Optimization solution method and device for intelligent manufacturing, electronic equipment and storage medium

Abstract

The invention is suitable for the technical field of intelligent manufacturing, and provides an optimization solution method and device for intelligent manufacturing, electronic equipment and a storage medium, and the method comprises the steps: carrying out the modeling according to the manufacturing parameters and target parameters of intelligent manufacturing, and obtaining a manufacturing model, the manufacturing model comprises a plurality of response functions, a desirability function of each response function and a comprehensive desirability function, the response functions are mapping functions between manufacturing parameters and target parameters, and the comprehensive desirability function is a weighted power average value of each desirability function; and carrying out iterative calculation by taking the maximized comprehensive satisfaction degree function as a target to obtain a target solution of intelligent manufacturing.

Description

Technical Field

[0001] The present application relates to the field of intelligent manufacturing, and in particular to an optimization solution method and device for intelligent manufacturing, an electronic device, and a computer-readable storage medium. Background Art

[0002] Intelligent manufacturing is a manufacturing method based on modern information technology and advanced manufacturing technology. It achieves a more efficient, flexible, sustainable and intelligent production process through the application of technologies such as digitalization, automation, Internet of Things, big data analysis and artificial intelligence.

[0003] The implementation of intelligent manufacturing is not only the application of technology, but also includes the optimization of the entire manufacturing system, such as the optimization of technology, equipment and process through data-driven methods, which may involve multiple parameters and multiple goals. Different goals may be independent or related. Traditional optimization methods are usually based on specific model assumptions, which may not hold true in practical applications. In the face of the above complex optimization problems, the reliability and effectiveness of the optimization results are often reduced, and even solutions that violate the laws of physics appear, thus affecting the promotion of intelligent manufacturing. Summary of the invention

[0004] The embodiments of the present application provide an optimization solution method and device for intelligent manufacturing, an electronic device and a computer-readable storage medium, which can solve the problem that the optimization methods in related technologies are difficult to meet the needs.

[0005] In a first aspect, an embodiment of the present application provides an optimization solution method for intelligent manufacturing, the method comprising: modeling according to manufacturing parameters and target parameters of intelligent manufacturing to obtain a manufacturing model, the manufacturing model comprising multiple response functions, desirability functions of each response function and a comprehensive desirability function, the response function being a mapping function between manufacturing parameters and target parameters, the comprehensive desirability function being a weighted power average of each desirability function; performing iterative calculations with the goal of maximizing the comprehensive desirability function to obtain a target solution for intelligent manufacturing.

[0006] In the second aspect, an embodiment of the present application provides an optimization solution device for intelligent manufacturing, which includes: a modeling module, which is used to model according to manufacturing parameters and target parameters of intelligent manufacturing to obtain a manufacturing model, wherein the manufacturing model includes multiple response functions, desirability functions of each response function and a comprehensive desirability function, the response function is a mapping function between manufacturing parameters and target parameters, and the comprehensive desirability function is a weighted power average of each desirability function; a calculation module, which is used to perform iterative calculations with the goal of maximizing the comprehensive desirability function to obtain a target solution for intelligent manufacturing.

[0007] In a third aspect, an embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable by the processor, wherein when the processor executes the computer program, the optimization solution method for intelligent manufacturing described in the first aspect above is implemented.

[0008] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the optimization solution method for intelligent manufacturing described in the first aspect above.

[0009] In a fifth aspect, an embodiment of the present application provides a computer program product. When the computer program product runs on an electronic device, the electronic device executes the optimization solution method for intelligent manufacturing described in the first aspect above.

