Material environment intelligent adjusting system for vacuum pug mill

Abstract

The invention discloses an intelligent material environment adjusting system for a vacuum pug mill, relates to the technical field of artificial intelligence, and solves the problems that the influence of environmental factors on pug stirring is not considered in the prior art, and the situation that finished pug meets the target requirement due to self-adaptive control over the vacuum pug mill is lacked. The finished product state of pug is poor; and the production efficiency of the vacuum pug mill is relatively low. The method comprises the steps of generating material data according to demand data and generating equipment initial parameters according to the material data; according to the material data, the to-be-finished product data and the environment data, to-be-finished product estimation parameters are generated; equipment adjustment parameters are generated according to the to-be-finished product pre-estimated parameters, the change influence of environmental factors on the to-be-finished product data in a stirrer is considered, the difference between the to-be-finished product data and the finished product data is predicted in real time, materials are added in advance or the equipment parameters are adjusted, and it is ensured that demand data can be obtained on time and according to quality at the minimum cost; and the accuracy and the efficiency of the vacuum pug mill are improved.

Description

Technical Field

[0001] The present application belongs to the field of artificial intelligence technology, and specifically is a material environment intelligent adjustment system for a vacuum clay kneading machine. Background Art

[0002] Vacuum clay machine is a special equipment used in ceramics, building materials and other industries. It is mainly used to degas, stir and homogenize the clay to improve its plasticity, density and molding performance. With the continuous advancement of technology, vacuum clay machine is developing in the direction of intelligence, energy saving and multi-function, providing more efficient and environmentally friendly solutions for the industry.

[0003] The prior art (invention patent application with publication number CN113370386A) discloses a method for preparing clay for ceramic rollers and a vacuum clay training machine thereof, wherein the method for preparing clay for ceramic rollers comprises the following steps: mixing ceramic roller clay, a binder and water; vacuuming and extruding the prepared clay mixture into strip-shaped clay; removing residual gas in the material by vacuuming and extruding while preparing the clay, and obtaining ceramic roller clay with high density, small and uniformly distributed embedded gas pores, thereby effectively improving the structural uniformity, strength and thermal shock resistance of the prepared ceramic roller; at the same time, the present invention also discloses a vacuum clay training machine, which is equipped with an extrusion and vacuuming device, optimizes the extrusion pressure and the extrusion head pore size, has a compact structure and reliable performance, and is conducive to reducing the complexity of the equipment while improving the granulation density and uniformity, thereby improving the production efficiency of the ceramic roller.

[0004] The above scheme mixes ceramic roller mud, binder and water and inputs them into the vacuum mud kneading machine for stirring. It does not consider the impact of environmental factors on mud stirring. At the same time, it lacks consideration of adaptive control of the vacuum mud kneading machine so that the finished mud meets the target requirements, resulting in poor finished mud state and low production efficiency of the vacuum mud kneading machine. Therefore, the material intelligent adjustment system of the vacuum mud kneading machine still needs further improvement. Summary of the invention

[0005] The present application aims to solve at least one of the technical problems existing in the prior art; to this end, the present application proposes a material environment intelligent adjustment system for a vacuum mud kneading machine, which is used to solve the technical problems that the prior art does not consider the impact of environmental factors on mud mixing, and at the same time lacks consideration of adaptive control of the vacuum mud kneading machine so that the finished mud meets the target requirements, resulting in poor finished mud state and low production efficiency of the vacuum mud kneading machine.

[0006] To achieve the above-mentioned purpose, the first aspect of the present application provides a material environment intelligent adjustment system for a vacuum clay kneading machine, comprising: a data acquisition module, a data analysis module, a control module and a database; The data acquisition module acquires environmental data and equipment parameters, finished product data and demand data through data acquisition equipment; The data analysis module: generates material data according to demand data; obtains material parameter data corresponding to the material data when entering the mixer; generates equipment initial parameters according to the material data and material parameter data; obtains the data of the finished product in the mixer in real time; generates estimated parameters of the finished product according to the material data, the data of the finished product and the environmental data; generates equipment adjustment parameters according to the estimated parameters of the finished product; The control module: transmits the corresponding materials to the mixer according to the material data; assigns the equipment adjustment parameters and controls the corresponding equipment parameters.

