Cold rolling unit process data acquisition, query and analysis system

Abstract

The invention belongs to the technical field of cold rolling unit computer software, and discloses a cold rolling unit process data acquisition, query and analysis system which comprises an intelligent data acquisition module, a data transmission module, a data storage module, a data query module, a data analysis module, a user management module and a system maintenance and upgrade module. The method is remarkable in effect in the field of cold rolling production, multi-link process parameters are obtained through accurate data collection, a foundation is laid for quality control, quality index association is mined through data analysis, abnormity is early warned in advance, the defective rate is reduced, the high-end product proportion is increased, and market competitiveness is enhanced; meanwhile, key information is provided for production scheduling by means of efficient and real-time data acquisition and transmission, and a production efficiency analysis module assists in finding out productivity bottlenecks and optimizing production scheduling; in addition, equipment data is monitored by means of a sensor, faults are predicted through machine learning, preventive maintenance is achieved, production continuity is guaranteed, cost is reduced, and efficiency is improved.

Description

Technical Field

[0001] The invention belongs to the technical field of computer software for cold rolling mills, and in particular relates to a process data collection, query and analysis system for cold rolling mills. Background Art

[0002] Steel rolling, as the main production process of steel enterprises, plays an increasingly important role in the modern metallurgical industry. In today's fiercely competitive steel industry, cold rolling units, as the core link in steel deep processing, have a process level that is directly related to the success or failure of enterprises. As the manufacturing industry moves towards high-end and refined production, the market's quality requirements for cold-rolled steel products are becoming more and more stringent. Not only does the plate thickness accuracy need to be controlled within an extremely small tolerance range, but indicators such as surface roughness and mechanical properties must also meet a variety of industrial standards. At the same time, production efficiency has become a key factor for enterprises to seize market share and reduce costs. Every minute of downtime means economic loss, and a fast and efficient rolling process is imminent.

[0003] Furthermore, cold rolling mill equipment is complex and expensive. Once a sudden failure occurs and the machine is shut down for maintenance, it will not only cause production stagnation, but may also trigger a chain reaction due to insufficient emergency repairs, damaging upstream and downstream related equipment, resulting in high maintenance costs and production delays. The traditional management model that relies on manual experience, post-maintenance and scattered data records is no longer able to cope with these challenges. In this context, the development of a system that can collect cold rolling mill process data in real time and accurately, and integrates convenient query and in-depth analysis, has become the only way for steel companies to break through development bottlenecks and move towards intelligent production, and provide strong support for process control, efficiency improvement and equipment maintenance optimization. Summary of the invention

[0004] The object of the present invention is to provide a cold rolling mill process data collection, query and analysis system to solve the problems raised in the above-mentioned background technology.

[0005] In order to achieve the above-mentioned purpose, the present invention provides the following technical solutions: a cold rolling mill process data collection, query and analysis system, the system comprising an intelligent data collection module, a data transmission module, a data storage module, a data query module, a data analysis module, a user management module, and a system maintenance and upgrade module;

[0006] Intelligent data acquisition module: deploy high-precision sensors at key locations of the cold rolling mill, set acquisition frequencies for different parameters, and use calibration algorithms and redundant sensor designs;

[0007] Data transmission module: Build workshop-level industrial Ethernet, introduce low-power Bluetooth or Wi-Fi wireless transmission technology, realize flexible networking, seamlessly connect with wired networks, ensure the continuity of collected data, follow the industrial standard OPCUA protocol, and realize the interaction of collected data between equipment from different manufacturers and different systems;

[0008] Data storage module: select a high-performance, distributed relational database, design a reasonable table structure and data fields, formulate a scheduled full backup and incremental backup strategy and store them in a specified path;

[0009] Data query module: Use VB.net to write the background query interface, extract the data files stored in the above specified path, and present the query results in the form of charts and tables;

[0010] Data analysis module: Use statistical analysis methods to mine process data and product quality indicators, through comprehensive analysis of rolling speed, downtime, and output data, based on vibration, temperature, and wear equipment operation data;

[0011] User management module: Define detailed user permissions according to different job roles, use multi-factor authentication to log in, and provide system operation training manuals and video tutorials for new users;

[0012] System maintenance and upgrade module: deploy system monitoring software, regularly collect user feedback and industry technology development trends, launch system upgrade versions in a timely manner, formulate regular inspection and maintenance plans for hardware equipment, and reserve key spare parts.

