CN-121979165-A - Production and environment data fusion and cooperative control method and system

CN121979165ACN 121979165 ACN121979165 ACN 121979165ACN-121979165-A

Abstract

The application discloses a production and environment data fusion and cooperative control method and system, and relates to the field of industrial informatization and intelligent manufacturing. The method comprises the steps of collecting production execution data and environment monitoring data, constructing a unified data structure, calculating space association degree according to a workshop topological structure, determining a monitoring point set and a weight coefficient, calculating a representative environment parameter value based on a time window, fusing process parameters and environment parameters to form a data set, extracting environment influence factors and process response factors, constructing an association matrix, establishing a constraint function, determining a process parameter value range and generating constraint rules, monitoring the environment parameters in real time, triggering the constraint rules when the environment parameters exceed a threshold value, calculating an adjustment value and generating a scheme, and realizing automatic parameter adjustment. The application takes environmental factors as constraint conditions of production scheduling and technological parameter setting, and optimizes production efficiency on the premise of environmental compliance.

Inventors

SHAO DANFENG
ZHU YEFENG
ZHU JIANFENG
WU YINGFEI
HUANG YONGGANG

Assignees

嘉兴市远东精密印刷有限公司

Dates

Publication Date: 20260505
Application Date: 20260409

Claims (10)

