Intelligent road condition emergency response system and method for simulation scene training

Abstract

The invention discloses an intelligent road condition emergency response system and method for simulation scene training, and relates to the related field of intelligent traffic systems, and the system comprises an emergency simulation model building module which is used for collecting sample traffic data and building an emergency simulation model; the burst simulation scene generation module is used for generating a burst simulation scene; the emergency processing information comparison module is used for obtaining emergency processing information of a user and comparing the emergency processing information with a standard to obtain a first emergency training score; the emergency training score calculation module is used for obtaining the processing time of user emergency processing, obtaining a second emergency training score in combination with the standard processing time, and obtaining an emergency training score in combination with the first emergency training score. The technical problems that the complexity of the actual road condition cannot be reflected, the training effect is limited and the defects of the user are difficult to find in the existing simulation scene training are solved, and the technical effects of providing a real training environment, improving the training effect and comprehensively evaluating the emergency response capability of the user are achieved.

Description

Technical Field

[0001] The present application relates to the field related to intelligent transportation systems, and in particular to an intelligent road condition emergency response system and method for simulated scenario training. Background Art

[0002] With the acceleration of urbanization and the continuous increase in traffic flow, road emergency response capabilities have become an important part of urban traffic management. In order to improve the emergency response capabilities of traffic management personnel, drivers, and emergency response teams, simulation scenario training has become an effective training method. In existing simulation scenario training, simulation training is usually carried out through preset simple scenarios, such as vehicle failures, traffic accidents, etc. Although this method can simulate some basic emergency situations, the scenario is too single and cannot truly reflect the complexity and variability of actual road conditions. It is impossible to comprehensively and objectively evaluate the user's emergency response capabilities and it is difficult to find the user's shortcomings in emergency handling, resulting in unsatisfactory training results.

[0003] Among the current related technologies, the simulated scenario training used for road emergency response cannot truly reflect the complexity and variability of actual road conditions, resulting in limited training effects and difficulty in discovering the shortcomings of users in emergency handling. Summary of the invention

[0004] The present application provides an intelligent road emergency response system and method for simulation scenario training, which collects sample traffic data corresponding to the simulation scenario, constructs an emergency simulation model, generates an emergency simulation scenario based on the emergency simulation model, obtains the emergency handling information fed back by the user in the emergency simulation scenario, compares it with the standard emergency handling information, obtains a first emergency training score, calculates a second emergency training score based on the user's emergency handling time and the standard handling time, and calculates the emergency training score based on the first emergency training score as the emergency response training result, and other technical means, thereby achieving the technical effect of providing a more realistic and complex training environment, improving training effects, and comprehensively evaluating the user's emergency response capabilities.

[0005] The present application provides an intelligent road emergency response system for simulation scenario training, including: an emergency simulation model construction module, used to collect sample traffic data corresponding to the simulation scenario and construct an emergency simulation model; a sudden simulation scenario generation module, used to generate a sudden simulation scenario according to the sudden event simulation model when a user enters the training of the simulation scenario; an emergency processing information comparison module, used to obtain the emergency processing information fed back by the user in the sudden simulation scenario, and compare it with the standard emergency processing information corresponding to the sudden simulation scenario to obtain a first emergency training score; an emergency training score calculation module, used to obtain the processing time of the user's emergency processing in the sudden simulation scenario, combined with the standard processing time of the standard emergency processing information, to calculate a second emergency training score, and combined with the first emergency training score, to calculate an emergency training score as an emergency response training result.

[0006] In a possible implementation, the emergency event simulation model construction module includes: a sample traffic scene matching unit, used to match multiple sample traffic scenes according to the simulation scene; an emergency event simulation model construction unit, used to construct the emergency event simulation model according to the sample traffic data of the multiple sample traffic scenes.

