A method and system for intelligent optimization of BOPP film making process parameters

Abstract

The present invention relates to a method and system for intelligently optimizing BOPP film production process parameters, comprising the following steps: obtaining historical data on BOPP film production, including process parameters and corresponding product performance data; constructing a product performance control model using the process parameters as input and the product performance data as output, formulating a control strategy based on demand, and solving to obtain first process parameters using a multi-objective optimization algorithm; preparing BOPP film using the first process parameters and obtaining equipment operation data; constructing an equipment operation control model using the first process parameters as input and the equipment operation data as output, and solving to obtain second process parameters; and producing BOPP film using the second process parameters. The method and system for intelligently optimizing BOPP film production process parameters of the present invention optimizes process parameters in two dimensions: product performance and equipment operation, ensuring high product quality while maintaining the optimal operating state of the equipment to the greatest extent possible.

Description

Technical Field

[0001] The present invention relates to the technical field of BOPP film making technology, and in particular to a method and system for intelligently optimizing BOPP film making process parameters. Background Art

[0002] In traditional BOPP (biaxially oriented polypropylene) film production, determining process parameters has always faced many challenges. Previously, operators relied heavily on past experience or trial and error to find the appropriate parameters, which resulted in the following significant deficiencies:

[0003] Efficiency constraints are obvious: relying on manual experience to adjust parameters, each trial and error process is extremely time-consuming, making the entire production preparation phase lengthy, seriously slowing down the production pace, and making it difficult to improve BOPP film production efficiency.

[0004] Difficulty in quality control: The experience of different operators varies, and the judgment of the same operator at different times also fluctuates. This makes the quality of the produced BOPP film products unstable and difficult to maintain a high quality level. Key quality indicators such as film thickness uniformity and tensile strength are prone to deviations.

[0005] Lack of comprehensive consideration: In the past, when determining process parameters, people often focused on a single aspect, such as yield or quality, without comprehensively and comprehensively weighing key dimensions such as optimal speed, optimal quality, and optimal yield. This made it difficult to accurately select the optimal process parameter combination that truly meets the production needs of BOPP film.

[0006] With the development of data analysis technology, some companies have begun to apply data analysis to optimize BOPP film production process parameters, but these applications are often limited. For example, simple correlation analysis is performed on selected process parameters and product performance data to identify parameters that significantly affect product quality or production efficiency. However, this analysis lacks systematicity and depth, fails to fully explore the inherent relationships between parameters, and fails to consider more dimensional factors such as energy consumption and equipment lifespan, making it impossible to achieve comprehensive parameter optimization and comprehensive multi-objective decision-making.

[0007] This initial application is often not in-depth and comprehensive enough, and cannot dynamically recommend the optimal process parameter combination based on different product types, order requirements and production stages, making it difficult to meet the company's needs for further improvement in production efficiency. Summary of the Invention

[0008] To this end, the technical problem to be solved by the present invention is to overcome the shortcomings of the BOPP film making process in the prior art, and to provide a BOPP film making process parameter intelligent optimization method and system, which optimizes the process parameters from the dimensions of product performance data and equipment operation data, and can ensure that the equipment is in the best working condition as much as possible while ensuring a high level of product production.

[0009] To solve the above technical problems, the present invention provides a method for intelligent optimization of BOPP film making process parameters, comprising the following steps:

[0010] Obtain historical data on BOPP film production, including various process parameters involved in the film production process and product performance data corresponding to the process parameters;

[0011] A product performance control model is constructed using various process parameters involved in historical data as input parameters and product performance data as output, wherein the product performance data includes multiple product performance evaluation indicators;

[0012] Among multiple product performance evaluation indicators, multiple control strategies are formulated according to needs, and the process parameters in each control strategy are solved based on a multi-objective optimization algorithm to obtain multiple first process parameters;

[0013] preparing a BOPP film using a plurality of first process parameters and obtaining equipment operation data during the film-making process;

[0014] An equipment operation control model is constructed using a plurality of first process parameters as input parameters and equipment operation data as output, wherein the equipment operation data includes a plurality of equipment operation evaluation indicators;

[0015] Among the multiple equipment operation evaluation indicators, taking one of the optimal equipment operation indicators as a target, solving the process parameters of the equipment operation control model to obtain a second process parameter;

[0016] The BOPP film is produced using the second process parameters.

