A large-scale unmanned aerial vehicle flight collision risk assessment method and device

Abstract

The application provides a large-scale unmanned aerial vehicle flight collision risk assessment method and device, and relates to the field of aircraft flight. The method comprises the following steps: obtaining the relative position, relative speed and track error of any two low-altitude aircrafts in a low-altitude aircraft cluster in dynamic flight at each moment; determining the collision parameter and the target collision probability algorithm corresponding to the corresponding collision parameter based on the relative position, the track error and the relative speed; and calculating the relative position and the track error by using the target collision probability algorithm to obtain the collision probability between the corresponding two low-altitude aircrafts. The core advantage of the application is that the lightweight algorithm architecture design significantly improves the calculation efficiency on the premise of ensuring the evaluation accuracy, and can accurately predict the conflict risk of the low-altitude aircraft cluster in the three-dimensional space running scene.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of aircraft flight, in particular, to a large-scale unmanned aerial vehicle flight collision risk assessment method and device. BACKGROUND

[0002] With the rapid development of low-altitude economy industry, diversified application scenarios have emerged in the low-altitude field, and various new aircrafts are frequently put into use, which makes the low-altitude airspace gradually evolve into a high-density, multi-dimensional complex operating environment. The aircraft flow in the low-altitude airspace continues to grow rapidly, and the flight conflict risk is significantly increased, so the demand for high-precision and real-time flight conflict risk assessment and control technology is increasingly urgent.

[0003] At present, the flight conflict risk assessment in the low-altitude airspace mostly relies on traditional algorithms and models, which can still maintain a certain assessment accuracy in small-scale and low-flow scenarios. However, in the face of the increasing scale of low-altitude aircraft and the complex three-dimensional operating space, these methods have exposed serious defects. On the one hand, as the number of aircraft in the airspace increases, the calculation time of the existing technology increases significantly, which makes it difficult to meet the timeliness requirements of real-time monitoring and risk disposal; on the other hand, its processing capacity is limited, which makes it difficult to adapt to the development trend of future low-altitude airspace with super-large-scale low-altitude aircraft collaborative operation. At the same time, dynamic factors such as the performance difference of low-altitude aircraft further increase the complexity of risk assessment, which makes it difficult for the existing technology to effectively guarantee the safe operation of the low-altitude airspace in actual application. SUMMARY

[0004] The purpose of the embodiments of the present application is to provide a large-scale unmanned aerial vehicle flight collision risk assessment method and device, which solves the above-mentioned problems existing in the prior art, can improve the calculation efficiency, and provide reliable safety protection strategies for low-altitude aircraft clusters, thereby effectively improving the overall safety and reliability of low-altitude airspace operation.

[0005] In a first aspect, a large-scale unmanned aerial vehicle flight collision risk assessment method is provided, which can include:

[0006] Obtaining the relative position, relative speed and track error of any two low-altitude aircrafts in a dynamic flight low-altitude aircraft cluster at each time; the track error is determined according to the actual flight trajectory data and the nominal track data of the corresponding two low-altitude aircrafts;

[0007] Based on the relative position, the track error and the relative speed, determining a collision parameter and a target collision probability algorithm corresponding to the corresponding collision parameter;

[0008] Using the target collision probability algorithm, calculating the relative position and track error to obtain the collision probability between the corresponding two low-altitude aircrafts.

[0009] In one possible implementation, the track error is represented by a covariance matrix;

[0010] Determine the collision parameters, including:

[0011] The position vector corresponding to the relative position, the inverse matrix of the covariance matrix, and the velocity vector corresponding to the relative velocity are processed to determine the approach characteristic values ​​of the two low-altitude aircraft.

[0012] The transpose of the velocity vector, the inverse of the covariance matrix, and the velocity vector are processed to determine the kinetic energy values ​​of the two low-altitude aircraft.

[0013] The collision parameters are determined based on the approaching characteristic value and the kinetic energy value.

[0014] In one possible implementation, the process of determining the target collision probability algorithm includes:

[0015] If the collision parameter is not greater than 0, then the configured first collision probability algorithm is determined as the target collision probability algorithm;

[0016] If the collision parameter is greater than 0, then the configured second collision probability algorithm is determined as the target collision probability algorithm.

