A temperature monitoring data analysis method for non-modulated steel controlled cooling process

Abstract

The present invention discloses a temperature monitoring data analysis method for controlling the cooling process of non-modulated steel, which relates to the technical field of data processing. The method comprises: extracting cooling features from available steel cooling temperature state monitoring data streams to obtain a steel cooling feature parameter set, establishing a steel cooling trend prediction adaptive network to predict trends for the steel cooling feature parameter set, outputting steel cooling trend feature prediction information to perform optimization analysis in the steel cooling parameter control space, and determining a steel cooling control parameter solution library; performing control differentiation analysis based on the steel cooling control parameter solution library, preferably obtaining a target steel cooling control parameter solution, and optimizing cooling control of non-modulated steel through the target steel cooling control parameter solution. Realizing real-time monitoring and efficient analysis and control of temperature data of non-modulated steel during the cooling process, improving analysis and control accuracy and cooling control efficiency, and thereby improving the quality of non-modulated steel products.

Description

Technical Field

[0001] The invention relates to the technical field of data processing, and in particular to a temperature monitoring data analysis method for controlling a cooling process of non-modulated steel. Background Art

[0002] In the complex process of steel smelting, the cooling process of non-modulated steel is a crucial link, which is directly related to the performance and quality of the final product. The control of the cooling process not only affects the crystal structure of non-modulated steel, but also determines the mechanical properties and physical properties of the steel. Therefore, efficient and accurate temperature monitoring data analysis methods are of great significance for improving the product quality of non-modulated steel. However, the existing cooling process temperature monitoring and analysis methods have the problems of low analysis and control accuracy and slow control speed, which in turn affects the quality of non-modulated steel products. Summary of the invention

[0003] The present application provides a temperature monitoring data analysis method for controlling the cooling process of non-modulated steel, thereby solving the technical problems of low analysis and control accuracy and slow control speed in the prior art, which in turn affect the quality of non-modulated steel products. The application achieves the technical effect of realizing real-time monitoring and efficient analysis and control of temperature data of non-modulated steel during the cooling process, improving the analysis and control accuracy and cooling control efficiency, and thus improving the quality of non-modulated steel products.

[0004] In view of the above problems, the present invention provides a temperature monitoring data analysis method for a non-modulated steel controlled cooling process.

[0005] In a first aspect, the present application provides a temperature monitoring data analysis method for controlling the cooling process of non-modulated steel, the method comprising: S1: installing a temperature sensing module on the non-modulated steel cooling equipment, collecting and acquiring the steel cooling temperature state monitoring data stream through the temperature sensing module, and standardizing the steel cooling temperature state monitoring data stream to obtain an available steel cooling temperature state monitoring data stream; S2: extracting cooling features from the available steel cooling temperature state monitoring data stream to obtain a set of steel cooling feature parameters, establishing a steel cooling trend prediction adaptive network to perform trend prediction on the steel cooling feature parameter set, and outputting steel cooling trend feature prediction information; S3: constructing a steel cooling parameter control space, using the steel cooling trend feature prediction information as a cooling constraint parameter to perform optimization analysis in the steel cooling parameter control space, and determining a steel cooling control parameter solution library, the steel cooling control parameter solution library including an optimal steel cooling control parameter solution and multiple applicable steel cooling control parameter solutions; S4: performing control differentiation analysis based on the steel cooling control parameter solution library, preferably obtaining a target steel cooling control parameter solution, and performing cooling optimization control on non-modulated steel through the target steel cooling control parameter solution.

[0006] On the other hand, the present application also provides a temperature monitoring data analysis system for controlling the cooling process of non-modulated steel, the system comprising: a data standardization processing module, used to install a temperature sensing module on the non-modulated steel cooling equipment, collect and obtain the steel cooling temperature state monitoring data stream through the temperature sensing module, standardize the steel cooling temperature state monitoring data stream, and obtain the available steel cooling temperature state monitoring data stream; a cooling characteristic trend prediction module, used to extract cooling characteristics from the available steel cooling temperature state monitoring data stream, obtain a steel cooling characteristic parameter set, establish a steel cooling trend prediction adaptive network for the steel cooling characteristic parameter set Perform trend prediction and output steel cooling trend feature prediction information; a parameter solution library determination module is used to construct a steel cooling parameter control space, and the steel cooling trend feature prediction information is used as a cooling constraint parameter to perform optimization analysis in the steel cooling parameter control space to determine a steel cooling control parameter solution library, wherein the steel cooling control parameter solution library includes an optimal steel cooling control parameter solution and multiple applicable steel cooling control parameter solutions; a cooling optimization control module is used to perform control differentiation analysis based on the steel cooling control parameter solution library, preferably obtain a target steel cooling control parameter solution, and perform cooling optimization control on non-modulated steel through the target steel cooling control parameter solution.

[0007] In a third aspect, the present application provides an electronic device, comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, and the computer program, when executed by the processor, implements the steps of any one of the above methods.

[0008] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps in any one of the above-mentioned methods when executed by a processor.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] Since a temperature sensing module is installed on the non-modulated steel cooling equipment, the temperature sensing module is used to collect and obtain the steel cooling temperature state monitoring data stream, and the available steel cooling temperature state monitoring data stream is obtained by standardized processing, and then the cooling characteristics of the available steel cooling temperature state monitoring data stream are extracted to obtain a steel cooling characteristic parameter set, and a steel cooling trend prediction adaptive network is established to perform trend prediction on the steel cooling characteristic parameter set, and steel cooling trend characteristic prediction information is output. The steel cooling trend characteristic prediction information is used as a cooling constraint parameter to perform optimization analysis in the steel cooling parameter control space, and a steel cooling control parameter solution library is determined. Then, a control differentiation analysis is performed based on the steel cooling control parameter solution library, and a target steel cooling control parameter solution is preferably obtained to perform cooling optimization control on non-modulated steel. Thus, the technical effect of realizing real-time monitoring and efficient analysis and control of temperature data of non-modulated steel during the cooling process is achieved, improving the analysis and control accuracy and cooling control efficiency, and thus improving the quality of non-modulated steel products is achieved.

