Safety performance detection method and system for new energy vehicle and storage medium

Abstract

The invention relates to the technical field of machine learning, in particular to a safety performance detection method and system for a new energy vehicle and a storage medium, and the method comprises the steps: S1, determining a plurality of to-be-determined historical records from all historical records of a historical record combination, and carrying out the modification processing of the plurality of to-be-determined historical records; s2, determining a historical record in the modified historical record combination, inputting historical performance data in the determined historical record into the detection model, and repeating the step under the condition that the historical record which is not determined exists; s3, judging whether the practice frequency is smaller than a practice frequency threshold value or not, if not, stopping all the steps, if yes, continuing to judge whether the requirement for stopping practice is met or not, if not, skipping to S2, and if yes, stopping all the steps; and S4, inputting the performance data of the to-be-detected vehicle into the practiced detection model. According to the method, errors in the historical record combination can be modified.

Description

Technical Field

[0001] The present application relates to the field of machine learning technology, and in particular to a safety performance detection method, system and storage medium for new energy vehicles. Background Art

[0002] With the development of computer application technology, it is becoming more and more common to collect performance data of new energy vehicles through performance testing equipment and to obtain analysis results about the performance of new energy vehicles through machine learning models.

[0003] A Chinese patent application with publication number CN119322980A discloses a method and system for analyzing the power performance of an automobile air conditioner based on machine learning. The system includes a data collection module for automobile air conditioner power influencing factors, a final random forest model construction module, a parameter tuning module, a first automobile air conditioner power performance level determination module, and a second automobile air conditioner power performance level determination module. The random forest model is trained by using the collected automobile air conditioner power influencing factor data set, and the data of the automobile air conditioner power influencing factors to be tested are input into the final random forest model for classification to obtain the first automobile air conditioner power performance level, and the cooling speed of the automobile air conditioner to be tested is calculated, and the second automobile air conditioner power performance level is obtained by analysis, and the final automobile air conditioner power performance level to be tested is determined. A Chinese patent application with publication number CN118133657A discloses a method for detecting the deployment speed of an automobile airbag, which includes collecting collision data; storing the collision data to form a data set; expanding the data set through data enhancement technology; inputting the data into a neural network model as a training set to establish a collision model; inputting the collision data into the neural network model to adjust the deployment speed of the airbag according to the prediction results, wherein the collision model based on machine learning can learn and extract features in the collision data through a large amount of experimental data, and predict the severity of the collision and the deployment speed of the airbag.

[0004] However, both of the above two patent applications do not consider the problem that erroneous training data may exist in the training data set. Summary of the invention

[0005] The present application determines several pending historical records in a historical record combination, modifies the several pending historical records, generates a detection model using the modified historical record combination, and stops practicing the detection model at an appropriate time. The present application aims to generate a detection model using a correct historical record combination.

[0006] The present application provides a safety performance testing method for new energy vehicles, comprising the following steps: S1. The preprocessing module obtains a historical record combination, wherein the historical record combination includes different historical records, and the historical records are composed of historical performance data and historical scores corresponding to the historical performance data. The preprocessing module determines a number of pending historical records from all the historical records of the historical record combination, and performs modification processing on the number of pending historical records. S2, the training module determines a historical record in the modified historical record combination, and inputs the historical performance data in the determined historical record into the detection model, adjusts the detection model based on the output score of the detection model, and further determines whether there is an undetermined historical record, and if yes, repeats this step, and if no, proceeds to the next step; S3, the exercise module determines whether the number of exercises is less than a preset exercise number threshold, if not, stops all steps, if yes, continues to determine whether the requirement for stopping the exercise is met, if not, jumps to S2, if yes, stops all steps; S4. The detection module obtains performance data of the vehicle to be detected, and inputs the performance data of the vehicle to be detected into the trained detection model, and the detection model outputs a score on the performance of the vehicle to be detected.

[0007] As a preferred technical solution of the present application, the exercise module determines whether the requirement for stopping exercise is met, including the following steps: S31, the practice module determines a historical record in the modified historical record combination, and inputs the historical performance data in the determined historical record into the current detection model, and obtains a first non-negative value of the difference between the output score of the current detection model and the historical score in the determined historical record, and a second non-negative value of the difference between the output score of the current detection model and the output score of the previous detection model with respect to the historical performance data in the determined historical record; S32, the exercise module determines whether the first non-negative value is less than a preset first threshold value. If yes, continue to determine whether there is a historical record that has not been determined. If yes, jump to S31. If no, it is considered that the requirement for stopping the exercise is met and all steps are stopped. If no, proceed to the next step. S33, the practice module determines whether the second non-negative value is less than the preset second threshold value. If so, continue to determine whether there is a historical record that has not been determined. If so, jump to S31. If not, it is considered that the requirements for stopping practice are met and all steps are stopped. If not, it is considered that the requirements for stopping practice are not met and all steps are stopped.

