Steel model production informatization management method and system and storage medium

Abstract

The invention relates to the field of production informatization management, in particular to a steel model production informatization management method and system and a storage medium, and aims to dynamically formulate an information acquisition strategy, realize intelligent adjustment of key parameter acquisition frequency, avoid resource redundancy acquisition under unnecessary conditions, remarkably improve data utilization efficiency, and improve production efficiency. Meanwhile, simulation verification is carried out through a virtual model based on the collected data, and the implementation effect of an adjustment scheme is rehearsed in a virtual production model, so that invalid or harmful decisions are screened out before the adjustment scheme is put into an entity production line, production fluctuation and resource waste caused by decision errors are greatly reduced, and the production efficiency is improved. And finally, continuously tracking the consistency of a virtual decision and an entity execution result, and starting a machine learning optimization process when simulation deviation occurs.

Description

Technical Field

[0001] This invention relates to the field of production information management, specifically to a method, system, and storage medium for the production information management of steel molds. Background Technology

[0002] In recent years, with the rapid development of intelligent manufacturing and industrial information technology, heavy industries such as steel and machinery are gradually introducing production management systems to achieve process optimization and efficiency improvement. Traditional steel model production management systems usually use data acquisition and monitoring systems or manufacturing execution systems as the core architecture to achieve real-time monitoring of production line equipment status, environmental parameters and product quality. Some systems also introduce digital twin technology to build a virtual model corresponding to the physical production line to simulate the production process and predict equipment failures.

[0003] However, when dealing with multi-objective and highly dynamic production tasks, there are usually obvious shortcomings. The data acquisition strategy of the system is mostly fixed frequency or manually set, and cannot be adaptively adjusted according to the real-time changes in production objectives. At the same time, traditional virtual models often lack feedback and self-learning mechanisms in the simulation verification stage, resulting in significant deviations between virtual decisions and actual production line execution results. Ultimately, this leads to insufficient data support or resource waste in high-speed production or high-precision environmental control scenarios, affecting the overall control accuracy and reliability of the system.

[0004] Therefore, there is an urgent need to develop an information management system for steel model production that can achieve adaptive data acquisition strategies, simulateable decision-making processes, and iterative model accuracy, in order to solve the aforementioned technical deficiencies. Summary of the Invention

[0005] This invention proposes a method, system, and storage medium for information management of steel model production by dynamically formulating information collection strategies to intelligently adjust the collection frequency of key parameters such as environmental information, product quality inspection, equipment operation, and raw material inventory. This ensures the data support strength for key production links while avoiding redundant resource collection in unnecessary situations, significantly improving data utilization efficiency. Simultaneously, based on the collected data, simulation verification is performed through a virtual model to pre-enact the implementation effect of the adjustment plan in the virtual production model. This screens out ineffective or harmful decisions before they are put into the physical production line, greatly reducing production fluctuations and resource waste caused by decision-making errors. Finally, by continuously tracking the consistency between virtual decisions and physical execution results, and initiating machine learning optimization processes when simulation deviations occur, this invention proposes a method, system, and storage medium for information management of steel model production.

[0006] The objective of this invention can be achieved through the following technical solution: an information management method for steel model production, comprising the following steps: Step 1: Obtain the production target of the steel model, and make collection and management decisions based on the production target to complete the collection of production information for the steel model; Step Two: Collect information from multiple sources regarding the steel model production process; Step 3: Construct a virtual production model using the collected inherent information from the production line; Step 4: Based on the steel model production information collected in Step 2, make judgments and conduct management decisions through a decision-making process to obtain virtual management decisions; Step 5: Apply the virtual management decisions to the virtual production model to obtain virtual decision results, and then evaluate the virtual decision results in production to obtain the reliability of the decision application; Step Six: Assess the reliability of the decision application. If the assessment is successful, transform the virtual management decision into a physical management decision and apply it to the physical production line. Step 7: Collect the entity decision results after applying entity management decisions, compare the similarity between the entity decision results and the virtual decision results, and verify and optimize the simulation degree of the virtual production model.