[0011] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 A schematic flow chart of a temperature monitoring data analysis method for a non-modulated steel controlled cooling process in the present application;

[0013] Figure 2 A schematic diagram of a process for installing a temperature sensing module in a temperature monitoring data analysis method for a non-modulated steel controlled cooling process in the present application;

[0014] Figure 3 This is a schematic diagram of the structure of a temperature monitoring data analysis system for non-modulated steel controlled cooling process in this application;

[0015] Figure 4 This is a schematic diagram of the structure of an exemplary electronic device of the present application.

[0016] Explanation of the accompanying drawings: data standardization processing module 11, cooling characteristic trend prediction module 12, parameter scheme library determination module 13, cooling optimization control module 14, bus 1110, processor 1120, transceiver 1130, bus interface 1140, memory 1150, operating system 1151, application 1152 and user interface 1160. DETAILED DESCRIPTION

[0017] In the description of this application, those skilled in the art should know that this application can be implemented as a method, an apparatus, an electronic device, and a computer-readable storage medium. Therefore, this application can be specifically implemented in the following forms: complete hardware, complete software (including firmware, resident software, microcode, etc.), a combination of hardware and software. In addition, in some embodiments, this application can also be implemented in the form of a computer program product in one or more computer-readable storage media, and the computer-readable storage medium contains computer program code.

[0018] The above-mentioned computer-readable storage medium may adopt any combination of one or more computer-readable storage media. Computer-readable storage media include: electrical, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or devices, or any combination of the above. More specific examples of computer-readable storage media include: portable computer disks, hard disks, random access memories, read-only memories, erasable programmable read-only memories, flash memories, optical fibers, optical disc read-only memories, optical storage devices, magnetic storage devices, or any combination of the above. In the present application, computer-readable storage media 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 device.

[0019] The acquisition, storage, use, and processing of data in the technical solution of this application comply with the relevant provisions of national laws.

[0020] The present application describes the provided methods, devices, and electronic devices through flowcharts and / or block diagrams.

[0021] It should be understood that each box in the flowchart and / or block diagram and the combination of boxes in the flowchart and / or block diagram can be implemented by computer-readable program instructions. These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer or other programmable data processing device to produce a machine, and these computer-readable program instructions are executed by a computer or other programmable data processing device to produce a device that implements the functions / operations specified by the boxes in the flowchart and / or block diagram.

[0022] These computer-readable program instructions may also be stored in a computer-readable storage medium that enables a computer or other programmable data processing device to work in a specific manner. In this way, the instructions stored in the computer-readable storage medium produce an instruction device product including functions / operations specified in the blocks in the flowchart and / or block diagram.

[0023] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device, so that a series of operational steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby enabling the instructions executed on the computer or other programmable data processing apparatus to provide a process for implementing the functions / operations specified in the blocks in the flowchart and / or block diagram.

[0024] The present application is described below in conjunction with the drawings in the present application.

[0025] Embodiment 1

[0026] like Figure 1 As shown, the present application provides a temperature monitoring data analysis method for a non-modulated steel controlled cooling process, the method comprising:

[0027] Step S1: installing a temperature sensing module on the non-modulated steel cooling equipment, collecting and acquiring a steel cooling temperature state monitoring data stream through the temperature sensing module, and performing standardization processing on the steel cooling temperature state monitoring data stream to obtain an available steel cooling temperature state monitoring data stream;

[0028] like Figure 2 As shown, further, in said S1, a temperature sensing module is installed on the non-modulated steel cooling device, and the steps of this application also include:

[0029] S11: feature classification and marking of attribute information of non-modulated steel cooling equipment to determine equipment structure dimensions, cooling medium, and steel cooling area characteristics;

[0030] S12: obtaining a steel cooling equipment sensor layout database, performing association screening with the steel cooling equipment sensor layout database according to the equipment structure size, cooling medium, and equipment cooling area, and constructing a temperature sensor design parameter solution space;

[0031] S13: Determine a set of temperature sensor sensing factors, where the set of temperature sensor sensing factors includes measurement accuracy, measurement timeliness, and threshold applicability;

[0032] S14: performing global optimization in the temperature sensor design parameter solution space based on the temperature sensor sensing index set, determining sensor design parameter information, and arranging the temperature sensing module on the non-modulated steel cooling equipment according to the sensor design parameter information.

[0033] Specifically, in order to realize the real-time monitoring of the temperature of the non-modulated steel controlled cooling process, a temperature sensing module is installed on the non-modulated steel cooling equipment, wherein the cooling equipment is an industrial device for reducing the temperature of non-modulated steel, and the temperature sensing module is composed of an integrated temperature sensor. The temperature sensor type used to detect the cooling temperature of the steel can preferably be a thermocouple sensor and a thermistor sensor, which has high measurement accuracy and response speed to ensure the accuracy and real-time performance of the temperature data. In order to improve the accuracy of temperature data acquisition, the attribute parameters of the non-modulated steel cooling equipment are characterized and classified, and its equipment structure size, cooling medium and steel cooling area characteristics are determined. The steel cooling area characteristics include parameters such as the layout of the non-modulated steel cooling area, the cooling temperature threshold, and the structural shape. The steel cooling equipment sensor layout database is obtained through historical data collection. The steel cooling equipment sensor layout database includes the historical sensor layout type, angle, quantity and other design parameters of the non-modulated steel cooling equipment and the layout effect data corresponding to each sensor design parameter, which is determined by the evaluation of the temperature sensor sensing factor set.

[0034] According to the equipment structure size, cooling medium and equipment cooling area, the steel cooling equipment sensor layout database is associated and screened. The sensor layout data in the steel cooling equipment sensor layout database that is within the preset similarity threshold with the non-modulated steel attribute parameters can be calculated using a similarity algorithm, and the temperature sensor design parameter solution space is constructed based on the associated matching sensor layout data composition to serve as the applicability optimization space for the sensor layout parameters. The temperature sensor sensing factor set is determined by the layout effect data. The temperature sensor sensing factor set is the sensor layout effect evaluation index, including measurement accuracy, that is, the measurement data accuracy error; measurement timeliness, that is, the sensor measurement sensitivity; threshold applicability, that is, the matching applicability of the sensor measurement threshold. Based on the temperature sensor sensing index set, a global optimization is performed in the temperature sensor design parameter solution space, and the sensor design parameter information with the best layout effect evaluation index is determined by evaluation and comparison. The temperature sensing module is laid on the non-modulated steel cooling equipment according to the sensor design parameter information to ensure the comprehensive coverage of the temperature sensor layout and the accuracy of temperature data collection.

