New energy fuel tank shell manufacturing process optimization method based on big data analysis

Abstract

The present invention discloses a method for optimizing the manufacturing process of a new energy fuel tank shell based on big data analysis, and relates to the field of intelligent manufacturing technology. The method is based on the collection of historical big data to determine the material that performs best in a specific use environment. After determining the most suitable material, the production process of the fuel tank shell is simulated and optimized using simulation technology, and the performance of the fuel tank shell is predicted under different process parameters. By simulating different production processes, the optimal process path is found, and the fuel tank shell is comprehensively inspected through quality inspection. If quality problems are found, the causes of the quality problems are automatically analyzed. The present invention can monitor and predict the impact of various process parameters on the quality of the fuel tank shell in real time during the production process through big data collection and analysis, combined with a machine learning model, and automatically adjust the process parameters to avoid the production of unqualified products.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent manufacturing technology, and in particular to a method for optimizing the manufacturing process of a new energy fuel tank shell based on big data analysis. Background Art

[0002] With the global energy crisis and the increasing environmental protection requirements, the new energy industry has developed rapidly. Among them, the manufacturing process of the fuel tank shell in new energy equipment plays a vital role in the operation of the entire system. Fuel tank shells are usually used to store and transmit liquids, and are widely used in new energy vehicles, wind power generation equipment, solar energy and other new energy equipment. The quality of the fuel tank shell is directly related to the safety, durability and operation efficiency of the equipment. In order to ensure the quality of the product, the manufacturing process of the fuel tank shell requires precise control of various process parameters, such as temperature, pressure, welding speed, etc. However, the traditional manufacturing process has the following technical problems, such as: the process parameter optimization is difficult, the production of the fuel tank shell involves the regulation of a variety of complex process parameters, how to balance various parameters to improve product quality without affecting production efficiency is an important challenge, and the existing manufacturing optimization systems based on big data analysis are mostly only applied to simple process flows. For the complex production process of new energy fuel tank shells with multiple parameters and multiple processes, there is still a lack of efficient and comprehensive process optimization solutions. Summary of the invention

[0003] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0004] In view of the above-mentioned problems existing in the existing new energy fuel tank shell manufacturing process optimization method based on big data analysis, the present invention is proposed.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a new energy fuel tank shell manufacturing process optimization method based on big data analysis, characterized in that the method comprises the following steps:

[0006] Step 1: Collect all historical data related to production, including: types of raw materials, suppliers, production equipment, process parameters, and quality data of the final product;

[0007] Step 2: Based on the historical big data collected, start big data analysis to analyze the performance of different materials under different process conditions and determine the material that performs best in a specific use environment;

[0008] Step 3: After selecting the most suitable material, use simulation technology to simulate and optimize the production process of the fuel tank shell, predict the performance of the fuel tank shell under different process parameters, and find the optimal process path by simulating different production processes;

[0009] Step 4: After each batch of production is completed, the fuel tank shell is comprehensively inspected through quality inspection. If quality problems are found, the causes of the quality problems are automatically analyzed and fed back to the process adjustment and material selection links. These feedback data will be input into the big data analysis system to optimize the next production process.

[0010] As a preferred solution of the new energy fuel tank shell manufacturing process optimization method based on big data analysis described in the present invention, the process of step 2 specifically includes:

[0011] S201: Determine the impact of each process parameter on material properties through big data analysis ;

[0012] S202: When multiple process parameters affect material properties, it is necessary to uniformly scale the effects of these process parameters in order to more fairly compare the performance of materials under different process conditions. The processing formula is: ,in, Indicates standard process parameters, Indicates the current process parameters;

[0013] S203: Construct a comprehensive evaluation function based on the normalized impact of all process parameters, material performance and environmental factors , select the optimal material, then The expression is: , where γ represents the intensity factor of the influence of process parameters on the material, and the value range of Y is , Y=1 means that the material performs best under given environmental and process conditions. The closer Y is to 0, the worse the material performs under specific process parameters and environmental conditions.

[0014] As a preferred solution of the new energy fuel tank shell manufacturing process optimization method based on big data analysis described in the present invention, wherein: The calculation formula is: ,in, Material and process parameters The interaction coefficient between Represents the process parameters, It is the degree of influence of process parameters on material properties, that is, the power factor, which indicates the sensitivity of the material to different parameters. represents the attenuation factor, is an exponential decay function that simulates the negative impact of process parameters on material properties, and D is the effect of ambient temperature on the material.

