A highway incident handling method, system, terminal and storage medium based on big data

Abstract

The present invention belongs to the technical field of road traffic management, and specifically relates to a highway event handling method, system, terminal and storage medium based on big data, including collecting real-time data on the highway, preprocessing the collected real-time highway data; analyzing the preprocessed real-time highway data to determine whether an emergency event occurs on the highway; if so, inputting the collected real-time highway data into a pre-constructed highway emergency event handling model to generate emergency event handling measures. The present invention collects vehicle driving data, road condition data and environmental data on the highway in real time through equipment such as vehicle speed sensors, meteorological sensors and cameras installed on the highway; and constructs a highway emergency event handling model using the Markov decision process, thereby realizing intelligent and automated emergency handling.

Description

Technical Field

[0001] The present invention belongs to the technical field of road traffic management, and specifically relates to a highway event handling method, system, terminal and storage medium based on big data. Background Art

[0002] With the continuous expansion of the highway network and the continuous increase in the number of vehicles, highway traffic safety and emergency event handling have become important issues in the field of traffic management. Emergency events on highways, such as traffic accidents, road damage, and bad weather, not only affect the smoothness of traffic, but may also pose a serious threat to personnel safety. Therefore, it is particularly important to develop an efficient and intelligent highway event handling method.

[0003] Traditional highway incident handling mainly relies on manual monitoring and alarm systems, which have problems such as slow response speed and low handling efficiency. When an emergency occurs, it often needs to be manually confirmed and reported to the relevant departments, and then the relevant departments dispatch personnel to handle it on site. The whole process is time-consuming and easily affected by human factors. In addition, traditional handling methods lack in-depth mining and analysis of emergency event data, making it difficult to form effective prevention and response measures. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides a highway event handling method, system, terminal and storage medium based on big data to solve the above-mentioned technical problems.

[0005] In a first aspect, the present invention provides a highway incident handling method based on big data, comprising:

[0006] Collect real-time data on highways, including vehicle driving data, road condition data, and environmental data;

[0007] Preprocess the collected real-time highway data, including data cleaning, exception processing and normalization;

[0008] Analyze the pre-processed real-time highway data to determine whether an emergency event occurs on the highway;

[0009] If so, the collected real-time highway data is input into a pre-built highway emergency event handling model to generate emergency event handling measures.

[0010] A further improvement of the technical solution is to collect real-time data of the highway through vehicle speed sensors, meteorological sensors and cameras including processors installed on the highway.

[0011] A further improvement of the technical solution is that the method for constructing a highway emergency event handling model includes:

[0012] Obtain relevant monitoring images and text reports of historical highway emergency events, including records of casualties at the scene of highway emergency events, vehicle types, road conditions, highway environmental information and related disposal information;

[0013] According to the relevant monitoring images and text reports of historical highway emergency events, a highway emergency event handling model is constructed based on the Markov decision process; wherein the Markov decision process is composed of state space, action space, state transition probability and reward generation mechanism;

[0014] Define the emergency response department of highway incidents as an intelligent agent in a Markov decision process;

[0015] According to the types of highway emergency events, emergency response tasks are divided into five categories: people, vehicles, roads, environment and information, and weights are set for emergency response tasks in each category;

[0016] According to the completion of the emergency response task, a five-row and five-column state matrix is ​​used to represent the state of the Markov decision process;

[0017] Design a reward mechanism to calculate the reward value based on the completion status of emergency response tasks, the category and weight of the corresponding task, and the value and weight of the evaluation index, and provide reward compensation when all emergency response tasks are completed to optimize the highway emergency event response model.

[0018] A further improvement of the technical solution is that the Markov decision process is represented by a tuple including four elements: state space, action space, state transition probability and reward generation mechanism: ;

[0019] Among them, S is the state space of the highway environment; A is the action space of the agent; is the state transition probability, It means that when the highway environment is in state s at time t, the action a of the agent causes the highway environment to be transformed into state at time t+1 probability; As a reward generation mechanism, Indicates that the highway environment changes from state s to state after action a. The reward value received by the agent.

