Unmanned aerial vehicle low-altitude economic management method and system based on digital airspace

Abstract

The invention discloses an unmanned aerial vehicle low-altitude economic management method and system based on a digital airspace, and relates to the technical field of unmanned aerial vehicle management.The unmanned aerial vehicle low-altitude economic management method comprises the steps that a charging device is arranged at the position of a signal station with the high conflict probability and the high charging requirement in the low-altitude airspace, and when an unmanned aerial vehicle executes a flight task and encounters a conflict or needs to be charged, the charging device is charged; according to the difficulty and risk of collision avoidance of the unmanned aerial vehicle and the difficulty and risk of arriving at the charging device, the corresponding charging device is allocated to the unmanned aerial vehicle as a temporary storage station or is charged, and by arranging the charging device, the cruising ability of the unmanned aerial vehicle can be guaranteed, the flight limitation is reduced, and the task execution effect of the unmanned aerial vehicle is improved; when avoiding conflicts occur, the charging device is used as a pause point, flight conflicts can be avoided, airspace congestion is avoided, the risk of collision is reduced, the time of adjustment in the air is shortened, and the accuracy and integrity of data collection of the unmanned aerial vehicle are guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle management, and particularly to a method and system for managing the low-altitude economy of unmanned aerial vehicles based on a digital airspace. Background Art

[0002] The low-altitude economy of unmanned aerial vehicles is an economic form formed by carrying out various economic activities relying on the low-altitude airspace with unmanned aerial vehicles as the core. However, the low-altitude space is a limited resource. Scientific management of unmanned aerial vehicles can optimize the allocation of low-altitude resources, improve resource utilization efficiency, avoid airspace congestion, and ensure that various low-altitude flight activities can be carried out safely and efficiently.

[0003] The prior art, such as a low-altitude airspace traffic management platform based on a three-dimensional digital air corridor disclosed in the patent application with the publication number CN109976375A, has a technical system including: low-altitude safety corridor demarcation technology, spatial traffic network generation technology, spatial traffic routing planning technology, flight dynamic monitoring technology, aircraft flight control technology, and key technologies of the integrated service platform. The system function levels are divided into: basic information access layer, data center platform layer, application layer, and user layer. The present invention adopts a three-dimensional digital air corridor system to establish a unified low-altitude airspace traffic management platform for real-time monitoring of flight activities, ensuring the smooth progress of aviation operation activities, preventing events such as misflight and collision, providing a scientific, standardized, comprehensive, and systematic management mode and operation mechanism for air traffic control departments, and at the same time providing the required information services for low-altitude aircraft to ensure the flight safety of aircraft in the low-altitude airspace and the safety during takeoff and landing.

[0004] The prior art, such as a method and system for monitoring the safety of unmanned aerial vehicles based on the Internet of Things disclosed in the patent application with the publication number CN118658344B, includes analyzing the similarity of flight paths between the unmanned aerial vehicle group in the current period and the historical obstacle avoidance unmanned aerial vehicle group to obtain the target unmanned aerial vehicle group; monitoring the flight of the target unmanned aerial vehicle group, obtaining the flight data of the target unmanned aerial vehicle group, and evaluating the obstacle avoidance risk of the flight of the unmanned aerial vehicles in the target unmanned aerial vehicle group to obtain the target unmanned aerial vehicle; obtaining the marked flight data in the target unmanned aerial vehicle group, evaluating the influence degree of different trajectory adjustment states of the target unmanned aerial vehicle on the unmanned aerial vehicles in the target unmanned aerial vehicle group to obtain the target trajectory adjustment route of the target unmanned aerial vehicle; obtaining the target trajectory adjustment route of the target unmanned aerial vehicle, optimizing and adjusting the flight paths of the unmanned aerial vehicles in the target unmanned aerial vehicle group based on the target trajectory adjustment route, and intelligently monitoring the flight safety of the unmanned aerial vehicles.

[0005] The above - mentioned solution has at least the following deficiencies: 1. In the above - mentioned solution, there is a lack of layout of charging devices for drones in the low - altitude airspace. The endurance of drones is limited. If charging devices are not set up in the low - altitude airspace, their flight radius will be greatly restricted and they can only operate within a limited range centered on the charging point, making it difficult to cover a large area. In addition, in case of an emergency or when the flight mission needs to be adjusted temporarily, due to inconvenient charging, the drone may not be able to reach the designated location in time, reducing the flexibility and effectiveness of emergency response.

