Intelligent model data processing method applied to metal smelting

Abstract

The invention discloses an intelligent model data processing method applied to metal smelting, and relates to the technical field of metal smelting, and the method comprises the following specific steps: S100, collecting metal smelting data in real time, S200, establishing different sub-models, S300, outputting a prediction result based on each sub-model, and carrying out uncertainty evaluation. The invention designs a multi-target model output fusion mechanism capable of simultaneously considering the product quality, the energy consumption and the production efficiency in the metal smelting process, and through a weighted average method, the optimal decision output is obtained through the intelligent fusion of the output results, and the comprehensive optimal decision output is obtained, and S500, the actual data in the production process is continuously monitored, and the operation parameters of equipment are adjusted. The invention designs a multi-target model output fusion mechanism capable of simultaneously considering the product quality, the energy consumption and the production efficiency in the metal smelting process. And the model prediction results corresponding to different targets are intelligently fused to obtain a comprehensive optimal decision output, so that the metal smelting process can realize balance and optimization among a plurality of key indexes, and the demand of an enterprise for maximizing comprehensive benefits is met.

Description

Technical Field

[0001] The present invention relates to the technical field of metal smelting, and in particular to a method for processing intelligent model data applied to metal smelting. Background Art

[0002] In today's industrial production field, metal smelting, as a basic and key industry, plays an important supporting role in economic development. With the increasingly fierce market competition and the continuous improvement of the requirements for sustainable development, metal smelting companies are facing many challenges and opportunities. On the one hand, customers have increasingly stringent quality requirements for metal products, not only requiring them to have good physical properties and chemical composition stability, but also expecting products to meet higher standards in terms of microstructure and surface quality to meet the precision needs of the high-end equipment manufacturing industry. On the other hand, the continuous rise in energy costs and the increasingly stringent environmental protection regulations require companies to attach great importance to the control of energy consumption and the improvement of production efficiency in order to reduce production costs and reduce the impact on the environment.

[0003] However, the existing metal smelting data processing technology has obvious limitations in dealing with these complex demands. In terms of data fusion and model construction, traditional methods mostly only optimize for a single goal. For example, when only focusing on improving product quality, they often ignore the sharp increase in energy consumption and the decline in production efficiency. They fail to fully explore the inherent correlation and mutual influence mechanism between various goals, and lack effective multi-objective optimization strategies. At the same time, the existing technology also has shortcomings in dealing with data uncertainty issues. Uncertain factors are not reasonably incorporated into the model decision-making process, making it difficult for the model to make robust and flexible decisions when facing complex and changeable smelting conditions, and cannot meet the urgent needs of modern metal smelting enterprises for refined and intelligent production management.

[0004] In summary, current metal smelting urgently needs an innovative data processing method that can comprehensively and effectively integrate multi-source data, establish accurate intelligent models, and use multi-objective optimization mechanisms while fully considering data uncertainty to enhance the adaptability of the model and the reliability of decision-making. Summary of the invention

[0005] The purpose of the present invention is to make up for the shortcomings of the prior art and to provide a method for processing intelligent model data applied to metal smelting, which can bring comprehensive benefit improvement to metal smelting enterprises through a multi-objective optimization intelligent model output fusion mechanism. By accurately integrating model prediction results in multiple aspects of product quality, energy consumption and production efficiency, enterprises can significantly reduce energy consumption while ensuring high-quality product output.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a method for processing intelligent model data applied to metal smelting, wherein the specific steps of the method are:

[0007] S100, collect data related to product quality, energy consumption, and production efficiency in all links of metal smelting in real time, and clean and remove noise from the collected data;

[0008] S200, establishing different sub-models for product quality, energy consumption and production efficiency targets in the metal smelting process, wherein the sub-models include a product quality sub-model, an energy consumption sub-model and a production efficiency sub-model, and independently training each sub-model using historical data and actual production data, so that each sub-model has the ability to predict and analyze the corresponding target;

[0009] S300, each sub-model performs prediction calculation based on the real-time collected data, outputs its own prediction results on product quality, energy consumption and production efficiency, and performs uncertainty assessment on the output results of each sub-model, where:

[0010] The uncertainty evaluation index of the product quality is set as the variance of the prediction result ;

[0011] The uncertainty evaluation index of energy consumption is set as the variance of the residual of the prediction result ;

[0012] The uncertainty evaluation index of the production efficiency is set as the mean of the prediction result accuracy ;

