CN115294487A - Unmanned aerial vehicle risk level obtaining method, storage medium and electronic equipment - Google Patents
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Abstract
The invention relates to the field of data acquisition, in particular to an unmanned aerial vehicle risk level acquisition method, a storage medium and electronic equipment, wherein the method comprises the following steps of; determining the central point coordinate of each sub-area in the target area D as a characteristic point coordinate to obtain a characteristic point coordinate set W = (W) 1 ,w 2 ,w 3 ,...,w q ) (ii) a Obtaining a differential model m 0 *L"(t)=m 0 * g-c | L ″ (t); to w j The following treatments were carried out: obtaining the differential model, the horizontal velocity distribution data, the vertical velocity distribution data and the flying height distribution dataw j Corresponding coordinate set COOR of falling place j (ii) a According to COOR j Determining P ris (x j ,y j ) (ii) a According to P ris (x j ,y j ) Determining P ris (x j ,y j ) A first target risk level for the corresponding sub-region. Thus, the accuracy of risk level assessment for the target area can be improved.
Description
Technical Field
The invention relates to the field of data acquisition, in particular to an unmanned aerial vehicle risk level acquisition method, a storage medium and electronic equipment.
Background
Unmanned aerial vehicle has nimble practical characteristics, is used for aerial photography, air transportation or city monitoring task etc. for example by wide application in military field and civilian field at present, in order to reduce the emergence that unmanned aerial vehicle falls, can carry out risk grade to unmanned aerial vehicle's flight area and assess.
At present, the risk level of a flight area of an unmanned aerial vehicle is evaluated, most of the risk evaluation is carried out when the unmanned aerial vehicle collides with the ground, and the risk evaluation research of the unmanned aerial vehicle for the ground collision only considers the ground information such as population density of the flight area so as to estimate the number of casualties caused by the ground collision of the unmanned aerial vehicle, and then the risk level of the flight area is evaluated according to the estimated number of casualties.
However, since there are few factors to be considered in the risk assessment of the ground collision of the unmanned aerial vehicle, the accuracy of the risk level assessment of the flight area of the unmanned aerial vehicle is low.
Disclosure of Invention
Aiming at the technical problems, the technical scheme adopted by the invention is as follows:
according to an aspect of the disclosure, there is provided an unmanned aerial vehicle risk level obtaining method, including:
determining the central point coordinate of each sub-area in the target area D as a characteristic point coordinate to obtain a characteristic point coordinate set W = (W) 1 ,w 2 ,w 3 ,...,w q ),w j =(x j ,y j ) (ii) a Wherein j =1,2, 3.. Q, q is the number of subregions, w j Is the jth feature point coordinate, x j Is w j Abscissa of (a), y j Is w j The ordinate of (c).
Acquiring differential model m corresponding to target unmanned aerial vehicle 0 *L"(t)=m 0 * g-c | L ″ (t); the unmanned aerial vehicle control method comprises the following steps of obtaining a target unmanned aerial vehicle, wherein t is the falling duration of the target unmanned aerial vehicle, L (t), L '(t) and L' (t) are all three-dimensional vectors, L (t) is a displacement vector of the target unmanned aerial vehicle corresponding to t, g is a gravity acceleration vector of the target unmanned aerial vehicle corresponding to t, L '(t) is a speed vector of the target unmanned aerial vehicle corresponding to t, and L' (t) is an acceleration vector of the target unmanned aerial vehicle corresponding to t; c is a resistance parameter c =0.5 rho AC of the target unmanned aerial vehicle D And ρ is an air density constant.
