A remote control system for intelligent manufacturing equipment based on the Internet

Abstract

This invention discloses an internet-based remote control system for intelligent manufacturing equipment, relating to the field of intelligent manufacturing technology. It constructs a manufacturing data set S by collecting environmental and product data in real time during the product manufacturing process. Based on this data set S, it obtains the product surface roughness score Ra, area change coefficient Dr, and thermal expansion coefficient Pz. The extracted feature data is analyzed to obtain a comprehensive quality score Qf for product quality assessment. Based on the assessment results, the intelligent manufacturing equipment is adjusted and controlled. Manufacturing data after the adjustment and control is collected to obtain the product qualification rate HG for the same batch of products. Combined with a preset product qualification rate threshold HGYZ, the optimization effect of the equipment adjustment and control is evaluated. When the optimization effect is normal, the stability of product quality is analyzed, thereby improving production efficiency, product qualification rate, and ensuring product quality.

Description

Technical Field

[0001] This invention relates to the field of intelligent manufacturing technology, specifically to an Internet-based remote control system for intelligent manufacturing equipment. Background Technology

[0002] "Intelligent manufacturing," as an important branch of the industrial field, encompasses the integrated application of various advanced technologies such as automation, informatization, digitalization, and intelligence. With the development of science and technology, intelligent manufacturing is gradually evolving towards greater efficiency, flexibility, and precision. It has enormous potential, especially in improving production line management, optimizing production processes, and enhancing production quality. In the field of intelligent manufacturing, quality control is particularly crucial, especially in large-scale production. Ensuring consistent product quality is a significant challenge for manufacturing enterprises. Specifically, in intelligent manufacturing equipment, quality inspection equipment serves as an important tool for ensuring production quality, undertaking tasks such as automated visual inspection and dimensional measurement. Through remote control systems, manufacturing equipment can monitor and adjust various quality parameters in the production process in real time, ensuring that every production link meets the predetermined quality standards.

[0003] Chinese invention patent application CN117793288A discloses a real-time on-site monitoring system for intelligent equipment in cloud manufacturing scenarios, and an additive manufacturing system based on remote control and cloud processing. The system includes: a video monitoring network unit for setting up a video monitoring network for N intelligent manufacturing devices; a video data monitoring unit for collecting video data from the N intelligent manufacturing devices through the video monitoring network; and an adjustment unit for adjusting the video monitoring network according to cloud-based fault video requirements. This invention can significantly reduce the bandwidth consumption of the production line when collecting and analyzing faults in batches of intelligent manufacturing equipment, thereby saving data bandwidth for the remote end of the production line based on CPS intelligent production line integration technology, such as the end requiring remote control of batch intelligent manufacturing equipment for complex process operations.

[0004] The above systems avoid the time-disruptive pressure on video monitoring equipment and its management system based on CPS intelligent production line integration technology when monitoring and uploading relevant videos of multiple faulty intelligent manufacturing equipment. However, in addition to this, existing remote control systems for intelligent manufacturing equipment typically monitor the manufacturing process of the intelligent equipment, analyze the faults of the intelligent manufacturing equipment, and adjust the intelligent equipment accordingly.

[0005] However, such systems are limited to monitoring the status of smart devices and lack production process control and quality optimization.

[0006] Therefore, the present invention provides an Internet-based remote control system for intelligent manufacturing equipment. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this invention provides an internet-based remote control system for intelligent manufacturing equipment. By collecting environmental and product data in real time during the manufacturing process, a manufacturing data set S is constructed. Based on this set S, the surface roughness score Ra, area change coefficient Dr, and thermal expansion coefficient Pz of the product are obtained. The extracted feature data are analyzed to obtain a comprehensive quality score Qf, enabling product quality assessment. Based on the assessment results, the intelligent manufacturing equipment is adjusted and controlled. Manufacturing data after the adjustment and control is collected to obtain the product qualification rate HG of the same batch of products. Combined with a preset product qualification rate threshold HGYZ, the optimization effect of the equipment adjustment and control is evaluated. When the optimization effect is normal, the stability of product quality is analyzed, thereby improving production efficiency, product qualification rate, ensuring product quality, and solving the problems mentioned in the background technology.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solution: a remote control system for intelligent manufacturing equipment based on the Internet, comprising a data acquisition module, a data preprocessing and feature extraction module, a quality detection and evaluation module, an adjustment control module, an equipment optimization effect evaluation module, and a stability analysis module;

[0009] The data acquisition module is used to deploy infrared temperature sensors and high-definition cameras on intelligent manufacturing equipment to collect environmental and product data in real time during the product manufacturing process and construct a manufacturing data set S.

[0010] The data preprocessing and feature extraction module is used to preprocess the manufacturing data set S and extract features based on the preprocessed manufacturing data set S to obtain the product surface roughness score Ra, area change coefficient Dr, and thermal expansion coefficient Pz.

[0011] The quality inspection and evaluation module is used to perform a summary calculation based on the product surface roughness score Ra, area change coefficient Dr, and thermal expansion coefficient Pz to obtain the product's comprehensive quality score Qf, and to perform product quality evaluation based on the comprehensive quality score Qf to generate a product quality evaluation report.

[0012] The adjustment control module is used to adjust and control the intelligent manufacturing equipment when the product quality is unqualified, based on the product quality assessment report. It generates a control signal u(t) based on the PID control algorithm and controls the intelligent manufacturing equipment to perform adjustment operations based on the control signal u(t).

[0013] The equipment optimization effect evaluation module is used to calculate the product qualification rate HG of the same batch of products based on the manufacturing data after equipment adjustment and control, and preset the product qualification rate threshold HGYZ. The product qualification rate threshold HGYZ and the product qualification rate HG are compared and analyzed to evaluate the optimization effect of equipment adjustment and control.

[0014] The stability analysis module is used to collect manufacturing data for a period of time after the equipment adjustment and control is in a normal state, draw a product quality control chart, and perform stability analysis based on the product quality control chart.

[0015] Preferably, the data acquisition module includes an environmental data acquisition unit and a product data acquisition unit;

[0016] The environmental data acquisition unit is used to deploy infrared temperature sensors on intelligent manufacturing equipment to collect the temperature change ΔT of the product during the product manufacturing process in real time.

[0017] The product data acquisition unit is used to deploy a high-definition camera on the intelligent manufacturing equipment to collect image data of the product in real time during the product manufacturing process. The image data includes the height h of different areas on the product surface, the area d of the product, and the length L of the product.

