A method and system for optimizing ultra-thin ceramic tile ingredients based on real-time data acquisition

Abstract

The present invention discloses a method and system for optimizing the ingredients of ultra-thin ceramic tiles based on real-time data acquisition, and relates to the technical field of ultra-thin ceramic tiles. The method comprises the following steps: S1: obtaining the final adjustment parameter according to the difference size and external environmental data; S2: comparing the simulation result corresponding to the adjustment parameter with the simulation result corresponding to the standard formula. When the comparison result is the same, the next step is directly executed, otherwise, the previous step is returned; S3: determining the amount of each raw material according to the raw material formula corresponding to the compared simulation result, and adjusting the production formula amount of the current batch of raw materials according to the amount. The present invention obtains the adjustment parameter and compares the current formula with the standard formula according to the adjustment parameter and the simulation model, so that the current batch of raw materials can determine the amount of each raw material in its corresponding formula while ensuring the production result.

Description

Technical Field

[0001] The present invention relates to the technical field of ultra-thin ceramic tiles, and in particular to an ultra-thin ceramic tile batching optimization method and system based on real-time data acquisition. Background Art

[0002] With the development of the construction industry and consumers' increasing demands for higher-quality building materials, ultra-thin ceramic tiles have become widely popular due to their lightweight, high-strength, and aesthetically pleasing appearance. However, ensuring batch-to-batch consistency, particularly color consistency, has always been a major challenge for manufacturers during the production process. This is primarily due to factors such as fluctuations in raw material composition and changing environmental conditions, which can affect the quality of the final product.

[0003] Traditionally, the production process of ceramic tiles relies on manual experience and fixed recipes to control the proportion of ingredients. This method has several significant problems:

[0004] Human error: Reliance on operator experience may lead to inaccurate batching, thus affecting product quality.

[0005] Slow response speed: When raw materials change, traditional adjustment methods often lag behind and cannot respond in time, resulting in low production efficiency.

[0006] High cost: In order to ensure product quality, sometimes more expensive or more stable raw materials have to be used, which increases production costs.

[0007] Waste of resources: Due to the lack of precise control, some materials may be overused, resulting in unnecessary waste.

[0008] In recent years, advancements in sensing technology and data analysis capabilities have led to a continuous increase in industrial automation. In the manufacturing industry in particular, the use of sensors for real-time monitoring, combined with advanced data analysis techniques, has become a key means of improving production efficiency and product quality. By collecting key parameters from the production process in real time and analyzing them using intelligent algorithms such as machine learning, more refined control of the production process can be achieved.

[0009] The Chinese invention patent with publication number CN113413973A discloses an intelligent batching device for high-strength ultra-thin ceramic tiles, comprising a storage mechanism, a weighing mechanism, a front sealing mechanism, a ball milling mechanism, a rear sealing mechanism, a fine grinding mechanism, and a control mechanism arranged in sequence according to the production process; the storage mechanism is used to store various raw materials after homogenization and add the raw materials separately; the weighing mechanism is used to weigh the raw materials added by the storage mechanism; the front sealing mechanism is used to add a certain amount of raw materials to the ball milling mechanism and seal the front opening of the ball milling mechanism; the ball milling mechanism is used to mix and ball-mill a certain amount of raw materials and prepare them into coarse powder; the rear sealing mechanism is used to seal the rear opening of the ball milling mechanism and spray the coarse powder after ball milling into the fine grinding mechanism; the fine grinding mechanism is used to finely grind the coarse powder and prepare it into fine powder. The intelligent batching device for high-strength ultra-thin ceramic tiles of the present invention ensures that the raw materials meet the production requirements and improves production efficiency.

[0010] The aforementioned and similar intelligent batching devices rely primarily on fixed recipe ratios and operator experience for practical adjustments. However, because the iron oxide content in different batches of raw materials cannot be exactly the same, slight variations can occur between batches. Consequently, without formula adjustments, significant color variations in the final product can occur, impacting the product's market competitiveness. Adjusting the recipe requires re-adjustment, which reduces production efficiency. Summary of the Invention

[0011] The purpose of the present invention is to provide a method and system for optimizing the ingredients of ultra-thin ceramic tiles based on real-time data acquisition to solve the problems raised in the above background technology.