[0007] In a possible implementation, the emergency simulation model construction unit includes: a sample emergency data set collection subunit, which is used to collect multiple sample emergency data sets in the multiple sample traffic scenes; a data set clustering subunit, which is used to cluster the multiple sample emergency data sets to obtain multiple sample category emergency data sets of multiple sample emergency categories; a ratio calculation subunit, which is used to calculate the ratio of the data volume in each sample category emergency data set to the sum of the data volume in the multiple sample category emergency data sets, and obtain multiple emergency probabilities; a database indexing subunit, which is used to index and obtain multiple standard emergency processing information of the multiple sample emergency categories in the emergency emergency database, wherein the emergency emergency database includes a mapping relationship between sample emergency categories and sample standard emergency processing information; a mapping relationship construction subunit, which is used to construct a mapping relationship between the multiple sample emergency categories, multiple sample category emergency data sets, multiple emergency probabilities and multiple standard emergency processing information to obtain a sudden event simulation model.

[0008] In a possible implementation, the emergency simulation scenario generation module includes: a simulated emergency event data acquisition unit, which is used to randomly select a sample category emergency event data within a sample emergency event category according to the multiple emergency event probabilities in the emergency event simulation model when the user enters the simulation scenario training, as simulated emergency event data; a data loading unit, which is used to load the simulated emergency event data into the simulation scenario to generate a emergency simulation scenario.

[0009] In a possible implementation, the emergency handling information comparison module includes: a user feedback information acquisition unit, used to obtain the emergency handling information fed back by the user in the emergency simulation scenario; a standard emergency handling information acquisition unit, used to obtain the standard emergency handling information corresponding to the sample emergency event category to which the simulated emergency event data belongs; an information comparison unit, used to compare whether the emergency handling information and the standard emergency handling information are consistent, and generate a first emergency training score, which is 1 or 0.

[0010] In a possible implementation, the emergency training score calculation module includes: a standard processing time acquisition unit, used to obtain the standard processing time of the standard emergency processing information; a user processing time acquisition unit, used to obtain the processing time of the user's emergency processing in the emergency simulation scenario; a second emergency training score calculation unit, used to calculate the ratio of the difference between the processing time and the standard processing time to the standard processing time, and subtract the ratio from 1 to obtain the second emergency training score.

[0011] In a possible implementation, the emergency training score calculation module includes: a weighted calculation unit, used to weightedly calculate the first emergency training score and the second emergency training score to obtain an emergency training score; and an emergency response training result generation unit, used to use the emergency training score as an emergency response training result.

[0012] The present application also provides an intelligent road emergency response method for simulation scenario training, including: collecting sample traffic data corresponding to the simulation scenario and constructing an emergency simulation model; when a user enters the simulation scenario training, generating an emergency simulation scenario according to the emergency simulation model; obtaining emergency processing information fed back by the user in the emergency simulation scenario, and comparing it with standard emergency processing information corresponding to the emergency simulation scenario to obtain a first emergency training score; obtaining the processing time of the user's emergency processing in the emergency simulation scenario, combining it with the standard processing time of the standard emergency processing information, and calculating a second emergency training score, and combining it with the first emergency training score to calculate an emergency training score as an emergency response training result.

[0013] The intelligent road emergency response system and method for simulation scenario training proposed in this application collects sample traffic data corresponding to the simulation scenario through the emergency simulation model construction module, constructs an emergency simulation model, and generates an emergency simulation scenario according to the emergency simulation model when the user enters the simulation scenario training through the emergency simulation scenario generation module. The emergency processing information comparison module obtains the emergency processing information feedback by the user in the emergency simulation scenario, and compares it with the standard emergency processing information corresponding to the emergency simulation scenario to obtain a first emergency training score. The emergency training score calculation module obtains the processing time of the user's emergency processing in the emergency simulation scenario, and combines the standard processing time of the standard emergency processing information to calculate the second emergency training score. Combined with the first emergency training score, the emergency training score is calculated as the emergency response training result, thereby achieving the technical effect of providing a more realistic and complex training environment, improving training effects, and comprehensively evaluating the user's emergency response capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solution of the embodiment of the present invention, the accompanying drawings of the embodiment of the present invention will be briefly introduced below. A flow chart is used in the present application to illustrate the operations performed by the system according to the embodiment of the present application. It should be understood that the preceding or following operations are not necessarily performed accurately in order. On the contrary, various steps can be processed in reverse order or simultaneously as needed. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0015] Figure 1 A schematic diagram of the structure of an intelligent road emergency response system for simulated scenario training provided in an embodiment of the present application.

