A method and system for detecting flight conflict of unmanned aerial vehicles applied to low-altitude airspace

Abstract

The application relates to the technical field of air traffic management, in particular to a UAV flight conflict detection method and system applied to low-altitude airspace, which comprises the following steps: when detecting that a new UAV enters an airspace unit, a conflict detection request is acquired; the conflict detection request comprises a planned flight path of the UAV in the airspace unit, and the planned flight path records grid occupation probability of the UAV in the airspace unit; airspace information is acquired, the airspace information records residual occupiable probability of the grid of the airspace unit, the airspace information is updated based on the planned flight path, the latest real-time airspace information is acquired, conflict detection is carried out based on the planned flight path and the latest real-time airspace information, and a conflict determination result is acquired; the application can improve the accuracy and calculation efficiency of flight conflict detection.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of air traffic management, in particular to a UAV flight conflict detection method and system applied to low-altitude airspace. BACKGROUND

[0002] The UAV flight conflict detection system for low-altitude airspace obtains the planned flight tracks of all relevant UAVs in the airspace through a common information service provider (CISP), detects potential flight conflicts by calculating the spatiotemporal position relationship between them, and provides decision-making basis for UAVs to adjust their flight tracks to avoid dangerous proximity, thereby improving the safety and efficiency of UAV operation in low-altitude airspace. In the two-dimensional space of the same flight altitude layer, flight conflict is defined as the distance between two UAVs at a certain time being less than the minimum safety distance.

[0003] Due to the need to consider the uncertainty of UAV position, this further increases the complexity of the model on the original nonlinear problem, making it difficult to accurately calculate the conflict probability within the required priority time in real time. Existing methods mainly include random sampling method, Monte Carlo method and overlapping area method.

[0004] The random sampling method is to randomly sample the uncertain predicted trajectory to obtain a deterministic trajectory, and then judge the conflict of these specific trajectories. Since there is an inevitable difference between the sampled trajectory and the actual trajectory, it cannot guarantee the accuracy of conflict detection.

[0005] The Monte Carlo method is to obtain the probability of conflict occurrence through the Monte Carlo method based on simulation. In high traffic density scenarios, its calculation speed is slow, making it difficult to meet the real-time operation requirements of fast calculation.

[0006] The overlapping area method is to calculate the volume or area of the overlapping region of the position distribution of two UAVs to represent the conflict probability. It needs to judge the conflict of each two UAVs in all aircrafts, which makes it difficult to effectively compatible with the grid-based occupancy model currently used in low-altitude airspace, and leads to slow calculation speed in high traffic density scenarios. SUMMARY

[0007] In view of the above problems, the present application provides a UAV flight conflict detection method and system applied to low-altitude airspace, which solves the technical problem of limited flight conflict detection efficiency caused by the uncertainty of UAV position in the prior art.

[0008] In one aspect, the present application provides a UAV flight conflict detection method applied to low-altitude airspace, comprising the following steps:

[0009] Step S1, determining a designated airspace as a space domain unit; dividing the space domain unit into a plurality of grid units as a grid of the space domain unit; when detecting that the space domain unit has a new unmanned aerial vehicle entering, obtaining a conflict detection request; the conflict detection request includes a planned flight path of the unmanned aerial vehicle in the space domain unit, and the planned flight path records a grid occupation probability of the unmanned aerial vehicle in the grid of the space domain unit;

[0010] Step S2, obtaining space domain information, the space domain information records a remaining occupation probability of the grid of the space domain unit, updating the space domain information based on the planned flight path, and obtaining the latest real-time space domain information,

[0011] Step S3, performing conflict detection based on the planned flight path and the latest real-time space domain information, and obtaining a conflict determination result

[0012] Preferably, the obtaining manner of the planned flight path comprises:

[0013] (1) obtaining a grid occupation probability distribution nominal map;

[0014] (2) in the space domain unit, for each grid at each time step in the planned flight path, obtaining a relative grid position of the grid and a grid where a planned flight point of the unmanned aerial vehicle is located;

[0015] (3) based on the relative grid position, obtaining an occupation probability of each grid at each time step in the grid occupation probability distribution nominal map by table lookup;

[0016] (4) taking information recording the occupation probability of each grid at each time step in the space domain unit as the planned flight path.