[0018] Based on the collected environmental and product data, a manufacturing data set S is constructed, and the manufacturing data set S is transmitted to the equipment data center for storage via wireless transmission technology.

[0019] Preferably, the data preprocessing and feature extraction module includes a preprocessing unit and a feature extraction unit;

[0020] The preprocessing unit is used to perform preprocessing based on the manufacturing data set S, including environmental data preprocessing and product data preprocessing.

[0021] The environmental data preprocessing refers to cleaning, denoising, and standardizing the environmental data in the manufacturing dataset S.

[0022] The product data preprocessing refers to using Gaussian filtering to smooth the image of the product data in the manufacturing dataset S, removing high-frequency noise from the image, using histogram equalization to evenly distribute the gray values ​​of the image, increasing the contrast, and using Canny edge detection to extract the edge contours of the image.

[0023] Preferably, the feature extraction unit is used to perform summary calculations based on the preprocessed manufacturing data set S to obtain the product surface roughness score Ra, area change coefficient Dr, and thermal expansion coefficient Pz.

[0024] The method for obtaining the product surface roughness score Ra is as follows:

[0025]

[0026] In the formula, L represents the length of the product, and y(x) represents the height of the surface position x of the product being measured. This represents the average height of the surface of the product being tested;

[0027] The height y(x) of position x on the surface of the product under test and the average height of the surface of the product under test. Acquired by using surface scanning equipment, which includes a laser scanner and a stylus profilometer;

[0028] The area change coefficient Dr is obtained as follows:

[0029]

[0030] In the formula, d i d represents the area measured at the i-th measurement point on the product. ref，i This represents the preset area size of the i-th measurement point on the product, where i = [1, 2, 3, ..., N] and N represents the number of measurement points;

[0031] The thermal expansion coefficient Pz is obtained as follows:

[0032]

[0033] In the formula, ΔL represents the change in length of the product during the heating process, L0 represents the initial length of the product, and ΔT represents the change in temperature of the material.

[0034] Preferably, the quality inspection and evaluation module includes a quality scoring calculation unit and a quality evaluation unit;

[0035] The quality scoring calculation unit is used to perform a summary calculation based on the product's surface roughness score Ra, area change coefficient Dr, and thermal expansion coefficient Pz to obtain the product's comprehensive quality score Qf. The comprehensive quality score Qf is obtained in the following way:

[0036]

[0037] In the formula, Ra max Dr max and Pz max ω1, ω2, and ω3 represent the maximum values ​​of the product surface roughness score Ra, area variation coefficient Dr, and thermal expansion coefficient Pz, respectively, and represent the weighting coefficients of the product surface roughness score Ra, area variation coefficient Dr, and thermal expansion coefficient Pz, respectively. and These represent the normalization processes performed on the product surface roughness score Ra, area variation coefficient Dr, and thermal expansion coefficient Pz, respectively, to eliminate differences in the dimensions and ranges of different characteristic indicators. C represents the first correction coefficient.

[0038] Preferably, the quality assessment unit is used to preset a quality scoring threshold QfYZ, and compare and analyze the quality scoring threshold QfYZ with the comprehensive quality score Qf to assess the product's qualification. The specific assessment content is as follows:

[0039] If the overall quality score Qf is less than the quality score threshold QfYZ, i.e. Qf < QfYZ, the product quality is judged to be in a qualified state. At this time, the product manufacturing continues and the product production process is continuously monitored.

[0040] If the overall quality score is greater than or equal to the quality score threshold QfYZ, i.e. Qf≥QfYZ, the product is judged to be in a non-conforming state, and an alarm mechanism is triggered to stop the operation of the intelligent manufacturing equipment, record the non-conforming product data, generate a product quality assessment report, and transmit the product quality assessment report to the equipment data center.

[0041] Preferably, the adjustment control module includes a control signal generation unit and a production parameter adjustment unit;

[0042] The control signal generation unit is used to obtain the surface roughness score deviation e of the defective product based on the recorded defective product data when the product quality is unqualified. Ra thermal expansion coefficient deviation e Pz and the deviation of the area change coefficient e Dr :

[0043] e Ra =Ra target -Ra;

[0044] e Pz =Pz target -Pz;

[0045] e Dr =Dr target -Dr;

[0046] In the formula, Ra target 、Pz target and Dr target These represent the standard values ​​of the product surface roughness score Ra, the coefficient of thermal expansion Pz, and the area change coefficient Dr, respectively. target 、Pz target and Dr target Obtained from relevant industry standards databases;

[0047] Based on the obtained product surface roughness score deviation e Ra thermal expansion coefficient deviation e Pz and the deviation of the area change coefficient e Dr The device control signal u(t) is generated using a PID control algorithm. The method for obtaining the device control signal u(t) is as follows:

[0048]

[0049] In the formula, e(t) represents the adjustment parameter error at the current time point t, e(t) = [e Ra e Pz e Dr ], K p K represents the strength of the control system's response to the error e(t) of the adjustment parameter at the current time point t. i K represents the cumulative effect of the error over a period of time. d It indicates the response speed of the control system to the rate of change of error.

[0050] Preferably, the production parameter adjustment unit is used to automatically map the equipment control signal u(t) obtained by the control signal generation unit into equipment operating parameter instructions according to the industrial automation control software TIA PROtal, and send the equipment operating parameter instructions to the intelligent manufacturing equipment through the PLC system of the production equipment to control the intelligent manufacturing equipment to perform adjustment operations.

[0051] Preferably, the equipment optimization effect evaluation module is used to collect manufacturing data after equipment adjustment and control, including environmental data and product data, and select several products from the same batch to construct a sample combination to be evaluated, calculate the comprehensive quality score Qf for each sample to be evaluated, evaluate the quality of the sample to be evaluated based on the comprehensive quality score Qf, and obtain the product qualification rate HG of the same batch of products. The product qualification rate HG is obtained in the following way:

[0052]

[0053] In the formula, q represents the number of qualified samples to be evaluated, and m represents the total number of samples in the combination of samples to be evaluated;

[0054] A preset product pass rate threshold HGYZ is established. The product pass rate HG is compared and analyzed with the product pass rate threshold HGYZ to evaluate the optimization effect of equipment adjustment and control. The specific evaluation content is as follows:

[0055] If the product qualification rate HG is greater than or equal to the product qualification rate threshold HGYZ, i.e. HG≥HGYZ, the optimization effect of equipment adjustment and control is judged to be normal. At this time, the equipment adjustment and control measures and equipment parameters are recorded, and an equipment optimization and control report is generated. The equipment optimization and control report is sent to relevant personnel as a basis for further optimization. At the same time, the manufacturing data during the product manufacturing process is continuously recorded. The equipment optimization and control report includes the optimization process and optimization results of equipment adjustment and control.

