A system and method for airspace management through mesh visual analytics

Abstract

The application discloses an airspace management system and method through grid visualization analysis, relates to the technical field of airspace management, and the system firstly divides the airspace into grids according to the flight conditions of unmanned aerial vehicles in the airspace, then performs a flight preliminary examination on the unmanned aerial vehicles aiming at the flight routes applied by the unmanned aerial vehicles, performs a reexamination on the submitted proof materials when the preliminary examination is unqualified, ensures that the conflict avoidance capability of the unmanned aerial vehicles is qualified, guarantees the safety of the unmanned aerial vehicle flight, effectively guarantees the safety of the flight of all the unmanned aerial vehicles in the airspace, and monitors the flight of the unmanned aerial vehicles when the unmanned aerial vehicles are flying, analyzes the real flight conditions of the unmanned aerial vehicles, ensures the safety of the real flight of the unmanned aerial vehicles and the authenticity of the proof materials, is favorable for maintaining the aviation order, reduces the flight risk of the unmanned aerial vehicles, reduces the threat to other aircraft in the airspace when the unmanned aerial vehicles are flying, and guarantees the safety of the airspace and the standardization of the management.

Description

Technical Field

[0001] This invention relates to the field of airspace management technology, and specifically to an airspace management system and method that uses grid visualization analysis. Background Technology

[0002] The widespread application of drones in various fields has led to a surge in demand for low-altitude flights, making drone airspace management crucial for ensuring flight safety and improving airspace utilization efficiency. By dividing airspace into grid units, real-time collection, processing, and visualization of drone flight data enable precise monitoring and analysis of drone operational status, effectively supporting decision-making processes such as flight plan approval, conflict warning and handling, and significantly improving airspace management efficiency and safety.

[0003] Existing technologies, such as the invention patent application CN118942287A, which discloses a method, system, device, and medium for drone control based on spatial grids, belong to the field of drone management technology. The technical problem to be solved by the drone control method is how to effectively manage the flight path of drones and ensure that drones fly within safe areas. The technical solution adopted is to divide the target airspace into multiple fine grid units and use geographic information systems and intelligent algorithm technology to achieve comprehensive, real-time, and refined control of drone flight activities. Specifically, this includes: spatial grid division; flight path application and approval, as well as the setting of safe airspace and dangerous areas; flight path optimization and conflict early warning; and flight activity monitoring.

[0004] The approval process for the above-mentioned technical solutions has at least the following shortcomings: 1. For autonomous drones, their ability to avoid conflicts during autonomous flight determines the safety of the flight. However, the above-mentioned solutions lack an assessment of the drone's conflict avoidance capabilities during the approval process, which cannot guarantee the safety of drone flight, leading to airspace congestion, increased risk of flight conflicts, and failure to ensure airspace safety.

[0005] 2. The supporting documents submitted during the drone flight approval process may be falsified. Testing the actual flight of the drone and assessing the authenticity of the submitted supporting documents would help maintain aviation order, reduce the risks of drone flights, and decrease the threat to other aircraft in the airspace. However, the above-mentioned solution does not assess the authenticity of the drone's supporting documents, thus failing to reduce the risks of drone flights, increasing the threat to other aircraft in the airspace, and failing to guarantee the safety and standardization of the airspace. Summary of the Invention

[0006] To address the aforementioned technical shortcomings, the present invention aims to provide an airspace management system and method based on grid visualization analysis.

[0007] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: In the first aspect, the present invention provides an airspace management system through grid visualization analysis, including the following modules: a flight preliminary review module, used to acquire flight data applied for by target UAVs in the airspace, acquire the approved records of current UAVs in the airspace, divide the airspace into multiple grid units, and conduct preliminary review of the flight of target UAVs.

[0008] The flight review module is used to prompt the applicant of the target drone to submit the target drone's flight test data when the initial flight review of the target drone fails. The flight test data is then used to conduct a review of the target drone's flight.

[0009] The flight monitoring module is used to monitor the flight data of the target drone and analyze its actual flight status when it is flying in the airspace after the initial or secondary flight review has been passed.

[0010] The execution module is used to stop the flight of the target drone when the target drone's actual flight status is in an abnormal flight state.

[0011] Secondly, the present invention provides an airspace management method through grid visualization analysis, including: S1, acquiring flight data applied for by a target UAV in the airspace, acquiring the approved records of the current UAV in the airspace, dividing the airspace into multiple grid units, and conducting a preliminary review of the flight of the target UAV.

[0012] S2. If the initial flight review of the target drone fails, the applicant for the target drone will be prompted to submit the target drone flight test data. The flight test data will be used to conduct a second review of the target drone's flight.