[0017] In one possible implementation, the first collision probability algorithm is:

[0018]

[0019] in, Let be the collision probability. Let be the constant of the integration interval. An algorithm for calculating collision probabilities less than or equal to a threshold. It is the inverse of the covariance matrix. This refers to the non-centralized parameter being The three-degree-of-freedom chi-square distribution, This is the position vector corresponding to the relative position.

[0020] In one possible implementation, the second collision probability algorithm is as follows:

[0021]

[0022] in, Let be the collision probability. Let be the constant of the integration interval. An algorithm for calculating collision probabilities less than or equal to a threshold. It is the inverse of the covariance matrix. This refers to the non-centralized parameter being a three-degree-of-freedom chi-square distribution, is a position vector corresponding to a relative position, is a velocity vector corresponding to a relative velocity.

[0023] In a possible implementation, the integral interval constant is determined by an eigenvalue of the covariance matrix.

[0024] In a possible implementation, the relative velocity is determined by the velocities of the two low-altitude aircrafts relative to the ground.

[0025] In a second aspect, a large-scale unmanned aerial vehicle collision risk assessment device is provided, which can include:

[0026] An acquisition unit is configured to acquire relative positions, relative velocities and track errors of any two low-altitude aircrafts in a cluster of low-altitude aircrafts flying dynamically at each time; the track error is determined according to actual flight trajectory data and nominal track data of the two low-altitude aircrafts.

[0027] A determination unit is configured to determine a collision parameter and a target collision probability algorithm corresponding to the collision parameter based on the relative positions, the track errors and the relative velocities.

[0028] A calculation unit is configured to calculate the relative positions and the track errors by using the target collision probability algorithm to obtain a collision probability between the two low-altitude aircrafts.

[0029] In a third aspect, an electronic device is provided, which includes a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory complete communication with each other through the communication bus.

[0030] The memory is configured to store a computer program.

[0031] The processor is configured to execute the program stored on the memory to implement the method steps of any of the first aspect.

[0032] In a fourth aspect, a computer readable storage medium is provided, which stores a computer program, and the computer program is executed by a processor to implement the method steps of any of the first aspect.

[0033] The application provides a large-scale unmanned aerial vehicle flight collision risk assessment method, which comprises the following steps: acquiring the relative position, relative speed and track error of any two low-altitude aircrafts in a low-altitude aircraft cluster in dynamic flight at each moment; determining the collision parameter and the target collision probability algorithm corresponding to the collision parameter based on the relative position, track error and relative speed; and calculating the relative position and track error by using the target collision probability algorithm to obtain the collision probability between the corresponding two low-altitude aircrafts. The application constructs a risk assessment model by fusing kinematic equations and three-degree-of-freedom non-central chi-square distribution. The method breaks through the calculation bottleneck of traditional Monte Carlo simulation, greatly compresses the risk assessment time from minutes to seconds or even milliseconds, and realizes real-time analysis of the dynamic trajectory of a high-density low-altitude aircraft cluster. The core advantage of the method is that the calculation efficiency is significantly improved under the premise of ensuring the evaluation accuracy through lightweight algorithm architecture design, which can accurately predict the conflict risk of a low-altitude aircraft cluster in a three-dimensional space running scenario, provide a safe protection scheme with theoretical innovation and engineering practicability for unmanned aerial vehicle logistics, urban air traffic and other application scenarios in the low-altitude economy background, and effectively promote the landing application of intelligent control technology in low-altitude airspace. BRIEF DESCRIPTION OF DRAWINGS

[0034] In order to more clearly illustrate the technical solutions of the embodiments of the application, the following will briefly introduce the drawings needed to be used in the embodiments of the application. It should be understood that the following drawings only show some embodiments of the application, and therefore should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0035] Figure 1 A system architecture diagram of a large-scale unmanned aerial vehicle flight collision risk assessment method provided by the embodiments of the application is provided.

[0036] Figure 2 A flowchart of a large-scale unmanned aerial vehicle flight collision risk assessment method provided by the embodiments of the application is provided.