[0017] Preferably, the obtaining of the grid occupation probability distribution nominal map comprises:

[0018] The grid occupation probability distribution nominal map is expressed as:

[0019]

[0020] Wherein, P i Map (x,y) is a value of the nominal map of the unmanned aerial vehicle i at index (x,y), P i C (x,y), P i T (x,y), P i R (x,y) and P i TR(x, y) respectively represent the value of the occupation probability distribution matrix of the UAV i at the central, top, right and upper right positions of the grid where it is located at index (x, y), N i X and N i Y respectively represent the maximum range of the grid occupied by the UAV i in the horizontal and vertical directions;

[0021] The maximum range of the grid occupied by the UAV i in the horizontal and vertical directions N i X and N i Y The range of the grid where the value of the occupation probability distribution matrix is greater than the preset occupation probability identification threshold is determined as the maximum range.

[0022] Preferably, the occupation probability distribution matrix of the central, top, right and upper right positions is calculated in the following manner:

[0023]

[0024] wherein P i (m,n) is the occupation probability of the grid (m, n) by the UAV i in the occupation probability distribution matrix, X′ n and X″ n respectively represent the coordinates of the starting point and the ending point of the grid (m, n) in the horizontal coordinate direction, Y′ n and Y″ n respectively represent the coordinates of the starting point and the ending point of the grid (m, n) in the vertical coordinate direction, f(x, y) represents the probability distribution function of the UAV at the coordinate (x, y) position, s is the standard deviation, and X and Y are the planned positions of the UAV.

[0025] The occupation probability of each grid by the UAV i is calculated by taking the central, top, right and upper right positions of the grid where the UAV i is located as the planned positions, respectively, and combining them into the occupation probability distribution matrix of the central, top, right and upper right positions.

[0026] Preferably, the occupation probability of each grid at each time step in the grid occupation probability distribution matrix is obtained by looking up the table based on the relative grid position, which specifically includes:

[0027] The occupation probability is calculated in the following manner:

[0028]

[0029] wherein, Let P represent the probability that drone i occupies grid (m,n). i Map (x,y) represents the value of the grid cell at position (x,y) in the nominal graph of the grid occupancy probability distribution. This is the grid number where the planned flight path of the drone is located.

[0030] Preferably, step S2, which involves updating the airspace information based on the planned flight path to obtain the latest real-time airspace information, specifically includes:

[0031] (1) When the grid (m,n) is not occupied by the drone in time step t, set r (m,n,t) =1;

[0032] (2) When a new UAV with the number New is detected entering the airspace unit, the following iterative calculations are performed sequentially:

[0033]

[0034]

[0035] in, Let be the probability that grid (m,n) is occupied by two or more drones at time step t. and Let represent the probabilities that grid (m,n) is occupied by exactly 0 and 1 drones at time step t, respectively. Let r be the probability of a newly entered drone (number New) occupying grid (m,n) at time step t. The left arrow (←) indicates that the value on the right is updated with the value on the left. (m,n,t) Let P be the probability that the remaining grid cell (m,n) is still occupied at time step τ. Safety The threshold for conflict determination;

[0036] (3) Update r (m,n,t) This is the latest real-time airspace information.

[0037] Preferably, step S3 specifically includes: using the following conflict discriminant to determine whether a conflict exists in each grid at each time step of the spatial unit:

[0038]

[0039] when Greater than r (m,n,t) If a grid (m,n) is determined to have a conflict at time step t, then the grid (m,n) is determined to be a conflicting grid. If the conflicting grid exists, the final determination result is that a conflict exists.