1. A method for fusing and cooperatively controlling production and environmental data, comprising the steps of: Collecting production execution data and environment monitoring data, wherein the production execution data comprises a production task identifier and a process parameter, and the environment monitoring data comprises an environment parameter value, a monitoring point position coordinate and a data acquisition time stamp; A multidimensional label system is adopted to construct a unified data structure for the production execution data and the environment monitoring data, standardized production execution data and environment monitoring data are generated, and the multidimensional label system comprises a time dimension label, a space dimension label, a production dimension label and an environment dimension label; Calculating the space association degree between the environmental monitoring points and the production operation areas according to the workshop topological structure, determining an associated monitoring point set and a weight coefficient for each production operation area, extracting environmental parameters of a time period corresponding to the production execution data from the environmental monitoring data based on a time window, and calculating representative environmental parameter values in the time window by adopting a weighted average method; Inquiring corresponding task technological requirements according to the production task identification, combining the associated monitoring point set and the weight coefficient, and fusing the technological parameters with corresponding representative environmental parameters to form a fused data set containing production information and environmental information; extracting environmental impact factors and process response factors in the fusion data set, constructing an environment-process association matrix, establishing constraint functions between the environmental parameters and the process parameters according to the environment-process association matrix, determining allowable value ranges of the process parameters in different environmental states, and generating an environment constraint rule set; Collecting current environment parameters in real time, matching the current environment parameters with the environment constraint rule set, triggering corresponding constraint rules when the current environment parameters exceed a preset threshold, calculating adjustment values of the process parameters according to the triggered constraint rules and the constraint functions, and generating a process parameter adjustment scheme; executing the technological parameter adjustment scheme to realize automatic adjustment of technological parameters.
2. The method of claim 1, further comprising, after generating the process parameter adjustment scheme: and when the task delay risk exceeds a preset risk threshold or the capacity is reduced to exceed a preset capacity reduction threshold, executing production scheduling optimization, and compensating the capacity change caused by the process parameter adjustment by adjusting a task execution sequence or an equipment allocation scheme.
3. The method of claim 1, further comprising, after performing the process parameter adjustment scheme: Monitoring environmental parameter changes and production process data after control execution, calculating control effectiveness indexes, and recording effective control cases to a knowledge base; and optimizing and updating the environment constraint function based on the control effect.
4. The method of claim 1, wherein the calculating the spatial association between the environmental monitoring points and the production job areas according to the plant topology and determining the associated monitoring point set and the weight coefficient for each production job area, extracting the environmental parameters corresponding to the time period of the production execution data from the environmental monitoring data based on the time window and calculating the representative environmental parameter values within the time window by using a weighted average method comprises: acquiring the space position of production equipment, the layout of an air supply and exhaust system, workshop partition information and airflow direction information in the workshop topological structure; Dividing the workshop space into areas according to production procedures according to the space position of the production equipment, the layout of the air supply and exhaust system, the partition information of the workshop and the flow direction information of the air flow, wherein each production operation area corresponds to a space range; calculating the space linear distance between each environment monitoring point and the center point of each production operation area, determining wind direction influence factors according to the layout of the air supply and exhaust system and the dominant wind direction, and determining airflow diffusion correction factors according to the distribution of obstacles and airflow paths in a workshop; Calculating the spatial association degree of each production operation area to each environment monitoring point based on the spatial linear distance, the wind direction influence factor and the airflow diffusion correction factor, and carrying out normalization processing on the spatial association degree to obtain the weight coefficient; for each production operation area, selecting a preset number of environment monitoring points with highest spatial association degree to form the association monitoring point set; Extracting, for each timestamp of the production record in the production execution data, an environmental parameter value within a time window centered on the timestamp from the environmental monitoring data; calculating time weight according to the time distance between the acquisition time of the environmental parameter value and the production record time stamp; And carrying out weighted average on a plurality of environment parameter values in the time window by adopting the time weight to obtain the representative environment parameter value.
5. The method of claim 4, wherein the querying the corresponding task process requirements according to the production task identifier, combining the set of associated monitoring points and the weight coefficient, fusing the process parameters with the corresponding representative environmental parameters to form a fused dataset comprising production information and environmental information, comprises: For each production record in the production execution data, inquiring corresponding task process requirements from a production process database according to a production task identifier in the production record, wherein the task process requirements comprise a standard process parameter range, a product quality standard and a process control requirement; inquiring an associated monitoring point set and a weight coefficient corresponding to the production operation area from a space mapping table according to the time stamp of the production record and the production operation area number; Extracting representative environmental parameter values of each associated monitoring point in a time window corresponding to the production record from the time alignment processing result; for a plurality of monitoring points associated with the production operation area, weighting and aggregating the representative environment parameter values of the monitoring points by adopting the weight coefficient to obtain a comprehensive environment parameter value; Respectively carrying out weighted aggregation on the representative environmental parameter values of all the monitoring points by adopting the weight coefficients to obtain an environmental parameter vector containing multiple types of environmental parameters, wherein the multiple types of environmental parameters comprise volatile organic compound concentration, temperature and humidity; And merging the environment parameter vector, the task process requirement and the process parameters, the production task identifier, the spatial position and the time stamp in the production record to form the fusion data set.
6. The method of claim 5, wherein the extracting the environmental impact factors and the process response factors in the fused dataset and constructing an environment-process correlation matrix, establishing a constraint function between the environmental parameters and the process parameters according to the environment-process correlation matrix, determining allowable value ranges of the process parameters in different environmental states and generating an environment constraint rule set, comprises: Calculating a correlation coefficient between each environmental impact factor and each process response factor, and constructing an environment-process correlation matrix, wherein the environment-process correlation matrix represents correlation strength between corresponding environment parameters and process parameters, the environmental impact factors comprise volatile organic compound concentration, peculiar smell index, temperature and humidity, and the process response factors comprise printing speed, drying temperature, air supply quantity and air exhaust quantity; Dividing the value range of the environmental parameter into a normal interval, an early warning interval and an overrun interval; establishing a sectional constraint function aiming at different areas, wherein the sectional constraint function determines an adjustment coefficient of a process parameter under the value of a corresponding environment parameter; and generating the environment constraint rule set based on the piecewise constraint function, wherein each constraint rule comprises a trigger condition, a corresponding technological parameter, an adjustment direction and an adjustment amplitude.
