A driving dynamic risk prediction method for hazardous chemicals transport vehicles

Abstract

The present invention discloses a method for predicting the driving dynamic risk of hazardous chemical transport vehicles, and relates to the technical field of risk prediction for hazardous chemical transport. The method comprises the following steps: collecting information related to hazardous chemical transport vehicles, establishing a database of hazardous chemical transport vehicles, and establishing files for each hazardous chemical transport; collecting information related to the driving of transport vehicles, including deceleration and deceleration time, and collecting additional relevant index parameters for hazardous chemicals in different states to predict the risk of hazardous chemical transport vehicles; collecting relevant environmental data during vehicle transportation based on a vehicle-mounted camera and a satellite positioning system, and training a standard simulation model based on historical excellent safe driving data. The present invention collects data and predicts risks during the transportation of hazardous chemical transport vehicles according to the state of the hazardous chemicals, and assists drivers in making risk decisions in combination with the actual environment of the transport vehicles, thereby enhancing practicality and functionality.

Description

Technical Field

[0001] The present invention relates to the technical field of risk prediction for hazardous chemicals transportation, and in particular to a method for predicting the driving dynamics risk of hazardous chemicals transportation vehicles. Background Art

[0002] Hazardous chemicals are chemical substances that may pose a danger to human safety, health, the environment or property during production, storage, transportation and use. Different hazardous chemicals exist in different states, including solid, liquid and gas, such as chlorine, ammonia, sulfur, methanol, etc.

[0003] A Chinese patent with publication number "CN113919642A" discloses "a method for predicting the risk of the driving status of a hazardous chemical transport vehicle, comprising the following steps: S1: using data acquisition equipment to collect and store driving status data of the hazardous chemical transport vehicle during driving". Although it can predict the risks of hazardous chemicals transportation, it is unable to collect relevant parameters according to the specific state of hazardous chemicals for further risk rating, and it is unable to combine the quality of hazardous chemicals and container-related parameters when transporting hazardous chemicals in different states to assist in correcting the risky behaviors that may occur in the driver, and has problems of low practicality and functionality.

[0004] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention

[0005] In view of the problems in the related technology, the present invention proposes a method for predicting the driving dynamic risks of hazardous chemicals transport vehicles to overcome the above-mentioned technical problems existing in the existing related technology.

[0006] To this end, the specific technical solution adopted by the present invention is as follows:

[0007] A method for predicting driving dynamic risks of a hazardous chemicals transport vehicle comprises the following steps:

[0008] S1. Collect information related to hazardous chemicals transportation vehicles, establish a hazardous chemicals transportation vehicle database, and create files for each hazardous chemicals transportation;

[0009] S2. Collect information related to the transportation vehicles, including deceleration and deceleration time. For hazardous chemicals in different states, additional relevant index parameters are collected to conduct risk prediction for hazardous chemicals transportation vehicles;

[0010] S3. Based on the on-board camera and satellite positioning system, relevant environmental data during vehicle transportation is collected. At the same time, based on the historical excellent safe driving data, the standard simulation model is trained. Based on the current actual data of hazardous chemicals transportation vehicles, behavior pattern prediction and output are carried out to make risk predictions.

[0011] As a preferred embodiment, the S1 includes the following sub-steps:

[0012] S11. Collect relevant information on hazardous chemicals transport vehicles, including vehicle model, load capacity, total mileage, and maintenance records, and establish a hazardous chemicals transport vehicle database;

[0013] S12. Collect relevant information of drivers of hazardous chemicals transport vehicles, including driving experience, driving records, and health status, and establish a database of drivers of hazardous chemicals transport vehicles;

[0014] S13. Collect information related to hazardous chemicals, including the names and status of hazardous chemicals, and establish a hazardous chemicals information database;

[0015] S14. Based on the hazardous chemicals transportation vehicle database, hazardous chemicals transportation driver database and hazardous chemicals information database, establish a hazardous chemicals transportation file for the day, which includes driving vehicle information, driver information, hazardous chemicals name, hazardous chemicals status and hazardous chemicals weight.