[0013] Preferably, the intelligent data acquisition module includes:

[0014] (1) Sensor layout: High-precision sensors, including pressure sensors, temperature sensors, speed sensors, and tension sensors, are rationally deployed at the key locations of the cold rolling mill rolls, cooling system, and tension device to ensure that process parameters are fully captured;

[0015] (2) Data collection frequency: Set the collection frequency of different parameters. The roll pressure can be collected every millisecond to accurately reflect the instantaneous changes in rolling. The cooling water temperature can be collected every 5 seconds to balance data accuracy and storage pressure.

[0016] (3) Using calibration algorithms and redundant sensor design, the sensor zero point and gain are automatically calibrated regularly. Multi-sensor data fusion is used to reduce errors in key data and ensure that the acquisition accuracy is controlled within a very small range.

[0017] Preferably, the data transmission module comprises:

[0018] (1) Wired transmission network: Build a workshop-level industrial Ethernet, use shielded twisted pair cables to connect each sensor and data acquisition terminal, ensure high-speed and stable data transmission, with strong anti-interference ability, and meet the real-time requirements;

[0019] (2) Wireless transmission supplement: For some mobile devices or areas where it is difficult to lay cables, introduce low-power Bluetooth or Wi-Fi wireless transmission technology to achieve flexible networking and seamless connection with the wired network to ensure data continuity;

[0020] (3) Transmission protocol: Follow the industrial standard OPC UA protocol to achieve data interaction between devices from different manufacturers and different systems, and ensure accurate data transmission between the acquisition end, transmission link, and storage server.

[0021] Preferably, the data storage module includes:

[0022] (1) Database selection: Select a high-performance, distributed relational database (MySQL Cluster), combined with a non-relational database (InfluxDB for storing time-series data), give full play to the advantages of both to meet the storage requirements of massive and multi-structured process data;

[0023] (2) Data storage structure: Design a reasonable table structure and data fields, classify and store according to the process flow, establish indexes to optimize query performance, and at the same time use data partitioning technology to partition by time and unit dimensions for easy data management and maintenance;

[0024] (3) Data backup and recovery: Develop a strategy for regular full backups and incremental backups, store the backup data in a remote disaster recovery center to ensure data security; in case of data loss or damage, it can be quickly restored to any historical node to ensure production continuity.

[0025] Preferably, the data query module includes:

[0026] (1) Query interface design: Use VB.net to write a background query interface, and users can flexibly select query conditions such as time range, unit number, and process parameter name through dropdown menus and text box interaction components;

[0027] (2) Query result display: Extract the data files of the data storage module and present the query results in the form of charts (line charts, bar charts, scatter plots) and tables, intuitively reflecting the data change trend and distribution characteristics, and supporting the export of data to Excel and CSV formats for further analysis and processing.

[0028] Preferably, the data analysis module includes:

[0029] (1) Quality correlation analysis: using statistical analysis methods to explore the potential relationship between process data and product quality indicators such as plate thickness accuracy, surface roughness, and mechanical properties, establish a quality prediction model, warn of quality anomalies in advance, and guide the adjustment of process parameters;

[0030] (2) Production efficiency analysis: Through comprehensive analysis of rolling speed, downtime, and output data, production bottlenecks can be identified and equipment overall efficiency (OEE) can be calculated to provide a basis for optimizing production scheduling and increasing production capacity;

[0031] (3) Equipment health analysis: Based on vibration, temperature, and wear equipment operation data, a machine learning algorithm is used to build an equipment failure prediction model to monitor the equipment health status in real time, implement preventive maintenance, and reduce equipment failure rates.