[0040] Preferably, the conflict detection request is sent by a ground control station; and the latest real-time airspace information is provided by a common information service provider

[0041] In one aspect, the present application provides a UAV flight conflict detection system applied to low-altitude airspace, comprising:

[0042] The acquisition module is configured to determine a designated air region as an airspace unit, divide the airspace unit into a plurality of grid units as a grid of the airspace unit, and acquire a conflict detection request when detecting that a new UAV enters the airspace unit, wherein the conflict detection request comprises a planned flight path of the UAV in the airspace unit, and the planned flight path records a grid occupancy probability of the UAV in the grid of the airspace unit.

[0043] The update module is configured to acquire airspace information, the airspace information records a remaining occupancy probability of the grid of the airspace unit, update the airspace information based on the planned flight path, and acquire latest real-time airspace information.

[0044] The determination module is configured to perform conflict detection based on the planned flight path and the latest real-time airspace information, and acquire a conflict determination result.

[0045] Compared with the prior art, the present application has at least the following beneficial effects:

[0046] (1) The present application significantly reduces the calculation amount required for rapid conflict detection of UAVs in low-altitude airspace by introducing a conflict discriminant based on iterative calculation and a grid occupancy probability distribution nominal map, so that the calculation time does not increase significantly with the increase of airspace traffic density, effectively improving the operation efficiency of the system.

[0047] (2) The present application provides a strict constraint-based occupancy probability calculation method, which realizes that a UAV only uses one grid occupancy probability distribution nominal map to cope with all position distribution situations, thereby greatly reducing the calculation amount.

[0048] (3) The present application sets a reasonable occupancy rate recognition threshold, avoids calculating the occupancy probability of a large number of grids that have little effect on actual operation, further reduces the calculation burden, and improves the response speed to rapidly changing airspace environment. BRIEF DESCRIPTION OF DRAWINGS

[0049] The accompanying drawings are only for the purpose of illustrating specific embodiments and are not considered as limiting the present application.

[0050] Figure 1 The present application provides a flowchart of a UAV flight conflict detection method applied to low-altitude airspace.

[0051] Figure 2 A time step-based discrete track point airspace occupation schematic diagram provided by the present application;

[0052] Figure 3 A grid occupation probability distribution nominal diagram provided by the present application;

[0053] Figure 4 A grid occupation probability calculation method schematic diagram based on a position probability distribution provided by the present application;

[0054] Figure 5 A conflict grid acquisition method schematic diagram provided by the present application;

[0055] Figure 6 A fast conflict detection system schematic diagram provided by the present application;

[0056] Figure 7 A schematic diagram of a UAV flight conflict detection system applied to low-altitude airspace provided by the present application.

[0057] Reference signs: 101 - acquisition module, 102 - update module, 103 - determination module. DETAILED DESCRIPTION

[0058] In order to enable the above-mentioned purposes, features and advantages of the present application to be more clearly understood, the present application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict. In addition, the present application can also be implemented in other ways different from those described herein, and therefore, the protection scope of the present application is not limited by the specific embodiments disclosed below.

[0059] On the two-dimensional space of the same flight altitude layer, flight conflict is defined as the distance between two UAVs (UAV i, UAV j) at a certain moment is less than the minimum safety distance S. The mathematical description is as follows:

[0060]

[0061] wherein, indicates "existence", T i and T j respectively indicate the flight time of UAV i and UAV j in the airspace, x i (t) and y i (t) are respectively the horizontal and vertical coordinates of the position of UAV i at time t, x j (t) and y j (t) are respectively the horizontal and vertical coordinates of the position of UAV i at time t. The present application provides a method and system for detecting the above-mentioned UAV flight conflict.

[0062] In order to illustrate the effectiveness of the method of the present application, the above technical solutions of the present application are described in detail below through a specific embodiment, as shown in Figure 1 The present application discloses a UAV flight conflict detection method applied to low-altitude airspace, comprising the following steps:

[0063] Step S1, a designated airspace is determined as an airspace unit; the airspace unit is divided into a plurality of grid units as the grid of the airspace unit; when a new UAV entering the airspace unit is detected, a conflict detection request is obtained; the conflict detection request includes a planned track of the UAV in the airspace unit, and the planned track records the grid occupation probability of the UAV in the airspace unit;

[0064] The present application performs conflict detection based on grid probability occupation, and the grid is a square sub-block in a map, and the edges thereof are respectively parallel to the horizontal and vertical coordinate axes of the map plane. The current planned track of the UAV is discretized into a plurality of plane position points based on time steps, and the occupation proportion of each UAV to each grid in the airspace is calculated, that is, the probability of any grid being occupied by the UAV at any time step is calculated.