7. The method of claim 6, wherein the matching the current environmental parameter with the set of environmental constraint rules, triggering a corresponding constraint rule when the current environmental parameter exceeds a preset threshold, calculating an adjustment value for the process parameter and generating a process parameter adjustment scheme according to the triggered constraint rule and the constraint function, comprises: Comparing the current environmental parameters acquired in real time with the triggering conditions of all constraint rules in the environmental constraint rule set one by one; When the current environmental parameter exceeds the preset threshold value, marking a constraint rule meeting a trigger condition as an activated state; Extracting the type of the technological parameter, the adjusting direction and the reference adjusting amplitude which need to be adjusted from the constraint rule of the activation state; Substituting the current environmental parameters into the corresponding constraint functions, and calculating target values of the process parameters; Calculating an adjustment value of the process parameter according to the difference between the current value of the process parameter and the target value; When a plurality of environment parameters exceed the preset threshold value at the same time, distributing constraint priority weights for the environment parameters, calculating comprehensive constraint intensity based on the constraint priority weights, and determining a joint adjustment value of the process parameters according to the comprehensive constraint intensity; summarizing all the technological parameters and adjustment values to be adjusted to form the technological parameter adjustment scheme.
8. The method of claim 2, wherein the step of evaluating the adjusted tact, unit production time, and risk of delay in task scheduling according to the process parameter adjustment scheme, and performing production scheduling optimization when the risk of delay in task scheduling exceeds a preset risk threshold or the capacity drop exceeds a preset capacity drop threshold, and compensating for capacity variation due to the process parameter adjustment by adjusting a task execution order or an equipment allocation scheme comprises: Calculating the adjusted production takt time and unit product production time according to the process parameter adjustment value in the process parameter adjustment scheme; Comparing the adjusted production time with the task intersection period in the current production plan, and judging whether an intersection delay risk exists or not; When the delay risk exists, starting production scheduling optimization, wherein the optimization strategy comprises the steps of adjusting the task execution sequence, starting a standby production line, increasing the equipment parallelism or optimizing the production batch; For task execution sequence adjustment, tasks with smaller influence on the environment are preferentially arranged, and high-pollution tasks are delayed or executed in a scattered manner; For the equipment parallel strategy, starting standby equipment or increasing the parallel quantity of the equipment, and compensating the productivity loss caused by the reduction of the single-line speed by improving the utilization rate of the equipment; For batch optimization, adjusting production batch and batch switching frequency, and reducing the change time; And establishing a scheduling optimization model based on equipment capacity constraint, material supply constraint, process flow constraint and the process parameter adjustment scheme, and solving an optimized production scheduling scheme.
9. The method of claim 3, wherein monitoring environmental parameter changes and production process data after control execution, calculating a control availability indicator, and recording availability control cases to a knowledge base, comprises: Continuously collecting environmental parameter change data and production process data after the control instruction is executed, wherein the production process data comprises an adjusted actual execution value of a process parameter, an equipment running state and a product quality index; Calculating the control effectiveness index, wherein the control effectiveness index comprises an environment improvement rate, an environment reaching time, control stability and production efficiency influence rate; When the environment improvement rate is larger than a preset improvement threshold value and the production efficiency influence rate is smaller than a preset influence threshold value, judging that the control measure is effective; recording effective control cases to the knowledge base, wherein the control cases comprise initial environment states, triggered constraint rules, the process parameter adjustment scheme, environment parameter change curves, production process data and the control effectiveness indexes; optimizing and updating threshold parameters and adjustment coefficients of the constraint functions based on the control cases; The environment improvement rate indicates the improvement degree of the environment parameter to the target value, and is calculated according to the environment parameter value before control, the environment parameter value after control and the target threshold value when the environment parameter is stable, the environment standard reaching time indicates the time required for the environment parameter to fall to the normal threshold value from the execution of control, the control stability indicates the fluctuation standard deviation of the environment parameter after control, and the production efficiency influence rate indicates the productivity reduction ratio caused by control measures.
10. A production and environmental data fusion and cooperative control system, comprising: The data acquisition module is used for acquiring production execution data and environment monitoring data, wherein the production execution data comprises a production task identifier and a process parameter, and the environment monitoring data comprises an environment parameter value, a monitoring point position coordinate and a data acquisition time stamp; The data modeling module is used for constructing a unified data structure for the production execution data and the environment monitoring data by adopting a multi-dimensional label system to generate standardized production execution data and environment monitoring data, wherein the multi-dimensional label system comprises a time dimension label, a space dimension label, a production dimension label and an environment dimension label; the space-time mapping module is used for calculating the space association degree between the environmental monitoring points and the production operation areas according to the workshop topological structure, determining an associated monitoring point set and a weight coefficient for each production operation area, extracting environmental parameters of a time period corresponding to the production execution data from the environmental monitoring data based on a time window, and calculating representative environmental parameter values in the time window by adopting a weighted average method; The data fusion module is used for inquiring corresponding task process requirements according to the production task identification, combining the associated monitoring point set and the weight coefficient, and fusing the process parameters with corresponding representative environment parameters to form a fusion data set containing production information and environment information; The constraint modeling module is used for extracting the environment influence factors and the process response factors in the fusion data set, constructing an environment-process association matrix, establishing constraint functions between the environment parameters and the process parameters according to the environment-process association matrix, determining the allowable value ranges of the process parameters in different environment states and generating an environment constraint rule set; The control decision module is used for collecting current environment parameters in real time, matching the current environment parameters with the environment constraint rule set, triggering corresponding constraint rules when the current environment parameters exceed a preset threshold, calculating adjustment values of the process parameters according to the triggered constraint rules and the constraint functions, and generating a process parameter adjustment scheme; And the instruction issuing module is used for executing the process parameter adjustment scheme to realize automatic adjustment of the process parameters.

