A steel coil performance data control method, device, medium and electronic equipment

Abstract

The application provides a steel coil performance data control method and device, a medium and an electronic equipment. The method comprises the following steps: obtaining historical production data of each steel coil production line in a historical production process as historical process parameters; obtaining performance parameters of steel coils produced by each steel coil production line in the history as historical performance parameters; constructing a steel performance prediction model by performing correlation analysis on the historical performance parameters and the historical production data; and controlling performance data of a steel coil to be produced by using the steel performance prediction model. The application solves the problem that the judgment of the steel performance in the prior art relies on a manual judgment method, which leads to low accuracy and efficiency of the judgment and difficulty in meeting different requirements for different steel materials. The scheme provided by the application improves the accuracy and efficiency of the judgment of the steel performance by performing correlation analysis on the steel performance and production data, improves the stability of the steel performance, the quality of the product and the production efficiency, and reduces the production cost.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of steel rolling, in particular to a steel coil performance data control method and device, medium and electronic equipment. BACKGROUND

[0002] People's pursuit of high-quality life has led to the rapid development of large buildings, bridges, vehicles, and small daily tools such as household appliances and kitchen utensils. Therefore, the demand for high-performance steel materials is increasing day by day. How to reduce the cost of steel production and improve production efficiency while ensuring the quality of steel has become an urgent task for steel enterprises.

[0003] With the rapid development of international steel industry, domestic and foreign steel enterprises have also begun to study the steel performance prediction technology, but the performance prediction model is less used in actual application. At present, most enterprises still adopt a relatively fixed artificial judgment method to improve or reduce certain process parameters according to the performance. Due to the uneven personal level of operators, and the lack of reasonable standard specifications for the operation of different process parameters, it is difficult to meet the dynamic changes of different demand steel materials in terms of accuracy and efficiency of steel performance judgment, and the high dependence on operators will lead to waste problems. There is no scientific and effective method for early warning of steel quality.

[0004] Therefore, how to effectively improve the stability of steel performance, improve the quality and production efficiency of steel products, and reduce production costs is a technical problem to be solved. SUMMARY

[0005] The purpose of the present application is to provide a steel coil performance data control method and device, medium and electronic equipment. The present application solves the problem that the judgment of steel performance in the prior art relies on artificial judgment method, resulting in low accuracy and efficiency of judgment, and difficulty in meeting different requirements for different steel materials. The scheme proposed in the present application improves the accuracy and efficiency of steel performance judgment by analyzing the correlation between steel performance and production data, improves the stability of steel performance, the quality and production efficiency of products, and reduces production costs.

[0006] Specifically, the present application adopts the following technical scheme:

[0007] According to an aspect of some embodiments of the present application, a method for controlling performance data of a steel coil is provided. The method comprises: obtaining historical production data of each steel coil production line in a historical production process, wherein the historical production data comprises a strip composition parameter and a plurality of process parameters in a process of producing a steel coil from a strip; obtaining performance parameters of the steel coils produced by each steel coil production line in history as historical performance parameters; constructing a steel performance prediction model by performing correlation analysis on the historical performance parameters and the historical production data; and controlling performance data of a steel coil to be produced in an online production process by using the steel performance prediction model.

[0008] In some embodiments of the present application, based on the foregoing scheme, before performing correlation analysis on the historical performance parameters and the historical production data, the method further comprises: performing correlation analysis on any two parameters in the historical production data and the historical performance parameters by using a lasso algorithm to obtain a correlation analysis result; and removing a noise parameter in the historical production data according to the correlation analysis result.

[0009] In some embodiments of the present application, based on the foregoing scheme, the step of constructing a steel performance prediction model by performing correlation analysis on the historical performance parameters and the historical production data comprises: performing correlation analysis on the historical performance parameters and the historical production data by using an adaptive boosting algorithm, an extreme tree algorithm, a gradient boosting algorithm and a random forest algorithm respectively to construct a plurality of candidate steel performance prediction models; and selecting the steel performance prediction model from the plurality of candidate steel performance prediction models.

