Production data management method and system for material processing

Abstract

The invention discloses a production data management method and system for material processing, and relates to the technical field of production data management. The production data management method for material processing comprises the following steps of production data acquisition, quality evaluation judgment, sharing evaluation judgment and scheduling evaluation judgment. According to the method, the acquisition quality of the processing and production data is evaluated to judge whether to execute the processing and production data sharing, then the effect of the processing and production data sharing is evaluated to obtain the production data sharing evaluation index, and whether to execute the production scheduling optimization is judged; according to the method, the production scheduling optimization evaluation index is obtained, then the scheduling optimization evaluation index after the production scheduling optimization is executed is obtained, and finally whether the data storage architecture optimization is executed or not is comprehensively judged, so that the effect of improving the production data management efficiency for material processing is achieved, and the problem that the production data management efficiency for material processing is not high in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of production data management, and in particular to a production data management method and system for material processing. Background Art

[0002] In modern manufacturing, the demand for production data management is increasing, especially in the field of material processing. With the advancement of intelligent manufacturing, Industry 4.0 and digital transformation, the amount of data in the material processing process continues to increase, and it is crucial to manage and utilize this data. In the field of material processing, production data includes everything from raw materials entering the factory, various parameters in the processing process, equipment operating status to quality inspection data of the final product. These data are of great significance for real-time monitoring of the production process, predicting equipment failures, and optimizing production scheduling.

[0003] Existing production data management methods for material processing usually integrate a variety of advanced technologies and systems to achieve data collection, storage, analysis and decision support. For example, big data technology is used to conduct in-depth analysis of massive data in the production process, extract valuable information from it, support production optimization, quality control and equipment management, thereby achieving real-time monitoring of the production process, quality optimization, improvement of resource utilization efficiency and intelligent decision support.

[0004] For example, a patent application with publication number: CN116308295A discloses an industrial production data management method and system, including: collecting initial data from an industrial intelligent machine, and performing data conversion and data transmission on the initial data through a communication gateway to obtain target data; performing data anomaly analysis on the target data according to a preset data anomaly analysis model, and determining the data anomaly analysis results corresponding to the target data, wherein the data anomaly analysis results include: anomaly evaluation indicators and anomaly fields; based on the anomaly evaluation indicators and anomaly fields, performing anomaly diagnosis on the target data to determine the anomaly root cause corresponding to the target data; performing operation and maintenance processing on the anomaly root cause to obtain operation and maintenance feedback information of the target data; classifying and processing the target data and the operation and maintenance feedback information to generate a corresponding data management log, and storing the data management log through the industrial intelligent machine.

[0005] For example, the invention patent with announcement number: CN118037012B announces a master data management method and system for comprehensive engineering scheduling, including: obtaining comprehensive scheduling data of shipbuilding projects, such as drawings, materials, personnel, contracts and construction data, and identifying master data; secondly, using SSIS technology to perform data integration operations and establish a data mart to store integrated master data; then, building a shared management platform for comprehensive engineering scheduling based on blockchain technology to achieve data docking with the data mart; finally, uniformly scheduling construction through the shared management platform for comprehensive engineering scheduling to generate a unified scheduling plan.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems: Material processing involves multiple production process links, such as welding, drying, inspection, etc. The production data of each link (such as temperature, pressure, vibration, etc.) needs to be collected and monitored in real time. However, traditional data collection methods have problems such as incomplete data collection. At the same time, the delay in the data transmission process also affects the data storage efficiency.

[0007] Another thing that needs to be considered is that as the amount of data increases in the material processing and production process, traditional data management and analysis methods find it difficult to use data for optimization in a timely manner, and there is a problem of low efficiency in production data management for material processing. Summary of the invention

[0008] The embodiments of the present application solve the problem of low efficiency of production data management for material processing in the prior art by providing a production data management method and system for material processing, thereby improving the efficiency of production data management for material processing.

[0009] The embodiment of the present application provides a production data management method for material processing, comprising the following steps: S1, collecting processing production data in a material processing process within a preset time interval, wherein the processing production data includes material-related data, production-related data and environment-related data; S2, evaluating the collection quality of the processing production data to obtain a production data quality evaluation index, and judging whether to execute processing production data sharing based on the production data quality evaluation index, wherein the production data quality evaluation index is used to quantify the collection quality of the processing production data; S3, after executing processing production data sharing, evaluating the effect of processing production data sharing to obtain a production data sharing evaluation index, and judging whether to execute production scheduling optimization based on the production data sharing evaluation index, wherein the production data sharing evaluation index is used to quantitatively evaluate the effect of processing production data sharing; S4, obtaining a scheduling optimization evaluation index after executing production scheduling optimization, and comprehensively judging whether to execute data storage architecture optimization based on the production data quality evaluation index and the production data sharing evaluation index, wherein the scheduling optimization evaluation index is used to quantitatively evaluate the effect of production scheduling optimization.

[0010] Furthermore, the specific steps of evaluating the collection quality of the processing production data to obtain the production data quality evaluation index are as follows: obtaining an accurate material evaluation value according to the material-related evaluation data within a preset time interval and the corresponding material-related data reference value obtained from a preset database, wherein the material-related evaluation data includes a maximum value of the material-related data, a minimum value of the material-related data, and a material-related data measurement value; obtaining an accurate production evaluation value according to the production-related measurement value at a preset time within the preset time interval combined with the production-related reference data obtained from the preset database, wherein the production-related measurement value includes an equipment humidity measurement value, an equipment time measurement value, and an equipment load measurement value; The environment-related values ​​and environment-related standard deviations within a preset time interval are processed to obtain an environment accuracy assessment value, wherein the environment-related values ​​include the maximum environment temperature, the minimum environment temperature, the maximum environment humidity, the minimum environment humidity, the maximum environment light intensity, and the minimum environment light intensity, and the environment-related standard deviations include the standard deviation of environment temperature, the standard deviation of environment humidity, and the standard deviation of environment light intensity; the amount of lost data collected in production within a preset time interval, the total amount of data collected in production, the material accuracy assessment value, the production accuracy assessment value, the environment accuracy assessment value, and the data integrity quality assessment weights and data accuracy quality assessment weights obtained from a preset database are combined to obtain a production data quality assessment index.

[0011] Furthermore, the method for obtaining the production data quality assessment index is as follows: ; In the formula, Represents the production data quality assessment index within a preset time interval, represents the data integrity quality assessment weight, Indicates the amount of lost data in production collection within the preset time interval. Indicates the total amount of production data collected within the preset time interval. represents the weight of data accuracy quality assessment, represents the material data influencing factor, Indicates the accurate assessment value of the material within a preset time interval. represents the production data impact factor, Indicates the accurate assessment value of production within a preset time interval, represents the environmental data impact factor, Indicates the accurate assessment value of the environment within the preset time interval.

