Airspace management system and method based on grid visual analysis

Abstract

The invention discloses an airspace management system and method based on grid visualization analysis, and relates to the technical field of airspace management.The system firstly performs grid division on an airspace according to the flight condition of an unmanned aerial vehicle in the airspace, then performs flight preliminary review on the unmanned aerial vehicle according to the flight route applied by the unmanned aerial vehicle, and when the preliminary review is not qualified, the unmanned aerial vehicle is reviewed. The submitted proof data is rechecked, the conflict avoidance capability of the unmanned aerial vehicle is ensured to be qualified, the flight safety of the unmanned aerial vehicle is ensured, the flight safety of all the unmanned aerial vehicles in the airspace is effectively ensured, the flight of the unmanned aerial vehicle is monitored when the unmanned aerial vehicle flies, and the real flight condition of the unmanned aerial vehicle is analyzed; the safety of the real flight of the unmanned aerial vehicle and the authenticity of the proof data are ensured, the aviation order can be maintained, the flight risk of the unmanned aerial vehicle is reduced, the threat to other aircrafts in the airspace during flight is reduced, and the safety of the airspace and the standardization of management are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of airspace management, and specifically relates to an airspace management system and method through grid visualization analysis. Background Art

[0002] The wide application of unmanned aerial vehicles (UAVs) in various fields has led to a sharp increase in low-altitude flight demand. UAV airspace management has become the key to ensuring flight safety and improving airspace utilization efficiency. By dividing the airspace into grid cells, collecting, processing, and visualizing UAV flight data in real time, precise monitoring and analysis of the operating state of UAVs can be achieved, effectively supporting decision-making such as flight plan approval, conflict early warning, and disposal, and effectively improving airspace management efficiency and safety.

[0003] The prior art, such as the invention patent with the publication number CN118942287A, discloses a UAV control method, system, device, and medium based on spatial grids, belonging to the technical field of UAV management. The technical problem to be solved by the UAV control method is how to effectively manage the flight path of UAVs to ensure that UAVs fly within a safe area. The adopted technical solution is as follows: dividing the target airspace into multiple fine grid cells, and using geographic information system and intelligent algorithm technologies to achieve comprehensive, real-time, and refined control of UAV flight activities; specifically as follows: spatial grid division; flight path application and approval, and setting of safe airspace and dangerous areas; flight path optimization and conflict early warning; flight activity supervision.

[0004] Regarding the approval process in the above technical solution, it has at least the following deficiencies: 1. For autonomously flying UAVs, the ability to avoid conflicts during their autonomous flight determines flight safety. However, in the above solution, there is a lack of assessment of the conflict avoidance ability of UAVs during approval, which cannot guarantee the flight safety of UAVs, resulting in an increase in airspace congestion and flight conflict risks, and also cannot ensure the safety of the airspace.

[0005] 2. There is a possibility of fraud in the certification materials submitted during UAV flight approval. Detecting the actual flight of UAVs and evaluating the authenticity of the certification materials submitted by UAVs is beneficial to maintaining air traffic order, reducing the risk of UAV flight, and reducing the threat to other aircraft in the airspace during flight. However, in the above solution, the authenticity of UAV certification materials is not evaluated, which cannot reduce the risk of UAV flight, increases the threat to other aircraft in the airspace during flight, and cannot guarantee the safety and standardization of the airspace. Summary of the Invention

[0006] Aiming at the above existing technical deficiencies, the purpose of the present invention is to provide an airspace management system and method through grid visualization analysis.

[0007] To solve the above technical problems, the present invention adopts the following technical solutions: In a first aspect, the present invention provides an airspace management system through grid visualization analysis, including the following modules: A flight preliminary review module, which is used to obtain the flight data applied for by a target unmanned aerial vehicle (UAV) in the airspace, and at the same time obtain the reviewed records of the current UAVs in the airspace, divide the airspace into multiple grid cells, and conduct a preliminary review of the flight of the target UAV.

[0008] A flight re-review module, which is used to prompt the applicant of the target UAV to submit the flight test materials of the target UAV when the preliminary review of the flight of the target UAV fails, and use the flight test materials of the target UAV to conduct a re-review of the flight of the target UAV.

[0009] A flight monitoring module, which is used to monitor the flight data of the target UAV and analyze the actual flight status of the target UAV when the target UAV is flying in the airspace after the preliminary review or re-review of the flight of the target UAV is passed.

[0010] An execution module, which is used to stop the flight of the target UAV when the actual flight status of the target UAV is in an abnormal flight state.

