Unmanned aerial vehicle flight area risk prediction method
Through detailed assessment of the risk of the drone's flight area, combined with the flight trajectory, wind speed, overlapping area and historical accident data, the regional risk value was calculated, which solved the problem of risk prediction during the drone's flight and improved the flight safety.
Patent Information
- Application Number
- CN202411889413.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-20
- Publication Date
- 2025-03-28
AI Technical Summary
As the number of drone flights increases, how to effectively predict the risks of the flight areas that pass through during drone flights, avoiding the impact between flight activities and threats to safety.
By obtaining the target flight trajectory and time period of the target drone, determining its range of flight activities, and combining the flight strategy, wind speed, overlapping area and flight density of the geographical location, the flight activity risk value is calculated. At the same time, based on the historical drone collision accident data and damage degree, the ground risk value and economic loss risk value are evaluated, and the regional risk value is finally added and determined.
It realizes effective prediction of the risk of the drone's flight area, provides a reference to reduce flight risks, and provides safety guarantees for operators and approvers.
Smart Images
Figure CN119723832A_ABST
Abstract
Description
Background Art
[0002] With the development of the drone industry, the number of drone flights is increasing. Due to the different management and control of flight areas in various geographical locations, such as some areas are no-fly zones, some areas are restricted areas, etc., and in order to avoid the impact of too many drone flight activities in the same area at the same time period, which may pose a threat to drone flight safety, it is necessary to conduct risk prediction for the flight areas that drones pass through during flight. Summary of the invention
[0003] In view of the above technical problems, the technical solution adopted by the present invention is:
[0004] According to one aspect of the present application, a method for predicting the risk of a drone flight area is provided, comprising the following steps:
[0005] Step S100: in response to receiving a flight activity request, obtaining a target flight trajectory and a target flight time period of a target UAV included in the flight activity request;
[0006] Step S200, determining the target flight activity range of the target UAV according to the target flight trajectory;
[0007] Step S300, determining the flight activity risk value of the target UAV according to the flight strategy at the geographical location corresponding to the target flight activity range, the wind speed at the geographical location corresponding to the target flight activity range within the target flight time period, the overlapping area between the target flight activity range and the key flight activity range of the key UAV, and the regional flight density of the target flight activity range within the historical time period; the key UAV is a UAV whose flight time is within the target flight time period;
[0008] Step S400: determining the ground risk value of the target UAV according to the collision accidents of historical UAVs of the same type as the target UAV that occurred in the historical period;
[0009] Step S500: determining the economic loss risk value of the target UAV according to the degree of damage to the UAV after a collision accident in history;
[0010] Step S600: The sum of the flight activity risk value, the ground risk value and the economic loss risk value of the target UAV is determined as the regional risk value of the target UAV within the target flight activity range.
[0011] In an exemplary embodiment of the present application, step S200 includes:
[0012] Step S210: Obtain a number of trajectory points on the target flight trajectory to obtain a first trajectory point list A=(A1, A2, ..., A i ,...,An ), where i = 1, 2, ..., n, and n is the number of trajectory points on the target flight trajectory; A i is the i-th trajectory point on the target flight trajectory;
[0013] Step S220: traverse the first trajectory point list A to obtain the geographical location corresponding to each trajectory point on the target flight trajectory, so as to obtain a first geographical location identifier list B = (B1, B2, ..., B i ,...,B n ), where B i A i an identifier of the corresponding geographical location;
[0014] Step S230: De-duplicate the first geographic location identifier list B to obtain a second geographic location identifier list C = (C1, C2, ..., C j ,...,C m ), wherein j=1, 2, ..., m; m is the number of geographic location identifiers obtained after deduplication processing is performed on the first geographic location identifier list B; C j is the jth geographical location identifier obtained after deduplication processing is performed on the first geographical location identifier list B;
[0015] Step S240: C1, C2, ..., C j ,...,C m The area composed of the corresponding geographical locations is determined as the target flight activity range of the target UAV.
[0016] In an exemplary embodiment of the present application, step S300 includes:
[0017] Step S310, determining a first flight risk value Z1 of the target UAV according to the flight strategy at the geographical location corresponding to the target flight activity range;
[0018] Step S320, determining a second flight risk value Z2 of the target UAV according to the wind speed of the geographical location corresponding to the target flight activity range within the target flight time period;
[0019] Step S330, determining a third flight risk value Z3 of the target UAV according to an overlapping area between the target flight activity range and the key flight activity range of the key UAV;
[0020] Step S340, determining a fourth flight risk value Z4 of the target UAV according to the regional flight density of the target flight activity range in the historical time period;
[0021] Step S350: determine MAX(Z1, Z2, Z3, Z4) as the flight activity risk value of the target UAV; wherein MAX() is a preset maximum value determination function.
[0022] In an exemplary embodiment of the present application, step S310 includes:
[0023] Step S311, traverse the second geographical location identifier list C, if C j If the corresponding geographical location is a restricted flight area, L1 is determined as the first flight risk value Z1 of the target UAV; otherwise, step S320 is executed;
[0024] Step S312: If the geographical location corresponding to each geographical location identifier in the second geographical location identifier list C is not a restricted flight area, obtain the distance between the geographical location corresponding to each geographical location identifier in the second geographical location identifier list C and the restricted flight area closest to the geographical location, so as to obtain the area interval distance list D = (D1, D2, ..., D j ,...,D m ), where D j C j The distance between the corresponding geographical location and the restricted flight area closest to the geographical location;
[0025] Step S313: If MIN(D)≤D0, L2 is determined as the first flight risk value Z1 of the target UAV; otherwise, L3 is determined as the first flight risk value Z1 of the target UAV; wherein MIN() is a preset minimum value determination function; D0 is a preset area interval distance threshold; L1>L2>L3>0.
