Intelligent control method for steel production process

Abstract

The present invention provides an intelligent control method for a steel production process, comprising the following steps: constructing a framework platform, creating a data management module and a plug-in management module in the framework platform, and obtaining a process model comprehensive control platform; deploying the process model in the steel production process to the process model comprehensive control platform in the form of a plug-in through the plug-in management module; after the process model is deployed, opening the process model comprehensive control platform, so that the process model obtains the data required for the calculation of the preset mathematical model inside the process model from the process model comprehensive control platform to complete the optimization of the control target data, and obtains the control target optimization data; uploading the control target optimization data to the process model comprehensive control platform, and sending the control target optimization data to the production line corresponding to the process model through the data management module. The present invention can solve the problems of information islands and fragmentation between current process models.

Description

Technical Field

[0001] The present invention relates to the technical field of iron and steel metallurgy, and more specifically, to an intelligent control method for a steel production process. Background Art

[0002] In the steel production process, the workshop process control level system based on basic automation has been increasingly widely adopted by steel companies, especially the process model software systems that directly participate in and guide the production of various steel processes, which have gradually become a must-have system for steel companies and production plants.

[0003] However, different process models are developed at different stages for different control objects or control targets, and the process technologies, development teams, service scopes, development platforms and development tools used are not unified, and information sharing is not considered, resulting in serious information islands and fragmentation of process models. On the one hand, in addition to focusing on the technical business itself, each process model developer must invest a lot of time, energy and funds to develop various interfaces in order to interact with on-site production line data, other metallurgical process data, production plan data, etc., resulting in a large communication load on the associated systems, a heavy network burden, high maintenance costs, and serious waste of resources; on the other hand, due to the island nature of the process model and the lack of standardized standards for management processes and management systems, the compatibility and integration between the various systems are poor, there is a lack of necessary communication and collaboration, user access is difficult, production maintenance is complicated, the overall applicability is poor, and the operating performance is unstable.

[0004] In summary, the information between the existing process models is isolated and fragmented. The data interaction between different process systems currently has problems such as heavy communication load, heavy network burden, high maintenance cost, serious waste of resources, poor compatibility and integration between systems, complicated production and maintenance, poor overall applicability and unstable operating performance. Summary of the invention

[0005] In view of the above problems, the purpose of the present invention is to provide an intelligent management and control method for steel production process to solve the problems of large communication load, heavy network burden, high maintenance cost, serious waste of resources, poor compatibility and integration between systems, complicated production and maintenance, poor overall applicability and unstable operating performance in data interaction between different process systems due to the existence of information islands and fragmentation between process models.

[0006] The present invention provides an intelligent control method for a steel production process, comprising the following steps:

[0007] Construct a framework platform, create a data management module and a plug-in management module in the framework platform, and obtain a comprehensive process model management and control platform; wherein the data management module is used for data interaction with real-time PLC production data of the production line and metallurgical process data in the steel production process;

[0008] Deploy the process model in the steel production process to the process model comprehensive management and control platform in the form of a plug-in through the plug-in management module; wherein the process model is used to design control target data in the steel production process;

[0009] After the process model is deployed, the process model integrated management and control platform is started, so that the process model obtains data required for calculation of the mathematical model preset inside the process model from the process model integrated management and control platform to optimize the control target data and obtain the control target optimization data;

[0010] The control target optimization data is uploaded to the process model comprehensive management and control platform, and the control target optimization data is sent to the production line corresponding to the process model through the data management module.

[0011] In addition, a preferred solution is that the plug-in management module includes: a plug-in interface for plugging in the process model, a preset plug-in interface specification set inside the plug-in interface, a directory storage unit for storing an information directory of the plugged-in process model, a search unit for searching the information directory in the directory storage unit, a verification unit for verifying the legitimacy of data information of the process model on the information directory searched by the search unit according to the preset plug-in interface specification, a content loading unit for loading content into the process model corresponding to the information directory verified by the verification unit, and a data calling unit for calling the content of the process model loaded by the content loading unit.

[0012] In addition, a preferred solution is that the preset plug-in interface specification includes: plug-in version number, plug-in name, plug-in type identifier, plug-in loading status, number of plug-in threads, plug-in operation cycle, plug-in data interface handle, plug-in initialization, plug-in mathematical model and plug-in resource release.

[0013] In addition, a preferred solution is that the plug-in interface is a unified and standardized plug-in interface.