[0016] As a preferred embodiment, S2 includes the following sub-steps:

[0017] S21. Collect speed, acceleration and braking status data of hazardous chemicals transport vehicles during driving through the on-board computer system;

[0018] S22. For the transportation of gaseous hazardous chemicals, based on the impact of temperature on gaseous hazardous chemicals, a gaseous prediction model is built to collect the direct sunlight temperature during transportation and calculate the transportation risk of gaseous hazardous chemicals;

[0019] S23. For the transportation of solid and liquid hazardous chemicals, based on the impact of impact force on containers, solid and liquid prediction models are built to collect the real-time emergency braking deceleration, deceleration time and quality of hazardous chemicals during transportation, and calculate the transportation risks of solid and liquid hazardous chemicals.

[0020] As a preferred embodiment, the S22 includes the following sub-steps:

[0021] S221. Collect the reference temperature inside the transport container through the temperature sensor , based on direct solar temperature , calculate the internal temperature , and its algorithm formula is:

[0022] ;

[0023] in, stands for influence coefficient, which indicates the degree of influence of direct sunlight temperature on the temperature inside the container;

[0024] S222. Calculate the gas pressure of hazardous chemicals according to the ideal gas equation. The algorithm formula is:

[0025] ;

[0026] in, Represents the gas pressure of hazardous chemicals under the current direct sunlight temperature. represents the volume of the container, represents the amount of gaseous substance, is the gas constant;

[0027] S223, for and Standardize, construct a weighted risk function, and predict the risk value. The specific steps are:

[0028] Based on the name of the gaseous hazardous chemicals, obtain the critical temperature of the gaseous hazardous chemicals Flash point temperature , safety pressure With ultimate pressure ,for and Standardize and obtain temperature standardized value Normalized to pressure :

[0029] when < hour:

[0030] ;

[0031] when > hour:

[0032] ;

[0033] when < hour:

[0034] ;

[0035] when > hour:

[0036] ;

[0037] Normalize values ​​based on temperature Normalized to pressure , construct a weighted risk function, and its algorithm formula is:

[0038] ;

[0039] in, represents the gaseous risk factor, Represent temperature weight and pressure weight respectively;

[0040] When 0< When ≤0.3, it indicates low risk;

[0041] When 0.3< When ≤0.7, it represents moderate risk;

[0042] When 0.7< When ≤1, it indicates high risk.

[0043] As a preferred embodiment, the S23 includes the following sub-steps:

[0044] S231, based on the vehicle's emergency braking deceleration during transportation , deceleration time and the quality of hazardous chemicals , calculate the deceleration speed of solid and liquid hazardous chemicals , the specific algorithm formula is:

[0045] ;

[0046] ;

[0047] ;

[0048] in, is the impact energy during rapid deceleration, The impact force during rapid deceleration is determined by the material of the hazardous chemicals transport container to obtain the maximum impact force. With maximum impact energy ;

[0049] S232, based on maximum impact force With maximum impact energy , construct a weighted risk function, and its algorithm formula is:

[0050] Impact safety factor ;

[0051] Energy safety factor ;

[0052] ;

[0053] in, Represents the solid and liquid risk factors, Represent the impact safety factor weight and energy safety factor weight respectively;

[0054] When 0< When ≤0.3, it indicates low risk;

[0055] When 0.3< When ≤0.7, it represents moderate risk;

[0056] When 0.7< When ≤1, it indicates high risk.

[0057] As a preferred embodiment, S3 includes the following sub-steps:

[0058] S31. Collect data related to the surrounding environment of hazardous chemicals transport vehicles during driving, including roads, traffic signs, other vehicles, and the latitude and longitude, real-time speed, and acceleration of the vehicle through on-board cameras and satellite positioning systems;

[0059] S32. Collect driving behavior data of historical excellent drivers, including speed, acceleration and deceleration, braking habits, steering, vehicle distance, and road conditions, and build a standard simulation model;

[0060] S33. Based on the standard simulation model and combined with the real-time data of hazardous chemicals transport vehicles, risk prediction for hazardous chemicals transportation is carried out.