[0017] In one embodiment of the present invention, the product performance data includes a plurality of product performance evaluation indicators, and the product performance evaluation indicators include at least: product quality, product production speed, and product yield.

[0018] In one embodiment of the present invention, among multiple product performance evaluation indicators, multiple control strategies are formulated according to demand, and the process parameters in each control strategy are solved based on a multi-objective optimization algorithm to obtain multiple first process parameters, including:

[0019] With the goal of optimal product quality, the product performance control model is optimized through a neural network algorithm to obtain a process parameter, including:

[0020] Go through the historical data, various process parameters and corresponding product quality data;

[0021] Design a neural network model to simulate the relationship between process parameters and product quality data, and train the neural network model with process parameters as input parameters and product quality data as output parameters;

[0022] Define the objective function of maximizing a certain quality indicator of a specific product, and use a global search algorithm to find the optimal solution based on the trained model.

[0023] In one embodiment of the present invention, among multiple product performance evaluation indicators, multiple control strategies are formulated according to demand, and the process parameters in each control strategy are solved based on a multi-objective optimization algorithm to obtain multiple first process parameters, including:

[0024] With the goal of optimizing product production speed, the product performance control model is optimized through a neural network algorithm to obtain a process parameter including:

[0025] Calculate the production speed under different parameter settings based on the time-related data and corresponding process parameters in the BOPP film production records;

[0026] Design a neural network model to simulate the relationship between process parameters and product production speed, and train the neural network model with process parameters as input parameters and product production speed as output parameters;

[0027] Use the neural network model to screen out the process parameter combination that can maximize the production process turnover while ensuring that the BOPP film product quality meets the standards;

[0028] Apply the multi-objective optimization algorithm, combine it with the trained neural network model, set the product yield as a constraint, and find the optimal solution based on the trained model.

[0029] In one embodiment of the present invention, among multiple product performance evaluation indicators, multiple control strategies are formulated according to demand, and the process parameters in each control strategy are solved based on a multi-objective optimization algorithm to obtain multiple first process parameters, including:

[0030] With the goal of achieving the optimal product yield, the product performance control model is optimized using a neural network algorithm to obtain a set of process parameters, including:

[0031] Go through the historical data, various process parameters and corresponding product yield data;

[0032] Design a neural network model to simulate the relationship between process parameters and product yield data, and train the neural network model with process parameters as input parameters and product yield data as output parameters;

[0033] Using a neural network model, a multi-channel calculation model is set up according to the process steps involved in the preparation process to analyze the process parameter combination that can achieve the highest production yield in each process step;

[0034] Apply a multi-objective optimization algorithm, combine it with a trained neural network model, set the product production speed as a constraint, and find the optimal solution based on the trained model.

[0035] In one embodiment of the present invention, the equipment operation data includes a plurality of equipment operation evaluation indicators, and the equipment operation evaluation indicators include at least: equipment energy consumption and equipment life.

[0036] In one embodiment of the present invention, among multiple equipment operation evaluation indicators, taking the optimal energy consumption of the equipment as the goal, the process parameters of the equipment operation control model are solved to obtain the second process parameters, including:

[0037] Traverse the historical data, first process parameters and corresponding equipment energy consumption data;

[0038] Designing a neural network model to simulate the relationship between the first process parameter and the equipment energy consumption, and training the neural network model with the first process parameter as an input parameter and the equipment energy consumption as an output parameter;

[0039] Define the objective function of minimizing the overall equipment energy consumption, combine it with the trained neural network model, set the equipment life as a constraint, and find the optimal solution based on the trained model.

[0040] In one embodiment of the present invention, among multiple equipment operation evaluation indicators, taking the optimal equipment life as the goal, the process parameters of the equipment operation control model are solved to obtain the second process parameters, including:

[0041] Traverse the historical data, first process parameters and corresponding equipment life data;

[0042] Designing a neural network model to simulate the relationship between the first process parameter and the equipment life, and training the neural network model with the first process parameter as an input parameter and the equipment life as an output parameter;

[0043] Using a neural network model, a multi-channel calculation model is set up according to the process steps involved in the preparation process to analyze the first process parameter combination that can maximize the equipment life in each process step;

[0044] Apply a multi-objective optimization algorithm, combine it with a trained neural network model, set equipment energy consumption as a constraint, and find the optimal solution based on the trained model.

