Space-time grid assessment method and system for flight accident risk of unmanned aerial vehicle

Abstract

The invention relates to the technical field of unmanned aerial vehicle electronic data processing, and particularly discloses an unmanned aerial vehicle flight accident risk space-time grid assessment method and system, and the method comprises the following steps: S1, creating a space-time grid network, and managing the space-time grid network; s2, inputting and extracting parameter information of the unmanned aerial vehicle, an operator and a planned route of the flight number of the unmanned aerial vehicle to be evaluated; s3, calculating sub-item risk assessment indexes, and determining sub-item risk assessment index data of the flight number of the to-be-assessed unmanned aerial vehicle; s4, calculating a risk assessment weight, and determining a calculation weight of each sub-item risk assessment index of the flight voyage of the to-be-assessed unmanned aerial vehicle; and S5, calculating a risk assessment index. According to the method, assessment indexes of the unmanned aerial vehicle accident risk are unified to a standard space-time framework for quantitative calculation, a quantitative assessment result is provided for the unmanned aerial vehicle accident risk, and support is provided for accurate design of an insurance scheme.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle electronic data processing, and in particular to a method and system for evaluating the spatiotemporal grid of unmanned aerial vehicle flight accident risks. Background Art

[0002] In recent years, the low-altitude economic industry, which is mainly based on drone applications, has developed rapidly, accompanied by a gradual increase in drone accidents. There are many ways to improve drone safety. One is to start from the supporting technology of drone flight, study various drone accident monitoring, evaluation, and analysis methods, and improve the safety of drone flight activities themselves; the second is to provide drone commercial insurance similar to auto insurance, such as units or individuals engaged in small, medium, and large drone flight activities and using drones for commercial activities to purchase drone liability insurance in accordance with the law to provide safety protection for drone flight activities.

[0003] At present, in the field of accident risk assessment for commercial drone insurance, the insurance plan is mainly designed based on the analysis of the drone's own parameters and the insured's situation, such as the drone equipment model parameters, the flight qualifications of the flight insured, etc. Although this model is simple and easy to operate, it does not evaluate and analyze specific drone flight missions in the actual time and space environment, resulting in a gap between insurance and claims. Therefore, it is necessary to combine the spatiotemporal characteristics of drone flight activities and use the drone flight activities themselves as the analysis basis for risk assessment to improve the safety and security capabilities of drone insurance plans. Summary of the invention

[0004] The purpose of the present invention is to address the deficiencies in the prior art and to provide a method and system for evaluating the flight accident risk of unmanned aerial vehicles (UAVs) in a spatiotemporal grid manner. The method and system can quantitatively analyze the flight risk of unmanned aerial vehicles in a spatiotemporal grid manner, and provide risk assessment support for the design of UAV insurance solutions.

[0005] The technical solution of one of the purposes of the present invention is:

[0006] A spatiotemporal grid assessment method for UAV flight accident risk includes the following steps:

[0007] Step S1: Create a space-time grid network and manage the space-time grid network;

[0008] Step S2: input and extract the information of the drone, operator, and planned route parameters of the drone flight to be evaluated;

[0009] Step S3: Calculate the sub-item risk assessment index and determine the sub-item risk assessment index data of the UAV flight to be assessed;

[0010] Step S4: Calculate the risk assessment weights and determine the calculation weights of each sub-item risk assessment indicator of the UAV flight to be assessed;

[0011] Step S5: Calculate risk assessment indicators.

[0012] Furthermore, the step S1: creating a space-time grid network and managing the space-time grid network specifically includes:

[0013] S101: In a spatial region, a certain point is selected as the coordinate origin O, and the space available for use by the UAV low-altitude airspace is divided into three-dimensional spatial grids with the same length, width and height spacing or different length, width and height spacing, and the three-dimensional spatial grids are sequentially numbered according to the distance from the coordinate origin O;

[0014] S102: Select a fixed time as the time starting point and a fixed duration as the end point, divide the fixed duration into segments according to the same or different time intervals, and number them in chronological order;

[0015] S103: Based on the three-dimensional space grids divided in S101, according to the time periods divided in S102, each three-dimensional space grid is divided into space-time grid units with a time dimension to form a unified space-time grid network;

[0016] S104: Manage the established space-time grid network, including naming, creating, deleting, etc. of the space-time grid network.