[0007] Through the above steps, the present application considers the impact of environmental factors on the changes of the data of the finished product in the mixer, and predicts the gap between the data of the finished product and the finished product data in real time, adds materials in advance or adjusts the equipment parameters, ensures that the required data can be obtained on time and in quality at the lowest cost, and improves the accuracy and efficiency of the vacuum mud kneading machine.

[0008] Further, generating material data according to demand data includes: Obtaining demand data; the demand data includes finished product name, finished product parameters, finished product weight and demand time; The material data is searched in the material data table according to the finished product name and the finished product weight; the material data table is constructed by experts according to the demand data; the material data includes several material names and their corresponding material weights.

[0009] Furthermore, generating the initial parameters of the equipment according to the material data and the material parameter data includes: Obtain material data, demand data and material parameter data corresponding to the material data when entering the mixer; Material data, demand data and material parameter data are input into a parameter estimation model to obtain initial equipment parameters; the parameter estimation model is constructed through an artificial intelligence model; the initial equipment parameters include operating power, operating time, stirring speed and equipment vacuum degree.

[0010] Through the above steps, the present application automatically generates the required material data according to the demand data, generates the equipment initial parameters according to the material data, material parameter data and demand data, mixes the materials with the equipment initial parameters, and automatically controls the vacuum mud kneading machine, thereby reducing manual operation and improving the efficiency of the vacuum mud kneading machine.

[0011] Furthermore, the parameter estimation model is constructed by an artificial intelligence model, including: Obtain some historical material data, demand data and material parameter data and their corresponding historical equipment initial parameters; Divide a number of historical material data, demand data and material parameter data and their corresponding historical equipment initial parameters into training data, verification data and test data; perform data preprocessing on the training data, verification data and test data to obtain a training set, a verification set and a test set; Select an AI model as the base model; Train the basic model with the training set, and adjust the learning rate and hyperparameters on the validation set to obtain the pre-trained model; By verifying the pre-trained model on the test set, we finally get the input material data, demand data and material parameter data, and output the parameter estimation model of the equipment initial parameters.

[0012] Furthermore, generating estimated parameters of the finished product according to the material data, the finished product data and the environmental data includes: Real-time acquisition of historical data of finished products, historical material data, historical environmental data, historical equipment parameters and recording time in the mixer; the equipment parameters are initial equipment parameters or equipment adjustment parameters; the environmental data include external environmental data and internal environmental data; Integrate a number of historical finished product data, historical material data, historical environmental data and historical equipment parameters into a number of forecast sequences according to the recording time; The prediction sequence is input into the parameter estimation model for the finished product to obtain the estimated parameters of the finished product; the parameter estimation model for the finished product is constructed by a machine learning model.

[0013] Furthermore, the parameter estimation model for the finished product is constructed by a machine learning model, including: Obtain some historical data on finished products, historical material data, historical environmental data, historical equipment parameters and recording time; Performing data preprocessing on a number of historical unfinished product data, historical material data, historical environmental data, historical equipment parameters and recording time to obtain a number of preprocessed historical unfinished product data, historical material data, historical environmental data, historical equipment parameters and recording time; Integrate the historical finished product data, historical material data, historical environmental data, and historical equipment parameters with the same recording time into a time feature vector; Use the principal component analysis method to reduce the features of several time feature vectors to obtain the reduced time feature vector; the reduced time feature vector is the time feature vector obtained by the principal component analysis method after the cumulative contribution rate reaches M%; where M is a constant, M∈[0,100]; Integrate each time feature vector into several prediction sequences according to the order of recording time; Divide several prediction sequences into training sets, test sets and validation sets in chronological order; Select a machine learning model as the base model; Train the basic model with the training set, and adjust the learning rate or other hyperparameters on the validation set to obtain the pre-trained model; By verifying the pre-trained model on the test set, the final input is a number of prediction sequences. The output is a parameter estimation model for the finished product, which is an estimated parameter of the finished product.

[0014] Furthermore, the step of generating equipment adjustment parameters according to the estimated parameters of the finished product includes: Obtain estimated parameters of unfinished products and parameters of finished products; Construct a cost minimization function based on the estimated parameters of the finished product and the parameters of the finished product; The deep reinforcement learning model is used to solve the minimization cost function to obtain the optimal solution, and then an adjustment plan is generated; Generate equipment adjustment parameters according to the adjustment plan.