[0045] In one embodiment of the present invention, after the BOPP film is produced with the second process parameters, the method further includes collecting product performance data and equipment operation data in real time, and updating the second process parameters according to the collected results.

[0046] The above technical solution of the present invention has the following advantages over the prior art:

[0047] The intelligent optimization method for BOPP film making process parameters described in the present invention first makes full use of the process parameters and corresponding product performance result data in the historical process of BOPP film making, and with the help of machine learning, accurately recommends the optimal process parameter combination from the dimension of product performance data, thereby effectively improving the production efficiency of BOPP film making, stabilizing product quality and ensuring that the yield reaches a better level; while ensuring a high product production level, the obtained process parameters and corresponding equipment operation data are used, with the help of machine learning, to further optimize the process parameters from the dimension of equipment operation data, to ensure that the equipment can also be in a long-term stable and optimal working state.

[0048] Compared with existing technologies, it abandons the inefficient traditional trial and error method and quickly locks the optimal parameters through intelligent recommendation, which greatly shortens the preparation time for BOPP film production, speeds up the entire BOPP film production, and improves the BOPP film output efficiency per unit time. It recommends parameters based on scientific data analysis, avoids the instability and subjective interference of human experience, and ensures that the quality of the produced BOPP film products can be stably maintained at a high level, reducing quality problems such as uneven film thickness and insufficient strength caused by parameter fluctuations. Moreover, it not only controls parameters from the product dimension, but also optimizes parameters from the equipment dimension, overcoming the limitations of previous single-dimensional decision-making, making the selection of BOPP film process parameters more scientific and reasonable, and helping BOPP film production to reach the best state in all aspects. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to make the content of the present invention more clearly understood, the present invention is further described in detail below based on specific embodiments of the present invention in conjunction with the accompanying drawings, wherein:

[0050] Figure 1 It is a flowchart of the steps of the intelligent optimization method of BOPP film making process parameters of the present invention;

[0051] Figure 2 is a flow chart of steps for obtaining a plurality of first process parameters using the method of the present invention;

[0052] Figure 3 is a flow chart of the steps of obtaining a plurality of second process parameters using the method of the present invention;

[0053] Figure 4 This is a step structure framework diagram of the BOPP film making process parameter intelligent optimization system of the present invention. DETAILED DESCRIPTION

[0054] The present invention will be further described below with reference to the accompanying drawings and specific embodiments so that those skilled in the art can better understand the present invention and implement it. However, the embodiments are not intended to limit the present invention.

[0055] Example 1

[0056] Reference Figure 1 As shown, the present invention discloses a method for intelligent optimization of BOPP film making process parameters, comprising the following steps:

[0057] S10. Obtain historical data of BOPP film production, including various process parameters involved in the film production process and product performance data corresponding to the process parameters;

[0058] Specifically, the BOPP film production process is a complex and precise process, mainly including the following key process steps: extrusion, cooling, longitudinal stretching, transverse stretching, heat setting treatment, cooling and winding. In this process, each process step has corresponding process parameters to guide the operation, such as extrusion temperature, stretch ratio, pulling speed, cooling temperature. In addition, the properties of polypropylene used as different raw materials are also different, such as parameters such as molecular weight distribution and isotacticity. Corresponding to different process parameters, the quality of the prepared BOPP film is also different. Correspondingly, the product performance result data of the produced BOPP film is collected, such as the thickness uniformity, tensile strength, elongation at break, haze, glossiness and yield rate of the BOPP film.

[0059] Specifically, in order to comprehensively evaluate product performance, it is necessary to collect the above-mentioned parameters from raw materials to each process step, enrich the data dimensions as much as possible, and ensure that there is sufficient and high-quality data as a basis to build an accurate and reliable prediction model. The quality of the data directly affects the effectiveness of all subsequent steps.

