Process monitoring and early warning method based on process specifications

Abstract

The invention relates to a process monitoring and early warning method based on process specifications, belongs to the field of metallurgy, and solves the problems that in the prior art, key indexes in the production process are difficult to monitor and record in real time, and the improvement of the production management level and the product quality stability are affected. According to the invention, digital process specifications are integrated to the monitoring system, so that real-time acquisition of production data and integration of material information are realized. The system compares data in real time and pre-warns deviation through a space-time monitoring mechanism, automatically generates a process discipline report to support quality control, and guides production operation and dynamically optimizes the process flow by using an algorithm. And a continuously improved closed-loop management system is formed by continuously optimizing a monitoring result, so that the production efficiency and the product quality are improved. The control capability of the production process is remarkably improved, the product quality is guaranteed, and finally the double improvement of the production efficiency and the economic benefit is achieved.

Description

Technical Field

[0001] The invention relates to a process monitoring and early warning method based on process specifications, and belongs to the field of metallurgy. Background Art

[0002] In the current industrial production field, although a large number of process specifications and production knowledge have been formulated, these valuable knowledge and experience are scattered in many different documents and materials. This dispersion makes it difficult to fully implement and implement these specifications and standards in the actual production process. The main difficulty lies in the lack of effective monitoring methods, which makes it impossible for these specifications to receive due attention and application in all aspects of production.

[0003] In addition, since process specifications and standards are distributed in various production processes, if each process cannot be carefully monitored individually and in real time, these specifications will lose their due guiding value. Relying solely on post-analysis and review, it is impossible to achieve in-process control of key indicators in the production process, which greatly affects the stability of product quality and the continuity of production.

[0004] In view of this situation, we realize that real-time monitoring and recording of quality processes plays a vital role in improving production management and ensuring product quality. Summary of the invention

[0005] The present invention proposes a process monitoring and early warning method based on process specifications. By integrating and applying advanced monitoring technologies, real-time and comprehensive monitoring of the production process is achieved, thereby ensuring that process specifications and production cognition can be effectively implemented in each process step, significantly improving the control capability of the production process, ensuring product quality, and ultimately achieving a dual improvement in production efficiency and economic benefits, so as to solve the problem that it is difficult to achieve real-time monitoring and recording of key indicators in the production process in the prior art, affecting the improvement of production management level and product quality stability.

[0006] A process monitoring and early warning method based on process specifications, the process monitoring and early warning method based on process specifications comprising the following steps:

[0007] S100, digitize process specifications and integrate them into the monitoring system, collect key production data in real time, and integrate material timing information to form a unified data set;

[0008] S200, establish a monitoring mechanism in time and space dimensions, and realize real-time monitoring of the production process by comparing real-time data with process specifications;

[0009] S300, once a deviation is detected, an alarm is triggered immediately and abnormal information is displayed. At the same time, potential quality defects or possible production accidents are predicted and early warning information is provided;

[0010] S400, generate process discipline reports containing key attributes, perform data storage, statistics and analysis to support real-time quality control;

[0011] S500, using algorithms to guide production operations, such as cutting the head and tail of strip steel, while optimizing the process based on monitoring data;

[0012] S600. Continuously optimize process specifications and monitoring methods based on monitoring results to form a closed-loop management system for continuous improvement.

[0013] Furthermore, in S100, the following steps are included:

[0014] S110. Convert existing process specifications and production standards into electronic format and establish a comprehensive process specification library;

[0015] S120, configure OPC UA communication protocol to ensure compatibility with production equipment and collect key production process parameters in real time, such as temperature, pressure, speed, etc.;

[0016] S130, interface with the inspection and testing system to obtain the chemical composition and physical property data of the material, communicate with the MES system, and collect the timing information of material flow and production scheduling;

[0017] S140, clean the collected data, remove outliers and noise, verify the accuracy and completeness of the data, and ensure that the data can be used for subsequent analysis;

[0018] S150. Apply the time-space conversion algorithm to integrate the real-time data and material information according to the time and space dimensions to ensure that the data sets are continuous in time and have a clear correspondence in space;

[0019] S160. Design an appropriate database structure to store collected data and converted data sets, and implement data indexing and query optimization to support fast data access and analysis.

