Deconstruction model maturity analysis method and system of hydrogen energy vehicle

Abstract

The invention provides a method and a system for analyzing maturity of a deconstruction model of a hydrogen energy vehicle, relates to the technical field of vehicle model deconstruction, and aims to classify comprehensive control data of the hydrogen energy vehicle, obtain control data of multiple stages and control data of multiple types of stages, train a vehicle control model and obtain a vehicle model. Performing first deconstruction and second deconstruction on the vehicle control model to obtain first deconstruction data and second deconstruction data, and calculating a first deconstruction evaluation coefficient and a second deconstruction evaluation coefficient; and calculating a comprehensive deconstruction coefficient, performing comprehensive deconstruction evaluation on the vehicle control model, and further performing comprehensive deconstruction maturity evaluation to obtain an evaluation result, so that the deconstruction performance maturity evaluation level of the hydrogen energy vehicle control system can be effectively improved.

Description

Technical Field

[0001] The present invention proposes a deconstruction model maturity analysis method and system for hydrogen energy vehicles, which relate to the technical field of vehicle model deconstruction, and specifically to the technical field of deconstruction model maturity analysis for hydrogen energy vehicles. Background Art

[0002] In the development process of hydrogen energy vehicles, the design and implementation of vehicle software control systems is one of the key links. In order to achieve efficient, stable and safe operation of hydrogen energy vehicles, it is necessary to conduct in-depth analysis and precise control of their comprehensive control data. This requires that the comprehensive control data be classified according to stages and stage types to obtain multiple stage control data and multiple types of stage control data. By establishing a mapping relationship between these data, an accurate vehicle software control system can be generated to achieve precise regulation of each control link of hydrogen energy vehicles. However, the complexity and diversity of vehicle control systems make it particularly difficult to evaluate their performance. Traditional methods can often only make a general evaluation of the overall performance of the vehicle control system, and it is difficult to deeply analyze its internal control mechanism and existing problems. In order to more comprehensively and accurately evaluate the performance of hydrogen energy vehicle control systems, a more sophisticated deconstruction evaluation method is needed, but the traditional deconstruction method has the problem of insufficient stability and accuracy. Summary of the invention

[0003] The present invention provides a deconstruction model maturity analysis method and system for hydrogen energy vehicles, which is used to solve the problem that the complexity and diversity of vehicle control systems make performance evaluation particularly difficult. Traditional methods can only generally evaluate the overall performance of vehicle control systems, and it is difficult to deeply analyze its internal control mechanism and existing problems. In order to more comprehensively and accurately evaluate the performance of hydrogen energy vehicle control systems, a more sophisticated deconstruction evaluation method is needed. However, traditional deconstruction methods have problems such as insufficient stability and accuracy:

[0004] The present invention proposes a deconstruction model maturity analysis method and system for hydrogen energy vehicles, the method comprising:

[0005] S1. Classify the comprehensive control data of hydrogen energy vehicles according to stages and stage types, obtain multiple stage control data and multiple types of stage control data, establish mapping relationships, and generate a vehicle software control system;

[0006] S2, training a vehicle control model, performing a first deconstruction on the vehicle control model to obtain first deconstruction data, calculating a first deconstruction evaluation coefficient, performing a first deconstruction evaluation according to the first deconstruction evaluation coefficient, and obtaining a first deconstruction evaluation result;

[0007] S3, performing a second deconstruction on the vehicle control model to obtain second deconstruction data, calculating a second deconstruction evaluation coefficient, performing a second deconstruction evaluation according to the second deconstruction evaluation coefficient, and obtaining a second deconstruction evaluation result;

[0008] S4. Calculate the comprehensive deconstruction coefficient, conduct a comprehensive deconstruction evaluation on the vehicle control model, and then conduct a comprehensive deconstruction maturity evaluation to obtain an evaluation result.

[0009] Furthermore, the S1 includes:

[0010] Acquire comprehensive control data of the hydrogen energy vehicle, classify the comprehensive control data according to the acquisition stage of the comprehensive control data, and obtain control data of multiple stages; classify the control data of each stage according to the data type, and obtain control data of multiple types of the stage;

[0011] Acquire multiple control stages of the hydrogen energy vehicle, classify each of the multiple control stages according to the control type, and obtain multiple control types of the stage;

[0012] Establishing a mapping relationship between multiple types of control data and multiple control types in each stage, and obtaining mapping relationship data;

[0013] A vehicle software control system corresponding to the control data is generated according to the mapping relationship data.

[0014] Further, the S2 includes:

[0015] Obtain comprehensive control data, multi-stage control data, and multi-type control data and combine them with vehicle software control system data to generate a control training set;

[0016] Training a neural network model according to the control training set to obtain a vehicle control model;

[0017] Acquire a first-level deconstruction target, and deconstruct the vehicle control model according to the first-level deconstruction target to obtain first deconstruction data;

[0018] A first deconstruction evaluation coefficient is calculated according to the first deconstruction data, the first deconstruction evaluation coefficient is compared with a preset first coefficient threshold to obtain a first deconstruction comparison result, and a first deconstruction evaluation is performed on the vehicle control model according to the first deconstruction comparison result to obtain a first deconstruction evaluation result.

[0019] Further, the S3 includes:

[0020] Acquire a second-level deconstruction target according to the first-level deconstruction target, and deconstruct the first-level deconstruction target of the vehicle control model according to the second-level structure target to obtain second deconstruction data;

[0021] A second deconstruction evaluation coefficient is calculated according to the second deconstruction data, the second deconstruction evaluation coefficient is compared with a preset second coefficient threshold to obtain a second deconstruction comparison result, and a second deconstruction evaluation is performed on the vehicle control model according to the second deconstruction comparison result to obtain a second deconstruction evaluation result.

[0022] Further, the S4 includes:

[0023] Calculating a comprehensive deconstruction coefficient based on the data of the first deconstruction evaluation result combined with the data of the second deconstruction evaluation result;

[0024] Comparing the comprehensive deconstruction coefficient with a preset comprehensive deconstruction threshold to obtain a comprehensive comparison result;

[0025] Performing a comprehensive deconstruction evaluation on the vehicle control model according to the comprehensive comparison result to obtain a comprehensive deconstruction evaluation result;

[0026] According to the comprehensive deconstruction evaluation result, the first deconstruction evaluation result combined with the second deconstruction evaluation result, a comprehensive deconstruction maturity evaluation, a first deconstruction maturity evaluation and a second deconstruction maturity evaluation are performed on the vehicle control model to obtain a comprehensive deconstruction maturity evaluation result, a first deconstruction maturity evaluation result and a second deconstruction maturity evaluation result.