[0035] The temperature sensing module is used to monitor and collect the steel cooling temperature status monitoring data stream in real time, and then the steel cooling temperature status monitoring data stream is standardized. The preprocessing process includes noise removal, smoothing, outlier detection and processing, etc. Through data preprocessing, a usable steel cooling temperature status monitoring data stream is obtained to improve the accuracy and reliability of temperature monitoring data, improve data processing quality, and provide a usable data basis for subsequent cooling feature extraction and cooling control analysis.

[0036] Step S2: extract cooling features from the available steel cooling temperature state monitoring data stream to obtain a steel cooling feature parameter set, establish a steel cooling trend prediction adaptive network to perform trend prediction on the steel cooling feature parameter set, and output steel cooling trend feature prediction information;

[0037] Furthermore, in the step S2, a set of steel cooling characteristic parameters is obtained, and the steps of the present application further include:

[0038] S21: spatially dividing the available steel cooling temperature state monitoring data stream according to monitoring areas based on the sensor design parameter information to obtain a plurality of steel cooling space area temperature state monitoring data streams;

[0039] S22: arranging and integrating the plurality of steel cooling space area temperature state monitoring data streams in chronological order to obtain a plurality of steel cooling space area temperature state chronological data streams;

[0040] S23: generating a plurality of steel cooling temperature space variation curves according to the plurality of steel cooling space region temperature state time series data streams, performing curvature calculation on the plurality of steel cooling temperature space variation curves, and obtaining a plurality of steel cooling temperature space trend curves;

[0041] S24: performing cooling feature extraction based on the plurality of steel cooling temperature spatial trend curves respectively, and determining a steel cooling feature parameter set, wherein the steel cooling feature parameter set includes a cooling rate, a temperature gradient, and a temperature cooling time node.

[0042] Furthermore, the step S2 of establishing a steel cooling trend prediction adaptive network also includes:

[0043] S25: acquiring a non-modulated steel cooling temperature data set, classifying and identifying the non-modulated steel cooling temperature data set based on the steel cooling characteristic parameter set, and obtaining a steel cooling characteristic sample data set;

[0044] S26: Performing supervised training verification on the steel cooling feature sample data set based on a recurrent neural network structure to generate a steel cooling trend feature branch network set and a branch network prediction accuracy set;

[0045] S27: taking the branch criticality factor of the steel cooling trend characteristic branch network set and the average of the branch network prediction accuracy set as the branch network integration coefficient;

[0046] S28: Integrate and fuse the steel cooling trend feature branch network set based on the branch network integration coefficient to construct the steel cooling trend prediction adaptive network.

[0047] Furthermore, the application steps also include:

[0048] Performing prediction verification on the steel cooling trend prediction adaptive network, and when the prediction verification result does not meet the benchmark accuracy, initializing the particle swarm parameters, the particle swarm parameters including parameter particle position and parameter particle velocity;

[0049] Define a fitness loss function to iteratively update the particle swarm parameters until a preset iteration condition is met, and optimize and determine the particle with the largest fitness as the optimal parameter solution;

[0050] The model parameters of the steel cooling trend prediction adaptive network are configured based on the optimal parameter solution to obtain the steel cooling trend optimization prediction adaptive network.

[0051] Specifically, the cooling characteristics of the available steel cooling temperature state monitoring data stream are extracted. First, the available steel cooling temperature state monitoring data stream is spatially divided according to the sensor monitoring area based on the sensor design parameter information to obtain multiple temperature state monitoring data streams of steel cooling space areas. Then, the multiple steel cooling space area temperature state monitoring data streams are arranged and integrated in sequence according to the monitoring time sequence to obtain multiple steel cooling space area temperature state time series data streams after time sequence integration. Multiple steel cooling temperature space change curves are generated based on the multiple steel cooling space area temperature state time series data streams, and the multiple steel cooling temperature space change curves represent the change of steel cooling temperature over time. The curvature of the multiple steel cooling temperature space change curves is calculated by derivatives to obtain multiple steel cooling temperature space trend curves, which represent the change of steel cooling rate and can analyze and predict the cooling behavior characteristics of steel.

[0052] Based on the multiple steel cooling temperature spatial trend curves, cooling characteristics are extracted respectively to determine the steel cooling characteristic parameter set, which includes cooling rate, that is, describing the rate at which the temperature of the steel changes over time, which is determined by the numerical value of the steel cooling temperature spatial trend curve; temperature gradient, that is, the temperature difference between various spatial positions inside the steel, obtained by calculating the temperature difference distribution of each spatial position to reflect the temperature distribution of each structural area of ​​non-modulated steel; temperature cooling time node, that is, the time node when the steel reaches a specific cooling temperature is predicted by the temperature cooling rate. By comprehensively extracting characteristic parameters related to the cooling process, the accuracy of grasping the cooling temperature distribution and change trend of the steel is improved, thereby accurately predicting the cooling trend of the steel and controlling the cooling process of the steel.

[0053] Establish a steel cooling trend prediction adaptive network to predict the trend of the steel cooling characteristic parameter set. First, obtain a non-modulated steel cooling temperature data set through a historical database. The non-modulated steel cooling temperature data set is the historical steel cooling temperature time series change data of the non-modulated steel cooling equipment. Classify and identify the non-modulated steel cooling temperature data set based on the steel cooling characteristic parameter set, and classify the data set identifier according to the steel cooling characteristic parameter type as a steel cooling characteristic sample data set. Based on the recurrent neural network structure, supervised training is performed on the steel cooling characteristic sample data set to generate a steel cooling trend characteristic branch network set. The steel cooling trend characteristic branch network set is a cooling trend prediction network corresponding to each steel cooling characteristic parameter type, which is used to predict the steel cooling characteristic parameter information in the future time period. Then, each branch network is tested and verified for accuracy to obtain a corresponding branch network prediction accuracy set. The branch criticality factor of the steel cooling trend characteristic branch network set, that is, the weight proportion of each branch network, can be set empirically. The larger the criticality factor, the greater the importance proportion of the branch network.

