Unmanned aerial vehicle falling risk assessment method based on collision damage and related device

Abstract

The invention provides an unmanned aerial vehicle falling risk assessment method based on collision damage and a related device. Collision simulation is performed according to an unmanned aerial vehicle simulation model, a human body simulation model and a simulation working condition, and a deviation vector between each grid center and a falling point in a corresponding grid division result is combined; determining a damage fatality rate index corresponding to each grid under various simulation working conditions; determining a risk assessment value corresponding to the simulation working condition according to the damage fatality rate index and the grid area corresponding to each grid under the simulation working condition; and determining a risk assessment comprehensive value corresponding to the unmanned aerial vehicle according to the risk assessment numerical values and the joint probability quality corresponding to the various simulation working conditions. By introducing a fatality rate index of damage caused by collision, more accurate and more efficient quantitative evaluation of the falling risk of the unmanned aerial vehicle is realized. Human body injury differences under different collision situations can be fully considered, the calculation burden cost of large-scale simulation can be effectively reduced, and the evaluation precision and the simulation cost are balanced.

Description

Technical Field

[0001] This invention relates to the field of transportation systems, and more specifically, to a method and related apparatus for assessing the crash risk of unmanned aerial vehicles (UAVs) based on collision damage. Background Technology

[0002] The Specific Operation Risk Assessment (SORA) method is widely used for assessing the operational safety risks of drones. In assessing the risk of crashing, it mainly considers two key indicators: the area of ​​the drone's crash zone and the mortality rate of personnel within that area. The product of these two indicators is used as the risk measurement dimension.

[0003] The area of ​​impact zone is typically estimated using the size and speed of the drone, with a conservative damage factor set at 100%. While this assessment method is simple, it is too conservative in many real-world scenarios, potentially leading to excessively high safety redundancy required for drone operation. This can negatively impact drone operational efficiency and the expansion of commercial applications.

[0004] With the increasing application of drones in logistics, surveying, inspection, and other fields, high-precision and high-efficiency risk assessment methods are becoming increasingly important. In order to improve the operational efficiency of drones while ensuring safety, it is necessary to conduct a more detailed analysis of the ground risk assessment process in the SORA method, so as to obtain more targeted and applicable risk assessment results. Summary of the Invention

[0005] The purpose of this invention is to provide a method and related apparatus for assessing the risk of drone crashes based on collision damage, so as to improve the above-mentioned problems.

[0006] To achieve the above objectives, the technical solutions adopted in the embodiments of the present invention are as follows: In a first aspect, embodiments of the present invention provide a method for assessing the crash risk of a drone based on collision damage, the method comprising: Based on the numerical range of the simulated collision parameters when the drone crashes, multiple simulation scenarios are generated. The simulated collision parameters include one or more of the following: simulated drone collision speed, simulated drone vertical plane collision angle, simulated drone collision three-dimensional attitude, and simulated human horizontal orientation angle. At least one simulated collision parameter has a different value between different simulation scenarios. The impact coverage area is determined based on the characteristics of the UAV model, and the impact coverage area is divided into grids according to contribution and sensitivity requirements under various simulation conditions to obtain the grid division results corresponding to various simulation conditions. The contribution requirement refers to the proportion of the overall mortality rate corresponding to the grid relative to the overall risk assessment value being lower than the first proportion threshold, and the sensitivity requirement refers to the degree of influence of the overall mortality rate corresponding to the grid on the overall risk assessment value being lower than the second influence threshold. Collision simulations are conducted based on UAV simulation models, human simulation models, and simulation conditions. By combining the deviation vector between the center of each grid and the landing point in the corresponding grid division results, the injury fatality rate index corresponding to each grid under various simulation conditions is determined. The injury fatality rate index includes head injury fatality rate, neck injury fatality rate, and viscous injury fatality rate. Based on the injury fatality rate index and grid area corresponding to each grid under the simulation condition, determine the risk assessment value corresponding to the simulation condition. Based on the risk assessment values ​​corresponding to various simulation conditions and the joint probability mass of the corresponding simulation collision parameters, the comprehensive risk assessment value corresponding to the UAV is determined.