[0012] Furthermore, the specific steps of judging whether to execute processing production data sharing based on the production data quality assessment index are as follows: compare the production data quality assessment index with the preset data quality assessment threshold range obtained from the preset database: if the production data quality assessment index is within the preset data quality assessment threshold range, then processing production data sharing is executed, and at the same time, the effect of processing production data sharing is evaluated to obtain the production data sharing evaluation index; if the production data quality assessment index exceeds the preset data quality assessment threshold range, then processing production data sharing is not executed, and at the same time, the preset personnel are reminded to collect processing production data again.

[0013] Furthermore, the specific steps of evaluating the effect of processing and production data sharing to obtain a production data sharing evaluation index are as follows: obtaining initial sharing-related data within a preset time interval to be shared before executing processing and production data sharing, the initial sharing-related data including the total shared data volume, data volume sending time, initial material hash value, initial production hash value, and initial environment hash value; obtaining sharing-related data within a preset time interval after executing processing and production data sharing, the sharing-related data including the shared transmission data volume, data volume receiving time, verification material hash value, verification production hash value, verification environment hash value, and shared access data volume; converting the shared transmission data volume, data volume sending time, and and data volume receiving time are processed to obtain the data volume transmission rate; the initial material hash value, the initial production hash value, the initial environment hash value are compared with the verification material hash value, the verification production hash value, and the verification environment hash value to obtain a shared hash verification value, wherein the shared hash verification value includes a material hash verification value, a production hash verification value, and an environment hash verification value; the initial shared related data, the shared related data, the shared hash verification value, the data volume transmission rate are combined with the shared rate reference value obtained from a preset database and the shared evaluation weight to obtain a production data sharing evaluation index, wherein the shared evaluation weight includes a shared rate evaluation weight, a shared accuracy evaluation weight, and a shared utilization evaluation weight.

[0014] Furthermore, the specific steps for determining whether to execute production scheduling optimization based on the production data sharing evaluation index are as follows: compare the production data sharing evaluation index with a preset sharing effect threshold range obtained from a preset database: if the production data sharing evaluation index is within the preset sharing effect threshold range, then execute production scheduling optimization, and simultaneously obtain a scheduling optimization evaluation index after executing production scheduling optimization; if the production data sharing evaluation index exceeds the preset sharing effect threshold range, then do not execute production scheduling optimization, and simultaneously remind the preset personnel to change the data sharing mechanism.

[0015] Furthermore, the specific steps of obtaining the scheduling optimization evaluation index after executing the production scheduling optimization are as follows: obtaining the initial scheduling optimization related values ​​within the preset time interval to be optimized before executing the production scheduling optimization, the initial scheduling optimization related values ​​including the initial material accuracy evaluation value, the initial production accuracy evaluation value, the initial environment accuracy evaluation value, and the initial production data sharing evaluation index; obtaining the scheduling optimization related values ​​within the preset time interval after executing the production scheduling optimization, the scheduling optimization related values ​​including the optimized material accuracy evaluation value, the optimized production accuracy evaluation value, the optimized environment accuracy evaluation value, and the optimized production data sharing evaluation index; combining the initial scheduling optimization related values ​​and the scheduling optimization related values ​​with the scheduling optimization evaluation weights obtained from the preset database, and then processing them to obtain the scheduling optimization evaluation index, the scheduling optimization evaluation weights including the quality optimization evaluation weights and the sharing optimization evaluation weights.

[0016] Furthermore, the specific steps of comprehensively judging whether to perform data storage architecture optimization based on the production data quality evaluation index and the production data sharing evaluation index are as follows: A1, judging whether the obtained production data quality evaluation index is within the preset data quality evaluation threshold range in the preset database, if so, executing A2, otherwise, not performing data storage architecture optimization; A2, judging whether the obtained production data sharing evaluation index is within the preset sharing effect threshold range, if the production data sharing evaluation index is within the preset sharing effect threshold range, executing A3, otherwise, not performing data storage architecture optimization; A3, performing production scheduling optimization and judging whether the obtained scheduling optimization evaluation index is within the preset scheduling optimization effect threshold range, if the scheduling optimization evaluation index is within the preset scheduling optimization effect threshold range, performing data storage architecture optimization, otherwise, not performing data storage architecture optimization.

[0017] The embodiment of the present application provides a production data management system for material processing, including: a production data acquisition module, a quality assessment and judgment module, a sharing assessment and judgment module, and a scheduling assessment and judgment module; wherein the production data acquisition module is used to collect processing production data in the material processing process within a preset time interval, and the processing production data includes material-related data, production-related data, and environment-related data; the quality assessment and judgment module is used to evaluate the collection quality of the processing production data to obtain a production data quality assessment index, and judge whether to perform processing production data sharing based on the production data quality assessment index, and the production data quality assessment index is used to quantify the collection quality of the processing production data; the sharing assessment and judgment module is used to evaluate the effect of processing production data sharing after performing processing production data sharing to obtain a production data sharing assessment index, and judge whether to perform production scheduling optimization based on the production data sharing assessment index, and the production data sharing assessment index is used to quantitatively evaluate the effect of processing production data sharing; the scheduling assessment and judgment module is used to obtain a scheduling optimization assessment index after performing production scheduling optimization, and comprehensively judge whether to perform data storage architecture optimization based on the production data quality assessment index and the production data sharing assessment index, and the scheduling optimization assessment index is used to quantitatively evaluate the effect of production scheduling optimization.

[0018] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. The collection quality of processing and production data is evaluated to determine whether to share processing and production data. Then, the effect of sharing processing and production data is evaluated to determine whether to perform production scheduling optimization. Then, the scheduling optimization evaluation index after the production scheduling optimization is obtained. Finally, a comprehensive judgment is made as to whether to perform data storage architecture optimization. This realizes the collection quality evaluation, sharing and production scheduling optimization of processing and production data, thereby improving the management efficiency of production data for material processing, and effectively solving the problem of low efficiency of production data management for material processing in the prior art.

[0019] 2. The production data quality assessment index is obtained by combining the amount of lost data collected during the preset time interval, the total amount of production data collected, the material accuracy assessment value, the production accuracy assessment value, the environmental accuracy assessment value, and the data integrity quality assessment weight and data accuracy quality assessment weight obtained from the preset database, thereby realizing a numerical assessment of the quality of processing and production data collection, and further realizing a more accurate assessment of the quality of processing and production data collection.