[0054] The mean of the branch criticality factor and the branch network prediction accuracy set is used as the branch network integration coefficient. The branch network integration coefficient is the decisive proportion of the model output result. The larger the branch network weight proportion, the higher the prediction accuracy, and the larger its corresponding decisive proportion. Based on the branch network integration coefficient, the steel cooling trend feature branch network set is integrated and fused to construct a steel cooling trend prediction adaptive network. The steel cooling trend prediction adaptive network is a recurrent neural network structure, which comprehensively predicts the steel cooling trend through temperature time series data. Based on the steel cooling trend prediction adaptive network, the steel cooling characteristic parameter set is trend predicted, and steel cooling trend characteristic prediction information is output. The steel cooling trend characteristic prediction information is the predicted change information of each steel cooling characteristic parameter, which provides a basis for cooling control, thereby ensuring the accuracy of steel cooling analysis and control.

[0055] In order to ensure the accuracy of steel cooling trend prediction, the steel cooling trend prediction adaptive network is predicted and verified. When the prediction verification result does not meet the benchmark accuracy, the particle swarm parameters are initialized. Each particle represents a potential solution of the steel cooling trend prediction adaptive network. The particle swarm parameters include parameter particle position, that is, the initial value of the model parameter; parameter particle speed, that is, the model parameter optimization step length. Define the fitness loss function to iteratively update the particle swarm parameters, wherein the fitness loss function can be preferably a mean square error loss function, which is used to describe the difference between the model prediction result and the true value. The fitness loss function needs to be minimized. The smaller the model loss value, the greater the corresponding model parameter fitness. The particle position is optimized and updated by the particle swarm optimization algorithm until the preset iteration condition is met. The preset iteration condition can be set by itself, such as the preset maximum number of iterations. When the preset iteration condition is met, the particle with the largest fitness is optimized and determined as the optimal model parameter solution. And the model parameters of the steel cooling trend prediction adaptive network are configured based on the optimal parameter solution to obtain the steel cooling trend optimization prediction adaptive network after the model parameters are optimized. Realize adaptive optimization and update of model parameters, improve the prediction and evaluation performance of the optimization model, and thus ensure prediction accuracy.

[0056] Step S3: constructing a steel cooling parameter control space, using the steel cooling trend characteristic prediction information as a cooling constraint parameter to perform optimization analysis in the steel cooling parameter control space, and determining a steel cooling control parameter solution library, wherein the steel cooling control parameter solution library includes an optimal steel cooling control parameter solution and multiple applicable steel cooling control parameter solutions;

[0057] Furthermore, the steel cooling control parameter solution library is determined in S3, and the steps of this application also include:

[0058] S31: obtaining a steel cooling control effect index, and constructing a steel cooling effect evaluation objective function based on the steel cooling control effect index;

[0059] S32: using the steel cooling trend characteristic prediction information as a cooling constraint parameter to perform global optimization in the steel cooling parameter control space to obtain multiple steel cooling control parameter solutions;

[0060] S33: evaluating and calculating the plurality of steel cooling control parameter schemes by using the steel cooling effect evaluation objective function to obtain a plurality of cooling control effect fitnesses;

[0061] S34: dividing and clustering the multiple steel cooling control parameter schemes based on the multiple cooling control effect fitnesses to obtain multiple steel cooling control parameter scheme clusters;

[0062] S35: performing iterative optimization on the plurality of steel cooling control parameter solution clusters respectively, and calculating the fitness sum of the plurality of optimized steel cooling control parameter solution clusters, thereby screening and obtaining the steel cooling control parameter solution library.

[0063] Specifically, a steel cooling parameter control space is constructed by big data technology, and the steel cooling parameter control space is the cooling control data of historical non-modulated steel, including steel cooling trend characteristic data, steel cooling control parameter schemes and steel cooling control effect data. The steel cooling trend characteristic prediction information is used as a cooling constraint parameter to perform optimization analysis in the steel cooling parameter control space. First, a steel cooling control effect index is formulated. The steel cooling control effect index is used to evaluate the steel cooling control effect, including hardness change, temperature uniformity, deformation, etc., and the steel cooling control effect data is evaluated and fitted based on the steel cooling control effect index to construct a steel cooling effect evaluation objective function. The steel cooling effect evaluation objective function is used to evaluate the effect of the steel cooling control parameter scheme, and the evaluation effect is measured by the fitness. The steel cooling trend characteristic prediction information is used as a cooling constraint parameter to perform global optimization in the steel cooling parameter control space to obtain multiple steel cooling control parameter schemes that match the similarity of the cooling constraint parameters.

[0064] The steel cooling effect evaluation objective function is used to evaluate and calculate the multiple steel cooling control parameter schemes to obtain the multiple cooling control effect fitnesses corresponding to each control parameter scheme. Based on the multiple cooling control effect fitnesses, the multiple steel cooling control parameter schemes are proportionally divided and clustered, and the preset number of control parameter schemes with the largest fitness are selected as the cluster center, and the remaining control parameter schemes are proportionally divided into the preset number of control parameter schemes. The larger the fitness, the more remaining control parameter schemes are divided. The clustered parameter schemes after division are integrated to obtain multiple steel cooling control parameter scheme clusters with the preset number of control parameter schemes as cluster centers. At the same time, iterative optimization is performed on the multiple steel cooling control parameter scheme clusters respectively, and the fitness sum is calculated for the optimized multiple steel cooling control parameter scheme clusters, and the parameter scheme cluster with the largest fitness sum is screened as the steel cooling control parameter scheme library, and the steel cooling control parameter scheme library includes the steel cooling control parameter scheme with the best fitness and the remaining multiple applicable steel cooling control parameter schemes in the cluster. Intelligent optimization analysis of the steel cooling control parameter scheme library is realized to ensure rapid convergence of control parameter optimization, thereby improving the accuracy of control parameter scheme optimization analysis and optimization analysis efficiency.