[0026] In an exemplary embodiment of the present application, step S320 includes:
[0027] Step S321: Obtain the wind speed of each geographical location corresponding to the geographical location identifier in the second geographical location identifier list C during the target flight time period, so as to obtain a flight wind speed list set E = (E1, E2, ..., E j ,...,E m );E j =(E j1 ,E j2 ,...,E jv ,...,E jb ), where E j C j The wind speed list of the corresponding geographical location within the target flight time period; v = 1, 2, ..., b; b is the number of wind speed collection moments included in the target flight time period; E jv C jThe wind speed corresponding to the vth wind speed collection time at the corresponding geographical location within the target flight time period;
[0028] Step S322: According to E j , determine C j The target wind speed value F of the corresponding geographical location during the target flight time period j =(∑ b v＝1 E jv ) / b;
[0029] Step S323: If MAX(F1, F2, ..., F j ,...,F m )≥G1, L1 is determined as the second flight risk value Z2 of the target UAV;
[0030] If G2≤MAX(F1,F2,...,F j ,...,F m )<G1, then L2 is determined as the second flight risk value Z2 of the target UAV;
[0031] If MAX(F1,F2,...,F j ,...,F m )<G2, then L3 is determined as the second flight risk value Z2 of the target UAV;
[0032] Among them, G1 is a preset first wind speed threshold; G2 is a preset second wind speed threshold.
[0033] In an exemplary embodiment of the present application, step S330 includes:
[0034] Step S331, determining the key flight activity range of each key UAV according to the key flight trajectory of each key UAV within the target flight time period;
[0035] Step S332: determine the area of the overlapping region between the region corresponding to the target flight activity range and the region corresponding to each key flight activity range, so as to obtain an overlapping region area list H=(H1, H2, ..., H g ,...,H f ), where g = 1, 2, ..., f, f is the number of key UAVs, H g is the area of the overlap between the area corresponding to the target flight activity range and the area corresponding to the key flight activity range of the g-th key UAV;
[0036] Step S333: traverse the overlapping area list H. If H g > 0, the g-th key UAV is determined as the target key UAV to obtain several target key UAVs;
[0037] Step S334: Determine the overlap factor K of the target UAV according to the overlap area list H = ∑ f g＝1 (H g / H0)×a; where H0 is the area corresponding to the target flight activity range; a is the number of target key UAVs;
[0038] Step S335: if K>M1, L1 is determined as the third flight risk value Z3 of the target UAV;
[0039] If M2≤K≤M1, L2 is determined as the third flight risk value Z3 of the target UAV;
[0040] If K<M2, L3 is determined as the third flight risk value Z3 of the target UAV;
[0041] Wherein, M1 is a preset first overlapping factor threshold; M2 is a preset second overlapping factor threshold.
[0042] In an exemplary embodiment of the present application, step S340 includes:
[0043] Step S341: the number of UAVs that have flown within the target flight activity range within the historical time period is determined as the target area flight density e corresponding to the target flight activity range; the end time of the historical time period is the current time; the length of the historical time period is a preset time length;
[0044] Step S342, obtaining the number d of UAVs flying within a preset key flight area within a historical time period; the key flight area includes the target flight activity range;
[0045] Step S343: if (e / d)>N1, L1 is determined as the fourth flight risk value Z4 of the target UAV;
[0046] If N2≤(e / d)≤N1, L2 is determined as the fourth flight risk value Z4 of the target UAV;
[0047] If (e / d) < N2, L3 is determined as the fourth flight risk value Z4 of the target UAV;
[0048] Among them, N1 is a preset first flight density ratio threshold; N2 is a preset second flight density ratio threshold; 0<N2<N1<1.
[0049] In an exemplary embodiment of the present application, step S400 includes:
[0050] Step S410: Obtain the ground cross-sectional areas of several historical UAVs when they collided during the historical period, and obtain a ground cross-sectional area list W1=(W 11 ,W 12 ,...,W 1p ,...,W 1q ), where p = 1, 2, ..., q, q is the number of historical drones, W 1p is the ground cross-sectional area of the pth historical UAV when a collision occurred in the historical period; the historical UAV is a UAV of the same type as the target UAV that has had a collision accident in the historical period;
[0051] Step S420: Obtain the crowd density of the area where several historical drone collision accidents occurred during the historical period, and obtain a crowd density list W2=(W 21 ,W 22 ,...,W 2p ,...,W 2q ), where W 2p is the population density in the area where the pth historical UAV collision occurred during the historical period;
[0052] Step S430: determine the crash risk rate of each historical UAV when a collision accident occurs in the historical period, and obtain a crash risk rate list W3=(W 31 ,W 32 ,...,W 3p ,...,W 3q );
[0053]
[0054] Among them, W 3p is the crash risk rate of the pth historical UAV when a collision accident occurs in the historical period; W 3p2 is the preset shielding coefficient when the pth historical UAV has a collision accident in the historical period; W 3p2 >0; α is the preset first impact energy parameter; β is the preset second impact energy parameter; λ is the preset adjustment correction coefficient; W 3p1 is the kinetic energy of the impact point of the pth historical UAV when a collision occurred in the historical period; W 3p3 is the mass of the pth historical drone; r is the gravitational acceleration constant; e is the preset natural exponential function; W 3p4 is the height above the ground when the pth UAV collided during the historical period; y is the preset drag coefficient; W 3p5 is the air density of the environment in which the pth historical UAV was located when a collision occurred in the historical period;
[0055] Step S440: Determine the historical ground risk value W of the pth historical UAV p =c×W 1p ×W 2p ×W 3p ; Where c is the preset mid-air collision probability;
[0056] Step S450: Determine the ground risk value of the target UAV as (∑ q p＝1 W p ) / q.