[0020] Furthermore, in S200, the following steps are included:

[0021] S210. Clarify the objectives of the monitoring system, including process parameters to be monitored, alarm conditions, user needs, etc.;

[0022] S220. Design the architecture of the monitoring system, including data flow, module division and user interface, and determine the performance indicators of the system, such as real-time performance, accuracy and reliability;

[0023] S230, develop data interfaces with production equipment, inspection and testing systems, and MES systems to ensure that data can be stably and securely transmitted to the monitoring system;

[0024] S240, implement a real-time data processing mechanism, including receiving, parsing and storing data, and performing necessary pre-processing on the data, such as unit conversion, filtering, etc.;

[0025] S250. Develop monitoring logic according to process specifications, including parameter verification, alarm condition setting, etc., to achieve dynamic association between monitoring points and process specifications;

[0026] S260. Design the trigger logic of the alarm, including threshold setting, alarm level classification, etc., to realize the recording, display and notification functions of the alarm information;

[0027] S270, develop user interface, including real-time data display, historical data query, alarm information viewing, etc., to ensure that the user interface is intuitive and easy to use and can meet the needs of different users;

[0028] S280. Conduct comprehensive testing on the monitoring system, including functional testing, performance testing and user testing, to verify the accuracy, stability and availability of the monitoring system;

[0029] S290. Deploy the monitoring system to the production environment, conduct a final check before going online, and develop an online plan to ensure a smooth transition and stable operation of the system.

[0030] Further, in S300, the deviation is the deviation compared with the process standard, including the deviation of the final composition of the converter, the deviation of the continuous casting billet drawing speed, the deviation of the superheat degree of the tundish, the deviation of the finished product composition, the deviation of the heating temperature of the steel rolling, and the deviation of the finished strip thickness;

[0031] The quality defects include inappropriate chemical composition of the finished product, risk of steel leakage, and excessive thickness of the finished strip steel.

[0032] Furthermore, in S300, the following steps are included:

[0033] S310. Set alarm thresholds according to process specifications and develop corresponding early warning logic;

[0034] S320, deploy a real-time monitoring system and automatically trigger an alert when an anomaly is detected;

[0035] S330. Set up a multi-channel notification mechanism to ensure that all relevant personnel can receive alarm information in a timely manner;

[0036] S340. Develop standardized response procedures and provide corresponding training to operators;

[0037] S350, promptly implement preventive measures or corrective actions based on early warning information;

[0038] S360, regularly test the alarm system, collect feedback, and continuously optimize the early warning mechanism based on data analysis.

[0039] Furthermore, in S400, the following steps are included:

[0040] S410, collect monitoring data and alarm records, and clean them to ensure data quality;

[0041] S420, using the cleaned data, automatically generating a report containing key process parameters and alarm statistics;

[0042] S430, conduct in-depth analysis of alarm and monitoring data to identify problems and improvement points in the production process;

[0043] S440. Develop and implement quality control measures based on the analysis results;

[0044] S450. Distribute the report to relevant personnel and collect their feedback for improvement;

[0045] S460, archive historical data and conduct long-term trend analysis;

[0046] S470. Continuously optimize monitoring systems and report generation processes based on feedback and trend analysis.

[0047] Furthermore, in S500, the following steps are included:

[0048] S510, analyzing the real-time monitoring data and dynamically adjusting the process parameters to adapt to production changes;

[0049] S520, based on real-time monitoring data, ensure that the production process can be automatically adjusted when deviations occur;

[0050] S530, evaluating the effect of the automatic adjustment operation, and collecting feedback from on-site operators, to further optimize the data analysis process of S510 and the automatic adjustment process of S520.

[0051] Furthermore, S600 includes the following steps:

[0052] S610, collect a wide range of information including process monitoring points and quality defect data, and analyze the correlation between them;

[0053] S620, evaluate the effectiveness of the current monitoring system in identifying and preventing quality defects;

[0054] S630, identify the specific links between process monitoring points and quality defects, and their impact on downstream processes;

[0055] S640. Develop measures to improve process monitoring and early warning mechanisms based on metallurgical principles and the correlation of quality defects;

[0056] S650, adjust the monitoring system, incorporate new process monitoring points and quality defect correlation, and optimize the guidance of downstream processes;

[0057] S660. Develop and improve process discipline reports to ensure they accurately reflect the status of process monitoring points and quality control;

[0058] S670, track the effects of improvement measures after implementation, especially the improvement of production guidance and process control capabilities of downstream processes;

[0059] S680. Record and share the knowledge of the relationship between process monitoring points and quality defects, as well as the experience and results of improvement measures.

[0060] A storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned process monitoring and early warning method based on process specifications.