[0011] In a second aspect, the present invention provides an airspace management method through grid visualization analysis, including: S1. Obtain the flight data applied for by a target UAV in the airspace, and at the same time obtain the reviewed records of the current UAVs in the airspace, divide the airspace into multiple grid cells, and conduct a preliminary review of the flight of the target UAV.

[0012] S2. When the preliminary review of the flight of the target UAV fails, prompt the applicant of the target UAV to submit the flight test materials of the target UAV, and use the flight test materials of the target UAV to conduct a re-review of the flight of the target UAV.

[0013] S3. When the preliminary review or re-review of the flight of the target UAV is passed, monitor the flight data of the target UAV and analyze the actual flight status of the target UAV when the target UAV is flying in the airspace.

[0014] S4. When the actual flight status of the target UAV is in an abnormal flight state, stop the flight of the target UAV.

[0015] The beneficial effects of the present invention are as follows: The present application provides an airspace management system and method through grid visualization analysis. First, the airspace is divided into grids according to the flight conditions of unmanned aerial vehicles (UAVs) in the airspace. Then, for the flight routes applied for by UAVs, a preliminary flight review is carried out on the UAVs. When the preliminary review fails, a review is carried out on the submitted supporting documents to ensure that the conflict avoidance ability of the UAVs is qualified, guarantee the safety of UAV flights, effectively ensure the safety of all UAV flights in the airspace, and monitor the flights of UAVs during flight, analyze the actual flight conditions of the UAVs, ensure the safety of the actual flights of the UAVs and the authenticity of the supporting documents, which is conducive to maintaining air traffic order, reducing the risks of UAV flights, reducing the threats to other aircraft in the airspace during flight, and ensuring the safety of the airspace and the standardization of management. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] 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-described drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

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

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

[0019] Figure 1 and Figure 2 In [the figure] YES indicates that the preliminary review fails and a review is required; NO indicates that the preliminary review passes and no review is required. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0021] Embodiment 1:

[0022] Refer to Figure 1 As shown, an airspace management system through grid visualization analysis includes the following modules: a flight preliminary review module, a flight review module, a flight monitoring module, and an execution module.

[0023] The flight preliminary review module is used to obtain the flight data applied for by the target UAV in the airspace, and at the same time obtain the reviewed records of the current UAV in the airspace, divide the airspace into multiple grid cells, and conduct a preliminary review of the flight of the target UAV.

[0024] It should be noted that the reviewed records of the current UAV in the airspace can be obtained from the review center.

[0025] In the above, the flight preliminary review module includes a grid division unit and a preliminary review unit.

[0026] The grid division unit is used to utilize the flight data applied for by the target UAV in the airspace and the reviewed records of the current UAV in the airspace to confirm the grid division parameters of the airspace, and conduct corresponding grid cell division to obtain each grid cell.

[0027] In a specific embodiment, the specific process of the grid division unit is as follows: obtain the flight period applied for by the target UAV from the flight data applied for by the target UAV in the airspace as the target period, and obtain each UAV that has passed the review and its flight trajectory during the target period from the reviewed records of the current UAV in the airspace. Based on the flight trajectories of each UAV that has passed the review during the target period, construct a flight trajectory heat map.

[0028] Utilize the flight trajectory heat map to analyze the flight complexity level of the airspace, and then obtain the flight complexity level, grid division parameters, and monitoring accuracy level of the airspace during each historical period from the historical supervision records. Select each historical period with the same flight complexity level as the airspace as each marked period, obtain the grid division parameters and monitoring accuracy level of each marked period, count the set of monitoring accuracy levels corresponding to each grid division parameter, calculate the monitoring effect priority value corresponding to each grid division parameter, select the grid division parameter with the largest monitoring effect priority value as the grid division parameter of the airspace, and then divide the airspace into multiple grid cells according to the grid division parameter.

[0029] Preferably, the process of analyzing the flight complexity level of the airspace is as follows: obtain the maximum color depth from the flight trajectory heat map, compare the maximum color depth with the color depth intervals corresponding to each flight complexity level. When the maximum color depth is within the color depth interval corresponding to a certain flight complexity level, then this flight complexity level is the flight complexity level of the airspace.

[0030] Among them, the higher the flight complexity level, the greater the corresponding color depth. The color depth intervals corresponding to each flight complexity level are set and modified by airspace management personnel according to the actual terrain and human activities of the airspace, etc., and specific data limitations are not provided here.