[0007] Secondly, embodiments of the present invention provide a device for assessing the crash risk of a drone based on collision damage, the device comprising: The first processing unit is used to generate multiple simulation conditions based on the numerical range of the simulated collision parameters when the UAV crashes; wherein, the simulated collision parameters include any one or more of the following: simulated UAV collision speed, simulated UAV vertical plane collision angle, simulated UAV collision three-dimensional attitude, and simulated human body horizontal orientation angle, and at least one simulated collision parameter has a different value between different simulation conditions. The first processing unit is also used to determine the landing point coverage area according to the aircraft type characteristics of the UAV, and to perform grid division on the landing point coverage area according to the contribution requirements and sensitivity requirements under various simulation conditions, so as to obtain the grid division results corresponding to various simulation conditions. The contribution requirement refers to the proportion of the overall mortality rate corresponding to the grid relative to the overall risk assessment value being lower than the first proportion threshold, and the sensitivity requirement refers to the degree of influence of the overall mortality rate corresponding to the grid on the overall risk assessment value being lower than the second influence threshold. The first processing unit is also used to perform collision simulation based on the UAV simulation model, the human simulation model and the simulation conditions, and combine the deviation vector between the center of each grid and the landing point in the corresponding grid division results to determine the injury fatality rate index corresponding to each grid under various simulation conditions. The injury fatality rate index includes head injury fatality rate, neck injury fatality rate and viscous injury fatality rate. The first processing unit is also used to determine the risk assessment value corresponding to the simulation condition based on the injury fatality rate index and the grid area corresponding to each grid under the simulation condition; The second processing unit is used to determine the comprehensive risk assessment value of the UAV based on the risk assessment values ​​corresponding to various simulation conditions and the joint probability mass of the corresponding simulation collision parameters.

[0008] Thirdly, embodiments of the present invention provide a storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method.

[0009] Fourthly, embodiments of the present invention provide an electronic device, the electronic device comprising: a processor and a memory, the memory being used to store one or more programs; when the one or more programs are executed by the processor, the above-described method is implemented.

[0010] Compared to existing technologies, the present invention provides a method and related apparatus for assessing the crash risk of unmanned aerial vehicles (UAVs) based on collision damage. This method generates multiple simulation scenarios based on the numerical range of simulated collision parameters during a UAV crash. It determines the crash site coverage area based on the UAV's model characteristics and, under each simulation scenario, divides the crash site coverage area into grids according to contribution and sensitivity requirements to obtain grid division results for each simulation scenario. Collision simulations are performed using a UAV simulation model, a human simulation model, and the simulation scenarios. Combining the deviation vector between the center of each grid and the crash site in the corresponding grid division results, the method determines the fatality rate index corresponding to each grid under each simulation scenario. Based on the fatality rate index and grid area of ​​each grid under each simulation scenario, the method determines the risk assessment value corresponding to that simulation scenario. Finally, based on the risk assessment values ​​for each simulation scenario and the joint probability mass of the corresponding simulated collision parameters, the method determines the comprehensive risk assessment value for the UAV. By introducing the fatality rate index caused by collision, a more accurate and efficient quantitative assessment of UAV crash risk is achieved. This method not only fully considers the differences in human injury under different collision scenarios but also effectively reduces the computational burden of large-scale simulations, balancing assessment accuracy and simulation costs. The risk results obtained through this method are more targeted and practical, providing technical support for the safe operation of drones in urban low-altitude transportation, logistics delivery, agricultural plant protection, and other fields, and has broad application and promotion prospects.

[0011] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0012] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0014] Figure 2 This is one of the flowcharts illustrating the method for assessing the risk of drone crashes based on collision damage, as provided in an embodiment of the present invention.