[0061] A computer device comprises: a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-mentioned process monitoring and early warning method based on process specifications.

[0062] Beneficial effects of the present invention: The process monitoring and early warning method based on process specifications of the present invention realizes precise control and timely early warning of the production process through real-time data collection and monitoring, and significantly improves product quality. It provides operators with comprehensive process control tools by integrating process specification libraries, real-time data and material information, thereby enhancing the ability to control the production process. In addition, the solution uses monitoring data to optimize the process flow, reduce resource waste, reduce production costs, and improve production efficiency. The generated process discipline reports and data analysis provide data support for management, helping them make more scientific decisions. At the same time, the solution also includes employee training and operation guidance to improve the skill level of operators. Through a closed-loop management system of continuous improvement, the solution can continuously optimize itself according to real-time feedback, achieve continuous improvement of the production process, and lay the foundation for the realization of intelligent manufacturing and Industry 4.0. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 The present invention is a method flow chart of a process monitoring and early warning method based on process specifications. DETAILED DESCRIPTION

[0064] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0065] Reference Figure 1 As shown, a process monitoring and early warning method based on process specifications, the process monitoring and early warning method based on process specifications includes the following steps:

[0066] S100, digitize process specifications and integrate them into the monitoring system, collect key production data in real time, and integrate material timing information to form a unified data set;

[0067] S200, establish a monitoring mechanism in time and space dimensions, and realize real-time monitoring of the production process by comparing real-time data with process specifications;

[0068] S300: Once a deviation is detected, an alarm is triggered immediately and abnormal information is displayed. At the same time, potential quality defects or possible production accidents are predicted and early warning information is provided;

[0069] S400, generate process discipline reports containing key attributes, perform data storage, statistics and analysis to support real-time quality control;

[0070] S500, using algorithms to guide production operations, such as cutting the head and tail of strip steel, while optimizing the process based on monitoring data;

[0071] S600. Continuously optimize process specifications and monitoring methods based on monitoring results to form a closed-loop management system for continuous improvement.

[0072] Specifically, a process monitoring and early warning method based on process specifications of the present invention realizes the real-time collection of key production data and the integration of material timing information by digitizing the process specifications and integrating them into the monitoring system, thereby forming a unified data set. The method compares the real-time data with the process specifications by establishing a monitoring mechanism in the time and space dimensions to realize real-time monitoring of the production process. When a deviation is detected, the system will immediately trigger an alarm, predict potential quality defects, and provide early warning information, so that preventive measures can be taken in advance. In addition, the method also involves generating a process discipline report containing key attributes, and performing data storage, statistics and analysis to support real-time quality control. Production operations, such as cutting the head and tail of the strip, are guided by algorithms, and the process flow is optimized according to the monitoring data to further improve production efficiency and product quality. Finally, the method forms a closed-loop management system of continuous improvement by continuously optimizing the process specifications and monitoring methods, which brings the following beneficial effects to the production enterprises:

[0073] 1. Improve production quality: Through real-time monitoring and timely alarm, quality defects in the production process can be effectively prevented and reduced.

[0074] 2. Enhanced process control capabilities: Unified data sets and multi-dimensional monitoring mechanisms provide operators with comprehensive process control means.

[0075] 3. Optimize the process flow: Use monitoring data to optimize the process flow, improve production efficiency and resource utilization.

[0076] 4. Reduce production costs: Reducing scrap rates and optimizing processes can help reduce production costs and improve the economic benefits of the enterprise.

[0077] 5. Improve response speed: The rapid alarm and early warning mechanism enables faster response to production anomalies and shortens problem solving time.

[0078] 6. Data-driven decision support: Process discipline reports and data analysis provide data support for management to help make more scientific decisions.

[0079] 7. Continuous improvement: The closed-loop management system ensures that the monitoring system can continuously optimize itself based on real-time feedback.

[0080] 8. Improve operator skills: Employee training and operation guidance improve operators' understanding of process flow and monitoring system.

[0081] 9. Promote Smart Manufacturing: The program is a step towards smart manufacturing and Industry 4.0, improving the level of intelligence in production.

[0082] 10. Environmentally friendly: By reducing resource waste and optimizing production processes, the solution helps achieve a more environmentally friendly production method.