[0065] Step S4: performing control differentiation analysis based on the steel cooling control parameter solution library, preferably obtaining a target steel cooling control parameter solution, and performing cooling optimization control on non-modulated steel through the target steel cooling control parameter solution.

[0066] Furthermore, the target steel cooling control parameter scheme is preferably obtained in S4, and the steps of this application also include:

[0067] S41: performing non-modulation steel cooling control based on the optimal steel cooling control parameter scheme to obtain steel cooling control feedback parameters;

[0068] S42: If the steel cooling control feedback parameter does not achieve the preset steel cooling effect, a simulation test is performed on the steel cooling control parameter solution library through a steel cooling simulation test module to obtain a set of remaining steel cooling simulation test results;

[0069] S43: traverse the remaining steel cooling simulation test result sets to perform parameter optimization compensation, and reverse match to determine the target steel cooling control parameter solution.

[0070] Specifically, in order to ensure the practical applicability of the steel cooling control parameter scheme, a control differentiation analysis is performed based on the steel cooling control parameter scheme library. First, non-modulation steel cooling control monitoring is performed based on the optimal steel cooling control parameter scheme to obtain steel cooling control feedback parameters, which include cooling temperature change information, steel hardness change, temperature uniformity, etc. If the steel cooling control feedback parameters do not reach the preset steel cooling effect, the remaining parameter schemes in the steel cooling control parameter scheme library are simulated and tested by the steel cooling simulation test module, wherein the steel cooling simulation test module is a computer simulation test simulation module, which is used to perform simulation effect test on the steel cooling control process to obtain the remaining steel cooling simulation test result sets, and the remaining steel cooling simulation test result sets are the steel cooling simulation effects of the remaining parameter schemes. The remaining steel cooling simulation test result sets are traversed to perform parameter optimization compensation, and reverse matching is performed to determine the target steel cooling control parameter scheme with the best steel cooling simulation effect. And the non-modulation steel is subjected to cooling optimization control through the target steel cooling control parameter scheme, so as to realize efficient analysis and control of temperature data in the non-modulation steel cooling process, improve the analysis and control accuracy and cooling control efficiency, and thus improve the quality of non-modulation steel products.

[0071] In summary, the temperature monitoring data analysis method for non-modulated steel controlled cooling process provided by the present application has the following technical effects:

[0072] Since a temperature sensing module is installed on the non-modulated steel cooling equipment, the temperature sensing module is used to collect and obtain the steel cooling temperature state monitoring data stream, and the available steel cooling temperature state monitoring data stream is obtained by standardized processing, and then the cooling characteristics of the available steel cooling temperature state monitoring data stream are extracted to obtain a steel cooling characteristic parameter set, and a steel cooling trend prediction adaptive network is established to perform trend prediction on the steel cooling characteristic parameter set, and steel cooling trend characteristic prediction information is output. The steel cooling trend characteristic prediction information is used as a cooling constraint parameter to perform optimization analysis in the steel cooling parameter control space, and a steel cooling control parameter solution library is determined. Then, a control differentiation analysis is performed based on the steel cooling control parameter solution library, and a target steel cooling control parameter solution is preferably obtained to perform cooling optimization control on non-modulated steel. Thus, the technical effect of realizing real-time monitoring and efficient analysis and control of temperature data of non-modulated steel during the cooling process is achieved, improving the analysis and control accuracy and cooling control efficiency, and thus improving the quality of non-modulated steel products is achieved.

[0073] Embodiment 2

[0074] Based on the same inventive concept as the temperature monitoring data analysis method for controlling the cooling process of non-modulated steel in the aforementioned embodiment, the present invention also provides a temperature monitoring data analysis system for controlling the cooling process of non-modulated steel, such as Figure 3 As shown, the system comprises:

[0075] The data standardization processing module 11 is used to install a temperature sensing module on the non-modulation steel cooling equipment, collect and obtain the steel cooling temperature state monitoring data stream through the temperature sensing module, and perform standardization processing on the steel cooling temperature state monitoring data stream to obtain the available steel cooling temperature state monitoring data stream;

[0076] A cooling characteristic trend prediction module 12 is used to extract cooling characteristics from the available steel cooling temperature state monitoring data stream, obtain a steel cooling characteristic parameter set, establish a steel cooling trend prediction adaptive network to perform trend prediction on the steel cooling characteristic parameter set, and output steel cooling trend characteristic prediction information;

[0077] A parameter solution library determination module 13 is used to construct a steel cooling parameter control space, use the steel cooling trend characteristic prediction information as a cooling constraint parameter to perform optimization analysis in the steel cooling parameter control space, and determine a steel cooling control parameter solution library, wherein the steel cooling control parameter solution library includes an optimal steel cooling control parameter solution and multiple applicable steel cooling control parameter solutions;

[0078] The cooling optimization control module 14 is used to perform control differentiation analysis based on the steel cooling control parameter solution library, preferably obtain a target steel cooling control parameter solution, and perform cooling optimization control on non-modulated steel through the target steel cooling control parameter solution.

[0079] Furthermore, the system also includes:

[0080] A feature classification and marking unit is used to perform feature classification and marking on the attribute information of the non-modulated steel cooling equipment, and determine the equipment structure size, cooling medium and steel cooling area characteristics;

[0081] A parameter solution space construction unit is used to obtain a steel cooling equipment sensor layout database, perform association screening with the steel cooling equipment sensor layout database according to the equipment structure size, cooling medium and equipment cooling area, and construct a temperature sensor design parameter solution space;

[0082] A sensing factor determination unit, used to determine a set of sensing factors of a temperature sensor, wherein the set of sensing factors of the temperature sensor includes measurement accuracy, measurement timeliness, and threshold applicability;

[0083] The sensor design parameter determination unit is used to perform global optimization in the temperature sensor design parameter solution space based on the temperature sensor sensing index set, determine the sensor design parameter information, and arrange the temperature sensing module on the non-modulated steel cooling equipment according to the sensor design parameter information.

