, resulting in a normalized time cost score of 0.2. Meanwhile, the mission priority value obtained from the mission beacon is linearly mapped to obtain a normalized mission priority score of 0.8. In calculating the comprehensive score, preset weighting coefficients are energy weight 0.4, mission weight 0.4, and time weight 0.2. The comprehensive score is calculated according to the formula... =0.4×0.9+0.4×0.8-0.2×0.2, and after obtaining the final comprehensive score, the airborne decision module compares it with the system's preset decision threshold. If the preset decision threshold is 0.60, since the comprehensive score of 0.64 is greater than the decision threshold, the system generates a relay decision, and the UAV immediately sends a confirmation signal to the intelligent relay station and is converted into a mission UAV to execute the task for this segment.

[0062] S5. When the decision result is relay, control the stationary UAV to send a confirmation signal to the smart relay station broadcasting the mission beacon, and convert it into a mission UAV to perform the flight mission of the flight segment corresponding to the mission beacon.

[0063] Optionally, when the decision result is a relay, controlling the stationary UAV to send a confirmation signal to the smart relay station broadcasting the mission beacon, and converting it into a mission UAV to perform the flight mission corresponding to the mission beacon's flight path segment includes:

[0064] The stationary drone sends a confirmation signal containing its own number, current status, and relay commitment to the smart relay station of the broadcast mission beacon;

[0065] After receiving the confirmation signal, the intelligent relay station marks that the mission for that flight segment has been taken over, and the stationed drone is simultaneously converted into a mission drone;

[0066] The mission-oriented UAV analyzes the route information and mission priority in the mission beacon, and completes the flight mission by planning the route segment according to the starting point coordinates, ending point coordinates, flight altitude, flight speed, and mission duration.

[0067] Specifically, the local decision-making result of the stationed drone is formally notified to the network, completing the contractual locking of the mission and preventing resource conflicts. The stationed drone that makes the relay decision immediately sends a structured confirmation signal to the source smart relay station that broadcast the mission beacon via its onboard data link communication module. This confirmation signal is a data packet that encapsulates three key fields: the drone's unique identification number, used to identify the mission executor; a summary of the drone's current status, including but not limited to its fully charged takeoff status and precise geographical coordinates; and a clear relay commitment field, which contains the unique identifier of the mission beacon it is responding to, to ensure that both parties are confirming the same mission.

[0068] The system status is updated synchronously, ensuring that task allocation takes effect simultaneously at both the network and individual drone levels. Once the intelligent relay station successfully receives and verifies the confirmation signal, it immediately marks the flight segment corresponding to the task beacon as "taken over" in its internal task distribution table and records the takeover drone's number. Afterward, the intelligent relay station typically stops broadcasting this task beacon to avoid redundant bidding calculations for other drones. Simultaneously, the drone that sent the confirmation signal also performs an internal state machine transition in its local flight control system, switching its operating mode from "standby" to "task execution." From this moment on, the drone officially becomes a mission drone.

[0069] The mission instructions are translated into an executable flight trajectory, and autonomous flight is initiated. Once the drone becomes a mission-oriented UAV, its mission management module immediately performs deep analysis of the stored mission beacons, extracting all specific parameters from the flight path information, including the starting point coordinates, ending point coordinates, mission duration, preset flight altitude, and cruising speed. Based on these parameters, the onboard path planning algorithm begins operation. It first generates an optimal path connecting the starting and ending points in three-dimensional space, taking into account factors such as terrain avoidance and flight economy, and discretizes it into a series of high-density waypoints according to the set flight altitude and speed. After path planning is completed, the flight control system takes over control, driving the UAV to take off autonomously and precisely execute the flight mission corresponding to the mission beacon along the generated path plan. Flight segment path planning algorithm: Based on the starting and ending coordinates of the flight segment in the mission beacon, a three-dimensional flight space is constructed under the constraints of flight altitude, cruise speed and mission duration. Flight constraints such as no-fly zone avoidance, maximum turning angle limit and smooth flight attitude are introduced. An optimization strategy based on energy consumption balance and path optimization is adopted to generate a smooth flight trajectory without abrupt changes and trackable. The trajectory is discretized into an ordered three-dimensional waypoint sequence, enabling the UAV to stably complete the autonomous flight of the flight segment according to the waypoints, meeting the requirements of flight accuracy and mission execution.