[0010] Compared with the prior art, the embodiments of the present application have the following beneficial effects: a manufacturing model is obtained by modeling according to the manufacturing parameters and target parameters of intelligent manufacturing, the manufacturing model includes multiple response functions, a desirability function of each response function and a comprehensive desirability function, the response function is a mapping function between the manufacturing parameters and the target parameters, and the comprehensive desirability function is a weighted power average of each desirability function; iterative calculation is performed with the goal of maximizing the comprehensive desirability function to obtain the target solution of intelligent manufacturing. In the manufacturing model, each response function represents a target parameter, and the comprehensive desirability function obtains the overall satisfaction by calculating the weighted power average of the desirability functions of each target parameter. The weight in the weighted power average reflects the importance of the target parameter, and the power index can control the influence of each desirability function on the overall satisfaction, so that the comprehensive satisfaction function can more truly reflect the balance between the various targets in the complex system, thereby effectively improving the quality of the optimization results. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0012] Figure 1 is a structural schematic diagram of an electronic device provided by an embodiment of the present application;

[0013] Figure 2 It is a flowchart of an optimization solution method for intelligent manufacturing provided by an embodiment of the present application;

[0014] Figure 3 In the case of using genetic algorithm Figure 2 The specific process diagram of S2 in FIG.

[0015] Figure 4 is a flowchart of weights in a more comprehensive desirability function provided by a specific embodiment of the present application;

[0016] Figure 5 is a flow chart of a more comprehensive power index in a desirability function provided by a specific embodiment of the present application;

[0017] Figure 6 is a flow chart of a more comprehensive power index in a desirability function provided by another specific embodiment of the present application;

[0018] Figure 7 When the annealing algorithm is used Figure 2 The specific process diagram of S2 in FIG.

[0019] Figure 8 It is a schematic diagram of the structure of an optimization solution device for intelligent manufacturing provided in one embodiment of the present application. DETAILED DESCRIPTION

[0020] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0021] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0022] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0023] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0024] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0025] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0026] The optimization solution method for intelligent manufacturing provided in the embodiments of the present application can be applied to electronic devices, including but not limited to servers, server clusters, mobile phones, tablet computers, laptop computers, desktop computers, personal digital assistants, wearable devices and other electronic devices with computing functions. The embodiments of the present application do not impose any restrictions on the specific types of electronic devices.

[0027] Figure 1 FIG. 1 is a block diagram of a partial structure of an electronic device provided in an embodiment of the present application. Figure 1 , the electronic device includes: a processor 10, a memory 20, a bus 30, an input device 40, an output device 50 and a communication device 60. The processor 10 and the memory 20 are connected to each other through the bus 30, and the input device 40, the output device 50 and the communication device 60 are also connected to the bus 30. Those skilled in the art can understand that Figure 1 The structure of the electronic device shown in the figure does not constitute a limitation of the electronic device, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0028] Combine the following Figure 1 A detailed introduction to the various components of electronic equipment:

[0029] The processor 10 is the control center of the electronic device, and can run the program stored in the memory 20 to perform various functions and process data. The processor 10 can be a central processing unit (CPU), and the processor 10 can 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 gates or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. In some embodiments, the processor 10 may include an AI (Artificial Intelligence) processor, which is used to process computing operations related to machine learning.

[0030] The memory 20 is used to store an operating system, an application program, a boot loader, data, and other programs, such as program codes of computer programs. The memory 20 can also be used to temporarily store data required for executing the program and generated. The memory 20 may include a high-speed random access memory, and may also include a non-volatile memory, such as a flash memory, a hard disk, a multimedia card, a card-type memory, etc. The memory 20 may include a storage unit disposed inside the electronic device, such as a hard disk of the electronic device, and / or a removable external storage unit, such as a mobile hard disk, a USB flash drive, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, etc.

[0031] The input device 40 may include at least one of a keyboard, a mouse, a touch panel, a joystick, etc., and is used to collect user input operations to generate corresponding operation instructions.

[0032] The output device 50 is used to output information to be provided to the user. The output device 50 generally includes a display, and optionally, a liquid crystal display (LCD), an organic light-emitting diode (OLED), etc. can be used. In addition, the output device can further include a speaker.

[0033] The communication device 60 may include a modem, a network card, etc., for establishing a network connection with other electronic devices and communicating with each other.

[0034] The optimization solution method of intelligent manufacturing provided by the embodiment of the present application can be implemented as a computer software program. For example, the embodiment of the present application provides a computer program product, which includes a computer program carried on a computer readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device 60, and / or installed from a removable external storage unit. When the computer program is executed by the processor 10, the various functions defined in the optimization solution method of intelligent manufacturing provided by the embodiment of the present application are realized.