[0017] Furthermore, the step S2: inputting and extracting the information of the drone, operator, and planned route parameters of the drone flight to be evaluated specifically includes:

[0018] S201: Input and extract the drone parameter information of the drone flight to be evaluated, including the drone model, body structure, flight performance, power system, navigation positioning, manufacturer and other information;

[0019] S202: Input and extract operator parameter information of the UAV flight to be evaluated, including the operator's name, identity certificate, qualification certificate, physical condition, etc.;

[0020] S203: Input and extract the planned route parameter information of the UAV flight to be evaluated, including the flight route and flight time of the planned route, where the planned route is stored in the GIS vector space element format, and the planned route and its flight time are superimposed and analyzed with the space-time grid created in step S1 to obtain the space-time grid set G = {G 1 , G 2 , G 3 ...,G n}, n is the total number of space-time grids in the set G.

[0021] Furthermore, the step S3: calculating the sub-item risk assessment index and determining the sub-item risk assessment index data of the UAV flight to be assessed specifically includes:

[0022] S301: Calculate route evaluation index Where j represents the number of a spatiotemporal grid in the spatiotemporal grid set G in step S203, and A j AW represents the route evaluation factor of the space-time grid j. The value is calculated as j =RA j ×WF j ;

[0023] Among them, RA j is the risk factor of the route in the space-time grid j, RA j =TA j / TF j , where TA j TF represents the total flight time of the flight with accidents in the space-time grid j; j Represents the total flight duration of all flights in the space-time grid j. This value is the sum of the flight durations of all flights in the space-time grid j.

[0024] Furthermore, WF j is the airworthiness factor of space-time grid j, TF represents the sum of all flight durations in all space-time grids, and N represents the total number of all flight trips;

[0025] S302: Calculate UAV evaluation indicators Where j represents the number of a spatiotemporal grid in the spatiotemporal grid set G in step S203, UW j represents the evaluation factor of the specific model x UAV in the space-time grid j. The value is calculated as UW j =UA j ×UF j ;

[0026] Among them, UA j is the risk factor of drone of specific model x in space-time grid j, UA j =TUA j / TUF j , where TUA j TUF represents the total flight time of the specific model x UAV flight flights in which accidents occurred in grid j; j represents the total flight time of all flights of a specific model x UAV in grid j;

[0027] Furthermore, UF jis the airworthiness factor of the specific model x UAV at the spatiotemporal grid j, TUF represents the sum of all flight times of all time-space grids of a specific model x drone, N x Represents the total number of all flights of a specific model x drone;

[0028] S303: Calculate flight control evaluation indicators Where j represents the number of a spatiotemporal grid in the spatiotemporal grid set G in step S203, j OW represents the flight control evaluation factor of a specific operator y in the space-time grid j. The value is calculated as j =OA j ×OF j ;

[0029] Among them, OA j is the flight control risk factor of the specific operator y in the space-time grid j, OA j =TOA j / TOF j , where TOA j TOF represents the total flight time of the flight of a specific operator y in which an accident occurred in the space-time grid j; j represents the total flight duration of all flights of a specific operator y in the space-time grid j;

[0030] Furthermore, OF j is the specific operator y airworthiness factor for space-time grid j, TOF represents the sum of all flight times of a specific operator y in all space-time grids, N y Represents the total number of all flights of a specific operator y.

[0031] Furthermore, the step S4: calculating the risk assessment weights, determining the calculation weights of the risk assessment indicators of each sub-item of the UAV flight to be assessed, specifically includes:

[0032] S401: Calculate route weight index S i Indicates the number of flights where accidents have occurred on the planned route to be evaluated, F i represents the number of historical flights on the route, S represents the number of flights with accidents on all routes, and F represents the total number of flights on all routes;

[0033] S402: Calculate drone weight index Where S y Indicates the number of flights in which accidents occurred with a specific model x drone, F x Indicates the total number of flights of a specific model x drone;

[0034] S403: Flight control weight index QO: Where S y represents the number of flights in which a specific operator y has an accident, F y Represents the total number of flights of a specific operator y.

[0035] Furthermore, the step S5: calculating the risk assessment index, the specific calculation method is:

[0036] Based on step S3 and step S4, the risk assessment index is calculated according to the sub-item risk assessment index and weight index. R xy It indicates the risk assessment index results of the flight mission performed by a specific model x UAV and a specific operator y, and is provided to insurance institutions as a quantitative basis for designing insurance plans.