[0020] A further improvement of the technical solution is that the action of the agent is determined by the strategy of the agent, and a strategy function is set for the strategy of the agent: , which represents the probability that the agent chooses action a when it is in state s in the highway environment.

[0021] A further improvement of the technical solution is that, when constructing the reward of the Markov decision process, a two-layer analysis matrix is ​​created to determine the weights of the emergency response tasks; wherein, the first layer defines the emergency response tasks including five categories of people, vehicles, roads, environment and information, and defines the weights corresponding to each category, and the second layer defines the emergency response tasks corresponding to each category, and defines the weights corresponding to each emergency response task in each category.

[0022] A further improvement of this technical solution is that the reward of the Markov decision process is defined as:

[0023] ;

[0024] ;

[0025] in, is the reward value at time t; and They are the number of categories of emergency response tasks at the highway emergency event handling site and the number of corresponding emergency response tasks in each category; is the state matrix s ij The value of indicates the completion status of the jth emergency response task in the i-th category; and They are the emergency response task categories and corresponding weights respectively; is the number of evaluation indicators; and are the value and weight of the kth evaluation index in the analysis matrix respectively; The amount of reward compensation for completing emergency response tasks; , and The factor that magnifies each reward component.

[0026] In a second aspect, the present invention provides a highway event handling system based on big data, comprising:

[0027] Data collection module, used to collect real-time data on highways, including vehicle driving data, road condition data and environmental data;

[0028] The data preprocessing module is used to preprocess the collected real-time highway data, including data cleaning, exception processing and normalization;

[0029] The emergency event determination module is used to analyze the pre-processed real-time highway data to determine whether an emergency event occurs on the highway;

[0030] The emergency event handling measures generation module is used to input the collected real-time highway data into the pre-built highway emergency event handling model to generate emergency event handling measures when the emergency event determination module determines that an emergency event has occurred on the highway.

[0031] In a third aspect, a terminal is provided, including:

[0032] processor, memory, wherein:

[0033] The memory is used to store computer programs.

[0034] The processor is used to call and run the computer program from the memory, so that the terminal executes the above-mentioned terminal method.

[0035] According to a fourth aspect, a computer storage medium is provided, wherein the computer-readable storage medium stores instructions, and when the instructions are executed on a computer, the computer executes the methods described in the above aspects.

[0036] The beneficial effects of the present invention are:

[0037] Real-time and high efficiency: Through the installation of speed sensors, meteorological sensors, cameras and other equipment on the highway, the present invention can collect vehicle driving data, road condition data and environmental data on the highway in real time. After pre-processing, these data can be quickly used to determine whether an emergency event has occurred on the highway. Once an emergency event is confirmed, the system can immediately generate disposal measures, greatly shortening the emergency response time and improving disposal efficiency.

[0038] Intelligence and automation: This invention uses the Markov decision process to construct a highway emergency event handling model, realizing the intelligence and automation of emergency handling. The model can automatically determine the type and severity of emergency events based on historical data and real-time data, and generate corresponding handling measures. This not only reduces the burden of manual monitoring and alarm systems, but also improves the accuracy and pertinence of handling.

[0039] Data-driven and decision-making optimization: By deeply mining and analyzing the relevant data of highway emergency events, the present invention can form effective prevention and response measures. At the same time, the model can also calculate the reward value and optimize the model according to the completion of the emergency response task, the category and the weight of the corresponding task, as well as the value and weight of the evaluation index. This data-driven decision-making method makes the emergency response measures more scientific, reasonable and effective.

[0040] In addition, the invention has a reliable design principle, a simple structure and a very broad application prospect. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] 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 or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0042] Figure 1 is a schematic flow chart of a method according to an embodiment of the present invention.

[0043] Figure 2 is a schematic block diagram of a system according to an embodiment of the present invention.

[0044] Figure 3 A schematic diagram of the structure of a terminal provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0045] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0046] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention.