[0037] Figure 3 A schematic diagram of two low-altitude aircrafts provided by the embodiments of the application is provided.

[0038] Figure 4 A structural schematic diagram of a large-scale unmanned aerial vehicle flight collision risk assessment device provided by the embodiments of the application is provided.

[0039] Figure 5 A structural schematic diagram of an electronic device provided by the embodiments of the application is provided. DETAILED DESCRIPTION

[0040] With reference to the accompanying drawings, the technical solutions in the embodiments of the present application will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present application, and not all the embodiments. Based on the embodiments of the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0041] The large-scale unmanned aerial vehicle flight collision risk assessment method provided by the embodiments of the present application can be applied in Figure 1 The system architecture is shown in FIG. 1, and the system can include a processor and a low-altitude aircraft cluster, as shown in FIG. 2. Figure 1

[0042] Each low-altitude aircraft in the low-altitude aircraft cluster sends its position and speed at each time to the processor. The low-altitude aircraft can be an unmanned aerial vehicle, a flying car, an electric vertical take-off and landing aircraft (eVTOL), and other low-altitude transportation tools.

[0043] The processor is configured to receive the position and speed of each low-altitude aircraft at each time to execute the large-scale unmanned aerial vehicle flight collision risk assessment method provided by the present application.

[0044] With the rapid development of the low-altitude economy industry, various application scenarios have emerged in the low-altitude field, and various new aircrafts are frequently put into use, making the low-altitude airspace gradually evolve into a high-density, multi-dimensional complex operating environment. The aircraft traffic in the low-altitude airspace continues to grow rapidly, and the flight conflict risk is significantly increased, making the demand for high-precision and real-time flight conflict risk assessment and control technology more urgent.

[0045] At present, the flight conflict risk assessment in the low-altitude airspace mostly relies on traditional algorithms and models, which can still maintain a certain assessment accuracy in small-scale, low-traffic scenarios. However, in the face of the growing scale of low-altitude aircraft and the complex three-dimensional operating space, these methods have exposed serious defects. On the one hand, as the number of aircraft in the airspace increases, the calculation time of the existing technology increases significantly, making it difficult to meet the timeliness requirements of real-time monitoring and risk disposal; on the other hand, its processing capacity is limited, and it is difficult to adapt to the development trend of future low-altitude airspace with ultra-large-scale low-altitude aircraft collaborative operation. At the same time, dynamic factors such as differences in aircraft performance further increase the complexity of risk assessment, making it difficult for existing technologies to effectively ensure the safe operation of low-altitude airspace in actual application.

[0046] Therefore, the present application provides a large-scale unmanned aerial vehicle flight collision risk assessment method to solve the above problems existing in the prior art, which can improve the overall safety and reliability of low-altitude airspace operation while improving the calculation efficiency.

[0047] ​The preferred embodiments of the present application are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to explain and illustrate the present application, and are not used to limit the present application, and the embodiments in the present application and the features in the embodiments can be combined with each other without conflict.

[0048] Figure 2 A flowchart of a large-scale unmanned aerial vehicle flight collision risk assessment method provided by an embodiment of the present application is shown in FIG. 1. As shown in FIG. 1, the method can include the following steps. Figure 2

[0049] In step S210, the relative position, relative speed and track error of any two low-altitude aircraft in the low-altitude aircraft cluster flying dynamically at each time are obtained.

[0050] Specifically, each low-altitude aircraft in the low-altitude aircraft cluster flying dynamically sends its position and speed at each time to the processor. It can be understood that although there is no displacement change during hovering of the low-altitude aircraft, the flight control system of the low-altitude aircraft will continuously adjust the motor speed to resist the influence of air flow and maintain a relatively stable position due to factors such as atmospheric environment interference (e.g., air flow fluctuation) and fine tuning of the low-altitude aircraft's own control system. At this time, the attitude and power output of the low-altitude aircraft are in a dynamic adjustment process, and therefore, the low-altitude aircraft is also in dynamic flight during hovering.

[0051] After the processor receives the position and speed of each low-altitude aircraft at each time, the position and speed of any two low-altitude aircraft at the corresponding time are calculated to obtain the relative position, relative speed and track error.