[0007] The present invention also proposes an information management system for steel model production, including a production line acquisition module, a central decision-making module, a virtual image construction module, a decision review module, a mapping comparison module, and a parameter optimization module. The central decision-making module can acquire the steel model production target and generate information acquisition decisions based on the production target. The production line acquisition module can collect information on the production line operation status of the steel model based on information acquisition decisions, and obtain production line operation information. At the same time, the production line acquisition module also collects inherent information of the production line. The virtual image construction module obtains the inherent information of the production line through the production line acquisition module and then constructs a virtual production model based on the inherent information of the production line. After obtaining production line operation information, the central decision-making module makes management decisions based on the production line operation information, obtains virtual management decisions, and sends the virtual management decisions to the decision review module. The decision review module applies virtual management decisions to a virtual production model, performs simulation based on the virtual production model, obtains virtual decision results, conducts risk assessment on the virtual decision results, generates decision application reliability based on the risk assessment results, and sends the decision application reliability to the central decision module when it meets the standard. The central decision module then transforms the virtual management decisions into physical management decisions and applies them to the physical production line. After the entity management decision is applied, the mapping comparison module collects the entity decision results of the entity production line and compares them with the virtual decision results to obtain the simulation similarity. Based on the simulation similarity, it makes a judgment, generates a simulation optimization signal or a simulation qualified signal, and feeds it back to the parameter optimization module.

[0008] In a preferred embodiment of the present invention, the production targets obtained by the central decision-making module include production speed, production environment requirements and product qualification rate. The central decision-making module determines the environmental information collection frequency based on the standard environmental range span in the production environment requirements, determines the product quality inspection sampling frequency based on the product qualification rate, and determines the collection frequency based on the equipment operation status and raw material inventory based on the production speed. The central decision-making module records the collection frequencies of environmental information, product quality inspection sampling, equipment operation status, and raw material inventory as information collection decisions.

[0009] In a preferred embodiment of the present invention, the production line operation information collected by the production line acquisition module includes real-time environmental information, product qualification rate, equipment failure probability, and raw material inventory. The production line acquisition module collects inherent information about the production line, including the layout of production line equipment, process flow, and production resource configuration. Specifically, the production resource configuration includes the operating speed of production equipment, the adjustment capability of environmental control equipment, and the allocation of human resources.

[0010] In a preferred embodiment of the present invention, the central decision module compares the production line operation information with the set production target, obtains the environmental deviation based on the real-time environmental information, obtains the qualification rate deviation based on the product qualification rate, obtains the expected production speed based on the equipment failure probability and raw material inventory, and compares it with the production speed in the production target to obtain the speed deviation. The central decision-making module statistically analyzes environmental deviations, pass rate deviations, and speed deviations, identifies items requiring adjustment through threshold judgments, obtains adjustment plans, and records them as virtual management decisions.

[0011] In a preferred embodiment of the present invention, the decision review module performs simulation on the virtual production model through virtual management decision-making to obtain the changes in the virtual production model after the implementation of the adjustment plan. Then, the virtual environmental deviation, pass rate deviation and speed deviation are compared with the deviations statistically recorded by the central decision module. Based on the comparison results, it is determined whether it is a positive feedback adjustment or a negative feedback adjustment. After determining that the decision review module is a positive feedback adjustment, it transforms the virtual management decision into a physical management decision and applies it to the physical production line. After determining that the adjustment is a negative feedback adjustment, the decision review module generates an abnormal adjustment reminder and outputs the reminder through a display device.

[0012] In a preferred embodiment of the present invention, the mapping comparison module collects the results of the entity decision-making of the physical production line, obtains all kinds of deviations of the physical production line, and compares them with the deviations after the virtual management decision is applied to the virtual production model to obtain the comparison similarity. Based on the comparison results, it is determined whether the model simulation is qualified or the model simulation has deviations, and a simulation qualified signal or a simulation optimization signal is generated accordingly.

[0013] The present invention also proposes a computer storage medium storing a computer program, which, when executed by a processor, implements the aforementioned information management method for steel model production.

[0014] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention dynamically formulates information collection strategies based on production targets, enabling intelligent adjustment of the collection frequency of key parameters such as environmental information, product quality inspection, equipment operation, and raw material inventory. It effectively overcomes the problem of fixed collection strategies in traditional systems that cannot adapt to high-precision production needs. It ensures the data support strength of key production links and avoids redundant resource collection in unnecessary situations, significantly improving data utilization efficiency.