[0084] Furthermore, the system also includes:

[0085] A data space division unit, configured to spatially divide the available steel cooling temperature state monitoring data stream according to monitoring areas based on the sensor design parameter information, to obtain a plurality of steel cooling space area temperature state monitoring data streams;

[0086] A time sequence arrangement and integration unit, used to arrange and integrate the plurality of steel cooling space area temperature state monitoring data streams in time sequence to obtain a plurality of steel cooling space area temperature state time sequence data streams;

[0087] A trend curve obtaining unit is used to generate a plurality of steel cooling temperature space variation curves according to the plurality of steel cooling space region temperature state time series data streams, and perform curvature calculation on the plurality of steel cooling temperature space variation curves to obtain a plurality of steel cooling temperature space trend curves;

[0088] The cooling feature extraction unit is used to extract cooling features based on the multiple steel cooling temperature spatial trend curves respectively, and determine a steel cooling feature parameter set, wherein the steel cooling feature parameter set includes a cooling rate, a temperature gradient, and a temperature cooling time node.

[0089] Furthermore, the system also includes:

[0090] A data set classification and identification unit, used for acquiring a non-modulated steel cooling temperature data set, classifying and identifying the non-modulated steel cooling temperature data set based on the steel cooling characteristic parameter set, and obtaining a steel cooling characteristic sample data set;

[0091] A supervised training verification unit, used to perform supervised training verification on the steel cooling feature sample data set based on a recurrent neural network structure, and generate a steel cooling trend feature branch network set and a branch network prediction accuracy set;

[0092] An integration coefficient obtaining unit, used to use the average of the branch criticality factor of the steel cooling trend characteristic branch network set and the branch network prediction accuracy set as the branch network integration coefficient;

[0093] The branch network integration and fusion unit is used to integrate and fuse the steel cooling trend feature branch network set based on the branch network integration coefficient to construct the steel cooling trend prediction adaptive network.

[0094] Furthermore, the system also includes:

[0095] A parameter initialization unit is used to perform prediction verification on the steel cooling trend prediction adaptive network, and when the prediction verification result does not meet the benchmark accuracy, initialize the particle swarm parameters, the particle swarm parameters including parameter particle position and parameter particle speed;

[0096] The optimal parameter solution determination unit is used to define a fitness loss function to iteratively update the particle swarm parameters until a preset iteration condition is met, and to optimize and determine the particle with the largest fitness as the optimal parameter solution;

[0097] The model parameter configuration unit is used to configure the model parameters of the steel cooling trend prediction adaptive network based on the optimal parameter solution to obtain the steel cooling trend optimization prediction adaptive network.

[0098] Furthermore, the system also includes:

[0099] An evaluation objective function construction unit is used to obtain a steel cooling control effect index, and to construct a steel cooling effect evaluation objective function based on the steel cooling control effect index;

[0100] A spatial global optimization unit, used for performing global optimization in the steel cooling parameter control space by using the steel cooling trend characteristic prediction information as a cooling constraint parameter to obtain multiple steel cooling control parameter solutions;

[0101] A parameter scheme evaluation and calculation unit, used to evaluate and calculate the multiple steel cooling control parameter schemes through the steel cooling effect evaluation objective function to obtain multiple cooling control effect fitness levels;

[0102] A parameter scheme division and clustering unit, used for dividing and clustering the plurality of steel cooling control parameter schemes based on the plurality of cooling control effect fitnesses to obtain a plurality of steel cooling control parameter scheme clusters;

[0103] The fitness sum calculation unit is used to perform iterative optimization on the multiple steel cooling control parameter solution clusters respectively, and perform fitness sum calculation on the optimized multiple steel cooling control parameter solution clusters to screen and obtain the steel cooling control parameter solution library.

[0104] Furthermore, the system also includes:

[0105] A control feedback parameter obtaining unit, used for performing non-modulation steel cooling control based on the optimal steel cooling control parameter scheme to obtain steel cooling control feedback parameters;

[0106] A parameter solution library simulation test unit, used for, if the steel cooling control feedback parameter fails to achieve a preset steel cooling effect, performing a simulation test on the steel cooling control parameter solution library through a steel cooling simulation test module to obtain a set of remaining steel cooling simulation test results;

[0107] The cooling control parameter scheme determining unit is used to traverse the remaining steel cooling simulation test result sets to perform parameter optimization compensation, and reverse matching to determine the target steel cooling control parameter scheme.

[0108] The foregoing Figure 1 The various variations and specific examples of the temperature monitoring data analysis method for the controlled cooling process of non-modulated steel in Example 1 are also applicable to the temperature monitoring data analysis system for the controlled cooling process of non-modulated steel in this embodiment. Through the above detailed description of the temperature monitoring data analysis method for the controlled cooling process of non-modulated steel, those skilled in the art can clearly know the implementation method of the temperature monitoring data analysis system for the controlled cooling process of non-modulated steel in this embodiment. Therefore, for the sake of brevity of the specification, it will not be described in detail here.

[0109] In addition, the present application also provides an electronic device, including a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and run on the processor. The transceiver, the memory, and the processor are respectively connected via a bus. When the computer program is executed by the processor, each process of the above-mentioned method embodiment for controlling output data is implemented, and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0110] Exemplary Electronic Devices

[0111] For details, see Figure 4As shown, the present application also provides an electronic device, which includes a bus 1110 , a processor 1120 , a transceiver 1130 , a bus interface 1140 , a memory 1150 and a user interface 1160 .

[0112] In the present application, the electronic device further includes: a computer program stored in the memory 1150 and executable on the processor 1120, and when the computer program is executed by the processor 1120, each process of the above-mentioned method embodiment for controlling output data is implemented.

[0113] The transceiver 1130 is configured to receive and send data under the control of the processor 1120 .

[0114] In the present application, a bus architecture (represented by bus 1110 ) may include any number of interconnected buses and bridges. Bus 1110 connects various circuits including one or more processors represented by processor 1120 and a memory represented by memory 1150 .