[0052] The determination process of the covariance matrix corresponding to A and the track error is as follows:

[0053] In the low-altitude aircraft ground monitoring system, the time stamp, latitude and longitude, height above ground, nominal speed and other information of the flight trajectory of the low-altitude aircraft at different stages (e.g., takeoff, hovering, climbing, cruising, descending and landing) can be obtained to obtain the nominal track data and actual flight trajectory data of the low-altitude aircraft at the cruising stage. The nominal track data includes the nominal speed, the position (i.e., latitude and longitude and height above ground) of the nominal track point at different time stamps; the actual flight trajectory data includes the actual speed, the position (i.e., latitude and longitude and height above ground) of the actual flight trajectory point at different time stamps.

[0054] The process of establishing the covariance matrix corresponding to the track error of the low-altitude aircraft can include:

[0055] First, a coordinate system is established, and a right-handed coordinate system is adopted. The aircraft center of the low-altitude aircraft is taken as the origin, the speed direction of the low-altitude aircraft is taken as the x-axis, the gravity direction is taken as the z-axis, and the direction perpendicular to the x-axis and the z-axis is taken as the y-axis.​

[0056] Any low-altitude aircraft in actual operation, due to the actual track exists uncertainty, using the covariance matrix The error distribution of the center of mass of the low-altitude aircraft relative to the nominal track data is described. The covariance matrix As shown in the following formula:

[0057]

[0058] Wherein, , , The main diagonal elements of the covariance matrix The track error variance in each coordinate direction is represented by , , The non-diagonal elements of the covariance matrix The error covariance between each direction is represented by the non-diagonal elements of the covariance matrix The eigenvalues of the covariance matrix , and not all 0.

[0059] The track error of the low-altitude aircraft A corresponds to the covariance matrix , the track error of the low-altitude aircraft B corresponds to the covariance matrix , and the covariance matrix of the relative position between the two unmanned aerial vehicles is .

[0060] That is, the track error can include the error value of the position of the actual flight trajectory point and the position of the nominal trajectory point in the x-axis direction, the error value of the position of the actual flight trajectory point and the position of the nominal trajectory point in the y-axis direction, and the error value of the position of the actual flight trajectory point and the position of the nominal trajectory point in the z-axis direction.

[0061] Further, it is assumed that the low-altitude aircraft is in the cruise phase, and the track error of the low-altitude aircraft is determined along the body coordinate system. First, according to the operation data, the actual flight trajectory data of the cruise phase is first screened; second, according to the actual trajectory data, the corresponding nominal trajectory point of the actual flight trajectory point of the cruise phase is found. The low-altitude aircraft in The n actual flight trajectory points in the x-axis direction are extracted respectively: , , , , , , and the corresponding n nominal trajectory points are: Similarly, the error value of the position of the actual flight trajectory point in the y-axis direction and the position of the nominal flight path point and the error value of the position of the actual flight trajectory point in the z-axis direction and the position of the nominal flight path point can be obtained by using the same algorithm, and the present application will not be described here.

[0062] The error covariance can include a first variance of the error value of the position of the actual flight trajectory point in the x-axis direction and the position of the nominal flight path point, a second variance of the error value of the position of the actual flight trajectory point in the y-axis direction and the position of the nominal flight path point, and a third variance of the error value of the position of the actual flight trajectory point in the z-axis direction and the position of the nominal flight path point; and a first covariance of the error value of the position of the actual flight trajectory point in the x-axis direction and the position of the nominal flight path point and the error value of the position of the actual flight trajectory point in the y-axis direction, a second covariance of the error value of the position of the actual flight trajectory point in the x-axis direction and the position of the nominal flight path point and the error value of the position of the actual flight trajectory point in the z-axis direction, and a third covariance of the error value of the position of the actual flight trajectory point in the y-axis direction and the position of the nominal flight path point and the error value of the position of the actual flight trajectory point in the z-axis direction.

[0063] In the above example, the first variance of the error value of the position of the actual flight trajectory point of the low-altitude aircraft in the x-axis direction and the position of the nominal flight path point can be represented as:

[0064] ;

[0065] Similarly, the second variance and the third variance of the low-altitude aircraft can also be obtained by a similar algorithm, and will not be described here.