[0057] In an exemplary embodiment of the present application, Among them, MIN[] is a preset minimum value determination function.
[0058] In an exemplary embodiment of the present application, step S500 includes:
[0059] Step S510: Obtain the loss value of each historical UAV after a collision accident, and obtain a loss value list W4=(W 41 ,W 42 ,...,W 4p ,...,W 4q ), where W 4p is the loss value of the pth historical UAV after a collision accident;
[0060] Step S520: Determine the economic loss risk value of the target UAV as ∑ q p＝1 (W 4p ×W 5p ) / q; where W 5p is the preset value loss rate based on the degree of damage of the pth historical UAV after a collision accident.
[0061] According to one aspect of the present application, a non-transitory computer-readable storage medium is provided, in which at least one instruction or at least one program is stored, and the at least one instruction or the at least one program is loaded and executed by a processor to implement the aforementioned UAV flight area risk prediction method.
[0062] According to one aspect of the present application, an electronic device is provided, including a processor and the aforementioned non-transitory computer-readable storage medium.
[0063] The present invention has at least the following beneficial effects:
[0064] The UAV flight area risk prediction method of the present invention determines the flight activity risk value of the target UAV according to the flight environment and target flight time period of the geographical location corresponding to the target flight trajectory of the target UAV; determines the ground risk value of the target UAV according to the collision accidents of historical UAVs of the same type as the target UAV in the historical time period; determines the economic loss risk value of the target UAV according to the degree of damage of the historical UAVs after the collision accidents; and determines the regional risk value of the target UAV within the target flight activity range as the sum of the flight activity risk value, the ground risk value and the economic loss risk value of the target UAV, so as to indicate the risk degree of the target UAV flying within the target flight activity range within the target flight time period, and provides a reference for the operating user of the target UAV and the approver of the flight activity request, so as to reduce the flight risk. BRIEF DESCRIPTION OF THE DRAWINGS
[0065] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.
[0066] Figure 1 A flowchart of a method for predicting risk of a UAV flight area provided in an embodiment of the present invention. DETAILED DESCRIPTION
[0067] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.
[0068] A method for predicting the risk of UAV flight areas, such as Figure 1 As shown, the following steps are included:
[0069] Step S100: in response to receiving a flight activity request, obtaining a target flight trajectory and a target flight time period of a target UAV included in the flight activity request;
[0070] Before a drone takes off, the operator needs to submit a flight activity request to the drone flight approver (such as the Unmanned Aerial Vehicle Integrated Supervision Platform) to indicate the area and time of the drone flight.
[0071] The target flight trajectory included in the flight activity request is the trajectory range of the target UAV to be flown, that is, the trajectory to be flown; the target flight time period is the time period of the target UAV to be flown in the future, that is, the target flight time period can be the next day.
[0072] When the flight activity request is approved, the target UAV can fly within the activity range corresponding to the target flight trajectory within the target flight time period.
[0073] Step S200, determining the target flight activity range of the target UAV according to the target flight trajectory;
[0074] The target flight activity range is the activity range corresponding to the target flight trajectory of the target UAV.
[0075] Further, step S200 includes steps S210 to S240:
[0076] Step S210: Obtain a number of trajectory points on the target flight trajectory to obtain a first trajectory point list A=(A1, A2, ..., A i ,...,A n ), where i = 1, 2, ..., n, and n is the number of trajectory points on the target flight trajectory; A i is the i-th trajectory point on the target flight trajectory;
[0077] Step S220: traverse the first trajectory point list A to obtain the geographical location corresponding to each trajectory point on the target flight trajectory, so as to obtain a first geographical location identifier list B = (B1, B2, ..., B i ,...,B n ), where B i A i an identifier of the corresponding geographical location;
[0078] Step S230: De-duplicate the first geographic location identifier list B to obtain a second geographic location identifier list C = (C1, C2, ..., C j ,...,C m ), wherein j=1, 2, ..., m; m is the number of geographic location identifiers obtained after deduplication processing is performed on the first geographic location identifier list B; C j is the jth geographical location identifier obtained after deduplication processing is performed on the first geographical location identifier list B;
[0079] Step S240: C1, C2, ..., C j ,...,C m The area composed of the corresponding geographical locations is determined as the target flight activity range of the target UAV.
[0080] Step S300, determining the flight activity risk value of the target UAV according to the flight strategy at the geographical location corresponding to the target flight activity range, the wind speed at the geographical location corresponding to the target flight activity range during the target flight time period, the overlapping area between the target flight activity range and the key flight activity range of the key UAV, and the regional flight density of the target flight activity range during the historical time period;
[0081] Key UAVs are UAVs whose flight time is within the target flight time period.
[0082] Further, step S300 includes steps S310 to S350:
[0083] Step S310, determining a first flight risk value Z1 of the target UAV according to the flight strategy at the geographical location corresponding to the target flight activity range;
[0084] The flight strategy may be a flight restriction rule at a geographical location or geographical area, such as if the geographical location is a no-fly zone, etc. The first flight risk value determined according to the flight strategy at the geographical location corresponding to the target flight activity range of the target UAV is the flight restriction risk value.
