Process card optimization method, device, equipment and storage medium for intelligent cloud simulation

Abstract

This application relates to the field of data processing and discloses a process card optimization method, device, equipment and storage medium for intelligent cloud simulation. The method involves obtaining equipment operation data in the production workshop; using big data processing technology in the cloud platform to clean the production workshop data and remove abnormal data in the production workshop data; establishing a multi-level simulation model for the process card based on the FEA finite element analysis model, setting production scenarios and process parameters based on the historical data of the production workshop, and using the multi-level simulation model for the process card to simulate and calculate the production scenarios and process parameters; learning the initial production workshop data and the initial process card production plan based on the improved GAN generative adversarial network; using the reinforcement algorithm Q learning to define the state, action and reward in the learning process of the GAN generative adversarial network; and adaptively adjusting the process card based on the production data to solve the problems of insufficient real-time performance, flexibility and intelligence in traditional methods.

Description

Technical Field

[0001] The present application relates to the field of intelligent manufacturing technology, and in particular to a process card optimization method, device, equipment and storage medium for intelligent cloud simulation. Background Art

[0002] As the manufacturing industry transitions toward intelligent and digital manufacturing, process cards are becoming increasingly important in the production process. Process cards are detailed descriptions of production processes, guiding production operations, quality control, and resource allocation. However, traditional process card optimization methods often rely on static data analysis and manual adjustments, failing to respond to changes in the production site in real time. Furthermore, process card optimization methods are mostly based on preset rules and historical data, lacking self-learning and adaptive capabilities, and are unable to automatically optimize the process based on real-time production data. In today's complex and ever-changing production demands, traditional process card optimization methods often struggle to meet flexibility requirements, resulting in reduced production efficiency and resource utilization. Therefore, improving process cards to address rapidly changing production demands and complex process environments is a pressing technical challenge. Summary of the Invention

[0003] This application provides a process card optimization method for intelligent cloud simulation, which can store asset registration in a database and audit asset change request information through a multiple audit mechanism to ensure the security of assets.

[0004] In a first aspect, an embodiment of the present application provides a process card optimization method for intelligent cloud simulation, the process card optimization method comprising the following steps:

[0005] Acquire equipment operation data, environmental data, and production progress data in the production workshop to obtain production workshop data, and transmit the production workshop data to the cloud platform via a wireless network;

[0006] Using big data processing technology in the cloud platform to clean the production workshop data, remove abnormal data in the production workshop data, and obtain initial production workshop data;

[0007] Establish a multi-level simulation model of the process card based on the FEA finite element analysis model, set the production scene and process parameters according to the historical data of the production workshop, and use the multi-level simulation model of the process card to simulate and calculate the production scene and process parameters to obtain an initial process card production plan;

[0008] Learning the initial production workshop data and the initial process card production plan based on an improved GAN generative adversarial network;

[0009] The reinforcement algorithm Q learning is used to define the state, action and reward of the GAN generative adversarial network learning process, and the target process card production plan is obtained;

[0010] The target process card production plan is managed and data shared in multiple threads through the Hadoop parallel computing framework, and is pushed to production equipment and operators for execution through the production control system.

[0011] In one possible implementation, acquiring equipment operation data, environmental data, and production progress data in a production workshop to obtain production workshop data, and transmitting the production workshop data to a cloud platform via a wireless network includes:

[0012] Temperature sensors, pressure sensors, and vibration sensors are deployed in the production workshop. Every 5 seconds, the sensors acquire the motor speed and temperature change parameters of equipment components. The collected data is organized according to equipment number and collection time information to obtain equipment operation data.

[0013] Use temperature and humidity sensors, light sensors, and air quality sensors to collect data from different locations in the workshop, annotate the collected data with spatial locations, and obtain environmental data;

[0014] Production progress data is obtained by reading the task completion mark of each production process node, the product flow counting device, and the manually entered time node information of material preparation and product quality inspection links;

[0015] Aligning and merging the equipment operation data, environmental data, and production progress data according to a unified timestamp to obtain production workshop data;

[0016] The production workshop data is encrypted using the AES encryption algorithm, and after encryption, the production workshop data is transmitted to the cloud platform via a wireless network.

[0017] In one possible implementation, the step of using big data processing technology on the cloud platform to clean the production workshop data, remove abnormal data from the production workshop data, and obtain initial production workshop data includes:

[0018] Acquire production workshop data, and use linear interpolation to supplement missing values ​​in the production workshop data to obtain first production workshop data;

[0019] The K-means clustering algorithm is used to cluster the first production workshop data.