[0013] S400, intelligently integrating the output results of each sub-model by weighted average to obtain the comprehensive optimal decision output;

[0014] The weighted average fusion method includes assigning weights to the output results of each sub-model according to the importance of each target in actual production, and multiplying the output results of each sub-model by the corresponding weights and adding them to obtain a weighted average fusion result, which is used as the final decision output to guide the adjustment of equipment operating parameters in the metal smelting process;

[0015] S500. Apply the fused decision output to the actual production process of metal smelting, continuously monitor the actual data in the production process, compare and analyze the actual production results with the predicted results of the fusion model, and make corresponding adjustments and optimizations to the equipment operating parameters and process control parameters to achieve continuous optimization and closed-loop control of the metal smelting process.

[0016] Furthermore, the S100 product quality-related data includes the hardness value of the metal product. , Strength value of metal products , Purity value of finished metal products , the number of internal defects in finished metal products , Types of internal defects in finished metal products , Surface roughness value of metal products ;

[0017] The energy consumption related data include the power consumption values ​​of each stage in the smelting process , fuel consumption flow value at each stage of the smelting process , the thermal energy utilization rate in the smelting process ;

[0018] The production efficiency-related data includes the metal output value per unit time. , Equipment operation time ratio , the residence time value of each link in the production process , Equipment failure downtime , Equipment failure downtime value .

[0019] Furthermore, the S200 establishes a sub-model based on neural network prediction for the product quality target, the model includes an input layer, a hidden layer and an output layer, wherein:

[0020] The input layer is composed of neurons with parameter data related to product quality, and all collected parameter data related to product quality are used as input values, that is, the input layer neurons include , , , , and ;

[0021] The hidden layer has neurons, extract features from the data in the input layer, and the weight matrix is ​​used between the input layer and the hidden layer. Connect, its dimension is , is the number of neurons in the input layer, each element Represents the input layer neurons and hidden layers The connection weights between neurons, and the hidden layer also has a bias vector , whose dimensions are , each element Represents the hidden layer The bias value of the neurons in the hidden layer is ,Right now , whose output The calculation formula is: ,in , The input layer The value of the neuron, that is, the parameter data value of the corresponding product quality , , , , and ;

[0022] The output layer is responsible for outputting the final product quality prediction value, which is connected with the hidden layer through the weight matrix Connected, dimension is , each element Represents the hidden layer The connection weights between neurons and the output layer neurons. The output layer also has a bias vector , the activation function of the output layer neurons is ,Right now , then the output of the output layer neuron is used as the product quality prediction value The calculation formula is: , adjust the weight matrix by inputting product quality parameter data , and the bias vector , , and continuously iterates and optimizes the model parameters so that the model can accurately predict the quality of metal products based on the input quality-related parameters, thereby providing a reliable decision-making basis for quality control and optimization in the metal smelting process.

[0023] Furthermore, the S200 establishes a sub-model based on a linear regression algorithm for the energy consumption target, and establishes a quantitative relationship model between energy consumption and these parameters by linearly combining the parameters related to energy consumption, thereby achieving accurate prediction of energy consumption and providing effective support for energy management and cost control. , , As the independent variable, the predicted energy consumption As the dependent variable, the constructed linear regression equation is: ,in, is the regression coefficient, which reflects the influence of each independent variable on energy consumption; is the intercept, representing the basic value of energy consumption when the independent variable is zero. The linear regression equation is fitted by the least squares method, the purpose of which is to minimize the difference between the actual energy consumption value and the predicted value. The sum of squared errors is used to find the best regression coefficient and intercept value so that the predicted value of the model is close to the actual value. The calculation formula of the sum of squared errors is: ,in, is the sample size, , , , Respectively The actual energy consumption value of each sample and the corresponding independent variable value are used to iteratively update the regression coefficient by minimizing the sum of squared errors using historical data and energy consumption-related data samples collected during the actual production process. , , and the intercept The estimated value of enables the model to accurately predict energy consumption based on the input energy consumption related data.

