Product quality index stability dynamic monitoring method and system

Abstract

The invention relates to a product quality index stability dynamic monitoring method and system, and the method comprises the steps: collecting various types of manufacturing data covering steelmaking, hot rolling and cold rolling procedures, and constructing a whole-process material quality history; constructing a layered quality index monitoring framework according to the whole-process material quality resume; the layered quality index monitoring architecture at least comprises a process index monitoring layer, a user side monitoring layer and a manufacturing side monitoring layer; according to a preset index rule engine, performing dynamic calculation on the process index monitoring layer, the user side monitoring layer and the manufacturing side monitoring layer, and obtaining monitoring indexes corresponding to each layer; and performing visual display and SPC process capability analysis on the monitoring indexes corresponding to each layer, and outputting a dynamic monitoring result corresponding to each layer. According to the invention, real-time monitoring and tracing of data of the whole process of steelmaking, hot rolling and cold rolling are realized, and the quality control precision and decision-making efficiency are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of product quality monitoring in the steel industry, and in particular to a product quality index stability dynamic monitoring method and system. BACKGROUND

[0002] In large-scale continuous manufacturing industries such as steel, the stability of product quality directly determines the core competitiveness of the enterprise. At present, enterprises generally rely on a quality monitoring system composed of multiple systems such as manufacturing execution system (MES) and laboratory information management system (LIMS), and realize the control of product production quality by designing core indicators for statistics.

[0003] However, the existing technical means still has obvious limitations, which is difficult to meet the higher requirements of modern intelligent manufacturing on quality management, mainly in the following aspects: first, information islands are formed between various production systems, the data formats are different and the collaboration is poor, which leads to the fact that defect data cannot be transmitted in real time between steelmaking, hot rolling, cold rolling and other processes, and the quality problem tracing is time-consuming and laborious. Second, the quality analysis is seriously lagging behind, mainly relying on manual statistics and reports after the event, and cannot respond and intervene in real time to the abnormalities in the production process. Third, the SPC process capability analysis capability for key process parameters is weak, and mainly relies on manual completion with external software, resulting in high process out-of-control missing rate. In addition, the existing quality index statistics has a coarse granularity, and cannot be layered and refined according to responsibility, customer or product level, making it difficult to accurately locate and control weak links. Finally, the data display is mainly in the form of tables and lacks visualization, and the management decision support is insufficient, making it difficult to realize the change from "after-event rectification" to "pre-event prevention". SUMMARY

[0004] In view of the above analysis, the embodiments of the present application aim to provide a product quality index stability dynamic monitoring method and system to solve the problems of the lack of layered and refined full-process quality index system, poor process collaboration, weak SPC control capability and insufficient decision support in the prior art.

[0005] In one aspect, the embodiments of the present application provide a product quality index stability dynamic monitoring method, comprising:

[0006] Collecting multiple types of manufacturing data covering steelmaking, hot rolling and cold rolling processes, and constructing a full-process material quality history;

[0007] According to the full-process material quality history, a layered quality index monitoring architecture is constructed; wherein the layered quality index monitoring architecture at least includes a process index monitoring layer, a user end monitoring layer and a manufacturing end monitoring layer;

[0008] According to the preset index rule engine, the process index monitoring layer, the user end monitoring layer and the manufacturing end monitoring layer are dynamically calculated, and the corresponding monitoring indexes of each layer are obtained;

[0009] The corresponding monitoring indexes of each layer are visually displayed and SPC process capability analyzed, and the dynamic monitoring results corresponding to each layer are output.

[0010] Further, the multiple manufacturing data covering the steelmaking, hot rolling and cold rolling processes are collected, and a full-process material quality history is constructed, including:

[0011] Based on the mixed distributed storage of MySQL and InfluxDB, the multiple manufacturing data are associated with the material number as the primary key, and the full-process material quality history is constructed according to the multi-dimensional association set obtained by association;

[0012] Among them, the multiple manufacturing data at least include: production data, process data, quality data, equipment data and business data;

[0013] The full-process material quality history at least includes: process dimension data integrated based on the production data, process data, quality data and equipment data, user end dimension data integrated based on the business data and quality data, and manufacturing end dimension data integrated based on the process data, quality data and equipment data.

[0014] Further, the hierarchical quality index monitoring architecture is constructed according to the full-process material quality history, including:

[0015] According to the process dimension in the full-process material quality history, a process index monitoring layer is constructed, which at least includes hot rolling process monitoring, steelmaking process monitoring and cold rolling process monitoring;

[0016] According to the user end dimension in the full-process material quality history, a user end monitoring layer is constructed, which at least includes contract monitoring, order monitoring and quality objection management;

[0017] According to the manufacturing end dimension in the full-process material quality history, a manufacturing end monitoring layer is constructed, which at least includes quality index evaluation, process capability evaluation, product capability evaluation and equipment capability evaluation.

[0018] Further, through the preset index rule engine, the process index monitoring layer is dynamically calculated, and the monitoring indexes are obtained, including:

[0019] According to the preset index rule engine, the process dimension data in the full-process material quality history are called to calculate and generate the following monitoring indexes:

[0020] The hot rolling process monitors corresponding hot rolling strip yield indicators, hot rolling one inspection pass rate indicators and rolling line yield indicators;

[0021] The steelmaking process monitors corresponding heat pass rate indicators and slab strip yield indicators;

[0022] And the cold rolling process monitors corresponding yield indicators, cold rolling one inspection pass rate indicators and cold rolling strip yield indicators.

[0023] Further, the user end monitoring layer is dynamically calculated by a preset index rule engine, and monitoring indicators are obtained, including:

[0024] According to the preset index rule engine, the user end dimension data in the full-process material quality history is called to calculate and generate the following monitoring indicators:

[0025] The contract monitoring corresponds to the contract realization rate indicators;

[0026] The order monitoring corresponds to the order one-time pass rate indicators;

[0027] And the quality objection management corresponds to the quality objection trend indicators and quality objection correlation analysis indicators.

[0028] Further, the manufacturing end monitoring layer is dynamically calculated by a preset index rule engine, and monitoring indicators are obtained, including:

[0029] According to the preset index rule engine, the manufacturing end dimension data in the full-process material quality history is called to calculate and generate the following monitoring indicators:

[0030] The quality index evaluation corresponds to the non-conformity defect trend indicators and abnormal change analysis indicators;

[0031] The process capability evaluation corresponds to the SPC control chart indicators and SPC anomaly indicators;

[0032] The product capability evaluation corresponds to the key product indicators and multi-dimensional capability evaluation indicators;

[0033] And the equipment capability evaluation corresponds to the failure rate indicators and key equipment precision indicators.

