Driving examination training driving school recommendation method and system

Abstract

The embodiment of the invention provides a driving examination training driving school recommendation method and system, and can solve the problem that the current driving school selection is mainly propagandized by the driving school, even some students only consider the principle of proximity, and cannot effectively select the driving school suitable for themselves. The method comprises the steps that the coping ability of a predictive name student to different driving scenes is evaluated through driving simulation training, the coping ability of the predictive name student to the different driving scenes is graded, and the driving simulation training comprises simulation training scenes of all the driving scenes; obtaining a plurality of target driving scenes associated with the predictive name trainee and having the coping ability level lower than a preset level and map information of a preset surrounding area range of each driving school of the city driving school to which the predictive name trainee belongs; and based on the matching degree of the plurality of target driving scenes and the map information, recommending a driving school for the predictive name student.

Description

Technical Field

[0001] This application relates to the field of computer technology, and particularly to a method and system for recommending driving schools for driving test training. Background Art

[0002] The driving test is an assessment system established by governments or relevant traffic management departments in various countries, aiming to evaluate the driving skills of drivers, their understanding of traffic regulations, and their ability to handle complex road conditions. The purpose of the driving test is to ensure that drivers have the ability to drive safely, reduce traffic accidents, and improve the overall safety of road traffic. Before taking the driving test, it is generally necessary to select a driving school. However, currently, the selection of driving schools mainly depends on the publicity of the driving schools. Some students even only consider the principle of proximity and cannot effectively select a driving school suitable for themselves. Summary of the Invention

[0003] The embodiments of this application provide a method and system for recommending driving schools for driving test training, which can solve the problem that currently, the selection of driving schools mainly depends on the publicity of the driving schools. Some students even only consider the principle of proximity and cannot effectively select a driving school suitable for themselves.

[0004] The first aspect of the embodiments of this application provides a method for recommending driving schools for driving test training, including:

[0005] Evaluating the coping ability of pre-registered students for different driving scenarios through driving simulation training, so as to classify the coping ability of the pre-registered students for different driving scenarios. The driving simulation training includes simulation training scenarios for all driving scenarios;

[0006] Obtaining multiple target driving scenarios with a coping ability level lower than the preset level associated with the pre-registered students and map information of the preset surrounding area range of each driving school in the city where the pre-registered students are located;

[0007] Recommending a driving school for the pre-registered students based on the matching degree between the multiple target driving scenarios and the map information.

[0008] Optionally, the recommending a driving school for the pre-registered students based on the matching degree between the multiple target driving scenarios and the map information includes:

[0009] Planning driving training routes based on the map information and the multiple target driving scenarios in the preset surrounding area range of each driving school, so that the same training route can include as many of the target driving scenarios as possible;

[0010] Recommending to the pre-registered students the driving school corresponding to the training route including the most types of the target driving scenarios.

[0011] Optionally, recommending a driving school for the pre-registered trainee based on the matching degree between multiple target driving scenarios and the map information includes:

[0012] Based on the map information and multiple target driving scenarios, plan driving training paths within the preset peripheral area range of each driving school, so that the same training path can include as many of the target driving scenarios as possible;

[0013] Recommend to the pre-registered trainee the driving school corresponding to the training path that includes the most types of the target driving scenarios and has the shortest path.

[0014] Optionally, recommending a driving school for the pre-registered trainee based on the matching degree between multiple target driving scenarios and the map information includes:

[0015] Based on the map information, evaluate the number of the target driving scenarios included in the preset peripheral area range of each driving school;

[0016] Recommend to the pre-registered trainee the driving school that includes the largest number of the target driving scenarios.

[0017] Optionally, it further includes:

[0018] Obtain the target city for the pre-registered trainee's exam registration to query the map information of the external road exam area range of the target city;

[0019] Recommend a driving school for the pre-registered trainee based on the matching degree between the map information of the external road exam area range and the map information of the preset peripheral area range of the driving school.

[0020] Optionally, recommending a driving school for the pre-registered trainee based on the matching degree between the map information of the external road exam area range and the map information of the preset peripheral area range of the driving school includes:

[0021] Extract multiple target exam scenarios based on the map information of the external road exam area range of the target city;

[0022] Recommend a driving school for the pre-registered trainee according to the number of target exam scenarios included in the preset peripheral area range of the driving school.

[0023] Optionally, it further includes:

[0024] Based on the multiple target driving scenarios and the map information of the external road exam area range of the target city, plan a target training path for the pre-registered trainee within the external road exam area range of the target city, and the target training path includes as many of the target driving scenarios as possible;

[0025] Based on the target training path, plan training paths within the preset peripheral area range of each driving school;

[0026] Recommend to the pre-registered students the driving schools corresponding to the area ranges where the training paths with the highest matching degree to the target training path are located.

[0027] In the second aspect of the embodiments of the present application, a driving test training driving school recommendation device is provided, including:

[0028] An evaluation unit, configured to evaluate the pre-registered students' coping abilities in different driving scenarios through driving simulation training, so as to classify the coping abilities of the pre-registered students in different driving scenarios. The driving simulation training includes simulation training scenarios for all driving scenarios;

[0029] An acquisition unit, configured to acquire multiple target driving scenarios with coping ability levels lower than a preset level associated with the pre-registered students and map information of the preset surrounding area ranges of each driving school of the driving schools in the city where the pre-registered students are located;

[0030] A recommendation unit, configured to recommend a driving school for the pre-registered students based on the matching degree between the multiple target driving scenarios and the map information.

[0031] In the third aspect of the embodiments of the present application, an electronic system is provided, including a memory and a processor. When the processor executes a computer program stored in the memory, the steps of the above-mentioned driving test training driving school recommendation method are implemented.

[0032] In the fourth aspect of the embodiments of the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned driving test training driving school recommendation method are implemented.

[0033] In summary, the driving test training driving school recommendation method provided by the embodiments of the present application evaluates the pre-registered students' coping abilities in different driving scenarios through driving simulation training, so as to classify the coping abilities of the pre-registered students in different driving scenarios. The driving simulation training includes simulation training scenarios for all driving scenarios; acquires multiple target driving scenarios with coping ability levels lower than a preset level associated with the pre-registered students and map information of the preset surrounding area ranges of each driving school of the driving schools in the city where the pre-registered students are located; recommends a driving school for the pre-registered students based on the matching degree between the multiple target driving scenarios and the map information. Through driving simulation training and intelligent matching algorithms, the problem that students only rely on publicity or geographical location to select driving schools is successfully solved, and the scientificity and accuracy of driving school selection are improved. Compared with the traditional method, students can match the driving school that best meets their own training needs, avoid learning irrelevant driving skills in an unsuitable environment, and thus improve learning efficiency. At the same time, special training for weak scenarios helps to improve the passing rate of the exam, enabling students to master real driving skills faster and more confidently.