[0015] Furthermore, the minimization cost function is constructed according to the estimated parameters of the finished product and the parameters of the finished product, including: Obtain estimated parameters of unfinished products and parameters of finished products; By subtracting the parameters of the finished product from the estimated parameters of the finished product, a number of parameter differences are obtained; Calculate the amount of material to be added based on the parameter difference; Generate estimated equipment energy change based on parameter differences; By formula Construct the minimization cost function FC; among them, TWL represents the amount of added material, DJ represents the unit price corresponding to the amount of added material, DNJ represents the unit energy price, and NYL represents the estimated change in equipment energy.

[0016] Further, the calculating the amount of added material according to the parameter difference includes: Acquire a number of parameter differences and data of finished products; the data of finished products include the weight DCZ of the finished products; Select a parameter difference CC corresponding to the maximum absolute value of several parameter differences; Determine whether the parameter difference is within the corresponding difference range; Yes, the amount of added material corresponding to the parameter difference TWL=0; No, when the parameter difference is greater than the maximum value in its corresponding difference range; By formula Calculate the amount of added material TWL; When the parameter difference is less than the minimum value in its corresponding difference range; By formula Calculate the added material amount TWL; where DTWL is expressed as the amount of added material required for the unit difference unit weight of the finished product.

[0017] Furthermore, generating the estimated change amount of equipment energy according to the parameter difference includes: Obtaining several parameter differences, the weight of the finished product and the estimated parameters of the finished product; the estimated parameters of the finished product include the estimated duration; Select the parameter difference corresponding to the maximum absolute value of several parameter differences; Determine whether the parameter difference is within the corresponding difference range; Yes, the estimated change in equipment energy corresponding to the parameter difference NYL=0; No, the parameter difference, the weight of the finished product and the estimated duration are input into the energy estimation model to obtain the estimated change in equipment energy; the energy estimation model is constructed through an artificial intelligence model.

[0018] This application calculates the corresponding amount of added material or the estimated change in equipment energy based on the parameter difference between the estimated parameters of the finished product and its corresponding finished product parameters. When adding material, different parameter differences require the addition of different materials, thereby providing accurate data support for finding the minimum cost and improving the quality of mud production.

[0019] Furthermore, the energy estimation model is constructed by an artificial intelligence model, including: Obtain the difference of several historical parameters, the weight of the finished product, the estimated duration and the corresponding estimated change in equipment energy; Divide several historical parameter differences, weight of finished products, estimated duration and their corresponding estimated changes in equipment energy into training data, verification data and test data; perform data preprocessing on the training data, verification data and test data to obtain a training set, a verification set and a test set; Select an AI model as the base model; Train the basic model with the training set, and adjust the learning rate and hyperparameters on the validation set to obtain the pre-trained model; By verifying the pre-trained model on the test set, we finally get the input parameter difference, the weight of the finished product and the estimated time, and output an energy estimation model that is the estimated change in equipment energy.

[0020] Compared with the prior art, the beneficial effects of this application are: The present application generates material data according to demand data; transmits corresponding materials to the mixer according to the material data; obtains material parameter data corresponding to the material data when entering the mixer; generates equipment initial parameters according to the material data and the material parameter data; obtains the data of unfinished products in the mixer in real time; generates estimated parameters of unfinished products according to the material data, the data of unfinished products and environmental data; generates equipment adjustment parameters according to the estimated parameters of unfinished products, controls the equipment adjustment parameter assignments and corresponding equipment parameters, considers the impact of environmental factors on the changes of the data of unfinished products in the mixer, and predicts the gap between the data of unfinished products and the finished product data in real time, adds materials in advance or adjusts the equipment parameters, ensures that the demand data can be obtained on time and with quality at the lowest cost, and improves the accuracy and efficiency of the vacuum mud kneading machine.

[0021] This application obtains the data of unfinished products, material data, environmental data and equipment parameters in the mixer in real time in chronological order, predicts the estimated parameters of the unfinished products when the demand time is reached, compares the finished product parameters to detect abnormal conditions in the data of unfinished products, makes dynamic decisions in advance, reduces time consumption, and improves the production efficiency and mud quality of the vacuum mud kneading machine.