[0034] Further, the monitoring indicators corresponding to each layer are visually displayed and SPC process capability analyzed, and dynamic monitoring results corresponding to each layer are output, including:

[0035] Based on the monitoring indicators corresponding to the process index monitoring layer, the user end monitoring layer and the manufacturing end monitoring layer, the monitoring indicators are visually displayed through a chart component, a dashboard component and a custom report component.

[0036] generate an SPC control chart based on the SPC control chart indicators in the manufacturing end monitoring layer, and perform abnormality judgment according to the SPC abnormality indicators in the manufacturing end monitoring layer and a preset abnormality rule;

[0037] In combination with the visual display result, the SPC control chart and the SPC abnormality judgment result, a dynamic monitoring result is output.

[0038] In another aspect, an embodiment of the present application provides a product quality indicator stability dynamic monitoring system, comprising:

[0039] A data acquisition and processing module is configured to acquire multiple types of manufacturing data covering steelmaking, hot rolling and cold rolling processes, and to construct a full-process material quality history;

[0040] A hierarchical monitoring construction module is configured to construct a hierarchical quality indicator monitoring architecture according to the full-process material quality history; wherein the hierarchical quality indicator monitoring architecture at least comprises a process indicator monitoring layer, a user end monitoring layer and a manufacturing end monitoring layer.

[0041] A monitoring indicator analysis module is configured to perform dynamic calculation on the process indicator monitoring layer, the user end monitoring layer and the manufacturing end monitoring layer according to a preset indicator rule engine, and to obtain corresponding monitoring indicators of each layer.

[0042] A monitoring result display module is configured to visually display and perform SPC process capability analysis on the corresponding monitoring indicators of each layer, and to output dynamic monitoring results corresponding to each layer.

[0043] Further, the process indicator monitoring layer is configured to realize a process monitoring function according to a process dimension in the full-process material quality history; the process monitoring function at least comprises hot rolling process monitoring, steelmaking process monitoring and cold rolling process monitoring.

[0044] The user end monitoring layer is configured to realize a user end monitoring function according to a user end dimension in the full-process material quality history; the user end monitoring function at least comprises contract monitoring, order monitoring and quality objection management.

[0045] The manufacturing end monitoring layer is configured to realize a manufacturing end monitoring function according to a manufacturing end dimension in the full-process material quality history; the manufacturing end monitoring function at least comprises quality indicator evaluation, process capability evaluation, product capability evaluation and equipment capability evaluation.

[0046] Further, the monitoring indicators output by the process monitoring function at least comprise:

[0047] The hot rolling process monitoring includes the hot-rolled strip yield rate, hot-rolled first inspection pass rate, and rolling line yield rate; the steelmaking process monitoring includes the furnace pass rate and slab strip yield rate; and the cold rolling process monitoring includes the yield rate, cold-rolled first inspection pass rate, and cold-rolled strip yield rate.

[0048] The monitoring metrics output by the client-side monitoring function include at least the following:

[0049] The contract fulfillment rate indicator corresponding to the contract monitoring; the order pass rate indicator corresponding to the order monitoring; and the quality objection trend indicator and quality objection correlation analysis indicator corresponding to the quality objection management;

[0050] The monitoring metrics output by the manufacturing end monitoring function include at least the following:

[0051] The quality indicator evaluation corresponds to the non-conformity trend indicators and abnormal change analysis indicators; the process capability evaluation corresponds to the SPC control chart indicators and SPC anomaly detection indicators; the product capability evaluation corresponds to the key product indicators and multi-dimensional capability evaluation indicators; and the equipment capability evaluation corresponds to the failure rate indicators and key equipment accuracy indicators.

[0052] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects:

[0053] Collect various types of manufacturing data covering steelmaking, hot rolling, and cold rolling processes, and build a full-process material quality history.

[0054] Based on the entire material quality history, a hierarchical quality indicator monitoring architecture is constructed; wherein, the hierarchical quality indicator monitoring architecture includes at least: a process indicator monitoring layer, a user-end monitoring layer, and a manufacturing-end monitoring layer;

[0055] According to the preset indicator rule engine, the process indicator monitoring layer, user terminal monitoring layer and manufacturing terminal monitoring layer are dynamically calculated, and the corresponding monitoring indicators for each layer are obtained.

[0056] The monitoring indicators corresponding to each layer are visualized and SPC process capability analysis is performed, and the dynamic monitoring results corresponding to each layer are output.

[0057] First, unlike related technologies where each process system operates independently, forming data silos, this invention constructs a full-process material quality history by collecting multiple types of manufacturing data covering steelmaking, hot rolling, and cold rolling processes. This breaks down data barriers between multiple systems, enabling real-time monitoring and traceability of data throughout the entire steelmaking, hot rolling, and cold rolling process, thus solving the problem of poor process coordination.

[0058] Secondly, unlike related technologies where monitoring methods are lagging and indicator management is relatively crude, resulting in insufficient control precision, this invention constructs a layered quality indicator monitoring architecture consisting of a process indicator monitoring layer, a user-end monitoring layer, and a manufacturing-end monitoring layer. This architecture enables real-time trend tracking and anomaly warning for core indicators such as yield rate and first-pass inspection rate. By dynamically calculating the monitoring indicators at each layer, the statistical logic of the indicators is refined, achieving refined control from "full-process process evaluation," "user-end multi-dimensional evaluation," to "manufacturing-end multi-dimensional evaluation." This shifts quality analysis from "post-event remediation" to "in-process intervention," eliminating analytical lag.

[0059] Third, unlike related technologies which suffer from weak SPC analysis capabilities and a lack of effective data support for decision-making, this invention provides a visual display of the monitoring indicators corresponding to each layer and SPC process capability analysis. It offers multi-dimensional visualization and customizable report functions, making the display more intuitive and easier for manual reading. This enables the system to pinpoint anomalies to specific production lines, work groups, and even steel grades and specifications. This not only achieves rapid response to product quality anomalies but also promotes a fundamental shift in enterprise quality management decisions from "post-event rectification" to "pre-event prevention." It provides technical support for achieving precise, real-time, and traceable integrated quality control throughout the entire steel production process.

[0060] In this invention, the above-described technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of this invention will be set forth in the following description, and some advantages may become apparent from the description or be learned by practicing the invention. The objects and other advantages of this invention can be realized and obtained from what is particularly pointed out in the description and drawings. Attached Figure Description

[0061] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts.