[0008] As a preferred technical solution of the present application, the preprocessing module determines a number of pending historical records from all historical records of the historical record combination, and performs modification processing on the number of pending historical records, including the following steps: S11, the preprocessing module generates a first verification model using all historical records, inputs historical performance data in all historical records into the generated first verification model in sequence, obtains output scores of the first verification model respectively, and calculates a first ratio of the number of historical records whose corresponding output scores are consistent with the included historical scores to the total number of all historical records; S12, the preprocessing module determines whether the first ratio is greater than a preset first ratio threshold, and if so, continues with subsequent processing, and if not, continues with the next step; S13, the preprocessing module determines whether the number of times of repeatedly generating the first test model and repeatedly calculating the first ratio is greater than or equal to a preset number threshold. If not, several historical records whose corresponding output scores are inconsistent with the included historical scores are regarded as several pending historical records, several pending historical records are modified, and the process goes to S11. If yes, several pending historical records are determined from all the historical records using a preset method, several pending historical records are modified, and the process goes to S11.

[0009] As a preferred technical solution of the present application, the subsequent processing includes the following steps: S121, the preprocessing module divides all historical records into several sub-combinations, selects one sub-combination from the several sub-combinations, uses all other sub-combinations to generate a second inspection model, inputs historical performance data in all historical records in the selected sub-combination into the generated second inspection model in sequence, obtains output scores of the second inspection model respectively, and calculates a second ratio of the number of historical records whose corresponding output scores are consistent with the included historical scores to the total number of all historical records in the selected sub-combination; S122, the preprocessing module determines whether the second ratio is greater than a preset second ratio threshold value, and if so, stops all steps, and if not, proceeds to the next step; S123, the preprocessing module determines whether the number of repeated generation of the second test model and repeated calculation of the second ratio is greater than or equal to a preset number threshold. If it is less than, several historical records whose corresponding output scores are inconsistent with the included historical scores are regarded as several pending historical records, and several pending historical records are modified, and the process jumps to S121. If it is greater than or equal to, a preset method is used to determine several pending historical records from all the historical records, and several pending historical records are modified, and the process jumps to S121.

[0010] As a preferred technical solution of the present application, the preset method comprises the following steps: S131, the preprocessing module generates a third inspection model using all historical records, inputs the historical performance data in all historical records into the generated third inspection model in sequence, obtains the output scores of the third inspection model respectively, and stores the historical records whose corresponding output scores are inconsistent with the included historical scores respectively for the first time; S132, the preprocessing module determines a number of historical records from all the historical records, generates a fourth inspection model using all the other historical records, sequentially inputs the historical performance data in all the other historical records into the generated fourth inspection model, obtains the output scores of the fourth inspection model respectively, and stores the corresponding historical records whose output scores are consistent with the included historical scores respectively for a second time; S133, the preprocessing module determines whether there are historical records that have been stored twice. If not, continue to determine whether there are historical records that have not been determined. If so, jump to S132. If not, stop all steps. If so, treat the determined historical records as pending historical records.

[0011] As a preferred technical solution of the present application, in the above S133, if yes, the following steps are also included: S1331, the preprocessing module selects a number of historical records from all determined historical records, and uses all other historical records except the selected number of historical records from all historical records to generate a fifth verification model; S1332, the preprocessing module sequentially inputs all the historical performance data in other historical records into the generated fifth inspection model, obtains the output scores of the fifth inspection model respectively, and stores the corresponding historical records that are consistent with the output scores and the included historical scores respectively for the third time; S1333, the preprocessing module determines whether there are historical records that are stored both at the first storage and the third storage. If so, the selected historical records are regarded as pending historical records. If not, continue to determine whether there are historical records that have not been selected among all the determined historical records. If so, jump to S1331. If not, stop all steps.