[0035] Figure 2 A schematic flowchart of an optimization solution method for intelligent manufacturing provided in an embodiment of the present application is shown. As an example but not a limitation, the method can be applied to the above-mentioned electronic device.

[0036] S1: Modeling is performed according to the manufacturing parameters and target parameters of intelligent manufacturing to obtain a manufacturing model.

[0037] The optimization of intelligent manufacturing involves multiple manufacturing parameters and multiple target parameters. Manufacturing parameters can also be understood as the input parameters of the manufacturing system, which can specifically include raw material parameters, production process parameters, inventory allocation parameters, etc.; target parameters can also be understood as the output parameters of the manufacturing system. The value of the target parameter is determined by the influence of the manufacturing parameters, and this influence can be described by a response function. In addition to the target parameter itself, it also includes the expected type of the target parameter, which is used to indicate the optimization direction of the target parameter. The expected type can be divided into maximization, minimization and targeting, which correspond to maximizing the target parameter, minimizing the target parameter and making the target parameter approach the target value.

[0038] For example, smart manufacturing is specifically for lithium battery cell manufacturing. The manufacturing parameters involved include the ratio of various raw materials, and the target parameters include cost, battery capacity and cycle life. The cost type is minimization, the cycle life type is maximization, and the battery capacity type is targeted. It can be seen that reducing the proportion of expensive raw materials, such as lithium, can effectively reduce costs, but this will reduce battery capacity.

[0039] Alternatively, for spare parts management of production equipment in intelligent manufacturing, the manufacturing parameters involved include inventory configuration and procurement configuration of various spare parts, and the target parameters include production efficiency and capital occupancy rate. The type of production efficiency is maximization, and the type of capital occupancy rate is minimization. It can be seen that when production equipment is regularly maintained or fails, spare parts need to be replaced. If the spare parts are not in stock, temporary purchases are required. During the waiting period, the production line cannot work, affecting production efficiency. Increasing the inventory of spare parts can improve production efficiency, but it will increase capital occupancy rate.

[0040] That is to say, a common situation in the optimization calculation of intelligent manufacturing is that different target parameters are related and the optimization directions are conflicting, which requires an overall evaluation of the scheme during the optimization calculation.

[0041] The manufacturing model includes a plurality of response functions, a desirability function of each response function and a comprehensive desirability function.

[0042] The response function, also known as the objective function, is a mapping function between manufacturing parameters and target parameters. The response function corresponds to the target parameters one by one, the independent variables of the response function are at least part of the manufacturing parameters, and the dependent variables are the corresponding target parameters. The response function can be modeled based on experimental data.

[0043] Specifically, a multivariate regression model can be constructed as follows:

[0044]

[0045] Among them, β0 is the intercept term, β i is the linear term x i The regression coefficient, β jk is the interaction term x j x k is the regression coefficient and ε is the error term.

[0046] The multivariate regression model given here only contains the product of two different independent variables, that is, the second-order interaction term. In practical applications, the order of the interaction term, that is, the number of different independent variables in the product, can be expanded according to actual needs.

[0047] According to the experimental data, the multivariate regression model can be fitted to obtain the model coefficients, including the intercept term and the regression coefficient. Specifically, the value of the target parameter in the experimental data is called the actual observed value, and the calculation result obtained by substituting the manufacturing parameters in the experimental data into the multivariate linear regression model is the model prediction value. The optimal value of the model parameter should make the error between the model prediction value and the actual observed value as small as possible. The model coefficients can be estimated by minimizing the error, such as the least squares method, the minimum absolute deviation, etc., without limitation here.

[0048] The desirability function, which can also be called the satisfaction function, corresponds one-to-one with the response function, that is, one-to-one with the target parameter. The desirability function of a response function can also be called the desirability function of the corresponding target parameter. The desirability function is determined by the response function value and the expected type.

[0049] Specifically, the input of the desirability function also includes a value range [L, U] of the response function value and a target value T, where L is a preset lower limit, U is a preset upper limit, and a weight parameter r for adjusting the function curve.