[0006] 2. During the peak monitoring period in the low - altitude airspace, multiple drones operate together, increasing the probability of flight conflicts among drones and causing airspace congestion. By setting up charging devices to provide pause points for drones with flight conflicts to avoid flight conflicts and airspace congestion, however, the above - mentioned solution lacks an analysis of the coordination between drones and charging devices when there are flight conflicts in the low - altitude airspace. It is impossible to provide charging devices as pause points for drones with greater avoidance difficulty during the peak period, resulting in drones with greater avoidance difficulty may not be able to find a suitable position to avoid in time, thus increasing the risk of collision with other drones or obstacles. In addition, due to the lack of coordinated analysis and pause points, when drones encounter flight conflicts, they may need to spend more time adjusting their flight routes in the air. Frequent avoidance adjustments of drones may affect the accuracy and integrity of data collection, and at the same time affect the normal flight of other drones, resulting in delays in other flight missions and reducing the overall utilization efficiency of the airspace. Moreover, drones hovering and avoiding in the airspace for a long time will consume more power, causing a waste of energy. Summary of the Invention

[0007] Aiming at the above - mentioned existing technical deficiencies, the purpose of the present invention is to provide a method and system for low - altitude economic management of drones based on a digital airspace.

[0008] To solve the above - mentioned technical problems, the present invention adopts the following technical solutions: In the first aspect, the present invention provides a method for low - altitude economic management of drones based on a digital airspace, including the following steps: S1. Data acquisition: Obtain the modeling and each monitoring trajectory of the low - altitude airspace, and obtain the monitoring data of low - altitude economic activities in the low - altitude airspace within a preset period.

[0009] S2. Charging station layout: Obtain the positions of each signal station in the low - altitude airspace, and based on the monitoring data of low - altitude economic activities in the low - altitude airspace within a preset period, confirm the positions of each charging station, deploy charging devices at the positions of each charging station, and form a charging network for the low - altitude airspace by each charging device and each signal station.

[0010] S3. Charging device coordination: When each unmanned aerial vehicle (UAV) conducts low-altitude economic activity monitoring, obtain the flight parameters of each UAV, judge the flight behavior, and based on the flight behavior of each UAV and the charging network in the low-altitude airspace, conduct coordinated management of the charging devices.

[0011] In a second aspect, the present invention provides a UAV low-altitude economic management system based on a digital airspace, including: a data acquisition module, configured to acquire the modeling of the low-altitude airspace and each monitoring trajectory, and acquire the low-altitude economic activity monitoring data in the low-altitude airspace within a preset period.

[0012] A charging station layout module, configured to acquire the positions of each signal station in the low-altitude airspace, and based on the low-altitude economic activity monitoring data in the low-altitude airspace within a preset period, confirm the positions of each charging station, deploy charging devices at the positions of each charging station, and form a charging network for the low-altitude airspace by each charging device and each signal station.

[0013] A charging device coordination module, configured to when each UAV conducts low-altitude economic activity monitoring, obtain the flight parameters of each UAV, judge the flight behavior, and based on the flight behavior of each UAV and the charging network in the low-altitude airspace, conduct coordinated management of the charging devices.

[0014] The beneficial effects of the present invention are as follows: In this application, charging devices are deployed at the positions of signal stations with a high conflict probability and a high charging demand in the low-altitude airspace. When the UAVs perform flight tasks and encounter conflicts or need to charge, according to the difficulty and risk of UAV conflict avoidance and the difficulty and risk of reaching the charging devices, corresponding charging devices are allocated to the UAVs as temporary storage stations or for charging. By setting up the charging devices, the endurance ability of the UAVs can be guaranteed, flight restrictions can be reduced, so that the UAV monitoring can cover a larger area, improving the effect of UAV task execution. In addition, when encountering avoidance conflicts, using the charging devices as pause points can avoid flight conflicts, prevent airspace congestion, reduce the risk of collision with other UAVs or obstacles. When the UAVs encounter flight conflicts, the time for in-air adjustment is reduced, ensuring the accuracy and integrity of UAV data collection, while reducing the impact on the normal flight of other UAVs and the risk of delay of other flight tasks, improving the overall utilization efficiency of the airspace, and effectively saving the power of the UAVs and reducing energy waste. Description of the Drawings

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0016] Figure 1 This is a schematic flow chart of the implementation steps of the method of the present invention.

[0017] Figure 2 This is a schematic connection diagram of the system structure of the present invention. Detailed implementation manners

[0018] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0019] Embodiment 1:

[0020] Refer to Figure 1 As shown, a method for managing the low-altitude economy of unmanned aerial vehicles based on a digital airspace includes the following steps: S1. Data acquisition: Obtain the modeling of the low-altitude airspace and each monitoring trajectory, and obtain the monitoring data of the low-altitude economic activities in the low-altitude airspace within a preset period.

[0021] It should be noted that the modeling of the low-altitude airspace and each monitoring trajectory are obtained from the monitoring center. Each monitoring trajectory is a plurality of unmanned aerial vehicle flight trajectories set by airspace management personnel according to monitoring requirements. The unmanned aerial vehicle can execute monitoring tasks in each monitoring trajectory and shall not deviate. A positioning system is arranged in the unmanned aerial vehicle. When the positioning system detects that the unmanned aerial vehicle deviates from the monitoring orbit and the monitoring center does not receive the conflict avoidance signal of the unmanned aerial vehicle, it is determined that the unmanned aerial vehicle deviates from the monitoring trajectory, and then the monitoring center immediately issues a control signal to stop the unmanned aerial vehicle to execute the stop of the unmanned aerial vehicle.