To w j The following treatments were carried out:
according to differential model, L ″ (0) = v 0 And L (0) = (x) j ,y j ,h 0 ) Obtaining L (t) = (L) a1j ,L b1j ,L c1j ) (ii) a Wherein, L ″ (0) = v ″ 0 And L (0) = (x) j ,y j ,h 0 ) All the initial conditions are corresponding to the differential model; l' (0) is a velocity vector of the target drone at t =0, and L (0) is a displacement vector of the target drone at t = 0; v. of 0 Is the initial velocity vector of the target drone, h 0 Is the flight altitude of the target drone; l (0) = (x) j ,y j ,h 0 ) Represents x j Is the component of L (0) along the horizontal axis, y j Is the component of L (0) in the direction of the longitudinal axis and h 0 Is the component of L (0) in the vertical direction; l is a1j Is L (t) along the horizontal axisComponent of (a), L b1j Is the component of L (t) along the longitudinal axis, L c1j Is the component of L (t) in the vertical direction; l is a radical of an alcohol a1j 、L b1j And L c1j All follow v 0 、h 0 And t, are varied.
According to L (t) = (L) a1j ,L b1j ,L c1j ) Obtaining w from the horizontal velocity distribution data, the vertical velocity distribution data and the flying height distribution data j Corresponding coordinate set COOR of falling place j =((a 1j ,b 1j ),(a 2j ,b 2j ),...,(a n(j)j ,b n(j)j ) ); wherein (a) ij ,b ij ) Is w j Corresponding ith drop point coordinate, i =1,2,3,.. N (j), n (j) being w j Number of corresponding drop location coordinates.
According to COOR j Determining P ris (x j ,y j ) (ii) a Wherein, P ris (x j ,y j ) Is w j A corresponding risk value;
；P 1 the probability of the target unmanned aerial vehicle failing is set; p 2 (a ij ,b ij ) For the target unmanned plane at (x) j ,y j ) After accident, fall to (a) ij ,b ij ) The probability of (d); p 2 (a ij ,b ij )=N ij /(n(j)),N ij Is COOR j Is located in (a) ij ,b ij ) The number of the coordinates of the falling position in the sub-area; p 3 (a ij ,b ij ) For the target unmanned plane at (x) j ,y j ) Is out of affairs and falls to (a) ij ,b ij ) Probability of rear impact to the target; p is 4 (a ij ,b ij ) For unmanned plane in (x) j ,y j ) Is in failure and falls to (a) ij ,b ij ) And the probability of switching the target object from the normal state to the abnormal state after the target object is collided.
Will P ris (x j ,y j ) Corresponding risk level as P ris (x j ,y j ) Corresponding sub-regionThe first target risk level.
According to another aspect of the present disclosure, there is also provided a non-transitory computer-readable storage medium, 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 above method for acquiring risk level of a drone.
According to another aspect of the present disclosure, there is also provided an electronic device comprising a processor and the non-transitory computer-readable storage medium described above.
The invention has at least the following beneficial effects:
the risk level of a target area of the target unmanned aerial vehicle is evaluated, a plurality of drop point coordinates corresponding to each characteristic point coordinate can be determined based on the differential model and the horizontal speed distribution data, the vertical speed distribution data and the flight height distribution data of the target unmanned aerial vehicle, and the P is determined according to the drop point coordinates corresponding to each characteristic point coordinate 2 (a ij ,b ij )、P 3 (a ij ,b ij ) And P 4 (a ij ,b ij ) Further, a risk value corresponding to each feature point coordinate can be determined, and at the moment, a first target risk level corresponding to each sub-region can be determined; in a word, the data of more dimensionalities need to be considered for determining the first target risk level of each sub-region, the determination process of the differential model and the risk value is obtained based on the falling principle of the unmanned aerial vehicle, the falling condition of the unmanned aerial vehicle can be accurately reflected, and then the accuracy of risk level evaluation on the target region can be improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart of an unmanned aerial vehicle risk level obtaining method according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention provides an unmanned aerial vehicle risk level obtaining method, wherein the method can be completed by any one or any combination of the following steps: a terminal, a server, or other devices with processing capability, which is not limited in this embodiment of the present invention.
The embodiment of the present invention takes a server as an example, and introduces an unmanned aerial vehicle risk level acquisition method with reference to a flowchart of the unmanned aerial vehicle risk level acquisition method shown in fig. 1.