[0085] Wherein, step S310 includes steps S311 to S313:
[0086] Step S311, traverse the second geographical location identifier list C, if C j If the corresponding geographical location is a restricted flight area, L1 is determined as the first flight risk value Z1 of the target UAV; otherwise, step S320 is executed;
[0087] Among them, restricted flight areas include obstacle restriction surfaces of civil aviation airports, no-fly zones, restricted areas, dangerous areas, etc.
[0088] If the geographical location is a restricted flight area, it means that the drone cannot take off at this geographical location, and the value with the highest risk level is directly determined as the first flight risk value.
[0089] Step S312: If the geographical location corresponding to each geographical location identifier in the second geographical location identifier list C is not a restricted flight area, obtain the distance between the geographical location corresponding to each geographical location identifier in the second geographical location identifier list C and the restricted flight area closest to the geographical location, so as to obtain the area interval distance list D = (D1, D2, ..., D j ,...,D m ), where D j C j The distance between the corresponding geographical location and the restricted flight area closest to the geographical location;
[0090] Step S313: If MIN(D)≤D0, L2 is determined as the first flight risk value Z1 of the target UAV; otherwise, L3 is determined as the first flight risk value Z1 of the target UAV; wherein MIN() is a preset minimum value determination function; D0 is a preset area interval distance threshold; L1>L2>L3>0.
[0091] L1 is the value corresponding to the high risk level, L2 is the value corresponding to the medium risk level, and L3 is the value corresponding to the low risk level.
[0092] Step S320, determining a second flight risk value Z2 of the target UAV according to the wind speed of the geographical location corresponding to the target flight activity range within the target flight time period;
[0093] When judging the risk of drone flight activities, the wind speed in the area where the drone is flying is also a factor affecting the flight risk of the drone. If the wind speed is too high, due to the influence of wind resistance, corresponding risks will also arise during the flight of the drone.
[0094] Wherein, step S320 includes steps S321 to S323:
[0095] Step S321: Obtain the wind speed of each geographical location corresponding to the geographical location identifier in the second geographical location identifier list C during the target flight time period, so as to obtain a flight wind speed list set E = (E1, E2, ..., E j ,...,E m );E j =(E j1 ,E j2 ,...,E jv ,...,E jb ), where E j C j The wind speed list of the corresponding geographical location within the target flight time period; v = 1, 2, ..., b; b is the number of wind speed collection moments included in the target flight time period; E jv C j The wind speed corresponding to the vth wind speed collection time at the corresponding geographical location within the target flight time period;
[0096] Step S322: According to E j , determine C j The target wind speed value F of the corresponding geographical location during the target flight time period j ;
[0097] Among them, F j It can be MAX(E j ), that is, E j The maximum wind speed value; it can also be ∑ bv＝1 (E jv ) / b, that is, E j The average wind speed value in .
[0098] Step S323: If MAX(F1, F2, ..., F j ,...,F m )≥G1, L1 is determined as the second flight risk value Z2 of the target UAV;
[0099] If G2≤MAX(F1,F2,...,F j ,...,F m )<G1, then L2 is determined as the second flight risk value Z2 of the target UAV;
[0100] If MAX(F1,F2,...,F j ,...,F m )<G2, then L3 is determined as the second flight risk value Z2 of the target UAV;
[0101] Among them, G1 is a preset first wind speed threshold, which may be 17.1 m / s; G2 is a preset second wind speed threshold, which may be 10.8 m / s.
[0102] Step S330, determining a third flight risk value Z3 of the target UAV according to an overlapping area between the target flight activity range and the key flight activity range of the key UAV;
[0103] The key UAV is a UAV whose flight time is within the target flight time period, that is, a UAV flying in the same period as the target UAV. Due to the excessive number of current UAVs, if a large number of UAVs take off at the same time and location, accidents may occur between these flying UAVs, such as collisions, signal interference, etc., which will cause risks to the flight of the UAV. Therefore, the third flight risk value of the target UAV is determined by determining whether the flight activity range of the key UAV overlaps with the flight activity range of the target UAV.
[0104] Wherein, step S330 includes steps S331 to S335:
[0105] Step S331, determining the key flight activity range of each key UAV according to the key flight trajectory of each key UAV within the target flight time period;
[0106] The method for determining the critical flight activity range is the same as the method for determining the target flight activity range, so it will not be repeated here.
[0107] Step S332: determine the area of the overlapping region between the region corresponding to the target flight activity range and the region corresponding to each key flight activity range, so as to obtain an overlapping region area list H=(H1, H2, ..., H g ,...,H f ), where g = 1, 2, ..., f, f is the number of key UAVs, H g is the area of the overlap between the area corresponding to the target flight activity range and the area corresponding to the key flight activity range of the g-th key UAV;
[0108] Step S333: traverse the overlapping area list H. If H g > 0, the g-th key UAV is determined as the target key UAV to obtain several target key UAVs;
[0109] A target critical UAV is a UAV that flies in the same geographical area at the same time as the target UAV.
[0110] Step S334: Determine the overlap factor K of the target UAV according to the overlap area list H = ∑ f g＝1 (H g / H0)×a; where H0 is the area corresponding to the target flight activity range; a is the number of target key UAVs;
[0111] Step S335: if K>M1, L1 is determined as the third flight risk value Z3 of the target UAV;
[0112] If M2≤K≤M1, L2 is determined as the third flight risk value Z3 of the target UAV;
[0113] If K<M2, L3 is determined as the third flight risk value Z3 of the target UAV;
[0114] Wherein, M1 is a preset first overlapping factor threshold value, which may be 0.6; M2 is a preset second overlapping factor threshold value, which may be 0.2.
