A method and system for predicting a credible airport fire rescue response time

Abstract

The application provides a credible airport fire rescue response time prediction method, which comprises the following steps: establishing an airport flight area fire rescue predetermined path response time model, measuring the predetermined path response time of a plurality of fire rescue most unfavorable points in the flight area by a high-precision vehicle driving tester, determining model parameter values by statistical analysis of the test data, assigning the model parameters to each most unfavorable point predetermined path to obtain a response time prediction value, performing error analysis on each prediction value and the measured value, optimizing the model parameters until the error rate of each prediction value is within the acceptance range, determining the credible model parameters matching the main airport fire truck and the driving habits of the driver, then decomposing the predetermined path of any point in the flight area into different road segments, assigning the credible model parameters to the predetermined path, and calculating the response time prediction value. The application predicts the response time of the main airport fire truck to any point in the flight area under the same best visibility and ground conditions.

Description

Technical Field

[0001] This invention relates to the field of airport fire and rescue response time prediction technology, and in particular to a reliable method and system for predicting airport fire and rescue response time. Background Technology

[0002] Response time prediction is a common problem in the planning and site selection of fire stations in airport flight zones and in the field of fire rescue emergency response. In particular, under the "emergency dispatch" order, the main fire trucks must arrive at the scene on time in accordance with regulations. Accurately predicting the response time of the main fire trucks has always been a challenge.

[0003] Geographic information technology-based response time prediction combines factors such as the number of airport runways, runway length, runway spacing, and aircraft operating areas to determine fire and rescue routes. Based on constraints such as fire truck factory parameters and speed limits on airfield roads, a geographic information technology-based airport fire and rescue response time prediction model is constructed. Compared with other prediction methods, geographic information technology-based prediction methods can quantitatively calculate the predicted response time value based on road network information and vehicle driving parameters. The prediction process can consider a wider range of constraints than other prediction methods, but it requires computer classification and processing of the constraint information.

[0004] Currently, research on response time prediction based on geographic information technology mainly focuses on prediction models built on GIS technology. The GIS technology architecture is based on traditional server GIS engines such as ArcGIS, which publish urban geographic data as various types of services, such as map services, spatial data services, and vehicle data services. The system has rich functions, but the urban geographic data used has problems such as mismatch with airport flight area maps, spatial data, and vehicle data, which cannot meet the prediction requirements of airport flight area fire and rescue response time.

[0005] The shortcomings of existing technologies are as follows: First, they are generally used for vehicle driving prediction on urban roads, and the road network rules are not adapted to the road network characteristics of the specific environment of airport flight zones; second, they are based on theoretical data of fire truck factory parameters, which deviates significantly from the actual performance of fire trucks; third, they are based on theoretical data of road driving and fail to reflect user factors such as the driving habits of fire truck drivers. Summary of the Invention

[0006] To address the aforementioned issues, this invention provides a reliable method and system for predicting airport fire and rescue response times. The method determines model parameter values ​​based on measured data, optimizes the model parameters through error analysis until the error rate is within acceptable limits, thereby determining reliable model parameters that match the airport's main fire trucks and drivers' driving habits. This improves the applicability and accuracy of the prediction, as detailed below: On the one hand, the present invention provides a reliable method for predicting airport fire and rescue response time, the method comprising the following steps: S1. Establish a model of the response time for predetermined fire and rescue routes in the airport's flight area, and match it with the driving habits of the airport's main fire trucks and drivers; S2. Identify several unfavorable points for fire and rescue operations in the airport's flight area; S3. Measure the response time of the given path at each most unfavorable point, record complete test data using a high-precision vehicle driving test instrument, obtain the measured values ​​of the model parameters from the test data, and determine the model parameters through statistical analysis. S4. Assign the model parameters to the predetermined path through the measured most unfavorable points to obtain the predicted response time. S5. Perform error analysis between each predicted value and the measured value, optimize the model parameters until the error rate of each predicted value is within the acceptable range, and determine the reliable model parameters that match the main fire trucks and drivers' driving habits of this airport. S6. Decompose the predetermined path at any point in the flight area into different road segments; S7. Assign the reliable model parameters to the road segment and calculate the predicted response time.