[0061] As a preferred embodiment, the S32 includes the following sub-steps:

[0062] S321. Collect driving behavior pattern data of historical excellent drivers, including normal driving, speed-changing driving, and emergency mode, and collect relevant data separately, including speed, acceleration and deceleration, braking intensity, steering angle, vehicle distance, and road conditions, and standardize the time series data to The data is divided into training set, validation set and test set.

[0063] S322, extracting a fixed-length time window from the time series as input data by using a sliding window technique, and converting the data into an LSTM model format;

[0064] S323. Build an LSTM simulation model using the training set, select mean square error as the loss function, and minimize the loss function using the Adam optimization algorithm.

[0065] S324. Use the validation set to evaluate the performance of the adjustment simulation model, and use the test set to make a final determination of the simulation model.

[0066] As a preferred embodiment, the S33 includes the following sub-steps:

[0067] S331, extracting relevant data obtained by the vehicle camera and the satellite positioning system through the target detection algorithm, quantizing the data into features and inputting them into the benchmark simulation model;

[0068] S332. Based on the current environmental data ahead of the hazardous chemicals transport vehicle, output prediction results, including normal driving, speed-changing driving, and emergency mode, and pop up warning signals through the vehicle computer, with a green signal for normal driving, a blue signal for speed-changing driving, and a red signal for emergency mode;

[0069] S333. Based on the acceleration and deceleration data generated by the speed change driving and emergency mode output by the benchmark simulation model, risk factor rating is performed for solid and liquid hazardous chemicals.

[0070] As a preferred implementation, the S333 includes the following sub-steps:

[0071] S3331, based on the acceleration and deceleration data generated by the speed change driving and emergency mode output of the reference simulation model, including acceleration and deceleration And acceleration and deceleration time ;

[0072] S3332. Calculate the solid and liquid risk factors based on the hazardous chemicals mass of hazardous chemicals transport vehicles ;

[0073] S3333, based on solid and liquid risk factors The value of further determines the risk level, and the steps are as follows:

[0074] When 0< When ≤0.3, it indicates low risk;

[0075] When 0.3< When ≤0.7, it represents moderate risk;

[0076] When 0.7< When ≤1, it indicates high risk;

[0077] S3334. For medium and high risks, the alarm signal is broadcast through the vehicle computer, and the solid and liquid risk coefficients are repeatedly calculated in combination with the driver's subsequent driving behavior until the solid and liquid risk coefficients are reduced to low risk, and the alarm signal is lifted.

[0078] The beneficial effects of the present invention are:

[0079] 1. The present invention records the state of hazardous chemicals during transportation, combines the characteristics of hazardous chemicals in different states, and additionally obtains relevant indicator data before and during the transportation of hazardous chemicals, so as to rate and predict the driving risks of hazardous chemical transportation vehicles. By comprehensively considering the different states and characteristics of hazardous chemicals, a more accurate risk assessment can be performed to provide a basis for transportation;

[0080] 2. The present invention collects relevant data of vehicles in different states when transporting hazardous chemicals, records possible state changes during the transportation of hazardous chemicals, and can respond more quickly when emergencies occur. It integrates the situation during the transportation of hazardous chemicals, facilitates safer operation methods in the loading and unloading process, and reduces possible accidents;

[0081] 3. The present invention collects real-time data on the environment of hazardous chemicals transport vehicles and establishes a standard simulation model in combination with historical excellent safe driving data to predict and output the upcoming situations of hazardous chemicals vehicles, including normal driving, variable speed driving, and emergency mode. At the same time, based on different output results and in combination with the vehicle computer, different colored alarm prompts pop up, which helps to quickly attract the driver's attention and take necessary actions, such as changing the driving mode or adjusting the speed;

[0082] 4. The present invention can provide intuitive visual feedback to the driver through alarm prompts of different colors, helping him to react and make decisions faster. It can also obtain relevant predicted behavior data in the standard model to generate a hazardous chemical risk coefficient to calculate the current risk level, and broadcast the alarm signal through the vehicle computer. Combined with the driver's subsequent remedial measures, the risk coefficient is continuously calculated until the risk level drops to a reasonable value and the alarm broadcast is stopped, thereby helping the driver to make reasonable driving decisions and enhancing the practicality and functionality of the method. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0084] Figure 1 It is a flow chart of a method for predicting driving dynamic risks of a hazardous chemicals transport vehicle according to an embodiment of the present invention. DETAILED DESCRIPTION

[0085] To further illustrate each embodiment, the present invention provides drawings, which are part of the disclosure of the present invention and are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these contents, ordinary technicians in the field should be able to understand other possible implementations and advantages of the present invention. The components in the figures are not drawn to scale, and similar component symbols are generally used to represent similar components.