[0016] Figure 2 A flowchart of an intelligent road emergency response method for simulated scenario training provided in an embodiment of the present application.

[0017] Explanation of the accompanying drawings: emergency simulation model building module 10, emergency simulation scenario generation module 20, emergency processing information comparison module 30, emergency training score calculation module 40. DETAILED DESCRIPTION

[0018] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below.

[0019] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present application. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of this application.

[0020] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict, and the terms "first\second" involved are merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "including" and "having" and any variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by technicians in the technical field of this application. The terms used herein are for the purpose of describing the embodiments of the present application only.

[0021] The present application embodiment provides an intelligent road emergency response system for simulated scenario training, such as Figure 1 As shown, the system comprises:

[0022] The emergency simulation model building module 10 is used to collect sample traffic data corresponding to the simulation scene and build an emergency simulation model.

[0023] Specifically, the emergency simulation model construction module 10 first collects sample traffic data corresponding to the simulation scenario by means of data crawlers, API interface calls or direct reading from the database. The sample traffic data is the basic data for building the emergency simulation model, which comes from the actual traffic monitoring system or historical data, including road flow, vehicle speed, weather conditions, traffic accident history records, etc. Based on the collected sample data, the emergency simulation model is constructed using machine learning, deep learning or statistical methods. This model can simulate various possible emergencies, such as traffic accidents, road closures, bad weather, etc.

[0024] In a possible implementation, the emergency event simulation model construction module 10 includes: a sample traffic scene matching unit, used to match multiple sample traffic scenes according to the simulation scene; an emergency event simulation model construction unit, used to construct the emergency event simulation model according to the sample traffic data of the multiple sample traffic scenes.

[0025] Specifically, the sample traffic scene matching unit first determines the specific type and characteristics of the simulation scene, which is a scene used to simulate the real traffic environment, such as urban traffic congestion, highway accidents, traffic management under severe weather conditions, etc. These scenes are the basis for matching the sample traffic scene.

[0026] Collect sample traffic data related to the simulation scenario from multiple sources such as traffic monitoring systems, historical accident records, and weather data. Clean, integrate, and format the collected data to ensure data accuracy and consistency. According to the characteristics of the simulation scenario, multiple sample traffic scenarios that match it are screened from the preprocessed data based on multiple dimensions such as scenario type, time, location, and weather conditions. That is, the sample traffic scenario is extracted from the actual traffic data and is similar to the simulation scenario.

[0027] The emergency simulation model construction unit extracts key features from the sample traffic data of the matched sample traffic scenes, such as traffic flow, vehicle speed changes, accident types, weather impacts, etc. The extracted features are combined with machine learning or deep learning algorithms to train the emergency simulation model to simulate the impact of various emergencies on traffic. This implementation method can collect and process data more targetedly by pre-defining simulation scenarios and matching sample traffic scenarios, thereby improving training efficiency. By matching multiple sample traffic scenes and extracting key features from them, it is ensured that the emergency simulation model can more accurately simulate emergencies in real traffic environments, thereby improving the accuracy of the model. By using multiple sample traffic scenes for training, the model has stronger adaptability and generalization capabilities when facing different types of emergencies.

[0028] In a possible implementation, the emergency simulation model construction unit includes: a sample emergency data set collection subunit, which is used to collect multiple sample emergency data sets in the multiple sample traffic scenes; a data set clustering subunit, which is used to cluster the multiple sample emergency data sets to obtain multiple sample category emergency data sets of multiple sample emergency categories; a ratio calculation subunit, which is used to calculate the ratio of the data volume in each sample category emergency data set to the sum of the data volume in the multiple sample category emergency data sets, and obtain multiple emergency probabilities; a database index subunit, which is used to index and obtain multiple standard emergency processing information of the multiple sample emergency categories in the emergency emergency database, wherein the emergency emergency database includes a mapping relationship between sample emergency categories and sample standard emergency processing information; a mapping relationship construction subunit, which is used to construct a mapping relationship between the multiple sample emergency categories, multiple sample category emergency data sets, multiple emergency probabilities and multiple standard emergency processing information to obtain an emergency simulation model.