[0115] Step S340, determining a fourth flight risk value Z4 of the target UAV according to the regional flight density of the target flight activity range in the historical time period;
[0116] The regional flight density is determined according to the number of drones in the target flight activity range within the historical time period. The regional flight density of the target flight activity range is used to judge whether the target flight activity range is a place where drones often fly, so as to determine the fourth flight risk value of the target drone.
[0117] Wherein, step S340 includes steps S341 to S343:
[0118] Step S341, determining the number of UAVs that have flown within the target flight activity range during the historical time period as the target area flight density e corresponding to the target flight activity range;
[0119] The end time of the historical time period is the current time; the length of the historical time period is the preset time length, and the historical time period can be the time period of the past week.
[0120] Step S342, obtaining the number d of UAVs flying within a preset key flight area within a historical time period;
[0121] The key flight area range includes the target flight activity range, and the key flight area range may be the flight area range of the whole country.
[0122] Step S343: if (e / d)>N1, L1 is determined as the fourth flight risk value Z4 of the target UAV;
[0123] If N2≤(e / d)≤N1, L2 is determined as the fourth flight risk value Z4 of the target UAV;
[0124] If (e / d) < N2, L3 is determined as the fourth flight risk value Z4 of the target UAV;
[0125] Among them, N1 is a preset first flight density ratio threshold; N2 is a preset second flight density ratio threshold; 0<N2<N1<1.
[0126] Step S350: determine MAX(Z1, Z2, Z3, Z4) as the flight activity risk value of the target UAV; wherein MAX() is a preset maximum value determination function.
[0127] Step S400: determining the ground risk value of the target UAV according to the collision accidents of historical UAVs of the same type as the target UAV that occurred in the historical period;
[0128] At present, the operational risk assessment of drones uses mortality as a measurement indicator. Therefore, the present invention will establish a risk assessment method based on accident fatalities as the criterion. The accident process in which drones endanger the lives of people on the ground is divided into three stages: the drone crashes when operating over a crowd, the falling drone hits people on the ground, and people die due to strong impact. Therefore, by statistically analyzing the data information of historical collision accidents of drones of the same type as the target drone in the historical period, the ground risk value of the target drone is determined.
[0129] Further, step S400 includes steps S410 to S450:
[0130] Step S410: Obtain the ground cross-sectional areas of several historical UAVs when they collided during the historical period, and obtain a ground cross-sectional area list W1=(W 11 ,W 12 ,...,W 1p ,...,W 1q ), where p = 1, 2, ..., q, q is the number of historical drones, W 1p is the ground cross-sectional area of the pth historical UAV when a collision occurred in the historical period, that is, the area of the UAV’s upward viewing angle when the collision occurred; the historical UAV is a UAV of the same type as the target UAV that has had a collision accident in the historical period;
[0131] Step S420: Obtain the crowd density of the area where several historical drone collision accidents occurred during the historical period, and obtain a crowd density list W2=(W 21 ,W 22 ,...,W 2p ,...,W 2q ), where W 2p is the population density in the area where the pth historical UAV collision occurred during the historical period;
[0132] The greater the population density in the area where the collision occurs, the greater the probability that the drone will hit a person when it collides with the ground, and the higher the ground risk value.
[0133] Drones of different masses and flight altitudes have different impact kinetic energy when they fall, resulting in different mortality rates; at the same time, buildings, trees, vehicles and other obstacles can shield and cushion people on the ground during the collision. Therefore, when determining the ground risk value of a drone, it is necessary to comprehensively consider the shielding factor and the impact kinetic energy.
[0134] Step S430: determine the crash risk rate of each historical UAV when a collision accident occurs in the historical period, and obtain a crash risk rate list W3=(W 31 ,W 32 ,...,W 3p ,...,W 3q );
[0135]
[0136] Among them, W 3p is the crash risk rate of the pth historical UAV when a collision accident occurs in the historical period; λ is the preset adjustment correction coefficient; MIN[] is the preset minimum value determination function; W 3p1 is the kinetic energy of the impact point of the pth historical UAV when a collision occurred in the historical period; W 3p3is the mass of the pth historical drone; r is the gravitational acceleration constant; e is the preset natural exponential function; W 3p4 is the height above the ground when the pth UAV collided during the historical period; y is the preset drag coefficient; W 3p5 is the air density of the environment in which the pth historical UAV was located when a collision occurred in the historical period;
[0137] W 3p2 is the preset shielding coefficient when the pth historical UAV has a collision accident in the historical period; W 3p2 >0;W 3p2 is the shielding factor during the drone falling process, and the value can be: If there is no shielding when a collision occurs, then W 3p2 =0; if there are sparse trees on the ground when a collision occurs, then W 3p2 = 10; if there are trees on the ground when a collision occurs, then W 3p2 =20; if there is a building on the ground when a collision occurs, then W 3p2 =40;
[0138] α is the preset first impact energy parameter, which can be taken as W 3p2 When the probability of death is 50%, the impact energy parameter is 6, that is, 1,000,000 joules;
[0139] β is the preset second impact energy parameter, which can be taken as W 3p2 The impact energy parameter that causes death is 0, which is 34 joules;
[0140] Step S440: Determine the historical ground risk value W of the pth historical UAV p =c×W 1p ×W 2p ×W 3p Where c is the preset probability of mid-air collision, which can be set to 10 per flight hour using the civil aviation safety risk standard. -7 accident;
[0141] Step S450: Determine the ground risk value of the target UAV as (∑ q p＝1 W p ) / q.
[0142] Step S500: determining the economic loss risk value of the target UAV according to the degree of damage to the UAV after a collision accident in history;
[0143] When a drone crashes in mid-air, it may cause a series of economic losses, which can be divided into direct economic losses and indirect economic losses. Direct economic losses include the loss of the drone's own value and the possible loss of the payload's value; indirect economic losses include the loss of social service agency expenses. Therefore, by statistically analyzing the economic losses caused by historical drone collisions, the economic loss risk value of the target drone can be determined.