[0062] Figure 1 This is a flowchart of the product quality indicator stability dynamic monitoring method according to an embodiment of the present invention;

[0063] Figure 2 This is a schematic diagram of the functional architecture of dynamic monitoring in an embodiment of the present invention;

[0064] Figure 3 This is a schematic diagram of the early warning process for dynamic monitoring according to an embodiment of the present invention;

[0065] Figure 4 This is a schematic diagram of the main modules of the product quality index stability dynamic monitoring system according to an embodiment of the present invention. Detailed Implementation

[0066] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0067] A specific embodiment of the present invention discloses a method for dynamic monitoring of the stability of product quality indicators, such as... Figure 1 As shown, the steps S1 to S4 are as follows:

[0068] Step S1: Collect various types of manufacturing data covering steelmaking, hot rolling and cold rolling processes, and construct a full-process material quality history.

[0069] In implementation, the various types of manufacturing data specifically include: a dataset covering the entire production process from product manufacturing-related systems. Specifically, data can be collected from production management systems, process control systems, and quality inspection systems through pre-configured standardized data interfaces. These standardized data interfaces include at least: a RESTful API interface for obtaining structured data from Manufacturing Execution Systems (MES), Enterprise Resource Planning (ERP), and Laboratory Information Management Systems (LIMS); an OPC UA interface for obtaining real-time time-series data from Process Control Systems (PCS) and equipment systems; and a JDBC / ODBC interface for obtaining historical data from relational databases. After preprocessing operations such as cleaning, transformation, and standardization, the various types of manufacturing data from different levels of systems will be linked and integrated using the material number as the primary key. This provides a data foundation for constructing a complete material quality history throughout the entire process and provides comprehensive data support for subsequent hierarchical indicator monitoring and dynamic analysis.

[0070] Reference Figure 2 As shown, the data acquisition scope of this embodiment covers the entire process of steelmaking, hot rolling, and cold rolling, and adopts a three-layer collaborative functional architecture consisting of the edge side, the data platform service layer, and the application side. The edge side is responsible for comprehensively collecting data from the manufacturing site. Its role is to obtain raw data in real time from various manufacturing-related systems, such as the manufacturing execution system, process control system, and quality inspection system of each process in steelmaking, hot rolling, and cold rolling, including process parameters, production status, equipment status, and quality inspection results. The data platform service layer cleans, transforms, and standardizes the raw data collected by the edge side, constructing a material quality history spanning the entire process and forming a unified data service center. The application side, based on the full-process material quality history provided by the data platform service layer, displays the analysis results of various monitoring indicators in the form of charts, dashboards, etc.

[0071] The specific types of data collected through the pre-configured standardized data interface are as follows:

[0072] The quality indicator data mainly comes from the MES system and can cover steelmaking, hot rolling and cold rolling processes. It includes at least the following data: first inspection quantity, production quantity, downgrade quantity, re-judgment quantity, scrap quantity, judgment code (such as qualified, downgraded, scrap, etc.).

[0073] Surface defect data mainly comes from the MES system and surface inspection system, and can cover hot rolling and cold rolling processes. It includes at least: defect type (such as iron oxide scale, foreign object indentation, scratches), judgment level, judgment code, steel grade (such as 700BL, Q235B), specifications (thickness × width, accuracy ±0.1mm) and production line (such as 2050 hot rolling line, 1550 cold rolling line) data;

[0074] The performance test data mainly comes from the LIMS system and can cover both hot rolling and cold rolling processes. It includes at least the following data: tensile strength (Rm), lower yield strength (Rp0.2), elongation after fracture (A), and impact energy (Akv, -40℃). At the same time, the data accuracy needs to meet the requirements of strength ±1MPa, elongation ±0.1%, and impact energy ±0.1J.

[0075] The process parameter data mainly comes from equipment-level systems and workshop-level systems, and includes at least the following data: converter endpoint C, endpoint oxygen, tapping temperature, tundish superheat in the steelmaking process; heating temperature, final rolling temperature, coiling temperature, rolling load in the hot rolling process; annealing temperature, pickling tank concentration, cold rolling reduction rate, etc. in the cold rolling process.

[0076] Equipment status data, which mainly originates from equipment-level systems, includes at least: downtime records accurate to the minute, fault classification data covering various types of faults such as mechanical, electrical, automation, hydraulic, and process faults, functional accuracy scores evaluated by region and equipment dimension, and data such as stiffness retention rate and contact difference in mill stiffness parameters.

[0077] Order planning data, which mainly comes from the MES system, includes at least: user information covering name and type, steel specifications, production cycle, order quantity and delivery date.

[0078] The specific process of constructing a full-process material quality history includes: using a distributed storage system combining MySQL and InfluxDB, with the material number as the primary key, associating the various types of manufacturing data, and constructing the full-process material quality history based on the multi-dimensional association set obtained from the association; wherein, the various types of manufacturing data include at least: production data, process data, quality data, equipment data, and business data; the full-process material quality history includes at least: process dimension data integrated based on the production data, process data, quality data, and equipment data, user-end dimension data integrated based on the business data and quality data, and manufacturing-end dimension data integrated based on the process data, quality data, and equipment data.

[0079] During implementation, a structured dataset can be obtained by using a MySQL relational database to store production data, quality data, process data, equipment data, and business data, resulting in a structured dataset with the material number as the primary key. Simultaneously, the time-series data from the production data, quality data, process data, equipment data, and business data can be integrated using an InfluxDB time-series database to obtain a time-series dataset. The structured dataset and the time-series dataset are then associated to construct the full-process material quality history.

[0080] For example, a hybrid architecture of MySQL and InfluxDB is used for data storage and management. MySQL is responsible for storing structured data divided by process, including steelmaking quality tables, hot rolling production tables, and cold rolling data tables. The data tables are designed based on process boundaries and data relationships. The steelmaking table mainly stores data related to smelting, continuous casting processes, and testing, including basic information, smelting parameters, continuous casting parameters, and quality testing data. The hot rolling table stores data on heating and rolling processes, including basic hot rolling information, heating and rolling processes, and post-rolling testing data. The cold rolling table stores data on pickling, rolling, annealing processes, and finished product testing. The table structure can include primary keys and foreign keys. Each process table uses the material number as the primary key and is linked through foreign keys to form a complete data chain. Primary keys include, for example, coil number, slab number, and furnace number. Foreign keys represent the relationships between tables, such as linking the steelmaking table through the slab number. InfluxDB is specifically designed for storing time-series data. It is indexed by device ID and time, and can store process parameters such as temperature profiles and rolling load profiles. The data retention period is set to three years, and expired data is automatically archived to an offline server. The time-series data comes directly from field parameters collected in real time by devices such as sensors and programmable logic controllers.