[0022] Obtain the monitoring data of the low-altitude economic activities in the low-altitude airspace within a preset period from the data center. The preset period can be within half a month or within a month. The specific value is set and adjusted according to airspace management requirements and is not limited here.

[0023] The monitoring data of low-altitude economic activities are all data generated when the unmanned aerial vehicle executes monitoring tasks, including but not limited to the positions, timestamps, economic activity types of each unmanned aerial vehicle flight conflict, the economic activity types, timestamps and positions of the economic activities monitored by each charging request of the unmanned aerial vehicle.

[0024] S2. Charging station layout: Obtain the positions of each signal station in the low-altitude airspace, and based on the monitoring data of the low-altitude economic activities in the low-altitude airspace within a preset period, confirm the positions of each charging station, arrange charging devices at the positions of each charging station, and form a charging network for the low-altitude airspace by each charging device and each signal station.

[0025] In a specific embodiment, the specific process of S2 is as follows: S2-1. Obtain the location, timestamp, economic activity type of each UAV flight conflict, the economic activity type, timestamp, and location monitored by each UAV for each charging request from the low-altitude economic activity monitoring data in the preset period, and then calculate the configuration requirement value of each signal station using the locations of each signal station.

[0026] It should be noted that the economic activity types include power inspection, agricultural inspection, environmental monitoring, etc. The UAV has a flight conflict detection and automatic obstacle avoidance function. When the UAV detects a flight conflict, it sends a conflict avoidance signal to the monitoring center, generates a conflict avoidance trajectory, and then displays it on the display terminal of the monitoring center. At the same time, when the UAV's power is insufficient, it sends a charging request to the monitoring center. After the monitoring center approves, the UAV can return for charging.

[0027] Preferably, the specific process of S2-1 is as follows: S2-1.1. According to the location of each UAV flight conflict and each monitoring trajectory, obtain the monitoring trajectory where each UAV flight conflict is located, and according to the timestamp and economic activity type of each UAV flight conflict, obtain the flight conflict frequency of each monitoring trajectory for each economic activity type.

[0028] It should be noted that according to the timestamps of each UAV flight conflict of each economic activity type monitored by each monitoring trajectory, the interval duration between each UAV flight conflict of each economic activity type monitored by each monitoring trajectory is obtained, and then the mean value is calculated to obtain the flight conflict interval duration of each monitoring trajectory for each economic activity type. The reciprocal of the flight conflict interval duration is the flight conflict frequency.

[0029] S2-1.2. Use the economic activity type, timestamp, and location monitored by each UAV for each charging request to obtain the charging request frequency of each monitoring trajectory for each economic activity type.

[0030] It should be noted that the calculation process of the charging request frequency is the same as that of the flight conflict frequency, and will not be elaborated here.

[0031] S2-1.3. Analyze the configuration requirement value of each monitoring trajectory using the flight conflict frequency and charging request frequency of each monitoring trajectory for each economic activity type, and based on the locations of each signal station, obtain the distance between the location of each signal station and each monitoring trajectory, and thus calculate the configuration requirement value of each signal station.

[0032] It should be noted that the locations of each signal station are obtained from the data center.

[0033] Among the above, the calculation process of the configuration requirement value of each signal station is as follows: normalize the flight conflict frequency and charging request frequency of each monitoring track for each economic activity type, and then denote them as f1 ij and f2 iw , where i represents the number of each monitoring track, w represents the number of each economic activity type, both i and w are positive integers. Using the calculation formula: obtain the configuration requirement value α1 of the i-th monitoring track i , in the formula, W represents the total number of economic activity types, γ w represents the weight factor of the w-th economic activity type, and η1 and η2 respectively represent the weight factors of the flight conflict frequency and the charging request frequency.

[0034] It should be noted that to obtain the importance value δ of each economic activity type w , where P1 and P2 respectively represent the total number of flight conflicts and the total number of charging requests.

[0035] Set the signal station service length threshold, denoted as L. If the distance between the position of a certain signal station and a certain monitoring track is less than L, it indicates that this monitoring track is the monitoring track served by this signal station, and this monitoring track is denoted as the target track. In this way, obtain each target track corresponding to each signal station, and obtain the distance between the position of each signal station and each target track, denoted as L xi′ , x represents the number of the position of each signal station, i′ represents the number of each target track, both x and i′ are positive integers.

[0036] It should be noted that the signal station service length threshold represents the farthest distance that the signal station can serve, which is a value set by technicians after testing.

[0037] Using the calculation formula: obtain the configuration requirement value β of the x-th signal station x , in the formula, I′ represents the number of target tracks, and α1 xi′ represents the configuration requirement value of the x-th signal station corresponding to the i′-th target track.