[0010] In some embodiments of the present application, based on the foregoing scheme, the step of controlling performance data of a steel coil to be produced by using the steel performance prediction model comprises: obtaining a target performance parameter of the steel coil to be produced; determining a target strip composition parameter and a plurality of target process parameters for producing the steel coil to be produced by using the steel performance prediction model based on the target performance parameter; and performing production of the steel coil to be produced according to the target strip composition parameter and the plurality of target process parameters.

[0011] In some embodiments of the present application, based on the foregoing scheme, the method further comprises:

[0012] In the process of performing production of the steel coil to be produced, if any one or more process parameters deviate from the corresponding target process parameters, the steel performance prediction model is used to adjust the process parameters other than the any one or more process parameters in the plurality of target process parameters, and the production of the steel coil to be produced is performed according to the adjusted process parameters.

[0013] In some embodiments of the present application, based on the foregoing scheme, the process parameters at least include hot rolling furnace time, discharge temperature, finish rolling temperature, coiling temperature, deformation amount, cold rolling annealing temperature.

[0014] In some embodiments of the present application, based on the foregoing scheme, the performance parameters at least include tensile strength, yield strength, elongation, hardness index.

[0015] According to an aspect of embodiments of the present application, there is provided a steel coil performance data control device, the device comprising: a first acquisition unit configured to acquire historical production data of each steel coil production line in a historical production process, the historical production data comprising strip steel composition parameters and a plurality of process parameters in the process of producing steel coils from strip steel as historical process parameters; a second acquisition unit configured to acquire performance parameters of steel coils produced by each steel coil production line in history as historical performance parameters; a modeling unit configured to construct a steel performance prediction model by performing correlation analysis on the historical performance parameters and the historical production data; and a control unit configured to control performance data of a steel coil to be produced in an online production process through the steel performance prediction model.

[0016] According to an aspect of embodiments of the present application, there is provided a computer readable storage medium having at least one program code stored therein, the at least one program code being loaded and executed by a processor to implement operations performed by the steel coil performance data control method as described above.

[0017] According to an aspect of embodiments of the present application, there is provided an electronic device comprising a memory and a processor, the memory storing a computer program, and the processor implementing operations performed by the steel coil performance data control method as described above when executing the computer program.

[0018] From the above technical solutions, the present application has at least the following advantages and positive effects:

[0019] The scheme proposed in the present application can solve the problem in the prior art that the judgment of steel performance relies on artificial judgment mode, resulting in low accuracy and efficiency of judgment, and the difficulty in meeting different requirements for different steel materials. The scheme proposed in the present application improves the accuracy and efficiency of steel performance judgment by performing correlation analysis on steel performance and its production data, improves the stability of steel performance, the quality and production efficiency of products, and reduces production cost. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the present application, the following will briefly introduce the drawings needed in the embodiments. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort based on these drawings.

[0021] Figure 1 A flow chart of a steel coil performance data control method in an embodiment of the present application is shown;

[0022] Figure 2 A similarity matching diagram of a steel coil coiling temperature in an embodiment of the present application is shown;

[0023] Figure 3 A similarity matching diagram of a steel coil finishing temperature in an embodiment of the present application is shown;

[0024] Figure 4 A tensile strength prediction display diagram of a steel coil in an embodiment of the present application is shown;

[0025] Figure 5 A structural block diagram of a steel coil performance data control device in an embodiment of the present application is shown;

[0026] Figure 6 A structural schematic diagram of a computer system of an electronic device suitable for implementing the embodiments of the present application is shown. DETAILED DESCRIPTION

[0027] Example implementations will now be described more fully with reference to the accompanying drawings. Example implementations may, however, be implemented in many different forms and should not be construed as limited to the implementations set forth herein; rather, these implementations are provided so that this disclosure will be thorough and complete, and will fully convey the inventive aspects to those skilled in the art. Like reference numerals may refer to like elements throughout.

