Method and device for adjusting smelting process data

Abstract

This invention provides a method and apparatus for adjusting smelting process data. The method includes: determining the correlation coefficient of related process data based on historical quality objection data; determining process data index values ​​based on process optimization data, quality objection optimization data, and the correspondence between process data and quality objection data; generating quality management analysis results based on real-time process data, correlation coefficients, and process data index values; determining abnormal process data index values ​​based on the quality management analysis results; and adjusting the corresponding abnormal process data based on the abnormal process data index values. This invention can dynamically realize the differences in process path selection under different production conditions, reduce dependence on process personnel, improve production efficiency, and stabilize production rhythm.

Description

Technical Field

[0001] This invention relates to the field of smelting technology, and more specifically, to a method and apparatus for adjusting smelting process data. Background Technology

[0002] In the current steelmaking process, to ensure product quality stability, production efficiency, cost control, and production rhythm, the production activities within each steelmaking stage are divided into multiple process paths. Standardized production process requirements are set for each specific process path. For example, during the electric furnace heating period, specific parameters such as voltage and current levels, target temperature range, and heating cycle are set. By breaking down the entire smelting cycle into specific process paths and setting process requirements, the specific production operations of operators are guided to ensure the standardization and stability of steelmaking production.

[0003] The aforementioned process paths and specific process parameters are gradually explored by the process and production personnel of each steel plant during actual production, and are eventually compiled into process standards. Currently, steelmaking production in the metallurgical industry uses pre-set process standards to adjust and select production process paths. Within existing information systems, process engineers need to pre-compile production process standards for different products to set different production process paths. During production, operators need to find the corresponding process standards according to the production plan and follow the guidance of the process standards. The information system can display the process standards on the operator's screen or directly send them to the control system. When a problem occurs in the production of a certain product or production rhythm, production process personnel need to collect and analyze the specific furnace data and manually update the corresponding production process path with the final analysis results.

[0004] Therefore, the updating and optimization process of existing technologies is influenced by various factors, including the personal abilities and experience of process and management personnel, and the condition of equipment. Subtle changes in the production process rely heavily on the individual experience and abilities of operators, making standardized production operations difficult. The frequency of process path selection and parameter adjustments also depends on the work efficiency and personal capabilities of process engineers. An experienced and responsible process engineer directly impacts the optimization results of the process path, thereby affecting the control of production quality, cost, and pace. With the overall planning and implementation of intelligent manufacturing in China's industry, the original production management model, primarily based on human experience, is no longer adequate to meet the demands of intelligent management. Summary of the Invention

[0005] The main objective of this invention is to provide a method and apparatus for adjusting smelting process data, so as to dynamically realize the differences in process path selection under different production conditions, reduce dependence on process personnel, improve production efficiency, and stabilize production rhythm.

[0006] To achieve the above objectives, embodiments of the present invention provide a method for adjusting smelting process data, including:

[0007] Determine the correlation coefficient of related process data based on historical quality dispute data;

[0008] The process data index values ​​are determined based on the process optimization data, the quality objection optimization data, and the correspondence between process data and quality objection data.

[0009] Quality management analysis results are generated based on real-time process data, correlation coefficients, and process data index values.

[0010] Determine the abnormal process data index values ​​based on the quality management analysis results, and adjust the corresponding abnormal process data according to the abnormal process data index values.

[0011] This invention also provides a smelting process data adjustment device, comprising:

[0012] The correlation coefficient determination module is used to determine the correlation coefficient of related process data based on historical quality dispute data;

[0013] The index value determination module is used to determine the process data index values ​​based on process optimization data, quality objection optimization data, and the correspondence between process data and quality objection data.

[0014] The analysis results generation module is used to generate quality management analysis results based on real-time process data, correlation coefficients, and process data index values.

[0015] The adjustment module is used to determine the abnormal process data index values ​​based on the quality management analysis results, and to adjust the corresponding abnormal process data according to the abnormal process data index values.

[0016] This invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of the smelting process data adjustment method.

[0017] This invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the smelting process data adjustment method.