[0032] Preferably, the user management module includes:

[0033] (1) User rights allocation: Define detailed user rights according to different job roles. Operators can only query production data on duty, engineers can perform data analysis, and administrators are responsible for system configuration and user management to ensure data security and compliance.

[0034] (2) Identity authentication mechanism, which uses password, fingerprint recognition, and dynamic token multi-factor authentication to prevent illegal logins and ensure system access security. It also records user operation logs for easy audit tracking;

[0035] (3) User training and support: provide new users with system operation training manuals and video tutorials, set up online customer service and technical support hotlines, and promptly resolve problems encountered by users during use to ensure smooth operation of the system.

[0036] Preferably, the system maintenance and upgrade module includes:

[0037] (1) Real-time monitoring and fault alarm: deploy system monitoring software to monitor key indicators of server performance, network status, and software processes in real time. Once an abnormality occurs, the operation and maintenance personnel will be notified in a timely manner via SMS or email for rapid response and processing;

[0038] (2) Software upgrade management: regularly collect user feedback and industry technology development trends, launch system upgrade versions in a timely manner, and use a combination of online upgrades and offline installation packages to ensure a smooth upgrade process without affecting normal production, and continuously improve system functions and performance;

[0039] (3) Hardware maintenance plan: formulate regular inspection and maintenance plans for hardware equipment sensors, servers, and network equipment, reserve key spare parts, extend equipment life, and ensure system hardware reliability.

[0040] The beneficial effects of the present invention are as follows:

[0041] 1. The present invention accurately captures the process parameters of multiple links such as rolling mill, cooling, tension, etc. through systematic data acquisition, laying a solid foundation for quality control. Data analysis provides in-depth mining of the relationship between process data and product quality indicators. Through correlation analysis of plate thickness accuracy, surface roughness, etc., early warning of quality anomalies is given. In actual production, enterprises adjust process parameters in time according to system prompts, which significantly reduces the defective rate of products and greatly increases the output ratio of high-precision products, meeting the stringent needs of the high-end market and enhancing market competitiveness.

[0042] 2. By relying on the efficient and real-time data collection and transmission, the present invention enables production scheduling personnel to accurately grasp the key dynamic information such as rolling speed, downtime, and time consumption of each process connection at the first time; the production efficiency analysis module is like an intelligent military advisor, which can quickly identify the bottleneck links that restrict the improvement of production capacity through in-depth analysis of the massive amount of stored data, and provide a scientific basis for optimizing the production scheduling plan.

[0043] 3. The present invention uses sensors to continuously monitor equipment vibration, temperature, and wear data, and the equipment health analysis module uses machine learning algorithms to predict faults. This changes the traditional post-maintenance model and implements preventive maintenance. For example, potential failure hazards of rolling mill rollers can be discovered in advance and replaced during non-production periods to avoid losses caused by sudden downtime. At the same time, it extends the overall service life of the equipment, reduces investment in equipment procurement, ensures production continuity, and helps companies reduce costs and increase efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 This is a diagram of the cold rolling mill process data collection, query and analysis system of the present invention. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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.