[0056] If the product qualification rate HG is less than the product qualification rate threshold HGYZ, i.e. HG < HGYZ, the optimization effect of the equipment adjustment control is determined to be abnormal. At this time, abnormal product data is recorded and fed back to the adjustment control module. The equipment adjustment parameter value u(t) is recalculated and iterative optimization is performed until the optimization effect of the equipment adjustment control is normal.

[0057] Preferably, the stability analysis module is used to perform product quality control chart analysis when the optimization effect of equipment adjustment and control is in a normal state. The specific analysis process is as follows:

[0058] The product manufacturing process is continuously monitored after the equipment is adjusted and controlled. Manufacturing data is collected at fixed intervals, and a certain number of products are randomly selected as test samples each time.

[0059] Calculate the mean of the characteristic indicators of the test samples at the same time point. And based on the mean of the characteristic indicators of the test samples at the same time point And obtain the total average value μ of the feature indicators of the test samples at all time points. The method for obtaining the total average value μ of the feature indicators is as follows:

[0060]

[0061] In the formula, g represents the total number of data collection time points. Let g represent the mean of the characteristic indicators of the sample to be tested at the j-th data collection time point, where j = [1, 2, 3, ..., g].

[0062] Based on the total average value μ of the characteristic indicators of the test samples at all time points, the upper control limit (UCL) and lower control limit (LCL) are obtained, and a product quality control chart is plotted. The horizontal axis of the product quality control chart represents the product production time, and the vertical axis represents the average value of the characteristic indicators of the test samples at the same time point. The upper control limit (UCL) and lower control limit (LCL) are obtained as follows:

[0063] UCL = μ + 3σ;

[0064] LCL = μ - 3σ;

[0065] In the formula, μ represents the total average value of the characteristic indicators of the test samples at all time points, and σ represents the standard deviation of the characteristic indicators of the test samples at all time points.

[0066] Based on the constructed product quality control chart, a stability analysis was conducted, and the specific analysis content is as follows;

[0067] If all data points in the product quality control chart are located between the upper control limit (UCL) and the lower control limit (LCL), the product quality is considered stable and without abnormalities.

[0068] If not all data points in the product quality control chart are located between the upper control limit (UCL) and the lower control limit (LCL), it is determined that the product quality is abnormal. The abnormal data is recorded and sent to the equipment adjustment and control personnel for intelligent equipment adjustment and control.

[0069] This invention provides an Internet-based remote control system for intelligent manufacturing equipment, which has the following advantages:

[0070] (1) By deploying infrared temperature sensors and high-definition cameras, intelligent manufacturing equipment can collect and monitor environmental and product data in real time, constructing a manufacturing dataset S. Based on this real-time data, the system extracts key quality indicators (such as surface roughness score, area change coefficient, and thermal expansion coefficient) through data preprocessing and feature extraction modules. Using this data, combined with the quality inspection and evaluation module, the system performs automated quality assessment of the product, generates a comprehensive quality score, and triggers intelligent adjustment control. This can greatly reduce human intervention, improve the automation and intelligence level of the production line, and ensure the consistency and stability of product quality. Furthermore, when non-conforming products are found during the quality assessment process, the system will automatically generate adjustment control signals to quickly adjust production parameters, ensuring that product quality is always at the expected level.

[0071] (2) By introducing an adjustment control module and a PID control algorithm, the system can automatically generate equipment control signals and precisely adjust the intelligent manufacturing equipment when product quality is unqualified. Through real-time monitoring of product quality, the PID control algorithm can effectively reduce errors and quickly optimize production parameters. The equipment optimization effect evaluation module compares the adjusted pass rate HG with the preset pass rate threshold HGYZ to assess the effectiveness of the equipment adjustment. If the pass rate HG is greater than or equal to the pass rate threshold HGYZ, the optimization measures are effective; otherwise, the system will iteratively optimize. This real-time data and feedback control approach significantly improves production efficiency, increases the pass rate, and reduces the scrap rate.

[0072] (3) Through the stability analysis module, the system can not only evaluate the equipment optimization effect in the short term, but also continuously monitor the equipment operation status when the optimization effect is normal, and conduct long-term stability analysis of product quality. Control chart analysis and standard deviation analysis can help monitor the quality fluctuation of each batch of products in real time and ensure the stability of the production process. In the production process after equipment adjustment, the stability analysis module will continuously collect manufacturing data and draw control charts to analyze whether the data is kept within the control limits. If the data points exceed the control limits, it indicates that abnormal fluctuations have occurred in the production process. The system will automatically record the abnormal data and promptly feed it back to the adjustment control module for further optimization. Such stability analysis ensures the continuous stability of product quality over a long period of time and avoids quality regression caused by factors such as equipment aging and environmental changes. Attached Figure Description

[0073] Figure 1 This is a block diagram of a remote control system for intelligent manufacturing equipment based on the Internet, according to the present invention. Detailed Implementation

[0074] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0075] Example 1

[0076] Please see Figure 1 This invention provides an Internet-based remote control system for intelligent manufacturing equipment, including a data acquisition module, a data preprocessing and feature extraction module, a quality detection and evaluation module, an adjustment control module, an equipment optimization effect evaluation module, and a stability analysis module.

[0077] The data acquisition module is used to deploy infrared temperature sensors and high-definition cameras on intelligent manufacturing equipment to collect environmental and product data in real time during the product manufacturing process and construct a manufacturing data set S.

[0078] The data preprocessing and feature extraction module is used to preprocess the manufacturing data set S and extract features based on the preprocessed manufacturing data set S to obtain the product surface roughness score Ra, area change coefficient Dr, and thermal expansion coefficient Pz.

[0079] The quality inspection and evaluation module is used to perform a summary calculation based on the product surface roughness score Ra, area change coefficient Dr, and thermal expansion coefficient Pz to obtain the product's comprehensive quality score Qf, and to perform product quality evaluation based on the comprehensive quality score Qf to generate a product quality evaluation report.