[0038] S2-2. Select each signal station with a configuration requirement value greater than the preset configuration requirement value threshold as each target signal station, use the positions of each target signal station as the positions of each charging station, and deploy each charging station at the positions of each target signal station.

[0039] S2-3. Use wireless communication technology to realize the communication between each charging device and each signal station, and form a charging network in the low-altitude airspace.

[0040] S3. Charging device coordination: When each UAV conducts low-altitude economic activity monitoring, obtain the flight parameters of each UAV, judge the flight behavior, and based on the flight behavior of each UAV and the charging network in the low-altitude airspace, conduct coordinated management of the charging devices.

[0041] In a specific embodiment, the specific process of S3 is as follows: S3-1. Obtain the flight parameters of each UAV from the monitoring center, judge whether there is a flight conflict between each UAV and other UAVs, and at the same time judge whether there is a charging requirement for each UAV. If there is a flight conflict, mark each UAV with the same flight conflict as each target UAV, and then execute S3-2. If there is a charging requirement, mark each UAV with a charging requirement as each UAV to be charged, and then execute S3-3.

[0042] It should be noted that the flight parameters include position, speed, remaining battery power, etc. Using machine learning algorithms, including neural networks and support vector machines, etc., learn and train a large number of UAV flight parameters and conflict cases to establish a mapping relationship between the flight state and conflicts, and then judge whether there is a flight conflict between each UAV and other UAVs. Among them, the method of using machine learning algorithms to learn and train data has been specifically disclosed and can be obtained by querying the Internet, so it will not be specifically described here.

[0043] Obtain the remaining battery power from the flight parameters. When the remaining battery power is less than the battery power threshold, it indicates that there is a charging requirement. The battery power threshold is set by technicians according to the monitoring requirements of the airspace, and no specific numerical limit is provided here.

[0044] S3-2. Obtain the monitoring trajectories and economic activity types of each target UAV, calculate the autonomous obstacle avoidance characteristic values of each target UAV, and obtain each idle charging device from the charging network in the low-altitude airspace. According to the autonomous obstacle avoidance characteristic values of each target UAV, select each temporary storage station from each idle charging device for UAV temporary storage.

[0045] Preferably, the specific process of S3-2 is as follows: S3-2.1. Obtain the monitoring trajectories and economic activity types of each target UAV, calculate the autonomous obstacle avoidance characteristic values of each target UAV. If there is only one autonomous obstacle avoidance characteristic value of the target UAV less than the autonomous obstacle avoidance characteristic value threshold, obtain each idle charging device from the charging network in the low-altitude airspace, and select the idle charging device closest to the target UAV as the temporary storage station of the target UAV.

[0046] Among the above, the specific process of calculating the autonomous obstacle avoidance eigenvalue of each target UAV is as follows: Obtain the flight parameters, the monitored trajectories where the UAVs are located, and the types of economic activities of each historical UAV in each historical conflict avoidance from the data center. Compare the flight parameters, monitored trajectories, and types of economic activities of each target UAV with those of each historical UAV in each historical conflict avoidance, screen out each reference conflict avoidance, and obtain the avoidance trajectories and avoidance records of each historical UAV in each reference conflict avoidance from the data center.

[0047] It should be noted that when the flight parameters, monitored trajectories, and types of economic activities of each target UAV correspond to those of each historical UAV in a certain historical conflict avoidance, then this historical conflict avoidance is taken as a reference conflict avoidance. At the same time, when the flight parameters, monitored trajectories, and types of economic activities of a certain target UAV correspond to those of a certain historical UAV in this reference conflict avoidance, then the avoidance trajectory and avoidance record of this historical UAV in this reference conflict avoidance are taken as the reference avoidance trajectory and the reference avoidance record of the reference avoidance trajectory of this target UAV, so as to screen out each reference conflict avoidance and obtain each reference avoidance trajectory corresponding to each target UAV and each reference avoidance record of each reference avoidance trajectory.

[0048] Among them, the avoidance record is all the data recorded by the UAV during the avoidance process, including flight speed, flight speed change rate, pitch angle change rate, roll angle change rate, yaw angle change rate, and avoidance duration, etc.

[0049] Extract each reference avoidance trajectory corresponding to each target UAV and each reference avoidance record of each reference avoidance trajectory, import each reference avoidance trajectory corresponding to each target UAV into the modeling of the low-altitude airspace, obtain the number of monitored trajectories crossed by each reference avoidance trajectory corresponding to each target UAV, and at the same time obtain from the monitoring center whether there are UAV flight tasks on each monitored trajectory crossed. Mark the monitored trajectories with UAV flight tasks as busy trajectories, and thus count the number of busy trajectories crossed by each reference avoidance trajectory corresponding to each target UAV.