[0015] 2. In this invention, by constructing a simulation test process from virtual decision generation to simulation verification, the system pre-simulates the implementation effect of adjustment schemes in a virtual production model. By comparing virtual deviations with actual deviations, positive or negative feedback is quickly identified, thereby screening out invalid or harmful decisions before putting them into the physical production line. This significantly reduces production fluctuations and resource waste caused by decision-making errors. At the same time, the abnormality reminder function enables human-machine collaborative intervention, enhancing the reliability of the system under complex working conditions.

[0016] 3. This invention can also continuously track the consistency between virtual decisions and physical execution results, and initiate machine learning optimization processes when simulation deviations occur, enabling the virtual production model to continuously approximate the real behavior of the physical production line, forming a virtuous cycle of iterative improvement in simulation accuracy, significantly improving the predictive accuracy of management decisions, and providing continuously optimized digital support for the refined and intelligent management of steel model production. Attached Figure Description

[0017] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0018] Figure 1 This is a system block diagram of the present invention; Figure 2 This is a system flowchart of the present invention. Detailed Implementation

[0019] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0020] Example 1: Please refer to Figure 1 - Figure 2 As shown, an information management method for steel model production includes the following steps: Step 1: Obtain the production objectives of the steel model, including the production environment requirements and pass rate requirements of the steel model, and make data collection and management decisions based on the production objectives to determine different data collection frequencies and quality inspection frequencies, thereby completing the collection of inherent production line information of the steel model production line. Step 2: Collect information from multiple aspects of the steel model production process according to the established collection and quality inspection frequencies, thereby collecting parameters such as production speed, production environment, and product qualification rate during the operation of the steel model production line; Step 3: Construct a virtual production model using the collected production line information to obtain a digitized virtual production line; Step 4: Based on the production line operation information of the steel model production line collected in Step 2, determine whether the current production line needs to be controlled and adjusted through the decision-making process, and generate a management decision if adjustment is required, thus obtaining a virtual management decision. Step 5: Apply virtual management decisions to the virtual production model, simulate the production line operation results after adjustment, obtain virtual decision results, and evaluate the virtual decision results to obtain the reliability of decision application. The reliability of decision application is divided into positive feedback adjustment and negative feedback adjustment. Step Six: Assess the reliability of the decision application. If it is a positive feedback adjustment, transform the virtual management decision into a physical management decision and apply it to the physical production line. If it is a negative feedback adjustment, issue an early warning and request management personnel to take the initiative to intervene. Step 7: Collect the entity decision results after applying entity management decisions, compare the similarity between the entity decision results and the virtual decision results, and verify and optimize the simulation degree of the virtual production model.

[0021] Example 2: Please refer to Figure 1 - Figure 2 As shown, a steel model production information management system includes a production line acquisition module, a central decision-making module, a virtual image construction module, a decision review module, a mapping comparison module, and a parameter optimization module. The central decision-making module obtains production targets from the host computer through the industrial network. These targets include production speed, production environment requirements, and product qualification rate. After obtaining the production targets, the central decision-making module determines the corresponding environmental information collection frequency based on the standard environmental range in the production environment requirements. That is, the lower the standard environmental range in the production environment requirements, the higher the environmental information collection frequency. It also determines the product quality inspection sampling frequency based on the product qualification rate. The higher the product qualification rate requirement, the higher the product quality inspection sampling frequency. Finally, it determines the collection frequency for equipment operation status and raw material inventory based on the production speed. The faster the production speed, the higher the collection frequency. Ultimately, the central decision-making module constructs information collection decisions by combining the collection frequencies of environmental information, product quality inspection sampling, equipment operation status, and raw material inventory.