[0080] The adjustment control module is used to adjust and control the intelligent manufacturing equipment when the product quality is unqualified, based on the product quality assessment report. It generates a control signal u(t) based on the PID control algorithm and controls the intelligent manufacturing equipment to perform adjustment operations based on the control signal u(t).

[0081] The equipment optimization effect evaluation module is used to calculate the product qualification rate HG of the same batch of products based on the manufacturing data after equipment adjustment and control, and preset the product qualification rate threshold HGYZ. The product qualification rate threshold HGYZ and the product qualification rate HG are compared and analyzed to evaluate the optimization effect of equipment adjustment and control.

[0082] The stability analysis module is used to collect manufacturing data for a period of time after the equipment adjustment and control is in a normal state, draw a product quality control chart, and perform stability analysis based on the product quality control chart.

[0083] In this embodiment, by integrating a data acquisition module, a data preprocessing and feature extraction module, a quality inspection and evaluation module, an adjustment control module, an equipment optimization effect evaluation module, and a stability analysis module, the remote control system for this intelligent manufacturing equipment can improve the intelligence, automation, and precision of the manufacturing process. The data acquisition module collects environmental and product data in real time by deploying infrared temperature sensors and high-definition cameras, constructing a manufacturing data set S to ensure the comprehensiveness and accuracy of the data sources. The data preprocessing and feature extraction module further cleans and analyzes the data, extracting the product's surface roughness score Ra, area change coefficient Dr, and thermal expansion coefficient Pz, providing a precise basis for subsequent quality assessment. The quality inspection and evaluation module obtains the product's comprehensive quality score Qf, automatically evaluates the product quality, and generates a product quality evaluation report, facilitating timely adjustments to production parameters. The adjustment control module, combined with a PID control algorithm, adjusts the production equipment parameters in real time to ensure that product quality meets requirements. The equipment optimization effect evaluation module provides feedback on the equipment adjustment effect through the evaluation of the product qualification rate HG, and monitors long-term production stability through the stability analysis module to ensure the continuous effectiveness of the optimization effect. The overall system improves the production efficiency of the manufacturing equipment, reduces the scrap rate, and ensures product quality stability.

[0084] Example 2

[0085] Please refer to Figure 1Specifically: the data acquisition module includes an environmental data acquisition unit and a product data acquisition unit;

[0086] The environmental data acquisition unit is used to deploy infrared temperature sensors on intelligent manufacturing equipment to collect the temperature change ΔT of the product during the product manufacturing process in real time.

[0087] The product data acquisition unit is used to deploy a high-definition camera on the intelligent manufacturing equipment to collect image data of the product in real time during the product manufacturing process. The image data includes the height h of different areas on the product surface, the area d of the product, and the length L of the product.

[0088] Based on the collected environmental and product data, a manufacturing data set S is constructed, and the manufacturing data set S is transmitted to the equipment data center for storage via wireless transmission technology.

[0089] In this embodiment, by introducing an environmental data acquisition unit and a product data acquisition unit, the intelligent manufacturing equipment can achieve comprehensive monitoring and data acquisition of the product manufacturing process. The environmental data acquisition unit, by deploying an infrared temperature sensor, can collect the temperature change ΔT generated by the product during the manufacturing process in real time, providing data for subsequent calculation of the coefficient of thermal expansion and analysis of the impact of temperature on product quality. The product data acquisition unit, through a high-definition camera, acquires the height h, area d, and length L of different areas on the product surface in real time. This data can effectively reflect the surface quality, dimensional accuracy, and shape changes of the product, thus providing an important basis for product quality assessment. The integrated data acquisition manufacturing data set S is transmitted to the equipment data center for centralized storage and management via wireless transmission technology, improving the visualization and intelligence level of the production process. It can monitor and adjust various parameters in the production process in real time, ensuring the stability and consistency of production quality. In addition, the system can also respond and adjust quickly based on real-time data, providing accurate data support for subsequent quality control and equipment optimization, helping enterprises achieve a more efficient and precise production process.

[0090] Example 3

[0091] Please refer to Figure 1 Specifically: the data preprocessing and feature extraction module includes a preprocessing unit and a feature extraction unit;

[0092] The preprocessing unit is used to perform preprocessing based on the manufacturing data set S, including environmental data preprocessing and product data preprocessing.

[0093] The environmental data preprocessing refers to cleaning, denoising, and standardizing the environmental data in the manufacturing dataset S.

[0094] The product data preprocessing refers to using Gaussian filtering to smooth the image of the product data in the manufacturing dataset S, removing high-frequency noise from the image, using histogram equalization to evenly distribute the gray values ​​of the image, increasing the contrast, and using Canny edge detection to extract the edge contours of the image.

[0095] The feature extraction unit is used to perform summary calculations based on the preprocessed manufacturing data set S to obtain the product surface roughness score Ra, area change coefficient Dr, and thermal expansion coefficient Pz.

[0096] The method for obtaining the product surface roughness score Ra is as follows:

[0097]

[0098] In the formula, L represents the length of the product, and y(x) represents the height of the surface position x of the product being measured. This represents the average height of the surface of the product being tested;

[0099] The height y(x) of position x on the surface of the product under test and the average height of the surface of the product under test. Acquired by using surface scanning equipment, which includes a laser scanner and a stylus profilometer;

[0100] The area change coefficient Dr is obtained as follows:

[0101]

[0102] In the formula, d i d represents the area measured at the i-th measurement point on the product. ref，i This represents the preset area size of the i-th measurement point on the product, where i = [1, 2, 3, ..., N] and N represents the number of measurement points;

[0103] The thermal expansion coefficient Pz is obtained as follows:

[0104]

[0105] In the formula, ΔL represents the change in length of the product during the heating process, L0 represents the initial length of the product, and ΔT represents the change in temperature of the material.

[0106] In this embodiment, environmental data preprocessing and product data preprocessing ensure the quality of the collected data. Environmental data preprocessing includes data cleaning, noise reduction, and standardization, effectively eliminating interference from the external environment and ensuring the accuracy of subsequent data analysis. Product data preprocessing uses techniques such as Gaussian filtering, histogram equalization, and Canny edge detection to optimize image data, remove image noise, enhance image features, and improve the accuracy and robustness of surface quality assessment. In terms of feature extraction, the feature extraction unit further refines the dimensions of product quality assessment by calculating the product surface roughness score Ra, area change coefficient Dr, and thermal expansion coefficient Pz. The product surface roughness score Ra accurately reflects the smoothness of the product surface, the area change coefficient Dr is used to assess the stability of product dimensions, and the thermal expansion coefficient Pz is an important parameter of material performance, reflecting the impact of temperature changes on product deformation. Through the comprehensive analysis of these feature indicators, the quality of the product can be more comprehensively assessed, helping to optimize the production process and adjust manufacturing parameters in a timely manner, ensuring product consistency and high pass rate, and ultimately improving the production efficiency and quality control capabilities of the intelligent manufacturing system.