[0013] S3. After the target drone passes the initial or secondary review of its flight, monitor the flight data of the target drone while it is flying in the airspace and analyze the actual flight status of the target drone.

[0014] S4. When the target drone's actual flight status is in an abnormal flight state, stop the target drone's flight.

[0015] The beneficial effects of this invention are as follows: This application provides an airspace management system and method through grid visualization analysis. First, the airspace is divided into grids based on the flight status of UAVs in the airspace. Then, a preliminary flight review is conducted on the flight routes applied for by the UAVs. If the preliminary review fails, the submitted supporting documents are reviewed to ensure that the UAVs' conflict avoidance capabilities are qualified, thus ensuring the safety of UAV flights and effectively protecting the safety of all UAV flights within the airspace. Furthermore, the flight of UAVs is monitored during flight, and the actual flight status of the UAVs is analyzed to ensure the safety of actual flight and the authenticity of supporting documents. This is conducive to maintaining aviation order, reducing the risks of UAV flights, reducing the threat to other aircraft in the airspace during flight, and ensuring the safety and standardization of airspace management. Attached Figure Description

[0016] 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 only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of the system structure connection of the present invention.

[0018] Figure 2 This is a schematic diagram of the implementation steps of the method of the present invention.

[0019] Figure 1 and Figure 2 YES indicates that the initial review has passed and no further review is required; NO indicates that the initial review has failed and a further review is required. Detailed Implementation

[0020] 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, and 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.

[0021] Example 1:

[0022] See Figure 1 As shown, an airspace management system based on grid visualization analysis includes the following modules: initial flight review module, secondary flight review module, flight monitoring module, and execution module.

[0023] The initial flight review module is used to acquire flight data of target UAVs applying for flights in the airspace, as well as the reviewed records of current UAVs in the airspace. It also divides the airspace into multiple grid units to conduct an initial review of the target UAVs' flights.

[0024] It should be noted that the current approved records of drones in the airspace can be obtained from the approval center.

[0025] The flight preliminary review module mentioned above includes a grid division unit and a preliminary review unit.

[0026] The grid division unit is used to determine the grid division parameters of the airspace by using the flight data applied for by the target UAV in the airspace and the approved records of the current UAV in the airspace, and to divide the airspace into corresponding grid units to obtain each grid unit.

[0027] In a specific embodiment, the specific process of the grid division unit is as follows: obtain the flight time period applied for by the target UAV from the flight data applied for by the target UAV in the acquisition airspace, and use it as the target time period; obtain each UAV that has been approved and its flight trajectory in the target time period from the approved records of the current UAV in the airspace; and construct a flight trajectory heat map based on the flight trajectory of each UAV that has been approved in the target time period.

[0028] By using flight trajectory heatmaps, the flight complexity level of the airspace is analyzed. Then, the flight complexity level, grid division parameters, and monitoring accuracy level of the airspace in each historical period are obtained from historical monitoring records. Historical periods with the same flight complexity level as the airspace are selected as marked periods. The grid division parameters and monitoring accuracy levels of each marked period are obtained. The set of monitoring accuracy levels corresponding to each grid division parameter is statistically analyzed. The monitoring effect priority value corresponding to each grid division parameter is calculated. The grid division parameter with the highest monitoring effect priority value is selected as the grid division parameter of the airspace. Then, the airspace is divided into multiple grid units according to the grid division parameters.

[0029] Preferably, the analysis of the flight complexity level of the airspace is carried out as follows: the maximum color depth is obtained from the flight trajectory heat map, and the maximum color depth is compared with the color depth range corresponding to each flight complexity level. When the maximum color depth is within the color depth range corresponding to a certain complexity level, the flight complexity level is the flight complexity level of the airspace.

[0030] The higher the flight complexity level, the greater the corresponding color depth. The color depth range corresponding to each flight complexity level is set and modified by airspace management personnel based on the actual terrain and human activities of the airspace, and no specific data restrictions are imposed here.

[0031] Preferably, the process of obtaining the airspace monitoring accuracy level is as follows: multiple markers with precise coordinates are set in the airspace, the position of each UAV in the airspace is collected using a GPS positioning system, the monitoring position coordinates of each UAV when it arrives at each marker are obtained, the coefficient of variation is calculated, and the coefficient of variation is compared with the coefficient of variation corresponding to each monitoring accuracy level. If the coefficient of variation is the same as a certain monitoring accuracy level, then the monitoring accuracy level is taken as the monitoring accuracy level of the airspace.

