A method and device for assessing the risk of a crash range of an unmanned aerial vehicle

Abstract

The application provides a method and device for evaluating the risk of the landing range of an unmanned aerial vehicle, and relates to the technical field of unmanned aerial vehicle risk evaluation, comprising: analyzing different failure forms of the unmanned aerial vehicle, and constructing an unmanned aerial vehicle failure model; extracting functional loss features according to the unmanned aerial vehicle failure model, combining a falling flight path to construct a failure falling flight path prediction model; simulating landing motion according to the failure falling flight path prediction model, calculating an impact area through the conditions of the landing motion and impact environment parameters of the landing, and obtaining an impact influence area; performing ground feature determination based on the latitude and longitude boundary and the impact influence area, correcting the landing contact parameters through the ground feature, and obtaining an optimized impact area result; performing grid division on the impact area based on the optimized impact area result and impact evaluation indexes, calculating a risk evaluation value, and obtaining a landing range risk evaluation result. The application solves the problem that the risk of unmanned aerial vehicle out-of-control landing cannot be evaluated.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) risk assessment technology, and more specifically, to a method and apparatus for assessing the crash range risk of unmanned aerial vehicles. Background Technology

[0002] In existing drone risk assessment technologies, due to the unmanned nature of drones, the focus of operational safety risks has shifted from threats to the safety of onboard personnel to threats to the safety of ground personnel. The drone regulation development consortium has explicitly defined harm caused by drones to third parties on the ground as a major operational risk. Currently, reducing the threat to ground personnel is key to mitigating drone crash risks. The scope of threatened ground personnel is determined by the drone's failure trajectory and impact area; therefore, the key to accident risk prediction and mitigation lies in accurately predicting the drone's failure trajectory. However, existing methods for calculating the ground impact range of drones often simplify the drone to a point mass. Given the diversity of drone types, significant performance differences, complex failure modes, and the influence of airspace and weather conditions, it is difficult to accurately simulate their trajectory after crashing to the ground, leading to the inability to assess the risk of uncontrolled drone crashes.

[0003] Therefore, there is an urgent need for a method and device for assessing the risk of unmanned aerial vehicles crashing to the ground, which solves the problem of being unable to assess the risk of uncontrolled drone crashes. Summary of the Invention

[0004] The purpose of this invention is to provide a method and apparatus for assessing the crash zone risk of unmanned aerial vehicles (UAVs) to improve the aforementioned problems. To achieve the above objective, the technical solution adopted by this invention is as follows:

[0005] Firstly, this application provides a method for assessing the crash zone risk of an unmanned aerial vehicle, including:

[0006] Analyze different failure modes of UAVs and construct a UAV failure model;

[0007] Based on the aforementioned UAV failure model, functional loss features are extracted, and a failure crash trajectory prediction model is constructed by combining crash trajectories of similar failure modes.

[0008] The impact area is obtained by simulating the ground motion based on the failure fall trajectory prediction model, and calculating the impact area by using the ground motion conditions and preset impact environment parameters.

[0009] Based on the latitude and longitude boundaries of the work area and the impact area, ground features are determined, and the impact contact parameters are corrected by ground features to obtain the optimized impact area result.

[0010] Based on the optimized impact area results and the preset impact assessment indicators, the impact area is divided into grids and the risk assessment value is calculated to obtain the risk assessment result of the impact range.

[0011] Secondly, this application also provides a device for assessing the crash radius risk of an unmanned aerial vehicle, comprising:

[0012] The failure analysis module is used to analyze different failure modes of UAVs and build UAV failure models.

[0013] The extraction module is used to extract functional loss features based on the UAV failure model and construct a failure crash trajectory prediction model by combining crash trajectories with similar failure modes.

[0014] The simulation module is used to simulate the impact motion based on the failure fall trajectory prediction model, and calculate the impact area by using the conditions of the impact motion and preset impact environment parameters.

[0015] The determination module is used to determine the ground features based on the latitude and longitude boundaries of the work area and the impact area, and to correct the impact contact parameters through the ground features to obtain the optimized impact area result.

[0016] The partitioning module is used to divide the impact area into grids based on the optimized impact area results and preset impact assessment indicators, and calculate the risk assessment value to obtain the risk assessment result of the impact range.

