An intelligent analysis and management method for industrial production data based on MES

Abstract

The present invention discloses an intelligent analysis and management method for industrial production data based on MES, which relates to the field of industrial automation and information technology. The specific steps of this method are as follows: S100 collects data during the industrial production process, S200 determines the characteristics of each data and performs slicing operations, S300 screens key features to form dynamic slices, S400 comprehensively analyzes the dynamic slices, mines association rules and locates key factors, S500 sends the analysis results to the MES system in a visual manner to provide a basis for production decisions. Through the unique dynamic data slicing technology, the present invention significantly improves the analysis efficiency and pertinence of production data. In complex industrial production processes, it can quickly focus on key data subsets according to the production task priority, equipment criticality, and real-time data fluctuation characteristics, avoiding the ineffective analysis of a large amount of irrelevant data by traditional methods and improving the overall production efficiency and product quality.

Description

Technical Field

[0001] The present invention relates to the field of industrial automation and information technology, and specifically provides an intelligent analysis and management method for industrial production data based on MES. Background Art

[0002] In today's highly industrialized era, manufacturing enterprises are facing increasingly fierce market competition and the challenge of growing customer demand diversification. The complexity and scale of the industrial production process continue to climb, generating a vast amount of production data. This data covers all aspects from raw material procurement, equipment operation status, production process parameters to product quality inspection. Effectively managing and analyzing this production data is crucial for enterprises to optimize production processes, improve product quality, reduce costs, and enhance market competitiveness.

[0003] However, the existing industrial production data management methods have significant defects when facing complex production scenarios. Among them, in terms of data slicing analysis, traditional methods adopt fixed and preset slicing methods, which cannot be flexibly adjusted according to the real-time priority of production tasks, the actual criticality of equipment, and the dynamic fluctuation characteristics of data. This static slicing method results in a large amount of irrelevant data being included in the analysis scope, while the truly critical data is submerged, greatly reducing the analysis efficiency and accuracy. In terms of production decision support, due to the lack of accurate data analysis, enterprises are difficult to quickly locate the root causes of problems in the production process and cannot make effective decision adjustments in a timely manner, often resulting in production delays, resource waste, and quality instability, seriously restricting the development potential and profitability of enterprises.

[0004] In summary, the existing industrial production data management technologies are difficult to meet the urgent needs of modern manufacturing enterprises for efficient and accurate production management. Therefore, there is an urgent need for an intelligent analysis and management method for industrial production data based on MES, which can dynamically slice and deeply analyze production data, provide timely and accurate decision-making basis for enterprises, and enhance the competitiveness and sustainable development ability of enterprises in the complex and changeable market environment. Summary of the Invention

[0005] The purpose of the present invention is to make up for the deficiencies of the existing technology and provide an intelligent analysis and management method for industrial production data based on MES. When performing dynamic data analysis for complex production processes, it can use dynamic data slicing technology to automatically perform dynamic slicing on production data according to the priority of production tasks, the criticality of equipment, and the fluctuation characteristics of real-time data, focusing on analyzing data subsets in specific time periods and specific production links, thereby quickly and accurately locating the key factors affecting production efficiency and product quality, and significantly improving the analysis efficiency and pertinence.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: An intelligent analysis and management method for industrial production data based on MES. The specific steps of the method are as follows:

[0007] S100. Real-time collect data during the production process through the MES system, perform data cleaning and integration to form a data information library, where the data information library includes production task information, equipment status information, and quality inspection information;

[0008] The production task information includes the urgency of delivery time , the order revenue coefficient , and the degree of closeness of cooperation with customers . By performing weighted summation on the parameters of different production tasks, obtain the priority value of the production task , which serves as the basis for data slicing operations;

[0009] The equipment status information includes the frequency of fault occurrence , the proportion of maintenance cost , and the equipment operation stability index . According to the weighted calculation of the equipment status information, obtain the key degree index of the equipment , and use this index as the basis for data slicing operations;

[0010] The quality inspection information includes the coefficient of variation of quality data , the growth rate of defective products , and the deviation degree of key quality characteristics . By performing weighted summation on the various data fluctuation indexes of the quality inspection information, obtain the comprehensive evaluation value of the real-time data fluctuation characteristics , which is used to identify and screen out data intervals with abnormal fluctuations;

[0011] S200. Automatically perform dynamic slicing operations on the production data according to the priority of the production task, the key degree index of the equipment, and the real-time data fluctuation characteristics;

[0012] The dynamic slicing operations include time slicing, production link slicing, and attribute slicing;

[0013] S300. Screen out data within high-priority production tasks, key equipment, and data intervals with abnormal fluctuations, that is, focus on analyzing a data subset of dynamic data slices formed by specific time periods, specific production links, and specific data attributes;

[0014] S400. Use data analysis methods to deeply analyze the dynamic data slices and locate key factors;

[0015] S500. Send the analysis results to the MES system in a visual manner and store them in the data information library to provide a basis for production decisions.