[0015] As a preferred solution of the new energy fuel tank shell manufacturing process optimization method based on big data analysis described in the present invention, in which: during the step three, when different process parameters are changed with the help of simulation software tools such as finite element analysis software or computational fluid dynamics software, the production process of the fuel tank shell is simulated, the performance of the fuel tank shell under different conditions is observed, the simulation results are obtained through multiple simulation process paths, and the comprehensive evaluation function is reused. , and calculate a The value represents the comprehensive performance score of the fuel tank shell under this path;

[0016] Among them, select The path with the largest value is taken as the optimal process path.

[0017] As a preferred solution of the new energy fuel tank shell manufacturing process optimization method based on big data analysis described in the present invention, wherein: the process of step 4 specifically includes:

[0018] S401: Collect various quality data of each batch of fuel tank shells from the quality inspection system, and combine these data with the process parameters in the production process to form a comprehensive production data set;

[0019] S402: Build a machine learning model or statistical model for quality problem analysis based on historical production data and quality inspection data;

[0020] S403: Analyze which process parameters or material factors lead to quality problems based on the model.

[0021] As a preferred solution of the new energy fuel tank shell manufacturing process optimization method based on big data analysis described in the present invention, the statistical model is a random forest classification model to predict whether the fuel tank shell meets the quality standards, the input features include various process parameters or quality data, and the output result is whether the batch is qualified;

[0022] set up: is the input feature;

[0023] The output is the quality assessment result, which is a binary classification problem, that is, whether the batch of products is qualified, that is: , wherein the model is trained through historical data, and the label data: qualified or unqualified, and feature data: various process parameters or quality data are used for training to generate a classification tree.

[0024] As a preferred solution of the new energy tank shell manufacturing process optimization method based on big data analysis described in the present invention, wherein: through the comprehensive evaluation function through the comprehensive evaluation function , influence of process parameters , Path Effect , classification tree prediction results Four dimensions are used to comprehensively evaluate the quality of the fuel tank shell;

[0025] Among them, if Above the set threshold, has a significant impact on quality, and is the positive path, and =1, the oil tank shell is determined to be qualified and can continue to be put into production;

[0026] If Y is lower than the set threshold, or No significant effect on quality, or is a negative path, and =0, the fuel tank shell is judged to be unqualified and production is stopped.

[0027] A preferred solution of a process optimization system applied to the above-mentioned new energy fuel tank shell manufacturing process optimization method based on big data analysis, wherein: the system includes:

[0028] The data acquisition and integration module is responsible for collecting process parameters and quality data from various sensors, testing equipment and quality control systems in the manufacturing process;

[0029] The data storage and management module is responsible for storing, organizing and managing all data collected from the production line to ensure data security and efficient access;

[0030] The data analysis and modeling module builds an analytical model for process optimization based on historical production data and quality inspection data using machine learning, statistical models and other methods;

[0031] The prediction and decision support module generates process optimization suggestions for the next batch of production based on data analysis results and provides support for production decisions;

[0032] and a process adjustment and feedback module that receives suggestions from the prediction and decision support module, automatically or manually adjusts process parameters, and feeds back the adjustment results to the system.

[0033] A computer device includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the above-mentioned new energy fuel tank shell manufacturing process optimization method based on big data analysis are implemented.

[0034] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of the above-mentioned method for optimizing the manufacturing process of a new energy fuel tank shell based on big data analysis are implemented.

[0035] Beneficial effects of the present invention:

[0036] 1. Through big data collection and analysis, combined with machine learning models, the impact of various process parameters on the quality of the fuel tank shell during the production process can be monitored and predicted in real time, and the process parameters can be automatically adjusted to avoid the production of unqualified products. A complete quality inspection feedback mechanism has been established, which can timely analyze the quality problems of each batch of products, trace the root causes of the problems and feedback to the process adjustment and material selection links, forming a closed-loop optimization.

[0037] 2. The present invention uses simulation technology to simulate the production process of the fuel tank shell, evaluates the impact of different process parameters on the product, finds the optimal process path before actual production, reduces the cost of trial and error, and through intelligent analysis and optimization means, not only improves production efficiency, but also significantly improves the quality of the fuel tank shell, meeting the demand for high-performance components of new energy equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. Among them:

[0039] Figure 1 This is a schematic diagram of the overall framework of the new energy fuel tank shell manufacturing process optimization method based on big data analysis proposed in the present invention. DETAILED DESCRIPTION

[0040] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0041] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0042] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0043] Example 1

[0044] Reference Figure 1 , is an embodiment of the present invention, and provides a new energy fuel tank shell manufacturing process optimization method based on big data analysis, the method comprising the following steps:

[0045] Step 1: Collect all historical data related to production, including: types of raw materials, suppliers, production equipment, process parameters, and quality data of the final product. These data can be obtained through sensors, equipment logs, quality inspection reports, etc.

