Driver driving ability analysis method and device, storage medium and electronic equipment

Abstract

The invention relates to a driver driving ability analysis method and device, a storage medium and electronic equipment, and relates to the technical field of driving analysis, and the method comprises the steps: obtaining the driving state information of a target trainee driving a target vehicle in the training of each sub-item of a to-be-evaluated driving test subject, and obtaining the subject score of the target trainee in each sub-item; based on the driving state information and the subject score corresponding to each sub-item, determining a first score of the corresponding sub-item in the first dimension, determining a second score of the corresponding sub-item in the second dimension, and determining a third score of the corresponding sub-item in the third dimension; performing weighted summation on the first score, the second score and the third score corresponding to the same sub-project to obtain a final score of the corresponding sub-project; and performing weighted summation on the final scores of the sub-items to obtain a comprehensive score of the target trainee in the training of the to-be-evaluated driving test subject. The method and the device have the effect of improving the accuracy of analyzing the driving ability of the driver.

Description

Technical Field

[0001] This application relates to the technical field of driving analysis, and particularly to a method, device, storage medium and electronic device for analyzing the driving ability of a driver. Background Art

[0002] Driver training refers to a series of systematic education and training for drivers through driver training institutions, such as driving schools, aiming to improve their driving skills and traffic safety awareness. Among them, driver training institutions mainly conduct systematic education and training for the second and third subjects in the driving test, and through an effective driving ability evaluation or analysis mechanism, assess the training of drivers in the driving test subjects, so that the driving ability of drivers in the second and third subjects can be improved, which is of great significance in improving the comprehensive quality of drivers and road traffic safety.

[0003] Currently, the commonly used method for analyzing the driving ability of a driver is to evaluate the driving ability of the driver according to the data of a single index during driving, for example, the number of brakes or fuel consumption during driving. However, in real life, the driving scenarios encountered during driving are complex and changeable. The analysis mode through a single index in this method has poor comprehensiveness in analyzing the driving ability of the driver, resulting in low accuracy in analyzing the driving ability of the driver. Summary of the Invention

[0004] In order to improve the accuracy of analyzing the driving ability of a driver, this application provides a method, device, storage medium and electronic device for analyzing the driving ability of a driver.

[0005] In the first aspect of this application, a method for analyzing the driving ability of a driver is provided, which specifically includes: Obtain the driving state information of the target vehicle driven by the target trainee during the training of each sub-item of the driving test subject to be evaluated, and obtain the subject score of the target trainee in each sub-item; Based on the driving state information and subject score corresponding to each sub-item, determine the first score of the corresponding sub-item in the first dimension, determine the second score of the corresponding sub-item in the second dimension, and determine the third score of the corresponding sub-item in the third dimension. The first dimension is the dimension for evaluating the driving safety of the target trainee in the sub-item, the second dimension is the dimension for evaluating the driving smoothness of the target trainee in the sub-item, and the third dimension is the dimension for evaluating the driving fluency of the target trainee in the sub-item; Perform a weighted sum of the first score, the second score, and the third score corresponding to the same sub-item to obtain the final score of the corresponding sub-item; The final scores of each of the sub-items are weighted and summed to obtain the comprehensive score of the target trainee in the training of the driving test subject to be evaluated.

[0006] By adopting the above technical solution, after obtaining the driving state information and subject scores of the target trainee in a single sub-item, the first score of each sub-item in the first dimension, the second score in the second dimension, and the third score in the third dimension are determined accordingly. Thus, for a single sub-item, the driving ability of the target trainee is evaluated from three dimensions of driving safety, smoothness, and fluency. Then, the first score, the second score, and the third score corresponding to a single sub-item are weighted and summed, so as to more accurately evaluate the overall driving ability of the target trainee in a single sub-item of the driving test subject to be evaluated. Finally, the final scores of each sub-item are weighted and summed, so as to comprehensively analyze the driving ability of the target trainee in the entire driving test subject to be evaluated. Based on comprehensively evaluating the driving ability of the target trainee in the sub-item from three dimensions, the driving ability of the target trainee is finally analyzed more comprehensively, and the accuracy of the analysis of the driving ability of the driver is improved.

[0007] Optionally, determining the first score of the corresponding sub-item in the first dimension based on the driving state information and subject scores corresponding to each sub-item specifically includes: According to the brake pedal monitoring information in the driving state information corresponding to a single sub-item, determine the number of emergency brakes of the target trainee in the corresponding sub-item, and determine the corresponding brake score according to the number of emergency brakes. The more the number of emergency brakes, the lower the corresponding brake score; According to the accelerator pedal monitoring information in the driving state information corresponding to a single sub-item, determine the number of emergency accelerations of the target trainee in the corresponding sub-item, and determine the corresponding acceleration score according to the number of emergency accelerations. The more the number of emergency accelerations, the lower the corresponding acceleration score; According to at least one driving speed in the driving state information corresponding to a single sub-item, determine the standard deviation and average value of the vehicle speed in the corresponding sub-item, and determine the corresponding speed variation score according to the ratio of the standard deviation of the vehicle speed to the average value of the vehicle speed. The larger the ratio, the lower the corresponding speed variation score; Weight and sum the brake score, acceleration score, speed variation score, and subject score corresponding to the same sub-item to obtain the first score of the corresponding sub-item in the first dimension.

[0008] By adopting the above technical solution, when determining the first score of the target trainee in the first dimension of a single sub-item, important indicators affecting driving safety such as hard braking, hard acceleration, and driving speed fluctuations during driving are comprehensively considered. The braking score, acceleration score, speed variation score, and subject score corresponding to each indicator are weighted and summed, so as to more comprehensively and reasonably analyze the driving ability of the target trainee in terms of driving safety in a single sub-item.

[0009] Optionally, the determination of the second score of the corresponding sub-item in the second dimension specifically includes: According to the acceleration data in the driving state information corresponding to a single sub-item, the acceleration smoothness of the vehicle driving in the corresponding sub-item is determined by a preset spectrum analysis method, and according to the acceleration smoothness, the corresponding acceleration score is determined. The higher the acceleration smoothness, the higher the corresponding acceleration score; According to the actual driving trajectory of the vehicle in the driving state information corresponding to a single sub-item, the corresponding trajectory score is determined; According to at least one steering wheel angle in the driving state information corresponding to a single sub-item, the maximum steering wheel angle within a preset driving distance is determined, and according to the maximum steering wheel angle, the corresponding steering wheel score is determined. The larger the maximum steering wheel angle, the lower the corresponding steering wheel score; The acceleration score, trajectory score, and steering wheel score corresponding to the same sub-item are weighted and summed to obtain the second score of the corresponding sub-item in the second dimension.