[0050] Obtain each avoidance performance data from each reference avoidance record of each reference avoidance trajectory corresponding to each target UAV. Thus, input the number of busy trajectories crossed by each reference avoidance trajectory corresponding to each target UAV and each avoidance performance data into the autonomous obstacle avoidance evaluation model, and output the autonomous obstacle avoidance eigenvalue of each target UAV.

[0051] It should be noted that the avoidance performance data includes flight speed change rate, pitch angle change rate, roll angle change rate, yaw angle change rate, and avoidance duration, etc.

[0052] Among them, the reaction duration is the duration from the start of avoidance to the completion of the avoidance action and the restoration to the stable flight state.

[0053] When the rates of change such as the flight speed change rate, pitch angle change rate, roll angle change rate, and yaw angle change rate are smaller, it indicates that the stability of the UAV during avoidance is stronger. When the avoidance duration is shorter, it indicates that the avoidance difficulty of the UAV is simpler. By analyzing the avoidance performance data, it is judged whether the UAV can maintain stability during the avoidance trajectory flight and whether the avoidance is simple, providing a reference for the subsequent analysis of the temporary placement station and reducing the avoidance difficulty of the UAV.

[0054] In the above, the average avoidance performance data of each target UAV corresponding to each reference avoidance trajectory is calculated by taking the mean value of the avoidance performance data of each target UAV corresponding to each reference avoidance trajectory. Then, the number of busy trajectories crossed by each target UAV corresponding to each reference avoidance trajectory and the average avoidance performance data are respectively denoted as M rq and B rq , r represents the number of each target UAV, q represents the number of each reference avoidance trajectory, and both r and q are positive integers; the autonomous obstacle avoidance evaluation model:

[0055] In the formula, represents the autonomous obstacle avoidance eigenvalue of the r-th target UAV. M and B respectively represent the average value of the number of busy trajectories crossed by each target UAV corresponding to each reference avoidance trajectory and the average value of the average avoidance performance data of each target UAV corresponding to each reference avoidance trajectory. Q represents the number of reference avoidance trajectories.

[0056] S3-2.2. If the autonomous obstacle avoidance eigenvalues of at least two target UAVs are all less than the autonomous obstacle avoidance eigenvalue threshold, then the target UAVs with autonomous obstacle avoidance eigenvalues less than the autonomous obstacle avoidance eigenvalue threshold are denoted as each marked UAV. Calculate the flight task importance value of each marked UAV, and obtain each idle charging device from the charging network in the low-altitude airspace. Calculate the flight difficulty coefficient between each marked UAV and each idle charging device, and then allocate an idle charging device to each marked UAV as a temporary storage station.

[0057] It should be noted that the autonomous obstacle avoidance eigenvalue threshold is 0.

[0058] In the above, the specific process of calculating the flight task importance value of each marked UAV is as follows: Obtain the proportion of UAV monitoring data in each economic activity type from the monitoring center, and at the same time obtain the monitoring frequency and risk warning times of each economic activity type. Normalize the proportion of UAV monitoring data, monitoring frequency, and risk warning times in each economic activity type, and denote the processed data as a1 w , a2 w and a3 w, where w represents the number of each economic activity type, w is a positive integer, and using the calculation formula: δ w = a1 w *μ1 + a2 w *μ2 + a3 w *μ3, the importance value δ of each economic activity type is obtained w , where μ1, μ2, and μ3 are the set first weight factor, second weight factor, and third weight factor respectively.

[0059] It should be noted that the proportion of UAV monitoring data represents the ratio of the amount of data monitored by UAVs to the total amount of monitored data. For example, in power line inspection, the total amount of monitored data includes the amount of data monitored by internal sensors in power equipment and the amount of data monitored by UAVs. The proportion of UAV monitoring data is the ratio of the amount of data monitored by UAVs to the total amount of monitored data.

[0060] Calculate the difference in the proportion of UAV monitoring data among each economic activity type, and select the largest difference as the maximum proportion difference, denoted as Δa1 max , and at the same time select the maximum proportion, denoted as a1 max , similarly obtain the maximum monitoring frequency difference, maximum monitoring frequency, maximum risk warning times difference, and maximum risk warning times, denoted as Δa2 max , a2 max , Δa3 max and a3 max ,

[0061]

[0062] Obtain the economic activity types monitored by each marked UAV, and use the importance value of the economic activity types monitored by each marked UAV as the flight task importance value of each marked UAV.

[0063] In the above, the specific process of calculating the flight difficulty coefficient between each marked UAV and each idle charging device is as follows: Obtain the flight trajectories of each marked UAV reaching each idle charging device from the monitoring center, and import them into the modeling of the low-altitude airspace. Obtain the number of busy trajectories crossed, the number of secondary avoidance times on the way, and the number of flight adjustment times of the flight trajectories of each marked UAV reaching each idle charging device, and then input them into the flight difficulty assessment model to output the flight difficulty coefficient between each marked UAV and each idle charging device.