[0022] This application calculates the gap between the estimated parameters of the finished product and the parameters of the finished product, and considers adding materials or adjusting equipment parameters to eliminate the gap. Adding materials or adjusting equipment will incur different cost consumptions, so the minimum cost that can eliminate the gap is found between material cost and energy cost, so as to achieve the target requirements for the parameters of the finished product, thereby reducing resource consumption on the basis of meeting the requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0024] Figure 1 This is a schematic diagram of the principle of a material environment intelligent adjustment system for a vacuum clay kneading machine of the present application; Figure 2 A flow chart of a material environment intelligent adjustment method for a vacuum clay kneading machine of the present application; Figure 3 Generate a flow chart for the estimated change in device energy for this application. DETAILED DESCRIPTION

[0025] The technical solution of the present application will be described clearly and completely in conjunction with the embodiments below. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application.

[0026] See also Figure 1-Figure 2 , the first aspect of the present application provides a material environment intelligent adjustment system for a vacuum clay machine, including: a data acquisition module, a data analysis module, a control module and a database; Data acquisition module: obtains environmental data and equipment parameters, finished product data and demand data through data acquisition equipment; data acquisition equipment includes various sensors, such as temperature sensors and humidity sensors; Data analysis module: Generate material data according to demand data. Material data refers to several raw material data that form demand data; obtain material parameter data corresponding to the material data when entering the mixer. Material parameter data refers to various parameters of the material, such as material temperature, material humidity, etc.; generate equipment initial parameters according to material data and material parameter data. Equipment initial parameters refer to the initial parameter settings of the mixing equipment; obtain the data of the finished product in the mixer in real time. The data of the finished product refers to the data of several raw materials in the mixer when preparing to form the demand data; generate estimated parameters of the finished product according to material data, data of the finished product and environmental data. The estimated parameters of the finished product refer to the parameter data that can be formed by the data of the finished product at the demand time, such as the temperature of the finished product, the humidity of the finished product and the viscosity of the finished product; generate equipment adjustment parameters according to the estimated parameters of the finished product. The equipment adjustment parameters refer to the equipment parameters that adjust the initial parameters of the equipment; Control module: transfer the corresponding materials to the mixer according to the material data; assign the equipment adjustment parameters and control the corresponding equipment parameters.

[0027] In this embodiment, generating material data according to demand data includes: Obtain demand data; demand data includes finished product name, finished product parameters, finished product weight and demand time; Find material data in the material data table according to the finished product name and finished product weight; the material data table is constructed by experts based on the demand data; the material data includes several material names and their corresponding material weights.

[0028] In this embodiment, generating the initial parameters of the equipment according to the material data and the material parameter data includes: Obtain material data, demand data and material parameter data corresponding to the material data when entering the mixer; Material data, demand data and material parameter data are input into the parameter estimation model to obtain the initial parameters of the equipment; the parameter estimation model is constructed through an artificial intelligence model; the initial parameters of the equipment include operating power, operating time, stirring speed and equipment vacuum degree.

[0029] The parameter estimation model in this embodiment is constructed by an artificial intelligence model, including: Obtain some historical material data, demand data and material parameter data and their corresponding historical equipment initial parameters; Divide a number of historical material data, demand data, material parameter data and their corresponding historical equipment initial parameters into training data, verification data and test data; perform data preprocessing on the training data, verification data and test data to obtain a training set, a verification set and a test set; the ratio between the training set, the test set and the verification set is 7:2:1; Select an artificial intelligence model as the basic model; the artificial intelligence model includes a neural network model, etc.; Train the basic model with the training set, and adjust the learning rate and hyperparameters on the validation set to obtain the pre-trained model; By verifying the pre-trained model on the test set, we finally get the input material data, demand data and material parameter data, and output the parameter estimation model of the equipment initial parameters.