[0010] By adopting the above technical solution, when determining the second score of the target trainee in the second dimension of a single sub-item, the acceleration smoothness, driving trajectory, and steering wheel angle of driving are comprehensively considered. These three important indicators affecting driving smoothness are weighted and summed with the acceleration score, trajectory score, and steering wheel score corresponding to the three indicators respectively, so as to more comprehensively and reasonably analyze the driving ability of the target trainee in terms of driving smoothness in a single sub-item.

[0011] Optionally, the determination of the third score of the corresponding sub-item in the third dimension specifically includes: According to at least one driving speed in the driving state information corresponding to a single sub-item, the average speed of the target vehicle in the corresponding sub-item is determined, and according to the average speed, the corresponding average speed score is determined. The larger the average speed, the higher the corresponding average speed score; According to the real-time speed and the gear position in the driving state information corresponding to a single sub-item, the duration of the mismatch between the gear and the speed is determined, and according to the duration, the gear-speed matching score is determined. The larger the duration, the lower the corresponding gear-speed matching score; Perform a weighted sum of the average speed score and the gear speed matching score corresponding to the same sub-item to obtain the third score of the corresponding sub-item in the third dimension.

[0012] By adopting the above technical solution, when determining the third score of a single sub-item of the target trainee in the third dimension, two important indicators affecting driving smoothness, namely the average driving speed and gear speed matching, are comprehensively considered. The average speed score and the gear speed matching score corresponding to the two indicators are weighted and summed, so as to more comprehensively and reasonably analyze the driving ability of the target trainee in terms of driving smoothness in a single sub-item.

[0013] Optionally, the method further includes: Send the comprehensive score to the in-vehicle device of the target vehicle and the terminal of the target trainee, and compare the comprehensive score with the score threshold of the to-be-evaluated driving test subject; If the comprehensive score is lower than the score threshold, send driving improvement suggestions for the to-be-evaluated driving test subject to the terminal of the target trainee.

[0014] By adopting the above technical solution, if the comprehensive score is lower than the score threshold, it means that the target trainee performs poorly in the to-be-evaluated driving test subject. Then, send driving improvement suggestions to the terminal of this target trainee, so as to help the target trainee improve driving skills during training and improve the driving level.

[0015] Optionally, the sending of the driving improvement suggestions for the to-be-evaluated driving test subject to the terminal of the target trainee specifically includes: Determine the target portrait of the target trainee, obtain the historical driving operations that occurred when the historical trainees of the target portrait were deducted points in the to-be-evaluated driving test subject, count the first occurrence times of each historical driving operation, and select the first number of historical driving operations from each historical driving operation in descending order of the first occurrence times to be determined as the target driving operations; Obtain the historical sub-items in the to-be-evaluated driving test subject where points were deducted under a single target driving operation, count the second occurrence times of each historical sub-item, and select the second number of historical sub-items from each historical sub-item in descending order of the second occurrence times to be determined as the target sub-items corresponding to the target driving operation; Determine the first weight of each target driving operation and the second weight of each target sub-item corresponding to each target driving operation. The first weight is the ratio of the first occurrence times of each target driving operation to the sum of the first occurrence times of all target driving operations, and the second weight is the ratio of the second occurrence times of a single target sub-item corresponding to the target driving operation to the sum of the second occurrence times of all target sub-items corresponding to the target driving operation; Determine driving improvement suggestions for the driving test subject to be evaluated according to the first weight and the corresponding second weights, and send the driving improvement suggestions to the terminal of the target trainee.

[0016] By adopting the above technical solution, the greater the first occurrence frequency, the more likely the corresponding historical driving operation is to cause deductions in the driving test subject, and thus the target driving operation is determined; the greater the second occurrence frequency, when the target trainee performs a single target driving operation, the more likely it is to induce deductions in the corresponding historical sub-item, and thus the target sub-item is determined. Finally, by combining the first weight of the target driving operation and the second weight of the corresponding target sub-item, analyze and determine the driving operations that are likely to cause deductions in the training of the target trainee, so as to facilitate the subsequent provision of targeted driving improvement suggestions.

[0017] Optionally, the determining driving improvement suggestions for the driving test subject to be evaluated according to the first weight and the corresponding second weights specifically includes: Determine the actual sub-items in which the target trainee has deductions during training, determine the target driving operations with at least one actual sub-item in the corresponding target sub-items as key driving operations, and calculate the product of the first weight of each key driving operation and the second weights of the corresponding actual sub-items; Select the maximum product from the products corresponding to the same actual sub-item, and determine the key driving operation corresponding to each maximum product as the operation to be improved; Based on each operation to be improved, determine driving improvement suggestions for the driving test subject to be evaluated.

[0018] By adopting the above technical solution, the larger the product, the more likely the corresponding actual sub-item is to have deduction situations when the key driving operation occurs. Select the maximum product from the products corresponding to the same actual sub-item. When the key driving operation corresponding to the maximum product occurs, the possibility of deductions in the corresponding actual sub-item is the greatest. Then determine the key driving operation corresponding to each maximum product as the operation to be improved. Finally, based on this operation to be improved, provide more targeted driving improvement suggestions for the target trainee, so as to better improve the driving ability.

[0019] In the second aspect of the present application, a driving ability analysis device for drivers is provided, specifically including: An information acquisition module, configured to acquire the driving state information of the target vehicle driven by the target trainee during the training of each sub-item of the driving test subject to be evaluated, and acquire the subject scores of the target trainee in each sub-item; The first scoring module is used to determine the first score of the corresponding sub-item in the first dimension, the second score of the corresponding sub-item in the second dimension, and the third score of the corresponding sub-item in the third dimension based on the driving state information and subject score corresponding to each sub-item. The first dimension is the dimension for evaluating the driving safety of the target trainee in the sub-item, the second dimension is the dimension for evaluating the driving smoothness of the target trainee in the sub-item, and the third dimension is the dimension for evaluating the driving fluency of the target trainee in the sub-item; The second scoring module is used to perform weighted summation on the first score, the second score, and the third score corresponding to the same sub-item to obtain the final score of the corresponding sub-item; The third scoring module is used to perform weighted summation on the final scores of each sub-item to obtain the comprehensive score of the target trainee in the training of the to-be-evaluated driving test subject.