[0007] Preferably, in step S1, a response time model is determined based on the characteristics of the established fire rescue routes in the airport flight area, and the model is matched with the performance of the airport's main fire trucks and the driving habits of the drivers.

[0008] Preferably, in step S2, the most unfavorable point for fire rescue in the airport flight area is determined based on the configuration of the airport flight area runways and taxiways and the layout of fire stations, taking into account the travel distance, turning radius and number of turns of fire trucks.

[0009] Preferably, in step S3, the response time required for each fire station in the most unfavorable point responsibility area to reach the most unfavorable point by traveling along the predetermined path is measured, and complete test data including time, speed and distance is recorded by professional instruments. The predetermined path for the fire truck to travel from the fire station to the most unfavorable point is decomposed into several straight segments and several turning segments at different angles. The linear acceleration of a given path at each most unfavorable point is represented by its maximum value, as shown in the following formula: a straight1 = max(a straight11 a straight12 , … a straight1p1 ), a straight2 = max(a straight21 a straight22 , … a straight2p2 ), … a straightm = max(a straightm1 a straightm2 , … a straightmpn ); The linear acceleration of the model parameters is represented by the average linear acceleration along a given path at each most unfavorable point, as shown in the following formula: , The maximum straight-line speed of a given path at each most unfavorable point is represented by its maximum value, as shown in the following formula: v max1 = max(v max 11 v max 12 , … v max 1p1 ), v max 2 = max(v max 21 v max 22 , … v max 2p2 ), … v max m = max(v max m1 v max m2 , … v max mpn ); The maximum speed of the model parameters is represented by the average of the maximum straight-line speeds along the given path at each most unfavorable point, as shown in the following formula: , The pre-deceleration ratio of a given path at each most unfavorable point is represented by its mean, as shown in the following formula: , The pre-deceleration ratio of the model parameters is represented by the average pre-deceleration ratio of the straight-line path at each most unfavorable point, as shown in the following formula: , The starting speed constraint for a 90° turn on a given path at each most unfavorable point is represented by its mean value, as shown in the following formula: , The 90° turning starting speed constraint of the model parameters is represented by the average of the 90° turning starting speed constraints of the given path at each most unfavorable point, as shown in the following formula: , The starting speed constraint for a 120° turn on a given path at each most unfavorable point is represented by its mean value, as shown in the following formula: , The starting speed constraint for the 120° turn in the model parameters is represented by the average starting speed constraint for the 120° turn on the given path at each most unfavorable point, as shown in the following formula: , The deceleration at the end of the forced stop in the model parameters is represented by the average deceleration at the end of the forced stop along the given path at each most unfavorable point, as shown in the following formula: .

[0010] Preferably, in step S4, the model parameters are assigned to the predetermined path of each most unfavorable point obtained through actual measurement, and the predicted response time is calculated. According to a typical predetermined path scenario, the calculation process is as follows: Typical scenario: Initial velocity is 0 → straight distance s straight1 →120° turning distance s 120 → Straight-line distance s straight2 →The final velocity is 0. (a) Accelerate to top speed Acceleration time: The initial velocity of the first straight segment is v0=0. Acceleration distance: s accel1 =v0⋅t accel1 +0.5⋅a⋅t accel1 2 , (b) Allocation of remaining straight segments Pre-deceleration distance: s pre_decay1 = s straight1 ⋅d pre_decay_ratio , The remaining distance that can be used for acceleration and constant speed: s remaining1 = s straigh1 - s pre_decay1 , Distance of remaining uniform velocity segment: s const1 = s straight1 - s accel 1- s pre_decay1 , (c) Calculation of the uniform speed segment Uniform speed time: , (d) Calculation of pre-deceleration section Deceleration: , Deceleration time: , Total time for the first straight segment: t straight1 =t accel1 +t const1 +t pre_decay1 , (e) Calculation of 120-degree turning section s 120 =v start_turn_120 ⋅t 120 +0.5⋅ a turn_120 ⋅t 120 2 , Solving the quadratic equation, we get t 120 , Final velocity: v end_turn = v start_turn_120 + a turn_120 ⋅ t 120 , (f) Accelerate to top speed Acceleration time: The initial velocity of the second straight segment is v0 = v end_turn , Acceleration distance: s accel2 =v0⋅t accel2 +0.5⋅a⋅t accel2 2 , (g) Allocation of remaining straight segments Pre-deceleration distance: s pre_decay2 = s straight2 ⋅d pre_decay_ratio , The remaining distance that can be used for acceleration and constant speed: s remaining2 = s1- s pre_decay2 , Distance of remaining uniform velocity segment: s const2 = s straight2 - s accel - s pre_decay2 , (h) Calculation of the uniform speed segment Uniform speed time: , (i) Calculation of pre-deceleration section Deceleration: , Deceleration time: , Total time for the second straight segment: t straight2 =t accel2 +t const2 +t pre_decay2 , (j) Total travel time: T total =t straight1 +t 120 +t straight2 .