[0029] Specifically, the sample emergency event data set collection subunit collects emergency event data from multiple sample traffic scenes, including the time, location, type, and scope of impact of the accident. The data set clustering subunit cleans, integrates, and formats the collected data to ensure the accuracy and consistency of the data. Clustering algorithms (such as K-means, hierarchical clustering, etc.) are applied to cluster multiple sample emergency event data sets to classify similar emergencies into one category. After clustering, each category forms a sample category emergency event data set, which represents different types of emergencies.

[0030] The ratio calculation subunit calculates the ratio of the amount of data in each sample category emergency data set to the sum of the amount of data in all sample category emergency data sets outside the category. This ratio represents the probability of occurrence of the emergency of this category. Through the above calculation, multiple emergency probabilities are obtained, which reflect the possibility of occurrence of different types of emergencies in the simulation scenario.

[0031] The emergency response database is a database containing the mapping relationship between sample emergency event categories and sample standard emergency response information. Among them, the standard emergency response information is the correct or recommended emergency response measures for specific emergencies formulated according to industry standards. In the emergency response database, the database index subunit obtains the corresponding standard emergency response information based on the sample emergency event categories obtained by clustering.

[0032] The mapping relationship construction subunit associates multiple sample emergency event categories, multiple sample category emergency event data sets, multiple emergency event probabilities, and multiple standard emergency handling information to construct a mapping relationship, which constitutes the core of the emergency event simulation model. This implementation method uses cluster analysis to classify similar emergencies into one category and calculates the probability of each category, which more accurately simulates the distribution and characteristics of emergencies in the real world and improves the accuracy of the model. By constructing a mapping relationship between emergency event categories and standard emergency handling information, the corresponding emergency handling information can be quickly obtained according to the type of emergency event, providing strong support for the acquisition of emergency handling information.

[0033] The emergency simulation scenario generation module 20 is used to generate an emergency simulation scenario according to the emergency event simulation model when the user enters the simulation scenario training.

[0034] Specifically, when the user enters the simulation scenario training, the emergency simulation scenario generation module 20 generates a specific emergency simulation scenario according to the emergency event simulation model. The emergency simulation scenario is a simulation environment generated based on the emergency event simulation model, which is used to train the user's emergency response ability. These scenarios can be dynamic and adjusted according to the user's real-time feedback.

[0035] In one possible implementation, the emergency simulation scenario generation module 20 includes: a simulated emergency event data acquisition unit, which is used to randomly select a sample category emergency event data within a sample emergency event category according to the multiple emergency event probabilities in the emergency event simulation model when the user enters the simulation scenario training, as simulated emergency event data; a data loading unit, which is used to load the simulated emergency event data into the simulation scenario to generate a emergency simulation scenario.

[0036] Specifically, when the user chooses to enter the simulation scenario for training, the system triggers the simulated emergency data acquisition unit to start working. In the emergency simulation model, the probabilities of multiple emergency categories have been calculated based on the sample traffic data. The simulated emergency data acquisition unit uses these probabilities and adopts a random selection algorithm (such as a roulette algorithm, etc.) to randomly select a category from multiple emergency categories. This selection process is based on probability, ensuring that the probability of each category being selected matches its probability of occurrence in the real world.

[0037] In the selected emergency event category, the simulated emergency event data acquisition unit further extracts the corresponding sample category emergency event data set as simulated emergency event data, which contains detailed information of the emergency event, such as occurrence time, location, impact range, etc.

[0038] The data loading unit loads the selected simulated emergency data into the simulation scene, including converting the data into a format that the simulation scene can understand and placing it in a suitable position in the scene. After loading the data, the simulation scene will render the corresponding emergency effects based on the data, such as traffic jams, vehicle accidents, road closures, etc. After the above steps, a simulation scene containing a specific emergency is generated, and users can conduct emergency response training in this scene. This implementation method selects the emergency category and specific data by probability, ensuring that the generated emergency simulation scene statistically matches the distribution of emergencies in the real world, thereby improving the authenticity of the simulation. At the same time, since the emergencies in the simulation scene are generated based on real data, training users' emergency response capabilities in these scenes is more targeted, which helps to improve users' response capabilities in real situations.

[0039] The emergency handling information comparison module 30 is used to obtain the emergency handling information fed back by the user in the emergency simulation scenario, and compare it with the standard emergency handling information corresponding to the emergency simulation scenario to obtain a first emergency training score.