[0020] By adopting the above technical solution, in each sub-item of the information acquisition module, the driving state information and subject score of the target vehicle are obtained. Then, the first scoring module determines the first score of the sub-item in the first dimension, the second score in the second dimension, and the third score in the third dimension. Then, the second scoring module performs weighted summation on the first score, the second score, and the third score corresponding to the same sub-item to obtain the final score. Finally, the third scoring module performs weighted summation on the final scores of each sub-item to obtain the comprehensive score of the target trainee.

[0021] In the third aspect of the present application, a computer-readable storage medium is provided. A computer program is stored in the computer-readable storage medium. When the computer program is loaded and executed by a processor, the method steps described in any one of the first aspect are executed.

[0022] In the fourth aspect of the present application, an electronic device is provided, which specifically includes: A processor, a memory, and a computer program stored in the memory and capable of running on the processor. The processor is used to load and execute the computer program stored in the memory so that the electronic device executes the method described in any one of the first aspect.

[0023] In summary, the present application includes at least one of the following beneficial technical effects: Determine the first score in the first dimension, the second score in the second dimension, and the third score in the third dimension for each sub-item. Thus, for a single sub-item, evaluate the driving ability of the target trainee from three dimensions: driving safety, smoothness, and fluency. Then, perform a weighted sum of the first score, the second score, and the third score corresponding to the single sub-item, so as to more accurately evaluate the overall driving ability of the target trainee in a single sub-item of the driving test subject to be evaluated. Finally, perform a weighted sum of the final scores of each sub-item, so as to comprehensively analyze the driving ability of the target trainee in the entire driving test subject to be evaluated. This solution is based on comprehensively evaluating the driving ability of the target trainee in sub-items from three dimensions, and finally more comprehensively analyzes the driving ability of the target trainee, improving the accuracy of the analysis of the driving ability of drivers. Description of the Drawings

[0024] Figure 1 is a schematic flowchart of a method for analyzing the driving ability of a driver provided by an embodiment of the present application; Figure 2 is a schematic flowchart of another method for analyzing the driving ability of a driver provided by an embodiment of the present application; Figure 3 is a schematic structural diagram of a device for analyzing the driving ability of a driver provided by an embodiment of the present application; Figure 4 is a schematic structural diagram of another device for analyzing the driving ability of a driver provided by an embodiment of the present application.

[0025] Description of the reference numerals: 11, information acquisition module; 12, first scoring module; 13, second scoring module; 14, third scoring module; 15, real-time feedback module. Detailed Embodiments

[0026] In order to enable those skilled in the art to better understand the technical solutions in this specification, the following will clearly and completely describe the technical solutions in the embodiments of this specification with reference to the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments.

[0027] In the description of the embodiments of the present application, words such as "exemplary", "for example", or "for illustration" are used to represent examples, illustrations, or explanations. Any embodiment or design solution described as "exemplary", "for example", or "for illustration" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Exactly speaking, using words such as "exemplary", "for example", or "for illustration" aims to present relevant concepts in a specific manner.

[0028] In the description of the embodiments of the present application, the term "and / or" is merely a description of the association relationship between associated objects, indicating that three relationships may exist. For example, A and / or B may represent three situations: A exists alone, B exists alone, and A and B exist simultaneously. Additionally, unless otherwise specified, the term "plural" means two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. Furthermore, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. The terms "comprise", "include", "have" and their variants all mean "including but not limited to", unless otherwise particularly emphasized in other ways.

[0029] See Figure 1 , the embodiments of the present application disclose a schematic flowchart of a method for analyzing a driver's driving ability, which can be implemented depending on a computer program or run on a driver's driving ability analysis device based on the von Neumann architecture. This computer program can be integrated into an application or run as an independent tool-type application, and specifically includes: S101: Obtain the driving state information of the target vehicle when the target trainee is driving the target vehicle during the training of each sub-item of the driving test subject to be evaluated, and obtain the subject score of the target trainee in each sub-item.

[0030] Specifically, in the embodiments of the present application, the driving test subject to be evaluated is the driving test subject that the target trainee has completed training for and needs to be scored for driving, which can specifically be Subject 2. In other embodiments, the driving test subject to be evaluated can also be Subject 3. The sub-items are a series of items for assessing a driver's driving ability included in the driving test subject to be evaluated. Exemplarily, if the driving test subject to be evaluated is Subject 2, then the corresponding sub-items are respectively: reverse parking, parallel parking, hill start parking, hill start, right-angle turn, and curve driving. In other embodiments, since the driving test content is different in different regions, the sub-items included in Subject 2 can also be other assessment items. The driving state information represents the driving information of the target vehicle and the operation information of the target trainee, etc. The driving state information includes but is not limited to the real-time driving speed of the target vehicle during the training of a single sub-item, the accelerator pedal monitoring information, the brake pedal monitoring information, the steering wheel angle, the gear information, the GPS data, and the acceleration, etc. The subject score refers to the real-time subject score of the target trainee in the driving test subject to be evaluated in a single sub-item. Exemplarily, if the first sub-item that the target trainee performs is reverse parking and there are no deductions during the process, then the subject score in the corresponding sub-item is 100 points.

[0031] Further, the execution subject of the driving ability analysis method disclosed in the embodiments of the present application is a cloud server. In other embodiments, it can also be an independent physical server. The cloud server is wirelessly connected to various sensors preset in the target vehicle. Further, during the sub-item training of the target trainee, the driving state information of the target vehicle in each sub-item can be obtained in real time through the sensors. Exemplarily, the driving speed is obtained in real time through the speed sensor, the throttle pedal monitoring information is obtained in real time through the throttle pedal position sensor, the brake pedal monitoring information is obtained in real time through the brake pedal position sensor, the real-time steering angle of the steering wheel is obtained through the steering wheel angle sensor, the gear information of the target vehicle is obtained in real time through the in-vehicle system of the target vehicle, the acceleration of the target vehicle is obtained in real time through the acceleration sensor, the GPS data of the target vehicle is obtained in real time through the preset GPS module, etc. In addition, the subject scores of the target trainee in each sub-item can be obtained through the existing second-level driving intelligent examination system installed in the target vehicle. It should be noted that the second-level driving intelligent examination system is a comprehensive examination management and evaluation system based on computer control.

[0032] S102: Based on the driving state information and subject scores corresponding to each sub-item, determine the first score of the corresponding sub-item in the first dimension, determine the second score of the corresponding sub-item in the second dimension, and determine the third score of the corresponding sub-item in the third dimension.