[0046] Step 2: Based on the historical big data collected, start big data analysis to analyze the performance of different materials under different process conditions and determine the material that performs best in a specific use environment;

[0047] Specifically:

[0048] S201: Determine the impact of each process parameter on material properties through big data analysis , the calculation formula is: ,in, Material and process parameters The interaction coefficient between Represents the process parameters, It is the degree of influence of process parameters on material properties, that is, the power factor, which indicates the sensitivity of the material to different parameters. represents the attenuation factor, is an exponential decay function that simulates the negative impact of process parameters on material properties, and D is the effect of ambient temperature on the material.

[0049] S202: When multiple process parameters affect material properties, it is necessary to uniformly scale the effects of these process parameters in order to more fairly compare the performance of materials under different process conditions. The processing formula is: ,in, Indicates standard process parameters, Represents the current process parameters; through this normalization function, it is ensured that the impact of all process parameters can be analyzed on the same scale.

[0050] S203: Construct a comprehensive evaluation function based on the normalized impact of all process parameters, material performance and environmental factors , select the optimal material, then The expression is: , where γ represents the intensity factor of the influence of process parameters on the material, and the value range of Y is , Y=1 means that the material performs best under given environmental and process conditions, and the closer Y is to 0, the worse the material performs under specific process parameters and environmental conditions. By adjusting process parameters, the material selection process is continuously optimized, and new production data is fed back to the big data analysis system, forming a closed loop of continuous optimization.

[0051] Step 3: After selecting the most suitable material, use simulation technology to simulate and optimize the production process of the fuel tank shell, predict the performance of the fuel tank shell under different process parameters, and find the optimal process path by simulating different production processes;

[0052] Specifically, by using simulation software tools such as finite element analysis software or computational fluid dynamics software to change different process parameters, simulate the production process of the fuel tank shell, observe the performance of the fuel tank shell under different conditions, obtain simulation results through multiple simulation process paths, and reuse the comprehensive evaluation function , and calculate a The value represents the comprehensive performance score of the fuel tank shell under this path;

[0053] Among them, select The path with the largest value is taken as the optimal process path.

[0054] Specifically, The calculation of only needs to consider the contribution value of a single path j, that is: ,if A certain set threshold, the path is a positive path, This sets the threshold,path to be a negative path.

[0055] Step 4: After each batch is produced, the fuel tank shell is fully inspected through quality inspection. If quality problems are found, the causes of the quality problems are automatically analyzed and fed back to the process adjustment and material selection links. These feedback data will be input into the big data analysis system to optimize the next production process. Specifically:

[0056] S401: First, set clear quality standards for the fuel tank shell, including strength, thickness, surface finish, dimensional accuracy, corrosion resistance and other testing requirements. These standards need to be set based on the use environment and safety requirements of the fuel tank;

[0057] Collect various quality data of each batch of fuel tank shells from the quality inspection system, and combine these data with the process parameters in the production process to form a comprehensive production data set;

[0058] S402: Build a machine learning model or statistical model for quality problem analysis based on historical production data and quality inspection data;

[0059] The statistical model is a random forest classification model to predict whether the fuel tank shell meets the quality standards. The input features include various process parameters or quality data, and the output result is whether the batch is qualified;

[0060] set up: is the input feature;

[0061] The output is the quality assessment result, which is a binary classification problem, that is, whether the batch of products is qualified, that is: , where the model is trained through historical data, using label data: qualified or unqualified, and feature data: various process parameters or quality data for training to generate classification

[0062] S403: Analyze which process parameters or material factors lead to quality problems based on the model.

[0063] In summary, through the comprehensive evaluation function , influence of process parameters , Path Effect , classification tree prediction results Four dimensions are used to comprehensively evaluate the quality of the fuel tank shell;

[0064] Among them, if Above the set threshold, has a significant impact on quality, and is the positive path, and =1, the oil tank shell is determined to be qualified and can continue to be put into production;

[0065] If Y is lower than the set threshold, or No significant effect on quality, or is a negative path, and =0, the fuel tank shell is judged to be unqualified and production is stopped.