[0060] S20, constructing a product performance control model using various process parameters involved in the historical data as input parameters and product performance data as output, wherein the product performance data includes multiple product performance evaluation indicators;

[0061] Specifically, by building a model, determining the relationship between process parameters and product performance, and selecting appropriate machine learning or deep learning models (such as neural networks, support vector machines, etc.) based on specific problems, historical data can be divided into training sets, test sets, and validation sets. The model can be trained using historical data, and the model performance can be evaluated through cross-validation and other techniques to ensure its generalization ability. Establishing a model that can accurately predict product quality under different process conditions is the core of the entire optimization process, and can provide a theoretical basis and technical means for subsequent process parameter optimization.

[0062] S30. Develop multiple control strategies based on demand among multiple product performance evaluation indicators, and solve the process parameters in each control strategy based on a multi-objective optimization algorithm to obtain multiple first process parameters.

[0063] Specifically, in the actual production process, a variety of BOPP films of different qualities will be generated based on different needs. For example, some customers need high-quality BOPP films, so we must focus on the quality of film production, and ensure that the optimal solution is achieved from raw materials to the control of each process parameter. Some customers need BOPP films urgently, so we must focus on the speed of film production, and increase the speed of each production process as soon as possible while ensuring the quality. In this way, when training the model, it is necessary to formulate multiple control strategies. Based on the needs of the enterprise (such as focusing on quality, speed or yield), weights are assigned to each product performance evaluation indicator to form a comprehensive objective function. Genetic algorithms, particle swarm optimization and other methods are used to find the process parameter combination that optimizes the objective function under given constraints. Multiple first process parameters can be obtained corresponding to different strategies.

[0064] S40, preparing a BOPP film using a plurality of first process parameters, and obtaining equipment operation data during the film-making process;

[0065] Specifically, the BOPP film prepared by the first process parameters is a product that can meet the demand and ensure the quality of the product. On this basis, sensors are set to monitor the operating parameters of various equipment in the production process, such as: equipment energy consumption, equipment operating temperature, equipment wear, etc., and record and analyze these data to understand the impact of the first process parameters on the equipment, providing a basis for subsequent modeling.

[0066] S50, constructing an equipment operation control model using a plurality of first process parameters as input parameters and equipment operation data as output, wherein the equipment operation data includes a plurality of equipment operation evaluation indicators;

[0067] Specifically, by building a model to determine the relationship between process parameters and equipment operation, the model is trained using the collected data to enable it to predict equipment performance under specific process settings.

[0068] S60, taking one of the optimal equipment operation indicators as a target among the multiple equipment operation evaluation indicators, solving the process parameters of the equipment operation control model to obtain a second process parameter;

[0069] Specifically, in the actual production process, the degree of equipment usage varies based on different needs. For example, different extrusion temperatures and cooling temperatures have different effects on the overall energy consumption; different stretching ratios and pulling speeds have different effects on the wear of equipment components such as the extruder screw and stretching roller. One or more key equipment operation evaluation indicators (such as minimum energy consumption and longest service life) are selected as optimization targets, and the optimization algorithm is used to find the process parameter configuration that makes the selected equipment operation indicators achieve the optimal value while meeting product quality requirements.

[0070] S70, producing a BOPP film using the second process parameters;

[0071] Specifically, after the BOPP film is produced with the second process parameters, the process also includes real-time collection of product performance data and equipment operation data, and updating the second process parameters according to the collection results to ensure that they are always in the best state.

[0072] Specifically, the product performance data includes multiple product performance evaluation indicators. In an embodiment, according to actual needs, the product performance evaluation indicators are set to at least include: product quality, product production speed and product yield. If there are other product performance evaluation indicators, they can also be expanded according to actual conditions. In this embodiment, the above three product performance evaluation indicators are used for illustration to obtain different first process parameters.

[0073] Reference Figure 2 As shown, in one of the product performance evaluation indicators, with the optimal product quality as the goal, the product performance control model is optimized by the neural network algorithm to obtain a process parameter, including:

[0074] Various process parameters and corresponding product quality data are traversed in historical data. In this embodiment, only the process parameters that determine product quality need to be collected, which involves screening the process parameters, cleaning the collected data, and removing outliers or incomplete records. Here, the process parameters with little impact on product quality are mainly removed to ensure the quality and integrity of the data. The process parameters of different magnitudes are normalized to facilitate subsequent model training.