[0028] Moreover, the described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a thorough understanding of embodiments of the application. One skilled in the relevant art will recognize, however, that the

[0029] The flow charts shown in the drawings are only illustrative, and do not necessarily include all the contents and operations / steps, nor do they have to be executed in the order described. For example, some operations / steps can be further decomposed, and some operations / steps can be combined or partially combined, so the actual execution order can be changed according to the actual situation.

[0030] It is to be noted that the terms "first", "second", and the like in the description and claims of the present application and above-described accompanying drawings are used to distinguish similar objects, and are not necessarily used to describe a particular sequential or chronological order. It should be understood that such objects used in this way can be interchanged, where appropriate, to enable the embodiments of the present application described herein to be carried out in other than the order illustrated or described.

[0031] With the advent of the data-driven information age, intelligent digital models have been applied in many fields, and people's mining and application of data are increasingly mature. The performance and correlation analysis of steel has important significance for improving the quality of steel, reducing costs, and improving production efficiency. The present application is a solution that uses intelligent algorithms to convert offline analysis into online autonomous intelligent analysis. From people to data, let data do it, focusing on in-depth comprehensive analysis of relevant process parameters of steel performance, quickly finding and positioning the root cause of problems affecting the quality of steel, shortening the time-consuming of problem processing, and assisting enterprises to master steel information, prevent steel quality defects, and improve steel quality.

[0032] The implementation details of the technical solutions of the embodiments of the present application are described in detail as follows:

[0033] Referring to Figure 1 , Figure 1 is a steel coil performance data control method flowchart in an embodiment of the present application.

[0034] According to a typical embodiment of the present application, a steel coil performance data control method is provided, which includes the following steps S1 to S4:

[0035] Step S1, obtaining historical production data of each steel coil production line in the historical production process, the historical production data including strip composition parameters and a plurality of process parameters in the process of producing steel coils from strips as historical process parameters.

[0036] In the present application, before performing the correlation analysis of the performance of the steel coil and the production data of the steel coil, a large amount of data needs to be based on digital analysis, so that the analysis result has data support. First, the historical production data of each steel coil in the historical production process is obtained from the steel coil production line, the historical production data can be saved to the control system after the strip is produced and coiled into a coil, the historical production data can include strip composition parameters and a plurality of process parameters in the process of producing steel coils from strips, and the plurality of process parameters are used as historical process parameters.

[0037] Step S2, obtaining performance parameters of the steel coils produced by each steel coil production line in history as historical performance parameters.

[0038] In the present application, in order to analyze the correlation between the performance of the steel coil and the production data of the steel coil, in addition to obtaining each historical production data on the steel coil production line, the performance parameter of each steel coil produced on the historical production line also needs to be obtained. In the historical production, each steel coil corresponds to a performance parameter (the performance parameter can include multiple parameters), and the performance parameter can represent the quality of the produced steel coil. After obtaining the performance parameter, the performance parameter is taken as the historical performance parameter.

[0039] Step S3: constructing a steel performance prediction model by performing correlation analysis on the historical performance parameter and the historical production data.

[0040] In the present application, after obtaining the historical performance parameter and the historical production data, a steel performance prediction model can be constructed by performing correlation analysis on the historical performance parameter and the historical production data. The steel performance prediction model can perform performance prediction on the steel coil.

[0041] Step S4: in the online production process, the performance data of the steel coil to be produced is controlled by the steel performance prediction model.

[0042] In the present application, after the steel performance prediction model is established, it can be applied to online production. In the online production process of the steel coil, the performance data of the steel coil to be produced can be controlled by the steel performance prediction model to ensure that the final quality of the steel coil to be produced meets the product quality standard.