[0017] The beneficial effects of this invention are as follows:

[0018] This invention analyzes different failure modes of UAVs and constructs failure models. It then combines these with similar failure trajectories to build a trajectory prediction model, abandoning traditional empirical estimation methods and ensuring that the predicted UAV crash trajectory closely matches real-world failure conditions. Furthermore, it integrates failure trajectory simulation, crash motion conditions, and preset impact environment parameters into the calculation, and corrects contact parameters based on the latitude and longitude boundaries of the operating area and ground features, improving the rationality and accuracy of the impact impact area. The optimized impact area is gridded, and risk assessment values ​​are calculated, achieving a leapfrog upgrade in risk assessment from macroscopic judgment of the entire area to precise quantification of risk at each point. This technology can be used for airspace delineation, flight restrictions, ground personnel evacuation, and safety liability determination. In summary, this invention solves the problem of the inability to assess the risk of uncontrolled UAV crashes.

[0019] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing embodiments of the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description

[0020] 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.

[0021] Figure 1 This is a schematic diagram of the risk assessment method for the crash range of an unmanned aerial vehicle as described in an embodiment of the present invention;

[0022] Figure 2 This is a schematic diagram of the structure of the crash range risk assessment device for the unmanned aerial vehicle described in this embodiment of the invention.

[0023] The markings in the diagram are: 800, Unmanned Aerial Vehicle (UAV) crash zone risk assessment equipment; 801, Processor; 802, Memory; 803, Multimedia component; 804, I / O interface; 805, Communication component. Detailed Implementation

[0024] 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, not all, of the embodiments of the present invention. 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. Therefore, the following detailed description of the embodiments of the present 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 present invention without inventive effort are within the scope of protection of the present invention.

[0025] 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.

[0026] In the context of the commercial application of drones in urban low-altitude logistics, inspection, and aerial photography, drones may experience various failures when operating in complex urban airspace, variable weather conditions, and densely populated areas. These failures can be caused by equipment malfunctions, environmental interference, and human error, leading to loss of altitude, loss of control, ground impact, or component drop. Furthermore, existing technologies often simplify drones as point masses for calculating the fall radius without fully considering the differences in drone structure, failure modes, aerodynamic characteristics, and ground environment. This results in the inability to accurately simulate the failure trajectory and impact range, leading to the technical problem of being unable to quantitatively assess the risk of injury to ground personnel and the impact range after a drone crashes out of control.

[0027] Example 1:

[0028] This embodiment provides a method for assessing the risk of unmanned aerial vehicle crash site.

[0029] See Figure 1 The figure shows that the method includes steps S1 to S5, including:

[0030] S1: Analyze different failure modes of the UAV and construct a UAV failure model;

[0031] To clarify the specific methods for obtaining the drone failure model, the following are included:

[0032] Based on the configuration, application scenarios, operation procedures, flight tests, and historical accident data of UAVs, we analyze the failure modes of UAVs, such as loss of altitude holding ability, loss of control ability, crashing to the ground due to operational errors, and falling system components. Then, we construct UAV failure models according to equipment causes, operating environment factors, and human factors.

[0033] In this step, based on existing unmanned aerial vehicle (UAV) configurations, application scenarios, and standardized operating procedures, and combined with flight test data and historical crash accident records, we first sorted out and summarized four core failure modes: loss of altitude maintenance capability, loss of control capability, crash due to operational errors, and system component drop. Then, according to three major causal dimensions—equipment malfunction, operating environment-induced failure, and human error—we classified, summarized, and mapped the causes of failure, and constructed a UAV failure model covering the three dimensions of equipment, environment, and personnel.

[0034] The expression for the UAV failure model is:

[0035] ;

[0036] In the above formula, For the first Type of failure causes and the first The correlation of failure modes For the existence of the first Type of triggers and the first Number of samples of different failure modes For the existence of the first The total number of samples for each type of failure cause. For the existence of the first Total number of samples for each type of failure.

[0037] S2: Extract functional loss features based on the UAV failure model, and construct a failure crash trajectory prediction model by combining crash trajectories with similar failure modes;

[0038] To clarify the specific method for obtaining the failure crash trajectory prediction model, step S2 includes S21 to S23, specifically:

[0039] S21: Based on the failure inductions and causes of the UAV failure model, the different airframe structures of the UAV are matched and associated to extract the functional loss features of the UAV failure.

[0040] In this step, the various failure inducing factors and causes in the UAV failure model are matched and associated with the key airframe structures of the UAV, such as flight control, power, rotor, navigation, and structure. By performing failure propagation logic analysis on each matching relationship, the types and degrees of airframe functional damage caused by different inducing factors are obtained, and the functional loss characteristics after UAV failure are extracted.

