A method for measuring and calculating highway network fire risk, a medium and a terminal

Abstract

The present application is suitable for the technical field of fire prevention and control, and relates to a highway network fire risk calculation method, medium and terminal, comprising: determining the composition and geographic location information of the highway network, obtaining the road network and satellite remote sensing image; dividing the risk unit and obtaining its latitude and longitude coordinates; identifying different risk unit vehicle types and quantities based on target detection, calculating the average length, spacing and speed of the vehicle types; combining historical fire data to calculate the annual average accident rate of each vehicle type under different road section types; calculating the number of times of different grade fires occurring in each risk unit per year; considering the influence of fire rescue accessibility on fire loss, calculating the loss amount adjustment value; finally, comprehensively calculating the fire risk value of each risk unit according to the annual average number of fires and the adjusted loss amount, and visually presenting according to the risk level. The present application realizes the visualization of the highway fire risk level, improves the accuracy of fire risk calculation, and provides a scientific basis for the site selection of fire rescue stations and risk prevention and control.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of fire prevention and control, and particularly relates to a highway network fire risk calculation method, a medium and a terminal. BACKGROUND

[0002] In the prior art, the research on fire risk is mostly focused on regional fire risk, and the research on highway fire risk is very little. The highway is linear and isolated from the outside space, forming an independent long linear space. Its fire risk characteristics are different from those of general regional fire. Therefore, it is necessary to calculate the fire risk of the highway network. In the fire risk calculation technology, most studies only calculate the fire risk level of the research object or risk unit, but do not visualize it, so that the fire risk cannot be presented in a clear and intuitive way, which is not conducive to the decision makers and drivers to understand the distribution of fire risk in a timely manner and take targeted prevention and control measures.

[0003] The patent with publication number CN117114423B provides an industrial plant fire risk prediction and loss assessment method and device, which includes: indoor and outdoor fire potential risk analysis and assessment based on the industrial plant; indoor and outdoor fire / smoke numerical simulation analysis based on the industrial plant; fire loss assessment based on fire influence range simulation. This patent belongs to the risk calculation of regional fire, which is different from the risk calculation of highway network fire. Moreover, this application only realizes the calculation of fire risk level and does not realize the visualization of risk level, so it still cannot solve the problems existing in the prior art.

[0004] Therefore, how to provide a highway network fire risk calculation method to realize the visualization of highway fire risk level in a clear and intuitive way and improve the accuracy of highway network fire risk calculation is a problem to be solved by those skilled in the art. SUMMARY

[0005] In view of the deficiencies of the prior art, the purpose of the present application is to provide a highway network fire risk calculation method to solve the problem that the risk level visualization is not realized in the prior art highway network fire risk calculation, resulting in low accuracy of fire risk calculation and being not conducive to the decision makers and drivers to understand the distribution of fire risk in a timely manner. In addition, the present application also provides a highway network fire risk calculation medium and terminal.

[0006] In order to solve the above technical problems, the present application adopts the following technical solutions:

[0007] In the first aspect, the present application provides a highway network fire risk calculation method, comprising the following steps:

[0008] S10, determine the composition and geographical location information of the expressway network, obtain the road network map and satellite remote sensing image of the expressway network, determine the terrain type and road section type of each expressway, and obtain the location information of the division;

[0009] S20, divide the risk unit of the expressway network, and obtain the latitude and longitude coordinates thereof;

[0010] S30, identify the satellite remote sensing image of different risk units by using a target detection method, count the types and quantities of vehicles in different risk units, calculate the average total quantity of different vehicle types in different risk units under different time type scenarios, the average driving speed of different vehicle types on the expressway under different time type scenarios, and the annual average vehicle flow of different risk units of the expressway network;

[0011] S40, calculate the number of times of different consequences risk level fires occurring in different road sections and different vehicle types per year, the average driving mileage of different vehicle types on the expressway network, the number of times of different consequence risk level fires occurring in different risk units of the expressway network per year, and the average loss amount score of different consequence risk level fires on the expressway network;

[0012] S50, calculate the fire rescue accessibility of different risk units of the expressway network;

[0013] S60, calculate the loss amount score adjustment value of the expressway fire accident considering the fire rescue accessibility, the loss value of the expressway fire accident occurring in different risk units of the expressway network per year, and the fire risk value of different risk units of the expressway network;

[0014] S70, determine the fire risk level of different risk units of the expressway network and the corresponding RGB value, the translation direction and position of the latitude and longitude coordinates of different risk units of the expressway network, and visualize the risk value of the risk unit of the expressway network.

[0015] Further, the specific steps in S10 are as follows:

[0016] S101, determine the composition and geographical location information of the expressway network: define that the expressway network is composed of N expressways, obtain the length L(n) of the nth expressway, regard the uplink and downlink of the expressway as different roads, the latitude and longitude coordinates of the uplink starting point are (x(n, ls), y(n, ls)), the latitude and longitude coordinates of the uplink terminal point are (x(n, lf), y(n, lf)), the latitude and longitude coordinates of the downlink starting point are (x(n, rs), y(n, rs)), and the latitude and longitude coordinates of the downlink terminal point are (x(n, rf), y(n, rf));

[0017] S102, acquire road network map and satellite remote sensing map historical image: based on the geographic location information, acquire the road network data of the highway network and the high-resolution satellite remote sensing image, and acquire the high-resolution satellite remote sensing map historical image of different time in the past three years or more, filter and integrate the historical image according to the time type that can reflect the change of traffic flow, define the number of satellite remote sensing map historical image of the Tth time type after integration as T(z);

[0018] S103, determine the terrain type, road section type and its boundary information: based on the satellite remote sensing image, road network map and field investigation, determine the terrain type and road section type of each highway, and the boundary line where the type changes, and acquire the longitude and latitude coordinates of the center point of each boundary line; define the road with the same uplink and downlink direction, same terrain type and same road section type in the same highway as the same road section type road, number the boundary line center point of the bth road section type of the nth highway according to the preset order, and acquire the longitude and latitude coordinates (x(b,k),y(b,k)) of the kth boundary line center point of the bth road section type.

[0019] Further, in the S20, the nth highway has B different road section types, the highways of different road section types are equally divided according to the interval l, and a plurality of risk units are divided, wherein the remaining part with a length less than l is separately regarded as a risk unit; each service area and each toll station are respectively regarded as an independent risk unit; all risk units on the same highway are numbered in the order of uplink highway first and downlink highway second, and in the order of small to large according to the stake number; acquire the longitude and latitude coordinates of each risk unit, wherein the longitude and latitude coordinates of the jth risk unit of the nth highway are (x(n,j),y(n,j)).

[0020] Further, in the S30, the specific steps are as follows:

[0021] S301, divide the highway satellite remote sensing historical image according to the boundary of the risk unit, construct a data set labeled with vehicle type and boundary box, and identify the vehicle type and quantity in each risk unit through a target detection algorithm; for the unit that cannot be identified due to shielding or tunnel, linear interpolation method is used to calculate the vehicle type quantity, the formula is:

[0022]

[0023] Wherein, q(z,α,a) and q(z,β,a) respectively represent the total number of the ath vehicle type on the front and rear two completely identifiable vehicle risk units adjacent to the jth risk unit on the nth highway at the zth time of the Tth time type;

[0024] S302, classify the data according to the time type, and calculate the average number of vehicle types in each risk unit, the formula is:

[0025]

[0026] Wherein, T(z) represents the number of historical images of the Tth time type in all expressway network satellite remote sensing map historical images, q(z,j,a) represents the total number of the a th vehicle type on the j th risk unit of the n th expressway at the z th time of the T th time type;

[0027] S303, through orthorectification and image rotation, the average vehicle body length l(a) of the vehicle is calculated based on the vehicle bounding box pixel coordinates and the map resolution, and the formula is as follows:

[0028]

[0029]