[0064] It should be noted that the number of secondary avoidance times on the way is the number of times the marked UAV needs to avoid other UAVs when traveling on the flight trajectory to reach each idle charging device, and the number of flight adjustment times is the number of times the marked UAV needs to adjust flight parameters when traveling on the flight trajectory to reach each idle charging device.

[0065] In the modeling of low-altitude airspace, simulate the flight of each marked unmanned aircraft along the flight trajectory to each idle charging device, so as to obtain the number of busy trajectories crossed by the flight trajectories of each marked unmanned aircraft reaching each idle charging device, the number of secondary avoidance times during the journey, and the number of flight adjustment times.

[0066] It should be noted that the number of busy trajectories crossed by the flight trajectories of each marked unmanned aircraft reaching each idle charging device, the number of secondary avoidance times during the journey, and the number of flight adjustment times are normalized, and the processed data are respectively denoted as c1 yu , c2 yu and c3 yu , where y represents the number of each marked unmanned aircraft, u represents the number of each idle charging device, and both y and u are positive integers. Using the flight difficulty evaluation model: ω yu = c1 yu *τ1 + c2 yu *τ2 + c3 yu *τ3, the flight difficulty coefficient ω yu between the y-th marked unmanned aircraft and the u-th idle charging device is obtained. In the formula, τ1, τ2, and τ3 are the weight factors of the number of busy trajectories, the weight factor of the number of secondary avoidance times during the journey, and the weight factor of the number of flight adjustment times respectively.

[0067] Among them, the calculation methods of τ1, τ2, and τ3 are the same as those of μ1, μ2, and μ3, and will not be elaborated here.

[0068] In the above, idle charging devices are allocated to each marked unmanned aircraft. The specific process is as follows: Select the marked unmanned aircraft with the largest flight task importance value as the first allocated unmanned aircraft, and then select the idle charging device with the largest flight difficulty coefficient from the flight difficulty coefficients between the first allocated unmanned aircraft and each idle charging device and allocate it to the first allocated unmanned aircraft. The allocation methods for the remaining marked unmanned aircraft are the same as those for the first allocated unmanned aircraft, and will not be elaborated here.

[0069] S3-4. Obtain the flight task importance values of each drone to be charged, and obtain each idle charging device from the charging network in the low-altitude airspace. Analyze the flight difficulty coefficients between each drone to be charged and each idle charging device, and allocate idle charging devices to each drone to be charged according to the flight task importance values of each drone to be charged and the flight difficulty coefficients between each drone to be charged and each idle charging device.

[0070] It should be noted that, in the same way as analyzing the flight task importance values of the marked drones and the flight difficulty coefficients between the marked drones and the idle charging devices, analyze the flight task importance values of the drones to be charged and the flight difficulty coefficients between the drones to be charged and the idle charging devices and the marked drones. At the same time, allocate idle charging devices to the drones to be charged in the same way as allocating idle charging devices to the marked drones.

[0071] It should also be noted that when an idle charging device is allocated to a marked drone and a drone to be charged at the same time, the idle charging device is preferentially allocated to the drone to be charged.

[0072] Embodiment 2:

[0073] Refer to Figure 2 As shown, a low-altitude economic management system for drones based on a digital airspace includes: a data acquisition module for acquiring the modeling of the low-altitude airspace and each monitoring trajectory, and acquiring the low-altitude economic activity monitoring data in the low-altitude airspace within a preset period.

[0074] A charging station layout module for acquiring the positions of each signal station in the low-altitude airspace, and based on the low-altitude economic activity monitoring data in the low-altitude airspace within a preset period, confirming the positions of each charging station, arranging charging devices at the positions of each charging station, and forming a charging network for the low-altitude airspace by each charging device and each signal station.

[0075] A charging device coordination module for, when each drone conducts low-altitude economic activity monitoring, acquiring the flight parameters of each drone, judging the flight behavior, and based on the flight behavior of each drone and the charging network of the low-altitude airspace, conducting coordinated management of the charging devices.

[0076] The above content is only an example and illustration of the concept of the present invention. Those skilled in the art of this technology can make various modifications or supplements to the described specific embodiments or use similar methods for substitution, as long as they do not deviate from the concept of the invention or exceed the scope defined by this specification, they should all fall within the protection scope of the present invention.

Claims

1. A method for managing the low-altitude economy of unmanned aerial vehicles based on a digital airspace, characterized in that, It includes the following steps: S1. Data acquisition: Acquire the modeling of the low-altitude airspace and each monitoring trajectory, and acquire the low-altitude economic activity monitoring data of the low-altitude airspace within a preset period; S2. Charging station layout: Acquire the positions of each signal station in the low-altitude airspace, and based on the low-altitude economic activity monitoring data of the low-altitude airspace within a preset period, confirm the positions of each charging station, and install charging devices at the positions of each charging station, and form a charging network for the low-altitude airspace by each charging device and each signal station; S3. Charging device coordination: When each unmanned aerial vehicle conducts low-altitude economic activity monitoring, acquire the flight parameters of each unmanned aerial vehicle, judge the flight behavior, and based on the flight behavior of each unmanned aerial vehicle and the charging network of the low-altitude airspace, conduct coordinated management of the charging devices.