[0012] To achieve the above objectives, the present invention provides the following technical solutions: a method for optimizing the ingredients of ultra-thin ceramic tiles based on real-time data acquisition, comprising:

[0013] S1: Obtain adjustment parameters: Obtain the difference in composition between the current batch of raw materials and the standard formula through the key parameters of the current batch of raw materials, and obtain the final adjustment parameters based on the difference and external environmental data;

[0014] S2: Obtain simulation results: According to the adjustment parameters and the simulation model, obtain simulation results corresponding to the adjustment parameters and the standard recipe, and compare the simulation results corresponding to the adjustment parameters with the simulation results corresponding to the standard recipe. If the comparison results are the same, directly execute the next step, otherwise return to the previous step;

[0015] S3: batching operation: determining the amount of each raw material according to the raw material formula corresponding to the compared simulation results, and adjusting the production formula amount of the current batch of raw materials according to the amount;

[0016] Get the final adjustment parameters, specifically:

[0017] S1.1: Using deployed sensors, obtain key parameters and external environmental data for the current batch of raw materials. The key parameters include chemical composition, and the external environmental data includes temperature and relative humidity in the current production environment, outdoor temperature and humidity, and wind speed.

[0018] S1.2: Establish multiple prediction models, and obtain an initial difference prediction value corresponding to each prediction model using the key parameters, and determine a final difference prediction value based on each of the initial difference prediction values;

[0019] S1.3: Establish an environmental monitoring model and obtain the impact parameters of environmental parameters on the current batch of raw materials through the external environmental data;

[0020] S1.4: Based on the difference prediction value and the influencing parameter, the final adjustment parameter is obtained through the adjustment model. The adjustment model is specifically:

[0021] ,in: is the final adjustment parameter, is the weighting coefficient, is the final difference prediction value, It is the impact parameter of environmental parameters on the current batch of raw materials.

[0022] Furthermore, the final difference prediction value is determined as follows:

[0023] M1: Establish a linear regression model to obtain the first initial difference prediction value, specifically:

[0024] ,in: is the first initial difference prediction value, is the weight matrix, is the key parameter after preprocessing, is the bias vector;

[0025] M2: Build a random forest model to obtain the second initial difference prediction value, specifically:

[0026] ,in: is the second initial difference prediction value, is the total number of decision trees, is the difference prediction value corresponding to the i-th decision tree, is the key parameter after preprocessing, is the index of the decision tree;

[0027] M3: Establish a support vector machine model to obtain the third initial difference prediction value, specifically:

[0028] ,in: is the third initial difference prediction value, is the Lagrange multiplier, is the index of the sample, is the total number of samples, is the label of the jth sample in the key parameters after preprocessing, is the bias term, is the kernel function, is the feature vector of the jth sample in the key parameters after preprocessing, is the feature vector of the jth sample in the key parameters after preprocessing currently in operation;

[0029] M4: Obtain a final difference prediction value based on the first initial difference prediction value, the second initial difference prediction value, and the third initial difference prediction value, specifically:

[0030] ,in: is the final difference prediction value, is the weight corresponding to the first initial difference prediction value, is the first initial difference prediction value, is the weight corresponding to the second initial difference prediction value, is the second initial difference prediction value, is the weight corresponding to the third initial difference prediction value, is the third initial difference prediction value.

[0031] Furthermore, the weight corresponding to the initial difference prediction value is obtained as follows:

[0032] N1: Obtain the root mean square error corresponding to each prediction model and normalize each of the root mean square errors;

[0033] N2: Determine the distribution weight corresponding to each of the initial difference prediction values ​​based on the normalized root mean square error, and obtain the final weight corresponding to each of the initial difference prediction values ​​using the distribution weight, specifically:

[0034] ,in: is the weight corresponding to the k-th prediction model, is the distribution weight corresponding to the root mean square error after normalization, is the initial weight corresponding to the m-th prediction model, is the index corresponding to the prediction model.