[0018] The smelting process data adjustment method and apparatus of this invention first determines the correlation coefficient of related process data based on historical quality objection data, then determines the process data index value based on process optimization data, quality objection optimization data, and the correspondence between process data and quality objection data, then generates quality management analysis results based on real-time process data, correlation coefficients, and process data index values ​​to determine abnormal process data index values, and finally adjusts the corresponding abnormal process data based on the abnormal process data index values. This can dynamically realize the differences in process path selection under different production conditions, reduce dependence on process personnel, improve production efficiency, and stabilize the production rhythm. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a schematic diagram of the data flow of the present invention;

[0021] Figure 2 This is a flowchart of the smelting process data adjustment method in an embodiment of the present invention;

[0022] Figure 3 This is a flowchart of S101 in an embodiment of the present invention;

[0023] Figure 4 This is a flowchart for determining abnormal process data index values ​​in an embodiment of the present invention;

[0024] Figure 5 This is a structural block diagram of the smelting process data adjustment device in an embodiment of the present invention;

[0025] Figure 6 This is a structural block diagram of the computer device in an embodiment of the present invention. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] Those skilled in the art will recognize that embodiments of the present invention can be implemented as a system, apparatus, device, method, or computer program product. Therefore, this disclosure can be specifically implemented in the following forms: entirely hardware, entirely software (including firmware, resident software, microcode, etc.), or a combination of hardware and software.

[0028] Given that the updating and optimization process of existing technologies is affected by various factors such as the individual capabilities and experience of process and management personnel, and the condition of equipment, it is difficult to adapt to the needs of intelligent management. This invention provides a method and apparatus for adjusting smelting process data, which uses computer technology to achieve self-learning, dynamic optimization, and automatic selection of process smelting paths and standards. While reducing the workload of process personnel and mitigating the coupling effect of individual capability differences, it can dynamically adjust the selection of process smelting paths and parameter settings based on detailed changes in production factors. The invention will be described in detail below with reference to the accompanying drawings.

[0029] This invention combines a big data acquisition platform and a steelmaking information system for real-time data access and processing. The big data acquisition platform collects real-time smelting process paths and parameters during steelmaking production, and, combined with the final product's production quality issues and deviations, adjusts the selection and standards of current standard smelting processes. Through data filtering of optimal heat runs, it collects more reasonable smelting path selections and operating parameters.

[0030] Figure 1 This is a schematic diagram of the data flow in this invention. For example... Figure 1As shown, the first row from top to bottom represents the main production processes, including EAF (electric furnace), LF (ladle furnace), RH (vacuum refining), CCM (continuous casting machine), heating furnace, and rolling line. The second row represents the L2 (process control system) or L1 (electrical control system) for each main production process. The third row contains quality risk warnings and subsequent process response strategies: The big data platform acquires production process data (such as electric furnace power consumption, LF soft blowing time, RH vacuum holding time, continuous casting speed, heating furnace soaking zone temperature, and rolling line opening temperature) from the control systems of various major processes. It compares various real-time process data with the indicator standard library (and historical double-excellent heat data). Based on the deviation of the process data, it identifies corresponding quality objection issues and feeds back potential quality objections to the front-end screen for quality risk warnings. It also sends quality objections that can be remedied in subsequent processes, auxiliary suggestions, and countermeasures to the control systems of subsequent processes to adjust abnormal process data. The fourth row optimizes multiple processes and process paths through parameter self-learning to improve the process knowledge base, while providing online quality risk prediction and judgment. After screening and calculating historical double-excellent heat data and heat data with quality objections, the calculated results are updated to the full-process process knowledge base. The fifth row establishes a full-process process knowledge base for the entire process, quality, and countermeasures to provide process data standards for historical high-quality heats.

[0031] Figure 2 This is a flowchart of the smelting process data adjustment method in an embodiment of the present invention. Figure 2 As shown, the methods for adjusting smelting process data include:

[0032] S101: Determine the correlation coefficient of related process data based on historical quality dispute data.

[0033] Before executing S101, the following is also included:

[0034] Establish data tables for the quality data knowledge base within the database (full-process process knowledge base).

