A method and system for quickly solving setting parameters for multi-step production processes

Abstract

The present invention provides a method and system for quickly solving setting parameters for a multi-process production process, relating to the field of data processing technology. The method comprises: constructing a mathematical model to describe the multiple processes and determining basic information of each process; constructing a multi-objective optimization model with the goal of maximizing production efficiency and production benefits; solving the multi-objective optimization model by using an NSGA-II algorithm and determining an optimal dynamic setting strategy; converting the multi-objective optimization model into a single-objective optimization model and determining constraints in the conversion process; adopting the optimal dynamic setting strategy and using a particle swarm optimization algorithm to determine optimal setting parameters under different input parameters and constructing a training set; fusing the constraints in the conversion process as physical information into a neural network to construct a physical information neural network model; and training the model using the training set; and determining setting parameter values ​​of the input parameters using the trained physical information neural network model.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and system for quickly solving setting parameters for a multi-step production process. Background Art

[0002] The multi-process production process is the core link in modern industrial manufacturing, covering multiple key steps from raw material processing to finished product assembly. This type of production process is widely used in many fields, including mechanical manufacturing, aerospace, automotive industry and electronic equipment manufacturing. In the multi-process production process, a variety of complex process steps are involved, such as melting, casting, rolling, heat treatment and surface treatment. The parameter settings of each process (such as temperature, time, pressure, etc.) will have an important impact on the final performance and quality of the product. For example, in the melting process, improper temperature control may lead to deviations in the alloy composition; in the casting process, slight changes in cooling rate may affect the grain size and distribution of the alloy; in the rolling and heat treatment processes, improper parameter settings will lead to uneven distribution of internal stress and hardness of the material. Therefore, the reasonable setting of process parameters has become the key to improving product quality and production efficiency.

[0003] However, traditional process parameter setting methods often rely on experience, with operators adjusting process parameters based on past experience or trial and error. This approach is not only inaccurate but also consumes significant manpower and time. Furthermore, this reliance on operator experience makes it difficult to adjust and optimize parameters in a complex and changing production environment, impacting production continuity and product consistency. Summary of the Invention

[0004] To address the problem that traditional process parameter setting methods typically rely on experience, requiring operators to adjust process parameters based on past experience or step-by-step trials. This method is not only inaccurate but also consumes a significant amount of manpower and time. Furthermore, this reliance on operator experience makes it difficult to adjust and optimize parameters in a complex and changing production environment, hindering production continuity and product consistency. The present invention provides a method and system for rapidly determining setting parameters for multi-process production.

[0005] The technical solutions provided by the embodiments of the present invention are as follows:

[0006] First aspect:

[0007] An embodiment of the present invention provides a method for quickly solving setting parameters for a multi-step production process, including:

[0008] S1: Construct a mathematical model to describe multiple processes and determine the basic information of each process;

[0009] S2: Build a multi-objective optimization model with the goal of maximizing production efficiency and production benefits;

[0010] S3: Solve the multi-objective optimization model using the NSGA-II algorithm to determine the optimal dynamic setting strategy;

[0011] S4: converting the multi-objective optimization model into a single-objective optimization model, and determining the constraints in the conversion process;

[0012] S5: using the optimal dynamic setting strategy and a particle swarm optimization algorithm to determine the optimal setting parameters under different input parameters;

[0013] S6: constructing a training data set according to each input parameter and the optimal setting parameter corresponding to the input parameter;

[0014] S7: Integrate the constraints in the transformation process into the neural network as physical information to build a physical information neural network model;

[0015] S8: Training the physical information neural network model using the training data set;

[0016] S9: Get input parameters;

[0017] S10: Determine the set parameter values ​​of the input parameters through the trained physical information neural network model.

[0018] Second aspect:

[0019] An embodiment of the present invention provides a system for quickly solving setting parameters for a multi-step production process, comprising:

[0020] processor;

[0021] A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the method for quickly solving setting parameters for a multi-step production process as described in the first aspect is implemented.

[0022] The third aspect:

[0023] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the method for quickly solving setting parameters for a multi-step production process as described in the first aspect is implemented.