[0043] In an embodiment of the present application, before performing correlation analysis on the historical performance parameter and the historical production data, the method further comprises:

[0044] The correlation analysis on any two parameters in the historical production data and the historical performance parameter is performed by using the lasso algorithm to obtain a correlation analysis result.

[0045] According to the correlation analysis result, the noise parameters in the historical production data are removed.

[0046] In the present application, since the obtained historical production data and historical performance parameters can be thousands of data, data redundancy and abnormal data can easily occur when obtained, therefore, before performing correlation analysis on the historical performance parameters and the historical production data, the disordered data needs to be formed into mean consistent data, integrated into plan standard value, integrated into performance data, constructed into curve alignment data or constructed into sampling position data (which can digitize steel, quickly lock process parameters affecting steel quality, and provide relevant data based on steel performance) according to different needs, and then the historical production data and the historical performance parameters are obtained from the sorted data.

[0047] In the present application, before performing correlation analysis on the historical performance parameters and the historical production data, the lasso algorithm can also be used to perform correlation analysis on any two parameters in the historical production data and the historical performance parameters to obtain a correlation analysis result. For example, when the historical production data contains three data A, B and C, and the historical performance parameters contain two parameters D and E, if data A increases, data B also increases, and the increase of data A and data B also affects parameter D, it indicates that data A, data B and parameter D have correlation, but since data A and data B also have correlation and belong to the same type of data, to avoid data redundancy, one data is selected from data A and data B, and the other is eliminated (the eliminated one can be a noise parameter); if data C increases or decreases, no matter how data C changes, it has little or even no effect on parameter D or parameter E, then data C (noise parameter) is also eliminated, so that the obtained historical production data and historical performance parameters can be simplified to avoid data redundancy and abnormal data.

[0048] In the present application, it is noted that the data A, data B and data C can be any item in the historical production data, such as tapping temperature, finishing temperature, tapping temperature, or other items, and the parameter D and parameter E can be any item in the historical performance parameters, such as yield strength and elongation, or other items, which are not particularly limited in the present application and can be adjusted according to actual production.

[0049] In the present application, after the historical production data is obtained, the historical process parameters in the historical production data can be matched by the operation system in terms of curve topography characteristics, and the matched curve topography characteristics can be stored in a curve feature library. The historical process parameter data can be intuitively displayed in a chart manner, and typical process parameter curves can be added to the curve feature library or the process parameter curves in the curve feature library can be changed, so as to facilitate analysis and comparison with subsequent curves, so as to realize data systematization and automation such as query, analysis, arrangement and export.

[0050] In an embodiment of the present application, the constructing a steel performance prediction model by performing correlation analysis on the historical performance parameters and the historical production data comprises:

[0051] The correlation analysis on the historical performance parameters and the historical production data is performed by an adaptive boosting algorithm, an extreme tree algorithm, a gradient boosting algorithm and a random forest algorithm respectively, and a plurality of candidate steel performance prediction models are constructed.

[0052] The steel performance prediction model is selected from the plurality of candidate steel performance prediction models.

[0053] In the present application, after the noise parameters in the historical production data are removed according to the correlation analysis results, the historical performance parameters become concise, which is beneficial to reduce the workload when constructing a model. If the historical performance parameters are more lengthy and complex, the constructed model is also more complex and redundant. Removing the noise parameters in the historical production data has a great beneficial effect on constructing a model.

[0054] In the present application, the correlation analysis on the historical performance parameters and the historical production data can be performed by a regression algorithm such as an adaptive boosting algorithm, an extreme tree algorithm, a gradient boosting algorithm and a random forest algorithm, and a plurality of candidate steel performance prediction models are constructed. After the candidate steel performance prediction models are established, they are displayed in three forms of key process parameters and importance, key process thermodynamic diagrams and modeling evaluation results. The plurality of candidate steel performance prediction models can be displayed by visualization to show which key process parameters they are composed of, and the importance of the key process in the plurality of processes in the entire steel strip production process, as well as the key process thermodynamic diagram and the modeling evaluation result. The steel performance prediction model can be selected from the plurality of candidate steel performance prediction models according to the modeling evaluation result. The evaluation result is used to evaluate the accuracy of the plurality of candidate steel performance prediction models in evaluating the performance of the steel coil. The steel performance prediction model with the optimal evaluation result is selected from the plurality of candidate steel performance prediction models.