[0016] Furthermore, in the S100 production task information:

[0017] The urgency of the delivery time is measured by the difference between the remaining delivery days of the current production task and the standard delivery days , that is ;

[0018] The order revenue coefficient is obtained by the ratio of the estimated revenue of the current production task to the average revenue of the enterprise's production tasks during the same period , that is ;

[0019] The degree of closeness of cooperation with the customer is comprehensively evaluated based on the product of the number of past collaborations with the customer and the total cooperation amount ;

[0020] Calculate the priority of the production task according to the production task information as: , where , , are the weight coefficients of the urgency of the delivery time, the order revenue coefficient, and the degree of closeness of cooperation with the customer, respectively, and satisfy .

[0021] Furthermore, in the S100 equipment status information:

[0022] The frequency of faults is the number of times the equipment fails within a specific time period , ;

[0023] The proportion of maintenance cost is obtained by calculating the ratio of the average cost of a single equipment maintenance to the equipment acquisition cost , that is ;

[0024] The equipment operation stability index is determined by monitoring the fluctuation range of the equipment operation parameters, using the ratio of the standard deviation of the operation parameters to the average value of this parameter, that is ;

[0025] Calculate the criticality index of the equipment according to the equipment status information as: , where , , are the weight coefficients of the failure occurrence frequency, the proportion of maintenance cost, and the equipment operation stability index, respectively, and satisfy .

[0026] Furthermore, in the S100 quality inspection information:

[0027] The coefficient of variation of the quality data is obtained by dividing the sample standard deviation of the quality inspection data by the sample mean , that is , and is used to measure the dispersion degree of the quality data;

[0028] The growth rate of defective products is calculated as the ratio of the growth value of the number of defective products in the current time period to the number of defective products in the previous time period, that is ;

[0029] The deviation degree of key quality characteristics is statistically calculated for the difference between the standard value of the key quality characteristics of the product and the actual measured value , that is ;

[0030] The real-time data fluctuation characteristics are calculated through the quality inspection information as: , where are the weight coefficients of the coefficient of variation of quality data, the growth rate of defective products, and the deviation degree of key quality characteristics, respectively, and satisfy .

[0031] Furthermore, when the S200 performs dynamic slicing operations, for the time slice, based on the start time and the end time of the production task, combined with the priority of the production task and the comprehensive evaluation value of the real-time data fluctuation characteristics to determine the specific slice time range, that is, when is higher than the priority threshold and is greater than the fluctuation threshold , the time slice range is reduced to , where and are time offsets dynamically adjusted according to the urgency of the production task and the data fluctuation situation, and their calculation methods are respectively: , where and is the time adjustment coefficient.

[0032] Furthermore, when the S200 performs slicing in the production process, according to the key degree index of the equipment classify each link in the production process, and classify the key degree index higher than the key degree threshold The production link where the equipment is located is determined as the key production link, and the data of the key production link is extracted and analyzed emphatically. At the same time, for the production links that have a direct up and down correlation with the key production link, the data extraction weight is adjusted according to the correlation tightness coefficient The adjustment of the data extraction weight is , where is the basic data extraction weight, is the weight adjustment coefficient, and the key production link and related links are extracted according to the adjusted weight, and the data of non-key links are filtered out.

[0033] Furthermore, when the S200 performs attribute slicing, based on various attribute data in the production task information, equipment status information, and quality inspection information, construct an attribute correlation matrix , for the production task priority , equipment key degree index and the comprehensive evaluation value of the real-time data fluctuation characteristics The attributes with a correlation higher than the correlation threshold are preferentially extracted and retained in the attribute slicing. The Pearson correlation coefficient method is used to calculate the attribute correlation. For the attributes and the comprehensive evaluation indexes P, K, V, their Pearson correlation coefficient The calculation formula is: , where is the value of the attribute in the th data sample, is the sample mean of the attribute , is the value of the corresponding comprehensive evaluation value of the production task priority, equipment key degree index, and real-time data fluctuation characteristics in the th data sample, is its sample mean, is the number of data samples.