[0033] Specifically, the first dimension is the dimension for evaluating the driving safety of the target trainee in the sub-item, the second dimension is the dimension for evaluating the driving smoothness of the target trainee in the sub-item, and the third dimension is the dimension for evaluating the driving fluency of the target trainee in the sub-item. In the embodiments of the present application, a feasible way to determine the first score is as follows: for a single sub-item, obtain the brake pedal monitoring information during the entire sub-item from the corresponding driving state information, that is, the brake pedal position change information, and accordingly determine the brake pedal position change rate when the target trainee steps on the brake once. If the brake pedal position change rate exceeds the preset first change rate, it indicates that the target trainee steps on the brake violently. Then, this brake is determined as an emergency brake, and further, the number of emergency brakes is counted. Further, match the brake score corresponding to this number of emergency brakes from the preset brake score matching table. The more the number of emergency brakes, the greater the potential safety hazard during vehicle driving, and the lower the corresponding brake score. Among them, the brake score matching table includes different ranges of the number of emergency brakes and the corresponding brake scores, and the brake score takes values from 0 to 100. Exemplarily, the brake score matching table includes the range of the number of emergency brakes: 5 to 6 times, and the corresponding brake score is 60 points; the range of the number of emergency brakes: 4 to 5 times, and the corresponding brake score is 70 points, and so on.

[0034] Further, obtain the throttle pedal monitoring information in the entire sub-item from the corresponding driving state information, that is, the throttle pedal position change information. Based on this, determine the throttle pedal position change rate when the target trainee steps on the throttle once. If it exceeds the preset second change rate, it indicates that the target trainee steps on the throttle violently. Then, determine this acceleration as a sharp acceleration, and further count the number of sharp accelerations. Finally, match the acceleration score corresponding to this number of sharp accelerations from the preset acceleration score matching table. The more the number of sharp accelerations, the lower the corresponding acceleration score. The acceleration score matching table includes different ranges of the number of sharp accelerations and the corresponding acceleration scores, which are set based on personnel experience. Next, obtain the various different driving speeds in the entire sub-item from the corresponding driving state information, determine the standard deviation and the average value of the vehicle speeds of each driving speed, and calculate the ratio of the standard deviation of the vehicle speed to the average value of the vehicle speed. Finally, based on this ratio, determine the corresponding speed variation score. The larger the ratio, the higher the degree of dispersion of the driving speed, the greater the fluctuation of the vehicle speed when the target trainee is driving, and the greater the potential safety hazard, and the lower the corresponding speed variation score. Among them, based on this ratio, match the corresponding speed variation score from the preset variation score matching table. The variation score matching table includes different ratio ranges and the corresponding speed variation scores.

[0035] Finally, for a single sub-item, perform a weighted sum of the corresponding brake score, acceleration score, speed variation score, and subject score to obtain the first score of this sub-item in the first dimension. Among them, the determination method of the weights corresponding to the brake score, acceleration score, speed variation score, and subject score is as follows: evaluate the importance of the brake score, acceleration score, speed variation score, and subject score for analyzing the driving ability in the corresponding sub-item through indicators such as information gain in the decision tree, and then determine the corresponding weights. Among them, the higher the first score, the higher the safety of driving the target vehicle in this sub-item.

[0036] After determining the first score of a single sub-item in the first dimension, obtain the acceleration data of the target vehicle from the driving state information corresponding to the single sub-item, and then determine the acceleration smoothness of the target vehicle in this sub-item through a preset spectrum analysis method. Specifically, input all the acceleration data into the Fourier transform, calculate the energy distribution of the spectrum, and determine the ratio of the low-frequency energy to the total energy as the acceleration smoothness. Finally, based on the acceleration smoothness, determine the corresponding acceleration score from the preset acceleration score matching table. The higher the acceleration smoothness, the smoother the driving process, and the higher the corresponding acceleration score. The acceleration score matching table includes different smoothness ranges and the corresponding acceleration scores, all of which are set based on human experience.

[0037] Obtain the GPS data of the target vehicle from the driving state information corresponding to a single sub-item. Since the GPS data can represent the position information of the target vehicle, the actual driving trajectory of the vehicle can be determined according to the GPS data. Then, calculate the similarity between the actual driving trajectory of the vehicle and the standard driving trajectory in this sub-item. Specifically, map the two to a high-dimensional vector space to obtain the corresponding vectors, and calculate the cosine similarity between the two vectors. The higher the similarity, the more standard the actual driving trajectory of the vehicle, and the higher the corresponding trajectory score. Further, obtain each steering wheel angle of the target vehicle from the driving state information corresponding to a single sub-item, screen out each steering wheel angle within the preset driving distance in this sub-item, and select the maximum steering wheel angle from them. The preset driving distance can be the first 100 meters in this sub-item. Among them, the steering wheel angle can reflect the target trainee's direction control ability during driving. The larger the maximum steering wheel angle, the worse the target trainee's direction control ability during driving, and the lower the corresponding steering wheel score. Finally, perform a weighted sum of the acceleration score, trajectory score, and steering wheel score corresponding to the same sub-item to obtain the second score of this sub-item in the second dimension. The higher the second score, the higher the driving smoothness of the target vehicle in this sub-item. Among them, the determination of the corresponding weights of the acceleration score, trajectory score, and steering wheel score can refer to the above-mentioned weight determination method, which will not be elaborated here.

[0038] Further, obtain at least one driving speed from the driving state information corresponding to a single sub-item, take the average of each driving speed to obtain the average speed of the target vehicle in the sub-item, and then determine the corresponding average speed score from the preset average speed score matching table according to the average speed. The average speed score matching table includes different average speed ranges and corresponding acceleration scores. The greater the average speed, the smoother the driving process in terms of vehicle speed, and the higher the corresponding average speed score. Further, obtain the real-time speed and the gear position of the target vehicle in this sub-item from the driving state information corresponding to a single sub-item, and judge whether the real-time speed matches the gear position according to the preset gear-speed matching table. The gear-speed matching table includes different speed ranges and corresponding matching gears. If they do not match, then count the duration of the mismatch. The longer the duration, the higher the degree of gear-speed mismatch, the worse the driving smoothness, and the lower the corresponding gear-speed matching score. Specifically, match the corresponding gear-speed matching score from the preset gear-speed score matching table according to this duration. The gear-speed score matching table includes different durations and corresponding gear-speed matching scores. Exemplarily, the gear-speed matching table includes a speed range of 0 - 20 km / h, and the matching gear is the first gear; a speed range of 20 - 30 km / h, and the matching gear is the second gear, and so on. Finally, perform a weighted sum of the average speed score and the gear-speed matching score corresponding to the same sub-item to obtain the third score of the corresponding sub-item in the third dimension. The higher the third score, the higher the driving smoothness of the target vehicle in this sub-item.