[0081] In terms of data partitioning and query optimization, MySQL partitions data by month, while InfluxDB partitions by day, and establishes multi-dimensional indexes for process, time, and material number to ensure that the response time for a single data query does not exceed 2 seconds. As a result, multi-table joint queries are realized, providing efficient data support for multi-dimensional refined management and full-process traceability of the hierarchical quality indicator monitoring architecture.

[0082] Step S2: Based on the entire process material quality history, construct a hierarchical quality indicator monitoring architecture; wherein the hierarchical quality indicator monitoring architecture includes at least: a process indicator monitoring layer, a user-end monitoring layer, and a manufacturing-end monitoring layer.

[0083] Specifically, this includes: constructing a process indicator monitoring layer that includes at least hot rolling process monitoring, steelmaking process monitoring, and cold rolling process monitoring based on the process dimension in the entire process material quality history; constructing a user-end monitoring layer that includes at least contract monitoring, order monitoring, and quality objection management based on the user-end dimension in the entire process material quality history; and constructing a manufacturing-end monitoring layer that includes at least quality indicator evaluation, process capability evaluation, product capability evaluation, and equipment capability evaluation based on the manufacturing-end dimension in the entire process material quality history.

[0084] In practical implementation, the hierarchical quality indicator monitoring architecture in this embodiment of the invention consists of three core layers: a process indicator monitoring layer, focusing on quality and efficiency monitoring within each production process, including specific monitoring dashboards for the three major processes of hot rolling, steelmaking, and cold rolling; a user-end monitoring layer, evaluating the satisfaction of external customer needs, covering functions such as contract performance, order execution, and quality objection management; and a manufacturing-end monitoring layer, focusing on the evaluation of the enterprise's internal manufacturing standards and capabilities, including a comprehensive evaluation of four dimensions: quality indicators, process capabilities, product capabilities, and equipment capabilities. By establishing a hierarchical quality indicator monitoring system, each layer supports each other and shares data, thereby solving the problem of poor process coordination and achieving real-time trend tracking and anomaly warning for core indicators such as yield rate and first-pass inspection rate, eliminating analytical lag.

[0085] Step S3: Based on the preset indicator rule engine, dynamically calculate the process indicator monitoring layer, user terminal monitoring layer and manufacturing terminal monitoring layer, and obtain the corresponding monitoring indicators for each layer.

[0086] In this embodiment of the invention, the monitoring indicators corresponding to each layer are obtained through dynamic calculation. These indicators refer to real-time monitoring results calculated based on specific data from the entire material quality history and through a configurable indicator rule engine. The preset indicator rule engine is a core software component that supports user-defined configuration. System administrators or authorized users can flexibly define, modify, and manage the calculation rules for various monitoring indicators according to actual business needs through a web configuration interface, without modifying the underlying source code or performing secondary system development. By refining the indicator statistical logic, indicator types such as process responsibility, user type, and product level can be distinguished, thereby achieving precise control.

[0087] The monitoring indicators of the process indicator monitoring layer specifically include: based on the preset indicator rule engine, calling the process dimension data in the entire process material quality history, calculating and generating the following monitoring indicators: the hot-rolled strip yield rate, hot-rolled first inspection pass rate, and rolling line yield rate corresponding to the hot rolling process monitoring; the furnace pass rate and slab strip yield rate corresponding to the steelmaking process monitoring; and the yield rate, cold-rolled first inspection pass rate, and cold-rolled strip yield rate corresponding to the cold rolling process monitoring.

[0088] For example, the hot-rolled strip yield is calculated by combining the production volume recorded in the hot rolling process with the weight of the material carried out based on quality assessment; the hot-rolled first-inspection pass rate is calculated based on the total number of hot-rolled inspections and the number of materials that passed the first inspection; the hot-rolled line yield is calculated based on the total weight of the slabs input and the final weight of the qualified hot-rolled coils. The steelmaking furnace pass rate is calculated by combining the number of production furnaces with the number of furnaces that met all chemical composition and temperature standards; the steelmaking slab strip yield is calculated based on the total slab production volume and the weight of unqualified slabs with surface cracks or internal inclusions. The cold-rolled yield is calculated by combining the weight of the hot-rolled raw material coils input with the weight of the qualified cold-rolled coils produced; the cold-rolled first-inspection pass rate is calculated based on the total number of cold-rolled inspections and the number of cold-rolled coils that passed the first inspection; the cold-rolled strip yield is calculated by combining the production volume recorded in the cold rolling process with the weight of the material carried out based on quality assessment.

[0089] As can be seen, the calculation of the aforementioned indicators is based on complete and accurate data in the entire process material quality history. The calculation is dynamically executed through the preset calculation formula in the indicator rule engine, which ensures the real-time and accuracy of the indicator results and provides precise data support for the quality control of each process.

[0090] Preferably, the monitoring indicators for the hot rolling process are mainly displayed through the following dashboard:

[0091] Output Dashboard: Used to display the hot-rolled strip output rate, which measures the proportion of defective products produced in the hot-rolling process. Specifically, it includes the process output rate, the output rate of responsibility for this process, and the output rate of responsibility for upstream products. The trend changes are presented through a line chart output daily. Each data point is marked with the specific values ​​of the daily production volume and output volume. For example, on June 1, the production volume was 1,000 tons and the output volume was 12 tons. Data can be filtered by shift and production line. It can be understood that the output rate is the product that cannot meet the order requirements and is forced to be produced due to reasons such as process, technology, equipment, and production organization and management when producing ordered products.

[0092] First Inspection Pass Rate Dashboard: Used to display the first inspection pass rate and final inspection pass rate indicators. It can measure the first-time pass rate of hot-rolled products after rolling and during the first inspection. It also displays a pie chart of the composition of first inspection non-conformities, such as iron oxide scale accounting for 30% and scratches accounting for 20%. The data points are marked with the daily inspection volume and the number of first inspection non-conformities.

[0093] Rolling line yield dashboard: Used to display the rolling line yield and overall yield, with a matching yield loss composition chart, such as scrap loss 30%, rework loss 20%, and other 50%, and marks values ​​below the abnormal value in red and indicates possible causes, such as issuing an alert when it is 10% below the historical average.