[0115] Bus 1110 represents one or more of any of several types of bus structures, including a memory bus and memory controller, a peripheral bus, an accelerated graphics port, a processor or a local bus using any of a variety of bus architectures. Such architectures include, by way of example and not limitation, an Industry Standard Architecture bus, a Micro Channel Architecture bus, an expansion bus, a Video Electronics Standards Association, a Peripheral Component Interconnect bus.

[0116] The processor 1120 may be an integrated circuit chip having signal processing capabilities. In the implementation process, each step of the above method embodiment may be completed by an integrated logic circuit of hardware in the processor or instructions in the form of software. The above processors include: a general-purpose processor, a central processing unit, a network processor, a digital signal processor, an application-specific integrated circuit, a field programmable gate array, a complex programmable logic device, a programmable logic array, a microcontroller unit or other programmable logic device, a discrete gate, a transistor logic device, and a discrete hardware component. The various methods, steps, and logic block diagrams disclosed in this application may be implemented or executed. For example, the processor may be a single-core processor or a multi-core processor, and the processor may be integrated into a single chip or located on multiple different chips.

[0117] Processor 1120 may be a microprocessor or any conventional processor. The method steps disclosed in the present application may be directly executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software module may be located in a readable storage medium known in the art, such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, an erasable programmable read-only memory, a register, etc. The readable storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0118] The bus 1110 may also connect various other circuits such as peripheral devices, voltage regulators or power management circuits, and the bus interface 1140 provides an interface between the bus 1110 and the transceiver 1130, which are well known in the art. Therefore, this application will not further describe them.

[0119] The transceiver 1130 may be one element or multiple elements, such as multiple receivers and transmitters, and provides a unit for communicating with various other devices on a transmission medium. For example, the transceiver 1130 receives external data from other devices, and the transceiver 1130 is used to send data processed by the processor 1120 to other devices. Depending on the nature of the computer device, a user interface 1160 may also be provided, such as a touch screen, a physical keyboard, a display, a mouse, a speaker, a microphone, a trackball, a joystick, or a stylus.

[0120] It should be understood that in the present application, the memory 1150 may further include a memory remotely arranged relative to the processor 1120, and these remotely arranged memories may be connected to the server through a network. One or more parts of the above network may be a self-organizing network, an intranet, an extranet, a virtual private network, a local area network, a wireless local area network, a wide area network, a wireless wide area network, a metropolitan area network, the Internet, a public switched telephone network, a plain old telephone service network, a cellular telephone network, a wireless network, a wireless fidelity network, and a combination of two or more of the above networks. For example, the cellular telephone network and the wireless network may be a global mobile communication device, a code division multiple access device, a global microwave interconnection access device, a general packet radio service device, a wideband code division multiple access device, a long term evolution device, a LTE frequency division duplex device, a LTE time division duplex device, an advanced long term evolution device, a universal mobile communication device, an enhanced mobile broadband device, a massive machine type communication device, an ultra-reliable low latency communication device, and the like.

[0121] It should be understood that the memory 1150 in the present application may be a volatile memory or a non-volatile memory, or may include both a volatile memory and a non-volatile memory. Among them, the non-volatile memory includes: a read-only memory, a programmable read-only memory, an erasable programmable read-only memory, an electrically erasable programmable read-only memory, or a flash memory.

[0122] Volatile memory includes random access memory, which is used as an external cache. By way of example but not limitation, many forms of RAM are available, such as static random access memory, dynamic random access memory, synchronous dynamic random access memory, double data rate synchronous dynamic random access memory, enhanced synchronous dynamic random access memory, synchronously linked dynamic random access memory, and direct memory bus random access memory. The memory 1150 of the electronic device described in the present application includes, but is not limited to, the above and any other suitable types of memory.

[0123] In the present application, the memory 1150 stores the following elements of the operating system 1151 and the application program 1152: executable modules, data structures, or subsets thereof, or extended sets thereof.

[0124] Specifically, the operating system 1151 includes various device programs, such as a framework layer, a core library layer, a driver layer, etc., which are used to implement various basic services and process hardware-based tasks. The application 1152 includes various application programs, such as a media player and a browser, which are used to implement various application services. The program implementing the method of the present application may be included in the application 1152. The application 1152 includes applets, objects, components, logic, data structures, and other computer device executable instructions that perform specific tasks or implement specific abstract data types.

[0125] In addition, the present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the various processes of the above-mentioned method embodiment for controlling output data are implemented and the same technical effect can be achieved. To avoid repetition, it will not be repeated here.

[0126] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A temperature monitoring data analysis method for controlling cooling process of non-modulated steel, characterized in that: The method comprises: S1: installing a temperature sensing module on the non-modulated steel cooling equipment, collecting and acquiring a steel cooling temperature state monitoring data stream through the temperature sensing module, and performing standardization processing on the steel cooling temperature state monitoring data stream to obtain an available steel cooling temperature state monitoring data stream; S2: extract cooling features from the available steel cooling temperature state monitoring data stream to obtain a steel cooling feature parameter set, establish a steel cooling trend prediction adaptive network to perform trend prediction on the steel cooling feature parameter set, and output steel cooling trend feature prediction information; S3: constructing a steel cooling parameter control space, using the steel cooling trend characteristic prediction information as a cooling constraint parameter to perform optimization analysis in the steel cooling parameter control space, and determining a steel cooling control parameter solution library, wherein the steel cooling control parameter solution library includes an optimal steel cooling control parameter solution and multiple applicable steel cooling control parameter solutions; S4: performing control differentiation analysis based on the steel cooling control parameter solution library, preferably obtaining a target steel cooling control parameter solution, and performing cooling optimization control on non-modulated steel through the target steel cooling control parameter solution.

