Mica material production optimization method and system based on big data analysis

Abstract

The application discloses a mica material production optimization method and system based on big data analysis, relates to the mica production process field, and the method comprises the following steps: acquiring a mica processing product type and a historical mica processing product type; acquiring initial equipment parameters of all mica processing equipment; acquiring mica processing data and historical mica processing data according to a plurality of types of sensors preset in the mica processing equipment; screening out core process parameters in the initial equipment parameters; calculating core process standard values corresponding to the core process parameters; calculating a mica raw material loss rate in a mica processing process; screening and outputting local correction factors and global correction factors; and correcting the initial equipment parameters to obtain optimal equipment parameters. The application has the effects of optimizing a mica processing process, improving mica processing raw material utilization and mica processing product quality.

Description

Technical Field

[0001] This application relates to the field of mica production processes, and in particular to a method and system for optimizing mica material production based on big data analysis. Background Technology

[0002] Mica possesses insulating and heat-insulating properties, excellent chemical stability, and resistance to strong acids, alkalis, and pressure. Therefore, it is a crucial raw material for manufacturing electrical equipment. With advancements in science and technology, mica materials are also widely used in other fields, such as the building materials and fire protection industries. Because mica plays an indispensable role in multiple industries, any production of mica materials that fails to meet relevant industry quality standards can have a significant economic impact on those industries. Therefore, it is necessary to optimize mica material production processes and improve and strictly control the quality of mica materials.

[0003] Current technologies for optimizing mica material production processes primarily involve updating and optimizing mica processing equipment. While this can improve the quality of mica materials to some extent, equipment upgrades are costly, and the mica processing is complex, with various factors affecting material quality. Therefore, a method is needed to optimize mica material production processes from multiple dimensions to improve mica production quality. Summary of the Invention

[0004] This application provides a method and system for optimizing mica material production based on big data analysis, which is used to solve the problem of insufficient quality of mica materials produced by existing mica production processes.

[0005] To achieve the above objectives, the embodiments of this application adopt the following technical solutions:

[0006] Firstly, a method for optimizing mica material production based on big data analysis is provided, which includes:

[0007] Obtain the mica processing product type of the current material order through the mica material order system, and select the sample to be processed that is of the same type as the mica processing product.

[0008] Retrieve historical processing data and initial equipment parameters of the same type as the mica processing product type from the preset mica processing database. The initial equipment parameters are the equipment parameters of all mica processing equipment in the mica processing technology corresponding to the mica processing product type.

[0009] Based on the initial equipment parameters, the sample to be processed is processed by the mica processing equipment. Multiple types of sensors preset in all the mica processing equipment are used to collect mica processing data during the processing of the sample to be processed, and the sample quality parameters after processing are detected.

[0010] The mica processing data is classified and subjected to time-series feature analysis, and the core process parameters in the initial equipment parameters are selected based on the analysis results of the time-series feature analysis.

[0011] The average value of the historical processing data of mica is calculated based on the core process parameters, and the average value is used as the core process standard value corresponding to the core process parameters.

[0012] By combining the core process standard values ​​with the mica processing data, the mica raw material loss rate during the mica processing process is calculated.

[0013] The core process parameters are screened by calculating the correlation coefficient between the mica raw material loss rate and the core process parameters, and the screened core process parameters are output as local correction factors.

[0014] The mica product quality parameters corresponding to the mica processing product type are obtained from a preset database. The influence degree dataset between the initial equipment parameters and the mica product quality parameters is calculated. The initial equipment parameters are prioritized based on the influence degree dataset. The initial equipment parameters that have completed the priority classification are output as global correction factors.

[0015] The initial equipment parameters are corrected by combining the local correction factors and the global correction factors to obtain the optimal equipment parameters.

[0016] Optionally, the step of classifying and performing time-series feature analysis on the mica processing data, and selecting the core process parameters from the initial equipment parameters based on the analysis results of the time-series feature analysis, includes the following steps:

[0017] Based on the sensor types of the various sensors, the mica processing data is classified by features to obtain mica weight change data, mica temperature change data, and mica pH change data.

[0018] The upper limit of the total number of inflection points is set according to the total number of processing steps included in the mica processing technology. The upper limit of the total number of inflection points is used as an algorithm constraint, and the inflection point detection algorithm is used to detect the inflection points of the mica weight change data to obtain the weight change inflection points distributed in the mica weight change data.

[0019] Based on the inflection point of weight change, the mica processing technology is divided into process nodes to obtain multiple mica processing sub-processes within the mica processing technology.

[0020] Extract the maximum and minimum temperature values ​​of the mica temperature change data within each mica processing sub-process, calculate the difference between the maximum and minimum temperature values, and obtain the temperature difference value.

[0021] Extract the maximum and minimum values ​​of the pH change data of mica within each mica processing sub-process, and calculate the difference between the maximum and minimum pH values ​​to obtain the pH difference.

[0022] If the temperature difference corresponding to the mica processing sub-process is greater than a preset temperature difference threshold, then analyze whether the pH difference corresponding to the mica processing sub-process is greater than a preset pH difference threshold.

[0023] If the pH difference corresponding to the mica processing sub-process is greater than the pH difference threshold, the initial equipment parameters corresponding to the mica processing sub-process are determined to be mica purification parameters.

[0024] If the pH difference is less than or equal to the pH difference threshold, then the initial equipment parameters corresponding to the mica processing sub-process are determined to be mica grinding parameters.

[0025] The core process parameters are obtained by integrating the mica purification parameters and the mica grinding parameters.

[0026] Optionally, calculating the average value of the historical mica processing data based on the core process parameters, and using the average value as the core process standard value corresponding to the core process parameters, includes the following steps:

[0027] Extract the historical purification weight change data from the historical processing data corresponding to the mica purification parameters to obtain the historical purification weight change data, calculate the average purification weight of the historical purification weight change data, and use the average purification weight as the standard value of the purification process.

[0028] Extract the historical grinding weight change data from the historical processing data corresponding to the mica grinding parameters, obtain the historical grinding weight change data, calculate the average grinding weight of the historical grinding weight change data, and use the average grinding weight as the standard value of the grinding process.

[0029] The standard values ​​of the purification process and the grinding process are integrated to obtain the standard values ​​of the core process parameters.

[0030] Optionally, calculating the correlation coefficient between the mica raw material loss rate and the core process parameters, screening the core process parameters based on the correlation coefficient, and outputting the screened core process parameters as local correction factors includes the following steps:

[0031] Calculate the core parameter correlation coefficient set between the core process parameters of mica and the mica raw material loss rate, filter out the core parameter correlation coefficients in the core parameter correlation coefficient set that are greater than the preset core parameter correlation coefficient threshold, and output the core process parameters corresponding to the core parameter correlation coefficients as local correction factors.

[0032] Based on multiple types of sensors pre-installed in the mica processing plant, mica processing environment data is obtained, including mica processing environment temperature data and mica processing environment humidity data.

[0033] A multiple linear regression equation was established with the mica raw material loss rate as the dependent variable. The environmental temperature data and environmental humidity data of the mica processing environment were respectively input into the multiple linear regression equation as independent variables, and the environmental temperature correlation coefficient and environmental humidity correlation coefficient were calculated.

[0034] If the environmental temperature correlation coefficient is greater than the preset environmental correlation coefficient threshold, the mica processing environmental temperature data will be output as a local correction factor.

[0035] If the environmental humidity correlation coefficient is greater than the environmental correlation coefficient threshold, then the environmental humidity data of the mica processing environment will be output as a local correction factor.

[0036] Optionally, calculating the core parameter correlation coefficient set between the core process parameters of mica and the mica raw material loss rate, filtering out the core parameter correlation coefficients in the core parameter correlation coefficient set that are greater than a preset core parameter correlation coefficient threshold, and outputting the core process parameters corresponding to the core parameter correlation coefficients as local correction factors includes the following steps:

[0037] A correlation coefficient model was established using the mica raw material loss rate as the independent variable;

[0038] The vibration frequency, grinding time, and grinding method included in the mica grinding parameters are input as independent variables into the correlation coefficient model to obtain a set of grinding correlation coefficients.

[0039] The grinding correlation coefficients that are greater than a preset correlation coefficient threshold are selected, and the mica grinding parameters corresponding to the grinding correlation coefficients are output as local correction factors.

[0040] The solution ratio, purification time, and purification screen parameters included in the mica purification parameters are respectively input into the correlation coefficient model as independent variables to obtain the purification correlation coefficient set;

[0041] The purification correlation coefficients that are greater than a preset correlation coefficient threshold are selected, and the mica purification parameters corresponding to the purification correlation coefficients are output as local correction factors.

[0042] Optionally, the following steps are included: obtaining mica product quality parameters from a preset database, calculating the influence dataset between the initial equipment parameters and the mica product quality parameters, prioritizing the initial equipment parameters based on the influence dataset, and outputting the prioritized initial equipment parameters as global correction factors:

[0043] The mica product quality parameters corresponding to the mica processing product type are obtained through a preset database.

[0044] Establish a mica regression model with the mica product quality parameters as the dependent variable and the initial equipment parameters as the independent variable;

[0045] The mica equipment parameters, mica raw material data, and mica ingredient ratio included in the initial equipment parameters are respectively input into the mica regression model as independent variables to obtain the influence degree data. The influence degree data is then integrated to obtain the influence degree dataset.

[0046] The initial device parameter corresponding to the largest impact value in the impact value dataset is output as a global correction factor with the first priority.

[0047] The initial device parameter corresponding to the second largest impact value in the impact value dataset is output as a global correction factor with the second priority.