[0094] Furthermore, the monitoring indicators for the steelmaking process are mainly displayed through the following dashboards:

[0095] Heat batch pass rate dashboard: Used to display the overall heat batch pass rate and the pass rate of special steel grades. It outputs a line chart on a daily basis, with data points marked with the number of heats produced and the number of heats that passed on the day. For example, on June 1, 50 heats were produced, 48 heats passed, and the pass rate was 96%.

[0096] Slab output dashboard: Used to output bar charts with output rates according to the casting machine dimension, such as 2% for casting machine #1 and 1.8% for casting machine #2, and supports steel grade screening function to facilitate comparison of quality differences between casting machines and steel grades.

[0097] Furthermore, the monitoring indicators for the cold rolling process are mainly displayed through the following dashboards:

[0098] Yield Dashboard: Outputs weekly line graphs of rolling line yield and overall yield, and marks weekly production volume and scrap volume.

[0099] First-round inspection pass rate dashboard: Outputs pass rate by annealing furnace batch, supports clicking on data points to view the composition of defects in that batch, such as roller marks accounting for 15% and pickling defects accounting for 10%.

[0100] With output dashboard: The processing logic is the same as that of the hot rolling process, but the responsibility allocation between this process and the upstream hot rolling process needs to be distinguished.

[0101] Meanwhile, when monitoring the process, the system can also be equipped with an indicator maintenance function according to the specific implementation scenario. This function can support adding and deleting indicators through a web-based visual interface. For example, you can add an indicator such as "cold rolling annealing temperature hit rate" or modify the calculation logic. For another example, the scope of modification can be controlled according to the different roles and permissions of administrators and technicians. Administrators can modify all indicators, while technicians can only modify indicators for their own process. Modification records are automatically archived.

[0102] The monitoring metrics of the user-side monitoring layer specifically include: based on the preset metric rule engine, calling the user-side dimension data in the full-process material quality history, calculating and generating the following monitoring metrics: the contract fulfillment rate metric corresponding to the contract monitoring; the order first-pass rate metric corresponding to the order monitoring; and the quality objection trend metric and quality objection correlation analysis metric corresponding to the quality objection management.

[0103] For example, the contract fulfillment rate is calculated by comparing the total monthly contract volume obtained from the Enterprise Resource Planning (ERP) system with the actual number of orders approved for shipment in the corresponding month obtained from the Manufacturing Execution System (MES); the order first-pass yield rate is calculated based on the total number of orders in the order management system with the number of orders that passed inspection and did not require rework in the production quality data; the quality objection trend indicator is calculated by performing trend analysis on the number of objections recorded in the quality objection management system with the corresponding compensation amount data in the financial compensation system; and the quality objection correlation analysis indicator compares and analyzes abnormal process parameters with normal process parameters by associating the objected steel coil number with the corresponding process parameter table in the production history, and calculates the correlation between quality anomalies and process parameters.

[0104] Preferably, the monitoring indicators for contract monitoring mainly include the following calculation logic:

[0105] Overall contract fulfillment rate = (Monthly approved issuance volume / Monthly contract volume × 100%), strategic user contract fulfillment rate = (Monthly strategic user approved issuance volume / Monthly strategic user contract volume × 100%), where strategic users are defined according to user standards;

[0106] The trend display includes: monthly line charts to compare the overall and strategic user fulfillment rates; unfulfilled contracts are displayed as pie charts by "defect type", such as 40% for dimensional deviations and 30% for surface defects. Based on user clicks, users can view the detailed steel coil numbers corresponding to the defect type.

[0107] Furthermore, the main monitoring metrics for order monitoring include the following calculation logic:

[0108] Overall order pass rate = (number of orders passed on the first attempt / total number of orders) × 100%; Strategic user order pass rate = (number of strategic user orders passed on the first attempt / total number of strategic user orders) × 100%.

[0109] Its analysis dimensions include: outputting bar charts for orders that failed by "steel type-specification", such as "700BL steel type, 1.5×1500mm, specification failure rate 2.5%", and supporting querying the reasons for failure (such as tensile strength not meeting the standard) and related process parameters by entering the order number.

[0110] Furthermore, the monitoring of quality objection management mainly includes the following:

[0111] Information import: Import objection information via Excel template, such as steel coil number, defect type, defect photos, defect pattern, steel type, specifications, compensation quantity, case filing / closing date, etc.

[0112] Trend Analysis: Based on dimensions such as user, steel type, and production line, output a line graph showing the number of objections and the amount of compensation paid. For example, user A has 3 objections in a month and has been compensated 100,000 yuan, thus forming the monitoring results of the quality objection trend indicator.

[0113] Correlation Analysis: Input the disputed steel coil number, and the system will automatically query the process parameters of the same batch of steel coils, such as heating temperature and descaling pressure. The disputed steel coil will be compared with the normal steel coil and an analysis report will be generated. The report will include a parameter comparison table, root cause diagnosis (such as insufficient descaling pressure leading to iron oxide scale), improvement measures, etc., and the monitoring results of the quality dispute correlation analysis indicators will be generated.

[0114] The monitoring indicators of the manufacturing end monitoring layer specifically include: based on the preset indicator rule engine, calling the manufacturing end dimension data in the entire process material quality history, calculating and generating the following monitoring indicators: the non-conformity defect trend indicators and abnormal change analysis indicators corresponding to the quality indicator evaluation; the SPC control chart indicators and SPC anomaly detection indicators corresponding to the process capability evaluation; the key product indicators and multi-dimensional capability evaluation indicators corresponding to the product capability evaluation; and the failure rate indicators and key equipment accuracy indicators corresponding to the equipment capability evaluation.

[0115] Preferably, the monitoring of quality indicator evaluation mainly includes the following:

[0116] The number of non-conformities in the first inspection is counted and sorted according to "defect name-steel type-specification". This serves as the monitoring result of the non-conformity trend indicator, and the top five non-conformity trend charts are output, such as iron oxide scale, foreign object indentation, etc.