[0015] Figure 3 This is the second flowchart illustrating the method for assessing the risk of UAV crashes based on collision damage, as provided in this embodiment of the invention.

[0016] Figure 4 This is a schematic diagram of a unit for a drone crash risk assessment device based on collision damage, provided in an embodiment of the present invention.

[0017] In the diagram: 10-Processor; 11-Memory; 12-Bus; 13-Communication interface; 301-First processing unit; 302-Second processing unit. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0019] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0020] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0021] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0022] The following detailed description of some embodiments of the present invention is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0023] This invention provides an electronic device, which may be a server device, a computer device, or a mobile phone device. Please refer to... Figure 1 This is a schematic diagram of the structure of an electronic device. The electronic device includes a processor 10, a memory 11, and a bus 12. The processor 10 and the memory 11 are connected via the bus 12. The processor 10 is used to execute executable modules, such as computer programs, stored in the memory 11.

[0024] Processor 10 can be an integrated circuit chip with signal processing capabilities. In its implementation, each step of the collision damage-based UAV crash risk assessment method can be completed through integrated logic circuits in the hardware or software instructions within processor 10. Processor 10 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0025] The memory 11 may include high-speed random access memory (RAM) and may also include non-volatile memory, such as at least one disk storage.

[0026] Bus 12 can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. Figure 1 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus 12 or one type of bus 12.

[0027] The memory 11 is used to store programs, such as programs corresponding to a drone crash risk assessment device based on collision damage. The drone crash risk assessment device based on collision damage includes at least one software functional module that can be stored in the memory 11 in the form of software or firmware or embedded in the operating system (OS) of the electronic device. Upon receiving an execution instruction, the processor 10 executes the program to implement the drone crash risk assessment method based on collision damage.

[0028] The electronic device provided in this embodiment of the invention may further include a communication interface 13. The communication interface 13 is connected to the processor 10 via a bus.

[0029] It should be understood that, Figure 1 The structure shown is only a partial schematic diagram of the electronic device; the electronic device may also include components that are larger than... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown. Figure 1 The components shown can be implemented using hardware, software, or a combination thereof.

[0030] The method for assessing the crash risk of unmanned aerial vehicles (UAVs) based on collision damage provided in this invention can be applied to, but is not limited to, [various applications]. Figure 1 For the specific process of the electronic devices shown, please refer to [link / reference]. Figure 2 The methods for assessing the risk of drone crashes based on collision damage include: S11, S12, S13, S14, and S15, which are described in detail below.

[0031] S11 generates various simulation scenarios based on the numerical range of the simulated collision parameters when the drone crashes.

[0032] The simulation collision parameters include one or more of the following: the collision speed of the simulated drone, the vertical plane collision angle of the simulated drone, the three-dimensional collision attitude of the simulated drone, and the horizontal orientation angle of the simulated human body. At least one simulation collision parameter will have a different value in different simulation conditions.

[0033] S12. Determine the impact coverage area based on the characteristics of the UAV model, and divide the impact coverage area into grids according to contribution and sensitivity requirements under various simulation conditions to obtain the grid division results corresponding to various simulation conditions.

[0034] Among them, the contribution requirement means that the proportion of the overall mortality rate corresponding to the grid to the overall risk assessment value is lower than the first proportion threshold, and the sensitivity requirement means that the impact of the overall mortality rate corresponding to the grid on the overall risk assessment value is lower than the second impact threshold.

[0035] It should be noted that the area of ​​each grid in the mesh generation result does not need to be uniform.

[0036] S13. Based on the UAV simulation model, human simulation model, and simulation conditions, collision simulation is performed. Combining the deviation vector between the center of each grid and the landing point in the corresponding grid division results, the injury fatality rate index corresponding to each grid under various simulation conditions is determined.

[0037] Among them, the injury mortality rate indicators include head injury mortality rate, neck injury mortality rate, and adhesive injury mortality rate.