[0032] It should be noted that the higher the monitoring accuracy level, the smaller the coefficient of variation. The coefficient of variation corresponding to each monitoring accuracy level is set by airspace management personnel according to the actual needs of the airspace, and no specific data restrictions are imposed here.

[0033] The calculation process of the coefficient of variation is as follows: Taking the horizontal coordinate as an example, firstly, the average horizontal coordinate of the monitoring position of each UAV when it arrives at each marker is calculated to obtain the average horizontal coordinate of the UAV corresponding to each marker. Then, the standard deviation of the horizontal coordinate of the UAV corresponding to each marker is calculated. The standard deviation of the horizontal coordinate of the UAV corresponding to each marker is divided by the average horizontal coordinate of the UAV corresponding to each marker to obtain the coefficient of variation of the horizontal coordinate of the UAV at each marker. Then, the average of the coefficients of variation of the horizontal coordinate of the UAV at each marker is calculated to obtain the coefficient of variation of the horizontal coordinate of the airspace. Following the calculation process of the coefficient of variation of the horizontal coordinate, the coefficients of variation of the vertical coordinate are calculated. The coefficients of variation of the horizontal coordinate, vertical coordinate, and vertical coordinate are averaged to obtain the coefficient of variation of the airspace.

[0034] The calculation of standard deviation mentioned above is a standard mathematical calculation method and will not be elaborated further here.

[0035] In a specific embodiment, the priority value of the monitoring effect corresponding to each grid division parameter is calculated. The specific process is as follows: extract the maximum monitoring accuracy level and the minimum monitoring accuracy level from the set of monitoring accuracy levels corresponding to each grid division parameter, and denot them as M respectively. imax and M imin Where i represents the number of each grid division parameter, and i is a positive integer, the formula for calculating the priority value of monitoring effect is:

[0036] In the formula This represents the priority value of the monitoring effect corresponding to the i-th grid division parameter, and I represents the total number of grid division parameters.

[0037] In the above, the meshing parameters are the size parameters of the mesh, including mesh length and width, etc.

[0038] The preliminary review unit is used to analyze whether there is a flight conflict for the target drone by using the flight data applied for by the target drone in the airspace and the review records of the current drone in the airspace, and then conduct a preliminary review of the drone's flight.

[0039] In a specific embodiment, the specific steps of the preliminary review unit are as follows: S11, obtain the flight trajectory applied for by the target drone from the flight data applied for by the target drone, map the flight trajectory applied for by the target drone and the flight trajectories of each drone that has been approved in the target time period to each grid cell in the airspace, if there is at least one grid cell in which the flight trajectory applied for by the target drone overlaps with the flight trajectory of at least one drone that has been approved, it indicates that there is a flight conflict of the target drone, and the grid cells in which the flight trajectories overlap are recorded as each marked grid cell, and the drones that have been approved in the same grid cell are recorded as each marked drone, and then S13 is executed; if the flight trajectory applied for by the target drone in each grid cell does not overlap with the flight trajectory of each drone that has been approved, it indicates that there is no flight conflict of the target drone, and then S12 is executed.

[0040] S12. Obtain the conflict avoidance data of the target drone from historical monitoring records, analyze the basic conflict avoidance capability of the target drone. If the basic conflict avoidance capability of the target drone is qualified, the initial flight review of the target drone is passed. Otherwise, if the basic conflict avoidance capability of the target drone is unqualified, the initial flight review of the target drone is not passed, and the reason for the failure of the initial flight review is that the basic conflict avoidance capability is unqualified.

[0041] Preferably, the analysis process of the target UAV's basic conflict avoidance capability is as follows: Obtain the initial avoidance distance and avoidance speed of the target UAV for each conflict avoidance from the conflict avoidance data, and set avoidance distance threshold and avoidance speed threshold, denoted as L and v respectively. Compare the initial avoidance distance and avoidance speed of the target UAV for each conflict avoidance with the avoidance distance threshold and avoidance speed threshold respectively. If the initial avoidance distance is less than the avoidance distance threshold or the avoidance speed is greater than the avoidance speed threshold, it indicates that the conflict avoidance is a dangerous conflict avoidance. If the initial avoidance distance is greater than or equal to the avoidance distance threshold and the avoidance speed is less than or equal to the avoidance speed threshold, it indicates that the conflict avoidance is a safe conflict avoidance. Count the number of dangerous and safe conflict avoidances of the target UAV, denoted as P1 and P2 respectively, using the analysis formula: Thus, the basic conflict avoidance capability of the target UAV is obtained.

[0042] When γ=1, it indicates that the target UAV's basic collision avoidance capability is qualified; when γ=0, the target UAV's basic collision avoidance capability is unqualified.