[0012] This application also provides a safety performance testing system for new energy vehicles, including the following modules: A preprocessing module is used to obtain a historical record combination, wherein the historical record combination includes different historical records, the historical records are composed of historical performance data and historical scores corresponding to the historical performance data, determine a number of pending historical records from all the historical records of the historical record combination, and perform modification processing on the several pending historical records; A practice module, used to determine a historical record in the modified historical record combination, input the historical performance data in the determined historical record into the detection model, adjust the detection model based on the output score of the detection model, determine whether there is an undetermined historical record, if yes, repeat the present process, and determine whether the number of practice times is less than a preset practice times threshold, if no, stop all processes, if yes, continue to determine whether the requirement for stopping practice is met, if no, repeat the previous process, if yes, stop all processes; The detection module is used to obtain the performance data of the vehicle to be detected, input the performance data of the vehicle to be detected into a trained detection model, and the detection model outputs a score about the performance of the vehicle to be detected.

[0013] The present application also provides a storage medium, wherein the storage medium stores program instructions, wherein when the program instructions are executed, the device where the storage medium is located is controlled to execute any one of the above methods.

[0014] Compared with the prior art, the beneficial effects of the present application are at least as follows: In the technical solution provided by the present application, first, a historical record combination is obtained, in which different historical records are included, and several pending historical records are determined from all the historical records of the historical record combination, and the pending historical records are modified. Secondly, a historical record is determined in the modified historical record combination, and the historical performance data in the determined historical record is input into the detection model, and the detection model is adjusted based on the output score of the detection model. It is also determined whether there are historical records that have not been determined. If yes, repeat this process, and if no, proceed to the next step. Thirdly, it is determined whether the number of exercises is less than the preset number of exercises threshold. If no, stop all steps. If yes, continue to determine whether the requirements for stopping the exercise are met. If no, jump to the previous process, and if yes, stop all steps. Finally, the performance data of the vehicle to be detected is obtained, and the performance data of the vehicle to be detected is input into the practiced detection model, and the detection model outputs a score about the performance of the vehicle to be detected. Through this application, not only can the pending historical records that may contain errors in the historical record combination be determined, and the pending historical records can be modified and processed, so that a detection model can be generated through the correct historical record combination, which is beneficial to the accuracy of the detection model, but also the appropriate time to stop practicing the detection model can be determined, thereby avoiding waste of computing power. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0016] Figure 1 This is a flow chart of a safety performance testing method for new energy vehicles in an embodiment of the present application; Figure 2 A flowchart for determining several pending historical records in an embodiment of the present application; Figure 3 This is a flowchart of the subsequent processing in the embodiment of this application; Figure 4 Schematic diagram of a safety performance testing system for new energy vehicles in an embodiment of the present application. DETAILED DESCRIPTION

[0017] The embodiments of the present application provide a safety performance detection method, system and storage medium for new energy vehicles. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described here can be implemented in an order other than that illustrated or described here. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0018] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 The safety performance detection method for new energy vehicles in the embodiment of the present application includes the following main steps: S1. The preprocessing module obtains a historical record combination, wherein the historical record combination includes different historical records, and the historical records are composed of historical performance data and historical scores corresponding to the historical performance data. The preprocessing module determines a number of pending historical records from all the historical records of the historical record combination, and performs modification processing on the number of pending historical records. S2, the exercise module determines a historical record in the modified historical record combination, and inputs the historical performance data in the determined historical record into the detection model, adjusts the detection model based on the output score of the detection model, and further determines whether there is an undetermined historical record. If yes, repeat this step, otherwise proceed to the next step; S3, the practice module determines whether the number of practice times is less than a preset practice number threshold. If not, stop all steps. If yes, continue to determine whether the requirement for stopping practice is met. If not, jump to S2. If yes, stop all steps. S4. The detection module obtains performance data of the vehicle to be detected, inputs the performance data of the vehicle to be detected into a trained detection model, and the detection model outputs a score on the performance of the vehicle to be detected.

[0019] Specifically, a large amount of historical records are usually accumulated first, and then the accumulated historical records are used to practice generating machine learning models. However, if there are erroneous historical records in the accumulated historical records, it will have a negative impact on the accuracy of the machine learning model. In addition, when practicing generating machine learning models, it is also necessary to judge the appropriate time to stop to avoid wasting computing power.