[0035] Furthermore, the influence parameters of environmental parameters on the current batch of raw materials are obtained, as follows:

[0036] W1: Establish an initial neural network model to obtain the initial environmental impact coefficient, specifically:

[0037] ,in: is the initial environmental impact coefficient, is the weight matrix corresponding to the neural network model, is the activation function, is the environmental parameter, is the bias vector;

[0038] W2: Optimize the initial neural network model through a sliding window algorithm to obtain an optimized neural network model, specifically:

[0039] ,in: is the optimized environmental impact coefficient, For the The weight corresponding to each environmental parameter, For the environmental parameters, is the index of the environment parameter, is the total number of environmental parameters;

[0040] W3: Based on the optimized neural network model, an environmental monitoring model is established to obtain the impact parameters of environmental parameters on the current batch of raw materials. The environmental monitoring model is specifically:

[0041] ,in: is the impact parameter of environmental parameters on the current batch of raw materials, For the The weight corresponding to each environmental parameter, For the environmental parameters, is the index of the environment parameter, is the total number of environmental parameters, For the The weights corresponding to the interaction terms between the environmental parameters, For the The interaction terms between the environmental parameters, is the total number of interaction terms between environmental parameters, is the index of the interaction term between environmental parameters.

[0042] Furthermore, the simulation results corresponding to the adjustment parameters are compared with the simulation results corresponding to the standard formulation, as follows:

[0043] S2.1: Using the adjustment parameters and the standard formula as inputs to a simulation model, and outputting simulation results corresponding to the adjustment parameters and the standard formula;

[0044] S2.2: Compare the simulation results corresponding to the adjustment parameters with the simulation results corresponding to the standard formula. If the comparison results are the same, execute step S3; otherwise, execute step S2.3;

[0045] S2.3: Determine the influencing factors from the environmental parameters and the weighting coefficients, repeat steps S1.4 to S2.2, and re-acquire the adjustment parameters based on the influencing factors.

[0046] Furthermore, the influencing factors are determined from the environmental parameters and weighting coefficients, as follows:

[0047] E1: Re-obtaining current environmental parameters and comparing the current environmental parameters with the test environmental parameters. If the current environmental parameters are the same as the test environmental parameters, executing step E2; otherwise, re-obtaining the parameters affecting the current batch of raw materials based on the current environmental parameters;

[0048] E2: Adjust the size of the weighting coefficient, and re-acquire the adjustment parameter according to the adjusted weighting coefficient and the adjustment model.

[0049] A system for optimizing ultra-thin ceramic tile ingredients based on real-time data acquisition is disclosed. The system for optimizing ultra-thin ceramic tile ingredients uses any of the above-mentioned methods for optimizing ultra-thin ceramic tile ingredients based on real-time data acquisition.

[0050] Compared with the prior art, the present invention has the following beneficial effects:

[0051] First, the present invention determines adjustment parameters by obtaining the differences in composition and environmental impact between the current batch of raw materials and the standard formula. Based on the adjustment parameters and the simulation model, the current formula is compared with the standard formula. This allows the amount of each raw material in the corresponding formula to be determined while ensuring production results, thereby improving the quality consistency of the final product.

[0052] Second, by collecting and processing data in real time, the present invention can quickly respond to changes in the production process and adjust ingredient parameters in a timely manner, reducing production interruptions caused by inaccurate recipes while also supporting flexible recipe switching;

[0053] Thirdly, the present invention further ensures the accuracy of raw material ratio by incorporating environmental factors into the production process, thereby reducing the waste of raw materials and lowering production costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 Schematic diagram of the process of optimizing the ingredients of ultra-thin ceramic tiles of the present invention;

[0055] Figure 2 A schematic diagram of a process for obtaining adjustment parameters of the present invention;

[0056] Figure 3 A schematic diagram of the process of obtaining influencing parameters of the present invention;

[0057] Figure 4 This is a flow chart of the present invention comparing simulation results corresponding to adjustment parameters with simulation results corresponding to a standard recipe. DETAILED DESCRIPTION

[0058] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. It is obvious that the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0059] In the process of specific implementation of the existing intelligent batching device, its batching method mainly relies on fixed formula ratios and the operator's work experience for actual adjustment. However, since the iron oxide content in different batches of raw materials cannot be kept exactly the same, there will be slight differences between the raw material batches. The technical solution of the present application, by real-time collection and processing of key parameters of the current batch of raw materials, can obtain the difference in composition and environmental impact between the current batch of raw materials and the standard formula, and obtain adjustment parameters based on the difference and environmental impact. Based on the adjustment parameters and the simulation model, the current formula is compared with the standard formula to determine the amount of each raw material in the corresponding formula of the current batch of raw materials while ensuring the production results.