[0034] Accordingly, the driving test training driving school recommendation device, electronic system, and computer-readable storage medium provided by the embodiments of the present invention also have the above technical effects. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1 FIG. is a schematic flowchart of a possible driving test training driving school recommendation method provided by an embodiment of the present application;

[0036] Figure 2 FIG. is a schematic structural block diagram of a possible driving test training driving school recommendation device provided by an embodiment of the present application;

[0037] Figure 3 FIG. is a schematic hardware structure diagram of a possible driving test training driving school recommendation device provided by an embodiment of the present application;

[0038] Figure 4 FIG. is a schematic structural block diagram of a possible electronic system provided by an embodiment of the present application;

[0039] Figure 5 FIG. is a schematic structural block diagram of a possible computer-readable storage medium provided by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0040] The embodiments of the present application provide a driving test training driving school recommendation method and system, which can solve the problem that the selection of driving schools currently mainly depends on the publicity of driving schools, and some students only consider the principle of proximity, and cannot effectively select a driving school suitable for themselves.

[0041] The terms "first", "second", "third", "fourth", etc. (if any) in the specification, claims, and drawings of the present application are used to distinguish similar objects, and do not necessarily describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments described herein can be implemented in an order different from that shown or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices. The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments.

[0042] Please refer to Figure 1, which is a flowchart of a method for recommending a driving school for driving test training provided in an embodiment of the present application, and may specifically include: S110-S130.

[0043] S110, evaluating the ability of the pre-registered students to cope with different driving scenarios through driving simulation training, so as to grade the ability of the pre-registered students to cope with different driving scenarios, wherein the driving simulation training includes simulation training scenarios of all driving scenarios.

[0044] S120, obtaining map information of a plurality of target driving scenarios of the pre-registered student whose coping ability grade is lower than a preset level and a preset surrounding area range of each driving school in the city to which the pre-registered student belongs.

[0045] S130: recommending a driving school to the pre-registered student based on the matching degree between the plurality of target driving scenarios and the map information.

[0046] It is understandable that the driving ability of students is evaluated through driving simulation training, and the most suitable driving school is recommended to students in combination with the matching degree of the real road environment around the driving school, so as to ensure that they can focus on the driving skills they need to improve during the training on the road. The core is to conduct a graded assessment of the driving ability of students and match them with driving schools that can provide targeted training according to their weak scenarios, so as to avoid students choosing driving schools based on geographical location or publicity alone, thereby optimizing the effect of driving training, improving the passing rate of driving tests and actual driving ability.

[0047] Exemplarily, a driving simulation system is used to comprehensively evaluate the driving ability of trainees, determine their ability to cope with different driving scenarios, and generate personalized driving ability grading reports through analysis. During the implementation process, a high-precision driving simulation system covering various driving scenarios is first built to simulate the real driving environment, including basic driving skills (such as acceleration, braking, and shifting), complex road driving (such as starting on a slope, sharp bends, and meeting on narrow roads), special traffic conditions (such as densely populated pedestrian scenes in commercial streets, sections with more temporary parking around campuses, and sections around hospitals and fire stations that need to avoid rescue vehicles). During the simulation training process, the system collects the driving operation data of trainees in various scenarios through sensors and data analysis, such as braking reaction time, steering accuracy, lane change timing, emergency avoidance ability, etc., and scores the driving performance of trainees in combination with artificial intelligence algorithms. Subsequently, according to the scoring results, the trainees' coping ability in each scenario is graded (for example, 1-5 levels), where level 1 indicates weak coping ability and level 5 indicates proficiency. Finally, the system generates a driving ability report, which lists in detail the scenarios in which the trainee performs poorly and marks the key points that need to be strengthened. For example, if the trainee's ability to cope with night driving is low (level 2), and the avoidance ability in areas with dense pedestrians is also insufficient (level 3), the system will focus on recommending driving schools that can provide night driving training and commercial street driving training to improve the trainee's weak skills in a targeted manner.

[0048] Exemplarily, after completing the assessment of the trainee's capabilities, the second step is to obtain information on all qualified driving schools in the city and conduct an in-depth analysis of the surrounding road environment of the driving schools to ensure that the recommended driving schools can provide training opportunities that match the trainee's weak scenarios. First, by connecting with relevant traffic management departments and driving school management systems, collect basic information on all driving schools in the city, including the geographical location of the driving schools, training ground configurations (such as whether there is a ramp training ground, night driving ground), off-road training routes, passing rates of examinations, trainee feedback scores, etc. At the same time, the system uses the map API to obtain real road data within a range of 5-10 kilometers around the driving schools, analyzes the road environment around the driving schools, and identifies whether it contains driving scenarios required by the trainees. For example, the system will scan whether there are steep slopes (suitable for ramp start training), sharp curves (suitable for complex curve driving training), narrow roads (suitable for passing vehicle training), commercial blocks (suitable for driving training in areas with dense pedestrians), areas around schools (suitable for dealing with temporary parking and student passage), areas around hospitals or fire stations (suitable for training to avoid rescue vehicles), etc. around a certain driving school. To ensure the accuracy of the data, the system not only relies on static map data but also combines real-time traffic flow data to evaluate the traffic characteristics of these areas at different time periods. For example, although a certain driving school is near a commercial street, its training period is from 6 am to 9 am, when there are fewer people, and it is not suitable for high-intensity pedestrian avoidance training. Therefore, the system will adjust the driving school matching priority according to the real traffic environment to ensure that the recommended driving schools can provide the best off-road training conditions.

[0049] Exemplarily, after obtaining the data on the classification of the trainee's driving capabilities and the road environment around the driving schools, the third step is to recommend the most suitable driving schools for the trainees based on an intelligent matching algorithm. The system first filters out the driving schools that cover the trainee's weak scenarios, that is, only the driving schools that can provide the training scenarios required by the trainees will enter the recommended list. Subsequently, the system calculates the driving school matching score, which consists of multiple factors, including the adaptability of the off-road training scenarios of the driving school (that is, whether the roads around the driving school have the types of roads required by the trainees), trainee evaluations of the driving school (such as teaching quality, coach attitude, etc.), passing rates of examinations (measuring the overall training quality of the driving school), the commuting distance of the trainees (considering the travel convenience of the trainees), etc. For example, assume that trainee A is weak in ramp start and driving in areas with dense pedestrians. Then the system will give priority to recommending driving schools with ramp training grounds and off-road training areas that include commercial areas and give them a higher score. If a certain driving school has a relatively high overall score, but its off-road training area is a highway and does not include a commercial area, its matching degree is relatively low and it will not be recommended first. Finally, the system generates a ranked list of recommended driving schools according to the matching scores and shows 3-5 of the best driving schools to the trainees for their selection.