[0066] The first covariance of the error value of the position of the actual flight trajectory point of the low-altitude aircraft in the x-axis direction and the position of the nominal flight path point and the error value of the position of the actual flight trajectory point in the y-axis direction can be represented as:

[0067] ;

[0068] Similarly, the second covariance and the third covariance of the low-altitude aircraft can also be obtained by a similar algorithm, and will not be described here.

[0069] In some embodiments, the number of eigenvalues of the relative covariance matrix in different directions can be 3, such as three different eigenvalues, and all greater than 0; wherein, respectively represent the first eigenvalue and the second eigenvalue in the horizontal direction, represent the eigenvalue in the vertical direction.

[0070] B. The determination process of the position vector corresponding to the relative position is as follows:

[0071] At the corresponding moment, the position of the low-altitude aircraft A is , and the position of the low-altitude aircraft B is ;

[0072] The position vector is denoted by μ, , which represents the relative position of the centers of mass (or geometric centers) of the two low-altitude aircrafts in a three-dimensional Cartesian coordinate system, with the unit being meters; wherein , , .

[0073] C. The determination process of the velocity vector corresponding to the relative velocity is as follows:

[0074] The velocity vector is denoted by ; wherein is the velocity of the low-altitude aircraft A relative to the ground, is the velocity of the low-altitude aircraft B relative to the ground. Step S220, based on the relative position, the track error, and the relative velocity, determine the collision parameter and the target collision probability corresponding to the corresponding collision parameter algorithm.

[0075] Specifically, the position vector μ corresponding to the relative position, the inverse matrix of the covariance matrix

[0076] corresponding to the track error, and the velocity vector corresponding to the relative velocity are processed to determine the approaching eigenvalue of the two low-altitude aircrafts; this approaching eigenvalue can be understood as the projection of the relative position and the relative velocity in the error space corresponding to the track error. The transpose matrix of the velocity vector

[0077] , the inverse matrix of the covariance matrix , and the velocity vector are processed to determine the kinetic energy value of the two low-altitude aircrafts; this kinetic energy value can be understood as the effective kinetic energy of the relative velocity in the error space corresponding to the track error. Based on the approaching eigenvalue and the kinetic energy value, the collision parameter c is determined.

[0078] Specifically, this process can be represented by the following formula:

[0079]

[0080]

[0081] Then, the collision parameter is compared with the configured parameter threshold value (usually set to 0):

[0082] ​If the collision parameter is not greater than 0, then the configured first collision probability algorithm is determined as the target collision probability algorithm;

[0083] The algorithm for the first collision probability can be expressed as:

[0084]

[0085] It can be converted to:

[0086]

[0087] in, Let be the collision probability. Let be the constant of the integration interval. An algorithm for calculating collision probabilities less than or equal to a threshold. It is the inverse of the covariance matrix. This refers to the non-centralized parameter being The three-degree-of-freedom chi-square distribution, This is the position vector corresponding to the relative position.

[0088] If the collision parameter is greater than 0, then the configured second collision probability algorithm will be determined as the target collision probability algorithm.

[0089] The second collision probability algorithm can be:

[0090]

[0091] It can be converted to:

[0092]

[0093] in, Let be the collision probability. Let be the constant of the integration interval. An algorithm for calculating collision probabilities less than or equal to a threshold. It is the inverse of the covariance matrix. This refers to the non-centralized parameter being The three-degree-of-freedom chi-square distribution, The position vector corresponding to the relative position. This is the velocity vector corresponding to the relative velocity.

[0094] Furthermore, Let be the constant of the integration interval, and its calculation formula can be expressed as: Among them, remove The value of 0 in It is the radius of the collision box;

[0095] The calculation method can be:

[0096] Each low-altitude aircraft is regarded as a point, and its collision protection zone is a minimum spherical region that wraps the low-altitude aircraft.

[0097] The center of the sphere o is the center of mass of the low-altitude aircraft, and the radius r of the sphere is the maximum distance from the center of mass of the low-altitude aircraft i during the operation of the low-altitude aircraft. i The maximum distance is used to define the radius of a sphere, which represents the range that the low-altitude aircraft will not exceed in any attitude.