[0043] It should be noted that the avoidance distance threshold and avoidance speed threshold are the minimum safe avoidance distance and maximum safe avoidance speed for drones when performing conflict avoidance in the airspace. These are set and modified by airspace management personnel according to the airspace conditions, and are not specifically limited here.

[0044] S13. Obtain terrain environment data for each grid cell, and use the terrain environment data and flight trajectory of each marked UAV to confirm the conflict avoidance scheme of the target UAV in each grid cell. Summarize the conflict avoidance schemes of the target UAV in flight, obtain the conflict avoidance data of the target UAV from historical monitoring records, and analyze the capability level of the target UAV in executing each conflict avoidance scheme. The capability level of the conflict avoidance scheme includes Level 1, Level 2 and Level 3.

[0045] Preferably, the analysis process for the target UAV's ability to execute each conflict avoidance scheme is as follows: S131, Obtain the terrain environment data of each grid cell from the airspace management center, and obtain the number of fixed obstacles from the terrain environment data of each grid cell. When the number of fixed obstacles in a grid cell is 1, the conflict avoidance scheme of the target UAV in that grid cell is single fixed obstacle avoidance. When the number of fixed obstacles in a grid cell is greater than 1, the conflict avoidance scheme of the target UAV in that grid cell is multiple fixed obstacle avoidance. When the number of fixed obstacles in a grid cell is 0, the target UAV in that grid cell does not need to avoid conflict.

[0046] S132. Based on the flight trajectory of each marked UAV, obtain the flight trajectory of each marked UAV in each marked grid cell. When there is only one marked UAV flight trajectory in a marked grid cell, the target UAV conflict avoidance scheme in the marked grid cell is single moving obstacle avoidance. When there is one marked UAV flight trajectory and one fixed number of obstacles in a marked grid cell, the target UAV conflict avoidance scheme in the marked grid cell is multi-type obstacle avoidance.

[0047] S133. If a marked grid cell contains multiple marked UAV flight paths and one or more fixed obstacles, then the target UAV conflict avoidance scheme in the marked grid cell is complex obstacle avoidance, thereby obtaining the target UAV conflict avoidance scheme in each grid cell.

[0048] S134. Obtain historical performance data, whether secondary avoidance was required, and whether safe avoidance was achieved for each conflict avoidance scheme from the conflict avoidance data of the target UAV. Count the number of secondary avoidances in each conflict avoidance scheme and record it as the number of secondary avoidances for each conflict avoidance scheme, denoted as U1. f The number of times each conflict avoidance plan failed to achieve a safe avoidance is recorded as the number of failed avoidances for each conflict avoidance plan, denoted as U2.f Simultaneously, based on historical performance data from the cloud data platform for each historical flight of drones and each conflict avoidance scheme where no secondary avoidance was required and safe avoidance was achieved, this historical performance data for each historical flight of drones and each conflict avoidance scheme where no secondary avoidance was required and safe avoidance was clustered and used as the performance data threshold for each conflict avoidance scheme, denoted as ZT'. f .

[0049] It should be noted that the conflict avoidance data includes historical performance data for each conflict avoidance scheme during its historical execution, whether a secondary avoidance was required, and whether the avoidance was safe. Visual sensors are used to examine images or videos captured by the sensors, and machine vision is used to determine if a new obstacle or hazard entered the field of view after the initial avoidance, causing the drone to perform a secondary avoidance maneuver. If the drone performs a secondary avoidance maneuver, it indicates a secondary avoidance. If the drone collides with an obstacle after avoiding it, or if its own equipment emits abnormal signals due to the avoidance maneuver, it is determined that the drone did not achieve safe avoidance. Performance data includes the number of attitude changes and the avoidance duration. The drone's trajectory, the number of attitude changes, and the avoidance duration are obtained from the visualization interface, and the trajectory includes information such as timestamps and coordinates.

[0050] S135. Using analytical formulas:

[0051] The capability level μ of the target UAV to execute the f-th conflict avoidance scheme is obtained. f In the formula, f represents the number of each conflict avoidance plan, y represents the number of each historical execution, and Y represents the number of historical executions. f, y, and Y are all positive integers. fy σ represents the historical performance data of the target drone during the y-th historical execution of the f-th conflict avoidance scheme. min σ max These are the preset lower limit and upper limit of the ability level assessment, respectively.