[0024] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0025] (1) In the present invention, a multi-objective optimization model is constructed with the goal of maximizing production efficiency and production benefits. This multi-objective optimization model is then solved using the NSGA-II algorithm to determine the optimal dynamic setting strategy. Using this optimal dynamic setting strategy, the particle swarm optimization algorithm is used to determine the optimal setting parameters for different input parameters. By constructing a multi-objective optimization model based on mathematical modeling, reliance on experience and experimentation is significantly reduced, extensive trial-and-error experiments are avoided, the accuracy of process parameter setting is improved, and labor costs are reduced.

[0026] (2) In the present invention, the constraints in the transformation process are integrated into a neural network as physical information to construct a physical information neural network model. This physical information neural network model is trained using a training data set, and the set parameter values ​​of the input parameters are determined based on the trained physical information neural network model. This method enables rapid and automatic parameter adjustment, ensuring production continuity and product consistency, effectively improving production efficiency and guaranteeing stable product quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0028] Figure 1 A schematic flow chart of a method for quickly solving setting parameters for a multi-step production process provided by an embodiment of the present invention;

[0029] Figure 2 A schematic diagram of the structure of a system for rapidly solving setting parameters for a multi-step production process provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0030] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0031] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0032] In the embodiments of the present invention, the terms "image" and "picture" may be used interchangeably. It should be noted that, when the distinction between them is not emphasized, their intended meanings are the same. The terms "of," "corresponding," and "corresponding" may be used interchangeably. It should be noted that, when the distinction between them is not emphasized, their intended meanings are the same.

[0033] In the embodiments of the present invention, sometimes a subscript such as W1 may be mistakenly written as a non-subscript form such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0034] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0035] Reference Manual Figure 1 , which shows a flow chart of a method for quickly solving setting parameters for a multi-step production process provided by an embodiment of the present invention.

[0036] An embodiment of the present invention provides a method for rapidly determining set parameters for a multi-step production process. This method can be implemented by a device for rapidly determining set parameters for a multi-step production process, which can be a terminal or a server. The process flow of the method for rapidly determining set parameters for a multi-step production process can include the following steps:

[0037] S1: Construct a mathematical model to describe multiple processes and determine the basic information of each process.

[0038] In a possible implementation, the multi-process description mathematical model is specifically:

[0039]

[0040]

[0041]

[0042]

[0043]

[0044] i=1,2,,m

[0045] Among them, Q i represents the output of the i-th process, C i represents the cost of the i-th process, T i represents the completion time of the i-th process, P irepresents the output quality of the i-th process, R i represents the resource consumption of the i-th process, Q i-1 represents the output of the i-1th process, C i-1 represents the cost of the i-1th process, T i-1 represents the completion time of the i-1th process, P i-1 Indicates the output quality of the i-1th process, R i-1 represents the resource consumption of the i-1th process, m represents the total number of processes, θ represents the input parameters, Indicates the kth setting parameter of the i-th process, k=1,2,…,n i , n i Represents the total number of setting parameters for the i-th process.

[0046] In this paper, by constructing a mathematical model to describe multiple processes, a clear and systematic structure is provided for the multi-process production process, helping to optimize the production process, reduce costs, and improve efficiency and quality. At the same time, it provides a solid foundation for subsequent parameter optimization and intelligent control.

[0047] S2: Build a multi-objective optimization model with the goal of maximizing production efficiency and production benefits.

[0048] In a possible implementation, the multi-objective optimization model is specifically:

[0049]

[0050]

[0051] stT i ≤T i_max ,

[0052] R i ≤R i_max ,

[0053] P i ≥P i_min ,i=1,2,…,m

[0054] Among them, max represents the maximum value, X represents the independent variable, Ω represents the range of X values, m represents the total number of processes, Q i represents the output of the i-th process, T i represents the completion time of the i-th process, P i represents the output quality of the i-th process, C i represents the cost of the i-th process, R i represents the resource consumption of the i-th process, T i-max represents the maximum completion time of the i-th process, R i-maxrepresents the maximum resource consumption of the i-th process, P i-min Represents the minimum output quality of the i-th process.