[0035] Table 1 includes the quality objection ID, quality objection type, quality objection name, quality objection resolution strategy, quality objection calculation rules, and quality objection standard value.

[0036] Table 2 includes process data ID, process data name, process data type, and process data index value.

[0037] Table 3 includes the quality objection ID, the associated process data ID, and the correlation coefficient of the associated process data.

[0038] Table 4 includes furnace number, time, associated process data ID, and real-time process data.

[0039] Table 5 includes furnace number, time, quality objection ID, and historical quality objection data.

[0040] The front end of the smelting process data adjustment device displays query and maintenance screens for Data Tables 1, 2, 3, 4, and 5. The back end of the device has data reading and updating programs. The maintenance screen can be used to initialize the process parameter values ​​for Data Tables 1 and 2, as well as the correlation coefficients of the associated process parameters in Data Table 3.

[0041] Figure 3 This is a flowchart of S101 in an embodiment of the present invention. Figure 2 As shown, S101 includes:

[0042] S201: Determine the corresponding real-time process data based on historical quality objection data.

[0043] Among them, historical quality dispute data may include the flaw detection results, number of flaw detection points and location data of the continuous casting billet of heat H1001.

[0044] Real-time process data includes electric furnace power consumption, LF soft blowing time, RH vacuum holding time, continuous casting speed, heating furnace soaking zone temperature, and rolling line start temperature, etc. This data is read from the control system via a data interaction interface at regular intervals (per second) or using a data update event mode. The data interaction interface is established with the electrical control system L1 and process control system L2 of each process through communication methods such as OPC (industry standard OLE for Process Control) and Socket.

[0045] In practice, first, the corresponding quality objection ID and furnace number are obtained from data table 5 based on historical quality objection data. Then, the corresponding associated process data ID is obtained from data table 3 based on the quality objection ID. Finally, the corresponding real-time process data is obtained from data table 4 based on the associated process data ID and furnace number.

[0046] S202: Determine the correlation coefficient of the associated process data based on the corresponding real-time process data.

[0047] The correlation coefficient is the correlation between process data (process data ID) and quality objection data (quality objection ID). For example, when the quality objection data is a surface crack on the billet, and the process data includes casting speed, crystallization temperature, and cooling water volume, the correlation coefficient between the surface crack on the billet and the casting speed is 1.01, the correlation coefficient between the surface crack on the billet and the crystallization temperature is 1.15, and the correlation coefficient between the surface crack on the billet and the cooling water volume is 1.23.

[0048] In practice, linear correlation coefficient, covariance analysis matrix and multiple linear regression model can be used to perform correlation and partial correlation analysis on real-time process data to determine the correlation coefficient of related process data, and the correlation coefficient in data table 3 can be updated according to the calculation results.

[0049] S102: Determine the process data index values ​​based on process optimization data, quality objection optimization data, and the correspondence between process data and quality objection data.

[0050] Specifically, the correspondence between process data and quality objection data can be obtained based on the quality objection ID and associated process data ID in Data Table 3. In practice, the simplicity algorithm can be used to perform regression calculations on the correlation coefficients of process optimization data, quality objection optimization data, and the correspondence between process data and quality objection data to obtain process data index values. The original process data index values ​​in Data Table 2 are then updated based on these process data index values.

[0051] In one embodiment, before performing S102, the method further includes:

[0052] Obtain the optimized furnace batches, determine the real-time process data corresponding to the optimized furnace batches as process optimization data, and determine the historical quality objection data corresponding to the optimized furnace batches as quality objection optimization data.

[0053] The optimized furnace batches are those ranked in the top 100 in terms of process quality, with fewer than 3 alarms and a comprehensive coefficient greater than a preset coefficient. Historical quality objection data corresponding to the optimized furnace batches are obtained from Table 5 as quality objection optimization data, and real-time process data corresponding to the optimized furnace batches are obtained from Table 4 as process optimization data.

[0054] S103: Generate quality management analysis results based on real-time process data, correlation coefficients, and process data index values.

[0055] In practice, SPC (Statistical Process Control) analysis and calculation can be performed on real-time process data, correlation coefficients, and process data index values ​​to generate quality management analysis results.