[0040] Specifically, the emergency handling information comparison module 30 obtains the emergency handling information fed back by the user in the emergency simulation scenario through user operation records or real-time input, that is, the information of the emergency handling measures taken by the user in the simulation scenario. The emergency handling information fed back by the user is compared with the standard emergency handling information to evaluate whether the user's emergency handling is correct or reasonable. According to the comparison result, a first emergency training score is given to reflect the accuracy of the user's emergency handling.

[0041] In a possible implementation, the emergency handling information comparison module 30 includes: a user feedback information acquisition unit, used to obtain the emergency handling information fed back by the user in the emergency simulation scenario; a standard emergency handling information acquisition unit, used to obtain the standard emergency handling information corresponding to the sample emergency event category to which the simulated emergency event data belongs; an information comparison unit, used to compare whether the emergency handling information is consistent with the standard emergency handling information, and generate a first emergency training score, which is 1 or 0.

[0042] Specifically, when a user performs emergency response training in an emergency simulation scenario, the user feedback information acquisition unit records the emergency handling operations performed by the user, including the selected emergency measures, the order of execution, the relevant instructions input, etc., and this information is used as the user's emergency handling information.

[0043] The standard emergency handling information acquisition unit retrieves the corresponding standard emergency handling information from the emergency database according to the sample emergency event category to which the simulated emergency event data belongs. The standard emergency handling information is pre-set and represents the best or recommended emergency response strategy for the emergency event of this category.

[0044] The information comparison unit compares the user's emergency handling information with the standard emergency handling information item by item, including steps such as information analysis, format unification, content matching, etc. During the comparison process, it is determined whether the user's emergency handling information is completely consistent with the standard emergency handling information. The consistency means that the emergency measures taken by the user, the order of execution, the relevant instructions input, etc. are completely consistent with the standard emergency handling information.

[0045] Based on the comparison results, a first emergency training score is generated. If the user's emergency handling information is completely consistent with the standard emergency handling information, the score is 1 (full score); if it is inconsistent, the score is 0 (zero score). This score reflects the user's emergency handling accuracy in the simulated scenario. This implementation method accurately evaluates the user's emergency handling accuracy in the simulated scenario by comparing the user's emergency handling information with the standard emergency handling information item by item. The binary evaluation mechanism of setting the first emergency training score to 1 or 0 clearly reflects whether the user has correctly implemented the emergency response strategy. This clear feedback helps users quickly identify their shortcomings and make improvements in subsequent training. At the same time, the comparison results of the emergency handling information are directly converted into a score form, which simplifies the evaluation process, improves the evaluation efficiency, and facilitates subsequent data statistics and analysis.

[0046] The emergency training score calculation module 40 is used to obtain the processing time of the user's emergency handling in the emergency simulation scenario, combine it with the standard processing time of the standard emergency handling information, calculate the second emergency training score, and combine it with the first emergency training score to calculate the emergency training score as the emergency response training result.

[0047] Specifically, the processing time of the user's emergency handling in the emergency simulation scene is recorded. The user's processing time is compared with the standard processing time of the standard emergency handling information (the recommended emergency handling time for specific emergencies formulated according to industry standards), the user's emergency handling efficiency is evaluated, and the second emergency training score is obtained. Combined with the first emergency training score (accuracy) and the second emergency training score (efficiency), the final emergency training score is calculated, which comprehensively reflects the user's emergency handling accuracy and efficiency, as an emergency response training result, used to evaluate the user's emergency response ability. The embodiment of the present application adopts the method of collecting sample traffic data corresponding to the simulation scene, constructing an emergency simulation model, generating an emergency simulation scene according to the emergency simulation model, obtaining the emergency handling information fed back by the user in the emergency simulation scene, comparing it with the standard emergency handling information, obtaining the first emergency training score, combining the user's emergency handling processing time and the standard processing time, calculating the second emergency training score, and combining the first emergency training score, calculating the emergency training score, as the emergency response training result and other technical means, to provide a more realistic and complex training environment, improve the training effect, and comprehensively evaluate the user's emergency response ability.