[0086] According to an embodiment of the present invention, a method for predicting driving dynamic risks of a hazardous chemicals transport vehicle is provided.

[0087] The present invention is further described with reference to the accompanying drawings and specific embodiments. Figure 1 As shown, according to an embodiment of the present invention, a method for predicting driving dynamic risks of a hazardous chemicals transport vehicle comprises the following steps:

[0088] S1. Collect information related to hazardous chemicals transportation vehicles, establish a hazardous chemicals transportation vehicle database, and create files for each hazardous chemicals transportation;

[0089] S11. Collect relevant information on hazardous chemicals transport vehicles, including vehicle model, load capacity, total mileage, and maintenance records, and establish a hazardous chemicals transport vehicle database;

[0090] S12. Collect relevant information of drivers of hazardous chemicals transport vehicles, including driving experience, driving records, and health status, and establish a database of drivers of hazardous chemicals transport vehicles;

[0091] S13. Collect information related to hazardous chemicals, including the names and status of hazardous chemicals, and establish a hazardous chemicals information database;

[0092] S14. Based on the hazardous chemicals transportation vehicle database, hazardous chemicals transportation driver database and hazardous chemicals information database, establish a hazardous chemicals transportation file for the day, which includes driving vehicle information, driver information, hazardous chemicals name, hazardous chemicals status and hazardous chemicals weight.

[0093] It should be noted that by building a database of hazardous chemical transport vehicles, hazardous chemical drivers, and hazardous chemical information, and by establishing archives to record the data of relevant vehicles, personnel, and hazardous chemicals involved in each hazardous chemical transportation, detailed information on specific transportation tasks, vehicles, drivers, or hazardous chemicals can be quickly retrieved.

[0094] S2. Collect information related to the transportation vehicles, including deceleration and deceleration time. For hazardous chemicals in different states, additional relevant index parameters are collected to conduct risk prediction for hazardous chemicals transportation vehicles;

[0095] S21. Collect speed, acceleration and braking status data of hazardous chemicals transport vehicles during driving through the on-board computer system;

[0096] S22. For the transportation of gaseous hazardous chemicals, based on the impact of temperature on gaseous hazardous chemicals, a gaseous prediction model is built to collect the direct sunlight temperature during transportation and calculate the transportation risk of gaseous hazardous chemicals;

[0097] S221. Collect the reference temperature inside the transport container through the temperature sensor , based on direct solar temperature , calculate the internal temperature , and its algorithm formula is:

[0098] ;

[0099] in, It stands for influence coefficient, which indicates the degree of influence of direct sunlight temperature on the temperature inside the container.

[0100] It should be noted that the influence coefficient is usually a constant and is based on the material of different containers. The values ​​can be determined through experimental data or set in combination with specific parameters of different materials.

[0101] S223, for and Standardize, construct a weighted risk function, and predict the risk value. The specific steps are:

[0102] Based on the name of the gaseous hazardous chemicals, obtain the critical temperature of the gaseous hazardous chemicals Flash point temperature , safety pressure With ultimate pressure ,for and Standardize and obtain temperature standardized value Normalized to pressure :

[0103] when < hour:

[0104] ;

[0105] when > hour:

[0106] ;

[0107] It should be noted that when < When the actual temperature is lower than the flash point, the normalized value is 0, indicating the lowest risk. > When it means the actual temperature is above the critical temperature, the normalized value is 1, indicating the highest risk.

[0108] when < hour:

[0109] ;

[0110] when > hour:

[0111] ;

[0112] It should be noted that when < When the actual pressure is lower than the safety pressure, the standardized value is 0, indicating the lowest risk. > When it means that the actual pressure is higher than the limit pressure, the normalized value is 1, indicating the highest risk.

