Flow chart automatic testing method, system and equipment of control system and storage medium

Abstract

The invention relates to the technical field of flow chart automatic testing, and provides a flow chart automatic testing method, system and device of a control system and a storage medium so as to solve the problems of low testing efficiency and poor accuracy. The method comprises the following steps: acquiring a film thickness and transverse tension fluctuation curve by using an intelligent textile sensor array, generating a tension compensation factor based on a phase difference to correct the film thickness, and establishing a thickness distribution topology model; performing non-contact scanning by using a terahertz wave emission array, and activating a defect mark cluster when Doppler frequency shift is detected; transmitting the thickness distribution model and the defect characteristics to a test platform, reconstructing a control logic and generating a test script, and realizing a virtual-real synchronous test through a communication protocol; and generating a process parameter correction factor according to a test result, and feeding back the process parameter correction factor to the distributed control system to optimize the tension closed-loop control module, and generating a matched terahertz verification map. According to the technical scheme provided by the invention, the efficiency and accuracy of automatic testing of the flow chart can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of flowchart automation testing, and in particular to a flowchart automation testing method, system, device and storage medium for a control system. Background Art

[0002] The fluorine chemical industry, particularly in the production of fluoropolymer films, places stringent requirements on film thickness consistency and uniformity. During the high-speed stretching process, to ensure final product quality, film thickness and transverse tension fluctuations must be monitored and adjusted in real time. Furthermore, potential defects (such as tiny holes or irregular areas) must be quickly and accurately identified and corrected. Therefore, developing automated flowsheet testing methods that improve production efficiency, ensure product quality, and achieve automation and intelligent control is crucial.

[0003] Currently, monitoring of fluoropolymer film production relies primarily on traditional sensor technologies, mechanical tension controllers, and basic distributed control systems. Traditional sensor technologies, such as capacitive or optical sensors, measure the thickness of the fluoropolymer film; mechanical tension controllers adjust the transverse tension to ensure uniform film stretching during production. Furthermore, basic distributed control systems perform process control tasks, enabling monitoring and adjustment of the production process to maintain stable operating parameters. These technologies together form the foundation of current fluoropolymer film production monitoring and control systems.

[0004] The main drawback of existing solutions lies in their limited adaptability and flexibility. Traditional sensors struggle to meet the demand for high-precision measurement of film thickness and tension distribution, especially under high-speed production conditions, which can lead to inaccurate or delayed data acquisition. Furthermore, traditional methods for detecting film surface defects often require downtime for manual inspection, which is not only time-consuming but also increases costs. More importantly, current distributed systems lack effective self-diagnosis and adaptive adjustment mechanisms, and are unable to quickly respond and adjust to real-time production conditions, limiting the efficiency of the entire production line and the consistency of products. Therefore, it is particularly urgent to develop a more intelligent and efficient method to solve these problems. Summary of the Invention

[0005] The embodiments of the present application provide a method and system for automated flow chart testing of a control system, which are used to solve the problems of low efficiency and poor accuracy in automated flow chart testing in the prior art.

[0006] In a first aspect, an embodiment of the present application provides a method for automated testing of a flow chart of a control system, comprising:

[0007] During the high-speed stretching process of the fluoropolymer film, the film thickness data and the transverse tension fluctuation curve are collected by an intelligent textile sensor array. A tension compensation factor is generated based on the phase difference of the transverse tension fluctuation curve, and the film thickness data is corrected to generate a thickness distribution topology model.

[0008] The thickness distribution topology model is non-contact scanned by a terahertz wave emission array, and a thin film defect characteristic marker cluster is activated when a Doppler frequency shift of the terahertz reflection interference fringes is detected;

[0009] The thickness distribution topology model and the thin film defect characteristic mark cluster are transmitted to the test platform, the control logic flow chart is reconstructed and a process parameter test script is generated, and a virtual-real synchronization test of the distributed control system is performed through a communication protocol;

[0010] A process parameter correction factor is generated according to the virtual-real synchronization test result and is reversely injected into the tension closed-loop control module of the distributed control system, and a terahertz characteristic verification map matching the corrected process parameter is generated at the same time.

[0011] Optionally, the transmitting the thickness distribution topology model and the thin film defect characteristic mark cluster to a test platform, reconstructing a control logic flow chart and generating a process parameter test script includes:

[0012] Analyzing the crystallinity distribution characteristics of the thickness distribution topology model, generating a tension gradient-thickness fluctuation correlation matrix, and establishing a control logic node mapping relationship based on the data mutation amplitude;

[0013] Performing spatial cluster analysis on the thin film defect signature cluster, calculating the radial distance distribution from the defect center area to the process section boundary, and generating a defect impact weight factor in combination with the stress concentration coefficient of the thickness distribution topology model;

[0014] Performing multi-dimensional matching on the control logic node mapping relationship and the defect impact weight factor to generate a process branch containing a crystallinity-tension gradient constraint condition;

[0015] Establishing a process parameter boundary equation based on the process branch, and generating a test case set covering the defect impact weight factor by traversing the combination space of film stretching speed and temperature compensation parameters;

[0016] Protocol encapsulation processing is performed on the test case set, and the real-time updated data stream of the thickness distribution topology model is injected into the test script execution engine to generate a process parameter test script.

[0017] Optionally, performing multi-dimensional matching on the control logic node mapping relationship and the defect impact weight factor to generate a process branch containing a crystallinity-tension gradient constraint condition includes:

[0018] Dividing the orthogonal process area grid based on the film stretching direction and the transverse tension gradient axis, performing grid space encoding on the control logic node mapping relationship, and generating a node distribution vector with coordinate attributes;

[0019] Normalizing the defect impact weight factors according to the process regions, analyzing the relationship between the radial distance from the defect center to the boundary of each region and the stress concentration factor, and generating a defect impact distribution matrix;

[0020] Performing a dynamic convolution operation on the node distribution vector and the defect impact distribution matrix to output a regional matching degree matrix reflecting the correlation strength between the control logic node and the defect area;

[0021] Extracting elements whose values exceed a dynamic threshold in the regional matching matrix, and mapping the elements to process parameter adjustment nodes in a control logic flow chart;

[0022] A crystallinity-tension gradient constraint condition is superimposed on the process parameter adjustment node to generate a process branch containing the crystallinity-tension gradient constraint condition.

[0023] Optionally, performing a dynamic convolution operation on the node distribution vector and the defect impact distribution matrix to output a region matching matrix reflecting the strength of association between the control logic node and the defect region includes:

[0024] Performing boundary extension filling on the node distribution vector, wherein the filling width is dynamically adjusted based on the process area grid spacing, and the filling value is generated by interpolation of adjacent grid node distribution vectors;

[0025] Align the padded node distribution vector with the defect impact distribution matrix, perform a convolution kernel sliding operation, calculate the product accumulation value of the node weight and the defect impact factor in each window, and generate the original matching degree matrix;

[0026] Performing adaptive filtering on the original matching degree matrix, dynamically adjusting the filtering strength based on the tension gradient change rate, suppressing discrete noise interference and retaining mutation characteristics;

[0027] The filtered original matching degree matrix is regionally normalized according to the crystallinity distribution characteristics, and the regional matching degree matrix reflecting the correlation strength between the control logic node and the defect area is output.

[0028] Optionally, the non-contact scanning of the thickness distribution topology model by a terahertz wave emission array and activating a thin film defect characteristic marker cluster when a Doppler frequency shift of terahertz reflection interference fringes is detected include:

[0029] Based on the crystallinity distribution characteristics of the thickness distribution topological model, the terahertz wave incident angle and scanning frequency are dynamically adjusted to perform multi-band scanning on the film surface and collect phase difference data of the reflected interference fringes;

[0030] Calculating the Doppler frequency shift of the reflected interference fringes, extracting the phase change matrix of adjacent scanning cycles, and generating a frequency shift trajectory map through a phase gradient accumulation algorithm;

[0031] Calculating the stress concentration coefficient of the distortion center area based on the geometric topological characteristics of the fringe distortion area in the frequency shift trajectory map, and performing spatial matching with the stress concentration area of the thickness distribution topological model;

[0032] A dynamic frequency shift threshold is set, and when the Doppler frequency shift exceeds the threshold and the stress concentration factor of the corresponding area matches, the thin film defect feature marker cluster is activated.