[0066] During the long production process, quality inspection data will continue to accumulate. Through trend analysis of historical data, it is possible to identify common quality problems in the production process and predict which process conditions are most likely to cause quality problems.

[0067] The process optimization system applied to the above-mentioned new energy fuel tank shell manufacturing process optimization method based on big data analysis includes: a data acquisition and integration module, which is responsible for collecting process parameters and quality data from various sensors, testing equipment and quality control systems in the manufacturing process; a data storage and management module, which is responsible for storing, organizing and managing all data collected from the production line to ensure data security and efficient access; a data analysis and modeling module, which uses machine learning, statistical models and other methods based on historical production data and quality inspection data to build an analysis model for process optimization.

[0068] The prediction and decision support module generates process optimization suggestions for the next batch of production based on data analysis results and provides support for production decisions; and the process adjustment and feedback module receives suggestions from the prediction and decision support module, automatically or manually adjusts process parameters, and feeds back the adjustment results to the system.

[0069] In summary, the present invention can monitor and predict the impact of various process parameters on the quality of the fuel tank shell in real time during the production process through big data collection and analysis combined with a machine learning model, automatically adjust the process parameters, avoid the production of unqualified products, and establish a complete quality inspection feedback mechanism, which can timely analyze the quality problems of each batch of products, trace the root causes of the problems and feedback to the process adjustment and material selection links to form a closed-loop optimization. The present invention uses simulation technology to simulate the production process of the fuel tank shell, evaluates the impact of different process parameters on the product, finds the optimal process path before actual production, reduces the cost of trial and error, and uses intelligent analysis and optimization methods to not only improve production efficiency, but also significantly improve the quality of the fuel tank shell, meeting the needs of new energy equipment for high-performance components.

[0070] This embodiment also provides a computer device, which is suitable for the case of a new energy fuel tank shell manufacturing process optimization method based on big data analysis, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the new energy fuel tank shell manufacturing process optimization method based on big data analysis as proposed in the above embodiment.

[0071] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0072] The present embodiment also provides a storage medium on which a computer program is stored. When the program is executed by the processor, the method for optimizing the manufacturing process of a new energy tank shell based on big data analysis proposed in the above embodiment is implemented; the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic memory, a flash memory, a disk or an optical disk.

[0073] Example 2

[0074] Referring to Table 1, in order to verify the beneficial effects of the present invention, a set of comparative experiments were designed to compare the new energy fuel tank shell manufacturing process optimization system based on big data analysis proposed in the present invention with the traditional manufacturing process under the same production environment and conditions.

[0075] Experimental material type: aluminum alloy material (the same batch of materials is used for both the traditional process and the present invention), sample quantity: 200 tank shells per batch, and 10 consecutive batches of comparative experiments are carried out, process parameter range: welding temperature, pressure, speed, etc., controlled within the standard range, quality indicators: product strength, dimensional accuracy, surface defect rate, and qualified rate.

[0076] Table 1 Experimental comparison data table

[0077] index Traditional crafts Optimization method of the present invention Improvement Product strength (MPa) 420 ± 15 435 ± 8 +3.6% Dimensional accuracy deviation (mm) ±0.6 ±0.2 +66.7% Surface defect rate (%) 7.5 2.8 -62.7% Pass rate (%) 91.5 98.2 +7.3% Production cycle (minutes / batch) 120 110 -8.3% Energy consumption (kWh / batch) 150 140 -6.7%

[0078] Comparative experiments in actual environments show that the present invention is superior to traditional manufacturing processes in terms of product quality, production efficiency and resource utilization. In particular, it performs outstandingly in terms of dimensional accuracy control and defect rate reduction, verifying its significant advantages in optimizing the manufacturing process of new energy fuel tank shells, and bringing actual economic benefits and technical value to industrial production.