[0055]

[0056] Where X is an r-dimensional vector, m represents the total number of processes, Indicates the kth setting parameter of the i-th process, k=1,2,…,n i , n i represents the total number of setting parameters for the i-th process, and T represents the transpose of the matrix.

[0057] In the present invention, by simultaneously considering multiple optimization objectives (such as output, completion time, output quality, cost, and resource consumption), the entire production process can be comprehensively optimized, balancing the conflicts between different objectives, ensuring that both production efficiency can be improved and production benefits can be maximized. At the same time, by setting multiple parameters for different processes, the model can flexibly adjust the set parameters of each process according to the production situation to find the optimal solution for a specific process or specific production task. This approach can achieve personalized optimization under different production conditions, ensuring that the system can quickly adapt to various complex production needs.

[0058] S3: Solve the multi-objective optimization model using the NSGA-II algorithm to determine the optimal dynamic setting strategy.

[0059] NSGA-II (Non-dominated Sorting Genetic Algorithm II) is a multi-objective evolutionary algorithm used to solve multi-objective optimization problems. Unlike traditional single-objective optimization, NSGA-II is suitable for scenarios where multiple conflicting objectives need to be optimized simultaneously. NSGA-II is an improved version of NSGA (Non-dominated Sorting Genetic Algorithm), which improves computational efficiency and convergence performance and is widely used in multi-objective optimization problems in engineering, economics, and production.

[0060] In this paper, by applying the NSGA-II algorithm, the multi-objective optimization model can simultaneously handle multiple conflicting production objectives, quickly and efficiently finding the optimal solution set, ensuring the accuracy, flexibility, and global optimality of production parameter settings. This process reduces reliance on manual experience, improves production efficiency and economic benefits, adapts to changing production environments, and implements more intelligent dynamic setting strategies.

[0061] S4: Convert the multi-objective optimization model into a single-objective optimization model and determine the constraints in the conversion process.

[0062] It should be noted that Input parameters for the constraint value of production benefit or production efficiency At different times, the optimal setting parameters also change accordingly, that is, it is necessary to In order to obtain the optimal setting parameters, it is necessary to transform the multi-objective optimization model into a single-objective optimization model.

[0063] In a possible implementation, the single-objective optimization model is specifically:

[0064]

[0065]

[0066] T i ≤T i_max ,

[0067] R i ≤R i_max ,

[0068] P i ≥P i_min ,i=1,2,…,m

[0069] Among them, m represents the total number of processes, Q i represents the output of the i-th process, T i represents the completion time of the i-th process, P i represents the output quality of the i-th process, C i represents the cost of the i-th process, R i represents the resource consumption of the i-th process, T i-max represents the maximum completion time of the i-th process, R i-max represents the maximum resource consumption of the i-th process, P i-min represents the minimum output quality of the i-th process, ε represents the constraint value, Ω represents the value range of X, and X represents the independent variable.

[0070] In the present invention, constraints are introduced into the optimization problem to ensure that the optimization results meet the limitations of the actual production process. The setting of such constraints can ensure the feasibility of the optimization solution and avoid generating solutions that do not meet the actual production requirements.

[0071] In a possible implementation, the constraint conditions in the conversion process in S4 are determined as follows:

[0072] Determine the first objective function in a single-objective optimization model:

[0073]

[0074] Among them, f1() represents the first objective function, m represents the total number of processes, Q i() represents the output function of the i-th process, P i () represents the output quality function of the i-th process, C i () represents the cost function of the i-th process.

[0075] Perform convexity analysis on the first objective function and determine that the set of independent variables for which the first-order derivative function of the first objective function exists and is continuous is:

[0076]

[0077] in, represents the set of independent variables for which the first-order derivative of the first objective function exists and is continuous, D1 represents the domain of the first objective function, and e j represents the jth standard basis vector, represents the partial derivative, h represents the change in the independent variable, and r represents the total number of independent variables.

[0078] Perform a second-order derivative based on the first-order derivative function, and determine the set of independent variables for which the second-order derivative function of the first objective function exists and is continuous:

[0079]

[0080] in, The set of independent variables that represents the existence and continuity of the second-order derivative function of the first objective function, x j represents the j-th independent variable of the first objective function.