[0027] Furthermore, the system comprises:

[0028] A classification mapping generation module is used to classify the comprehensive control data of hydrogen energy vehicles according to stages and stage types, obtain multiple stage control data and multiple types of stage control data, establish mapping relationships, and generate a vehicle software control system;

[0029] A first deconstruction evaluation module is used to train a vehicle control model, perform a first deconstruction on the vehicle control model, obtain first deconstruction data, calculate a first deconstruction evaluation coefficient, perform a first deconstruction evaluation according to the first deconstruction evaluation coefficient, and obtain a first deconstruction evaluation result;

[0030] A second deconstruction evaluation module, used to perform a second deconstruction on the vehicle control model, obtain second deconstruction data, calculate a second deconstruction evaluation coefficient, perform a second deconstruction evaluation according to the second deconstruction evaluation coefficient, and obtain a second deconstruction evaluation result;

[0031] The comprehensive deconstruction evaluation module is used to calculate the comprehensive deconstruction coefficient, conduct a comprehensive deconstruction evaluation on the vehicle control model, and then conduct a comprehensive deconstruction maturity evaluation to obtain the evaluation results.

[0032] Furthermore, the classification map generation module includes:

[0033] The data stage classification module is used to obtain the comprehensive control data of the hydrogen energy vehicle, classify the comprehensive control data according to the acquisition stage of the comprehensive control data, and obtain multiple stages of control data; classify the control data of each stage according to the data type, and obtain multiple types of control data of the stage;

[0034] A data type classification module is used to obtain multiple control stages of a hydrogen energy vehicle, classify each of the multiple control stages according to the control type, and obtain multiple control types of the stage;

[0035] A mapping system generation module is used to establish a mapping relationship between multiple types of control data and multiple control types in each stage, and obtain the mapping relationship data;

[0036] A vehicle software control system corresponding to the control data is generated according to the mapping relationship data.

[0037] Furthermore, the first deconstruction evaluation module includes:

[0038] Model training module, used to obtain comprehensive control data, multi-stage control data, and multi-type control data combined with vehicle software control system data to generate a control training set;

[0039] Training a neural network model according to the control training set to obtain a vehicle control model;

[0040] A first deconstruction module, used for acquiring a first-level deconstruction target, deconstructing the vehicle control model according to the first-level deconstruction target, and obtaining first deconstruction data;

[0041] A first evaluation module is used to calculate a first deconstruction evaluation coefficient based on the first deconstruction data, compare the first deconstruction evaluation coefficient with a preset first coefficient threshold to obtain a first deconstruction comparison result, and perform a first deconstruction evaluation on the vehicle control model based on the first deconstruction comparison result to obtain a first deconstruction evaluation result.

[0042] Furthermore, the second deconstruction evaluation module includes:

[0043] A second deconstruction module is used to obtain a second-level deconstruction target according to the first-level deconstruction target, and deconstruct the first-level deconstruction target of the vehicle control model according to the second-level structure target to obtain second deconstruction data;

[0044] A second evaluation module is used to calculate a second deconstruction evaluation coefficient based on the second deconstruction data, compare the second deconstruction evaluation coefficient with a preset second coefficient threshold to obtain a second deconstruction comparison result, and perform a second deconstruction evaluation on the vehicle control model based on the second deconstruction comparison result to obtain a second deconstruction evaluation result.

[0045] Furthermore, the comprehensive deconstruction assessment module includes:

[0046] A comprehensive deconstruction evaluation module, used to calculate a comprehensive deconstruction coefficient based on the data of the first deconstruction evaluation result and the data of the second deconstruction evaluation result;

[0047] Comparing the comprehensive deconstruction coefficient with a preset comprehensive deconstruction threshold to obtain a comprehensive comparison result;

[0048] Performing a comprehensive deconstruction evaluation on the vehicle control model according to the comprehensive comparison result to obtain a comprehensive deconstruction evaluation result;

[0049] The maturity assessment module is used to perform comprehensive deconstruction maturity assessment, first deconstruction maturity assessment and second deconstruction maturity assessment on the vehicle control model according to the comprehensive deconstruction assessment result, the first deconstruction assessment result combined with the second deconstruction assessment result, and obtain the comprehensive deconstruction maturity assessment result, the first deconstruction maturity assessment result and the second deconstruction maturity assessment result.

[0050] Beneficial effects of the present invention: Through stage-by-stage and type-by-category data classification and mapping, as well as multi-level deconstruction evaluation, the performance and efficiency of hydrogen-powered vehicles under different operating conditions can be more accurately reflected, thereby improving the accuracy of the evaluation. Through deconstruction evaluation, problems and bottlenecks in the vehicle control system can be discovered. For example, the configuration of the power system can be adjusted, the battery management strategy can be optimized, etc., according to the evaluation results, to improve the overall performance and efficiency of the vehicle. By optimizing vehicle design and control strategies, the driving experience and ride comfort of hydrogen-powered vehicles can be improved. At the same time, reducing failure rates and improving safety are also important aspects of improving user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 A schematic diagram of a deconstruction model maturity analysis method for hydrogen energy vehicles;

[0052] Figure 2 Schematic diagram of a deconstruction model maturity analysis system for a hydrogen energy vehicle. DETAILED DESCRIPTION

[0053] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0054] In one embodiment of the present invention, a deconstruction model maturity analysis method and system for hydrogen energy vehicles are provided, and the method comprises:

[0055] S1. Classify the comprehensive control data of hydrogen energy vehicles according to stages and stage types, obtain multiple stage control data and multiple types of stage control data, establish mapping relationships, and generate a vehicle software control system;

[0056] S2, training a vehicle control model, performing a first deconstruction on the vehicle control model to obtain first deconstruction data, calculating a first deconstruction evaluation coefficient, performing a first deconstruction evaluation according to the first deconstruction evaluation coefficient, and obtaining a first deconstruction evaluation result;

[0057] S3, performing a second deconstruction on the vehicle control model to obtain second deconstruction data, calculating a second deconstruction evaluation coefficient, performing a second deconstruction evaluation according to the second deconstruction evaluation coefficient, and obtaining a second deconstruction evaluation result;

[0058] S4. Calculate the comprehensive deconstruction coefficient, conduct a comprehensive deconstruction evaluation on the vehicle control model, and then conduct a comprehensive deconstruction maturity evaluation to obtain an evaluation result.

[0059] The working principle of the above technical solution is: the system collects comprehensive control data of hydrogen energy vehicles, including but not limited to vehicle operating status, energy consumption, power output, battery status, etc. These data are classified according to different stages (such as starting, driving, parking, etc.) and stage types (such as normal driving, acceleration, deceleration, etc.) to form multiple stage control data and multiple types of stage control data. Through data analysis, a mapping relationship between these control data and the vehicle software control system is established to ensure that each stage and type of control data can be accurately reflected in the vehicle software control system. Based on the classified data, the vehicle control model is trained to accurately simulate the actual operation of hydrogen energy vehicles. The trained vehicle control model is first deconstructed to split the model into smaller components (such as power system, battery management system, control system, etc.), and the first deconstruction data is obtained. The first deconstruction evaluation coefficient is calculated based on these data to evaluate the performance and efficiency of each part. On the basis of the first deconstruction, a deeper second deconstruction is carried out to further refine the sub-modules or functional units of each part, and the second deconstruction data is obtained. Similarly, the second deconstruction evaluation coefficient is calculated for a more detailed evaluation. The first deconstruction evaluation result and the second deconstruction evaluation result are weighted to calculate the comprehensive deconstruction coefficient. This coefficient comprehensively reflects the performance and maturity of the vehicle control model at different levels. According to the comprehensive deconstruction coefficient, the vehicle control model is comprehensively deconstructed to evaluate its maturity, and finally the evaluation result is obtained. The evaluation result can intuitively reflect the maturity and optimization space of hydrogen energy vehicles in design, manufacturing, control and other aspects.