[0144] Further, step S500 includes step S510-step S520:
[0145] Step S510: Obtain the loss value of each historical UAV after a collision accident, and obtain a loss value list W4=(W 41 ,W 42 ,...,W 4p ,...,W 4q ), where W 4p is the loss value of the pth historical UAV after a collision accident;
[0146] Step S520: Determine the economic loss risk value of the target UAV as ∑ q p＝1 (W 4p ×W 5p ) / q; where W 5p is the preset value loss rate based on the degree of damage of the pth historical UAV after a collision accident.
[0147] The direct economic loss of drones is mainly composed of the loss of the value of the drone itself and the economic loss caused by the damage to the payload it carries. The economic loss of payload is affected by factors such as payload type, market price, and damage rate. Since the payload conditions of drones in different missions are different, this invention does not consider the payload loss condition for the time being.
[0148] The loss of the drone's own value depends on the number of drones involved in the collision, the price of the drones, and the degree of damage to the drones. Drawing on the classification of direct loss registration for road vehicles, the present invention divides the degree of damage to drones into five levels: consumable damage, minor damage, general damage, severe damage, and scrap.
[0149] Among them, the classification standard for consumable damage is that the fuselage is not damaged, and the replaceable consumables are damaged, such as propellers, landing gear, etc., and the preset value loss rate is 10%; the classification standard for minor damage is that the damage does not affect the safe flight performance, and the preset value loss rate is 20%; the classification standard for general damage is that the damage affects the safe flight performance, and the preset value loss rate is 40%; the classification standard for severe damage is that the damage requires replacement of important components such as the frame and motor or requires major repairs, and the preset value loss rate is 60%; the classification standard for scrapping is that it cannot be repaired, and the preset value loss rate is 100%.
[0150] In addition, when a drone crashes to the ground, it will not only cause casualties and direct economic losses, but also cause cost losses to government social service agencies, such as the public security system, medical system, and public opinion system. After the accident, all government social agencies need to invest manpower and material resources to resolve the impact of the accident, that is, the indirect economic loss of the drone. The economic loss at this stage is determined by checking the results of historical case processing, or it can be calculated by presetting the loss functions of different agencies. Therefore, if it is not necessary to calculate the indirect economic loss of the drone, the economic loss risk value of the target drone is calculated through steps S510-S520. If it is necessary to calculate the indirect economic loss of the drone, the indirect economic loss of the drone is added to the direct economic loss of step S520 to obtain the economic loss risk value of the target drone.
[0151] Step S600: The sum of the flight activity risk value, the ground risk value and the economic loss risk value of the target UAV is determined as the regional risk value of the target UAV within the target flight activity range.
[0152] The regional risk value of the target UAV within the target flight activity range is the final flight risk value of the determined target UAV. The approver of the UAV flight request can determine whether the flight request for the target UAV is approved by checking the regional risk value. The operator of the target UAV can also determine whether the flight of the target UAV is safe by checking the various flight risk values.
[0153] The UAV flight area risk prediction method of the present invention determines the flight activity risk value of the target UAV according to the flight environment and target flight time period of the geographical location corresponding to the target flight trajectory of the target UAV; determines the ground risk value of the target UAV according to the collision accidents of historical UAVs of the same type as the target UAV in the historical time period; determines the economic loss risk value of the target UAV according to the degree of damage of the historical UAVs after the collision accidents; and determines the regional risk value of the target UAV within the target flight activity range as the sum of the flight activity risk value, the ground risk value and the economic loss risk value of the target UAV, so as to indicate the risk degree of the target UAV flying within the target flight activity range within the target flight time period, and provides a reference for the operating user of the target UAV and the approver of the flight activity request, so as to reduce the flight risk.
[0154] An embodiment of the present invention further provides a computer program product, which includes program code. When the program product is run on an electronic device, the program code is used to enable the electronic device to execute the steps of the method according to various exemplary embodiments of the present invention described above in this specification.
[0155] In addition, although the steps of the method in the present disclosure are described in a specific order in the drawings, this does not require or imply that the steps must be performed in this specific order, or that all the steps shown must be performed to achieve the desired results. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step, and / or one step may be decomposed into multiple steps, etc.
[0156] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the implementation of the present disclosure.
[0157] In an exemplary embodiment of the present disclosure, an electronic device capable of implementing the above method is also provided.
[0158] It will be appreciated by those skilled in the art that various aspects of the present invention may be implemented as a system, method or program product. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software, which may be collectively referred to herein as a "circuit", "module" or "system".
[0159] The electronic device according to this embodiment of the present invention is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.
[0160] The electronic device is presented in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: the at least one processor mentioned above, the at least one storage device mentioned above, and a bus connecting different system components (including storage devices and processors).
[0161] The storage stores program codes, which can be executed by the processor, so that the processor executes the steps according to various exemplary embodiments of the present invention described in the above “Exemplary Method” section of this specification.
[0162] The memory may include readable media in the form of volatile memory, such as random access memory (RAM) and / or cache memory, and may further include read only memory (ROM).
[0163] The storage may also include a program / utility having a set (at least one) of program modules, such program modules including but not limited to: an operating system, one or more application programs, other program modules and program data, each of which or some combination may include an implementation of a network environment.
[0164] The bus may represent one or more of several types of bus structures including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus architectures.