[0011] Preferably, in step S5, the error analysis between each predicted value and the measured value is calculated, and the calculation formula is as follows: △ i = T totali – T i , When the predicted value of the given path at the most unfavorable point is outside the error range, optimize the statistical analysis method of the measured data in step S3, optimize the model parameters, and recalculate each predicted value and error value until the predicted values ​​of the given paths at all most unfavorable points are within the error range, that is: When △ i ≤ △ trust When i∈{0,1,2,⋯,n}, Determined as acceptability △ trust The reliable model parameters, namely: Linear acceleration: a straight_trust ; Top speed: v max_trust ; Pre-deceleration ratio: d pre_decay_ratio_trust ; 90° turn starting speed constraint: v start_turn_90_trust ; 90° deceleration: a turn_90_trust ; 120° turn starting speed constraint: v start_turn_120_trust ; 120° turn deceleration: a turn_120_trust ; Final stop deceleration: a last_trust .

[0012] Preferably, in step S6, the predetermined path for the fire truck to travel from the fire station to any point in the flight area is decomposed into multiple straight segments and turning segments at different angles.

[0013] Preferably, in step S7, the acceptance level is △ trust Assign values ​​to the reliable model parameters of the road segment, calculate the predicted response time, and follow the calculation steps in step S4.

[0014] On the other hand, the present invention provides a reliable prediction system for airport fire and rescue response time, the system comprising the following apparatus: Response time model management device; Response time data acquisition device; A computational device for segmenting and extracting data along a predetermined path; Response time measured data preprocessing device; Error analysis calculation device; Response time prediction calculation device.

[0015] Preferably, the response time model management device establishes a response time model that matches the performance of the airport's main fire trucks and the driving habits of the drivers, based on the characteristics of the established fire rescue routes in the airport's flight zone.

[0016] Preferably, the response time data acquisition device uses a high-precision vehicle driving test instrument, which has a time accuracy of not less than 0.01s and a speed accuracy of not less than 0.1 km / h.

[0017] Preferably, the predetermined path segment extraction calculation device decomposes the predetermined path of the fire truck from the fire station to the most unfavorable point and any point in the flight zone into several straight segments and several turning segments at different angles.

[0018] Preferably, the response time measured data preprocessing device is used to decompose the complete test data of time, speed and distance into measured data corresponding to several straight segments and several turning segments at different angles, and obtain model parameter values ​​through statistical analysis.

[0019] Preferably, the error analysis calculation device is used to perform error analysis between each predicted value and the measured value, and optimize the model parameters until the error rate of each predicted value is within the acceptable range.

[0020] Preferably, the response time prediction calculation device is used to calculate the predicted value of the response time by assigning model parameters to a predetermined path at any point in the flight area through a response time model.

[0021] Based on the characteristics of the established fire and rescue routes in the airport's flight zone, a response time model matching the performance of the airport's main fire trucks and the driving habits of the drivers was established. A high-precision vehicle driving test instrument was used to collect accurate test data of the response process. After the measured data was preprocessed, the model parameter values ​​were obtained through statistical analysis. The model parameters were optimized through error analysis until the error rate of each predicted value was within the acceptable range, thus obtaining reliable model parameters. The model parameter values ​​were then assigned to the established route at any point in the flight zone to complete the calculation of the predicted response time.

[0022] The present invention has the following beneficial effects: 1. Establish a response time model that matches the performance of the airport's main fire trucks and the driving habits of the drivers. Optimize the model parameters through error analysis between predicted and measured data until the error rate of each predicted value is within an acceptable range, ensuring the reliability of the model parameters and providing users with a more applicable and accurate prediction method for airport flight zone fire and rescue response time.