[0075] A neural network model is designed to simulate the relationship between process parameters and product quality data, and the neural network model is trained with the process parameters as input parameters and the product quality data as output parameters. A suitable neural network architecture is selected according to the complexity of the problem and the data characteristics. In this embodiment, due to the presence of multiple parameters of multiple processes, a multi-layer perceptron is more appropriate. The preprocessed historical data is divided into a training set, a validation set, and a test set, usually in a ratio of 70% training set, 15% validation set, and 15% test set.

[0076] Define an objective function that maximizes a certain quality indicator for a specific product, and use a global search algorithm to find the optimal solution based on the trained model. For example, if the focus is on product transparency, the objective function can be set to maximize the transparency score, and other relevant product quality indicators can be used as constraints to ensure that other important attributes are not affected while pursuing the main goal; run a global search algorithm, including genetic algorithm (GA), particle swarm optimization (PSO) or Bayesian optimization, until the optimal process parameter combination that meets the termination criteria (such as reaching a predetermined number of iterations or the improvement is below a threshold) is found.

[0077] Reference Figure 2 As shown, in one of the product performance evaluation indicators, with the optimal product production speed as the goal, the product performance control model is optimized by the neural network algorithm to obtain a process parameter including:

[0078] Calculate the production speed under different parameter settings based on the time-related data and corresponding process parameters in the BOPP film production records;

[0079] A neural network model is designed to simulate the relationship between process parameters and product production speed. The neural network model is trained with process parameters as input parameters and product production speed as output parameters.

[0080] The neural network model is used to screen out the process parameter combination that can make the production process turnover the fastest under the premise of ensuring that the quality of the BOPP film product meets the standards. In this embodiment, it is necessary to ensure that the quality of the BOPP film product meets the standards and screen out the process parameter combination that can achieve the fastest production speed. Combined with the neural network model of product quality, it is checked whether each predicted process parameter combination meets the pre-set quality standards (such as transparency, strength, etc.), and those parameter combinations that meet the quality requirements and provide a higher production speed are retained.

[0081] It should be noted that, in this embodiment, a multi-objective optimization algorithm is applied, combined with a trained neural network model, the product yield is set as a constraint condition, and the optimal solution is found based on the trained model. Specifically, when training the neural network model, the objective function of the multi-objective optimization is defined, where the main goal is to minimize the production time (i.e., maximize the production speed), and the product yield is set as a constraint condition.

[0082] Reference Figure 2 As shown, in one of the product performance evaluation indicators, with the optimal product yield as the goal, the product performance control model is optimized by the neural network algorithm to obtain a process parameter including:

[0083] Go through the historical data, various process parameters and corresponding product yield data.

[0084] A neural network model is designed to simulate the relationship between process parameters and product yield data. The neural network model is trained with process parameters as input parameters and product yield data as output parameters.

[0085] Using a neural network model, a multi-channel calculation model is set up according to the process steps involved in the preparation process. The process parameter combination that can achieve the highest production yield in each process step is analyzed. In the actual production process, each process step has a yield test. In order to deeply analyze the impact of the process parameters in each specific process step on the yield, the entire production process is broken down into multiple independent but interrelated sub-processes (i.e., "channels"), which can more accurately identify and optimize the key process parameters at each stage, thereby improving the overall yield. The specific process is as follows:

[0086] Decompose the production process steps and regard each production process step as an independent "channel". For each process step, design an independent calculation model, which aims to simulate the relationship between process parameters and yield under a specific process step. For each channel, use historical data to train the corresponding model. Based on the trained model, analyze the optimal process parameter combination for each process step, analyze the influence weight of each channel on the yield, comprehensively consider the results of all channels, and configure the globally optimal process parameters according to the influence weight.

[0087] It should be noted that, in this embodiment, a multi-objective optimization algorithm is applied, combined with a trained neural network model, the production speed of the product is set as a constraint, and the optimal solution is found based on the trained model.