[0020] 3. By obtaining the initial scheduling optimization related values ​​within the preset time interval to be optimized before executing the production scheduling optimization, and then obtaining the scheduling optimization related values ​​within the preset time interval after executing the production scheduling optimization, and finally combining the initial scheduling optimization related values, the scheduling optimization related values ​​and the scheduling optimization evaluation weights obtained from the preset database, the scheduling optimization evaluation index is obtained, thereby realizing the numerical evaluation of the production scheduling optimization effect, and then realizing a more accurate evaluation of the production scheduling optimization effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A flowchart of a production data management method for material processing provided in an embodiment of the present application; Figure 2 A schematic diagram of the change of the production data sharing evaluation index provided in the embodiment of the present application, wherein (a) is a schematic diagram of the change of the production data sharing evaluation index with the data volume transmission rate, and (b) is a schematic diagram of the change of the production data sharing evaluation index with the shared access data volume; Figure 3 A schematic diagram of the structure of a production data management system for material processing provided in an embodiment of the present application. DETAILED DESCRIPTION

[0022] The embodiment of the present application solves the problem of low efficiency of production data management for material processing in the prior art by providing a production data management method and system for material processing. The method collects processing production data in the material processing process within a preset time interval, evaluates the collection quality of the processing production data to obtain a production data quality evaluation index, and then determines whether to execute processing production data sharing based on the production data quality evaluation index. After executing processing production data sharing, the effect of processing production data sharing is evaluated to obtain a production data sharing evaluation index and determine whether to execute production scheduling optimization. Finally, the scheduling optimization evaluation index after executing production scheduling optimization is obtained. The method determines whether to execute data storage architecture optimization based on the production data quality evaluation index and the production data sharing evaluation index, thereby improving the efficiency of production data management for material processing.

[0023] The technical solution in the embodiment of the present application is to solve the problem of low efficiency of production data management for material processing mentioned above, and the overall idea is as follows: The collection quality of processing and production data is evaluated to determine whether to implement processing and production data sharing, and then the effect of processing and production data sharing is evaluated to determine whether to implement production scheduling optimization. Finally, the scheduling optimization evaluation index after the production scheduling optimization is obtained and a comprehensive judgment is made whether to implement data storage architecture optimization, thereby achieving the effect of improving the efficiency of production data management used for material processing.

[0024] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0025] like Figure 1 As shown, it is a flowchart of a production data management method for material processing provided by an embodiment of the present application, the method comprising the following steps: S1, collecting processing production data in the material processing process within a preset time interval, the processing production data including material-related data, production-related data and environment-related data; S2, evaluating the collection quality of the processing production data to obtain a production data quality evaluation index, judging whether to perform processing production data sharing based on the production data quality evaluation index and a preset data quality evaluation threshold range, and the production data quality evaluation index is used to quantify the collection quality of the processing production data; S3, after performing processing production data sharing, evaluating the effect of processing production data sharing to obtain a production data sharing evaluation index, judging whether to perform production scheduling optimization based on the production data sharing evaluation index and a preset sharing effect threshold range, and the production data sharing evaluation index is used to quantitatively evaluate the effect of processing production data sharing; S4, obtaining a scheduling optimization evaluation index after performing production scheduling optimization, making a comprehensive judgment on whether to perform data storage architecture optimization based on the production data quality evaluation index and the production data sharing evaluation index, and the scheduling optimization evaluation index is used to quantitatively evaluate the effect of production scheduling optimization.

[0026] In this embodiment, the material-related data includes material specification length, material specification width, material specification density and material temperature; the material specification length and material specification width are obtained through automated sensors such as a laser rangefinder or a photoelectric sensor, the material specification density is obtained through a standard density table, and the material temperature is obtained through an infrared thermal imager.

[0027] Production-related data includes processing equipment operating speed, processing equipment operating time and processing equipment load; production-related data is obtained through a programmable logic controller (PLC, Programmable Logic Controller).

[0028] Environmental-related data include ambient temperature data, ambient humidity data, and ambient light intensity; ambient temperature data, ambient humidity data, and ambient light intensity are obtained through temperature sensors, humidity sensors, and light sensors deployed on processing equipment, where processing equipment refers to equipment used in the material processing process, such as CNC equipment (CNC lathes).

[0029] By acquiring processing production data to conduct comprehensive monitoring of data in the material processing process, the reliability of processing production data in the material processing process is improved, thereby achieving improved accuracy in quality assessment of processing production data collection.

[0030] Furthermore, the specific steps of evaluating the collection quality of processing production data to obtain the production data quality evaluation index are as follows: according to the material-related evaluation data within the preset time interval and the corresponding material-related data reference value obtained from the preset database, the material accurate evaluation value (i.e., the production data quality evaluation index) is obtained. ), material-related evaluation data include the maximum value of material-related data, the minimum value of material-related data and the measured value of material-related data; the accurate production evaluation value (i.e., the value in the production data quality evaluation index) is obtained by combining the production-related measured value at a preset time within a preset time interval with the production-related reference data obtained from the preset database. ), production-related measurement values ​​include equipment humidity measurement values, equipment time measurement values, and equipment load measurement values; the environment-related values ​​and environment-related standard deviations within the preset time interval are processed to obtain accurate environmental assessment values ​​(i.e., the production data quality assessment index ), environment-related values ​​include maximum ambient temperature, minimum ambient temperature, maximum ambient humidity, minimum ambient humidity, maximum ambient light intensity, minimum ambient light intensity; environment-related standard deviations include ambient temperature standard deviation, ambient humidity standard deviation and ambient light intensity standard deviation; the amount of lost data collected during a preset time interval, the total amount of data collected during production are combined with the material accuracy assessment value, production accuracy assessment value, environmental accuracy assessment value, and the data integrity quality assessment weights and data accuracy quality assessment weights obtained from the preset database to obtain the production data quality assessment index.

[0031] The method for obtaining the production data quality assessment index is as follows: ; ; ; ; In the formula, Represents the production data quality assessment index within a preset time interval, represents the data integrity quality assessment weight, Indicates the amount of lost data in production collection within the preset time interval. Indicates the total amount of production data collected within the preset time interval. represents the weight of data accuracy quality assessment, represents the material data influencing factor, Indicates the accurate assessment value of the material within a preset time interval. represents the production data impact factor, Indicates the accurate assessment value of production within a preset time interval, represents the environmental data impact factor, Indicates the accurate assessment value of the environment within the preset time interval. The category number indicating the material-related data, , Indicates the maximum value of material-related data of the ath category within the preset time interval. Indicates the minimum value of the material-related data of the ath category within the preset time interval. Indicates the number of the preset time in the preset time interval, , Indicates the total number of preset moments in a preset time interval. Indicates the material-related data measurement value of the a-th category at the n-th preset time within the preset time interval, Indicates the reference value of material-related data for the ath category, Indicates the device speed measurement value at the nth preset time within the preset time interval. Indicates the device speed reference value, Indicates the device speed reference deviation, Indicates the device time measurement value at the nth preset time within the preset time interval. Indicates the device time reference value, Indicates the device time reference deviation, Indicates the equipment load measurement value at the nth preset time within the preset time interval, Indicates the equipment load reference value, Indicates the equipment load reference deviation, Indicates the standard deviation of the ambient temperature at a preset time interval, Indicates the maximum value of the ambient temperature during the preset time interval. Indicates the minimum value of the ambient temperature during the preset time interval. Indicates the standard deviation of ambient humidity at a preset time interval, Indicates the maximum value of the ambient humidity at the preset time interval. Indicates the minimum value of ambient humidity at a preset time interval. Indicates the standard deviation of ambient light intensity at a preset time interval. Indicates the maximum value of the ambient light intensity during the preset time interval. Indicates the minimum value of the ambient light intensity during the preset time interval.