[0039] S103: Perform weighted summation on the first score, second score, and third score corresponding to the same sub-item to obtain the final score of the corresponding sub-item.

[0040] Specifically, after the first score, second score, and third score of each sub-item in the three dimensions are determined, use indicators such as information gain in the decision tree to evaluate the importance of the first dimension, second dimension, and third dimension for analyzing the driving ability of the target trainee in this sub-item, and then determine the corresponding weights. Finally, perform weighted summation on the first score, second score, and third score of the same sub-item to obtain the final score of the target trainee in this sub-item during training. Exemplarily, the first score is 60 points, the corresponding weight is 0.6, the second score is 80 points, the corresponding weight is 0.2, the third score is 90 points, the corresponding weight is 0.2, and the full score is 100 points. Then the calculation process of the final score is: 60 * 0.6 + 80 * 0.2 + 90 * 0.2 = 70.

[0041] S104: Perform weighted summation on the final scores of each sub-item to obtain the comprehensive score of the target trainee in the training of the to-be-evaluated driving test subject.

[0042] Specifically, after the final score of each sub-item is determined, still use indicators such as information gain in the decision tree to evaluate the importance of each sub-item for analyzing the driving ability of the target trainee in this to-be-evaluated driving test subject, and then determine the corresponding weights. Finally, perform weighted summation on the final scores of each sub-item to obtain the comprehensive score of the target trainee in the training of this to-be-evaluated driving test subject, and more accurately analyze the driving ability of the target trainee based on the comprehensive consideration of the three dimensions.

[0043] See Figure 2 , the embodiment of the present application discloses a flowchart of another method for analyzing the driving ability of a driver, which can be implemented depending on a computer program or run on a driving ability analysis device based on the von Neumann architecture. This computer program can be integrated in an application or run as an independent tool application, and specifically includes: S201: Obtain the driving state information of the target trainee driving the target vehicle during the training of each sub-item of the to-be-evaluated driving test subject, and obtain the subject scores of the target trainee in each sub-item.

[0044] S202: Based on the driving state information and subject scores corresponding to each sub-item, determine the first score of the corresponding sub-item in the first dimension, determine the second score of the corresponding sub-item in the second dimension, and determine the third score of the corresponding sub-item in the third dimension.

[0045] S203: Perform a weighted sum of the first score, the second score, and the third score corresponding to the same sub-item to obtain the final score of the corresponding sub-item.

[0046] S204: Perform a weighted sum of the final scores of each sub-item to obtain the comprehensive score of the target trainee in the training of the driving test subject to be evaluated.

[0047] Specifically, refer to steps S101 - S104, which will not be elaborated here.

[0048] S205: Send the comprehensive score to the in-vehicle device of the target vehicle and the terminal of the target trainee, and compare the comprehensive score with the score threshold of the driving test subject to be evaluated.

[0049] S206: If the comprehensive score is lower than the score threshold, send driving improvement suggestions for the driving test subject to be evaluated to the terminal of the target trainee.

[0050] Specifically, send the comprehensive score of the target trainee in the training of the driving test subject to be evaluated to the in-vehicle device of the target vehicle for display, so that the target trainee can intuitively understand their driving performance after the training. Then compare the comprehensive score with the preset score threshold of the driving test subject to be evaluated. If the comprehensive score is lower than the score threshold, it means that the target trainee performs poorly in this driving test subject to be evaluated. Then send driving improvement suggestions to the terminal of this target trainee to help the target trainee improve their driving skills during the training process and thus improve their driving level. Among them, the terminal can be a smart phone or a tablet computer. Further, the implementation method for sending driving improvement suggestions for the driving test subject to be evaluated is as follows: Obtain the basic information of the target trainee when signing up for driving training. The basic information includes but is not limited to information such as the age, gender, and occupation of the target trainee. Input this basic information into the portrait prediction model to obtain the corresponding target portrait. The portrait prediction model can be a trained recurrent neural network model. The training process is briefly described as: Use the basic information of historical trainees marked with trainee portraits as training samples to train the model, and adjust the model parameters through the reverse gradient algorithm to finally obtain the portrait prediction model.

[0051] Further, retrieve the historical monitoring records of the training performance of historical students in the driving test subject to be evaluated. The historical monitoring records include, but are not limited to, information such as the student portraits of historical students, the sub-items with deductions, and the driving operations at the time of deductions. Based on this historical monitoring record, when a historical student with a target portrait has deductions in the driving test subject to be evaluated, obtain the historical driving operations that occurred, and count the first occurrence times of each historical driving operation. The larger the first occurrence time, the more likely the corresponding historical driving operation is to cause deductions in the driving test subject. Select the first number of historical driving operations from each historical driving operation in descending order of the first occurrence times and determine them as target driving operations, that is, the driving operations that are likely to cause deductions in the driving test subject. Among them, historical driving operations can be captured in real time through an in-vehicle camera, specifically by turning on the in-vehicle camera after obtaining the consent and authorization of the target student.

[0052] Further, based on the above historical monitoring record, obtain the historical sub-items with deductions in the driving test subject to be evaluated under a single target driving operation, and count the second occurrence times of each historical sub-item. The larger the second occurrence time, the more likely the target student is to induce deductions in the corresponding historical sub-item when performing a single target driving operation. Then, select the second number of historical sub-items from each historical sub-item in descending order of the second occurrence times and determine them as the target sub-items corresponding to the target driving operation, that is, the sub-items that are likely to cause deductions.

[0053] Further, determine the first weight of each target driving operation. The first weight is the ratio of the first occurrence time of each target driving operation to the sum of the first occurrence times of all target driving operations. And determine the second weight of each target sub-item corresponding to each target driving operation. The second weight is the ratio of the second occurrence time of a single target sub-item corresponding to the target driving operation to the sum of the second occurrence times of all target sub-items corresponding to the target driving operation. Then, based on the first weight and the corresponding second weights, determine the driving improvement suggestions and send these driving improvement suggestions to the terminal of the target student. The determination method of the driving improvement suggestions is as follows: According to the deducted sub-items sent by the terminal of the target student, determine the actual sub-items, and determine the key driving operations as the target driving operations in which at least one actual sub-item exists in the corresponding target sub-items. Then calculate the product of the first weight of each key driving operation and the second weights of the corresponding actual sub-items. The larger the product, the more likely the corresponding actual sub-item is to have a deduction when the key driving operation occurs. Then select the largest product from the products corresponding to the same actual sub-item. When the key driving operation corresponding to the largest product occurs, the possibility of deduction in the corresponding actual sub-item is the greatest. Then determine each key driving operation corresponding to the largest product as the operation to be improved. Finally, use the ChatGPT large language model to generate the corresponding driving improvement suggestions based on this operation to be improved.

