Process control parameter dynamic calculation method, system and equipment based on customer demands and medium

Abstract

The invention provides a process control parameter dynamic calculation method, system and device based on customer requirements and a medium. The method comprises the steps that S1, process control parameters and corresponding constraint condition sets are set according to the process requirements of steel products to be produced; s2, according to the acquired process control parameters and the corresponding constraint condition set, performing simulation verification of a preset scene based on customer information, product information and historical material big data, and evaluating and optimizing the priority of constraint conditions based on a verification result; and S3, based on the set process control parameters and the optimized constraint condition set, obtaining process control parameters based on customer demands.

Description

Technical Field

[0001] The present invention relates to the field of quality management information technology in smart manufacturing in the metallurgical steel industry, and specifically to a method, system, equipment and medium for dynamically calculating process control parameters based on customer needs. Background Art

[0002] The steel industry has a long production and manufacturing process, uses large-scale manufacturing equipment, has complex process control, has a wide variety of products, and its product applications involve many fields such as automobiles, construction, machinery, new energy, and aerospace, with a very large customer base. With the development of the market and the trend of customers constantly putting forward special requirements for the technical conditions of delivered products, as well as energy conservation and environmental protection requirements, the manufacturing end must take active measures and methods to respond, not only to differentiate the market and customer needs, but also to improve product quality through technological innovation and process improvement.

[0003] Process control in the manufacturing process is crucial to the performance and quality of the product. If the process execution standards are released to the site in the form of official documents, and the personalized process adjustment requirements rely on manual coordination, telephone or email, paper printing and other means of transmission, it will be difficult to achieve timely and accurate information transmission, and the actual implementation on site will be difficult to supervise, and even cause quality complaints from downstream processes or customers. Therefore, not only should a complete quality standard implementation specification be formulated in the product design stage, but also effective means should be available to quickly transmit the special requirements of customers to the site, and in the manufacturing process, abnormal fluctuations in the previous process can be monitored in real time, and lean corrections can be made in advance for the subsequent process and on-site production can be guided, and even the process control can automatically execute the adjusted process setting values. Transform from passive firefighting after the event to active prevention mode before the event, realize lean quality control, improve product quality, reduce quality costs, and improve customer satisfaction. Summary of the invention

[0004] In view of the defects in the prior art, the object of the present invention is to provide a method, system, equipment and medium for dynamically estimating process control parameters based on customer needs.

[0005] A method for dynamically estimating process control parameters based on customer needs provided by the present invention includes:

[0006] Step S1: setting process control parameters and corresponding constraint condition sets according to the process requirements of the steel product to be produced;

[0007] Step S2: According to the acquired process control parameters and corresponding constraint condition sets, a preset scenario is simulated and verified based on customer information, product information, and material history big data, and the priority of the constraint conditions is evaluated and optimized based on the verification results;

[0008] Step S3: Obtaining the preferred process standard based on the set process control parameters and the optimized constraint condition set.

[0009] Preferably, the step S1 adopts:

[0010] Step S1.1: Define the process parameters for manufacturing process control according to the process requirements of the steel product to be produced;

[0011] Step S1.2: Establish a constraint set based on multiple constraint conditions that affect a certain process parameter, and determine the initial screening priority of the multiple constraint conditions.

[0012] Preferably, the step S2 adopts:

[0013] Step S2.1: Collect multi-dimensional data including customers, products, uses, specifications, ingredients and processes;

[0014] Step S2.2: Based on the collected multi-dimensional data, according to the initial screening priority of the constraint conditions, screen the target data that meets the constraint conditions;

[0015] Step S2.3: Update the screening priority of the constraint conditions, and screen the target data based on the updated screening priority, and record the number of screening steps. Repeat step S2.3 until the screening priority sequence of the constraint conditions is fully verified; select the constraint condition priority with the least number of screening steps as the most optimized constraint condition priority.

[0016] Preferably, step S3 comprises:

[0017] Step S3.1: Collect valid customer information, product information, material history data, and unit production plan data in the online system;

[0018] Step S3.2: Output the preferred process standard based on the optimized constraint priority.

[0019] According to the present invention, a process control parameter dynamic estimation system based on customer needs is provided, comprising:

[0020] Module M1: Set process control parameters and corresponding constraint condition sets according to the process requirements of the steel products to be produced;

[0021] Module M2: Based on the acquired process control parameters and corresponding constraint condition sets, simulation verification of preset scenarios is performed based on customer information, product information, and material history big data, and the priority of constraint conditions is evaluated and optimized based on the verification results;

[0022] Module M3: Obtain the optimal process standard based on the set process control parameters and the optimized constraint condition set.