[0031] Preferably, the process for obtaining the monitoring accuracy level of the airspace is as follows: Set multiple punctuation marks with precise coordinates in the airspace, use the GPS positioning system to collect the positions of each UAV in the airspace, obtain the monitoring position coordinates of each UAV when reaching each punctuation mark, calculate the coefficient of variation, compare the coefficient of variation with the coefficient of variation corresponding to each monitoring accuracy level, and if the coefficient of variation is the same as a certain monitoring accuracy level, then use this monitoring accuracy level 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 the airspace management personnel according to the actual requirements of the airspace, and no specific data limit is imposed here.

[0033] Among them, the calculation process of the coefficient of variation: Taking the abscissa as an example: First, calculate the mean value of the abscissas of the monitoring positions of each UAV when reaching each punctuation mark to obtain the average abscissa of the UAV corresponding to each punctuation mark, then calculate the standard deviation of the abscissas of the UAV corresponding to each punctuation mark, divide the standard deviation of the abscissas of the UAV corresponding to each punctuation mark by the average abscissa of the UAV corresponding to each punctuation mark to obtain the coefficient of variation of the abscissa of the UAV for each punctuation mark, and then calculate the mean value of the coefficients of variation of the abscissas of the UAV for each punctuation mark to obtain the coefficient of variation of the abscissa of the airspace. According to the calculation process of the coefficient of variation of the abscissa, calculate the coefficients of variation of the ordinate and the vertical coordinate, and perform an average calculation on the coefficient of variation of the abscissa, the coefficients of variation of the ordinate and the vertical coordinate to obtain the coefficient of variation of the airspace.

[0034] In the above, the calculation of the standard deviation is the conventional calculation method in mathematics and will not be elaborated here.

[0035] In a specific embodiment, calculate the monitoring effect priority value corresponding to each grid division parameter. 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 denote them as M imax and M imin , where i represents the number of each grid division parameter, i is a positive integer, and the calculation formula for the monitoring effect priority value is:

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

[0037] In the above, the grid division parameter is the size parameter of grid division, including grid length, width, etc.

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

[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 UAV from the flight data applied for by the target UAV, map the flight trajectory applied for by the target UAV and the flight trajectories of the UAVs that have passed the review in the target time period to each grid unit in the airspace. When there is at least one grid unit where the flight trajectory applied for by the target UAV coincides with the flight trajectories of at least one UAV that has passed the review, it indicates that there is a flight conflict for the target UAV. Record the grid units where the flight trajectories coincide as the marked grid units, and record the UAVs that have passed the review in the same grid unit as the marked UAVs, and then execute S13; if the flight trajectory applied for by the target UAV does not coincide with the flight trajectories of the UAVs that have passed the review in each grid unit, it indicates that there is no flight conflict for the target UAV, and then execute S12.

[0040] S12. Obtain the conflict avoidance data of the target UAV from the historical supervision records, and analyze the basic conflict avoidance ability of the target UAV. If the basic conflict avoidance ability of the target UAV is qualified, the preliminary flight review of the target UAV passes; otherwise, if the basic conflict avoidance ability of the target UAV is unqualified, the preliminary flight review of the target UAV fails, and the reason for the failure of the preliminary flight review is that the basic conflict avoidance ability is unqualified.

[0041] Preferably, the analysis process of the basic conflict avoidance ability of the target UAV is as follows: Obtain the start avoidance distance and avoidance speed of the target UAV during each conflict avoidance from the conflict avoidance data of the target UAV, and set the avoidance distance threshold and avoidance speed threshold, denoted as L and v respectively. Compare the start avoidance distance and avoidance speed of the target UAV during each conflict avoidance with the avoidance distance threshold and avoidance speed threshold respectively. If the start avoidance distance during a certain conflict avoidance is less than the avoidance distance threshold or the avoidance speed is greater than the avoidance speed threshold, it indicates that this conflict avoidance is a dangerous conflict avoidance; if the start avoidance distance during a certain conflict avoidance 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 this conflict avoidance is a safe conflict avoidance. Thus, count the number of dangerous conflict avoidances and safe avoidances of the target UAV, denoted as P1 and P2 respectively, and use the analysis formula: Obtain the basic conflict avoidance ability γ of the target UAV.

[0042] Where when γ = 1, it indicates that the basic conflict avoidance ability of the target UAV is qualified; when γ = 0, the basic conflict avoidance ability of the target UAV is unqualified.

[0043] It should be noted that the avoidance distance threshold and the avoidance speed threshold are the minimum safe avoidance distance and the maximum safe avoidance speed when the UAV conducts conflict avoidance in the airspace. They are specifically set and modified by the airspace management personnel according to the situation of the airspace, and no specific restrictions are imposed here.