[0038] The collision simulation is performed with the human body simulation model centered on the grid. During the simulation, the human body simulation model is positioned at the grid center. Based on the UAV simulation model, the human body simulation model, and the simulation conditions, and considering the deviation vector between the center of each grid and the impact point in the grid division results, the fatality rate index corresponding to each grid under various simulation conditions is determined. The human body simulation model is used to simulate the response of different body parts (head, neck, chest, etc.) to impact, thereby determining the fatality rate.

[0039] S14. Based on the injury fatality rate index and grid area corresponding to each grid under the simulation conditions, determine the risk assessment value corresponding to the simulation conditions.

[0040] S15. Based on the risk assessment values ​​corresponding to various simulation conditions and the joint probability mass of the corresponding simulation collision parameters, determine the comprehensive risk assessment value corresponding to the UAV.

[0041] Optionally, the formula for calculating the comprehensive risk assessment value for drones is:

[0042] in, This indicates the overall risk assessment value corresponding to the drone. The value representing the collision velocity of the simulated drone. This represents the value of the vertical plane collision angle of the simulated drone. This represents the values ​​of the three-dimensional attitude of the simulated drone during collision. This indicates the value of the horizontal orientation angle of the simulated human body. The simulation collision parameters are represented as ( ). , , , The risk assessment value corresponding to the simulated working conditions. The simulation collision parameters are ( , , , The joint probability mass corresponding to the simulation conditions of ) The value representing the collision speed of the simulated drone is... Furthermore, the value of the vertical plane collision angle of the simulated drone. The probability density at time , The values ​​representing the 3D attitude of the simulated drone during collision are: The probability density at time , The value represents the horizontal orientation angle of the simulated human body. The probability density at time , The value representing the collision speed of the simulated drone is... The standard area of ​​the integral grid at that time, This represents the value of the vertical plane collision angle of the simulated drone. The standard area of ​​the integral grid at that time, The values ​​representing the 3D attitude of the simulated drone during collision are: The standard area of ​​the integral grid at that time, The value represents the horizontal orientation angle of the simulated human body. The standard area of ​​the integral grid at that time.

[0043] The collision-damage-based UAV crash risk assessment method provided in this invention introduces a collision-induced fatality rate index, enabling a more accurate and efficient quantitative assessment of UAV crash risk. This method fully considers the differences in human injury under different collision scenarios while effectively reducing the computational burden of large-scale simulations, thus balancing assessment accuracy and simulation costs. The risk results obtained through this method are more targeted and practical, providing technical support for the safe operation of UAVs in urban low-altitude transportation, logistics delivery, and agricultural plant protection, and possesses broad application and promotion prospects.

[0044] exist Figure 2Based on the above, regarding the content in S14, this embodiment of the invention also provides an optional implementation method, please refer to the following. S14, based on the injury fatality rate index and grid area corresponding to each grid under the simulation condition, determines the risk assessment value corresponding to the simulation condition, including: S141 and S143, which are specifically described below.

[0045] S141, Determine the overall mortality rate of the grid based on the damage mortality rate index corresponding to the grid.

[0046] Optionally, the formula for the overall lethality rate corresponding to the grid is:

[0047] in, The simulation collision parameters are ( The overall lethality of the mesh under the simulated operating conditions. The simulation collision parameters are ( The head injury fatality rate corresponding to the mesh under the simulated working conditions. The simulation collision parameters are ( The neck injury fatality rate corresponding to the mesh under the simulated working conditions. The simulation collision parameters are ( The lethality rate of viscous damage corresponding to the mesh under simulated working conditions. The simulation collision parameters are ( The deviation vector between the grid center and the impact point under the simulation conditions.

[0048] S143, the comprehensive lethality and grid area corresponding to each grid under the simulation condition, determine the risk assessment value corresponding to the simulation condition.