[0030] Suppose that in the Tth time type, the number of samples of the a th vehicle type in the data set is V on the n th expressway, wherein the upper left corner pixel coordinates of the bounding box in the v th sample are (X(v,lu),Y(v,lu)), the right lower corner pixel coordinates are (X(v,rd),Y(v,rd)), the upper left corner pixel coordinates of the bounding box in the v+1 th sample are (X(v+1,lu),Y(v+1,lu)), the right lower corner pixel coordinates are (X(v+1,rd),Y(v+1,rd)), the satellite remote sensing map resolution obtained is e meters / pixel, if the vehicle distance between the v th sample and the v+1 th sample is D(v), the average distance of the a th vehicle type on the n th expressway in the Tth time type is D(T,a), and the calculation formula is as follows:

[0031]

[0032] ;

[0033] S304, through expert experience or interval speed measurement historical data, the average driving speed of each vehicle type in the risk unit on the expressway is obtained, and the nearest principle is used for non-speed measurement unit:

[0034] Suppose that in the Tth time type, the number of samples of the a th vehicle type driving speed is I(a) on the j th risk unit of the n th expressway, wherein the driving speed of the vehicle in the i(a) th data is V(i(a)), and the average driving speed of the a th vehicle type on the j th risk unit of the n th expressway in the Tth time type is defined as V(T,j,a) calculated by the above method, and the calculation formula is as follows:

[0035] ;

[0036] S305, in combination with time type, average speed, vehicle body length and vehicle spacing, the daily average traffic volume Q(T, j, a) and the annual average traffic volume Q(n, j, a) of the vehicle type are calculated, and the formulas are as follows:

[0037]

[0038]

[0039] Wherein, 24 represents 24h / d, V(T, j, a) unit is km / h, D(T, a) and l(a) unit is km, the unit of daily average traffic volume is veh / d, D(1), D(2) and D(3) respectively represent the total number of working days, weekends and statutory holidays in the historical satellite remote sensing map image statistical period of the entire expressway, Y(n) represents the number of years in the historical satellite remote sensing map image statistical period of the entire expressway.

[0040] Further, in the S40, the specific steps are as follows:

[0041] S401, based on the historical fire data, the number of times F(b, a, c) of the a kind of vehicle type on the b type road section of the nth expressway in the q year period is calculated, and the annual average fire number AF(b, a, c) is calculated, and the formula is as follows:

[0042]

[0043] S402, summing up all road section types of the nth expressway, the annual average fire number YAF(n, a, c) of the a kind of vehicle type on the nth expressway is calculated, and the formula is as follows:

[0044]

[0045] Wherein, B represents the total number of road section types on the nth expressway;

[0046] S403, according to the length L(n, i) of each toll station interval of the nth expressway and the daily average traffic volume Q(n, i, a) of the vehicle type, the average driving mileage L(n, a) of the vehicle type is calculated, and the formula is as follows:

[0047]

[0048] Wherein, m is the number of toll stations, including the starting point and the ending point;

[0049] S404, the annual average accident rate P(n, a, c) of the a kind of vehicle type on the nth expressway is defined, and the formula is as follows:

[0050]

[0051] Wherein, the unit of P(n, a, c) is times per million vehicle kilometers;

[0052] S404, define the number of times of the a-th vehicle type of the c-th level fire occurred on the j-th risk unit of the n-th highway in a year as T(j, a, c), the formula is as follows:

[0053]

[0054] Wherein, P(n, a, c) is the annual accident rate of the a-th vehicle type of the c-th level fire occurred on the n-th highway, Q(n, j, a) is the annual vehicle flow of the a-th vehicle type on the j-th risk unit of the n-th highway, and l(n, j) represents the length of the j-th risk unit of the n-th highway;

[0055] S405, convert the loss caused by the fire accident into a loss score by using linear interpolation method, assuming that the period of the fire history data is q years, and the fire accidents in the period are numbered in chronological order, if the number of seriously injured people caused by the h-th fire accident is Z(h), the number of deaths is S(h), and the direct property loss is C(h), then the personnel casualty and direct property loss are converted into a loss score according to the following formula:

[0056]

[0057]

[0058]

[0059] Wherein, DZ(h), DS(h) and DC(h) are respectively the values of the number of seriously injured people, the number of deaths and the direct property loss caused by the fire accident converted into a loss score, define the loss score D(h) of the h-th fire accident, the formula is as follows:

[0060] ;

[0061] S406, assuming that the total number of times of the a-th vehicle type of the c-th level fire occurred on the n-th highway in a period is G(a, c), wherein the loss score of the g-th time of the a-th vehicle type of the c-th level fire occurred is D(a, g, c), define the average loss score of the a-th vehicle type of the c-th level fire occurred on the n-th highway as AD(a, c), the formula is as follows:

[0062] .

[0063] Further, in the S50, all time scene samples are classified according to time types, and time samples of the same type are numbered in chronological order. It is assumed that the samples of the Tth time type are composed of z time scenes, wherein the accessibility score corresponding to the jth risk unit in the Tth(t) time scene is A(T(t)), and the average accessibility score corresponding to the jth risk unit in the Tth time type is A(T,j). The formula is as follows:

[0064]

[0065] It is assumed that the Tth time type has T(d) days in the entire calculation period, and thus the average accessibility score of the jth risk unit is A(j). The formula is as follows:

[0066]

[0067] It is assumed that in the expressway network, the total number of risk units on the nth expressway is N(j), and the total average fire rescue accessibility is TA(N). The formula is as follows:

[0068] .

[0069] Further, in the S60, the specific steps are as follows:

[0070] S601, it is assumed that the adjustment coefficient of the fire rescue accessibility on the jth risk unit of the nth expressway on the fire accident loss amount is f(j), and the average loss amount score adjustment value of the ac type of fire of the ath vehicle type on the risk unit is TD(a,c). The formula is as follows:

[0071]

[0072] S602, the annual average loss value of the ac type of fire of the ath vehicle type on the jth risk unit of the nth expressway is defined as S(j,a,c). The formula is as follows:

[0073]

[0074] The annual average loss value of the fire accident on the jth risk unit of the nth expressway is defined as S(n,j). The formula is as follows:

[0075]

[0076] wherein 5 represents the risk level of the expressway fire accident, including minor fire, general fire, larger fire, major fire and particularly major fire, and A represents the number of vehicle types on the nth expressway;

[0077] S603, define the average annual risk value of fire accident in the jth risk unit of the nth highway as R(n, j), the formula is as follows:

[0078] When S(n, j)≤β(1), the fire risk level of the risk unit is I level, and the corresponding risk value is:

[0079]

[0080] When β(1)<S(n, j)≤β(2), the fire risk level of the risk unit is II level, and the corresponding risk value is:

[0081]

[0082] When β(2)<S(n, j)≤β(3), the fire risk level of the risk unit is III level, and the corresponding risk value is:

[0083]

[0084] When S(n, j) > β(3), the fire risk level of the risk unit is IV level, and the corresponding risk value is:

[0085]

[0086] Wherein, the value of β(1), β(2) and β(3) is determined according to the loss amount score corresponding to different fire risk level of highway, β(1)=3, β(2)=6, β(3)=8.

[0087] Further, in the S70, the specific steps are as follows:

[0088] S701, according to the preset risk value interval, the highway network risk unit is divided into low, general, larger and major fire risk unit, and based on the RGB color coordinates of key risk value 0, 40, 60, 80, 100, blue, green, yellow, orange and red, the RGB value of any risk value is calculated by nonlinear interpolation method, and the dynamic mapping of risk value and color is realized;

[0089] S702, using the longitude and latitude coordinate translation method, assuming that the jth risk unit of the nth highway corresponds to point E j , whose longitude and latitude coordinates are (x j , y j ), the risk unit of the highway in the uplink direction, and the corresponding risk unit in the downlink direction is point E p , whose longitude and latitude coordinates are (x p , y p ), and point E j is translated along the direction of vector ​ point E p along the direction of vector translation distance , According to the scale of the map, if the azimuth angle of the direction of vector is θ, then when θ∈[0°, 180°), the azimuth angle of the direction of vector is (θ+180°), and when θ∈[180°, 360°), the azimuth angle of the direction of vector is (θ-180°). The formula of the azimuth angle θ is as follows:

[0090]

[0091] Suppose point E j is translated along the direction of vector by a distance of , and becomes point , whose longitude and latitude coordinates are point E p is translated along the direction of vector by a distance of , and becomes point , whose longitude and latitude coordinates are The formula of the longitude and latitude coordinates after translation is as follows:

[0092]

[0093]

[0094]

[0095]

[0096] S703、According to the longitude and latitude coordinates of each risk unit after coordinate translation, mark the position of each risk unit in the satellite remote sensing map, mark the color in the corresponding position according to the corresponding RGB value coordinates of the risk value of the risk unit and save, so as to realize the visualization of the risk values of different risk units of the expressway.