[0050] In summary, the driving school recommendation method provided by the above embodiments evaluates the response capabilities of pre-registered students to different driving scenarios through driving simulation training, grades the response capabilities of the pre-registered students to different driving scenarios, and the driving simulation training includes simulation training scenarios for all driving scenarios; obtains multiple target driving scenarios with response capability grades lower than the preset grade associated with the pre-registered students and map information of the preset surrounding area range of each driving school in the city where the pre-registered students are located; recommends a driving school for the pre-registered students based on the matching degree between the multiple target driving scenarios and the map information. Through driving simulation training and intelligent matching algorithms, the problem that students only rely on publicity or geographical location to select driving schools is successfully solved, and the scientificity and accuracy of driving school selection are improved. Compared with the traditional method, students can match the driving school that best meets their training needs, avoid learning irrelevant driving skills in an unsuitable environment, and thus improve learning efficiency. At the same time, specialized training for weak scenarios helps to improve the passing rate of the exam and enables students to master real driving skills faster and more confidently.

[0051] Exemplarily, after the intelligent matching is completed, the system will present the recommended driving school information to the students and provide detailed driving school training content, so that the students can make more reasonable choices. Specifically, the system will display the detailed information of the recommended driving school, including the basic information of the driving school (name, location), training strengths (such as "providing special night driving training"), student ratings, an overview of the off-road training area (such as "there is a hill start practice point near this driving school, 2 kilometers away from the commercial area, suitable for pedestrian avoidance training"), passing rate of the exam, training fees, etc. In addition, to help the students make better decisions, the system also provides a "trial training reservation" function. The students can make an appointment in advance to experience the class and feel the training method of the driving school, the teaching quality of the coaches and the situation of the training ground. If the students are not satisfied with the recommended results, the system also allows adjusting the recommendation weight. For example, the students can manually increase the weight of "hill training" or "commercial area driving training", and the system will update the list of recommended driving schools in real time to provide more personalized recommendations. Finally, the students can select the driving school that best meets their own needs according to the recommendation list and detailed information and sign up.

[0052] In one embodiment, the recommending a driving school for the pre-registered students based on the matching degree between the multiple target driving scenarios and the map information includes:

[0053] Based on the map information and the multiple target driving scenarios, plan driving training paths within the preset surrounding area range of each driving school, so that the same training path can include as many of the target driving scenarios as possible;

[0054] Recommend to the pre-registered students the driving school corresponding to the training path that includes the most types of the target driving scenarios.

[0055] Exemplarily, after obtaining the training needs of the trainees and the information on the road environment around the driving schools, the system needs to plan the optimal training route within the preset off-road training area of each driving school based on the trainees' weak driving scenarios, so as to ensure that the trainees can cover as many target driving scenarios as possible during one off-road training. Specifically, the system will calculate the number of target driving scenarios included in each available training route and use a path optimization algorithm (such as the Dijkstra algorithm search algorithm) to find the best driving route that can cover the most types of training scenarios within the shortest driving training time. During the path optimization process, the system will comprehensively consider various factors, such as the target scenario coverage rate, traffic flow, road complexity, etc., to ensure that the trainees can obtain the best driving training effect within the limited training time. For example, if a certain trainee is weak in starting on a slope, meeting vehicles on a narrow road, and avoiding pedestrians, the system will prefer to select an off-road training route that includes multiple slopes, narrow roads, and commercial areas, rather than a training route that only covers one of these scenarios. In addition, the system will also consider the traffic conditions during the training period to ensure that the training route will not affect the training efficiency due to congestion. For example, even if there are rich training scenarios around a certain driving school, if this area often has severe congestion during the trainees' available training period (such as 3 pm to 5 pm), the matching score of this driving school may be reduced to ensure that the recommended driving school can provide a good training experience.

[0056] Exemplarily, after the training path planning is completed, the system will rate the driving schools according to the target scene coverage rate of the training path and recommend the most suitable driving schools to the trainees. First, the system will calculate the training path matching degree of each driving school and use the following scoring formula for ranking: Driving school matching degree = Training path score × Training demand matching degree + Driving school score + Exam passing rate. Among them, the training path score measures whether the off-road training route of the driving school can provide the training scenarios required by the trainees, and the training demand matching degree is weighted and calculated according to the weak-point training needs of the trainees. For example, if the trainee needs to focus on improving the ability of hill start and pedestrian avoidance, and the training route of a driving school can cover multiple slopes and commercial area sections, the matching degree of this driving school will be higher than that of a driving school that can only provide slope training. The system will finally rank according to the matching degree, recommend the top 3-5 driving schools, and provide detailed information to the trainees, including the training ground configuration of the driving school (such as whether there is a slope or a night training area), the details of the off-road training route (such as "including slopes + narrow roads + commercial areas"), the evaluations of past trainees, the exam passing rate, the training fees, etc. In addition, the trainee can reserve a trial training course, experience the teaching quality of the driving school, and adjust the recommendation weight according to personal needs, such as increasing the emphasis on certain specific training scenarios, and the system will recalculate the recommendation list in real time. Finally, this method ensures that the trainee can choose the driving school that best meets their driving training needs, optimize their learning experience, improve the passing rate of the driving test, and enhance their driving safety in the real road environment. Through intelligent training path planning, it is ensured that the trainees can efficiently cover multiple target driving scenarios during off-road training, avoiding the problems of traditional driving school recommendation methods that only consider geographical location or driving school scale. Compared with the traditional method, this method is more accurate, has higher training efficiency, more reliable teaching quality, and better trainee experience, and finally effectively improves the driving ability and road safety awareness of the trainees.

[0057] In one embodiment, recommending a driving school for the pre-registered trainee based on the matching degree between the multiple target driving scenarios and the map information includes:

[0058] Performing driving training path planning based on the map information and the multiple target driving scenarios within the preset peripheral area range of each driving school, so that the same training path can include as many of the target driving scenarios as possible;

[0059] Recommending to the pre-registered trainee the driving school corresponding to the training path that includes the most types of the target driving scenarios and has the shortest path.