[0060] refer to Figures 1-4 This embodiment provides a method for optimizing the ingredients of ultra-thin ceramic tiles based on real-time data collection, comprising the following steps:

[0061] Step S1: Acquire adjustment parameters. This involves using deployed sensors to collect key parameters of the current batch of raw materials. Based on these key parameters, the difference in composition between the current batch of raw materials and the standard formula, as well as the environmental impact, are determined to obtain the final adjustment parameters. The details are as follows:

[0062] Step S1.1: Deployed sensors are used to obtain key parameters of the current batch of raw materials. Specifically, a spectrometer is used to obtain the chemical composition of the current batch of raw materials, such as iron oxide and silicon dioxide. Temperature and humidity sensors are used to monitor the temperature and relative humidity of the current production environment, and an external weather service API is used to obtain outdoor temperature, humidity, and wind speed.

[0063] Furthermore, the key parameters of the current batch of raw materials obtained above are subjected to corresponding preprocessing. The preprocessing operations in this embodiment include data cleaning, feature extraction and data standardization. Among them, data cleaning and removing outliers from the key parameters obtained are performed, and corresponding smoothing operations are performed. Feature extraction is to extract the absorbance value at a specific wavelength from the spectral data, because the size of the absorbance value is related to the chemical composition in the raw material. Data cleaning is to perform corresponding standardization operations on the key data through Z-score standardization operations. Specifically, the data cleaning, feature extraction and data standardization are all existing mature conventional data processing methods, so in this embodiment, they will not be further elaborated.

[0064] Step S1.2: Establish multiple prediction models, and use the key parameters preprocessed in step S1.1 as the input of each prediction model, output the initial difference prediction value corresponding to each prediction model, and extract the final difference prediction value from the multiple initial difference prediction values. The details are as follows:

[0065] Step M1: Establish a linear regression model, and use the key parameters pre-processed in step S1.1 as the input of the linear regression model, and output the first initial difference prediction value. In this embodiment, the linear regression model is specifically:

[0066] ,in: is the first initial difference prediction value, is the weight matrix, is the key parameter after preprocessing, is the bias vector.

[0067] In the specific implementation process, the key parameters of the pre-treated raw materials in the current batch are: absorbance value A is 0.75, temperature T is 25℃, relative humidity RH is 60%, outdoor temperature is 20℃, outdoor relative humidity RH0 is 70%, wind speed V w 5m / s.

[0068] That is to say, based on the above data, the following key parameters after preprocessing can be obtained: , weight matrix and the bias vector , specifically:

[0069] , , Therefore, in this embodiment, the linear regression model outputs the first initial difference prediction values ​​obtained as 7.8 and 6.1875. 7.8 is the difference between the current batch and the standard formula in the first component (iron oxide), and 6.1875 is the difference between the current batch and the standard formula in the second component (silicon dioxide).

[0070] Step M2: Establish a random forest model, and use the key parameters pre-processed in step S1.1 as the input of the random forest, and output the second initial difference prediction value. In this embodiment, the random forest model is specifically:

[0071] ,in: is the second initial difference prediction value, is the total number of decision trees, is the difference prediction value corresponding to the i-th decision tree, is the key parameter after preprocessing, is the index of the decision tree.

[0072] In the specific implementation process, the key parameters of the pre-treated raw materials in the current batch are: absorbance value A is 0.75, temperature T is 25℃, relative humidity RH is 60%, outdoor temperature is 20℃, outdoor relative humidity RH0 is 70%, wind speed V w At the same time, the number of decision trees in the random forest model in this embodiment is set to 100.