[0113] Normalize values ​​based on temperature Normalized to pressure , construct a weighted risk function, and its algorithm formula is:

[0114] ;

[0115] in, represents the gaseous risk factor, Represent temperature weight and pressure weight respectively;

[0116] When 0< When ≤0.3, it indicates low risk;

[0117] When 0.3< When ≤0.7, it represents moderate risk;

[0118] When 0.7< When ≤1, it indicates high risk.

[0119] It should be noted that the specific weight coefficient can be obtained by consulting experts in related fields or conducting experiments in combination with the properties of different hazardous chemicals, and the value of the weight coefficient can be adjusted according to actual conditions to meet different usage requirements. .

[0120] S23. For the transportation of solid and liquid hazardous chemicals, based on the impact of impact force on containers, solid and liquid prediction models are built to collect the real-time emergency braking deceleration, deceleration time and quality of hazardous chemicals during transportation, and calculate the transportation risks of solid and liquid hazardous chemicals;

[0121] S231, based on the vehicle's emergency braking deceleration during transportation , deceleration time and the quality of hazardous chemicals , calculate the deceleration speed of solid and liquid hazardous chemicals , the specific algorithm formula is:

[0122] ;

[0123] ;

[0124] ;

[0125] in, is the impact energy during rapid deceleration, The impact force during rapid deceleration is determined by the material of the hazardous chemicals transport container to obtain the maximum impact force. With maximum impact energy ;

[0126] S232, based on maximum impact force With maximum impact energy , construct a weighted risk function, and its algorithm formula is:

[0127] Impact safety factor ;

[0128] Energy safety factor ;

[0129] ;

[0130] in, Represents the solid and liquid risk factors, Represent the impact safety factor weight and energy safety factor weight respectively;

[0131] When 0< When ≤0.3, it indicates low risk;

[0132] When 0.3< When ≤0.7, it represents moderate risk;

[0133] When 0.7< When ≤1, it indicates high risk.

[0134] It should be noted that The value is usually set to 0.5, 0.5, and can also be set individually according to specific circumstances. , It reflects the importance of impact safety factor and energy safety factor in the overall risk assessment. Under normal circumstances, it will not be greater than 1. It is to integrate different risk factors into a relative risk measurement. This measurement value should generally be between 0 and 1. When the measurement value is greater than 1, the impact safety factor and energy safety factor must be less than 1. When the impact safety factor and energy safety factor are less than 1, it usually means that there is a problem with the container itself.

[0135] S3. Based on the on-board camera and satellite positioning system, relevant environmental data during vehicle transportation is collected. At the same time, based on the historical excellent safe driving data, the standard simulation model is trained. Based on the current real-time data of hazardous chemicals transportation vehicles, the behavior pattern prediction output is carried out to make risk prediction;

[0136] S31. Collect data related to the surrounding environment of hazardous chemicals transport vehicles during driving, including roads, traffic signs, other vehicles, and the latitude and longitude, real-time speed, and acceleration of the vehicle through on-board cameras and satellite positioning systems;

[0137] It should be noted that the satellite positioning system includes the Beidou satellite navigation system and the GPS system, which are used to collect data related to the surrounding environment during the driving of hazardous chemical vehicles.

[0138] S32. Collect driving behavior data of historical excellent drivers, including speed, acceleration and deceleration, braking habits, steering, vehicle distance, and road conditions, and build a standard simulation model;

[0139] S321. Collect driving behavior pattern data of historical excellent drivers, including normal driving, speed-changing driving, and emergency mode, and collect relevant data separately, including speed, acceleration and deceleration, braking intensity, steering angle, vehicle distance, and road conditions, and standardize the time series data to The data is divided into training set, validation set and test set.

[0140] S322, extracting a fixed-length time window from the time series as input data by using a sliding window technique, and converting the data into an LSTM model format;

[0141] S323. Build an LSTM simulation model using the training set, select mean square error as the loss function, and minimize the loss function using the Adam optimization algorithm.

[0142] S324. Use the validation set to evaluate the performance of the adjustment simulation model, and use the test set to make a final determination of the simulation model.