[0023] Preferably, the module M1 adopts:

[0024] Module M1.1: Define the process parameters for manufacturing process control according to the process requirements of the steel products to be produced;

[0025] Module M1.2: Establish a constraint set based on multiple constraints that affect a process parameter and determine the initial screening priority of multiple constraints.

[0026] Preferably, the module M2 adopts:

[0027] Module M2.1: Collect multi-dimensional data including customers, products, uses, specifications, ingredients and processes;

[0028] Module M2.2: Based on the collected multi-dimensional data, select the target data that meets the constraints according to the initial screening priority of the constraints;

[0029] Module M2.3: Update the screening priority of the constraint conditions, and screen the target data based on the updated screening priority, and record the number of screening steps. Repeatedly trigger module M2.3 until the screening priority sequence of the constraint conditions is fully verified; select the constraint condition with the least screening steps as the most optimized screening priority.

[0030] Preferably, the module M3 comprises:

[0031] Module M3.1: Collect valid customer information, product information, material history data, and unit production plan data in the online system;

[0032] Module M3.2: Output the preferred process standard based on the optimized constraint priority.

[0033] According to an electronic device provided by the present invention, the electronic device includes a memory and at least one processor, and instructions are stored in the memory;

[0034] The at least one processor calls the instructions in the memory to enable the electronic device to execute the various steps of the method for dynamically estimating process control parameters based on customer requirements as described above.

[0035] According to a computer-readable storage medium provided by the present invention, instructions are stored on the computer-readable storage medium, and when the instructions are executed by a processor, the various steps of the method for dynamically estimating process control parameters based on customer requirements as described above are implemented.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] 1. The present invention realizes the systematic structural design of conventional standards and special needs of customers and the digital quantitative design of process standards, which is conducive to the accumulation and inheritance of the enterprise's knowledge base;

[0038] By adopting this method and process of dynamic optimization design of process standards for steel manufacturing, we can achieve online process control optimization, quickly respond to the latest market / customer needs, efficiently deliver them to on-site production, implement lean quality control, improve product quality, reduce quality costs, and increase customer satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0040] Figure 1 It is a dynamic calculation method and flow chart of process control parameters.

[0041] Figure 2 Manage functional diagrams for rule structures.

[0042] Figure 3 Function diagram for rule + algorithm feasibility verification and evaluation.

[0043] Figure 4 Output function diagram for dynamic extrapolation results of process parameter items. DETAILED DESCRIPTION

[0044] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several changes and improvements can also be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0045] Example 1

[0046] According to a method for dynamically calculating process control parameters based on customer needs provided by the present invention, Figure 1 As shown, including:

[0047] Step 1: According to the production requirements of steel products, a combination of key constraint condition sets is constructed for one or a group of process control parameter items in the manufacturing process control process to form an initial base table rule structure;

[0048] Step 2: Based on the design of structured rules and constraints, simulation verification of various scenarios is carried out through customer information, product information, and material history big data; the verification results are used to evaluate and optimize the priority algorithm of multiple constraints, and automatically convert conventional standards and customer special needs into synthetic process standards.

[0049] Step 3: After one or a group of process control parameters have been determined and the combined values ​​have been determined, the latest version will be automatically released after the approval process to support the dynamic optimization design of the process standard requirements. The system will automatically match and recommend the optimal process standard requirements.

[0050] Specifically, the step 1 includes:

[0051] Step 1.1: Define one or a group of process parameter items, design a set of key constraint conditions based on expert knowledge and empirical data, and determine the priority order among multiple conditions.

[0052] Step 1.2: Collect and integrate data combinations of these conditions;

[0053] Step 1.3: Determine the logic judgment rules for each constraint condition in advance. The logic between multiple conditions is "and", the same condition supports the logic of complete matching or fuzzy matching, and the logic between multiple data in the same project supports "or", forming a set of dynamic optimization standard rule algorithms with parameters based on combined conditions.

[0054] Through software design, step 1.1 forms the initial rule structure; step 1.2 collects, sorts and imports data to support data change operations; step 1.3 maintains the logical rule algorithm with parameters.

[0055] Specifically, step 2 includes: feasibility verification and evaluation of structured rules and key constraint condition data, and automatic optimization of priority algorithms.

[0056] Step 2.1: Collect data on multiple dimensions such as customers, products, uses, specifications, ingredients, processes, etc.

[0057] Step 2.2: Select the data in step 2.1 and perform simulation verification of different scenarios. The verification results are used to evaluate the accuracy of the recommendation results and optimize and improve the data index priority value.

[0058] Step 2.3: Determine the final logical rule structure, maintain the "Standard + Special Requirements (α)" rule data, and complete the approval process. After approval, the latest version will be automatically released. Historical versions are automatically archived to support traceability.