[0050] In the case where the expected type is maximization, if the response function value y is less than the preset lower limit L, the satisfaction function is 0; when y is in the interval [L, T], its satisfaction function is calculated according to the formula ; if y is greater than T, the satisfaction function is 1.

[0051] In the case where the expected type is minimization, if the response function value y exceeds the preset upper limit U, the satisfaction function is 0; when y is in the interval [T, U], the satisfaction function is calculated according to the formula ; if y is lower than the response function value T, the satisfaction function is 1.

[0052] In the case where the expected type is targeting: for the response function value y, if it is outside the interval [L, U], the satisfaction function is 0; if y is in the interval [L, T], the satisfaction function is calculated according to the formula ; if y is in the interval [T, U], the satisfaction function is calculated according to the formula calculate.

[0053] Considering the possible boundary conditions in practical applications, in the specific calculation process, an epsilon parameter can be introduced. For the denominator part in the above satisfaction function calculation formula, if the absolute value of the calculation result is less than the epsilon parameter, it is set to the epsilon parameter to avoid the situation of 0 as the denominator and ensure the stability and accuracy of the calculation.

[0054] The comprehensive satisfaction function is the weighted power mean of each satisfaction function, specifically:

[0055]

[0056] Among them, D is the comprehensive satisfaction function, w i is the weight of the i-th satisfaction function and satisfies d i is the i-th satisfaction function, i = 1, 2,..., k, k is the total number of satisfaction functions, and p is the power exponent.

[0057] The power exponent p determines the non-linearity degree of the comprehensive satisfaction function.

[0058] When p > 1, the comprehensive satisfaction function is more sensitive to higher satisfaction values, that is, it is more inclined to optimize those objective functions with high satisfaction.

[0059] When p = 1, the comprehensive satisfaction function degenerates into a weighted average.

[0060] When 0 < p < 1, the comprehensive satisfaction function pays more attention to those lower satisfaction values, highlighting the sensitivity of the system to low satisfaction.

[0061] When p approaches -∞, the comprehensive desirability function is closer to the minimum desirability value (min function).

[0062] When p approaches +∞, the comprehensive desirability function is closer to the maximum desirability value (max function).

[0063] For example, suppose there are three target parameters with corresponding desirability values ​​of 0.8, 0.5 and 0.9, and weights w1 = 0.4, w2 = 0.3, w3 = 0.3 respectively. For different power exponents p, the comprehensive desirability D is calculated as follows:

[0064] a. Linear combination (p = 1): D = 0.4 × 0.8 + 0.3 × 0.5 + 0.3 × 0.9 = 0.71

[0065] b. Convex combination (p = 2): D = (0.4 × 0.8^2 + 0.3 × 0.5^2 + 0.3 × 0.9^2)^1 / 2 ≈ 0.74

[0066] c. Concave combination (p = 0.5): D = (0.4 × 0.8^0.5 + 0.3 × 0.5^0.5 + 0.3 × 0.9^0.5)^2≈0.68 It can be seen that by selecting different p values, the degree of attention paid to different types of target parameters in the optimization process can be flexibly adjusted. This nonlinear combination method enables the comprehensive satisfaction function to more truly reflect the balance between various objectives in a complex system, thereby effectively improving the quality of the optimization results.

[0067] S2: Perform iterative calculations with the goal of maximizing the comprehensive desirability function to obtain the target solution for intelligent manufacturing.

[0068] The specific iterative algorithm is not limited here, and the following is explained using the genetic algorithm and the annealing algorithm as examples.

[0069] like Figure 3 As shown, in a specific embodiment of the present application, when a genetic algorithm is used, S2 specifically includes the following parts.

[0070] S11: Initialize the population.

[0071] The population includes a plurality of candidate solutions, each of which includes a set of manufacturing parameters.

[0072] Each manufacturing parameter has its own range of values. By quantizing the range of values ​​according to the set step size, the value set of the manufacturing parameter can be obtained. A parameter space can be defined, which contains all possible combinations of manufacturing parameters. Each combination is a set of manufacturing parameters. Multiple combinations can be randomly selected from the parameter space as candidate solutions.