[0065] The probability of the UAV occupying the grid is obtained by integrating the position probability distribution function of the UAV in the airspace grid. At a certain time step, the occupation probability of the UAV i to the grid (m, n) is represented as:

[0066]

[0067] Wherein, X' n and X" n are the coordinates of the starting point and the ending point of the grid (m, n) in the horizontal coordinate direction, Y' n and Y" n respectively represent the coordinates of the starting point and the ending point of the grid (m, n) in the vertical coordinate direction. f(x, y) is the position probability distribution function of the UAV, which is used to represent the uncertainty of the position of the UAV in the low-altitude airspace, and the uncertainty can be represented by a two-dimensional Gaussian distribution with a covariance of 0. The mathematical description is as follows:

[0068]

[0069] Wherein, f(x, y) represents the probability distribution function of the UAV at the coordinate (x, y) position, s is the standard deviation, which is determined according to the degree of uncertainty of the position of the UAV, and X and Y are respectively the planned position of the UAV at the time.

[0070] In order to detect the conflict of the whole flight path of the UAV, all the grids in all time steps during the flight of the UAV need to be judged for conflict, that is, each grid in each time step corresponds to an integral calculation, which is very time-consuming. Therefore, the present application establishes a nominal map of grid occupation probability distribution for quickly obtaining the calculation of the required grid occupation probability in operation, as shown in the following formula (1). Figure 3 In actual operation, when the occupation probability of a certain grid of a certain UAV is required, the occupation probability of the grid is directly obtained by table lookup in the nominal map through the relative grid position of the grid and the grid where the planned path point of the UAV is located.

[0071] Before the operation conflict detection, the nominal map is generated according to the airspace grid model and the position probability distribution function of the UAV. Since the occupation probability of other grids corresponding to the different positions of the UAV in a grid on the plane is different, in order to use a nominal map to deal with the different positions of the UAV, a strict constraint-based occupation probability calculation method is adopted, that is, for any position of the UAV in a grid, the occupation probability of the grid in the maximum case is calculated.

[0072] The specific implementation is that, when calculating the occupation probability of the grid (m, n) by the UAV i, the planned path point of the UAV i is set to be the position closest to the grid (m, n) in the grid (m i L ,n i L ) where the UAV i is located. First, the occupation probability distribution maps of the planned path point of the UAV respectively assuming to be located in the center, the top, the right side and the upper right are constructed, and the planned path points of the UAV are and (L G ,L G ), respectively. The corresponding occupation probability distribution map matrices are represented as P i C , P i T , P i R and P i TR , respectively, wherein L G is the grid length.

[0073] The nominal map P i Map (x, y) can be represented as:

[0074]

[0075] wherein P i Map ​​(x,y) is the value of the nominal map of the UAV i at index (x,y), P i C (x,y), and P i TR (x,y) respectively represent the values of the central, top, right and right upper occupancy probability distribution map matrix at index (x,y), and respectively represent the maximum range of the grid occupied by the UAV i in the horizontal and vertical directions.

[0076] As shown in Figure 3 , when the planned track point of the UAV is located at the top, the part with the maximum occupancy probability of x=0, y=31 is determined as the occupancy probability in the nominal map, when the planned track point is located at the right side, the part with the maximum occupancy probability of x=31, y=0 is determined as the occupancy probability in the nominal map, when the planned track point of the UAV is located at the right upper, the part with the maximum occupancy probability of x=31, y=31 is determined as the occupancy probability in the nominal map, and finally the part located in the center is added to the nominal map. In the above manner, the part with the maximum occupancy probability of each case is spliced to obtain the probability distribution nominal map. For any position of the UAV in the grid, the case with the maximum grid occupancy probability is calculated, and the occupancy probability calculation mode based on strict constraints can be realized.