[0030] In this embodiment, the estimated parameters of the finished product are generated according to the material data, the finished product data and the environmental data, including: Real-time acquisition of historical data of unfinished products, historical material data, historical environmental data, historical equipment parameters and recording time in the mixer; equipment parameters are equipment initial parameters or equipment adjustment parameters; environmental data include external environmental data and internal environmental data; when the material is predicted for the first time in the mixer, the corresponding equipment parameters are the equipment initial parameters; when the material is predicted for the Nth time in the mixer, the corresponding equipment parameters are the equipment adjustment parameters corresponding to the N-1th prediction, where N is an integer and N>1; external environmental data refers to environmental data outside the mixer, and internal environmental data refers to environmental data inside the mixer; Integrate a number of historical finished product data, historical material data, historical environmental data and historical equipment parameters into a number of forecast sequences according to the recording time; The prediction sequence is input into the parameter estimation model for the finished product to obtain the estimated parameters of the finished product; the parameter estimation model for the finished product is constructed through a machine learning model.

[0031] The parameter estimation model for the finished product in this embodiment is constructed by a machine learning model, including: Obtain some historical data on finished products, historical material data, historical environmental data, historical equipment parameters and recording time; Performing data preprocessing on a number of historical unfinished product data, historical material data, historical environmental data, historical equipment parameters and recording time to obtain a number of preprocessed historical unfinished product data, historical material data, historical environmental data, historical equipment parameters and recording time; Integrate the historical finished product data, historical material data, historical environmental data, and historical equipment parameters with the same recording time into a time feature vector; Use the principal component analysis method to reduce the characteristics of several time feature vectors to obtain a reduced time feature vector; the reduced time feature vector is a time feature vector obtained by the principal component analysis method after the cumulative contribution rate reaches M%; where M is a constant, M∈[0,100], the specific value is set according to experience, and in this embodiment, M is set to 95; Integrate each time feature vector into several prediction sequences according to the order of recording time; Divide several prediction sequences into training set, test set and validation set in chronological order; the ratio between training set, test set and validation set is 7:2:1; Select a machine learning model as the basic model; machine learning models include LSTM models, etc. Train the basic model with the training set, and adjust the learning rate or other hyperparameters on the validation set to obtain the pre-trained model; By verifying the pre-trained model on the test set, we finally obtain a parameter estimation model for the finished product whose input is a number of prediction sequences and whose output is the estimated parameters of the finished product.

[0032] This embodiment collects the data of unfinished products, material data, environmental data and equipment parameters in the mixer in real time in chronological order, predicts the estimated parameters of the unfinished products at the target time point, and compares them with the finished product parameters, thereby detecting abnormal conditions in the data of unfinished products; through dynamic adjustment and decision-making in advance, time consumption is effectively reduced, and the production efficiency and mud quality of the vacuum mud kneading machine are significantly improved.

[0033] In this embodiment, the device adjustment parameters are generated according to the estimated parameters of the finished product, including: Obtain estimated parameters of unfinished products and parameters of finished products; A minimization cost function is constructed based on the estimated parameters of the finished product and the parameters of the finished product. The minimization cost function is to set the minimum cost required for the raw materials in the mixer to reach the parameters of the finished product at the required time, including adding materials and adjusting equipment parameters. The deep reinforcement learning model is used to solve the optimal solution for minimizing the cost function and generate an adjustment plan based on it. The deep reinforcement learning model is a pre-trained model that can effectively obtain the optimal solution for minimizing the cost function and generate an adjustment plan based on it. Generate equipment adjustment parameters according to the adjustment plan.

[0034] In this embodiment, the minimization cost function is constructed based on the estimated parameters of the finished product and the parameters of the finished product, including: Obtain estimated parameters of unfinished products and parameters of finished products; By subtracting the parameters of the finished product from the estimated parameters of the finished product, a number of parameter differences are obtained; Calculate the amount of material to be added based on the parameter difference; Generate the estimated change of equipment energy according to the parameter difference; the estimated change of equipment energy refers to the estimated amount of energy consumed or saved by adjusting the equipment parameters, wherein, when a certain amount of energy is consumed when the equipment parameters are adjusted, NYL>0; otherwise, NYL<0; By formula Construct a minimization cost function FC; wherein TWL represents the amount of added material, DJ represents the unit price corresponding to the amount of added material, DNJ represents the unit energy price, and NYL represents the estimated change in equipment energy; the amount of added material has an upper limit, that is, 0≤TWL≤TWLmax, and TWLmax represents the maximum value of the amount of added material; when adding materials or adjusting equipment parameters, ensure that the adjusted parameters of the finished product are equal to the finished product parameters; this embodiment is to solve the optimal TWL and NYL, so that the target requirements are achieved with the minimum cost and cost consumption is reduced.