[0060] The technical effect of the above technical solution is: through stage-by-stage and type-by-type data classification and mapping, as well as multi-level deconstruction evaluation, it can more accurately reflect the performance and efficiency of hydrogen energy vehicles under different operating conditions and improve the accuracy of the evaluation. Through deconstruction evaluation, problems and bottlenecks in the vehicle control system can be discovered. For example, the configuration of the power system can be adjusted and the battery management strategy can be optimized according to the evaluation results to improve the overall performance and efficiency of the vehicle. By optimizing vehicle design and control strategies, the driving experience and ride comfort of hydrogen energy vehicles can be improved. At the same time, reducing failure rates and improving safety are also important aspects of improving user experience.

[0061] In one embodiment of the present invention, the S1 includes:

[0062] Acquire comprehensive control data of the hydrogen energy vehicle, classify the comprehensive control data according to the acquisition stage of the comprehensive control data, and obtain multiple stage control data; classify the control data of each stage according to the data type, and obtain multiple types of stage control data; the stage control data includes startup stage data, driving stage data and parking stage data, and the data types include speed data, temperature data, pressure data and battery status data, etc.;

[0063] Acquire multiple control stages of the hydrogen energy vehicle, classify each of the multiple control stages according to the control type, and obtain multiple control types of the stages; the control stages include a start stage, a driving stage, and a parking stage, and the control types include speed control, temperature control, pressure control, and battery status control, etc.;

[0064] Establishing a mapping relationship between multiple types of control data and multiple control types in each stage, and obtaining mapping relationship data;

[0065] A vehicle software control system corresponding to the control data is generated according to the mapping relationship data.

[0066] The working principle of the above technical solution is: the system obtains the comprehensive control data of hydrogen energy vehicles in real time through sensors, on-board computers and other equipment. These data cover various parameters of the vehicle under different operating conditions. The comprehensive control data is classified according to the data acquisition stage (such as the start-up stage, the driving stage, and the parking stage) to obtain the control data of each stage. In this way, the data can be organized in order according to the time sequence and the vehicle operation state. In each stage, the data is further classified according to the type (such as speed data, temperature data, pressure data, battery status data, etc.) to obtain multiple types of control data for each stage. This classification is helpful for subsequent data analysis and processing. The system simultaneously identifies multiple control stages (start-up, driving, and parking) of hydrogen energy vehicles, which correspond to the acquisition stages of comprehensive control data. For each control stage, it is classified according to the control type (such as speed control, temperature control, pressure control, battery status control, etc.). This classification reflects the various control operations that the vehicle needs to perform at different stages. Based on the above classification results, the system establishes a mapping relationship between multiple types of control data and multiple control types in each stage. This mapping relationship clarifies the correspondence between different data types and control types, and provides a basis for the generation of vehicle software control systems. The mapping relationship data is usually displayed in the form of a database, table or graphical interface to facilitate subsequent software design and development. Based on the mapping relationship data, the system generates a vehicle software control system that matches the actual operation of the hydrogen energy vehicle. This system can receive real-time data from sensors and accurately control and manage the vehicle according to the preset control logic and algorithm. The generation of the vehicle software control system includes multiple links such as software architecture design, algorithm writing, and interface definition, aiming to achieve efficient, safe, and stable operation of the vehicle.

[0067] The technical effect of the above technical solution is: by classifying the comprehensive control data into stages and types, the organization and utilization efficiency of the data are improved. This makes subsequent data analysis and processing more convenient and efficient. Establishing a mapping relationship between control data and control types helps to optimize the vehicle's control strategy. The system can adjust control parameters according to real-time data to achieve more accurate and efficient vehicle control. The generation of the vehicle software control system is based on detailed mapping relationship data, which can ensure that the system closely matches the actual vehicle operation. This helps to improve the reliability and stability of the system and reduce the possibility of failure. By improving the control accuracy and stability of the vehicle, the user's driving experience and ride comfort can be significantly improved. At the same time, reducing the failure rate and improving safety are also important aspects of enhancing user experience.

[0068] In one embodiment of the present invention, S2 includes:

[0069] Obtain comprehensive control data, multi-stage control data, and multi-type control data and combine them with vehicle software control system data to generate a control training set;

[0070] Training a neural network model according to the control training set to obtain a vehicle control model;

[0071] Acquire a first-level deconstruction target, deconstruct the vehicle control model according to the first-level deconstruction target, and obtain first deconstruction data; the first-level deconstruction target includes a preset deconstruction target for each control stage;

[0072] A first deconstruction evaluation coefficient is calculated according to the first deconstruction data, the first deconstruction evaluation coefficient is compared with a preset first coefficient threshold to obtain a first deconstruction comparison result, and a first deconstruction evaluation is performed on the vehicle control model according to the first deconstruction comparison result to obtain a first deconstruction evaluation result.

[0073] When the first deconstruction evaluation coefficient is greater than a preset first coefficient threshold, the first deconstruction of the vehicle control model is evaluated as a good deconstruction, otherwise, it is a poor deconstruction.

[0074] The calculation formula of the first deconstruction evaluation coefficient is:

[0075]

[0076] Among them, G py is the first deconstruction evaluation coefficient, k is the number of control stages, m is the number of preset deconstruction targets, B sjn Deconstruct the target data for the nth preset deconstruction target, S sjsn The actual deconstructed data for the nth preset deconstruction target, Evaluate the mean coefficient of variance for the deconstruction of the dth control period.

[0077] The working principle of the above technical solution is to obtain comprehensive control data from the vehicle software control system, sensors and other data sources of the hydrogen energy vehicle. These data include control data of multiple stages (such as starting, driving, parking) and multiple types of control data (such as speed, temperature, pressure, battery status, etc.). Combine these data with the operation log, parameter configuration and other information of the vehicle software control system to generate a control training set. This training set contains the control data of the vehicle under different operating conditions and its corresponding control results or effects. Use this training set to train the neural network model, and through multiple iterations and optimizations, finally obtain a vehicle control model that can accurately reflect the vehicle control logic. Set the first level of deconstruction goals, which are usually specific requirements and expected effects for the vehicle control model at each control stage. For example, in the driving stage, the model may be required to accurately control the vehicle speed and maintain a stable battery state. According to these preset deconstruction goals, the vehicle control model is deconstructed, that is, the performance and internal mechanism of the model at each control stage are analyzed to obtain the first deconstruction data. These data reflect the operation and performance parameters of the model under specific goals. Calculate the first deconstruction evaluation coefficient using the first deconstruction data. Compare the calculated first deconstruction evaluation coefficient with the preset first coefficient threshold. If the evaluation coefficient is greater than the threshold, the model is considered to perform well under the deconstruction target, and the deconstruction evaluation result is "good deconstruction"; otherwise, the performance is considered to be poor, and the deconstruction evaluation result is "poor deconstruction".