[0014] In addition, a preferred solution is that the process model in the steel production process is deployed to the process model comprehensive management and control platform in the form of a plug-in through the plug-in management module, including:

[0015] Inserting the process model into the plug-in interface in the form of a plug-in, placing the model data folder corresponding to the process model into the directory storage unit for storage, and forming an information directory recording the model data file in the directory storage unit;

[0016] The information directory is searched by the search unit, and the legitimacy of the searched information directory is verified by the verification unit according to the preset plug-in interface specification;

[0017] The content of the model data file that has passed the legality verification is loaded into the process model comprehensive management and control platform through the content loading unit, and the content of the model data file in the process model comprehensive management and control platform is called through the data calling unit to complete the deployment of the process model.

[0018] In addition, a preferred solution is that the plug-in management module further includes:

[0019] An operation monitoring unit is used to periodically monitor the operation status of a process model corresponding to an information catalog that has been qualified by the verification unit.

[0020] In addition, a preferred solution is that in the process of deploying the process model in the steel production process to the process model comprehensive management and control platform in the form of a plug-in through the plug-in management module,

[0021] When the validity verification of the information directory fails, the loading of the content of the model data file corresponding to the information directory is stopped, and verification failure information is generated.

[0022] In addition, a preferred solution is that before deploying the process model in the steel production process in the form of a plug-in to the process model comprehensive management and control platform through the plug-in management module, it also includes:

[0023] The control target of the process model is simplistically processed so that the process model designs control target data for only one control target.

[0024] In addition, a preferred solution is that a calculation data selection rule table for determining data required for calculation of the mathematical model is provided in the process model.

[0025] In addition, a preferred solution is that after the process model deployment is completed, the process model comprehensive management and control platform is started, so that the process model obtains the data required for the calculation of the mathematical model preset inside the process model from the process model comprehensive management and control platform to complete the optimization of the control target data, and the control target optimization data obtained includes:

[0026] According to the preset optimization cycle, the process model obtains the data required for the calculation of the internal preset mathematical model from the process model comprehensive management and control platform according to the calculation data selection rule table;

[0027] The control target data is re-optimized and calculated according to the data required for the mathematical model calculation to obtain the control target optimization data.

[0028] It can be seen from the above technical scheme that the intelligent control method of the steel production process provided by the present invention establishes a logical relationship between process models by adopting a framework platform combined with a plug-in architecture design of a process model, strengthens the management function of the entire steel production system, and realizes intelligent communication between process models in the steel production process; by constructing a comprehensive control platform for process models, the comprehensive management of process models is realized. The process model adopts a plug-and-play framework design, provides a standardized interface for process models, supports user process model access, realizes intelligent communication and collaboration between process models and unifies the model development platform, meets the needs of user personalized customization and multi-scenario application, solves the problems of technology isolation, platform clutter, waste of development resources, etc., improves the efficiency of control model development, and improves the applicability, control reliability and operating performance stability of the process control model; can better adapt to the current information, digitalization and intelligent industrial development requirements.

[0029] In order to achieve the above and related purposes, one or more aspects of the present invention include the features that will be described in detail later. The following description and the accompanying drawings describe some exemplary aspects of the present invention in detail. However, these aspects indicate only some of the various ways in which the principles of the present invention can be used. In addition, the present invention is intended to include all these aspects and their equivalents. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] By referring to the following description in conjunction with the accompanying drawings, and with a more comprehensive understanding of the present invention, other objects and results of the present invention will become more clear and easy to understand. In the accompanying drawings:

[0031] Figure 1 A schematic diagram of a process of intelligent management and control of a steel production process according to an embodiment of the present invention;

[0032] Figure 2 This is a schematic diagram of the framework structure of the process model comprehensive management and control platform in Example 1 of the present invention;

[0033] Figure 3 This is a diagram of the plug-in management and control logic relationship in Example 1 of the present invention.

[0034] The same reference numerals in the drawings indicate similar or corresponding features or functions. DETAILED DESCRIPTION

[0035] In the following description, for the purpose of explanation, numerous specific details are set forth in order to provide a thorough understanding of one or more embodiments. However, it is apparent that these embodiments may also be implemented without these specific details.

[0036] In response to the aforementioned problems that the information between process models is isolated and fragmented, which leads to heavy communication load, heavy network burden, high maintenance cost, serious waste of resources, poor compatibility and integration between systems, complicated production and maintenance, poor overall applicability and unstable operating performance in data interaction between different process systems, a method for intelligent control of steel production process is proposed.