[0117] The system supports refined comparative analysis at the production line and work group levels. For example, the work group's iron oxide scale defect rate is calculated by correlating the work group's production records with the surface defect detection results from the laboratory information management system. For instance, it calculates the proportion of defective products identified as having iron oxide scale defects in batches produced by work group A, showing a defect rate of 1.2% for work group A and 1.8% for work group B. When the system detects an abnormal surge in a certain indicator, it automatically marks the specific time period of the anomaly using a time-series anomaly detection algorithm and simultaneously correlates it with process parameter change data obtained from the process control system during that time period. This generates monitoring results for anomaly change analysis indicators, such as a 20°C increase in the average heating temperature. Thus, by establishing a mapping between production line / work group production data tables and real-time process parameter tables, precise tracing of quality anomalies and process fluctuations is achieved, providing data support for on-site quality management.

[0118] Furthermore, the monitoring of process capability evaluation includes: supporting the selection of key process parameters, such as hot rolling final rolling temperature, steelmaking ladle superheat, cold rolling annealing temperature, etc., and using them as SPC control chart indicators or SPC anomaly detection indicators for subsequent control chart generation and anomaly detection steps.

[0119] In some ways, such as Figure 3 As shown, when the calculated monitoring indicators violate the SPC judgment rules, the abnormal process and related process parameters are located, and closed-loop management and control are completed. Preferably, a predictive model can be constructed based on historical data in the material quality history of the entire process to provide early warning of quality trends and generate process parameter optimization suggestions. Then, the improvement status is fed back and the monitoring indicators are dynamically evaluated in the next stage, thereby forming a closed-loop management from quality monitoring to process optimization.

[0120] For example, based on continuously collected process parameter data, and with a sample size of at least 30 days and at least 30 data points per shift, the process capability index Ppk is calculated. This calculation involves using statistical analysis methods to calculate the mean and standard deviation of the process parameters and comparing them with product specifications to obtain specific values. Simultaneously, the process parameter sequence can be automatically analyzed according to eight custom-configured SPC anomaly detection rules. For example, it can identify abnormal patterns such as seven consecutive data points showing an upward trend or a single data point exceeding three times the standard deviation, and mark the corresponding out-of-control points. When an out-of-control point is identified, it automatically links to maintenance records in the equipment management system to determine the cause of the anomaly. For example, it links to equipment maintenance data such as thermocouple calibration records, and makes a preliminary determination of the cause by comparing the out-of-control point timestamp with the equipment maintenance timestamp. Furthermore, the index data calculated during the process capability evaluation stage will be used to generate corresponding statistical process control charts, providing a quantitative basis for the manufacturing process capability evaluation.

[0121] Furthermore, monitoring of product capability evaluation includes:

[0122] Key product selection: For example, select products such as automotive outer panels, high-strength steel plates, and pipeline steel according to user needs, and calculate the key product indicators for the corresponding products;

[0123] Multi-dimensional evaluation includes quality capability evaluations such as surface pass rate, downgrade rate, and yield; dimensional capability evaluations such as thickness and width deviation hit rate and SPC dimensional parameter analysis; and composition capability evaluations such as C / Mn content hit rate and SPC composition parameter analysis. These results generate monitoring results of multi-dimensional capability evaluation indicators, which are displayed in a comprehensive radar chart to visually represent the compliance rate. For example, the surface pass rate of automotive outer panels is 99.5%, and the dimensional hit rate is 98.8%.

[0124] Furthermore, monitoring of equipment capability evaluation includes:

[0125] Fault downtime monitoring: Statistics on downtime and frequency are compiled according to fault type and the fault rate index is calculated. This generates a trend chart of downtime for each production line and a pie chart showing the percentage of fault types. For example, mechanical faults account for 40% and electrical faults account for 25%.

[0126] Key equipment function accuracy management: Monitor the function / accuracy scores of equipment such as heating furnace burners and rolling mill work rolls, and then output trend charts on a daily basis. Deduct points for functional deficiencies according to their level to serve as the monitoring results of key equipment accuracy indicators. For example, deduct 10 points for level A, 7 points for level B, and 3 points for level C. An early warning is triggered when the total score of the entire line is below 90 points.

[0127] Meanwhile, the manufacturing-side monitoring layer also includes rule parameter maintenance functions, specifically including:

[0128] SPC rule configuration: Supports users to customize the selection of outlier detection rules and modify parameter specification limits, such as adjusting the final rolling temperature specification limit from ±20℃ to ±15℃;

[0129] Indicator logic maintenance: Supports modification of core indicators, such as the statistical logic of indicators like yield rate and first-pass pass rate. Modifications take effect immediately, and historical data can be re-analyzed.

[0130] Indicator type maintenance: Control indicators can be adjusted, and key control indicator items can be dynamically adjusted according to different production periods of the production line. This enables the adjustment and updating of indicator parameters, ensuring the flexibility of manufacturing end monitoring, with strong versatility and higher scalability.

[0131] It should be noted that the improvement of this invention lies in constructing a monitoring system according to the three layers of "process indicators - user end - manufacturing end", and thereby realizing real-time trend tracking of core indicators, and then issuing early warnings based on the identified abnormal indicators; there are no restrictions on the indicator rule engine, the specific calculation formulas of various indicators, etc., which can be referred to existing technologies and user-defined implementations, and will not be elaborated here.

[0132] Step S4: Visualize the monitoring indicators corresponding to each layer and perform SPC process capability analysis, and output the dynamic monitoring results corresponding to each layer.

[0133] Based on the monitoring indicators corresponding to the process indicator monitoring layer, user terminal monitoring layer, and manufacturing end monitoring layer, the data is visualized using chart components, dashboard components, and custom report components. An SPC control chart is generated based on the SPC control chart indicators in the manufacturing end monitoring layer, and anomaly judgment is performed according to the SPC anomaly judgment indicators and preset anomaly judgment rules in the manufacturing end monitoring layer. Combining the visualization results, SPC control chart, and SPC anomaly judgment results, dynamic monitoring results are output and real-time feedback is provided.

[0134] During implementation, in the visualization stage, multi-dimensional visualization and custom report functions are provided to support rapid decision-making in quality control.

[0135] For example, specific chart types include, but are not limited to: line charts, used to display indicator trends, such as daily trends in yield; bar charts, used to compare data from different dimensions, such as yield for each shift; pie charts, used to analyze the composition of defects and failures, such as the percentage of defects that fail the first inspection; scatter plots, used to display the distribution trends between parameters, such as the relationship between heating temperature and defect rate; and heat maps, used to present multivariate correlation coefficients, such as the correlation between process parameters and performance indicators. The dashboard component allows management to quickly grasp the production status and can be used to display KPI indicators, such as yield rate, pass rate, and equipment functional accuracy scores. Simultaneously, users can select data dimensions and chart types to generate customized personalized reports, such as the June strategic user contract fulfillment rate report, monthly quality reports, and objection analysis reports.