[0070] For example, after the stationary UAV outputs the relay result from its local autonomous bidding decision module, it immediately sends a structured confirmation signal to the source intelligent relay station that broadcast the task beacon via its onboard data link communication module. This confirmation signal encapsulates the UAV's unique identification number, current state summary, and a clear relay commitment field to ensure the uniqueness and contractual locking of the task execution entity. Upon successful reception and verification of the confirmation signal, the intelligent relay station marks the route segment status as taken over in its internal task distribution table and stops broadcasting the task beacon to avoid resource conflicts. Simultaneously, the stationary UAV that sent the confirmation signal undergoes a transition in its internal state machine, officially switching its operating mode from stationary standby to task execution, becoming a task UAV. Subsequently, the task management module performs deep analysis of the route information in the task beacon, extracting parameters such as the start-point coordinates, end-point coordinates, suggested flight altitude, cruising speed, and task duration. The onboard path planning algorithm generates the optimal path based on these parameters and discretizes it into a series of high-density waypoints. Finally, the flight control system drives the UAV to take off and precisely execute the corresponding route segment's flight task along the planned path.

[0071] S6. During the execution of the flight mission, monitor the remaining battery power of the mission drone in real time and obtain the battery power monitoring results;

[0072] Specifically, during the mission of the UAV executing the flight segment assigned by the intelligent relay station, the onboard energy management system initiates a real-time monitoring program to obtain high-frequency power monitoring results. This monitoring process is achieved through current sensors and voltage sampling circuits integrated into the battery pack output, collecting the battery's discharge current and output voltage in real time. The onboard processor uses the coulomb accounting method combined with an open-circuit voltage prediction algorithm to perform time integration on the collected physical current signal and make real-time corrections based on voltage fluctuations, thereby accurately calculating the current remaining power. To quantitatively assess the power status, the system compares the real-time remaining power with the rated total capacity of the battery pack, generating a normalized score reflecting the degree of power adequacy. This monitoring result is input into the flight control system in real time at a preset frequency, serving as the core basis for triggering subsequent low-power return-to-home logic or generating an incomplete mission beacon.

[0073] S7. When the power monitoring triggers a preset low power threshold and the safe return-to-home condition is met, the mission drone generates an incomplete mission beacon and broadcasts it.

[0074] Optionally, when the battery monitoring triggers a preset low battery threshold and the safe return-to-home condition is met, the task drone generates an incomplete mission beacon and broadcasts it, including:

[0075] Obtain the remaining battery power of the mission drone and the location information of all surrounding smart relay stations;

[0076] Based on the remaining battery power and the location information, a set of smart relay stations that can be safely reached is calculated, which serves as the optional set of smart relay stations;

[0077] When the number of smart relay stations included in the optional smart relay station set meets the preset redundancy threshold for ensuring redundant alternate landing, it is determined that the return-to-home condition is met.

[0078] Obtain the flight mission progress information of the current flight path segment executed by the mission UAV, and encapsulate the flight mission progress information into the incomplete mission beacon;

[0079] The drone is controlled to continuously broadcast the unfinished mission beacon during its return journey.

[0080] Specifically, the mission drone's onboard flight control system periodically queries the battery management system to obtain the precise remaining battery power, serving as a quantitative benchmark for energy reserves. Simultaneously, the flight control system retrieves and loads the location information of all pre-set smart relay stations in the vicinity from its internally stored global geographic information database. This database contains the precise three-dimensional coordinates of each smart relay station, forming the basis for reachability analysis.

[0081] Based on real-time energy status, all theoretically safe landing points are precisely selected, forming a dynamic set of selectable smart relay stations. The mission UAV will perform an reachability calculation for each smart relay station in the list. This calculation follows the energy conservation constraints:

[0082] ,

[0083] in, This represents the real-time remaining battery power provided by the battery management system. The current position of the mission drone to the [number]th The straight-line spatial distance between each intelligent relay station is calculated in real time by the airborne navigation system. This parameter represents the energy consumption rate per unit distance of the drone. It is obtained by fitting a large amount of flight test data or by estimating through aerodynamic models. Typically, its value ranges from 0.05 to 0.15 kWh per kilometer, and the specific value is related to flight speed, payload and weather conditions. The preset safety redundancy power is a reserve of unusable power to cope with emergencies such as strong headwinds or navigation errors. It is usually set to 15% to 20% of the total battery capacity. Any smart relay station that meets this inequality is considered to be safely reachable and is added to the set of selectable smart relay stations.

[0084] By verifying the redundancy of alternate landing points, the system ultimately determines whether the triggering conditions for a safe return to base are met. The mission drone calculates the number of members in the set of optional smart relay stations and compares this number with a preset redundancy threshold. This threshold is a key safety parameter ensuring that the drone still has backup options in the event of a sudden failure or excessively long queue at the primary target charging station. Only when the number of optional smart relay stations is greater than or equal to this redundancy threshold is the return-to-base condition officially met, which greatly improves the robustness of the return-to-base decision.