[0020] In S1, the preprocessing module obtains a historical record combination, which includes different historical records, wherein the historical records are composed of historical performance data and historical scores corresponding to the historical performance data. It should be noted that the method for obtaining the historical records is to first set a historical score, and then set the historical performance data corresponding to the historical score. Therefore, it is considered that the historical score is accurate. The preprocessing module determines a number of pending historical records from all the historical records of the historical record combination. The pending historical records are historical records that contain historical performance data that may need to be modified. Modification processing is performed on the several pending historical records. It should be noted that the modification processing can be performed manually, and the pending historical records may not be modified when it is manually determined that modification is not necessary. The detailed process will be described below. In S2, the practice module determines a historical record in the modified historical record combination, inputs the historical performance data in the determined historical record into the detection model, and adjusts the detection model based on the output score of the detection model. The detection model can be a neural network model, and the parameters of the neural network model can be adjusted based on the deviation value between the output score of the neural network model and the corresponding historical score. The practice module determines whether there is an undetermined historical record. If it exists, determine an undetermined historical record and repeat this step. If it does not exist, it is considered that one practice is ended and the next step is continued. In S3, the practice module determines whether the number of exercises is less than the preset number of exercises threshold. The number of exercises threshold is set according to the actual application scenario. If it is greater than or equal to, stop all steps. If it is less than, continue to determine whether the requirements for stopping the practice are met. The requirements for stopping the practice will be described below. If not, jump to S2. If it is met, stop all steps. In S4, the detection module obtains the performance data of the vehicle to be detected from the new energy vehicle performance detection equipment, inputs the performance data of the vehicle to be detected into the practiced detection model, and the detection model outputs the score of the performance of the vehicle to be detected.

[0021] Furthermore, the exercise module determines whether the requirement for stopping the exercise is met, including the following steps: S31, the training module determines a historical record in the modified historical record combination, and inputs the historical performance data in the determined historical record into the current detection model, and obtains a first non-negative value of the difference between the output score of the current detection model and the historical score in the determined historical record, and a second non-negative value of the difference between the output score of the current detection model and the output score of the previous detection model with respect to the historical performance data in the determined historical record; S32, the exercise module determines whether the first non-negative value is less than a preset first threshold value. If yes, continue to determine whether there is a historical record that has not been determined. If yes, jump to S31. If no, it is considered that the requirement for stopping the exercise is met and all steps are stopped. If no, proceed to the next step. S33, the practice module determines whether the second non-negative value is less than the preset second threshold value. If so, continue to determine whether there is a historical record that has not been determined. If so, jump to S31. If not, it is considered that the requirements for stopping practice are met and all steps are stopped. If not, it is considered that the requirements for stopping practice are not met and all steps are stopped.

[0022] Specifically, the process of the practice module judging whether the requirement of stopping practice is met is introduced. In S31, the practice module determines a historical record in the combination of historical records that have been modified, inputs the historical performance data in the determined historical record into the current detection model, and the current detection model has been practiced several times, and obtains the first non-negative value of the difference between the output score of the current detection model and the historical score in the determined historical record, and at the same time obtains the second non-negative value of the difference between the output score of the current detection model and the output score of the previous detection model with respect to the historical performance data in the determined historical record, and the previous detection model refers to the detection model when the practice before the current practice ends. In S32, the practice module judges whether the first non-negative value is less than a preset first threshold value, and the first threshold value is set according to the actual application scenario. If it is less than, continue to judge whether there is a historical record that has not been determined. If it exists, jump to S31. If it does not exist, it is considered that the requirement of stopping practice is met, and the method from S31 to S33 is stopped. If it is greater than or equal to, proceed to the next step. In S33, the practice module determines whether the second non-negative value is less than a preset second threshold value, and the second threshold value is set according to the actual application scenario. If it is less than, continue to determine whether there is a historical record that has not been determined. If it exists, jump to S31. If it does not exist, it is considered that the requirement for stopping practice is met, and the method from S31 to S33 is stopped. If it is greater than or equal to, it is considered that the requirement for stopping practice is not met, and the method from S31 to S33 is stopped. According to the above method, for all historical records, if the first non-negative values ​​are all less than the first threshold value, or the second non-negative values ​​are all less than the second threshold value, then it is considered that the requirement for stopping practice is met.