[0023] 2. Employing a high-precision vehicle driving test instrument provides more accurate test data on the actual performance of fire trucks, ensuring data validity and offering higher prediction accuracy.

[0024] 3. It adapts to the road network and established route characteristics of the specific environment of the airport flight area, and has a significant advantage in predicting the response time of fire rescue in the airport flight area. Attached Figure Description

[0025] Figure 1 This is a flowchart illustrating the response time prediction process of the present invention. Figure 2 This is a schematic diagram of the predetermined path decomposition in the flight area of ​​this invention; Figure 3 This is a speed-time curve for predicting the response time of the present invention; Figure 4 This is a distance-time curve for the predicted response time of the present invention; Figure 5 This is a schematic diagram illustrating the process of optimizing model parameters through error analysis between predicted and measured data until the error rate of each predicted value is within an acceptable range, as described in this invention. Figure 6 This is a structural diagram of the reliable airport fire rescue response time prediction system of the present invention. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] like Figure 1 A reliable method for predicting airport fire and rescue response time is described below: Step S1: Based on the characteristics of the established fire and rescue routes in the airport's flight area, determine the response time model, matching it with the performance of the airport's main fire trucks and the driving habits of the drivers. The model parameters include: Linear acceleration: a straight ; Top speed: v max ; Pre-deceleration ratio: d pre_decay_ratio ; 90° turn starting speed constraint: v start_turn_90 ; 90° deceleration: a turn_90 ; 120° turn starting speed constraint: v start_turn_120 ; 120° turn deceleration: a turn_120 ; Deceleration at the final stop: a last , Error tolerance: △; Step S2: Based on the configuration of the airport's runways and taxiways and the layout of fire stations, and considering factors such as the travel distance of fire trucks, turning radius, and number of turns, determine the most unfavorable point for fire rescue in the airport's flight area. Step S3: Measure the response time required for each fire station in the most unfavorable responsibility area to reach the most unfavorable point by traveling along the predetermined path. Record complete test data including time, speed, and distance using professional instruments. Decompose the predetermined path for the fire truck to travel from the fire station to the most unfavorable point into several straight segments and several turning segments at different angles.

[0028] The linear acceleration of a given path at each most unfavorable point is represented by its maximum value, as shown in the following formula: a straight1 = max(a straight11 a straight12 , … a straight1p1 ), a straight2 = max(a straight21 a straight22 , … a straight2p2 ), … a straightm = max(a straightm1 a straightm2 , … a straightmpn ); The linear acceleration of the model parameters is represented by the average linear acceleration along a given path at each most unfavorable point, as shown in the following formula: , The maximum straight-line speed of a given path at each most unfavorable point is represented by its maximum value, as shown in the following formula: v max1 = max(v max 11 v max 12 , … v max 1p1 ), v max 2 = max(v max 21 v max 22 , … v max 2p2 ), … v max m = max(v max m1 v max m2 , … v max mpn ); The maximum speed of the model parameters is represented by the average of the maximum straight-line speeds along the given path at each most unfavorable point, as shown in the following formula: , The pre-deceleration ratio of a given path at each most unfavorable point is represented by its mean, as shown in the following formula: ,