[0024] Furthermore, the S200 establishes a sub-model based on decision tree evaluation for the production efficiency target. The model is constructed based on a decision tree algorithm. The decision tree is a method of constructing a tree structure model by recursively dividing data features. It can effectively handle complex classification and regression problems. In the present invention, it is used to evaluate and predict production efficiency based on multiple production efficiency-related parameters in the metal smelting process, and provide an intuitive, easy-to-understand and decision-making guiding model output for the optimization and management of the production process. The sub-model is based on , , , and As the splitting feature of the decision tree, the information gain of each feature is calculated, and the information entropy is used as an indicator to measure the uncertainty of the data set. The larger the information gain, the more the uncertainty of the data set can be reduced by using this feature for division, and the more conducive it is to classification or prediction tasks. The data set is set to ,feature The value set of , , according to the characteristics The dataset Divide into Subset , , then the feature Information gain for: ,in, For the dataset The information entropy of ,in, is the number of production efficiency categories, For Category In the dataset The probability in the data set is calculated by calculating the information gain of each feature, selecting the feature with the largest information gain as the root node for splitting, and dividing the data set into different subsets. For each subset, the above process is repeated recursively, and the feature with the largest information gain is continued to be split until the stopping condition is met. The stopping condition is: the information gain of all features is less than the threshold information gain , thus constructing a complete decision tree sub-model;

[0025] When the parameter values ​​of production efficiency-related data are input, starting from the root node of the decision tree, judgments are made step by step along the branches of the decision tree according to the characteristic judgment conditions of each node. Each internal node of the decision tree represents a test condition of a feature, the branch represents different values ​​of the feature, and the leaf node represents the final predicted value, which is the predicted result of production efficiency. .

[0026] Furthermore, the S300 predicts the quality of metal smelting products based on the neural network sub-model. During the model training phase, some output values ​​of the hidden layer neurons are randomly set to 0, and the probability is set to In the prediction phase, forward propagation, we get Different product quality prediction results , calculate the prediction results The mean and variance , the variance As an uncertainty measure of product quality prediction results, a large variance indicates high uncertainty.

[0027] Furthermore, the S300 estimates the uncertainty of the prediction sub-model for metal smelting energy consumption based on linear regression by using the variance of the residual, that is, by calculating the residual of each sample ,in , is the sample size, and the variance of the residual is calculated , As an uncertainty measure of energy consumption prediction results, the variance is large and the uncertainty is high.

[0028] Furthermore, the S200 establishes a sub-model based on decision tree evaluation for the production efficiency target. When constructing the decision tree, for the data set For each sample in the dataset, one-third of the data that does not participate in the construction of the leaf node where the sample is located is used as external data. The sub-model is verified using the external data. For each sample, its prediction accuracy on the external data is calculated. , the accuracy is low, indicating that the prediction result of the sample is highly uncertain. The average prediction accuracy of all samples is As the overall uncertainty measure of the production efficiency prediction results, s is the number of samples in the data set S, As a quantitative indicator of the degree of uncertainty, the larger the value, the higher the uncertainty.

[0029] Furthermore, the S400 determines the initial weight of product quality based on the importance of each target in the metal smelting production process: , the initial weight of energy consumption is , the initial weight of production efficiency is , and satisfies , comprehensively considering the impact of the uncertainty of the output results of each sub-model on the decision, the initial weights are dynamically adjusted, that is, the output result for product quality is , the uncertainty measure is the variance , the output result of energy consumption is , the uncertainty measure is the variance The output result of production efficiency is , the uncertainty measure is , using the weight adjustment formula: ,in, Represents the weights of product quality, energy consumption and production efficiency set at the initial stage, is the uncertainty measure of the output results of each sub-model, ;

[0030] By adjusting the weights, the output results of the sub-models with low uncertainty have a relatively large influence on the final fusion decision, making the decision stable. The output results of each sub-model are multiplied by their corresponding adjusted weights and then added to obtain the weighted average fusion result. ,Right now ,The fusion result is used as the final decision output to guide the adjustment of equipment operating parameters in the metal smelting process, to achieve precise control of the metal smelting process and effective balance and optimization among multiple objectives.

[0031] Compared with the prior art, the processing method of intelligent model data applied to metal smelting has the following beneficial effects:

[0032] 1. The present invention designs a multi-objective model output fusion mechanism that can simultaneously consider product quality, energy consumption and production efficiency in the metal smelting process. Through the weighted average method, the model prediction results corresponding to different objectives are intelligently integrated to obtain the comprehensive optimal decision output. This innovative fusion mechanism overcomes the limitations of traditional single-objective optimization models, enables the metal smelting process to achieve balance and optimization among multiple key indicators, meets the company's needs for maximizing comprehensive benefits, and improves the sustainability and competitiveness of the production process.