[0033] Optionally, calculating the stress concentration coefficient of the distortion center region according to the geometric topological features of the fringe distortion region in the frequency shift trajectory map, and performing spatial matching with the stress concentration region of the thickness distribution topological model includes:

[0034] Extracting geometric topological parameters of the fringe distortion area of the frequency shift trajectory map, calculating the density of the distortion boundary curvature mutation points and the fractal dimension value, and generating a geometric feature vector representing the dynamic deformation of the defect;

[0035] Based on the crystallinity gradient distribution of the thickness distribution topological model, a three-dimensional stress analysis grid is divided along the film stretching direction, and a covariance matrix of the thickness fluctuation rate and the transverse tension gradient in each three-dimensional stress analysis grid unit is calculated;

[0036] Mapping the geometric eigenvector to the three-dimensional stress analysis grid, generating a dynamic weight factor according to a spatial distance attenuation function from the distortion center to the grid unit boundary, and combining the covariance matrix to obtain a dynamic stress concentration factor;

[0037] Performing spatial cluster analysis on the stress concentration area of the thickness distribution topology model, extracting cluster center coordinates and area coverage radius, and constructing a static stress distribution map;

[0038] The dynamic stress concentration factor is spatially matched with the static stress distribution map, and an overlap index of the dynamic-static stress region is calculated. When the overlap index exceeds a matching threshold, a cross-modal association flag is triggered.

[0039] Optionally, generating a tension compensation factor based on the phase difference of the transverse tension fluctuation curve and correcting film thickness data to generate a thickness distribution topology model includes:

[0040] Performing a multi-channel sliding window analysis on the transverse tension fluctuation curve, extracting a phase difference sequence of adjacent sensor nodes, and generating a phase difference matrix with a timestamp;

[0041] Calculating the phase correlation coefficients of adjacent windows in the phase difference matrix, identifying abnormal tension fluctuation intervals based on the correlation coefficient decay rate, and generating dynamic phase compensation weights;

[0042] Performing spatial interpolation correction on the film thickness data, where the interpolation weight is determined jointly by the dynamic phase compensation weight and the spatial distribution density of the sensor nodes, to generate an initial corrected film thickness distribution map;

[0043] Based on the gradient variation characteristics of the initial corrected film thickness distribution map, three-dimensional grid cells are divided along the film stretching direction, and the coupling coefficient between the thickness fluctuation rate and the transverse tension gradient in each cell is calculated;

[0044] The coupling coefficient is mapped to the vertices of the three-dimensional grid, and combined with the crystallinity variation trend of the initial corrected film thickness distribution map to generate a thickness distribution topology model with thermal field properties.

[0045] In a second aspect, an embodiment of the present application provides a flow chart automation testing system for a control system, comprising:

[0046] An acquisition module collects film thickness data and a transverse tension fluctuation curve through an intelligent textile sensor array during the high-speed stretching process of the fluoropolymer film, generates a tension compensation factor based on the phase difference of the transverse tension fluctuation curve, and corrects the film thickness data to generate a thickness distribution topology model;

[0047] a detection module, which performs non-contact scanning on the thickness distribution topology model through a terahertz wave emission array, and activates a thin film defect characteristic marker cluster when a Doppler frequency shift of the terahertz reflection interference fringes is detected;

[0048] A reconstruction module transmits the thickness distribution topology model and the thin film defect characteristic mark cluster to the test platform, reconstructs the control logic flow chart and generates a process parameter test script, and performs a virtual-real synchronization test of the distributed control system through a communication protocol;

[0049] The correction module generates a process parameter correction factor according to the virtual-real synchronization test result and injects the correction factor back into the tension closed-loop control module of the distributed control system, and simultaneously generates a terahertz characteristic verification map matching the corrected process parameter.

[0050] In a third aspect, an embodiment of the present application provides a computing device comprising a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a flow chart automation testing method for a control system as described in the first aspect above.

[0051] In a fourth aspect, an embodiment of the present application provides a computer storage medium storing a computer program. When the computer program is executed by a computer, it implements a flow chart automated testing method for a control system as described in the first aspect.

[0052] In an embodiment of the present application, in the high-speed stretching process section of the fluoropolymer film, the film thickness data and the transverse tension fluctuation curve are collected by an intelligent textile sensor array, and a tension compensation factor is generated based on the phase difference of the transverse tension fluctuation curve, and the film thickness data is corrected to generate a thickness distribution topological model; the thickness distribution topological model is non-contact scanned by a terahertz wave emission array, and the film defect feature marker cluster is activated when a Doppler frequency shift of the terahertz reflection interference fringes is detected; the thickness distribution topological model and the film defect feature marker cluster are transmitted to a test platform, the control logic flow chart is reconstructed and a process parameter test script is generated, and the virtual-real synchronization test of the distributed control system is executed through the communication protocol; the process parameter correction factor is generated according to the virtual-real synchronization test result and reversely injected into the tension closed-loop control module of the distributed control system, and a terahertz feature verification map matching the corrected process parameters is generated.

[0053] The technical solution of this application has the following beneficial effects:

[0054] This application uses an intelligent textile sensor array to obtain film thickness data and transverse tension fluctuation curves, and generates a tension compensation factor based on the phase difference to correct the film thickness data to form a thickness distribution topology model. Then, a terahertz wave emission array is used to perform non-contact scanning of the thickness distribution model, and a defect marker cluster is activated when a Doppler frequency shift is detected. Then, a parameter correction factor is generated based on the test results and fed back to the distributed control system, and a matching verification map is generated at the same time. This method significantly improves the ability to monitor and adjust quality during the manufacturing process of fluoropolymer films, and realizes a fully automated process from data acquisition, analysis to real-time adjustment. It not only improves the consistency of film thickness and production efficiency, but also enhances the accuracy and reliability of defect detection by introducing terahertz wave technology.

[0055] Furthermore, the thickness distribution model and defect feature marker clusters are analyzed, and the defect impact weight factors are generated through spatial cluster analysis, and the control logic node mapping relationship is established in combination with the crystallinity-tension gradient constraint conditions. Based on this, a detailed process parameter boundary equation and a test case set are generated, which are injected into the test script execution engine after protocol encapsulation processing to optimize the process parameter adjustment strategy, enhance the system's ability to cope with complex working conditions, and improve the level of intelligence and production efficiency. This method refines the data analysis and processing mechanism in the film production process, making the control logic more accurate and adaptable. By deeply analyzing the thickness distribution and defect characteristics, the film production process parameters can be more accurately predicted and adjusted to ensure the stability and consistency of product quality. In addition, by constructing a detailed set of test cases and conducting virtual and real synchronous tests, the system's ability to cope with complex working conditions has been greatly improved, further enhancing the intelligence level and production efficiency of the entire production line.

[0056] These and other aspects of the present application will become more readily apparent from the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0058] Figure 1 A flowchart of a flow chart automation testing method for a control system provided by the present application is shown;

[0059] Figure 2 A flow chart of a control system provided by the present application is shown, which is a schematic structural diagram of an automated test system;

[0060] Figure 3 A schematic structural diagram of a computing device provided by the present application is shown. DETAILED DESCRIPTION

[0061] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0062] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any order of execution. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to being different types.

[0063] This solution aims to use intelligent sensors to collect thickness and tension data, analyze phase differences, generate compensation factors to correct thickness data, and construct a topological model. Terahertz wave scanning is then used to detect and mark defects. This data is then fed into a testing platform, where control logic is reconfigured and virtual-real-simultaneous testing is performed. Finally, process parameters are adjusted based on the test results, and the optimization results are verified. The entire process, from data collection and defect detection to feedback optimization, systematically improves film quality and production efficiency.

[0064] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making creative efforts are within the scope of protection of this application.

[0065] Figure 1 The present invention provides a flowchart of a control system automation test method, such as Figure 1 As shown, the method includes:

[0066] 101. In the high-speed stretching process section of the fluoropolymer film, the film thickness data and the transverse tension fluctuation curve are collected by the intelligent textile sensor array, a tension compensation factor is generated based on the phase difference of the transverse tension fluctuation curve, and the film thickness data is corrected to generate a thickness distribution topology model;

[0067] In this step, a tension compensation factor is generated based on the phase difference of the transverse tension fluctuation curve and is used to correct the film thickness data.

[0068] The intelligent textile sensor array collects film thickness data and transverse tension fluctuation curves, which together form the basis for analysis.

[0069] The transverse tension fluctuation curve depicts the tension variation in the transverse direction of the film during production, while the film thickness data directly reflects the thickness of each part of the film. By calculating the phase difference between these data and adjusting the film thickness data accordingly, an accurate thickness distribution topology model is ultimately formed.

[0070] In this application example, a smart textile sensor array is first deployed in the high-speed stretching process of a fluoropolymer film to acquire real-time film thickness and transverse tension data. Next, an algorithm analyzes the transverse tension fluctuation curve to determine the phase difference, thereby generating a tension compensation factor. This factor is applied to the raw thickness data for correction. By integrating all data, a thickness distribution topology model is generated to ensure that the film's actual thickness variations are reflected.

[0071] In a new fluoropolymer production line, engineers installed an intelligent textile sensor array to monitor film thickness and tension. As the line operates, the system automatically records and processes data, using a specific algorithm to calculate a tension compensation factor. This allows for precise correction of film thickness data, successfully constructing a topological model that details actual film thickness variations, providing a reliable basis for subsequent testing.

[0072] 102. Performing non-contact scanning on the thickness distribution topology model through a terahertz wave emission array, and activating a thin film defect characteristic marker cluster when a Doppler frequency shift of the terahertz reflection interference fringes is detected;

[0073] In this step, the terahertz wave emission array is used to non-contact scan the thickness distribution topology model, and when the Doppler frequency shift of the terahertz reflection interference fringes is detected, the thin film defect feature marker cluster is activated.