[0029] The pre-deceleration ratio of the model parameters is represented by the average pre-deceleration ratio of the straight-line path at each most unfavorable point, as shown in the following formula: , The starting speed constraint for a 90° turn on a given path at each most unfavorable point is represented by its mean value, as shown in the following formula: , The 90° turning starting speed constraint of the model parameters is represented by the average of the 90° turning starting speed constraints of the given path at each most unfavorable point, as shown in the following formula: , The starting speed constraint for a 120° turn on a given path at each most unfavorable point is represented by its mean value, as shown in the following formula: , The starting speed constraint for the 120° turn in the model parameters is represented by the average starting speed constraint for the 120° turn on the given path at each most unfavorable point, as shown in the following formula: , The deceleration at the end of the forced stop in the model parameters is represented by the average deceleration at the end of the forced stop along the given path at each most unfavorable point, as shown in the following formula: ; Step S4: Assign the model parameters to the predetermined path of each most unfavorable point measured in the actual test, and calculate the predicted response time value. Step S5: Calculate the error analysis between each predicted value and the measured value. The calculation formula is as follows: △ i = T totali – T i , When the predicted value of the given path at the most unfavorable point is outside the error range, optimize the statistical analysis method of the measured data in step S3, optimize the model parameters, and recalculate each predicted value and error value until the predicted values ​​of the given paths at all most unfavorable points are within the error range, that is: When △ i ≤ △, where i∈{0,1,2,⋯,n} Determined as acceptability △ trustParameters of the reliable model; Step S6: Decompose the predetermined path of the fire truck from the fire station to any point in the flight area into several straight segments and several turning segments at different angles; Step S7, with an acceptance level of △ trust Assign values ​​to the reliable model parameters of the road segment, calculate the predicted response time, and follow the calculation steps as in step S4. like Figures 2-4 The decomposition process of the predetermined path in the flight area: As in step S4, a typical scenario for a given path: initial velocity is 0 → straight distance s straight1 →120° turning distance s 120 → Straight-line distance s straight2 →The final velocity is 0. The predetermined route for fire trucks to travel from the fire station to the most unfavorable point is broken down into multiple straight segments and turning segments at different angles; The straight section is divided into the acceleration to maximum speed section, the constant speed section, and the pre-deceleration section; The turning sections are mainly 120-degree turns and 90-degree turns; Based on the decomposition of the predetermined path in the flight area for typical predetermined path scenarios, predictive speed-time curves and predictive distance-time curves are established. Taking the location of the main fire truck as the starting point, the calculation of the main fire truck's journey to the most unfavorable point is performed. The calculation process is as follows: (a) Accelerate to top speed Acceleration time: The initial velocity of the first straight segment is v0=0. Acceleration distance: s accel1 =v0⋅t accel1 +0.5⋅a⋅t accel1 2 , (b) Allocation of remaining straight segments Pre-deceleration distance: s pre_decay1 = s straight1 ⋅d pre_decay_ratio , The remaining distance that can be used for acceleration and constant speed: s remaining1 = s straigh1 - s pre_decay1 , Distance of remaining uniform velocity segment: s const1 = s straight1 - s accel 1- s pre_decay1 , (c) Calculation of the uniform speed segment Uniform speed time: , (d) Calculation of pre-deceleration section Deceleration: , Deceleration time: , Total time for the first straight segment: t straight1 =t accel1 +t const1 +t pre_decay1 , (e) Calculation of 120-degree turning section s 120 =v start_turn_120 ⋅t 120 +0.5⋅ a turn_120 ⋅t 120 2 , Solving the quadratic equation, we get t 120 , Final velocity: v end_turn = v start_turn_120 + a turn_120 ⋅ t 120 , (f) Accelerate to top speed Acceleration time: The initial velocity of the second straight segment is v0 = v end_turn , Acceleration distance: s accel2 =v0⋅t accel2 +0.5⋅a⋅t accel2 2 , (g) Allocation of remaining straight segments Pre-deceleration distance: s pre_decay2 = s straight2 ⋅d pre_decay_ratio , The remaining distance that can be used for acceleration and constant speed: s remaining2 = s1- s pre_decay2 , Distance of remaining uniform velocity segment: s const2 = s straight2 - s accel - s pre_decay2 , (h) Calculation of the uniform speed segment Uniform speed time: , (i) Calculation of pre-deceleration section Deceleration: , Deceleration time: ,

[0030] Total time for the second straight segment: t straight2 =t accel2 +tconst2 +t pre_decay2 , (j) Total travel time: T total =t straight1 +t 120 +t straight2 .