[0107] Example 4

[0108] Please refer to Figure 1 Specifically: the quality inspection and evaluation module includes a quality scoring calculation unit and a quality evaluation unit;

[0109] The quality scoring calculation unit is used to perform a summary calculation based on the product's surface roughness score Ra, area change coefficient Dr, and thermal expansion coefficient Pz to obtain the product's comprehensive quality score Qf. The comprehensive quality score Qf is obtained in the following way:

[0110]

[0111] In the formula, Ra max Dr max and Pz max ω1, ω2, and ω3 represent the maximum values ​​of the product surface roughness score Ra, area variation coefficient Dr, and thermal expansion coefficient Pz, respectively, and represent the weighting coefficients of the product surface roughness score Ra, area variation coefficient Dr, and thermal expansion coefficient Pz, respectively. and The surface roughness score Ra, area variation coefficient Dr, and thermal expansion coefficient Pz of the product are respectively normalized to eliminate differences in the dimensions and ranges of different characteristic indicators, and C represents the first correction coefficient; the maximum value Ra of the product surface roughness score Ra, area variation coefficient Dr, and thermal expansion coefficient Pz is... max Dr max and Pz maxThe quality control requirements stipulated by relevant industry standards are obtained, where the weighting coefficients ω1, ω2 and ω3 are set by the customer according to the actual situation, and ω1+ω2+ω3=1.

[0112] The quality assessment unit is used to preset a quality scoring threshold QfYZ, and compare and analyze the quality scoring threshold QfYZ with the comprehensive quality score Qf to assess the product's qualification. The specific assessment content is as follows:

[0113] If the overall quality score Qf is less than the quality score threshold QfYZ, i.e. Qf < QfYZ, the product quality is judged to be in a qualified state. At this time, the product manufacturing continues and the product production process is continuously monitored.

[0114] If the overall quality score is greater than or equal to the quality score threshold QfYZ, i.e. Qf≥QfYZ, the product is judged to be in a non-conforming state, and an alarm mechanism is triggered to stop the operation of the intelligent manufacturing equipment, record the non-conforming product data, generate a product quality assessment report, and transmit the product quality assessment report to the equipment data center.

[0115] In this embodiment, by introducing a quality scoring calculation unit and a quality assessment unit, the system can achieve accurate and real-time product quality assessment and automatically adjust based on the assessment results. The quality scoring calculation unit comprehensively considers the product surface roughness score Ra, area change coefficient Dr, and thermal expansion coefficient Pz, and weights and summarizes each characteristic indicator using weighting coefficients to generate a comprehensive quality score Qf. To eliminate differences in the dimensions and ranges of different indicators, the system normalizes these indicators, making the scoring more scientific and fair. By setting a quality scoring threshold QfYZ, the system can automatically determine whether the product meets the quality standards. When the comprehensive quality score Qf is less than the quality scoring threshold QfYZ, the system considers the product quality to be qualified, continues production, and monitors the production process. When the comprehensive quality score Qf is greater than or equal to the quality scoring threshold QfYZ, the system immediately stops the equipment operation, triggers an alarm mechanism, and prevents the continued production of unqualified products, thereby ensuring product quality. This mechanism effectively reduces human error, automatically optimizes the production process, ensures the consistency and reliability of product quality, and provides a reliable quality assurance means for intelligent manufacturing equipment.

[0116] Example 5

[0117] Please refer to Figure 1 Specifically: the adjustment control module includes a control signal generation unit and a production parameter adjustment unit;

[0118] The control signal generation unit is used to obtain the surface roughness score deviation e of the defective product based on the recorded defective product data when the product quality is unqualified. Rathermal expansion coefficient deviation e Pz and the deviation of the area change coefficient e Dr :

[0119] e Ra =Ra target -Ra;

[0120] e Pz =Pz target -Pz;

[0121] e Dr =Dr target -Dr;

[0122] In the formula, Ra target 、Pz target and Dr target These represent the standard values ​​of the product surface roughness score Ra, the coefficient of thermal expansion Pz, and the area change coefficient Dr, respectively. target 、Pz target and Dr target Obtained from relevant industry standards databases;

[0123] Based on the obtained product surface roughness score deviation e Ra thermal expansion coefficient deviation e Pz and the deviation of the area change coefficient e Dr The device control signal u(t) is generated using a PID control algorithm. The method for obtaining the device control signal u(t) is as follows:

[0124]

[0125] In the formula, e(t) represents the adjustment parameter error at the current time point t, e(t) = [e Ra e Pz e Dr ], K p K represents the strength of the control system's response to the error e(t) of the adjustment parameter at the current time point t. i K represents the cumulative effect of the error over a period of time. d The response speed of the control system to the rate of change of error is represented by K, where K is the response intensity of the control system to the error e(t) of the adjustment parameter at the current time point t. p The cumulative impact of error over a period of time, K i And the response speed K of the control system to the rate of error change d Determined using the Ziegler-Nichols method;

[0126] The Ziegler-Nichols method is a classic PID control parameter tuning method, mainly used for adjusting PID controller parameters in industrial automation, including the proportional gain K. p Integral gain K i Differential gain K d The core idea behind determining the gain is to find the critical parameters that bring the closed-loop system close to stability through experiments or system models, and then calculate the appropriate PID controller gain value based on these parameters.

[0127] In a specific example, suppose there is an industrial heating furnace that needs to maintain an internal temperature of 200℃. However, in actual production, the furnace temperature will fluctuate due to factors such as raw materials, ambient temperature, and opening / closing operations. PID control is used to automatically adjust the heating power to stabilize the furnace temperature at the target value. If the actual measured temperature is 180℃, which is lower than the set temperature, the proportional term of the PID controller will immediately increase the heating power. The integral term will further accumulate and continuously increase the heating intensity when the temperature remains low for an extended period. Conversely, when the temperature rapidly approaches the target value, the derivative term will prematurely weaken the heating signal to prevent temperature overshoot. The Ziegler-Nichols method allows for the experimental acquisition of the critical gain and critical oscillation period, and the calculation of the appropriate proportional gain K. p Integral gain K i Differential gain K d This allows the controller to restore the furnace temperature to the set value and maintain stability in the shortest possible time, avoiding frequent oscillations or insufficient adjustment.