[0041] The expression for the functional loss characteristics of the UAV failure is:

[0042] ;

[0043] In the above formula, For the first Standardized values ​​of each functional loss feature For the first The original measurements of each functional loss feature, The minimum measured value of the functional loss characteristic. This represents the maximum measured value of the functional loss characteristic.

[0044] S22: Based on the aforementioned UAV failure model, UAV flight test data, and historical crash accident data, different failure modes with similar trajectory characteristics are integrated to obtain similar failure crash trajectories;

[0045] In this step, based on the aforementioned UAV failure model, and combined with the UAV flight test trajectory data and real crash accident playback data, the crash trajectory characteristics under different failure modes are clustered and summarized. Failure modes with similar crash attitude, speed changes, and displacement trends are integrated and classified to form standardized similar failure crash tracks.

[0046] ;

[0047] In the above formula, For the first Type of failure and the first Similarity of flight paths in failure-like scenarios In order to achieve a sum, The number of track feature parameters, For the first The first type of failure trajectory Each feature parameter value, For the first The first type of failure trajectory Each feature parameter value, For the first The maximum value of each feature parameter. For the first The minimum value of each characteristic parameter.

[0048] S23: Match the similar failure crash trajectories with the functional loss features of the UAV failure one by one, and perform differentiated kinematic modeling and dynamic modeling respectively to obtain the failure crash trajectory prediction model.

[0049] In this step, the similar failure crash tracks are matched and associated with the functional loss characteristics under the failure scenario. Different kinematic equations and dynamic equations are established for different matching combinations. The model parameters are calibrated and verified considering the differences in parameters such as body mass, aerodynamic characteristics, and stress state, so as to form a failure crash track prediction model for different failure scenarios.

[0050] The expression for the kinematic equations is:

[0051] ;

[0052] In the above formula, and for The center of gravity of the drone is always in , , Velocity in the direction (m / s) For the current moment, , and for , , Initial moment of directional failure ( The speed (m / s) For definite integral operations, , and for The center of gravity of the drone is always in , , Acceleration in the direction of For integration dummy variables, For an integral infinitesimal element, , and for The center of gravity of the drone is always in , , Position of direction (m) , and for , , The position (m) at the initial moment of directional failure (t=0).

[0053] The expression for the dynamic equation is:

[0054] ;

[0055] In the above formula, The mass of the drone (kg) is the weight of the drone body. The acceleration vector of the UAV's center of mass (m / s²) 2 ), The acceleration vector due to gravity (m / s²) 2 ), N is the rotor lift vector. N is the air resistance vector. The ambient wind force vector (N) is used.

[0056] S3: Simulate the ground motion based on the failure fall trajectory prediction model, calculate the impact area by using the ground motion conditions and preset impact environment parameters, and obtain the impact impact area;

[0057] To clarify the specific method for obtaining the impact-affected area, step S3 includes S31 to S34, specifically:

[0058] S31: Based on the failure crash trajectory prediction model, the crash motion of the UAV after failure is decomposed into vertical and horizontal directions, and the air resistance motion equation is established in combination with air resistance.

[0059] In this step, based on the failure crash trajectory prediction model, the crash motion of the UAV after failure is decomposed into vertical descent motion and horizontal drift motion according to the spatial dimension. Combined with the aerodynamic characteristics of the UAV, an air resistance term is added to obtain the air resistance motion equation including vertical and horizontal components.

[0060] The expression for the vertical air resistance motion is:

[0061] ;

[0062] In the above formula, Vertical air resistance motion, For the quality of drones, The acceleration is in the vertical direction. It is the acceleration due to gravity. air density, The air drag coefficient, For windward area, The velocity is in the vertical direction. This represents the vertical displacement.

[0063] The expression for the equation of motion for horizontal air resistance is as follows:

[0064] ;

[0065] In the above formula, The motion is due to horizontal air resistance. For the quality of drones, For horizontal acceleration, air density, The air drag coefficient, For windward area, The velocity is in the horizontal direction. This represents horizontal displacement.

[0066] S32: Substitute the preset flight dynamic environment parameters and the drag characteristic parameters of the UAV into the air resistance motion equation to derive the failure equation;

[0067] In this step, the flight dynamic environment parameters and the drag characteristic parameters are substituted into the air resistance motion equation for mathematical derivation, and irrelevant variables are eliminated to form a failure fall motion equation applicable to different failure scenarios.