[0023] Furthermore, the preprocessing module determines a number of pending historical records from all historical records of the historical record combination, and performs modification processing on the number of pending historical records, including the following steps: S11, the preprocessing module generates a first inspection model using all historical records, inputs historical performance data in all historical records into the generated first inspection model in sequence, obtains output scores of the first inspection model respectively, and calculates a first ratio of the number of historical records whose corresponding output scores are consistent with the included historical scores to the total number of all historical records; S12, the preprocessing module determines whether the first ratio is greater than a preset first ratio threshold value, and if so, continues with the subsequent processing, and if not, continues with the next step; S13, the preprocessing module determines whether the number of repeated generation of the first test model and repeated calculation of the first ratio is greater than or equal to a preset number threshold. If not, several historical records whose corresponding output scores are inconsistent with the included historical scores are regarded as several pending historical records, and several pending historical records are modified, and the process goes to S11. If so, several pending historical records are determined from all the historical records using a preset method, and several pending historical records are modified, and the process goes to S11.

[0024] Specifically, see Figure 2, introduces how the preprocessing module determines several pending historical records from all the historical records of the historical record combination, and performs modification processing on several pending historical records. In S11, the preprocessing module uses all the historical records to generate a first test model. The first test model has the same function as the detection model, and can output performance scores based on the input historical performance data. After the first test model is generated, the historical performance data in all the historical records are sequentially input into the generated first test model, and the output scores of the first test model are obtained respectively. The preprocessing module calculates the first ratio of the number of historical records that are consistent with the corresponding output score and the included historical score to the total number of all historical records. The consistency of the corresponding output score and the included historical score can mean that the two are the same, or the difference between the two is less than the preset difference threshold, and the difference threshold is set according to the actual application scenario. In S12, the preprocessing module determines whether the first ratio is greater than the preset first ratio threshold, and the first ratio threshold is set according to the actual application scenario. If it is greater than, continue with the subsequent processing, and the steps of the subsequent processing will be described below. If it is less than or equal to, continue with the next step. In S13, the preprocessing module determines whether the number of times the first test model is repeatedly generated and the number of times the first ratio is repeatedly calculated is greater than or equal to a preset number threshold, and the number threshold is set according to the actual application scenario. If it is less than, several historical records whose corresponding output scores are inconsistent with the included historical scores are regarded as several pending historical records, and several pending historical records are modified, specifically, the historical performance data in several pending historical records are modified, and the process jumps to S11. If it is greater than or equal to, a preset method is used to determine several pending historical records in all historical records, and several pending historical records are modified, and the process jumps to S11. The steps of the preset method will be described below.

[0025] Further, the subsequent processing includes the following steps: S121, the preprocessing module divides all historical records into several sub-combinations, selects one sub-combination from the several sub-combinations, uses all other sub-combinations to generate a second inspection model, inputs historical performance data in all historical records in the selected sub-combination into the generated second inspection model in sequence, obtains output scores of the second inspection model respectively, and calculates a second ratio of the number of historical records whose corresponding output scores are consistent with the included historical scores to the total number of all historical records in the selected sub-combination; S122, the preprocessing module determines whether the second ratio is greater than a preset second ratio threshold value, and if so, stops all steps, and if not, proceeds to the next step; S123, the preprocessing module determines whether the number of repeated generation of the second test model and repeated calculation of the second ratio is greater than or equal to a preset number threshold. If it is less than, several historical records whose corresponding output scores are inconsistent with the included historical scores are regarded as several pending historical records, and several pending historical records are modified, and the process goes to S121. If it is greater than or equal to, a preset method is used to determine several pending historical records from all the historical records, and several pending historical records are modified, and the process goes to S121.

[0026] Specifically, see Figure 3 , the steps of subsequent processing are introduced. In S121, the preprocessing module divides all historical records into several sub-combinations, selects a sub-combination from the several sub-combinations, and uses all other sub-combinations to generate a second test model. The second test model has the same function as the detection model, and can output performance scores based on the input historical performance data. After the second test model is generated, the historical performance data of all historical records in the selected sub-combination are sequentially input into the generated second test model, and the output scores of the second test model are obtained respectively. The preprocessing module calculates the second ratio of the number of historical records that are consistent with the corresponding output score and the historical score to the total number of all historical records in the selected sub-combination. The consistency here has the same meaning as the consistency mentioned above. In S122, the preprocessing module determines whether the second ratio is greater than the preset second ratio threshold. The second ratio threshold is set according to the actual application scenario. If it is greater than, stop all steps. If it is less than or equal to, continue to the next step. In S123, the preprocessing module determines whether the number of times the second test model is repeatedly generated and the number of times the second ratio is repeatedly calculated is greater than or equal to a preset number threshold. If so, several historical records whose corresponding output scores are inconsistent with the included historical scores are regarded as several pending historical records, and several pending historical records are modified, specifically the historical performance data in the several pending historical records are modified, and the process jumps to S121. If so, a preset method is used to determine several pending historical records in all the historical records, and several pending historical records are modified, and the process jumps to S121. The steps of the preset method will be described below.