[0074] Terahertz reflection interference fringes are reflection phenomena caused by inhomogeneities inside or on the surface of a material.

[0075] Doppler shift refers to the change in received frequency caused by the movement of an object and is used here to identify potential defect locations.

[0076] The thin film defect feature marker cluster is a series of marks automatically generated based on these changes to indicate possible defect areas.

[0077] In this embodiment, a terahertz wave transmitting array is used to meticulously scan the thickness distribution topology model obtained in step 101. During this process, if Doppler shifts in the reflected interference fringes are detected due to internal or surface inhomogeneities in the material, the corresponding defect signature cluster is immediately activated. This method can efficiently locate microscopic defects in thin films, ensuring effective quality control.

[0078] After obtaining a detailed topological model of the thickness distribution, the production line activates the terahertz wave transmission array to perform non-contact scanning of the film. When the system detects Doppler shifts in certain areas, it quickly activates the corresponding defect signature cluster, marking the location of possible defects and providing clear guidance for further quality inspection.

[0079] 103. Transmit the thickness distribution topology model and the thin film defect characteristic mark cluster to a test platform, reconstruct a control logic flow chart and generate a process parameter test script, and perform a virtual-real synchronization test of a distributed control system through a communication protocol;

[0080] In this step, the test platform reconstructs the control logic flow chart and generates a process parameter test script to achieve virtual-real synchronous testing of the distributed control system.

[0081] Thickness distribution topology models and thin film defect signature clusters are used as input to help the platform understand the current process status.

[0082] Control logic flow charts guide the entire production process, while process parameter test scripts are used to verify the effects of different parameter settings. These scripts are executed through communication protocols to ensure accurate synchronization between virtual and physical environments.

[0083] In this embodiment, the thickness distribution topology model and thin film defect signature clusters are transmitted to the testing platform, which then reconstructs the control logic flow chart and writes process parameter test scripts to simulate various scenarios. These scripts are executed using a pre-defined communication protocol to ensure consistency between the physical production line and the digital simulation. This step aims to verify and optimize existing process parameters and improve product quality.

[0084] After completing the initial defect identification, all relevant data was uploaded to the testing platform. Engineers used the platform to redesign control logic and adjust production process parameters to address new issues. They then initiated a new round of testing using the communication protocol to ensure consistency between the physical production line and the digital simulation, further improving production stability and product reliability.

[0085] 104. Generate a process parameter correction factor based on the virtual-real synchronization test result and reversely inject it into the tension closed-loop control module of the distributed control system, and simultaneously generate a terahertz characteristic verification map matching the corrected process parameter.

[0086] In this step, the tension closed-loop control module generates process parameter correction factors based on the virtual-real synchronization test results and injects them back into the system. Simultaneously, a terahertz signature verification map is generated to confirm the effectiveness of the optimized process parameters.

[0087] The process parameter correction factor is used to adjust the original process settings to make them more in line with actual production needs.

[0088] The terahertz characteristic verification map shows how the optimized process parameters affect the quality characteristics of the film, ensuring that each step of improvement will bring positive results.

[0089] In this example, the necessary process parameter correction factors were calculated based on the synchronized test results provided by the test platform and applied to the tension closed-loop control module. Furthermore, a terahertz signature verification map was created to evaluate the effectiveness of the optimization measures. This step not only adjusted key parameters in the production process but also scientifically verified whether these adjustments had truly improved film quality.

[0090] After a rigorous series of simultaneous virtual and real-world tests, engineers adjusted tension settings and other key parameters on the production line based on the feedback. Terahertz signature verification mapping then verified the effectiveness of these changes, confirming a significant improvement in film quality under the new settings, demonstrating the success of the optimization measures. This process significantly improved the overall quality and production efficiency of fluoropolymer films.

[0091] In summary, the process from steps 101 to 104, from initial data collection to final process parameter optimization, not only improves the precision and efficiency of fluoropolymer film production but also enhances product quality monitoring capabilities. Through intelligent data processing technology and sophisticated testing methods, a high degree of controllability and predictability in the film manufacturing process is achieved, laying a solid foundation for the development of future advanced materials. Each step is closely linked, forming a complete closed-loop control system, effectively promoting continuous process improvement and technological advancement.

[0092] To further improve the quality control of fluoropolymer films, this solution analyzes the thickness distribution topology model to generate a tension gradient-thickness fluctuation correlation matrix and control logic node mapping. It then performs spatial cluster analysis on defect marker clusters to calculate defect impact weight factors, formulates process parameter boundary equations, and generates a set of test cases. This protocol encapsulates and processes data streams in real time to generate test scripts, achieving seamless integration of data analysis and application, and optimizing production processes. In some embodiments, step 103 involves transmitting the thickness distribution topology model and the film defect signature clusters to a test platform, reconstructing the control logic flow chart, and generating a process parameter test script, including:

[0093] 201. Analyze the crystallinity distribution characteristics of the thickness distribution topology model, generate a tension gradient-thickness fluctuation correlation matrix, and establish a control logic node mapping relationship according to the data mutation amplitude;

[0094] In step 201, the tension gradient-thickness fluctuation correlation matrix contains crystallinity distribution characteristic data based on the thickness distribution topology model. This matrix is used to identify tension variation trends in different regions and establish control logic node mapping relationships based on the magnitude of data mutations, guiding subsequent process parameter adjustments. The tension gradient represents the tension variation in different parts of the film, while the thickness fluctuation refers to the change in film thickness. The matrix generated by combining the two helps identify critical control points.

[0095] In this application's examples, we first analyze the thickness distribution topology model to determine its crystallinity distribution characteristics. We then calculate the correlation between tension gradients and thickness fluctuations to form a correlation matrix. Based on this matrix, we analyze data mutation points to define control logic nodes, providing fundamental data support for process optimization. Ultimately, these nodes are used to construct a control logic diagram tailored to the production line's needs.

[0096] 202. Perform spatial cluster analysis on the thin film defect signature cluster, calculate the radial distance distribution from the defect center area to the process section boundary, and generate a defect impact weight factor in combination with the stress concentration coefficient of the thickness distribution topology model;

[0097] In step 202, the defect impact weight factor is derived from a spatial cluster analysis. This analysis considers the radial distance distribution from the defect center to the process section boundary and combines it with the stress concentration factor to assess the impact of each defect on the overall structure. This factor is then generated as an important reference for optimizing process parameters. The stress concentration factor reflects the local stress increase caused by internal or surface inhomogeneities in the material.

[0098] In this embodiment, a spatial clustering algorithm is used to process clusters of thin film defect signatures, calculating the radial distance from each defect's center to the process section boundary. Furthermore, based on the stress concentration factors in the thickness distribution topology model, defect impact weighting factors are comprehensively considered to ensure that critical defects are prioritized. These weighting factors are then applied to process parameter adjustments to improve product quality stability.

[0099] 203. Perform multi-dimensional matching on the control logic node mapping relationship and the defect impact weight factor to generate a process branch including a crystallinity-tension gradient constraint condition;

[0100] In step 203, multidimensional matching technology is used to combine the control logic node mapping relationship with the defect impact weight factor to generate a process branch containing crystallinity-tension gradient constraints. This aims to precisely control key variables in the production process and improve product quality stability. Multidimensional matching technology is a method that can simultaneously process data from multiple dimensions, ensuring that all factors are fully considered.

[0101] In this example, a multidimensional matching algorithm was used to integrate the results of the first two steps to create a process branch with crystallinity and tension gradient constraints. This step ensures that each control node in the production process can accurately respond to specific defects, improving the adaptability and flexibility of the production line. In this way, precise control of the production process can be achieved, ensuring that every link reaches the optimal state.

[0102] 204. Establishing a process parameter boundary equation based on the process branch, and generating a test case set covering the defect impact weight factor by traversing the combination space of film stretching speed and temperature compensation parameters;

[0103] In step 204, process parameter boundary equations are established based on the process branches. By traversing different combinations of film stretching speed and temperature compensation parameters, a set of test cases covering defect impact weight factors is generated to ensure that every possible operating condition is fully verified. The process parameter boundary equations define the safe operating range of the process parameters, ensuring a stable and reliable production process.

[0104] In this application's examples, boundary conditions were set based on process branching, and a test plan was designed to cover all potential process parameter combinations. By comprehensively exploring film stretching speed and temperature compensation parameters, a series of test cases were generated to ensure that all key variables were tested within controlled ranges. The ultimate result was to ensure that production process parameters met quality requirements under every operating condition.

[0105] 205. Perform protocol encapsulation processing on the test case set, inject the real-time updated data stream of the thickness distribution topology model into the test script execution engine, and generate a process parameter test script.