[0068] The expression for the vertical failure fall motion equation is:

[0069] ;

[0070] In the above formula, The acceleration is in the vertical direction. For the quality of drones, It is the acceleration due to gravity. air density, The air drag coefficient, For windward area, The velocity is in the vertical direction. This represents the vertical displacement.

[0071] The expression for the horizontal failure fall motion equation is:

[0072] ;

[0073] In the above formula, For horizontal acceleration, For the quality of drones, air density, The air drag coefficient, For windward area, The velocity is in the horizontal direction. This represents horizontal displacement.

[0074] The failure fall motion equation is used as the failure equation. The flight dynamic environment parameters include air density, wind speed, flight attitude parameters and flight altitude in meteorological conditions. The drag characteristic parameters include air drag coefficient and frontal area.

[0075] S33: Substitute the impact environmental parameters into the failure equation to perform a failure fall simulation and obtain the fall motion characteristics;

[0076] To clarify the specific method for obtaining the characteristics of the fall motion, step S33 includes S331 to S334, specifically:

[0077] S331: Substitute the initial velocity boundary conditions of the impact environment parameters into the failure equation to solve for vertical motion and zero displacement, and integrate to obtain the UAV impact point.

[0078] In this step, the initial velocity boundary conditions at the moment of UAV failure are substituted into the failure equation, and the Runge-Kutta method is used for numerical solution. The vertical motion equation is obtained, and the vertical displacement is set to zero (i.e., ), determine the time of impact Corresponding horizontal displacement The impact point of the drone was obtained by integration. .

[0079] S332: Based on the initial vertical velocity of the UAV at the time of failure, the operating altitude of the impact environment parameters, and the drag characteristic parameters in the vertical direction, a dynamic calculation is performed to obtain the fall landing time;

[0080] In this step, based on the initial vertical velocity component at the time of drone failure and the actual operating altitude at the time of failure, the vertical windward area and air resistance coefficient are substituted into the vertical failure equation, and the fall landing time is obtained by dynamic integration.

[0081] The specific integral expression for the fall landing time is:

[0082] ;

[0083] In the above formula, For the time it took to fall to the ground, For drones The instantaneous velocity in the vertical direction at time t. Let be the initial vertical velocity at the moment of drone failure. The time of failure Until the moment of impact , For the quality of drones, It is the acceleration due to gravity. air density, The air drag coefficient, For windward area, For any time within the integration interval The instantaneous velocity in the vertical direction, The velocity is in the vertical direction. This represents the vertical displacement.

[0084] S333: Calculate the horizontal distance of the crash landing based on the horizontal component of the initial velocity of the UAV at the time of crash landing and the time of crash landing;

[0085] In this step, the time of impact is used as the time reference. The horizontal component of the initial velocity at the moment of drone failure is substituted into the failure equation in the horizontal direction. The relationship between the horizontal velocity and time is solved through dynamic integration. Then, the relationship is analyzed over the time interval. The system performs an integral to obtain the horizontal distance from the point of failure of the UAV to the point of impact.

[0086] The specific integral expression for the horizontal distance of the failed fall is:

[0087] ;

[0088] In the above formula, The horizontal distance from the failure initiation position to the point of impact on the ground is the failure impact distance. For definite integral operations, For the time it took to fall to the ground, for The instantaneous horizontal velocity at a given moment, It is an integral dummy variable.

[0089] Wherein, the horizontal distance from the failure initiation position to the impact point is the failure fall horizontal distance.

[0090] S334: Based on the UAV's impact point, the landing time, and the horizontal distance of the failed landing, the falling motion characteristics are constructed to obtain the falling motion characteristics.

[0091] In this step, the impact point of the UAV, the time of the fall and the horizontal distance of the failed fall are integrated, and a complete set of fall motion features including position, time, distance, speed and kinetic energy is constructed by combining the changes in fall speed and kinetic energy.

[0092] S34: Based on the falling motion characteristics and the resistance characteristic parameters, assess the impact range of the impact accident and construct the impact area.