[0085] Once the return-to-home conditions are met, a standardized incomplete mission beacon containing handover point information is generated and broadcast to notify other UAVs in the area. The mission UAV immediately retrieves the flight mission progress information for the current flight segment from its mission management module. This information precisely records key handover data such as the coordinates of the last waypoint passed, the coordinates of the next waypoint to be flown, and the original mission priority. This flight mission progress information is then encapsulated into a structured data packet, i.e., the incomplete mission beacon, according to a predefined protocol format.

[0086] As the mission drone begins its return journey to the target smart relay station, it controls its communication module to continuously broadcast the unfinished mission beacon to the surrounding airspace at a fixed frequency, such as 1 to 2 times per second, to ensure that the mission handover information can be stably received by potential relay drones, thereby minimizing mission interruption time.

[0087] For example, during the inspection mission, the onboard battery management system of the mission drone collects and outputs the precise remaining battery power in real time. The power consumption is 15.00 kWh. At this time, the flight control system loads the location information of three preset intelligent relay stations in the surrounding area from the global geographic information database, and the airborne navigation system calculates the straight-line spatial distance from the current location to each station. They are respectively Equal to 50 kilometers Equal to 80 kilometers Equals 120 kilometers. The system sets the drone's energy consumption rate per unit distance. The preset safety redundancy is 0.12 kWh per kilometer. This is 20% of the total battery capacity, or 3.00 kWh. Verification was performed on each of the three relay stations. For station number 1, the calculation result was... and The inequality is satisfied. For station 2, the calculation result is... If the value is less than 12.00, the inequality is still satisfied. For station 3, the calculation result is... The value is greater than 12.00, which does not meet the constraint. Therefore, the system selects two smart relay stations as the optional set: Station 1 and Station 2, with a total of 2 members per station. If the preset redundancy threshold for ensuring redundant alternate landings is 2, then the current set size meets the threshold requirement, and the system determines that the return-to-home condition is met. Subsequently, the mission management module obtains the flight mission progress information for the current execution route segment, including the coordinates of the last waypoint passed and the original mission priority, and encapsulates it as an incomplete mission beacon. While the mission UAV is turning towards Station 1 or Station 2 to return to home, the control communication module continuously broadcasts this incomplete mission beacon at a fixed frequency of twice per second to trigger a new round of relay decisions for nearby UAVs.

[0088] S8. Obtain charging queuing information broadcast by multiple smart relay stations, including real-time queue length and estimated waiting time, and select a target smart relay station for the mission drone based on the charging queuing information, and control the mission drone to return to the target smart relay station.

[0089] Optionally, selecting a target smart relay station for the mission drone based on the charging queuing information includes:

[0090] Obtain the charging queuing information for each smart relay station in the set of optional smart relay stations;

[0091] Obtain the estimated flight time from the current location of the mission drone to each smart relay station;

[0092] The total time is obtained by summing the estimated flight time corresponding to each smart relay station with the estimated waiting time obtained from its charging queue information.

[0093] The total time consumption is sorted by numerical value, and the smart relay station with the smallest total time consumption is selected as the target smart relay station.

[0094] Specifically, the drone about to return to base sends or listens for information requests to each member of its previously identified set of selectable smart relay stations via its data link communication module. This allows it to obtain real-time charging queue information broadcast by each selectable smart relay station. This information is a structured data packet containing at least two core fields: real-time queue length, i.e., the number of drones currently waiting to charge; and estimated waiting time, a prediction dynamically calculated by the smart relay station based on the queue length and its own average charging service duration.

[0095] Meanwhile, the mission drone's onboard navigation system calculates the estimated flight time for each potential return path based on its real-time precise position acquired via GNSS and the position coordinates of each available smart relay station retrieved from a local database. This calculation divides the straight-line spatial distance by a preset return cruise speed that prioritizes energy efficiency, typically set between 12 and 18 meters per second to maximize range.

[0096] Establish a unified, quantifiable evaluation metric for each optional return destination: total time. For the first [number] smart relay station in the set of optional smart relay stations... At each intelligent relay station, the mission drone sums the two key time parameters it has just acquired or calculated to obtain its total time. This calculation follows a linear summation model:

[0097] ,

[0098] In this formula, Represents flying to the The total time cost required to establish a smart relay station and complete charging preparation. Fly from the current location to the The estimated flight time of each intelligent relay station is calculated locally and in real time by the drone. From the first The estimated waiting time is directly parsed from the charging queue information of each smart relay station.