[0027] Furthermore, the preset method includes the following steps: S131, the preprocessing module generates a third inspection model using all historical records, inputs the historical performance data in all historical records into the generated third inspection model in sequence, obtains the output scores of the third inspection model respectively, and stores the historical records whose corresponding output scores are inconsistent with the included historical scores respectively for the first time; S132, the preprocessing module determines a number of historical records from all the historical records, generates a fourth inspection model using all the other historical records, sequentially inputs the historical performance data in all the other historical records into the generated fourth inspection model, obtains the output scores of the fourth inspection model respectively, and stores the corresponding historical records whose output scores are consistent with the included historical scores respectively for a second time; S133, the preprocessing module determines whether there are historical records that have been stored twice. If not, continue to determine whether there are historical records that have not been determined. If so, jump to S132. If not, stop all steps. If so, treat the determined historical records as pending historical records.

[0028] Specifically, the steps of the preset method are introduced. In S131, the preprocessing module uses all the historical records to generate a third test model. The third test model has the same function as the detection model, and can output performance scores based on the input historical performance data. After the third test model is generated, the historical performance data in all the historical records are sequentially input into the generated third test model, and the output scores of the third test model are obtained respectively. For the first time, multiple historical records whose corresponding output scores are inconsistent with the included historical scores are stored. The consistency here has the same meaning as the consistency mentioned above. In S132, the preprocessing module determines several historical records from all the historical records, and uses all the other historical records to generate a fourth test model. The fourth test model has the same function as the detection model, and can output performance scores based on the input historical performance data. After the fourth test model is generated, the historical performance data in all the other historical records are sequentially input into the generated fourth test model, and the output scores of the fourth test model are obtained respectively. For the second time, multiple historical records whose corresponding output scores are consistent with the included historical scores are stored. The consistency here has the same meaning as the consistency mentioned above. In S133, the preprocessing module determines whether there are historical records that are stored twice. For example, a historical record is recorded both when executing S131 and when executing S132. If not, continue to determine whether there are historical records that have not been determined. If so, jump to S132, determine a number of historical records that have not been determined from all historical records and continue to execute subsequent steps. If not, stop all steps. If so, it is considered that the reason for this situation is that the determined historical records were not used when generating the fourth verification model in S132, and therefore the determined historical records are regarded as pending historical records.

[0029] Furthermore, in S133, if yes, the following steps are also included: S1331: The preprocessing module selects a number of historical records from all determined historical records, and uses all other historical records except the selected number of historical records from all historical records to generate a fifth inspection model; S1332, the preprocessing module sequentially inputs all the historical performance data in other historical records into the generated fifth inspection model, obtains the output scores of the fifth inspection model respectively, and stores the corresponding historical records that are consistent with the output scores and the included historical scores respectively for the third time; S1333, the preprocessing module determines whether there are historical records that are stored both at the first storage and the third storage. If so, the selected historical records are regarded as pending historical records. If not, continue to determine whether there are historical records that have not been selected among all the determined historical records. If so, jump to S1331. If not, stop all steps.

[0030] Specifically, the steps of the preset method are continued to be introduced. By executing S131 to S133, several pending historical records can be determined, but the total number of the pending historical records may be relatively large. It takes a long time to modify the pending historical records. Therefore, S1331 to S1333 are proposed. In S133, when the judgment result is that there are historical records that have been stored twice, it is executed. In S1331, the preprocessing module selects several historical records from all the determined historical records. The determined historical records here refer to the several historical records determined when S132 is executed. All other historical records except the selected historical records are used to generate a fifth inspection model. The fifth inspection model has the same function as the detection model, and can output performance scores according to the input historical performance data. In S1332, after the fifth inspection model is generated, the preprocessing module sequentially inputs the historical performance data in all other historical records into the generated fifth inspection model, and obtains the output scores of the fifth inspection model respectively. The third storage of multiple corresponding historical records with consistent output scores and the included historical scores is the same. The consistency here has the same meaning as the consistency in the above text. In S1333, the preprocessing module determines whether there are historical records that are stored both at the first storage and the third storage. If so, it is considered that the reason for this situation is that when the fifth verification model is generated in S131, several historical records selected from all the determined historical records are not used, and therefore the selected several historical records are regarded as several pending historical records. If not, continue to determine whether there are historical records that have not been selected in all the determined historical records. If so, jump to S1331, select several historical records that have not been selected from all the determined historical records and continue to execute subsequent steps. If not, stop all steps.