[0077] The maximum range of the grid occupied by the UAV i in the horizontal and vertical directions and The range of the occupied grid is determined by the size relationship between the occupancy probability and the preset occupancy probability identification threshold.

[0078] The occupancy probability of the two-dimensional Gaussian distribution in any grid is greater than 0, and in actual operation, the occupancy probability is negligible when it is too small. The present application pre-sets an occupancy probability identification threshold, and when the actual occupancy probability is less than the threshold, the occupancy probability is taken as 0, so as to reduce the number of grids that need to be iteratively calculated for the remaining probability that can be occupied, thereby further improving the calculation speed.

[0079] After having the nominal map, the probability of the grid (m, n) occupied by the UAV i can be obtained by looking up the value of the corresponding position of the nominal map. The mathematical expression is as follows:

[0080]

[0081] Where, P i Map (x,y) represents the occupancy probability value of the grid at position (x,y) in the nominal map.

[0082] The occupancy information of the airspace grid of the UAV i at time step t can be represented as:

[0083]

[0084] where A i denotes the set of cells that the UAV i is going to enter, T i denotes the set of time steps that the UAV i can fly in the sector it is going to enter.

[0085] Step S2, obtaining airspace information, the airspace information records the remaining probability of occupancy of the grid of the airspace unit, updating the airspace information based on the planned flight path, obtaining the latest real-time airspace information.

[0086] Discretize the current planned flight path of the UAV into several planar position points based on time steps and calculate the occupancy proportion of each grid in the airspace by each UAV, that is, the probability that any grid is occupied by two or more UAVs at any time step is greater than a set conflict determination threshold, that is, it is determined to be a conflict. The discrimination method is represented as:

[0087]

[0088] wherein, is the probability that the grid (m, n) is occupied by two or more UAVs at time step t, P Safety is the conflict determination threshold, and m and n are the horizontal and vertical coordinates of the grid, respectively.

[0089] The probability that the grid (m, n) is occupied by two or more UAVs at time step t is:

[0090]

[0091] wherein and denote the probability that the grid (m, n) is occupied by 0 and 1 UAV at time step t, respectively, and the calculation methods are as follows:

[0092]

[0093] wherein I (m,n,t) denotes the set of all UAVs, is the probability that the UAV i is located in the grid (m, n) at time step t.

[0094] By the above calculation, since the position distribution of the UAVs can be assumed to be independent, the product of the probabilities that all UAVs are not in the grid (m, n) is calculated to obtain the probability that the grid is occupied by exactly 0 UAVs at time step t; the product of the probability that UAV i is in the grid and the probability that all other UAVs i' are not in the grid is calculated to obtain the probability that each UAV occupies the grid (m, n) alone, and the probabilities are accumulated to obtain the probability that the grid (m, n) is occupied by exactly 1 UAV at time step t.

[0095] Further, the probability that the grid (m, n) is occupied by two or more UAVs at time step t can be expressed as:

[0096]

[0097] By the above calculation, the probability that the grid (m, n) is occupied by two or more UAVs at time step t is obtained by subtracting the probabilities of being occupied by 0 and 1 UAVs from 1.

[0098] Directly calculating by the above method requires processing the combination of the occupation probabilities of all UAVs on the grid, which becomes very complex and time-consuming as the number of UAVs increases. The present application provides an iterative calculation method, which simplifies the calculation process by considering the influence of each newly entering UAV on the grid occupation probability step by step. The probability of not being occupied, the probability of being occupied by two or more UAVs, the probability of being occupied by one UAV, and the probability of remaining to be occupied are updated each time, thereby effectively reducing the calculation amount. The iteration process is:

[0099] (1) When the grid (m, n) is not occupied by a UAV at time step t, r (m,n,t) = 1, where r (m,n,t) = 1 is the probability of the grid (m, n) remaining to be occupied at time step t;

[0100] (2) When a new UAV numbered New is detected to enter the airspace unit, the following iterative calculation is performed in turn:

[0101]

[0102] wherein, is the occupation probability of the newly entering UAV numbered New on the grid (m, n) at time step t, and <- means updating the value on the left side with the value on the right side.