[0049] Optionally, the formula for the risk assessment value corresponding to the simulation condition is:

[0050] in, The simulation collision parameters are ( The risk assessment values ​​corresponding to the simulated working conditions. The simulation collision parameters are ( The overall lethality of the mesh under the simulated operating conditions. The deviation vector between the grid center and the impact point is: The grid area, This indicates the area covered by the point of impact.

[0051] This invention also provides an optional implementation method, please refer to... Figure 3After determining the comprehensive risk assessment value of the UAV based on the risk assessment values ​​corresponding to various simulation conditions and the joint probability mass of the corresponding simulation collision parameters, the UAV crash risk assessment method based on collision damage also includes: S16, which is described in detail below.

[0052] S16. Based on the comprehensive risk assessment value, the accident rate of unmanned aerial vehicle (UAV) system failure and crash per unit operating hour, the population density of the area over which the UAV flies, and the probability that ground personnel are spared from UAV crash injury, generate the estimated number of ground personnel casualties per unit operating hour.

[0053] Optionally, the formula for estimating the number of ground personnel casualties per unit operating hour is as follows:

[0054] in, This indicates the estimated number of casualties among ground personnel per unit of operating hour. This indicates the accident rate of unmanned aerial vehicle (UAV) system failures and crashes per unit of operating hours. This indicates the population density of the area over which the drone flies. This indicates the probability that ground personnel will be protected from injury by a drone crash. This represents the overall risk assessment value.

[0055] In an optional implementation, the UAV simulation model includes a multibody system model and a finite element coupling model. Before conducting collision simulation based on the UAV simulation model, the human simulation model, and the simulation conditions, the UAV crash risk assessment method based on collision damage also includes the construction of the UAV simulation model. The specific construction process is as follows.

[0056] Step 1: Obtain the material and structural characteristics of the UAV.

[0057] Material characteristics can be obtained through actual testing or authoritative material databases, including material type, material stiffness, material strength and other characteristic parameters of the materials used in the drone. Structural characteristics can be obtained through measurement, including the overall geometric characteristics of the drone (body size, weight distribution, moment of inertia), the connection relationship of different parts of the drone and structural stiffness, etc.

[0058] Step 2: Model the multi-body system (MBS) based on the material and structural characteristics to establish the multi-body system model.

[0059] Among them, the multibody system model mainly focuses on the connection relationships and dynamic characteristics of different components of the UAV.

[0060] Step 3: Combining the material and structural characteristics of key components on the UAV, perform finite element (FE) modeling on the key components and establish a finite element coupled model; Among them, the finite element coupling model is used to describe the stress-strain distribution and deformation data of the key component under impact load. The key component is any component pre-specified on the UAV.

[0061] By using high-precision multibody system (MBS) and finite element (FE) modeling and simulation, and by comprehensively considering multiple factors such as UAV material and structural characteristics, failure scenarios, and human collision models, a refined and quantitative assessment of crash risk has been achieved.

[0062] Please see Figure 4 , Figure 4 The present invention provides a device for assessing the risk of drone crashing based on collision damage. Optionally, the device for assessing the risk of drone crashing based on collision damage is applied to the electronic device described above.

[0063] The drone crash risk assessment device based on collision damage includes: a first processing unit 301 and a second processing unit 302.