[0022] The production line data acquisition module obtains information acquisition decisions through the central decision module and collects production line operation information based on the information acquisition decisions. The production line operation information includes real-time environmental information, product qualification rate, equipment failure probability, and raw material inventory. The production line data acquisition module supplements the production line's inherent information according to the set inherent acquisition frequency. The inherent information of the production line includes the production line equipment layout, process flow, and production resource configuration. The production line equipment layout and process flow are manually entered by personnel, while the production resource configuration is acquired in real time, specifically including the operating speed of production equipment, the adjustment capability of environmental control equipment, and human resource configuration. The production line acquisition module sends the inherent information of the production line to the virtual image construction module, which then creates a digital virtual production model based on the inherent information of the production line. The central decision-making module obtains real-time production line operation information through the production line acquisition module and compares the production line operation information with the set production targets. Specifically, it compares the real-time environmental information with the production environment requirements to obtain the environmental deviation, compares the product qualification rate with the product qualification rate of the production target to obtain the qualification rate deviation, and simulates the equipment failure probability and raw material inventory to obtain the expected production speed, and compares it with the production speed in the production target to obtain the speed deviation. The central decision-making module statistically analyzes environmental deviations, pass rate deviations, and speed deviations, compares them based on adjustment thresholds, identifies items that need adjustment, imports these items into a knowledge graph, generates path simulations through the knowledge graph, and obtains adjustment schemes, such as environmental temperature control, humidity control, production speed adjustment, equipment maintenance, and raw material replenishment schemes, which are recorded as virtual management decisions. The central decision-making module sends virtual management decisions to the decision review module. After receiving the virtual management decisions, the decision review module performs simulations on the virtual production model based on the virtual management decisions to obtain the changes in the virtual production model after implementing the adjustment plan. The changes are then compared with the set production targets to obtain the virtual environmental deviation, pass rate deviation, and speed deviation. The virtual deviations are then compared with the deviations statistically recorded by the central decision-making module. If all types of deviations decrease, it is determined to be a positive feedback adjustment; if any deviation increases, it is determined to be a negative feedback adjustment. After determining that the decision review module is a positive feedback adjustment, it transforms the virtual management decision into a physical management decision and applies it to the physical production line. After the decision review module determines that the adjustment is a negative feedback, it generates an abnormal adjustment reminder and outputs the reminder through the display device, thereby reminding human intervention to realize manual intervention and adjustment of the production line; After the entity management decision is applied to the entity production line, the mapping comparison module collects the results of the entity decision on the entity production line, thereby obtaining all kinds of deviations of the entity production line after the entity management decision is applied to the entity production line, and compares them with the deviations after the virtual management decision is applied to the virtual production model to obtain the comparison similarity. If the comparison similarity is less than or equal to the set threshold, the model simulation is judged to be qualified and a simulation qualified signal is generated. If the comparison similarity is greater than the set threshold, the model simulation is judged to have deviations and a simulation optimization signal is generated. After obtaining the simulation deviation of the model, the mapping comparison module feeds back the simulation optimization signal to the parameter optimization module. The parameter optimization module performs machine learning optimization on the virtual production model, thereby continuously improving the simulation level of the virtual production model.

[0023] Example 3: Please refer to Figure 1 - Figure 2 As shown, a computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the aforementioned information management method for steel model production.

[0024] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods.

[0025] Any references to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory.

[0026] By way of illustration and not limitation, RAM is available in many forms, such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), Rambus Direct RAM (RDRAM), Direct Memory Bus Dynamic RAM (DRDRAM), and Memory Bus Dynamic RAM (RDRAM), etc. Thresholds, preset values, or preset ranges are set for result comparison and analysis to determine whether they are good or bad. The value of these thresholds is determined by a combination of large-scale model analysis of sample data and human experience. They can also be adjusted appropriately based on seasonal or rational factors.

[0027] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. An information management method for steel model production, characterized in that, Includes the following steps: Step 1: Obtain the production target of the steel model, and make collection and management decisions based on the production target to complete the collection of production information for the steel model; Step Two: Collect information from multiple sources regarding the steel model production process; Step 3: Construct a virtual production model using the collected inherent information from the production line; Step 4: Based on the steel model production information collected in Step 2, make judgments and conduct management decisions through a decision-making process to obtain virtual management decisions; Step 5: Apply the virtual management decisions to the virtual production model to obtain virtual decision results, and then evaluate the virtual decision results in production to obtain the reliability of the decision application; Step Six: Assess the reliability of the decision application. If the assessment is successful, transform the virtual management decision into a physical management decision and apply it to the physical production line. Step 7: Collect the entity decision results after applying entity management decisions, compare the similarity between the entity decision results and the virtual decision results, and verify and optimize the simulation degree of the virtual production model.