[0031] like Figure 5 The process of optimizing model parameters through error analysis between predicted and measured data until the error rate of each predicted value is within an acceptable range is as follows: (a) Assign the model parameters to the predetermined path of each most unfavorable point through actual measurement, and calculate the predicted response time value according to step S4. (b) Calculate the error between each predicted value and the measured value. The calculation formula is as follows: △ i = T totali – T i , When the predicted value of the given path at the most unfavorable point is outside the error range, optimize the statistical analysis method of the measured data in step S3 and optimize the model parameters. (c) Recalculate all predicted values ​​and error values ​​until the predicted values ​​for all the most unfavorable points along the given path are within the error range, i.e.: When △ i ≤ △, where i∈{0,1,2,⋯,n} The acceptance level for matching the airport's main fire trucks and drivers' driving habits was determined to be △. trust The reliable model parameters, namely: Linear acceleration: a straight_trust ; Top speed: v max_trust ; Pre-deceleration ratio: d pre_decay_ratio_trust ; 90° turn starting speed constraint: v start_turn_90_trust ; 90° deceleration: a turn_90_trust ; 120° turn starting speed constraint: v start_turn_120_trust ; 120° turn deceleration: a turn_120_trust ; Deceleration at the final stop: a last_trust .

[0032] Based on the characteristics of the established fire and rescue routes in the airport's flight zone, a response time model matching the performance of the airport's main fire trucks and the driving habits of the drivers was established. A high-precision vehicle driving test instrument was used to collect accurate test data of the response process. After the measured data was preprocessed, the model parameter values ​​were obtained through statistical analysis. The model parameters were optimized through error analysis until the error rate of each predicted value was within the acceptable range, thus obtaining reliable model parameters. The model parameter values ​​were then assigned to the established route at any point in the flight zone to complete the calculation of the predicted response time.

[0033] In practice, better prediction accuracy can be obtained by improving the manufacturing and integration precision of vehicle driving test instruments, as well as the precision of fire rescue route decomposition, and raising the acceptable numerical requirements.

[0034] Based on the above method, a system can be programmed accordingly.

[0035] like Figure 6 A reliable system for predicting airport fire and rescue response times, including: Response time model management device: Based on the characteristics of the established fire rescue routes in the airport flight area, a response time model is established that matches the performance of the airport's main fire trucks and the driving habits of the drivers. Such a model can more purely reflect the actual performance of the airport's main fire trucks and the actual driving habits of the drivers, rather than theoretical road network geographic information and vehicle performance factory parameters. It has higher credibility, stronger repeatability, and can provide reliable prediction values. High-precision vehicle driving test instrument: During operation, the test instrument has a time accuracy of no less than 0.01s and a speed accuracy of no less than 0.1 km / h. This results in higher sensitivity, higher accuracy, and smaller errors during vehicle driving tests. It can provide accurate test data covering the entire process and can decompose and study local features. Predetermined path segment extraction calculation device: decomposes the predetermined path of the fire truck from the fire station to the most unfavorable point and any point in the flight zone into several straight segments and several turning segments at different angles; Response time measured data preprocessing device: used to decompose the complete test data of time, speed and distance into measured data corresponding to several straight segments and several turning segments at different angles, and obtain model parameter values ​​through statistical analysis; Error analysis calculation device: used to perform error analysis between each predicted value and the measured value, and optimize the model parameters until the error rate of each predicted value is within the acceptable range; Response time prediction calculation device: used to calculate the predicted response time by assigning model parameters to the road segment through the response time model.

[0036] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A reliable method for predicting airport fire and rescue response time, characterized in that, The method includes the following steps: S1. Establish a model of the response time for predetermined fire and rescue routes in the airport's flight area, and match it with the driving habits of the airport's main fire trucks and drivers; S2. Identify several unfavorable points for fire and rescue operations in the airport's flight area; S3. Measure the response time of the given path at each most unfavorable point, record complete test data using a high-precision vehicle driving test instrument, obtain the measured values ​​of the model parameters from the test data, and determine the model parameters through statistical analysis. S4. Assign the model parameters to the predetermined path through the measured most unfavorable points to obtain the predicted response time. S5. Perform error analysis between each predicted value and the measured value, optimize the model parameters until the error rate of each predicted value is within the acceptable range, and determine the reliable model parameters that match the main fire trucks and drivers' driving habits of this airport. S6. Decompose the predetermined path at any point in the flight area into different road segments; S7. Assign the reliable model parameters to the road segment and calculate the predicted response time.