[0020]

[0021] Where E is the total clustering error sum of squares, k is the number of clusters, and Ci represents the i-th cluster, x is cluster C i The data points in u i The centroid of the cluster;

[0022] The data points with similar characteristics in the production workshop data are divided into the same category, and the centroid positions are continuously updated iteratively to redistribute the data points to the cluster with the nearest centroid until convergence to the minimum value, thereby obtaining normal data clustering. The data points far away from the centroids of each cluster are removed to obtain the second production workshop data;

[0023] The equipment operation data, environmental data and production progress data in the second production workshop data are stored in separate tables to obtain initial production workshop data.

[0024] In one possible implementation, a multi-level simulation model of a process card is established based on the FEA finite element analysis model, production scenarios and process parameters are set according to historical data of the production workshop, and the multi-level simulation model of the process card is used to simulate and calculate the production scenarios and process parameters to obtain an initial process card production plan, including:

[0025] According to the product characteristics and process flow of the workshop production, a multi-level simulation model of the process card is established based on the FEA finite element analysis principle in ANSYS simulation software;

[0026] Obtain historical production data from the production workshop, analyze the production scenario details and process parameters of different order products in the historical production data, and extract production scenario patterns;

[0027] Setting corresponding initial values ​​of process parameters according to the production scenario mode, and starting the process card multi-level simulation model to perform simulation calculations according to the initial values ​​of process parameters;

[0028] Numerical calculations are performed on the material deformation index, processing energy consumption index, and production cycle index in the initial values ​​of the process parameters according to the mechanical equations and thermodynamic equations in the multi-level simulation model of the process card;

[0029] Combining production efficiency, product quality and cost control, the process parameter combination and production process arrangement are screened out to obtain the initial process card production plan.

[0030] In one possible implementation, the improved GAN generative adversarial network is used to learn the initial production workshop data and the initial process card production plan, including:

[0031] A fully connected neural network structure is used to establish a GAN generator, which includes at least an input layer, an intermediate layer, and an output layer. Multiple hidden layers are set in the intermediate layer, and the Relu function is used as the activation function. Nonlinear transformation is introduced, and the number of neurons in the output layer matches the dimension of the process card production plan data.

[0032] A fully connected neural network structure is used to establish the discriminator of the GAN generative adversarial network. The input layer is used to receive data with the same dimension as the generator output. The middle layer is set as a hidden layer, the LeakyRelu function is set as the activation function, and the output layer uses the Sigmoid function to compress the output to between (0,1);

[0033] The gradient penalty is introduced to improve the established GAN generative adversarial network. The gradient penalty term is added to the discriminator loss function to obtain the WGAN-GP generative adversarial network.

[0034] Randomly extract a batch of real production workshop data and process card production plans from the initial production workshop data and the initial process card production plan, calculate the output and loss of the discriminator for the real data, collect a batch of noise vectors from the prior noise distribution, obtain generated data through the generator, and calculate the output and loss of the discriminator for the generated data;

[0035] The total loss of the discriminator is calculated based on the gradient penalty, and the discriminator weights are updated based on the gradient backpropagation. The discriminator weights are kept unchanged, and the generator loss is calculated again by sampling from the noise distribution, and the generator weights are updated by backpropagation.

[0036] Continuously adjust the learning rate, the ratio of discriminator and generator training times, and the noise distribution parameters, and optimize the hyperparameter settings by monitoring the loss curve and the quality of generated samples during training.

[0037] In one possible implementation, the reinforcement algorithm Q learning is used to define the state, action, and reward of the GAN generative adversarial network learning process to obtain the target process card production plan, including:

[0038] Create a Q-table, where rows correspond to different states in the state space S and columns correspond to various actions in the action space A. The initial value of the Q-table is randomly set in the range of (-0.1, 0.1);

[0039] In each round of learning iteration, the agent is in the current state s t ; Select the greedy strategy according to the Q table and randomly select an action a with probability э t Explore and select the task with the largest Q value in the current state with probability 1-э, that is:

[0040]

[0041] Among them, the initial value of э is 0.5;

[0042] Execute action a t , if the process parameters are adjusted in the GAN generative adversarial network, the parameters of the generator generating the process card are modified according to the set step size. If the generator structure adjustment is involved, the generator structure is changed;

[0043] Get new status s by simulating production or applying the newly generated process card in a small range in the actual production environment t+1 and instant rewards t+1 ;

[0044] Update the Q table according to the Q learning update formula,

[0045]

[0046] Where α is the learning rate, which determines how quickly new information overwrites old Q values, and its value range is between (0.01, 0.1). γ is the discount factor, which measures the importance of future rewards, and its value range is between (0, 1).

[0047] Through continuous cycles, the Q table gradually converges, and the optimal actions are taken for different production states, guiding the GAN to generate adversarial networks to generate results close to the target process card production plan, and thus obtain the target process card production plan.