[0035] This embodiment calculates the deviation between the estimated parameters of the finished product and the parameters of the finished product, and comprehensively considers eliminating the deviation by adding materials or adjusting equipment parameters; since adding materials and adjusting equipment parameters will incur different cost consumptions, such as material costs and energy costs, the system searches for the minimum cost solution between the two that can eliminate the deviation, thereby adjusting the parameters of the finished product to the target requirements; on the basis of meeting production needs, it effectively reduces resource consumption and improves the economy and sustainability of production.

[0036] In this embodiment, the amount of added material is calculated according to the parameter difference, including: Obtaining several parameter differences and data of finished products; the data of finished products includes the weight DCZ of finished products; Select the parameter difference CC corresponding to the maximum absolute value of several parameter differences; that is, solve the factors with the largest gap between the current unfinished product data and the finished product data in turn; Determine whether the parameter difference is within the corresponding difference range; the difference range is set based on experience; Yes, the amount of added material corresponding to the parameter difference TWL=0; this embodiment considers that if the parameters of the finished product are not much different from the parameters of the corresponding finished product, the obtained finished product parameters will not have a significant impact on the subsequent process, so there is no need to add materials for adjustment; No, when the parameter difference is greater than the maximum value in its corresponding difference range; By formula Calculate the amount of added material TWL; When the parameter difference is less than the minimum value in its corresponding difference range; By formula Calculate the amount of added material TWL; wherein DTWL is expressed as the amount of added material required per unit difference per unit weight of the finished product; this embodiment takes into account that when the positive and negative values ​​of the parameter differences are different, different materials need to be added for adjustment. For example, when the viscosity in the parameters of the finished product is large, a material that reduces the viscosity needs to be added for adjustment. When the viscosity in the parameters of the finished product is small, a material that increases the viscosity needs to be added for adjustment. These two materials are not the same material, so they need to be calculated separately. When the amount of added material required per unit weight of the finished product is fixed, the larger the parameter difference and the larger the weight of the finished product, the more material will be required, and thus the amount of added material will increase accordingly.

[0037] This embodiment determines the required amount of material to be added or the estimated change in equipment energy by calculating the difference between the estimated parameters of the finished product and its corresponding finished product parameters; when adding materials, the system accurately matches the corresponding material type and quantity according to the different parameter differences, providing reliable data support for finding the cost-minimizing solution; it not only optimizes resource utilization efficiency, but also significantly improves the quality stability of mud production.

[0038] See also Figure 3 In this embodiment, the estimated change amount of equipment energy is generated according to the parameter difference, including: Obtaining several parameter differences, the weight of the finished product and the estimated parameters of the finished product; the estimated parameters of the finished product include the estimated duration; the estimated duration is the duration between the current estimated time and the mixing completion time; Select the parameter difference corresponding to the maximum absolute value of several parameter differences; Determine whether the parameter difference is within the corresponding difference range; the difference range is set based on experience; Yes, the estimated change in equipment energy corresponding to the parameter difference is NYL=0; this embodiment considers that if the finished product parameters are not much different from the corresponding finished product parameters, the obtained finished product parameters will not have a significant impact on the subsequent process, so there is no need to adjust the equipment parameters, and the equipment energy will not change; No, the parameter difference, weight of finished products and estimated duration are input into the energy estimation model to obtain the estimated change in equipment energy; the energy estimation model is constructed through an artificial intelligence model.

[0039] The energy estimation model in this embodiment is constructed by an artificial intelligence model, including: Obtain the difference of several historical parameters, the weight of the finished product, the estimated duration and the corresponding estimated change in equipment energy; Divide several historical parameter differences, weight of finished products, estimated duration and their corresponding estimated changes in equipment energy into training data, verification data and test data; perform data preprocessing on the training data, verification data and test data to obtain training set, verification set and test set; the ratio of training set, test set and verification set is 7:2:1; Select an artificial intelligence model as the basic model; the artificial intelligence model includes a BP network model, etc.; Train the basic model with the training set, and adjust the learning rate and hyperparameters on the validation set to obtain the pre-trained model; By verifying the pre-trained model on the test set, we finally get the input parameter difference, the weight of the finished product and the estimated time, and output an energy estimation model that is the estimated change in equipment energy.