2. The reliable method for predicting airport fire and rescue response time as described in claim 1, characterized in that, In step S1, based on the characteristics of the established fire and rescue routes in the airport's flight zone, a response time model is determined, matching the performance of the airport's main fire trucks and the drivers' driving habits. The model parameters include: Linear acceleration: a straight ; Top speed: v max ; Pre-deceleration ratio: d pre_decay_ratio ; 90° turn starting speed constraint: v start_turn_90 ; 90° deceleration: a turn_90 ; 120° turn starting speed constraint: v start_turn_120 ; 120° turn deceleration: a turn_120 ; Deceleration at the final stop: a last ; Error tolerance: △.

3. The reliable method for predicting airport fire and rescue response time as described in claim 1, characterized in that, In step S2, based on the configuration of the airport's runways and taxiways and the layout of fire stations, and considering the travel distance, turning radius, and number of turns of fire trucks, the most unfavorable point for fire rescue in the airport's flight area is determined.

4. The reliable airport fire and rescue response time prediction method as described in claim 1, characterized in that, In step S3, the response time required for each fire station in the most unfavorable responsibility area to reach the most unfavorable point by traveling along the predetermined path is measured. Complete test data including time, speed and distance are recorded by professional instruments. The predetermined path for the fire truck to travel from the fire station to the most unfavorable point is decomposed into several straight segments and several turning segments at different angles. The linear acceleration of a given path at each most unfavorable point is represented by its maximum value, as shown in the following formula: a straight1 = max(a straight11 ,a straight12 , … a straight1p1 ), a straight2 = max(a straight21 ,a straight22 , … a straight2p2 ), … a straightm = max(a straightm1 ,a straightm2 , … a straightmpn ); The linear acceleration of the model parameters is represented by the average linear acceleration along a given path at each most unfavorable point, as shown in the following formula: ， The maximum straight-line speed of a given path at each most unfavorable point is represented by its maximum value, as shown in the following formula: v max1 = max(v max 11 ,v max 12 , … v max 1p1 ), v max 2 = max(v max 21 ,v max 22 , … v max 2p2 ), … v max m = max(v max m1 ,v max m2 , … v max mpn ); The maximum speed of the model parameters is represented by the average of the maximum straight-line speeds along the given path at each most unfavorable point, as shown in the following formula: ， The pre-deceleration ratio of a given path at each most unfavorable point is represented by its mean, as shown in the following formula: ， The pre-deceleration ratio of the model parameters is represented by the average pre-deceleration ratio of the straight-line path at each most unfavorable point, as shown in the following formula: ， The starting speed constraint for a 90° turn on a given path at each most unfavorable point is represented by its mean value, as shown in the following formula: ， The 90° turning starting speed constraint of the model parameters is represented by the average of the 90° turning starting speed constraints of the given path at each most unfavorable point, as shown in the following formula: ， The starting speed constraint for a 120° turn on a given path at each most unfavorable point is represented by its mean value, as shown in the following formula: ， The starting speed constraint for the 120° turn in the model parameters is represented by the average starting speed constraint for the 120° turn on the given path at each most unfavorable point, as shown in the following formula: ， The deceleration at the end of the forced stop in the model parameters is represented by the average deceleration at the end of the forced stop along the given path at each most unfavorable point, as shown in the following formula: 。 5. The reliable method for predicting airport fire and rescue response time as described in claim 1, characterized in that, In step S4, the model parameters are assigned to the predetermined path of each most unfavorable point measured in the actual test, and the predicted response time is calculated. The calculation process is as follows, based on a typical predetermined path scenario: Typical scenario: Initial velocity is 0 → straight distance s straight1 →120° turning distance s 120 → Straight-line distance s straight2 →The final velocity is 0. (a) Accelerate to top speed Acceleration time: The initial velocity of the first straight segment is v0=0. Acceleration distance: s accel1 =v0⋅t accel1 +0.5⋅a⋅t accel1 2 , (b) Allocation of remaining straight segments Pre-deceleration distance: s pre_decay1 = s straight1 ⋅d pre_decay_ratio , The remaining distance that can be used for acceleration and constant speed: s remaining1 = s straigh1 - s pre_decay1 , Distance of remaining uniform velocity segment: s const1 = s straight1 - s accel 1- s pre_decay1 , (c) Calculation of the uniform speed segment Uniform speed time: , (d) Calculation of pre-deceleration section Deceleration: ， Deceleration time: ， Total time for the first straight segment: t straight1 =t accel1 +t const1 +t pre_decay1 , (e) Calculation of 120-degree turning section s 120 =v start_turn_120 ⋅t 120 +0.5⋅ a turn_120 ⋅t 120 2 , Solving the quadratic equation, we get t 120 , Final velocity: v end_turn = v start_turn_120 + a turn_120 ⋅ t 120 , (f) Accelerate to top speed Acceleration time: The initial velocity of the second straight segment is v0 = v end_turn , Acceleration distance: s accel2 =v0⋅t accel2 +0.5⋅a⋅t accel2 2 , (g) Allocation of remaining straight segments Pre-deceleration distance: s pre_decay2 = s straight2 ⋅d pre_decay_ratio , The remaining distance that can be used for acceleration and constant speed: s remaining2 = s1- s pre_decay2 , Distance of remaining uniform velocity segment: s const2 = s straight2 - s accel - s pre_decay2 , (h) Calculation of the uniform speed segment Uniform speed time: , (i) Calculation of pre-deceleration section Deceleration: , Deceleration time: , Total time for the second straight segment: t straight2 =t accel2 +t const2 +t pre_decay2 , (j) Total travel time: T total =t straight1 +t 120 +t straight2。 6. The reliable method for predicting airport fire and rescue response time as described in claim 1, characterized in that, The method includes the following steps: In step S5, error analysis is performed between each predicted value and the measured value, using the following formula: △ i = T totali – T i , When the predicted value of the given path at the most unfavorable point is outside the error range, optimize the statistical analysis method of the measured data in step S3, optimize the model parameters, and recalculate each predicted value and error value until the predicted values ​​of the given paths at all most unfavorable points are within the error range, that is: When △ i ≤ △ trust When i∈{0,1,2,⋯,n}, Determined as acceptability △ trust The reliable model parameters, namely: Linear acceleration: a straight_trust ; Top speed: v max_trust ; Pre-deceleration ratio: d pre_decay_ratio_trust ; 90° turn starting speed constraint: v start_turn_90_trust ; 90° deceleration: a turn_90_trust ; 120° turn starting speed constraint: v start_turn_120_trust ; 120° turn deceleration: a turn_120_trust ; Deceleration at the final stop: a last_trust .