[0054] In other embodiments, calculate the weight products of each first weight and the corresponding second weights, sum up the weight products corresponding to the same target sub-item to obtain the sum of weight products. The larger the sum of weight products, the greater the possibility of deduction in the corresponding target sub-item during the training of the target trainee. Based on the sums of weight products, determine the weights of the final scores of the corresponding sub-items. The larger the sum of weight products, the greater the weight of the final score of the corresponding sub-item, thereby improving the rationality of the weight of the final score and further improving the accuracy of the comprehensive score of the target trainee. In yet another embodiment, sum up the weight products corresponding to each target driving operation to obtain the corresponding sum result. If the sum result is greater than the preset sum result threshold, it indicates that the corresponding target driving operation is more likely to cause deductions in the driving test subject. Then, determine the corresponding target driving operation as an operation to be recorded. When this operation to be recorded appears in the driving video of the target trainee recorded by the in-vehicle camera obtained in real time, mark the corresponding video time node to obtain the marked driving video. Once the training score of the target trainee in the driving test subject to be evaluated is poor and the reasons need to be reviewed, play from the corresponding video time node in the order from largest to smallest of each sum result. The larger the sum result, the earlier the corresponding video time node is played, so as to quickly and accurately find out the specific reasons for the deductions.

[0055] The implementation principle of a method for analyzing the driving ability of a driver in an embodiment of the present application is as follows: Determine the first score of each sub-item in the first dimension, the second score in the second dimension, and the third score in the third dimension, so as to evaluate the driving ability of the target trainee in terms of the three dimensions of driving safety, smoothness, and fluency for a single sub-item. Then, perform a weighted sum of the first score, the second score, and the third score corresponding to a single sub-item, so as to more accurately evaluate the overall driving ability of the target trainee in a single sub-item of the driving test subject to be evaluated. Finally, perform a weighted sum of the final scores of each sub-item, so as to comprehensively analyze the driving ability of the target trainee in the entire driving test subject to be evaluated. This solution comprehensively evaluates the driving ability of the target trainee in the sub-items from three dimensions, and finally more comprehensively analyzes the driving ability of the target trainee, improving the accuracy of the analysis of the driving ability of the driver.

[0056] The following is an embodiment of the device of the present application, which can be used to execute the method embodiment of the present application. For the details not disclosed in the embodiment of the device of the present application, please refer to the method embodiment of the present application.

[0057] Please refer to Figure 3, which is a schematic structural diagram of the driver driving ability analysis device provided by the embodiment of the present application. The device applied to the driver driving ability analysis can be implemented as all or part of the device through software, hardware, or a combination of both. The device includes an information acquisition module 11, a first scoring module 12, a second scoring module 13, and a third scoring module 14.

[0058] The information acquisition module 11 is used to obtain the driving state information of the target trainee driving the target vehicle during the training of each sub-item of the driving test subject to be evaluated, and obtain the subject score of the target trainee in each sub-item; The first scoring module 12 is used to determine the first score of the corresponding sub-item in the first dimension, determine the second score of the corresponding sub-item in the second dimension, and determine the third score of the corresponding sub-item in the third dimension based on the driving state information and subject score corresponding to each sub-item. The first dimension is the dimension for evaluating the driving safety of the target trainee in the sub-item, the second dimension is the dimension for evaluating the driving smoothness of the target trainee in the sub-item, and the third dimension is the dimension for evaluating the driving fluency of the target trainee in the sub-item; The second scoring module 13 is used to perform a weighted sum of the first score, the second score, and the third score corresponding to the same sub-item to obtain the final score of the corresponding sub-item; The third scoring module 14 is used to perform a weighted sum of the final scores of each sub-item to obtain the comprehensive score of the target trainee during the training of the driving test subject to be evaluated.

[0059] Optionally, the first scoring module 12 is specifically used for: According to the brake pedal monitoring information in the driving state information corresponding to a single sub-item, determine the number of emergency brakes of the target trainee in the corresponding sub-item, and determine the corresponding brake score according to the number of emergency brakes. The more the number of emergency brakes, the lower the corresponding brake score; According to the accelerator pedal monitoring information in the driving state information corresponding to a single sub-item, determine the number of emergency accelerations of the target trainee in the corresponding sub-item, and determine the corresponding acceleration score according to the number of emergency accelerations. The more the number of emergency accelerations, the lower the corresponding acceleration score; According to at least one driving speed in the driving state information corresponding to a single sub-item, determine the standard deviation and average value of the vehicle speed in the corresponding sub-item, and determine the corresponding speed variation score according to the ratio of the standard deviation of the vehicle speed to the average value of the vehicle speed. The larger the ratio, the lower the corresponding speed variation score; Perform a weighted sum of the brake score, acceleration score, speed variation score, and subject score corresponding to the same sub-item to obtain the first score of the corresponding sub-item in the first dimension.

[0060] Optionally, the first scoring module 12 is specifically used for: According to the acceleration data in the driving state information corresponding to a single sub-item, determine the acceleration smoothness of the vehicle driving in the corresponding sub-item through a preset spectrum analysis method, and determine the corresponding acceleration score according to the acceleration smoothness. The higher the acceleration smoothness, the higher the corresponding acceleration score; Determine the corresponding trajectory score according to the actual driving trajectory of the vehicle in the driving state information corresponding to a single sub-item; According to at least one steering wheel angle in the driving state information corresponding to a single sub-item, determine the maximum steering wheel angle within a preset driving distance, and determine the corresponding steering wheel score according to the maximum steering wheel angle. The larger the maximum steering wheel angle, the lower the corresponding steering wheel score; Perform weighted summation on the acceleration score, trajectory score, and steering wheel score corresponding to the same sub-item to obtain the second score of the corresponding sub-item in the second dimension.

[0061] Optionally, the first scoring module 12 is specifically configured to: According to at least one driving speed in the driving state information corresponding to a single sub-item, determine the average speed of the target vehicle in the corresponding sub-item, and determine the corresponding average speed score according to the average speed. The larger the average speed, the higher the corresponding average speed score; According to the real-time speed and the gear position in the driving state information corresponding to a single sub-item, determine the duration of non-matching between the gear and the speed, and determine the gear-speed matching score according to the duration. The larger the duration, the lower the corresponding gear-speed matching score; Perform weighted summation on the average speed score and the gear-speed matching score corresponding to the same sub-item to obtain the third score of the corresponding sub-item in the third dimension.