[0106] In step 205, protocol encapsulation performs standardized encapsulation on the test case set, injecting the real-time updated data stream of the thickness distribution topology model into the test script execution engine to generate the final process parameter test script, achieving a seamless transition from data analysis to actual application. Protocol encapsulation ensures that the test cases can be efficiently executed by the automated tool, while the injection of real-time data streams ensures that the test script always reflects the latest production status.

[0107] In the embodiments of this application, protocol encapsulation technology is used to process and standardize a collection of test cases, allowing them to be recognized and executed by an automated test script execution engine. By continuously monitoring changes in the thickness distribution model and updating the test script in real time, the production process is ensured to be always in optimal condition. The end result is a dynamically adjusted and highly responsive production control system.

[0108] Here's a specific example:

[0109] In a new fluoropolymer film production line project, engineers first used smart sensors to collect film thickness and tension data and construct a thickness distribution topology model. Next, they used a terahertz wave emission array to detect and mark microscopic defects on the film surface. This information was then input into the test platform to analyze the model's crystallinity distribution characteristics, generate a tension gradient-thickness fluctuation correlation matrix, and establish a control logic node mapping relationship. Subsequently, they performed spatial clustering analysis on the defect feature marker clusters and calculated the defect impact weight factors. Combining these two sets of results, multidimensional matching technology was used to generate process branches containing crystallinity-tension gradient constraints. Based on this, process parameter boundary equations were formulated, a test case set was generated, and the real-time data stream was injected into the test script execution engine through protocol encapsulation processing to complete the generation of process parameter test scripts, achieving automated management of the entire process from data collection and analysis to feedback optimization.

[0110] In summary, through steps 201 to 205, closed-loop management from data collection and analysis to feedback optimization is achieved in the entire production process, which greatly improves the accuracy and efficiency of fluoropolymer film production, enhances product quality monitoring capabilities, ensures that each production link can be optimally configured, and promotes continuous process improvement and technological progress.

[0111] To further improve the precise control of the fluoropolymer film production process, this solution uses a dynamic convolution operation to output a regional matching matrix, superimposing crystallinity-tension gradient constraints on these nodes to generate optimized process branches. This process achieves closed-loop management from data analysis to precise control, optimizes the production process, and improves product quality and efficiency. In some embodiments, the multi-dimensional matching of the control logic node mapping relationship and the defect impact weight factor described in step 203 to generate a process branch containing crystallinity-tension gradient constraints includes:

[0112] 301. Divide the orthogonal process area grid based on the film stretching direction and the transverse tension gradient axis, perform grid spatial encoding on the control logic node mapping relationship, and generate a node distribution vector with coordinate attributes;

[0113] In step 301, a node distribution vector is created based on the orthogonal process area grid, which divides the film stretching direction and the transverse tension gradient axis. This vector spatially encodes the mapping relationship between the control logic nodes and generates a dataset with coordinate attributes. This vector not only identifies the specific location of each control logic node but also reflects the control requirements within each area, providing basic data support for subsequent analysis. The orthogonal process area grid, created by dividing the film stretching direction and the transverse tension gradient axis, is used to precisely identify the control logic nodes within each area.

[0114] In the examples of this application, an orthogonal grid is first created based on the film stretching direction and the transverse tension gradient axis. The control logic nodes are then mapped onto this grid, forming node distribution vectors with coordinates. These vectors not only contain the spatial information of the nodes but also reflect the control requirements within different areas, providing an accurate data source for subsequent processing. Ultimately, a node distribution vector is generated that can guide subsequent optimization measures.

[0115] 302. Normalize the defect impact weight factors according to the process regions, analyze the relationship between the radial distance from the defect center to the boundary of each region and the stress concentration factor, and generate a defect impact distribution matrix;

[0116] In step 302, a defect impact distribution matrix is generated by normalizing the defect impact weight factors by process region and combining them with the relationship between radial distance and stress concentration factor. This matrix illustrates the impact of defects within each process region, helping to identify areas requiring focus and ensuring that critical defects are effectively addressed. Normalization allows for direct comparison of defect impacts across different regions, while the stress concentration factor reflects the localized stress increases caused by internal or surface inhomogeneities within the material.

[0117] In this embodiment, a normalization algorithm is used to process the defect impact weight factors and calculate the relationship between the radial distance from each defect center to each region boundary and the stress concentration factor to generate a defect impact distribution matrix. This step helps clarify the specific impact of defects in each region and guide subsequent optimization measures. The final result is a matrix that reflects the degree of defect impact in each region.

[0118] 303. Perform a dynamic convolution operation on the node distribution vector and the defect impact distribution matrix, and output a region matching matrix reflecting the correlation strength between the control logic node and the defect region;

[0119] In step 303, a dynamic convolution operation is applied between the node distribution vector and the defect impact distribution matrix, outputting a regional matching matrix reflecting the strength of the correlation between the two. This matrix shows the strength of the interaction between the control logic nodes and the defect regions, providing a basis for precise adjustment of process parameters. Dynamic convolution is a method that can simultaneously process data from multiple dimensions, ensuring that all factors are fully considered.

[0120] In this embodiment, dynamic convolution technology is used to process node distribution vectors and defect impact distribution matrices to generate a regional matching matrix. By evaluating the strength of the association between control logic nodes and defect regions, it determines which regions require priority treatment, thereby achieving more refined process control. The final result is a regional matching matrix that can guide subsequent optimization measures.

[0121] 304. Extracting elements whose values exceed a dynamic threshold in the regional matching matrix, and mapping the elements to process parameter adjustment nodes in a control logic flow chart;

[0122] In step 304, a dynamic threshold is used to filter elements with high values in the regional matching matrix and map these elements to specific process parameter adjustment nodes in the control logic flow graph. This process ensures that critical defect areas receive targeted optimization treatment, improving overall process efficiency. The dynamic threshold sets a certain standard, and only elements that exceed this standard are selected for further processing.

[0123] In the embodiments of this application, dynamic thresholds are set to filter out highly matched elements and map them to corresponding nodes in the control logic diagram to facilitate subsequent adjustment of process parameters. In this way, key issues can be quickly located and resolved, improving the flexibility and responsiveness of the production line. The end result is a control logic diagram that can guide actual production.

[0124] 305. Superimpose a crystallinity-tension gradient constraint condition on the process parameter adjustment node to generate a process branch including the crystallinity-tension gradient constraint condition.

[0125] In step 305, the crystallinity-tension gradient constraint is added to the selected process parameter adjustment node, generating a process branch containing specific constraints. These constraints are designed to ensure that all parameters in the production process meet expected standards, thereby improving product quality stability. This overlay of constraints ensures that each node operates according to optimal parameters, thereby achieving refined control over the entire production process.

[0126] In this application's examples, crystallinity-tension gradient constraints are added to selected process parameter adjustment nodes to ensure that each node operates according to optimal parameters. This approach enables refined control of the entire production process, significantly improving product consistency and reliability. The end result is a process branch that ensures the stability of the production process.

[0127] Here's a specific example:

[0128] In a new fluoropolymer production line project, engineers first divided the orthogonal process area grid based on the film stretching direction and the transverse tension gradient axis, spatially encoded the control logic nodes to generate node distribution vectors with coordinate attributes, and ensured that the spatial information of each control point was accurate. Next, the defect impact weight factors were normalized according to the process area to generate a defect impact distribution matrix and identify key defect areas. Then, dynamic convolution operations were used to process the node distribution vectors and the defect impact distribution matrix, and a regional matching matrix reflecting the strength of the correlation between the two was output. By setting dynamic thresholds to screen high-matching elements and mapping them to process parameter adjustment nodes, it was ensured that key defect areas could be targeted and optimized. Finally, the crystallinity-tension gradient constraint conditions were superimposed on the selected nodes to generate optimization process branches, realizing closed-loop management from data analysis to precise control, greatly improving production accuracy and efficiency, enhancing product quality monitoring capabilities, and promoting continuous process improvement and technological advancement.

[0129] In summary, steps 301 to 305 achieve closed-loop management from data analysis and defect identification to precise control in the fluoropolymer film production process. This significantly improves production precision and efficiency, enhances the ability to monitor product quality, ensures optimal configuration of every link, and promotes continuous process improvement and technological advancement. Through a series of precise data processing and analysis steps, a high degree of controllability and refined management of the production process is achieved, ensuring the high quality standards of the final product.

[0130] To further improve the precise control of the fluoropolymer film production process, the solution first performs boundary expansion and filling on the node distribution vector, dynamically adjusts the filling width, and interpolates to generate the filling value. The original matching degree matrix is generated through a convolution kernel sliding operation. Finally, a regional matching degree matrix reflecting the correlation strength between the control logic node and the defect area is output to achieve precise control and efficient management, and improve product quality and production efficiency. In some embodiments, the dynamic convolution operation performed on the node distribution vector and the defect impact distribution matrix in step 303 to output a regional matching degree matrix reflecting the correlation strength between the control logic node and the defect area includes:

[0131] 401. Perform boundary extension filling on the node distribution vector, wherein the filling width is dynamically adjusted based on the process area grid spacing, and the filling value is generated by interpolation of adjacent grid node distribution vectors;

[0132] In step 401, boundary extension and padding involve interpolating the process area grid spacing and generating padding values. Dynamically adjusting the padding width ensures that the node distribution vectors have sufficient edge information before the convolution operation, thus avoiding errors caused by edge effects. The padding width is dynamically adjusted based on the process area grid spacing, while the padding value is generated by interpolating the node distribution vectors of adjacent grids.