[0143] It should be noted that the LSTM unit consists of an input gate, a forget gate, and an output gate, and its specific algorithm formula is:

[0144] Input Gate: ;

[0145] in, is the activation value of the input gate, is the weight matrix of the input gate, is the bias term of the input gate, Represents the Sigmoid activation function, which compresses the result of the linear combination to between 0 and 1, indicating the degree of opening and closing of the input gate;

[0146] Forget Gate: ;

[0147] in, is the activation value of the forget gate, is the weight matrix of the forget gate, is the bias term of the forget gate;

[0148] Output Gate: ;

[0149] in, is the activation value of the output gate, is the weight matrix of the output gate, is the bias term of the output gate;

[0150] Unit status update: ;

[0151] in, is the candidate unit state, through the current input and the hidden state at the previous moment The linear combination of is the weight matrix for unit state update, is the bias term for updating the cell state, is the hyperbolic tangent activation function;

[0152] New unit status: ;

[0153] in, represents the cell state at the previous time step, is the cell state at the current time step;

[0154] Hidden state: ;

[0155] in, is the hidden state at the current time step.

[0156] S33. Based on the standard simulation model and combined with the real-time data of hazardous chemicals transport vehicles, risk prediction for hazardous chemicals vehicles is carried out;

[0157] S331, extracting relevant data obtained by the vehicle camera and the satellite positioning system through the target detection algorithm, quantizing the data into features and inputting them into the benchmark simulation model;

[0158] S332. Based on the current environmental data ahead of the hazardous chemicals transport vehicle, output prediction results, including normal driving, speed-changing driving, and emergency mode, and pop up warning signals through the vehicle computer, with a green signal for normal driving, a blue signal for speed-changing driving, and a red signal for emergency mode;

[0159] S333. Rating the risk coefficients of solid and liquid hazardous chemicals based on the acceleration and deceleration data generated by the speed change driving and emergency mode output by the benchmark simulation model;

[0160] S3331, based on the acceleration and deceleration data generated by the speed change driving and emergency mode output of the reference simulation model, including acceleration and deceleration And acceleration and deceleration time ;

[0161] S3332. Calculate the solid and liquid risk factors based on the hazardous chemicals mass of hazardous chemicals transport vehicles ;

[0162] S3333, based on solid and liquid risk factors The value of further determines the risk level, and the steps are as follows:

[0163] When 0< When ≤0.3, it indicates low risk;

[0164] When 0.3< When ≤0.7, it represents moderate risk;

[0165] When 0.7< When ≤1, it indicates high risk;

[0166] S3334. For medium and high risks, the alarm signal is broadcast through the vehicle computer, and the solid and liquid risk coefficients are repeatedly calculated in combination with the driver's subsequent driving behavior until the solid and liquid risk coefficients are reduced to low risk, and the alarm signal is lifted.

[0167] It should be noted that by obtaining the acceleration and deceleration data of variable speed driving and emergency mode from the benchmark simulation model, these data can help understand the risk impact of hazardous chemicals in impending situations, comprehensively consider the physical and chemical properties of hazardous chemicals and the driving behavior of the vehicle, and continuously calculate the risk factor until the risk factor of solid and liquid hazardous chemicals drops to the low risk range and then the alarm signal is lifted. This can indirectly affect the driver's behavior during hazardous chemicals transportation and assist the driver in making safe driving behaviors.

[0168] In summary, the present invention records the state of hazardous chemicals during transportation, combines the characteristics of hazardous chemicals in different states, and additionally obtains relevant indicator data before and during the transportation of hazardous chemicals, so as to rate and predict the driving risks of hazardous chemical transportation vehicles. By comprehensively considering the different states and characteristics of hazardous chemicals, a more accurate risk assessment can be performed to provide a basis for transportation;

[0169] By collecting relevant data of vehicles transporting hazardous chemicals in different states and recording possible state changes during the transportation of hazardous chemicals, we can respond more quickly when emergencies occur. By integrating the situation during the transportation of hazardous chemicals, it is convenient to adopt safer operating methods in the loading and unloading links to reduce possible accidents. In addition, for the collection of real-time data on the environment of hazardous chemical transportation vehicles, a standard simulation model is established in combination with historical excellent safe driving data, and predictions are output for upcoming situations of hazardous chemical vehicles, including normal driving, variable speed driving, and emergency mode. At the same time, based on different output results and combined with the car computer, different colored alarm prompts are popped up. Through different colored alarm prompts, intuitive visual feedback can be provided to the driver, helping him to react and make decisions faster, which helps to quickly attract the driver's attention and take necessary actions, such as changing the driving mode or adjusting the speed, helping the driver to make reasonable driving decisions, thereby enhancing the practicality and functionality of this method.