[0048] The initial device parameter corresponding to the third largest impact data in the impact dataset is output as a global correction factor with a third priority, where the first priority is greater than the second priority, and the second priority is greater than the third priority.

[0049] Optionally, the initial device parameters are corrected by combining the local correction factor and the global correction factor to obtain the optimal device parameters, including the following steps:

[0050] After analyzing the local correction factors, the core process parameters and mica processing environment data contained in the primary equipment parameters are locally corrected.

[0051] The initial equipment parameters, which have undergone local correction, are globally corrected based on the global correction factors to obtain the optimal equipment parameters.

[0052] Optionally, to obtain the optimal device parameters by globally correcting the initial device parameters that have completed local correction based on the global correction factors, the following steps are included:

[0053] Obtain the mica product quality standard parameters corresponding to the mica processing product type from the preset database;

[0054] Calculate the standard deviation between the mica product quality parameters and the mica product quality standard parameters;

[0055] The initial device parameters corresponding to the global correction factors of the first priority are optimized to obtain the first device parameters;

[0056] Calculate the first difference between the quality parameters of the mica product obtained from processing based on the first equipment parameters and the quality parameters of the mica production standard. If the first difference is less than a preset difference threshold, then output the first equipment parameters as the optimal equipment parameters.

[0057] If the first difference is greater than or equal to the difference threshold, the initial device parameters corresponding to the global correction factor of the second priority are optimized to obtain the second device parameters;

[0058] Calculate the second difference between the quality parameters of the mica product obtained from processing according to the second equipment parameters and the quality parameters of the mica production standard. If the second difference is less than the difference threshold, then output the second mica equipment parameters as the optimal equipment parameters.

[0059] If the second difference is greater than or equal to the difference threshold, the initial device parameters corresponding to the global correction factor of the third priority are optimized to obtain the third device parameters, and the third device parameters are output as the optimal device parameters.

[0060] Secondly, a mica material production optimization system based on big data analysis includes:

[0061] The memory is configured to store instructions; and

[0062] The processor is configured to retrieve the instructions from the memory and, when executing the instructions, to implement the mica material production optimization method based on big data analysis as described in any of the first aspects.

[0063] Thirdly, a computer-readable storage medium is provided, on which instructions are stored, which, when executed by a processor, cause the processor to perform the mica material production optimization method based on big data analysis according to any one of the first aspects.

[0064] The above technical solution utilizes a mica material order system to obtain the types of mica processed products and historical mica processed product types. Initial equipment parameters are obtained based on these product types. Multiple types of sensors are used to collect mica processing data and historical data during the processing. The mica processing data is then categorized after time-series feature extraction. An inflection point detection algorithm is used to detect inflection points in the categorized mica weight change data. These weight change inflection points are used to divide the mica processing process into flow nodes. The standard deviation is calculated based on the division results. Core process parameters are selected from the initial equipment parameters based on the standard deviation. Core process standard values ​​are calculated based on these core process parameters. The mica raw material loss rate is obtained based on the correlation coefficient between the mica raw material loss rate and the core process standard value. Local correction factors are selected based on the influence of the initial equipment parameters and mica product quality parameters. Global correction factors are selected based on the influence data between the initial equipment parameters and the global correction factors. The equipment parameters are then optimized by combining the local and global correction factors to obtain the optimal equipment parameters.

[0065] As described above, this invention obtains information on factors affecting raw material loss rate and mica processed product quality from multiple dimensions, and performs local and global optimization of primary equipment parameters, achieving comprehensive optimization of primary equipment parameters. In summary, this application comprehensively considers various factors affecting raw material loss rate and mica processed product quality, while also taking into account the influence of environmental temperature and humidity, greatly improving the utilization rate of mica raw materials and the quality of mica processed products, and reducing mica production costs.

[0066] Other features and advantages of the embodiments of this application will be described in detail in the following detailed description section. Attached Figure Description

[0067] Figure 1 A flowchart illustrating a method for optimizing mica material production based on big data analysis, provided for an embodiment of this application;

[0068] Figure 2 A flowchart illustrating a method for filtering core process parameters from initial equipment parameters, provided in an embodiment of this application;

[0069] Figure 3 This is a flowchart illustrating a method for globally correcting initial device parameters, provided in an embodiment of this application. Detailed Implementation

[0070] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for illustration and explanation of the embodiments of this application and are not intended to limit the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0071] It should be noted that if the embodiments of this application involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.

[0072] Furthermore, if the embodiments of this application involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0073] Figure 1 The illustration shows a flowchart of a method for optimizing mica material production based on big data analysis according to an embodiment of this application. Figure 2 The illustration shows a flowchart of a method for selecting core process parameters from initial equipment parameters according to an embodiment of this application. Figure 3 The illustration shows a flowchart of a method for globally correcting initial device parameters according to an embodiment of this application.

[0074] like Figure 1 As shown in the embodiments of this application, a method for optimizing mica material production based on big data analysis is provided. This method may include the following steps:

[0075] S101. Obtain the mica processing product type of the current material order through the mica material order system, and select the sample to be processed that is the same type as the mica processing product.

[0076] In this embodiment, the mica material order system can store and update orders from customers cooperating with mica factories in real time. The orders display the types of mica products the customer requires for processing, such as mica sheets, mica powder, mica plates, and irregularly shaped mica parts. The system allows users to query the mica product types for current material orders and select samples of the same type for processing. Processing these samples first effectively reduces waste of mica raw materials and saves costs. Furthermore, the system can also query historical mica product types from past material orders placed by the mica processing factory.

[0077] S102. Retrieve historical processing data and initial equipment parameters of the same type as the mica processing product from the preset mica processing database. The initial equipment parameters are the equipment parameters of all mica processing equipment in the mica processing process corresponding to the mica processing product type.

[0078] In this embodiment, the initial equipment parameters include core process parameters, mica equipment parameters, mica raw material data, and mica batching ratio. Core process parameters refer to mica purification parameters and mica grinding parameters. Mica equipment parameters refer to the processing equipment parameters used in the mica processing, such as the stroke and frequency of the jig used for coarse screening of mica. Mica raw material data includes the type of mica ore (e.g., biotite, phlogopite, muscovite, etc.) and mica density (e.g., muscovite density is 2.7 g / cm³). 3 -2.9g / cm 3 (etc.) The mica batching ratio refers to the proportion of mica ore and other raw materials required for processing mica materials. For example, in addition to mica, processing mica strips requires binders, solvents, silane coupling agents, catalysts, stabilizers, and other raw materials. The addition of these raw materials requires accurate proportioning. Insufficient addition of any one raw material will lead to a decrease in the electrical properties of the produced mica sheets, failing to meet the delivery standards of mica material orders, resulting in financial losses for the mica processing plant. First, historical processing data and initial equipment parameters of the same type as the mica product being processed will be retrieved from the preset mica processing database to prepare for subsequent correction of the initial equipment parameters.

[0079] S103. Based on the initial equipment parameters, process the sample to be processed using the mica processing equipment, collect mica processing data during the processing of the sample using multiple types of sensors preset in all the mica processing equipment, and detect the sample quality parameters after processing.

[0080] In this embodiment, the mica processing equipment first processes the sample to be processed using initial equipment parameters. Simultaneously, multiple types of sensors pre-installed within all the mica processing equipment collect mica processing data during the processing, and detect the sample quality parameters after processing. The main purpose of this step is to detect processing parameters that need to be modified in subsequent steps. These multiple types of sensors, including temperature sensors, weight sensors, and pH sensors, are installed inside all the mica processing equipment to detect temperature changes, weight changes, and pH changes during the mica processing. The mica processing data includes temperature change data, weight change data, and pH change data. In addition, all sensors also upload the detected data to a database in real time for storage.

[0081] S104. Classify and perform time-series feature analysis on the mica processing data, and select the core process parameters from the initial equipment parameters based on the analysis results of the time-series feature analysis.

[0082] In this embodiment, mica processing data refers to temperature, weight, and pH data during the mica processing process. First, this data is arranged according to the order of collection, converting it into time-series data. Then, the time-series data is classified to obtain mica weight change data, mica temperature change data, and mica pH change data. Since weight changes occur at each step of the mica processing process, the mica processing steps can be divided into nodes based on the mica weight change data. Then, based on the mica temperature and pH change data, the core process parameters in the initial equipment parameters are selected, namely, the mica purification parameters and the mica grinding parameters. The mica purification and grinding parameters are the core steps for subsequent correction. Because temperature and pH changes occur during the mica purification and grinding processes, these parameters can be selected based on the magnitude of these temperature and pH changes. This step aims to identify the parameters corresponding to steps in the mica processing process that are prone to mica raw material waste, facilitating the reduction of mica raw material loss rates in subsequent steps.

[0083] S105. Calculate the average value of historical mica processing data based on core process parameters, and use the average value as the core process standard value corresponding to the core process parameters.

[0084] In this embodiment, corresponding historical mica processing data is selected based on core process parameters. The mica weight change data involving the two steps of mica purification and mica grinding in the historical mica processing data is extracted. The average purification weight of the historical purification weight change data is calculated and used as the standard value of the purification process. Similarly, the average grinding weight of the historical grinding weight change data is calculated and used as the standard value of the grinding process. The core process standard value is obtained by integrating the purification process standard value and the grinding process standard value.

[0085] S106. Combining the core process standard values ​​with mica processing data, the mica raw material loss rate during the mica processing process is calculated.

[0086] In this embodiment, the difference between the standard value of the purification process and the historical purification weight change data in the mica processing data is calculated. Then, the difference is divided by the historical purification weight change data to obtain the mica raw material loss rate during the mica purification process. Similarly, the difference between the standard value of the grinding process and the historical grinding weight change data in the mica processing data is calculated. Then, the difference is divided by the historical grinding weight change data to obtain the mica raw material loss rate during the mica grinding process.