[0099] Based on the calculated total time metric, the optimal decision is executed and the final return target is locked. The onboard decision module of the mission UAV calculates the total time for all available smart relay stations. The data is sorted in ascending order. The smart relay station with the smallest total time consumption is selected from the sorted results and uniquely designated as the final target smart relay station for this return journey. Once the target is determined, the flight control system immediately plans the return route based on the coordinates of the target smart relay station and controls the mission UAV to fly autonomously along the new route until it reaches the target for charging operations.

[0100] For example, the mission drone that triggers the return-to-home decision obtains charging queuing information for each station in the set of optional smart relay stations via a data link communication module. Assuming this set includes station A and station B, the estimated waiting time is parsed from the data packets broadcast by station A. The estimated waiting time is 600 seconds, which is obtained by parsing the data packets broadcast by Bilibili. The estimated flight time is 300 seconds. Simultaneously, the onboard navigation system calculates the estimated flight time based on the current coordinates and station location. The drone is set to fly at a return cruise speed of 15 meters per second. The distance to station A is measured to be 6000 meters, and the estimated flight time is calculated accordingly. The estimated flight time was calculated based on a distance of 12,000 meters from Bilibili, with a time limit of 400 seconds. The time is 800 seconds. The total time for each station is calculated using the linear summation formula. For station A, its total time is... Seconds. For Bilibili, the total time spent... The onboard decision module sorts the calculated total time in ascending order. Since 1000 seconds is less than 1100 seconds, the system selects station A, with the smallest value, as the target intelligent relay station. The mission UAV then locks onto the coordinates of station A and plans its return route, controlling the flight control system to autonomously fly to the station for charging. Through this quantitative evaluation metric, the system achieves the goal of avoiding charging congestion and minimizing non-operational time for energy replenishment.

[0101] S9. Enable other stationary drones in the stationary state to listen to the unfinished mission beacon and trigger a new round of autonomous relay decision-making.

[0102] Optionally, the step of enabling other stationary drones in a stationary state to listen to the unfinished mission beacon and triggering a new round of autonomous relay decision-making includes:

[0103] The stationary drone continuously listens to and receives the unfinished beacons, and parses them to obtain the route information, task priority and task progress information of the remaining route segments;

[0104] The stationary drone combines its own status information with the local bidding rules to autonomously score and assess the feasibility of relaying uncompleted beacons;

[0105] Based on the feasibility assessment results, a new round of decision results are generated, and the decision is that the relay drone sends a confirmation to the corresponding smart relay station and takes over the task.

[0106] Specifically, this enables all eligible stationed UAVs within the area to accurately capture and understand a handover request for an ongoing mission. During standby, the onboard data link communication system of the stationed UAVs continuously monitors and decodes data packets broadcast within the airspace. When a mission UAV returning due to low battery begins broadcasting an incomplete mission beacon, nearby stationed UAVs will receive this beacon. This beacon is a specially formatted data packet, and its parsing process is similar to that of the initial mission beacon, but more crucially, it extracts mission progress information. This includes the precise three-dimensional coordinates of the mission UAV when it aborted its mission—the new mission handover point—as well as the endpoint coordinates of the remaining flight segment and the original mission priority.

[0107] This dynamically emerging task relay point serves as a new bidding target, allowing each monitored drone to independently and quickly assess its own suitability for the relay. Upon successfully parsing the incomplete task beacon, the drone immediately activates its internal autonomous relay decision-making module. This module uses the same local bidding rules and algorithms as those used for the initial task beacon. It acquires its real-time status information, primarily its current location and ensures full battery power, and combines this with the task priority parsed from the incomplete task beacon, along with the estimated arrival time from its current location to the task relay point. These factors are then used in a standard comprehensive scoring formula to generate a comprehensive score for the incomplete task.

[0108] The new round of local decision-making results is translated into actual takeover actions, completing the closed-loop handover of the mission. Based on the feasibility assessment results, the drone with the highest comprehensive score or the first stationed drone to exceed the decision threshold will generate a new decision result of "relay". Subsequently, the drone immediately executes the same confirmation protocol as during the initial mission relay, that is, sending a confirmation signal containing its own number and relay commitment to the corresponding smart relay station responsible for that route segment. After receiving this confirmation for the unfinished mission, the smart relay station will update the mission status and formally assign the remaining mission to this new drone. The drone that made the relay decision then becomes a mission drone, takes off autonomously, flies to the mission handover point, and continues to execute the unfinished route segment mission in a seamless manner.