[0093] In this step, based on the characteristics of the fall motion, the impact area of ​​the vertical fall accident and the impact area of ​​the lateral sliding accident are calculated by combining the drag characteristic parameters, the angle between the fall trajectory and the ground, the ground friction coefficient, and the impact kinetic energy constant. Then, the impact areas of the vertical fall accident and the lateral sliding accident are merged based on spatial geometric operations. Overlapping areas are eliminated by geometric spatial judgment and intersection calculation to avoid duplicate measurement. At the same time, the edge omission areas are supplemented by combining the sliding offset, angle deviation and drag attenuation characteristics. The realism and accuracy of the range calculation are improved by correcting the kinetic energy constant and friction coefficient with ground features. Finally, the complete impact area after the UAV failure and crash is constructed.

[0094] S4: Based on the latitude and longitude boundaries of the work area and the impact area, ground features are determined, and the impact contact parameters are corrected through ground features to obtain the optimized impact area result;

[0095] To clarify the specific method for obtaining the optimized impact area results, step S4 includes S41 to S43, specifically:

[0096] S41: Based on the latitude and longitude boundaries, perform region-by-region ground feature determination of the UAV failure area to obtain different ground feature types for different operating areas;

[0097] In this step, based on the latitude and longitude boundaries, the entire spatial range where the UAV failure occurred is divided and retrieved by grid using a grid division method. The surface physical attribute characteristics of each divided area are determined by combining geographic information data and field survey data to obtain different ground feature types of the operation area. The different ground feature types of the operation area include hardened paved roads, soil and grassland, vegetation and woodland, buildings and water areas, etc.

[0098] S42: Based on the different ground feature types of the operation area, the impact kinetic energy constant and ground friction coefficient of the impact-affected area are corrected according to the ground feature, to obtain the corrected impact dynamic parameters;

[0099] In this step, the surface material property database is retrieved according to the different ground feature types of the operation area. The original impact kinetic energy constant and ground friction coefficient of the impact-affected area are graded and numerically corrected according to the physical and mechanical properties of different surface features to obtain corrected impact dynamic parameters that fit the actual impact environment.

[0100] S43: Calculate the impact area of ​​the accident based on the corrected impact dynamics parameters to obtain the optimized impact area result.

[0101] In this step, the corrected impact dynamics parameters are re-substituted into the preset impact impact range assessment model to recalculate the vertical impact area and the lateral sliding impact area. The boundary contour of the impact area is smoothed and its shape is adjusted in combination with the surface features. Finally, the optimized impact area result is output, which takes into account both the prediction accuracy and the actual situation of the ground surface.

[0102] S5: Based on the optimized impact area results and the preset impact assessment indicators, the impact area is divided into grids and the risk assessment value is calculated to obtain the risk assessment result of the impact range.

[0103] To clarify the specific method for obtaining the risk assessment results of the impact area, step S5 includes S51 to S55, specifically:

[0104] S51: Obtain population density data;

[0105] In this step, the population density data refers to high-precision population density data within the drone operation area and the optimized impact area, including resident population density, dynamic population density, and population distribution data at different times.

[0106] S52: Based on the optimized impact area results and the population density data, calculate the number of people in the area affected by the accident to obtain the degree of harm of the ground impact accident;

[0107] In this step, the estimated number of people in the affected area is calculated based on the optimized impact area results and the population density data. The degree of harm caused by the ground impact accident is further quantified by combining the estimated number of people, thus forming a quantified result of the degree of harm.

[0108] S53: Calculate the probability of casualties based on the crash energy of the drone, the preset shielding protection coefficient, and the personnel injury threshold to obtain the probability of casualties in the accident.

[0109] In this step, based on key parameters such as the crash energy of the drone, the protection coefficient of the shield, and the personnel injury threshold, coupled calculations are performed using ground features and impact kinetic energy to obtain the probability of casualties per unit area and within the overall area.

[0110] S54: Based on the severity of the ground impact accident and the probability of casualties, select scenario elements as the main factors affecting the probability of ground impact accidents, and construct an accident risk probability model.

[0111] In this step, based on the severity of the ground impact accident and the probability of casualties, scene elements such as meteorological elements, geographical elements, and communication elements that have a significant impact on the probability of ground impact accidents are selected as the main factors to construct an accident risk probability model.

[0112] S55: Based on the accident risk probability model, the impact area is divided into grids and the crash risk assessment value of the UAV within the grid is calculated to obtain the crash range risk assessment result.

[0113] In this step, based on the accident risk probability model, the impact area and the surrounding safety control range are divided into several small-scale grids of equal area. The corresponding crash risk assessment value is calculated for each grid and a spatial distribution is formed. Finally, the crash range risk assessment result for route planning and safety control is obtained.