[0165] The electronic device may also communicate with one or more external devices (e.g., keyboards, pointing devices, Bluetooth devices, etc.), one or more devices that enable a user to interact with the electronic device, and / or any device that enables the electronic device to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface. Furthermore, the electronic device may also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter.
[0166] In an exemplary embodiment of the present disclosure, a computer-readable storage medium is also provided, on which a program product capable of implementing the above method of the present specification is stored. In some possible implementations, various aspects of the present invention can also be implemented in the form of a program product, which includes a program code, and when the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps according to various exemplary embodiments of the present invention described in the above "Exemplary Method" section of the present specification.
[0167] The program product may use any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.
[0168] Computer readable signal media may include data signals propagated in baseband or as part of a carrier wave, in which readable program code is carried. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Readable signal media may also be any readable medium other than a readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.
[0169] The program code embodied on the readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the foregoing.
[0170] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages such as Java, C++, etc., and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).
[0171] In addition, the above-mentioned figures are only schematic illustrations of the processes included in the method according to an exemplary embodiment of the present invention, and are not intended to be limiting. It is easy to understand that the processes shown in the above-mentioned figures do not indicate or limit the time sequence of these processes. In addition, it is also easy to understand that these processes can be performed synchronously or asynchronously, for example, in multiple modules.
[0172] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be embodied.
[0173] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims
1. A method for predicting the risk of an unmanned aerial vehicle flight area, characterized in that: The method comprises the following steps: Step S100: in response to receiving a flight activity request, obtaining a target flight trajectory and a target flight time period of a target UAV included in the flight activity request; Step S200: determining the target flight activity range of the target UAV according to the target flight trajectory; Step S300, determining the flight activity risk value of the target UAV according to the flight strategy at the geographical location corresponding to the target flight activity range, the wind speed at the geographical location corresponding to the target flight activity range during the target flight time period, the overlapping area between the target flight activity range and the key flight activity range of the key UAV, and the regional flight density of the target flight activity range during the historical time period; the key UAV is a UAV whose flight time is within the target flight time period; Step S400, determining the ground risk value of the target UAV according to historical UAV collision accidents of the same type as the target UAV that occurred within a historical period; Step S500: determining the economic loss risk value of the target UAV according to the degree of damage to the historical UAV after the collision accident; Step S600: Determine the regional risk value of the target UAV within the target flight activity range as the sum of the flight activity risk value, the ground risk value and the economic loss risk value of the target UAV.
2. The method according to claim 1, characterized in that The step S200 includes: Step S210: Acquire a number of trajectory points on the target flight trajectory to obtain a first trajectory point list A=(A1, A2, ..., A i ,...,A n ), wherein i=1, 2, ..., n, and n is the number of trajectory points on the target flight trajectory; A i is the i-th trajectory point on the target flight trajectory; Step S220: traverse the first trajectory point list A to obtain the geographical location corresponding to each trajectory point on the target flight trajectory, so as to obtain a first geographical location identifier list B = (B1, B2, ..., B i ,...,B n ), where B i A i an identifier of the corresponding geographical location; Step S230: De-duplicate the first geographic location identifier list B to obtain a second geographic location identifier list C = (C1, C2, ..., C j ,...,C m ), wherein j=1, 2, ..., m; m is the number of geographic location identifiers obtained after deduplication processing is performed on the first geographic location identifier list B; C j is the jth geographical location identifier obtained after deduplication processing is performed on the first geographical location identifier list B; Step S240: C1, C2, ..., C j ,...,C m The area composed of the corresponding geographical locations is determined as the target flight activity range of the target UAV.
3. The method according to claim 2, characterized in that The step S300 includes: Step S310: determining a first flight risk value Z1 of the target UAV according to the flight strategy at the geographical location corresponding to the target flight activity range; Step S320, determining a second flight risk value Z2 of the target UAV according to the wind speed of the geographical location corresponding to the target flight activity range during the target flight time period; Step S330: determining a third flight risk value Z3 of the target UAV according to an overlapping area between the target flight activity range and the key flight activity range of the key UAV; Step S340, determining a fourth flight risk value Z4 of the target UAV according to the regional flight density of the target flight activity range in a historical time period; Step S350: determine MAX(Z1, Z2, Z3, Z4) as the flight activity risk value of the target UAV; wherein MAX() is a preset maximum value determination function.
4. The method according to claim 3, characterized in that The step S310 includes: Step S311: traverse the second geographical location identifier list C. If C j If the corresponding geographical location is a restricted flight area, L1 is determined as the first flight risk value Z1 of the target UAV; otherwise, step S320 is executed; Step S312: If the geographical location corresponding to each geographical location identifier in the second geographical location identifier list C is not a restricted flight area, obtain the distance between the geographical location corresponding to each geographical location identifier in the second geographical location identifier list C and the restricted flight area closest to the geographical location, so as to obtain a regional interval distance list D = (D1, D2, ..., D j ,...,D m ), where D j C j The distance between the corresponding geographical location and the restricted flight area closest to the geographical location; Step S313: If MIN(D)≤D0, L2 is determined as the first flight risk value Z1 of the target UAV; otherwise, L3 is determined as the first flight risk value Z1 of the target UAV; wherein MIN() is a preset minimum value determination function; D0 is a preset area interval distance threshold; L1>L2>L3>0.