[0037] The technical solution of the second object of the present invention is:

[0038] A spatiotemporal grid assessment system for UAV flight accident risk, comprising a spatiotemporal grid management module, an information input and extraction module, a sub-item risk assessment indicator module, a risk assessment weight module, and a risk assessment indicator module;

[0039] The space-time grid management module is used to create a space-time grid network and manage the space-time grid network;

[0040] The information input and extraction module is used to input and extract the information of the drone, operator, and planned route parameters of the drone flight to be evaluated;

[0041] The sub-item risk assessment indicator module is used to calculate the sub-item risk assessment indicators and determine the sub-item risk assessment indicator data of the UAV flight to be assessed;

[0042] The risk assessment weight module is used to calculate the risk assessment weight and determine the calculation weight of each sub-item risk assessment indicator of the UAV flight to be assessed;

[0043] The risk assessment index module is used to calculate the risk assessment index of a specific flight;

[0044] Furthermore, the spatiotemporal grid management module has the following specific functions:

[0045] S101: In a spatial region, a certain point is selected as the coordinate origin O, and the space available for use by the UAV low-altitude airspace is divided into three-dimensional spatial grids with the same length, width and height spacing or different length, width and height spacing, and the three-dimensional spatial grids are sequentially numbered according to the distance from the coordinate origin O;

[0046] S102: Select a fixed time as the time starting point and a fixed duration as the end point, divide the fixed duration into segments according to the same or different time intervals, and number them in chronological order;

[0047] S103: Based on the three-dimensional space grids divided in S101, according to the time periods divided in S102, each three-dimensional space grid is divided into space-time grid units with a time dimension to form a unified space-time grid network;

[0048] S104: Manage the established space-time grid network, including naming, creating, deleting, etc. of the space-time grid network.

[0049] Furthermore, the information input and extraction module has the following specific functions:

[0050] S201: Input and extract the drone parameter information of the drone flight to be evaluated, including the drone model, body structure, flight performance, power system, navigation positioning, manufacturer and other information;

[0051] S202: Input and extract operator parameter information of the UAV flight to be evaluated, including the operator's name, identity certificate, qualification certificate, physical condition, etc.;

[0052] S203: Input and extract the planned route parameter information of the UAV flight to be evaluated, including the flight route and flight time of the planned route, where the planned route is stored in the GIS vector space element format, and the planned route and its flight time are superimposed and analyzed with the space-time grid created by the space-time grid management module to obtain the space-time grid set G = {G 1 , G 2 , G 3 ...,G n}, n is the total number of space-time grids in the set G.

[0053] Furthermore, the sub-item risk assessment indicator module has the following specific functions:

[0054] S301: Calculate route evaluation index Where j represents the number of a spatiotemporal grid in the spatiotemporal grid set G generated by the information input and extraction module, AW j AW represents the route evaluation factor of the space-time grid j. The value is calculated as j =RA j ×WF j ;

[0055] Among them, RA j is the route risk factor of space-time grid j, RA j =TAj / TF j , T.A. j TF represents the total flight time of the flight with accidents in the space-time grid j; j represents the total flight duration of all flights in the space-time grid j;

[0056] Furthermore, WF j is the airworthiness factor of space-time grid j, TF represents the sum of all flight durations in all space-time grids, and N represents the total number of all flight trips;

[0057] S302: Calculate UAV evaluation indicators Where j represents the number of a spatiotemporal grid in the spatiotemporal grid set G generated by the information input and extraction module, UW j represents the evaluation factor of the specific model x UAV in the space-time grid j. The value is calculated as UW j =UA j ×UF j ;

[0058] Among them, UA j is the risk factor of drone of specific model x in space-time grid j, UA j =TUA j / TUF j , TUA j TUF represents the total flight time of the specific model x UAV flight flights in which accidents occurred in grid j; j represents the total flight time of all flights of a specific model x UAV in grid j;

[0059] Furthermore, UF j is the airworthiness factor of the specific model x UAV at the spatiotemporal grid j, TUF represents the sum of all flight times of a specific model x drone in all space-time grids, N x Represents the total number of all flights of a specific model x drone;

[0060] S303: Calculate flight control evaluation indicators Where j represents the number of a spatiotemporal grid in the spatiotemporal grid set G generated by the information input and extraction module, j OW represents the flight control evaluation factor of a specific operator y in the space-time grid j. The value is calculated as j =OA j ×OF j ;

[0061] Among them, OA j is the flight control risk factor of the specific operator y in the space-time grid j, OA j =TOAj / TOF j， TOA j TOF represents the total flight time of the flight of a specific operator y in which an accident occurred in the space-time grid j; j represents the total flight duration of all flights of a specific operator y in the space-time grid j;

[0062] Furthermore, OF j is the specific operator y airworthiness factor for space-time grid j, TOF represents the sum of all flight times of a specific operator y in all space-time grids, N y Represents the total number of all flights of a specific operator y.