[0034] Further, the S300 filters out high-priority production tasks, corresponding key devices, and data within the abnormal data fluctuation range from the dynamically sliced data, and further integrates the filtered data to form a dynamic data slice for analysis, that is, the data within the time interval of the time slice The production link slice determines the production link set , including key links and associated links, and the attribute slice determines the attribute set . The data subset of the dynamic data slice represents the th time point within the time interval, the th production link in the production link set, and the data value of the th attribute corresponding thereto.

[0035] Further, the S400 performs standardized processing on the data subset through the principal component analysis (PCA) of the data analysis method to obtain . For each attribute in the standardized processing, its mean value and standard deviation are calculated, then ;

[0036] A covariance matrix Cov is constructed, and the element in its variance matrix is: , where is the number of data points, is the mean value of the attribute on the production link , is the mean value of the attribute on the production link ;

[0037] The covariance matrix is subjected to eigenvalue decomposition to obtain eigenvalues and the corresponding eigenvectors , , is the number of attributes. The eigenvectors are sorted in descending order of eigenvalues, and the first eigenvectors are selected to form a projection matrix . Through , the dimension-reduced data set is obtained;

[0038] The data set is regarded as a transaction database, where each row represents a transaction and each column represents an attribute. The minimum support threshold and the minimum confidence threshold , scan the dataset , count the number of occurrences of each attribute, and select the frequent itemsets that meet the minimum support threshold Connect and generate candidate item sets , scan the dataset again ,statistics The support of each item in the set is frequently , repeatedly search for frequent item sets, generate high-order candidate sets and frequent sets, until no new frequent sets can be generated;

[0039] For the final , calculate its confidence ,in Representing Item Sets The support degree of is the proportion of the number of occurrences to the total number of transactions, so as to extract the association rules whose confidence is greater than the minimum confidence threshold.

[0040] Compared with the existing technology, this MES-based industrial production data intelligent analysis and management method has the following beneficial effects:

[0041] 1. The present invention significantly improves the analysis efficiency and pertinence of production data through unique dynamic data slicing technology. In complex industrial production processes, it can quickly focus on key data subsets based on production task priority, equipment criticality and real-time data fluctuation characteristics, avoiding the ineffective analysis of massive irrelevant data by traditional methods, and improving overall production efficiency and product quality.

[0042] 2. The present invention conducts in-depth analysis of dynamic data slices to discover the potential patterns and correlations in production data, so that enterprise managers can clearly understand the overall picture of the production process and the mutual influence of various factors. This data-driven decision-making method effectively reduces the uncertainty and risk in the production process, enabling enterprises to respond to market changes more flexibly and optimize resource allocation.

[0043] Other advantages, objectives and features of the present invention will be set forth in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0045] Figure 1 It is an operation diagram of an intelligent analysis and management method for industrial production data based on MES;

[0046] Figure 2 It is the implementation flow chart of the second embodiment. Specific implementation manners

[0047] To further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation manners, structures, features and their effects of the present invention as follows.

[0048] Embodiment 1

[0049] As Figure 1 shown, this embodiment details the application process of an intelligent analysis and management method for industrial production data based on MES. By collecting production data in real time, performing data cleaning and integration to build a data information library, determining relevant indicators and weights based on production task information, equipment status information and quality inspection information, and then performing dynamic slicing operations, screening out dynamic data slices and using data analysis methods to deeply explore key factors, and visualizing the results to assist production decision-making.