2. The method for managing the low-altitude economy of drones based on a digital airspace according to claim 1, characterized in that The specific process of S2 is as follows: S2-1. Obtain the positions, timestamps, economic activity types of each UAV flight conflict, the economic activity types, timestamps and positions monitored by each UAV for each charging request from the low-altitude economic activity monitoring data of the low-altitude airspace within a preset period, and then use the positions of each signal station to calculate the configuration demand values of each signal station; S2-2. Select each signal station with a configuration demand value greater than the preset configuration demand value threshold as each target signal station, use the positions of each target signal station as the positions of each charging station, and install each charging station at the positions of each target signal station; S2-3. Use wireless communication technology to realize the communication between each charging device and each signal station, and form a charging network for the low-altitude airspace.

3. The method for managing the low-altitude economy of unmanned aerial vehicles based on a digital airspace according to claim 2, characterized in that, The specific process of S2-1 is as follows: S2-1.

1. According to the positions of each UAV flight conflict and each monitoring trajectory, obtain the monitoring trajectory where each UAV flight conflict is located, and according to the timestamp and economic activity type of each UAV flight conflict, obtain the flight conflict frequency of each monitoring trajectory for monitoring each economic activity type; S2-1.

2. Use the economic activity types, timestamps and positions monitored by each UAV for each charging request to obtain the charging request frequency of each monitoring trajectory for monitoring each economic activity type; S2-1.

3. Use the flight conflict frequency and charging request frequency of each monitoring trajectory for monitoring each economic activity type to analyze the configuration demand value of each monitoring trajectory, and based on the positions of each signal station, obtain the distance between the position of each signal station and each monitoring trajectory, and thus calculate the configuration demand value of each signal station.

4. The method for managing the low-altitude economy of unmanned aerial vehicles based on digital airspace according to claim 3, wherein The calculation process of the configuration demand value of each signal station is as follows: Normalize the flight conflict frequency and charging request frequency of each monitoring track for each economic activity type, and then denote them as f1 ij and f2 iw , where i represents the number of each monitoring track, w represents the number of each economic activity type, both i and w are positive integers, and use the calculation formula: to obtain the configuration requirement value α1 of the i-th monitoring track i , where W represents the total number of economic activity types, and γ w represents the weight factor of the w-th economic activity type, and η1 and η2 respectively represent the weight factors of the flight conflict frequency and the charging request frequency; Set a signal station service length threshold, denoted as L. If the distance between the position of a signal station and a monitoring trajectory is less than L, it indicates that the monitoring trajectory is the monitoring trajectory served by the signal station, and the monitoring trajectory is denoted as the target trajectory. In this way, each target trajectory corresponding to each signal station is obtained, and the distance between each signal station position and each target trajectory is denoted as L xi′ , where x represents the number of each signal station position, i' represents the number of each target trajectory, and both x and i' are positive integers; Using the calculation formula: Obtain the configuration requirement value β of the x-th signal station x , where I′ represents the number of target trajectories, and α1 xi′ represents the configuration requirement value of the x-th signal station corresponding to the i′-th target trajectory.

5. The method for managing the low-altitude economy of drones based on digital airspace according to claim 1, characterized in that, The specific process of S3 is as follows: S3-1. Acquire the flight parameters of each UAV from the monitoring center, judge whether each UAV has a flight conflict with other UAVs, and at the same time judge whether each UAV has a charging demand. If there is a flight conflict, mark each UAV in the same flight conflict as each target UAV, and then execute S3-2. If there is a charging demand, mark each UAV with a charging demand as each UAV to be charged, and then execute S3-3; S3-2. Obtain the monitoring trajectories and economic activity types of each target UAV, calculate the autonomous obstacle avoidance eigenvalue of each target UAV, obtain each idle charging device from the charging network in the low-altitude airspace, and select each temporary station from each idle charging device according to the autonomous obstacle avoidance eigenvalue of each target UAV for UAV temporary placement; S3-4. Obtain the flight task importance value of each UAV to be charged, obtain each idle charging device from the charging network in the low-altitude airspace, analyze the flight difficulty coefficient between each UAV to be charged and each idle charging device, and allocate idle charging devices for each UAV to be charged according to the flight task importance value of each UAV to be charged and the flight difficulty coefficient between each UAV to be charged and each idle charging device.

6. A method for managing the low-altitude economy of unmanned aerial vehicles based on a digital airspace according to claim 4, characterized in that The specific process of the above S3-2 is as follows: S3-2.