[0170] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for predicting the driving dynamic risk of hazardous chemicals transport vehicles, characterized in that: The method comprises the following steps: S1. Collect information related to hazardous chemicals transportation vehicles, establish a hazardous chemicals transportation vehicle database, and create files for each hazardous chemicals transportation; S11. Collect relevant information on hazardous chemicals transport vehicles, including vehicle model, load capacity, total mileage, and maintenance records, and establish a hazardous chemicals transport vehicle database; S12. Collect relevant information of drivers of hazardous chemicals transport vehicles, including driving experience, driving records, and health status, and establish a database of drivers of hazardous chemicals transport vehicles; S13. Collect information related to hazardous chemicals, including the names and status of hazardous chemicals, and establish a hazardous chemicals information database; S14. Based on the hazardous chemicals transportation vehicle database, hazardous chemicals transportation driver database and hazardous chemicals information database, establish a hazardous chemicals transportation file for the day, which includes driving vehicle information, driver information, hazardous chemicals name, hazardous chemicals status and hazardous chemicals weight; S2. Collect information related to the transportation vehicles, including deceleration and deceleration time. For hazardous chemicals in different states, additional relevant index parameters are collected to conduct risk prediction for hazardous chemicals transportation vehicles; S21. Collect speed, acceleration and braking status data of hazardous chemicals transport vehicles during driving through the on-board computer system; S22. For the transportation of gaseous hazardous chemicals, based on the impact of temperature on gaseous hazardous chemicals, a gaseous prediction model is built to collect the direct sunlight temperature during transportation and calculate the transportation risk of gaseous hazardous chemicals; S23. For the transportation of solid and liquid hazardous chemicals, based on the impact of impact force on containers, solid and liquid prediction models are built to collect the real-time emergency braking deceleration, deceleration time and quality of hazardous chemicals during transportation, and calculate the transportation risks of solid and liquid hazardous chemicals; S3. Based on the on-board camera and satellite positioning system, relevant environmental data during vehicle transportation is collected. At the same time, based on the historical excellent safe driving data, the standard simulation model is trained. Based on the current real-time data of hazardous chemicals transportation vehicles, the behavior pattern prediction output is carried out to make risk prediction; S31. Collect data related to the surrounding environment of hazardous chemicals transport vehicles during driving, including roads, traffic signs, other vehicles, and the latitude and longitude, real-time speed, and acceleration of the vehicle through on-board cameras and satellite positioning systems; S32. Collect driving behavior data of historical excellent drivers, including speed, acceleration and deceleration, braking habits, steering, vehicle distance, and road conditions, and build a standard simulation model; S33. Based on the standard simulation model and combined with the real-time data of hazardous chemicals transport vehicles, risk prediction for hazardous chemicals transportation is carried out.

2. The method for predicting driving dynamic risk of hazardous chemicals transport vehicles according to claim 1, characterized in that: The S22 comprises the following sub-steps: S221. Collect the reference temperature inside the transport container through the temperature sensor , based on direct solar temperature , calculate the internal temperature , and its algorithm formula is: ; in, stands for influence coefficient, which indicates the degree of influence of direct sunlight temperature on the temperature inside the container; S222. Calculate the gas pressure of hazardous chemicals according to the ideal gas equation. The algorithm formula is: ; in, Represents the gas pressure of hazardous chemicals under the current direct sunlight temperature. represents the volume of the container, represents the amount of gaseous substance, is the gas constant; S223, for and Standardize, construct a weighted risk function, and predict the risk value. The specific steps are: Based on the name of the gaseous hazardous chemicals, obtain the critical temperature of the gaseous hazardous chemicals Flash point temperature , safety pressure With ultimate pressure ,for and Standardize and obtain temperature standardized value Normalized to pressure : when < hour: ; when > hour: ; when < hour: ; when > hour: ; Normalize values ​​based on temperature Normalized to pressure , construct a weighted risk function, and its algorithm formula is: ; in, represents the gaseous risk factor, Represent temperature weight and pressure weight respectively; When 0< When ≤0.3, it indicates low risk; When 0.3< When ≤0.7, it represents moderate risk; When 0.7< When ≤1, it indicates high risk.