[0033] Second, the present invention has strong adaptability and decision reliability. Considering the complexity and uncertainty of the metal smelting process, the present invention evaluates the uncertainty of the output results of each sub-model and incorporates it into the fusion decision-making process, so that the final decision can be more robust to cope with various emergencies and changes in working conditions.

[0034] Other advantages, objectives and features of the present invention will be set forth in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0036] Figure 1 An operation diagram of a method for processing intelligent model data applied to metal smelting;

[0037] Figure 2 This is a flowchart of the steps of user classification in Example 2. DETAILED DESCRIPTION

[0038] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation mode, structure, characteristics and effects of the present invention are described in detail below in combination with the accompanying drawings and preferred embodiments.

[0039] Embodiment 1

[0040] like Figure 1As shown, this embodiment aims to demonstrate the specific application process of a method for processing intelligent model data applied to metal smelting in a steel smelting plant. By collecting, processing and analyzing data related to product quality, energy consumption and production efficiency in the smelting process of the plant, and utilizing the established multi-objective intelligent model and fusion mechanism, precise control and optimization of the smelting process can be achieved, thereby effectively improving the overall benefits.

[0041] On the production line, we collect comprehensive data closely related to product quality, energy consumption, and production efficiency. Data related to product quality includes the hardness value of metal products. , Strength value of metal products , Purity value of finished metal products , the number of internal defects in finished metal products , Types of internal defects in finished metal products , Surface roughness value of metal products ; The energy consumption related data include the power consumption value of each stage in the smelting process , fuel consumption flow value at each stage of the smelting process , the thermal energy utilization rate in the smelting process ; The data related to the production efficiency include the metal output value per unit time , Equipment operation time ratio , the residence time value of each link in the production process , Equipment failure downtime , Equipment failure downtime value The collected raw data often contains noise and outliers. These interference factors will mislead the training and decision-making of subsequent models. Therefore, the data is cleaned and denoised to ensure the accuracy and reliability of the data and provide a high-quality data source for subsequent model training.

[0042] Different sub-models are established for the product quality, energy consumption and production efficiency goals in the metal smelting process, and each sub-model is independently trained using historical data and actual production data, so that each sub-model has the ability to predict and analyze the corresponding goals. A sub-model based on neural network prediction is established for the product quality goal. The input layer of the model is composed of neurons with parameter data related to product quality. , , , , and As input value, the hidden layer has The function of neurons is to extract complex nonlinear features from the input layer data. The input layer and the hidden layer are connected by a weight matrix. To connect, each element of the weight matrix Represents the input layer neurons and hidden layers The connection weights between neurons determine the scaling and weighting of the input data when it is passed to the hidden layer. At the same time, the hidden layer also has a bias vector , providing an initial offset for the hidden layer neurons to increase the expressive power of the model. The activation function of the hidden layer neurons is , accelerate the convergence of model training, and introduce nonlinear factors so that the model can learn the complex relationship between input data, and its output The calculation formula is: ,in , The input layer The value of each neuron is the parameter data value of the corresponding product quality. The output layer is responsible for outputting the final product quality prediction value and is connected with the hidden layer through the weight matrix Connected, each element Represents the hidden layer The connection weights between neurons and the output layer neurons. The output layer also has a bias vector , the activation function of the output layer neurons is , then the output of the output layer neuron is used as the product quality prediction value The calculation formula is: , through a large amount of historical data and actual production data, the weight matrix is ​​constantly adjusted , and the bias vector , , so that the model can accurately predict the quality of steel products based on the input product quality related parameters. A sub-model based on linear regression algorithm is established for energy consumption targets. As the independent variable, the predicted energy consumption As the dependent variable, the constructed linear regression equation is: ,in, are regression coefficients, which reflect the influence of each independent variable on energy consumption, for example, It indicates the change in the predicted energy consumption value when the power consumption value changes by one unit; is the intercept, which represents the basic value of energy consumption when the independent variable is zero. It includes the basic energy consumption of the equipment, the energy consumption caused by factors such as environmental heat dissipation, etc. The linear regression equation is fitted by the least squares method, and its purpose is to minimize the difference between the actual energy consumption value and the predicted value. The sum of squared errors between ,in, is the sample size, , , , Respectively The actual energy consumption value of each sample and the corresponding independent variable value are used. In the actual training process, the historical data and energy consumption related data samples collected in the actual production process are used to iteratively update the regression coefficient by solving the problem of minimizing the sum of squared errors. and the intercept The estimated value of enables the model to accurately predict energy consumption based on the input energy consumption related data; a sub-model based on decision tree evaluation is established for the production efficiency target. , , , and As the splitting feature of the decision tree, we first calculate the information gain of each feature, and use information entropy as an indicator to measure the uncertainty of the data set. Suppose the data set is ,feature ( The value set of , according to the characteristics The dataset Divide into Subset ( ), then the feature Information gain for: ,in, For the dataset The information entropy of is the number of production efficiency categories, For Category In the dataset By calculating the information gain of each feature, the feature with the largest information gain is selected as the root node for splitting, and the data set is divided into different subsets. For example, if the metal production value per unit time is found The information gain is the largest, so Split the root node and divide the data set into different branches according to its different values. For each subset, recursively repeat the above process and continue to select the feature with the largest information gain for splitting until the stopping condition is met, that is, the information gain of all features is less than the threshold information gain. , thus constructing a complete decision tree sub-model. When the parameter values ​​of production efficiency-related data are input, starting from the root node of the decision tree, according to the characteristic judgment conditions of each node, judgment is made step by step along the branches of the decision tree. Each internal node of the decision tree represents a test condition of a feature, the branch represents the different values ​​of the feature, and the leaf node represents the final predicted value, which is the predicted result of production efficiency. .