[0063] Furthermore, the risk assessment weight module has the following specific functions:

[0064] S401: Calculate route weight index S i Indicates the number of flights where accidents have occurred on the planned route to be evaluated, F i represents the number of historical flights on the route, S represents the number of flights with accidents on all routes, and F represents the total number of flights on all routes;

[0065] S402: Calculate drone weight index Where S y Indicates the number of flights in which accidents occurred with a specific model x drone, F x Indicates the total number of flights of a specific model x drone;

[0066] S403: Flight control weight index QO: Where S y represents the number of flights in which a specific operator y has an accident, F y represents the total number of flights of a specific operator y;

[0067] Furthermore, the risk assessment indicator module has the following specific functions:

[0068] Calculate the risk assessment index based on the sub-item risk assessment index and weight index R xy It indicates the risk assessment index results of the flight mission performed by a specific model x UAV and a specific operator y, and is provided to insurance institutions as a quantitative basis for designing insurance plans.

[0069] The above technical solution has the following beneficial effects:

[0070] The method and system for assessing the risk of unmanned aerial vehicle (UAV) flight accidents of the present invention are based on the characteristic that the flight status of an UAV is a combination of the flight route status of the UAV, the status of the UAV equipment and the status of the UAV operator. Under a unified spatiotemporal grid framework, the route assessment index, the UAV assessment index and the flight control assessment index values ​​of the UAV accident risk are calculated, and combined with the corresponding index weight values, the accident risk of UAV flight missions in specific areas and specific environments, specific UAVs and specific operators is quantitatively analyzed, so as to provide support for the design of insurance plans and realize the quantitative assessment of the risk of UAV accidents.

[0071] Therefore, the present invention achieves:

[0072] 1. Establish a unified space-time grid framework, unify the assessment indicators of drone accident risks into a standard space-time framework for calculation, and ensure the uniformity of accident risk assessment standards.

[0073] 2. Using the space-time grid as the calculation unit, the route assessment index, drone assessment index and flight control assessment index values ​​of drone accident risks are quantitatively calculated to provide quantitative analysis results for drone accident risks and provide support for the accurate design of insurance plans.

[0074] Further description is given below in conjunction with the accompanying drawings and specific implementation methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0075] Figure 1 This is a flow chart of a method for assessing the risk of unmanned aerial vehicle flight accidents in specific embodiment 1;

[0076] Figure 2 This is a principle block diagram of specific embodiment 2. DETAILED DESCRIPTION Specific embodiment 1:

[0078] See also Figure 1 As shown in FIG. 1 , the spatiotemporal grid assessment method for UAV flight accident risk includes the following steps:

[0079] Step S1: Create a space-time grid network and manage the space-time grid network;

[0080] Specifically include:

[0081] S101: In a spatial region, a certain point is selected as the coordinate origin O, and the space available for use by drones in low-altitude airspace is divided into three-dimensional spatial grids with the same length, width and height spacing or different length, width and height spacing, and the three-dimensional spatial grids are sequentially numbered according to the distance from the coordinate origin O. The spatial region can be an urban area or an administrative area; the length, width and height spacing can be the same spacing of 50 meters, 100 meters, 200 meters, etc., or different lengths, widths and heights can be used;

[0082] S102: Select a fixed time as the time starting point and a fixed duration as the end point, divide the fixed duration into segments according to the same or different time intervals, and number them in chronological order to form a time dimension; the fixed duration can be different options such as one day, one week, one month, one year, etc.; the time interval can be the same interval such as 1 minute, 1 hour, etc., or different time intervals can be used;

[0083] S103: Based on the three-dimensional space grids divided in S101, according to the time periods divided in S102, each three-dimensional space grid is divided into space-time grid units with time dimensions to form a unified space-time grid network; for example, a city area is divided into 20,000 space grids with a length, width, and height interval of 100 meters in space, and a fixed time length of one day and a time interval of one hour are used, so that one day is divided into 24 time scales, thereby forming a space-time grid network consisting of 480,000 space-time grids;

[0084] S104: managing the established space-time grid network, including naming, creating, deleting, etc. of the space-time grid network, and creating a new space-time grid network as needed;

[0085] Step S2: input and extract the information of the drone, operator, and planned route parameters of the drone flight to be evaluated;

[0086] Specifically include:

[0087] S201: Input and extract the drone parameter information of the drone flight to be evaluated, including the drone model, body structure, flight performance, power system, navigation positioning, manufacturer and other information;