[0073] S12: Calculate the comprehensive desirability function value of each candidate solution in the population.

[0074] For each candidate solution, each manufacturing parameter is substituted into the response function in the manufacturing model to obtain the response function value, and then the response function value is substituted into the desirability function to obtain the desirability function value, and then each desirability function value is substituted into the comprehensive desirability function to obtain the comprehensive desirability function value of the candidate solution. The comprehensive desirability function value is used as the fitness value in the genetic algorithm to evaluate the candidate solution.

[0075] S13: Update the population according to the comprehensive desirability function value of the population.

[0076] The updating operations include retaining the best solution, blending and adjusting.

[0077] A certain proportion of candidate solutions can be randomly selected from the current population for mixing operations. The mixing operation simulates the sexual reproduction of organisms. Specifically, a single mixing operation can include randomly selecting two candidate solutions, determining the mixing point, and exchanging parameters at the mixing point to generate two new candidate solutions to join the population. For example, after mixing the original candidate solutions A and B, the mixed candidate solutions C and D are added to the population. The original candidate solutions A and B can be deleted or retained; the part before the mixing point p in the mixed candidate solution C comes from A, and the part after the mixing point p comes from B; the part before the mixing point p in the mixed candidate solution D comes from B, and the part after the mixing point p comes from A.

[0078] The adjustment operation simulates biological mutations to ensure the diversity of candidate solutions. The perturbation rate can be used to determine whether a candidate solution needs to be adjusted; if so, a manufacturing parameter of the candidate solution is randomly selected for modification. The new value of this manufacturing parameter is randomly selected within the allowed range of values, while ensuring that the new value does not overlap with the existing value. This random fine-tuning can help explore new possible solutions and avoid all solutions from concentrating on a certain area too early, thereby increasing the chance of finding the global optimal solution.

[0079] In order to prevent the excellent solutions from being affected by the above operations and causing the global optimal solution to be lost, in each round of iteration, the excellent solutions, that is, the specified number or proportion of candidate solutions with the largest comprehensive desirability function value in the population, are retained.

[0080] S14: Update the weight and / or power index in the comprehensive desirability function.

[0081] In some embodiments, this step may be omitted.

[0082] In a complex system, the priority of the target parameter may change with time, external conditions or the progress of the optimization process. Therefore, a dynamic adjustment mechanism can be introduced so that the weight and / or power index can be adaptively adjusted according to the current state or historical performance of the system, which can more accurately reflect the actual needs and improve the optimization effect. There is no restriction on the execution order between this step and S13.

[0083] like Figure 4 As shown, in a specific embodiment of the present application, updating the weight may specifically include the following parts.

[0084] S101: Calculate the overall comprehensive desirability of the population in the current round based on the comprehensive desirability function value of each candidate solution in the population.

[0085] The overall comprehensive desirability is the arithmetic mean of the comprehensive desirability function values ​​of each candidate solution in the population.

[0086] S102: Calculate the difference between the overall comprehensive desirability of the population in the current round and the previous round.

[0087] The difference is obtained by subtracting the overall comprehensive desirability of the current round from the overall comprehensive desirability of the previous round.

[0088] S103: Calculate the weight adjustment amount according to the difference.

[0089] Specifically, w i Adjustment amount Δw i It can be expressed as:

[0090]

[0091] Among them, α is the first learning rate, is the comprehensive desirability function for w i The derivative of , ΔD is the difference between the overall comprehensive desirability of the current round and the previous round.

[0092] S104: Update the weight using the weight adjustment amount.

[0093] Since the weights need to sum to 1, they need to be normalized. The update process can be specifically expressed by the following formula, where ← represents assignment.

[0094] w i ←w i +Δw i

[0095]

[0096] There are three ways to update the power index, which are described below with examples.

[0097] like Figure 5 As shown, in a specific embodiment of the present application, updating the power index may specifically include the following parts.

[0098] S111: Calculate the response statistics of the response function according to the response function value of each candidate solution in the population.