[0064] The first processing unit 301 is used to generate multiple simulation conditions based on the numerical range of the simulated collision parameters when the UAV crashes. The simulated collision parameters include any one or more of the following: simulated UAV collision speed, simulated UAV vertical plane collision angle, simulated UAV collision three-dimensional attitude, and simulated human horizontal orientation angle. At least one simulated collision parameter has a different value between different simulation conditions. The first processing unit 301 is also used to determine the landing point coverage area according to the aircraft characteristics of the UAV, and to perform grid division on the landing point coverage area according to the contribution requirements and sensitivity requirements under various simulation conditions, so as to obtain the grid division results corresponding to various simulation conditions. Among them, the contribution requirement means that the proportion of the overall mortality rate corresponding to the grid relative to the overall risk assessment value is lower than the first proportion threshold, and the sensitivity requirement means that the impact of the overall mortality rate corresponding to the grid on the overall risk assessment value is lower than the second impact threshold. The first processing unit 301 is also used to perform collision simulation based on the UAV simulation model, the human simulation model and the simulation conditions, and combine the deviation vector between the center of each grid and the landing point in the corresponding grid division results to determine the injury fatality rate index corresponding to each grid under various simulation conditions. The injury fatality rate index includes head injury fatality rate, neck injury fatality rate and viscous injury fatality rate. The first processing unit 301 is also used to determine the risk assessment value corresponding to the simulation condition based on the injury fatality rate index and the grid area corresponding to each grid under the simulation condition. The second processing unit 302 is used to determine the comprehensive risk assessment value of the UAV based on the risk assessment values ​​corresponding to various simulation conditions and the joint probability mass of the corresponding simulation collision parameters.

[0065] Optionally, the first processing unit 301 is also used to generate an estimated number of ground personnel casualties per unit operating hour based on the comprehensive risk assessment value, the accident rate of unmanned aerial vehicle system failure and crash per unit operating hour, the population density of the area over which the unmanned aerial vehicle flies, and the probability that ground personnel are spared from injury from the unmanned aerial vehicle crash.

[0066] It should be noted that the collision-damage-based UAV crash risk assessment device provided in this embodiment can execute the method flow shown in the above-described method flow embodiment to achieve the corresponding technical effects. For the sake of brevity, any parts not mentioned in this embodiment can be referred to the corresponding content in the above-described embodiments.

[0067] This invention also provides a storage medium storing computer instructions and programs. When read and executed, these instructions and programs perform the collision-damage-based drone crash risk assessment method described in the above embodiments. The storage medium may include memory, flash memory, registers, or a combination thereof.

[0068] The following provides an electronic device, which may be a server device, a computer device, or a mobile phone device. This electronic device, as shown below... Figure 1 As shown, the above-described method for assessing the drone crash risk based on collision damage can be implemented. Specifically, the electronic device includes: a processor 10, a memory 11, and a bus 12. The processor 10 may be a CPU. The memory 11 is used to store one or more programs, which, when executed by the processor 10, execute the drone crash risk assessment method based on collision damage described in the above embodiment.

[0069] In summary, the present invention provides a method and related apparatus for assessing the crash risk of unmanned aerial vehicles (UAVs) based on collision damage. This method generates multiple simulation scenarios based on the numerical range of simulated collision parameters during a UAV crash. It determines the crash site coverage area based on the UAV's model characteristics and, under various simulation scenarios, divides the crash site coverage area into grids according to contribution and sensitivity requirements to obtain grid division results corresponding to each simulation scenario. Collision simulations are performed using a UAV simulation model, a human simulation model, and the simulation scenarios. Combining the deviation vector between the center of each grid and the crash site in the corresponding grid division results, the method determines the fatality rate index corresponding to each grid under various simulation scenarios. Based on the fatality rate index and grid area of ​​each grid under each simulation scenario, the method determines the risk assessment value corresponding to the simulation scenario. Finally, based on the risk assessment values ​​corresponding to various simulation scenarios and the joint probability mass of the corresponding simulated collision parameters, the method determines the comprehensive risk assessment value for the UAV. By introducing the fatality rate index caused by collision, a more accurate and efficient quantitative assessment of UAV crash risk is achieved. This method not only fully considers the differences in human injury under different collision scenarios but also effectively reduces the computational burden of large-scale simulations, balancing assessment accuracy and simulation costs. The risk results obtained through this method are more targeted and practical, providing technical support for the safe operation of drones in urban low-altitude transportation, logistics delivery, agricultural plant protection, and other fields, and has broad application and promotion prospects.