[0136] During the SPC monitoring refinement phase, the specific control chart types that can be generated include, but are not limited to: X-bar-R chart, used to monitor subgroup mean and range, suitable for batch production; X-bar-S chart, used to monitor subgroup mean and standard deviation, suitable for scenarios with large sample sizes; XR chart, used to monitor individual observations and range, suitable for scenarios with small sample sizes; P chart, used to monitor nonconforming rate, suitable for piece-rate production; NP chart, used to monitor the number of nonconforming items, suitable for piece-rate production; C chart, used to monitor the number of defects, suitable for defect counting; and U chart, used to monitor the number of defects per unit, suitable for defect counting per unit product.

[0137] Preferably, SPC analysis includes full-process analysis functions and integrates comprehensive functions such as data collection and integration, real-time monitoring and early warning, data analysis and reporting, and identification of abnormal factors and improvement suggestions. For example, by configuring a preset SPC analysis engine, historical process data and real-time process data in the full-process material quality history can be called to generate control charts and obtain analysis results covering the entire process of steelmaking, hot rolling, and cold rolling. This enables the transformation of relevant parameters from passive recording to active optimization, improving decision-making accuracy and efficiency.

[0138] It is understood that this invention is applicable to the entire process of quality control in the steel industry and can also be extended to similar manufacturing fields. The above embodiments are only for ease of understanding and simplification and should not be construed as limiting the invention. Furthermore, this invention does not specifically limit data collection, monitoring indicators, indicator analysis, and dashboard display.

[0139] Therefore, this invention constructs a complete closed-loop quality control system encompassing "data acquisition, hierarchical monitoring, trend analysis, and visualization output." First, through multi-source data acquisition and integration, data from different manufacturing levels is standardized, and a full-process material quality history is constructed using the material number as the primary key. Second, based on this full-process material quality history, a configurable indicator rule engine is used to monitor and dynamically evaluate process, user-end, and manufacturing-end indicators in real time. Finally, optimization solutions are fed back to each system for execution, thus forming a closed-loop management mechanism of "monitoring, early warning, analysis, optimization, and verification." This not only enables rapid handling of individual quality issues, significantly reducing the incidence of defective batches and product quality losses, but also provides technical support for achieving precise, real-time, and traceable integrated quality control throughout the entire steel production process through continuously accumulated quality data and iterative calculations.

[0140] In another embodiment of the present invention, a dynamic monitoring system for the stability of product quality indicators is proposed, such as... Figure 4 As shown, it specifically includes the following modules:

[0141] The data acquisition and processing module is used to collect various types of manufacturing data covering steelmaking, hot rolling and cold rolling processes, and to build a full-process material quality history.

[0142] A hierarchical monitoring construction module is used to construct a hierarchical quality indicator monitoring architecture based on the entire process material quality history; wherein, the hierarchical quality indicator monitoring architecture includes at least: a process indicator monitoring layer, a user-end monitoring layer, and a manufacturing-end monitoring layer.

[0143] The monitoring indicator analysis module is used to dynamically calculate the process indicator monitoring layer, user terminal monitoring layer and manufacturing terminal monitoring layer according to the preset indicator rule engine, and obtain the corresponding monitoring indicators for each layer.

[0144] The monitoring results display module is used to visualize the monitoring indicators corresponding to each layer and perform SPC process capability analysis, and output the dynamic monitoring results corresponding to each layer.

[0145] The process indicator monitoring layer is used to realize process monitoring functions based on the process dimensions in the entire process material quality history; the process monitoring functions include at least hot rolling process monitoring, steelmaking process monitoring and cold rolling process monitoring.

[0146] The user-end monitoring layer is used to implement user-end monitoring functions based on the user-end dimensions in the full-process material quality history. The user-end monitoring functions include at least contract monitoring, order monitoring, and quality objection management.

[0147] The manufacturing end monitoring layer is used to realize the manufacturing end monitoring function based on the manufacturing end dimension in the entire process material quality history. The manufacturing end monitoring function includes at least quality indicator evaluation, process capability evaluation, product capability evaluation and equipment capability evaluation.

[0148] The monitoring indicators output by the process monitoring function include at least the following: the hot-rolled strip yield, hot-rolled first inspection pass rate, and rolling line yield corresponding to the hot rolling process monitoring; the furnace pass rate and slab strip yield corresponding to the steelmaking process monitoring; and the yield, cold-rolled first inspection pass rate, and cold-rolled strip yield corresponding to the cold rolling process monitoring.

[0149] The monitoring metrics output by the user-side monitoring function include at least: the contract fulfillment rate metric corresponding to the contract monitoring; the order first-pass rate metric corresponding to the order monitoring; and the quality objection trend metric and quality objection correlation analysis metric corresponding to the quality objection management.

[0150] The monitoring indicators output by the manufacturing end monitoring function include at least the following: the non-conformity trend indicators and abnormal change analysis indicators corresponding to the quality indicator evaluation; the SPC control chart indicators and SPC anomaly detection indicators corresponding to the process capability evaluation; the key product indicators and multi-dimensional capability evaluation indicators corresponding to the product capability evaluation; and the failure rate indicators and key equipment accuracy indicators corresponding to the equipment capability evaluation.

[0151] The above-described method and apparatus embodiments are based on the same principle, and their related aspects can be referenced from each other to achieve the same technical effect. For specific implementation processes, please refer to the foregoing embodiments, which will not be repeated here.

[0152] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0153] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for dynamic monitoring of the stability of product quality indicators, characterized in that, include: Collect various types of manufacturing data covering steelmaking, hot rolling, and cold rolling processes, and build a full-process material quality history. Based on the entire material quality history, a hierarchical quality indicator monitoring architecture is constructed; wherein, the hierarchical quality indicator monitoring architecture includes at least: a process indicator monitoring layer, a user-end monitoring layer, and a manufacturing-end monitoring layer; According to the preset indicator rule engine, the process indicator monitoring layer, user terminal monitoring layer and manufacturing terminal monitoring layer are dynamically calculated, and the corresponding monitoring indicators for each layer are obtained. The monitoring indicators corresponding to each layer are visualized and SPC process capability analysis is performed, and the dynamic monitoring results corresponding to each layer are output.