[0073] In other words, based on the above data, the sum of the difference predictions for the first component (iron oxide) from all decision trees is 760, and the sum of the difference predictions for the second component (silicon dioxide) from all decision trees is 660. This outputs the second initial difference predictions of 7.6 and 6.6, where 7.6 is the difference between the current batch and the standard formula for the first component (iron oxide), and 6.6 is the difference between the current batch and the standard formula for the second component (silicon dioxide).

[0074] Step M3: Establish a support vector machine model, and use the key parameters pre-processed in step S1.1 as input to the support vector machine model, and output the third initial difference prediction value. In this embodiment, the support vector machine model is specifically:

[0075] ,in: is the third initial difference prediction value, is the Lagrange multiplier, is the index of the sample, is the total number of samples, is the label of the jth sample in the key parameters after preprocessing, is the bias term, is the kernel function, is the feature vector of the jth sample in the key parameters after preprocessing, is the feature vector of the jth sample in the key parameters after preprocessing currently in operation.

[0076] In the specific implementation process, the key parameters of the pre-treated raw materials in the current batch are: absorbance value A is 0.75, temperature T is 25℃, relative humidity RH is 60%, outdoor temperature is 20℃, outdoor relative humidity RH0 is 70%, wind speed V w is 5m / s. At the same time, the bias term in this embodiment Set to 0.1, kernel function Set to 0.1.

[0077] That is, the support vector machine model outputs the third initial difference prediction values ​​of 7.6 and 6.385, where 7.6 is the difference between the current batch and the standard formula in the first component (iron oxide), and 6.385 is the difference between the current batch and the standard formula in the second component (silicon dioxide).

[0078] Step M4: Combine the first initial difference prediction value obtained in step M1, the second initial difference prediction value obtained in step M2, and the third initial difference prediction value obtained in step M3 to obtain a final difference prediction value, specifically:

[0079] ,in: is the final difference prediction value, is the weight corresponding to the first initial difference prediction value, is the first initial difference prediction value, is the weight corresponding to the second initial difference prediction value, is the second initial difference prediction value, is the weight corresponding to the third initial difference prediction value, is the third initial difference prediction value.

[0080] In this embodiment, the weight corresponding to the first initial difference prediction value is , the weight corresponding to the second initial difference prediction value The weight corresponding to the third initial difference prediction value The sum of 3 is 1. At the same time, the specific size of each weight is calculated as follows:

[0081] Step N1: Obtain the root mean square error corresponding to the linear regression model in step M1, the root mean square error corresponding to the linear regression model in step M2, and the root mean square error corresponding to the support vector machine model in step M3, and normalize the root mean square error corresponding to each model.

[0082] Step N2: Based on the root mean square error after normalization in step N1, a weight is assigned to each initial difference prediction value, and the assigned weight is normalized. The normalized weight value corresponding to each initial difference prediction value is the final corresponding weight. In this embodiment, the formula for obtaining the weight is specifically:

[0083] ,in: is the weight corresponding to the k-th prediction model, is the distribution weight corresponding to the root mean square error after normalization, is the initial weight corresponding to the m-th prediction model, is the index corresponding to the prediction model.

[0084] In the specific implementation process, the root mean square error corresponding to the linear regression model in step M1 is 0.5, the root mean square error corresponding to the linear regression model in step M2 is 0.4, and the root mean square error corresponding to the support vector machine model in step M3 is 0.6. In other words, the normalized root mean square error corresponding to the linear regression model in step M1 is 0.333, the normalized root mean square error corresponding to the linear regression model in step M2 is 0.267, and the normalized root mean square error corresponding to the support vector machine model in step M3 is 0.4.

[0085] Furthermore, according to the normalized root mean square error corresponding to each of the above-mentioned prediction models, the assigned weight corresponding to the linear regression model in step M1 is 3, the assigned weight corresponding to the linear regression model in step M2 is 3.75, and the assigned weight corresponding to the support vector machine model in step M3 is 2.5. Therefore, after further normalizing the assigned weights, the final weight corresponding to each prediction model can be obtained. Specifically, the final weight corresponding to the linear regression model in step M1 is 0.324, the final weight corresponding to the linear regression model in step M2 is 0.405, and the final weight corresponding to the support vector machine model in step M3 is 0.271. In other words, the final difference prediction values ​​obtained in this embodiment are 7.7 and 6.4.