[0097] In a second aspect, the present application further provides a computer readable storage medium, wherein the storage medium stores a computer program, and the computer program is executed by a processor to realize the method as described above.

[0098] In a third aspect, the present application further provides an electronic terminal, comprising a processor and a memory; the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory to make the terminal execute the method as described above.

[0099] Compared with the prior art, the expressway network fire risk calculation method, medium and terminal provided by the present application have at least the following beneficial effects:

[0100] Most of the researches in the prior art only calculate the fire risk level of the research object or risk unit, but do not visualize it, so that the fire risk cannot be presented in a clear and intuitive manner, which is not conducive to the decision makers and the drivers and passengers to timely understand the distribution of the fire risk and take targeted prevention and control measures. The present application comprehensively considers various dynamic and static factors, constructs a multi-dimensional and multi-scenario risk assessment model, and significantly improves the scientificity and accuracy of the fire risk calculation. The present application combines the complex calculation results with the geographic information system (GIS) and satellite remote sensing map, uses the longitude and latitude coordinate translation and RGB color mapping technology, realizes the visualization and intuitive presentation of the fire risk level of different risk units along the expressway, greatly facilitates the decision makers, managers and drivers to master the distribution of the risk. The calculation results of the present application not only give the risk level of the overall road network, but also are more refined to the risk value of each expressway, each specific road section (risk unit), different vehicle types and different consequence risk levels, which helps the traffic management department and the fire rescue agency to make scientific decisions and fine management, and effectively improves the overall fire prevention and control capability and emergency response efficiency of the expressway network. BRIEF DESCRIPTION OF DRAWINGS

[0101] In order to more clearly illustrate the scheme of the present application, the drawings needed in the following embodiment description will be briefly introduced. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0102] Figure 1 A flowchart of an expressway network fire risk calculation method provided by an embodiment of the present application;

[0103] Figure 2 A vehicle body length and vehicle spacing calculation method based on a satellite remote sensing image in an expressway network fire risk calculation method provided by an embodiment of the present application;

[0104] Figure 3 A longitude and latitude coordinate translation diagram of an expressway risk unit in an expressway network fire risk calculation method provided by an embodiment of the present application. DETAILED DESCRIPTION

[0105] For the purpose of facilitating the understanding of the present application, a more comprehensive description of the present application will be given below with reference to the relevant drawings. The preferred embodiments of the present application are shown in the drawings. However, the present application can be realized in many different forms and is not limited to the embodiments described herein. On the contrary, these embodiments are provided for the purpose of making the disclosure of the present application more thorough and comprehensive.

[0106] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs. The terms used in the specification of the present application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application.

[0107] The present application provides a highway network fire risk calculation method, which is applied to the research process of highway fire risk. The highway network fire risk calculation method comprises the following steps:

[0108] S10, determine the composition and geographical location information of the highway network, obtain the road network map and satellite remote sensing image of the highway network, determine the terrain type and road section type of each highway, and obtain the location information of the dividing point; S20, divide the highway network risk unit and obtain the latitude and longitude coordinates thereof; S30, identify the satellite remote sensing image of different risk units by using a target detection method, count the types and quantities of vehicles in different risk units, calculate the average total number of different vehicle types in each risk unit under different time type scenarios, the average driving speed of each vehicle type on the highway under different time type scenarios, and the annual average vehicle flow of different risk units of the highway network; S40, calculate the number of times of different vehicle types on different road sections under different consequence risk level fires, the average driving mileage of different vehicle types of the highway network, the number of times of different consequence risk level fires in different risk units of the highway network, and the average loss amount score of different consequence risk level fires in the highway network; S50, calculate the fire rescue accessibility of different risk units of the highway network; S60, calculate the loss amount score adjustment value of the highway fire accident considering the fire rescue accessibility, the loss value of the highway fire accident in different risk units of the highway network, and the fire risk value of different risk units of the highway network; S70, determine the fire risk grade of different risk units of the highway network and the corresponding RGB value, the translation direction and position of the latitude and longitude coordinates of different risk units of the highway network, and visualize the risk value of the highway network risk unit.

[0109] The present application realizes the visualization of the highway fire risk grade, improves the accuracy of fire risk calculation, and provides a scientific basis for the site selection of fire rescue stations and risk prevention and control.

[0110] In order for those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings.

[0111] The application provides a highway network fire risk calculation method applied to the research process of highway fire risk, in combination with Figures 1 to 3 In the embodiment, the highway network fire risk calculation method comprises the following steps:

[0112] S10, determine the highway network composition and geographic location information, obtain the road network map and satellite remote sensing image of the highway network, determine the terrain type and road section type of each highway, and obtain the location information of the dividing point.

[0113] Specifically, in the embodiment, the specific steps of step S10 are as follows:

[0114] S101, the highway network can be determined according to administrative division, or can be composed of multiple highways defined by the user, the name, number, length and geographic boundary of each highway in the highway network are determined by using an online map and combining field investigation, and the longitude and latitude coordinates of the center point of the geographic boundary line are obtained by using a measuring instrument or an online map longitude and latitude query platform.

[0115] Specifically, the number of highways in the highway network is defined as N, wherein the length of the nth highway is L(n), the uplink of the highway is regarded as a different road, the longitude and latitude coordinates of the uplink starting point are (x(n, ls), y(n, ls)), the longitude and latitude coordinates of the uplink ending point are (x(n, lf), y(n, lf)), the longitude and latitude coordinates of the downlink starting point are (x(n, rs), y(n, rs)), and the longitude and latitude coordinates of the downlink ending point are (x(n, rf), y(n, rf)).

[0116] S102, according to the location information of the highway network, obtain the road network data of the highway network in the road network map open platform, obtain the high-resolution satellite remote sensing image of each highway in the highway network by using a satellite remote sensing map open platform, and need to clearly identify the type of vehicle driving on the highway, obtain the satellite remote sensing map historical image of each highway at different time in the past three years or more to be calculated, and filter and integrate the historical image according to the time sequence and different time types, and the division of the time type should reflect the obvious change of the vehicle flow on the same road section of the highway.

[0117] Specifically, after filtering and integrating the satellite remote sensing map historical image of the highway according to different time types by the above method, the number of highway network historical images of the Tth time type is defined as T(z).

[0118] In this embodiment, the road network map of the expressway network can be obtained by OpenStreetMap, the 0.5m high-definition satellite remote sensing map historical image of the expressway network to be measured can be obtained by Jilin No. 1, Tianditu, Xingtudong, etc. Open platform, the historical image cycle is from January 2022 to October 2025, the historical image is sorted according to time sequence, and the historical image is classified according to working day, weekend and statutory holiday, so the type of time type is 3, at this time T e {1, 2, 3}, the numbers in the set respectively represent the time type {working day, weekend, statutory holiday}, if after screening, there are 6 groups of historical images in all historical images, which belong to the time type of statutory holiday, then 3(z) = 6 groups.

[0119] S103, according to the satellite remote sensing image of the expressway network to be evaluated and the road network map, determine which region the expressway belongs to, and determine the boundary line of the change of the terrain type and the road section type, which cannot be determined by satellite remote sensing image or road network map, and is determined by field investigation. The longitude and latitude coordinates of the center point of the boundary line are obtained by using measuring instrument or online map latitude and longitude query platform.

[0120] Specifically, the same road section type road in the same expressway with the same uplink and downlink direction, the same terrain type and the same road section type is defined, and the nth expressway has B different road section types. According to the order of small to large according to the stake number, the boundary line of each road section type is numbered in turn, and the longitude and latitude coordinates of the center point of the kth boundary line of the bth road section type are (x(b, k), y(b, k)).