[0032] In this embodiment, the maximum value and minimum value of the material-related data are obtained through the statistical toolbox in MATLAB (such as max(data), min(data)); the material-related data measurement value is the measurement value of the material-related data at any preset time within the preset time interval. It should be understood that the material specification length, material specification width, material specification density and material temperature are the first, second, third and fourth categories corresponding to the material-related data respectively. Among them, the result of summing and averaging the various types of data in the collected historical categories of material-related data represents the corresponding material-related data reference value.

[0033] The production-related measurement value is the measurement value of the production-related data at any preset time within a preset time interval; the production-related reference value includes an equipment speed reference value, an equipment time reference value and an equipment load reference value. Specifically, the equipment speed reference value is represented by summing and averaging the collected historical processing equipment operating speeds, the equipment time reference value is represented by summing and averaging the collected historical processing equipment operating times, and the equipment load reference value is represented by summing and averaging the collected historical processing equipment loads.

[0034] Production-related reference deviations include equipment speed reference deviations, equipment time reference deviations and equipment load reference deviations. Specifically, the equipment speed reference deviation is represented by summing and averaging the collected historical processing equipment operating speed deviations (i.e., the difference between the historical processing equipment operating speed and the equipment standard operating speed). The equipment time reference deviation is represented by summing and averaging the collected historical processing equipment operating time deviations (i.e., the difference between the processing equipment operating time and the equipment standard operating time). The equipment load reference deviation is represented by summing and averaging the collected historical processing equipment load deviations (i.e., the difference between the processing equipment operating load and the equipment standard operating load).

[0035] Obtain material-related values ​​through the statistical toolbox in MATLAB (such as max(data), min(data)), and obtain material-related standard deviations through the statistical toolbox in MATLAB (such as std(data)); obtain the amount of lost data and the total amount of production collection data through network monitoring tools (such as Wireshark).

[0036] Among them, the data integrity quality assessment weight is used to describe the influence of the integrity of processing and production data collection on the production data quality assessment index. Specifically, the data integrity quality assessment weight is the weight corresponding to the data integrity quality assessment preset in the preset database, which represents the numerical value of the influence of the integrity of processing and production data collection on the production data quality assessment index. When used, the weight corresponding to the preset data integrity quality assessment can be directly obtained from the preset database, and the corresponding relationship can be a pre-set mapping relationship. For example, the integrity of processing and production data collection (that is, the complement of the ratio of missing data points of processing and production data to the total data points of processing and production data) and the weight corresponding to the data integrity quality assessment preset in the preset database form a mapping set, and the integrity of real-time processing and production data collection is input into the mapping set to obtain the corresponding weight. The mapping relationship can be one-to-one or many-to-one. In this example, the value range is [0, 1].

[0037] In this example, the sum of the data integrity quality assessment weight and the data accuracy quality assessment weight is 1. The data accuracy quality assessment weight is used to describe the degree of influence of the accuracy of processing and production data collection on the production data quality assessment index.

[0038] The production data quality assessment index is used to quantify the collection quality of processing production data; among them, the material accuracy assessment value, production accuracy assessment value and environmental accuracy assessment value all have an impact on the production data quality assessment index; specifically, the production data quality assessment index is obtained through a comprehensive analysis of the correlation and mutual influence between multiple parameters. For example, environmental factors such as ambient temperature, ambient humidity and ambient light intensity have an impact on the properties of the material (for example, as the ambient humidity increases, water and oxygen react easily on the iron surface to form rust), that is, a decrease in the environmental accuracy assessment value will cause a decrease in the material accuracy assessment value; at the same time, the material-related data measurement value also affects the equipment speed measurement value and the equipment load measurement value, which in turn leads to a decrease in the production accuracy assessment value. The value changes accordingly; in addition, the production accuracy assessment value is also affected by environmental factors (such as temperature). For example, as the temperature increases, the performance of the processing equipment may decline, which in turn affects the equipment speed measurement value, the equipment load measurement value and the equipment time measurement value. It is also necessary to consider that changes in ambient temperature may cause the material to expand or contract, and changes in ambient humidity may affect the corrosion rate of the material. Therefore, through a quantitative approach, a numerical evaluation of the quality of processing and production data collection is achieved, and the quality of processing and production data collection is judged through numerical evaluation, thereby achieving an improvement in the reliability of the judgment of the quality of processing and production data collection, and thereby achieving an improvement in the accuracy of judging abnormal conditions in the quality of processing and production data collection used in the material processing process.

[0039] Furthermore, the specific steps for determining whether to execute processing production data sharing based on the production data quality assessment index are as follows: compare the production data quality assessment index with the preset data quality assessment threshold range obtained from the preset database: if the production data quality assessment index is within the preset data quality assessment threshold range, then processing production data sharing is executed, and at the same time, the effect of processing production data sharing is evaluated to obtain the production data sharing evaluation index; if the production data quality assessment index exceeds the preset data quality assessment threshold range, then processing production data sharing is not executed, and at the same time, the preset personnel are reminded to collect processing production data again.

[0040] In this embodiment, processing and production data are shared through data sharing platforms such as data lakes and data warehouses; wherein, the preset data quality assessment threshold range is set by professionals according to standards in the field. For example, the amount of lost data in production collection is generally set to 2% to 3% of the total amount of production collection data, the material accuracy assessment value range (generally 0.7 to 1), the production accuracy assessment value range (generally 0.7 to 1), and the environment accuracy assessment value range (generally 0.7 to 1), thereby determining the preset data quality assessment threshold range; by making judgments based on the preset data quality assessment threshold range, a more accurate assessment of the quality of processing and production data collection is achieved.