[0070] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0071] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A method for assessing the crash risk of unmanned aerial vehicles (UAVs) based on collision damage, characterized in that, The method includes: Based on the numerical range of the simulated collision parameters when the drone crashes, multiple simulation scenarios are generated. The simulated collision parameters include one or more of the following: simulated drone collision speed, simulated drone vertical plane collision angle, simulated drone collision three-dimensional attitude, and simulated human horizontal orientation angle. At least one simulated collision parameter has a different value between different simulation scenarios. The impact coverage area is determined based on the characteristics of the UAV model, and the impact coverage area is divided into grids according to contribution and sensitivity requirements under various simulation conditions to obtain the grid division results corresponding to various simulation conditions. The contribution requirement refers to the proportion of the overall mortality rate corresponding to the grid relative to the overall risk assessment value being lower than the first proportion threshold, and the sensitivity requirement refers to the degree of influence of the overall mortality rate corresponding to the grid on the overall risk assessment value being lower than the second influence threshold. Collision simulations are conducted based on UAV simulation models, human simulation models, and simulation conditions. By combining the deviation vector between the center of each grid and the landing point in the corresponding grid division results, the injury fatality rate index corresponding to each grid under various simulation conditions is determined. The injury fatality rate index includes head injury fatality rate, neck injury fatality rate, and viscous injury fatality rate. Based on the injury fatality rate index and grid area corresponding to each grid under the simulation condition, determine the risk assessment value corresponding to the simulation condition. Based on the risk assessment values ​​corresponding to various simulation conditions and the joint probability mass of the corresponding simulation collision parameters, the comprehensive risk assessment value corresponding to the UAV is determined.

2. The method for assessing the crash risk of unmanned aerial vehicles based on collision damage as described in claim 1, characterized in that, The formula for calculating the comprehensive risk assessment value corresponding to the drone is: in, This indicates the overall risk assessment value corresponding to the aforementioned drone. The value representing the collision velocity of the simulated drone. This represents the value of the vertical plane collision angle of the simulated drone. This represents the values ​​of the three-dimensional attitude of the simulated drone during collision. This indicates the value of the horizontal orientation angle of the simulated human body. The simulation collision parameters are represented as ( ). , , , The risk assessment value corresponding to the simulated working conditions. The simulation collision parameters are ( , , , The joint probability mass corresponding to the simulation conditions of ) The value representing the collision speed of the simulated drone is... Furthermore, the value of the vertical plane collision angle of the simulated drone. The probability density at time , The values ​​representing the 3D attitude of the simulated drone during collision are: The probability density at time , The value represents the horizontal orientation angle of the simulated human body. The probability density at time , The value representing the collision speed of the simulated drone is... The standard area of ​​the integral grid at that time, This represents the value of the vertical plane collision angle of the simulated drone. The standard area of ​​the integral grid at that time, The values ​​representing the 3D attitude of the simulated drone during collision are: The standard area of ​​the integral grid at that time, The value represents the horizontal orientation angle of the simulated human body. The standard area of ​​the integral grid at that time.

3. The method for assessing the crash risk of unmanned aerial vehicles based on collision damage as described in claim 1, characterized in that, The step of determining the risk assessment value corresponding to the simulation condition based on the injury fatality rate index and grid area corresponding to each grid under the simulation condition includes: Determine the overall mortality rate of the grid based on the damage mortality rate index corresponding to the grid. The comprehensive lethality and grid area corresponding to each grid under the simulation condition are used to determine the risk assessment value corresponding to the simulation condition.

4. The method for assessing the crash risk of unmanned aerial vehicles based on collision damage as described in claim 3, characterized in that, The formula for the overall lethality rate corresponding to the grid is: in, The simulation collision parameters are ( The overall lethality of the mesh under the simulated operating conditions. The simulation collision parameters are ( The head injury fatality rate corresponding to the mesh under the simulated working conditions. The simulation collision parameters are ( The neck injury fatality rate corresponding to the mesh under the simulated working conditions. The simulation collision parameters are ( The lethality rate of viscous damage corresponding to the mesh under simulated working conditions. The simulation collision parameters are ( The deviation vector between the grid center and the impact point under the simulation conditions.