[0088] S202: Input and extract operator parameter information of the UAV flight to be evaluated, including the operator's name, identity certificate, qualification certificate, physical condition, etc.;

[0089] S203: Input and extract the planned route parameter information of the UAV flight to be evaluated, including the flight route and flight time of the planned route, where the planned route is stored in the GIS vector space element format, and the planned route and its flight time are superimposed and analyzed with the space-time grid created in step S1 to obtain the space-time grid set G = {G 1 , G 2 , G 3 ...,G n}, n is the total number of space-time grids in the set G. The flight route of the planned route can be expressed by linear graphic elements of GIS standard, and the overlay analysis can be realized by using the basic analysis functions provided by GIS. First, the spatial grids overlapping the flight route are obtained, and then the space-time grids of the corresponding time dimension are obtained according to the flight time information;

[0090] Step S3: Calculate the sub-item risk assessment index and determine the sub-item risk assessment index data of the UAV flight to be assessed;

[0091] Specifically include:

[0092] S301: Calculate route evaluation index Where j represents the number of a spatiotemporal grid in the spatiotemporal grid set G in step S203, and A j AW represents the route evaluation factor of the space-time grid j. The value is calculated as j =RA j ×WF j ;

[0093] Among them, RA j is the risk factor of the route in the space-time grid j, RA j =TA j / TF j , T.A. j TF represents the total flight time of the flight with accidents in the space-time grid j; j represents the total flight duration of all flights in the space-time grid j;

[0094] Furthermore, WF j is the airworthiness factor of space-time grid j, TF represents the sum of all flight durations in all space-time grids, and N represents the total number of all flight trips;

[0095] S302: Calculate UAV evaluation indicators Where j represents the number of a spatiotemporal grid in the spatiotemporal grid set G in step S203, UW j represents the evaluation factor of the specific model x UAV in the space-time grid j. The value is calculated as UW j =UA j ×UF j ;

[0096] Among them, UA j is the risk factor of drone of specific model x in space-time grid j, UA j =TUA j / TUF j , TUA j TUF represents the total flight time of the flight of a specific model x UAV in which an accident occurred in the space-time grid j;j represents the total flight time of all flights of a specific model x UAV in grid j;

[0097] Furthermore, UF j is the airworthiness factor of the specific model x UAV at the spatiotemporal grid j, TUF represents the sum of all flight times of a specific model x drone in all space-time grids, N x Represents the total number of all flights of a specific model x drone;

[0098] S303: Calculate flight control evaluation indicators Where j represents the number of a spatiotemporal grid in the spatiotemporal grid set G in step S203, j OW represents the flight control evaluation factor of a specific operator y in the space-time grid j. The value is calculated as j =OA j ×OF j ;

[0099] Among them, OA j is the flight control risk factor of the specific operator y in the space-time grid j, OA j =TOA j / TOF j , TOA j TOF represents the total flight time of the flight of a specific operator y in which an accident occurred in the space-time grid j; j represents the total flight duration of all flights of a specific operator y in the space-time grid j;

[0100] Furthermore, OF j is the specific operator y airworthiness factor for space-time grid j, TOF represents the sum of all flight times of a specific operator y in all space-time grids, N y Represents the total number of all flights of a specific operator y.

[0101] Step S4: Calculate the risk assessment weights and determine the calculation weights of each sub-item risk assessment indicator of the UAV flight to be assessed;

[0102] Specifically include:

[0103] S401: Calculate route weight index S i Indicates the number of flights where accidents have occurred on the planned route to be evaluated, F i represents the number of historical flights on the route, S represents the number of flights with accidents on all routes, and F represents the total number of flights on all routes;

[0104] S402: Calculate drone weight index Where Sy Indicates the number of flights in which accidents occurred with a specific model x drone, F x Indicates the total number of flights of a specific model x drone;

[0105] S403: Flight control weight index QO: Where S y represents the number of flights in which a specific operator y has an accident, F y represents the total number of flights of a specific operator y;

[0106] Step S5: Calculate risk assessment indicators;

[0107] Specifically include:

[0108] Based on step S3 and step S4, the risk assessment index is calculated according to the sub-item risk assessment index and weight index. R xy It indicates the risk assessment index results of the flight mission performed by a specific model x UAV and a specific operator y, and is provided to insurance institutions as a quantitative basis for designing insurance plans. Specific embodiment 2:

[0110] like Figure 2 As shown, a spatiotemporal grid assessment system for UAV flight accident risk includes a spatiotemporal grid management module 1, an information input and extraction module 2, a sub-item risk assessment indicator module 3, a risk assessment weight module 4, and a risk assessment indicator module 5;

[0111] The space-time grid management module 1 is used to create a space-time grid network and manage the space-time grid network;

[0112] The specific functions are as follows:

[0113] S101: In a spatial region, a certain point is selected as the coordinate origin O, and the space available for use by drones in low-altitude airspace is divided into three-dimensional spatial grids with the same length, width and height spacing or different length, width and height spacing, and the three-dimensional spatial grids are sequentially numbered according to the distance from the coordinate origin O. The spatial region can be an urban area or an administrative area; the length, width and height spacing can be the same spacing of 50 meters, 100 meters, 200 meters, etc., or different lengths, widths and heights can be used;

[0114] S102: Select a fixed time as the time starting point and a fixed duration as the end point, divide the fixed duration into segments according to the same or different time intervals, and number them in chronological order to form a time dimension; the fixed duration can be different options such as one day, one week, one month, one year, etc.; the time interval can be the same interval such as 1 minute, 1 hour, etc., or different time intervals can be used;

[0115] S103: Based on the three-dimensional space grid divided in S101, according to the time period divided in S102, each three-dimensional space grid is divided into space-time grid units with time dimensions to form a unified space-time grid network; for example, a city area is divided into 20,000 space grids with a length, width and height interval of 100 meters in space, and a fixed time length of one day and a time interval of one hour are used, so that one day is divided into 24 time scales, thereby forming a space-time grid network composed of 480,000 space-time grids;

[0116] S104: managing the established space-time grid network, including naming, creating, deleting, etc. of the space-time grid network, and creating a new space-time grid network as needed;

[0117] The information input and extraction module 2 is used to input and extract the information of the drone, operator, and planned route parameters of the drone flight to be evaluated;

[0118] The specific functions are as follows:

[0119] S201: Input and extract the drone parameter information of the drone flight to be evaluated, including the drone model, body structure, flight performance, power system, navigation positioning, manufacturer and other information;

[0120] S202: Input and extract operator parameter information of the UAV flight to be evaluated, including the operator's name, identity certificate, qualification certificate, physical condition, etc.;

[0121] S203: Input and extract the planned route parameter information of the UAV flight to be evaluated, including the flight route and flight time of the planned route, where the planned route is stored in the GIS vector space element format, and the planned route and its flight time are superimposed and analyzed with the space-time grid created in step S1 to obtain the space-time grid set G = {G 1 , G 2 , G 3 ...,G n}, n is the total number of space-time grids in the set G. The flight route of the planned route can be expressed by linear graphic elements of GIS standard, and the overlay analysis can be realized by using the basic analysis functions provided by GIS. First, the space-time grids overlapping in the space of the flight route are obtained, and then the space-time grids of the corresponding time dimension are obtained according to the flight time information;

[0122] The sub-item risk assessment indicator module 3 is used to calculate the sub-item risk assessment indicators and determine the sub-item risk assessment indicator data of the UAV flight to be assessed;

[0123] The specific functions are as follows:

[0124] S301: Calculate route evaluation index Where j represents the number of a spatiotemporal grid in the spatiotemporal grid set G in step S203, and A j AW represents the route evaluation factor of the space-time grid j. The value is calculated as j =RA j ×WF j ;

[0125] Among them, RA j is the route risk factor of space-time grid j, RA j =TA j / TF j , T.A. j TF represents the total flight time of the flight with accidents in the space-time grid j; j represents the total flight duration of all flights in the space-time grid j;

[0126] Furthermore, WF j is the airworthiness factor of space-time grid j, TF represents the sum of all flight durations in all space-time grids, and N represents the total number of all flight trips;

[0127] S302: Calculate UAV evaluation indicators Where j represents the number of a spatiotemporal grid in the spatiotemporal grid set G in step S203, UW j represents the evaluation factor of the specific model x UAV in the space-time grid j. The value is calculated as UW j =UA j ×UF j ;

[0128] Among them, UA j is the risk factor of drone of specific model x in space-time grid j, UA j =TUA j / TUF j , TUA jTUF represents the total flight time of the specific model x UAV flight flights in which accidents occurred in grid j; j represents the total flight time of all flights of a specific model x UAV in grid j;

[0129] Furthermore, UF j is the airworthiness factor of the specific model x UAV at the spatiotemporal grid j, TUF represents the sum of all flight times of a specific model x drone in all space-time grids, N x Represents the total number of all flights of a specific model x drone;