[0114] Example 2:

[0115] This embodiment provides a device for assessing the crash zone risk of an unmanned aerial vehicle, the device comprising:

[0116] The failure analysis module is used to analyze different failure modes of UAVs and build UAV failure models.

[0117] The extraction module is used to extract functional loss features based on the UAV failure model and construct a failure crash trajectory prediction model by combining crash trajectories with similar failure modes.

[0118] To clarify the specific methods for obtaining the extraction module, the following are included:

[0119] The association unit is used to match and associate the failure inducing factors and causes of the UAV failure model with different airframe structures of the UAV to extract the functional loss features of the UAV failure.

[0120] The integration unit is used to integrate different failure modes with similar trajectory characteristics based on the UAV failure model, UAV flight test data and crash accident history data to obtain similar failure crash trajectories.

[0121] The matching unit is used to match the similar failure crash tracks with the functional loss features of the UAV failure one by one, and perform differentiated kinematic modeling and dynamic modeling respectively to obtain the failure crash track prediction model.

[0122] The simulation module is used to simulate the impact motion based on the failure fall trajectory prediction model, and calculate the impact area by using the conditions of the impact motion and preset impact environment parameters.

[0123] To clarify the specific methods for obtaining the simulation module, the following are included:

[0124] The decomposition unit is used to decompose the fall motion of the UAV after failure into vertical and horizontal directions according to the failure fall trajectory prediction model, and to establish the air resistance motion equation in combination with air resistance.

[0125] The derivation unit is used to substitute preset flight dynamic environment parameters and UAV drag characteristic parameters into the air resistance motion equation to derive the failure equation.

[0126] The deduction unit is used to substitute the impact environmental parameters into the failure equation to perform failure fall deduction and obtain the fall motion characteristics.

[0127] The evaluation unit is used to assess the impact range of an impact accident based on the fall motion characteristics and the drag characteristic parameters, and to construct the impact impact area.

[0128] The determination module is used to determine the ground features based on the latitude and longitude boundaries of the work area and the impact area, and to correct the impact contact parameters through the ground features to obtain the optimized impact area result.

[0129] The partitioning module is used to divide the impact area into grids based on the optimized impact area results and preset impact assessment indicators, and calculate the risk assessment value to obtain the risk assessment result of the impact range.

[0130] To clarify the specific methods for obtaining the modules, the following are included:

[0131] Acquisition unit, used to acquire population density data;

[0132] The population calculation unit is used to calculate the number of people in the area affected by the accident based on the optimized impact area results and the population density data, so as to obtain the degree of harm of the ground impact accident.

[0133] The probability calculation unit is used to calculate the probability of casualties based on the crash energy of the drone, the preset shield protection coefficient, and the personnel injury threshold, and to obtain the probability of casualties in the accident.

[0134] The selection unit is used to select scene elements as the main factors affecting the probability of ground impact accidents based on the severity of the ground impact accident and the probability of casualties, and to construct an accident risk probability model.

[0135] The partitioning unit is used to divide the impact area into grids based on the accident risk probability model and calculate the drone crash risk assessment value within the grid to obtain the crash range risk assessment result.

[0136] It should be noted that the specific manner in which each module performs its operation in the apparatus described in the above embodiments has been described in detail in the embodiments of the method, and will not be elaborated here.

[0137] Example 3:

[0138] Corresponding to the above method embodiments, this embodiment also provides a crash range risk assessment device for unmanned aerial vehicles. The crash range risk assessment device for unmanned aerial vehicles described below and the crash range risk assessment method for unmanned aerial vehicles described above can be referred to in correspondence.

[0139] Figure 2 This is a block diagram illustrating a crash range risk assessment device 800 for an unmanned aerial vehicle according to an exemplary embodiment. Figure 2 As shown, the crash zone risk assessment device 800 for the unmanned aerial vehicle may include: a processor 801 and a memory 802. The crash zone risk assessment device 800 for the unmanned aerial vehicle may also include one or more of a multimedia component 803, an I / O interface 804, and a communication component 805.