[0056] S104: Determine the abnormal process data index values ​​based on the quality management analysis results, and adjust the corresponding abnormal process data according to the abnormal process data index values.

[0057] In practice, the electrical control system L1 or the process control system L2 can adjust the corresponding abnormal process data based on the abnormal process data index values.

[0058] Figure 4 This is a flowchart illustrating the determination of abnormal process data index values ​​in an embodiment of the present invention. For example... Figure 4As shown, the abnormal process data index values ​​determined based on the quality management analysis results include:

[0059] S301: Determine the correlation coefficient of abnormal process data based on the results of quality management analysis.

[0060] In practice, the median of the standard deviation of the quality management analysis results can be calculated using the standard deviation formula, and the correlation coefficient of real-time process data that is greater than the median of the standard deviation can be determined as the correlation coefficient of abnormal process data.

[0061] S302: Determine the current quality objection data based on the correlation coefficient of the abnormal process data and the correspondence between the process data and the quality objection data.

[0062] In practice, the quality objection data corresponding to the maximum value of the correlation coefficient between abnormal process data and quality objection data is determined as the current quality objection data.

[0063] For example, if the median standard deviation of casting speed within 1 minute is 3, save the casting speed values ​​greater than 3 at the corresponding time points, and filter out the current quality objection data (cracks on the surface of the cast billet) corresponding to the maximum value of the casting speed correlation coefficient.

[0064] S303: Determine the abnormal process data index value based on the current quality objection data and the correspondence between process data and quality objection data.

[0065] In practice, current quality objection data and abnormal process data index values ​​can be returned to the system screen or control system. For example, the system front-end screen can be displayed with a prompt that excessive casting speed may cause surface cracks in the billet, as well as the standard casting speed (1.4 m / min), through the real-time data interaction service. At the same time, the standard casting speed can be sent to the data exchange interface of the L2 / L1 control system through the real-time data interaction service.

[0066] After executing S104, it may also include: creating a real-time online comparison and display screen, comparing the abnormal process data index values ​​with the real-time process data in the graphic screen and text box (including deviation degree, difference and deviation rate per unit time, etc.), and displaying the comparison results.

[0067] Figure 1 The smelting process data adjustment method shown can be implemented by a self-learning feedback adjustment system. Figure 1As shown in the flowchart, the smelting process data adjustment method of this embodiment first determines the correlation coefficient of related process data based on historical quality objection data, then determines the process data index value based on process optimization data, quality objection optimization data, and the correspondence between process data and quality objection data, then generates quality management analysis results based on real-time process data, correlation coefficients, and process data index values ​​to determine abnormal process data index values, and finally adjusts the corresponding abnormal process data based on the abnormal process data index values. This can dynamically realize the differences in process path selection under different production conditions, reduce dependence on process personnel, improve production efficiency, and stabilize the production rhythm.

[0068] The specific process of this invention embodiment is as follows:

[0069] 1. Determine the corresponding real-time process data based on historical quality dispute data, and determine the correlation coefficient of related process data based on the corresponding real-time process data.

[0070] 2. Obtain the optimized furnace batches, determine the real-time process data corresponding to the optimized furnace batches as process optimization data, and determine the historical quality objection data corresponding to the optimized furnace batches as quality objection optimization data.

[0071] 3. Determine the correlation coefficient of related process data based on historical quality dispute data.

[0072] 4. Determine the process data index values ​​based on the process optimization data, quality objection optimization data, and the correspondence between process data and quality objection data.

[0073] 5. Generate quality management analysis results based on real-time process data, correlation coefficients, and process data index values.

[0074] 6. Determine the correlation coefficient of abnormal process data based on the results of quality management analysis.

[0075] 7. Determine the current quality dispute data based on the correlation coefficient of abnormal process data and the correspondence between process data and quality dispute data.

[0076] 8. Determine the abnormal process data index values ​​based on the current quality objection data and the correspondence between process data and quality objection data.

[0077] 9. Adjust the corresponding abnormal process data according to the abnormal process data index values.