[0109] For example, a mission-performing UAV detects that its remaining battery power has reached a low threshold and immediately broadcasts an incomplete mission beacon containing remaining path information along its return route. A stationary UAV within the beacon's communication range captures this signal and parses the start and end coordinates of the incomplete segment, as well as the current mission priority. At this point, the stationary UAV acquires its own status data and records its current normalized battery score. The score is 0.85. The airborne navigation system calculates the spatial distance between its current position and the starting point of the unfinished mission as 1500 meters, sets its relay response cruising speed at 10 meters per second, and calculates the arrival time as 150 seconds. The system then performs reverse normalization on this time within a preset range of 0 to 1000 seconds to obtain the normalized time cost score. The score is 0.15. Simultaneously, the task priorities extracted from the beacon are mapped to obtain a normalized score. The score is 0.70. The overall score is calculated based on the local bidding rules formula. The weighting coefficients are set as energy weights. Task weight Time weight Substituting the parameters into the formula yields... The airborne decision module will calculate the results. The value is compared with the preset decision threshold of 0.55. Since 0.59 is greater than 0.55, the decision result is determined to be a relay. The stationary UAV immediately sends a relay confirmation acknowledgment to the original mission UAV, and after receiving the mission confirmation, it changes its status to that of the mission UAV, and then takes off to the starting point of the unfinished segment to take over the subsequent flight mission.

[0110] Optionally, the method further includes:

[0111] The stationary drone is equipped with a power selection module, which is used to select and switch between external power supply and onboard battery power supply.

[0112] When the stationary drone is in standby mode, it is connected to a ground power source through an external power supply interface, and the power selection module selects the ground power source to power the drone system.

[0113] When the stationary UAV receives the mission beacon command, the power selection module automatically performs a power supply switch, switching from external power supply to onboard battery power supply. The external power supply interface automatically disconnects during takeoff.

[0114] Specifically, after the drone lands on the smart relay station's helipad, its external power supply interface on its belly or fuselage automatically connects to the corresponding interface of the ground power supply. This interface typically employs a highly reliable electromagnetic engagement or mechanical locking structure to ensure a secure connection. Once the connection is established, the drone's internal power selection module, an electronic switching circuit composed of a high-power relay or MOSFET, immediately detects a valid external power input. After confirming that the external power voltage is stable, the module automatically disconnects the drone's internal main power supply bus from the onboard battery and switches to the external power supply interface.

[0115] When the drone decides to execute a mission, it achieves an instantaneous and seamless switch from external power supply to onboard battery power, coupled with physical detachment, to complete autonomous takeoff. This process is triggered when the drone's flight controller successfully receives and parses the mission beacon and makes a "relay" decision based on local bidding rules. This decision signal is simultaneously sent to the power selection module. Upon receiving the instruction, the power selection module immediately executes the power switching action. Its internal control logic ensures that this is a "disconnect-then-connect" switching process, i.e., first disconnecting the connection to the external power source, then closing the connection to the onboard battery. The entire switching process is controlled within milliseconds to prevent the onboard system from restarting due to power interruption. At the same moment or immediately after the power switching is completed, the flight controller sends an instruction to the detachment mechanism of the external power interface.

[0116] For example, when a stationary UAV lands at a smart relay station and is in standby mode, its external power supply interface is connected to a ground power source. The power selection module selects the ground power source to supply power to the UAV system, maintaining combat readiness and preventing battery drain and lifespan degradation. When the stationary UAV receives a mission beacon and makes a relay decision based on local bidding rules, this decision signal is simultaneously sent to the power selection module, triggering it to automatically perform a power supply switch. The power selection module, through its internal electronic switching circuitry, ensures a seamless switch from external power to the onboard battery within milliseconds, preventing the onboard system from restarting due to power interruption. Simultaneously with the power switch, the flight controller sends a command to the disengagement mechanism, causing the external power supply interface to automatically disconnect physically upon takeoff.

[0117] Optionally, the method further includes:

[0118] When a fault is detected in a smart relay station, stop broadcasting the task beacon and charging queue information for that smart relay station.

[0119] This allows drones originally stationed within the communication range of the smart relay station to become task beacons that listen to broadcasts from other normal smart relay stations and participate in their autonomous relay decision-making.

[0120] The mission drone, originally scheduled to return to the smart relay station, recalculates and selects a new target smart relay station based on the charging queue information received from other normal smart relay stations.

[0121] Specifically, faulty nodes should be isolated promptly to prevent their erroneous information from misleading drones in the network. When the system backend or other smart relay stations within the area detect a communication interruption or functional malfunction of a smart relay station through a heartbeat detection mechanism, a fault tolerance process will be immediately triggered. The first action of this process is to send a shutdown command to the control system of the faulty station, or revoke its broadcast privileges at the network level, so that it immediately stops broadcasting any task beacons and charging queue information.