[0041] Furthermore, the specific steps for evaluating the effect of processing and production data sharing to obtain a production data sharing evaluation index are as follows: obtaining initial sharing-related data within a preset time interval to be shared before executing processing and production data sharing (i.e., the preset time interval before executing processing and production data sharing), the initial sharing-related data including the total shared data volume, data volume sending time, initial material hash value, initial production hash value, and initial environment hash value; obtaining sharing-related data within a preset time interval after executing processing and production data sharing, the sharing-related data including the shared transmission data volume, data volume receiving time, verification material hash value, verification production hash value, verification environment hash value, and shared access data volume; processing the shared transmission data volume, data volume sending time, and data volume receiving time to obtain the data volume transmission rate (i.e., the production data sharing evaluation index in the production data sharing evaluation index); ); compare the initial material hash value, initial production hash value, initial environment hash value with the verification material hash value, verification production hash value, and verification environment hash value to obtain a shared hash verification value, which includes the material hash verification value (i.e., the production data sharing evaluation index ), produce hash check values ​​(i.e., produce data sharing evaluation index ), the environment hash check value (i.e., the production data sharing evaluation index ); the initial sharing-related data, sharing-related data, sharing hash check value, data volume transmission rate, sharing rate reference value obtained from the preset database and sharing assessment weight are processed to obtain the production data sharing assessment index. The sharing assessment weight includes sharing rate assessment weight, sharing accuracy assessment weight and sharing utilization assessment weight.

[0042] The method for obtaining the production data sharing assessment index is as follows: ; In the formula, Indicates the sharing evaluation index of production data to be shared within the preset time interval. represents the shared rate evaluation weight, Indicates the data transmission rate. Indicates the shared rate reference value, represents the shared accurate evaluation weight, Indicates the material hash check value, Indicates the generation of hash check value, Represents the environment hash check value, represents the shared utilization assessment weight, Indicates the amount of shared access data. Indicates the total amount of shared data, Indicates the preset data quality assessment threshold range.

[0043] In this embodiment, the total shared data volume, data volume sending time, shared transmission data volume, data volume receiving time and shared access data volume are obtained through network monitoring tools (such as PRTG, Wireshark). The initial material hash value, initial production hash value, initial environment hash value, verification material hash value, verification production hash value and verification environment hash value are obtained by performing hash operation on the processing production data using a hash algorithm such as SHA-256.

[0044] Among them, the initial material hash value, initial production hash value, and initial environment hash value are compared with the corresponding verification material hash value, verification production hash value, and verification environment hash value. If they are equal, the material hash verification value, production hash verification value, and environment hash verification value are recorded as 1, otherwise they are recorded as 0.

[0045] The data volume transmission rate is the result of calculating the absolute value of the difference between the data volume sending time and the data volume receiving time and the ratio of the shared transmission data volume, wherein the result of summing and averaging the collected historical data shared transmission rates represents the shared rate reference value.

[0046] It should be understood that the sharing rate assessment weight is used to describe the degree of influence of the sharing rate of processing production data on the sharing assessment index of production data. Specifically, the sharing rate assessment weight is the weight corresponding to the sharing rate assessment preset in the preset database, which represents the numerical value of the influence of the sharing rate of processing production data on the sharing assessment index of production data. When used, the weight corresponding to the preset sharing rate assessment can be directly obtained from the preset database, and the corresponding relationship can be a pre-set mapping relationship. For example, the sharing rate of processing production data and the weight corresponding to the sharing rate assessment preset in the preset database form a mapping set, and the sharing rate of real-time processing production data is input into the mapping set to obtain the corresponding weight. The mapping relationship can be one-to-one or many-to-one. In this example, the value range is [0, 1].

[0047] The shared accuracy assessment weight is used to describe the degree of influence of the accuracy of processing and production data sharing on the production data sharing assessment index. Specifically, the shared accuracy assessment weight is the weight corresponding to the shared accuracy assessment preset in the preset database, which represents the numerical value of the degree of influence of the accuracy of processing and production data sharing on the production data sharing assessment index. When used, the weight corresponding to the preset shared accuracy assessment can be directly obtained from the preset database, and the corresponding relationship can be a pre-set mapping relationship. For example, the accuracy of processing and production data sharing and the weight corresponding to the shared accuracy assessment preset in the preset database form a mapping set, and the accuracy of real-time processing and production data sharing is input into the mapping set to obtain the corresponding weight. The mapping relationship can be one-to-one or many-to-one. In this example, the value range is [0, 1].

[0048] In this example, the sum of the sharing rate assessment weight, the sharing accuracy assessment weight, and the sharing utilization assessment weight is 1. The sharing utilization assessment weight is used to describe the impact of the sharing and utilization of processing production data on the production data sharing assessment index.

[0049] like Figure 2 As shown, it is a schematic diagram of the change of the production data sharing evaluation index provided in the embodiment of the present application, wherein: the sharing rate evaluation weight is set to 0.5, the sharing accuracy evaluation weight is set to 0.3, the sharing utilization evaluation weight is set to 0.2, wherein the material hash check value, the production hash check value and the environment hash check value are all 1, the sharing rate reference value is 200 bits per second, and the total shared data volume is 1000 bytes; wherein (a) is a schematic diagram of the change of the production data sharing evaluation index with the data volume transmission rate, specifically, the shared access data volume is set to 500 bytes, and as the data volume transmission rate increases, the production data sharing evaluation index increases accordingly; (b) is a schematic diagram of the change of the production data sharing evaluation index with the shared access data volume, specifically, the data volume transmission rate is set to 100 bits per second, and as the shared access data volume increases, the production data sharing evaluation index also increases accordingly.

[0050] The production data sharing evaluation index is used to quantitatively evaluate the effect of processing production data sharing. Specifically, the production data sharing evaluation index includes parameters from multiple aspects, and the various parameters are related to each other and do not exist independently. For example, with the increase of data transmission rate, more data may be accessed due to faster data transmission, which in turn leads to an increase in the amount of shared access data; similarly, when the data transmission rate decreases, it may lead to an inability to quickly respond to data requests, which in turn leads to a decrease in the amount of shared access data. In addition, with the increase of data transmission rate, more errors may occur during the transmission process, which in turn reduces the shared hash check value. Therefore, through a quantitative approach, the correlation and mutual influence between various factors are taken into account, and the production data sharing evaluation index is obtained through comprehensive analysis; the numerical evaluation of the processing production data sharing effect is realized, and the judgment of the processing production data sharing effect is made through numerical evaluation, thereby achieving an improvement in the accuracy of the evaluation of the processing production data sharing effect.

[0051] Furthermore, the specific steps for determining whether to execute production scheduling optimization based on the production data sharing evaluation index are as follows: compare the production data sharing evaluation index with the preset sharing effect threshold range obtained from the preset database: if the production data sharing evaluation index is within the preset sharing effect threshold range, then execute the production scheduling optimization, and at the same time obtain the scheduling optimization evaluation index after executing the production scheduling optimization; if the production data sharing evaluation index exceeds the preset sharing effect threshold range, then do not execute the production scheduling optimization, and at the same time remind the preset personnel to change the data sharing mechanism.