[0078] The technical effect of the above technical solution is: by generating a control training set and training a neural network model, a more accurate and reliable vehicle control model can be obtained. This model can more accurately reflect the actual control requirements of the vehicle and improve driving safety and comfort. The process of model deconstruction and evaluation helps to deeply understand the internal mechanism and control logic of the model. By setting deconstruction goals and calculating deconstruction evaluation coefficients, the performance of the model in different aspects can be quantified, providing strong support for the optimization and improvement of the model. The setting of the first-level deconstruction goal makes the model deconstruction and evaluation process more flexible and customizable. According to different application scenarios and requirements, different deconstruction goals and evaluation criteria can be set to meet specific control requirements. The deconstruction evaluation results can provide important reference information for decision makers. By understanding the performance of the model under different deconstruction goals, decision makers can more accurately evaluate the applicability and reliability of the model, thereby making more informed decisions. In one embodiment of the present invention, S3 includes:

[0079] Acquire a second-level deconstruction target according to the first-level deconstruction target, deconstruct the first-level deconstruction target of the vehicle control model according to the second-level structure target to obtain second deconstruction data; the second-level deconstruction target includes a preset deconstruction target for each control type in each control stage;

[0080] A second deconstruction evaluation coefficient is calculated according to the second deconstruction data, the second deconstruction evaluation coefficient is compared with a preset second coefficient threshold to obtain a second deconstruction comparison result, and a second deconstruction evaluation is performed on the vehicle control model according to the second deconstruction comparison result to obtain a second deconstruction evaluation result.

[0081] When the second deconstruction evaluation coefficient is greater than a preset second coefficient threshold, the second deconstruction of the vehicle control model is evaluated as a good deconstruction, otherwise, it is a poor deconstruction.

[0082] The calculation formula of the second deconstruction evaluation coefficient is:

[0083]

[0084] Among them, G pr is the second deconstruction evaluation coefficient, z is the total number of control types, E is the total number of preset deconstruction targets for each control type, B sji The target deconstruction data for the i-th preset deconstruction target, S sjsi The actual deconstructed data for the i-th preset deconstruction target. The coefficient of variance of the deconstructed evaluation of the ith preset control target of the oth control category in the dth control stage.

[0085] The working principle of the above technical solution is as follows: after completing the first-level deconstruction evaluation, in order to further analyze and optimize the vehicle control model, the second-level deconstruction target is set according to the feedback of the first-level deconstruction target and the actual needs of vehicle control. These targets are more specific and detailed, and preset deconstruction targets are set for each control type in each control stage. For example, in the speed control of the driving stage, the model may be required to have different control accuracy and response speed in different speed ranges. Based on the second-level deconstruction target, the performance of the vehicle control model under the first-level deconstruction target is more carefully deconstructed. This process involves a detailed analysis of the specific behavior of each control type in the control stage to obtain the second deconstruction data. These data provide detailed performance parameters and performance of the model under specific control types and stages. Using the second deconstruction data, the second deconstruction evaluation coefficient is calculated according to the preset evaluation method. This coefficient comprehensively considers the performance of the model under multiple control types and stages, and the calculated second deconstruction evaluation coefficient is compared with the preset second coefficient threshold. According to the comparison result, the vehicle control model is subjected to a second deconstruction evaluation, and a conclusion of "good deconstruction" or "poor deconstruction" is obtained. This evaluation result is more specific and comprehensive, and can more accurately reflect the performance of the model under different control types and stages.

[0086] The technical effect of the above technical solution is as follows: the second-level deconstruction evaluation improves the accuracy of the vehicle control model performance evaluation by refining the deconstruction goals and evaluation methods. It can more accurately identify the advantages and disadvantages of the model under different control types and stages, and provide more detailed and specific guidance for the optimization and improvement of the model. By performing a second-level deconstruction evaluation on the model, the performance bottlenecks and potential problems of the model under specific control types and stages can be found. This helps developers to carry out targeted optimization and improvement on these problems and improve the overall performance and stability of the model. The second-level deconstruction evaluation method enables the vehicle control model to better adapt to different driving scenarios and control requirements. By refining the deconstruction goals and evaluation methods, it can be ensured that the model can maintain high control accuracy and stability under different conditions. By performing a detailed performance evaluation and optimization of the vehicle control model, the driving comfort and safety can be significantly improved. This helps to improve the user's driving experience and enhance the user's trust and satisfaction with hydrogen energy vehicles. The second-level deconstruction evaluation promotes continuous improvement and innovation in control algorithms, model design, etc.

[0087] In one embodiment of the present invention, the S4 includes:

[0088] Calculating a comprehensive deconstruction coefficient based on the data of the first deconstruction evaluation result combined with the data of the second deconstruction evaluation result;

[0089] The calculation formula of the comprehensive deconstruction coefficient is:

[0090]

[0091] Among them, H zj is the comprehensive deconstruction coefficient;

[0092] Comparing the comprehensive deconstruction coefficient with a preset comprehensive deconstruction threshold to obtain a comprehensive comparison result;

[0093] Performing a comprehensive deconstruction evaluation on the vehicle control model according to the comprehensive comparison result to obtain a comprehensive deconstruction evaluation result;

[0094] When the comprehensive deconstruction coefficient is greater than a preset comprehensive deconstruction threshold, the comprehensive deconstruction of the vehicle control model is evaluated as a good deconstruction, otherwise, it is a poor deconstruction.

[0095] According to the comprehensive deconstruction evaluation result, the first deconstruction evaluation result combined with the second deconstruction evaluation result, a comprehensive deconstruction maturity evaluation, a first deconstruction maturity evaluation and a second deconstruction maturity evaluation are performed on the vehicle control model to obtain a comprehensive deconstruction maturity evaluation result, a first deconstruction maturity evaluation result and a second deconstruction maturity evaluation result.

[0096] The working principle of the above technical solution is as follows: the comprehensive deconstruction coefficient is a comprehensive evaluation index that combines the data of the first deconstruction evaluation result and the second deconstruction evaluation result. A comprehensive index reflecting the overall deconstruction performance of the vehicle control model is obtained through a calculation formula. The calculated comprehensive deconstruction coefficient is then compared with a preset comprehensive deconstruction threshold. This threshold is set according to the performance requirements, industry standards or historical data of the vehicle control model, and is used to determine whether the comprehensive deconstruction performance of the model reaches the expected level. According to the comparison results, the vehicle control model is comprehensively deconstructed and evaluated to obtain a conclusion of "good deconstruction" or "poor deconstruction". This evaluation result comprehensively considers the performance of the model at different deconstruction levels and provides a comprehensive evaluation of the overall performance of the model. On the basis of the comprehensive deconstruction evaluation, the vehicle control model is further deconstructed and evaluated for maturity based on the comprehensive deconstruction evaluation result, the first deconstruction evaluation result and the second deconstruction evaluation result. The deconstruction maturity evaluation not only focuses on the current performance level of the model, but also on its development potential and degree of perfection at different deconstruction levels. By analyzing the model's scores at different deconstruction levels, room for improvement, and comparison with other models, the maturity level of the model at each deconstruction level can be evaluated and the corresponding evaluation results can be given.