[0121] In this embodiment, the terrain type of the expressway can be divided into plain, plateau, hilly, basin and mountain, which can be increased or decreased according to actual conditions. The road section type of the expressway can be divided into tunnel, bridge, ramp, service area, toll station and ordinary road, which can be increased or decreased according to actual conditions. It is assumed that according to the satellite remote sensing image of the expressway network and field investigation, it is determined that the terrain type of a certain expressway network is mainly plain and mountain, and the road section of the expressway can be divided into tunnel, bridge, ramp, service area, toll station and ordinary road, so the type of road section is 12, which is plain area tunnel, bridge, ramp, service area, toll station, ordinary road and mountain area tunnel, bridge, ramp, service area, toll station, ordinary road. The boundary line of each road section type in the expressway network is determined by means of Baidu online map and through field investigation, and the longitude and latitude coordinates of each boundary point are obtained by using Baidu map coordinate picking system.

[0122] S20, divide the risk unit of the expressway network, and obtain the longitude and latitude coordinates.

[0123] Specifically, in this embodiment, the same highway is divided into equal risk units at a certain fixed interval based on the boundary of different section types of each highway, and the risk units less than the specified interval in each section are regarded as one risk unit. Each service area and toll station is regarded as an independent risk unit. The latitude and longitude coordinates of each risk unit are obtained by using measuring instruments or online map latitude and longitude query platform.

[0124] In this embodiment, the nth highway has B different section types, and the risk units are divided according to the interval l. The risk units on the same highway are numbered in the order of ascending pile number from small to large, from the upstream highway to the downstream highway. The latitude and longitude coordinates of the jth risk unit of the nth highway are (x(n,j), y(n,j)).

[0125] S30, using target detection method to identify satellite remote sensing images of different risk units, and counting the number of different types of vehicles in different risk units, and measuring the average total number of different types of vehicles in different risk units under different time types, the average driving speed of different types of vehicles on the highway network under different time types, and the annual average traffic volume of different risk units of the highway network.

[0126] Specifically, in this embodiment, the specific steps of step S30 are as follows:

[0127] S301, the satellite remote sensing map historical image of the screened and integrated highway is divided according to the boundary of the divided risk unit to obtain the satellite remote sensing image of different risk units. The satellite remote sensing images of different risk units are screened to establish an initial data set. The vehicle type and its bounding box in the satellite remote sensing image are labeled by using a labeling tool to establish a highway vehicle type recognition data set. Then, the data set is trained by using a target detection algorithm (such as YOLO, SSD) to generate a highway vehicle type recognition model. The model is used in combination with Python programming to identify the vehicle type of the satellite remote sensing map historical image of the highway risk unit, and the total number of vehicles of different types of vehicles on the highway network in different risk unit sections is counted. If the satellite remote sensing image of the highway risk unit is blocked by trees, covered by shadows, or the section type is a tunnel, etc., the number of vehicles cannot be accurately identified, and the linear interpolation method is used to measure the number of vehicles.

[0128] Specifically, the total number of the a-th vehicle type on the j-th risk unit of the n-th expressway at the z-th moment of the T-th time type is defined as q(z, j, a), and if the satellite remote sensing image of the expressway risk unit is blocked by trees, covered by shadows, or the road section type is a tunnel, etc., so that the number of vehicles cannot be accurately identified, the total number of the a-th vehicle type on the risk unit is q(z, j, a), which is calculated according to the following formula:

[0129]

[0130] wherein q(z, a, a) and q(z, b, a) represent the total number of the a-th vehicle type on the two risk units adjacent to the j-th risk unit of the n-th expressway before and after the j-th risk unit, which can completely identify vehicles at the z-th moment of the T-th time type.

[0131] In this embodiment, the vehicle types running on the expressway can be divided into small cars, buses, small trucks, large trucks, trailer trucks, tank trucks and other vehicles, and the initial data set is labeled according to different vehicle types on different risk unit satellite remote sensing images by using the labeling tool Labelimg to establish the expressway vehicle type identification data set. The total number of small cars on the two risk units adjacent to the A tunnel risk unit of a certain expressway is 6 and 10 respectively, and the total number of small cars on the A tunnel risk unit is (6+10) / 2=8.

[0132] S302, the number data of different vehicle types on each risk unit of the expressway network at different moments are divided into T time types according to the time sequence. The average value of the data of the same time type is obtained by using the arithmetic mean method, and thus the average total number of each vehicle type on each risk unit under different time type scenarios can be obtained.

[0133] Specifically, the average total number of the a-th vehicle type on the j-th risk unit of the n-th expressway at the T-th time type is defined as q(T, j, a). The calculation formula is as follows:

[0134]

[0135] wherein T(z) represents the number of historical images of the T-th time type in all historical satellite remote sensing images of the expressway network, and q(z, j, a) represents the total number of the a-th vehicle type on the j-th risk unit of the n-th expressway at the z-th moment of the T-th time type.

[0136] S303, as Figure 2The data set of the risk unit of the straight section of the expressway is shown in the figure. The terrain distortion of the satellite remote sensing map image is eliminated by orthorectification. The direction of all satellite remote sensing images in the data set is rotated to the up-down direction. The vehicle type and its bounding box in the data set are obtained by using the trained expressway vehicle type recognition model. The left upper corner pixel coordinates and the right lower corner pixel coordinates of the vehicle type a and its bounding box are output. The satellite map resolution is obtained from the satellite remote sensing map open platform. The average length of the vehicle and the average distance between different vehicles are calculated by using the above data.

[0137] Specifically, assuming that on the nth expressway, the number of samples of the ath vehicle type in the data set is V, wherein the left upper corner pixel coordinates of the bounding box in the vth sample are (X(v,lu), Y(v,lu)), the right lower corner pixel coordinates of the bounding box are (X(v,rd), Y(v,rd)), the satellite remote sensing map resolution obtained is e meters / pixel, and if the length of the ath vehicle type in the vth sample is l(a,v), the average length of the ath vehicle type on the nth expressway is l(a), and the calculation formula is as follows:

[0138]

[0139]

[0140] Assuming that the Tth time type, on the nth expressway, the number of samples of the ath vehicle type in the data set is V. The left upper corner pixel coordinates of the bounding box in the vth sample are (X(v,lu), Y(v,lu)), the right lower corner pixel coordinates of the bounding box are (X(v,rd), Y(v,rd)), the left upper corner pixel coordinates of the bounding box in the v+1th sample are (X(v+1,lu), Y(v+1,lu)), the right lower corner pixel coordinates of the bounding box are (X(v+1,rd), Y(v+1,rd)), the satellite remote sensing map resolution obtained is e meters / pixel, if the distance between the vth sample and the v+1th sample is D(v), the average distance of the ath vehicle type on the nth expressway in the Tth time type is D(T,a), and the calculation formula is as follows:

[0141]

[0142] .

[0143] S304, the average driving speed of each type of vehicle on the expressway in different time type scenarios can be estimated and determined by expert experience, or the driving speed of different vehicle types in different time type scenarios can be obtained by historical data of the expressway interval speed measurement system of the transportation department, and the average driving speed of each type of vehicle in different time type scenarios is obtained.

[0144] Specifically, if the average speed of the a-th vehicle type on the j-th risk unit of the n-th highway under the T-th time type is determined according to the estimation of the expert, the average speed of the a-th vehicle type on the j-th risk unit of the n-th highway under the T-th time type is determined according to the estimation of the expert. If the average speed is determined by the historical data of the traffic department, it is assumed that the data of the a-th vehicle speed on the j-th risk unit of the n-th highway under the T-th time type is obtained, wherein the i(a)-th data of the vehicle speed is V(i(a)), and the average speed of the a-th vehicle type on the j-th risk unit of the n-th highway under the T-th time type is defined as V(T,j,a) calculated by the above method. The calculation formula is as follows:

[0145]

[0146] If the risk unit on the highway does not belong to the interval speed measurement section, the average speed of the nearest interval speed measurement section of the same type of road section is selected as the average speed of the risk unit according to the nearest principle.