[0044] S13. Obtain the terrain environment data of each grid cell, and use the terrain environment data of each grid cell and the flight trajectories of each marked UAV to confirm the conflict avoidance plans of the target UAV in each grid cell, summarize the conflict avoidance plans of the target UAV during flight, obtain the conflict avoidance data of the target UAV from the historical supervision records, and analyze the ability levels of the target UAV to execute each conflict avoidance plan, where the conflict avoidance ability levels include level 1, level 2, and level 3.

[0045] Preferably, the analysis process of the ability levels of the target UAV to execute each conflict avoidance plan 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 certain grid cell is 1, the conflict avoidance plan of the target UAV in this grid cell is single fixed obstacle avoidance; when the number of fixed obstacles in a certain grid cell is greater than 1, the conflict avoidance plan of the target UAV in this grid cell is multi-fixed obstacle avoidance; when the number of fixed obstacles in a certain grid cell is 0, the target UAV in this grid cell does not need conflict avoidance.

[0046] S132. Based on the flight trajectories of each marked UAV, obtain the flight trajectories of each marked UAV in each marked grid cell. When there is only 1 flight trajectory of a marked UAV in a certain marked grid cell, the conflict avoidance plan of the target UAV in this marked grid cell is single moving obstacle avoidance; when there is 1 flight trajectory of a marked UAV and 1 number of fixed obstacles in a certain marked grid cell, the conflict avoidance plan of the target UAV in this marked grid cell is multi-class obstacle avoidance.

[0047] S133. If there are multiple flight trajectories of marked UAVs and 1 or more numbers of fixed obstacles in a certain marked grid cell, the conflict avoidance plan of the target UAV in this marked grid cell is complex obstacle avoidance, and thus obtain the conflict avoidance plans of the target UAV in each grid cell.

[0048] S134. Obtain the historical performance data, whether there is secondary avoidance, and whether there is safe avoidance during each historical execution of each conflict avoidance plan from the conflict avoidance data of the target UAV, count the historical execution times of secondary avoidance in each conflict avoidance plan, denoted as the secondary avoidance times of each conflict avoidance plan, denoted as U1 f , count the historical execution times of non-safe avoidance in each conflict avoidance plan, denoted as the failed avoidance times of each conflict avoidance plan, denoted as U2f ; meanwhile, from the historical performance data during each historical execution of each conflict avoidance plan for each historical flying drone in the cloud data platform, where there is no secondary avoidance and the avoidance is safe, cluster the historical performance data during each historical execution of each conflict avoidance plan for each historical flying drone where there is no secondary avoidance and the avoidance is safe, and use it as the performance data threshold for each conflict avoidance plan, denoted as ZT f ′.

[0049] It should be noted that the conflict avoidance data includes the historical performance data during each historical execution of each conflict avoidance plan, whether there is secondary avoidance, and whether the avoidance is safe; use the visual sensor to check the images or videos captured by the sensor, and use machine vision to determine whether, after the first avoidance, new obstacles or dangerous situations enter the field of view, resulting in the drone making a secondary avoidance action. If the drone makes a secondary avoidance action, it indicates that the drone has made a secondary avoidance; when the drone collides with an obstacle after avoiding it or emits an abnormal signal due to the avoidance, it is determined that the drone has not made a safe avoidance; the performance data includes the number of attitude changes and the avoidance duration, etc. Obtain the trajectory of the drone from the visualization interface, and obtain the number of attitude changes and the avoidance duration, where the trajectory includes information such as timestamps and coordinates.

[0050] S135. Use the analysis formula:

[0051] Obtain the ability level μ of the target drone when executing the f-th conflict avoidance plan f , where f represents the number of each conflict avoidance plan, y represents the number of each historical execution, Y represents the number of historical executions, and f, y, and Y are all positive integers. ZT fy represents the historical performance data during the y-th historical execution of the f-th conflict avoidance plan of the target drone, and σ min , σ max are respectively the preset lower limit value and upper limit value for evaluating the ability level.

[0052] Among the above, σ min , σ max are respectively the lower limit reference value and upper limit reference value for evaluating the ability level of executing the conflict avoidance plan. When the calculated data is less than σ min , it indicates that the ability to execute the conflict avoidance plan is low, and the ability level is classified as level 1. When the calculated data is greater than or equal to σ min and less than σ max , it indicates that the ability to execute the conflict avoidance plan is high, and the ability level is classified as level 2. When the calculated data is greater than or equal to σ max , it indicates that the ability to execute the conflict avoidance plan is very high, and the ability level is classified as level 3. σ min , σ maxIt is set and modified by airspace management personnel according to the situation of the airspace, and data restrictions are carried out here.