[0133] In this example, the grid spacing of the process area is first calculated, and the fill width is dynamically adjusted accordingly. Interpolation is used to generate fill values, expanding the node distribution vector to the appropriate size. This step ensures that the subsequent convolution operation can accurately process all node data, avoiding data loss or distortion due to edge effects. The end result is that the integrity of the original data set is enhanced, providing a solid foundation for subsequent analysis.

[0134] 402. Align the padded node distribution vector with the defect impact distribution matrix, perform a convolution kernel sliding operation, calculate the product accumulation value of the node weight and the defect impact factor in each window, and generate an original matching degree matrix;

[0135] In step 402, the original matching matrix is generated by aligning the padded node distribution vectors with the defect impact distribution matrix and performing a sliding convolution operation. This process quantifies the strength of the association between each node and the defect region, providing a basis for subsequent optimization. The product of the node weights and the defect impact factors within each window is accumulated to generate the original matching matrix.

[0136] In this embodiment, after aligning the padded node distribution vectors with the defect impact distribution matrix, a sliding convolution kernel operation is used to calculate the product of the node weights and the defect impact factors on a window-by-window basis to generate the original matching matrix. This process helps identify which nodes have strong correlations with defective areas, providing critical data support for subsequent analysis. The end result is a raw matching matrix that can guide subsequent optimization measures.

[0137] 403. Perform adaptive filtering on the original matching degree matrix, dynamically adjust the filtering strength based on the tension gradient change rate, suppress discrete noise interference and retain mutation characteristics;

[0138] In step 403, adaptive filtering dynamically adjusts the filter strength based on the rate of change of the tension gradient, suppressing discrete noise interference while preserving the sudden change characteristics. This processing method can remove unnecessary noise while maintaining important features, improving data quality and ensuring the accuracy of subsequent analysis results.

[0139] In this embodiment, the filter strength is dynamically adjusted based on the rate of change of the tension gradient, and adaptive filtering technology is applied to the original matching degree matrix, effectively suppressing discrete noise interference while preserving important mutation characteristics. This process improves data purity and makes the final analysis results more reliable. The final result is a high-quality original matching degree matrix that has been filtered.

[0140] 404. Perform regional normalization on the filtered original matching degree matrix according to the crystallinity distribution characteristics, and output a regional matching degree matrix reflecting the correlation strength between the control logic node and the defect area.

[0141] In step 404, regional normalization normalizes the filtered raw matching matrix based on the crystallinity distribution characteristics, outputting a regional matching matrix that reflects the strength of the association between the control logic node and the defective region. This step ensures that the matching degrees between different regions can be directly compared, providing a basis for precise control.

[0142] In the examples of this application, the filtered raw matching matrix is normalized according to the crystallinity distribution characteristics to generate a final regional matching matrix. This process ensures that the matching degrees between different regions can be directly compared, providing a solid foundation for subsequent precise control. The final result is a regional matching matrix that can guide actual production.

[0143] Here's a specific example:

[0144] In a new fluoropolymer production line project, engineers first performed boundary expansion filling on the node distribution vector, dynamically adjusted the filling width based on the grid spacing of the process area, and used interpolation of adjacent grid nodes to generate filling values to ensure data integrity. Next, the convolution kernel sliding operation was used to calculate the product accumulation value of the node weight and the defect impact factor in each window to generate the original matching degree matrix and identify key control points and high defect impact areas. Then, the filter strength was dynamically adjusted based on the tension gradient change rate, and the original matching degree matrix was adaptively filtered to suppress noise interference and retain mutation characteristics to improve data quality. Finally, the filtered matrix was regionally normalized according to the crystallinity distribution characteristics, and the regional matching degree matrix reflecting the correlation strength between the control logic node and the defect area was output, providing a solid foundation for precise control.

[0145] In summary, steps 401 to 404 optimize the entire fluoropolymer film production process, from data processing and defect identification to precise control. Through a series of precise data processing steps, such as boundary expansion and filling, convolution kernel sliding operations, adaptive filtering, and regional normalization, each process parameter adjustment node is optimally configured, significantly improving product quality and production efficiency, and promoting continuous process improvement and technological advancement. This process ensures optimal configuration of each production link, enhancing product quality and production efficiency.

[0146] To further improve the accuracy of defect detection in fluoropolymer film production, the solution uses a terahertz wave emission array to perform non-contact scanning of the thickness distribution topology model, dynamically adjust scanning parameters, calculate Doppler frequency shift, generate a frequency shift trajectory map, calculate the stress concentration factor, and set a dynamic frequency shift threshold. This achieves efficient and accurate defect detection and marking, optimizes the production process, and improves product quality and production efficiency. This process ensures that all aspects of production are optimally configured, improving product quality and production efficiency. In some embodiments, the non-contact scanning of the thickness distribution topology model by the terahertz wave emission array described in step 102, activating the film defect feature marker cluster when a Doppler frequency shift is detected in the terahertz reflection interference fringes, includes:

[0147] 501. Based on the crystallinity distribution characteristics of the thickness distribution topological model, dynamically adjust the terahertz wave incident angle and scanning frequency, perform multi-band scanning on the film surface, and collect phase difference data of reflected interference fringes;

[0148] In step 501, the phase difference data is collected by dynamically adjusting the terahertz wave incident angle and scanning frequency based on the crystallinity distribution characteristics of the thickness distribution topology model. A multi-band scan is performed on the film surface, collecting phase difference data of the reflected interference fringes. This phase difference data reflects the variations in reflectivity within different regions, helping to identify potential defects. The terahertz wave incident angle and scanning frequency are dynamically adjusted based on the crystallinity distribution characteristics to ensure optimal scanning results.

[0149] In this application's example, the incident angle and scanning frequency of the terahertz wave were dynamically adjusted based on the crystallinity distribution characteristics of the thickness distribution topology model. A multi-band scan was then performed on the film surface to collect phase difference data from the reflected interference fringes. This step ensured that subtle variations in different regions could be captured, providing accurate data support for subsequent analysis. Ultimately, a detailed phase difference dataset was generated.

[0150] 502. Calculate the Doppler frequency shift of the reflected interference fringes, extract the phase change matrix of adjacent scanning cycles, and generate a frequency shift trajectory map using a phase gradient accumulation algorithm;

[0151] In step 502, a frequency shift trajectory map is generated by calculating the Doppler shift of the reflected interference fringes, extracting the phase change matrix between adjacent scanning cycles, and applying a phase gradient accumulation algorithm. This map shows the path of Doppler shift changes, helping to identify defective areas. The Doppler shift reflects the change in the received frequency caused by object movement. The phase gradient accumulation algorithm processes these changes and generates a frequency shift trajectory map.

[0152] In this embodiment, a Doppler frequency shift calculation method is used to process the reflected interference fringes, extract the phase change matrix between adjacent scanning cycles, and generate a frequency shift trajectory map using a phase gradient accumulation algorithm. This step helps identify areas with significant frequency shift changes, providing key data support for further analysis. Ultimately, a clear frequency shift trajectory map is generated.

[0153] 503. Calculate the stress concentration coefficient of the distortion center region based on the geometric topological characteristics of the fringe distortion region in the frequency shift trajectory map, and perform spatial matching with the stress concentration region of the thickness distribution topological model;

[0154] In step 503, the stress concentration factor (SCF) is calculated based on the geometric topological characteristics of the fringe distortion region in the frequency shift trajectory map and spatially matched with the stress concentration region in the thickness distribution topological model. The SCF reflects the localized stress increase caused by internal or surface inhomogeneities in the material and helps locate potential defects. The geometric topological characteristics of the fringe distortion region are used to determine the specific value of the SCF and match it with the spatial information of the thickness distribution model.

[0155] In this embodiment, the stress concentration factor is calculated based on the geometric topological characteristics of the fringe distortion regions in the frequency shift trajectory map, and then spatially matched with the stress concentration regions in the thickness distribution topology model. This step helps identify areas where potential defects may exist, providing a basis for subsequent optimization measures. The ultimate result is a stress concentration factor map that can guide actual production.

[0156] 504. Set a dynamic frequency shift threshold. When the Doppler frequency shift exceeds the threshold and the stress concentration coefficient of the corresponding area matches, activate the thin film defect feature marker cluster.

[0157] In step 504, a dynamic frequency shift threshold is used to select regions where the Doppler frequency shift exceeds the threshold and the corresponding stress concentration factor matches, activating the thin film defect signature cluster. This process ensures that only areas with true defects are marked, improving detection accuracy. The dynamic frequency shift threshold sets a certain standard, and only regions exceeding this standard are selected for further processing.