[0043] Each sub-model performs prediction calculations based on the real-time collected data, outputs its own prediction results on product quality, energy consumption and production efficiency, and performs uncertainty assessment on the output results of each sub-model. For the prediction sub-model of metal smelting product quality based on neural network, during the model training stage, some output values ​​of hidden layer neurons are randomly set to 0, and the probability is set to This is to simulate the uncertainty of the model under different training conditions and increase the generalization ability of the model. In the prediction stage, forward propagation, we get Different product quality prediction results , calculate the prediction results The mean and variance ,variance As a measure of uncertainty in product quality prediction results, the larger the variance, the greater the degree of dispersion of the prediction results in different forward propagation processes, that is, the higher the prediction uncertainty of the model for the input data. For example, if the variance is small, it means that the model is relatively stable in multiple predictions and the prediction reliability of the product quality is high; on the contrary, if the variance is large, further data analysis or model improvement is required; for the prediction sub-model of metal smelting energy consumption based on linear regression, the variance of the residual is used to estimate the uncertainty, and the residual of each sample is calculated. ,in , is the number of samples, the residual reflects the deviation between the model prediction value and the actual value, and the variance of the residual is calculated ,variance As a measure of uncertainty in the energy consumption prediction results, the larger the variance, the greater the deviation fluctuation between the model's prediction of energy consumption and the actual value, and the higher the uncertainty; for the evaluation sub-model of metal smelting production efficiency based on the decision tree, when constructing the decision tree, for each sample, one-third of the data that does not participate in the construction of the leaf node where the sample is located is used as external data, and the sub-model is verified using the external data. For each sample, its prediction accuracy on the external data is calculated. , the accuracy is low, indicating that the prediction result of the sample is highly uncertain. The average prediction accuracy of all samples is As a measure of the overall uncertainty of the production efficiency prediction results, As a quantitative indicator of the degree of uncertainty, the larger the value, the higher the uncertainty.

[0044] Based on the importance of each target in the metal smelting production process, combined with the actual production needs and strategic priorities of the steel plant, the initial weight of product quality is determined as , the initial weight of energy consumption is , the initial weight of production efficiency is , and satisfies , comprehensively considering the impact of the uncertainty of the output results of each sub-model on the decision, dynamically adjusting the initial weights, the output result for product quality is , the uncertainty measure is the variance , the output result of energy consumption is , the uncertainty measure is the variance The output result of production efficiency is , the uncertainty measure is , using the weight adjustment formula , , In these formulas, the denominator It plays a normalization role to ensure that the sum of the adjusted weights is still 1. For the numerator, taking product quality as an example, Indicates that the weights are adjusted based on the uncertainty measure based on the initial weights. If the variance of the prediction results of the product quality sub-model is is small, indicating that its uncertainty is low, then Larger, which will increase the weight of product quality when adjusting the weight Relatively increase, that is, let the output results of the sub-models with low uncertainty have a relatively large influence in the final fusion decision, so that the decision tends to be stable, and the output results of each sub-model are multiplied by their corresponding adjusted weights and then added to obtain the weighted average fusion result ,Right now ,The fusion result is used as the final decision output to guide the adjustment of equipment operating parameters in the metal smelting process, such as adjusting the furnace temperature, feeding speed, and rolling pressure, so as to achieve optimized control of the steel smelting process.