[0097] The technical effect of the above technical solution is: the comprehensive deconstruction evaluation method provides a comprehensive and accurate performance evaluation for the vehicle control model by combining the evaluation results of multiple deconstruction levels. This method can more comprehensively reflect the performance of the model under different control stages and types, avoiding the one-sidedness that may be caused by a single deconstruction level evaluation. The deconstruction maturity evaluation results provide a clear direction and basis for the optimization of the vehicle control model. By analyzing the maturity level of the model at different deconstruction levels, the advantages and disadvantages of the model can be identified, and targeted optimization strategies and improvement measures can be formulated accordingly. Through continuous deconstruction evaluation and optimization, the quality of the vehicle control model will be improved. The adaptability, stability and accuracy of the model in different control scenarios will be enhanced, thereby improving driving safety and comfort. The comprehensive deconstruction evaluation method promotes the continuous innovation and development of vehicle control technology. Through the comprehensive evaluation and optimization of model performance, continuous improvement and innovation in control algorithms, model design, etc. can be promoted. Through comprehensive deconstruction evaluation and optimization of the vehicle control model, the user's driving experience can be significantly improved.

[0098] In one embodiment of the present invention, the system comprises:

[0099] A classification mapping generation module is used to classify the comprehensive control data of hydrogen energy vehicles according to stages and stage types, obtain multiple stage control data and multiple types of stage control data, establish mapping relationships, and generate a vehicle software control system;

[0100] A first deconstruction evaluation module is used to train a vehicle control model, perform a first deconstruction on the vehicle control model, obtain first deconstruction data, calculate a first deconstruction evaluation coefficient, perform a first deconstruction evaluation according to the first deconstruction evaluation coefficient, and obtain a first deconstruction evaluation result;

[0101] A second deconstruction evaluation module, used to perform a second deconstruction on the vehicle control model, obtain second deconstruction data, calculate a second deconstruction evaluation coefficient, perform a second deconstruction evaluation according to the second deconstruction evaluation coefficient, and obtain a second deconstruction evaluation result;

[0102] The comprehensive deconstruction evaluation module is used to calculate the comprehensive deconstruction coefficient, conduct a comprehensive deconstruction evaluation on the vehicle control model, and then conduct a comprehensive deconstruction maturity evaluation to obtain the evaluation results.

[0103] The working principle of the above technical solution is: the system collects comprehensive control data of hydrogen energy vehicles, including but not limited to vehicle operating status, energy consumption, power output, battery status, etc. These data are classified according to different stages (such as starting, driving, parking, etc.) and stage types (such as normal driving, acceleration, deceleration, etc.) to form multiple stage control data and multiple types of stage control data. Through data analysis, a mapping relationship between these control data and the vehicle software control system is established to ensure that each stage and type of control data can be accurately reflected in the vehicle software control system. Based on the classified data, the vehicle control model is trained to accurately simulate the actual operation of hydrogen energy vehicles. The trained vehicle control model is first deconstructed to split the model into smaller components (such as power system, battery management system, control system, etc.), and the first deconstruction data is obtained. The first deconstruction evaluation coefficient is calculated based on these data to evaluate the performance and efficiency of each part. On the basis of the first deconstruction, a deeper second deconstruction is carried out to further refine the sub-modules or functional units of each part, and the second deconstruction data is obtained. Similarly, the second deconstruction evaluation coefficient is calculated for a more detailed evaluation. The first deconstruction evaluation result and the second deconstruction evaluation result are weighted to calculate the comprehensive deconstruction coefficient. This coefficient comprehensively reflects the performance and maturity of the vehicle control model at different levels. According to the comprehensive deconstruction coefficient, the vehicle control model is comprehensively deconstructed to evaluate its maturity, and finally the evaluation result is obtained. The evaluation result can intuitively reflect the maturity and optimization space of hydrogen energy vehicles in design, manufacturing, control and other aspects.

[0104] The technical effect of the above technical solution is: through stage-by-stage and type-by-type data classification and mapping, as well as multi-level deconstruction evaluation, it can more accurately reflect the performance and efficiency of hydrogen energy vehicles under different operating conditions and improve the accuracy of the evaluation. Through deconstruction evaluation, problems and bottlenecks in the vehicle control system can be discovered. For example, the configuration of the power system can be adjusted and the battery management strategy can be optimized according to the evaluation results to improve the overall performance and efficiency of the vehicle. By optimizing vehicle design and control strategies, the driving experience and ride comfort of hydrogen energy vehicles can be improved. At the same time, reducing failure rates and improving safety are also important aspects of improving user experience.

[0105] In one embodiment of the present invention, the classification map generation module includes:

[0106] The data stage classification module is used to obtain the comprehensive control data of the hydrogen energy vehicle, classify the comprehensive control data according to the acquisition stage of the comprehensive control data, and obtain multiple stage control data; classify the control data of each stage according to the data type, and obtain multiple types of stage control data; the stage control data includes the start-up stage data, the driving stage data and the parking stage data, and the data types include speed data, temperature data, pressure data and battery status data, etc.;

[0107] A data type classification module is used to obtain multiple control stages of a hydrogen energy vehicle, classify each of the multiple control stages according to the control type, and obtain multiple control types of the stages; the control stages include a start-up stage, a driving stage, and a parking stage, and the control types include speed control, temperature control, pressure control, and battery status control, etc.;

[0108] A mapping system generation module is used to establish a mapping relationship between multiple types of control data and multiple control types in each stage, and obtain the mapping relationship data;

[0109] A vehicle software control system corresponding to the control data is generated according to the mapping relationship data.

[0110] The working principle of the above technical solution is: the system obtains the comprehensive control data of hydrogen energy vehicles in real time through sensors, on-board computers and other equipment. These data cover various parameters of the vehicle under different operating conditions. The comprehensive control data is classified according to the data acquisition stage (such as the start-up stage, the driving stage, and the parking stage) to obtain the control data of each stage. In this way, the data can be organized in order according to the time sequence and the vehicle operation state. In each stage, the data is further classified according to the type (such as speed data, temperature data, pressure data, battery status data, etc.) to obtain multiple types of control data for each stage. This classification is helpful for subsequent data analysis and processing. The system simultaneously identifies multiple control stages (start-up, driving, and parking) of hydrogen energy vehicles, which correspond to the acquisition stages of comprehensive control data. For each control stage, it is classified according to the control type (such as speed control, temperature control, pressure control, battery status control, etc.). This classification reflects the various control operations that the vehicle needs to perform at different stages. Based on the above classification results, the system establishes a mapping relationship between multiple types of control data and multiple control types in each stage. This mapping relationship clarifies the correspondence between different data types and control types, and provides a basis for the generation of vehicle software control systems. The mapping relationship data is usually displayed in the form of a database, table or graphical interface to facilitate subsequent software design and development. Based on the mapping relationship data, the system generates a vehicle software control system that matches the actual operation of the hydrogen energy vehicle. This system can receive real-time data from sensors and accurately control and manage the vehicle according to the preset control logic and algorithm. The generation of the vehicle software control system includes multiple links such as software architecture design, algorithm writing, and interface definition, aiming to achieve efficient, safe, and stable operation of the vehicle.