[0140] The processor 801 controls the overall operation of the unmanned aerial vehicle (UAV) crash range risk assessment device 800 to complete all or part of the steps in the aforementioned UAV crash range risk assessment method. The memory 802 stores various types of data to support the operation of the UAV crash range risk assessment device 800. This data may include, for example, instructions for any application or method operating on the UAV crash range risk assessment device 800, as well as application-related data such as contact data, sent and received messages, images, audio, video, etc. The memory 802 can be implemented using any type of volatile or non-volatile storage device or a combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The multimedia component 803 may include a screen and an audio component. The screen may be, for example, a touchscreen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 802 or transmitted via the communication component 805. The audio component also includes at least one speaker for outputting audio signals. I / O interface 804 provides an interface between processor 801 and other interface modules, such as a keyboard, mouse, and buttons. These buttons can be virtual or physical. Communication component 805 is used for wired or wireless communication between the unmanned aerial vehicle's crash range risk assessment device 800 and other devices. Wireless communication includes Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination thereof. Therefore, the corresponding communication component 805 may include a Wi-Fi module, a Bluetooth module, or an NFC module.

[0141] In an exemplary embodiment, the crash range risk assessment device 800 for an unmanned aerial vehicle may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the aforementioned crash range risk assessment method for unmanned aerial vehicles.

[0142] Example 4:

[0143] Corresponding to the above method embodiments, this embodiment also provides a medium. The medium described below can be referred to in conjunction with the above-described method for assessing the crash range risk of an unmanned aerial vehicle.

[0144] A medium storing a computer program, which, when executed by a processor, implements the steps of the method for assessing the crash range risk of an unmanned aerial vehicle as described in the above method embodiments.

[0145] The medium can specifically be any medium capable of storing program code, such as a USB flash drive, external hard drive, read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0146] 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.

[0147] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for assessing the risk of a drone's crash site, characterized in that, include: Analyze different failure modes of UAVs and construct a UAV failure model; Based on the aforementioned UAV failure model, functional loss features are extracted, and a failure crash trajectory prediction model is constructed by combining crash trajectories of similar failure modes. The impact area is obtained by simulating the ground motion based on the failure fall trajectory prediction model, and calculating the impact area by using the ground motion conditions and preset impact environment parameters. Based on the latitude and longitude boundaries of the work area and the impact area, ground features are determined, and the impact contact parameters are corrected by ground features to obtain the optimized impact area result. Based on the optimized impact area results and the preset impact assessment indicators, the impact area is divided into grids and the risk assessment value is calculated to obtain the risk assessment result of the impact range.

2. The method for assessing the crash zone risk of an unmanned aerial vehicle according to claim 1, characterized in that, Different failure modes of UAVs were analyzed, and a UAV failure model was constructed, including: Based on the configuration, application scenarios, operation procedures, flight tests, and historical accident data of UAVs, we analyze the failure modes of UAVs, such as loss of altitude holding ability, loss of control ability, crashing to the ground due to operational errors, and falling system components. Then, we construct UAV failure models according to equipment causes, operating environment factors, and human factors.

3. The method for assessing the crash zone risk of an unmanned aerial vehicle according to claim 1, characterized in that, Based on the aforementioned UAV failure model, functional loss features are extracted, and a failure fall trajectory prediction model is constructed by combining fall trajectories of similar failure modes, including: Based on the failure inductions and causes of the aforementioned UAV failure model, and by matching and associating them with different airframe structures of the UAV, functional loss features of UAV failure are extracted. Based on the aforementioned UAV failure model, UAV flight test data, and historical crash accident data, different failure modes with similar trajectory characteristics are integrated to obtain similar failure crash trajectories. The similar failure crash trajectories are matched one by one with the functional loss characteristics of the UAV failure, and differentiated kinematic and dynamic modeling is performed respectively to obtain the failure crash trajectory prediction model.

4. The method for assessing the crash zone risk of an unmanned aerial vehicle according to claim 1, characterized in that, The impact zone is calculated by simulating the ground motion using the failure fall trajectory prediction model and preset impact environmental parameters. The impact zone includes: Based on the failure crash trajectory prediction model, the crash motion of the UAV after failure is decomposed into vertical and horizontal directions, and the air resistance motion equation is established in combination with air resistance. By substituting the preset flight dynamic environment parameters and the drag characteristic parameters of the UAV into the air resistance motion equation, the failure equation is obtained. Substitute the impact environmental parameters into the failure equation to perform a failure fall simulation and obtain the fall motion characteristics. The impact range of an impact accident is assessed based on the fall motion characteristics and the drag characteristic parameters, and the impact impact area is constructed.