[0062] Optionally, as Figure 4 shown, the device further includes a real-time feedback module 15, which is specifically configured to: Send the comprehensive score to the in-vehicle device of the target vehicle and the terminal of the target trainee, and compare the comprehensive score with the scoring threshold of the driving test subject to be evaluated; If the comprehensive score is lower than the scoring threshold, send driving improvement suggestions for the driving test subject to be evaluated to the terminal of the target trainee.

[0063] Optionally, the real-time feedback module 15 is specifically configured to: Determine the target portrait of the target trainee, obtain the historical driving operations that occurred when the historical trainees with the target portrait had points deducted in the driving test subject to be evaluated, count the first occurrence times of each historical driving operation, and select the first number of historical driving operations from each historical driving operation in descending order of the first occurrence times to determine the target driving operations; Obtain the historical sub-items with deductions in the driving test subject to be evaluated under a single target driving operation, count the second occurrence times of each historical sub-item, and select the second quantity of historical sub-items from each historical sub-item in descending order of the second occurrence times to determine the target sub-items of the corresponding target driving operation; Determine the first weight of each target driving operation, and determine the second weights of the respective target sub-items corresponding to each target driving operation. The first weight is the ratio of the first occurrence times of each target driving operation to the sum of the first occurrence times of all target driving operations, and the second weight is the ratio of the second occurrence times of a single target sub-item corresponding to a target driving operation to the sum of the second occurrence times of all target sub-items corresponding to the target driving operation; Determine the driving improvement suggestions for the driving test subject to be evaluated according to the first weight and the respective second weights, and send the driving improvement suggestions to the terminal of the target student.

[0064] Optionally, the real-time feedback module 15 is specifically used for: Determine the actual sub-items with deductions of the target student during training, determine the target driving operations with at least one actual sub-item in the respective corresponding target sub-items as the key driving operations, and calculate the product of the first weight of each key driving operation and the second weights of the respective actual sub-items; Select the maximum product from the products corresponding to the same actual sub-item, and determine the key driving operations corresponding to each maximum product as the operations to be improved; Based on the respective operations to be improved, determine the driving improvement suggestions for the driving test subject to be evaluated.

[0065] It should be noted that when the driving ability analysis device for a driver provided in the above embodiment executes the driving ability analysis method for a driver, only the above division of each functional module is used for illustration. In practical applications, the above functions can be allocated to different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the driving ability analysis device for a driver provided in the above embodiment and the embodiment of the driving ability analysis method for a driver belong to the same concept, and the implementation process thereof is detailed in the method embodiment, which will not be repeated here.

[0066] The embodiment of the present application also discloses a computer-readable storage medium, and the computer-readable storage medium stores a computer program, wherein when the computer program is executed by a processor, the driving ability analysis method for a driver in the above embodiment is adopted.

[0067] Among them, the computer program can be stored in a computer-readable medium. The computer program includes computer program code, which can be in the form of source code, object code, executable file, or some middleware form, etc. The computer-readable medium includes any entity or device capable of carrying the computer program code, recording medium, USB flash drive, mobile hard disk, magnetic disk, optical disc, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the computer-readable medium includes but is not limited to the above components.

[0068] Among them, through this computer-readable storage medium, a method for analyzing a driver's driving ability in the above embodiment is stored in the computer-readable storage medium, and is loaded and executed on a processor to facilitate the storage and application of the above method.

[0069] The embodiment of the present application also discloses an electronic device. When a computer program stored in the computer-readable storage medium is loaded and executed by a processor, the above method for analyzing a driver's driving ability is adopted.

[0070] Among them, the electronic device can be a desktop computer, a laptop computer, or a cloud server, etc. And the electronic device includes but is not limited to a processor and a memory. For example, the electronic device may also include input / output devices, network access devices, and a bus, etc.

[0071] Among them, the processor can adopt a central processing unit (CPU). Of course, according to the actual usage situation, other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. can also be adopted. The general-purpose processor can adopt a microprocessor or any conventional processor, etc. The present application does not make any restrictions on this.

[0072] Among them, the memory can be an internal storage unit of the electronic device. For example, the hard disk or memory of the electronic device, or it can also be an external storage device of the electronic device. For example, a plug-in hard disk, a smart media card (SMC), a secure digital card (SD), or a flash card (FC), etc. equipped on the electronic device. And the memory can also be a combination of the internal storage unit and the external storage device of the electronic device. The memory is used to store the computer program and other programs and data required by the electronic device. The memory can also be used to temporarily store the data that has been output or will be output. The present application does not make any restrictions on this.

[0073] Among them, through this electronic device, a method for analyzing the driving ability of a driver in the above embodiment is stored in the memory of the electronic device and is loaded and executed on the processor of the electronic device for convenient use.

[0074] The foregoing are only exemplary embodiments of the present disclosure, and the scope of the present disclosure cannot be limited thereby. That is, any equivalent changes and modifications made in accordance with the teachings of the present disclosure still fall within the scope covered by the present disclosure. This application aims to cover any variations, uses, or adaptive changes of the present disclosure, and these variations, uses, or adaptive changes follow the general principles of the present disclosure and include common general knowledge or conventional technical means in the technical field not recorded in the present disclosure. The description and the embodiments are only regarded as exemplary, and the scope and spirit of the present disclosure are defined by the claims.

Claims

1. A method for analyzing a driver's driving ability, characterized in that: The method comprises: Obtaining driving status information of a target student driving a target vehicle during training of each sub-item of the driving test subject to be evaluated, and obtaining subject scores of the target student in each of the sub-items; Based on the driving state information and the subject score corresponding to each of the sub-items, determining a first score of the corresponding sub-item in a first dimension, determining a second score of the corresponding sub-item in a second dimension, and determining a third score of the corresponding sub-item in a third dimension, wherein the first dimension is a dimension for evaluating the driving safety of the target learner in the sub-item, the second dimension is a dimension for evaluating the driving stability of the target learner in the sub-item, and the third dimension is a dimension for evaluating the driving fluency of the target learner in the sub-item; Performing a weighted summation on the first score, the second score, and the third score corresponding to the same sub-item to obtain a final score of the corresponding sub-item; The final scores of each sub-item are weighted and summed to obtain the comprehensive score of the target student in the training of the driving test subject to be evaluated.