[0158] In this embodiment, a dynamic frequency shift threshold is set to filter out areas where the Doppler frequency shift exceeds the threshold and the corresponding stress concentration factor matches, activating the thin film defect signature cluster. This step ensures that only high-risk areas are marked, thereby improving detection efficiency and accuracy. Ultimately, a precise defect signature cluster is generated.

[0159] Here's a specific example:

[0160] In a new fluoropolymer production line project, engineers first performed boundary expansion filling on the node distribution vector, dynamically adjusted the filling width based on the grid spacing of the process area, and used interpolation of adjacent grid nodes to generate filling values to ensure data integrity. Next, the filled node distribution vector was aligned with the defect impact distribution matrix, and the product accumulation value of the node weight and the defect impact factor in each window was calculated through convolution kernel sliding operation to generate the original matching matrix. Then, the filter strength was dynamically adjusted based on the tension gradient change rate to suppress noise interference and retain mutation characteristics to improve data quality. Finally, the filtered matrix was regionally normalized according to the crystallinity distribution characteristics, and the regional matching matrix reflecting the correlation strength between the control logic node and the defect area was output, providing a solid foundation for precise control. The entire process optimized the production process, significantly improved film quality and production efficiency, and promoted continuous process improvement and technological advancement.

[0161] In summary, steps 501 to 504 optimize the entire fluoropolymer film production process, from data acquisition and defect identification to precise marking. Through a series of precise data processing steps, such as dynamically adjusting the terahertz wave incident angle and scanning frequency, calculating Doppler shift, using a phase gradient accumulation algorithm, and calculating stress concentration factors, each process parameter adjustment node is optimally configured, significantly improving product quality and production efficiency, and promoting continuous process improvement and technological advancement. This process ensures optimal configuration of each production link, enhancing product quality and production efficiency.

[0162] To further improve the accuracy of defect detection in fluoropolymer film production, this solution calculates the stress concentration coefficient based on the geometric topological characteristics of the fringe distortion region in the frequency shift trajectory map. By generating geometric feature vectors, calculating the covariance matrix, generating dynamic weight factors, constructing a static stress distribution map, and performing spatial matching, efficient and accurate defect detection and marking are achieved, optimizing the production process, improving product quality and production efficiency, and ensuring that all production links are optimally configured, thereby improving product quality and production efficiency. In some embodiments, the step 503 of calculating the stress concentration coefficient of the center of the distortion based on the geometric topological characteristics of the fringe distortion region in the frequency shift trajectory map and performing spatial matching with the stress concentration region of the thickness distribution topology model includes:

[0163] 601. Extract geometric topological parameters of the fringe distortion region of the frequency shift trajectory map, calculate the density of the curvature mutation points and the fractal dimension value of the distortion boundary, and generate a geometric feature vector representing the dynamic deformation of the defect;

[0164] In step 601, the geometric feature vector extracts geometric topological parameters from the fringe distortion region of the frequency shift trajectory, including the density of curvature mutation points and fractal dimension values at the distortion boundary, to characterize the dynamic deformation characteristics of the defect. These parameters help accurately describe the specific morphology and severity of the defect, providing key data support for subsequent analysis.

[0165] In this embodiment, we first extract geometric topological parameters from the fringe distortion region of the frequency shift trajectory map, calculate the density of curvature mutation points and the fractal dimension of the distortion boundary, and generate a geometric feature vector representing the dynamic deformation of the defect. This step ensures that the specific form and severity of the defect are captured, providing an accurate data source for subsequent analysis. Ultimately, a detailed geometric feature vector is generated.

[0166] 602. Based on the crystallinity gradient distribution of the thickness distribution topological model, divide the three-dimensional stress analysis grid along the film stretching direction, and calculate the covariance matrix of the thickness fluctuation rate and the transverse tension gradient in each three-dimensional stress analysis grid unit;

[0167] In step 602, the covariance matrix is used to divide the three-dimensional stress analysis grid along the film stretching direction based on the crystallinity gradient distribution of the thickness distribution topology model. The covariance matrix of the thickness fluctuation rate and the transverse tension gradient within each grid cell is calculated. This matrix reflects the stress variation within different regions and provides a basis for identifying potential stress concentration areas.

[0168] In the examples of this application, based on the crystallinity gradient distribution of the thickness distribution topology model, a three-dimensional stress analysis grid was divided along the film stretching direction, and the covariance matrix of the thickness fluctuation rate and the transverse tension gradient within each grid cell was calculated. This step helped identify areas with significant stress changes, providing key data support for further analysis. Ultimately, a detailed covariance matrix was generated.

[0169] 603. Map the geometric eigenvector to the three-dimensional stress analysis grid, generate a dynamic weight factor according to a spatial distance attenuation function from the distortion center to the grid unit boundary, and obtain a dynamic stress concentration factor in combination with the covariance matrix;

[0170] In step 603, dynamic weighting factors are mapped to the 3D stress analysis grid based on the geometric eigenvectors. These factors are generated using a spatial distance decay function from the center of distortion to the grid cell boundary and combined with the covariance matrix to yield a dynamic stress concentration factor. The dynamic weighting factors ensure accurate assessment of stress variations between regions, providing a foundation for precise control.

[0171] In this embodiment, the geometric eigenvectors are mapped to a three-dimensional stress analysis grid. A dynamic weighting factor is generated based on the spatial distance decay function from the center of distortion to the grid cell boundary. This is combined with the covariance matrix to obtain a dynamic stress concentration factor. This step helps identify areas where potential defects may exist, providing a basis for subsequent optimization measures. The end result is a dynamic stress concentration factor that can guide actual production.

[0172] 604. Perform spatial cluster analysis on the stress concentration area of the thickness distribution topological model, extract cluster center coordinates and area coverage radius, and construct a static stress distribution map;

[0173] In step 604, a static stress distribution map is constructed by performing spatial cluster analysis on the stress concentration areas of the thickness distribution topology model, extracting the cluster center coordinates and region coverage radius. The static stress distribution map shows the stress concentration in different regions, providing a reference for identifying potential defects.

[0174] In this embodiment, spatial cluster analysis is performed on the stress concentration areas of the thickness distribution topology model, and the cluster center coordinates and regional coverage radius are extracted to construct a static stress distribution map. This step helps identify areas with significant stress concentration, providing key data support for further analysis. Ultimately, a detailed static stress distribution map is generated.

[0175] 605. Spatially match the dynamic stress concentration factor with the static stress distribution map, calculate the overlap index of the dynamic-static stress region, and trigger a cross-modal association flag when the overlap index exceeds a matching threshold.

[0176] In step 605, the overlap index is calculated by spatially matching the dynamic stress concentration factor with the static stress distribution map to determine the overlap index of the dynamic-static stress region. When the overlap index exceeds a matching threshold, cross-modal correlation marking is triggered, ensuring that only areas with true defects are marked, improving detection accuracy.

[0177] In this embodiment, the dynamic stress concentration factor is spatially matched with the static stress distribution map, and the overlap index of the dynamic and static stress regions is calculated. When the overlap index exceeds the matching threshold, cross-modal correlation tagging is triggered. This step ensures that only high-risk areas are marked, thereby improving detection efficiency and accuracy. Ultimately, a precise defect signature cluster is generated.

[0178] Here's a specific example:

[0179] In a new fluoropolymer production line project, engineers first expanded and filled the node distribution vectors, dynamically adjusted the filling width based on the grid spacing of the process area, and used interpolation of adjacent grid nodes to generate filling values. Next, the original matching degree matrix was generated through convolution kernel sliding operation to identify key control points and high defect-affected areas. Then, the filter strength was dynamically adjusted based on the tension gradient change rate to suppress noise and retain mutation characteristics to improve data quality. Finally, the filtered matrix was regionally normalized according to the crystallinity distribution characteristics, and the output was a regional matching degree matrix reflecting the correlation strength between the control logic node and the defect area, providing a solid foundation for precise control. The entire process optimized the production process, significantly improved film quality and production efficiency, and promoted continuous process improvement and technological advancement. For example, in actual operation, engineers used these steps to accurately optimize multiple key control points, effectively improving the overall performance of the production line and product consistency.

[0180] In summary, steps 601 to 605 optimize the entire fluoropolymer film production process, from data collection and defect identification to precise marking. Through a series of precise data processing steps, including geometric topology parameter extraction, covariance matrix calculation, dynamic weight factor generation, and spatial cluster analysis, each process parameter adjustment node is optimally configured, significantly improving product quality and production efficiency, and promoting continuous process improvement and technological advancement. This process ensures optimal configuration of all production links, enhancing product quality and production efficiency.