[0050] Using the MES system to comprehensively collect various data in the production process, covering production task information, equipment status information and quality inspection information. Among the production task information, the delivery time urgency is obtained by taking the difference between the remaining delivery days of the current production task and the standard delivery days , that is , this difference intuitively reflects the time urgency of the task, and the smaller the difference, the more urgent the task; the order revenue coefficient is obtained by dividing the expected revenue of the current production task by the average revenue of the enterprise's production tasks during the same period, that is , the larger this coefficient, the higher the potential contribution of this task to the enterprise's revenue; the degree of closeness of customer cooperation is comprehensively evaluated based on the product of the number of past cooperation times with the customer and the total cooperation amount , the more cooperation times and the larger the amount, the higher the degree of closeness; according to the formula (where ) calculate the priority of the production task, and the weight coefficients , , Represents the importance that the enterprise attaches to each factor. It accurately quantifies the task priority through weighted summation, providing a basis for subsequent data screening. In terms of equipment status information, the frequency of faults Count the number of times a device fails within a specific time period ,Right now , the more failures there are, the worse the equipment stability is; the maintenance cost accounts for Average cost of equipment maintenance Equipment purchase cost Divide them and we get The higher the ratio, the heavier the equipment maintenance cost burden; equipment operation stability index Determined by monitoring the fluctuation range of the operating parameters of the equipment, the standard deviation of the operating parameters is used With the average The ratio calculation is The larger the ratio, the lower the running stability. Calculate the criticality index of the equipment , weight coefficient , , Represents the importance of the equipment in the production process and the company's maintenance strategy, so as to accurately determine the criticality of the equipment and guide the direction of data screening; in quality inspection information, the coefficient of variation of quality data The sample standard deviation of the quality inspection data Divide by the sample mean Calculate, that is It effectively measures the degree of dispersion of quality data. The larger the coefficient of variation, the more drastic the quality fluctuation. The growth rate of defective products is the growth value of the number of defective products in the current time period Compared with the number of defective products in the previous period The ratio of The higher the growth rate, the worse the production quality; the deviation of key quality characteristics Standard values ​​for key quality characteristics of products The actual detection value The difference of The larger the difference, the farther the key quality characteristics of the product deviate from the standard. ) to obtain the comprehensive evaluation value of the real-time data fluctuation characteristics , weight system , , Determine the importance of quality factors on production. The comprehensive evaluation value is used to accurately lock the abnormal range of data fluctuations, screen key data. The collected raw data is cleaned to remove noise and incorrect data, and then integrated and stored in the data information library as the basis for subsequent analysis of the basic data.

[0051] Using the collected basic data, perform dynamic slicing operations. The dynamic slicing operations include time slicing, production process slicing, and attribute slicing. Among them, for time slicing, starting from the start time and end time of the production task, combined with the production task priority and the comprehensive evaluation value of real-time data fluctuation characteristics to determine the slicing range. When is higher than the priority threshold and is greater than the fluctuation threshold , according to the formulas and ( , are time adjustment coefficients to calculate the time offset), narrow the time slicing range to . By dynamically adjusting the time slicing, it is possible to accurately focus on the key time periods, exclude data in irrelevant time intervals, greatly improve the analysis efficiency and pertinence, and ensure that the analysis focuses on key time nodes. For production process slicing, classify each link of the production process according to the equipment criticality index , and delimit the links where the equipment higher than the criticality threshold is located as the key production links, and focus on extracting and deeply analyzing their data. For the links directly upstream and downstream related to the key production links, adjust the data extraction weight according to the correlation intensity coefficient . The formula is ( is the basic data extraction weight, is the weight adjustment coefficient). This is because the data of key links and closely related links have a significant impact on production. By reasonably allocating weights, it is possible to ensure the full extraction of key data, avoid waste of analysis resources, and make the analysis focus on the core production area, jointly constituting the production process slicing data; for attribute slicing, construct an attribute correlation matrix based on various attribute data in production task information, equipment status information, and quality inspection information, and use the Pearson correlation coefficient method to calculate the correlation between attributes and production task priority , equipment criticality index and the comprehensive evaluation value of real-time data fluctuation characteristics . For attributes with a correlation higher than the correlation threshold , preferentially extract and retain them in the attribute slicing. Pearson correlation coefficient The calculation formula is ( is the attribute at the th data sample, is the sample mean of the attribute , is the corresponding , , at the th data sample, is its sample mean, is the number of data samples). In this way, the attributes closely related to the key factors are screened out, the irrelevant attributes are discarded, and the focus of analysis is further narrowed down, laying a foundation for exploring the deep - level laws of data.