[0083] Furthermore, in S100, the following steps are included:

[0084] S110. Convert existing process specifications and production standards into electronic format and establish a comprehensive process specification library;

[0085] S120, configure OPC UA communication protocol to ensure compatibility with production equipment and collect key production process parameters in real time, such as temperature, pressure, speed, etc.;

[0086] S130, interface with the inspection and testing system to obtain the chemical composition and physical property data of the material, communicate with the MES system, and collect the timing information of material flow and production scheduling;

[0087] S140, clean the collected data, remove outliers and noise, verify the accuracy and completeness of the data, and ensure that the data can be used for subsequent analysis;

[0088] S150. Apply the time-space conversion algorithm to integrate the real-time data and material information according to the time and space dimensions to ensure that the data sets are continuous in time and have a clear correspondence in space;

[0089] S160. Design an appropriate database structure to store collected data and converted data sets, and implement data indexing and query optimization to support fast data access and analysis.

[0090] Specifically, the process monitoring and early warning method based on process specifications of the present invention significantly improves the efficiency of the production monitoring system through carefully designed steps. First, the process specifications and production standards are electronically integrated into the monitoring system, laying the foundation for real-time data collection and material information integration. The configured OPC UA communication protocol ensures seamless docking with production equipment and realizes real-time acquisition of key parameters such as temperature, pressure, and speed. In addition, the interface docking with the inspection and testing system and the MES system further enriches the dimensions of production data, including the chemical composition, physical properties, and flow and scheduling information of the materials. The data cleaning step removes outliers and noise, ensuring the accuracy and integrity of the data used for analysis. The application of the spatiotemporal conversion algorithm ensures the continuity and correspondence of the data set in time and space, providing the possibility for in-depth analysis. Finally, the optimized database design supports rapid access and analysis of data, greatly improving the efficiency of data processing. The comprehensive application of these steps not only improves the monitoring accuracy of the production process, but also reduces production risks through precise early warning and alarm mechanisms, and contributes to the optimization of process flow, the strengthening of decision support, continuous improvement and adaptability improvement, the promotion of intelligent manufacturing, and environmental and social sustainability.

[0091] Furthermore, in S200, the following steps are included:

[0092] S210. Clarify the objectives of the monitoring system, including process parameters to be monitored, alarm conditions, user needs, etc.;

[0093] S220. Design the architecture of the monitoring system, including data flow, module division and user interface, and determine the performance indicators of the system, such as real-time performance, accuracy and reliability;

[0094] S230, develop data interfaces with production equipment, inspection and testing systems, and MES systems to ensure that data can be stably and securely transmitted to the monitoring system;

[0095] S240, implement a real-time data processing mechanism, including receiving, parsing and storing data, and performing necessary pre-processing on the data, such as unit conversion, filtering, etc.;

[0096] S250. Develop monitoring logic according to process specifications, including parameter verification, alarm condition setting, etc., to achieve dynamic association between monitoring points and process specifications;

[0097] S260. Design the trigger logic of the alarm, including threshold setting, alarm level classification, etc., to realize the recording, display and notification functions of the alarm information;

[0098] S270, develop user interface, including real-time data display, historical data query, alarm information viewing, etc., to ensure that the user interface is intuitive and easy to use and can meet the needs of different users;

[0099] S280. Conduct comprehensive testing on the monitoring system, including functional testing, performance testing and user testing, to verify the accuracy, stability and availability of the monitoring system;

[0100] S290. Deploy the monitoring system to the production environment, conduct a final check before going online, and develop an online plan to ensure a smooth transition and stable operation of the system.

[0101] Specifically, in the S200 stage, the beneficial effects of the present invention are concentrated on the careful design and comprehensive testing of the monitoring system, ensuring the efficiency and stability of the system. By clarifying the monitoring objectives, including key process parameters, alarm conditions and user needs, we have established the precise monitoring direction of the system. The reasonable design of the system architecture, including data flow, module division and user interface, and the strict determination of performance indicators such as real-time, accuracy and reliability, provide architectural guarantees for the excellent performance of the monitoring system. In addition, the development of stable data interfaces with production equipment, inspection and testing systems and MES systems ensures the real-time and secure transmission of data. The establishment of a real-time data processing mechanism and the pre-processing of data, such as unit conversion and filtering, further improve the efficiency of data processing and the accuracy of monitoring. Dynamic monitoring logic and refined alarm triggering logic enable the system to flexibly respond to different monitoring points and process specifications, while ensuring accurate recording and timely notification of alarm information. The intuitive design of the user interface meets the needs of different users and improves the ease of use of the system. Comprehensive system testing, including functional, performance and user testing, verifies the stability and reliability of the monitoring system. Finally, by developing a detailed launch plan, the deployment of the monitoring system achieved a smooth transition in the production environment and ensured stable operation. These comprehensive measures greatly improved the transparency of production monitoring, provided a solid step for the implementation of intelligent manufacturing, and laid the foundation for the continuous improvement and upgrading of the system.