[0087] S107. Core process parameters are selected by calculating the correlation coefficient between mica raw material loss rate and core process parameters, and the selected core process parameters are output as local correction factors.

[0088] In this embodiment, the correlation coefficients between mica purification parameters, mica grinding parameters, and mica raw material loss rate are calculated respectively. Based on the correlation coefficients, parameters with a high mica raw material loss rate are selected, and then these selected parameters are output as local correction factors to facilitate subsequent adjustments to the core process parameters in the initial equipment parameters. Since mica processing environmental data (ambient temperature and humidity data) has a certain impact on the mica raw material loss rate, it is necessary to calculate the correlation coefficient between the mica processing environmental data and [other parameters], and determine whether the mica processing environmental data should be output as a local correction factor based on the obtained correlation coefficients.

[0089] S108. Obtain the mica product quality parameters corresponding to the mica processing product type through the preset database, calculate the influence degree dataset between the initial equipment parameters and the mica product quality parameters, prioritize the initial equipment parameters based on the influence degree dataset, and output the initial equipment parameters that have completed the priority classification as global correction factors.

[0090] In this embodiment, mica product quality parameters are obtained through a pre-set database. These parameters are used to measure the quality of the finished mica product. For example, the quality parameters for mica powder include sand content, loss on ignition, bulk density, moisture content, and whiteness. A dataset of the influence between the initial equipment parameters (including mica equipment parameters, mica raw material data, mica ingredient ratios, and mica product quality parameters) and the initial equipment parameters is calculated. Based on this dataset, the initial equipment parameters are prioritized, and high-priority parameters are corrected first. If the mica product quality standard parameters are met, further correction is unnecessary, saving time and reducing unnecessary resource waste. After prioritization, the initial equipment parameters are output as global correction factors.

[0091] S109. Combine local and global correction factors to correct the initial equipment parameters and obtain the optimal equipment parameters.

[0092] In this embodiment, the core process parameters and mica processing environment data are first corrected based on local correction factors. After the local correction is completed, the initial equipment parameters are corrected based on the global correction factors and their corresponding priorities. If the mica product quality standard parameters are met or three corrections are completed, the final correction result is output as the optimal equipment parameters.

[0093] In one embodiment, reference is made to Figure 2 The process of classifying and analyzing the time-series characteristics of mica processing data, and then selecting the core process parameters from the initial equipment parameters based on the analysis results, includes the following steps:

[0094] S201. Based on the sensor type of multiple types of sensors, perform feature classification on the mica processing data to obtain mica weight change data, mica temperature change data, and mica pH change data.

[0095] S202. Set an upper limit for the total number of inflection points based on the total number of processing steps included in the mica processing technology. Use the upper limit for the total number of inflection points as an algorithm constraint, and use the inflection point detection algorithm to detect inflection points in the mica weight change data to obtain the weight change inflection points distributed in the mica weight change data.

[0096] S203. Based on the inflection point of weight change, the process nodes of the mica processing technology are divided to obtain multiple mica processing sub-processes within the mica processing technology.

[0097] S204. Extract the maximum and minimum temperatures of mica temperature changes in each mica processing sub-process, calculate the difference between the maximum and minimum temperatures, and obtain the temperature difference.

[0098] S205. Extract the maximum and minimum values ​​of the acidity / alkalinity change data of mica in each mica processing sub-process, calculate the difference between the maximum and minimum values ​​of acidity / alkalinity, and obtain the acidity / alkalinity difference.

[0099] S206. If the temperature difference is greater than the preset temperature difference threshold, analyze whether the pH difference corresponding to the mica processing sub-process is greater than the preset pH difference threshold.

[0100] S207. If the pH difference corresponding to the mica processing sub-process is greater than the pH difference threshold, the initial equipment parameters corresponding to the mica processing sub-process are determined to be mica purification parameters.

[0101] S208. If the pH difference is less than or equal to the pH difference threshold, the initial equipment parameters corresponding to the mica processing sub-process are determined to be the mica grinding parameters.

[0102] S209. Integrate the mica purification parameters and mica grinding parameters to obtain the core process parameters.

[0103] In this embodiment, mica processing data refers to temperature, weight, and pH data during the mica processing process. First, these data are arranged according to the order of collection time and converted into data with time-series characteristics. Then, the time-series characteristic data are classified according to the sensor type of multiple types of sensors to obtain mica weight change data, mica temperature change data, and mica pH change data. Since weight changes occur at every step in the mica processing process, the mica processing technology can be divided into nodes based on the mica weight change data.

[0104] Inflection point detection algorithms are used to detect inflection points in mica weight change data. A control function is constructed based on the mica processing data. The process of detecting inflection points is to minimize the value of the control function. Since the loss function value of the control function is lower in uniform subsequences and higher in non-uniform subsequences, the total number of inflection points and their distribution in the mica weight change data can be calculated based on this principle. To prevent the total number of inflection points from being too large, a threshold for the total number of inflection points needs to be set based on the total number of processing steps included in the mica processing technology. This limits the total number of inflection points, for example, by setting the threshold to three times the total number of processing steps. Finally, the weight change inflection points distributed in the mica weight change data can be obtained.

[0105] Based on the obtained weight change inflection point, the mica processing technology is divided into process nodes, resulting in multiple mica processing sub-processes. The maximum and minimum temperatures of the mica temperature change data corresponding to each mica processing sub-process are extracted, and the difference between the maximum and minimum temperatures is calculated to obtain the temperature difference. The maximum and minimum pH values ​​of the mica pH change data corresponding to each mica processing sub-process are extracted, and the difference between the maximum and minimum pH values ​​is calculated to obtain the pH difference. Based on the temperature difference and pH difference, the mica purification parameters and mica grinding parameters included in the initial equipment parameters are selected.

[0106] If the temperature difference exceeds a preset threshold, the mica processing sub-processes with pH differences exceeding the preset threshold are selected, and their initial equipment parameters are determined to be mica purification parameters. This is because acidic or alkaline solutions or organic solvents are added during mica purification, resulting in chemical reactions that cause temperature and pH changes. Based on these changes, the initial equipment parameters for these sub-processes are identified as mica purification parameters. Conversely, mica processing sub-processes with pH differences less than or equal to a preset threshold are selected, and their initial equipment parameters are determined to be mica grinding parameters. This is because continuous friction during mica grinding causes temperature changes, and these changes are used to determine the initial equipment parameters for these sub-processes. Specifically, mica grinding parameters refer to the vibration frequency, grinding time, and grinding method involved in the mica grinding process, while mica purification parameters refer to the solution ratio, purification time, and purification screen parameters involved in the mica purification process. Finally, the mica purification parameters and mica grinding parameters are integrated to obtain the core process parameters.

[0107] In one embodiment, calculating the average value of historical mica processing data based on core process parameters and using the average value as the core process standard value corresponding to the core process parameters includes the following steps:

[0108] Extract historical purification weight change data from the historical processing data corresponding to the mica purification parameters, obtain historical purification weight change data, calculate the average purification weight of the historical purification weight change data, and use the average purification weight as the standard value of the purification process.

[0109] Extract historical grinding weight variation data from the historical processing data corresponding to the mica grinding parameters, obtain historical grinding weight variation data, calculate the average grinding weight of historical grinding weight variation data, and use the average grinding weight as the standard value of the grinding process;

[0110] The standard values ​​of the purification process and the grinding process are integrated to obtain the standard values ​​of the core process parameters.

[0111] In this embodiment, the corresponding historical mica processing data is selected based on the core process parameters. The historical processing data is first arranged in chronological order, and time-series features are extracted. The mica weight change data involving the two steps of mica purification and mica grinding in the historical mica processing data with completed time-series feature extraction are selected. The average purification weight of the historical purification weight change data is calculated, and the calculated average purification weight is used as the standard value of the purification process. Similarly, the average grinding weight of the historical grinding weight change data is calculated, and the calculated average grinding weight is used as the standard value of the grinding process. The core process standard value is obtained by integrating the purification process standard value and the grinding process standard value.

[0112] In one embodiment, the process of calculating the correlation coefficient between the mica raw material loss rate and the core process parameters, screening the core process parameters based on the correlation coefficient, and outputting the screened core process parameters as local correction factors includes the following steps:

[0113] Calculate the core parameter correlation coefficient set between mica core process parameters and mica raw material loss rate, screen out the core parameter correlation coefficients in the core parameter correlation coefficient set that are greater than the preset core parameter correlation coefficient threshold, and output the mica core process parameters corresponding to the core parameter correlation coefficients as local correction factors.

[0114] Based on various types of sensors pre-installed in the mica processing plant, mica processing environment data is obtained, including mica processing environment temperature data and mica processing environment humidity data.

[0115] A multiple linear regression equation with mica raw material loss rate as the dependent variable was established. Mica processing ambient temperature data and mica processing ambient humidity data were input into the multiple linear regression equation as independent variables, and the ambient temperature correlation coefficient and ambient humidity correlation coefficient were calculated.

[0116] If the environmental temperature correlation coefficient is greater than the preset environmental correlation coefficient threshold, the mica processing environmental temperature data will be output as a local correction factor.

[0117] If the environmental humidity correlation coefficient is greater than the environmental correlation coefficient threshold, the environmental humidity data of mica processing will be output as a local correction factor.