The method comprises the following steps:
step S100, determining the center point coordinate of each sub-region in the target region D as a feature point coordinate, to obtain a feature point coordinate set W = (W) 1 ,w 2 ,w 3 ,...,w q ),w j =(x j ,y j )。
Wherein j =1,2, 3.. Q, q is the number of subregions, w j Is the jth feature point coordinate, x j Is w j Abscissa of (a), y j Is w j The ordinate of (a); d has a corresponding planar rectangular coordinate system.
In a possible implementation manner, a flight area corresponding to the target unmanned aerial vehicle may be used as a target area D, a plurality of sub-areas of D are obtained by equally dividing D, and when the server determines a feature point coordinate of each sub-area, the server may obtain coordinates of a plurality of boundary points on a boundary of the sub-area, then average values of abscissa of the plurality of boundary points are obtained, and the average value of the abscissa of the plurality of boundary points is set as the abscissa of the feature point coordinate of the sub-area, then average values of ordinate of the plurality of boundary points are obtained, and the average value of ordinate of the plurality of boundary points is set as the ordinate of the feature point coordinate of the sub-area, so as to obtain a feature point coordinate set W.
Step S200, obtaining a differential model m corresponding to the target unmanned aerial vehicle 0 *L"(t)=m 0 *g-c|L´(t)|L´(t)。
The method comprises the following steps that t is the falling duration of a target unmanned aerial vehicle which is in accident falling, L (t), L '(t) and L' (t) are all three-dimensional vectors, L (t) is a displacement vector of the target unmanned aerial vehicle corresponding to t, g is a gravity acceleration vector of the target unmanned aerial vehicle corresponding to t, L '(t) is a speed vector of the target unmanned aerial vehicle corresponding to t, and L' (t) is an acceleration vector of the target unmanned aerial vehicle corresponding to t; c is a resistance parameter c =0.5 rho AC of the target unmanned aerial vehicle D ρ is an air density constant; l 'is the first derivative of L (t), L' is the modulus of the first derivative of L (t), and L "(t) is the second derivative of L (t).
In a possible implementation, according to newton's second law, the corresponding differential model of the target drone may be determined.
To w j The following steps S300 to S600 are performed:
step S300, according to the differential model, L = v ″ (0) = v ″ 0 And L (0) = (x) j ,y j ,h 0 ) Obtaining L (t) = (L) a1j ,L b1j ,L c1j )。
Wherein, L ″ (0) = v ″ 0 And L (0) = (x) j ,y j ,h 0 ) All the initial conditions are corresponding to the differential model; l' (0) is a velocity vector of the target drone at t =0, and L (0) is a displacement vector of the target drone at t = 0; v. of 0 Is the initial velocity vector of the target drone, h 0 Is the flight altitude of the target drone; l (0) = (x) j ,y j ,h 0 ) Denotes x j Is the component of L (0) along the horizontal axis direction of the rectangular plane coordinate system, y j Is the component of L (0) along the longitudinal axis of the rectangular plane coordinate system and h 0 Is the component of L (0) in the vertical direction; l is a radical of an alcohol a1j Is the component of L (t) along the direction of the horizontal axis of the rectangular plane coordinate system, L b1j Is the component of L (t) in the direction of the longitudinal axis of the rectangular plane coordinate system, L c1j Is the component of L (t) in the vertical direction; l is a1j 、L b1j And L c1j All follow v 0 、h 0 And t, are varied.
In a possible implementation, the server may respond to the differential model by applying the corresponding initial conditions L ″ (0) = v ″ 0 And L (0) = (x) j ,y j ,h 0 ) Solving the differential model can obtain L (t) = (L) a1j ,L b1j ,L c1j )。
Step S400, according to L (t) = (L) a1j ,L b1j ,L c1j ) Obtaining w from horizontal velocity distribution data, vertical velocity distribution data and flying height distribution data j Corresponding set of coordinates of the point of falling COOR j =((a 1j ,b 1j ),(a 2j ,b 2j ),...,(a n(j)j ,b n(j)j ))。
Wherein (a) ij ,b ij ) Is w j Corresponding ith drop location coordinate, i =1,2,3 j The number of corresponding drop location coordinates.