[0060] It is understandable that through map information analysis and intelligent route planning, the optimal driving school is recommended to pre-registered students, enabling students to be exposed to as many target driving scenarios as possible during off-road training and ensuring that the training route is as short as possible to improve training efficiency and learning experience. The core of this method lies in using an intelligent training route optimization algorithm to find the best driving training route within the surrounding area of each driving school, ensuring that students can cover a variety of driving skill training scenarios during one training session, such as hill starts, sharp curves, narrow roads, pedestrian avoidance in commercial areas, parking responses around schools, and avoidance of rescue vehicles in hospitals. On this basis, the driving school with the shortest route but the most scene coverage is selected for recommendation, thus optimizing the scientific nature of driving school selection and improving students' driving ability and passing rate of the exam.

[0061] Exemplarily, after understanding the training needs of students and the road environment around driving schools, the system will plan the optimal driving training route within the preset training area of each driving school based on map information and target driving scenarios to ensure that students cover the most diverse driving scenarios within the shortest route. First, the system will identify the available training routes around the driving school and calculate the number of target driving scenarios included in each training route. For example, one training route may include hills, narrow roads, and commercial areas, while another may cover sharp curves, complex intersections, and areas around schools. The system will score these routes and preferentially select the route with the most scene coverage. Then, the system uses the Dijkstra shortest path algorithm to find the best training route that can cover the most target driving scenarios in the shortest time. During the route optimization process, the system will comprehensively consider multiple factors, such as the coverage rate of target scenarios, road smoothness, and traffic light density. For example, if a route contains multiple target training scenarios but the training efficiency is reduced due to too many traffic lights, the system may lower the score of this route. In addition, to ensure the continuity of training, the system will design a closed-loop training route, that is, starting from the driving school, students can train along a reasonable route and finally return to the driving school, rather than a simple round-trip route, to improve the coherence of training and practical driving adaptability.

[0062] Exemplarily, after completing the training path planning, the system will rate the driving schools based on the target scenario coverage rate and path length of the training path, and recommend the optimal driving schools to the trainees. The score calculation is as follows: Driving school matching degree = Training path scenario coverage rate × Demand matching degree + Driving school score + Passing rate - Path length weight. Among them, the training path scenario coverage rate measures the number of target driving scenarios included in the training path, the demand matching degree is weighted and calculated according to the trainee's weak-point training needs, and the path length weight is used to penalize driving schools with overly long paths to ensure that the recommended driving schools not only have rich training scenarios but also do not cause excessive waste of training time due to overly long paths. For example, if a trainee needs to focus on training hill starts and driving in commercial areas, and the training path of a certain driving school can cover multiple hill and commercial area sections and is relatively short, then the matching degree of this driving school will be higher than that of driving schools that can only provide a single training scenario or have a longer path. The system finally ranks by the matching degree and recommends the top 3 - 5 optimal driving schools, and displays detailed information to the trainees, such as the training ground configuration of the driving school, details of the off-road training route, trainee evaluations, exam passing rates, commuting times, etc. In addition, trainees can reserve trial training courses to experience the teaching quality of the driving school and adjust the recommendation weights according to their personal needs, such as focusing more on hill training or pedestrian avoidance training, and the system will recalculate the recommendation list in real time. Finally, this method ensures that trainees can select the driving school that best meets their driving training needs, optimize their learning experience, improve the passing rate of the driving test, and enhance their driving safety in the real road environment. Thus, through intelligent training path optimization, it is ensured that trainees can efficiently cover multiple target driving scenarios during off-road training, while shortening the training path and avoiding unnecessary time waste. Compared with traditional driving school recommendation methods, this solution recommends the training content that trainees truly need based on the evaluation of their personal driving abilities, rather than providing non-targeted training. Through the path optimization algorithm, it is ensured that the maximum number of driving skills can be trained in the shortest time, improving the training efficiency. Combining multi-dimensional information such as the passing rate of the driving school and trainee feedback, it is ensured that the recommended driving schools have high teaching quality and good training effects. Trainees can select training focuses personalized, and the system will dynamically adjust the recommendation strategy to better meet the needs of trainees.

[0063] In one embodiment, recommending a driving school for the pre-registered trainee based on the matching degree between the multiple target driving scenarios and the map information includes:

[0064] Evaluating, based on the map information, the number of the target driving scenarios included in the preset surrounding area range of each driving school;

[0065] Recommending to the pre-registered trainee the driving school that includes the largest number of the target driving scenarios.

[0066] It can be understood that, based on the map information assessment, the preset surrounding area range of each driving school is analyzed to determine whether it contains the target driving scenarios that the trainees need to train, and the driving school that covers the largest number of target scenarios is recommended to the trainees. Different from the traditional driving school selection methods (such as based on geographical location or advertising), this method accurately analyzes the road environment in the area where the driving school is located to ensure that when trainees choose a driving school, they can train as many driving skills as possible in the real road environment (such as hill start, sharp turn driving, meeting vehicles on narrow roads, avoiding pedestrians in commercial areas, coping with parking around schools, avoiding ambulances in hospitals, etc.). The core technologies of this method include map data analysis, road scene recognition, driving school area assessment, and intelligent matching algorithms, and finally provide driving school recommendations that best meet the training needs of the trainees.

[0067] Exemplarily, after collecting the data of the surrounding environment of the driving school, the system will calculate the coverage of the target driving scenarios of each driving school to determine whether the training environment of the driving school can meet the needs of the trainees. For each driving school, the system will list all the target driving scenarios (such as hills, sharp turns, commercial areas, etc.) within a range of 5-10 kilometers from the driving school. Calculate the types of target driving scenarios that each driving school can cover and assign weights. For example: hill start (weight 1.2), sharp turn (weight 1.1), meeting vehicles on narrow roads (weight 1.3), avoiding pedestrians in the commercial area (weight 1.4). The weights of the target scenarios can be dynamically adjusted according to the training needs of the trainees. For example, if the trainee is weak in hill driving, the system can increase the weight of the hill training scenario to ensure that the recommended driving school can provide a training environment that better meets the needs of the trainees. Sort the scores of the target scenario coverage of all driving schools, and give priority to recommending the driving school with the largest number of target driving scenarios.

[0068] In one embodiment, it further includes:

[0069] Obtain the target city for the pre-registered trainee's exam registration to query the map information of the off-road examination area range of the target city;

[0070] Recommend a driving school for the pre-registered trainee based on the matching degree between the map information of the off-road examination area range and the map information of the preset surrounding area range of the driving school.