[0048] In one possible implementation, the target process card production plan is managed and data shared through multi-threading in a Hadoop parallel computing framework, and the target process card production plan is pushed to production equipment and operators for execution through a production control system, including:

[0049] Determine the number and specifications of computing nodes and storage nodes of the Hadoop cluster based on production scale and data processing volume;

[0050] Organizing various data in the target process card production plan, the target process card production plan at least including numerical sequences of cutting speed and feed rate for machining, timestamp records of temperature and pressure setting values ​​for chemical processes, product design specification documents, and equipment adaptation information, into structured data;

[0051] Analyze the data processing logic of the target process card production plan and determine the MapReduce task structure. The YARN resource manager allocates the Map and Reduce subtasks of the MapReduce task to multiple threads on different computing nodes for execution based on the idle resources of the cluster nodes and the task priority.

[0052] Through the built-in communication module of the production control system, the Kafka message queue mechanism is used to push the process card plan data to the production equipment controller and operator terminal.

[0053] In a second aspect, the present application provides a process card optimization device for intelligent cloud simulation, the process card optimization device comprising the following modules:

[0054] A data acquisition module is used to acquire equipment operation data, environmental data, and production progress data in the production workshop, obtain production workshop data, and transmit the production workshop data to the cloud platform via a wireless network;

[0055] A data processing module is used to clean the production workshop data using big data processing technology in the cloud platform, remove abnormal data in the production workshop data, and obtain initial production workshop data;

[0056] A process card acquisition module is used to establish a multi-level simulation model of the process card based on the FEA finite element analysis model, set the production scene and process parameters according to the historical data of the production workshop, and use the multi-level simulation model of the process card to simulate and calculate the production scene and process parameters to obtain an initial process card production plan;

[0057] A process card optimization module, configured to learn the initial production workshop data and the initial process card production plan based on an improved GAN generative adversarial network;

[0058] A process card acquisition module is used to define the state, action and reward of the GAN generative adversarial network learning process using the reinforcement algorithm Q learning to obtain the target process card production plan;

[0059] The process card execution module is used to perform multi-threaded management and data sharing on the target process card production plan through the Hadoop parallel computing framework, and push the target process card production plan to production equipment and operators for execution through the production control system.

[0060] In the third aspect, the present application provides a process card optimization device for intelligent cloud simulation, comprising: a memory and at least one processor, wherein instructions are stored in the memory; the at least one processor calls the instructions in the memory so that the process card optimization device for intelligent cloud simulation executes each step of the above-mentioned process card optimization method for intelligent cloud simulation.

[0061] In a fourth aspect, the present application provides a computer-readable storage medium, which stores instructions. When the computer-readable storage medium is run on a computer, it enables the computer to execute the various steps of the above-mentioned intelligent cloud simulation process card optimization method.

[0062] Based on the method provided in the present application, the production workshop data is obtained by acquiring the equipment operation data, environmental data and production progress data in the production workshop, and the production workshop data is transmitted to the cloud platform via a wireless network; the production workshop data is cleaned by using big data processing technology in the cloud platform, and abnormal data in the production workshop data is removed to obtain the initial production workshop data; a multi-level simulation model of the process card is established based on the FEA finite element analysis model, and the production scene and process parameters are set according to the historical data of the production workshop, and the production scene and process parameters are simulated and calculated using the multi-level simulation model of the process card to obtain the initial process card production plan; the initial production workshop data and the initial process card production plan are learned based on the improved GAN generative adversarial network; the reinforcement algorithm Q learning is used to define the state, action and reward in the learning process of the GAN generative adversarial network to obtain the target process card production plan; the target process card production plan is multi-threaded managed and data shared through the Hadoop parallel computing framework, and the target process card production plan is pushed to the production equipment and operators for execution through the production control system. It can not only optimize the production process in real time, but also adaptively adjust the process card according to production data, solving the problems of insufficient real-time, flexibility and intelligence in traditional methods, improving production efficiency, reducing costs and ensuring product quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 This is a flow chart of an embodiment of the first intelligent cloud simulation process card optimization method provided in the embodiments of the present application;

[0064] Figure 2 This is a flow chart of an embodiment of the second intelligent cloud simulation process card optimization method provided in the embodiments of the present application;

[0065] Figure 3 This is a flow chart of an embodiment of the third intelligent cloud simulation process card optimization method provided in the embodiments of the present application;

[0066] Figure 4 A schematic diagram of the structure of the process card optimization device for the first intelligent cloud simulation provided in an embodiment of the present application;

[0067] Figure 5 A structural schematic diagram of a process card optimization device for intelligent cloud simulation provided in an embodiment of the present application. DETAILED DESCRIPTION

[0068] An embodiment of the present application provides a process card optimization method for intelligent cloud simulation, which can store asset registrations in a database and audit asset change request information through a multiple audit mechanism, thereby solving the problem of poor security of assets in daily management.