In a possible implementation manner, the horizontal velocity distribution data, the vertical velocity distribution data and the flying height distribution data can be normal distribution data, historical data of a plurality of unmanned aerial vehicles can be obtained through statistics, and the historical data are manually input into a server; the server is based on L (t) = (L) a1j ,L b1j ,L c1j ) And L c1j =0, w can be determined j Corresponding falling time t 1j Displacement vector L (t) corresponding to drop location 1j )=(L a2j ,L b2j ,L c2j )；L a2j Is L (t) 1j ) Component in the direction of the transverse axis, L b2j Is L (t) 1j ) Component in the direction of the longitudinal axis, L c2j Is L (t) 1j ) Component in the vertical direction, L a2j 、L b2j And L c2j All follow v 0 And h 0 Is changed;
randomly determining w based on the horizontal velocity profile data and the vertical velocity profile data j Corresponding set of target velocity vectors V = (V) 1 ,v 2 ,v 3 ,...,v n(j) ) (ii) a Wherein v is i Is the ith in VA target velocity vector which is any velocity vector corresponding to any horizontal velocity in the horizontal velocity distribution data and any vertical velocity in the vertical velocity distribution data; i =1,2, 3.. N (j), n (j) being w j The number of corresponding target velocity vectors;
randomly determining a target flying height set H = (H) according to flying height distribution data 1 ,h 2 ,h 3 ,...,h n(j) ) (ii) a Wherein h is i The ith target flying height in the H is set, and the target flying height is any flying height in the flying height distribution data;
according to V, H and L (t) 1j ) Can determine w j Corresponding target displacement vector set DIS j =(L 1j ,L 2j ,L 3j ,...,L nj )；L ij Is DIS j Of the ith target displacement vector, L ij To satisfy v 0 =v i And h 0 =h i L (t) of 1j )；
According to DIS j To obtain w j Corresponding set of coordinates of the point of falling COOR j =((a 1j ,b 1j ),(a 2j ,b 2j ),...,(a n(j)j ,b n(j)j ) ); wherein (a) ij ,b ij ) Is w j Corresponding ith location coordinate, a ij Is L ij Component in the direction of the transverse axis, b ij Is L ij Component in the direction of the longitudinal axis.
In another possible embodiment, the method further comprises: obtaining a wind velocity vector v wind ；
To w j The following processes are also performed: determining t 1j Corresponding wind stroke vector L wind =v wind* t 1j ；
Based on this, the above a ij Is L ij Component in the direction of the transverse axis and L wind Sum of components in the direction of the transverse axis, b ij Is L ij Component along the longitudinal axis and L wind The sum of the components along the longitudinal axis.
Step S500, according to COOR j Determining P ris (x j ,y j )。
Wherein, P ris (x j ,y j ) Is w j A corresponding risk value; p is ris (x j ,y j ) The following conditions may be satisfied:
；
P 1 is the probability of the failure of the target unmanned aerial vehicle; p is 2 (a ij ,b ij ) For the target unmanned plane at (x) j ,y j ) After accident, fall to (a) ij ,b ij ) Probability of (P) 2 (a ij ,b ij ) Is COOR j Is located in (a) ij ,b ij ) The ratio of the number of the coordinates of the falling points in the sub-area to n (j); p 3 (a ij ,b ij ) For the target unmanned plane at (x) j ,y j ) Is out of affairs and falls to (a) ij ,b ij ) Probability of rear impact to the target; p is 3 (a ij ,b ij ) The following conditions are satisfied: p is 4 (a ij ,b ij ) For unmanned aerial vehicle at (x) j ,y j ) Is in failure and falls to (a) ij ,b ij ) And the probability that the target object is switched from the normal state to the abnormal state after the collision to the target object.
In a possible implementation mode, the target object can be a person or a tree, and correspondingly, the target object is switched from a normal state to an abnormal state, namely the person dies or the tree is knocked down, and the like; the server is based on P 1 、P 2 (a ij ,b ij )、P 3 (a ij ,b ij ) And P 4 (a ij ,b ij ) Can determine w j Corresponding risk value P ris (x j ,y j )。
In another possible embodiment, P ris (x j ,y j ) The following conditions may also be satisfied:
；
P 1 (a ij ,b ij ) To the eyesMarking unmanned plane at (a) ij ,b ij ) The probability of the sub-region in which it is in trouble.