[0079] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A new energy fuel tank shell manufacturing process optimization method based on big data analysis, characterized in that: The method comprises the following steps: Step 1: Collect all historical data related to production, including: types of raw materials, suppliers, production equipment, process parameters, and quality data of the final product; Step 2: Based on the historical big data collected, start big data analysis to analyze the performance of different materials under different process conditions and determine the material that performs best in a specific use environment; Specifically include: S201: Determine the influence of each process parameter on material properties through big data analysis i ; S202: When multiple process parameters affect material properties, it is necessary to uniformly scale the effects of these process parameters in order to more fairly compare the performance of materials under different process conditions. The processing formula is: Among them, P0 represents the standard process parameters, P j Indicates the current process parameters; S203: Based on the normalized influence of all process parameters, the performance of the material and environmental factors, a comprehensive evaluation function Y is constructed to select the optimal material. The expression of Y is: Among them, γ represents the intensity factor of the influence of process parameters on the material, C j is the material and process parameter P j The interaction coefficient between them, the value range of Y is [0,1], Y = 1 means that the material performs best under given environmental and process conditions, and the closer Y is to 0, the worse the performance of the material under specific process parameters and environmental conditions; The M i The calculation formula is: Among them, P j represents the process parameters, a j is the influence of process parameters on material properties, that is, the power factor, which indicates the sensitivity of the material to different parameters, β j represents the attenuation factor, is an exponential decay function, simulating the negative impact of process parameters on material properties, and D is the effect of ambient temperature on the material; Step 3: After selecting the most suitable material, use simulation technology to simulate and optimize the production process of the fuel tank shell, predict the performance of the fuel tank shell under different process parameters, and find the optimal process path by simulating different production processes; Step 4: After each batch of production is completed, the fuel tank shell is comprehensively inspected through quality inspection. If quality problems are found, the causes of the quality problems are automatically analyzed and fed back to the process adjustment and material selection links. These feedback data will be input into the big data analysis system to optimize the next production process.

2. The method for optimizing the manufacturing process of a new energy fuel tank shell based on big data analysis according to claim 1 is characterized in that: In the process of step 3, when different process parameters are changed by using simulation software tools such as finite element analysis software or computational fluid dynamics software, the production process of the fuel tank shell is simulated, and the performance of the fuel tank shell under different conditions is observed. The simulation results are obtained through multiple simulation process paths, and the comprehensive evaluation function Y is reused to calculate a Y path The value represents the comprehensive performance score of the fuel tank shell under this path; Among them, select Y path The path with the largest value is taken as the optimal process path.

3. The method for optimizing the manufacturing process of a new energy fuel tank shell based on big data analysis according to claim 2 is characterized in that: The process of step 4 specifically includes: S401: Collect various quality data of each batch of fuel tank shells from the quality inspection system, and combine these data with the process parameters in the production process to form a comprehensive production data set; S402: Build a machine learning model or statistical model for quality problem analysis based on historical production data and quality inspection data; S403: Analyze which process parameters or material factors lead to quality problems based on the model.

4. The method for optimizing the manufacturing process of a new energy fuel tank shell based on big data analysis according to claim 3 is characterized in that: The statistical model is a random forest classification model to predict whether the fuel tank shell meets the quality standards. The input features include various process parameters or quality data, and the output result is whether the batch is qualified. Setting: X = [X1, X2, X3,,, X i ] is the input feature; The output is the quality assessment result, which is a binary classification problem, that is, whether the batch of products is qualified, that is: The model is trained through historical data, and the label data: qualified or unqualified, and feature data: various process parameters or quality data are used for training to generate a classification tree.

5. The method for optimizing the manufacturing process of a new energy fuel tank shell based on big data analysis according to claim 4 is characterized in that: Through the comprehensive evaluation function Y, the influence of process parameters M i , Path Effect Y path , classification tree prediction result Y quality Four dimensions are used to comprehensively evaluate the quality of the fuel tank shell; If Y is higher than the set threshold, M i has a significant impact on quality, and Y path is a positive path, and Y quality =1, the oil tank shell is determined to be qualified and can continue to be put into production; If Y is lower than the set threshold, or M i No significant effect on quality, or Y path is a negative path, and Y quality =0, the fuel tank shell is judged to be unqualified and production is stopped.

6. The process optimization system applied to the new energy fuel tank shell manufacturing process optimization method based on big data analysis as described in claim 5 is characterized by: The system includes: The data acquisition and integration module is responsible for collecting process parameters and quality data from various sensors, testing equipment and quality control systems in the manufacturing process; The data storage and management module is responsible for storing, organizing and managing all data collected from the production line to ensure data security and efficient access; The data analysis and modeling module builds an analytical model for process optimization based on historical production data and quality inspection data using machine learning, statistical models and other methods; The prediction and decision support module generates process optimization suggestions for the next batch of production based on data analysis results and provides support for production decisions; and a process adjustment and feedback module that receives suggestions from the prediction and decision support module, automatically or manually adjusts process parameters, and feeds back the adjustment results to the system.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the new energy fuel tank shell manufacturing process optimization method based on big data analysis as described in any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for optimizing the manufacturing process of a new energy fuel tank shell based on big data analysis as described in any one of claims 1 to 5 are implemented.

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