[0031] According to another aspect of the embodiment of the present application, refer to Figure 4 As shown, the present application also provides a safety performance detection system for new energy vehicles, including a preprocessing module, a training module, and a detection module to implement the safety performance detection method for new energy vehicles described above.

[0032] The functions of each module are as follows: A preprocessing module is used to obtain a historical record combination, wherein the historical record combination includes different historical records, the historical records are composed of historical performance data and historical scores corresponding to the historical performance data, determine a number of pending historical records from all the historical records of the historical record combination, and perform modification processing on the several pending historical records; A practice module, used to determine a historical record in the modified historical record combination, input the historical performance data in the determined historical record into the detection model, adjust the detection model based on the output score of the detection model, determine whether there is an undetermined historical record, if yes, repeat the present process, and determine whether the number of practice times is less than a preset practice times threshold, if no, stop all processes, if yes, continue to determine whether the requirement for stopping practice is met, if no, repeat the previous process, if yes, stop all processes; The detection module is used to obtain the performance data of the vehicle to be detected, input the performance data of the vehicle to be detected into a trained detection model, and the detection model outputs a score about the performance of the vehicle to be detected.

[0033] According to another aspect of an embodiment of the present application, a storage medium is further provided, wherein the storage medium stores program instructions, wherein when the program instructions are executed, a device where the storage medium is located is controlled to execute any one of the above methods.

[0034] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0035] If the 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 this understanding, the technical solution of the present application is essentially 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, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program codes.

[0036] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A safety performance testing method for new energy vehicles, characterized in that: The method comprises the following steps: S1. The preprocessing module obtains a historical record combination, wherein the historical record combination includes different historical records, and the historical records are composed of historical performance data and historical scores corresponding to the historical performance data. The preprocessing module determines a number of pending historical records from all the historical records of the historical record combination, and performs modification processing on the number of pending historical records. S2, the training module determines a historical record in the modified historical record combination, and inputs the historical performance data in the determined historical record into the detection model, adjusts the detection model based on the output score of the detection model, and further determines whether there is an undetermined historical record, and if yes, repeats this step, and if no, proceeds to the next step; S3, the exercise module determines whether the number of exercises is less than a preset exercise number threshold, if not, stops all steps, if yes, continues to determine whether the requirement for stopping the exercise is met, if not, jumps to S2, if yes, stops all steps; S4. The detection module obtains performance data of the vehicle to be detected, and inputs the performance data of the vehicle to be detected into the trained detection model, and the detection model outputs a score on the performance of the vehicle to be detected.

2. The method according to claim 1, characterized in that: The exercise module determines whether the requirement for stopping the exercise is met, including the following steps: S31, the practice module determines a historical record in the modified historical record combination, and inputs the historical performance data in the determined historical record into the current detection model, and obtains a first non-negative value of the difference between the output score of the current detection model and the historical score in the determined historical record, and a second non-negative value of the difference between the output score of the current detection model and the output score of the previous detection model with respect to the historical performance data in the determined historical record; S32, the exercise module determines whether the first non-negative value is less than a preset first threshold value. If yes, continue to determine whether there is a historical record that has not been determined. If yes, jump to S31. If no, it is considered that the requirement for stopping the exercise is met and all steps are stopped. If no, proceed to the next step. S33, the practice module determines whether the second non-negative value is less than the preset second threshold value. If so, continue to determine whether there is a historical record that has not been determined. If so, jump to S31. If not, it is considered that the requirements for stopping practice are met and all steps are stopped. If not, it is considered that the requirements for stopping practice are not met and all steps are stopped.

3. The method according to claim 1, characterized in that: The preprocessing module determines a number of pending historical records from all historical records of the historical record combination, and performs modification processing on the number of pending historical records, including the following steps: S11, the preprocessing module generates a first verification model using all historical records, inputs historical performance data in all historical records into the generated first verification model in sequence, obtains output scores of the first verification model respectively, and calculates a first ratio of the number of historical records whose corresponding output scores are consistent with the included historical scores to the total number of all historical records; S12, the preprocessing module determines whether the first ratio is greater than a preset first ratio threshold, and if so, continues with subsequent processing, and if not, continues with the next step; S13, the preprocessing module determines whether the number of times of repeatedly generating the first test model and repeatedly calculating the first ratio is greater than or equal to a preset number threshold. If not, several historical records whose corresponding output scores are inconsistent with the included historical scores are regarded as several pending historical records, several pending historical records are modified, and the process goes to S11. If yes, several pending historical records are determined from all the historical records using a preset method, several pending historical records are modified, and the process goes to S11.