[0099] The response statistic is the variance or standard deviation of the corresponding response function values ​​of each candidate solution. For a certain response function, the response function values ​​obtained by substituting each candidate solution in the population into the response function form an array, and the variance or standard deviation of the array is calculated as the response statistic of the response function.

[0100] S112: Calculate the power exponent adjustment amount according to the response statistics.

[0101] Specifically, the adjustment amount Δp of p can be expressed as:

[0102]

[0103] Among them, β is the second learning rate, σ i is the response statistic of the ith response function, k is the total number of response functions, σ target is the target statistic.

[0104] S113: Update the power exponent using the power exponent adjustment amount.

[0105] The power exponent does not need to be normalized, and the update can be specifically expressed by the following formula, where ← represents assignment.

[0106] p i ←p i +Δp i

[0107] like Figure 6 As shown, in another specific embodiment of the present application, updating the power index may specifically include the following parts.

[0108] S121: Calculate the overall comprehensive desirability of the population in the current round based on the comprehensive desirability function value of each candidate solution in the population.

[0109] The overall comprehensive desirability is the arithmetic mean of the comprehensive desirability function values ​​of each candidate solution in the population.

[0110] S122: Calculate the difference between the overall comprehensive desirability of the population in the current round and the previous round.

[0111] The difference is obtained by subtracting the overall comprehensive desirability of the current round from the overall comprehensive desirability of the previous round.

[0112] S123: Update the power exponent according to the difference.

[0113] The goal of updating the power index is to continuously improve the overall comprehensive desirability of the population by adjusting the power index, thereby increasing the difference.

[0114] Specifically, the adjustment of the power index is positively correlated with the difference. When the difference between the overall comprehensive desirability of the current round and the previous round increases, it means that the optimization process has made good progress. At this time, the value of the power index should be increased to strengthen its influence on the desirability function and further promote the optimization of the solution. If the difference decreases, it means that the optimization progress slows down, and the power index should be reduced to reduce the influence on the comprehensive desirability and avoid over-reliance on the current weight configuration.

[0115] In addition, the power exponent can be dynamically adjusted according to the size of the difference. The size of the power exponent adjustment is proportional to the change in the difference. That is, the more significant the difference change, the larger the adjustment of the power exponent can be; conversely, the adjustment amount can be appropriately reduced to ensure smooth and rhythmic updates.

[0116] Specifically, the power exponent and its adjustment amount are both scalars and do not need to be normalized.

[0117] In another specific embodiment of the present application, the power index can be updated using reinforcement learning (e.g., Q learning). For example, the optimization process can be modeled as a Markov decision process (MDP), where the state is the current comprehensive target performance, which can be calculated based on the response function value and the weight, the action is to adjust p, specifically including increasing and decreasing p, the reward function is the improvement of the comprehensive desirability function value, and the optimal p value adjustment strategy is selected through Q learning.

[0118] S15: Determine whether the stop condition is met.

[0119] If satisfied, the iterative calculation ends and the process jumps to S16; otherwise, the process jumps to S12.

[0120] The stopping condition may include the number of iterations reaching a set value. In addition, in order to improve the optimization speed, an early stopping mechanism may be introduced, that is, the difference between the optimal fitness of the current round and the previous round of the population (that is, the maximum value of the comprehensive desirability function value of each candidate solution) is calculated. If the difference between the two is continuously less than the preset threshold for a specified number of rounds, it means that the optimal fitness has not been significantly improved for a certain number of rounds, and the early stopping mechanism is triggered to terminate the iteration in advance. The stopping condition corresponding to the early stopping mechanism includes the difference between the optimal fitness of the current round and the previous round of the population being continuously less than the preset threshold for a specified number of rounds.

[0121] S16: Determine the target solution from the excellent solutions.

[0122] Specifically, the one with the largest comprehensive desirability function value can be selected from the excellent solutions, that is, the candidate solution corresponding to the optimal fitness as the target solution.

[0123] Optionally, the excellent solutions can be visualized after principal component analysis technique, and scatter plots and / or surface plots can be drawn for presentation.

[0124] like Figure 7 As shown, in a specific embodiment of the present application, when the annealing algorithm is adopted, S2 specifically includes the following parts.