2. An information management system for steel model production, applicable to the information management method for steel model production as described in claim 1, characterized in that, It includes a production line acquisition module, a central decision-making module, a virtual image construction module, a decision review module, a mapping comparison module, and a parameter optimization module. The central decision-making module can acquire the production target of the steel model and generate information acquisition decisions based on the production target. The production line acquisition module can collect information on the production line operation status of the steel model based on information acquisition decisions, and obtain production line operation information. At the same time, the production line acquisition module also collects inherent information of the production line. The virtual image construction module obtains the inherent information of the production line through the production line acquisition module and then constructs a virtual production model based on the inherent information of the production line. After obtaining production line operation information, the central decision-making module makes management decisions based on the production line operation information, obtains virtual management decisions, and sends the virtual management decisions to the decision review module. The decision review module applies virtual management decisions to a virtual production model, performs simulation based on the virtual production model, obtains virtual decision results, conducts risk assessment on the virtual decision results, generates decision application reliability based on the risk assessment results, and sends the decision application reliability to the central decision module when it meets the standard. The central decision module then transforms the virtual management decisions into physical management decisions and applies them to the physical production line. After the entity management decision is applied, the mapping comparison module collects the entity decision results of the entity production line and compares them with the virtual decision results to obtain the simulation similarity. Based on the simulation similarity, it makes a judgment, generates a simulation optimization signal or a simulation qualified signal, and feeds it back to the parameter optimization module.

3. The information management system for steel model production according to claim 2, characterized in that, The production targets obtained by the central decision-making module include production speed, production environment requirements, and product qualification rate. The central decision-making module determines the environmental information collection frequency based on the standard environmental range in the production environment requirements, the product quality inspection sampling frequency based on the product qualification rate, and the collection frequency based on the equipment operation status and raw material inventory based on the production speed. The central decision-making module records the collection frequencies of environmental information, product quality inspection sampling, equipment operation status, and raw material inventory as information collection decisions.

4. The information management system for steel model production according to claim 2, characterized in that, The production line data acquisition module collects production line operation information including real-time environmental information, product qualification rate, equipment failure probability, and raw material inventory. The production line acquisition module collects inherent information about the production line, including the layout of production line equipment, process flow, and production resource configuration. Specifically, the production resource configuration includes the operating speed of production equipment, the adjustment capability of environmental control equipment, and the allocation of human resources.

5. The information management system for steel model production according to claim 2, characterized in that, The central decision-making module compares the production line operation information with the set production target, obtains the environmental deviation based on real-time environmental information, obtains the qualification rate deviation based on the product qualification rate, obtains the expected production speed based on the equipment failure probability and raw material inventory, and compares it with the production speed in the production target to obtain the speed deviation. The central decision-making module statistically analyzes environmental deviations, pass rate deviations, and speed deviations, identifies items requiring adjustment through threshold judgments, obtains adjustment plans, and records them as virtual management decisions.

6. The information management system for steel model production according to claim 2, characterized in that, The decision review module simulates the virtual production model through virtual management decisions, obtains the changes in the virtual production model after the implementation of the adjustment plan, and then compares the virtual environmental deviation, pass rate deviation and speed deviation with the deviations counted by the central decision module. Based on the comparison results, it determines whether it is a positive feedback adjustment or a negative feedback adjustment. After determining that the decision review module is a positive feedback adjustment, it transforms the virtual management decision into a physical management decision and applies it to the physical production line. After determining that the adjustment is a negative feedback adjustment, the decision review module generates an abnormal adjustment reminder and outputs the reminder through a display device.

7. The information management system for steel model production according to claim 2, characterized in that, The mapping comparison module collects the results of the entity decision-making of the physical production line, obtains all kinds of deviations of the physical production line, and compares them with the deviations after the virtual management decision is applied to the virtual production model to obtain the comparison similarity. Based on the comparison results, it determines whether the model simulation is qualified or the model simulation has deviations, and generates a simulation qualified signal or a simulation optimization signal accordingly.

8. A computer storage medium, characterized in that, It stores a computer program, which, when executed by a processor, implements the information management method for steel model production as described in claim 1.

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