[0181] To further improve the thickness control accuracy in fluoropolymer film production, the solution generates a tension compensation factor based on the phase difference of the transverse tension fluctuation curve, corrects the film thickness data, and generates a thickness distribution topology model. Through multi-channel sliding window analysis, phase correlation coefficient calculation, spatial interpolation correction, coupling coefficient calculation, and three-dimensional grid unit division, efficient and accurate thickness data correction and model generation are achieved, optimizing the production process and improving product quality and production efficiency. In some embodiments, the generation of the tension compensation factor based on the phase difference of the transverse tension fluctuation curve and the correction of the film thickness data to generate the thickness distribution topology model in step 101 include:

[0182] 701. Perform a multi-channel sliding window analysis on the transverse tension fluctuation curve, extract a phase difference sequence of adjacent sensor nodes, and generate a phase difference matrix with a timestamp;

[0183] In step 701, a phase difference matrix is generated by performing a multi-channel sliding window analysis on the transverse tension fluctuation curve to extract the phase difference sequence between adjacent sensor nodes and generate a timestamped data set. The phase difference matrix contains information about phase differences at different time points and locations, providing a foundation for subsequent calculations. Multi-channel sliding window analysis is used to extract these phase differences and ensure the temporal synchronization of the data.

[0184] In this embodiment, a multi-channel sliding window analysis is first performed on the transverse tension fluctuation curve to extract the phase difference sequence between adjacent sensor nodes and generate a timestamped phase difference matrix. This step ensures that subtle changes across different regions and time periods can be captured, providing accurate data support for subsequent analysis. Ultimately, a detailed phase difference matrix is generated, laying the foundation for subsequent processing.

[0185] 702. Calculate the phase correlation coefficients of adjacent windows in the phase difference matrix, identify abnormal tension fluctuation intervals based on the correlation coefficient decay rate, and generate dynamic phase compensation weights;

[0186] In step 702, dynamic phase compensation weights are calculated based on the phase correlation coefficients of adjacent windows in the phase difference matrix. The decay rate of the correlation coefficients is used to identify abnormal tension fluctuation intervals. This weight is used in subsequent spatial interpolation correction of the film thickness data to ensure that the corrected data more closely resembles actual conditions. The phase correlation coefficient reflects the degree of correlation between phase differences between different windows, while the decay rate is used to identify abnormal tension fluctuation intervals.

[0187] In this embodiment, the phase correlation coefficients of adjacent windows in the phase difference matrix are calculated. The decay rate of the correlation coefficients is used to identify abnormal tension fluctuation intervals and generate dynamic phase compensation weights. This step helps identify areas with significant abnormal tension fluctuations, providing critical data support for further analysis. The ultimate result is a dynamic phase compensation weight that can guide actual production.

[0188] 703. Perform spatial interpolation correction on the film thickness data, where the interpolation weight is determined by combining the dynamic phase compensation weight and the spatial distribution density of the sensor nodes to generate an initial corrected film thickness distribution map;

[0189] In step 703, the initial corrected film thickness distribution map is generated by performing spatial interpolation on the film thickness data. The interpolation weights are determined by combining the dynamic phase compensation weights with the spatial density of the sensor nodes. This process ensures that the corrected thickness distribution map more accurately reflects the actual film thickness distribution. Spatial interpolation correction utilizes the dynamic phase compensation weights and the spatial density of the sensor nodes to adjust the interpolation weights, thereby improving correction accuracy.

[0190] In this embodiment, spatial interpolation correction is performed on the film thickness data. The interpolation weights are determined by combining the dynamic phase compensation weights with the spatial distribution density of the sensor nodes to generate an initial corrected film thickness distribution map. This step ensures that the corrected thickness distribution map more accurately reflects the actual film thickness distribution, providing a basis for subsequent optimization measures. Ultimately, an accurate initial corrected film thickness distribution map is generated.

[0191] 704. Based on the gradient variation characteristics of the initial corrected film thickness distribution map, divide the film into three-dimensional grid units along the film stretching direction, and calculate the coupling coefficient between the thickness fluctuation rate and the transverse tension gradient in each unit;

[0192] In step 704, the coupling coefficient is calculated based on the gradient variation characteristics of the initial modified film thickness profile. The three-dimensional grid is divided along the film stretching direction, and the coupling coefficient between the thickness fluctuation rate and the transverse tension gradient within each cell is calculated. The coupling coefficient reflects the relationship between thickness fluctuation and transverse tension, providing a key parameter for subsequently generating a thickness distribution topology model.

[0193] In this example, based on the gradient variation characteristics of the initial corrected film thickness profile, the three-dimensional grid cells were divided along the film stretching direction, and the coupling coefficient between the thickness fluctuation rate and the transverse tension gradient within each cell was calculated. This step helped identify areas with significant thickness fluctuations and tension variations, providing key data support for further analysis. Ultimately, a detailed set of coupling coefficients was generated.

[0194] 705. Map the coupling coefficient to the three-dimensional grid vertices, and generate a thickness distribution topology model with thermal field properties by combining the crystallinity variation trend of the initial corrected film thickness distribution map.

[0195] In step 705, a thickness distribution topology model with thermal field attributes is generated by mapping the coupling coefficients to the vertices of the three-dimensional mesh and combining it with the crystallinity variation trend of the initial modified film thickness distribution map. This model not only reflects the film thickness distribution but also incorporates thermal field information, providing comprehensive data support for subsequent optimization.

[0196] In this example, the coupling coefficients were mapped to the vertices of the three-dimensional mesh and combined with the crystallinity variation trends of the initial modified film thickness distribution map to generate a thickness distribution topology model with thermal field attributes. This step ensured that the model fully reflected the film thickness distribution and its related physical properties, providing a solid foundation for subsequent optimization measures. Ultimately, a complete thickness distribution topology model was generated.

[0197] Here's a specific example:

[0198] The engineers first performed a multi-channel sliding window analysis on the transverse tension fluctuation curve to generate a phase difference matrix. They then calculated the phase correlation coefficients of adjacent windows in the phase difference matrix, identified abnormal tension fluctuation intervals, and generated dynamic phase compensation weights. The dynamic phase compensation weights and the spatial distribution density of the sensor nodes were then used to perform spatial interpolation correction on the film thickness data to generate an initial corrected film thickness distribution map. Subsequently, based on the gradient variation characteristics of the initial corrected film thickness distribution map, the three-dimensional grid cells were divided, and the coupling coefficient between the thickness fluctuation rate and the transverse tension gradient within each cell was calculated. Finally, the coupling coefficients were mapped to the three-dimensional grid vertices and, combined with the crystallinity variation trend, a thickness distribution topology model with thermal field properties was generated, significantly improving the accuracy of film thickness measurement and control.

[0199] In summary, steps 701 to 705 optimize the entire fluoropolymer film production process, from data acquisition and thickness correction to the generation of a thickness distribution topology model. Through a series of precise data processing steps, including multi-channel sliding window analysis, phase correlation coefficient calculation, spatial interpolation correction, and coupling coefficient calculation, each process parameter adjustment node is optimally configured, significantly improving product quality and production efficiency, and promoting continuous process improvement and technological advancement. This process ensures optimal configuration of all production links, enhancing product quality and production efficiency.

[0200] Figure 2 The present invention provides a flow chart of a control system and a schematic diagram of the structure of an automated test system. Figure 2 As shown, the system includes:

[0201] Acquisition module 21, during the high-speed stretching process of the fluoropolymer film, collects film thickness data and a transverse tension fluctuation curve through an intelligent textile sensor array, generates a tension compensation factor based on a phase difference of the transverse tension fluctuation curve, and corrects the film thickness data to generate a thickness distribution topology model;

[0202] A detection module 22 performs non-contact scanning on the thickness distribution topology model through a terahertz wave emission array, and activates a thin film defect characteristic marker cluster when a Doppler frequency shift of the terahertz reflection interference fringes is detected;

[0203] A reconstruction module 23 transmits the thickness distribution topology model and the thin film defect characteristic mark cluster to the test platform, reconstructs the control logic flow chart and generates a process parameter test script, and performs a virtual-real synchronization test of the distributed control system through a communication protocol;

[0204] The correction module 24 generates a process parameter correction factor according to the virtual-real synchronization test result and injects it back into the tension closed-loop control module of the distributed control system, and simultaneously generates a terahertz characteristic verification map matching the corrected process parameter.

[0205] Figure 2 The automated test system for a control system can execute Figure 1 The implementation principle and technical effects of the automated flow chart testing method for a control system described in the illustrated embodiment will not be elaborated on here. The specific manner in which each module and unit performs operations in the automated flow chart testing system for a control system in the above embodiment has been described in detail in the embodiments of the method and will not be elaborated on here.

[0206] In one possible design, Figure 2 The flow chart of a control system of the embodiment shown in FIG. 1 shows an automated test system that can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0207] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0208] The processing component 32 is used for the above Figure 1 The embodiment provides a method for automated testing of a flow chart of a control system.

[0209] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above method.

[0210] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0211] Of course, a computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0212] The input / output interface provides an interface between the processing component and the peripheral interface module, which can be an output device, an input device, etc.

[0213] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0214] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0215] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 A flowchart of an automated testing method for a control system according to the embodiment shown.