7. The reliable method for predicting airport fire and rescue response time as described in claim 1, characterized in that, In step S6, the predetermined path for the fire truck to travel from the fire station to any point in the flight area is decomposed into several straight segments and several turning segments at different angles.

8. The reliable method for predicting airport fire and rescue response time as described in claim 1, characterized in that, In step S7, the acceptance level is △ trust Assign values ​​to the reliable model parameters of the road segment, calculate the predicted response time, and follow the calculation steps in step S4.

9. A reliable system for predicting airport fire and rescue response time, characterized in that, The system includes the following devices: Response time model management device; Response time data acquisition device; Predetermined path segmentation extraction computing device; Response time measured data preprocessing device; Error analysis calculation device; Response time prediction calculation device.

10. The reliable airport fire and rescue response time prediction system as described in claim 9, characterized in that, The response time model management device establishes a response time model that matches the performance of the airport's main fire trucks and the driving habits of the drivers, based on the characteristics of the established fire rescue routes in the airport's flight zone.

11. The reliable airport fire and rescue response time prediction system as described in claim 9, characterized in that, The response time data acquisition device uses a high-precision vehicle driving test instrument, which has a time accuracy of not less than 0.01s and a speed accuracy of not less than 0.1 km / h.

12. The reliable airport fire and rescue response time prediction system as described in claim 9, characterized in that: The predetermined path segment extraction calculation device decomposes the predetermined path of the fire truck from the fire station to the most unfavorable point and any point in the flight zone into several straight segments and several turning segments at different angles. The response time measured data preprocessing device is used to decompose the complete test data of time, speed and distance into measured data corresponding to several straight segments and several turning segments at different angles, and obtain model parameter values ​​through statistical analysis; The error analysis calculation device is used to perform error analysis between each predicted value and the measured value, and optimize the model parameters until the error rate of each predicted value is within the acceptable range. The response time prediction calculation device is used to calculate the predicted value of the response time by assigning model parameters to a predetermined path at any point in the flight area using a response time model.

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