[0081] It should be noted that the positive definiteness of the Hessian moment can be used to determine that if the Hessian matrix is ​​positive semi-definite, then the first objective function is a convex function.

[0082] Construct the Hessian matrix based on the second-order derivative function of the first objective function:

[0083]

[0084] in, Represents the Hessian matrix constructed from the second-order derivative of the first objective function.

[0085] Based on the Hessian matrix, for all non-zero vectors All have v T H f (a) v>0, determine the set of positive Hessian matrices as constraints in the transformation process:

[0086]

[0087] in, The Hessian matrix of the first objective function is positive definite set, X 0 represents a point in the domain of the first objective function, represents the domain of the first objective function, represents any vector in n-dimensional space, represents the positive definiteness condition of the Hessian matrix.

[0088] In a possible implementation, the single-objective optimization model is specifically:

[0089]

[0090]

[0091] T i ≤T i_max ,

[0092] R i ≤R i_max ,

[0093] P i ≥P i_min ,i=1,2,…,m

[0094] Among them, m represents the total number of processes, Q i represents the output of the i-th process, T i represents the completion time of the i-th process, P i represents the output quality of the i-th process, C i represents the cost of the i-th process, R i represents the resource consumption of the i-th process, T i-max represents the maximum completion time of the i-th process, R i-max represents the maximum resource consumption of the i-th process, P i-min represents the minimum output quality of the i-th process, ε represents the constraint value, Ω represents the value range of X, and X represents the independent variable.

[0095] In a possible implementation, the constraint conditions in the conversion process in S4 are determined as follows:

[0096] Determine the second objective function in a single-objective optimization model:

[0097]

[0098] Among them, f1() represents the second objective function, m represents the total number of processes, Q i () represents the output function of the i-th process, T i () represents the completion time function of the i-th process.

[0099] Perform convexity analysis on the second objective function and determine that the set of independent variables for which the first-order derivative function of the second objective function exists and is continuous is:

[0100]

[0101] in, The first-order derivative function of the second objective function exists and is continuous. D2 represents the domain of the second objective function. q represents the qth standard basis vector, h represents the change in the independent variable, and r represents the total number of independent variables;

[0102] Perform a second-order derivative based on the first-order derivative function, and determine the set of independent variables for which the second-order derivative function of the second objective function exists and is continuous:

[0103]

[0104] in, The set of independent variables that represents the existence and continuity of the second-order derivative function of the second objective function, represents the partial derivative, x q represents the qth independent variable of the second objective function.

[0105] Construct the Hessian matrix based on the second-order derivative function of the second objective function:

[0106]

[0107] in, Represents the Hessian matrix constructed from the second-order derivative function of the second objective function.

[0108] Based on the Hessian matrix, for all non-zero vectors All have v T H f (a) v>0, determine the set of positive Hessian matrices as constraints in the transformation process:

[0109]

[0110] in, The Hessian matrix of the second objective function is a positive definite set, X 0 represents a point in the domain of the second objective function, represents the domain of the second objective function, represents any vector in n-dimensional space, represents the positive definiteness condition of the Hessian matrix.

[0111] In this invention, convexity analysis enables the optimization algorithm to stably converge to the optimal solution, avoiding local optima, thereby improving the robustness and efficiency of the solution. Furthermore, input parameters can change over time or with environmental changes. By combining the optimization model with constraints, the optimal setting parameters of the production process can be dynamically adjusted based on different input parameter values, thereby improving the flexibility and adaptability of the production system.

[0112] Furthermore, after being converted into a single-objective optimization model, various key parameters in production can be systematically controlled. This systematic control method avoids the uncertainty brought about by the traditional method of relying on experience to set parameters, making the production process more stable and controllable, and can greatly improve overall production efficiency.

[0113] S5: Adopt the best dynamic setting strategy and determine the optimal setting parameters under different input parameters through the particle swarm optimization algorithm.