[0052] In this embodiment, production scheduling optimization can be performed through a scheduling optimization algorithm such as a genetic algorithm. Specifically, the individual with the highest fitness is obtained through a fitness function to obtain a global optimal solution. The specific goals of the fitness function include minimizing production time, maximizing production efficiency, and achieving it through real-time tracking of production progress through a manufacturing execution system, etc.; wherein, the preset sharing effect threshold range is set by professionals according to standards in the field, for example, the preset sharing effect threshold range is generally set to 0.6 to 1; by combining the preset sharing effect threshold range for judgment, a more accurate judgment of the sharing effect of processing production data is achieved.

[0053] Furthermore, the specific steps for obtaining the scheduling optimization evaluation index after executing the production scheduling optimization are as follows: obtaining the initial scheduling optimization related values ​​within the preset time interval to be optimized before executing the production scheduling optimization, the initial scheduling optimization related values ​​including the initial material accuracy evaluation value, the initial production accuracy evaluation value, the initial environment accuracy evaluation value, and the initial production data sharing evaluation index; obtaining the scheduling optimization related values ​​within the preset time interval after executing the production scheduling optimization, the scheduling optimization related values ​​including the optimized material accuracy evaluation value, the optimized production accuracy evaluation value, the optimized environment accuracy evaluation value, and the optimized production data sharing evaluation index; combining the initial scheduling optimization related values ​​and the scheduling optimization related values ​​with the scheduling optimization evaluation weights obtained from the preset database, and then processing them to obtain the scheduling optimization evaluation index, the scheduling optimization evaluation weights including the quality optimization evaluation weights and the sharing optimization evaluation weights.

[0054] The method for obtaining the scheduling optimization evaluation index is as follows: ; In the formula, Indicates the scheduling optimization evaluation index within the preset time interval to be optimized. represents the quality optimization evaluation weight, Indicates the accurate evaluation value of optimized materials, Indicates the accurate assessment value of the initial material, Indicates accurate evaluation value of optimized production, Indicates the initial production accurate assessment value, Indicates the accurate evaluation value of the optimization environment, represents the accurate assessment value of the initial environment, represents the shared optimization evaluation weight, represents the optimization of production data sharing evaluation index, represents the initial production data sharing assessment index, Indicates the preset sharing effect threshold range.

[0055] It should be understood that the preset time interval to be optimized is used to describe the preset time interval before the production scheduling optimization is performed; and the optimized preset time interval is used to describe the preset time interval after the production scheduling optimization is performed.

[0056] Among them, the initial material accuracy assessment value refers to the material accuracy assessment value within the preset time interval to be optimized, the initial production accuracy assessment value refers to the production accuracy assessment value within the preset time interval to be optimized, the initial environment accuracy assessment value refers to the environment accuracy assessment value within the preset time interval to be optimized, and the initial production data sharing assessment index refers to the production data sharing assessment index within the preset time interval to be optimized.

[0057] Optimizing the material accuracy assessment value refers to optimizing the material accuracy assessment value within a preset time interval, optimizing the production accuracy assessment value refers to optimizing the production accuracy assessment value within a preset time interval, optimizing the environment accuracy assessment value refers to optimizing the environment accuracy assessment value within a preset time interval, and optimizing the production data sharing assessment index refers to optimizing the production data sharing assessment index within a preset time interval.

[0058] Among them, the quality optimization evaluation weight is used to describe the influence of the quality collection optimization of processing and production data on the scheduling optimization evaluation index. Specifically, the quality optimization evaluation weight is the weight corresponding to the quality optimization evaluation preset in the preset database, which represents the numerical value of the influence of the quality collection optimization of processing and production data on the scheduling optimization evaluation index. When used, the weight corresponding to the preset quality optimization evaluation can be directly obtained from the preset database, and the corresponding relationship can be a pre-set mapping relationship. For example, the quality collection optimization of processing and production data (such as the difference between the accurate evaluation value of the optimized material and the accurate evaluation value of the initial material) and the weight corresponding to the quality optimization evaluation preset in the preset database form a mapping set, and the real-time processing and production data quality collection optimization is input into the mapping set to obtain the corresponding weight. The mapping relationship can be one-to-one or many-to-one. In this example, the value range is [0, 1].

[0059] In this example, the sum of the quality optimization evaluation weight and the shared optimization evaluation weight is 1. The shared optimization evaluation weight is used to describe the degree of influence of the shared optimization of processing production data on the scheduling optimization evaluation index.

[0060] Specific assumptions: the quality optimization evaluation weight is 0.5, the sharing optimization evaluation weight is 0.5, the initial environment accuracy evaluation value is 0.35, and the initial production data sharing evaluation index is 0.7; the scheduling optimization evaluation index can be calculated by the above method, and the statistical table of the changes in the scheduling optimization evaluation index is shown in Table 1: Table 1 Statistics of changes in scheduling optimization evaluation index It can be seen from Table 1 that changes in the optimized material accuracy assessment value, optimized production accuracy assessment value, optimized environment accuracy assessment value and optimized production data sharing assessment index all have an impact on the scheduling optimization assessment index; such as the second and third groups of data, when the initial material accuracy value, the optimized material accuracy assessment value and the initial production accuracy assessment value are fixed values, as the optimized production accuracy assessment value and the optimized environment accuracy assessment value increase, the optimized production accuracy assessment value increases accordingly, and the scheduling optimization assessment index also increases accordingly.

[0061] The scheduling optimization evaluation index is used to quantitatively evaluate the effect of production scheduling optimization, and the various parameters are related to each other and do not exist independently. For example, with the increase of the accurate evaluation value of optimized materials, the accurate evaluation value of optimized production, and the accurate evaluation value of optimized environment, that is, the collection quality of processing and production data increases, the sharing effect of processing and production data increases, and then the accurate evaluation value of optimized production also increases. Therefore, considering the correlation and mutual influence between various factors, the scheduling optimization evaluation index is obtained through comprehensive analysis, which realizes the numerical evaluation of the production scheduling optimization effect. The production scheduling optimization effect is judged through numerical evaluation, thereby achieving a more accurate evaluation of the production scheduling optimization effect.

[0062] Furthermore, the specific steps for comprehensively judging whether to perform data storage architecture optimization based on the production data quality evaluation index and the production data sharing evaluation index are as follows: A1, judging whether the obtained production data quality evaluation index is within the preset data quality evaluation threshold range in the preset database. If so, execute A2, otherwise, do not perform data storage architecture optimization; A2, judging whether the obtained production data sharing evaluation index is within the preset sharing effect threshold range. If the production data sharing evaluation index is within the preset sharing effect threshold range, execute A3, otherwise, do not perform data storage architecture optimization; A3, perform production scheduling optimization and judge whether the obtained scheduling optimization evaluation index is within the preset scheduling optimization effect threshold range. If the scheduling optimization evaluation index is within the preset scheduling optimization effect threshold range, perform data storage architecture optimization, otherwise, do not perform data storage architecture optimization.