[0086] Step S1.3: Establish an environmental monitoring model and obtain the impact parameters of the environmental parameters on the current batch of raw materials based on the temperature and relative humidity in the current production environment, outdoor temperature, humidity and wind speed obtained in step S1.1. The details are as follows:

[0087] Step W1: Establish an initial neural network model, process the acquired environmental parameters, and obtain the initial environmental impact coefficient. The initial neural network model in this embodiment is specifically:

[0088] ,in: is the initial environmental impact coefficient, is the weight matrix corresponding to the neural network model, is the activation function, is the environmental parameter, is the bias vector.

[0089] Step W2: Optimize the initial neural network model in step W1 through the sliding window algorithm, specifically:

[0090] ,in: is the optimized environmental impact coefficient, For the The weight corresponding to each environmental parameter, For the environmental parameters, is the index of the environment parameter, is the total number of environmental parameters.

[0091] Step W3: Further optimize the optimized neural network model through the mutual influence of environmental parameters to obtain the final environmental parameters affecting the current batch of raw materials, specifically:

[0092] ,in: is the impact parameter of environmental parameters on the current batch of raw materials, For the The weight corresponding to each environmental parameter, For the environmental parameters, is the index of the environment parameter, is the total number of environmental parameters, For the The weights corresponding to the interaction terms between the environmental parameters, For the The interaction terms between the environmental parameters, is the total number of interaction terms between environmental parameters, is the index of the interaction term between environmental parameters.

[0093] During the specific implementation process, the data shown in Table 1 below was obtained, specifically:

[0094] Table 1

[0095] time Temperature / ℃ humidity / % Outdoor temperature / ℃ Outdoor humidity / % Wind speed / m / s Production results T1 25 60 20 70 5 7.6 T2 26 62 21 72 6 7.8 T3 24 59 19 69 4 7.5 T4 27 63 22 73 7 8.0 T5 23 57 18 67 3 7.3 T6 28 65 23 75 8 8.2 T7 23 58 19 68 4 7.5 According to the data in the table above, the current environmental parameters include indoor temperature, indoor humidity, outdoor temperature, outdoor humidity, and wind speed, with the corresponding weights for each environmental parameter being 0.2, 0.15, 0.1, 0.1, and 0.05, respectively. In this example, only the interaction term between indoor temperature and indoor humidity is considered, and the weight for this interaction term is 0.01. Therefore, the impact parameter of the environmental parameters on the current batch of raw materials in this example is 38.25.

[0096] Step S1.4: Combine the final difference prediction values ​​(7.7 and 6.4) obtained in step M4 with the environmental parameter impact parameter (38.25) obtained in step W3 to obtain the final adjustment parameter. In this embodiment, the final adjustment parameter is obtained through the adjustment model, which is specifically:

[0097] ,in: is the final adjustment parameter, is the weighting coefficient, is the final difference prediction value, It is the impact parameter of environmental parameters on the current batch of raw materials.

[0098] Furthermore, the weighting coefficient in this embodiment is There is no specific requirement for the size setting, which can be adjusted according to the production results of ultra-thin tiles in the actual production process. It is worth noting that the weighting coefficient The initial value of the size is set to 0.5, and its specific size will be adjusted according to specific production.

[0099] In the specific implementation process, the weighting coefficient in this embodiment The size of is set to 0.8, so the final adjustment parameters obtained in this embodiment are 13.81 and 12.77.

[0100] Step S2: Obtain simulation results. That is, based on the final adjustment parameters (13.81 and 12.77) obtained in step S1.4, use the final adjustment parameters as the input of the simulation model, output the simulation results corresponding to the adjustment parameters, and compare the simulation results with the simulation results corresponding to the standard formula. If the comparison results are consistent, execute the next step S3. Otherwise, return to the previous step S1.4, re-obtain the adjustment parameters, and repeat the current step S2 until the comparison results are consistent. The details are as follows:

[0101] Step S2.1: Use the final adjustment parameters (13.81 and 12.77) obtained in Step S1.4 as input to the simulation model. Using an existing process model (e.g., water-based tape casting), simulate the corresponding ultra-thin ceramic tile. Furthermore, using the same process model (e.g., water-based tape casting), simulate the corresponding ultra-thin ceramic tile with the standard formulation.