[0114] Among them, the particle swarm optimization algorithm (PSO) is an intelligent optimization algorithm that simulates group behavior. Inspired by the group behavior of bird flocks or fish schools in nature, it uses the mutual cooperation and information sharing of individuals in the group to find the optimal solution to the problem.

[0115] In this invention, PSO dynamically adjusts set parameters, allowing the optimal solution to be found flexibly based on different input parameters. This dynamic optimization improves the flexibility of the production system, ensuring optimal efficiency and effectiveness under different conditions. Furthermore, the automated optimization of the particle swarm algorithm significantly reduces reliance on human experience and intervention. PSO's ability to automatically adjust set parameters through its algorithm reduces the uncertainty associated with manually set parameters and enables more precise control of the production process.

[0116] S6: Construct a training data set based on each input parameter and the optimal setting parameters corresponding to the input parameters.

[0117] S7: Integrate the constraints in the transformation process into the neural network as physical information to build a physical information neural network model.

[0118] Physics-Informed Neural Networks (PINNs) are a type of model that combines physical laws with neural networks to solve partial differential equations (PDEs) and related problems in the physical field. PINNs combine the powerful function approximation capabilities of neural networks with prior knowledge of physical systems, enabling model training and prediction while satisfying physical constraints.

[0119] In this paper, constraints are integrated into neural networks as physical information to construct physical information neural network models (PINNs). This allows the model to make efficient and accurate predictions while satisfying physical constraints. This approach not only improves the physical plausibility and accuracy of predictions, but also reduces reliance on large amounts of data, improving the model's generalization capabilities.

[0120] S8: Train the physical information neural network model using the training dataset.

[0121] In one possible implementation, training the physical information neural network model in S8 specifically includes:

[0122] Initialize the physical information neural network parameters, which include learning rate, number of iterations, and initial weight threshold.

[0123] Get the training sample set.

[0124] Each training sample in the training sample set is input into the input layer of the physical information neural network model, and the output value of each neuron is calculated.

[0125] Based on the output value, determine the error of each neuron.

[0126] Update the weights and thresholds of the physical information neural network based on the error.

[0127] Repeat the iterations and end the training when the constraint loss function converges.

[0128] In a possible implementation, the constraint loss function is specifically:

[0129]

[0130] Among them, L eq represents the constraint loss function, K represents the number of sample points used to evaluate the constraint, g() represents the constraint function, y() represents the neural network output function, and x k represents the kth input feature vector.

[0131] In this invention, by initializing the neural network's parameters and automatically adjusting the weights during training, the neural network can converge to the optimal solution after multiple iterations. This reduces the need for manual adjustment of network parameters and improves the automation of model optimization. Furthermore, the physical information neural network learns the model using training data, always adhering to the laws of physics during training. In this way, even if the data set is noisy or insufficient, the model can still provide reasonable predictions within physical constraints, ensuring the credibility of the results.

[0132] S9: Get input parameters.

[0133] S10: Determine the set parameter values ​​of the input parameters through the trained physical information neural network model.

[0134] In this invention, a trained physical information neural network model is used to determine the set parameter values ​​for input parameters. This can significantly improve the accuracy, stability, and efficiency of setting parameters, reduce the trial-and-error process, and enhance the automation and intelligent level of production. The use of the physical information neural network model ensures that the system rationally optimizes the set parameters within physical constraints, making it adaptable to complex and dynamic production environments, greatly improving production flexibility, reliability, and economic benefits.

[0135] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0136] (1) In the present invention, a multi-objective optimization model is constructed with the goal of maximizing production efficiency and production benefits. This multi-objective optimization model is then solved using the NSGA-II algorithm to determine the optimal dynamic setting strategy. Using this optimal dynamic setting strategy, the particle swarm optimization algorithm is used to determine the optimal setting parameters for different input parameters. By constructing a multi-objective optimization model based on mathematical modeling, reliance on experience and experimentation is significantly reduced, extensive trial-and-error experiments are avoided, the accuracy of process parameter setting is improved, and labor costs are reduced.

[0137] (2) In the present invention, the constraints in the transformation process are integrated into a neural network as physical information to construct a physical information neural network model. This physical information neural network model is trained using a training data set, and the set parameter values ​​of the input parameters are determined based on the trained physical information neural network model. This method enables rapid and automatic parameter adjustment, ensuring production continuity and product consistency, effectively improving production efficiency and guaranteeing stable product quality.