[0048] In a possible implementation, the emergency training score calculation module 40 includes: a standard processing time acquisition unit, used to obtain the standard processing time of the standard emergency processing information; a user processing time acquisition unit, used to obtain the processing time of the user's emergency processing in the emergency simulation scenario; a second emergency training score calculation unit, used to calculate the ratio of the difference between the processing time and the standard processing time to the standard processing time, and subtract the ratio from 1 to obtain the second emergency training score.

[0049] Specifically, the standard processing time acquisition unit first retrieves the standard processing time of the standard emergency processing information corresponding to the sample emergency event category to which the simulated emergency event data belongs from the emergency database. At the same time, the user processing time acquisition unit records the total time from the start to the end of the emergency processing of the user in the emergency simulation scenario as the user's processing time, which includes the time required for all steps such as the user to identify the emergency, make decisions, and execute emergency measures.

[0050] The second emergency training score calculation unit calculates the difference between the user processing time and the standard processing time, and then divides this difference by the standard processing time to obtain a ratio. This ratio reflects the degree of deviation between the user processing time and the standard processing time. The result of subtracting the above ratio from 1 is used as the second emergency training score. The value range of this score is between 0 and 1. The higher the score, the closer the user's emergency processing efficiency is to the standard level. If the user's processing time is exactly the same as the standard processing time, the ratio is 0 and the second emergency training score is 1 (full score); if the user's processing time far exceeds the standard processing time, the ratio is close to 1, and the second emergency training score is close to 0 (zero score). This implementation method calculates the ratio of the user's processing time to the standard processing time and converts it into a second emergency training score, intuitively evaluates the user's emergency processing efficiency in the simulation scenario, provides users with quantitative feedback indicators, and facilitates subsequent data analysis and statistical work.

[0051] In a possible implementation, the emergency training score calculation module 40 includes: a weighted calculation unit, used to perform weighted calculation on the first emergency training score and the second emergency training score to obtain an emergency training score; and an emergency response training result generation unit, used to use the emergency training score as an emergency response training result.

[0052] Specifically, the weights of the first emergency training score and the second emergency training score are determined. The weights may be determined based on various factors such as historical data, user needs, etc. For example, if the accuracy of emergency processing (i.e., the first emergency training score) is more important than the processing time (i.e., the second emergency training score), a higher weight may be assigned to the first emergency training score.

[0053] The weighted calculation unit obtains the first emergency training score and the second emergency training score, and uses the determined weight to perform weighted calculation on the first emergency training score and the second emergency training score, that is, multiplying the first emergency training score by its weight, multiplying the second emergency training score by its weight, and then adding the two weighted scores to obtain the emergency training score. The emergency response training result generation unit outputs the calculated emergency training score as the emergency response training result, which is used to evaluate the user's emergency response ability in the simulated scenario and serves as a reference for subsequent training. This implementation method obtains a comprehensive emergency training score by weighted calculation of the first emergency training score and the second emergency training score. This score comprehensively reflects the user's emergency response ability in the simulated scenario, taking into account both the accuracy of emergency handling and the processing time, and providing the user with a more comprehensive evaluation result.

[0054] In the above, refer to Figure 1 The intelligent road emergency response system according to the simulated scenario training of the embodiment of the present invention is described in detail. Figure 2 An intelligent road emergency response method for simulation scenario training according to an embodiment of the present invention is described.

[0055] The intelligent road emergency response method for simulated scenario training according to an embodiment of the present invention is used to solve the technical problems that the existing simulated scenario training for road emergency response cannot truly reflect the complexity and variability of actual road conditions, resulting in limited training effects and difficulty in discovering users' deficiencies in emergency handling, so as to provide a more realistic and complex training environment, improve training effects, and comprehensively evaluate users' emergency response capabilities.

[0056] The intelligent road emergency response method for simulation scenario training includes: collecting sample traffic data corresponding to the simulation scenario and constructing an emergency simulation model; when a user enters the simulation scenario training, generating an emergency simulation scenario according to the emergency simulation model; obtaining emergency processing information fed back by the user in the emergency simulation scenario, and comparing it with the standard emergency processing information corresponding to the emergency simulation scenario to obtain a first emergency training score; obtaining the processing time of the user's emergency processing in the emergency simulation scenario, combining it with the standard processing time of the standard emergency processing information, and calculating a second emergency training score, and combining it with the first emergency training score to calculate an emergency training score as an emergency response training result.