[0052] In the above, σ min σ max These are the lower and upper benchmark values ​​for assessing the capability level of implementing conflict avoidance strategies. When the calculated data is less than σ... min If the ability to implement conflict avoidance strategies is low, the ability level is classified as Level 1, and the calculated data is greater than or equal to σ. min And less than σ max If the ability to implement conflict avoidance strategies is high, then the ability level is divided into two levels, and the calculated data is greater than or equal to σ. max If this indicates a very high ability to implement conflict avoidance strategies, then the capability level is classified into 3 levels. σ min σ maxThe settings and modifications are made by airspace management personnel based on the airspace conditions, and data restrictions are imposed here.

[0053] S14. If the target UAV's ability level in executing each conflict avoidance scheme is greater than or equal to level 2, the target UAV's initial flight review is passed. If the target UAV's ability level in executing at least one conflict avoidance scheme is level 1, the target UAV's initial flight review is failed, and the reason for the failure is that the ability to execute the conflict avoidance scheme is unqualified.

[0054] The flight review module is used to prompt the applicant of the target drone to submit the target drone's flight test data when the initial flight review of the target drone fails. The flight test data is then used to conduct a review of the target drone's flight.

[0055] The flight review module mentioned above includes a first-class review unit and a second-class review unit.

[0056] The first-class review unit is used to conduct a first-class review of the target UAV when the reason for the failure of the initial flight review is that the basic conflict avoidance capability is not up to standard. It uses the target UAV flight test data and the target UAV conflict avoidance data to confirm whether the target UAV's flight review has passed.

[0057] In a specific embodiment, the specific process of the review unit is as follows: extract the performance data of each conflict avoidance scheme in the flight test of the target UAV from the flight test data of the target UAV; use the performance data of each conflict avoidance scheme in the flight test of the target UAV and the conflict avoidance data of the target UAV to analyze the basic conflict avoidance capability of the target UAV and the rationality of the flight test data; when the basic conflict avoidance capability of the target UAV is qualified and the rationality of the flight test data is high, the flight review of the target UAV is passed; if the basic conflict avoidance capability of the target UAV is unqualified or the rationality of the flight test data is low, the flight review of the target UAV is not passed.

[0058] Preferably, the specific process for analyzing the basic conflict avoidance capability of the target UAV and the rationality of the flight test data is as follows: The conflict avoidance data from the target UAV flight test is analyzed according to the analysis method of the target UAV's basic conflict avoidance capability γ to obtain the target UAV's basic conflict avoidance capability. Simultaneously, the historical performance data of each conflict avoidance scheme during each historical execution is obtained from the target UAV's conflict avoidance data. The performance data of each conflict avoidance scheme in the target UAV flight test is recorded as ZT''. f Using the analytical formula: The rationality of obtaining flight test data ,when =1 indicates that the flight test data is highly reasonable. =0 indicates that the flight test data has low rationality.

[0059] The second-class review unit is used to conduct a second-class review of the target UAV when the reason for the failure of the initial flight review is that the ability to execute the conflict avoidance plan is not up to standard. This review uses the target UAV's flight test data and conflict avoidance data to confirm whether the target UAV's flight review has passed.

[0060] Preferably, the specific process of the second type of review unit is as follows: using the performance data of each conflict avoidance scheme and the conflict avoidance data of the target UAV in the flight test, analyze the capability level of the target UAV in executing each conflict avoidance scheme and the rationality of the flight test data.

[0061] Among them, the conflict avoidance data in the target UAV flight test is classified according to the target UAV's capability level μ for executing the f-th conflict avoidance scheme. f The analysis method yields the target UAV's capability level in executing various conflict avoidance schemes, and the rationality of the flight test data is determined. The analysis method is the same, so it will not be repeated here.

[0062] The flight review of the target UAV will pass if the target UAV's ability to execute each conflict avoidance scheme is greater than or equal to level 2 and the flight test data is reasonably accurate. If the target UAV's ability to execute at least one conflict avoidance scheme is level 1, or the flight test data is reasonably accurate, the flight review of the target UAV will fail.

[0063] It should be noted that when the flight review of the target drone fails due to the low reasonableness of the flight test data, the applicant of the target drone will be notified by SMS or email, and the applicant can apply for manual review and communication.

[0064] The flight monitoring module is used to monitor the flight data of the target drone and analyze its actual flight status when it is flying in the airspace after the initial or secondary flight review has been passed.

[0065] In a specific embodiment, the analysis of the target UAV's actual flight state specifically involves the following flight process: obtaining whether the target UAV performs conflict avoidance in each grid cell from its flight data; classifying grid cells that do not perform conflict avoidance as Class I cells and those that do as Class II cells; extracting the target UAV's flight trajectory in each Class I cell from its flight data and comparing it with the flight trajectory applied for by the target UAV; and analyzing the target UAV's flight trajectory deviation.