5. The method for assessing the crash risk of unmanned aerial vehicles based on collision damage as described in claim 3, characterized in that, The formula for calculating the risk assessment value corresponding to the simulated working condition is: in, The simulation collision parameters are ( The risk assessment values ​​corresponding to the simulated operating conditions. The simulation collision parameters are ( The overall lethality of the mesh under the simulated operating conditions. The deviation vector between the grid center and the impact point is: The grid area, This indicates the area covered by the point of impact.

6. The method for assessing the crash risk of unmanned aerial vehicles based on collision damage as described in claim 1, characterized in that, After determining the comprehensive risk assessment value of the UAV based on the joint probabilistic mass of the risk assessment values ​​corresponding to various simulation conditions and the corresponding simulation collision parameters, the method further includes: Based on the aforementioned risk assessment composite value, the accident rate of unmanned aerial vehicle (UAV) system failure and crash per unit operating hour, the population density of the area over which the UAV flies, and the probability that ground personnel are spared from UAV crash injury, the estimated number of ground personnel casualties per unit operating hour is generated.

7. The method for assessing the crash risk of unmanned aerial vehicles based on collision damage as described in claim 6, characterized in that, The formula for estimating the number of ground personnel casualties per unit operating hour is as follows: in, This indicates the estimated number of casualties among ground personnel per unit of operating hour. This indicates the accident rate of unmanned aerial vehicle (UAV) system failures and crashes per unit of operating hours. This indicates the population density of the area over which the drone flies. This indicates the probability that ground personnel will be protected from injury by a drone crash. This represents the overall risk assessment value.

8. A device for assessing the crash risk of unmanned aerial vehicles (UAVs) based on collision damage, characterized in that, The device includes: The first processing unit is used to generate multiple simulation conditions based on the numerical range of the simulated collision parameters when the UAV crashes; wherein, the simulated collision parameters include any one or more of the following: simulated UAV collision speed, simulated UAV vertical plane collision angle, simulated UAV collision three-dimensional attitude, and simulated human body horizontal orientation angle, and at least one simulated collision parameter has a different value between different simulation conditions. The first processing unit is also used to determine the landing point coverage area according to the aircraft type characteristics of the UAV, and to perform grid division on the landing point coverage area according to the contribution requirements and sensitivity requirements under various simulation conditions, so as to obtain the grid division results corresponding to various simulation conditions. The contribution requirement refers to the proportion of the overall mortality rate corresponding to the grid relative to the overall risk assessment value being lower than the first proportion threshold, and the sensitivity requirement refers to the degree of influence of the overall mortality rate corresponding to the grid on the overall risk assessment value being lower than the second influence threshold. The first processing unit is also used to perform collision simulation based on the UAV simulation model, the human simulation model and the simulation conditions, and combine the deviation vector between the center of each grid and the landing point in the corresponding grid division results to determine the injury fatality rate index corresponding to each grid under various simulation conditions. The injury fatality rate index includes head injury fatality rate, neck injury fatality rate and viscous injury fatality rate. The first processing unit is also used to determine the risk assessment value corresponding to the simulation condition based on the injury fatality rate index and the grid area corresponding to each grid under the simulation condition; The second processing unit is used to determine the comprehensive risk assessment value of the UAV based on the risk assessment values ​​corresponding to various simulation conditions and the joint probability mass of the corresponding simulation collision parameters.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method as described in any one of claims 1-7.

10. An electronic device, characterized in that, include: Processor and memory, the memory being used to store one or more programs; When the one or more programs are executed by the processor, the method as described in any one of claims 1-7 is implemented.

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