[0098] The collision box radius of the spherical collision box of two low-altitude aircrafts The radius of the collision protection zone of the two low-altitude aircrafts is the sum of the radii of the two collision protection zones:

[0099]

[0100] When modeling the spherical collision box, the two low-altitude aircrafts are processed as shown in Figure 3 , where one low-altitude aircraft is abstracted as a point, and the coordinates of the point are the center of mass (or geometric center) of the low-altitude aircraft; the other low-altitude aircraft is regarded as a sphere, and the center of the sphere is the center of mass (or geometric center) of the low-altitude aircraft, and the collision box radius .

[0101] Step S230, using the target collision probability algorithm, calculates the relative position and track error to obtain the collision probability between the corresponding two low-altitude aircrafts.

[0102] Specifically, when the target collision probability algorithm is the first collision probability algorithm, the position vector corresponding to the relative position and the covariance matrix corresponding to the track error are calculated to obtain the collision probability of the two low-altitude aircrafts.

[0103] When the target collision probability algorithm is the second collision probability algorithm, the position vector corresponding to the relative position, the velocity vector corresponding to the relative velocity, and the covariance matrix corresponding to the track error are calculated to obtain the collision probability of the two low-altitude aircrafts.

[0104] The application provides a large-scale unmanned aerial vehicle flight collision risk assessment method, which comprises the following steps: acquiring the relative position, relative speed and track error of any two low-altitude aircrafts in a low-altitude aircraft cluster in dynamic flight at each moment; determining a collision parameter and a target collision probability algorithm corresponding to the collision parameter based on the relative position, track error and relative speed; and calculating the relative position and track error by using the target collision probability algorithm to obtain the collision probability between the corresponding two low-altitude aircrafts. The application constructs a risk assessment model by fusing a kinematic equation and a three-degree-of-freedom non-central chi-square distribution. The method breaks through the calculation bottleneck of traditional Monte Carlo simulation, greatly compresses the risk assessment time from minutes to seconds or even milliseconds, and realizes real-time analysis of the dynamic trajectory of a high-density low-altitude aircraft cluster. The core advantage of the method is that the calculation efficiency is significantly improved under the premise of ensuring the assessment accuracy through lightweight algorithm architecture design, and the collision risk of a low-altitude aircraft cluster in a three-dimensional space running scenario can be accurately predicted, thereby providing a safe protection scheme with theoretical innovation and engineering practicability for unmanned aerial vehicle logistics, urban air traffic and other application scenarios under the background of low-altitude economy, and effectively promoting the landing application of intelligent control technology in low-altitude airspace.

[0105] Corresponding to the above method, the application also provides a large-scale unmanned aerial vehicle flight collision risk assessment device, as shown in Figure 4 The device comprises:

[0106] An acquisition unit 410 is configured to acquire the relative position, relative speed and track error of any two low-altitude aircrafts in a low-altitude aircraft cluster in dynamic flight at each moment; the track error is determined according to the actual flight trajectory data and the nominal track data of the corresponding two low-altitude aircrafts;

[0107] A determination unit 420 is configured to determine a collision parameter and a target collision probability algorithm corresponding to the collision parameter based on the relative position, the track error and the relative speed;

[0108] A calculation unit 430 is configured to calculate the relative position and track error by using the target collision probability algorithm to obtain the collision probability between the corresponding two low-altitude aircrafts.

[0109] The functions of each functional unit of the large-scale unmanned aerial vehicle flight collision risk assessment device provided in the above embodiments of the application can be realized through the above method steps, and therefore, the specific working process and beneficial effects of each unit in the large-scale unmanned aerial vehicle flight collision risk assessment device provided in the embodiments of the application will not be described here.

[0110] The application also provides an electronic device, as shown in Figure 5As shown, the electronic device includes a processor 510, a communication interface 520, a memory 530, and a communication bus 540, wherein the processor 510, the communication interface 520, and the memory 530 communicate with each other through the communication bus 540.

[0111] The memory 530 is configured to store a computer program.

[0112] The processor 510 is configured to execute the program stored in the memory 530 to implement the following steps.