[0066] In the above context, the flight data of the target drone refers to the data acquired through tracking and monitoring of the target drone during its flight. This includes, but is not limited to, the real-time position, attitude, and flight video of the target drone within each grid cell. When the drone is flying in the airspace, monitoring equipment such as positioning systems and visual sensors needs to be installed on the drone to monitor its flight data.

[0067] It can perform machine vision analysis on flight videos to determine whether the target drone is avoiding collisions in each grid cell.

[0068] Preferably, the analysis process for the flight trajectory offset of the target UAV is as follows: based on the flight trajectory applied for by the target UAV, the reference position coordinates of each point on the flight trajectory of the target UAV in each type of unit are obtained, denoted as... Where j represents the number of each class of units, and q represents the number of each point, both j and q are positive integers. The actual position coordinates of each point are obtained from the flight trajectory of the target UAV in each class of units, denoted as . Using the calculation formula:

[0069] Obtain the offset distance d of the target UAV at the q-th point on its flight trajectory in the j-th class 1 cell. jq The maximum offset distance is selected as the flight trajectory offset of the target UAV.

[0070] Extract the target UAV's conflict avoidance schemes and performance data in each Class II unit from the target UAV's flight data, and analyze the target UAV's true conflict avoidance capability level.

[0071] The analysis process for the target UAV's actual conflict avoidance capability level described above is as follows: If the target UAV has not undergone review, the conflict avoidance schemes and performance data of the target UAV in each Class II unit are calculated according to the target UAV's capability level μ for executing the f-th conflict avoidance scheme. f The analysis method is used to obtain the target drone's true conflict avoidance capability level.

[0072] When the target UAV undergoes a review, its conflict avoidance strategies and performance data in each Category II unit will be classified according to its capability level μ for executing the f-th conflict avoidance strategy. f The analysis method is used to obtain the target UAV's Class I conflict avoidance capability level; simultaneously, the conflict avoidance schemes and performance data of the target UAV in each Class II unit are compared with the performance data of each conflict avoidance scheme in the target UAV's flight tests, based on the reasonableness of the flight test data. The second type of rationality for obtaining flight test data through analytical methods.

[0073] When the Type II rationality of the flight test data is 1, the Type I conflict avoidance capability level of the target UAV is the actual conflict avoidance capability level. When the Type II rationality of the flight test data is 0, the actual conflict avoidance capability level of the target UAV is output as 0.

[0074] This invention assesses the authenticity of the information submitted by drones, ensuring the standardization of the review process. It also clarifies the responsibilities of all parties and facilitates follow-up handling in case of problems or accidents during flight. Furthermore, it promptly intercepts drones with insufficient information, reducing the flight risks to other drones in the airspace and improving airspace management effectiveness.

[0075] Based on the target UAV's flight trajectory deviation and actual conflict avoidance capability level, the target UAV's actual flight characteristic value is calculated. The actual flight characteristic value includes the values ​​of 1 and 0. When the actual flight characteristic value is 1, it indicates that the target UAV's actual flight status is normal. When the actual flight characteristic value is 0, it indicates that the target UAV's actual flight status is abnormal.

[0076] Preferably, the analysis process of the target UAV's true flight characteristic value is as follows: the flight trajectory offset of the target UAV is denoted as d, and an offset threshold is set as d1. When d < d1 and the target UAV's true conflict avoidance capability level is greater than 1, the target UAV's true flight characteristic value is 1. When d ≥ d1 or the target UAV's true conflict avoidance capability level is less than or equal to 1, the target UAV's true flight characteristic value is 0.

[0077] It should be noted that the offset threshold is the maximum distance that the drone is allowed to deviate from during flight. It is set by airspace management personnel according to the actual situation of the airspace and is not restricted here.

[0078] The execution module is used to stop the flight of the target drone when the target drone's actual flight status is in an abnormal flight state.

[0079] Example 2:

[0080] See Figure 2 As shown, an airspace management method using grid visualization analysis includes: S1, acquiring flight data of target UAVs applied for in the airspace, acquiring the approved records of current UAVs in the airspace, dividing the airspace into multiple grid units, and conducting preliminary review of the flight of the target UAVs.

[0081] S2. If the initial flight review of the target drone fails, the applicant for the target drone will be prompted to submit the target drone flight test data. The flight test data will be used to conduct a second review of the target drone's flight.

[0082] S3. After the target drone passes the initial or secondary review of its flight, monitor the flight data of the target drone while it is flying in the airspace and analyze the actual flight status of the target drone.

[0083] S4. When the target drone's actual flight status is in an abnormal flight state, stop the target drone's flight.

[0084] The above description is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they should all fall within the protection scope of the present invention.