[0102] Further, in S300, the deviation is the deviation compared with the process standard, including the deviation of the final composition of the converter, the deviation of the continuous casting billet drawing speed, the deviation of the superheat degree of the tundish, the deviation of the finished product composition, the deviation of the heating temperature of the steel rolling, the deviation of the finished strip thickness, etc.;

[0103] The quality defects include inappropriate chemical composition of the finished product, risk of steel leakage, and excessive thickness of the finished strip steel.

[0104] Furthermore, in S300, the following steps are included:

[0105] S310. Set alarm thresholds according to process specifications and develop corresponding early warning logic;

[0106] S320, deploy a real-time monitoring system and automatically trigger an alert when an anomaly is detected;

[0107] S330. Set up a multi-channel notification mechanism to ensure that all relevant personnel can receive alarm information in a timely manner;

[0108] S340. Develop standardized response procedures and provide corresponding training to operators;

[0109] S350, promptly implement preventive measures or corrective actions based on early warning information;

[0110] S360, regularly test the alarm system, collect feedback, and continuously optimize the early warning mechanism based on data analysis.

[0111] Specifically, in the S300 stage, the present invention realizes accurate identification of production process deviations by setting alarm thresholds based on process specifications and developing corresponding early warning logic. The deployed real-time monitoring system can automatically trigger an early warning when an abnormality occurs, and cooperate with a multi-channel notification mechanism to ensure that all relevant personnel can receive the alarm information in a timely manner. In addition, standardized response processes and training for operators improve the ability to quickly handle emergencies. Based on the early warning information, preventive measures or corrective actions can be quickly implemented to minimize production risks. Regular system testing and feedback collection, combined with data analysis, enable the early warning mechanism to be continuously optimized and improve the adaptability and flexibility of the system. These measures work together to not only improve production efficiency and quality control, but also strengthen production safety, ensure the long-term stability and reliability of the monitoring system, and provide a solid foundation for the continuous improvement and development of production.

[0112] Furthermore, in S400, the following steps are included:

[0113] S410, collect monitoring data and alarm records, and clean them to ensure data quality;

[0114] S420, using the cleaned data, automatically generating a report containing key process parameters and alarm statistics;

[0115] S430, conduct in-depth analysis of alarm and monitoring data to identify problems and improvement points in the production process;

[0116] S440. Develop and implement quality control measures based on the analysis results;

[0117] S450. Distribute the report to relevant personnel and collect their feedback for improvement;

[0118] S460, archive historical data and conduct long-term trend analysis;

[0119] S470. Continuously optimize monitoring systems and report generation processes based on feedback and trend analysis.

[0120] Specifically, in the S400 stage, the present invention significantly improves the data quality and decision support capabilities of production monitoring through a series of refined data processing and analysis steps. First, the monitoring data and alarm records are collected and cleaned to ensure the accuracy of the data, laying a solid foundation for generating high-quality process parameters and alarm statistical reports. Using these cleaned data, the automatically generated reports not only improve the efficiency of report generation, but also enhance the accuracy of the report content. In-depth analysis of alarm and monitoring data helps to identify problems and potential improvement points in the production process, thereby providing data support for the formulation of effective quality control measures. In addition, distributing reports to relevant personnel and collecting feedback further promotes the continuous improvement of the monitoring system and reporting process. At the same time, archiving historical data and conducting long-term trend analysis not only improves the transparency of the production process, but also helps to predict potential risks and formulate preventive measures. Based on feedback and trend analysis, the continuous optimization of the monitoring system and report generation process demonstrates the adaptability and flexibility of the system, and provides solid data and technical support for the continuous improvement of production management and the sustainable development of the enterprise.

[0121] Furthermore, in S500, the following steps are included:

[0122] S510, analyzing the real-time monitoring data and dynamically adjusting the process parameters to adapt to production changes;

[0123] S520, based on real-time monitoring data, ensure that the production process can be automatically adjusted when deviations occur;

[0124] S530, evaluating the effect of the automatic adjustment operation, and collecting feedback from on-site operators, to further optimize the data analysis process of S510 and the automatic adjustment process of S520.