[0053] S14. When the ability levels of the target UAV to execute each conflict avoidance plan are all greater than or equal to level 2, the initial flight review of the target UAV passes. When there is at least one conflict avoidance plan with an ability level of 1 among the conflict avoidance plans executed by the target UAV, the initial flight review of the target UAV fails, and the reason for the failure of the initial flight review is the unqualified ability to execute the conflict avoidance plan.

[0054] The flight re-review module is used to prompt the applicant of the target UAV to submit the flight test data of the target UAV when the initial flight review of the target UAV fails, and use the flight test data of the target UAV to conduct a re-review of the flight of the target UAV.

[0055] Among the above, the flight re-review module includes a first-class re-review unit and a second-class re-review unit.

[0056] The first-class re-review unit is used to conduct a first-class re-review of the target UAV by using the flight test data of the target UAV and the conflict avoidance data of the target UAV when the reason for the failure of the initial flight review is the unqualified basic conflict avoidance ability, and confirm whether the flight re-review of the target UAV passes;

[0057] In a specific embodiment, the specific process of the first-class re-review unit is as follows: Extract the performance data of each conflict avoidance plan in the flight test of the target UAV from the flight test data of the target UAV, and use the performance data of each conflict avoidance plan in the flight test of the target UAV and the conflict avoidance data of the target UAV to analyze the basic conflict avoidance ability of the target UAV and the rationality of the flight test data. When the basic conflict avoidance ability of the target UAV is qualified and the rationality of the flight test data is relatively high, the flight re-review of the target UAV passes. If the basic conflict avoidance ability of the target UAV is unqualified or the rationality of the flight test data is relatively low, the flight re-review of the target UAV fails.

[0058] Preferably, the specific process of analyzing the basic conflict avoidance ability of the target UAV and the rationality of the flight test data is: Analyze the performance data of each conflict avoidance plan in the flight test of the target UAV according to the analysis method of the basic conflict avoidance ability γ of the target UAV to obtain the basic conflict avoidance ability of the target UAV. At the same time, obtain the historical performance data of each historical execution of each conflict avoidance plan from the conflict avoidance data of the target UAV, and record the performance data of each conflict avoidance plan in the flight test of the target UAV as ZT f ″, and use the analysis formula: to obtain the rationality of the flight test data When it indicates that the rationality of the flight test data is relatively high. When Indicates that the rationality of the flight test data is relatively low.

[0059] The second-class review unit is used to conduct a second-class review of the target UAV by using the flight test data of the target UAV and the conflict avoidance data of the target UAV when the reason for the failure of the initial flight review is the inability to execute the conflict avoidance plan, and confirm whether the flight review of the target UAV passes.

[0060] Preferably, the specific process of the second-class review unit is as follows: analyze the ability level of the target UAV to execute each conflict avoidance plan and the rationality of the flight test data by using the performance data of each conflict avoidance plan in the flight test of the target UAV and the conflict avoidance data of the target UAV.

[0061] Among them, the performance data of each conflict avoidance plan in the flight test of the target UAV is analyzed according to the ability level μ of the target UAV to execute the f-th conflict avoidance plan f to obtain the ability level of the target UAV to execute each conflict avoidance plan. The analysis method of the rationality of the flight test data is the same as that of the rationality of the flight test data and will not be elaborated here.

[0062] When the ability level of the target UAV to execute each conflict avoidance plan is greater than or equal to level 2 and the rationality of the flight test data is relatively high, the flight review of the target UAV passes. If there is at least one conflict avoidance plan with an ability level of 1 in the conflict avoidance plans executed by the target UAV, or the rationality of the flight test data is relatively low, the flight review of the target UAV fails.

[0063] It should be noted that when the flight review of the target UAV fails due to the relatively low rationality of the flight test data, the applicant of the target UAV will be informed by text message 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 UAV and analyze the real flight state of the target UAV when the target UAV is flying in the airspace after the initial flight review or the review passes.

[0065] In a specific embodiment, the specific flight process of analyzing the real flight state of the target UAV is as follows: obtain whether the target UAV conducts conflict avoidance in each grid unit from the flight data of the target UAV, record each grid unit without conflict avoidance as each first-class unit, and record each grid unit with conflict avoidance as each second-class unit. Then, extract the flight trajectory of the target UAV in each first-class unit from the flight data of the target UAV, and compare it with the applied flight trajectory of the target UAV to analyze the flight trajectory deviation degree of the target UAV.