[0071] It is understandable that, on the basis of the foregoing matching of driving schools based on the target driving scenario, the target city for the student to apply for the exam is further considered, and the map information of the off-road test site area of the city is combined to ensure that the driving school selected by the student can not only provide comprehensive driving skill training, but also conduct adaptive training on the road environment of the off-road test site. The core technologies of this method include map data analysis, off-road test site area matching, driving school geographical scope comparison, and intelligent recommendation algorithms. Finally, the driving school that best matches the off-road test environment of the target city is recommended for the student, enabling the student to be more familiar with the road environment of the actual test site during the exam, thereby improving the passing rate and ensuring that they receive training that better meets future actual driving needs.

[0072] Exemplarily, the information of the target city for the student to apply for the exam is obtained, and the detailed map data of the off-road test site area of the city is queried to provide a reference for subsequent calculation of the matching degree of the driving school. First, the system obtains the target exam city of the student through the exam registration information of the student, accesses the official traffic management platform or the map API, queries the scope of the off-road test site area of the city, and collects the road environment data of the area. The core assessment points of the off-road test site include road infrastructure, dynamic traffic environment, specific exam requirements, etc. In terms of road infrastructure, the system analyzes whether the off-road test site includes steep slopes or ramps (for ramp start and stop tests), narrow roads (for meeting vehicle ability tests), sharp curves (for curve driving training), and other key scenarios; in terms of the dynamic traffic environment, the system evaluates whether the area passes through commercial blocks with dense pedestrians (examining the ability to avoid pedestrians), around schools (examining the ability to park and slow down), near hospitals or fire stations (examining the ability to avoid emergency vehicles); in terms of specific exam requirements, the system identifies whether the test site includes special training content such as lane change, overtaking point, highway ramp or expressway passing. Finally, this step generates an off-road test site map database and stores it in the system to provide data support for subsequent driving school matching.

[0073] For example, after clarifying the student's target test city and test site map information, the system needs to collect all qualified driving school information in the city and analyze the road environment around it to ensure that the recommended driving school can not only provide basic driving training, but also create a training environment as close to the external test site as possible for the students. First, the system obtains the basic information of the driving school from the driving school management database or the government supervision system, including the geographical location of the driving school, the configuration of the training ground (whether it contains ramps, night driving areas, etc.), the test pass rate, student evaluation, coach rating, etc. Next, the system calls the map API to analyze the road environment within 5-10 kilometers around the driving school, focusing on whether the area contains road types similar to the external test site. For example, the system will analyze whether there are ramp training grounds (for ramp start training), sharp bends (for curve control training), narrow streets (for meeting training), commercial blocks (for pedestrian avoidance training), and school surroundings (for simulating real test parking and slow traffic). In addition, the system will combine real-time traffic flow data to analyze the traffic conditions of training roads around driving schools at different time periods to ensure that the recommended driving schools not only match the test environment, but also provide efficient training under reasonable traffic conditions. For example, if a driving school has a good training environment around it, but the area is long-term congested during the time period when students can train (such as 3 pm to 5 pm), the system may lower its matching score to ensure that students have an efficient training experience. Finally, this step generates a database of road environments around driving schools for subsequent calculation of matching.

[0074] Exemplarily, after obtaining the data of the off-road examination site area and the roads around the driving school, the system needs to calculate the matching degree between the driving school and the target examination site to ensure that the training environment of the driving school selected by the students is as close as possible to the real examination environment, thereby improving the adaptability of the students and the passing rate of the examination. This step mainly adopts two matching calculation methods: target driving scenario matching calculation and geographical location matching calculation. In the target driving scenario matching calculation, the system will count the road environment around each driving school and calculate its coincidence degree with the off-road examination site. The scenario matching degree is the number of scenarios in the driving school training area that are the same as those of the off-road examination site divided by the total number of scenarios of the target examination site. If the training area of a certain driving school can cover most of the examination scenarios such as slopes, narrow roads, commercial blocks, and complex intersections of the off-road examination site, the matching degree of this driving school will be higher. In the geographical location matching calculation, the system uses a path planning algorithm to calculate the shortest training path from the driving school to the examination site and evaluate the number of target driving scenarios that can be trained on this path to further optimize the matching result. Finally, the system synthesizes the two calculation results to generate the final matching score of the driving school: Driving school matching degree = Scenario matching degree × 0.6 + Geographical matching degree × 0.4. This score will be used for subsequent intelligent recommendation sorting. After calculating the off-road examination site matching degrees of all driving schools, the system will rank the driving schools according to the matching scores and recommend the top 3-5 optimal driving schools to the students. The recommendation results not only include the basic information of the driving school (such as location, passing rate of the examination, student evaluation, etc.), but also provide a detailed matching degree analysis, including: training site information (whether it includes slopes, night driving, highway training areas), off-road training routes (whether they cover the same training scenarios as the examination site), geographical distance between the driving school and the examination site (whether it is convenient to adapt to the examination environment), passing rate of the examination, and student feedback scores, etc. In addition, to meet the personalized needs of different students, the system provides a dynamic weight adjustment function, allowing students to manually adjust the weights of the matching factors. For example: if a student pays more attention to the matching degree of the off-road examination site, the weight of "scenario matching degree" can be increased; if a student pays more attention to the commuting convenience of the driving school, the weight of "geographical matching degree" can be increased; if a student values the passing rate of the examination more, they can give priority to choosing a driving school with a high historical passing rate. Finally, the student can select the driving school that best meets their examination needs based on the recommended driving school information and reserve a trial training course to experience the teaching method and the matching degree of the examination environment of the driving school, so as to make the best decision. Based on the matching degree of the off-road examination site area in the target city, it is ensured that the students can train in the driving school closest to the examination environment, thereby optimizing the preparation for the driving test, improving the passing rate of the examination, and enhancing the actual driving ability of the students. Compared with the traditional driving school recommendation method, this solution is more accurate, efficient, and intelligent, can improve the passing rate of the examination, avoid losing points due to unfamiliarity with the examination site roads, improve the training efficiency, reduce unnecessary waste of training time, enhance the driving adaptability, and enable the students to drive more safely and confidently after obtaining the driver's license.

[0075] According to some embodiments, recommending a driving school for the pre-registered students based on the matching degree between the map information of the off-road test site area range of the target city and the map information of the preset surrounding area range of the driving school includes:

[0076] Extracting a plurality of target test scenarios based on the map information of the off-road test site area range of the target city;

[0077] Recommending a driving school for the pre-registered students according to the number of target test scenarios included in the preset surrounding area range of the driving school.