[0125] Specifically, in the S500 stage, the present invention realizes the automation of strip cutting, tail cutting and continuous overrun operations by embedding the algorithm into the monitoring system, which greatly improves production efficiency and reduces manual intervention. The algorithm analyzes real-time monitoring data, dynamically adjusts process parameters to quickly adapt to production changes, and enhances production flexibility. In addition, the training of operators ensures that they can effectively use the algorithm-assisted system, further improving the efficiency of human-machine collaboration. The implementation of algorithm-controlled monitoring improves the self-regulation ability of the system and ensures automatic adjustment when production deviations occur. Regular effect evaluation and feedback collection provide data support for the continuous optimization of the algorithm, and promote the improvement of production efficiency and product quality. In addition, the application of the algorithm promotes the intelligence of the production process, strengthens quality control, reduces production costs, and improves the skill level of operators. The improvement of production transparency and the enhancement of system adaptability lay the foundation for enterprises to cope with future production challenges, thereby providing strong support for the sustainable development and market competitiveness of enterprises.

[0126] The algorithms in S500 are used to perform operations such as cutting the head and tail of the strip, and continuous overrun. These algorithms are based on existing technologies. In the field of industrial automation and process control, there are many mature algorithms and technologies that can be used for such applications, including but not limited to:

[0127] 1. Threshold detection algorithm: used to identify whether production parameters exceed preset safety or performance thresholds.

[0128] 2. Control algorithms: such as PID (proportional-integral-derivative) controllers, which are used to automatically adjust process variables based on deviations.

[0129] 3. Machine learning model: It can learn and predict possible deviations in the production process based on historical data, so as to make adjustments in advance.

[0130] 4. Pattern recognition algorithms: used to identify specific patterns in production data, such as continuous over-limit situations.

[0131] 5. Optimization algorithm: used to find the optimal process parameter settings under multiple production constraints.

[0132] 6. Adaptive algorithm: can automatically adjust the control strategy based on real-time feedback.

[0133] 7. Real-time data processing algorithms: Rapidly process and analyze large amounts of real-time data to facilitate timely decision making.

[0134] 8. Decision support system: Combines multiple algorithms to provide decision support for operators.

[0135] Furthermore, S600 includes the following steps:

[0136] S610, collect a wide range of information including process monitoring points and quality defect data, and analyze the correlation between them;

[0137] S620, evaluate the effectiveness of the current monitoring system in identifying and preventing quality defects;

[0138] S630, identify the specific links between process monitoring points and quality defects, and their impact on downstream processes;

[0139] S640. Develop measures to improve process monitoring and early warning mechanisms based on metallurgical principles and the correlation of quality defects;

[0140] S650, adjust the monitoring system, incorporate new process monitoring points and quality defect correlation, and optimize the guidance of downstream processes;

[0141] S660. Develop and improve process discipline reports to ensure they accurately reflect the status of process monitoring points and quality control;

[0142] S670, track the effects of improvement measures after implementation, especially the improvement of production guidance and process control capabilities of downstream processes;

[0143] S680. Record and share the knowledge of the relationship between process monitoring points and quality defects, as well as the experience and results of improvement measures.

[0144] Specifically, in the S600 stage, the present invention collects process monitoring points and quality defect data extensively, deeply analyzes the correlation between them, and carefully evaluates the effectiveness of the monitoring system in identifying and preventing quality defects. By identifying the specific connection between process monitoring points and quality defects and their impact on downstream processes, the present invention formulates and implements measures to improve process monitoring and early warning mechanisms based on metallurgical principles and the correlation between quality defects. In addition, the adjustment of the monitoring system incorporates new monitoring points and quality defect correlations, optimizing the guidance of downstream processes. The development and improvement of process discipline reports ensures accurate reflection of the status of process monitoring points and quality control, and at the same time, tracks the effects of improvement measures after implementation, especially improving the production guidance and process control capabilities of downstream processes. Importantly, the present invention also includes recording and sharing the knowledge of the correlation between process monitoring points and quality defects and the experience of improvement measures, forming a closed loop of continuous improvement, which not only improves the optimization of production processes and the level of quality management, but also enhances the adaptability and flexibility of the monitoring system, providing strong support for the continuous improvement and knowledge accumulation of enterprises.

[0145] A storage medium stores a computer program, which, when executed by a processor, implements the above-mentioned process monitoring and early warning method based on process specifications.