[0078] In summary, the smelting process data adjustment method provided by the embodiments of the present invention has the following beneficial effects:

[0079] 1. Based on the production characteristics of metallurgical enterprises, and combined with a big data platform, achieve efficient management of process tooling, strict implementation of product quality, selection of the best process path for steelmaking production, and optimization of process parameters.

[0080] 2. Provide real-time guidance and recommendations for production operations during the production process, dynamically adjust process paths and optimize process parameters based on real-time differences in front-line production, and graphically display process paths and parameters to help operators intuitively understand production deviations and process path adjustment plans, thereby improving the controllability of product quality and production rhythm.

[0081] 3. It reduces the labor intensity of process personnel, decreases reliance on their knowledge, increases product qualification rate by 2%, stabilizes production rhythm, and has significant effects on reducing product quality problems and improving production efficiency.

[0082] Based on the same inventive concept, this invention also provides a smelting process data adjustment device. Since the principle of this device in solving the problem is similar to that of the smelting process data adjustment method, the implementation of this device can refer to the implementation of the method, and the repeated parts will not be described again.

[0083] Figure 5 This is a structural block diagram of the smelting process data adjustment device in an embodiment of the present invention. Figure 5 As shown, the smelting process data adjustment device includes:

[0084] The correlation coefficient determination module is used to determine the correlation coefficient of related process data based on historical quality dispute data;

[0085] The index value determination module is used to determine the process data index values ​​based on process optimization data, quality objection optimization data, and the correspondence between process data and quality objection data.

[0086] The analysis results generation module is used to generate quality management analysis results based on real-time process data, correlation coefficients, and process data index values.

[0087] The adjustment module is used to determine the abnormal process data index values ​​based on the quality management analysis results, and to adjust the corresponding abnormal process data according to the abnormal process data index values.

[0088] In one embodiment, the index value determination module includes:

[0089] The abnormal correlation coefficient determination unit is used to determine the correlation coefficient of abnormal process data based on the results of quality management analysis.

[0090] The quality objection data determination unit is used to determine the current quality objection data based on the correlation coefficient of abnormal process data and the correspondence between process data and quality objection data.

[0091] The abnormal indicator value determination unit is used to determine the abnormal process data indicator value based on the current quality objection data and the correspondence between the process data and the quality objection data.

[0092] In one embodiment, the correlation coefficient determination module includes:

[0093] The real-time process data determination unit is used to determine the corresponding real-time process data based on historical quality dispute data.

[0094] The correlation coefficient determination unit is used to determine the correlation coefficient of the associated process data based on the corresponding real-time process data.

[0095] In one embodiment, it further includes:

[0096] The Quality Objection Optimization Data Determination Module is used to acquire optimized furnace batches, determine the real-time process data corresponding to the optimized furnace batches as process optimization data, and determine the historical quality objection data corresponding to the optimized furnace batches as quality objection optimization data.

[0097] In summary, the smelting process data adjustment device of this invention first determines the correlation coefficient of related process data based on historical quality objection data, then determines the process data index value based on process optimization data, quality objection optimization data, and the correspondence between process data and quality objection data. Next, it generates quality management analysis results based on real-time process data, correlation coefficients, and process data index values ​​to determine abnormal process data index values. Finally, it adjusts the corresponding abnormal process data based on the abnormal process data index values. This allows for dynamic adjustment of process path selection differences under different production conditions, reducing reliance on process personnel, improving production efficiency, and stabilizing production rhythm.

[0098] The present invention also provides a specific implementation of a computer device capable of implementing all steps in the smelting process data adjustment method described in the above embodiments. Figure 6 This is a structural block diagram of the computer device in an embodiment of the present invention, see below. Figure 6 The computer equipment specifically includes the following:

[0099] Processor 601 and memory 602.

[0100] The processor 601 is used to call the computer program in the memory 602. When the processor executes the computer program, it implements all the steps in the smelting process data adjustment method in the above embodiments. For example, when the processor executes the computer program, it implements the following steps:

[0101] Determine the correlation coefficient of related process data based on historical quality dispute data;

[0102] The process data index values ​​are determined based on the process optimization data, the quality objection optimization data, and the correspondence between process data and quality objection data.