[0138] Reference Manual Figure 2 , which shows a structural schematic diagram of a system for quickly solving setting parameters for a multi-step production process provided by the present invention.

[0139] The present invention further provides a system 20 for rapidly solving setting parameters for a multi-step production process, which is applied to the aforementioned method for rapidly solving setting parameters for a multi-step production process, comprising:

[0140] Processor 201;

[0141] The memory 202 stores computer-readable instructions. When the computer-readable instructions are executed by the processor 201 , the method for quickly solving setting parameters for a multi-step production process as described in the method embodiment is implemented.

[0142] The system 20 for quickly solving setting parameters for a multi-process production process provided by the present invention can execute the above-mentioned method for quickly solving setting parameters for a multi-process production process and achieve the same or similar technical effects. To avoid repetition, the present invention will not go into details.

[0143] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0144] (1) In the present invention, a multi-objective optimization model is constructed with the goal of maximizing production efficiency and production benefits. This multi-objective optimization model is then solved using the NSGA-II algorithm to determine the optimal dynamic setting strategy. Using this optimal dynamic setting strategy, the particle swarm optimization algorithm is used to determine the optimal setting parameters for different input parameters. By constructing a multi-objective optimization model based on mathematical modeling, reliance on experience and experimentation is significantly reduced, extensive trial-and-error experiments are avoided, the accuracy of process parameter setting is improved, and labor costs are reduced.

[0145] (2) In the present invention, the constraints in the transformation process are integrated into a neural network as physical information to construct a physical information neural network model. This physical information neural network model is trained using a training data set, and the set parameter values ​​of the input parameters are determined based on the trained physical information neural network model. This method enables rapid and automatic parameter adjustment, ensuring production continuity and product consistency, effectively improving production efficiency and guaranteeing stable product quality.

[0146] It should be understood that the processor in the embodiments of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0147] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0148] The above embodiments can be implemented in whole or in part through software, hardware (such as circuits), firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with the embodiments of the present invention are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired method (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, or magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0149] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0150] In this disclosure, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, "at least one of a, b, or c" can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0151] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0152] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0153] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0154] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of the device or unit, which can be electrical, mechanical or other forms.

[0155] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0156] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0157] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0158] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the method for quickly solving setting parameters for a multi-step production process as described in the method embodiment is implemented.

[0159] The computer-readable storage medium provided by the present invention can implement the steps and effects of the method for quickly solving the setting parameters for a multi-step production process of the above-mentioned method embodiment. To avoid repetition, the present invention will not go into details.

[0160] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0161] (1) In the present invention, a multi-objective optimization model is constructed with the goal of maximizing production efficiency and production benefits. The multi-objective optimization model is solved using the NSGA-II algorithm to determine the optimal dynamic setting strategy. Using the optimal dynamic setting strategy, the particle swarm optimization algorithm is used to determine the optimal setting parameters under different input parameters. By constructing a multi-objective optimization model based on mathematical modeling, the reliance on experience and experiments is significantly reduced, a large number of trial and error experiments are avoided, the accuracy of process parameter setting is improved, and labor costs are reduced.

[0162] (2) In the present invention, the constraints in the transformation process are integrated into a neural network as physical information to construct a physical information neural network model. This physical information neural network model is trained using a training data set, and the set parameter values ​​of the input parameters are determined based on the trained physical information neural network model. This method enables rapid and automatic parameter adjustment, ensuring production continuity and product consistency, effectively improving production efficiency and guaranteeing stable product quality.

[0163] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

[0164] There are a few points to note:

[0165] (1) The drawings of the embodiments of the present invention only relate to the structures related to the embodiments of the present invention. Other structures may refer to conventional designs.

[0166] (2) For the sake of clarity, the thickness of layers or regions in the drawings used to describe the embodiments of the present invention are exaggerated or reduced, that is, these drawings are not drawn to scale. It is understood that when an element such as a layer, film, region, or substrate is referred to as being "on" or "under" another element, the element may be "directly" "on" or "under" the other element or intervening elements may be present.