[0078] It can be understood that by analyzing the map information of the off-road test site area range of the target city, extracting a plurality of target test scenarios, and matching them with the preset surrounding area range of the driving school, the most suitable driving school for the students can be recommended. The traditional driving school recommendation method is usually based on the geographical location of the students or the passing rate of the driving school, while this method further considers the similarity of the test environment to ensure that the students can adapt to the training in the real test environment in advance when training in the driving school. Through technical means such as map data analysis, target test scenario extraction, driving school surrounding environment matching, and intelligent recommendation algorithms, this method can improve the passing rate of the students' exams and help them adapt to the traffic conditions more quickly in the future actual driving environment.

[0079] Exemplarily, through map data analysis, key driving scenarios within the off-road driving test area outside the target city are extracted as the benchmark for subsequent driving school recommendations. First, the system obtains the target test city through the registration information of the trainee and accesses the official traffic management platform or map API to obtain the detailed map data of the off-road driving test site outside the target city. This data includes the test routes, road condition characteristics, traffic environment, etc. of the off-road driving test site. Next, the system extracts the target test scenarios from the map data of the off-road driving test site. After completing the scenario extraction, the system stores the list of target test scenarios in the database and marks the different test contents involved in this test area for subsequent matching calculations. After determining the target test scenarios, the system needs to collect the geographical information of all driving schools within the target city and analyze whether the surrounding areas contain these target test scenarios to ensure that the recommended driving schools can provide training conditions highly similar to the test environment. First, the system extracts driving school information from the driving school management database or government supervision system, including the driving school address, training ground facilities (such as whether it includes ramps, night driving training areas, etc.), passing rates of exams, trainee evaluations, coach ratings, etc. Subsequently, the system calls the map API to analyze the road environment within a 5 - 10 km radius of the driving school and identify the target test scenarios it contains. For example, the system will analyze whether this area has a ramp training ground (for the ramp start exam), sharp turn roads (for curve control training), commercial areas (for pedestrian avoidance exams), complex intersections (for traffic light passing rule training), etc. In addition, the system will combine real-time traffic flow data to evaluate the traffic conditions around the driving school to ensure that trainees can be exposed to the real test environment during training and will not be affected by excessive congestion or insufficient traffic flow. Finally, this step generates a database of target test scenarios around the driving school for subsequent matching calculations. After obtaining the target test scenario data of the driving school, the system needs to calculate the coverage of each driving school for the target test scenarios to ensure that the training environment of the trainee is closest to the real test scenario to the greatest extent. Calculate the number of scenarios that match the target test site within a 5 - 10 km radius around the driving school. For example, if the training area of a driving school includes ramps, commercial areas, and complex intersections, and the target test site also includes these scenarios, then the matching degree of this driving school is relatively high. The scenario coverage can be equal to the result of dividing the number of target test scenarios included in the surrounding area of the driving school by the total number of scenarios of the target test site. If a target test site includes 8 types of test scenarios and the training area of a driving school includes 6 of them, then the scenario coverage of this driving school is 75%. The higher this value, the closer the training environment of the driving school is to the actual test environment. Different test scenarios have different importance, and the system will assign different weights. For example: ramp exam weight: 1.2, narrow road exam weight: 1.1, commercial area exam weight: 1.3, night driving exam weight: 1.0.Driving school scene matching degree = ∑(number of target exam scenes × weight). For example, if a driving school covers more target exam scenes such as hill start exam and commercial area exam, the weighted matching degree of this driving school is higher, and the system will be more inclined to recommend this driving school. After calculating the coverage of target exam scenes of all driving schools, the system will rank according to the matching degree, recommend the most suitable driving school, and display detailed information to the students, including: basic information of the driving school (geographical location, training ground configuration), matching degree of exam scenes around the driving school (whether it covers the scenes of the target exam venue), passing rate of the exam and students' evaluations. In addition, the system provides a function to dynamically adjust the weights. Students can adjust the recommendation rules according to their personal needs. For example, if a student pays more attention to the matching degree of the target exam scenes, the student can increase the weight of "scene coverage degree"; if a student pays more attention to the passing rate of the driving school, the student can adjust the weight of "historical passing rate"; if a student hopes to be unaffected by traffic congestion during the training process, the student can increase the weight of "smoothness of the training environment". Finally, the student can choose the driving school that best meets the exam requirements and reserve a trial training course to experience the matching degree between the training method and the exam environment of the driving school, so as to ensure that the student obtains the best training experience and improves the passing rate of the exam. Thus, the student can master the driving skills in the exam area proficiently before the exam and improve the adaptability. Through intelligent matching, it is ensured that the student can complete the training of the most exam scenes in the shortest time. Allowing students to dynamically adjust the recommendation rules according to their own needs to ensure the best learning experience.

[0080] In one embodiment, it further includes:

[0081] Based on the map information of the multiple target driving scenes and the range of the off-road exam venue area in the target city, plan a target training path for the pre-registered student within the range of the off-road exam venue area in the target city, and the target training path includes as many of the target driving scenes as possible;

[0082] Based on the target training path, conduct training path planning within the preset peripheral area range of each driving school;

[0083] Recommend to the pre-registered student the driving school corresponding to the area range where the training path with the highest matching degree with the target training path is located.

[0084] It is understandable that the map information of the off-road test site outside the target city is used to plan the optimal target training path, ensuring that trainees can be exposed to as many target driving scenarios as possible during the training process. Further, the target training path is matched with the preset surrounding area ranges of each driving school to recommend the driving school with the highest matching degree to the target training path. Compared with the traditional driving school recommendation method that only selects based on geographical location or driving school passing rate, this solution uses an intelligent path optimization algorithm, combines multi-dimensional data such as off-road test environment, target driving scenarios, and driving school training areas, to ensure that trainees can cover the most target test scenarios in the shortest training time, thereby improving the passing rate of the driving test and at the same time enhancing the safety and adaptability of trainees in the future actual driving environment.