[0103] After each iteration, the obtained airspace information can be expressed as:

[0104] R i = {r (m,n,t) |(m, n) ∈ Ai ,t∈T i}

[0105] Where R i A represents the airspace information used by UAV i for conflict determination. i T represents the set of grid cells that UAV i is about to enter. i This represents the set of time steps that the drone i may take in the sector it is about to enter.

[0106] Using the above iterative method, the probability that the grid is not occupied by any drone at time step t is multiplied by... This indicates that the probability of a new drone not occupying this grid cell has been taken into account; the original... The probability of a grid being occupied by two or more drones is updated by multiplying the probability of a new drone occupying the grid by the probability of only one drone occupying it previously; the probability of a grid being occupied by two or more drones is updated by subtracting the probability of no drone occupying the grid from the probability of two or more drones occupying it. This ensures that the sum of probabilities is 1; finally, based on the conflict determination threshold P... Safety Update the remaining probability r that can be occupied (m,n,t) This iterative approach effectively calculates the different probabilities of a grid being occupied, thus simplifying the originally complex calculation process.

[0107] After completing the iteration, use the following conflict criterion to determine whether a conflict exists:

[0108]

[0109] A conflict is determined when the probability of a newly entered drone occupying the slot is greater than the remaining probability of the drone being available for occupancy.

[0110] The set of conflicting grids can be represented as:

[0111]

[0112] In some embodiments, such as Figure 6 As shown, the application scenarios of the UAV flight conflict detection method provided by this invention include Public Information Service Providers (CISP), Unmanned Aircraft System Traffic Management Service Providers (UTMSP), Ground Control Stations (GCS), and UAVs. The UAV flight conflict detection method is executed by the UTMSP, which obtains the planned flight path information of all UAVs in the airspace from the CISP to perform conflict detection for the UAVs controlled by the GCS it serves.

[0113] The UAV sends its location information to its GCS in real time. When the GCS detects that the UAV is about to enter a new airspace unit, it sends a conflict detection request to the UTMSP for the UAV.

[0114] An airspace cell is an airspace area composed of multiple grids. The detection request includes the UAV's planned flight path within that airspace cell. The planned flight path is specifically a matrix representing the occupancy probabilities of the grids within the airspace cell at each time step. For example... Figure 2 As shown, the planned trajectory is represented as the occupancy probability of each grid cell in the airspace at each relevant time step, with the time interval between each time step being ΔT. The value of ΔT is set according to the collision detection accuracy required in actual operation; the smaller the value, the higher the accuracy.

[0115] Upon receiving a conflict detection request from the GCS, the UTMSP acquires the UAV occupancy probability information of the grid from the GCS, extracts the latest real-time airspace information from the CISP to perform conflict detection, and obtains the conflicting grids. The detection results are then sent to the conflict resolution module to activate subsequent conflict-free path planning.

[0116] like Figure 7 As shown, the present invention provides a UAV flight conflict detection system for low-altitude airspace, the system comprising:

[0117] The acquisition module 101 is used to determine a specified airspace area as an airspace unit; divide the airspace unit into multiple grid units as the grid of the airspace unit; when a new UAV is detected entering the airspace unit, acquire a conflict detection request; the conflict detection request includes the planned flight path of the UAV in the airspace unit, and the planned flight path records the grid occupancy probability of the UAV in the airspace unit.

[0118] Update module 102 is used to acquire airspace information, which records the probability that the remaining grid cells of the airspace unit can be occupied. Based on the planned flight path, the airspace information is updated to obtain the latest real-time airspace information.

[0119] The determination module 103 is used to perform conflict detection based on the planned flight path and the latest real-time airspace information, and obtain the conflict determination result.