[0059] After executing step 1, execute step 2.1 to collect multi-dimensional and representative data topics such as customers, products, uses, specifications, ingredients, and processes; execute step 2.2 to determine whether there are any abnormalities according to the logical rule algorithm with parameters in step 1, and output the result: if there are abnormalities, first determine whether the rule data needs to be adjusted or corrected, and then determine whether the rules and algorithms need to be adjusted or corrected, and support repeated debugging and verification; if there are no abnormalities, it is judged as passed; continue to step 3.

[0060] Specifically, the step 3 includes: using standards / special requirements (α) + rules + algorithms to go through the approval process, and automatically take effect after approval, and can be used to support dynamic design by unit and material and output the preferred process standards.

[0061] Step 3.1: Collect valid customer information, product information, material history data, unit production plan and other data in the online system.

[0062] Step 3.2: Combine the parameterized logic algorithm of step 1 and the latest effective version data of step 2 to automatically and dynamically design and output the optimal process standard.

[0063] The starting point can be set after the process production path of the order is determined, or the intermediate work-in-progress is incorporated into the downstream unit plan. Combined with the algorithm of step 1 and the latest effective version of step 2, step 3.1 is executed to automatically collect valid customer information, product information, material data, unit production plan and other data in the online system; step 3.2 is executed to dynamically design and output the results in combination with the parameterized logic algorithm of step 1 and the latest effective version of step 2. The preferred process standard can be used as an application to support on-site production automatic control, unit process control over-standard warning, unit process compliance analysis and other work links.

[0064] The present invention provides a system for dynamically estimating process control parameters based on customer needs. The system for dynamically estimating process control parameters based on customer needs can be implemented by executing the process steps of the method for dynamically estimating process control parameters based on customer needs, that is, those skilled in the art can understand the method for dynamically estimating process control parameters based on customer needs as a preferred implementation of the system for dynamically estimating process control parameters based on customer needs.

[0065] Example 2

[0066] Embodiment 2 is a preferred embodiment of Embodiment 1

[0067] Step S1: Initial rule structure design module

[0068] Step S1.1: Define a standard specification for one or a group of process parameters, such as the temperature process requirements for the preheating section of a heating furnace. The standard specification design includes a lower temperature limit, an upper temperature limit, and a temperature target. Based on experience accumulation and expert advice, a set of combined key constraint condition sets is established, and the initial priority of some of the key conditions is determined. For example, the unit, variety, steel grade, and customer are determined in this order, and the numerical conditions of each condition determine the priority score calculation. See Figure 2 .

[0069] Step S1.2: Collect and integrate various data sets used for such process parameters, such as index, unit, variety, steel grade, customer, furnace opening times, raw material thickness, raw material width, target furnace discharge temperature, lower temperature limit, temperature target, upper temperature limit, etc. Figure 2 .

[0070] Step S1.3: Determine the logic judgment rules for all constraint conditions. The logic between multiple conditions is "and", the value within a condition supports the logic of complete matching or fuzzy matching, and the rule data of multiple conditions supports the logic of "or". Based on the priority algorithm, a set of optimal standards with dynamic calculation process parameters based on combined conditions is formed. Figure 2 .

[0071] Step S2: feasibility verification and evaluation of initial structured rules and key constraints, and priority algorithm tuning module;

[0072] Step S2.1: Collect multi-dimensional data sets such as customers, products, uses, specifications, ingredients, processes, etc. For example: product variety, customer name, production process path, unit number, material number, steel type, specification, standard protocol, etc.

[0073] Step S2.2: Perform multi-scenario simulation verification, and use the verification results to evaluate, optimize, and determine the project priority improvement requirements involved in key constraints. Figure 3 .

[0074] Step S2.3: Determine the final rule structure of the process, maintain the rule data of standards and special requirements, complete the approval process, and automatically release the latest version. The historical version is automatically archived for traceability verification. Figure 3 .

[0075] Step S3: After the standard or special requirement (α) + rule + algorithm is approved and becomes effective, the optimal standard requirement is dynamically pushed for the process parameters of any unit or material;

[0076] Step S3.1: Collect valid customer order data, in-stock material data (including historical process data, component data, etc.), unit production plan data, etc. in the online system. For example: product variety, customer name, production process path, unit number, material number, steel type, specification, standard agreement, etc.

[0077] Step S3.2: Combine the parameterized logic algorithm of step S1 and the latest effective version of the data of step S2, and dynamically design the standard requirements of the process parameters as the output result. For example, steel coil 4A5678900 needs to pass through the R031 (heating + rolling) unit. According to the order contract, the final product needs to be supplied to a certain customer. The variety belongs to special steel. The standard specification control requirement of "preheating section temperature" is 30-40℃, and the target is 35℃. Then, through the dynamic calculation method of this set of rules + algorithms, any steel coil including steel coil 4A5678900 that passes through the R031 unit and is a special steel variety, "preheating section temperature" can obtain the optimal process standard requirements in real time. Similarly, if other customers have new control requirements for this variety in the future, the standard specification of "preheating section temperature" can be expanded by adding new combination condition values. See. Figure 4 .