[0046] like Figure 1 As shown, an embodiment of the present invention provides a cold rolling mill process data collection, query, and analysis system, which includes an intelligent data collection module, a data transmission module, a data storage module, a data query module, a data analysis module, a user management module, and a system maintenance and upgrade module;

[0047] Intelligent data acquisition module: deploy high-precision sensors at key locations of the cold rolling mill, set acquisition frequencies for different parameters, and use calibration algorithms and redundant sensor designs;

[0048] Data transmission module: Build workshop-level industrial Ethernet, introduce low-power Bluetooth or Wi-Fi wireless transmission technology, realize flexible networking, seamlessly connect with wired networks, ensure the continuity of collected data, follow the industrial standard OPCUA protocol, and realize the interaction of collected data between equipment from different manufacturers and different systems;

[0049] Data storage module: select a high-performance, distributed relational database, design a reasonable table structure and data fields, formulate a scheduled full backup and incremental backup strategy and store them in a specified path;

[0050] Data query module: Use VB.net to write the background query interface, extract the data files stored in the above specified path, and present the query results in the form of charts and tables;

[0051] Data analysis module: Use statistical analysis methods to mine process data and product quality indicators, through comprehensive analysis of rolling speed, downtime, and output data, based on vibration, temperature, and wear equipment operation data;

[0052] User management module: Define detailed user permissions according to different job roles, use multi-factor authentication to log in, and provide system operation training manuals and video tutorials for new users;

[0053] System maintenance and upgrade module: deploy system monitoring software, regularly collect user feedback and industry technology development trends, launch system upgrade versions in a timely manner, formulate regular inspection and maintenance plans for hardware equipment, and reserve key spare parts.

[0054] In the intelligent manufacturing system of cold rolling mills, each module works closely together. The intelligent data acquisition module deploys high-precision sensors at key parts of the cold rolling mill, sets the acquisition frequency according to parameter characteristics, and calibrates the algorithm and redundant design to ensure data accuracy. The data transmission module builds a workshop-level industrial Ethernet, integrates low-power Bluetooth and Wi-Fi wireless technologies, and uses the OPCUA protocol to seamlessly connect wired and wireless networks to ensure continuous data interaction. The data storage module selects a high-performance distributed database, carefully plans the table structure and fields, and regularly backs up the full and incremental data to the specified path. The data query module uses VB.net to create a background interface to extract data and present the results in charts and tables. The data analysis module uses statistical methods to dig deep into the relationship between process and quality. User management determines permissions according to positions, logs in with multi-factor authentication, and provides training materials. The system maintenance and upgrade module relies on monitoring software, user feedback, and industry dynamics for timely upgrades, and hardware and key spare parts are maintained according to the inspection plan.

[0055] Wherein, the intelligent data acquisition module includes:

[0056] (1) Sensor layout: High-precision sensors, including pressure sensors, temperature sensors, speed sensors, and tension sensors, are rationally deployed at the key locations of the cold rolling mill rolls, cooling system, and tension device to ensure that process parameters are fully captured;

[0057] (2) Data collection frequency: Set the collection frequency of different parameters. The roll pressure can be collected every millisecond to accurately reflect the instantaneous changes in rolling. The cooling water temperature can be collected every 5 seconds to balance data accuracy and storage pressure.

[0058] (3) Using calibration algorithms and redundant sensor design, the sensor zero point and gain are automatically calibrated regularly. Multi-sensor data fusion is used to reduce errors in key data and ensure that the acquisition accuracy is controlled within a very small range.

[0059] In the construction of the intelligent control system of the cold rolling mill, the sensor layout plays a key role; in the core parts such as the roll, cooling system, and tension device, a variety of high-precision sensors are reasonably placed according to their process characteristics. The pressure sensor monitors the force of the roll in real time, the temperature sensor keeps a close eye on the water temperature of the cooling link, the speed sensor controls the operation rhythm, and the tension sensor ensures the stability of the tension, capturing the process parameters in all directions. In terms of data collection frequency, both accuracy and efficiency are taken into account. The roll pressure changes rapidly and is collected every millisecond; the cooling water temperature is relatively stable and is collected every 5 seconds. At the same time, the calibration algorithm and redundant sensor design are used to periodically and automatically calibrate the sensor zero point and gain, and multiple sensors fuse key data to minimize the collection accuracy error.