[0102] Step S2.2: Compare the simulation results of the ultra-thin ceramic tiles corresponding to the adjustment parameters obtained in step S2.1 with the simulation results of the ultra-thin ceramic tiles corresponding to the standard formula. If the comparison results are consistent, proceed to the next step S3, otherwise proceed to step S2.3 to adjust the weighting coefficients. The size of the raw material is adjusted or the environmental parameters are modified to modify the parameters affecting the current batch of raw materials, and the process returns to step S1.4 to re-acquire the adjustment parameters and repeat steps S2.1-S2.2.

[0103] During implementation, the simulation results for the ultra-thin ceramic tile corresponding to the adjusted parameters were: density (2.7g / cm³), flexural strength (150MPa), and water absorption (0.5%). The simulation results for the ultra-thin ceramic tile corresponding to the standard formulation were: density (2.8g / cm³), flexural strength (160MPa), and water absorption (0.4%). This data indicates a significant difference in flexural strength and water absorption, necessitating a new adjustment parameter.

[0104] Step S2.3: Determine the environmental parameters and weighting coefficients respectively, and identify the influencing factors that need to be adjusted. Based on the determined influencing factors, return to step S1.4, re-acquire the adjustment parameters, and repeat steps S2.1-S2.2. The details are as follows:

[0105] Step E1: Reacquire the current environmental parameters and compare them with the test environmental parameters acquired in step 1.3. Specifically, if the current environmental parameters and the test environmental parameters are identical, proceed directly to step E2. Otherwise, adjust the parameters affecting the current batch of raw materials based on the current environmental parameters. The final adjustment parameters are then adjusted based on the adjusted parameters affecting the current batch of raw materials.

[0106] Step E2: Adjust weighting coefficients The size of the weighting coefficient is increased or decreased by the same multiple. , and according to the weighted coefficient after increase or decrease , adjust the adjustment model and the final adjustment parameters.

[0107] Step S3: Batching operation. This involves determining the corresponding recipe based on the simulation results compared in step S2.2. The new dosage of each raw material is calculated based on the ratio of the raw materials in the recipe. The parameters of the current production equipment and the dosage of the production ingredients are adjusted based on the new dosage of each raw material.

[0108] This embodiment also provides an ultra-thin ceramic tile ingredient optimization system based on real-time data acquisition. The ultra-thin ceramic tile ingredient optimization system uses an ultra-thin ceramic tile ingredient optimization method based on real-time data acquisition in the above embodiment.

[0109] Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is limited by the accompanying embodiments and their equivalents.

Claims

1. A method for optimizing ultra-thin ceramic tile ingredients based on real-time data acquisition, characterized in that: Includes: S1: Obtain adjustment parameters: Obtain the composition difference between the current batch of raw materials and the standard formula, and obtain the final adjustment parameters based on the composition difference and external environmental data, including: S1.1: Obtain key parameters and external environmental data of the current batch of raw materials; S1.2: Establish multiple prediction models, obtain an initial difference prediction value corresponding to each prediction model, and determine a final difference prediction value based on the initial difference prediction value; S1.3: Obtaining the impact parameters of environmental parameters on the current batch of raw materials through the external environmental data and the environmental monitoring model; S1.4: Obtaining final adjustment parameters based on the difference prediction value and the influencing parameters; S2: Get simulation results: Get the simulation results corresponding to the adjustment parameters and the standard recipe, and compare the simulation results. If the comparison results are the same, execute the next step; otherwise, return to the previous step, including: S2.1: Using the adjustment parameters and the standard recipe as inputs to a simulation model to obtain corresponding simulation results; S2.2: Compare the simulation results. If the comparison results are the same, execute step S3. Otherwise, execute step S2.