[0111] The technical effect of the above technical solution is: by classifying the comprehensive control data into stages and types, the organization and utilization efficiency of the data are improved. This makes subsequent data analysis and processing more convenient and efficient. Establishing a mapping relationship between control data and control types helps to optimize the vehicle's control strategy. The system can adjust control parameters according to real-time data to achieve more accurate and efficient vehicle control. The generation of the vehicle software control system is based on detailed mapping relationship data, which can ensure that the system closely matches the actual vehicle operation. This helps to improve the reliability and stability of the system and reduce the possibility of failure. By improving the control accuracy and stability of the vehicle, the user's driving experience and ride comfort can be significantly improved. At the same time, reducing the failure rate and improving safety are also important aspects of enhancing user experience.

[0112] In one embodiment of the present invention, the first deconstruction evaluation module includes:

[0113] Model training module, used to obtain comprehensive control data, multi-stage control data, and multi-type control data combined with vehicle software control system data to generate a control training set;

[0114] Training a neural network model according to the control training set to obtain a vehicle control model;

[0115] A first deconstruction module, used for acquiring a first-level deconstruction target, deconstructing the vehicle control model according to the first-level deconstruction target, and obtaining first deconstruction data; the first-level deconstruction target includes a preset deconstruction target for each control stage;

[0116] A first evaluation module is used to calculate a first deconstruction evaluation coefficient based on the first deconstruction data, compare the first deconstruction evaluation coefficient with a preset first coefficient threshold to obtain a first deconstruction comparison result, and perform a first deconstruction evaluation on the vehicle control model based on the first deconstruction comparison result to obtain a first deconstruction evaluation result.

[0117] When the first deconstruction evaluation coefficient is greater than a preset first coefficient threshold, the first deconstruction of the vehicle control model is evaluated as a good deconstruction, otherwise, it is a poor deconstruction.

[0118] The calculation formula of the first deconstruction evaluation coefficient is:

[0119]

[0120] Among them, G py is the first deconstruction evaluation coefficient, k is the number of control stages, m is the number of preset deconstruction targets, B sjn Deconstruct the target data for the nth preset deconstruction target, S sjsn The actual deconstructed data for the nth preset deconstruction target, Evaluate the coefficient of variance for the deconstruction of the dth control phase.

[0121] The working principle of the above technical solution is to obtain comprehensive control data from the vehicle software control system, sensors and other data sources of hydrogen energy vehicles. These data include control data of multiple stages (such as starting, driving, parking) and multiple types of control data (such as speed, temperature, pressure, battery status, etc.). Combine these data with the operation log, parameter configuration and other information of the vehicle software control system to generate a control training set. This training set contains the control data of the vehicle under different operating conditions and its corresponding control results or effects. Use this training set to train the neural network model, and through multiple iterations and optimizations, finally obtain a vehicle control model that can accurately reflect the vehicle control logic. Set the first level of deconstruction goals, which are usually specific requirements and expected effects for the vehicle control model at each control stage. For example, in the driving stage, the model may be required to accurately control the vehicle speed and maintain a stable battery state. According to these preset deconstruction goals, the vehicle control model is deconstructed, that is, the performance and internal mechanism of the model at each control stage are analyzed to obtain the first deconstruction data. These data reflect the operation and performance parameters of the model under specific goals. The first deconstruction evaluation coefficient is calculated using the first deconstruction data. This coefficient is a quantitative indicator used to evaluate the performance of the model under a specific deconstruction goal. The calculation formula may involve the difference between the model output and the preset target, control accuracy, stability and other aspects. The calculated first deconstruction evaluation coefficient is compared with the preset first coefficient threshold. If the evaluation coefficient is greater than the threshold, it is considered that the model performs well under the deconstruction target, and the deconstruction evaluation result is "good deconstruction"; otherwise, it is considered that the performance is poor, and the deconstruction evaluation result is "poor deconstruction".

[0122] The technical effect of the above technical solution is: by generating a control training set and training a neural network model, a more accurate and reliable vehicle control model can be obtained. This model can more accurately reflect the actual control requirements of the vehicle and improve driving safety and comfort. The process of model deconstruction and evaluation helps to deeply understand the internal mechanism and control logic of the model. By setting deconstruction goals and calculating deconstruction evaluation coefficients, the performance of the model in different aspects can be quantified, providing strong support for the optimization and improvement of the model. The setting of the first-level deconstruction goal makes the model deconstruction and evaluation process more flexible and customizable. According to different application scenarios and requirements, different deconstruction goals and evaluation criteria can be set to meet specific control requirements. The deconstruction evaluation results can provide important reference information for decision makers. By understanding the performance of the model under different deconstruction goals, decision makers can more accurately evaluate the applicability and reliability of the model, thereby making more informed decisions. In one embodiment of the present invention, the second deconstruction evaluation module includes:

[0123] A second deconstruction module is used to obtain a second-level deconstruction target according to the first-level deconstruction target, and deconstruct the first-level deconstruction target of the vehicle control model according to the second-level structure target to obtain second deconstruction data; the second-level deconstruction target includes a preset deconstruction target for each control type in each control stage;

[0124] A second evaluation module is used to calculate a second deconstruction evaluation coefficient based on the second deconstruction data, compare the second deconstruction evaluation coefficient with a preset second coefficient threshold to obtain a second deconstruction comparison result, and perform a second deconstruction evaluation on the vehicle control model based on the second deconstruction comparison result to obtain a second deconstruction evaluation result.

[0125] When the second deconstruction evaluation coefficient is greater than a preset second coefficient threshold, the second deconstruction of the vehicle control model is evaluated as a good deconstruction, otherwise, it is a poor deconstruction.

[0126] The calculation formula of the second deconstruction evaluation coefficient is:

[0127]

[0128] Among them, G pr is the second deconstruction evaluation coefficient, z is the total number of control types, E is the total number of preset deconstruction targets for each control type, B sji The target deconstruction data for the i-th preset deconstruction target, S sjsi The actual deconstructed data for the i-th preset deconstruction target. The coefficient of variance of the deconstructed evaluation of the ith preset control target of the oth control category in the dth control stage.