[0060] Wherein, the data transmission module includes:

[0061] (1) Wired transmission network: Building workshop-level industrial Ethernet, using shielded twisted pair cables to connect sensors and data acquisition terminals, ensuring high-speed and stable data transmission, strong anti-interference capability, and meeting real-time requirements;

[0062] (2) Wireless transmission supplement: For some mobile devices or areas where wiring is difficult, low-power Bluetooth or Wi-Fi wireless transmission technology is introduced to achieve flexible networking, seamless connection with wired networks, and ensure data continuity;

[0063] (3) Transmission protocol: Follow the industrial standard OPC UA protocol to achieve data interaction between equipment from different manufacturers and different systems, and ensure that data is accurately transmitted between the collection end, transmission link, and storage server.

[0064] In terms of data transmission, the system has been planned and designed in multiple dimensions. First, the wired transmission network builds a workshop-level industrial Ethernet and uses shielded twisted pair cables to connect various sensors and data acquisition terminals. This method can ensure high-speed and stable data transmission, has strong anti-interference capabilities, and can fully meet the strict requirements of real-time performance. Secondly, considering the special circumstances of some mobile devices or areas with difficult wiring, wireless transmission technologies such as low-power Bluetooth or Wi-Fi are introduced. They can achieve flexible networking and can be seamlessly connected with wired networks to ensure the continuity of data transmission. Finally, the transmission protocol follows the industrial standard OPC UA protocol, which supports data interaction between equipment from different manufacturers and different systems, ensuring accurate transmission of data between the acquisition end, transmission link, and storage server.

[0065] Wherein, the data storage module comprises:

[0066] (1) Database selection: A high-performance, distributed relational database (MySQLCluster) is selected in combination with a non-relational database (InfluxDB to store time series data) to give full play to the advantages of both and meet the needs of storing massive, multi-structure process data;

[0067] (2) Data storage structure: design a reasonable table structure and data fields, classify and store data according to the process flow, establish indexes to optimize query performance, and use data partitioning technology to partition data by time and unit dimensions to facilitate data management and maintenance;

[0068] (3) Data backup and recovery: formulate regular full backup and incremental backup strategies, store backup data in an off-site disaster recovery center to ensure data security; when data is lost or damaged, it can be quickly restored to any historical node to ensure production continuity.

[0069] In the data management system of the cold rolling mill, the construction of the database link is particularly critical. First, the database selection is unique, and the high-performance, distributed MySQLCluster is selected as the main relational database, responsible for structured data storage. At the same time, InfluxDB is introduced to deal with time series data. The two are combined to calmly deal with massive and complex process data; second, the data storage structure is carefully crafted, the table structure and fields are carefully designed according to the process flow, the classified storage is orderly, and the query is optimized with indexes. Using data partitioning technology, the data is accurately divided according to time and units, making data management handy; third, the data backup and recovery strategy is rigorous, with scheduled full and incremental backups, and the off-site disaster recovery center builds a solid security line of defense. When the data is damaged, it can be instantly traced back to ensure uninterrupted production.

[0070] Wherein, the data query module includes:

[0071] (1) Query interface design: Use VB.net to write the backend query interface. Users can flexibly select query conditions, such as time range, unit number, and process parameter name, through drop-down menus and text box interactive components;

[0072] (2) Display of query results: extract the data files from the data storage module and present the query results in the form of line graphs, bar graphs, scatter plots, and tables to intuitively reflect the data change trends and distribution characteristics. It also supports exporting data to Excel and CSV formats for further analysis and processing.

[0073] The query and display module, a key link in the data processing of the cold rolling mill, is carefully designed and practical. On the one hand, the background query interface carefully built with VB.net fully considers the convenience of user operation, and sets interactive components such as drop-down menus and text boxes, so that users can flexibly filter query conditions from dimensions such as time range, unit number, and process parameter name according to their own needs. On the other hand, for the query results, not only can the data files in the data storage module be accurately extracted, but also presented in a variety of forms. Line charts, bar charts, and scatter plots intuitively show the dynamic changes and distribution trends of the data, and tables are used to list the data details in detail, and it supports export to Excel and CSV formats, which provides great convenience for subsequent in-depth analysis.