3. The method for predicting driving dynamic risk of hazardous chemicals transport vehicles according to claim 1, characterized in that: The S23 comprises the following sub-steps: S231, based on the vehicle's emergency braking deceleration during transportation , deceleration time and the quality of hazardous chemicals , calculate the deceleration speed of solid and liquid hazardous chemicals , the specific algorithm formula is: ； ； ； in, is the impact energy during rapid deceleration, The impact force during rapid deceleration is determined by the material of the hazardous chemicals transport container to obtain the maximum impact force. With maximum impact energy ; S232, based on maximum impact force With maximum impact energy , construct a weighted risk function, and its algorithm formula is: Impact safety factor ; Energy safety factor ; ； in, Represents the solid and liquid risk factors, Represent the impact safety factor weight and energy safety factor weight respectively; When 0< When ≤0.3, it indicates low risk; When 0.3< When ≤0.7, it represents moderate risk; When 0.7< When ≤1, it indicates high risk.

4. The method for predicting driving dynamic risk of hazardous chemicals transport vehicles according to claim 1, characterized in that: The S32 includes the following sub-steps: S321. Collect driving behavior pattern data of historical excellent drivers, including normal driving, speed-changing driving, and emergency mode, and collect relevant data separately, including speed, acceleration and deceleration, braking intensity, steering angle, vehicle distance, and road conditions, and standardize the time series data to The data is divided into training set, validation set and test set. S322, extracting a fixed-length time window from the time series as input data by using a sliding window technique, and converting the data into an LSTM model format; S323. Build an LSTM simulation model using the training set, select mean square error as the loss function, and minimize the loss function using the Adam optimization algorithm. S324. Use the validation set to evaluate the performance of the adjustment simulation model, and use the test set to make a final determination of the simulation model.

5. The method for predicting driving dynamic risk of hazardous chemicals transport vehicles according to claim 1, characterized in that: The S33 comprises the following sub-steps: S331, extracting relevant data obtained by the vehicle camera and the satellite positioning system through the target detection algorithm, quantizing the data into features and inputting them into the benchmark simulation model; S332. Based on the current environmental data ahead of the hazardous chemicals transport vehicle, output prediction results, including normal driving, speed-changing driving, and emergency mode, and pop up warning signals through the vehicle computer, with a green signal for normal driving, a blue signal for speed-changing driving, and a red signal for emergency mode; S333. Based on the acceleration and deceleration data generated by the speed change driving and emergency mode output by the benchmark simulation model, risk factor rating is performed for solid and liquid hazardous chemicals.

6. The method for predicting driving dynamic risk of hazardous chemicals transport vehicles according to claim 5, characterized in that: The S333 includes the following sub-steps: S3331, based on the acceleration and deceleration data generated by the speed change driving and emergency mode output of the reference simulation model, including acceleration and deceleration And acceleration and deceleration time ; S3332. Calculate the solid and liquid risk factors based on the hazardous chemicals mass of hazardous chemicals transport vehicles ; S3333, based on solid and liquid risk factors The value of further determines the risk level, and the steps are as follows: When 0< When ≤0.3, it indicates low risk; When 0.3< When ≤0.7, it represents moderate risk; When 0.7< When ≤1, it indicates high risk; S3334. For medium and high risks, the alarm signal is broadcast through the vehicle computer, and the solid and liquid risk coefficients are repeatedly calculated in combination with the driver's subsequent driving behavior until the solid and liquid risk coefficients are reduced to low risk, and the alarm signal is lifted.

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