[0052] According to the priority weight values of production tasks, the production data corresponding to high - priority production tasks are screened out, the relevant data intervals in the time dimension are determined, combined with the critical level of equipment, and the data generated by critical equipment within the above - mentioned data intervals are further focused on. Referring to the characteristics of real - time data fluctuations, the screened data are screened again, and the data subsets within the abnormal data fluctuation intervals are extracted, so as to integrate the time slice (data within the time interval ), the production link slice (the determined set of production links , including critical and related links) and the attribute slice (the determined set of attributes results, forming a data subset of the dynamic data slice , which represents the specific attribute data values of specific production links within a specific time interval, accurately focusing on the key data in the production process.

[0053] For the data subset of the generated dynamic data slice perform principal component analysis (PCA). First, perform standardization processing. For each attribute calculate the mean and the standard deviation , and obtain the standardized data set through the formula , eliminating the dimensional differences of different attributes and making the data comparable. Construct the covariance matrix Cov, and the elements in its matrix are calculated according to the formula to reflect the linear relationship between attributes. Subsequently, perform eigenvalue decomposition on the covariance matrix to obtain the eigenvalues and eigenvectors ( , is the number of attributes), arrange them in descending order of eigenvalues, and select the first eigenvectors to form the projection matrix , finally, through to obtain the dimensionality-reduced data set , effectively reducing the data dimension, retaining key information, facilitating subsequent analysis and processing. Consider the dimensionality-reduced data set as a transaction database, and set the minimum support threshold and the minimum confidence threshold . Scan the data set to count the occurrence times of each attribute, and find the frequent item sets that meet the minimum support threshold , connect to generate candidate item sets , scan again to count the support to obtain frequent item sets , perform loop operations until no new frequent sets can be generated, calculate the confidence for the final frequent item sets. For example , extract the association rules with confidence exceeding the threshold, and mine the potential associations between attributes. For example, analyze the correlation between the key equipment with abnormal data fluctuations during the execution of high-priority production tasks and product quality defects, so as to quickly and accurately locate the key factors affecting production efficiency and product quality.

[0054] Send the key factors and association relationships obtained from the analysis to the MES system in an intuitive visual form. Production managers can thus clearly understand the overall production situation, accurately grasp the interaction of various factors. Based on the visual results, managers can timely adjust the production plan, optimize the equipment maintenance strategy, and improve the process parameters to achieve intelligent control and continuous optimization of the production process and enhance the enterprise's market competitiveness.

[0055] Embodiment 2

[0056] As Figure 2 shown, on the basis of Embodiment 1, this embodiment details the specific implementation process of an intelligent analysis and management method for industrial production data based on MES:

[0057] First, enter the data collection and integration stage (S100). In the industrial production environment, the MES system undertakes the key task of data collection. The collected data includes production task information, equipment status information, and quality inspection information, and integrates and preprocesses these multi-source heterogeneous data to ensure the accuracy and integrity of the data, and stores it in the data information library.

[0058] Then, enter the dynamic data slice definition stage (S200), determine the priority evaluation index system of production tasks according to the characteristics of the production process and the needs of enterprise production management, comprehensively consider factors such as order delivery time, product profit, and customer importance, and assign corresponding priority weight values ​​to each production task; evaluate the criticality of production equipment, and divide the equipment into different critical levels according to factors such as the scope of impact of equipment failures, maintenance costs, and the degree of bottleneck constraints on the production process; monitor the fluctuation characteristics of production data in real time, identify abnormal changes in data, such as sudden changes, periodic fluctuation anomalies, etc., and record the time period of fluctuations.

[0059] Subsequently, the dynamic data slice generation stage (S300) is entered. According to the priority weight value of the production task, the production data corresponding to the high-priority production task is screened out, and its relevant data interval in the time dimension is determined; combined with the critical level of the equipment, the data generated by the key equipment in the above data interval is further focused on; with reference to the real-time data fluctuation characteristics, the filtered data is screened again, and the data subset in the data fluctuation abnormal interval is extracted to form the final dynamic data slice.

[0060] Next, the data subset analysis phase (S400) is entered. For the generated dynamic data slices, statistical analysis methods are used to conduct in-depth analysis on the data subsets of a specific time period and a specific production link, and to explore the potential patterns and correlations behind the data. For example, the correlation between abnormal data fluctuations of key equipment during the execution of high-priority production tasks and product quality defects is analyzed, so as to quickly and accurately locate the key factors affecting production efficiency and product quality.