[0040] Part of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is a formula closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0041] The working principle of the present application is as follows: by acquiring environmental data and equipment parameters, data of unfinished products and demand data; generating material data according to the demand data; transmitting the corresponding materials to the mixer according to the material data; acquiring the material parameter data corresponding to the material data when entering the mixer; generating the initial parameters of the equipment according to the material data and the material parameter data; acquiring the data of unfinished products in the mixer in real time; generating estimated parameters of unfinished products according to the material data, the data of unfinished products and environmental data; generating equipment adjustment parameters according to the estimated parameters of unfinished products, assigning the equipment adjustment parameters and controlling the corresponding equipment parameters, considering the influence of environmental factors on the changes of the data of unfinished products in the mixer, predicting the gap between the data of unfinished products and the data of finished products in real time, adding materials in advance or adjusting the equipment parameters, ensuring that the demand data can be obtained on time and in quality at the lowest cost, thereby improving the accuracy and efficiency of the vacuum mud kneading machine, avoiding the problem that the prior art does not consider the influence of environmental factors on mud mixing, and lacks consideration of adaptive control of the vacuum mud kneading machine so that the finished mud meets the target requirements, resulting in poor finished state of the mud and low production efficiency of the vacuum mud kneading machine.

[0042] The above embodiments are only used to illustrate the technical method of the present application and are not intended to limit it. Although the present application has been described in detail with reference to the preferred embodiments, a person of ordinary skill in the art should understand that the technical method of the present application may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present application.

Claims

1. A material environment intelligent adjustment system for a vacuum clay machine, characterized in that: include: Data acquisition module, data analysis module, control module and database; The data acquisition module acquires environmental data and equipment parameters, finished product data and demand data through data acquisition equipment; The data analysis module: generates material data according to demand data; obtains material parameter data corresponding to the material data when entering the mixer; generates equipment initial parameters according to the material data and material parameter data; obtains the data of the finished product in the mixer in real time; generates estimated parameters of the finished product according to the material data, the data of the finished product and the environmental data; generates equipment adjustment parameters according to the estimated parameters of the finished product; The control module: transmits the corresponding materials to the mixer according to the material data; assigns the equipment adjustment parameters and controls the corresponding equipment parameters.

2. The material environment intelligent adjustment system for a vacuum clay machine according to claim 1, characterized in that: The generating of material data according to demand data includes: Obtaining demand data; the demand data includes finished product name, finished product parameters, finished product weight and demand time; Search material data in the material data table according to the finished product name and finished product weight; the material data includes several material names and their corresponding material weights.

3. The material environment intelligent adjustment system for a vacuum clay machine according to claim 1, characterized in that: The generating of the equipment initial parameters according to the material data and the material parameter data comprises: Obtain material data, demand data and material parameter data corresponding to the material data when entering the mixer; Material data, demand data and material parameter data are input into a parameter estimation model to obtain initial equipment parameters; the parameter estimation model is constructed through an artificial intelligence model; the initial equipment parameters include operating power, operating time, stirring speed and equipment vacuum degree.

4. The material environment intelligent adjustment system for a vacuum clay extruder according to claim 3, characterized in that: The parameter estimation model is constructed by an artificial intelligence model, including: Obtain some historical material data, demand data and material parameter data and their corresponding historical equipment initial parameters; Divide a number of historical material data, demand data and material parameter data and their corresponding historical equipment initial parameters into training data, verification data and test data; perform data preprocessing on the training data, verification data and test data to obtain a training set, a verification set and a test set; Select an AI model as the base model; Train the basic model with the training set, and adjust the learning rate and hyperparameters on the validation set to obtain the pre-trained model; By verifying the pre-trained model on the test set, we finally get the input material data, demand data and material parameter data, and output the parameter estimation model of the equipment initial parameters.

5. The material environment intelligent adjustment system for a vacuum clay machine according to claim 1, characterized in that: The generating of estimated parameters of the finished product according to the material data, the finished product data and the environmental data includes: Real-time acquisition of historical data of finished products, historical material data, historical environmental data, historical equipment parameters and recording time in the mixer; the equipment parameters are initial equipment parameters or equipment adjustment parameters; the environmental data include external environmental data and internal environmental data; Integrate a number of historical finished product data, historical material data, historical environmental data and historical equipment parameters into a number of forecast sequences according to the recording time; The prediction sequence is input into the parameter estimation model for the finished product to obtain the estimated parameters of the finished product; the parameter estimation model for the finished product is constructed by a machine learning model.