[0055] In the present application, it is noted that the user or operator can autonomously select the data required for model establishment and the key process parameters of the model when performing manual operation to perform online modeling. The established model can also be displayed in three forms of key process parameters and importance, key process thermodynamic map and modeling evaluation results.

[0056] In an embodiment of the present application, the performance data of the to-be-produced steel coil controlled by the steel performance prediction model comprises:

[0057] Obtaining a target performance parameter of a to-be-produced steel coil;

[0058] Based on the target performance parameter, determining a target strip steel composition parameter and each target process parameter for producing the to-be-produced steel coil by the steel performance prediction model;

[0059] Performing production of the to-be-produced steel coil according to the target strip steel composition parameter and the each target process parameter.

[0060] In the present application, after the steel performance prediction model is established, the performance data of the to-be-produced steel coil can be controlled by the steel performance prediction model. In actual use, the target performance parameter of the to-be-produced steel coil, i.e., the performance parameter of the final product required by the production plan, can be obtained first. After the target performance parameter is obtained, the target strip steel composition parameter and each target process parameter for producing the to-be-produced steel coil are determined based on the target performance parameter by the steel performance prediction model, so that the production line performs production of the to-be-produced steel coil according to the target strip steel composition parameter and the each target process parameter. When the production of the to-be-produced steel coil is performed according to the target strip steel composition parameter and the each target process parameter, the product corresponding to the previously obtained target performance parameter can be obtained, and the quality of the steel coil product can be a qualified product.

[0061] In an embodiment of the present application, the method further comprises:

[0062] During the production of the to-be-produced steel coil, if any one or more process parameters deviate from the corresponding target process parameter, the steel performance prediction model is used to adjust the process parameters other than the any one or more process parameters in the each target process parameter, and the production of the to-be-produced steel coil is performed according to the adjusted process parameters.

[0063] In the present application, if the production of the target steel strip composition parameters and the target process parameters is performed, the final product will be a qualified product, but in actual production, various interference factors may cause any one or more process parameters to deviate from the corresponding target process parameters during production. If the parameters are not adjusted, the final product may be a non-qualified product, which may also affect the production efficiency of the product or cause production failure, causing damage to the equipment of the production line.

[0064] In the present application, after obtaining the target performance parameters to be produced, the target steel strip composition parameters and the target process parameters for producing the target steel strip are determined based on the target performance parameters through the steel performance prediction model, the target process parameter curve is generated through the operation system, the curve data in the feature library are extracted through the high-frequency time sequence data feature extraction algorithm, the minimum distance is calculated through the multi-template comprehensive similarity distance, and the curve features are automatically matched (as shown in Figure 2 、 Figure 3 The corresponding curve with the highest similarity can be selected for production according to the process parameters of the corresponding curve. The performance prediction result, the level of the curve, and the automatic matching of the curve level in the standard library can be displayed in the operation system.

[0065] In the present application, in order to avoid the above problems, if any one or more process parameters deviate from the corresponding target process parameters during the production of the target steel strip, the process parameters other than the any one or more process parameters are adjusted through the steel performance prediction model, for example, three process flows are required for producing the target steel strip, the first process is performed from the beginning to the end of the process, and there is no phenomenon of process parameter deviation from the corresponding target process parameter, but the phenomenon of process parameter deviation from the corresponding target process parameter occurs during the second process. In order to ensure that the target performance parameters are not affected, the process parameters of the third process need to be adjusted through the steel performance prediction model before the third process is performed, so that the final target performance parameters do not change, and the third process is performed according to the adjusted process parameters to produce the target steel strip, so that the final product of the target steel strip is a qualified product.