[0122] The system guides the most directly affected drones to reintegrate into the effective mission network. A group of drones previously docked at and powered by the faulty smart relay station will trigger an automatic redirection logic within their internal programs after detecting the cessation of beacon broadcasts from the local relay station. These drones will proactively expand their communication monitoring range or switch monitoring channels to scan for and receive mission beacons broadcast from other normally functioning smart relay stations in the vicinity. Once they receive new mission beacons, they will participate in the autonomous relay decision-making process for the corresponding mission, just like any other drone, thereby dynamically reallocating this idle capacity back into the network.

[0123] For mission drones already in the air and originally scheduled to return to the faulty site, a safe alternative landing route is replanned. For mission drones that have selected the faulty smart relay station as their target in the return decision and are en route, their onboard flight control system, while continuously monitoring charging queue information, will detect the disappearance of the target station's broadcast signal. This event will trigger its internal return route replanning algorithm. The drone will immediately abandon its original return plan and, based on its current position and remaining battery power, re-execute the decision-making process for selecting a target smart relay station. It will acquire and evaluate the charging queue information broadcast by all other normal smart relay stations at this moment, and, combined with the estimated flight time of these stations, recalculate the total time of all feasible options, ultimately selecting an optimal new target smart relay station and adjusting its course to fly to that new target.

[0124] For example, after triggering the low-battery return-to-home logic, the mission drone performing an inspection task originally intended to return to station A. During the return journey, the onboard flight control system detected that the mission beacon and charging queue information broadcasts at station A had stopped, determining that station A had malfunctioned. At this point, the mission drone immediately initiated a return path replanning program, obtaining real-time queue information from nearby normally functioning stations B and C via its communication module. The estimated waiting time was then parsed from the data packets broadcast by station B. The estimated waiting time is 200 seconds, calculated from station C. The estimated flight time is 500 seconds. Simultaneously, the onboard navigation system calculates the estimated flight time to both stations based on the current coordinates. The drone's constant return speed is set at 10 meters per second, and the distance to station B is measured to be 6000 meters. The estimated flight time is then calculated. The estimated flight time is calculated based on a distance of 600 seconds to station C, a distance of 2000 meters to station C. The total time is 200 seconds. The total time is calculated for each candidate site according to the formula. For site B, the total time is... Seconds. For station C, the total time taken... Seconds. The onboard decision module sorts the calculated total time values. Since 700 seconds is less than 800 seconds, the system selects station C, which has the smallest total time value, as the new target intelligent relay station. The mission UAV then adjusts its course and flies to station C, thus safely and efficiently completing the redirection of the energy supply target through a dynamic reselection mechanism in the event that the original landing point fails.

[0125] Based on the same inventive concept, such as Figure 4 As shown, the present invention also provides a system for relaying and dynamically planning flight routes for stationary UAV missions based on energy relay, the system comprising:

[0126] The macro task instruction receiving and distribution module is used to obtain macro task instructions issued by the task scheduling center and send the macro task instructions to the intelligent relay stations in the region.

[0127] The task dynamic segmentation and beacon generation module is used to dynamically segment the macro task instruction into multiple route segments using the intelligent relay station, and generate a task beacon containing route information and task priority for each route segment.

[0128] The mission beacon broadcast module is used to broadcast the mission beacon within the communication range of the smart relay station;

[0129] The drone autonomous bidding decision module is used for the stationary drone in the stationary state to listen to the mission beacon and make autonomous relay decisions based on the preset local bidding rules and the stationary drone's own state information to obtain the decision result;

[0130] The task confirmation and state transition control module is used to control the stationed UAV to send a confirmation signal to the smart relay station broadcasting the task beacon when the decision result is relay, and to convert it into a task UAV to perform the flight mission corresponding to the task beacon's flight path segment.

[0131] The real-time battery monitoring module is used to monitor the remaining battery power of the mission drone in real time during the execution of the flight mission and obtain the battery monitoring results;

[0132] The unfinished mission beacon generation module is used to generate an unfinished mission beacon and broadcast it when the power monitoring triggers a preset low power threshold and the safe return conditions are met.

[0133] The intelligent selection module for charging relay stations is used to acquire charging queuing information broadcast by multiple intelligent relay stations, including real-time queue length and estimated waiting time, and select a target intelligent relay station for the mission drone based on the charging queuing information, and control the mission drone to return to the target intelligent relay station.

[0134] The mission relay re-trigger module is used to enable other stationary drones in the stationary state to listen to the unfinished mission beacon and trigger a new round of autonomous relay decision-making.

[0135] It should be noted that the electrical connections between the various units described above do not necessarily represent direct or indirect connections. Any indirect connection method can be applied to the embodiments of the present invention as long as it achieves the purpose of the present invention. The above descriptions are merely exemplary embodiments of the present invention and should not be construed as limiting the scope of the present invention.