[0085] Exemplarily, within the scope of the off-road driving test area in the target city, an optimal training path is planned that includes as many target driving scenarios as possible to ensure that trainees can adapt to various possible driving environments before the exam. First, the system obtains the scope of the off-road driving test area in the target city from the official traffic management system or map API, and extracts the road structure information, dynamic traffic characteristics, and exam scenario data in this area. Subsequently, the system conducts a scenario analysis of the test area and extracts the key driving environments involved in the exam. For example, road structure-related scenarios: ramps (ramp start, ramp stop), sharp curves (curve driving, steering accuracy), narrow roads (meeting vehicles, low-speed driving), complex intersections (multi-lane merging, traffic signal response); dynamic traffic-related scenarios: commercial areas (pedestrian avoidance, complex traffic flow control), areas around schools (low-speed driving, response to emergencies), hospitals, areas around fire stations (emergency ambulance avoidance); special driving environment scenarios: night driving test area, highway ramp and main road merging, bridge, tunnel driving. After obtaining the target driving scenario data of the test area, the system uses the A* search algorithm or Dijkstra path optimization algorithm to calculate the optimal route that can cover the most target driving scenarios within the shortest driving time, forming the target training path. This path will serve as an important matching basis for subsequent driving school recommendations. After obtaining the target training path, the system needs to analyze the preset surrounding areas of each driving school and calculate whether it can provide training conditions close to the target training path. First, the system extracts data such as the geographical information of the driving school, training site facilities (such as ramps, night driving areas, etc.), exam pass rates, and trainee evaluations from the driving school management database. Subsequently, the system calls the map API to analyze the road environment within a 5-10 kilometer range around the driving school, focusing on identifying whether the target driving scenarios involved in the target training path are included in this area. For example, the system will analyze whether this area has a ramp training area (for ramp exam training), sharp curve roads (for curve driving training), commercial areas (for pedestrian avoidance exams), complex intersections (for traffic light passing rule training), etc. Then, the system will calculate the preset training paths of each driving school in sequence according to the driving scenarios of the target training path, ensuring that the training routes of each driving school include as many target exam scenarios as possible, and select the optimal route in the area with the highest matching degree. During the path planning process, the system uses path optimization algorithms (such as A* or Dijkstra algorithms) to ensure that: the training path does not repeat and detour, improving training efficiency; the target scenarios on the training path are evenly distributed, and trainees can have sufficient time to adapt; the training path does not pass through long-term congested sections, ensuring smooth training. Finally, each driving school will generate an optimal training path, which needs to match the target training path as much as possible and provide trainees with training opportunities in a similar exam environment before the exam.

[0086] After generating the training paths of each driving school, the system needs to calculate the matching degree between the target training path and the driving school training path to determine which driving schools can provide the training plan closest to the test examination room environment. Target scenario coverage calculation: Calculate the number of target driving scenarios overlapping with the target training path within the driving school training path. The scenario matching degree can be equal to the ratio of the number of target driving scenarios included in the driving school training path to the total number of target scenarios in the target training path. If the target training path involves 8 test scenarios, and a driving school training path only covers 5 of them, the scenario matching degree of this driving school is 62.5%. Calculate the geographical location similarity between the driving school training path and the target training path. If the training path of the driving school passes near the target test area, the matching degree is higher. Use the dynamic time warping (DTW) algorithm to calculate the shape similarity of the training path to ensure that the driving mode of the driving school training path is as close as possible to the test examination room. Different test scenarios have different importance, and the system will assign different weights. For example: Ramp test scenario: 1.2, Narrow road test scenario: 1.1, Commercial area test scenario: 1.3, Night driving test scenario: 1.0. Training matching degree = ∑(target driving scenario matching degree × scenario weight) + path similarity. Finally, the driving school with the highest matching degree will be recommended to the students first. Thus, ensure that students take the exam in a familiar test environment and reduce mistakes caused by unfamiliar road conditions. Intelligent planning of the training path to ensure that students can cover the most test scenarios in the shortest time. Students can adjust the recommended parameters to make the training content more in line with personal needs.

[0087] Please refer to Figure 2 , an embodiment of the driving test training driving school recommendation device in the embodiment of the present application may include:

[0088] An evaluation unit 201, configured to evaluate the coping ability of pre-registered students for different driving scenarios through driving simulation training, so as to classify the coping ability of the pre-registered students for different driving scenarios. The driving simulation training includes simulation training scenarios for all driving scenarios;

[0089] An acquisition unit 202, configured to acquire multiple target driving scenarios whose coping ability levels associated with the pre-registered students are lower than a preset level and map information of a preset surrounding area range of each driving school in the city where the pre-registered students are located;

[0090] A recommendation unit 203, configured to recommend a driving school for the pre-registered student based on the matching degree between the multiple target driving scenarios and the map information.

[0091] In summary, the driving test training driving school recommendation device provided by the above embodiments evaluates the response capabilities of pre-registered students to different driving scenarios through driving simulation training, so as to classify the response capabilities of the pre-registered students to different driving scenarios. The driving simulation training includes simulation training scenarios for all driving scenarios; obtains multiple target driving scenarios with response capabilities lower than the preset level associated with the pre-registered students and map information of the preset surrounding area ranges of each driving school in the city where the pre-registered students are located; recommends driving schools for the pre-registered students based on the matching degree between the multiple target driving scenarios and the map information. Through driving simulation training and intelligent matching algorithms, the problem that students only rely on publicity or geographical location to select driving schools is successfully solved, and the scientificity and accuracy of driving school selection are improved. Compared with the traditional method, students can match the driving school that best meets their own training needs, avoid learning irrelevant driving skills in an unsuitable environment, and thus improve learning efficiency. At the same time, specialized training for weak scenarios helps to improve the passing rate of the exam, enabling students to master real driving skills faster and more confidently.

[0092] Above Figure 2 The driving test training driving school recommendation device in the embodiments of the present application has been described from the perspective of modular functional entities. Next, the driving test training driving school recommendation device in the embodiments of the present application will be described in detail from the perspective of hardware processing. Please refer to Figure 3 , an embodiment of the driving test training driving school recommendation device 300 in the embodiments of the present application includes:

[0093] An input device 301, an output device 302, a processor 303, and a memory 304. Among them, the number of processors 303 can be one or more, Figure 3 Taking one processor 303 as an example. In some embodiments of the present application, the input device 301, the output device 302, the processor 303, and the memory 304 can be connected by a bus or other means. Among them, Figure 3 Taking the connection by bus as an example.

[0094] Among them, by calling the operation instructions stored in the memory 304, the processor 303 is used to execute the above method steps.

[0095] By calling the operation instructions stored in the memory 304, the processor 303 is also used to execute Figure 1 Any one of the corresponding embodiments.

[0096] Please refer to Figure 4 , Figure 4 is a schematic diagram of an embodiment of an electronic system provided by the embodiments of the present application.