Step S600, adding P ris (x j ,y j ) Corresponding risk level as P ris (x j ,y j ) A first target risk level for the corresponding sub-region.
In one possible implementation, a plurality of risk levels are stored in the server, each candidate risk level has a corresponding risk value range, and any two different candidate risk levels have no overlapping parts in the corresponding risk value ranges; based on this, the server can determine P among several candidate risk levels ris (x j ,y j ) Corresponding candidate risk level as P ris (x j ,y j ) A first target risk level for the corresponding sub-region; p ris (x j ,y j ) Within a range of risk values corresponding to the first target risk level.
Therefore, the risk level of the target area of the target unmanned aerial vehicle is evaluated, a plurality of drop point coordinates corresponding to each characteristic point coordinate can be determined based on the differential model and the horizontal speed distribution data, the vertical speed distribution data and the flight height distribution data of the target unmanned aerial vehicle, and the P is determined according to the plurality of drop point coordinates corresponding to each characteristic point coordinate 2 (a ij ,b ij )、P 3 (a ij ,b ij ) And P 4 (a ij ,b ij ) Further, a risk value corresponding to each feature point coordinate can be determined, and at the moment, a first target risk level corresponding to each sub-area can be determined; in a word, the data with more dimensionalities need to be considered for determining the first target risk level of each sub-area, the determination process of the differential model and the risk value is obtained based on the falling principle of the unmanned aerial vehicle, the falling condition of the unmanned aerial vehicle can be accurately reflected, and the accuracy of risk level assessment on the target area can be improved.
Optionally, P 1 The following conditions are satisfied:
P 1 =N acc /N fly (ii) a Wherein, N acc For several drones flying in DTotal number of crashes in D, N fly The total number of times that a plurality of unmanned aerial vehicles fly in D.
In one possible embodiment, the target drone is any one of a plurality of drones flying in D, and the plurality of drones may each be a drone of the same model as the target drone; the server can take the historical failure probability of a plurality of unmanned aerial vehicles in D as P 1 。
In another possible embodiment, the above P 1 (a ij ,b ij ) Can be a plurality of unmanned planes flying in D (a) ij ,b ij ) The ratio of the total number of crashes in the sub-area to the total number of flights of the drones in D.
Optionally, P 3 (a ij ,b ij ) The following conditions are satisfied:
P 3 (a ij ,b ij )=s ij *（2(r p +r f )(h p /tan(γ ij ))+π(r p +r f ) 2 ) (ii) a Wherein r is p Is the average radius of the object, r f Maximum radius of target drone, h p Is the average height of the object, gamma ij For the target unmanned plane at (x) j ,y j ) After taking accident, fall to (a) ij ,b ij ) Angle of falling on the ground, gamma ij According to L (t) = (L) a1j ,L b1j ,L c1j ) Can be determined; s is ij Is (a) ij ,b ij ) The density of the target in the sub-region.
In one possible embodiment, r p Can be the average radius of the human body, h p The target density can be the human mouth density, which is the average height of the human body; r is p 、h p 、r f And s ij All can be manually input into the server, gamma ij Can be according to the above L (t) 1j )=(L a2j ,L b2j ,L c2j ) And (5) determining.
Optionally, P 4 (a ij ,b ij ) The following conditions are satisfied:
P 4 (a ij ,b ij )= 
(ii) a Where α is a first constant threshold parameter, α =36, β is a second constant threshold parameter, β =100,e ij For the target unmanned plane at (x) j ,y j ) After taking accident, fall to (a) ij ,b ij ) Kinetic energy of impact in time; p is a radical of ij Is (a) ij ,b ij ) The shading factor of the sub-area; the shading factor is used to represent the degree of shading of the corresponding sub-region.