[0167] (3) In the absence of conflict, the embodiments of the present invention and the features therein may be combined with each other to form new embodiments.

[0168] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for quickly solving setting parameters for a multi-step production process, characterized in that: include: S1: Construct a mathematical model to describe multiple processes and determine the basic information of each process; S2: Build a multi-objective optimization model with the goal of maximizing production efficiency and production benefits; S3: Solve the multi-objective optimization model using the NSGA-II algorithm to determine the optimal dynamic setting strategy; S4: converting the multi-objective optimization model into a single-objective optimization model, and determining the constraints in the conversion process; S5: using the optimal dynamic setting strategy and a particle swarm optimization algorithm to determine the optimal setting parameters under different input parameters; S6: constructing a training data set according to each input parameter and the optimal setting parameter corresponding to the input parameter; S7: Integrate the constraints in the transformation process into the neural network as physical information to build a physical information neural network model; S8: Training the physical information neural network model using the training data set; S9: Get input parameters; S10: Determine the set parameter value of the input parameter through the trained physical information neural network model; The single-objective optimization model is specifically: T i ≤T i_max , R i ≤R i_max , P i ≥P i_min ,i=1,2,…,m Among them, m represents the total number of processes, Q i represents the output of the i-th process, T i represents the completion time of the i-th process, P i represents the output quality of the i-th process, C i represents the cost of the i-th process, R i represents the resource consumption of the i-th process, T i-max represents the maximum completion time of the i-th process, R i-max represents the maximum resource consumption of the i-th process, P i-min represents the minimum output quality of the i-th process, ε represents the constraint value, Ω represents the value range of X, and X represents the independent variable; The specific method for determining the constraint conditions in the conversion process in S4 is as follows: Determine the first objective function in the single-objective optimization model: Among them, f1() represents the first objective function, m represents the total number of processes, Q i () represents the output function of the i-th process, P i () represents the output quality function of the i-th process, C i () represents the cost function of the i-th process; Perform convexity analysis on the first objective function to determine that the set of independent variables for which the first-order derivative function of the first objective function exists and is continuous is: in, represents the set of independent variables for which the first-order derivative of the first objective function exists and is continuous, D1 represents the domain of the first objective function, and e j represents the jth standard basis vector, represents the partial derivative, h represents the change in the independent variable, and r represents the total number of independent variables; A second-order derivative is performed on the basis of the first-order derivative function, and the set of independent variables for which the second-order derivative function of the first objective function exists and is continuous is determined to be: in, The set of independent variables that represents the existence and continuity of the second-order derivative function of the first objective function, x j represents the j-th independent variable of the first objective function; The Hessian matrix is ​​constructed according to the second-order derivative function of the first objective function: in, A Hessian matrix constructed by the second-order derivative function representing the first objective function; Based on the Hessian matrix, for all non-zero vectors All have v T H f (a) v>0, determine the set of positive Hessian matrices, which are used as constraints in the transformation process: in, The Hessian matrix of the first objective function is positive definite set, X 0 represents a point in the domain of the first objective function, represents the domain of the first objective function, represents any vector in n-dimensional space, represents the positive definiteness condition of the Hessian matrix.

2. The method for quickly solving setting parameters for a multi-step production process according to claim 1 is characterized in that: The multi-process description mathematical model is specifically: i=1,2,…,m Among them, Q i represents the output of the i-th process, C i represents the cost of the i-th process, T i represents the completion time of the i-th process, P i represents the output quality of the i-th process, R i represents the resource consumption of the i-th process, Q i-1 represents the output of the i-1th process, C i-1 represents the cost of the i-1th process, T i-1 represents the completion time of the i-1th process, P i-1 Indicates the output quality of the i-1th process, R i-1 represents the resource consumption of the i-1th process, m represents the total number of processes, θ represents the input parameters, Indicates the kth setting parameter of the i-th process, k=1,2,…,n i , n i Represents the total number of setting parameters for the i-th process.