[0057] Among them, sample traffic data corresponding to the simulation scene is collected, an emergency simulation model is constructed, and the following processing is performed: according to the simulation scene, multiple sample traffic scenes are matched; according to the sample traffic data of the multiple sample traffic scenes, the emergency simulation model is constructed.

[0058] Wherein, the emergency simulation model is constructed according to the sample traffic data of the multiple sample traffic scenes, and the following processing is performed: multiple sample emergency data sets in the multiple sample traffic scenes are collected; the multiple sample emergency data sets are clustered to obtain multiple sample category emergency data sets of multiple sample emergency categories; the ratio of the data volume in each sample category emergency data set to the sum of the data volume in the multiple sample category emergency data sets is calculated to obtain multiple emergency probabilities; multiple standard emergency processing information of the multiple sample emergency categories is obtained by indexing in the emergency emergency database, wherein the emergency emergency database includes a mapping relationship between sample emergency categories and sample standard emergency processing information; a mapping relationship between the multiple sample emergency categories, the multiple sample category emergency data sets, the multiple emergency probabilities and the multiple standard emergency processing information is constructed to obtain the emergency simulation model.

[0059] Among them, when the user enters the training of the simulation scene, a sudden emergency simulation scene is generated according to the sudden event simulation model, and the following processing is performed: when the user enters the training of the simulation scene, in the sudden event simulation model, according to the multiple sudden event probabilities, a sample category sudden event data within a sample sudden event category is randomly selected as simulated sudden event data; the simulated sudden event data is loaded into the simulation scene to generate a sudden emergency simulation scene.

[0060] Among them, the emergency handling information fed back by the user in the emergency simulation scenario is obtained, and compared with the standard emergency handling information corresponding to the emergency simulation scenario to obtain a first emergency training score, and the following processing is performed: the emergency handling information fed back by the user in the emergency simulation scenario is obtained; the standard emergency handling information corresponding to the sample emergency event category to which the simulated emergency event data belongs is obtained; the emergency handling information and the standard emergency handling information are compared for consistency, and a first emergency training score is generated, and the first emergency training score is 1 or 0.

[0061] Among them, the processing time of the user's emergency handling in the sudden simulation scenario is obtained, combined with the standard processing time of the standard emergency handling information, to calculate the second emergency training score, and perform the following processing: obtain the standard processing time of the standard emergency handling information; obtain the processing time of the user's emergency handling in the sudden simulation scenario; calculate the ratio of the difference between the processing time and the standard processing time to the standard processing time, and subtract the ratio from 1 to obtain the second emergency training score.

[0062] Among them, in combination with the first emergency training score, an emergency training score is calculated as an emergency response training result, and the following processing is performed: weighted calculation of the first emergency training score and the second emergency training score to obtain an emergency training score; and the emergency training score is used as the emergency response training result.

[0063] The intelligent road condition emergency response system for simulated scenario training provided by the embodiment of the present invention can execute the intelligent road condition emergency response method for simulated scenario training provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0064] Although the present application makes various references to certain modules in the system according to the embodiments of the present application, any number of different modules may be used and run on the user terminal and / or server, and the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention.

[0065] The above specific implementation manner does not constitute a limitation to the protection scope of the present application. It should be understood by those skilled in the art that various modifications, combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the protection scope of the present application. In some cases, the actions or steps recorded in the present application can be performed in an order different from that in the embodiment and can still achieve the desired results. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. Intelligent road emergency response system for simulated scenario training, characterized by: The system comprises: The emergency simulation model building module is used to collect sample traffic data corresponding to the simulation scenario and build an emergency simulation model; An emergency simulation scenario generation module is used to generate an emergency simulation scenario according to the emergency event simulation model when the user enters the simulation scenario training; An emergency handling information comparison module is used to obtain the emergency handling information fed back by the user in the emergency simulation scenario, and compare it with the standard emergency handling information corresponding to the emergency simulation scenario to obtain a first emergency training score; The emergency training score calculation module is used to obtain the processing time of the user's emergency handling in the emergency simulation scenario, combine it with the standard processing time of the standard emergency handling information, calculate the second emergency training score, and combine it with the first emergency training score to calculate the emergency training score as the emergency response training result.