[0037] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0038] In order to illustrate the intelligent control method of steel production process provided by the present invention, Figure 1 The process of the intelligent management and control method of the steel production process according to an embodiment of the present invention is shown; Figure 2 The framework structure of the process model comprehensive management and control platform in Example 1 of the present invention is shown; Figure 3 The diagram shows the logical relationship between plug-in management and control in Embodiment 1 of the present invention.

[0039] like Figures 1 to 3 As shown together, the intelligent control method for steel production process provided by the present invention comprises the following steps:

[0040] S1. Build a framework platform, create a data management module and a plug-in management module in the framework platform, and obtain a comprehensive control platform for the process model; wherein the data management module is used for data interaction with the real-time PLC production data of the production line and the metallurgical process data in the steel production process.

[0041] Specifically, to build a basic framework platform, it can be completed by adopting the construction method of ordinary data management platform. Then, create data management module and plug-in management module in the framework platform. The plug-in management module can be used to introduce data from external plug-ins into the framework platform, and manage the data in the referenced plug-ins. The data management module can be used to obtain the real-time PLC production data of the production line and the metallurgical process data of the steel production process, and the optimized data obtained in the plug-in can be fed back to the production line, so as to realize data interaction between the platform and the production line.

[0042] As a preferred solution of the present invention, the plug-in management module includes: a plug-in interface for plugging in a process model, a preset plug-in interface specification set inside the plug-in interface, a directory storage unit for storing an information directory of a plugged in process model, a search unit for searching the information directory in the directory storage unit, a verification unit for verifying the legitimacy of data information of the process model on the information directory searched by the search unit according to the preset plug-in interface specification, a content loading unit for loading content into the process model corresponding to the information directory verified by the verification unit, and a data calling unit for calling the content of the process model loaded by the content loading unit.

[0043] As a preferred solution of the present invention, the preset plug-in interface specification includes: plug-in version number, plug-in name, plug-in type identification, plug-in loading status, number of plug-in threads, plug-in operation cycle, plug-in data interface handle, plug-in initialization, plug-in mathematical model and plug-in resource release.

[0044] As a preferred solution of the present invention, the plug-in interface is a unified and standardized plug-in interface.

[0045] As a preferred solution of the present invention, the plug-in management module further includes: an operation monitoring unit for periodically monitoring the operation status of the process model corresponding to the information catalog verified by the verification unit.

[0046] As a preferred solution of the present invention, before the process model in the steel production process is deployed to the process model comprehensive management and control platform in the form of a plug-in through the plug-in management module, it also includes:

[0047] The control target of the process model is simplified so that the process model only designs control target data for one control target.

[0048] Specifically, the plug-in management module searches, verifies, loads, and calls the data in the plug-in through various units. In order to facilitate the production of unified process model plug-ins, the plug-in interface can be set as a standardized interface, making the plug-in design more convenient and quick; each process model is first processed as a plug-in, so that each process model controls only one control target, for example, the optimal quenching time calculation model only calculates the optimal quenching time through its internal optimal quenching time calculation model; in order for the platform to better understand the operating status of each process model, an operation monitoring unit can be added to monitor the operation of the plug-in of each process model according to the detection cycle, for example, once a week.

[0049] S2. Deploy the process model in the steel production process in the form of a plug-in through the plug-in management module to the process model comprehensive management and control platform; wherein the process model is used to design the control target data in the steel production process.

[0050] As a preferred solution of the present invention, deploying the process model in the steel production process in the form of a plug-in to the process model comprehensive management and control platform through the plug-in management module includes:

[0051] Inserting the process model into the plug-in interface in the form of a plug-in, placing the model data folder corresponding to the process model into the directory storage unit for storage, and forming an information directory recording the model data files in the directory storage unit;

[0052] The information directory is searched through the search unit, and the legitimacy of the searched information directory is verified through the verification unit according to the preset plug-in interface specification;

[0053] The content of the model data file that has passed the legality verification is loaded into the process model comprehensive management and control platform through the content loading unit, and the content of the model data file in the process model comprehensive management and control platform is called through the data calling unit to complete the process model deployment.

[0054] As a preferred solution of the present invention, in the process of deploying the process model in the steel production process to the process model comprehensive management and control platform in the form of a plug-in through the plug-in management module,

[0055] When the validity verification of the information directory fails, the loading of the content of the model data file corresponding to the information directory is stopped, and verification failure information is generated.