In one possible embodiment, α, β and p ij The number of trees and buildings in a certain subregion is more, the corresponding shading factor is larger, and the number of vacant areas in a certain subregion is more, the corresponding shading factor is smaller; e ij =0.5*m 0 *v t1j ，v t1j =L´(t 1j )。
Optionally, a plurality of no-fly areas are arranged in the step D; based on this, the method further comprises:
taking each subarea which has a superposition part with the plurality of no-fly areas as a first subarea;
taking each sub-area except the first sub-areas as a second sub-area;
taking the highest risk grade as a second target risk grade corresponding to each first sub-area;
taking the lowest risk level as a second target risk level corresponding to each second sub-area;
and for each sub-area, taking the highest risk grade in the corresponding first target risk grade and the second target risk grade as the first comprehensive risk grade corresponding to the sub-area.
In a possible implementation manner, the server may take each sub-region having an overlapping portion with the plurality of no-fly regions as a first sub-region according to manually input boundary data of each no-fly region and boundary data of each sub-region; the boundary data may be a plurality of discrete coordinate points on the boundary line of the corresponding region, or continuous data for representing the boundary line of the corresponding region; the server can further determine a plurality of second sub-areas, then the highest candidate risk level in the plurality of candidate risk levels is used as a second target risk level corresponding to each first sub-area, and the lowest candidate risk level in the plurality of candidate risk levels is used as a second target risk level corresponding to each second sub-area; and finally, the higher one of the first target risk level and the second target risk level corresponding to each sub-area can be used as the first comprehensive risk level corresponding to the area. Therefore, the no-fly area can be set according to actual conditions, and the unmanned aerial vehicle flies in D according to the first comprehensive risk level of each sub-area, so that the flying safety of the unmanned aerial vehicle can be improved. Wherein any two different no-fly areas have no overlapping part.
Optionally, the D has a plurality of preset static areas; based on the method, a plurality of no-fly areas are determined by the following method:
acquiring the minimum value of the flight height of the target unmanned aerial vehicle;
acquiring the maximum building height of each sub-area;
taking each sub-area with the corresponding building height maximum value smaller than the flight height minimum value as a dynamic area;
and taking each dynamic area and each static area as a no-fly area.
In a possible implementation manner, the server may determine boundary data of each dynamic area according to a manually input minimum value of the flight height of the target unmanned aerial vehicle, a manually input maximum value of the building height of each sub-area, and boundary data of each sub-area; the minimum value of the flight height can be the minimum flight height after the target unmanned aerial vehicle flies stably in the current flight plan; the server can also obtain manually input boundary data of a plurality of static areas, and then the boundary data of each dynamic area and the boundary data of each static area are both used as the boundary data of the no-fly area to determine the no-fly area in D.
Optionally, a plurality of control areas are arranged in the step D; based on this, the method further comprises:
taking each sub-area which has a superposition part with the plurality of control areas as a third sub-area; each control area has a corresponding preset risk level;
taking each sub-area except the plurality of third sub-areas as a fourth sub-area;
taking the preset risk level of the control area corresponding to each third sub-area as a third target risk level;
taking the lowest risk level as a third target risk level corresponding to each fourth sub-area;
and for each sub-area, taking the highest risk grade in the corresponding first target risk grade, second target risk grade and third target risk grade as the second comprehensive risk grade corresponding to the sub-area.
In a possible implementation manner, one preset risk level is the same as one candidate risk level in the plurality of candidate risk levels, and the server may take each sub-region having a portion overlapping with the plurality of control regions as a third sub-region according to manually input boundary data of each control region, the preset risk level corresponding to each control region, and the boundary data of each sub-region; the server can determine a plurality of fourth sub-areas, then the preset risk level of the control area corresponding to each third sub-area is used as a third target risk level, and the lowest candidate risk level in the plurality of candidate risk levels is used as a third target risk level corresponding to each fourth sub-area; and finally, the highest one of the first target risk level, the second target risk level and the third target risk level corresponding to each sub-area can be used as a second comprehensive risk level corresponding to the area. Therefore, the no-fly area and the control area can be set according to actual conditions, and the unmanned aerial vehicle flies in D according to the second comprehensive risk level of each sub-area, so that the flying safety of the unmanned aerial vehicle can be further improved. Wherein any two different governing areas do not have an overlapping portion.