[0118] In this embodiment, firstly, the set of correlation coefficients between the core process parameters of mica and the mica raw material loss rate is calculated. This step aims to quantify the influence of the core process parameters on the raw material loss rate. Specifically, the Pearson correlation coefficient method can be used. For each core process parameter, such as grinding time, vibration frequency, and solution ratio, a correlation coefficient is calculated with the raw material loss rate. The calculation formula is as follows:

[0119]

[0120] Where X represents the core process parameter, Y represents the raw material loss rate, and X and These are the average values ​​of the corresponding variables. For example, suppose the correlation coefficient between grinding time and raw material loss rate is 0.85, and the correlation coefficient between vibration frequency and 0.72. After calculation, a set containing the correlation coefficients of all core process parameters will be obtained. Next, a suitable correlation coefficient threshold is set, such as 0.7, and correlation coefficients greater than this threshold and their corresponding core process parameters are selected. In this example, both grinding time and vibration frequency will be selected. These selected parameters will be output as local correction factors for subsequent process optimization. The effect of this step is to identify the core process parameters that have the most significant impact on raw material loss rate, providing a clear direction for subsequent parameter optimization and helping to improve the efficiency of mica processing and reduce raw material loss.

[0121] Then, based on various types of sensors pre-installed in the mica processing plant, environmental data for the mica processing environment is acquired. This data mainly includes temperature and humidity data for the mica processing environment. During implementation, temperature and humidity sensors can be installed at key locations in the processing workshop, such as the raw material storage area, grinding area, and purification area. These sensors need to have high accuracy and stability, capable of recording environmental parameters continuously in real time. For example, a PT100 platinum resistance temperature sensor can be used to measure temperature with an accuracy of ±0.1℃; a capacitive humidity sensor can be used to measure relative humidity with an accuracy of ±2%RH. The sensor data acquisition frequency can be set to once per minute to capture subtle changes in environmental parameters. The data acquisition system preprocesses this raw data, including removing outliers and smoothing, and then stores it in a database. The final mica processing environment dataset includes the following information: timestamp, location identifier, temperature value (℃), and relative humidity value (%RH). The effect of this step is to provide high-quality and comprehensive environmental parameter data for subsequent analysis, reflecting the actual environmental conditions during the mica processing process.

[0122] Next, a multiple linear regression equation is established with the mica raw material loss rate as the dependent variable. The purpose of this step is to quantify the impact of ambient temperature and humidity on the raw material loss rate. The general form of the multiple linear regression equation is: Y = β0 + β1X1 + β2X2 + ε, where Y represents the mica raw material loss rate, X1 represents the ambient temperature, X2 represents the ambient humidity, β0 is the intercept, β1 and β2 are the regression coefficients, and ε is the error term. In implementation, sufficient historical data is first required, including temperature and humidity records under different environmental conditions and the corresponding raw material loss rates. Then, the regression coefficients are estimated using the least squares method. Statistical software such as R or the statsmodels library in Python can be used for calculation. For example, suppose the obtained regression equation is: Loss Rate (%) = 2.5 + 0.15 * Temperature (°C) + 0.08 * Humidity (%). This means that for every 1°C increase in temperature, the loss rate increases by an average of 0.15%; for every 1% increase in relative humidity, the loss rate increases by an average of 0.08%. Next, the ambient temperature and humidity data from mica processing are input as independent variables into this regression equation. By calculating the t-statistic of the partial regression coefficients and their corresponding p-values, the correlation coefficients for ambient temperature and humidity can be obtained.

[0123] Next, it is determined whether ambient temperature has a significant impact on the raw material loss rate. In implementation, a suitable environmental correlation coefficient threshold needs to be set first. This threshold is usually chosen based on industry experience or statistical standards, for example, it can be set to 0.3. Then, the ambient temperature correlation coefficient calculated in the previous step is compared with this threshold. If the ambient temperature correlation coefficient is greater than the threshold, for example, a correlation coefficient of 0.42, which is greater than the set threshold of 0.3, then the ambient temperature is considered to have a significant impact on the raw material loss rate. In this case, the mica processing ambient temperature data is output as a local correction factor. This means that in subsequent process optimization, the temperature of the processing environment needs to be carefully monitored and controlled. Specific outputs include information such as the temperature measurement range, fluctuation range, and optimal temperature range. For example, the conclusion might be that the processing ambient temperature should be controlled between 20-25℃, and the temperature fluctuation should not exceed ±2℃.

[0124] Finally, it is determined whether the environmental humidity correlation coefficient is greater than the environmental correlation coefficient threshold. The purpose and principle of this step are similar to determining the temperature correlation coefficient, aiming to determine whether environmental humidity has a significant impact on the raw material loss rate. In practice, the same environmental correlation coefficient threshold (e.g., 0.3) is used to compare the calculated environmental humidity correlation coefficient. If the environmental humidity correlation coefficient is greater than the threshold, for example, a correlation coefficient of 0.38, which is greater than the set threshold of 0.3, then environmental humidity is considered to have a significant impact on the raw material loss rate. In this case, the mica processing environmental humidity data is output as a local correction factor. The output includes the appropriate humidity range and control accuracy requirements. For example, the conclusion is that the relative humidity of the processing environment should be controlled between 45-55%, and the humidity fluctuation should not exceed ±5% RH. The effect of this step is to identify environmental humidity as another potential key influencing factor, providing a scientific basis for the subsequent humidity control strategy. Combining the two factors of temperature and humidity allows for a more comprehensive optimization of the environmental conditions for mica processing, thereby effectively reducing the raw material loss rate and improving product quality and production efficiency.

[0125] In one embodiment, calculating the set of core parameter correlation coefficients between mica core process parameters and mica raw material loss rate, selecting core parameter correlation coefficients in the set that are greater than a preset core parameter correlation coefficient threshold, and outputting the core process parameters corresponding to the core parameter correlation coefficients as local correction factors includes the following steps:

[0126] A correlation coefficient model was established using the mica raw material loss rate as the independent variable;

[0127] The vibration frequency, grinding time, and grinding method included in the mica grinding parameters are input as independent variables into the correlation coefficient model to obtain the grinding correlation coefficient set;

[0128] Filter out grinding correlation coefficients that are concentrated above the preset correlation coefficient threshold, and output the mica grinding parameters corresponding to the grinding correlation coefficients as local correction factors.

[0129] The solution ratio, purification time, and purification screen parameters included in the mica purification parameters were respectively input into the correlation coefficient model as independent variables to obtain the purification correlation coefficient set;

[0130] The purification correlation coefficients that are concentrated above the preset correlation coefficient threshold are selected, and the mica purification parameters corresponding to the purification correlation coefficients are output as local correction factors.

[0131] In this embodiment, it is first necessary to define the mica grinding parameters, including vibration frequency, grinding time, and grinding method. Vibration frequency is typically measured in Hertz (Hz), with a possible range of 20-100 Hz; grinding time is measured in minutes, with a possible range of 30-180 minutes; grinding methods may include dry grinding, wet grinding, or a combination of both. The correlation coefficient model typically employs the Pearson correlation coefficient method. In practice, it is necessary to first collect a large amount of historical data, including mica processing results under different vibration frequencies, grinding times, and grinding methods. For example, (50 Hz, 60 min, dry grinding, 95% purity), (75 Hz, 90 min, wet grinding, 97% purity), etc. Then, the correlation coefficient between each grinding parameter and the processing result is calculated. Assuming the calculated correlation coefficients are: vibration frequency 0.75, grinding time 0.82, and grinding method (after numerical conversion) 0.68, these correlation coefficients constitute the grinding correlation coefficient set. The effect of this step is to quantify the degree of influence of each grinding parameter on the mica processing result. The obtained set of correlation coefficients clearly shows which parameters have a greater impact on the processing results. For example, in the hypothetical data above, the correlation coefficient for grinding time is the highest, meaning it is likely the most critical grinding parameter. This provides a clear direction for subsequent parameter optimization, helping to improve the efficiency of the mica grinding process and product quality.

[0132] Then, a suitable correlation coefficient threshold is set to distinguish between parameters with significant and insignificant effects. During implementation, the correlation coefficient of each grinding parameter obtained in the previous step is compared with the set threshold. Assuming the threshold is set to 0.7, based on the assumed data from the previous step, the correlation coefficients of vibration frequency (0.75) and grinding time (0.82) both exceed the threshold, while the grinding method (0.68) does not. Therefore, vibration frequency and grinding time will be selected as parameters with significant effects and output as local correction factors. The output local correction factors may include the following information: vibration frequency: correlation coefficient 0.75, suggested adjustment range 50-80Hz; grinding time: correlation coefficient 0.82, suggested adjustment range 60-120 minutes. These outputs will be used for subsequent process parameter optimization. For example, adjusting the grinding time can be prioritized because it has the highest correlation coefficient and has the greatest impact on the processing results. Adjusting the vibration frequency can be considered secondarily. The grinding method, due to its low correlation coefficient, does not need to be adjusted for the time being. The effect of this step is to select the most critical parameters from numerous grinding parameters, simplifying the subsequent optimization process. By focusing on these key parameters, the mica grinding process can be improved more effectively, product quality enhanced, and unnecessary parameter adjustments reduced, saving time and resources. Furthermore, this data-driven parameter selection method provides a scientific basis for process optimization, reducing errors that may arise from subjective judgment.