[0125] S21: Initialize the current temperature and the current solution, calculate the response function value and the comprehensive desirability function value of the current solution, and add the current solution and its response function value and the comprehensive desirability function value to the candidate set.

[0126] The initial value of the current solution can be randomly generated. The initial value of the temperature can be pre-set.

[0127] S22: Generate a neighborhood solution of the current solution, and calculate the response function value and the comprehensive desirability function value of the neighborhood solution.

[0128] Neighborhood solutions can be generated by randomly selecting a manufacturing parameter from the current solution and modifying it.

[0129] S23: Try to update the candidate set using the neighborhood solution and its response function value and comprehensive desirability function value.

[0130] Specifically, it can be determined whether the neighborhood solution already exists in the candidate set; if not, it is further determined whether the number of candidate solutions in the candidate set has reached an upper limit; if not, the neighborhood solution and its response function value and comprehensive desirability function value are directly added to the candidate set; otherwise, it is further determined whether the comprehensive desirability function value of the neighborhood solution is greater than the minimum value of the comprehensive desirability function value in the candidate set; if greater, the candidate solution with the smallest comprehensive desirability function value in the candidate set and its response function value and comprehensive desirability function value are replaced with the neighborhood solution and its response function value and comprehensive desirability function value; otherwise, the candidate set is not updated.

[0131] S24: If the comprehensive desirability function value of the neighborhood solution is greater than the comprehensive desirability function value of the current solution, or the generated random number is less than the acceptance probability, the current solution is updated to the neighborhood solution, otherwise the current solution is not updated.

[0132] The current solution is the starting point of the search in each iteration. Updating the current solution to a neighborhood solution means choosing to continue searching in the direction of the neighborhood solution. In the judgment condition for updating the current solution to a neighborhood solution, if the comprehensive desirability function value of the neighborhood solution is greater than the comprehensive desirability function value of the current solution, it means that a better solution is selected, and the introduction of the acceptance probability means that there is a probability of selecting a worse neighborhood solution rather than a better current solution; the acceptance probability is positively correlated with the current temperature, that is, as the iteration proceeds, the current temperature decreases, and the acceptance probability decreases accordingly, which means that the optimization system is more inclined to select a better solution rather than a worse solution, and the system tends to be stable, thereby reducing the possibility of finding a local optimal solution.

[0133] S25: Lower the current temperature.

[0134] The current temperature is reduced according to a preset annealing rate. This step may be performed in each iteration, or may be performed only when the acceptance probability is effective.

[0135] There is no restriction on the execution order of S23, S24 and S25.

[0136] S26: Determine whether the stop condition is met.

[0137] The stop condition may include that the number of iterations reaches a set value and / or the current temperature is lower than a preset value.

[0138] If satisfied, the iterative calculation ends and the process jumps to S27; otherwise, the process jumps to S22.

[0139] S27: Determine the target solution from the candidate set.

[0140] Specifically, a candidate solution with the largest comprehensive desirability function value can be selected from the candidate set as the target solution.

[0141] Through the implementation of this embodiment, a manufacturing model is obtained by modeling according to the manufacturing parameters and target parameters of intelligent manufacturing. The manufacturing model includes multiple response functions, the desirability function of each response function and the comprehensive desirability function. The response function is a mapping function between the manufacturing parameters and the target parameters, and the comprehensive desirability function is the weighted power average of each desirability function. Iterative calculation is performed with the goal of maximizing the comprehensive desirability function to obtain the target solution of intelligent manufacturing. In the manufacturing model, each response function represents a target parameter. The comprehensive desirability function calculates the weighted power average of the desirability function of each target parameter to obtain the overall satisfaction. The weight in the weighted power average reflects the importance of the target parameter. The power index can control the influence of each desirability function on the overall satisfaction, so that the comprehensive satisfaction function can more truly reflect the balance between the various goals in the complex system, thereby effectively improving the quality of the optimization results.

[0142] Figure 8A schematic structural diagram of an optimization solution device for intelligent manufacturing provided in an embodiment of the present application is shown. The optimization solution device for intelligent manufacturing includes a modeling module 11 and a calculation module 12.