[0045] Finally, the fused decision output Applied to the actual production process of steel smelting, the equipment operating parameters and process control parameters are adjusted and optimized accordingly according to the decision results. At the same time, the actual data in the production process is continuously monitored, and the actual production results are compared and analyzed with the predicted results of the fusion model. Through continuous feedback and adjustment, the prediction accuracy of the model and the effectiveness of the decision are improved, and the continuous optimization and closed-loop control of the steel smelting process are achieved, ensuring that the production process is always in an efficient, stable and high-quality state.

[0046] In summary, this embodiment demonstrates in detail the entire application process of a method for processing intelligent model data applied to metal smelting in a steel smelting plant. By comprehensively acquiring key data and preprocessing it in the data collection stage, the foundation for model training is laid; in the model construction stage, suitable sub-models are established for different objectives and trained and optimized; in the model output and fusion stage, the prediction results and uncertainty assessment are accurately calculated, and multi-objective fusion decision-making is achieved through scientific weight adjustment; finally, in the decision execution and feedback adjustment stage, the decision is applied to production and the model is continuously improved. This method can effectively integrate multiple factors such as product quality, energy consumption and production efficiency, overcome the limitations of traditional methods, make robust and flexible decisions under complex and changeable steel smelting conditions, and significantly improve the comprehensive benefits and sustainability of the production process.

[0047] Embodiment 2

[0048] like Figure 2 As shown, this embodiment mainly demonstrates the application process of the intelligent model data processing method applied to metal smelting in production, applies decision-making to production to achieve continuous optimization, effectively improves the comprehensive benefits and competitiveness of smelting enterprises, and fully verifies the feasibility and practicality of this method in complex industrial scenarios.

[0049] First, enter the data collection stage (S100), using sensors distributed in various links of metal smelting to collect various data related to multiple goals such as product quality, energy consumption, and production efficiency. The collected data is pre-processed by cleaning and denoising to remove abnormal and invalid data, ensure the accuracy and reliability of the data, and provide a high-quality data source for subsequent model training and analysis.

[0050] Then, the multi-objective intelligent model establishment stage (S200) is entered. Corresponding sub-models are established for the goals of product quality, energy consumption and production efficiency in the metal smelting process. For example, a product quality prediction model is established using a neural network, energy consumption is analyzed using a linear regression model, and a production efficiency evaluation model is constructed using a decision tree algorithm. Each sub-model is independently trained according to its corresponding goal, and the complex relationship and rules between each goal and related input parameters are learned through a large amount of historical data and actual production data, so as to have the ability to predict and analyze their respective goals.

[0051] Subsequently, the model output and fusion preparation stage (S300) is entered, and each sub-model performs prediction calculations based on the real-time collected data to obtain respective prediction results on targets such as product quality, energy consumption and production efficiency. These prediction results are output in a standardized data format for subsequent fusion processing. At the same time, the output results of each sub-model are evaluated for uncertainty. For example, by calculating the variance and probability distribution of the prediction results, the reliability and uncertainty of each sub-model output are quantified, providing an important reference for subsequent fusion decisions.

[0052] Next, the multi-objective fusion decision-making stage (S400) is entered. According to the importance of each objective in actual production, corresponding weights are assigned to the output results of sub-models such as product quality, energy consumption and production efficiency. The output results of each sub-model are multiplied by their corresponding weights and then added together to obtain the weighted average fusion result as the final decision output. This method can adjust the weight of each objective according to the actual needs of the enterprise and achieve emphasis and balance on different objectives.

[0053] Finally, the decision execution and feedback adjustment stage (S500) is entered, and the fused decision output is applied to the actual production process of metal smelting, and the equipment operating parameters, process control parameters, etc. are adjusted and optimized accordingly. At the same time, the actual data in the production process is continuously monitored, and the actual production results are compared and analyzed with the prediction results of the fusion model to evaluate the effect of decision execution. If it is found that there is a large deviation between the actual production results and the expectations, each sub-model is retrained and optimized according to the deviation information, and the model parameters, fusion weights or fuzzy rules are adjusted to improve the prediction accuracy of the model and the effectiveness of the decision, so as to achieve continuous optimization and closed-loop control of the metal smelting process.

[0054] To sum up, this embodiment demonstrates in detail the complete implementation process of the metal smelting intelligent model data processing method, realizes the effective optimization and comprehensive balance of product quality, energy consumption and production efficiency in the metal smelting process, and enhances the economic benefits and market competitiveness of the enterprise.