[0147] S305, the average speed of the vehicle on the highway is related to the time, the average speed of the vehicle, the average length of the vehicle and the average distance between the vehicles. The data obtained is brought into the traffic formula to calculate the traffic of different vehicles on different highways and their risk units.

[0148] Specifically, the average daily traffic of the a-th vehicle type on the j-th risk unit of the n-th highway under the T-th time type is defined as Q(T,j,a).

[0149]

[0150] wherein 24 represents 24h / d, V(T,j,a) is in units of km / h, D(T,a) and l(a) are in units of km, and the unit of the average daily traffic is veh / d.

[0151] The average annual traffic of the a-th vehicle type on the j-th risk unit of the n-th highway is defined as Q(n,j,a), and the calculation formula is as follows:

[0152]

[0153] wherein D(1), D(2) and D(3) represent the total number of working days, weekends and statutory holidays in the statistical period of the satellite remote sensing map historical image of the entire highway, and Y(n) represents the number of years in the statistical period of the satellite remote sensing map historical image of the entire highway.

[0154] Assume that the nth highway has J risk units, and define the daily traffic volume of the a-th vehicle type on the nth highway under the T-th time type as Q(T, n, a), which is calculated as follows:

[0155]

[0156] Define the annual traffic volume of the a-th vehicle type on the nth highway as Q(n, a), which is calculated as follows:

[0157]

[0158] S40, calculate the number of times of different consequences risk level fires of different vehicle types on different road sections, the average driving mileage of different vehicle types on the highway network, the number of times of different consequence risk level fires of different vehicle types on different risk units of the highway network, and the average loss amount score of different consequence risk level fires of the highway network.

[0159] Specifically, in this embodiment, the specific steps of step S40 are as follows:

[0160] S401, obtain the historical data of vehicle fires on each highway in the region to be studied in the past five years or more from the fire rescue authorities and the transportation department. The data includes the time, location, vehicle type causing the fire, personnel casualties and property losses of each fire, and the road section type of the accident according to the location of the fire. According to the personnel casualties and property losses, the consequence level of the highway fire accident is determined according to the “Fire Statistics Management Regulations”, and the fires of the same road section type and the same vehicle type are screened and integrated according to the different fire consequence risk levels, so as to obtain the number of times of different consequence risk level fires of different vehicle types on different road sections.

[0161] Specifically, assume that the period of statistical fire history data is q years, and through statistics, the number of times of the a-th vehicle type causing the c-th level fire on the b-th type road section of the nth highway is F(b, a, c), and the annual average number of fires of the a-th vehicle type causing the c-th level fire on the b-th type road section of the nth highway is AF(b, a, c), where c∈{1, 2, 3, 4, 5}, the numbers in this set represent {minor fire, general fire, larger fire, major fire, and particularly major fire} respectively. The calculation formula is as follows:

[0162]

[0163] Define the annual average number of fires of the a-th vehicle type causing the c-th level fire on the nth highway as YAF(n, a, c), which is calculated as follows:

[0164] ​

[0165] wherein B represents the total number of road section types on the nth highway.

[0166] S402, the road section between two toll stations on each highway is regarded as a whole, if the starting point and the ending point of the highway are not toll stations, they are regarded as a toll station, assuming that the highway network to be measured has N highways, m toll stations (including the starting point and the ending point), the toll stations of each highway are numbered in ascending order according to the stake number, then the road section between the ith toll station and the (i+1)th toll station on the nth highway is numbered as i, the length of the road section is L(n,i), the daily average number of the ath vehicle type on the road section is counted by the intelligent monitoring device set on the road section, which is Q(n,i,a), the average driving mileage of the ath vehicle type on the nth highway is defined as L(n,a), and the calculation formula is as follows:

[0167] .

[0168] S403, the fire accident rate of the highway can represent the frequency of the fire accident of the highway, so as to evaluate the fire risk degree of the highway, according to the accident rate calculation formula, the annual average accident rate of the fire of different consequences risk levels of different vehicle types on the highway can be obtained.

[0169] Specifically, the annual average accident rate of the fire of the cth level of the ath vehicle type on the nth highway is defined as P(n,a,c), and the calculation formula is as follows:

[0170]

[0171] wherein the unit of P(n,a,c) is times / million vehicle kilometers.

[0172] S404, according to the annual average traffic volume of different risk units in the highway network, the length of the risk unit and the annual average fire accident rate of the highway, the number of times of the fire of different risk levels of different risk units in the highway network can be calculated.

[0173] Specifically, the number of times of the fire of the cth level of the ath vehicle type on the jth risk unit of the nth highway is defined as T(j,a,c), and the calculation formula is as follows:

[0174]

[0175] Wherein, P(n, a, c) is the annual average accident rate of the a-th vehicle type of the c-th fire class on the n-th highway, Q(n, j, a) is the annual average traffic volume of the a-th vehicle type on the j-th risk unit of the n-th highway, and l(n, j) represents the length of the j-th risk unit of the n-th highway.

[0176] In S405, the loss types of the highway fire accidents mainly include personnel casualty and direct property loss. In order to convert different types of losses into a unified standard measurement, different types of losses caused by the fire accidents are uniformly converted into loss score according to the risk class. The risk class of the highway fire accident consequence and the corresponding loss score division table are shown in Table 1.

[0177] Table 1

[0178] Fire class Number of severely injured Number of deaths Direct property loss (yuan) Loss fraction Minor fire 0 0 [0,1000) [0,3) General fire [1,10) [1,3) [1000,30000000) [3,6) Large fire [10,50) [3,10) [30000000,100000000) [6,8) Major fire [50,100) [10,30) [100000000,300000000) [8,10) Extra major fire [100,+∞) [30,+∞) [300000000,+∞) 10

[0179] The specific conversion method of the loss score of the fire accident is as follows: according to the fire class and the corresponding loss score standard divided in Table 1, the linear interpolation method is used to convert the loss caused by the fire accident into the loss score. Assuming that the period of the statistical fire history data is q years, the fire accidents in the period are numbered in chronological order. If the number of seriously injured persons caused by the h-th fire accident is Z(h), the number of deaths is S(h), and the direct property loss is C(h), then the personnel casualty and the direct property loss are converted into the loss score according to the following formula:

[0180]

[0181]

[0182]

[0183] Wherein, DZ(h), DS(h) and DC(h) are respectively the values of the loss score converted from the number of seriously injured persons, the number of deaths and the direct property loss caused by the fire accident.

[0184] When determining the fire accident class, as long as any one of the number of seriously injured persons, the number of deaths and the property loss reaches the corresponding fire accident class, it is determined that the fire accident belongs to the class. Thus, the loss score of the h-th fire accident is defined as D(h), and the calculation formula is as follows:

[0185]

[0186] Using the above formula, the loss fraction of all fire accidents in the statistical period of fire history data is calculated, and the fire accidents are classified according to different expressways, different road section types, different vehicle types and different fire accident grades, and the same class is numbered in turn. Assuming that the total number of times of the nth expressway, the a-th vehicle type and the c-th class of fire is G(a,c) in the entire statistical period, wherein the loss fraction of the g-th time of the a-th vehicle type and the c-th class of fire is D(a,g,c). The average loss fraction of the a-th vehicle type and the c-th class of fire on the nth expressway is defined as AD(a,c), and the calculation formula is as follows:

[0187] .

[0188] S50, measuring the fire rescue accessibility of different risk units of the expressway network.

[0189] Specifically, in the embodiment, when measuring the loss of fire accidents, the influence of the difference in fire rescue accessibility of different risk units needs to be considered, different time scene samples are determined, the determination of the time scene needs to be representative and can reflect the dynamic changes of the expressway traffic conditions, the time scene samples are classified according to different time types, the request parameters are constructed by using the online map API interface service and the latitude and longitude coordinates of the fire rescue station to be measured and the latitude and longitude coordinates of each risk unit of the expressway network, the fire rescue travel time of the nearest fire rescue station to different risk units of the expressway network is obtained through the online map API batch route calculation service, and the fire rescue accessibility of different risk units under each time scene is calculated. The average fire rescue accessibility of different risk units of the expressway network under different time scenes is calculated by using the time weighting method.