[0133] Next, the mica purification parameters are input as independent variables into the correlation coefficient model to obtain a set of purification correlation coefficients. The main mica purification parameters include solution ratio, purification time, and purification sieve parameters. Solution ratio typically refers to the concentration of the pickling solution, which may range from 5% to 30%; purification time is measured in hours, with a possible range of 2 to 12 hours; and purification sieve parameters refer to the mesh size of the sieve, which may range from 100 to 400 mesh. The correlation coefficient model also uses the Pearson correlation coefficient method. In implementation, it is first necessary to collect a large amount of historical data, including mica purification results under different solution ratios, purification times, and purification sieve parameters. For example, (15%, 6h, 200 mesh, 98% purity), (20%, 8h, 300 mesh, 99% purity), etc. Then, the correlation coefficient between each purification parameter and the purification result is calculated separately. Assuming the calculated correlation coefficients are: solution ratio 0.78, purification time 0.85, and purification sieve parameter 0.72, these coefficients constitute the purification correlation coefficient set. Several special cases need to be considered during the calculation. For example, if the purification sieve parameter is a discrete mesh size, it needs to be converted to a continuous pore size (in micrometers) before correlation calculation. Furthermore, if there are interactions between certain parameters, interaction terms should be considered for analysis. This step quantifies the influence of each purification parameter on the mica purification result. The obtained correlation coefficient set clearly shows which parameters have a greater impact on the purification result. For example, in the hypothetical data above, the purification time has the highest correlation coefficient, meaning it is likely the most critical purification parameter. This provides a clear direction for subsequent parameter optimization, helping to improve the efficiency of the mica purification process and product quality.

[0134] Next, the correlation coefficients of each purification parameter are compared with the set thresholds. Assuming a threshold of 0.75, based on the hypothetical data in S3, the correlation coefficients of solution ratio (0.78) and purification time (0.85) both exceed the threshold, while the purification sieve parameter (0.72) does not. Therefore, solution ratio and purification time will be selected as significantly influencing parameters and output as local correction factors. The output local correction factors may include the following information: Solution ratio: correlation coefficient 0.78, suggested adjustment range 15%-25%; Purification time: correlation coefficient 0.85, suggested adjustment range 6-10 hours. These outputs will be used for subsequent process parameter optimization. For example, adjusting the purification time can be prioritized because it has the highest correlation coefficient and has the greatest impact on the purification results. Adjusting the solution ratio should be considered second. The purification sieve parameter, due to its low correlation coefficient, does not require adjustment for now. In practical applications, the feasibility and economy of parameter adjustments also need to be considered. For example, while increasing the purification time may improve purity, it will also increase energy consumption and production costs. Therefore, when determining the final parameter adjustment plan, a balance needs to be found between product quality and production efficiency.

[0135] In one embodiment, the process involves obtaining mica product quality parameters from a pre-set database, calculating the influence dataset between initial equipment parameters and mica product quality parameters, prioritizing the initial equipment parameters based on the influence dataset, and outputting the prioritized initial equipment parameters as global correction factors. The steps include:

[0136] Obtain mica product quality parameters corresponding to the mica processing product type from a pre-set database;

[0137] Establish a mica regression model with mica product quality parameters as the dependent variable and initial equipment parameters as the independent variable;

[0138] The mica equipment parameters, mica raw material data, and mica batching ratio included in the initial equipment parameters are respectively input into the mica regression model as independent variables to obtain the influence degree data. The influence degree data are then integrated to obtain the influence degree dataset.

[0139] The initial device parameter corresponding to the largest impact value in the impact value dataset is output as the first priority global correction factor.

[0140] The initial device parameters corresponding to the second largest impact data in the impact dataset are output as the second priority global correction factors.

[0141] The initial device parameters corresponding to the three largest impact data in the impact dataset are output as the third priority global correction factor, where the first priority is greater than the second priority, and the second priority is greater than the third priority.

[0142] In this embodiment, the preset database is typically a structured information storage system, employing a relational database (such as MySQL or Oracle) or a non-relational database (such as MongoDB). The database stores detailed information on various mica processed products, including but not limited to product type, quality parameters, and production processes. Mica product quality parameters include multiple aspects, such as purity, flake size, thickness, heat resistance, and insulation. For example, for mica powder products, quality parameters include: purity (≥98%), particle size distribution (D50≤45μm), whiteness (≥85%), and moisture content (≤0.5%). For mica flake products, these include: purity (≥99%), flake size (10-100mm), thickness (0.02-0.1mm), and heat resistance temperature (≥800℃). During implementation, it is first necessary to determine the type of mica product being processed, such as mica powder, mica flakes, or mica paper. Then, the quality parameters corresponding to that product type are retrieved from the preset database using SQL queries or other database query methods. These acquired quality parameters will serve as the dependent variables in subsequent steps for building regression models, analyzing the impact of initial equipment parameters on product quality. This not only provides reliable basic data for subsequent regression analysis but also helps ensure the practicality and reliability of the final optimization results. Furthermore, this database-based parameter acquisition method facilitates future data updates and maintenance, ensuring that the analysis process always uses the latest and most accurate quality standards.

[0143] Establish a mica regression model with mica product quality parameters as the dependent variable and initial equipment parameters as the independent variable. The choice of regression model depends on the complexity of the relationship between the variables; multiple linear regression, multinomial regression, or more complex nonlinear regression models may be used. For complex industrial processes like mica processing, multiple linear regression is usually the first choice because it is simple to understand and provides a sufficiently good approximation in many cases. In implementation, it is first necessary to collect sufficient historical data, including product quality data obtained under different equipment parameter settings. For example, consider the following dataset:

[0144] (Temperature: 200℃, Pressure: 5MPa, Rotation speed: 1000rpm) → Purity: 98.5%

[0145] (Temperature: 220℃, Pressure: 6MPa, Rotation speed: 1200rpm) → Purity: 99.0%

[0146] (Temperature: 180℃, Pressure: 4MPa, Rotation speed: 800rpm) → Purity: 97.8%

[0147] Then, the regression coefficients are estimated using the least squares method or other regression analysis methods. Statistical software such as R, Python's statsmodels library, or SPSS can be used for calculation. Assume the resulting regression equation is:

[0148] Purity (%) = 90 + 0.03 * Temperature (°C) + 0.5 * Pressure (MPa) + 0.001 * Rotation speed (rpm)

[0149] This equation shows that for every 1°C increase in temperature, the purity increases by an average of 0.03%; for every 1 MPa increase in pressure, the purity increases by an average of 0.5%; and for every 1 rpm increase in rotational speed, the purity increases by an average of 0.001%.

[0150] After the model is built, model diagnostics are needed, including checking the normality, homoscedasticity, and multicollinearity of the residuals. The coefficient of determination R-squared also needs to be calculated. 2 To evaluate the goodness of fit of the model.

[0151] Next, the mica equipment parameters, mica raw material data, and mica ingredient ratios included in the initial equipment parameters are input as independent variables into the mica regression model to obtain the influence level data. This influence level data is then integrated to obtain the influence level dataset. The core of this step is to use the regression model established in the previous steps to quantify the influence of each initial equipment parameter on the quality of the mica product. The initial equipment parameters can be divided into three categories:

[0152] Mica equipment parameters: such as grinding mill speed, pressure, temperature, etc.

[0153] Data on mica raw materials: such as purity, moisture content, and particle size distribution.

[0154] Mica ingredient ratios: such as the mixing ratio of different grades of mica, the proportion of additives, etc.

[0155] During implementation, the specific indicators for each type of parameter must first be determined. For example:

[0156] Mica equipment parameters: grinding mill speed (500-2000rpm), pressure (1-10MPa), temperature (100-300℃).

[0157] Mica raw material data: raw material purity (95-99%), moisture content (0.1-1%), average particle size (10-100μm).

[0158] Mica ingredient ratio: high-purity mica (60-90%), additives (0.5-2%).

[0159] The following results can be obtained through calculation using the mica regression model:

[0160] Grinding mill speed: β = 0.002 (purity increases by 0.002% for every 1 rpm increase)

[0161] Pressure: β = 0.5 (purity increases by 0.5% for every 1 MPa increase)

[0162] Temperature: β = 0.03 (purity increases by 0.03% for every 1°C increase)

[0163] Raw material purity: β = 0.8 (for every 1% increase, product purity increases by 0.8%)

[0164] Moisture content: β = -0.3 (For every 1% increase, product purity decreases by 0.3%)

[0165] Average particle size: β = -0.01 (for every 1 μm increase, product purity decreases by 0.01%)

[0166] High-purity mica ratio: β = 0.1 (for every 1% increase, product purity increases by 0.1%)

[0167] Additive ratio: β = -0.2 (for every 1% increase, product purity decreases by 0.2%)

[0168] These beta values ​​constitute the impact dataset.

[0169] The effect of this step is to obtain a quantified dataset of impact levels, clearly demonstrating the magnitude and direction of the influence of each initial equipment parameter on product quality. This provides direct data support for subsequent parameter optimization, helping to identify the parameters with the most significant impact on product quality, thereby enabling the development of targeted optimization strategies.

[0170] The initial device parameter corresponding to the largest impact value in the impact dataset is output as the first priority global correction factor. The core of this step is to identify the device parameter that has the most significant impact on mica product quality and to prioritize it as the most important factor in the optimization process. In implementation, the impact dataset obtained in the above steps first needs to be sorted. The sorting is based on the absolute value of the impact value of each parameter; because whether the impact is positive or negative, a large impact indicates that the parameter has a significant influence on product quality.

[0171] Based on the calculated data from the above steps, the impact datasets are ranked as follows:

[0172] 1. Raw material purity: β = 0.8

[0173] 2. Pressure: β = 0.5

[0174] 3. Moisture content: β = -0.3

[0175] 4. Temperature: β = 0.03

[0176] 5. Additive ratio: β = -0.2

[0177] 6. Proportion of high-purity mica: β = 0.1

[0178] 7. Average particle size: β = -0.01

[0179] 8. Grinding mill speed: β = 0.002

[0180] According to the ranking results, the influence of raw material purity is the greatest (β=0.8), so raw material purity is output as the first priority global correction factor.