[0130] S303: Calculate flight control evaluation indicators Where j represents the number of a spatiotemporal grid in the spatiotemporal grid set G in step S203, j OW represents the flight control evaluation factor of a specific operator y in the space-time grid j. The value is calculated as j =OA j ×OF j ;

[0131] Among them, OA j is the flight control risk factor of the specific operator y in the space-time grid j, OA j =TOA j / TOF j , TOA j TOF represents the total flight time of the flight of a specific operator y in which an accident occurred in the space-time grid j; j represents the total flight duration of all flights of a specific operator y in the space-time grid j;

[0132] Furthermore, OF j is the specific operator y airworthiness factor for space-time grid j, TOF represents the sum of all flight times of a specific operator y in all space-time grids, N y Represents the total number of all flights of a specific operator y;

[0133] The risk assessment weight module 4 is used to calculate the risk assessment weight and determine the calculation weight of each sub-item risk assessment indicator of the UAV flight to be assessed;

[0134] The specific functions are as follows:

[0135] S401: Calculate route weight index S i Indicates the number of flights where accidents have occurred on the planned route to be evaluated, F i represents the number of historical flights on the route, S represents the number of flights with accidents on all routes, and F represents the total number of flights on all routes;

[0136] S402: Calculate drone weight index Where S y Indicates the number of flights in which accidents occurred with a specific model x drone, F x Indicates the total number of flights of a specific model x drone;

[0137] S403: Flight control weight index QO: Where S y represents the number of flights in which a specific operator y has an accident, F y represents the total number of flights of a specific operator y;

[0138] The risk assessment index module 5 is used to calculate the risk assessment index of a specific flight;

[0139] The specific functions are as follows:

[0140] Calculate the risk assessment index based on the sub-item risk assessment index and weight index R xy It indicates the risk assessment index results of the flight mission performed by a specific model x UAV and a specific operator y, and is provided to insurance institutions as a quantitative basis for designing insurance plans.

[0141] The technical solution provided by the present invention is described in detail above. Specific examples are used herein to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. It should be pointed out that for ordinary technicians in this technical field, without departing from the principle of the present invention, the present invention can also be improved and modified in a number of ways, and these improvements and modifications also fall within the scope of protection of the claims of the present invention.

Claims

1. A spatiotemporal grid assessment method for UAV flight accident risk, characterized by: The steps include: Step S1: Create a space-time grid network and manage the space-time grid network; Step S2: input and extract the information of the drone, operator, and planned route parameters of the drone flight to be evaluated; Step S3: Calculate the sub-item risk assessment index and determine the sub-item risk assessment index data of the UAV flight to be assessed; Specifically include: S301: Calculate route evaluation index Where j represents the number of the jth space-time grid in the space-time grid set where the planned route to be evaluated is located, AW j AW represents the route evaluation factor of the space-time grid j. The value is calculated as j =RA j ×WF j , R.A. j is the route risk factor of space-time grid j, RA j =TA j / TF j , T.A. j represents the total flight time of the flight with accidents in the space-time grid j, TF j WF represents the total flight duration of all flights in the space-time grid j; j is the airworthiness factor of space-time grid j, Where TF represents the total flight duration of all flights in all time-space grids, and N represents the total number of all flights; S302: Calculate UAV evaluation indicators j represents the number of the jth space-time grid in the space-time grid set where the planned route to be evaluated is located, UW j represents the evaluation factor of the specific model x UAV in the space-time grid j, and the value is calculated as UW j =UA j ×UF j ,UA j is the risk factor of drone of specific model x in space-time grid j, UA j =TUA j / TUF j , TUA j TUF represents the total flight time of the specific model x UAV flight in which the accident occurred in the space-time grid j. j represents the total flight time of all flights of a specific model x UAV in the space-time grid j; UF j is the airworthiness factor of the specific model x UAV at the spatiotemporal grid j, TUF represents the sum of all flight times of a specific model x drone in all space-time grids, N x Represents the total number of all flights of a specific model x drone; S303: Calculate flight control evaluation indicators j represents the number of the jth space-time grid in the space-time grid set where the planned route to be evaluated is located. j OW represents the flight control evaluation factor of a specific operator y in the space-time grid j. The value is calculated as j =OA j ×OF j , OA j is the flight control risk factor of the specific operator y in the space-time grid j, OA j =TOA j / TOF j , TOA j TOF represents the total flight time of the flight of a specific operator y in which an accident occurred in the space-time grid j. j represents the total flight duration of all flights of a specific operator y in the space-time grid j; OF j is the specific operator y airworthiness factor for space-time grid j, TOF represents the sum of all flight times of a specific operator y in all space-time grids, N y Represents the total number of all flights of a specific operator y; Step S4: Calculate the risk assessment weights and determine the calculation weights of each sub-item risk assessment indicator of the UAV flight to be assessed; Specifically include: S401: Calculate route weight index S i Indicates the number of flights where accidents have occurred on the planned route to be evaluated, F i represents the number of historical flights on the route, S represents the number of flights with accidents on all routes, and F represents the total number of flights on all routes; S402: Calculate drone weight index S y Indicates the number of flights in which accidents occurred with a specific model x drone, F x Indicates the total number of flights of a specific model x drone; S403: Flight control weight index QO: S y represents the number of flights in which a specific operator y has an accident, F y represents the total number of flights of a specific operator y; Step S5: Calculate the risk assessment index based on the sub-item risk assessment index and weight index R xy It indicates the risk assessment index result of the flight mission performed by the specific model x UAV and the specific operator y.