[0088] Specifically, the equipment operation data includes multiple equipment operation evaluation indicators. In an embodiment, according to actual needs, the equipment operation evaluation indicators are set to at least include: equipment energy consumption and equipment life. If there are other equipment operation evaluation indicators, they can also be expanded according to actual conditions. In this embodiment, the above two equipment operation evaluation indicators are used for illustration to obtain different second process parameters.

[0089] Reference Figure 3 As shown, in one embodiment, with the goal of optimizing the energy consumption of the equipment, the process parameters of the equipment operation control model are solved to obtain the second process parameters, including:

[0090] The first process parameter and the corresponding equipment energy consumption data are traversed in the historical data.

[0091] A neural network model is designed to simulate the relationship between the first process parameter and the equipment energy consumption, and the neural network model is trained with the first process parameter as the input parameter and the equipment energy consumption as the output parameter. In this embodiment, the product prepared by the first process parameter can meet the performance requirements of the product. On this basis, the process parameters that affect the equipment energy consumption in the first process parameter are collected, and a secondary screening is performed in the first process parameter.

[0092] Define the objective function of minimizing the overall equipment energy consumption, combine it with the trained neural network model, set the equipment life as a constraint, and find the optimal solution based on the trained model.

[0093] Reference Figure 3 As shown, in another embodiment, the process parameters of the equipment operation control model may be solved with the optimal life of the equipment as the goal to obtain the second process parameters, including:

[0094] Traverse the historical data, first process parameters and corresponding equipment life data;

[0095] Designing a neural network model to simulate the relationship between the first process parameter and the equipment life, and training the neural network model with the first process parameter as an input parameter and the equipment life as an output parameter;

[0096] Using a neural network model, a multi-channel calculation model is set up according to the process steps involved in the preparation process to analyze the first process parameter combination that can maximize the equipment life in each process step;

[0097] Apply a multi-objective optimization algorithm, combine it with a trained neural network model, set equipment energy consumption as a constraint, and find the optimal solution based on the trained model.

[0098] The present invention provides an intelligent optimization method for BOPP film-making process parameters. By analyzing historical data and building a product performance control model, the method can accurately identify key process parameters that affect product performance, provide at least three product performance evaluation indicators, and use a multi-objective optimization algorithm to find the best compromise solution to ensure that the optimal product performance is achieved while meeting all quality standards. On this basis, by building an equipment operation control model, the performance of the equipment under different process conditions can be predicted and optimized. The optimized process parameters not only focus on product quality and production efficiency, but also focus on reducing energy consumption, which is of great significance for energy conservation and emission reduction. At the same time, preventive maintenance measures are taken to avoid excessive wear and extend the service life of the equipment.

[0099] The intelligent process parameter optimization method of the present invention allows for dynamic adjustment of optimization strategies based on changes in market or customer demand. For example, when the market demands high-end products, the focus is on quality optimization, while under conventional demand, more attention is paid to cost-effectiveness. Due to its high flexibility and adaptability, this method can support diverse production needs and meet the requirements of different customer groups.

[0100] Example 2

[0101] On the basis of the above embodiment 1, the present invention also discloses a BOPP film making process parameter intelligent optimization system, which can execute the above method, referring to Figure 4 As shown, including:

[0102] A data acquisition unit, configured to acquire historical data of BOPP film production, wherein the historical data includes at least various process parameters involved in the film production process and product performance data corresponding to the process parameters;

[0103] a product performance control model building unit, connected to the data acquisition unit, for building a product performance control model using various process parameters in the historical data as input parameters and product performance data as output parameters, wherein the product performance data includes a plurality of product performance evaluation indicators;

[0104] a first process parameter solving unit, connected to the product performance control model building unit, for solving the process parameters in the product performance control model with the optimal product performance evaluation index as a target among multiple product performance evaluation indexes, to obtain multiple first process parameters;

[0105] an equipment operation data acquisition unit, configured to prepare the BOPP film according to the plurality of first process parameters and acquire equipment operation data during the film-making process;

[0106] an equipment operation control model building unit, connected to the equipment operation data acquisition unit, for building an equipment operation control model using the plurality of first process parameters as input parameters and the equipment operation data as output parameters, wherein the equipment operation data includes a plurality of equipment operation evaluation indicators;

[0107] a second process parameter solving unit, connected to the equipment operation control model building unit, for selecting an optimal equipment operation indicator from a plurality of equipment operation evaluation indicators as a target, solving the process parameters of the equipment operation control model to obtain a second process parameter;

[0108] A production execution unit is used to produce the BOPP film according to the second process parameters.