Claims

1. An airspace management system based on grid visualization analysis, characterized in that, Includes the following modules: The initial flight review module is used to acquire flight data of target UAVs in the airspace, acquire the review records of current UAVs in the airspace, divide the airspace into multiple grid units, and conduct an initial review of the flight of the target UAVs. The flight preliminary review module includes a grid division unit and a preliminary review unit; The grid division unit is used to determine the grid division parameters of the airspace by using the flight data applied for by the target UAV in the airspace and the approved records of the current UAV in the airspace, and to divide the airspace into corresponding grid units to obtain each grid unit. The preliminary review unit is used to analyze whether there is a flight conflict for the target drone by using the flight data applied for by the target drone in the airspace and the review records of the current drone in the airspace, and then conduct a preliminary review of the drone's flight. Obtain conflict avoidance data of target drones from historical monitoring records, and analyze the basic conflict avoidance capabilities and the level of ability to execute various conflict avoidance schemes of the target drones; The conflict avoidance data includes the initial avoidance distance and avoidance speed of the target drone in each conflict avoidance, as well as the historical performance data of each conflict avoidance scheme in each historical execution, whether a second avoidance was required, and whether the avoidance was safe. The flight review module is used to prompt the applicant of the target drone to submit the target drone's flight test data when the initial flight review of the target drone fails. The flight test data of the target drone is then used to conduct a review of the target drone's flight. The flight review module includes a type of review unit; The first-class review unit is used to conduct a first-class review of the target UAV when the reason for the failure of the initial flight review is that the basic conflict avoidance capability is not up to standard. It uses the target UAV flight test data and the target UAV conflict avoidance data to confirm whether the target UAV's flight review has passed. The specific process for the first type of review unit is as follows: The performance data of each conflict avoidance scheme in the flight test of the target UAV is extracted from the flight test data of the target UAV. Using the performance data of each conflict avoidance scheme in the flight test of the target UAV and the conflict avoidance data of the target UAV, the basic conflict avoidance capability of the target UAV and the rationality of the flight test data are analyzed. When the basic conflict avoidance capability of the target UAV is qualified and the rationality of the flight test data is high, the flight review of the target UAV is passed. If the basic conflict avoidance capability of the target UAV is unqualified or the rationality of the flight test data is low, the flight review of the target UAV is not passed. The flight monitoring module is used to monitor the flight data of the target UAV and analyze its actual flight status when it is flying in the airspace after the initial or secondary flight review has been passed. The execution module is used to stop the flight of the target drone when the target drone's actual flight status is in an abnormal flight state.

2. The airspace management system based on grid visualization analysis according to claim 1, characterized in that, The specific process of dividing the grid into units is as follows: The flight time period of the target UAV is obtained from the flight data of the target UAV application in the airspace and used as the target time period. The approved UAVs and their flight trajectories in the target time period are obtained from the approved records of the current UAVs in the airspace. Based on the flight trajectories of the approved UAVs in the target time period, a flight trajectory heat map is constructed. By using flight trajectory heatmaps, the flight complexity level of the airspace is analyzed. Then, the flight complexity level, grid division parameters, and monitoring accuracy level of the airspace in each historical period are obtained from historical monitoring records. Historical periods with the same flight complexity level as the airspace are selected as marked periods. The grid division parameters and monitoring accuracy levels of each marked period are obtained. The set of monitoring accuracy levels corresponding to each grid division parameter is statistically analyzed. The monitoring effect priority value corresponding to each grid division parameter is calculated. The grid division parameter with the highest monitoring effect priority value is selected as the grid division parameter of the airspace. Then, the airspace is divided into multiple grid units according to the grid division parameters.