[0074] Wherein, the data analysis module includes:

[0075] (1) Quality correlation analysis: using statistical analysis methods to explore the potential relationship between process data and product quality indicators such as plate thickness accuracy, surface roughness, and mechanical properties, establish a quality prediction model, warn of quality anomalies in advance, and guide the adjustment of process parameters;

[0076] (2) Production efficiency analysis: Through comprehensive analysis of rolling speed, downtime, and output data, production bottlenecks can be identified and equipment overall efficiency (OEE) can be calculated to provide a basis for optimizing production scheduling and increasing production capacity;

[0077] (3) Equipment health analysis: Based on vibration, temperature, and wear equipment operation data, a machine learning algorithm is used to build an equipment failure prediction model to monitor the equipment health status in real time, implement preventive maintenance, and reduce equipment failure rates.

[0078] In the field of intelligent data analysis of cold rolling mills, it covers multiple key aspects to help optimize production; the first is quality correlation analysis, which uses professional statistical methods to deeply explore the intrinsic relationship between process data and core product quality indicators, such as plate thickness accuracy, surface roughness, and mechanical properties, and then builds an accurate quality prediction model to detect quality risks in advance and issue warnings, and provide directions for adjusting process parameters; secondly, it focuses on production efficiency analysis, comprehensively integrates data such as rolling speed, downtime, and output, accurately locates bottlenecks in the production process, and calculates the overall efficiency of the equipment OEE, laying a solid foundation for scientific production scheduling and capacity leap; finally, relying on the vibration, temperature, and wear data during equipment operation, with the help of machine learning algorithms, an equipment failure prediction model is created to control the health status of the equipment in real time, opening a new chapter of preventive maintenance and significantly reducing the risk of equipment failure.

[0079] Wherein, the user management module includes:

[0080] (1) User rights allocation: Define detailed user rights according to different job roles. Operators can only query production data on duty, engineers can perform data analysis, and administrators are responsible for system configuration and user management to ensure data security and compliance.

[0081] (2) Identity authentication mechanism, which uses password, fingerprint recognition, and dynamic token multi-factor authentication to prevent illegal logins and ensure system access security. It also records user operation logs for easy audit tracking;

[0082] (3) User training and support: provide new users with system operation training manuals and video tutorials, set up online customer service and technical support hotlines, and promptly resolve problems encountered by users during use to ensure smooth operation of the system.

[0083] In the operational support link of the cold rolling mill data management system, the user management system is of vital importance. First, the user rights are allocated in a detailed and reasonable manner, and are precisely defined according to the characteristics of the positions. The operator's rights are limited to querying the production data of the shift and focusing on his or her job. Engineers can conduct in-depth data analysis and explore the value of data. Administrators control the system configuration and user management. Each role performs its duties, strictly abides by the data security red line, and ensures compliance with regulations. Second, identity authentication adopts a multi-factor fusion mode, with passwords, fingerprint recognition, and dynamic tokens to prevent illegal intrusions. At the same time, detailed operation logs are recorded for audit traceability. Third, considerate user training and support, new users are provided with training manuals and video tutorials, and online customer service and technical hotlines are on standby at any time to ensure the smooth operation of the system.

[0084] Wherein, the system maintenance and upgrade module includes:

[0085] (1) Real-time monitoring and fault alarm: deploy system monitoring software to monitor key indicators of server performance, network status, and software processes in real time. Once an abnormality occurs, the operation and maintenance personnel will be notified in a timely manner via SMS or email for rapid response and processing;

[0086] (2) Software upgrade management: regularly collect user feedback and industry technology development trends, launch system upgrade versions in a timely manner, and use a combination of online upgrades and offline installation packages to ensure a smooth upgrade process without affecting normal production, and continuously improve system functions and performance;

[0087] (3) Hardware maintenance plan: formulate regular inspection and maintenance plans for hardware equipment sensors, servers, and network equipment, reserve key spare parts, extend equipment life, and ensure system hardware reliability.