2. The monitoring method according to claim 1, characterized in that, The collection covers multiple types of manufacturing data from steelmaking, hot rolling, and cold rolling processes, and constructs a complete material quality history, including: Based on a distributed storage system that combines MySQL and InfluxDB, the material number is used as the primary key to associate the various types of manufacturing data, and the full-process material quality history is constructed based on the multi-dimensional association set obtained from the association. The various types of manufacturing data include at least: production data, process data, quality data, equipment data, and business data; The full-process material quality history includes at least: process dimension data integrated based on the production data, process data, quality data and equipment data; user-end dimension data integrated based on the business data and quality data; and manufacturing-end dimension data integrated based on the process data, quality data and equipment data.

3. The monitoring method according to claim 2, characterized in that, The step of constructing a hierarchical quality indicator monitoring architecture based on the entire material quality history includes: Based on the process dimension in the entire material quality history, a process indicator monitoring layer is constructed that includes at least hot rolling process monitoring, steelmaking process monitoring, and cold rolling process monitoring. Based on the user-side dimensions in the entire material quality history, a user-side monitoring layer is constructed that includes at least contract monitoring, order monitoring, and quality objection management. Based on the manufacturing dimension of the entire material quality history, a manufacturing monitoring layer is constructed that includes at least quality indicator evaluation, process capability evaluation, product capability evaluation, and equipment capability evaluation.

4. The monitoring method according to claim 3, characterized in that, The process indicator monitoring layer is dynamically calculated using a preset indicator rule engine to obtain monitoring indicators, including: Based on the preset indicator rule engine, the process dimension data in the entire process material quality history is called to calculate and generate the following monitoring indicators: The hot rolling process monitoring corresponds to the hot-rolled strip yield rate, hot-rolled first inspection pass rate, and rolling line yield rate. The furnace pass rate and slab strip yield indicators are monitored for the steelmaking process. In addition, the yield rate, cold rolling first inspection pass rate and cold rolling strip output rate indicators corresponding to the monitoring of the cold rolling process.

5. The monitoring method according to claim 3, characterized in that, The user-side monitoring layer is dynamically calculated using a preset indicator rule engine to obtain monitoring indicators, including: Based on the preset indicator rule engine, the user-side dimension data in the entire process material quality history is called to calculate and generate the following monitoring indicators: The contract fulfillment rate indicator corresponding to the contract monitoring; The order monitoring corresponds to the order first-pass rate metric. In addition, the quality objection trend indicators and quality objection correlation analysis indicators corresponding to the quality objection management.

6. The monitoring method according to claim 3, characterized in that, The manufacturing end monitoring layer is dynamically calculated using a preset indicator rule engine to obtain monitoring indicators, including: Based on the preset indicator rule engine, the manufacturing-end dimension data in the entire process material quality history is called to calculate and generate the following monitoring indicators: The quality indicator evaluation corresponds to the non-conformity trend indicators and abnormal change analysis indicators; The SPC control chart indicators and SPC anomaly detection indicators corresponding to the process capability evaluation; The key product indicators and multi-dimensional capability evaluation indicators corresponding to the product capability evaluation; In addition, the failure rate index and key equipment accuracy index corresponding to the equipment capability evaluation.

7. The monitoring method according to claim 6, characterized in that, The visualization and SPC process capability analysis of the monitoring indicators corresponding to each layer, and the output of the dynamic monitoring results corresponding to each layer, include: Based on the monitoring indicators corresponding to the process indicator monitoring layer, user terminal monitoring layer and manufacturing terminal monitoring layer, the indicators are visualized through chart components, dashboard components and custom report components. An SPC control chart is generated based on the SPC control chart indicators in the manufacturing end monitoring layer, and anomaly judgment is performed according to the SPC anomaly detection indicators and preset anomaly detection rules in the manufacturing end monitoring layer. By combining the visualization results, SPC control charts, and SPC anomaly detection results, dynamic monitoring results are output.

8. A dynamic monitoring system for the stability of product quality indicators, characterized in that, include: The data acquisition and processing module is used to collect various types of manufacturing data covering steelmaking, hot rolling and cold rolling processes, and to build a full-process material quality history. A hierarchical monitoring construction module is used to construct a hierarchical quality indicator monitoring architecture based on the entire process material quality history; wherein, the hierarchical quality indicator monitoring architecture includes at least: a process indicator monitoring layer, a user-end monitoring layer, and a manufacturing-end monitoring layer. The monitoring indicator analysis module is used to dynamically calculate the process indicator monitoring layer, user terminal monitoring layer and manufacturing terminal monitoring layer according to the preset indicator rule engine, and obtain the corresponding monitoring indicators for each layer. The monitoring results display module is used to visualize the monitoring indicators corresponding to each layer and perform SPC process capability analysis, and output the dynamic monitoring results corresponding to each layer.

9. The monitoring system according to claim 8, characterized in that, The process indicator monitoring layer is used to realize process monitoring functions based on the process dimensions in the entire process material quality history; the process monitoring functions include at least hot rolling process monitoring, steelmaking process monitoring and cold rolling process monitoring. The user-end monitoring layer is used to implement user-end monitoring functions based on the user-end dimensions in the full-process material quality history. The user-end monitoring functions include at least contract monitoring, order monitoring, and quality objection management. The manufacturing end monitoring layer is used to realize the manufacturing end monitoring function based on the manufacturing end dimension in the entire process material quality history. The manufacturing end monitoring function includes at least quality indicator evaluation, process capability evaluation, product capability evaluation and equipment capability evaluation.

10. The monitoring system according to claim 9, characterized in that, The monitoring indicators output by the process monitoring function include at least the following: The hot rolling process monitoring includes the hot-rolled strip yield rate, hot-rolled first inspection pass rate, and rolling line yield rate; the steelmaking process monitoring includes the furnace pass rate and slab strip yield rate; and the cold rolling process monitoring includes the yield rate, cold-rolled first inspection pass rate, and cold-rolled strip yield rate. The monitoring metrics output by the client-side monitoring function include at least the following: The contract fulfillment rate indicator corresponding to the contract monitoring; the order pass rate indicator corresponding to the order monitoring; and the quality objection trend indicator and quality objection correlation analysis indicator corresponding to the quality objection management; The monitoring metrics output by the manufacturing end monitoring function include at least the following: The quality indicator evaluation corresponds to the non-conformity trend indicators and abnormal change analysis indicators; the process capability evaluation corresponds to the SPC control chart indicators and SPC anomaly detection indicators; the product capability evaluation corresponds to the key product indicators and multi-dimensional capability evaluation indicators; and the equipment capability evaluation corresponds to the failure rate indicators and key equipment accuracy indicators.

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