[0128] The production parameter adjustment unit is used to automatically map the equipment control signal u(t) obtained by the control signal generation unit into equipment operating parameter instructions based on the industrial automation control software TIA PROtal, and send the equipment operating parameter instructions to the intelligent manufacturing equipment through the PLC system of the production equipment to control the intelligent manufacturing equipment to perform adjustment operations.

[0129] The PLC system is a dedicated computer system for industrial automation control. It can monitor and automatically control mechanical equipment or production processes in real time and is widely used in industries such as manufacturing, energy, and transportation. It can perform complex logical operations and sequential control through pre-written programs.

[0130] The industrial automation control software TIA PROtal is a fully integrated automation software platform that integrates all the commonly used design, programming, debugging and operation and maintenance tools in industrial automation into one environment. When used in industrial production lines and intelligent manufacturing systems, it can be used for equipment control logic programming, parameter setting, signal mapping and equipment debugging.

[0131] In this embodiment, by adjusting the control module, the system can effectively respond to product quality defects and automatically adjust and optimize. The control signal generation unit calculates the deviations of the product surface roughness score Ra, thermal expansion coefficient Pz, and area change coefficient Dr from the standard values ​​based on real-time collected product quality data, including product surface roughness score Ra, thermal expansion coefficient Pz, and area change coefficient Dr. It then generates the equipment control signal u(t) through a PID control algorithm. The PID control algorithm can adjust production parameters in real time and precisely adjust the operating state of the production equipment to optimize and stabilize product quality. The system determines the PID parameters using the Ziegler-Nichols method, making the equipment control signal u(t) more sensitive and stable to errors, thereby effectively reducing quality fluctuations. The system maps the equipment control signal u(t) into equipment operating parameter instructions and sends them to the intelligent manufacturing equipment through the PLC system for automatic equipment parameter adjustment. This process enables rapid and precise equipment adjustment, avoiding delays and errors caused by manual intervention, improving the automation level of the production process, reducing the generation of defective products, and maintaining product quality stability during long-term operation.

[0132] Example 6

[0133] Please refer to Figure 1 Specifically: The equipment optimization effect evaluation module is used to collect manufacturing data after equipment adjustment and control, including environmental data and product data, and select several products from the same batch to construct a sample combination to be evaluated. It calculates the comprehensive quality score Qf for each sample to be evaluated, evaluates the quality of the samples based on the comprehensive quality score Qf, and obtains the product qualification rate HG for the same batch of products. The product qualification rate HG is obtained as follows:

[0134]

[0135] In the formula, q represents the number of qualified samples to be evaluated, and m represents the total number of samples in the combination of samples to be evaluated;

[0136] A preset product pass rate threshold HGYZ is established. The product pass rate HG is compared and analyzed with the product pass rate threshold HGYZ to evaluate the optimization effect of equipment adjustment and control. The specific evaluation content is as follows:

[0137] If the product qualification rate HG is greater than or equal to the product qualification rate threshold HGYZ, i.e. HG≥HGYZ, the optimization effect of equipment adjustment and control is judged to be normal. At this time, the equipment adjustment and control measures and equipment parameters are recorded, and an equipment optimization and control report is generated. The equipment optimization and control report is sent to relevant personnel as a basis for further optimization. At the same time, the manufacturing data during the product manufacturing process is continuously recorded. The equipment optimization and control report includes the optimization process and optimization results of equipment adjustment and control.

[0138] If the product qualification rate HG is less than the product qualification rate threshold HGYZ, i.e. HG < HGYZ, the optimization effect of the equipment adjustment control is determined to be abnormal. At this time, abnormal product data is recorded and fed back to the adjustment control module. The equipment adjustment parameter value u(t) is recalculated and iterative optimization is performed until the optimization effect of the equipment adjustment control is normal.

[0139] In this embodiment, by continuously monitoring and evaluating production data after equipment adjustment and control, the system ensures that the adjustment measures of the intelligent manufacturing equipment can effectively improve product quality in actual production. This module first calculates the comprehensive quality score Qf of products in the same batch and obtains the product qualification rate HG based on the product qualification rate HG. If the product qualification rate HG is greater than or equal to the product qualification rate threshold HGYZ, the system determines that the optimization effect of the equipment adjustment and control is normal, generates an optimization report, and records the adjustment process, providing a basis for subsequent optimization decisions. Simultaneously, if the product qualification rate HG is less than the product qualification rate threshold HGYZ, the system automatically identifies the optimization effect of the equipment adjustment and control as abnormal, records the non-conforming data, and readjusts the equipment parameters through the feedback adjustment unit, initiating an iterative optimization process until the expected effect is achieved. This evaluation and feedback mechanism can promptly identify deficiencies in equipment adjustment, avoid quality fluctuations in continuous production, and further optimize equipment performance through precise feedback adjustment, ultimately achieving continuous improvement and stability of quality during the production process. This not only improves the product qualification rate but also enhances the adaptability and intelligence level of the production system, ensuring the stable effect of equipment adjustment across different batches and time periods.

[0140] Example 7

[0141] Please refer to Figure 1 Specifically: The stability analysis module is used to perform product quality control chart analysis when the optimization effect of equipment adjustment and control is in a normal state. The specific analysis process is as follows:

[0142] The product manufacturing process is continuously monitored after the equipment is adjusted and controlled. Manufacturing data is collected at fixed intervals, and a certain number of products are randomly selected as test samples each time.

[0143] Calculate the mean of the characteristic indicators of the test samples at the same time point. And based on the mean of the characteristic indicators of the test samples at the same time point And obtain the total average value μ of the feature indicators of the test samples at all time points. The method for obtaining the total average value μ of the feature indicators is as follows:

[0144]

[0145] In the formula, g represents the total number of data collection time points. Let g represent the mean of the characteristic indicators of the sample to be tested at the j-th data collection time point, where j = [1, 2, 3, ..., g].