[0120] The detailed description of the application describes and illustrates with reference to certain specific embodiments. However, the description and illustrations are intended to be merely illustrative and not restrictive of the application. While the application has been described and illustrated with reference to specific embodiments, it will be recognized that variations and modifications can be made by persons skilled in the art depending upon the overall teachings of the present application. In particular, those skilled in the art will recognize that elements of the present application can readily be combined to provide further embodiments of the present application. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific embodiments described herein. It is, therefore, to be understood that the application is not to be limited to the specific embodiments disclosed and that modifications and / or substitutions are intended to be included within the scope of the present application. Such equivalents are considered within the scope of the present application.

[0121] The detailed description of the application describes and illustrates with reference to certain specific embodiments. However, the description and illustrations are intended to be merely illustrative and not restrictive of the application. While the application has been described and illustrated with reference to specific embodiments, it will be recognized that variations and modifications can be made by persons skilled in the art depending upon the overall teachings of the present application. In particular, those skilled in the art will recognize that elements of the present application can readily be combined to provide further embodiments of the present application. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific embodiments described herein. It is, therefore, to be understood that the application is not to be limited to the specific embodiments disclosed and that modifications and / or substitutions are intended to be included within the scope of the present application. Such equivalents are considered within the scope of the present application.

Claims

1. A method for detecting unmanned aerial vehicle (UAV) flight conflicts in low-altitude airspace, characterized in that, Includes the following steps: Step S1: Define the specified airspace area as an airspace unit; divide the airspace unit into multiple grid units as the grid of the airspace unit; when a new UAV is detected entering the airspace unit, obtain a conflict detection request; the conflict detection request includes the planned flight path of the UAV in the airspace unit, and the planned flight path records the grid occupancy probability of the UAV in the airspace unit. Step S2: Obtain airspace information, which records the probability that the remaining grid cells of the airspace unit can be occupied. Update the airspace information based on the planned flight path to obtain the latest real-time airspace information. Step S3: Based on the planned flight path and the latest real-time airspace information, perform conflict detection and obtain conflict determination results; The methods for obtaining the planned flight path include: (1) Obtain the nominal map of the grid occupancy probability distribution; (2) In the airspace unit, for each grid at each time step in the planned trajectory, obtain the relative grid position of the grid and the grid where the UAV planned trajectory point is located; (3) Based on the relative grid position, the occupancy probability of each grid at each time step is obtained by looking up a table in the nominal graph of grid occupancy probability distribution; (4) The information recording the occupancy probability of each grid at each time step in the airspace unit is used as the planned track; Step S2, which involves updating the airspace information based on the planned flight path and obtaining the latest real-time airspace information, specifically includes: (1) When the grid At time step When the space is not occupied by drones, set , , ; (2) When a new UAV with the number New is detected entering the airspace unit, the following iterative calculations are performed sequentially: in, For grid At time step The probability of being occupied by two or more drones. and Representing grids At time step The probability that it is occupied by exactly 0 and 1 drones. For the newly entered drone numbered New, at time step For grid The probability of occupancy. This means updating the value on the left with the value on the right side of the arrow. For grid At time step The remaining probability that can be occupied, The threshold for conflict determination; (3) Update the As the latest real-time airspace information; Step S3 specifically includes: The following conflict criterion is used to determine whether a conflict exists in each grid at each time step of the spatial unit: when Greater than At that time, determine the grid At time step There is a conflict; determine the grid. If a conflicting grid exists, the final determination result is that a conflict exists.

2. The method for detecting UAV flight conflicts in low-altitude airspace according to claim 1, characterized in that, The acquisition of the nominal map of grid occupancy probability distribution specifically includes: The nominal map representing the grid occupancy probability distribution is as follows: in, For drones At index The value of the nominal graph at that location, , , and They represent drones The occupancy probability distribution matrix at the center, top, right, and upper right positions of the grid cell is at index [index missing]. The value at that location, and drones The maximum area of ​​the grid occupied in the horizontal and vertical directions; The drone The maximum area occupied by the grid in the horizontal and vertical directions. and The maximum range is determined by the relationship between the value of the occupancy probability distribution map matrix and the preset occupancy probability identification threshold. The range of grid cells whose value of the occupancy probability distribution map matrix is ​​greater than the preset occupancy probability identification threshold is defined as the maximum range.