[0146] Specifically, the storage medium and the computer program stored therein have greatly improved the standardization and automation level of production monitoring by efficiently implementing process monitoring and early warning methods based on process specifications. It is easy to deploy and update, can effectively collect, clean and analyze production data, and automatically generate key process parameter reports, thereby strengthening data processing capabilities and decision support. In addition, the continuous improvement and adaptability of the program ensure that the monitoring system can quickly respond to production changes, and the promotion of intelligent manufacturing and the reduction of operational complexity have further improved production efficiency and safety. At the same time, the program promotes the sharing of process knowledge, reduces production costs, improves the economic benefits of enterprises, and promotes the realization of environmentally friendly production by optimizing process flows. Overall, the storage medium and its computer program provide strong technical support for improving the level of production management, promoting the intelligent transformation of enterprises and sustainable development.

[0147] A computer device comprises: a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-mentioned process monitoring and early warning method based on process specifications.

[0148] Specifically, the computer device is equipped with a memory, a processor and a dedicated computer program. By executing the program stored in the memory, it provides an efficient and integrated solution for realizing process monitoring and early warning methods based on process specifications. It enhances the automation of production monitoring, allows fast and accurate processing of large amounts of production data, and realizes real-time monitoring and early warning, thereby improving production efficiency and product quality. At the same time, the device is easy to deploy and maintain, ensuring the standardization and reliability of process management. In addition, it supports continuous improvement and adapts to changes in production needs, and is an indispensable part of the intelligent manufacturing system. By optimizing the process flow, the device helps to reduce production costs, improve production safety, and promote the sharing and learning of process knowledge. Ultimately, it promotes the realization of environmentally friendly production and provides a solid technical foundation for promoting the sustainable development of enterprises and enhancing market competitiveness.

[0149] The process monitoring and early warning method based on process specifications of the present invention realizes accurate control and timely early warning of the production process through real-time data collection and monitoring, and significantly improves product quality. It provides operators with comprehensive process control tools by integrating process specification libraries, real-time data and material information, thereby enhancing the ability to control the production process. In addition, the solution uses monitoring data to optimize the process flow, reduce resource waste, reduce production costs, and improve production efficiency. The generated process discipline reports and data analysis provide data support for management, helping them make more scientific decisions. At the same time, the solution also includes employee training and operation guidance to improve the skill level of operators. Through a closed-loop management system of continuous improvement, the solution can continuously optimize itself according to real-time feedback, achieve continuous improvement of the production process, and lay the foundation for the realization of intelligent manufacturing and Industry 4.0.

Claims

1. A process monitoring and early warning method based on process specifications, characterized in that: The process monitoring and early warning method based on process specifications includes the following steps: S100, digitize process specifications and integrate them into the monitoring system, collect key production data in real time, and integrate material timing information to form a unified data set; S200, establish a monitoring mechanism in time and space dimensions, and realize real-time monitoring of the production process by comparing real-time data with process specifications; S300: Once a deviation is detected, an alarm is triggered immediately and abnormal information is displayed. At the same time, potential quality defects or possible production accidents are predicted and early warning information is provided; S400, generate process discipline reports containing key attributes, perform data storage, statistics and analysis to support real-time quality control; S500, using algorithms to guide production operations, such as cutting the head and tail of strip steel, while optimizing the process based on monitoring data; S600. Continuously optimize process specifications and monitoring methods based on monitoring results to form a closed-loop management system for continuous improvement.

2. A process monitoring and early warning method based on process specifications according to claim 1, characterized in that: In S100, the following steps are included: S110. Convert existing process specifications and production standards into electronic format and establish a comprehensive process specification library; S120, configure OPC UA communication protocol to ensure compatibility with production equipment and collect key production process parameters in real time, such as temperature, pressure, speed, etc.; S130, interface with the inspection and testing system to obtain the chemical composition and physical property data of the material, communicate with the MES system, and collect the timing information of material flow and production scheduling; S140, clean the collected data, remove outliers and noise, verify the accuracy and completeness of the data, and ensure that the data can be used for subsequent analysis; S150. Apply the time-space conversion algorithm to integrate the real-time data and material information according to the time and space dimensions to ensure that the data sets are continuous in time and have a clear correspondence in space; S160. Design an appropriate database structure to store collected data and converted data sets, and implement data indexing and query optimization to support fast data access and analysis.