[0066] Among the above, the flight data of the target UAV is the data obtained by tracking and monitoring the target UAV during flight, including but not limited to the real-time position, attitude and flight video of the target UAV in each grid cell. When the UAV flies in the airspace, monitoring devices such as a positioning system and a vision sensor need to be set on the UAV to monitor the flight data of the target UAV.

[0067] Machine vision analysis can be performed on the flight video to obtain whether the target UAV performs conflict avoidance in each grid cell.

[0068] Preferably, the analysis process of the flight trajectory deviation degree of the target UAV is as follows: Based on the flight trajectory applied by the target UAV, the reference position coordinates of each point on the flight trajectory of the target UAV in each first-class unit are obtained and denoted as Q j ′ q (x′ jq ,y′ jq ,z′ jq ), where j represents the number of each first-class unit, q represents the number of each point, and 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 first-class unit and denoted as Q j ″ q (x′ j ′ q ,y′ j ′ q ,z′ j ′ q ); Using the calculation formula: The offset distance d of the q-th point on the flight trajectory of the target UAV in the j-th first-class unit is obtained jq , and the maximum offset distance is selected as the flight trajectory deviation degree of the target UAV.

[0069] Extract the conflict avoidance plan and performance data of the target UAV in each second-class unit from the flight data of the target UAV, and analyze the true conflict avoidance ability level of the target UAV.

[0070] Among the above, the analysis process of the true conflict avoidance ability level of the target UAV is as follows: When the target UAV does not undergo a review, the conflict avoidance plan and performance data of the target UAV in each second-class unit are analyzed according to the ability level μ of the target UAV to execute the f-th conflict avoidance plan f to obtain the true conflict avoidance ability level of the target UAV.

[0071] When the target UAV undergoes a review, the conflict avoidance plan and performance data of the target UAV in each second-class unit are analyzed according to the ability level μ of the target UAV to execute the f-th conflict avoidance plan fAnalysis method to obtain a class of conflict avoidance ability levels of the target UAV; at the same time, compare the conflict avoidance solutions and performance data of the target UAV in each secondary unit with the performance data of each conflict avoidance solution in the flight test of the target UAV according to the rationality of the flight test materials The analysis method is used to obtain the secondary rationality of the flight test materials.

[0072] When the secondary rationality of the flight test materials is 1, the class of conflict avoidance ability level of the target UAV is the true conflict avoidance ability level. When the secondary rationality of the flight test materials is 0, the true conflict avoidance ability level of the target UAV is output as 0.

[0073] The embodiments of the present invention evaluate the authenticity of the materials submitted by the UAV, which can ensure the standardization of the review. At the same time, in case of flight problems or accidents, it can clarify the responsibilities of all parties and handle subsequent matters. When the authenticity of the UAV materials is insufficient, it can be stopped in time, which can reduce the flight risks of other UAVs in the airspace and improve the management effect of the airspace.

[0074] According to the flight trajectory deviation degree and the true conflict avoidance ability level of the target UAV, calculate the true flight characteristic value of the target UAV. The true flight characteristic value includes values of 1 and 0. When the true flight characteristic value is 1, it indicates that the true flight state of the target UAV is normal. When the true flight characteristic value is 0, it indicates that the true flight state of the target UAV is abnormal.

[0075] Preferably, the analysis process of the true flight characteristic value of the target UAV is as follows: Denote the flight trajectory deviation degree of the target UAV as d, and set the deviation threshold, denoted as d1. When d < d1 and the true conflict avoidance ability level of the target UAV is greater than level 1, the true flight characteristic value of the target UAV is 1. When d ≥ d1 or the true conflict avoidance ability level of the target UAV is less than or equal to level 1, the true flight characteristic value of the target UAV is 0.

[0076] It should be noted that the deviation threshold is the maximum distance allowed for the UAV to deviate during flight, which is set by the airspace management personnel according to the actual situation of the airspace and is not limited here.

[0077] An execution module is used to stop the flight of the target UAV when the true flight state of the target UAV is in an abnormal flight state.

[0078] Embodiment 2:

[0079] Refer to Figure 2As shown, an airspace management method through grid visualization analysis includes: S1. Obtain the flight data applied for by the target unmanned aerial vehicle (UAV) in the airspace, and at the same time obtain the audited records of the current UAVs in the airspace, divide the airspace into multiple grid cells, and conduct a preliminary review of the flight of the target UAV.

[0080] S2. If the preliminary review of the flight of the target UAV fails, prompt the applicant of the target UAV to submit the flight test materials of the target UAV, and use the flight test materials of the target UAV to conduct a re-review of the flight of the target UAV.