[0143] The modeling module 11 is used to obtain a manufacturing model based on the manufacturing parameters and target parameters of intelligent manufacturing. The manufacturing model includes multiple response functions, a desirability function of each response function and a comprehensive desirability function. The response function is a mapping function between the manufacturing parameters and the target parameters, and the comprehensive desirability function is a weighted power average of each desirability function.

[0144] The calculation module 12 is used to perform iterative calculations with the goal of maximizing the comprehensive desirability function to obtain a target solution for intelligent manufacturing.

[0145] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / modules / units are based on the same concept as the method embodiment of the present application. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0146] It should be understood that the size of the serial numbers of the steps in the above embodiments 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 application.

[0147] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0148] An embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0149] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned various method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device that can carry the computer program code to the camera / electronic device, recording medium, computer memory, read-only memory (ROM, Read-Only Memory), random access memory (RAM, RandomAccess Memory), electric carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0150] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0151] Those of ordinary skill 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. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0152] In the embodiments provided in the present application, it should be understood that the disclosed devices / network equipment and methods can be implemented in other ways. For example, the device / network equipment embodiments described above are merely schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, 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 interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0153] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0154] The embodiments described above are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. An optimization solution method for intelligent manufacturing, characterized in that: The method comprises: A manufacturing model is obtained by modeling according to manufacturing parameters and target parameters of intelligent manufacturing, wherein the manufacturing model includes a plurality of response functions, a desirability function of each of the response functions, and a comprehensive desirability function, wherein the response function is a mapping function between the manufacturing parameters and the target parameters, and the comprehensive desirability function is a weighted power average of the desirability functions; Iterative calculation is performed with the goal of maximizing the comprehensive desirability function to obtain the target solution of the intelligent manufacturing.

2. The method according to claim 1, characterized in that The iterative calculation with the goal of maximizing the comprehensive desirability function includes: A genetic algorithm is used to perform iterative calculations with the goal of maximizing the comprehensive desirability function.

3. The method according to claim 2, characterized in that The iterative calculation using a genetic algorithm with the goal of maximizing the comprehensive desirability function includes: Initializing a population, wherein the population includes a plurality of candidate solutions, and each candidate solution includes a set of the manufacturing parameters; Calculating the comprehensive desirability function value of each candidate solution in the population; Performing an update operation on the population according to the comprehensive desirability function value of the population, the update operation comprising retaining excellent solutions, mixing and adjusting, the excellent solutions being the candidate solutions of the specified number or proportion with the largest comprehensive desirability function value in the population; Repeat the steps of calculating the comprehensive desirability function value of each candidate solution in the population and subsequent steps until a stopping condition is met.

4. The method according to claim 3, characterized in that After calculating the comprehensive desirability function value of each candidate solution in the population, the method further includes: The weights and / or power exponents in the comprehensive desirability function are updated.

5. The method according to claim 4, characterized in that The updating of the weight in the comprehensive desirability function comprises: Calculating the overall comprehensive desirability of the population in the current round according to the comprehensive desirability function value of each candidate solution in the population; Calculating the difference between the overall comprehensive desirability of the population in the current round and the previous round; Calculate a weight adjustment amount according to the difference; The weight is updated using the weight adjustment amount.

6. The method according to claim 4, characterized in that The updating of the power index in the comprehensive desirability function comprises: Calculate the response statistic of the response function according to the response function value of each candidate solution in the population, wherein the response statistic is the variance or standard deviation of the corresponding response function value of each candidate solution; Calculating a power exponent adjustment amount according to the response statistic; The power exponent is updated using the power exponent adjustment amount.

7. The method according to claim 4, characterized in that The updating of the power index in the comprehensive desirability function comprises: Calculating the overall comprehensive desirability of the population in the current round according to the comprehensive desirability function value of each candidate solution in the population; Calculating the difference between the overall comprehensive desirability of the population in the current round and the previous round; The power exponent is updated according to the difference.

8. The method according to claim 4, characterized in that The updating of the power index in the comprehensive desirability function comprises: The power exponent is updated using reinforcement learning.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable by the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 8 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 8 is implemented.

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