[0181] The output information includes:

[0182] Parameter Name: Raw Material Purity

[0183] Impact level: 0.8

[0184] Impact direction: Positive (increasing the purity of raw materials will improve the purity of the product).

[0185] Suggested adjustment range: 95-99%

[0186] Optimization suggestion: While ensuring economic feasibility, improve the purity of raw materials as much as possible.

[0187] The effect of this step is to identify the equipment parameters that have the most significant impact on product quality, providing a clear direction for subsequent process optimization. By prioritizing the adjustment of this parameter, product quality can be improved most quickly and effectively. This data-driven prioritization method not only improves the efficiency of the optimization process but also reduces errors caused by subjective judgment. Furthermore, treating the most important parameter as a global correction factor means that this parameter needs to be closely monitored and controlled throughout the entire production process, which helps maintain the stability and consistency of product quality.

[0188] Next, the initial equipment parameter corresponding to the second-largest impact data in the impact dataset will be output as the second-priority global correction factor. The core of this step is to identify the second most significant equipment parameter affecting mica product quality and consider it as the second-priority factor in the optimization process. Continuing with the ranking results from the previous steps, the parameter corresponding to the second-largest impact data is pressure (β = 0.5). Therefore, pressure will be output as the second-priority global correction factor. Using pressure as the second-priority global correction factor means that throughout the entire production process, in addition to focusing on raw material purity, it is also necessary to focus on controlling and monitoring the pressure parameter.

[0189] Next, the initial equipment parameter corresponding to the third most influential data point in the impact dataset will be output as the third priority global correction factor, while ensuring that the first priority is greater than the second, and the second priority is greater than the third. The core of this step is to identify the third most significant equipment parameter affecting mica product quality and include it in the optimization considerations, while establishing a clear priority structure. Continuing with the previous ranking results, the parameter corresponding to the third most influential data point is moisture content (β = -0.3). Therefore, moisture content will be output as the third priority global correction factor. During implementation, it is important to note that adjusting the moisture content will affect other parameters, such as material flowability or processing temperature. Therefore, when adjusting the moisture content, its impact on the entire process needs to be comprehensively considered. Establishing a clear priority structure (first priority > second priority > third priority) helps in making reasonable decisions with limited resources.

[0190] In one embodiment, the process of correcting the initial device parameters by combining local and global correction factors to obtain the optimal device parameters includes the following steps:

[0191] After analyzing the local correction factors, local corrections are made to the core process parameters and mica processing environment data contained in the primary equipment parameters;

[0192] The initial equipment parameters, which have undergone local correction, are globally corrected based on global correction factors to obtain the optimal equipment parameters.

[0193] In this embodiment, the previously determined local correction factors need to be analyzed in detail first. These local correction factors include core process parameters such as grinding time, vibration frequency, solution ratio, purification time, and purification screen parameters, as well as environmental data such as temperature and humidity. The analysis process involves an in-depth analysis of the influence mechanism, adjustment range, and interaction of each factor. For example, for the core process parameter of grinding time, the analysis includes: (1) Influence mechanism: Grinding time directly affects the fineness and uniformity of mica particles. Too short a time will lead to insufficient grinding, while too long a time will cause over-grinding and energy waste. (2) Adjustment range: Based on historical data and equipment capabilities, the adjustment range is 30-180 minutes. (3) Interaction: Grinding time interacts with parameters such as vibration frequency and grinding media, and needs to be considered in a coordinated manner. For the processing environment data such as ambient temperature, the analysis includes: (1) Influence mechanism: Temperature affects the brittleness of mica and the performance of processing equipment. Too high a temperature will cause mica to become over-dried and brittle, while too low a temperature will affect the normal operation of the equipment. (2) Adjustment range: Based on the physical properties of mica and equipment requirements, the adjustment range is 18-28℃. (3) Interaction: Temperature and humidity are closely related and need to be considered at the same time.

[0194] After analysis, local corrections are performed. This process employs a fine-tuning method, involving small, gradual adjustments to the original parameters. Specific implementation methods can include orthogonal experimental design or response surface methodology. Taking grinding time as an example, a three-level orthogonal experiment can be designed: L9(3^4), where the three grinding time levels can be set to 60 minutes, 90 minutes, and 120 minutes. Three other relevant factors are also considered, such as vibration frequency, grinding media size, and grinding media material. Through these nine sets of experiments, the optimal grinding time level can be determined. For ambient temperature, response surface methodology can be used. A central composite design is designed with a temperature range of 18-28℃, while also considering humidity factors (e.g., 40-60% RH). By fitting a quadratic response surface model, the optimal temperature and humidity combination can be obtained. The effectiveness of the correction is evaluated by comparing product quality parameters before and after the correction. For example, the purity, particle size distribution, and whiteness of mica before and after the correction can be compared. If the corrected parameters significantly improve these indicators, the local correction is considered effective.

[0195] The initial equipment parameters, after local correction, are globally corrected based on global correction factors to obtain the optimal equipment parameters. This step combines the results of the previous local correction with the global correction factors, comprehensively considering the influence of each parameter, and finally determining the optimal combination of equipment parameters. Global correction factors typically include several key parameters that have the most significant impact on product quality, such as raw material purity, pressure, and temperature. The priority of these factors has been determined in previous steps. The purpose of global correction is to optimize all parameters as a whole while meeting the requirements of these key factors. An effective method for implementing global correction is to use a multi-objective optimization algorithm, such as a genetic algorithm or particle swarm optimization algorithm. Taking a genetic algorithm as an example, its implementation steps are as follows:

[0196] 1. Encoding: Encode all parameters that need optimization into a chromosome. For example, parameters such as raw material purity, pressure, temperature, and grinding time can be encoded into a chromosome.

[0197] 2. Initialize the population: Generate a set of initial solutions (usually 100-200) based on the locally corrected parameters.

[0198] 3. Fitness Evaluation: Define the fitness function, which typically includes product quality indicators (such as purity, particle size) and production efficiency indicators (such as output, energy consumption). For example:

[0199] Fit = w1 * Purity + w2 * (1 / Particle Size) + w3 * Yield - w4 * Energy Consumption

[0200] Among them, w1, w2, w3, and w4 are weighting coefficients, which need to be determined according to actual production needs.

[0201] 4. Selection: Select superior individuals based on fitness. A roulette wheel selection method can be used.

[0202] 5. Crossover and mutation: New parameter combinations are generated through crossover operations, and new properties are introduced through mutation operations.

[0203] 6. Repeat steps 3-5 until the termination condition is met (such as the number of iterations reaching a preset value or no significant improvement in the optimal solution over multiple generations).

[0204] Finally, the effectiveness of this set of optimal parameters needs to be verified in actual production. A small-scale trial production can be conducted first; if the results meet expectations, it can be gradually expanded to full-scale production. Simultaneously, a continuous monitoring mechanism should be established to promptly identify and resolve any problems that arise. The effect of this step is to obtain a set of globally optimal equipment parameters. These parameters not only consider fine-tuning in local areas but also take into account key global factors, enabling the optimization of production efficiency and costs while ensuring product quality.

[0205] In one embodiment, reference is made to Figure 3 The process of globally correcting the initial equipment parameters that have undergone local correction based on global correction factors to obtain the optimal equipment parameters includes the following steps:

[0206] S301. Obtain the mica product quality standard parameters corresponding to the mica processing product type from the preset database;

[0207] S302. Calculate the standard deviation between the quality parameters of mica products and the standard parameters of mica products;

[0208] S303. Optimize the initial device parameters corresponding to the global correction factors of the first priority to obtain the first device parameters;

[0209] S304. Calculate the first difference between the quality parameters of the mica product obtained from processing according to the first equipment parameters and the quality parameters of the mica production standard. If the first difference is less than the preset difference threshold, the first equipment parameters are output as the optimal equipment parameters.

[0210] S305. If the first difference is greater than or equal to the difference threshold, the initial device parameters corresponding to the global correction factor of the second priority are optimized to obtain the second device parameters.

[0211] S306. Calculate the second difference between the quality parameters of the mica product obtained from processing according to the second equipment parameters and the quality parameters of the mica production standard. If the second difference is less than the difference threshold, the second mica equipment parameters are output as the optimal equipment parameters.

[0212] S307. If the second difference is greater than or equal to the difference threshold, the initial device parameters corresponding to the global correction factor of the third priority are optimized to obtain the third device parameters, and the third device parameters are output as the optimal device parameters.

[0213] In this embodiment, the preset database is typically a structured information storage system, employing a relational database (such as MySQL or Oracle) or a non-relational database (such as MongoDB). The database stores detailed quality standards for various mica processed products, derived from industry specifications, customer requirements, or internal company quality control standards. During implementation, the type of mica product to be processed, such as mica powder, mica flakes, or mica paper, must first be determined. Then, the quality standard parameters corresponding to this product type are retrieved from the preset database using SQL queries or other database query methods. These obtained quality standard parameters will serve as the benchmark for evaluating product quality in subsequent steps.

[0214] Next, we calculate the standard deviation between the mica product quality parameters and the standard mica product quality parameters. The purpose of this step is to quantify the gap between the current product quality and the standard, providing a basis for subsequent parameter optimization. Standard deviation is a statistical concept used to measure the dispersion of a set of data. Here, it reflects the degree of difference between the actual product quality and the standard quality. The formula for calculating the standard deviation is as follows:

[0215]

[0216] Where s is the standard deviation and xi is the value of each sample. is the sample mean, and n is the sample size.