[0055] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technical personnel in this field can make some changes or modify the technical contents disclosed above into equivalent embodiments without departing from the scope of the technical solution of the present invention. However, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention are still within the scope of the technical solution of the present invention.

Claims

1. A method for processing intelligent model data applied to metal smelting, characterized in that: The specific steps of this method are: S100, collect data related to product quality, energy consumption, and production efficiency in all links of metal smelting in real time, and clean and remove noise from the collected data; S200, establishing different sub-models for product quality, energy consumption and production efficiency targets in the metal smelting process, wherein the sub-models include a product quality sub-model, an energy consumption sub-model and a production efficiency sub-model, and independently training each sub-model using historical data and actual production data, so that each sub-model has the ability to predict and analyze the corresponding target; S300, each sub-model performs prediction calculation based on the real-time collected data, outputs its own prediction results on product quality, energy consumption and production efficiency, and performs uncertainty assessment on the output results of each sub-model, where: The uncertainty evaluation index of the product quality is set as the variance of the prediction result ; The uncertainty evaluation index of energy consumption is set as the variance of the residual of the prediction result ; The uncertainty evaluation index of the production efficiency is set as the mean of the prediction result accuracy ; S400, intelligently integrating the output results of each sub-model by weighted average to obtain the comprehensive optimal decision output; The weighted average fusion method includes assigning weights to the output results of each sub-model according to the importance of each target in actual production, and multiplying the output results of each sub-model by the corresponding weights and adding them to obtain a weighted average fusion result, which is used as the final decision output to guide the adjustment of equipment operating parameters in the metal smelting process; S500. Apply the fused decision output to the actual production process of metal smelting, continuously monitor the actual data in the production process, compare and analyze the actual production results with the predicted results of the fusion model, and make corresponding adjustments and optimizations to the equipment operating parameters and process control parameters to achieve continuous optimization and closed-loop control of the metal smelting process.

2. The method for processing intelligent model data applied to metal smelting according to claim 1, characterized in that: The S100 product quality related data includes the hardness value of the metal finished product , Strength value of metal products , Purity value of finished metal products , the number of internal defects in finished metal products , Types of internal defects in finished metal products , Surface roughness value of metal products ; The energy consumption related data include the power consumption values ​​of each stage in the smelting process , fuel consumption flow value at each stage of the smelting process , thermal energy utilization rate in the smelting process ; The production efficiency-related data includes the metal output value per unit time. , Equipment operation time ratio , the residence time value of each link in the production process , Equipment failure downtime , Equipment failure downtime value .

3. The method for processing intelligent model data applied to metal smelting according to claim 2, characterized in that: The S200 establishes a sub-model based on neural network prediction for the product quality target, the model includes an input layer, a hidden layer and an output layer, wherein: The input layer is composed of neurons with parameter data related to product quality, and all collected parameter data related to product quality are used as input values, that is, the input layer neurons include , , , , and ; The hidden layer has neurons, extract features from the data in the input layer, and the weight matrix is ​​used between the input layer and the hidden layer. Connect, its dimension is , is the number of neurons in the input layer, each element Represents the input layer neurons and hidden layers The connection weights between neurons, and the hidden layer also has a bias vector , whose dimensions are , each element Represents the hidden layer The bias value of the neurons in the hidden layer is ,Right now , whose output The calculation formula is: ,in , The input layer The value of the neuron, that is, the parameter data value of the corresponding product quality , , , , and ; The output layer is responsible for outputting the final product quality prediction value, which is connected with the hidden layer through the weight matrix Connected, dimension is , each element Represents the hidden layer The connection weights between neurons and the output layer neurons. The output layer also has a bias vector , the activation function of the output layer neurons is ,Right now , then the output of the output layer neuron is used as the product quality prediction value The calculation formula is: , adjust the weight matrix by inputting product quality parameter data , and the bias vector , , and continuously iterate and optimize model parameters.