Embodiments of the present invention also provide a non-transitory computer-readable storage medium, which may be disposed in an electronic device to store at least one instruction or at least one program for implementing a method of the method embodiments, where the at least one instruction or the at least one program is loaded into and executed by a processor to implement the method provided by the above embodiments.
Embodiments of the present invention also provide an electronic device comprising a processor and the aforementioned non-transitory computer-readable storage medium.
Embodiments of the present invention also provide a computer program product comprising program code means for causing an electronic device to carry out the steps of the method according to various exemplary embodiments of the invention described above in the present description, when said program product is run on the electronic device.
Although some specific embodiments of the present invention have been described in detail by way of example, it should be understood by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the invention. It will also be appreciated by those skilled in the art that various modifications may be made to the embodiments without departing from the scope and spirit of the invention. The scope of the invention is defined by the appended claims.
Claims (9)
1. An unmanned aerial vehicle risk level obtaining method is characterized by comprising the following steps:
determining the central point coordinate of each sub-area in the target area D as a characteristic point coordinate to obtain a characteristic point coordinate set W = (W) 1 ,w 2 ,w 3 ,...,w q ),w j =(x j ,y j ) (ii) a Wherein j =1,2, 3.. Q, q is the number of sub-regions, w j Is the jth feature point coordinate, x j Is w j Abscissa of (a), y j Is w j The ordinate of (a);
acquiring differential model m corresponding to target unmanned aerial vehicle 0 *L"(t)=m 0 * g-c | L ″ (t); wherein t is a duration of falling after the target unmanned aerial vehicle loses an wreck, L (t), L '(t) and L "(t) are all three-dimensional vectors, L (t) is a displacement vector of the target unmanned aerial vehicle corresponding to t, g is a gravity acceleration vector of the target unmanned aerial vehicle, and L' (t) is a velocity vector of the target unmanned aerial vehicle corresponding to tAnd L "(t) is the acceleration vector of the target unmanned aerial vehicle corresponding to t; c is a resistance parameter c =0.5 ρ AC of the target unmanned aerial vehicle D ρ is an air density constant;
to w j The following treatments were carried out:
according to the differential model, L ″ (0) = v 0 And L (0) = (x) j ,y j ,h 0 ) To obtain L (t) = (L) a1j ,L b1j ,L c1j ) (ii) a Wherein, L ″ (0) = v ″ 0 And L (0) = (x) j ,y j ,h 0 ) All the initial conditions are corresponding to the differential model; l' (0) is a velocity vector of the target drone when t =0, and L (0) is a displacement vector of the target drone when t = 0; v. of 0 Is the initial velocity vector, h, of the target drone 0 Is the flight altitude of the target drone; l (0) = (x) j ,y j ,h 0 ) Represents x j Is the component of L (0) along the horizontal axis, y j Is the component of L (0) along the longitudinal axis and h 0 Is the component of L (0) in the vertical direction; l is a1j Is the component of L (t) along the horizontal axis, L b1j Is the component of L (t) along the longitudinal axis, L c1j Is the component of L (t) in the vertical direction; l is a1j 、L b1j And L c1j All follow v 0 、h 0 And t;
according to L (t) = (L) a1j ,L b1j ,L c1j ) Obtaining w from the horizontal velocity distribution data, the vertical velocity distribution data and the flying height distribution data j Corresponding set of coordinates of the point of falling COOR j =((a 1j ,b 1j ),(a 2j ,b 2j ),...,(a n(j)j,bn(j)j ) ); wherein (a) ij ,b ij ) Is w j Corresponding ith drop location coordinate, i =1,2,3 j The number of corresponding drop location coordinates;
according to COOR j Determining P ris (x j ,y j ) (ii) a Wherein, P ris (x j ,y j ) Is w j A corresponding risk value;
；P 1 is the probability of the target unmanned aerial vehicle failing; p 2 (a ij ,b ij ) For the target drone in (x) j ,y j ) After taking accident, fall to (a) ij ,b ij ) The probability of (d); p 2 (a ij ,b ij )=N ij /(n(j)),N ij Is COOR j Is located in (a) ij ,b ij ) The number of the coordinates of the falling position in the sub-area; p 3 (a ij ,b ij ) For the target drone in (x) j ,y j ) Get out of affairs and fall to (a) ij ,b ij ) Probability of rear impact to the target; p is 4 (a ij ,b ij ) For unmanned aerial vehicle at (x) j ,y j ) Is in failure and falls to (a) ij ,b ij ) And the probability that the target object is switched from a normal state to an abnormal state after the target object is collided is obtained;
will P ris (x j ,y j ) Corresponding risk level as P ris (x j ,y j ) A first target risk level for the corresponding sub-region.