5. The method according to claim 4, characterized in that The step S320 includes: Step S321: Obtain the wind speed of the geographical location corresponding to each geographical location identifier in the second geographical location identifier list C during the target flight time period, so as to obtain a flight wind speed list set E=(E1, E2, ..., E j ,...,E m );E j =(E j1 ,E j2 ,...,E jv ,...,E jb ), where E j C j The wind speed list of the corresponding geographical location within the target flight time period; v = 1, 2, ..., b; b is the number of wind speed collection moments included in the target flight time period; E jv C j The wind speed corresponding to the vth wind speed collection time at the corresponding geographical location within the target flight time period; Step S322: According to E j , determine C j The target wind speed value F of the corresponding geographical location during the target flight time period j =(∑ b v＝1 E jv ) / b; Step S323: If MAX(F1, F2, ..., F j ,...,F m )≥G1, L1 is determined as the second flight risk value Z2 of the target UAV; If G2≤MAX(F1,F2,...,F j ,...,F m )<G1, then L2 is determined as the second flight risk value Z2 of the target UAV; If MAX(F1,F2,...,F j ,...,F m )<G2, then L3 is determined as the second flight risk value Z2 of the target UAV; Among them, G1 is a preset first wind speed threshold; G2 is a preset second wind speed threshold.
6. The method according to claim 5, characterized in that The step S330 includes: Step S331, determining the key flight activity range of each key UAV according to the key flight trajectory of each key UAV in the target flight time period; Step S332: determine the overlapping area of the area corresponding to the target flight activity range and the area corresponding to each key flight activity range, so as to obtain an overlapping area list H=(H1, H2, ..., H g ,...,H f ), where g = 1, 2, ..., f, and f is the number of the key UAVs; H g is the area of the overlapping area between the area corresponding to the target flight activity range and the area corresponding to the key flight activity range of the g-th key UAV; Step S333: traverse the overlapping area list H. If H g >0, the g-th key UAV is determined as the target key UAV to obtain a number of target key UAVs; Step S334: Determine the overlap factor K of the target UAV according to the overlapping area list H = ∑ f g＝1 (H g / H0)×a; where H0 is the area corresponding to the target flight activity range; a is the number of the target key UAVs; Step S335: if K>M1, L1 is determined as the third flight risk value Z3 of the target UAV; If M2≤K≤M1, L2 is determined as the third flight risk value Z3 of the target UAV; If K<M2, L3 is determined as the third flight risk value Z3 of the target UAV; Wherein, M1 is a preset first overlapping factor threshold; M2 is a preset second overlapping factor threshold.
7. The method according to claim 6, characterized in that The step S340 includes: Step S341, determining the number of UAVs that have flown within the target flight activity range within a historical time period as the target area flight density e corresponding to the target flight activity range; the end time of the historical time period is the current time; the length of the historical time period is a preset time length; Step S342, obtaining the number d of UAVs flying within a preset key flight area within a historical time period; the key flight area includes the target flight activity range; Step S343: if (e / d)>N1, L1 is determined as the fourth flight risk value Z4 of the target UAV; If N2≤(e / d)≤N1, L2 is determined as the fourth flight risk value Z4 of the target UAV; If (e / d) < N2, L3 is determined as the fourth flight risk value Z4 of the target UAV; Among them, N1 is a preset first flight density ratio threshold; N2 is a preset second flight density ratio threshold; 0<N2<N1<1.
8. The method according to claim 7, characterized in that The step S400 includes: Step S410: Obtain the ground cross-sectional areas of several historical UAVs when they collided during the historical period, and obtain a ground cross-sectional area list W1=(W 11 ,W 12 ,...,W 1p ,...,W 1q ), where p = 1, 2, ..., q, q is the number of historical drones, W 1p is the ground cross-sectional area of the pth historical UAV when a collision accident occurred in the historical period; the historical UAV is a UAV of the same type as the target UAV and which had a collision accident in the historical period; Step S420: Obtain the crowd density of the area where several historical drone collision accidents occurred during the historical period, and obtain a crowd density list W2=(W 21 ,W 22 ,...,W 2p ,...,W 2q ), where W 2p is the population density in the area where the pth historical UAV collision occurred during the historical period; Step S430: determine the crash risk rate of each historical UAV when a collision accident occurs in the historical period, and obtain a crash risk rate list W3=(W 31 ,W 32 ,...,W 3p ,...,W 3q ); Among them, W 3p is the crash risk rate of the pth historical UAV when a collision accident occurs in the historical period; W 3p2 is the preset shielding coefficient when the pth historical UAV has a collision accident in the historical period; W 3p2 >0; α is the preset first impact energy parameter; β is the preset second impact energy parameter; λ is the preset adjustment correction coefficient; W 3p1 is the kinetic energy of the impact point of the pth historical UAV when a collision occurs within the historical period; W 3p3 is the mass of the pth historical drone; r is the gravitational acceleration constant; e is the preset natural exponential function; W 3p4 is the height above the ground when the pth UAV in the history had a collision accident during the history period; y is the preset drag coefficient; W 3p5 is the air density of the environment in which the pth historical UAV was located when a collision occurred within the historical period; Step S440: Determine the historical ground risk value W of the pth historical UAV p =c×W 1p ×W 2p ×W 3p ; Where c is the preset mid-air collision probability; Step S450: Determine the ground risk value of the target UAV as (∑ q p＝1 W p ) / q.
9. The method according to claim 8, characterized in that Among them, MIN[] is a preset minimum value determination function.
10. The method according to claim 9, characterized in that The step S500 includes: Step S510: Obtain the loss value of each historical UAV after a collision accident, and obtain a loss value list W4=(W 41 ,W 42 ,...,W 4p ,...,W 4q ), where W 4p is the loss value of the pth historical drone after a collision accident; Step S520: determine the economic loss risk value of the target UAV as ∑ q p＝1 (W 4p ×W 5p ) / q; where W 5p is the value loss rate preset according to the degree of damage of the pth historical UAV after a collision accident.