5. The method for assessing the crash zone risk of an unmanned aerial vehicle according to claim 4, characterized in that, Substituting the impact environmental parameters into the failure equation to perform a failure fall simulation, the fall motion characteristics are obtained, including: Substitute the initial velocity boundary conditions of the impact environment parameters into the failure equation to solve for vertical motion and zero displacement, and integrate to obtain the UAV impact point. The landing time is obtained by performing dynamic calculations based on the initial vertical velocity of the UAV at the time of failure, the operating altitude of the impact environment parameters, and the drag characteristic parameters in the vertical direction. The horizontal distance of the crash landing is calculated based on the horizontal component of the initial velocity at the time of the crash and the time of the crash. The fall motion characteristics are constructed based on the drone's impact point, the fall landing time, and the horizontal distance of the failed fall.

6. The method for assessing the crash zone risk of an unmanned aerial vehicle according to claim 1, characterized in that, Based on the optimized impact area results and preset impact assessment indicators, the impact area is divided into grids and risk assessment values ​​are calculated to obtain the impact range risk assessment results, including: Obtain population density data; Based on the optimized impact zone results and the population density data, the number of people in the area affected by the accident is calculated to obtain the degree of harm of the ground impact accident. The probability of casualties in the accident is calculated based on the crash energy of the drone, the preset shielding protection coefficient, and the personnel injury threshold. Based on the severity of the ground impact accident and the probability of casualties, scenario elements are selected as the main factors affecting the probability of ground impact accidents, and an accident risk probability model is constructed. Based on the accident risk probability model, the impact area is divided into grids and the crash risk assessment value of the UAV within the grid is calculated to obtain the crash range risk assessment result.

7. A device for assessing the crash zone risk of an unmanned aerial vehicle, characterized in that, include: The failure analysis module is used to analyze different failure modes of UAVs and build UAV failure models. The extraction module is used to extract functional loss features based on the UAV failure model and construct a failure crash trajectory prediction model by combining crash trajectories with similar failure modes. The simulation module is used to simulate the impact motion based on the failure fall trajectory prediction model, and calculate the impact area by using the conditions of the impact motion and preset impact environment parameters. The determination module is used to determine the ground features based on the latitude and longitude boundaries of the work area and the impact area, and to correct the impact contact parameters through the ground features to obtain the optimized impact area result. The partitioning module is used to divide the impact area into grids based on the optimized impact area results and preset impact assessment indicators, and calculate the risk assessment value to obtain the risk assessment result of the impact range.

8. The crash zone risk assessment device for unmanned aerial vehicles according to claim 7, characterized in that, The extraction module includes: The association unit is used to match and associate the failure inducing factors and causes of the UAV failure model with different airframe structures of the UAV to extract the functional loss features of the UAV failure. The integration unit is used to integrate different failure modes with similar trajectory characteristics based on the UAV failure model, UAV flight test data and crash accident history data to obtain similar failure crash trajectories. The matching unit is used to match the similar failure crash tracks with the functional loss features of the UAV failure one by one, and perform differentiated kinematic modeling and dynamic modeling respectively to obtain the failure crash track prediction model.

9. The crash zone risk assessment device for unmanned aerial vehicles according to claim 7, characterized in that, The simulation module includes: The decomposition unit is used to decompose the fall motion of the UAV after failure into vertical and horizontal directions according to the failure fall trajectory prediction model, and to establish the air resistance motion equation in combination with air resistance. The derivation unit is used to substitute preset flight dynamic environment parameters and UAV drag characteristic parameters into the air resistance motion equation to derive the failure equation. The deduction unit is used to substitute the impact environmental parameters into the failure equation to perform failure fall deduction and obtain the fall motion characteristics. The evaluation unit is used to assess the impact range of an impact accident based on the fall motion characteristics and the drag characteristic parameters, and to construct the impact impact area.

10. The crash zone risk assessment device for unmanned aerial vehicles according to claim 7, characterized in that, The partitioning module includes: Acquisition unit, used to acquire population density data; The population calculation unit is used to calculate the number of people in the area affected by the accident based on the optimized impact area results and the population density data, so as to obtain the degree of harm of the ground impact accident. The probability calculation unit is used to calculate the probability of casualties based on the crash energy of the drone, the preset shield protection coefficient, and the personnel injury threshold, and to obtain the probability of casualties in the accident. The selection unit is used to select scene elements as the main factors affecting the probability of ground impact accidents based on the severity of the ground impact accident and the probability of casualties, and to construct an accident risk probability model. The partitioning unit is used to divide the impact area into grids based on the accident risk probability model and calculate the drone crash risk assessment value within the grid to obtain the crash range risk assessment result.

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