[0066] In the present application, not only can the steel performance result be predicted according to the position of a point of the steel strip (a point can be selected in the steel strip to obtain the corresponding process and process parameters of the point), but also the steel performance can be predicted through the mean data of the steel strip, the length direction curve of the steel strip is predicted, and the mean prediction of the steel strip and the prediction curve display of the length direction performance index are displayed (as shown in Figure 4The tensile strength prediction display of the steel coil shows that the prediction model evaluation result is displayed by accuracy, mean square error, sample size, bias scatter plot and bias distribution histogram, and the abnormal data existing in the model prediction can be inquired. The application also establishes the adjustment suggestion combined with the calculation rate of the system process adjustment scheme and the artificial suggestion strategy, to ensure the accuracy and efficiency of the steel performance judgment.

[0067] In an embodiment of the present application, the process parameters can at least include hot rolling furnace time, discharge temperature, finishing temperature, coiling temperature, deformation amount, cold rolling annealing temperature. It should be noted here that the process parameters can also include other process parameters for producing steel strips, such as coiling temperature, rolling temperature, etc., which are not particularly limited in the present application.

[0068] In an embodiment of the present application, the performance parameters can at least include tensile strength, yield strength, elongation, hardness index. It should be noted here that the performance parameters can also include nominal thickness, etc., which are not particularly limited in the present application.

[0069] In the present application, data can also be maintained, such as setting the upper and lower limits of the data when acquiring the data, so as to accurately acquire the data and avoid abnormal data from pouring in, and the performance item prediction accuracy threshold condition can also be maintained, and the data can be modified. When the accuracy of the steel performance prediction model is lower than the prediction accuracy threshold, the steel performance prediction model can be maintained and the data can be modified.

[0070] In the present application, the performance prediction of the steel coil to be produced by the steel prediction model can also be pushed through the enterprise WeChat, and the production state of the steel coil can be understood in real time

[0071] The specific embodiments of the present application will be further described below through specific embodiments, but the specific embodiments of the present application are not limited to the following embodiments.

[0072] Figure 5 The structural block diagram of the steel coil performance data control device according to the embodiment of the present application is shown.

[0073] Referring to Figure 5 As shown, the steel coil performance data control device 500 according to an embodiment of the present application includes a first acquisition unit 501, a second acquisition unit 502, a modeling unit 503 and a control unit 504.

[0074] The first acquisition unit 501 is used to acquire historical production data of each steel coil production line during the historical production process. The historical production data includes strip steel composition parameters and multiple process parameters in the process of producing steel coils from strip steel, which are used as historical process parameters.

[0075] The second acquisition unit 502 is used to acquire the performance parameters of steel coils produced by each steel coil production line in history, as historical performance parameters.

[0076] Modeling unit 503 is used to construct a steel performance prediction model by performing correlation analysis on the historical performance parameters and the historical production data.

[0077] The control unit 504 is used in the online production process to control the performance data of the steel coil to be produced through the steel performance prediction model.

[0078] Reference Figure 6 , Figure 6 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown.

[0079] like Figure 6 As shown, the computer system 600 includes a Central Processing Unit (CPU) 601, which can perform various appropriate actions and processes based on programs stored in Read-Only Memory (ROM) 602 or programs loaded from Storage Unit 608 into Random Access Memory (RAM) 603, such as performing the methods described in the above embodiments. The RAM 603 also stores various programs and data required for system operation. The CPU 601, ROM 602, and RAM 603 are interconnected via a bus 604. An Input / Output (I / O) interface 605 is also connected to the bus 604.