2. A spatiotemporal grid assessment system for UAV flight accident risk, characterized by: It includes space-time grid management module, information input and extraction module, sub-item risk assessment indicator module, risk assessment weight module and risk assessment indicator module. The space-time grid management module is used to create a space-time grid network and manage the space-time grid network; The information input and extraction module is used to input and extract the information of the drone, operator, and planned route parameters of the drone flight to be evaluated; The sub-item risk assessment indicator module is used to calculate the sub-item risk assessment indicators and determine the sub-item risk assessment indicator data of the UAV flight to be assessed; the specific functions are as follows: Calculate the route assessment indicator j represents the number of the jth space-time grid in the space-time grid set where the planned route to be evaluated is located. j AW represents the route evaluation factor of the space-time grid j. The value is calculated as j =RA j ×WF j , where RA j is the route risk factor of space-time grid j, RA j =TA j / TF j , where TA j represents the total flight time of the flight with accidents in the space-time grid j, TF j WF represents the total flight duration of all flights in the space-time grid j; j is the airworthiness factor of space-time grid j, TF represents the sum of all flight times in all time-space grids, and N represents the total number of all flights; Calculate the drone evaluation index j represents the number of the jth space-time grid in the space-time grid set where the planned route to be evaluated is located, UW j represents the evaluation factor of the specific model x UAV in the space-time grid j, and the value is calculated as UW j =UA j ×UF j ,UA j is the risk factor of drone of specific model x in space-time grid j, UA j =TUA j / TUF j , TUA j TUF represents the total flight time of the specific model x UAV flight flights in which accidents occurred in grid j; j represents the total flight time of all flights of a specific model x UAV in grid j; UF j is the airworthiness factor of the specific model x UAV at the spatiotemporal grid j, TUF represents the sum of all flight times of all time-space grids of a specific model x drone, N x Represents the total number of all flights of a specific model x drone; calculates flight control evaluation indicators j represents the number of the jth space-time grid in the space-time grid set where the planned route to be evaluated is located. j OW represents the flight control evaluation factor of a specific operator y in the space-time grid j. The value is calculated as j =OA j ×OF j OA j is the flight control risk factor of the specific operator y in the space-time grid j, OA j =TOA j / TOF j , where TOA j TOF represents the total flight time of the flight of a specific operator y in which an accident occurred in the space-time grid j. j represents the total flight duration of all flights of a specific operator y in the space-time grid j; OF j is the specific operator y airworthiness factor for space-time grid j, TOF represents the sum of all flight times of a specific operator y in all space-time grids, N y Represents the total number of all flights of a specific operator y; The risk assessment weight module is used to calculate the weight of each sub-item risk assessment indicator. The functions are as follows: Calculate route weight indicators S i Indicates the number of flights where accidents have occurred on the planned route to be evaluated, F i represents the number of historical flights on the route, S represents the number of flights with accidents on all routes, and F represents the total number of flights on all routes; calculate the drone weight index Where S y Indicates the number of flights in which accidents occurred with a specific model x drone, F x Indicates the total number of flights of a specific model x drone; Flight control weight index QO: Where S y represents the number of flights in which a specific operator y has an accident, F y represents the total number of flights of a specific operator y; The risk assessment indicator module calculates the risk assessment indicator based on the sub-item risk assessment indicator and the weight indicator. R xy It indicates the risk assessment index result of the flight mission performed by the specific model x UAV and the specific operator y.

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