2. The method according to claim 1, characterized in that In S1, a temperature sensing module is installed on the non-modulated steel cooling device, including: S11: feature classification and marking of attribute information of non-modulated steel cooling equipment to determine equipment structure dimensions, cooling medium, and steel cooling area characteristics; S12: obtaining a steel cooling equipment sensor layout database, performing association screening with the steel cooling equipment sensor layout database according to the equipment structure size, cooling medium, and equipment cooling area, and constructing a temperature sensor design parameter solution space; S13: Determine a set of temperature sensor sensing factors, where the set of temperature sensor sensing factors includes measurement accuracy, measurement timeliness, and threshold applicability; S14: performing global optimization in the temperature sensor design parameter solution space based on the temperature sensor sensing index set, determining sensor design parameter information, and arranging the temperature sensing module on the non-modulated steel cooling equipment according to the sensor design parameter information.

3. The method according to claim 2, characterized in that The steel cooling characteristic parameter set obtained in S2 includes: S21: spatially dividing the available steel cooling temperature state monitoring data stream according to monitoring areas based on the sensor design parameter information to obtain multiple steel cooling space area temperature state monitoring data streams; S22: arranging and integrating the plurality of steel cooling space area temperature state monitoring data streams in chronological order to obtain a plurality of steel cooling space area temperature state chronological data streams; S23: generating a plurality of steel cooling temperature space variation curves according to the plurality of steel cooling space region temperature state time series data streams, performing curvature calculation on the plurality of steel cooling temperature space variation curves, and obtaining a plurality of steel cooling temperature space trend curves; S24: performing cooling feature extraction based on the plurality of steel cooling temperature spatial trend curves respectively, and determining a steel cooling feature parameter set, wherein the steel cooling feature parameter set includes a cooling rate, a temperature gradient, and a temperature cooling time node.

4. The method according to claim 1, characterized in that The S2 establishes a steel cooling trend prediction adaptive network, including: S25: acquiring a non-modulated steel cooling temperature data set, classifying and identifying the non-modulated steel cooling temperature data set based on the steel cooling characteristic parameter set, and obtaining a steel cooling characteristic sample data set; S26: Performing supervised training verification on the steel cooling feature sample data set based on a recurrent neural network structure to generate a steel cooling trend feature branch network set and a branch network prediction accuracy set; S27: taking the branch criticality factor of the steel cooling trend characteristic branch network set and the average of the branch network prediction accuracy set as the branch network integration coefficient; S28: Integrate and fuse the steel cooling trend feature branch network set based on the branch network integration coefficient to construct the steel cooling trend prediction adaptive network.

5. The method according to claim 4, characterized in that The method comprises: Performing prediction verification on the steel cooling trend prediction adaptive network, and when the prediction verification result does not meet the benchmark accuracy, initializing the particle swarm parameters, the particle swarm parameters including parameter particle position and parameter particle velocity; Define a fitness loss function to iteratively update the particle swarm parameters until a preset iteration condition is met, and optimize and determine the particle with the largest fitness as the optimal parameter solution; The model parameters of the steel cooling trend prediction adaptive network are configured based on the optimal parameter solution to obtain the steel cooling trend optimization prediction adaptive network.

6. The method according to claim 1, characterized in that The steel cooling control parameter solution library is determined in S3, including: S31: obtaining a steel cooling control effect index, and constructing a steel cooling effect evaluation objective function based on the steel cooling control effect index; S32: using the steel cooling trend characteristic prediction information as a cooling constraint parameter to perform global optimization in the steel cooling parameter control space to obtain multiple steel cooling control parameter solutions; S33: evaluating and calculating the plurality of steel cooling control parameter schemes by using the steel cooling effect evaluation objective function to obtain a plurality of cooling control effect fitnesses; S34: dividing and clustering the multiple steel cooling control parameter schemes based on the multiple cooling control effect fitnesses to obtain multiple steel cooling control parameter scheme clusters; S35: performing iterative optimization on the plurality of steel cooling control parameter solution clusters respectively, and calculating the fitness sum of the plurality of optimized steel cooling control parameter solution clusters, thereby screening and obtaining the steel cooling control parameter solution library.

7. The method according to claim 1, characterized in that The target steel cooling control parameter scheme is preferably obtained in S4, including: S41: performing non-modulation steel cooling control based on the optimal steel cooling control parameter scheme to obtain steel cooling control feedback parameters; S42: If the steel cooling control feedback parameter does not achieve the preset steel cooling effect, a simulation test is performed on the steel cooling control parameter solution library through a steel cooling simulation test module to obtain a set of remaining steel cooling simulation test results; S43: traverse the remaining steel cooling simulation test result sets to perform parameter optimization compensation, and reverse match to determine the target steel cooling control parameter solution.

8. A temperature monitoring data analysis system for controlling cooling process of non-modulated steel, characterized in that: A method for analyzing temperature monitoring data for controlling cooling of non-modulated steel according to any one of claims 1 to 7, the system comprising: A data standardization processing module is used to install a temperature sensing module on the non-modulation steel cooling equipment, collect and obtain a steel cooling temperature state monitoring data stream through the temperature sensing module, and perform standardization processing on the steel cooling temperature state monitoring data stream to obtain a usable steel cooling temperature state monitoring data stream; A cooling characteristic trend prediction module is used to extract cooling characteristics from the available steel cooling temperature state monitoring data stream, obtain a steel cooling characteristic parameter set, establish a steel cooling trend prediction adaptive network to perform trend prediction on the steel cooling characteristic parameter set, and output steel cooling trend characteristic prediction information; A parameter solution library determination module is used to construct a steel cooling parameter control space, use the steel cooling trend characteristic prediction information as a cooling constraint parameter to perform optimization analysis in the steel cooling parameter control space, and determine a steel cooling control parameter solution library, wherein the steel cooling control parameter solution library includes an optimal steel cooling control parameter solution and multiple applicable steel cooling control parameter solutions; The cooling optimization control module is used to perform control differentiation analysis based on the steel cooling control parameter solution library, preferably obtain a target steel cooling control parameter solution, and perform cooling optimization control on non-modulated steel through the target steel cooling control parameter solution.

9. An electronic device, comprising a bus, a transceiver, a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the transceiver, the memory, and the processor are connected via the bus, wherein: When the computer program is executed by the processor, the steps of a temperature monitoring data analysis method for a controlled cooling process of non-modulated steel as claimed in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a temperature monitoring data analysis method for a non-modulated steel controlled cooling process as described in any one of claims 1 to 7 are implemented.

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