2. The method of claim 1, wherein P is 1 The following conditions are satisfied:
P 1 =N acc /N fly (ii) a Wherein N is acc Total number of crashes in D for several drones flying in D, N fly And D, the total number of the flying times of the unmanned aerial vehicles in D is a plurality of.
3. The method of claim 1, wherein P is 3 (a ij ,b ij ) The following conditions are satisfied:
P 3 (a ij ,b ij )=s ij *（2(r p +r f )(h p /tan(γ ij ))+π(r p +r f ) 2 ) (ii) a Wherein r is p Is the average radius of the object, r f Is the maximum radius, h, of the target drone p Is the average height of the object, gamma ij For the target drone at (x) j ,y j ) After accident, fall to (a) ij ,b ij ) Angle of falling on the ground, gamma ij According to L (t) = (L) a1j ,L b1j ,L c1j ) Can be determined; s ij Is (a) ij ,b ij ) The density of the target in the sub-region.
4. The method of claim 1, wherein P is 4 (a ij ,b ij ) The following conditions are satisfied:
P 4 (a ij ,b ij )=
(ii) a Where α is a first constant threshold parameter, α =36, β is a second constant threshold parameter, β =100,e ij For the target drone in (x) j ,y j ) After taking accident, fall to (a) ij ,b ij ) Kinetic energy of impact in time; p is a radical of ij Is (a) ij ,b ij ) The shading factor of the sub-area; the shading factor is used to represent the degree of shading of the corresponding sub-region.
5. The method of claim 1, wherein D has several no-fly regions therein;
the method further comprises the following steps:
taking each subarea which has a superposition part with the plurality of no-fly areas as a first subarea;
taking each subarea except a plurality of first subareas as a second subarea;
taking the highest risk level as a second target risk level corresponding to each first sub-area;
taking the lowest risk level as a second target risk level corresponding to each second sub-area;
and for each sub-area, taking the highest risk level in the corresponding first target risk level and the second target risk level as a first comprehensive risk level corresponding to the sub-area.
6. The method of claim 5, wherein D has a predetermined number of static areas;
a number of the no-fly zones are determined by:
acquiring the minimum value of the flight height of the target unmanned aerial vehicle;
acquiring the maximum building height of each sub-area;
taking each sub-area with the corresponding building height maximum value smaller than the flight height minimum value as a dynamic area;
and taking each dynamic area and each static area as the no-fly area.
7. The method of claim 6, wherein D has a number of regulatory domains;
the method further comprises the following steps:
taking each sub-area which has a superposition part with the plurality of control areas as a third sub-area; each control area has a corresponding preset risk level;
taking each subarea except a plurality of third subareas as a fourth subarea;
taking the preset risk level of the control area corresponding to each third sub-area as a third target risk level;
taking the lowest risk level as a third target risk level corresponding to each fourth sub-area;
and for each sub-area, taking the highest risk level in the corresponding first target risk level, second target risk level and third target risk level as a second comprehensive risk level corresponding to the sub-area.
8. A non-transitory computer readable storage medium having stored therein at least one instruction or at least one program, the at least one instruction or the at least one program being loaded and executed by a processor to implement the method of any one of claims 1-7.
9. An electronic device comprising a processor and the non-transitory computer readable storage medium of claim 8.
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