[0217] In practice, it is necessary to calculate the standard deviation for multiple quality parameters separately. For example, suppose we have the following data:

[0218] Actual purity: 98.5%, standard purity: ≥99%; actual particle size distribution (D50): 48μm, standard particle size distribution (D50): ≤45μm; actual whiteness: 88%, standard whiteness: ≥90%; actual moisture content: 0.6%, standard moisture content: ≤0.5%.

[0219] For each parameter, calculate its difference from the standard value, and then calculate the standard deviation of these differences. Note that for standards of type "≥" or "≤", the standard value can be used as a benchmark, and the deviation of the actual value from the benchmark can be calculated. The calculated standard deviations are as follows: Purity standard deviation: 0.5%; Particle size distribution standard deviation: 3μm; Whiteness standard deviation: 2%; Moisture content standard deviation: 0.1%.

[0220] Then, the initial equipment parameters corresponding to the first priority global correction factors are optimized to obtain the first equipment parameters. The core of this step is to optimize the parameters with the greatest impact in order to quickly improve product quality. The first priority global correction factors are usually the parameters with the most significant impact on product quality, as determined in the previous analysis, such as raw material purity or key process parameters. The optimization process can employ various methods, such as gradient descent, Newton's method, or more complex optimization algorithms. Taking gradient descent as an example, its basic idea is to iterate along the gradient direction of the objective function until a local optimum is found. The specific implementation steps are as follows:

[0221] 1. Define the objective function: usually the difference between product quality parameters and standard parameters, such as mean square error.

[0222] 2. Calculate the gradient of the objective function with respect to the optimization parameters.

[0223] 3. Update parameters along the gradient direction: Where α is the learning rate. It is the gradient.

[0224] 4. Repeat steps 2-3 until the termination condition is met (such as the number of iterations reaching a preset value or the gradient change being very small).

[0225] For example, suppose the first priority global correction factor is raw material purity, with an initial value of 98%. Through optimization, the following results are obtained: Iteration 1: 98.2% (objective function value: 0.8); Iteration 2: 98.5% (objective function value: 0.5); Iteration 3: 98.8% (objective function value: 0.2); Iteration 4: 99.0% (objective function value: 0.1). In the final first equipment parameter, the raw material purity is optimized to 99.0%. The effectiveness of this step lies in optimizing the most critical parameter; because it focuses on the parameter with the greatest impact, the optimization effect is often quite significant.

[0226] Next, the first difference between the mica product quality parameters obtained from the first equipment parameters and the standard mica production quality parameters is calculated. If this first difference is less than a preset difference threshold, the first equipment parameters are output as the optimal equipment parameters. The core of this step is to evaluate the effect of optimizing the first priority parameters and determine whether further optimization is needed. First, trial production or simulation calculations are performed using the optimized first equipment parameters to obtain new mica product quality parameters. Then, these parameters are compared with the standard quality parameters, and the difference is calculated. The difference can be calculated using various methods, such as Euclidean distance or weighted average difference. For example, suppose the following results are obtained after optimization:

[0227] Actual purity: 99.1%, Standard purity: ≥99%; Actual particle size distribution (D50): 46μm, Standard particle size distribution (D50): ≤45μm; Actual whiteness: 89%, Standard whiteness: ≥90%; Actual moisture content: 0.5%, Standard moisture content: ≤0.5%

[0228] The relative difference between each parameter can be calculated, and then the average can be taken:

[0229] Purity difference: (99.1% - 99%) / 99% = 0.1%; Particle size difference: (46μm - 45μm) / 45μm = 2.2%; Whiteness difference: (90% - 89%) / 90% = 1.1%; Moisture content difference: (0.5% - 0.5%) / 0.5% = 0%; Average difference = (0.1% + 2.2% + 1.1% + 0%) / 4 = 0.85%.

[0230] Then, this average difference is compared with a preset difference threshold. Assuming the preset difference threshold is 1%, then 0.85% < 1%, so the first equipment parameter can be output as the optimal equipment parameter. If the first difference is greater than or equal to the difference threshold, the initial equipment parameter corresponding to the second priority global correction factor is optimized to obtain the second equipment parameter. The second priority global correction factor is usually a parameter with a secondary impact on product quality, determined in previous analyses, such as pressure, temperature, or a key process parameter. The optimization process can use methods similar to step S303, such as gradient descent, Newton's method, or other optimization algorithms. For example, assuming the second priority global correction factor is pressure, with an initial value of 5 MPa, the optimization yields the following results: Iteration 1: 5.5 MPa (objective function value: 0.7); Iteration 2: 6.0 MPa (objective function value: 0.4); Iteration 3: 6.3 MPa (objective function value: 0.2); Iteration 4: 6.5 MPa (objective function value: 0.1). In the final second set of equipment parameters, the pressure was optimized to 6.5 MPa. The effect of this step is that, when the optimization of the first priority parameter is insufficient, optimizing the less important parameter further improves product quality.

[0231] Calculate the second difference between the mica product quality parameters obtained from processing using the second equipment parameters and the standard mica production quality parameters. If the second difference is less than a threshold, the second mica equipment parameters are output as the optimal equipment parameters. Specifically, first, trial production or simulation calculations are performed using the optimized second equipment parameters to obtain new mica product quality parameters. Then, these parameters are compared with the standard quality parameters, and the difference is calculated. The method for calculating the difference can follow the method in step S304. For example, assume the following result is obtained after optimization:

[0232] Actual purity: 99.2%, Standard purity: ≥99%; Actual particle size distribution (D50): 44μm, Standard particle size distribution (D50): ≤45μm; Actual whiteness: 90%, Standard whiteness: ≥90%; Actual moisture content: 0.4%, Standard moisture content: ≤0.5%

[0233] The relative difference between each parameter can be calculated, and then the average can be taken:

[0234] Purity difference: (99.2% - 99%) / 99% = 0.2%; Particle size difference: (44μm - 45μm) / 45μm = -2.2% (negative value indicates better than standard); Whiteness difference: (90% - 90%) / 90% = 0%; Moisture content difference: (0.4% - 0.5%) / 0.5% = -20% (negative value indicates better than standard); Average difference = (0.2% + (-2.2%) + 0% + (-20%)) / 4 = -5.5%.

[0235] Then, the absolute value of this average difference is compared with a preset difference threshold. Assuming the preset difference threshold is still 1%, then |-5.5%|>1%. However, it should be noted that a negative value actually indicates that the product quality exceeds the standard requirements. Therefore, in this case, although the absolute value of the difference is greater than the threshold, since it is a positive exceedance, the second equipment parameter can still be output as the optimal equipment parameter.

[0236] The effect of this step is to comprehensively evaluate the product quality after optimizing the second priority parameter, and determine the optimal equipment parameters when the effect meets or exceeds expectations. This method not only ensures that product quality meets requirements, but also identifies situations where product quality exceeds standards. In actual production, this "exceeding the standard" means higher product value or greater room for process adjustments. At the same time, this step also demonstrates the flexibility and adaptability of the optimization process. By progressively optimizing parameters of different priorities and evaluating the effect at each step, the target quality can be achieved within the minimum necessary optimization steps, thereby improving optimization efficiency. This method ensures product quality while avoiding the waste of time and resources caused by over-optimization.

[0237] If the second difference is greater than or equal to the difference threshold, the initial equipment parameters corresponding to the global correction factor of the third priority are optimized to obtain the third equipment parameters, which are then output as the optimal equipment parameters. The global correction factor of the third priority is usually a parameter that has a relatively small impact on product quality but is still important, as determined in the previous analysis, such as certain auxiliary process parameters or environmental factors. The optimization process can use a method similar to the first two steps, but considering that this is the final optimization step, a more complex or comprehensive optimization algorithm will be used, such as a genetic algorithm or particle swarm optimization algorithm. Taking the genetic algorithm as an example, the specific implementation steps are as follows:

[0238] 1. Encoding: Encode the third priority parameter into a chromosome.

[0239] 2. Initialize the population: Generate a set of initial solutions (usually 50-100) based on the current parameter values.

[0240] 3. Fitness evaluation: Define the fitness function, which is usually the difference between product quality parameters and standards.

[0241] 4. Selection: Select superior individuals based on fitness.

[0242] 5. Crossover and mutation: New parameter combinations are generated through crossover operations, and new properties are introduced through mutation operations.

[0243] 6. Repeat steps 3-5 until the termination condition is met (such as the number of iterations reaching a preset value or no significant improvement in the optimal solution over multiple generations).

[0244] For example, suppose the third priority global correction factor is grinding time, with an initial value of 60 minutes. Through optimization, the following results are obtained: Generation 1 optimal: 65 minutes (fitness: 0.85); Generation 5 optimal: 72 minutes (fitness: 0.92); Generation 10 optimal: 75 minutes (fitness: 0.97); Generation 15 optimal: 78 minutes (fitness: 0.99). In the final third equipment parameter, the grinding time is optimized to 78 minutes. The effect of this step is to make final fine-tuning and improvement to product quality by optimizing the third priority parameter. Although the impact of this parameter is relatively small, it is a key factor in achieving or exceeding quality standards based on the optimization in the first two steps. At the same time, this step also reflects the comprehensiveness and depth of the optimization process, ensuring that all important parameters are fully optimized. Outputting the third equipment parameter as the optimal equipment parameter means that the optimization process is now complete. This final output parameter combination should be the best solution after multi-level and multi-angle optimization, capable of ensuring product quality while taking into account production efficiency and cost control.

[0245] This application also discloses a mica material production optimization system based on big data analysis, characterized in that it includes:

[0246] The memory is configured to store instructions; and

[0247] The processor is configured to retrieve instructions from memory and, when executing instructions, to implement the aforementioned big data analytics-based mica material production optimization method.