[0047] The highway event handling method based on big data provided in the embodiment of the present invention is executed by a computer device, and accordingly, the highway event handling system based on big data runs in the computer device.

[0048] Figure 1 is a schematic flow chart of a method according to an embodiment of the present invention. Figure 1 The execution subject may be a highway incident handling system based on big data. According to different requirements, the order of the steps in the flowchart may be changed, and some may be omitted.

[0049] like Figure 1 As shown, the method includes:

[0050] Step 110, collecting real-time data on the highway, including vehicle driving data, road condition data and environmental data;

[0051] Step 120, preprocessing the collected real-time highway data, the preprocessing includes cleaning, abnormality processing and normalization processing of the data;

[0052] Step 130, analyzing the pre-processed real-time highway data to determine whether an emergency event occurs on the highway; if so, go to step 130;

[0053] Step 140, input the collected real-time highway data into a pre-built highway emergency event handling model to generate emergency event handling measures.

[0054] To facilitate understanding of the present invention, the highway event handling method based on big data provided by the present invention is further described below based on the principle of the highway event handling method based on big data of the present invention and in combination with the process of handling highway events based on big data in the embodiment.

[0055] Specifically, real-time data of the highway is collected through vehicle speed sensors, meteorological sensors and cameras containing processors installed on the highway; among them, vehicle driving data includes traffic flow (monitored by traffic flow monitors), vehicle speed and vehicle type.

[0056] In addition, the methods for constructing a highway emergency event handling model include:

[0057] S141. Obtain relevant monitoring images and text reports of historical highway emergency events, including records of casualties at the scene of the highway emergency event, vehicle types, road conditions, highway environment information and relevant disposal information;

[0058] S142. Constructing a highway emergency event handling model based on the Markov decision process according to the acquired relevant monitoring images and text reports of historical highway emergency events; wherein the Markov decision process is composed of a state space, an action space, a state transition probability and a reward generation mechanism;

[0059] S143. Define the emergency response department of highway incidents as an intelligent agent in a Markov decision process;

[0060] S144. Classify emergency response tasks into five categories: people, vehicles, roads, environment and information according to the types of highway emergency events, and set weights for emergency response tasks in each category;

[0061] S145. According to the completion status of the emergency response task, a state matrix of five rows and five columns is used to represent the state of the Markov decision process;

[0062] S146. Design a reward mechanism to calculate the reward value based on the completion status of the emergency response tasks, the category and weight of the corresponding task, and the value and weight of the evaluation index, and provide reward compensation when all emergency response tasks are completed to optimize the highway emergency event response model.

[0063] First, the relevant monitoring images and text reports of highway emergency events in the past five years were obtained from the database. These reports recorded in detail the casualties at the scene of each emergency, the types of vehicles involved, road conditions, highway environmental information (such as weather, visibility, etc.), and the disposal measures and results taken by relevant departments. Based on the historical data obtained, the Markov decision process was used to construct a highway emergency event disposal model. The model consists of the following four key elements:

[0064] State space S: defines the state space of the highway environment, including different categories of emergency events (such as traffic accidents, road damage, bad weather, etc.) and their severity (such as minor, moderate, and severe). These states are identified and represented through information in monitoring images and text reports.

[0065] Action space A: The emergency response department of highway incidents is defined as an intelligent agent of the Markov decision process, and the possible action space is defined for it; the emergency response department includes the traffic law enforcement department, the road fire department, the emergency rescue department, the emergency transportation department and the road management department, and the actions include dispatching rescue vehicles, clearing road obstacles, issuing traffic information, adjusting traffic lights, etc.

[0066] State transition probability : By analyzing the emergency response process and results in historical data, the probability of the environment state transferring to a new state after taking different actions in different states is estimated. These probability values ​​are used for state transfer and decision optimization in the model.