[0081] S3. When the target UAV passes the preliminary review or re-review and conducts flight in the airspace, monitor the flight data of the target UAV and analyze the actual flight state of the target UAV.

[0082] S4. When the actual flight state of the target UAV is in an abnormal flight state, stop the flight of the target UAV.

[0083] 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, supplements, or use similar methods to replace the specific embodiments described, as long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they should fall within the protection scope of the present invention.

Claims

1. An airspace management system through grid visualization analysis, characterized in that, It includes the following modules: The flight preliminary review module is used to obtain the flight data applied for by the target UAV in the airspace, and at the same time obtain the reviewed records of the current UAV in the airspace, divide the airspace into multiple grid cells, and conduct a preliminary review of the flight of the target UAV; The flight re-review module is used to prompt the applicant of the target UAV to submit the flight test materials of the target UAV when the preliminary review of the flight of the target UAV fails, and use the flight test materials of the target UAV to conduct a re-review of the flight of the target UAV; The flight monitoring module is used to monitor the flight data of the target UAV and analyze the true flight state of the target UAV when the target UAV is flying in the airspace after the preliminary review or re-review of the flight of the target UAV is passed; The execution module is used to stop the flight of the target UAV when the true flight state of the target UAV is in an abnormal flight state.

2. The airspace management system through grid visualization analysis according to claim 1, characterized in that, The flight preliminary review module includes a grid division unit and a preliminary review unit; The grid division unit is used to confirm the grid division parameters of the airspace by using the flight data applied for by the target UAV in the airspace and the reviewed records of the current UAV in the airspace, and conduct corresponding grid cell division to obtain each grid cell; The preliminary review unit is used to analyze whether there is a flight conflict of the target UAV by using the flight data applied for by the target UAV in the airspace and the reviewed records of the current UAV in the airspace, and then conduct a preliminary review of the flight of the UAV.

3. The airspace management system through grid visualization analysis according to claim 2, characterized in that The specific process of the grid division unit is as follows: Obtain the flight period applied for by the target UAV from the flight data applied for by the target UAV in the airspace as the target period, and obtain each UAV that has passed the review and its flight trajectory during the target period from the reviewed records of the current UAV in the airspace. Based on the flight trajectories of each UAV that has passed the review during the target period, construct a flight trajectory heat map; Use the flight trajectory heat map to analyze the flight complexity level of the airspace, and then obtain the flight complexity level, grid division parameters, and monitoring accuracy level of the airspace during each historical period from the historical supervision records. Select each historical period with the same flight complexity level as the airspace as each marked period, obtain the grid division parameters and monitoring accuracy level of each marked period, count the set of monitoring accuracy levels corresponding to each grid division parameter, calculate the monitoring effect priority value corresponding to each grid division parameter, select the grid division parameter with the largest monitoring effect priority value as the grid division parameter of the airspace, and then divide the airspace into multiple grid cells according to the grid division parameter.

4. An airspace management system through 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 applied for by the target UAV from the flight data applied for by the target UAV, map the flight trajectory applied for by the target UAV and the flight trajectories of the approved UAVs in the target time period to each grid cell in the airspace. When there is at least one grid cell where the flight trajectory applied for by the target UAV coincides with the flight trajectories of at least one approved UAV, it indicates that there is a flight conflict for the target UAV. Denote the grid cells where the flight trajectories coincide as the marked grid cells, and denote the approved UAVs in the same grid cell as the marked UAVs. Then execute S13; if the flight trajectory applied for by the target UAV does not coincide with the flight trajectories of the approved UAVs in each grid cell, it indicates that there is no flight conflict for the target UAV. Then execute S12; S12. Obtain the conflict avoidance data of the target UAV from the historical supervision records, and analyze the basic conflict avoidance ability of the target UAV. If the basic conflict avoidance ability of the target UAV is qualified, the initial flight review of the target UAV passes; otherwise, if the basic conflict avoidance ability of the target UAV is unqualified, the initial flight review of the target UAV fails, and the reason for the failure of the initial flight review is that the basic conflict avoidance ability is unqualified; S13. Obtain the terrain environment data of each grid cell, and use the terrain environment data of each grid cell and the flight trajectories of each marked UAV to confirm the conflict avoidance plan of the target UAV in each grid cell, summarize the conflict avoidance plans of the target UAV during flight, obtain the conflict avoidance data of the target UAV from the historical supervision records, and analyze the ability level of the target UAV to execute each conflict avoidance plan, where the conflict avoidance ability level includes level 1, level 2, and level 3; S14. When the ability level of the target UAV to execute each conflict avoidance plan is greater than or equal to level 2, the initial flight review of the target UAV passes; when there is at least one conflict avoidance plan for which the ability level of the target UAV to execute is level 1, the initial flight review of the target UAV fails, and the reason for the failure of the initial flight review is that the ability to execute the conflict avoidance plan is unqualified.