[0088] The maintenance and management of the cold rolling mill system includes many important aspects. First, real-time monitoring and fault alarm mechanisms are crucial. By deploying system monitoring software, key indicators such as server performance, network status and software processes are monitored in real time. Once an abnormality occurs, the operation and maintenance personnel will be notified by SMS or email for rapid response and processing. Secondly, in terms of software upgrade management, user feedback and industry technology trends are collected regularly, and upgraded versions are launched in a timely manner. A combination of online upgrades and offline installation packages is used to ensure that the upgrade process is stable and does not affect normal production, so as to continuously improve system performance. Finally, hardware maintenance plans are indispensable. Regular inspection and maintenance plans are formulated for sensors, servers, and network equipment to reserve key spare parts, extend equipment life, and ensure hardware reliability.

[0089] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0090] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A cold rolling mill process data collection, query and analysis system, characterized by: The system consists of an intelligent data acquisition module, a data transmission module, a data storage module, a data query module, a data analysis module, a user management module, and a system maintenance and upgrade module; Intelligent data acquisition module: deploy high-precision sensors at key locations of the cold rolling mill, set acquisition frequencies for different parameters, and use calibration algorithms and redundant sensor designs; Data transmission module: Build workshop-level industrial Ethernet, introduce low-power Bluetooth or Wi-Fi wireless transmission technology, realize flexible networking, seamlessly connect with wired networks, ensure the continuity of collected data, follow the industrial standard OPCUA protocol, and realize the interaction of collected data between equipment from different manufacturers and different systems; Data storage module: select a high-performance, distributed relational database, design a reasonable table structure and data fields, formulate a scheduled full backup and incremental backup strategy and store them in a specified path; Data query module: Use VB.net to write the background query interface, extract the data files stored in the above specified path, and present the query results in the form of charts and tables; Data analysis module: Use statistical analysis methods to mine process data and product quality indicators, through comprehensive analysis of rolling speed, downtime, and output data, based on vibration, temperature, and wear equipment operation data; User management module: Define detailed user permissions according to different job roles, use multi-factor authentication to log in, and provide system operation training manuals and video tutorials for new users; System maintenance and upgrade module: deploy system monitoring software, regularly collect user feedback and industry technology development trends, launch system upgrade versions in a timely manner, formulate regular inspection and maintenance plans for hardware equipment, and reserve key spare parts.

2. A cold rolling mill process data collection, query and analysis system according to claim 1, characterized in that: The intelligent data acquisition module comprises: (1) Sensor layout: High-precision sensors, including pressure sensors, temperature sensors, speed sensors, and tension sensors, are rationally deployed at the key locations of the cold rolling mill rolls, cooling system, and tension device to ensure that process parameters are fully captured; (2) Data collection frequency: Set the collection frequency of different parameters. The roll pressure can be collected every millisecond to accurately reflect the instantaneous changes in rolling. The cooling water temperature can be collected every 5 seconds to balance data accuracy and storage pressure. (3) Using calibration algorithms and redundant sensor design, the sensor zero point and gain are automatically calibrated regularly. Multi-sensor data fusion is used to reduce errors in key data and ensure that the acquisition accuracy is controlled within a very small range.

3. The cold rolling mill process data collection, query and analysis system according to claim 1, characterized in that: The data transmission module comprises: (1) Wired transmission network: Building workshop-level industrial Ethernet, using shielded twisted pair cables to connect sensors and data acquisition terminals, ensuring high-speed and stable data transmission, strong anti-interference capability, and meeting real-time requirements; (2) Wireless transmission supplement: For some mobile devices or areas where wiring is difficult, low-power Bluetooth or Wi-Fi wireless transmission technology is introduced to achieve flexible networking, seamless connection with wired networks, and ensure data continuity; (3) Transmission protocol: Follow the industrial standard OPC UA protocol to achieve data interaction between equipment from different manufacturers and different systems, and ensure that data is accurately transmitted between the collection end, transmission link, and storage server.