[0061] Finally, the result presentation and decision support stage (S500) is entered, where the results of data analysis are presented to enterprise managers and production decision makers in an intuitive and easy-to-understand manner, providing them with accurate decision-making basis so that they can take corresponding measures in time to make production adjustments, equipment maintenance, quality improvement and other operations, thereby realizing intelligent management and optimization of industrial production processes.

[0062] In summary, this embodiment comprehensively demonstrates the implementation steps of the intelligent analysis and management method for industrial production data based on MES. By systematically collecting and integrating production data, performing multi-dimensional dynamic slicing operations, forming accurate dynamic data slicing, deeply analyzing the data and locating key factors, and finally visualizing the results to support production decisions, this method helps enterprises fully utilize the value of production data, optimize production processes, improve production efficiency and product quality, reduce costs and risks, and enhance the competitiveness of enterprises in the market.

[0063] The above are only the preferred embodiments of the present invention, and do not impose any form of limitation on the present invention. Although the present invention has been disclosed above with the preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to equivalent embodiments with equivalent changes within the scope of the technical solution of the present invention. However, as long as it does not depart from the content of the technical solution of the present invention, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A method for intelligent analysis and management of industrial production data based on MES, characterized in that: The specific steps of this method are: S100, collecting data from the production process in real time through the MES system, cleaning and integrating the data, and forming a data information database, wherein the data information database includes production task information, equipment status information, and quality inspection information; The production task information includes the urgency of the delivery time , Order Revenue Coefficient , the degree of close cooperation with customers , by weighted summing the production task information, the priority value of the production task is obtained , as the basis for data slicing operations; The device status information includes the frequency of failure , Maintenance cost ratio , Equipment operation stability indicators , the criticality index of the equipment is calculated based on the weighted calculation of the equipment status information , and use this indicator as the basis for data slicing operations; The quality detection information includes the coefficient of variation of quality data , Defective product growth rate , Critical Quality Characteristics Deviation By weighting and summing the various data fluctuation indicators of quality inspection information, a comprehensive evaluation value of the real-time data fluctuation characteristics is obtained. , used to identify and filter out data intervals with abnormal fluctuations; S200, automatically perform dynamic slicing operations on production data based on the priority of production tasks, the criticality index of equipment, and the comprehensive evaluation value of real-time data fluctuation characteristics; The dynamic slicing operation includes time slicing, production link slicing and attribute slicing; S300, filter out data of high-priority production tasks, key equipment and data within abnormal data fluctuation range, that is, focus on analyzing data subsets of specific time periods, specific production links and specific data attributes to form dynamic data slices; S400, use data analysis methods to conduct in-depth analysis on the data subsets of dynamic data slices to locate key factors; S500, the analysis results are sent to the MES system in a visual manner and stored in the data information database to provide a basis for production decisions.

2. According to claim 1, a method for intelligent analysis and management of industrial production data based on MES is characterized in that: In the S100 production task information: The urgency of the delivery time The remaining delivery days of the current production task Standard delivery days The difference is measured by ; The order profit coefficient The expected revenue from the current production task The average revenue of the enterprise's production tasks during the same period The ratio is given, that is ; The degree of close cooperation with the client Based on the number of past cooperation with customers Total amount of cooperation Comprehensive evaluation of the product of Calculate the priority of production tasks based on production task information for: ,in, , , They are the weight coefficients of delivery time urgency, order revenue coefficient, and customer cooperation closeness, and they meet .

3. The method for intelligent analysis and management of industrial production data based on MES according to claim 1, characterized in that: In the S100 device status information: The frequency of the fault Count the number of times a device fails within a specific time period , ; The proportion of maintenance costs By calculating the average cost of a single equipment repair Equipment purchase cost The ratio of ; The equipment operation stability index It is determined by monitoring the fluctuation range of the equipment operating parameters and using the standard deviation of the operating parameters The average value of this parameter The ratio of ; Calculate the criticality index of the equipment based on the equipment status information for: ,in, , , They are the weight coefficients of failure frequency, maintenance cost ratio, and equipment operation stability index, and meet the requirements. .