[0063] In this embodiment, the data storage architecture optimization can be achieved by using a distributed database (such as MongoDB, Google Bigtable) and a cache (such as Redis, Memcached) to accelerate data access; wherein, the preset scheduling optimization effect threshold range is obtained from the database, and specifically, the preset scheduling optimization effect threshold range is set by professionals according to standards in the field, for example, the preset scheduling optimization effect threshold range is set to 1.5 to 3; by combining the preset scheduling optimization effect threshold range for judgment, a more accurate judgment of the production scheduling optimization effect is achieved.

[0064] Furthermore, data storage architecture optimization is performed, which also includes the following steps: continuously monitoring the access frequency of preset data classification, and migrating data based on the access frequency of preset data classification within a preset time interval, the data classification includes preset high-access data, preset medium-access data, and preset low-access data; continuously monitoring the storage capacity of the preset storage layer, and load balancing the preset storage layer through a load balancing algorithm, the preset storage layer includes a preset high-speed storage layer, a preset medium-speed storage layer, and a preset low-speed storage layer.

[0065] In this embodiment, the preset high-access data is used to describe the updated or read data, for example, the machine operation status monitoring data; the preset medium-access data is used to describe the data that is regularly accessed or read, for example, the machine performance data; the preset low-access data refers to the backup and archived data, such as historical data; it is implemented through data migration tools (such as AWS DataSync); the preset storage levels are load balanced through a load balancing algorithm, specifically including load balancing based on the minimum load, by monitoring the load of the preset storage levels, and selecting the storage level with the lowest load for data storage; by continuously monitoring the access frequency of the data hierarchy and the storage capacity of the preset storage levels, the storage management of the data storage architecture is realized, thereby achieving the improvement of the data processing efficiency in the material processing process.

[0066] like Figure 3 As shown, it is a structural schematic diagram of a production data management system for material processing provided by an embodiment of the present application. A production data management system for material processing provided by an embodiment of the present application includes: a production data acquisition module, a quality assessment judgment module, a sharing assessment judgment module, and a scheduling assessment judgment module; wherein the production data acquisition module is used to collect processing production data in the material processing process within a preset time interval, and the processing production data includes material-related data, production-related data, and environment-related data; the quality assessment judgment module is used to evaluate the collection quality of the processing production data to obtain a production data quality assessment index, and judge whether to perform processing production data sharing based on the production data quality assessment index, and the production data quality assessment index is used to quantify the collection quality of the processing production data; the sharing assessment judgment module is used to evaluate the effect of processing production data sharing after executing processing production data sharing to obtain a production data sharing assessment index, and judge whether to perform production scheduling optimization based on the production data sharing assessment index, and the production data sharing assessment index is used to quantitatively evaluate the effect of processing production data sharing; the scheduling assessment judgment module is used to obtain a scheduling optimization assessment index after executing production scheduling optimization, and comprehensively judge whether to perform data storage architecture optimization based on the production data quality assessment index and the production data sharing assessment index, and the scheduling optimization assessment index is used to quantitatively evaluate the effect of production scheduling optimization.

[0067] In this embodiment, by collecting processing and production data, it is possible to evaluate the quality of processing and production data collection and determine whether to share, optimize production scheduling and data storage architecture, thereby improving the utilization efficiency of processing and production data in the material processing process.

[0068] To summarize, the embodiment of the present application evaluates the collection quality of processing and production data to determine whether to execute processing and production data sharing, then evaluates the effect of processing and production data sharing and determines whether to execute production scheduling optimization, then obtains the scheduling optimization evaluation index after executing production scheduling optimization, and finally comprehensively determines whether to execute data storage architecture optimization, thereby realizing processing and production data collection quality evaluation, sharing and production scheduling optimization, and further realizing the improvement of production data management efficiency for material processing, effectively solving the problem of low efficiency of production data management for material processing in the prior art.

[0069] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented 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.

[0070] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0071] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0072] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0073] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0074] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A production data management method for material processing, characterized in that: The following steps are involved: S1, collecting processing and production data during material processing within a preset time interval, wherein the processing and production data includes material-related data, production-related data and environment-related data; S2, evaluating the collection quality of the processing and production data to obtain a production data quality evaluation index, and judging whether to execute the processing and production data sharing based on the production data quality evaluation index, wherein the production data quality evaluation index is used to quantify the collection quality of the processing and production data; S3, after executing the processing and production data sharing, evaluating the effect of the processing and production data sharing to obtain a production data sharing evaluation index, and judging whether to execute the production scheduling optimization based on the production data sharing evaluation index, wherein the production data sharing evaluation index is used to quantitatively evaluate the effect of the processing and production data sharing; S4, obtaining a scheduling optimization evaluation index after executing production scheduling optimization, and comprehensively judging whether to execute data storage architecture optimization based on the production data quality evaluation index and the production data sharing evaluation index. The scheduling optimization evaluation index is used to quantitatively evaluate the effect of production scheduling optimization.

2. A production data management method for material processing as claimed in claim 1, characterized in that: The specific steps of evaluating the collection quality of the processed production data to obtain the production data quality evaluation index are as follows: Obtaining an accurate material evaluation value based on material-related evaluation data within a preset time interval and a corresponding material-related data reference value obtained from a preset database, wherein the material-related evaluation data includes a maximum value of material-related data, a minimum value of material-related data, and a measured value of material-related data; Obtaining an accurate production assessment value based on production-related measurement values ​​at a preset time within a preset time interval combined with production-related reference data obtained from a preset database, wherein the production-related measurement values ​​include equipment humidity measurement values, equipment time measurement values, and equipment load measurement values; Processing the environment-related values ​​and environment-related standard deviations within a preset time interval to obtain an accurate environmental assessment value, wherein the environment-related values ​​include a maximum value of an ambient temperature, a minimum value of an ambient temperature, a maximum value of an ambient humidity, a minimum value of an ambient humidity, a maximum value of an ambient light intensity, and a minimum value of an ambient light intensity; and the environment-related standard deviations include a standard deviation of an ambient temperature, a standard deviation of an ambient humidity, and a standard deviation of an ambient light intensity; The production data quality assessment index is obtained by combining the amount of lost production collection data at preset time intervals, the total amount of production collection data, the material accuracy assessment value, the production accuracy assessment value, the environmental accuracy assessment value, and the data integrity quality assessment weights and data accuracy quality assessment weights obtained from the preset database.