[0190] Specifically, all time scene samples are classified according to different time types, the same type of time sample is numbered in chronological order, assuming that the sample of the T-th time type is composed of z time scenes, wherein the accessibility fraction corresponding to the j-th risk unit in the T(t)-th time scene is A(T(t)), and the average accessibility fraction corresponding to the j-th risk unit in the T-th time type is A(T,j), and the calculation formula is as follows:

[0191]

[0192] Assuming that there are T(d) days in the T-th time type in the entire measurement period, the average accessibility fraction of the j-th risk unit is A(j), and the calculation formula is as follows:

[0193]

[0194] Assuming in the highway network, the total number of risk units on the nth highway is N(j), and the total average fire rescue accessibility is TA(N), the calculation formula is as follows:

[0195] .

[0196] S60, measure the loss score adjustment value of the highway fire accident considering the fire rescue accessibility, the loss value of the annual average fire accident of different risk units in the highway network, and the fire risk value of different risk units in the highway network.

[0197] Specifically, in this embodiment, the specific steps of step S60 are as follows:

[0198] S601, introduce an adjustment coefficient f(j) to reflect the difference between the fire rescue accessibility and the average fire rescue accessibility of the jth risk unit in the highway network, which causes the adjustment percentage of the fire accident loss amount. The adjustment coefficient f(j) can be determined by comparing the influence value of the fire rescue accessibility difference in the fire rescue historical data on the fire loss amount, or by simulating the influence value of the fire accident loss amount under different accessibility scenarios. The adjustment value of the highway fire accident loss score can be calculated by the average loss amount and the adjustment coefficient.

[0199] Specifically, assuming that the adjustment coefficient of the fire rescue accessibility on the jth risk unit of the nth highway on the fire accident loss amount is f(j). In this risk unit, the average loss score adjustment value of the ac type fire of the a type vehicle is TD(a,c), and the calculation formula is as follows:

[0200] .

[0201] S601, using the obtained annual average number of different consequence risk level fires of different vehicles on different highways and the average loss score adjustment value of different consequence risk level fires of different vehicles on different highways, the loss value of the annual average different vehicle different risk level fire of different risk units on different highways is obtained.

[0202] Specifically, the annual average loss value of the ac type fire of the a type vehicle on the jth risk unit of the nth highway is defined as S(j,a,c), and the calculation formula is as follows:

[0203]

[0204] The annual average loss value of the fire accident on the jth risk unit of the nth highway is defined as S(n,,j), and the calculation formula is as follows:

[0205]

[0206] Wherein, 5 represents the risk level of highway fire accident, corresponding to slight fire, general fire, larger fire, major fire and particularly major fire respectively, and A represents the number of vehicle types on the nth highway.

[0207] S603、According to the annual loss value of the fire consequences of each risk unit in the obtained highway network, the fire consequence risk value of each risk unit of the to-be-tested highway network is calculated respectively.

[0208] Specifically, the annual risk value of a fire accident occurring on the jth risk unit of the nth highway is defined as R(n,j), and the calculation formula is as follows:

[0209] When S(n,j)≤β(1), the fire risk level of this risk unit is I level, and the corresponding risk value is:

[0210]

[0211] When β(1)<S(n,j)≤β(2), the fire risk level of this risk unit is II level, and the corresponding risk value is:

[0212]

[0213] When β(2)<S(n,j)≤β(3), the fire risk level of this risk unit is III level, and the corresponding risk value is:

[0214]

[0215] When S(n,j)>β(3), the fire risk level of this risk unit is IV level, and the corresponding risk value is:

[0216]

[0217] Wherein, the values of β(1), β(2) and β(3) are determined according to the loss amount score corresponding to different fire risk levels of the highway in Table 1, β(1)=3, β(2)=6, β(3)=8.

[0218] Similarly, the fire risk value of the a type of vehicle on the jth risk unit of the nth highway can be obtained by using the above formula.

[0219] S70, determine the fire risk level of different risk units of the highway network, and the corresponding RGB value, the longitude and latitude coordinate translation direction and position of different risk units of the highway network, and visualize the risk value of the risk unit of the highway network.

[0220] Specifically, in this embodiment, the specific steps of step S70 are as follows:

[0221] S701. Based on the risk values ​​of different risk units in the highway network, risk units are divided into major fire risk units, relatively large fire risk units, general fire risk units, and low fire risk units, with their corresponding risk value ranges shown in Table 2. To present the dynamic changes between the risk values ​​of different risk units, a correspondence between risk values ​​and RGB color values ​​is established, using different colors to represent the changes in risk values ​​of different risk units.

[0222] Table 2

[0223] Fire risk class Corresponding risk value Risk unit type Class I [0,40) Low fire risk unit Class II [40,60) General fire risk unit Class III [60,80) Large fire risk unit Class IV [80,100] Major fire risk unit

[0224] The risk unit is defined as follows: a risk value of 0 corresponds to blue (RGB coordinates: (0, 0, 255)); a risk value of 40 corresponds to green (RGB coordinates: (0, 255, 0)); a risk value of 60 corresponds to yellow (RGB coordinates: (255, 255, 0)); a risk value of 80 corresponds to orange (RGB coordinates: (255, 128, 0)); and a risk value of 100 corresponds to red (RGB coordinates: (255, 0, 0)). These three RGB coordinates correspond to the brightness values ​​of the red, green, and blue channels, respectively. The RGB coordinates for other risk units can be calculated using nonlinear interpolation based on the known correspondence between risk values ​​and RGB coordinates. The colors corresponding to different risk values ​​are then determined based on the calculated RGB coordinates.

[0225] S702, such as Figure 3 As shown, by using the latitude and longitude coordinate translation method, the position of the risk units in the up and down directions of the highway in the satellite remote sensing map is changed, so that when the highway fire risk is visualized, the difference in risk level of the risk units in the up and down directions of the highway can be presented.

[0226] Specifically, the proposed latitude and longitude coordinate translation method assumes that the j-th risk unit of the n-th highway corresponds to point E on the map. j Its latitude and longitude coordinates are (x j ,y j Point E represents the risk unit in the upbound direction of the highway, while point E represents the corresponding risk unit in the downbound direction. p Its latitude and longitude coordinates are (x p ,y p In order to clearly show the difference in risk levels between uplink and downlink risk units in satellite remote sensing maps, it is proposed to divide point E... j Along vector Translation distance in the direction Point E p Along vector Translation distance in the direction , According to the scale of the map, it is ensured that the trend of the risk level of the uplink and downlink risk units can be clearly seen in the risk visualization. If the azimuth of the direction of the vector is θ, then the azimuth of the direction of the vector is (θ+180°) when θ∈[180°, 360°), and the azimuth of the direction of the vector is (θ-180°) when θ∈[0°, 180°). The calculation formula of the azimuth θ is as follows:

[0227]

[0228] Suppose that point E j is translated along the direction of the vector by a distance of , and becomes point , whose longitude and latitude coordinates are , and point E p is translated along the direction of the vector by a distance of , and becomes point , whose longitude and latitude coordinates are , then the calculation formula of the longitude and latitude coordinates after translation is as follows:

[0229]

[0230]

[0231]

[0232] .

[0233] S703, according to the longitude and latitude coordinates of each risk unit after coordinate translation, mark the position of each risk unit in the satellite remote sensing map, mark the color in the corresponding position according to the corresponding RGB value coordinates of the risk value of the risk unit and save, so as to realize the visualization of the risk values of different risk units of the expressway.

[0234] In the embodiment, a dataset containing latitude and longitude coordinates and corresponding RGB value coordinates of different risk units is prepared, a DataFrame can be constructed using the pandas library, and then a basic map object is created using the folium.Map() function. Each row in the data is traversed, and the folium.CircleMarker() or folium.Marker() function is used to add a marker point on the map. The RGB value in the data is converted into a color format recognizable by folium, and is applied to the color, fillColor and other attributes of the marker. Finally, the save() method is used to save the map as an HTML file.