[0080] The following components are connected to the I / O interface 605: an input part 606 including a keyboard, a mouse, etc.; an output part 607 including a display such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), etc., and a speaker, etc.; a storage part 608 including a hard disk, etc.; and a communication part 609 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication part 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to the I / O interface 605 as necessary. A removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc. is attached to the drive 610 as necessary, so that a computer program read out therefrom is installed in the storage part 608 as necessary.

[0081] In particular, according to embodiments of the present application, the processes described above with reference to the flowcharts can be implemented as a computer software program. For example, embodiments of the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for executing the methods illustrated by the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network by the communication part 609, and / or installed from the removable medium 611. When the computer program is executed by the central processing unit (CPU) 601, various functions defined in the system of the present application are executed.

[0082] It should be noted that the computer-readable medium in the embodiments of the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination thereof. The computer-readable storage medium may, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or apparatus, or any combination thereof. More specific examples of the computer-readable storage medium can include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disk read-only memory (Compact Disc Read-Only Memory, CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In the present application, the computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, device or apparatus. In the present application, the computer-readable signal medium can include a data signal carrying a computer-readable program code in a baseband or as a part of a carrier wave. Such a propagated data signal can take various forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination thereof. The computer-readable signal medium can also be any computer-readable medium other than the computer-readable storage medium, which can send, transmit, propagate or transport a program for use by or in conjunction with an instruction execution system, device or apparatus. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, or the like, or any suitable combination thereof.

[0083] The flowcharts and block diagrams in the drawings illustrate the possible implementation architectures, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In the flowcharts or block diagrams, each block can represent a module, a program segment or a part of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions noted in the blocks can occur in different orders than that shown in the drawings. For example, two blocks that are shown in succession can actually be executed substantially in parallel, and sometimes in reverse order, depending on the involved functions. It should also be noted that each block in the block diagrams or flowcharts, and the combination of blocks in the block diagrams or flowcharts, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0084] The units described in the embodiments of the present application can be implemented in the form of software, or can be implemented in the form of hardware, and the described units can also be arranged in a processor. In some cases, the names of the units do not constitute a limitation on the units themselves.

[0085] According to a typical embodiment of the present application, the present application further provides a computer readable storage medium, the computer readable storage medium stores at least one program code, the at least one program code is loaded and executed by a processor to implement the operations performed by the steel coil performance data control method as described above.

[0086] According to a typical embodiment of the present application, the present application further provides an electronic device, the electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the operations performed by the steel coil performance data control method as described above when executing the computer program.

[0087] It should be noted that although several modules or units of the device for action execution are mentioned in the above detailed description, such division is not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into a plurality of modules or units.

[0088] From the above technical solutions, the present application has at least the following advantages and positive effects:

[0089] Firstly, the scheme proposed by the present application can solve the problem that the judgment of steel performance in the prior art relies on artificial judgment method, resulting in low accuracy and efficiency of judgment, and difficulty in meeting different requirements for different steel materials. The scheme proposed by the present application improves the accuracy and efficiency of steel performance judgment by analyzing the correlation between steel performance and production data, improves the stability of steel performance, the quality of products and production efficiency, and reduces production cost.

[0090] Secondly, the scheme proposed by the present application can ensure high-quality production of the production line, improve the quality and production efficiency of the products, increase market competitiveness and capital income.

[0091] Thirdly, the scheme proposed by the present application improves the model running precision by developing and applying the whole-process data alignment technology, realizes the data processing, alignment and analysis application technology of the whole-process key process, and realizes the running precision of the model.

[0092] Fourthly, the scheme provided in the application is based on massive data fusion, data mining, multiple algorithm intelligent analysis and prediction, and is free from the fetters of the copyright of the analysis means limited by software, complex operation of the analysis software and the like, and realizes the idea of independent analysis.

[0093] Fifthly, the scheme provided in the application is developed and applied through the whole-process data alignment technology, improves the model operation precision, realizes the data processing, alignment and analysis application technology of the whole-process key process, and realizes the operation precision of the model.