4. The cold rolling mill process data collection, query and analysis system according to claim 1, characterized in that: The data storage module comprises: (1) Database selection: Use a high-performance, distributed relational database in combination with a non-relational database to give full play to the advantages of both and meet the needs of storing massive, multi-structured process data; (2) Data storage structure: design a reasonable table structure and data fields, classify and store data according to the process flow, establish indexes to optimize query performance, and use data partitioning technology to partition data by time and unit dimensions to facilitate data management and maintenance; (3) Data backup and recovery: formulate regular full backup and incremental backup strategies, store backup data in an off-site disaster recovery center to ensure data security; when data is lost or damaged, it can be quickly restored to any historical node to ensure production continuity.

5. The cold rolling mill process data collection, query and analysis system according to claim 1, characterized in that: The data query module includes: (1) Query interface design: Use VB.net to write the backend query interface. Users can flexibly select query conditions, such as time range, unit number, and process parameter name, through drop-down menus and text box interactive components; (2) Display of query results: extract the data files from the data storage module and present the query results in the form of line graphs, bar graphs, scatter plots, and tables to intuitively reflect the data change trends and distribution characteristics. It also supports exporting data to Excel and CSV formats for further analysis and processing.

6. The cold rolling mill process data collection, query and analysis system according to claim 1, characterized in that: The data analysis module includes: (1) Quality correlation analysis: using statistical analysis methods to explore the potential relationship between process data and product quality indicators such as plate thickness accuracy, surface roughness, and mechanical properties, establish a quality prediction model, warn of quality anomalies in advance, and guide the adjustment of process parameters; (2) Production efficiency analysis: Through comprehensive analysis of rolling speed, downtime, and output data, production bottlenecks can be identified and equipment overall efficiency (OEE) can be calculated to provide a basis for optimizing production scheduling and increasing production capacity; (3) Equipment health analysis: Based on vibration, temperature, and wear equipment operation data, a machine learning algorithm is used to build an equipment failure prediction model to monitor the equipment health status in real time, implement preventive maintenance, and reduce equipment failure rates.

7. The cold rolling mill process data collection, query and analysis system according to claim 1, characterized in that: The user management module comprises: (1) User rights allocation: Define detailed user rights according to different job roles. Operators can only query production data on duty, engineers can perform data analysis, and administrators are responsible for system configuration and user management to ensure data security and compliance. (2) Identity authentication mechanism, which uses password, fingerprint recognition, and dynamic token multi-factor authentication to prevent illegal logins and ensure system access security. It also records user operation logs for easy audit tracking; (3) User training and support: provide new users with system operation training manuals and video tutorials, set up online customer service and technical support hotlines, and promptly resolve problems encountered by users during use to ensure smooth operation of the system.

8. The cold rolling mill process data collection, query and analysis system according to claim 1, characterized in that: The system maintenance and upgrade module includes: (1) Real-time monitoring and fault alarm: deploy system monitoring software to monitor key indicators of server performance, network status, and software processes in real time. Once an abnormality occurs, the operation and maintenance personnel will be notified in a timely manner via SMS or email for rapid response and processing; (2) Software upgrade management: regularly collect user feedback and industry technology development trends, launch system upgrade versions in a timely manner, and use a combination of online upgrades and offline installation packages to ensure a smooth upgrade process without affecting normal production, and continuously improve system functions and performance; (3) Hardware maintenance plan: formulate regular inspection and maintenance plans for hardware equipment sensors, servers, and network equipment, reserve key spare parts, extend equipment life, and ensure system hardware reliability.

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