[0146] Based on the total average value μ of the characteristic indicators of the test samples at all time points, the upper control limit (UCL) and lower control limit (LCL) are obtained, and a product quality control chart is plotted. The horizontal axis of the product quality control chart represents the product production time, and the vertical axis represents the average value of the characteristic indicators of the test samples at the same time point. The upper control limit (UCL) and lower control limit (LCL) are obtained as follows:

[0147] UCL = μ + 3σ;

[0148] LCL = μ - 3σ;

[0149] In the formula, μ represents the total average value of the characteristic indicators of the test samples at all time points, and σ represents the standard deviation of the characteristic indicators of the test samples at all time points.

[0150] Based on the constructed product quality control chart, a stability analysis was conducted, and the specific analysis content is as follows;

[0151] If all data points in the product quality control chart are located between the upper control limit (UCL) and the lower control limit (LCL), the product quality is considered stable and without abnormalities.

[0152] If not all data points in the product quality control chart are located between the upper control limit (UCL) and the lower control limit (LCL), it is determined that the product quality is abnormal. The abnormal data is recorded and sent to the equipment adjustment and control personnel for intelligent equipment adjustment and control.

[0153] In this embodiment, by conducting long-term monitoring and analysis of product quality, the stability of product quality during the production process is ensured. This module continuously collects manufacturing data and periodically extracts several product samples for characteristic index calculation, generating a product quality control chart. Through real-time analysis of the control chart, it can determine whether there are abnormal fluctuations in product quality. Specifically, by calculating the average of characteristic indicators at all time points and drawing the control chart, the module can set an upper control limit (UCL) and a lower control limit (LCL) and determine whether the data points fall within the control limits. When all data points are within the control limits, it indicates that the product quality is stable and no adjustment is needed. However, if the data points exceed the control limits, it indicates a quality anomaly. The system automatically records the abnormal data and feeds it back to the adjustment control personnel, promptly initiating equipment adjustments. This analysis can not only detect production fluctuations in the short term but also track the quality of products after equipment adjustments in the long term, ensuring the lasting effectiveness of optimization measures and effectively avoiding quality regression caused by changes in equipment or environment. This improves the stability of the production process and the consistency of products, enhances the self-adjustment capability of the intelligent manufacturing system, reduces manual intervention, and improves production efficiency and product qualification rate.

[0154] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A remote control system for intelligent manufacturing equipment based on the Internet, characterized in that: It includes a data acquisition module, a data preprocessing and feature extraction module, a quality detection and evaluation module, an adjustment and control module, an equipment optimization effect evaluation module, and a stability analysis module; The data acquisition module is used to deploy infrared temperature sensors and high-definition cameras on intelligent manufacturing equipment to collect environmental and product data in real time during the product manufacturing process and construct a manufacturing data set S. The data preprocessing and feature extraction module is used to preprocess the manufacturing data set S and extract features based on the preprocessed manufacturing data set S to obtain the product surface roughness score Ra, area change coefficient Dr, and thermal expansion coefficient Pz. The quality inspection and evaluation module is used to perform a summary calculation based on the product surface roughness score Ra, area change coefficient Dr, and thermal expansion coefficient Pz to obtain the product's comprehensive quality score Qf, and to perform product quality evaluation based on the comprehensive quality score Qf to generate a product quality evaluation report. The adjustment control module is used to adjust and control the intelligent manufacturing equipment when the product quality is unqualified, based on the product quality assessment report. It generates a control signal u(t) based on the PID control algorithm and controls the intelligent manufacturing equipment to perform adjustment operations based on the control signal u(t). The equipment optimization effect evaluation module is used to calculate the product qualification rate HG of the same batch of products based on the manufacturing data after equipment adjustment and control, and preset the product qualification rate threshold HGYZ. The product qualification rate threshold HGYZ and the product qualification rate HG are compared and analyzed to evaluate the optimization effect of equipment adjustment and control. The stability analysis module is used to collect manufacturing data for a period of time after the equipment adjustment and control is in a normal state, draw a product quality control chart, and perform stability analysis based on the product quality control chart. The area change coefficient Dr is obtained as follows: ; In the formula, This represents the measured area of ​​the i-th measurement point on the product. This represents the preset area size of the i-th measurement point on the product, where i = [1, 2, 3, ..., N] and N represents the number of measurement points. The thermal expansion coefficient Pz is obtained as follows: ; In the formula, ∆L represents the change in length of the product during the heating process. The initial length of the product is represented by ∆T, and the temperature change of the material is represented by ∆T. ; In the formula, These represent the maximum values ​​of the product's surface roughness score Ra, area variation coefficient Dr, and thermal expansion coefficient Pz, respectively. These represent the weighting coefficients for the product's surface roughness score Ra, area variation coefficient Dr, and thermal expansion coefficient Pz, respectively. These represent the normalization processes performed on the product surface roughness score Ra, area variation coefficient Dr, and thermal expansion coefficient Pz, respectively, to eliminate differences in the dimensions and ranges of different characteristic indicators. C represents the first correction coefficient.

2. The remote control system for intelligent manufacturing equipment based on the Internet according to claim 1, characterized in that: The data acquisition module includes an environmental data acquisition unit and a product data acquisition unit; The environmental data acquisition unit is used to deploy infrared temperature sensors on intelligent manufacturing equipment to collect the temperature change ∆T of the product in real time during the product manufacturing process. The product data acquisition unit is used to deploy a high-definition camera on the intelligent manufacturing equipment to collect image data of the product in real time during the product manufacturing process. The image data includes the height h of different areas on the product surface, the area d of the product, and the length L of the product. Based on the collected environmental and product data, a manufacturing data set S is constructed, and the manufacturing data set S is transmitted to the equipment data center for storage via wireless transmission technology.

3. The remote control system for intelligent manufacturing equipment based on the Internet according to claim 2, characterized in that: The data preprocessing and feature extraction module includes a preprocessing unit and a feature extraction unit; The preprocessing unit is used to perform preprocessing based on the manufacturing data set S, including environmental data preprocessing and product data preprocessing. The environmental data preprocessing refers to cleaning, denoising, and standardizing the environmental data in the manufacturing dataset S. The product data preprocessing refers to using Gaussian filtering to smooth the image of the product data in the manufacturing dataset S, removing high-frequency noise from the image, using histogram equalization to evenly distribute the gray values ​​of the image, increasing the contrast, and using Canny edge detection to extract the edge contours of the image.