[0248] The processor can be a central processing unit (CPU). Of course, depending on the actual use, it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor, etc., and this application does not limit it in this regard.

[0249] The memory can be an internal storage unit of a computer device, such as a hard disk or RAM, or an external storage device, such as a plug-in hard disk, smart memory card (SMC), secure digital card (SD), or flash memory card (FC) provided on the computer device. Furthermore, the memory can be a combination of internal storage units and external storage devices of a computer device. The memory is used to store computer programs and other programs and data required by the computer device. The memory can also be used to temporarily store data that has been output or will be output. This application does not limit this.

[0250] This application also provides a machine-readable storage medium storing instructions that cause a machine to execute the above-described method for optimizing mica material production based on big data analysis.

[0251] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0252] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0253] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0254] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0255] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0256] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, like read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0257] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0258] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0259] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for optimizing mica material production based on big data analysis, characterized in that, The method includes the following steps: Obtain the mica processing product type of the current material order through the mica material order system, and select the sample to be processed that is of the same type as the mica processing product. Retrieve historical processing data and initial equipment parameters of the same type as the mica processing product type from the preset mica processing database. The initial equipment parameters are the equipment parameters of all mica processing equipment in the mica processing technology corresponding to the mica processing product type. Based on the initial equipment parameters, the sample to be processed is processed by the mica processing equipment. Multiple types of sensors preset in all the mica processing equipment are used to collect mica processing data during the processing of the sample to be processed, and the sample quality parameters after processing are detected. Based on the sensor types of the various sensors, the mica processing data is classified by features to obtain mica weight change data, mica temperature change data, and mica pH change data. The upper limit of the total number of inflection points is set according to the total number of processing steps included in the mica processing technology. The upper limit of the total number of inflection points is used as an algorithm constraint, and the inflection point detection algorithm is used to detect the inflection points of the mica weight change data to obtain the weight change inflection points distributed in the mica weight change data. Based on the inflection point of weight change, the mica processing technology is divided into process nodes to obtain multiple mica processing sub-processes within the mica processing technology. Extract the maximum and minimum temperature values ​​of the mica temperature change data within each mica processing sub-process, calculate the difference between the maximum and minimum temperature values, and obtain the temperature difference value. Extract the maximum and minimum values ​​of the pH change data of mica within each mica processing sub-process, and calculate the difference between the maximum and minimum pH values ​​to obtain the pH difference. If the temperature difference corresponding to the mica processing sub-process is greater than a preset temperature difference threshold, then analyze whether the pH difference corresponding to the mica processing sub-process is greater than a preset pH difference threshold. If the pH difference corresponding to the mica processing sub-process is greater than the pH difference threshold, the initial equipment parameters corresponding to the mica processing sub-process are determined to be mica purification parameters. If the pH difference is less than or equal to the pH difference threshold, then the initial equipment parameters corresponding to the mica processing sub-process are determined to be mica grinding parameters. The core process parameters are obtained by integrating the mica purification parameters and the mica grinding parameters. The average value of the historical processing data of mica is calculated based on the core process parameters, and the average value is used as the core process standard value corresponding to the core process parameters. By combining the core process standard values ​​with the mica processing data, the mica raw material loss rate during the mica processing process is calculated. Calculate the core parameter correlation coefficient set between the core process parameters of mica and the mica raw material loss rate, filter out the core parameter correlation coefficients in the core parameter correlation coefficient set that are greater than the preset core parameter correlation coefficient threshold, and output the core process parameters corresponding to the core parameter correlation coefficients as local correction factors. Based on multiple types of sensors pre-installed in the mica processing plant, mica processing environment data is obtained, including mica processing environment temperature data and mica processing environment humidity data. A multiple linear regression equation was established with the mica raw material loss rate as the dependent variable. The environmental temperature data and environmental humidity data of the mica processing environment were respectively input into the multiple linear regression equation as independent variables, and the environmental temperature correlation coefficient and environmental humidity correlation coefficient were calculated. If the environmental temperature correlation coefficient is greater than the preset environmental correlation coefficient threshold, the mica processing environmental temperature data will be output as a local correction factor. If the environmental humidity correlation coefficient is greater than the environmental correlation coefficient threshold, then the environmental humidity data of the mica processing environment will be output as a local correction factor. The mica product quality parameters corresponding to the mica processing product type are obtained from a preset database. The influence degree dataset between the initial equipment parameters and the mica product quality parameters is calculated. The initial equipment parameters are prioritized based on the influence degree dataset. The initial equipment parameters that have completed the priority classification are output as global correction factors. The initial equipment parameters are corrected by combining the local correction factors and the global correction factors to obtain the optimal equipment parameters.

2. The method according to claim 1, characterized in that, The step of calculating the average value of the historical mica processing data based on the core process parameters and using the average value as the core process standard value corresponding to the core process parameters includes the following steps: Extract the historical purification weight change data from the historical processing data corresponding to the mica purification parameters to obtain the historical purification weight change data, calculate the average purification weight of the historical purification weight change data, and use the average purification weight as the standard value of the purification process. Extract the historical grinding weight change data from the historical processing data corresponding to the mica grinding parameters, obtain the historical grinding weight change data, calculate the average grinding weight of the historical grinding weight change data, and use the average grinding weight as the standard value of the grinding process. The standard values ​​of the purification process and the grinding process are integrated to obtain the standard values ​​of the core process parameters.

3. The method according to claim 1, characterized in that, The process of calculating the core parameter correlation coefficient set between the core process parameters of mica and the mica raw material loss rate, selecting the core parameter correlation coefficients in the set that are greater than a preset core parameter correlation coefficient threshold, and outputting the core process parameters corresponding to the core parameter correlation coefficients as local correction factors includes the following steps: A correlation coefficient model was established using the mica raw material loss rate as the independent variable; The vibration frequency, grinding time, and grinding method included in the mica grinding parameters are input as independent variables into the correlation coefficient model to obtain a set of grinding correlation coefficients. The grinding correlation coefficients that are greater than a preset correlation coefficient threshold are selected, and the mica grinding parameters corresponding to the grinding correlation coefficients are output as local correction factors. The solution ratio, purification time, and purification screen parameters included in the mica purification parameters are input as independent variables into the correlation coefficient model to obtain a set of purification correlation coefficients. The purification correlation coefficients that are greater than a preset correlation coefficient threshold are selected, and the mica purification parameters corresponding to the purification correlation coefficients are output as local correction factors.

4. The method according to claim 1, characterized in that, The steps of obtaining mica product quality parameters corresponding to the mica processing product type from a preset database, calculating the influence degree dataset between the initial equipment parameters and the mica product quality parameters, prioritizing the initial equipment parameters based on the influence degree dataset, and outputting the prioritized initial equipment parameters as global correction factors include the following steps: The mica product quality parameters corresponding to the mica processing product type are obtained through a preset database. Establish a mica regression model with the mica product quality parameters as the dependent variable and the initial equipment parameters as the independent variable; The mica equipment parameters, mica raw material data, and mica ingredient ratio included in the initial equipment parameters are respectively input into the mica regression model as independent variables to obtain the influence degree data. The influence degree data is then integrated to obtain the influence degree dataset. The initial device parameter corresponding to the largest impact value in the impact value dataset is output as a global correction factor with the first priority. The initial device parameter corresponding to the second largest impact value in the impact value dataset is output as a global correction factor with the second priority. The initial device parameter corresponding to the third largest impact data in the impact dataset is output as a global correction factor with a third priority, where the first priority is greater than the second priority, and the second priority is greater than the third priority.

5. The method according to claim 4, characterized in that, The step of combining the local correction factor and the global correction factor to correct the initial equipment parameters and obtain the optimal equipment parameters includes the following steps: After analyzing the local correction factors, the core process parameters and mica processing environment data contained in the initial equipment parameters are locally corrected. The initial equipment parameters, which have undergone local correction, are globally corrected based on the global correction factors to obtain the optimal equipment parameters.

6. The method according to claim 5, characterized in that, The step of globally correcting the initial device parameters that have undergone local correction based on the global correction factors to obtain the optimal device parameters includes the following steps: Obtain the mica product quality standard parameters corresponding to the mica processing product type from the preset database; Calculate the standard deviation between the mica product quality parameters and the mica product quality standard parameters; The initial device parameters corresponding to the global correction factors of the first priority are optimized to obtain the first device parameters; Calculate the first difference between the quality parameters of the mica product obtained from processing based on the first equipment parameters and the quality parameters of the mica production standard. If the first difference is less than a preset difference threshold, then output the first equipment parameters as the optimal equipment parameters. If the first difference is greater than or equal to the difference threshold, the initial device parameters corresponding to the global correction factor of the second priority are optimized to obtain the second device parameters; Calculate the second difference between the quality parameters of the mica product obtained from processing according to the second equipment parameters and the quality parameters of the mica production standard. If the second difference is less than the difference threshold, then the second equipment parameters are output as the optimal equipment parameters. If the second difference is greater than or equal to the second difference threshold, the initial device parameters corresponding to the global correction factor of the third priority are optimized to obtain the third device parameters, and the third device parameters are output as the optimal device parameters.

7. A mica material production optimization system based on big data analysis, characterized in that, include: The memory is configured to store instructions; as well as The processor is configured to retrieve the instructions from the memory and, when executing the instructions, to implement the mica material production optimization method based on big data analysis according to any one of claims 1 to 6.

8. A computer-readable storage medium storing instructions thereon, characterized in that, When executed by a processor, this instruction causes the processor to be configured to perform the mica material production optimization method based on big data analysis according to any one of claims 1 to 6.

Images