[0235] The embodiment of the application further provides a computer readable storage medium, which has a computer program stored thereon, and the computer program is executed by a processor to implement any of the methods in the embodiment.

[0236] The embodiment of the application further provides an electronic terminal, which comprises a processor and a memory, the memory is used for storing a computer program, and the processor is used for executing the computer program stored in the memory, so that the terminal executes any of the methods in the embodiment.

[0237] The computer readable storage medium in the embodiment can be understood by those skilled in the art that all or part of the steps of the above-mentioned method embodiments can be completed by a computer program related hardware. The computer program can be stored in a computer readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the storage medium includes ROM, RAM, magnetic disc or optical disc and various storage medium that can store program codes.

[0238] The electronic terminal provided in the embodiment comprises a processor, a memory, a transceiver and a communication interface, the memory and the communication interface are connected with the processor and the transceiver and complete communication between each other, the memory is used for storing a computer program, the communication interface is used for communication, and the processor and the transceiver are used for running the computer program, so that the electronic terminal executes each step of the method.

[0239] Compared with the prior art, most of the researches in the prior art only calculate the fire risk grade of the research object or the risk unit, but do not visualize the same, so that the fire risk cannot be presented in a clear and intuitive manner, which is not conducive to the decision makers and the drivers and passengers to understand the fire risk distribution in a timely manner and take targeted prevention and control measures. The present application comprehensively considers various dynamic and static factors, and constructs a multi-dimensional and multi-scenario risk assessment model, which significantly improves the scientificity and accuracy of the fire risk calculation. The present application combines the complex calculation results with the geographic information system (GIS) and the satellite remote sensing map, uses the longitude and latitude coordinate translation and the RGB color mapping technology, realizes the visualization and intuitive presentation of the fire risk grade of different risk units along the expressway, greatly facilitates the decision makers, the management personnel and the drivers and passengers to master the risk distribution, and the calculation results of the present application not only give the risk grade of the whole road network, but also are refined to the risk value of each expressway, each specific road section (risk unit), different vehicle types and different consequence risk grades, which is helpful for the traffic management department and the fire rescue agency to make fine management and scientific decision, and effectively improves the overall fire prevention and control capability and the emergency response efficiency of the expressway network.

[0240] Obviously, the above-described embodiments are only the preferred embodiments of the present application, but not all the embodiments, and the drawings show the preferred embodiments of the present application, but do not limit the patent scope of the present application. The present application can be realized in many different forms, and conversely, the purpose of providing these embodiments is to make the disclosure of the present application more thorough and comprehensive. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions recorded in the foregoing embodiments, or equivalently replace some of the technical features. Any equivalent structure made by using the content of the present application specification and drawings, directly or indirectly applied to other related technical fields, is also within the patent protection scope of the present application.

Claims

1. A method for assessing fire risk in a highway network, characterized in that, Includes the following steps: S10. Determine the composition and geographical location information of the expressway network, obtain the road network map and satellite remote sensing images of the expressway network, determine the terrain type and road segment type of each expressway, and obtain the location information of their boundaries. S20. Divide the expressway network into risk units and obtain their latitude and longitude coordinates; S30. Use target detection to identify satellite remote sensing images of different risk units, count the vehicle types and their quantities in different risk units, and calculate the average total number of different vehicle types in each risk unit under different time type scenarios, the average driving speed of various vehicle types on highways under different time type scenarios, and the average annual traffic flow of different risk units on the highway network. S40. Calculate the annual average number of fires with different consequences and risk levels for different vehicle types on different road sections, the average mileage of different vehicle types on the highway network, the annual average number of fires with different consequences and risk levels in different risk units of the highway network, and the average loss per fire with different consequences and risk levels on the highway network. S50, Calculate the accessibility of fire and rescue services in different risk units of the expressway network; S60. Calculate the fractional adjustment value of highway fire accident loss taking into account fire rescue accessibility, the annual average loss value of fire accidents in different risk units of the highway network, and the fire risk value of different risk units of the highway network. S70. Determine the fire risk level of different risk units in the expressway network and their corresponding RGB values, as well as the translation direction and location of the latitude and longitude coordinates of different risk units in the expressway network, and visualize the risk values ​​of the risk units in the expressway network.

2. The method for assessing fire risk in a highway network according to claim 1, characterized in that, In step S10, the specific steps are as follows: S101. Determine the composition and geographical location information of the expressway network: Define the expressway network as consisting of N expressways, obtain the length L(n) of the nth expressway, and treat the up and down directions of the expressway as different roads. The latitude and longitude coordinates of the starting position of the up road are (x(n,ls),y(n,ls)), and the latitude and longitude coordinates of its ending position are (x(n,lf),y(n,lf)). The latitude and longitude coordinates of the starting position of the down road are (x(n,rs),y(n,rs)), and the latitude and longitude coordinates of its ending position are (x(n,rf),y(n,rf)). S102. Obtain road network map and satellite remote sensing map historical images: Based on the geographical location information, obtain road network data of the expressway network and obtain high-resolution satellite remote sensing map historical images of different times in the past three years or more. Filter and integrate the historical images according to the time type that can reflect changes in traffic flow, and define the number of satellite remote sensing map historical images of the Tth time type after integration as T(z). S103. Determine the terrain type, road segment type and their boundary information: Based on the satellite remote sensing images, road network maps and field surveys, determine the terrain type and road segment type of each expressway, as well as the boundary lines where the types change, and obtain the latitude and longitude coordinates of the center point of each boundary line; define roads with the same up and down directions, the same terrain type and the same road segment type in the same expressway as roads of the same road segment type, number the boundary lines of different road segment types of the nth expressway in a preset order, and obtain the latitude and longitude coordinates (x(b,k),y(b,k)) of the center point of the kth boundary line of the bth road segment type.

3. The method for calculating fire risk in a highway network according to claim 2, characterized in that, In S20, the nth expressway is defined to have B different road segment types. The expressways of different road segment types are divided equally according to the interval l to form multiple risk units, where the remaining part with a length less than l is a separate risk unit. Each service area and each toll station is a separate risk unit. All risk units on the same expressway are numbered in ascending order according to the order of the uphill expressway and the downhill expressway. The latitude and longitude coordinates of each risk unit are obtained, where the latitude and longitude coordinates of the jth risk unit of the nth expressway are (x(n,j),y(n,j)).

4. The method for calculating fire risk in a highway network according to claim 3, characterized in that, The specific steps in S30 are as follows: S301. Divide the historical images of highway satellite remote sensing maps into risk unit boundaries, construct a dataset labeled with vehicle types and bounding boxes, and identify the vehicle types and quantities in each risk unit through target detection algorithms; For units that cannot be identified due to obstruction or tunnels, the number of vehicle types is calculated using linear interpolation, with the following formula: ； Where q(z,α,a) and q(z,β,a) represent the total number of type a vehicles in the two completely identifiable risk units adjacent to the j-th risk unit on the n-th highway at the z-th time of the T-th time type; S302. Classify data by time type and calculate the average number of vehicle types within each risk unit. The formula is as follows: ； Where T(z) represents the number of historical images of the Tth time type in all satellite remote sensing map historical images of the highway network, and q(z,j,a) represents the total number of vehicle types of the ath type on the jth risk unit of the nth highway at the zth time of the Tth time type. S303. Through orthorectification and image rotation, based on the vehicle bounding box pixel coordinates and map resolution, the average vehicle body length l(a) is calculated using the following formula: ； ； Assuming the time type is T, the total number of samples of type a vehicle in the dataset obtained on the nth highway is V. The top-left pixel coordinates of the bounding box in the vth sample are (X(v,lu),Y(v,lu)), and the bottom-right pixel coordinates are (X(v,rd),Y(v,rd)). The top-left pixel coordinates of the bounding box in the (v+1)th sample are (X(v+1),Y(v+1,lu)), and the bottom-right pixel coordinates are (X(v+1),Y(v+1,rd)). The resolution of the obtained satellite remote sensing map is e meters / pixel. If the vehicle spacing between the vth sample and the (v+1)th sample is D(v), and the average spacing of type a vehicle on the nth highway in the Tth time type is D(T,a), then the calculation formula is as follows: ； ； S304. The average speed of each vehicle type on different risk units of the highway is obtained through expert experience or historical speed measurement data. For non-speed measurement units, the nearest value is used for assignment. Suppose that in the T-th time type scenario, a total of I(a) data points on the speed of vehicle type a are obtained in the j-th risk unit of the n-th highway, where the speed of the vehicle in the i(a)-th data point is V(i(a)). The average speed of vehicle type a in the j-th risk unit of the n-th highway in the T-th time type is defined as V(T,j,a), calculated using the above method. Its calculation formula is as follows: ； S305. Taking into account time type, average speed, vehicle length, and vehicle spacing, calculate the daily average traffic flow Q(T,j,a) and annual average traffic flow Q(n,j,a) for each vehicle type. The formulas are as follows: ； ； Where 24 represents 24h / d, V(T,j,a) is in km / h, D(T,a) and l(a) are in km, the average daily traffic flow is in veh / d, D(1), D(2) and D(3) represent the total number of working days, weekends and statutory holidays in the entire historical image statistical period of the highway satellite remote sensing map, respectively, and Y(n) represents the number of years in the entire historical image statistical period of the highway satellite remote sensing map.