[0067] Reward generation mechanism : Design a reward mechanism to evaluate the quality of the actions taken by the agent in different states. The reward value is calculated based on the completion of the emergency response task, the category and the weight of the corresponding task, and the value and weight of the evaluation indicators (such as response time, response efficiency, personnel safety, etc.).

[0068] In order to reflect the importance and priority of different emergency response tasks in each emergency event, the emergency response tasks are divided into five categories: people, vehicles, roads, environment and information, and weights are set for the emergency response tasks in each category.

[0069] In order to represent the state of the Markov decision process, a state matrix with five rows and five columns is used. Each row of the state matrix represents an emergency response task category, and each column represents the completion status of an emergency response task (such as completed, in progress, not started, etc.). Through the changes in the state matrix, the progress and state transition of the emergency response task can be intuitively tracked.

[0070] Secondly, the reward value is calculated based on the completion of the emergency response tasks, the category and the weight of the corresponding task, as well as the value and weight of the evaluation index. When all emergency response tasks are completed, an additional reward compensation is provided to encourage the agent to complete the emergency response tasks as quickly and effectively as possible.

[0071] Finally, the model was trained and verified using historical data, and the highway emergency response model was optimized by continuously adjusting the state transition probability and parameters in the reward mechanism. The model can accurately identify emergency events in real-time data and generate effective emergency response measures, improving highway traffic safety and the efficiency and accuracy of emergency response.

[0072] Specifically, the Markov decision process is represented by a tuple containing four elements: state space, action space, state transition probability, and reward generation mechanism: ; Where S is the state space of the highway environment; A is the action space of the agent; is the state transition probability, It means that when the highway environment is in state s at time t, the action a of the agent causes the highway environment to be transformed into state at time t+1 probability; As a reward generation mechanism, Indicates that the highway environment changes from state s to state after action a. The reward value received by the agent.

[0073] Furthermore, the actions of the agent are determined by the agent's strategy, and a strategy function is set for the agent's strategy: , which represents the probability that the agent chooses action a when it is in state s in the highway environment.

[0074] In addition, when constructing the reward of the Markov decision process, a two-layer analysis matrix is ​​created to determine the weight of the emergency response task; the first layer defines the emergency response tasks including five categories of people, vehicles, roads, environment and information, and defines the weight corresponding to each category, and the second layer defines the emergency response tasks corresponding to each category, and defines the weight corresponding to each emergency response task in each category. Among them, people are the most important element in the emergency response task, followed by vehicles, roads, environment and information in decreasing order of importance.

[0075] Specifically, the reward of the Markov decision process is defined as:

[0076] ;

[0077] ;

[0078] in, is the reward value at time t; and They are the number of categories of emergency response tasks at the highway emergency event handling site and the number of corresponding emergency response tasks in each category; is the state matrix s ij The value of indicates the completion status of the jth emergency response task in the i-th category; and They are the emergency response task categories and corresponding weights respectively; is the number of evaluation indicators; and are the value and weight of the kth evaluation index in the analysis matrix respectively; The amount of reward compensation for completing emergency response tasks; , and The factor that magnifies each reward component.

[0079] The present invention uses the Markov decision process to construct a highway emergency event handling model, realizing the intelligent and automated emergency handling. The model can automatically determine the type and severity of the emergency event based on historical data and real-time data, and generate corresponding handling measures. This not only reduces the burden of manual monitoring and alarm systems, but also improves the accuracy and pertinence of the handling. In addition, by deeply mining and analyzing the relevant data of highway emergency events, the present invention can form effective prevention and response measures. At the same time, the model can also calculate the reward value and optimize the model based on the completion status of the emergency handling task, the category and the weight of the corresponding task, as well as the value and weight of the evaluation index. This data-driven decision-making method makes the emergency handling measures more scientific, reasonable and effective.

[0080] In some embodiments, the highway incident handling system 200 based on big data may include a plurality of functional modules composed of computer program segments. The computer programs of the various program segments in the highway incident handling system 200 based on big data may be stored in a memory of a computer device and executed by at least one processor to perform (see Figure 1 Description) Functions of highway incident handling based on big data.