[0129] The working principle of the above technical solution is as follows: after completing the first-level deconstruction evaluation, in order to further analyze and optimize the vehicle control model, the second-level deconstruction target is set according to the feedback of the first-level deconstruction target and the actual needs of vehicle control. These targets are more specific and detailed, and preset deconstruction targets are set for each control type in each control stage. For example, in the speed control of the driving stage, the model may be required to have different control accuracy and response speed in different speed ranges. Based on the second-level deconstruction target, the performance of the vehicle control model under the first-level deconstruction target is more carefully deconstructed. This process involves a detailed analysis of the specific behavior of each control type in the control stage to obtain the second deconstruction data. These data provide detailed performance parameters and performance of the model under specific control types and stages. Using the second deconstruction data, the second deconstruction evaluation coefficient is calculated according to the preset evaluation method. This coefficient comprehensively considers the performance of the model under multiple control types and stages. The calculated second deconstruction evaluation coefficient is compared with the preset second coefficient threshold. According to the comparison result, the vehicle control model is subjected to a second deconstruction evaluation, and a conclusion of "good deconstruction" or "poor deconstruction" is obtained. This evaluation result is more specific and comprehensive, and can more accurately reflect the performance of the model under different control types and stages.

[0130] The technical effect of the above technical solution is as follows: the second-level deconstruction evaluation improves the accuracy of the vehicle control model performance evaluation by refining the deconstruction goals and evaluation methods. It can more accurately identify the advantages and disadvantages of the model under different control types and stages, and provide more detailed and specific guidance for the optimization and improvement of the model. By performing a second-level deconstruction evaluation on the model, the performance bottlenecks and potential problems of the model under specific control types and stages can be found. This helps developers to carry out targeted optimization and improvement on these problems and improve the overall performance and stability of the model. The second-level deconstruction evaluation method enables the vehicle control model to better adapt to different driving scenarios and control requirements. By refining the deconstruction goals and evaluation methods, it can be ensured that the model can maintain high control accuracy and stability under different conditions. By performing a detailed performance evaluation and optimization of the vehicle control model, the driving comfort and safety can be significantly improved. This helps to improve the user's driving experience and enhance the user's trust and satisfaction with hydrogen energy vehicles. The second-level deconstruction evaluation promotes continuous improvement and innovation in control algorithms, model design, etc.

[0131] In one embodiment of the present invention, the comprehensive deconstruction evaluation module includes:

[0132] A comprehensive deconstruction evaluation module, used to calculate a comprehensive deconstruction coefficient based on the data of the first deconstruction evaluation result and the data of the second deconstruction evaluation result;

[0133] The calculation formula of the comprehensive deconstruction coefficient is:

[0134]

[0135] Among them, H zj is the comprehensive deconstruction coefficient;

[0136] Comparing the comprehensive deconstruction coefficient with a preset comprehensive deconstruction threshold to obtain a comprehensive comparison result;

[0137] Performing a comprehensive deconstruction evaluation on the vehicle control model according to the comprehensive comparison result to obtain a comprehensive deconstruction evaluation result;

[0138] When the comprehensive deconstruction coefficient is greater than a preset comprehensive deconstruction threshold, the comprehensive deconstruction of the vehicle control model is evaluated as a good deconstruction, otherwise, it is a poor deconstruction.

[0139] The maturity assessment module is used to perform comprehensive deconstruction maturity assessment, first deconstruction maturity assessment and second deconstruction maturity assessment on the vehicle control model according to the comprehensive deconstruction assessment result, the first deconstruction assessment result combined with the second deconstruction assessment result, and obtain the comprehensive deconstruction maturity assessment result, the first deconstruction maturity assessment result and the second deconstruction maturity assessment result.

[0140] The working principle of the above technical solution is as follows: the comprehensive deconstruction coefficient is a comprehensive evaluation index that combines the data of the first deconstruction evaluation result and the second deconstruction evaluation result. A comprehensive index reflecting the overall deconstruction performance of the vehicle control model is obtained through a calculation formula. The calculated comprehensive deconstruction coefficient is then compared with a preset comprehensive deconstruction threshold. This threshold is set according to the performance requirements, industry standards or historical data of the vehicle control model, and is used to determine whether the comprehensive deconstruction performance of the model reaches the expected level. According to the comparison results, the vehicle control model is comprehensively deconstructed and evaluated to obtain a conclusion of "good deconstruction" or "poor deconstruction". This evaluation result comprehensively considers the performance of the model at different deconstruction levels and provides a comprehensive evaluation of the overall performance of the model. On the basis of the comprehensive deconstruction evaluation, the vehicle control model is further deconstructed and evaluated for maturity based on the comprehensive deconstruction evaluation result, the first deconstruction evaluation result and the second deconstruction evaluation result. The deconstruction maturity evaluation not only focuses on the current performance level of the model, but also on its development potential and degree of perfection at different deconstruction levels. By analyzing the model's scores at different deconstruction levels, room for improvement, and comparison with other models, the maturity level of the model at each deconstruction level can be evaluated and the corresponding evaluation results can be given.

[0141] The technical effect of the above technical solution is: the comprehensive deconstruction evaluation method provides a comprehensive and accurate performance evaluation for the vehicle control model by combining the evaluation results of multiple deconstruction levels. This method can more comprehensively reflect the performance of the model under different control stages and types, avoiding the one-sidedness that may be caused by a single deconstruction level evaluation. The deconstruction maturity evaluation results provide a clear direction and basis for the optimization of the vehicle control model. By analyzing the maturity level of the model at different deconstruction levels, the advantages and disadvantages of the model can be identified, and targeted optimization strategies and improvement measures can be formulated accordingly. Through continuous deconstruction evaluation and optimization, the quality of the vehicle control model will be improved. The adaptability, stability and accuracy of the model in different control scenarios will be enhanced, thereby improving driving safety and comfort. The comprehensive deconstruction evaluation method promotes the continuous innovation and development of vehicle control technology. Through the comprehensive evaluation and optimization of model performance, continuous improvement and innovation in control algorithms, model design, etc. can be promoted. Through comprehensive deconstruction evaluation and optimization of the vehicle control model, the user's driving experience can be significantly improved.

[0142] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A deconstruction model maturity analysis method for hydrogen energy vehicles, characterized in that: The method comprises: S1. Classify the comprehensive control data of hydrogen energy vehicles according to stages and stage types, obtain multiple stage control data and multiple types of stage control data, establish mapping relationships, and generate a vehicle software control system; S2, training a vehicle control model, performing a first deconstruction on the vehicle control model to obtain first deconstruction data, calculating a first deconstruction evaluation coefficient, performing a first deconstruction evaluation according to the first deconstruction evaluation coefficient, and obtaining a first deconstruction evaluation result; S3, performing a second deconstruction on the vehicle control model to obtain second deconstruction data, calculating a second deconstruction evaluation coefficient, performing a second deconstruction evaluation according to the second deconstruction evaluation coefficient, and obtaining a second deconstruction evaluation result; S4. Calculate the comprehensive deconstruction coefficient, conduct a comprehensive deconstruction evaluation on the vehicle control model, and then conduct a comprehensive deconstruction maturity evaluation to obtain an evaluation result.

2. According to claim 1, a deconstruction model maturity analysis method for hydrogen energy vehicles is characterized in that: The S1 includes: Acquire comprehensive control data of the hydrogen energy vehicle, classify the comprehensive control data according to the acquisition stage of the comprehensive control data, and obtain control data of multiple stages; classify the control data of each stage according to the data type, and obtain control data of multiple types of the stage; Acquire multiple control stages of the hydrogen energy vehicle, classify each of the multiple control stages according to the control type, and obtain multiple control types of the stage; Establishing a mapping relationship between multiple types of control data and multiple control types in each stage, and obtaining mapping relationship data; A vehicle software control system corresponding to the control data is generated according to the mapping relationship data.