4. The method for processing intelligent model data applied to metal smelting according to claim 2, characterized in that: The S200 establishes a sub-model based on a linear regression algorithm for the energy consumption target. , , As the independent variable, the predicted energy consumption As the dependent variable, the constructed linear regression equation is: ,in, is the regression coefficient, which reflects the influence of each independent variable on energy consumption; is the intercept, representing the basic value of energy consumption when the independent variable is zero. The linear regression equation is fitted by the least squares method, the purpose of which is to minimize the difference between the actual energy consumption value and the predicted value. The sum of squared errors is used to find the best regression coefficient and intercept value so that the predicted value of the model is close to the actual value. The calculation formula of the sum of squared errors is: ,in, is the sample size, , , , Respectively The actual energy consumption value of each sample and the corresponding independent variable value are used to iteratively update the regression coefficient by minimizing the sum of squared errors using historical data and energy consumption-related data samples collected during the actual production process. , , and the intercept The estimated value of enables the model to accurately predict energy consumption based on the input energy consumption related data.

5. The method for processing intelligent model data applied to metal smelting according to claim 2, characterized in that: The S200 establishes a sub-model based on decision tree evaluation for the production efficiency target. , , , and As the splitting feature of the decision tree, the information gain of each feature is calculated, and the information entropy is used as an indicator to measure the uncertainty of the data set, and the data set is set to ,feature The value set of , , according to the characteristics The dataset Divide into Subset , , then the feature Information gain for: ,in, For the dataset The information entropy of ,in, is the number of production efficiency categories, For Category In the dataset The probability in the data set is calculated by calculating the information gain of each feature, selecting the feature with the largest information gain as the root node for splitting, and dividing the data set into different subsets. For each subset, the above process is repeated recursively, and the feature with the largest information gain is continued to be split until the stopping condition is met. The stopping condition is: the information gain of all features is less than the threshold information gain , thus constructing a complete decision tree sub-model; When the parameter values ​​of production efficiency-related data are input, starting from the root node of the decision tree, judgments are made step by step along the branches of the decision tree according to the characteristic judgment conditions of each node. Each internal node of the decision tree represents a test condition of a feature, the branch represents different values ​​of the feature, and the leaf node represents the final predicted value, which is the predicted result of production efficiency. .

6. The method for processing intelligent model data applied to metal smelting according to claim 3 is characterized in that: The S300 predicts the quality of metal smelting products based on a neural network sub-model. During the model training phase, some output values ​​of hidden layer neurons are randomly set to 0, with a probability of In the prediction phase, forward propagation, we get Different product quality prediction results , calculate the prediction results The mean and variance , the variance As an uncertainty measure of product quality prediction results, a large variance indicates high uncertainty.

7. The method for processing intelligent model data applied to metal smelting according to claim 4, characterized in that: The S300 predicts the energy consumption of metal smelting based on linear regression, and estimates the uncertainty by using the variance of the residual, that is, by calculating the residual of each sample ,in , is the sample size, and the variance of the residual is calculated , As an uncertainty measure of energy consumption prediction results, the variance is large and the uncertainty is high.

8. The method for processing intelligent model data applied to metal smelting according to claim 5, characterized in that: The S200 establishes a sub-model based on decision tree evaluation for the production efficiency target. When constructing the decision tree, for the data set For each sample in the dataset, one-third of the data that does not participate in the construction of the leaf node where the sample is located is used as external data. The sub-model is verified using the external data. For each sample, its prediction accuracy on the external data is calculated. , the accuracy is low, indicating that the prediction result of the sample is highly uncertain. The average prediction accuracy of all samples is As the overall uncertainty measure of the production efficiency prediction results, s is the number of samples in the data set S, As a quantitative indicator of the degree of uncertainty, the larger the value, the higher the uncertainty.

9. The method for processing intelligent model data applied to metal smelting according to claim 1, characterized in that: The S400 determines the initial weight of product quality based on the importance of each target in the metal smelting production process: , the initial weight of energy consumption is , the initial weight of production efficiency is , and satisfies , comprehensively considering the impact of the uncertainty of the output results of each sub-model on the decision, the initial weights are dynamically adjusted, that is, the output result for product quality is , the uncertainty measure is the variance , the output result of energy consumption is , the uncertainty measure is the variance The output result of production efficiency is , the uncertainty measure is , using the weight adjustment formula: ,in, Represents the weights of product quality, energy consumption and production efficiency set at the initial stage, is the uncertainty measure of the output results of each sub-model, ; By adjusting the weights, the output results of the sub-models with low uncertainty have a relatively large influence on the final fusion decision, making the decision stable. The output results of each sub-model are multiplied by their corresponding adjusted weights and then added to obtain the weighted average fusion result. ,Right now ,The fusion result is used as the final decision output to guide the adjustment of equipment operating parameters in the metal smelting process.

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