[0109] Specifically, the specific process of implementing the above method by using the intelligent optimization system for BOPP film-making process parameters of the present invention is described in detail in Example 1 and will not be repeated here.

[0110] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0111] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0112] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0113] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0114] Obviously, the above embodiments are merely examples for clarity of explanation and are not intended to limit the implementation methods. Those skilled in the art will appreciate that other variations or modifications can be made based on the above description. It is not necessary and impossible to enumerate all implementation methods here. Obvious variations or modifications arising therefrom remain within the scope of protection of the present invention.

Claims

1. A method for intelligent optimization of BOPP film making process parameters, characterized by: The following steps are involved: Obtain historical data on BOPP film production, including various process parameters involved in the film production process and product performance data corresponding to the process parameters; A product performance control model is constructed using various process parameters involved in historical data as input parameters and product performance data as output, wherein the product performance data includes multiple product performance evaluation indicators, and the product performance evaluation indicators include at least product quality, product production speed, and product yield rate; Among multiple product performance evaluation indicators, multiple control strategies are formulated according to needs, and the process parameters in each control strategy are solved based on a multi-objective optimization algorithm to obtain multiple first process parameters, including: taking optimal product quality as the goal, optimizing the variables of the product performance control model through a neural network algorithm to obtain a process parameter, including: traversing various process parameters and corresponding product quality data in historical data; designing a neural network model to simulate the relationship between the process parameters and product quality data, training the neural network model with the process parameters as input parameters and the product quality data as output parameters; defining an objective function that maximizes a quality indicator of a specific product, and using a global search algorithm to find the optimal solution based on the trained model; preparing a BOPP film using a plurality of first process parameters and obtaining equipment operation data during the film-making process; An equipment operation control model is constructed using a plurality of first process parameters as input parameters and equipment operation data as output, wherein the equipment operation data includes a plurality of equipment operation evaluation indicators, and the equipment operation evaluation indicators include at least equipment energy consumption and equipment life; Among multiple equipment operation evaluation indicators, taking one of the optimal equipment operation indicators as a target, solving the process parameters of the equipment operation control model to obtain a second process parameter, including: taking the optimal equipment energy consumption as a target, solving the process parameters of the equipment operation control model to obtain the second process parameter, including: traversing the first process parameter and the corresponding equipment energy consumption data in historical data; designing a neural network model to simulate the relationship between the first process parameter and equipment energy consumption, training the neural network model with the first process parameter as an input parameter and the equipment energy consumption as an output parameter; defining an objective function for minimizing overall equipment energy consumption, combining the trained neural network model, setting the equipment life as a constraint condition, and finding the optimal solution based on the trained model; The BOPP film is produced using the second process parameters.

2. The method for intelligent optimization of BOPP film-making process parameters according to claim 1, characterized in that: Among multiple product performance evaluation indicators, multiple control strategies are formulated according to needs. The process parameters in each control strategy are solved based on the multi-objective optimization algorithm to obtain multiple first process parameters, including: With the goal of optimizing product production speed, the product performance control model is optimized through a neural network algorithm to obtain a process parameter including: Calculate production speeds under different parameter settings based on time-related data and corresponding process parameters in BOPP film production records; Design a neural network model to simulate the relationship between process parameters and product production speed, and train the neural network model with process parameters as input parameters and product production speed as output parameters; Use a neural network model to identify the process parameter combination that can maximize production process turnover while ensuring that BOPP film product quality meets standards; Apply the multi-objective optimization algorithm, combine it with the trained neural network model, set the product yield as a constraint, and find the optimal solution based on the trained model.