[0097] As Figure 4As shown in the figure, an embodiment of the present application provides an electronic system, including a memory 410, a processor 420, and a computer program 411 stored on the memory 420 and executable on the processor 420. When the processor 420 executes the computer program 411, the above method steps are implemented.

[0098] In the specific implementation process, when the processor 420 executes the computer program 411, it can implement Figure 1 any implementation manner in the corresponding embodiment.

[0099] Since the electronic system introduced in this embodiment is the device adopted by a driving test training driving school recommendation device in an embodiment of the present application, based on the method introduced in the embodiment of the present application, those skilled in the art can understand the specific implementation manner and various variation forms of the electronic system in this embodiment. Therefore, the specific implementation of how this electronic system implements the method in the embodiment of the present application will not be described in detail here. As long as the device adopted by those skilled in the art to implement the method in the embodiment of the present application belongs to the scope protected by the present application.

[0100] Please refer to Figure 5 , Figure 5 which is a schematic diagram of an embodiment of a computer-readable storage medium provided by an embodiment of the present application.

[0101] As Figure 5 shown in the figure, this embodiment provides a computer-readable storage medium 500, on which a computer program 511 is stored. When the computer program 511 is executed by a processor, the above method steps are implemented.

[0102] In the specific implementation process, when the computer program 511 is executed by a processor, it can implement Figure 1 any implementation manner in the corresponding embodiment.

[0103] It should be noted that in the above embodiments, the descriptions of each embodiment have their own focuses. For the parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0104] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0105] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, as well as the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded computers, or other programmable data processing devices to generate a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices produce a means for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0106] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including an instruction means that implements the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0107] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one flow Figure 1 one flow or multiple flows and / or blocks Figure 1 or multiple blocks.

[0108] Embodiments of the present application also provide a computer program product, which includes computer software instructions. When the computer software instructions run on a processing device, the processing device is caused to execute the processes in the driving test training driving school recommendation method in the corresponding embodiment Figure 1 as described.

[0109] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from a website, computer, server, or data center to another website, computer, server, or data center in a wired manner (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or a wireless manner (such as infrared, wireless, microwave, etc.). The computer-readable storage medium may be any available medium that can be stored by a computer or a data storage device such as a server or a data center that includes one or more integrated available media. The available medium may be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium (such as a solid state disk (SSD)), etc.

[0110] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be described herein again.

[0111] In several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other may be indirect couplings or communication connections through some interfaces, devices, or units, and may be in an electrical, mechanical, or other form.

[0112] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0113] In addition, in each embodiment of the present application, each functional unit may be integrated into one processing unit, or each unit may exist physically alone, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of a software functional unit.

[0114] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical discs that can store program codes.

[0115] As described above, the above embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of each embodiment of the present application.

Claims

1. A method for recommending driving test training driving schools, characterized in that, including: evaluating the response capabilities of pre-registered trainees to different driving scenarios through driving simulation training, so as to classify the response capabilities of the pre-registered trainees to different driving scenarios, and the driving simulation training includes simulation training scenarios for all driving scenarios; obtaining multiple target driving scenarios with response capability levels lower than the preset level associated with the pre-registered trainee and map information of the preset peripheral area range of each driving school in the city where the pre-registered trainee belongs; recommending a driving school for the pre-registered trainee based on the matching degree between the multiple target driving scenarios and the map information.

2. The method according to claim 1, wherein The recommending a driving school for the pre-registered trainee based on the matching degree between the multiple target driving scenarios and the map information includes: planning a driving training path based on the map information and the multiple target driving scenarios within the preset peripheral area range of each driving school, so that the same training path can include as many of the target driving scenarios as possible; recommending to the pre-registered trainee the driving school corresponding to the training path including the most types of the target driving scenarios.

3. The method according to claim 1, wherein The recommending a driving school for the pre-registered trainee based on the matching degree between the multiple target driving scenarios and the map information includes: planning a driving training path based on the map information and the multiple target driving scenarios within the preset peripheral area range of each driving school, so that the same training path can include as many of the target driving scenarios as possible; recommending to the pre-registered trainee the driving school corresponding to the training path including the most types of the target driving scenarios and having the shortest path.

4. The method according to claim 1, characterized in that, The recommending a driving school for the pre-registered trainee based on the matching degree between the multiple target driving scenarios and the map information includes: evaluating the number of the target driving scenarios included in the preset peripheral area range of each driving school based on the map information; recommending to the pre-registered trainee the driving school including the largest number of the target driving scenarios.

5. The method according to any one of claims 1 to 4, characterized in that, also including: obtaining the target city for the pre-registered trainee's exam registration to query the map information of the outer road exam area range of the target city; recommending a driving school for the pre-registered trainee based on the matching degree between the map information of the outer road exam area range and the map information of the preset peripheral area range of the driving school.

6. The method according to claim 5, wherein The recommending a driving school for the pre-registered trainee based on the matching degree between the map information of the outer road exam area range and the map information of the preset peripheral area range of the driving school includes: extracting multiple target exam scenarios based on the map information of the outer road exam area range of the target city; recommending a driving school for the pre-registered trainee according to the number of target exam scenarios included in the preset peripheral area range of the driving school.

7. The method according to claim 5, wherein also including: planning a target training path for the pre-registered trainee within the outer road exam area range of the target city based on the multiple target driving scenarios and the map information of the outer road exam area range of the target city, and the target training path includes as many of the target driving scenarios as possible; planning a training path based on the target training path within the preset peripheral area range of each driving school; recommending to the pre-registered trainee the driving school corresponding to the area range where the training path with the highest matching degree to the target training path is located.

8. A driving test training driving school recommendation device, characterized in that, including: An evaluation unit for evaluating the response capabilities of pre-registered trainees to different driving scenarios through driving simulation training, so as to classify the response capabilities of the pre-registered trainees to different driving scenarios, and the driving simulation training includes simulation training scenarios for all driving scenarios; An acquisition unit for acquiring a plurality of target driving scenarios in which the response capability level associated with the pre-registered trainee is lower than a preset level and map information of a preset peripheral area range of each driving school of the driving schools in the city where the pre-registered trainee is located; A recommendation unit for recommending a driving school for the pre-registered trainee based on the matching degree between the plurality of target driving scenarios and the map information.

9. An electronic system, comprising a memory and a processor, characterized in that, The processor is used to implement the steps of the driving test training driving school recommendation method as described in any one of claims 1 to 7 when executing the computer program stored in the memory.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: The computer program, when executed by the processor, implements the steps of the driving test training driving school recommendation method as described in any one of claims 1 to 7.

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