1. Obtain the monitoring trajectories and economic activity types of each target UAV, calculate the autonomous obstacle avoidance eigenvalue of each target UAV. If there is only one autonomous obstacle avoidance eigenvalue of the target UAV less than the autonomous obstacle avoidance eigenvalue threshold, obtain each idle charging device from the charging network in the low-altitude airspace, and select the idle charging device closest to the target UAV as the temporary station of the target UAV; S3-2.

2. If there are at least two target UAVs whose autonomous obstacle avoidance eigenvalues are both less than the autonomous obstacle avoidance eigenvalue threshold, record the target UAVs whose autonomous obstacle avoidance eigenvalues are both less than the autonomous obstacle avoidance eigenvalue threshold as each marked UAV, calculate the flight task importance value of each marked UAV, obtain each idle charging device from the charging network in the low-altitude airspace, calculate the flight difficulty coefficient between each marked UAV and each idle charging device, and then allocate idle charging devices for each marked UAV as temporary stations.

7. A method for managing the low-altitude economy of drones based on a digital airspace according to claim 6, characterized in that, The specific process of calculating the autonomous obstacle avoidance eigenvalue of each target UAV is as follows: Obtain the flight parameters, monitoring trajectories and economic activity types of each historical UAV in each historical conflict avoidance from the data center, compare the flight parameters, monitoring trajectories and economic activity types of each target UAV with those of each historical UAV in each historical conflict avoidance, screen out each reference conflict avoidance, and obtain the avoidance trajectories and avoidance records of each historical UAV in each reference conflict avoidance from the data center; Extract the reference avoidance trajectories corresponding to each target UAV and the reference avoidance records of each reference avoidance trajectory, import the reference avoidance trajectories corresponding to each target UAV into the modeling of the low-altitude airspace, obtain the number of monitoring trajectories crossed by each reference avoidance trajectory corresponding to each target UAV, and at the same time obtain from the monitoring center whether the UAV flight task is carried out on each crossed monitoring trajectory, and record the monitoring trajectory on which the UAV flight task is carried out as a busy trajectory, so as to count the number of busy trajectories crossed by each reference avoidance trajectory corresponding to each target UAV; Obtain each avoidance performance data from the reference avoidance records of each reference avoidance trajectory corresponding to each target UAV, and thus input the number of busy trajectories crossed by each reference avoidance trajectory corresponding to each target UAV and each avoidance performance data into the autonomous obstacle avoidance evaluation model to output the autonomous obstacle avoidance eigenvalue of each target UAV.

8. A method for managing the low-altitude economy of unmanned aerial vehicles based on a digital airspace according to claim 6, wherein, The process of calculating the flight mission importance value of each marked drone is as follows: Obtain the proportion of UAV monitoring data in each economic activity type from the monitoring center. At the same time, obtain the monitoring frequency and the number of risk warnings for each economic activity type. Normalize the proportion of UAV monitoring data, the monitoring frequency, and the number of risk warnings in each economic activity type, and denote the processed data as a1 w , a2 w and a3 w , where w represents the number of each economic activity type, and w is a positive integer. Using the calculation formula: δ w = a1 w *μ1 + a2 w *μ2 + a3 w *μ3, obtain the importance value δ w of each economic activity type. In the formula, μ1, μ2, and μ3 are the set first weight factor, second weight factor, and third weight factor respectively; Obtain the types of economic activities monitored by each marked drone, and use the importance value of the types of economic activities monitored by each marked drone as the flight mission importance value of each marked drone.

9. The method for managing the low-altitude economy of unmanned aerial vehicles based on a digital airspace according to claim 6, characterized in that, The process of calculating the flight difficulty coefficient between each marked drone and each idle charging device is as follows: Obtain the flight trajectories of each marked drone reaching each idle charging device from the monitoring center, import them into the modeling of the low-altitude airspace, obtain the number of busy trajectories crossed, the number of secondary avoidance times in the middle, and the number of flight adjustment times of the flight trajectories of each marked drone reaching each idle charging device, and then input them into the flight difficulty evaluation model to output the flight difficulty coefficient between each marked drone and each idle charging device.

10. An unmanned aerial vehicle (UAV) low-altitude economic management system for the UAV low-altitude economic management method based on digital airspace according to any one of claims 1-9, characterized in that, It includes: A data acquisition module, which is used to acquire the modeling of the low-altitude airspace and each monitoring trajectory, and acquire the low-altitude economic activity monitoring data in the low-altitude airspace within a preset period; A charging station layout module, which is used to obtain the positions of each signal station in the low-altitude airspace, confirm the positions of each charging station based on the low-altitude economic activity monitoring data in the low-altitude airspace within a preset period, deploy charging devices at the positions of each charging station, and form a charging network for the low-altitude airspace by each charging device and each signal station; A charging device coordination module, which is used to obtain the flight parameters of each drone when each drone conducts low-altitude economic activity monitoring, judge the flight behavior, and perform coordinated management of the charging devices based on the flight behavior of each drone and the charging network of the low-altitude airspace.

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