[0094] Although the application has been described with reference to several exemplary embodiments, it will be understood that the terms used are illustrative and not restrictive, and that the scope of the application is not limited to any of the foregoing details. Since the application can be embodied in many different forms, it should be understood that the application is not limited to the specific details thereof, but is to be interpreted within the spirit and scope of the appended claims, and all changes and modifications that fall within the ranges of equivalents of the claims are therefore to be embraced by the foregoing disclosure and their legal equivalents.

Claims

1. A method of controlling performance data of a steel coil, characterized by, The method comprises: obtaining historical production data of each steel coil production line in the historical production process, the historical production data comprising strip steel composition parameters and a plurality of process parameters in the process of producing steel coils from strip steel as historical process parameters; obtaining performance parameters of the steel coils produced by each steel coil production line in history as historical performance parameters; performing correlation analysis on any two parameters in the historical production data and the historical performance parameters by a lasso algorithm to obtain a correlation analysis result; according to the correlation analysis result, eliminating noise parameters in the historical production data; constructing a steel performance prediction model by performing correlation analysis on the historical performance parameters and the historical production data; in the online production process, controlling the performance data of the steel coil to be produced by the steel performance prediction model, comprising: obtaining target performance parameters of the steel coil to be produced; based on the target performance parameters, determining target strip steel composition parameters and each target process parameter for producing the steel coil to be produced by the steel performance prediction model; and performing production of the steel coil to be produced according to the target strip steel composition parameters and the target process parameters; after obtaining the historical production data, performing curve topography feature matching on the historical process parameters in the historical production data by a control system, and storing the matched curve topography features into a curve feature library; after obtaining the target performance parameters of the steel coil to be produced, determining target strip steel composition parameters and each target process parameter for producing the steel coil to be produced based on the target performance parameters by the steel performance prediction model, generating a curve of the target process parameters by the control system, extracting feature parameters of the curve by a high-frequency time series data feature extraction algorithm, calculating the minimum distance by combining multi-template comprehensive similarity distance for automatic curve feature matching, and selecting the corresponding curve with the highest similarity to perform production according to the process parameters of the corresponding curve.

2. The method of claim 1, wherein, The method further comprises: in the process of performing production of the steel coil to be produced, if any one or more process parameters deviate from the corresponding target process parameters, adjusting the process parameters other than the any one or more process parameters in the target process parameters by the steel performance prediction model, and performing production of the steel coil to be produced according to the adjusted process parameters. The process parameters at least comprise hot rolling in-furnace time, out-of-furnace temperature, final rolling temperature, coiling temperature, deformation amount, and cold rolling annealing temperature.

3. The method of claim 1, wherein, The performance parameters at least comprise tensile strength, yield strength, elongation, and hardness index. The device comprises:

4. The method according to any one of claims 1 to 3, characterized in that, ​ 5. The method according to any one of claims 1 to 3, characterized in that, ​ 6. A steel coil property data control apparatus applying the steel coil property data control method according to any one of claims 1 to 5, characterized by ​ A first obtaining unit is configured to obtain historical production data of each steel coil production line in a historical production process, wherein the historical production data comprises strip composition parameters and a plurality of process parameters in a process of producing a steel coil from a strip, as historical process parameters; A second obtaining unit is configured to obtain performance parameters of steel coils produced by each steel coil production line in history, as historical performance parameters; A modeling unit is configured to construct a steel performance prediction model by performing correlation analysis on the historical performance parameters and the historical production data; A control unit is configured to control performance data of a steel coil to be produced in an online production process by using the steel performance prediction model.

7. A computer readable storage medium characterized in that, The computer readable storage medium stores at least one program code, and the at least one program code is loaded and executed by the processor to implement operations performed by the method according to any one of claims 1 to 5. 8.An electronic device comprising a memory and a processor, the memory storing a computer program, wherein, The processor executes the computer program to implement operations performed by the method according to any one of claims 1 to 5.

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