[0081] In this embodiment, the highway incident handling system 200 based on big data can be divided into multiple functional modules according to the functions it performs, such as Figure 2 As shown. The functional modules may include: a data acquisition module 210, a data preprocessing module 220, an emergency event determination module 230 and an emergency event handling measures generation module 240. The module referred to in the present invention refers to a series of computer program segments that can be executed by at least one processor and can complete fixed functions, which are stored in a memory. In this embodiment, the functions of each module will be described in detail in subsequent embodiments.

[0082] Specifically, the data acquisition module is used to collect real-time data on the highway, including vehicle driving data, road condition data and environmental data; the data preprocessing module is used to preprocess the collected real-time highway data, and the preprocessing includes data cleaning, exception processing and normalization processing; the emergency event determination module is used to analyze the preprocessed real-time highway data to determine whether an emergency event occurs on the highway; the emergency event handling measure generation module is used to input the collected real-time highway data into a pre-built highway emergency event handling model when the emergency event determination module determines that an emergency event occurs on the highway, so as to generate emergency event handling measures.

[0083] Figure 3 A schematic diagram of the structure of a terminal 300 provided in an embodiment of the present invention. The terminal 300 can be used to execute the highway event handling method based on big data provided in an embodiment of the present invention.

[0084] The terminal 300 may include: a processor 310, a memory 320 and a communication module 330. These components communicate via one or more buses. Those skilled in the art will appreciate that the server structure shown in the figure does not limit the present invention, and it may be a bus structure or a star structure, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0085] The memory 320 can be used to store the execution instructions of the processor 310, and the memory 320 can be implemented by any type of volatile or non-volatile storage terminal or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. When the execution instructions in the memory 320 are executed by the processor 310, the terminal 300 can perform some or all of the steps in the following method embodiments.

[0086] The processor 310 is the control center of the storage terminal, and uses various interfaces and lines to connect various parts of the entire electronic terminal. It runs or executes software programs and / or modules stored in the memory 320, and calls data stored in the memory to perform various functions of the electronic terminal and / or process data. The processor can be composed of an integrated circuit (IC), for example, it can be composed of a single packaged IC, or it can be composed of multiple packaged ICs with the same or different functions. For example, the processor 310 can only include a central processing unit (CPU). In the embodiment of the present invention, the CPU can be a single computing core or multiple computing cores.

[0087] The communication module 330 is used to establish a communication channel so that the storage terminal can communicate with other terminals, receive user data sent by other terminals or send user data to other terminals.

[0088] The present invention also provides a computer storage medium, wherein the computer storage medium may store a program, and when the program is executed, the program may include some or all of the steps in each embodiment provided by the present invention. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM).

[0089] Those skilled in the art can clearly understand that the technology in the embodiments of the present invention can be implemented by means of software plus a necessary general hardware platform. Based on this understanding, the technical solution in the embodiments of the present invention, in essence or in other words, the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, and other media that can store program codes, including several instructions for enabling a computer terminal (which can be a personal computer, a server, or a second terminal, a network terminal, etc.) to execute all or part of the steps of the methods described in each embodiment of the present invention.

[0090] In this specification, the same or similar parts between the various embodiments can be referred to each other. In particular, for the terminal embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the description in the method embodiment.

[0091] In the several embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of systems or modules, which can be electrical, mechanical or other forms.

[0092] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0093] In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0094] Although the present invention has been described in detail with reference to the accompanying drawings and in combination with the preferred embodiments, the present invention is not limited thereto. Without departing from the spirit and essence of the present invention, a person of ordinary skill in the art may make various equivalent modifications or substitutions to the embodiments of the present invention, and these modifications or substitutions shall be within the scope of the present invention. Any person of ordinary skill in the art may easily think of changes or substitutions within the technical scope disclosed by the present invention, and these shall be within the scope of protection of the present invention.