[0216] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0217] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0218] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0219] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A control system flow chart automation testing method, characterized in that: include: During the high-speed stretching process of the fluoropolymer film, the film thickness data and the transverse tension fluctuation curve are collected through an intelligent textile sensor array. A tension compensation factor is generated based on the phase difference of the transverse tension fluctuation curve. The film thickness data is corrected to generate a thickness distribution topology model. The thickness distribution topology model is non-contact scanned by a terahertz wave emission array, and when a Doppler frequency shift of the terahertz reflection interference fringes is detected, a thin film defect characteristic marker cluster is activated; The thickness distribution topology model and the thin film defect characteristic mark cluster are transmitted to the test platform, the control logic flow chart is reconstructed and a process parameter test script is generated, and a virtual-real synchronization test of the distributed control system is performed through a communication protocol; A process parameter correction factor is generated according to the virtual-real synchronization test result, and is reversely injected into the tension closed-loop control module of the distributed control system, and a terahertz characteristic verification map matching the corrected process parameter is generated.

2. The control system flow chart automation testing method according to claim 1, characterized in that: The method of transmitting the thickness distribution topology model and the thin film defect characteristic mark cluster to the test platform, reconstructing the control logic flow chart and generating a process parameter test script includes: Analyzing the crystallinity distribution characteristics of the thickness distribution topology model, generating a tension gradient-thickness fluctuation correlation matrix, and establishing a control logic node mapping relationship based on the data mutation amplitude; Performing spatial cluster analysis on the thin film defect signature cluster, calculating the radial distance distribution from the defect center area to the process section boundary, and generating a defect impact weight factor in combination with the stress concentration coefficient of the thickness distribution topology model; Performing multi-dimensional matching on the control logic node mapping relationship and the defect impact weight factor to generate a process branch containing a crystallinity-tension gradient constraint condition; Establishing a process parameter boundary equation based on the process branch, and generating a test case set covering the defect impact weight factor by traversing the combination space of film stretching speed and temperature compensation parameters; Protocol encapsulation processing is performed on the test case set, and the real-time updated data stream of the thickness distribution topology model is injected into the test script execution engine to generate a process parameter test script.

3. The control system flow chart automation testing method according to claim 2, characterized in that: The multi-dimensional matching of the control logic node mapping relationship and the defect impact weight factor to generate a process branch containing a crystallinity-tension gradient constraint condition includes: Dividing the orthogonal process area grid based on the film stretching direction and the transverse tension gradient axis, performing grid space encoding on the control logic node mapping relationship, and generating a node distribution vector with coordinate attributes; Normalizing the defect impact weight factors according to the process regions, analyzing the relationship between the radial distance from the defect center to the boundary of each region and the stress concentration factor, and generating a defect impact distribution matrix; Performing a dynamic convolution operation on the node distribution vector and the defect impact distribution matrix to output a regional matching degree matrix reflecting the correlation strength between the control logic node and the defect area; Extracting elements whose values exceed a dynamic threshold in the regional matching matrix, and mapping the elements to process parameter adjustment nodes in a control logic flow chart; A crystallinity-tension gradient constraint condition is superimposed on the process parameter adjustment node to generate a process branch containing the crystallinity-tension gradient constraint condition.

4. The control system flow chart automation testing method according to claim 3, characterized in that: The performing of a dynamic convolution operation on the node distribution vector and the defect impact distribution matrix to output a regional matching degree matrix reflecting the correlation strength between the control logic node and the defective region includes: Performing boundary extension filling on the node distribution vector, wherein the filling width is dynamically adjusted based on the process area grid spacing, and the filling value is generated by interpolation of adjacent grid node distribution vectors; Align the padded node distribution vector with the defect impact distribution matrix, perform a convolution kernel sliding operation, calculate the product accumulation value of the node weight and the defect impact factor in each window, and generate the original matching degree matrix; Performing adaptive filtering on the original matching degree matrix, dynamically adjusting the filtering strength based on the tension gradient change rate, suppressing discrete noise interference and retaining mutation characteristics; The filtered original matching degree matrix is regionally normalized according to the crystallinity distribution characteristics, and the regional matching degree matrix reflecting the correlation strength between the control logic node and the defect area is output.

5. The control system flow chart automation testing method according to claim 1, characterized in that: The non-contact scanning of the thickness distribution topology model by the terahertz wave emission array and activation of the thin film defect characteristic marker cluster when Doppler frequency shift of the terahertz reflection interference fringes is detected include: Based on the crystallinity distribution characteristics of the thickness distribution topological model, the terahertz wave incident angle and scanning frequency are dynamically adjusted to perform multi-band scanning on the film surface and collect phase difference data of the reflected interference fringes; Calculating the Doppler frequency shift of the reflected interference fringes, extracting the phase change matrix of adjacent scanning cycles, and generating a frequency shift trajectory map through a phase gradient accumulation algorithm; Calculating the stress concentration coefficient of the distortion center area based on the geometric topological characteristics of the fringe distortion area in the frequency shift trajectory map, and performing spatial matching with the stress concentration area of the thickness distribution topological model; A dynamic frequency shift threshold is set, and when the Doppler frequency shift exceeds the threshold and the stress concentration factor of the corresponding area matches, the thin film defect feature marker cluster is activated.

6. The control system flow chart automation testing method according to claim 5, characterized in that: The step of calculating the stress concentration coefficient of the distortion center region according to the geometric topological characteristics of the fringe distortion region in the frequency shift trajectory map and spatially matching the stress concentration coefficient with the stress concentration region of the thickness distribution topological model includes: Extracting geometric topological parameters of the fringe distortion area of the frequency shift trajectory map, calculating the density of the distortion boundary curvature mutation points and the fractal dimension value, and generating a geometric feature vector representing the dynamic deformation of the defect; Based on the crystallinity gradient distribution of the thickness distribution topological model, a three-dimensional stress analysis grid is divided along the film stretching direction, and a covariance matrix of the thickness fluctuation rate and the transverse tension gradient in each three-dimensional stress analysis grid unit is calculated; Mapping the geometric eigenvector to the three-dimensional stress analysis grid, generating a dynamic weight factor according to a spatial distance attenuation function from the distortion center to the grid unit boundary, and combining the covariance matrix to obtain a dynamic stress concentration factor; Performing spatial cluster analysis on the stress concentration area of the thickness distribution topology model, extracting cluster center coordinates and area coverage radius, and constructing a static stress distribution map; The dynamic stress concentration factor is spatially matched with the static stress distribution map, and an overlap index of the dynamic-static stress region is calculated. When the overlap index exceeds a matching threshold, a cross-modal association flag is triggered.

7. The control system flow chart automation testing method according to claim 1, characterized in that: The generating of the tension compensation factor based on the phase difference of the transverse tension fluctuation curve and the correction of the film thickness data to generate the thickness distribution topology model include: Performing a multi-channel sliding window analysis on the transverse tension fluctuation curve, extracting a phase difference sequence of adjacent sensor nodes, and generating a phase difference matrix with a timestamp; Calculating the phase correlation coefficients of adjacent windows in the phase difference matrix, identifying abnormal tension fluctuation intervals based on the correlation coefficient decay rate, and generating dynamic phase compensation weights; Performing spatial interpolation correction on the film thickness data, where the interpolation weight is determined jointly by the dynamic phase compensation weight and the spatial distribution density of the sensor nodes, to generate an initial corrected film thickness distribution map; Based on the gradient variation characteristics of the initial corrected film thickness distribution map, three-dimensional grid cells are divided along the film stretching direction, and the coupling coefficient between the thickness fluctuation rate and the transverse tension gradient in each cell is calculated; The coupling coefficient is mapped to the vertices of the three-dimensional grid, and combined with the crystallinity variation trend of the initial corrected film thickness distribution map to generate a thickness distribution topology model with thermal field properties.

8. A flow chart automation test system for a control system, characterized in that: include: An acquisition module collects film thickness data and a transverse tension fluctuation curve through an intelligent textile sensor array during the high-speed stretching process of the fluoropolymer film, generates a tension compensation factor based on the phase difference of the transverse tension fluctuation curve, and corrects the film thickness data to generate a thickness distribution topology model; a detection module, which performs non-contact scanning on the thickness distribution topology model through a terahertz wave emission array, and activates a thin film defect characteristic marker cluster when a Doppler frequency shift of the terahertz reflection interference fringes is detected; A reconstruction module transmits the thickness distribution topology model and the thin film defect characteristic mark cluster to the test platform, reconstructs the control logic flow chart and generates a process parameter test script, and performs a virtual-real synchronization test of the distributed control system through a communication protocol; The correction module generates a process parameter correction factor according to the virtual-real synchronization test result and injects the correction factor back into the tension closed-loop control module of the distributed control system, and simultaneously generates a terahertz characteristic verification map matching the corrected process parameter.

9. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a flow chart automation testing method for a control system as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that A computer program is stored, and when the computer program is executed by a computer, the flow chart automation testing method of a control system according to any one of claims 1 to 7 is implemented.

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