[0113] Obtain the relative position, the relative speed, and the track error of any two low-altitude aircrafts in the dynamic flight low-altitude aircraft cluster at each time; the track error is determined according to the actual flight track data and the nominal track data of the corresponding two low-altitude aircrafts.

[0114] Determine the collision parameter and the target collision probability algorithm corresponding to the corresponding collision parameter based on the relative position, the track error, and the relative speed.

[0115] Calculate the relative position and the track error by using the target collision probability algorithm to obtain the collision probability between the corresponding two low-altitude aircrafts.

[0116] The communication bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc. For the convenience of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or only one type of bus.

[0117] The communication interface is configured to communicate between the electronic device and other devices.

[0118] The memory can include a Random Access Memory (RAM) and can also include a Non-Volatile Memory (NVM), such as at least one disk memory. Optionally, the memory can also be at least one storage device located away from the aforementioned processor.

[0119] The processor described above can be a general processor, including a central processing unit (CPU), a network processor (NP), etc.; can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component.

[0120] The implementation manners and beneficial effects of the electronic device in the above embodiments can be achieved by referring to the steps in the above embodiments, and thus, the specific working process and beneficial effects of the electronic device provided by the embodiments of the present application are not repeated here. Figure 2 The implementation manners and beneficial effects of the electronic device in the above embodiments can be achieved by referring to the steps in the above embodiments, and thus, the specific working process and beneficial effects of the electronic device provided by the embodiments of the present application are not repeated here.

[0121] In another embodiment provided by the present application, a computer readable storage medium is provided, and the computer readable storage medium stores instructions, when the instructions are run on a computer, the computer executes the large-scale unmanned aerial vehicle flight collision risk assessment method in any of the above embodiments.

[0122] In another embodiment provided by the present application, a computer program product containing instructions is provided, when the instructions are run on a computer, the computer executes the large-scale unmanned aerial vehicle flight collision risk assessment method in any of the above embodiments.

[0123] Those skilled in the art should understand that the embodiments in the embodiments of the present application can be provided as a method, a system or a computer program product. Therefore, the embodiments in the present application can be in the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the embodiments in the present application can be in the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program codes.

[0124] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 The flowchart and / or block diagram in the embodiments of the application can describe any device, system or components of the computer program product. It should be understood that each block of the flowchart and / or block diagram and combinations of blocks in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart and / or block diagram block or blocks.

[0125] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 The flowchart and / or block diagram in the embodiments of the application can describe any device, system or components of the computer program product. It should be understood that each block of the flowchart and / or block diagram and combinations of blocks in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart and / or block diagram block or blocks.

[0126] These computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 The flowchart and / or block diagram in the embodiments of the application can describe any device, system or components of the computer program product. It should be understood that each block of the flowchart and / or block diagram and combinations of blocks in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart and / or block diagram block or blocks. Figure 1 These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart and / or block diagram block or blocks.

[0127] Unless otherwise defined, technical terms and scientific terms used in the present application shall have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs. The words "first", "second", and similar words of degree express quantities of matter, not quality, and are used only to distinguish one component from another. The words "include" and "contain" and similar words of degree mean encompassing the elements listed thereafter, and equivalent thereof, but do not exclude other elements. The words "connect", "couple", or "connected", "coupled", and similar words of degree are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. The words "upper", "lower", "left", "right", and the like are only used to indicate relative positions, and when the absolute positions of the described objects are changed, the relative positions may

[0128] While the preferred embodiments in the application have been described, additional modifications and changes can occur to those skilled in the art once they learn of the basic creative principles contained herein. Therefore, the present embodiments are to be regarded as including all modifications and variations that fall within the scope of the present embodiments.

[0129] Obviously, numerous modifications and variations of the embodiments in the present application are possible in light of the above teachings. It is therefore to be understood that within the scope of the embodiments in the present application, the embodiments can be practiced otherwise than as specifically described.