5. The method for calculating fire risk in a highway network according to claim 4, characterized in that, The specific steps in S40 are as follows: S401. Based on historical fire data, count the number of times, F(b,a,c), a type of vehicle type occurs in a fire of class c consequences on a type b section of the nth highway within a q-year period, and calculate the average annual number of fires, AF(b,a,c), as follows: ； S402. Summing all road segment types of the nth expressway, calculate the average annual number of fires YAF(n,a,c) of type a vehicle and class c fires on that expressway, using the following formula: ； Where B represents the total number of road segment types on the nth expressway; S403. Based on the length L(n,i) of each toll station section of the nth expressway and the average daily traffic volume Q(n,i,a) of each vehicle type, calculate the average mileage L(n,a) of each vehicle type, using the following formula: ； Where m represents the number of toll stations, including the starting point and the ending point; S404. The annual accident rate P(n,a,c) for a type a vehicle causing a fire of class c on the nth highway is defined by the following formula: ； Where P(n,a,c) is in units of times per million vehicle-kilometers; S404. Define the number of times a type a vehicle experiences a fire of class c per year in the j-th risk unit of the n-th highway as T(j,a,c), as follows: ； Wherein, P(n,a,c) is the annual accident rate of type a fire of type c on type a vehicle on type n highway, Q(n,j,a) is the annual traffic volume of type a vehicle on type j risk unit of type n highway, and l(n,j) represents the length of type j risk unit of type n highway. S405. Using linear interpolation, convert the losses caused by fire accidents into loss fractions. Assume the period for statistically analyzing historical fire data is q years. Number the fire accidents within this period in chronological order. If the number of seriously injured people in the h-th fire accident is Z(h), the number of deaths is S(h), and the direct property loss is C(h), then convert the casualties and direct property losses into loss fractions according to the following formula: ； ； ； Where DZ(h), DS(h), and DC(h) are the values ​​of the number of seriously injured, the number of dead, and the direct property loss caused by the fire accident after being converted into loss fractions, respectively. The loss fraction D(h) for the h-th fire accident is defined by the following formula: ； S406. Assume that during the entire statistical period, the total number of times a type a fire of class c occurs on the nth highway is G(a,c), and the loss fraction of the gth occurrence of a type a fire of class c is D(a,g,c). Define the average loss fraction per occurrence of a type a fire of class c on the nth highway as AD(a,c), as follows: 。 6. The method for calculating fire risk in a highway network according to claim 5, characterized in that, In step S50, all time scenario samples are classified according to time type, and time samples of the same type are numbered according to their chronological order. Assuming that the samples of the Tth time type consist of z time scenarios, and the reachability score corresponding to the jth risk unit in the T(t)th time scenario is A(T(t)), then the average reachability score corresponding to the jth risk unit in the Tth time type is A(T,j), as shown in the following formula: ； Assuming that the Tth time type has a total of T(d) days during the entire measurement period, the average accessibility score of the jth risk unit can be obtained as A(j), as shown in the following formula: ； Suppose that in a highway network, the total number of risk units on the nth highway is N(j), and its overall average fire and rescue accessibility is TA(N), as shown in the following formula: 。 7. The method for assessing fire risk in a highway network according to claim 6, characterized in that, In S60, the specific steps are as follows: S601. Assume that in the j-th risk unit of the n-th highway, the adjustment coefficient for fire rescue accessibility to the amount of fire accident loss is f(j). In this risk unit, the average loss fraction adjustment value for a type a fire of class c is TD(a,c), as shown in the following formula: ； S602. Define the average annual loss value of a Class c fire occurring on the a-type vehicle in the j-th risk unit of the n-th highway as S(j,a,c), as follows: ； The average annual loss value of a fire accident in the j-th risk unit of the n-th highway is defined as S(n,j), and is expressed as follows: ； Here, 5 represents the risk level of a highway fire accident, including minor fire, general fire, major fire, serious fire and extremely serious fire, and A represents the number of vehicle types traveling on the nth highway. S603. Define the average annual risk value of a fire accident occurring in the j-th risk unit of the n-th highway as R(n,j), as follows: When S(n,j)≤β(1), the fire risk level of this risk unit is Level I, and the corresponding risk value is: ； When β(1)<S(n,j)≤β(2), the fire risk level of this risk unit is Level II, and the corresponding risk value is: ； When β(2)<S(n,j)≤β(3), the fire risk level of this risk unit is Level III, and the corresponding risk value is: ； When S(n,j)>β(3), the fire risk level of this risk unit is Level IV, and the corresponding risk value is: ； The values ​​of β(1), β(2) and β(3) are determined according to the loss fractions corresponding to different fire risk levels of highways, β(1)=3, β(2)=6, β(3)=8.

8. The method for calculating fire risk in a highway network according to claim 7, characterized in that, In S70, the specific steps are as follows: S701. Based on the preset risk value range, the highway network risk units are divided into low, general, relatively large, and major fire risk units. Based on the RGB color coordinates (blue, green, yellow, orange, and red) corresponding to the key risk values ​​0, 40, 60, 80, and 100, the RGB values ​​of any risk value are calculated using a nonlinear interpolation method to achieve dynamic mapping between risk values ​​and colors. S702. Using the latitude and longitude coordinate translation method, assume that the j-th risk unit of the n-th highway corresponds to point E on the map. j Its latitude and longitude coordinates are (x j ,y j Point E represents the risk unit in the upbound direction of the highway, while point E represents the corresponding risk unit in the downbound direction. p Its latitude and longitude coordinates are (x p ,y p ), point E j Along vector Translation distance in the direction Point E p Along vector Translation distance in the direction , Determined based on the map scale, if the vector Let θ be the azimuth angle of the direction in which the vector is located. Then, when θ ∈ [0°, 180°), the vector... The azimuth angle of the direction is (θ+180°). When θ∈[180°,360°), then the vector... The azimuth angle of the direction is (θ-180°), and the formula for the azimuth angle θ is as follows: ； Assume point E j Along vector Translation distance in the direction Afterwards, it becomes a point. Its latitude and longitude coordinates are Point E p Along vector Translation distance in the direction Afterwards, it becomes a point. Its latitude and longitude coordinates are The formula for the translated latitude and longitude coordinates is as follows: ； ； ； ； S703. Based on the latitude and longitude coordinates of each risk unit after coordinate translation, mark the position of each risk unit in the satellite remote sensing map, mark the corresponding position with color according to the RGB value coordinates of the risk value of the risk unit, and save it, so as to realize the visualization of the risk values ​​of different risk units of the highway.

9. A computer-readable storage medium, characterized in that, The storage medium stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1 to 8.

10. An electronic terminal, characterized in that, include: Processor and memory; The memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory to cause the terminal to perform the method as described in any one of claims 1 to 8.

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