[0056] Specifically, the process model is plugged into the plug-in interface in the form of a plug-in. At this time, the processor inside the platform places the model data folder corresponding to the process model into the directory storage unit for storage, wherein the directory storage unit can also be a folder, for example, the platform Plugins folder. Then, an information directory of the model data folder placed therein is formed in the platform Plugins folder, the information directory is searched by the search unit, and the searched information directory is verified for legitimacy according to the preset plug-in interface specification. If the verification is successful, the content of the model data file is loaded into the process model comprehensive management and control platform as data that can be called by other process models. If it is unsuccessful, the user can be reminded by generating a verification failure message, wherein the legitimacy verification is completed according to the preset plug-in interface specification. For example, the rules stipulate that some process model plug-in information must be contained, but after verification, a certain piece of information is missing in a certain model data file, indicating that the verification is unqualified.

[0057] S3. After the process model deployment is completed, the process model comprehensive management and control platform is opened, so that the process model obtains the data required for the calculation of the preset mathematical model inside the process model from the process model comprehensive management and control platform to complete the optimization of the control target data and obtain the control target optimization data.

[0058] Specifically, after the process model is deployed, the process model comprehensive management and control platform is turned on and put into operation. At this time, each process model begins to obtain the corresponding data from the platform for calculation based on the data required for calculation of its internal mathematical model, thereby optimizing its previous control target data and obtaining control target optimization data.

[0059] As a preferred solution of the present invention, a calculation data selection rule table for determining data required for mathematical model calculation is provided in the process model.

[0060] As a preferred solution of the present invention, after the process model is deployed, the process model integrated management and control platform is started, so that the process model obtains the data required for the calculation of the mathematical model preset inside the process model from the process model integrated management and control platform to optimize the control target data, and the control target optimization data obtained includes:

[0061] According to the preset optimization cycle, the process model obtains the data required for the calculation of its internal preset mathematical model from the process model comprehensive management and control platform according to the calculation data selection rule table;

[0062] The control target data is re-optimized and calculated based on the data required for mathematical model calculation to obtain the control target optimization data.

[0063] According to the preset optimization cycle, for example, half an hour, the process model obtains the corresponding data from the platform according to the calculation data selection rule table set internally, and then recalculates the control target data through the calculation model to obtain the control target optimization data. Among them, the calculation data selection rule is used to determine the data required for the mathematical model calculation. For example, when calculating the amount of cooling water added, if the cooling water amount calculation model can complete the calculation according to the current water temperature or according to the quenching time without the current water temperature, then a calculation method is preferably selected according to the calculation data selection rule, and then the required calculation data is determined.

[0064] S4. Upload the control target optimization data to the process model comprehensive management and control platform, and send the control target optimization data to the production line corresponding to the process model through the data management module.

[0065] By uploading the target optimization data completed by the process model to the platform, and then continuing to interact with the production line through the data management module.

[0066] The following examples will further illustrate the present invention so that those skilled in the art can better understand the advantages and features of the present invention.

[0067] Example 1

[0068] Taking the continuous casting process in the steel production process as an example, in the actual production process, in order to prevent the occurrence of steel leakage accidents and avoid quality problems such as cracks, center looseness, center segregation during the solidification of molten steel, it is necessary to configure continuous casting process control process models such as crystallizer steel leakage prediction, billet temperature field thermal tracking, dynamic secondary cooling water distribution, dynamic light pressure, and quality tracking and judgment.

[0069] With current technology, each process model is an independent system that needs to interact with the outside world for data, resulting in heavy communication load on the data system, heavy network burden, high maintenance cost, and serious waste of resources. In addition, there is a lack of necessary communication and collaboration between systems, user access is difficult, and production and maintenance are complicated, resulting in poor overall applicability and unstable operating performance.

[0070] After adopting the method provided by the present invention, each process model is deployed on the process model comprehensive management and control platform (i.e., platform MF) in a plug-in manner. By creating a data management module and a plug-in management module on the platform MF, it can be used for data management and plug-in management. The process model performs parameter optimization control according to the control target. The data interaction of the real-time PLC production data of the on-site production line and other metallurgical process data is realized by the data management function of the platform MF. The MF platform periodically obtains production data from the PLC and other metallurgical processes, and sends the optimized control object data to the PLC and other metallurgical processes. Each process model plug-in only needs to obtain the required process data from the platform MF and send its control object data to the MF, which avoids the disadvantages of each process model interacting with the external PLC and database, resulting in a large system communication load, a heavy network burden, and serious waste of resources.