[0136] All equivalent changes and modifications made in accordance with the teachings of this invention are still within the scope of this invention. Those skilled in the art will readily conceive of other embodiments of this invention upon considering the specification and the disclosure of practical truth. This application is intended to cover any variations, uses, or adaptations of this invention that follow the general principles of this invention and include common knowledge or conventional techniques in the art not described herein.

Claims

1. A method for relaying and dynamically planning flight routes for stationary UAV missions based on energy relay, characterized in that, The method includes: Obtain macro-level task instructions issued by the task scheduling center and send the macro-level task instructions to the intelligent relay stations within the region; Using the intelligent relay station, the macro-task command is dynamically divided into multiple route segments, and a task beacon containing route information and task priority is generated for each route segment. The mission beacon is broadcast within the communication range of the intelligent relay station; The stationary drone in the stationary state listens to the mission beacon and makes autonomous relay decisions based on the preset local bidding rules and the stationary drone's own state information to obtain the decision result; When the decision result is relay, the stationary UAV is controlled to send a confirmation signal to the smart relay station broadcasting the mission beacon, and then converts into a mission UAV to perform the flight mission corresponding to the mission beacon's flight path segment. During the flight mission, the remaining battery power of the mission drone is monitored in real time to obtain the battery monitoring results; When the battery monitoring triggers a preset low battery threshold and the safe return-to-home conditions are met, the mission drone generates an incomplete mission beacon and broadcasts it. The system acquires charging queuing information broadcast by multiple smart relay stations, including real-time queue length and estimated waiting time, and selects a target smart relay station for the mission drone based on the charging queuing information, and controls the mission drone to return to the target smart relay station. This enables other stationary drones in a stationary state to listen for the unfinished mission beacon and trigger a new round of autonomous relay decision-making.

2. The method for relaying and dynamically planning flight routes for stationary UAV missions based on energy relay as described in claim 1, characterized in that, The autonomous relay decision-making based on preset local bidding rules and the self-state information of the stationary drone yields the following decision results: Obtain the self-status information of the stationary drone, including its current battery level and current location, and obtain the task priority from the task beacon; Based on the current location and the flight path information in the mission beacon, the estimated arrival time to the mission handover point is calculated. Based on the current battery level, the estimated arrival time, and the task priority, a comprehensive score for the stationing drone relative to the task beacon is calculated. The comprehensive score is compared with a preset decision threshold used to balance response enthusiasm and resource consumption to generate a decision result.

3. The method for relay and dynamic route planning of stationary UAV missions based on energy relay as described in claim 1, characterized in that, When the battery monitoring triggers a preset low battery threshold and the safe return-to-home conditions are met, the task drone generates an incomplete mission beacon and broadcasts it, including: Obtain the remaining battery power of the mission drone and the location information of all surrounding smart relay stations; Based on the remaining battery power and the location information, a set of smart relay stations that can be safely reached is calculated, which serves as the optional set of smart relay stations; When the number of smart relay stations included in the optional smart relay station set meets the preset redundancy threshold for ensuring redundant alternate landing, it is determined that the return-to-home condition is met. Obtain the flight mission progress information of the current flight path segment executed by the mission UAV, and encapsulate the flight mission progress information into the incomplete mission beacon; The drone is controlled to continuously broadcast the unfinished mission beacon during its return journey.

4. The method for relaying and dynamically planning flight routes for stationary UAV missions based on energy relay as described in claim 3, characterized in that, The step of selecting a target smart relay station for the mission drone based on the charging queuing information includes: Obtain the charging queuing information for each smart relay station in the set of optional smart relay stations; Obtain the estimated flight time from the current location of the mission drone to each smart relay station; The total time is obtained by summing the estimated flight time corresponding to each smart relay station with the estimated waiting time obtained from its charging queue information. The total time consumption is sorted by numerical value, and the smart relay station with the smallest total time consumption is selected as the target smart relay station.

5. The method for relay and dynamic route planning of stationary UAV missions based on energy relay as described in claim 1, characterized in that, The method further includes: The stationary drone is equipped with a power selection module, which is used to select and switch between external power supply and onboard battery power supply. When the stationary drone is in standby mode, it is connected to a ground power source through an external power supply interface, and the power selection module selects the ground power source to power the drone system. When the stationary UAV receives the mission beacon command, the power selection module automatically performs a power supply switch, switching from external power supply to onboard battery power supply. The external power supply interface automatically disconnects during takeoff.