2. The method for analyzing the driving ability of a driver according to claim 1, characterized in that: The determining, based on the driving state information and the subject score corresponding to each of the sub-items, a first score of the corresponding sub-item in the first dimension specifically includes: According to the brake pedal monitoring information in the driving state information corresponding to a single sub-item, determining the number of emergency braking of the target trainee in the corresponding sub-item, and determining the corresponding braking score according to the number of emergency braking, the more the number of emergency braking, the lower the corresponding braking score; According to the accelerator pedal monitoring information in the driving state information corresponding to a single sub-item, determining the number of sudden accelerations of the target student in the corresponding sub-item, and determining the corresponding acceleration score according to the number of sudden accelerations, wherein the more the number of sudden accelerations, the lower the corresponding acceleration score; According to at least one driving speed in the driving state information corresponding to a single sub-item, determine the vehicle speed standard deviation and the vehicle speed average value in the corresponding sub-item, and according to the ratio of the vehicle speed standard deviation to the vehicle speed average value, determine the corresponding speed variation score, the larger the ratio, the lower the corresponding speed variation score; The braking score, acceleration score, speed variation score and subject score corresponding to the same sub-item are weightedly summed to obtain the first score of the corresponding sub-item in the first dimension.

3. The method for analyzing the driving ability of a driver according to claim 1, characterized in that: Determining the second score of the corresponding sub-item in the second dimension specifically includes: According to the acceleration data in the driving state information corresponding to a single sub-item, the acceleration smoothness of the vehicle driving in the corresponding sub-item is determined by a preset spectrum analysis method, and according to the acceleration smoothness, a corresponding acceleration score is determined, wherein the higher the acceleration smoothness, the higher the corresponding acceleration score; Determine a corresponding trajectory score according to the actual driving trajectory of the vehicle in the driving state information corresponding to a single sub-item; determining a maximum steering wheel angle within a preset driving distance according to at least one steering wheel angle in the driving state information corresponding to a single sub-item, and determining a corresponding steering wheel score according to the maximum steering wheel angle, wherein the larger the maximum steering wheel angle, the lower the corresponding steering wheel score; The acceleration score, the trajectory score and the steering wheel score corresponding to the same sub-item are weightedly summed to obtain a second score of the corresponding sub-item in the second dimension.

4. The method for analyzing the driving ability of a driver according to claim 1, characterized in that: The determining of the third score of the corresponding sub-item in the third dimension specifically includes: According to at least one driving speed in the driving state information corresponding to a single sub-item, determine the average speed of the target vehicle in the corresponding sub-item, and determine the corresponding average speed score according to the average speed, wherein the greater the average speed, the higher the corresponding average speed score; According to the real-time vehicle speed and the gear position in the driving status information corresponding to the single sub-item, determine the duration of the mismatch between the gear position and the vehicle speed, and determine the gear speed matching score according to the duration, wherein the longer the duration, the lower the corresponding gear speed matching score; The average speed score and the gear speed matching score corresponding to the same sub-item are weightedly summed to obtain a third score of the corresponding sub-item in the third dimension.

5. The method for analyzing the driving ability of a driver according to claim 1, characterized in that: The method further comprises: Sending the comprehensive score to the on-board device of the target vehicle and the terminal of the target student, and comparing the comprehensive score with the score threshold of the driving test subject to be evaluated; If the comprehensive score is lower than the score threshold, driving improvement suggestions for the driving test subject to be evaluated are sent to the terminal of the target student.

6. The method for analyzing the driving ability of a driver according to claim 5, characterized in that: The sending of the driving improvement suggestions for the driving test subject to be evaluated to the terminal of the target student specifically includes: Determine a target profile of the target student, obtain historical driving operations of the historical students of the target profile when points are deducted in the driving test subject to be evaluated, count the first occurrence number of each of the historical driving operations, and select a first number of historical driving operations from each of the historical driving operations in descending order of the first occurrence number to determine as target driving operations; Obtaining historical sub-items in the subject of the driving test to be evaluated for which points are deducted under the single target driving operation, counting the second number of occurrences of each of the historical sub-items, and selecting a second number of historical sub-items from each of the historical sub-items in descending order of the second number of occurrences as target sub-items for the corresponding target driving operation; Determine a first weight for each of the target driving operations, and determine a second weight for each of the target sub-items corresponding to each of the target driving operations, wherein the first weight is a ratio of a first occurrence number of each target driving operation to a sum of first occurrence numbers of all target driving operations, and the second weight is a ratio of a second occurrence number of a single target sub-item corresponding to the target driving operation to a sum of second occurrence numbers of all corresponding target sub-items; According to the first weight and the corresponding second weights, driving improvement suggestions for the driving test subject to be evaluated are determined, and the driving improvement suggestions are sent to the terminal of the target student.

7. The method for analyzing the driving ability of a driver according to claim 1, characterized in that: The determining, according to the first weight and the corresponding second weights, driving improvement suggestions for the driving test subject to be evaluated specifically includes: Determine the actual sub-items in which the target trainee has deductions during training, determine the target driving operations in which at least one actual sub-item exists in the corresponding target sub-items as key driving operations, and calculate the product of the first weight of each key driving operation and the second weight of the corresponding actual sub-items; Selecting a maximum product from the products corresponding to the same actual sub-item, and determining the key driving operation corresponding to each maximum product as the operation to be improved; Based on each of the operations to be improved, driving improvement suggestions for the driving test subject to be evaluated are determined.

8. A device for analyzing a driver's driving ability, characterized in that: include: An information acquisition module (11) is used to obtain driving status information of a target student driving a target vehicle during training of each sub-item of a driving test subject to be evaluated, and to obtain subject scores of the target student in each of the sub-items; A first scoring module (12) is used to determine, based on the driving state information and subject score corresponding to each of the sub-items, a first score of the corresponding sub-item in a first dimension, a second score of the corresponding sub-item in a second dimension, and a third score of the corresponding sub-item in a third dimension, wherein the first dimension is a dimension for evaluating the driving safety of the target student in the sub-item, the second dimension is a dimension for evaluating the driving stability of the target student in the sub-item, and the third dimension is a dimension for evaluating the driving fluency of the target student in the sub-item; A second scoring module (13) is used to perform a weighted summation of the first score, the second score and the third score corresponding to the same sub-item to obtain a final score of the corresponding sub-item; The third scoring module (14) is used to perform weighted summation on the final scores of each of the sub-items to obtain a comprehensive score of the target student in the training of the driving test subject to be evaluated.

9. A computer-readable storage medium having a computer program stored therein, characterized in that: When the computer program is loaded and executed by a processor, the method according to any one of claims 1 to 7 is adopted.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and capable of running on the processor, characterized in that: When the processor loads and executes the computer program, the method according to any one of claims 1 to 7 is adopted.

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