3. S2.3: Determine the influencing factors and repeat steps S1.4 to S2.2 to re-obtain the adjustment parameters, including: E1: Re-obtain the current environmental parameters and compare them with the test environmental parameters. If the environmental parameters are the same, execute step E2. Otherwise, re-obtain the parameters affecting the environmental parameters on the current batch of raw materials based on the current environmental parameters. E2: Adjust the size of the weighting coefficient and re-obtain the adjustment parameter; S3: batching operation: according to the raw material formula corresponding to the compared simulation results, the dosage of each raw material is determined, and the production formula dosage of the current batch of raw materials is adjusted according to the dosage.

2. The method for optimizing ultra-thin ceramic tile ingredients based on real-time data acquisition according to claim 1, characterized in that: The final difference prediction value is determined as follows: M1: Establish a linear regression model to obtain the first initial difference prediction value, specifically: ,in: is the first initial difference prediction value, is the weight matrix, is the key parameter after preprocessing, is the bias vector; M2: Build a random forest model to obtain the second initial difference prediction value, specifically: ,in: is the second initial difference prediction value, is the total number of decision trees, is the difference prediction value corresponding to the i-th decision tree, is the key parameter after preprocessing, is the index of the decision tree; M3: Establish a support vector machine model to obtain the third initial difference prediction value, specifically: ， ， in: is the third initial difference prediction value, is the Lagrange multiplier, is the index of the sample, is the total number of samples, is the label of the jth sample in the key parameters after preprocessing, is the bias term, is the kernel function, is the feature vector of the jth sample in the key parameters after preprocessing, is the feature vector of the jth sample in the key parameters after preprocessing currently in operation; M4: Obtain a final difference prediction value based on the first initial difference prediction value, the second initial difference prediction value, and the third initial difference prediction value, specifically: ,in: is the final difference prediction value, is the weight corresponding to the first initial difference prediction value, is the first initial difference prediction value, is the weight corresponding to the second initial difference prediction value, is the second initial difference prediction value, is the weight corresponding to the third initial difference prediction value, is the third initial difference prediction value.

3. The method for optimizing ultra-thin ceramic tile ingredients based on real-time data acquisition according to claim 2, characterized in that: The method for obtaining the weight corresponding to the initial difference prediction value is as follows: N1: Obtain the root mean square error corresponding to each prediction model and normalize each of the root mean square errors; N2: Determine the distribution weight corresponding to each of the initial difference prediction values ​​based on the normalized root mean square error, and obtain the final weight corresponding to each of the initial difference prediction values ​​using the distribution weight, specifically: ,in: is the weight corresponding to the k-th prediction model, is the distribution weight corresponding to the root mean square error after normalization, is the initial weight corresponding to the m-th prediction model, is the index corresponding to the prediction model.

4. The method for optimizing ultra-thin ceramic tile ingredients based on real-time data acquisition according to claim 2, characterized in that: Get the impact of environmental parameters on the current batch of raw materials, as follows: W1: Establish an initial neural network model to obtain the initial environmental impact coefficient, specifically: ,in: is the initial environmental impact coefficient, is the weight matrix corresponding to the neural network model, is the activation function, is the environmental parameter, is the bias vector; W2: Optimize the initial neural network model through a sliding window algorithm to obtain an optimized neural network model, specifically: ,in: is the optimized environmental impact coefficient, For the The weight corresponding to each environmental parameter, For the environmental parameters, is the index of the environment parameter, is the total number of environmental parameters; W3: Based on the optimized neural network model, an environmental monitoring model is established to obtain the impact parameters of environmental parameters on the current batch of raw materials. The environmental monitoring model is specifically: ,in: is the impact parameter of environmental parameters on the current batch of raw materials, For the The weight corresponding to each environmental parameter, For the environmental parameters, is the index of the environment parameter, is the total number of environmental parameters, For the The weights corresponding to the interaction terms between the environmental parameters, For the The interaction terms between the environmental parameters, is the total number of interaction terms between environmental parameters, is the index of the interaction term between environmental parameters.

5. An ultra-thin ceramic tile batching optimization system based on real-time data acquisition, characterized in that: The ultra-thin ceramic tile ingredient optimization system uses the ultra-thin ceramic tile ingredient optimization method based on real-time data acquisition as described in any one of claims 1-4.

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