Claims

1. A highway incident handling method based on big data, characterized in that: include: Collect real-time data on highways, including vehicle driving data, road condition data, and environmental data; Preprocess the collected real-time highway data, including data cleaning, exception processing and normalization; Analyze the pre-processed real-time highway data to determine whether an emergency event occurs on the highway; If yes, the collected real-time highway data is input into a pre-built highway emergency event handling model to generate emergency event handling measures; The methods for constructing a highway emergency event handling model include: Obtain relevant monitoring images and text reports of historical highway emergency events, including records of casualties at the scene of highway emergency events, vehicle types, road conditions, highway environmental information and related disposal information; According to the relevant monitoring images and text reports of historical highway emergency events, a highway emergency event handling model is constructed based on the Markov decision process; wherein the Markov decision process is composed of state space, action space, state transition probability and reward generation mechanism; Define the emergency response department of highway incidents as an intelligent agent in a Markov decision process; According to the types of highway emergency events, emergency response tasks are divided into five categories: people, vehicles, roads, environment and information, and weights are set for emergency response tasks in each category; According to the completion of the emergency response task, a five-row and five-column state matrix is ​​used to represent the state of the Markov decision process; Design a reward mechanism to calculate the reward value based on the completion of emergency response tasks, the category and weight of the corresponding task, and the value and weight of the evaluation index, and provide reward compensation when all emergency response tasks are completed to optimize the highway emergency event response model; When constructing the reward of the Markov decision process, a two-layer analysis matrix is ​​created to determine the weights of emergency response tasks; the first layer defines emergency response tasks in five categories, including people, vehicles, roads, environment, and information, and defines the weights corresponding to each category; the second layer defines the emergency response tasks corresponding to each category, and defines the weights corresponding to each emergency response task in each category; The reward of the Markov decision process is defined as: ； ； in, is the reward value at time t; and They are the number of categories of emergency response tasks at the highway emergency event handling site and the number of corresponding emergency response tasks in each category; is the state matrix s ij The value of indicates the completion status of the jth emergency response task in the i-th category; and They are the emergency response task categories and corresponding weights respectively; is the number of evaluation indicators; and are the value and weight of the kth evaluation index in the analysis matrix respectively; The amount of reward compensation for completing emergency response tasks; , and It is the factor that magnifies each reward component.

2. The highway incident handling method based on big data according to claim 1 is characterized in that: Real-time data of the highway is collected through vehicle speed sensors, weather sensors and cameras containing processors installed on the highway.

3. The highway incident handling method based on big data according to claim 1 is characterized in that: The Markov decision process is represented by a tuple containing four elements: state space, action space, state transition probability, and reward generation mechanism: ; Among them, S is the state space of the highway environment; A is the action space of the agent; is the state transition probability, It means that when the highway environment is in state s at time t, the action a of the agent causes the highway environment to be transformed into state at time t+1 probability; As a reward generation mechanism, Indicates that the highway environment changes from state s to state after action a. The reward value received by the agent.

4. The highway incident handling method based on big data according to claim 3 is characterized in that: The actions of the agent are determined by the agent's strategy, and a policy function is set for the agent's strategy: , which represents the probability that the agent chooses action a when it is in state s in the highway environment.

5. A highway incident handling system based on big data, characterized in that: The method for handling highway events based on big data according to any one of claims 1 to 4 is implemented, comprising: Data collection module, used to collect real-time data on highways, including vehicle driving data, road condition data and environmental data; The data preprocessing module is used to preprocess the collected real-time highway data, including data cleaning, exception processing and normalization; The emergency event determination module is used to analyze the pre-processed real-time highway data to determine whether an emergency event occurs on the highway; The emergency event handling measures generation module is used to input the collected real-time highway data into the pre-built highway emergency event handling model to generate emergency event handling measures when the emergency event determination module determines that an emergency event has occurred on the highway.

6. A terminal, characterized in that: include: processor; A memory for storing execution instructions of the processor; The processor is configured to execute the method according to any one of claims 1 to 4.

7. A computer-readable storage medium storing a computer program, characterized in that: When the program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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