3. The airspace management system based on grid visualization analysis according to claim 2, characterized in that, The specific steps of the preliminary review unit are as follows: S11. Obtain the flight trajectory requested by the target UAV from the flight data requested by the target UAV. Map the flight trajectory requested by the target UAV and the flight trajectories of each UAV that have been approved during the target time period to each grid cell in the airspace. If there is at least one grid cell in which the flight trajectory requested by the target UAV overlaps with the flight trajectory of at least one approved UAV, it indicates that there is a flight conflict between the target UAV and the target UAV. Mark each grid cell in which the flight trajectories overlap as a marked grid cell, and mark each approved UAV in the same grid cell as a marked UAV. Then proceed to S13. If the flight trajectory requested by the target UAV in each grid cell does not overlap with the flight trajectories of each approved UAV, it indicates that there is no flight conflict between the target UAV and the target UAV. Then proceed to S12. S12. Obtain the conflict avoidance data of the target drone from the historical monitoring records, analyze the basic conflict avoidance capability of the target drone. If the basic conflict avoidance capability of the target drone is qualified, the initial flight review of the target drone is passed. Otherwise, if the basic conflict avoidance capability of the target drone is unqualified, the initial flight review of the target drone is not passed, and the reason for the failure of the initial flight review is that the basic conflict avoidance capability is unqualified. S13. Obtain terrain environment data for each grid cell, and use the terrain environment data for each grid cell and the flight trajectory of each marked UAV to confirm the conflict avoidance scheme of the target UAV in each grid cell, summarize the conflict avoidance schemes of the target UAV in flight, obtain the conflict avoidance data of the target UAV from historical monitoring records, and analyze the capability level of the target UAV to execute each conflict avoidance scheme, where the capability level of the conflict avoidance scheme includes level 1, level 2 and level 3. S14. If the target UAV's ability level in executing each conflict avoidance scheme is greater than or equal to level 2, the target UAV's initial flight review is passed. If the target UAV's ability level in executing at least one conflict avoidance scheme is level 1, the target UAV's initial flight review is failed, and the reason for the failure is that the ability to execute the conflict avoidance scheme is unqualified.

4. The airspace management system based on grid visualization analysis according to claim 3, characterized in that, The flight review module also includes two types of review units; The second-class review unit is used to conduct a second-class review of the target UAV when the reason for the failure of the initial flight review is that the ability to execute the conflict avoidance plan is not up to standard. This review uses the target UAV's flight test data and conflict avoidance data to confirm whether the target UAV's flight review has passed.

5. The airspace management system based on grid visualization analysis according to claim 4, characterized in that, The specific process for the second type of review unit is as follows: By utilizing the performance data of various conflict avoidance schemes and the conflict avoidance data of the target UAV during flight tests, we can analyze the capability level of the target UAV in executing each conflict avoidance scheme and the rationality of the flight test data. The flight review of the target UAV will pass if the target UAV's ability to execute each conflict avoidance scheme is greater than or equal to level 2 and the flight test data is reasonably accurate. If the target UAV's ability to execute at least one conflict avoidance scheme is level 1, or the flight test data is reasonably accurate, the flight review of the target UAV will fail.

6. The airspace management system based on grid visualization analysis according to claim 1, characterized in that, The analysis focuses on the actual flight state of the target drone, specifically the flight process as follows: The flight data of the target UAV is used to determine whether the target UAV avoids conflict in each grid cell. Grid cells that do not avoid conflict are recorded as Class I cells, and grid cells that avoid conflict are recorded as Class II cells. Then, the flight trajectory of the target UAV in each Class I cell is extracted from the flight data of the target UAV and compared with the flight trajectory applied for by the target UAV to analyze the flight trajectory deviation of the target UAV. Extract the conflict avoidance schemes and performance data of the target UAV in each type II unit from the flight data of the target UAV, and analyze the actual conflict avoidance capability level of the target UAV. Based on the target UAV's flight trajectory deviation and actual conflict avoidance capability level, the target UAV's actual flight characteristic value is calculated. The actual flight characteristic value includes the values ​​of 1 and 0. When the actual flight characteristic value is 1, it indicates that the target UAV's actual flight status is normal. When the actual flight characteristic value is 0, it indicates that the target UAV's actual flight status is abnormal.

7. The airspace management system based on grid visualization analysis according to claim 6, characterized in that, The analysis process of the target UAV's actual flight characteristic values ​​is as follows: The flight trajectory deviation of the target UAV is denoted as d, and a deviation threshold is set as d1. When d < d1 and the actual conflict avoidance capability level of the target UAV is greater than 1, the actual flight characteristic value of the target UAV is 1. When d ≥ d1 or the actual conflict avoidance capability level of the target UAV is 1, the actual flight characteristic value of the target UAV is 0.

8. A method for airspace management via grid visualization analysis executed using the airspace management system via grid visualization analysis as described in any one of claims 1-7, characterized in that, include: S1. Obtain the flight data of the target UAV in the airspace, and at the same time obtain the approved records of the current UAV in the airspace. Divide the airspace into multiple grid units and conduct a preliminary review of the flight of the target UAV. S2. If the initial flight review of the target drone fails, the applicant of the target drone will be prompted to submit the flight test data of the target drone. The flight test data of the target drone will be used to conduct a second review of the flight of the target drone. S3. After the target drone passes the initial or secondary review of its flight, monitor the flight data of the target drone while it is flying in the airspace and analyze the actual flight status of the target drone. S4. When the target drone's actual flight status is in an abnormal flight state, stop the target drone's flight.

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