[0071] Taking the dynamic secondary cooling water distribution process model as an example, during development, according to the preset plug-in interface specification of the MF platform, the plug-in information of the preset dynamic secondary cooling water distribution process model is shown in Table 1.

[0072]

[0073]

[0074] Table 1

[0075] After the development of the dynamic secondary cooling water distribution process model plug-in is completed, the published plug-in SecondaryCooling folder is placed in the Plugins folder directory of the platform MF. When the platform MF is started, the plug-in management module of the platform MF is used for searching, legitimacy verification, loading and calling, and status tracking and monitoring of the process model plug-in. The plug-in management module searches for plug-in files in the Plugins directory, and does not load plug-ins that do not meet the preset plug-in specification interface. For plug-ins that meet the plug-in interface specification, they are loaded and called according to the plug-in configuration information. At the same time, the running status of the plug-in is marked on the platform MF, and the running status of the plug-in is periodically monitored.

[0076] When the platform MF starts loading each process model plug-in, for the dynamic secondary cooling water distribution process model plug-in, the dynamic secondary cooling water distribution process model plug-in realizes the loading of the cooling process system during initialization, realizes data reading, data processing and data distribution in the mathematical model within the dynamic secondary cooling water distribution process model, and realizes the recovery of various resources during resource release. In actual control, the dynamic secondary cooling water distribution process model plug-in reads the required billet drawing speed, molten steel superheat, actual water volume in each secondary cooling zone, and temperature field data of thermal tracking simulation of the billet temperature field from the platform MF, and calculates the optimal water volume in each secondary cooling zone that conforms to the heat transfer law by comparing the temperature field data with the target surface temperature of the cooling process system, and uploads it to the platform MF as the set water volume. The platform MF automatically sends the data to the production line PLC periodically to achieve optimal control of the secondary cooling water volume.

[0077] Similarly, if you uninstall or deactivate the dynamic secondary cooling water distribution process model plug-in, just delete the SecondaryCooling folder in the Plugins folder directory of the MF platform.

[0078] In addition, in the actual production process, if the user summarizes a method for achieving accurate control of the weight of the billet based on production experience data, in order to quickly model this production experience and control the weight of the billet in real time, the method provided by the present invention can be used. First, according to the platform MF preset plug-in interface specification, an intelligent weight determination process model plug-in is developed, and the preset intelligent weight determination process model plug-in information is as follows:

[0079] As shown in Table 2.

[0080] Plugin interface Plugin Information Version Number 1.0.0.0 Plugin Name Intelligent weighing system Plugin type identifier ENUM_APPLICATIONTYPE_SWS Plugin loading status TRUE Number of plugin threads Casting strand related, automatically adapted according to the number of casting strands of the continuous casting machine Plugin operation cycle 1000ms Plugin data interface handle pIDataAccess Plugin initialization Initialize() Plug-in Mathematical Model SingleAction() Plug-in resource release Uninitialize()

[0081] Table 2

[0082] Similarly, the intelligent fixed-weight process model plug-in implements the loading of the fixed-weight calculation process during initialization; in its mathematical model, the intelligent fixed-weight process model plug-in reads the required data such as molten steel composition, billet drawing speed, molten steel superheat, and cooling water volume from the platform MF, and calculates the optimal billet length that meets the target weight through the user's empirical calculation method, which is uploaded to the platform MF as the set fixed length. The platform MF automatically and periodically sends the data to the production line PLC to achieve precise control of the billet weight.

[0083] After the development of the intelligent weighing process model plug-in is completed, the published plug-in SmartWeightSystem folder is placed in the Plugins folder directory of the platform MF. After the platform MF is started, the intelligent weighing process model plug-in will be automatically loaded and applied to production control. In this way, enterprise users can also quickly process model various production experiences, mechanism research, data analysis and other results, and apply them to production process control and process parameter optimization.

[0084] At the same time, each process model can be independently developed and deployed according to different business needs. The production site can combine and integrate process models according to actual application scenarios and user customization needs, thereby strengthening the management function of the entire process control system and improving the overall applicability and operational stability of the process model.

[0085] It can be seen from the above specific implementation methods that the intelligent control method of the steel production process provided by the present invention establishes a logical relationship between process models by adopting a framework platform combined with a plug-in architecture design of a process model, strengthens the management function of the entire steel production system, and realizes intelligent communication between process models in the steel production process; by constructing a comprehensive control platform for process models, the comprehensive management of process models is realized. The process model adopts a plug-and-play framework design, provides a standardized interface for process models, supports user process model access, realizes intelligent communication and collaboration between process models and unifies the model development platform, meets the needs of user personalized customization and multi-scenario application, solves the problems of technology isolation, platform clutter, waste of development resources, etc., improves the efficiency of control model development, and improves the applicability, control reliability and operating performance stability of the process control model; can better adapt to the current information, digitalization and intelligent industrial development requirements.