3. The method for intelligent optimization of BOPP film-making process parameters according to claim 1, characterized in that: Among multiple product performance evaluation indicators, multiple control strategies are formulated according to needs. The process parameters in each control strategy are solved based on the multi-objective optimization algorithm to obtain multiple first process parameters, including: With the goal of achieving the optimal product yield, the product performance control model is optimized using a neural network algorithm to obtain a set of process parameters, including: Go through the historical data, various process parameters and corresponding product yield data; Design a neural network model to simulate the relationship between process parameters and product yield data, and train the neural network model with process parameters as input parameters and product yield data as output parameters; Using a neural network model, a multi-channel calculation model is set up according to the process steps involved in the preparation process to analyze the process parameter combination that can achieve the highest production yield in each process step; Apply a multi-objective optimization algorithm, combine it with a trained neural network model, set the product production speed as a constraint, and find the optimal solution based on the trained model.

4. The method for intelligent optimization of BOPP film-making process parameters according to claim 1, characterized in that: Among multiple equipment operation evaluation indicators, with the equipment optimal life as the goal, the process parameters of the equipment operation control model are solved to obtain second process parameters, including: Traverse the historical data, the first process parameter and the corresponding equipment life data; Designing a neural network model to simulate the relationship between the first process parameter and the equipment life, and training the neural network model with the first process parameter as an input parameter and the equipment life as an output parameter; Using a neural network model, a multi-channel calculation model is set up according to the process steps involved in the preparation process to analyze the first process parameter combination that can maximize the equipment life in each process step; Apply a multi-objective optimization algorithm, combine it with a trained neural network model, set equipment energy consumption as a constraint, and find the optimal solution based on the trained model.

5. The method for intelligent optimization of BOPP film-making process parameters according to claim 1, characterized in that: After the BOPP film is produced with the second process parameters, the method further includes collecting product performance data and equipment operation data in real time, and updating the second process parameters according to the collected results.

6. A BOPP film-making process parameter intelligent optimization system, characterized by: include: A data acquisition unit, configured to acquire historical data of BOPP film production, wherein the historical data includes at least various process parameters involved in the film production process and product performance data corresponding to the process parameters; a product performance control model building unit, connected to the data acquisition unit, for building a product performance control model using various process parameters in the historical data as input parameters and product performance data as output parameters, wherein the product performance data includes a plurality of product performance evaluation indicators, and the product performance evaluation indicators include at least: product quality, product production speed, and product yield; A first process parameter solving unit, connected to the product performance control model building unit, is used to solve the process parameters in the product performance control model with the optimal product performance evaluation index as the target among multiple product performance evaluation indexes to obtain multiple first process parameters, including: with the optimal product quality as the target, performing variable optimization on the product performance control model through a neural network algorithm to obtain a process parameter, including: traversing various process parameters and corresponding product quality data in historical data; designing a neural network model to simulate the relationship between the process parameters and the product quality data, training the neural network model with the process parameters as input parameters and the product quality data as output parameters; defining an objective function for maximizing a quality index of a specific product, and using a global search algorithm to find the optimal solution based on the trained model; an equipment operation data acquisition unit, configured to prepare the BOPP film according to the plurality of first process parameters and acquire equipment operation data during the film-making process; an equipment operation control model building unit, connected to the equipment operation data acquisition unit, for building an equipment operation control model using the plurality of first process parameters as input parameters and the equipment operation data as output parameters, wherein the equipment operation data includes a plurality of equipment operation evaluation indicators, and the equipment operation evaluation indicators include at least: equipment energy consumption and equipment life; A second process parameter solving unit is connected to the equipment operation control model building unit and is used to select one of the optimal equipment operation indicators from multiple equipment operation evaluation indicators as a target, solve the process parameters of the equipment operation control model to obtain the second process parameters, including: solving the process parameters of the equipment operation control model with the optimal equipment energy consumption as the target to obtain the second process parameters, including: traversing the first process parameter and the corresponding equipment energy consumption data in the historical data; designing a neural network model to simulate the relationship between the first process parameter and the equipment energy consumption, training the neural network model with the first process parameter as the input parameter and the equipment energy consumption as the output parameter; defining an objective function for minimizing the overall equipment energy consumption, combining the trained neural network model, setting the equipment life as a constraint condition, and finding the optimal solution based on the trained model; A production execution unit is used to produce the BOPP film according to the second process parameters.

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