[0103] Quality management analysis results are generated based on real-time process data, correlation coefficients, and process data index values.

[0104] Determine the abnormal process data index values ​​based on the quality management analysis results, and adjust the corresponding abnormal process data according to the abnormal process data index values.

[0105] In summary, the computer equipment in this embodiment of the invention first determines the correlation coefficient of the associated process data based on historical quality objection data, then determines the process data index value based on process optimization data, quality objection optimization data, and the correspondence between process data and quality objection data. Next, it generates quality management analysis results based on real-time process data, correlation coefficients, and process data index values ​​to determine abnormal process data index values. Finally, it adjusts the corresponding abnormal process data based on the abnormal process data index values. This allows for dynamic adjustment of process path selection differences under different production conditions, reducing reliance on process personnel, improving production efficiency, and stabilizing production rhythm.

[0106] This invention also provides a computer-readable storage medium capable of implementing all steps of the smelting process data adjustment method in the above embodiments. The computer-readable storage medium stores a computer program that, when executed by a processor, implements all steps of the smelting process data adjustment method in the above embodiments. For example, when the processor executes the computer program, it implements the following steps:

[0107] Determine the correlation coefficient of related process data based on historical quality dispute data;

[0108] The process data index values ​​are determined based on the process optimization data, the quality objection optimization data, and the correspondence between process data and quality objection data.

[0109] Quality management analysis results are generated based on real-time process data, correlation coefficients, and process data index values.

[0110] Determine the abnormal process data index values ​​based on the quality management analysis results, and adjust the corresponding abnormal process data according to the abnormal process data index values.

[0111] In summary, the computer-readable storage medium of this invention first determines the correlation coefficient of the associated process data based on historical quality objection data, then determines the process data index value based on process optimization data, quality objection optimization data, and the correspondence between process data and quality objection data. Next, it generates quality management analysis results based on real-time process data, correlation coefficients, and process data index values ​​to determine abnormal process data index values. Finally, it adjusts the corresponding abnormal process data based on the abnormal process data index values. This allows for dynamic adjustment of process path selection differences under different production conditions, reducing reliance on process personnel, improving production efficiency, and stabilizing production rhythm.

[0112] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0113] Those skilled in the art will also understand that the various illustrative logical blocks, units, and steps listed in the embodiments of the present invention can be implemented by electronic hardware, computer software, or a combination of both. To clearly demonstrate the interchangeability of hardware and software, the functions of the various illustrative components, units, and steps described above have been generally described. Whether such functionality is implemented through hardware or software depends on the specific application and the overall system design requirements. Those skilled in the art can implement the described functions using various methods for each specific application, but such implementation should not be construed as exceeding the scope of protection of the embodiments of the present invention.

[0114] The various illustrative logic blocks, units, or devices described in the embodiments of this invention can be implemented or operate the described functions using a general-purpose processor, digital signal processor, application-specific integrated circuit (ASIC), field-programmable gate array or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof. The general-purpose processor can be a microprocessor; alternatively, it can be any conventional processor, controller, microcontroller, or state machine. The processor can also be implemented using a combination of computing devices, such as a digital signal processor and a microprocessor, multiple microprocessors, one or more microprocessors combined with a digital signal processor core, or any other similar configuration.

[0115] The steps of the methods or algorithms described in the embodiments of this invention can be directly embedded in hardware, a software module executed by a processor, or a combination of both. The software module can be stored in RAM, flash memory, ROM, EPROM, EEPROM, registers, hard disk, removable disk, CD-ROM, or any other form of storage medium in the art. Exemplarily, the storage medium can be connected to the processor so that the processor can read information from and write information to the storage medium. Optionally, the storage medium can also be integrated into the processor. The processor and storage medium can be housed in an ASIC, which can be housed in a user terminal. Optionally, the processor and storage medium can also be housed in different components of the user terminal.