3. The method for detecting UAV flight conflicts in low-altitude airspace according to claim 2, characterized in that, The calculation method for the occupancy probability distribution matrix of the center, top, right, and upper right positions is as follows: ， in, For the occupancy probability distribution matrix of UAVs For grid The probability of occupancy. and They are grids The coordinates of the starting and ending points along the horizontal axis. and Representing grids The coordinates of the starting and ending points along the vertical axis. Indicates the coordinates of the drone The probability distribution function of location. Standard deviation, and These are the locations of the drone project; The drones The planned positions for the drone are determined by using the center, top, right, and upper right positions within the grid. The occupancy probabilities of each grid cell are combined to form occupancy probability distribution matrices for the center, top, right, and upper right positions, respectively.

4. The method for detecting UAV flight conflicts in low-altitude airspace according to claim 3, characterized in that, The step of obtaining the occupancy probability of each grid at each time step by looking up a table in the nominal graph of grid occupancy probability distribution based on the relative grid position specifically includes: The occupancy probability is calculated in the following way: in, Indicates drone For grid The probability of occupancy. The position in the nominal graph representing the grid occupancy probability distribution is... The value of the raster. This is the grid number where the planned flight path of the drone is located.

5. The method for detecting unmanned aerial vehicle (UAV) flight conflicts in low-altitude airspace according to claim 1, characterized in that: The conflict detection request is sent by the ground control station; the latest real-time airspace information is provided by a public information service provider.

6. A UAV flight conflict detection system for low-altitude airspace, characterized in that, include: The acquisition module is used to identify a specified airspace area as an airspace unit; and to divide the airspace unit into multiple grid units as the grid of the airspace unit; When a new UAV is detected entering the airspace unit, a conflict detection request is obtained; the conflict detection request includes the planned flight path of the UAV in the airspace unit, and the planned flight path records the grid occupancy probability of the UAV in the airspace unit; The update module is used to acquire airspace information, which records the probability that the remaining grid cells of the airspace unit can be occupied. Based on the planned flight path, the airspace information is updated to obtain the latest real-time airspace information. The determination module is used to perform conflict detection based on the planned flight path and the latest real-time airspace information, and obtain the conflict determination result; The methods for obtaining the planned flight path include: (1) Obtain the nominal map of the grid occupancy probability distribution; (2) In the airspace unit, for each grid at each time step in the planned trajectory, obtain the relative grid position of the grid and the grid where the UAV planned trajectory point is located; (3) Based on the relative grid position, the occupancy probability of each grid at each time step is obtained by looking up a table in the nominal graph of grid occupancy probability distribution; (4) The information recording the occupancy probability of each grid at each time step in the airspace unit is used as the planned track; The step of updating the airspace information based on the planned flight path and obtaining the latest real-time airspace information specifically includes: (1) When the grid At time step When the space is not occupied by drones, set , , ; (2) When a new UAV with the number New is detected entering the airspace unit, the following iterative calculations are performed sequentially: in, For grid At time step The probability of being occupied by two or more drones. and Representing grids At time step The probability that it is occupied by exactly 0 and 1 drones. For the newly entered drone numbered New, at time step For grid The probability of occupancy. This means updating the value on the left with the value on the right side of the arrow. For grid At time step The remaining probability that can be occupied, The threshold for conflict determination; (3) Update the As the latest real-time airspace information; The step of performing conflict detection based on the planned flight path and the latest real-time airspace information, and obtaining the conflict determination result, specifically includes: The following conflict criterion is used to determine whether a conflict exists in each grid at each time step of the spatial unit: when Greater than At that time, determine the grid At time step There is a conflict; determine the grid. If a conflicting grid exists, the final determination result is that a conflict exists.

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