6. The intelligent material environment adjustment system for a vacuum clay extruder according to claim 5, characterized in that: The parameter estimation model for the finished product is constructed by a machine learning model, including: Obtain some historical data on finished products, historical material data, historical environmental data, historical equipment parameters and recording time; Performing data preprocessing on a number of historical unfinished product data, historical material data, historical environmental data, historical equipment parameters and recording time to obtain a number of preprocessed historical unfinished product data, historical material data, historical environmental data, historical equipment parameters and recording time; Integrate the historical finished product data, historical material data, historical environmental data, and historical equipment parameters with the same recording time into a time feature vector; Use the principal component analysis method to reduce the features of several time feature vectors to obtain the reduced time feature vector; the reduced time feature vector is the time feature vector obtained by the principal component analysis method after the cumulative contribution rate reaches M%; where M is a constant, M∈[0,100]; Integrate each time feature vector into several prediction sequences according to the order of recording time; Divide several prediction sequences into training sets, test sets and validation sets in chronological order; Select a machine learning model as the base model; Train the basic model with the training set, and adjust the learning rate or other hyperparameters on the validation set to obtain the pre-trained model; By verifying the pre-trained model on the test set, we finally obtain a parameter estimation model for the finished product whose input is a number of prediction sequences and whose output is the estimated parameters of the finished product.

7. The material environment intelligent adjustment system for a vacuum clay machine according to claim 1, characterized in that: The generating of equipment adjustment parameters according to the estimated parameters of the finished product includes: Obtain estimated parameters of unfinished products and parameters of finished products; Construct a cost minimization function based on the estimated parameters of the finished product and the parameters of the finished product; The deep reinforcement learning model is used to solve the minimization cost function to obtain the optimal solution, and then an adjustment plan is generated; Generate equipment adjustment parameters according to the adjustment plan.

8. The material environment intelligent adjustment system for a vacuum clay machine according to claim 1, characterized in that: The minimization cost function is constructed according to the estimated parameters of the finished product and the parameters of the finished product, including: Obtain estimated parameters of unfinished products and parameters of finished products; By subtracting the parameters of the finished product from the estimated parameters of the finished product, a number of parameter differences are obtained; Calculate the amount of material to be added based on the parameter difference; Generate estimated equipment energy change based on parameter differences; By formula Construct the minimization cost function FC; where TWL represents the amount of added material, DJ represents the unit price corresponding to the amount of added material, DNJ represents the unit energy price, and NYL represents the estimated change in equipment energy.

9. The intelligent material environment adjustment system for a vacuum clay machine according to claim 8, characterized in that: The method of calculating the amount of added material according to the parameter difference includes: Acquire a number of parameter differences and data of finished products; the data of finished products include the weight DCZ of the finished products; Select a parameter difference CC corresponding to the maximum absolute value of several parameter differences; Determine whether the parameter difference is within the corresponding difference range; Yes, the amount of added material corresponding to the parameter difference TWL=0; No, when the parameter difference is greater than the maximum value in its corresponding difference range; By formula Calculate the amount of added material TWL; When the parameter difference is less than the minimum value in its corresponding difference range; By formula Calculate the added material amount TWL; where DTWL is expressed as the amount of added material required for the unit difference unit weight of the finished product.

10. The material environment intelligent adjustment system for a vacuum clay extruder according to claim 8, characterized in that: The generating of the estimated change amount of equipment energy according to the parameter difference includes: Obtaining several parameter differences, the weight of the finished product and the estimated parameters of the finished product; the estimated parameters of the finished product include the estimated duration; Select the parameter difference corresponding to the maximum absolute value of several parameter differences; Determine whether the parameter difference is within the corresponding difference range; Yes, the estimated change in equipment energy corresponding to the parameter difference NYL=0; No, the parameter difference, the weight of the finished product and the estimated duration are input into the energy estimation model to obtain the estimated change in equipment energy; the energy estimation model is constructed through an artificial intelligence model.

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