4. The Internet-based intelligent manufacturing equipment remote control system according to claim 3, characterized in that: The feature extraction unit is used to perform summary calculations based on the preprocessed manufacturing data set S to obtain the product surface roughness score Ra, area change coefficient Dr, and thermal expansion coefficient Pz. The method for obtaining the product surface roughness score Ra is as follows: ; In the formula, L represents the length of the product, and y(x) represents the height of the surface position x of the product to be measured. This represents the average height of the surface of the product being tested; The height y(x) of position x on the surface of the product under test and the average height of the surface of the product under test. It is obtained by using a surface scanning device, which includes a laser scanner and a stylus profilometer.

5. The Internet-based intelligent manufacturing equipment remote control system according to claim 4, characterized in that: The quality assessment unit is used to preset a quality score threshold QfYZ, and compares and analyzes the quality score threshold QfYZ with the comprehensive quality score Qf to assess the product's qualification. The specific assessment content is as follows: If the overall quality score Qf is less than the quality score threshold QfYZ, i.e. Qf < QfYZ, the product quality is judged to be in a qualified state. At this time, the product manufacturing continues and the product production process is continuously monitored. If the overall quality score is greater than or equal to the quality score threshold QfYZ, i.e. Qf≥QfYZ, the product is judged to be in a non-conforming state, and an alarm mechanism is triggered to stop the operation of the intelligent manufacturing equipment, record the non-conforming product data, generate a product quality assessment report, and transmit the product quality assessment report to the equipment data center.

6. The Internet-based intelligent manufacturing equipment remote control system according to claim 5, characterized in that: The adjustment control module includes a control signal generation unit and a production parameter adjustment unit; The control signal generation unit is used to obtain the surface roughness score deviation of the defective product based on the recorded defective product data when the product quality is unqualified. deviation of thermal expansion coefficient and deviation of area change coefficient : ; In the formula, These represent the standard values ​​of the product surface roughness score Ra, the coefficient of thermal expansion Pz, and the area change coefficient Dr, respectively. Obtained from relevant industry standards databases; Based on the obtained product surface roughness score deviation deviation of thermal expansion coefficient and deviation of area change coefficient The device control signal u(t) is generated using a PID control algorithm. The method for obtaining the device control signal u(t) is as follows: ; In the formula, e(t) represents the error of the adjustment parameter at the current time point t. , This represents the strength of the control system's response to the error e(t) of the adjustment parameter at the current time point t. This indicates the cumulative effect of the error over a period of time. It indicates the response speed of the control system to the rate of change of error.

7. The Internet-based intelligent manufacturing equipment remote control system according to claim 6, characterized in that: The production parameter adjustment unit is used to automatically map the equipment control signal u(t) obtained by the control signal generation unit into equipment operating parameter instructions according to the industrial automation control software, and send the equipment operating parameter instructions to the intelligent manufacturing equipment through the PLC system of the production equipment to control the intelligent manufacturing equipment to perform adjustment operations.

8. The Internet-based intelligent manufacturing equipment remote control system according to claim 7, characterized in that: The equipment optimization effect evaluation module is used to collect manufacturing data after equipment adjustment and control, including environmental data and product data. It selects several products from the same batch to construct a sample combination to be evaluated, calculates the comprehensive quality score Qf for each sample, evaluates the quality of the samples based on the comprehensive quality score Qf, and obtains the product qualification rate HG for the same batch of products. The product qualification rate HG is obtained as follows: ; In the formula, q represents the number of qualified samples to be evaluated, and m represents the total number of samples in the combination of samples to be evaluated; A preset product pass rate threshold HGYZ is established. The product pass rate HG is compared and analyzed with the product pass rate threshold HGYZ to evaluate the optimization effect of equipment adjustment and control. The specific evaluation content is as follows: If the product qualification rate HG is greater than or equal to the product qualification rate threshold HGYZ, i.e. HG≥HGYZ, the optimization effect of equipment adjustment and control is determined to be normal. At this time, the measures and equipment parameters of equipment adjustment and control are recorded, and an equipment optimization and control report is generated. The equipment optimization and control report is sent to relevant personnel. At the same time, the manufacturing data during the product manufacturing process is continuously recorded. The equipment optimization and control report includes the optimization process and optimization results of equipment adjustment and control. If the product qualification rate HG is less than the product qualification rate threshold HGYZ, i.e., HG < HGYZ, the optimization effect of the equipment adjustment control is determined to be abnormal. At this time, abnormal product data is recorded and fed back to the adjustment control module. The equipment adjustment parameter value ut is recalculated and iterative optimization is performed until the optimization effect of the equipment adjustment control is normal.

9. The Internet-based intelligent manufacturing equipment remote control system according to claim 8, characterized in that: The stability analysis module is used to perform product quality control chart analysis when the optimization effect of equipment adjustment and control is in a normal state. The specific analysis process is as follows: The product manufacturing process is continuously monitored after the equipment is adjusted and controlled. Manufacturing data is collected at fixed intervals, and a certain number of products are randomly selected as test samples each time. Calculate the mean of the characteristic indicators of the test samples at the same time point. And based on the mean of the characteristic indicators of the test samples at the same time point. And obtain the total average value μ of the feature indicators of the test samples at all time points. The method for obtaining the total average value μ of the feature indicators is as follows: ; In the formula, g represents the total number of data collection time points. Let g represent the mean of the characteristic indicators of the sample to be tested at the j-th data collection time point, where j = [1, 2, 3, ..., g]. Based on the total average value μ of the characteristic indicators of the test samples at all time points, the upper control limit (UCL) and lower control limit (LCL) are obtained, and a product quality control chart is plotted. The horizontal axis of the product quality control chart represents the product production time, and the vertical axis represents the mean value X of the characteristic indicators of the test samples at the same time point. The upper control limit (UCL) and lower control limit (LCL) are obtained as follows: ; In the formula, μ represents the total average value of the characteristic indicators of the test samples at all time points, and σ represents the standard deviation of the characteristic indicators of the test samples at all time points. Based on the constructed product quality control chart, a stability analysis was conducted, and the specific analysis content is as follows; If all data points in the product quality control chart are located between the upper control limit (UCL) and the lower control limit (LCL), the product quality is considered stable and without abnormalities. If not all data points in the product quality control chart are located between the upper control limit (UCL) and the lower control limit (LCL), it is determined that the product quality is abnormal. The abnormal data is recorded and sent to the equipment adjustment and control personnel for intelligent equipment adjustment and control.

Images