[0086] As described above, the intelligent control method for the steel production process proposed by the present invention is described by way of example with reference to the accompanying drawings. However, those skilled in the art should understand that various improvements can be made to the intelligent control method for the steel production process proposed by the present invention without departing from the content of the present invention. Therefore, the protection scope of the present invention should be determined by the content of the attached claims.

Claims

1. A method for intelligent control of steel production process, characterized in that: The steps include: Construct a framework platform, create a data management module and a plug-in management module in the framework platform, and obtain a comprehensive control platform for process models; wherein the data management module is used for data interaction with real-time PLC production data of the production line and metallurgical process data in the steel production process; the plug-in management module includes: a plug-in interface for plugging in the process model, a preset plug-in interface specification set inside the plug-in interface, a directory storage unit for storing an information directory of the plugged-in process model, a search unit for searching the information directory in the directory storage unit, a verification unit for verifying the legitimacy of data information of the process model on the information directory searched by the search unit according to the preset plug-in interface specification, a content loading unit for loading content into the process model corresponding to the information directory verified by the verification unit, and a data calling unit for calling the content of the process model loaded by the content loading unit; Deploy the process model in the steel production process to the process model comprehensive management and control platform in the form of a plug-in through the plug-in management module, including: Inserting the process model into the plug-in interface in the form of a plug-in, placing the model data folder corresponding to the process model into the directory storage unit for storage, and forming an information directory recording the model data file in the directory storage unit; wherein the process model is used to design control target data in the steel production process; The information directory is searched by the search unit, and the legitimacy of the searched information directory is verified by the verification unit according to the preset plug-in interface specification; The content of the model data file that has passed the legality verification is loaded into the process model comprehensive management and control platform through the content loading unit, and the content of the model data file in the process model comprehensive management and control platform is called through the data calling unit to complete the deployment of the process model; After the process model is deployed, the process model integrated management and control platform is started, so that the process model obtains data required for calculation of the mathematical model preset inside the process model from the process model integrated management and control platform to optimize the control target data and obtain the control target optimization data; The control target optimization data is uploaded to the process model comprehensive management and control platform, and the control target optimization data is sent to the production line corresponding to the process model through the data management module.

2. The intelligent control method for steel production process according to claim 1, characterized in that: The preset plug-in interface specification includes: plug-in version number, plug-in name, plug-in type identifier, plug-in loading status, plug-in thread number, plug-in operation cycle, plug-in data interface handle, plug-in initialization, plug-in mathematical model and plug-in resource release.

3. The intelligent control method for steel production process according to claim 1, characterized in that: The plug-in interface is a unified and standardized plug-in interface.

4. The intelligent control method for steel production process according to claim 1, characterized in that: The plug-in management module also includes: An operation monitoring unit is used to periodically monitor the operation status of the process model corresponding to the information catalog that has been qualified by the verification unit.

5. The intelligent control method for steel production process according to claim 1, characterized in that: In the process of deploying the process model in the steel production process to the process model comprehensive management and control platform in the form of a plug-in through the plug-in management module, When the validity verification of the information directory fails, the loading of the content of the model data file corresponding to the information directory is stopped, and verification failure information is generated.

6. The intelligent control method for steel production process according to claim 1, characterized in that: Before deploying the process model in the steel production process to the process model comprehensive management and control platform in the form of a plug-in through the plug-in management module, it also includes: The control target of the process model is simplistically processed so that the process model designs control target data for only one control target.

7. The intelligent control method for steel production process according to claim 1, characterized in that: A calculation data selection rule table for determining data required for calculation of the mathematical model is provided in the process model.

8. The intelligent control method for steel production process according to claim 7, characterized in that: After the process model deployment is completed, the process model integrated management and control platform is started, so that the process model obtains the data required for the mathematical model calculation preset in the process model from the process model integrated management and control platform to optimize the control target data. The control target optimization data obtained includes: According to the preset optimization cycle, the process model obtains the data required for the calculation of the internal preset mathematical model from the process model comprehensive management and control platform according to the calculation data selection rule table; The control target data is re-optimized and calculated according to the data required for the mathematical model calculation to obtain the control target optimization data.

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