5. An airspace management system for grid visualization analysis according to claim 4, characterized in that, The flight re-review module includes a first-type re-review unit and a second-type re-review unit; The first-type re-review unit is used to conduct a first-type re-review of the target UAV using the flight test data of the target UAV and the conflict avoidance data of the target UAV when the reason for the failure of the initial flight review is that the basic conflict avoidance ability is unqualified, and confirm whether the flight re-review of the target UAV passes; The second-type re-review unit is used to conduct a second-type re-review of the target UAV using the flight test data of the target UAV and the conflict avoidance data 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 unqualified, and confirm whether the flight re-review of the target UAV passes.

6. The airspace management system through grid visualization analysis according to claim 5, characterized in that The specific process of the first-type re-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. 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, analyze the basic conflict avoidance ability of the target UAV and the rationality of the flight test data. When the basic conflict avoidance ability of the target UAV is qualified and the rationality of the flight test data is relatively high, the flight review of the target UAV passes. If the basic conflict avoidance ability of the target UAV is unqualified or the rationality of the flight test data is relatively low, the flight review of the target UAV fails.

7. An airspace management system for grid visualization analysis according to claim 5, characterized in that, The specific process of the second-class review unit is as follows: 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, analyze the ability level of the target UAV to execute each conflict avoidance scheme and the rationality of the flight test data; When the ability level of the target UAV to execute each conflict avoidance scheme is greater than or equal to level 2 and the rationality of the flight test data is relatively high, the flight review of the target UAV passes. If there is at least one conflict avoidance scheme in which the ability level of the target UAV to execute each conflict avoidance scheme is level 1, or the rationality of the flight test data is relatively low, the flight review of the target UAV fails.

8. The airspace management system through grid visualization analysis according to claim 1, characterized in that, The specific flight process of analyzing the real flight state of the target UAV is as follows: Obtain whether the target UAV performs conflict avoidance in each grid cell from the flight data of the target UAV. Denote each grid cell without conflict avoidance as each first-class cell, and denote each grid cell with conflict avoidance as each second-class cell. Then extract the flight trajectory of the target UAV in each first-class cell from the flight data of the target UAV, and compare it with the applied flight trajectory of the target UAV to analyze the flight trajectory deviation degree of the target UAV; Extract the conflict avoidance scheme and performance data of the target UAV in each second-class cell from the flight data of the target UAV, and analyze the real conflict avoidance ability level of the target UAV; According to the flight trajectory deviation degree and the real conflict avoidance ability level of the target UAV, calculate the real flight characteristic value of the target UAV. The real flight characteristic value includes values of 1 and 0. When the real flight characteristic value is 1, it indicates that the real flight state of the target UAV is normal. When the real flight characteristic value is 0, it indicates that the real flight state of the target UAV is abnormal.

9. An airspace management system through grid visualization analysis according to claim 8, characterized in that, The analysis process of the real flight characteristic value of the target UAV is as follows: Denote the flight trajectory deviation degree of the target UAV as d, and set the deviation degree threshold, denoted as d1. When d < d1 and the real conflict avoidance ability level of the target UAV is greater than level 1, the real flight characteristic value of the target UAV is 1. When d ≥ d1 or the real conflict avoidance ability level of the target UAV is level 1, the real flight characteristic value of the target UAV is 0.

10. An airspace management method through grid visualization analysis executed by the airspace management system through grid visualization analysis according to any one of claims 1-9, characterized in that, Include: S1. Obtain the flight data applied by the target UAV in the airspace, and at the same time obtain the reviewed records of the current UAV in the airspace, and divide the airspace into multiple grid cells to conduct a preliminary review of the flight of the target UAV; S2. When the initial flight review of the target UAV fails, prompt the applicant of the target UAV to submit the flight test materials of the target UAV, and use the flight test materials of the target UAV to conduct a review of the flight of the target UAV; S3. When the initial flight review or the review of the target UAV passes, monitor the flight data of the target UAV during flight in the airspace and analyze the actual flight state of the target UAV; S4. When the actual flight state of the target UAV is in an abnormal flight state, stop the flight of the target UAV.

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