Claims

1. A method for large-scale drone flight collision risk assessment, characterized in that, The method comprises: acquiring relative positions, relative velocities and track errors of any two low-altitude aircrafts in a low-altitude aircraft cluster flying dynamically at each time; the track error is determined according to actual flight track data and nominal track data of the two low-altitude aircrafts; determining a collision parameter and a target collision probability algorithm corresponding to the collision parameter based on the relative positions, the track errors and the relative velocities; calculating the relative positions and the track errors by using the target collision probability algorithm to obtain a collision probability between the two low-altitude aircrafts; the track error is represented by a covariance matrix; the collision parameter is determined by: processing a position vector corresponding to the relative positions, an inverse matrix of the covariance matrix and a velocity vector corresponding to the relative velocities to determine an approaching eigenvalue of the two low-altitude aircrafts; the approaching eigenvalue is a projection of the relative positions and the relative velocities in an error space corresponding to the track errors; processing a transposed matrix of the velocity vector, the inverse matrix of the covariance matrix and the velocity vector to determine a kinetic energy value of the two low-altitude aircrafts; the kinetic energy value is an effective kinetic energy of the relative velocities in the error space corresponding to the track errors; determining the collision parameter based on the approaching eigenvalue and the kinetic energy value; the collision parameter C is represented as: ； wherein μ is a position vector corresponding to the relative position, is a covariance matrix corresponding to the track error, is a velocity vector corresponding to the relative velocity, is an approaching eigenvalue, is a kinetic energy value; the determination process of the target collision probability algorithm comprises: if the collision parameter is not greater than 0, a first collision probability algorithm configured is determined as the target collision probability algorithm; if the collision parameter is greater than 0, a second collision probability algorithm configured is determined as the target collision probability algorithm; the first collision probability algorithm is: the second collision probability algorithm is: wherein, is a first collision probability, is a second collision probability, is an integration interval constant, is an algorithm to calculate the collision probability less than or equal to a threshold value, refers to a three-degree-of-freedom chi-square distribution with a non-central parameter of 0.

2. The method of claim 1, wherein, the integral interval constant is determined by eigenvalues of the covariance matrix.

3. The method of claim 1, wherein, the relative velocity is determined by velocities of the two low-altitude aircrafts relative to the ground.

4. A large-scale unmanned aerial vehicle flight collision risk assessment device, characterized by, The device comprises: an acquisition unit configured to acquire relative positions, relative velocities and track errors of any two low-altitude aircrafts in a low-altitude aircraft cluster flying dynamically at each time; the track error is determined according to actual flight track data and nominal track data of the two low-altitude aircrafts; a determination unit configured to determine a collision parameter and a target collision probability algorithm corresponding to the collision parameter based on the relative positions, the track errors and the relative velocities; a calculation unit configured to calculate the relative positions and the track errors by using the target collision probability algorithm to obtain a collision probability between the two low-altitude aircrafts; the track error is represented by a covariance matrix; the collision parameter is determined by: processing a position vector corresponding to the relative positions, an inverse matrix of the covariance matrix and a velocity vector corresponding to the relative velocities to determine an approaching eigenvalue of the two low-altitude aircrafts; the approaching eigenvalue is a projection of the relative positions and the relative velocities in an error space corresponding to the track errors; processing a transposed matrix of the velocity vector, the inverse matrix of the covariance matrix and the velocity vector to determine a kinetic energy value of the two low-altitude aircrafts; the kinetic energy value is an effective kinetic energy of the relative velocities in the error space corresponding to the track errors; determining the collision parameter based on the approaching eigenvalue and the kinetic energy value; The collision parameter C is expressed as: ； wherein μ is a position vector corresponding to the relative position, is a covariance matrix corresponding to the track error, is a velocity vector corresponding to the relative velocity, is an approaching eigenvalue, is a kinetic energy value; The determination process of the target collision probability algorithm includes: If the collision parameter is not greater than 0, a first collision probability algorithm configured is determined as the target collision probability algorithm; If the collision parameter is greater than 0, a second collision probability algorithm configured is determined as the target collision probability algorithm; The first collision probability algorithm is: The second collision probability algorithm is: wherein, is a first collision probability, is a second collision probability, is an integral interval constant, is an algorithm to calculate the collision probability less than or equal to a threshold value, refers to a three-degree-of-freedom chi-square distribution with a non-central parameter of 0.

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