3. A process monitoring and early warning method based on process specifications according to claim 2, characterized in that: In S200, the following steps are included: S210. Clarify the objectives of the monitoring system; S220. Design the architecture of the monitoring system, including data flow, module division and user interface, and determine the performance indicators of the system; S230, develop data interfaces with production equipment, inspection and testing systems, and MES systems to ensure that data can be stably and securely transmitted to the monitoring system; S240, implement a real-time data processing mechanism, including receiving, parsing and storing data, and performing necessary pre-processing on the data; S250. Develop monitoring logic according to process specifications, including parameter verification, alarm condition setting, etc., to achieve dynamic association between monitoring points and process specifications; S260. Design the trigger logic of the alarm, including threshold setting, alarm level classification, etc., to realize the recording, display and notification functions of the alarm information; S270, develop user interface, including real-time data display, historical data query, alarm information viewing, etc., to ensure that the user interface is intuitive and easy to use and can meet the needs of different users; S280. Conduct comprehensive testing on the monitoring system, including functional testing, performance testing and user testing, to verify the accuracy, stability and availability of the monitoring system; S290. Deploy the monitoring system to the production environment, conduct a final check before going online, formulate an online plan, and ensure a smooth transition and stable operation of the system.

4. A process monitoring and early warning method based on process specifications according to claim 3, characterized in that: In S300, the deviation is the deviation compared with the process standard, including the deviation of the final composition of the converter, the deviation of the continuous casting billet drawing speed, the deviation of the superheat degree of the tundish, the deviation of the finished product composition, the deviation of the heating temperature of the steel rolling, and the deviation of the finished strip thickness; The quality defects include inappropriate chemical composition of the finished product, risk of steel leakage, and excessive thickness of the finished strip steel.

5. A process monitoring and early warning method based on process specifications according to claim 4, characterized in that: In S300, the following steps are included: S310. Set alarm thresholds according to process specifications and develop corresponding early warning logic; S320, deploy a real-time monitoring system and automatically trigger an alert when an anomaly is detected; S330. Set up a multi-channel notification mechanism to ensure that all relevant personnel can receive alarm information in a timely manner; S340. Develop standardized response procedures and provide corresponding training to operators; S350, promptly implement preventive measures or corrective actions based on early warning information; S360, regularly test the alarm system, collect feedback, and continuously optimize the early warning mechanism based on data analysis.

6. A process monitoring and early warning method based on process specifications according to claim 5, characterized in that: In S400, the following steps are included: S410, collect monitoring data and alarm records, and clean them to ensure data quality; S420, using the cleaned data, automatically generating a report containing key process parameters and alarm statistics; S430, conduct in-depth analysis of alarm and monitoring data to identify problems and improvement points in the production process; S440. Develop and implement quality control measures based on the analysis results; S450. Distribute the report to relevant personnel and collect their feedback for improvement; S460, archive historical data and conduct long-term trend analysis; S470. Continuously optimize monitoring system and report generation process based on feedback and trend analysis.

7. A process monitoring and early warning method based on process specifications according to claim 6, characterized in that: In S500, the following steps are included: S510, analyzing the real-time monitoring data and dynamically adjusting the process parameters to adapt to production changes; S520, based on real-time monitoring data, ensure that the production process can be automatically adjusted when deviations occur; S530, evaluating the effect of the automatic adjustment operation, and collecting feedback from on-site operators, to further optimize the data analysis process of S510 and the automatic adjustment process of S520.

8. A process monitoring and early warning method based on process specifications according to claim 7, characterized in that: S600 includes the following steps: S610, collect a wide range of information including process monitoring points and quality defect data, and analyze the correlation between them; S620, evaluate the effectiveness of the current monitoring system in identifying and preventing quality defects; S630, identify the specific links between process monitoring points and quality defects, and their impact on downstream processes; S640. Develop measures to improve process monitoring and early warning mechanisms based on metallurgical principles and the correlation of quality defects; S650, adjust the monitoring system, incorporate new process monitoring points and quality defect correlation, and optimize the guidance of downstream processes; S660. Develop and improve process discipline reports to ensure they accurately reflect the status of process monitoring points and quality control; S670, track the effects of improvement measures after implementation, especially the improvement of production guidance and process control capabilities of downstream processes; S680. Record and share the knowledge of the relationship between process monitoring points and quality defects, as well as the experience and results of improvement measures.

9. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the process monitoring and early warning method based on process specifications described in any one of claims 1 to 8 is implemented.

10. A computer device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement a process monitoring and early warning method based on process specifications as described in any one of claims 1 to 8.

Images