3. A production data management method for material processing as claimed in claim 2, characterized in that: The method for obtaining the production data quality assessment index is as follows: ； In the formula, Represents the production data quality assessment index within a preset time interval, represents the data integrity quality assessment weight, Indicates the amount of lost data in production collection within the preset time interval. Indicates the total amount of production data collected within the preset time interval. represents the weight of data accuracy quality assessment, represents the material data influencing factor, Indicates the accurate assessment value of the material within a preset time interval. represents the production data impact factor, Indicates the accurate assessment value of production within a preset time interval, represents the environmental data impact factor, Indicates the accurate assessment value of the environment within the preset time interval.

4. A production data management method for material processing as claimed in claim 1, characterized in that: The specific steps of judging whether to execute processing production data sharing based on the production data quality evaluation index are as follows: Compare the production data quality assessment index with the preset data quality assessment threshold range obtained from the preset database: If the production data quality assessment index is within the preset data quality assessment threshold, the processing production data sharing is executed, and the effect of the processing production data sharing is evaluated to obtain the production data sharing assessment index; If the production data quality assessment index exceeds the preset data quality assessment threshold range, the processing and production data sharing will not be executed, and the preset personnel will be reminded to collect the processing and production data again.

5. A production data management method for material processing as claimed in claim 4, characterized in that: The specific steps of evaluating the effect of processing production data sharing to obtain the production data sharing evaluation index are as follows: Acquire the initial sharing related data to be shared within a preset time interval before executing the processing and production data sharing, wherein the initial sharing related data includes the total amount of shared data, the time when the data is sent, the initial material hash value, the initial production hash value, and the initial environment hash value; Acquire the sharing-related data within a preset time interval after the processing and production data sharing is performed, wherein the sharing-related data includes the amount of shared transmission data, the time when the data is received, the verification material hash value, the verification production hash value, the verification environment hash value, and the amount of shared access data; The shared transmission data volume, the data volume sending time and the data volume receiving time are processed to obtain the data volume transmission rate; Compare the initial material hash value, the initial production hash value, and the initial environment hash value with the verification material hash value, the verification production hash value, and the verification environment hash value to obtain a shared hash check value, wherein the shared hash check value includes the material hash check value, the production hash check value, and the environment hash check value; The initial sharing-related data, sharing-related data, sharing hash check value, data volume transmission rate are combined with the sharing rate reference value obtained from the preset database and the sharing evaluation weight and processed to obtain the production data sharing evaluation index. The sharing evaluation weight includes the sharing rate evaluation weight, the sharing accuracy evaluation weight and the sharing utilization evaluation weight.

6. A production data management method for material processing as claimed in claim 1, characterized in that: The specific steps of judging whether to perform production scheduling optimization based on the production data sharing evaluation index are as follows: Compare the production data sharing assessment index with the preset sharing effect threshold range obtained from the preset database: If the production data sharing evaluation index is within the preset sharing effect threshold range, the production scheduling optimization is performed, and the scheduling optimization evaluation index after the production scheduling optimization is performed is obtained; If the production data sharing evaluation index exceeds the preset sharing effect threshold range, the production scheduling optimization will not be performed, and the preset personnel will be reminded to change the data sharing mechanism.

7. A production data management method for material processing as claimed in claim 6, characterized in that: The specific steps of obtaining the scheduling optimization evaluation index after executing the production scheduling optimization are as follows: Obtaining initial scheduling optimization related values ​​within a preset time interval to be optimized before executing production scheduling optimization, wherein the initial scheduling optimization related values ​​include an initial material accuracy assessment value, an initial production accuracy assessment value, an initial environment accuracy assessment value, and an initial production data sharing assessment index; Obtaining scheduling optimization related values ​​within a preset time interval after executing production scheduling optimization, wherein the scheduling optimization related values ​​include an optimization material accuracy evaluation value, an optimization production accuracy evaluation value, an optimization environment accuracy evaluation value, and an optimization production data sharing evaluation index; The initial scheduling optimization related values, the scheduling optimization related values ​​and the scheduling optimization evaluation weights obtained from the preset database are processed to obtain a scheduling optimization evaluation index, wherein the scheduling optimization evaluation weights include quality optimization evaluation weights and sharing optimization evaluation weights.

8. A production data management method for material processing as claimed in claim 1, characterized in that: The specific steps of comprehensively judging whether to perform data storage architecture optimization based on the production data quality evaluation index and the production data sharing evaluation index are as follows: A1, determine whether the obtained production data quality assessment index is within the preset data quality assessment threshold range in the preset database, if so, execute A2, otherwise, do not perform data storage architecture optimization; A2, judging whether the obtained production data sharing evaluation index is within the preset sharing effect threshold range, if the production data sharing evaluation index is within the preset sharing effect threshold range, executing A3, otherwise, not executing data storage architecture optimization; A3, execute production scheduling optimization and determine whether the obtained scheduling optimization evaluation index is within the preset scheduling optimization effect threshold range. If the scheduling optimization evaluation index is within the preset scheduling optimization effect threshold range, perform data storage architecture optimization; otherwise, do not perform data storage architecture optimization.

9. A production data management method for material processing as claimed in claim 1, characterized in that: Also includes: Continuously monitoring the access frequency of preset data classifications, and performing data migration based on the access frequency of preset data classifications within a preset time interval, wherein the data classifications include preset high access data, preset medium access data, and preset low access data; Continuously monitor the storage capacity of the preset storage tiers and perform load balancing on the preset storage tiers through a load balancing algorithm.

10. A production data management system for material processing, characterized in that: include: Production data collection module, quality assessment and judgment module, sharing assessment and judgment module, scheduling assessment and judgment module; Wherein, the production data acquisition module is used to collect processing production data during the material processing process within a preset time interval, and the processing production data includes material-related data, production-related data and environment-related data; The quality assessment judgment module is used to evaluate the collection quality of the processing production data to obtain a production data quality assessment index, and judge whether to execute the processing production data sharing based on the production data quality assessment index, and the production data quality assessment index is used to quantify the collection quality of the processing production data; The sharing evaluation judgment module is used to evaluate the effect of processing and production data sharing after executing processing and production data sharing to obtain a production data sharing evaluation index, and judge whether to execute production scheduling optimization based on the production data sharing evaluation index, and the production data sharing evaluation index is used to quantitatively evaluate the effect of processing and production data sharing; The scheduling evaluation judgment module is used to obtain the scheduling optimization evaluation index after executing the production scheduling optimization, and comprehensively judge whether to execute the data storage architecture optimization based on the production data quality evaluation index and the production data sharing evaluation index. The scheduling optimization evaluation index is used to quantitatively evaluate the effect of the production scheduling optimization.

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