3. According to the deconstruction model maturity analysis method of a hydrogen energy vehicle according to claim 1, it is characterized in that: The S2 includes: Obtain comprehensive control data, multi-stage control data, and multi-type control data and combine them with vehicle software control system data to generate a control training set; Training a neural network model according to the control training set to obtain a vehicle control model; Acquire a first-level deconstruction target, and deconstruct the vehicle control model according to the first-level deconstruction target to obtain first deconstruction data; A first deconstruction evaluation coefficient is calculated according to the first deconstruction data, the first deconstruction evaluation coefficient is compared with a preset first coefficient threshold to obtain a first deconstruction comparison result, and a first deconstruction evaluation is performed on the vehicle control model according to the first deconstruction comparison result to obtain a first deconstruction evaluation result.

4. According to claim 1, a deconstruction model maturity analysis method for hydrogen energy vehicles is characterized in that: The S3 includes: Acquire a second-level deconstruction target according to the first-level deconstruction target, and deconstruct the first-level deconstruction target of the vehicle control model according to the second-level structure target to obtain second deconstruction data; A second deconstruction evaluation coefficient is calculated according to the second deconstruction data, the second deconstruction evaluation coefficient is compared with a preset second coefficient threshold to obtain a second deconstruction comparison result, and a second deconstruction evaluation is performed on the vehicle control model according to the second deconstruction comparison result to obtain a second deconstruction evaluation result.

5. According to claim 1, a deconstruction model maturity analysis method for hydrogen energy vehicles is characterized in that: The S4 includes: Calculating a comprehensive deconstruction coefficient based on the data of the first deconstruction evaluation result combined with the data of the second deconstruction evaluation result; Comparing the comprehensive deconstruction coefficient with a preset comprehensive deconstruction threshold to obtain a comprehensive comparison result; Performing a comprehensive deconstruction evaluation on the vehicle control model according to the comprehensive comparison result to obtain a comprehensive deconstruction evaluation result; According to the comprehensive deconstruction evaluation result, the first deconstruction evaluation result combined with the second deconstruction evaluation result, a comprehensive deconstruction maturity evaluation, a first deconstruction maturity evaluation and a second deconstruction maturity evaluation are performed on the vehicle control model to obtain a comprehensive deconstruction maturity evaluation result, a first deconstruction maturity evaluation result and a second deconstruction maturity evaluation result.

6. A deconstruction model maturity analysis system for hydrogen energy vehicles, characterized in that: The system comprises: A classification mapping generation module is used to classify the comprehensive control data of hydrogen energy vehicles according to stages and stage types, obtain multiple stage control data and multiple types of stage control data, establish mapping relationships, and generate a vehicle software control system; A first deconstruction evaluation module is used to train a vehicle control model, perform a first deconstruction on the vehicle control model, obtain first deconstruction data, calculate a first deconstruction evaluation coefficient, perform a first deconstruction evaluation according to the first deconstruction evaluation coefficient, and obtain a first deconstruction evaluation result; A second deconstruction evaluation module, used to perform a second deconstruction on the vehicle control model, obtain second deconstruction data, calculate a second deconstruction evaluation coefficient, perform a second deconstruction evaluation according to the second deconstruction evaluation coefficient, and obtain a second deconstruction evaluation result; The comprehensive deconstruction evaluation module is used to calculate the comprehensive deconstruction coefficient, conduct a comprehensive deconstruction evaluation on the vehicle control model, and then conduct a comprehensive deconstruction maturity evaluation to obtain the evaluation results.

7. According to claim 6, a deconstruction model maturity analysis system for hydrogen energy vehicles is characterized in that: The classification map generation module includes: The data stage classification module is used to obtain the comprehensive control data of the hydrogen energy vehicle, classify the comprehensive control data according to the acquisition stage of the comprehensive control data, and obtain multiple stages of control data; classify the control data of each stage according to the data type, and obtain multiple types of control data of the stage; A data type classification module is used to obtain multiple control stages of a hydrogen energy vehicle, classify each of the multiple control stages according to the control type, and obtain multiple control types of the stage; A mapping system generation module is used to establish a mapping relationship between multiple types of control data and multiple control types in each stage, and obtain the mapping relationship data; A vehicle software control system corresponding to the control data is generated according to the mapping relationship data.

8. According to claim 6, a deconstruction model maturity analysis system for hydrogen energy vehicles is characterized in that: The first deconstruction assessment module includes: Model training module, used to obtain comprehensive control data, multi-stage control data, and multi-type control data combined with vehicle software control system data to generate a control training set; Training a neural network model according to the control training set to obtain a vehicle control model; A first deconstruction module, used for acquiring a first-level deconstruction target, deconstructing the vehicle control model according to the first-level deconstruction target, and obtaining first deconstruction data; A first evaluation module is used to calculate a first deconstruction evaluation coefficient based on the first deconstruction data, compare the first deconstruction evaluation coefficient with a preset first coefficient threshold to obtain a first deconstruction comparison result, and perform a first deconstruction evaluation on the vehicle control model based on the first deconstruction comparison result to obtain a first deconstruction evaluation result.

9. According to claim 6, a deconstruction model maturity analysis system for hydrogen energy vehicles is characterized in that: The second deconstruction assessment module includes: A second deconstruction module is used to obtain a second-level deconstruction target according to the first-level deconstruction target, and deconstruct the first-level deconstruction target of the vehicle control model according to the second-level structure target to obtain second deconstruction data; A second evaluation module is used to calculate a second deconstruction evaluation coefficient based on the second deconstruction data, compare the second deconstruction evaluation coefficient with a preset second coefficient threshold to obtain a second deconstruction comparison result, and perform a second deconstruction evaluation on the vehicle control model based on the second deconstruction comparison result to obtain a second deconstruction evaluation result.

10. The deconstruction model maturity analysis system for hydrogen energy vehicles according to claim 6, characterized in that: The comprehensive deconstruction assessment module includes: A comprehensive deconstruction evaluation module, used to calculate a comprehensive deconstruction coefficient based on the data of the first deconstruction evaluation result and the data of the second deconstruction evaluation result; Comparing the comprehensive deconstruction coefficient with a preset comprehensive deconstruction threshold to obtain a comprehensive comparison result; Performing a comprehensive deconstruction evaluation on the vehicle control model according to the comprehensive comparison result to obtain a comprehensive deconstruction evaluation result; The maturity assessment module is used to perform comprehensive deconstruction maturity assessment, first deconstruction maturity assessment and second deconstruction maturity assessment on the vehicle control model according to the comprehensive deconstruction assessment result, the first deconstruction assessment result combined with the second deconstruction assessment result, and obtain the comprehensive deconstruction maturity assessment result, the first deconstruction maturity assessment result and the second deconstruction maturity assessment result.

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