[0078] Those skilled in the art know that, in addition to realizing the system and its various devices, modules, and units provided by the present invention in a purely computer-readable program code, it is entirely possible to realize the same functions in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, the system and its various devices, modules, and units provided by the present invention can be considered as a hardware component, and the devices, modules, and units included therein for realizing various functions can also be regarded as structures within the hardware component; the devices, modules, and units for realizing various functions can also be regarded as both software modules for realizing the method and structures within the hardware component.

[0079] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. In the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. A method for dynamically estimating process control parameters based on customer needs, characterized in that: include: Step S1: setting process control parameters and corresponding constraint condition sets according to the process requirements of the steel product to be produced; Step S2: According to the acquired process control parameters and corresponding constraint condition sets, a preset scenario is simulated and verified based on customer information, product information, and material history big data, and the priority of the constraint conditions is evaluated and optimized based on the verification results; Step S3: Obtaining the preferred process standard based on the set process control parameters and the optimized constraint condition set.

2. The method for dynamically estimating process control parameters based on customer needs according to claim 1, characterized in that: The step S1 adopts: Step S1.1: Define the process parameters for manufacturing process control according to the process requirements of the steel product to be produced; Step S1.2: Establish a constraint set based on multiple constraint conditions that affect a certain process parameter, and determine the initial screening priority of the multiple constraint conditions.

3. The method for dynamically estimating process control parameters based on customer needs according to claim 1, characterized in that: The step S2 adopts: Step S2.1: Collect multi-dimensional data including customers, products, uses, specifications, ingredients and processes; Step S2.2: Based on the collected multi-dimensional data, according to the initial screening priority of the constraint conditions, screen the target data that meets the constraint conditions; Step S2.3: Update the screening priority of the constraint conditions, and screen the target data based on the updated screening priority, and record the number of screening steps, and repeatedly trigger step S2.3 until the screening priority sequence of the constraint conditions is fully verified; Select the constraint condition with the least number of filtering steps and the constraint condition priority with the most optimized filtering priority.

4. The method for dynamically estimating process control parameters based on customer needs according to claim 1, characterized in that: The step S3 comprises: Step S3.1: Collect valid customer information, product information, material history data, and unit production plan data in the online system; Step S3.2: Output the preferred process standard based on the optimized constraint priority.

5. A dynamic estimation system for process control parameters based on customer needs, characterized in that: include: Module M1: Set process control parameters and corresponding constraint condition sets according to the process requirements of the steel products to be produced; Module M2: Based on the acquired process control parameters and corresponding constraint condition sets, simulation verification of preset scenarios is performed based on customer information, product information, and material history big data, and the priority of constraint conditions is evaluated and optimized based on the verification results; Module M3: Obtain the optimal process standard based on the set process control parameters and the optimized constraint condition set.

6. The process control parameter dynamic estimation system based on customer demand according to claim 5 is characterized in that: The module M1 adopts: Module M1.1: Define the process parameters for manufacturing process control according to the process requirements of the steel products to be produced; Module M1.2: Establish a constraint set based on multiple constraints that affect a process parameter and determine the initial screening priority of multiple constraints.

7. The process control parameter dynamic estimation system based on customer demand according to claim 5 is characterized in that: The module M2 adopts: Module M2.1: Collect multi-dimensional data including customers, products, uses, specifications, ingredients and processes; Module M2.2: Based on the collected multi-dimensional data, select the target data that meets the constraints according to the initial screening priority of the constraints; Module M2.3: Update the screening priority of the constraint conditions, and screen the target data based on the updated screening priority, and record the number of screening steps. Repeat the triggering of module M2.3 until the screening priority sequence of the constraint conditions is fully verified; Select the constraint condition with the least number of filtering steps and the constraint condition priority with the most optimized filtering priority.

8. The process control parameter dynamic estimation system based on customer demand according to claim 5, characterized in that: The module M3 comprises: Module M3.1: Collect valid customer information, product information, material history data, and unit production plan data in the online system; Module M3.2: Output the preferred process standard based on the optimized constraint priority.

9. An electronic device, comprising a memory and at least one processor, wherein instructions are stored in the memory; The at least one processor calls the instructions in the memory to enable the electronic device to execute the various steps of the method for dynamically estimating process control parameters based on customer requirements as described in any one of claims 1 to 4.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the various steps of the method for dynamically estimating process control parameters based on customer needs as described in any one of claims 1 to 4 are implemented.

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