4. The method for intelligent analysis and management of industrial production data based on MES according to claim 1, characterized in that: In the S100 quality inspection information: The coefficient of variation of the quality data The sample standard deviation of the quality inspection data Divide by the sample mean Get, that is , used to measure the discreteness of quality data; The defective product growth rate Calculate the growth value of the number of defective products in the current time period Compared with the number of defective products in the previous period The ratio of ; The critical quality characteristic deviation Standard values ​​for key quality characteristics of products The actual detection value The difference of ; Calculate the comprehensive evaluation value of real-time data fluctuation characteristics through quality detection information for: ,in, They are the coefficient of variation of quality data, the growth rate of defective products, and the weight coefficient of the deviation of key quality characteristics, and they meet .

5. The method for intelligent analysis and management of industrial production data based on MES according to claim 1, characterized in that: When the S200 performs the dynamic slicing operation, for the time slice, the starting time of the production task is used. and end time Based on the priority of production tasks and comprehensive evaluation of real-time data fluctuation characteristics To determine the specific slicing time range, that is, when Above priority threshold and Greater than the fluctuation threshold When , the time slice range is reduced to ,in and The time offset is dynamically adjusted according to the urgency of the production task and the data fluctuation. The calculation methods are as follows: ,in and is the time adjustment factor.

6. The method for intelligent analysis and management of industrial production data based on MES according to claim 1, characterized in that: When the S200 performs slicing in the production process, according to the criticality index of the equipment Classify each link in the production process and set the key indicators Above criticality threshold The production link where the equipment is located is determined as the key production link, and the data of the key production link is extracted and analyzed. At the same time, for the production link that has a direct upper and lower relationship with the key production link, its data extraction weight is According to the correlation coefficient Adjust the data extraction weight to ,in Extract weights for basic data, is the weight adjustment coefficient. The key production links and related links are extracted according to the adjusted weights, and the data of non-key links are screened out.

7. The method for intelligent analysis and management of industrial production data based on MES according to claim 1, characterized in that: When performing attribute slicing, the S200 constructs an attribute association matrix based on various attribute data in the production task information, equipment status information, and quality inspection information. , for production task priorities , Equipment criticality index and comprehensive evaluation of real-time data fluctuation characteristics Correlation is above the correlation threshold The attributes are extracted and retained first in the attribute slices. The attribute correlation is calculated using the Pearson correlation coefficient method. and comprehensive evaluation indicators P, K, V, and their Pearson correlation coefficient The calculation formula is: ,in, For attributes In the The values ​​in the data samples are For attributes The sample mean of The comprehensive evaluation value of the corresponding production task priority, equipment criticality index and real-time data fluctuation characteristics is in the first The values ​​in the data samples are is its sample mean, is the number of data samples.

8. The method for intelligent analysis and management of industrial production data based on MES according to claim 5, characterized in that: The S300 selects high-priority production tasks, corresponding key equipment, and data in the abnormal data fluctuation range from the data after dynamic slicing, and further integrates the selected data to form dynamic data slices for analysis, that is, the time slices are in the time interval The data within the production link is sliced ​​to determine the production link set , including key links and associated links, attribute slices determine attribute sets , the data subset of the dynamic data slice Indicates the time interval The first At a certain time point, the first The corresponding production link The data value of an attribute.

9. The method for intelligent analysis and management of industrial production data based on MES according to claim 8, characterized in that: The S400 uses principal component analysis (PCA) of the data analysis method to analyze the data subsets. Standardized processing to obtain , the standardization process is for each attribute , calculate its mean and standard deviation ,but ; Construct the covariance matrix Cov, whose elements in the variance matrix for: ,in is the number of data points, Is an attribute In the production stage The mean on Is an attribute In the production stage The mean on ; Perform eigenvalue decomposition on the covariance matrix to obtain the eigenvalue and the corresponding eigenvector , , is the number of attributes, sort the eigenvectors in descending order of eigenvalues, and select the first The eigenvectors form the projection matrix ,pass Get the reduced dimension dataset ; The dataset Consider it as a transaction database, where each row represents a transaction and each column represents an attribute. Determine the minimum support threshold and the minimum confidence threshold , scan the dataset , count the number of occurrences of each attribute, and select the frequent itemsets that meet the minimum support threshold Connect and generate candidate item sets , scan the dataset again ,statistics The support of each item in the set is frequently obtained , repeatedly search for frequent item sets, generate high-order candidate sets and frequent sets, until no new frequent sets can be generated; For the final , calculate its confidence ,in Representing Item Sets The support degree of is the proportion of the number of occurrences to the total number of transactions, so as to extract the association rules whose confidence is greater than the minimum confidence threshold.

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