2. The intelligent road emergency response system for simulated scenario training according to claim 1 is characterized in that: The emergency simulation model building module includes: A sample traffic scene matching unit, used to match a plurality of sample traffic scenes according to the simulation scene; The emergency event simulation model construction unit is used to construct the emergency event simulation model according to the sample traffic data of the multiple sample traffic scenes.

3. The intelligent road emergency response system for simulated scenario training according to claim 2 is characterized in that: The emergency simulation model construction unit comprises: A sample emergency event data set collection subunit, used to collect a plurality of sample emergency event data sets in the plurality of sample traffic scenes; A data set clustering subunit, used for clustering the plurality of sample emergency event data sets to obtain a plurality of sample category emergency event data sets of a plurality of sample emergency event categories; A ratio calculation subunit, used to calculate the ratio of the data volume in each sample category emergency event data set to the sum of the data volumes in the multiple sample category emergency event data sets, to obtain multiple emergency event probabilities; A database indexing subunit, used for indexing and acquiring a plurality of standard emergency handling information of the plurality of sample emergency event categories in an emergency response database, wherein the emergency response database includes a mapping relationship between the sample emergency event categories and the sample standard emergency handling information; The mapping relationship construction subunit is used to construct the mapping relationship among the multiple sample emergency event categories, multiple sample category emergency event data sets, multiple emergency event probabilities and multiple standard emergency handling information to obtain an emergency event simulation model.

4. The intelligent road emergency response system for simulated scenario training according to claim 3 is characterized in that: The emergency simulation scenario generation module comprises: A simulated emergency event data acquisition unit, configured to randomly select a sample category emergency event data within a sample emergency event category according to the plurality of emergency event probabilities in the emergency event simulation model when a user enters the training of the simulated scenario as simulated emergency event data; A data loading unit is used to load the simulated emergency event data into the simulation scene to generate an emergency simulation scene.

5. The intelligent road emergency response system for simulated scenario training according to claim 4 is characterized in that: The emergency handling information comparison module includes: A user feedback information acquisition unit, used to acquire emergency handling information fed back by the user in the emergency simulation scenario; A standard emergency handling information acquisition unit, used to acquire standard emergency handling information corresponding to the sample emergency event category to which the simulated emergency event data belongs; The information comparison unit is used to compare whether the emergency handling information is consistent with the standard emergency handling information, and generate a first emergency training score, where the first emergency training score is 1 or 0.

6. The intelligent road emergency response system for simulated scenario training according to claim 5 is characterized in that: The emergency training score calculation module includes: A standard processing time acquisition unit, used to acquire the standard processing time of the standard emergency processing information; A user processing time acquisition unit, used to acquire the processing time of the user's emergency processing in the emergency simulation scenario; The second emergency training score calculation unit is used to calculate the ratio of the difference between the processing time and the standard processing time to the standard processing time, and subtract the ratio from 1 to obtain a second emergency training score.

7. The intelligent road emergency response system for simulated scenario training according to claim 1 is characterized in that: The emergency training score calculation module includes: A weighted calculation unit, configured to perform weighted calculation on the first emergency training score and the second emergency training score to obtain an emergency training score; The emergency response training result generating unit is used to use the emergency response training score as the emergency response training result.

8. An intelligent road emergency response method for simulated scenario training, characterized in that: The method is implemented by the intelligent road emergency response system for simulation scenario training according to any one of claims 1 to 7, and the method comprises: Collect sample traffic data corresponding to the simulation scenario and build an emergency simulation model; When the user enters the training of the simulation scenario, an emergency simulation scenario is generated according to the emergency simulation model; Acquire the emergency handling information fed back by the user in the emergency simulation scenario, compare it with the standard emergency handling information corresponding to the emergency simulation scenario, and obtain a first emergency training score; The processing time of the user's emergency handling in the emergency simulation scenario is obtained, and the second emergency training score is calculated in combination with the standard processing time of the standard emergency handling information. The emergency training score is calculated in combination with the first emergency training score as the emergency response training result.

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