4. The method according to claim 3, characterized in that The subsequent processing includes the following steps: S121, the preprocessing module divides all historical records into several sub-combinations, selects one sub-combination from the several sub-combinations, uses all other sub-combinations to generate a second inspection model, inputs historical performance data in all historical records in the selected sub-combination into the generated second inspection model in sequence, obtains output scores of the second inspection model respectively, and calculates a second ratio of the number of historical records whose corresponding output scores are consistent with the included historical scores to the total number of all historical records in the selected sub-combination; S122, the preprocessing module determines whether the second ratio is greater than a preset second ratio threshold value, and if so, stops all steps, and if not, proceeds to the next step; S123, the preprocessing module determines whether the number of repeated generation of the second test model and repeated calculation of the second ratio is greater than or equal to a preset number threshold. If it is less than, several historical records whose corresponding output scores are inconsistent with the included historical scores are regarded as several pending historical records, and several pending historical records are modified, and the process jumps to S121. If it is greater than or equal to, a preset method is used to determine several pending historical records from all the historical records, and several pending historical records are modified, and the process jumps to S121.

5. The method according to claim 4, characterized in that The preset method comprises the following steps: S131, the preprocessing module generates a third inspection model using all historical records, inputs the historical performance data in all historical records into the generated third inspection model in sequence, obtains the output scores of the third inspection model respectively, and stores the historical records whose corresponding output scores are inconsistent with the included historical scores respectively for the first time; S132, the preprocessing module determines a number of historical records from all the historical records, generates a fourth inspection model using all the other historical records, sequentially inputs the historical performance data in all the other historical records into the generated fourth inspection model, obtains the output scores of the fourth inspection model respectively, and stores the corresponding historical records whose output scores are consistent with the included historical scores respectively for a second time; S133, the preprocessing module determines whether there are historical records that have been stored twice. If not, continue to determine whether there are historical records that have not been determined. If so, jump to S132. If not, stop all steps. If so, treat the determined historical records as pending historical records.

6. The method according to claim 5, characterized in that In the above S133, if yes, the following steps are also included: S1331, the preprocessing module selects a number of historical records from all determined historical records, and uses all other historical records except the selected number of historical records from all historical records to generate a fifth verification model; S1332, the preprocessing module sequentially inputs all the historical performance data in other historical records into the generated fifth inspection model, obtains the output scores of the fifth inspection model respectively, and stores the corresponding historical records that are consistent with the output scores and the included historical scores respectively for the third time; S1333, the preprocessing module determines whether there are historical records that are stored both at the first storage and the third storage. If so, the selected historical records are regarded as pending historical records. If not, continue to determine whether there are historical records that have not been selected among all the determined historical records. If so, jump to S1331. If not, stop all steps.

7. A safety performance testing system for new energy vehicles, used to implement the method according to any one of claims 1 to 6, characterized in that: Includes the following modules: A preprocessing module is used to obtain a historical record combination, wherein the historical record combination includes different historical records, the historical records are composed of historical performance data and historical scores corresponding to the historical performance data, determine a number of pending historical records from all the historical records of the historical record combination, and perform modification processing on the several pending historical records; A practice module, used to determine a historical record in the modified historical record combination, input the historical performance data in the determined historical record into the detection model, adjust the detection model based on the output score of the detection model, determine whether there is an undetermined historical record, if yes, repeat the present process, and determine whether the number of practice times is less than a preset practice times threshold, if no, stop all processes, if yes, continue to determine whether the requirement for stopping practice is met, if no, repeat the previous process, if yes, stop all processes; The detection module is used to obtain the performance data of the vehicle to be detected, input the performance data of the vehicle to be detected into a trained detection model, and the detection model outputs a score about the performance of the vehicle to be detected.

8. A storage medium, characterized in that: The storage medium stores program instructions, wherein when the program instructions are executed, the device where the storage medium is located is controlled to execute the method according to any one of claims 1 to 6.

Images