6. The method for relay and dynamic route planning of stationary UAV missions based on energy relay as described in claim 1, characterized in that, When the decision result is a relay, controlling the stationary UAV to send a confirmation signal to the smart relay station broadcasting the mission beacon, and converting it into a mission UAV to perform the flight mission corresponding to the mission beacon's flight path segment includes: The stationary drone sends a confirmation signal containing its own number, current status, and relay commitment to the smart relay station of the broadcast mission beacon; After receiving the confirmation signal, the intelligent relay station marks that the mission for that flight segment has been taken over, and the stationed drone is simultaneously converted into a mission drone; The mission-oriented UAV analyzes the route information and mission priority in the mission beacon, and completes the flight mission by planning the route segment according to the starting point coordinates, ending point coordinates, flight altitude, flight speed, and mission duration.

7. The method for relay and dynamic route planning of stationary UAV missions based on energy relay as described in claim 1, characterized in that, The step of enabling other stationary drones in a stationary state to listen for the unfinished mission beacon and triggering a new round of autonomous relay decision-making includes: The stationary drone continuously listens to and receives the unfinished beacons, and parses them to obtain the route information, task priority and task progress information of the remaining route segments; The stationary drone combines its own status information with the local bidding rules to autonomously score and assess the feasibility of relaying uncompleted beacons; Based on the feasibility assessment results, a new round of decision results are generated, and the decision is that the relay drone sends a confirmation to the corresponding smart relay station and takes over the task.

8. The method for relay and dynamic route planning of stationary UAV missions based on energy relay as described in claim 1, characterized in that, The step of dynamically dividing the macro-level task instruction into multiple route segments and generating a task beacon containing route information and task priority for each route segment includes: The macro-level task instructions are analyzed to obtain the target area, task duration, task type, task urgency, and total flight path. Based on the distribution location of intelligent relay stations in the region, the communication coverage range, and the typical range of the stationed UAVs, the total route is divided into several continuous and non-overlapping route segments, with the endpoint of each route segment located within the communication coverage range of the corresponding intelligent relay station. Based on the mission duration, mission type, mission urgency, and importance of the target area, assign corresponding mission priorities to each flight segment; The starting coordinates, ending coordinates, flight altitude, flight speed, and mission duration of each flight segment are encapsulated as flight information, and the flight information is combined with the assigned mission priority to generate a mission beacon.

9. The method for relay and dynamic route planning of stationary UAV missions based on energy relay as described in claim 1, characterized in that, The method further includes: When a fault is detected in a smart relay station, stop broadcasting the task beacon and charging queue information for that smart relay station. This allows drones originally stationed within the communication range of the smart relay station to become task beacons that listen to broadcasts from other normal smart relay stations and participate in their autonomous relay decision-making. The mission drone, originally scheduled to return to the smart relay station, recalculates and selects a new target smart relay station based on the charging queue information received from other normal smart relay stations.

10. A system for relaying and dynamically planning flight paths for stationary UAV missions based on energy relays, applied to the method for relaying and dynamically planning flight paths for stationary UAV missions based on energy relays as described in any one of claims 1-9, characterized in that, The system includes: The macro task instruction receiving and distribution module is used to obtain macro task instructions issued by the task scheduling center and send the macro task instructions to the intelligent relay stations in the region. The task dynamic segmentation and beacon generation module is used to dynamically segment the macro task instruction into multiple route segments using the intelligent relay station, and generate a task beacon containing route information and task priority for each route segment. The mission beacon broadcast module is used to broadcast the mission beacon within the communication range of the smart relay station; The drone autonomous bidding decision module is used for the stationary drone in the stationary state to listen to the mission beacon and make autonomous relay decisions based on the preset local bidding rules and the stationary drone's own state information to obtain the decision result; The task confirmation and state transition control module is used to control the stationed UAV to send a confirmation signal to the smart relay station broadcasting the task beacon when the decision result is relay, and to convert it into a task UAV to perform the flight mission corresponding to the task beacon's flight path segment. The real-time battery monitoring module is used to monitor the remaining battery power of the mission drone in real time during the execution of the flight mission and obtain the battery monitoring results; The unfinished mission beacon generation module is used to generate an unfinished mission beacon and broadcast it when the power monitoring triggers a preset low power threshold and the safe return conditions are met. The intelligent selection module for charging relay stations is used to acquire charging queuing information broadcast by multiple intelligent relay stations, including real-time queue length and estimated waiting time, and select a target intelligent relay station for the mission drone based on the charging queuing information, and control the mission drone to return to the target intelligent relay station. The mission relay re-trigger module is used to enable other stationary drones in the stationary state to listen to the unfinished mission beacon and trigger a new round of autonomous relay decision-making.

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