Description

Production and environment data fusion and cooperative control method and system Technical Field The application relates to the technical field of industrial informatization and intelligent manufacturing, in particular to a production and environment data fusion and cooperative control method and system. Background In the printing manufacturing industry, the production process generally relies on a production execution system (Manufacturing Execution System, MES) to manage and schedule production tasks, equipment states, and process parameters, thereby achieving fine control of the production process. Meanwhile, in order to meet Environmental supervision requirements, enterprises generally deploy an Environmental management system (Environmental MANAGEMENT SYSTEM, EMS) to monitor Environmental parameters such as volatile organic compounds (volatile organic compounds, VOC), concentration of peculiar smell gas, temperature and humidity in a workshop in real time. In the prior art, the MES and the EMS usually operate as two independent systems, the MES focuses on production process control and scheduling optimization, and the EMS focuses on environmental data acquisition and alarm management. The two differ significantly in system architecture, data structure, and application goals, resulting in a lack of efficient correlation and fusion between production data and environmental data. Further, in a printing workshop, the substances such as ink, solvent and the like used in the production process release volatile pollutants with certain concentration, and environmental factors not only affect the air quality of the workshop, but also have potential influence on the production stability, the product quality and the running state of equipment. However, when the existing MES system performs production scheduling and parameter setting, environmental factors are not considered as constraint conditions, and environmental data collected by the EMS system are only used for out-of-standard alarm, and do not participate in dynamic regulation and control of the production process. Disclosure of Invention The application aims to provide a production and environment data fusion and cooperative control method and system, which are used for solving the problems in the background technology. In a first aspect, an embodiment of the present application provides a method for fusing and cooperatively controlling production and environmental data, the method comprising collecting production execution data and environmental monitoring data, the production execution data including a production task identifier and process parameters, the environmental monitoring data including environmental parameter values, monitoring point position coordinates and data acquisition time stamps, constructing a unified data structure for the production execution data and the environmental monitoring data by using a multi-dimensional tag system, generating standardized production execution data and environmental monitoring data, the multi-dimensional tag system including a time dimension tag, a space dimension tag, a production dimension tag and an environmental dimension tag, calculating a spatial association degree between environmental monitoring points and production operation areas according to a workshop topology structure, determining an associated monitoring point set and a weight coefficient for each production operation area, extracting environmental parameters corresponding to the production execution data from the environmental monitoring data based on a time window, calculating representative environmental parameter values in the time window by using a weighted average method, inquiring corresponding task process requirements according to the production task identifier, combining the associated monitoring point set and the weight coefficient, fusing the process parameters with the corresponding representative environmental parameters to form a fusion data set including production information and environmental constraint factors, constructing a fusion data set including the production information and the environmental constraint factor, and the current process factor, and the current environment constraint factor is set, determining an environmental constraint factor and an environmental constraint factor, and an environmental constraint factor is set, and an environmental constraint is set based on a real-time constraint is established, triggering corresponding constraint rules when the current environment parameters exceed a preset threshold, calculating adjustment values of the process parameters according to the triggered constraint rules and constraint functions, generating a process parameter adjustment scheme, and executing the process parameter adjustment scheme to realize automatic adjustment of the process parameters. With reference to the first aspect, in some implementations of the first aspect, after the process parameter adjust