[0116] In one or more exemplary designs, the functions described in the embodiments of the present invention can be implemented in hardware, software, firmware, or any combination of these three. If implemented in software, these functions can be stored on a computer-readable medium or transmitted on a computer-readable medium in the form of one or more instructions or code. Computer-readable media include computer storage media and communication media that facilitate the transfer of computer programs from one place to another. Storage media can be any available media that can be accessed by a general-purpose or special-purpose computer. For example, such computer-readable media can include, but is not limited to, RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store program code in the form of instructions or data structures and other forms that can be read by a general-purpose or special-purpose computer, or a general-purpose or special-purpose processor. Furthermore, any connection can be suitably defined as a computer-readable medium, for example, if the software is transmitted from a website, server or other remote resource via a coaxial cable, fiber optic cable, twisted pair, digital subscriber line (DSL) or wirelessly, such as infrared, wireless and microwave, it is also included in the defined computer-readable medium. The disks and discs mentioned include compressed disks, laser discs, optical discs, DVDs, floppy disks, and Blu-ray discs. Disks typically copy data magnetically, while disks typically copy data optically using lasers. Combinations of the above can also be contained in computer-readable media.

Claims

1. A method for adjusting smelting process data, characterized in that, include: Determine the correlation coefficient of related process data based on historical quality dispute data; The process data index values ​​are determined based on the process optimization data, the quality objection optimization data, and the correspondence between process data and quality objection data. Quality management analysis results are generated based on real-time process data, the correlation coefficient, and the process data index values. Determine the abnormal process data index values ​​based on the quality management analysis results, and adjust the corresponding abnormal process data according to the abnormal process data index values. The determination of abnormal process data index values ​​based on quality management analysis results includes: The correlation coefficients of the abnormal process data are determined based on the quality management analysis results. The current quality objection data is determined based on the correlation coefficient of the abnormal process data and the correspondence between the process data and the quality objection data. The abnormal process data index value is determined based on the current quality objection data and the correspondence between the process data and the quality objection data.

2. The method for adjusting smelting process data according to claim 1, characterized in that, The correlation coefficients of related process data determined based on historical quality dispute data include: Determine the corresponding real-time process data based on the historical quality objection data; The correlation coefficient of the associated process data is determined based on the corresponding real-time process data.

3. The method for adjusting smelting process data according to claim 1, characterized in that, Also includes: The optimized furnace batch is obtained, and the real-time process data corresponding to the optimized furnace batch is determined as the process optimization data. The historical quality objection data corresponding to the optimized furnace batch is determined as the quality objection optimization data.

4. A smelting process data adjustment device, characterized in that, include: The correlation coefficient determination module is used to determine the correlation coefficient of related process data based on historical quality dispute data; The index value determination module is used to determine the process data index values ​​based on process optimization data, quality objection optimization data, and the correspondence between process data and quality objection data. The analysis result generation module is used to generate quality management analysis results based on real-time process data, the correlation coefficient, and the process data index values. The adjustment module is used to determine the abnormal process data index value based on the quality management analysis result, and adjust the corresponding abnormal process data according to the abnormal process data index value. The indicator value determination module includes: An abnormality correlation coefficient determination unit is used to determine the correlation coefficient of abnormal process data based on the quality management analysis results. The quality objection data determination unit is used to determine the current quality objection data based on the correlation coefficient of the abnormal process data and the correspondence between the process data and the quality objection data. An abnormal indicator value determination unit is used to determine the abnormal process data indicator value based on the current quality objection data and the correspondence between the process data and the quality objection data.

5. The smelting process data adjustment device according to claim 4, characterized in that, The correlation coefficient determination module includes: A real-time process data determination unit is used to determine the corresponding real-time process data based on the historical quality objection data. The correlation coefficient determination unit is used to determine the correlation coefficient of the associated process data based on the corresponding real-time process data.

6. The smelting process data adjustment device according to claim 4, characterized in that, Also includes: The quality objection optimization data determination module is used to acquire the optimized furnace batch, determine the real-time process data corresponding to the optimized furnace batch as the process optimization data, and determine the historical quality objection data corresponding to the optimized furnace batch as the quality objection optimization data.

7. A computer device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the smelting process data adjustment method according to any one of claims 1 to 3.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the smelting process data adjustment method according to any one of claims 1 to 3.

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