3. The method for quickly solving setting parameters for a multi-step production process according to claim 1 is characterized in that: The multi-objective optimization model is specifically: s.t.T i ≤T i_max , R i ≤R i_max , P i ≥P i_min ,i=1,2,…,m Among them, max represents the maximum value, X represents the independent variable, Ω represents the range of X values, m represents the total number of processes, Q i represents the output of the i-th process, T i represents the completion time of the i-th process, P i represents the output quality of the i-th process, C i represents the cost of the i-th process, R i represents the resource consumption of the i-th process, T i-max represents the maximum completion time of the i-th process, R i-max represents the maximum resource consumption of the i-th process, P i-min represents the minimum output quality of the i-th process; Where X is an r-dimensional vector, m represents the total number of processes, Indicates the kth setting parameter of the i-th process, k=1,2,…,n i , n i represents the total number of setting parameters for the i-th process, and T represents the transpose of the matrix.

4. The method for quickly solving setting parameters for a multi-step production process according to claim 1 is characterized in that: The single-objective optimization model is specifically: T i ≤T i_max , R i ≤R i_max , P i ≥P i_min ,i=1,2,…,m Among them, m represents the total number of processes, Q i represents the output of the i-th process, T i represents the completion time of the i-th process, P i represents the output quality of the i-th process, C i represents the cost of the i-th process, R i represents the resource consumption of the i-th process, T i-max represents the maximum completion time of the i-th process, R i-max represents the maximum resource consumption of the i-th process, P i-min represents the minimum output quality of the i-th process, ε represents the constraint value, Ω represents the value range of X, and X represents the independent variable.

5. The method for quickly solving setting parameters for a multi-step production process according to claim 4 is characterized in that: The specific method for determining the constraint conditions in the conversion process in S4 is: Determine the second objective function in the single-objective optimization model: Among them, f2() represents the second objective function, m represents the total number of processes, Q i () represents the output function of the i-th process, T i () represents the completion time function of the i-th process; Perform convexity analysis on the second objective function to determine that the set of independent variables for which the first-order derivative function of the second objective function exists and is continuous is: in, The first-order derivative function of the second objective function exists and is continuous. D2 represents the domain of the second objective function. q represents the qth standard basis vector, h represents the change of the independent variable, and r represents the total number of independent variables; A second-order derivative is performed on the basis of the first-order derivative function, and the set of independent variables for which the second-order derivative function of the second objective function exists and is continuous is determined to be: in, The set of independent variables that represents the existence and continuity of the second-order derivative function of the second objective function, represents the partial derivative, x q represents the qth independent variable of the second objective function; The Hessian matrix is ​​constructed according to the second-order derivative function of the second objective function: in, A Hessian matrix constructed by the second-order derivative function representing the second objective function; Based on the Hessian matrix, for all non-zero vectors All have v T H f (a) v>0, determine the set of positive Hessian matrices, which are used as constraints in the transformation process: in, The Hessian matrix of the second objective function is a positive definite set, X 0 represents a point in the domain of the second objective function, represents the domain of the second objective function, represents any vector in n-dimensional space, represents the positive definiteness condition of the Hessian matrix.

6. The method for quickly solving setting parameters for a multi-step production process according to claim 1 is characterized in that: The physical information neural network model is trained in S8, specifically including: Initializing physical information neural network parameters, wherein the physical information neural network parameters include a learning rate, a number of iterations, and an initial weight threshold; Obtaining the training data set; Inputting each training sample in the training data set into the input layer of the physical information neural network model, and calculating the output value of each neuron; determining an error for each neuron based on the output value; updating the weights and thresholds of the physical information neural network according to the error; Repeat the iterations and end the training when the constraint loss function converges.

7. The method for quickly solving setting parameters for a multi-step production process according to claim 6, characterized in that: The constraint loss function is specifically: Among them, L eq represents the constraint loss function, K represents the number of sample points used to evaluate the constraint, g() represents the constraint function, y() represents the neural network output function, and x k represents the kth input feature vector.

8. A system for quickly solving setting parameters for a multi-step production process, characterized by: include: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the method for quickly solving setting parameters for a multi-step production process according to any one of claims 1 to 7 is implemented.

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