[0069] The technical solutions in the embodiments of the present application will be described below in conjunction with the accompanying drawings in the embodiments of the present application. Wherein, the terms "first", "second", "third", "fourth" etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0070] It is understood that any part of this application related to data acquisition or collection has been authorized by the user.

[0071] It is understandable that the execution entity of this application can be a process card optimization device for intelligent cloud simulation, or a mobile terminal or server, which is not limited here.

[0072] The specific process of the embodiment of this application is described below. Figure 1 , Figure 1 This is a flow chart of an embodiment of a process card optimization method for intelligent cloud simulation provided in an embodiment of the present application, including:

[0073] 101. Obtain equipment operation data, environmental data, and production progress data in the production workshop, obtain production workshop data, and transmit the production workshop data to the cloud platform via wireless network;

[0074] 102. Use big data processing technology on the cloud platform to clean the production workshop data, remove abnormal data in the production workshop data, and obtain initial production workshop data;

[0075] It is understandable that temperature sensors, pressure sensors, and vibration sensors are deployed in the production workshop. The motor speed and temperature change parameters of equipment components are obtained through the sensors every 5 seconds. The collected data is sorted according to the equipment number and collection time information to obtain the equipment operation data;

[0076] Use temperature and humidity sensors, light sensors, and air quality sensors to collect data from different locations in the workshop, annotate the collected data with spatial locations, and obtain environmental data;

[0077] Production progress data is obtained by reading the task completion mark of each production process node, the product flow counting device, and the manually entered time node information of material preparation and product quality inspection links;

[0078] Align and merge equipment operation data, environmental data, and production progress data based on a unified timestamp to obtain production workshop data;

[0079] The AES encryption algorithm is used to encrypt the production workshop data, and after encryption, the production workshop data is transmitted to the cloud platform via a wireless network.

[0080] 103. Establish a multi-level simulation model of the process card based on the FEA finite element analysis model, set the production scenario and process parameters according to the historical data of the production workshop, and use the multi-level simulation model of the process card to simulate and calculate the production scenario and process parameters to obtain the initial process card production plan;

[0081] It can be understood that the production workshop data is obtained, and the missing values ​​of the production workshop data are supplemented by linear interpolation to obtain the first production workshop data;

[0082] The K-means clustering algorithm is used to cluster the data of the first production workshop.

[0083]

[0084] Where E is the total clustering error sum of squares, k is the number of clusters, and C i represents the i-th cluster, x is cluster C i The data points in u i The centroid of the cluster;

[0085] Data points with similar characteristics in the production workshop data are divided into the same category. By continuously iteratively updating the centroid position, the data points are redistributed to the cluster with the nearest centroid until convergence to the minimum value, thus obtaining normal data clustering. Data points far away from the centroid of each cluster are removed to obtain the second production workshop data.

[0086] The equipment operation data, environmental data and production progress data in the second production workshop data are stored in separate tables to obtain the initial production workshop data.

[0087] 104. Based on the improved GAN generative adversarial network, the initial production workshop data and the initial process card production plan are learned;

[0088] It can be understood that according to the characteristics of the products and process flow produced in the workshop, a multi-level simulation model of the process card is established based on the FEA finite element analysis principle in the ANSYS simulation software;

[0089] Obtain historical production data from the production workshop, analyze the production scenario details and process parameters of different order products in the historical production data, and extract production scenario patterns;

[0090] Set the corresponding initial values ​​of process parameters according to the production scenario mode, and start the multi-level simulation model of the process card to perform simulation calculations based on the initial values ​​of process parameters;

[0091] According to the mechanical equations and thermodynamic equations in the multi-level simulation model of the process card, the material deformation index, processing energy consumption index and production cycle index in the initial value of the process parameters are numerically calculated;

[0092] Combining production efficiency, product quality and cost control, the process parameter combination and production process arrangement are screened out to obtain the initial process card production plan.

[0093] 105. Use the reinforcement algorithm Q learning to define the state, action and reward in the GAN generative adversarial network learning process to obtain the target process card production plan;

[0094] It can be understood that the generator of the GAN generative adversarial network is established using a fully connected neural network structure, which includes at least an input layer, an intermediate layer, and an output layer; multiple hidden layers are set in the intermediate layer, the Relu function is used as the activation function, a nonlinear transformation is introduced, and the number of neurons in the output layer is matched with the dimension of the process card production plan data;

[0095] A fully connected neural network structure is used to establish the discriminator of the GAN generative adversarial network. The input layer is used to receive data with the same dimension as the generator output. The middle layer is set as a hidden layer, the LeakyRelu function is set as the activation function, and the output layer uses the Sigmoid function to compress the output to between (0,1);

[0096] The gradient penalty is introduced to improve the established GAN generative adversarial network. The gradient penalty term is added to the discriminator loss function to obtain the WGAN-GP generative adversarial network.

[0097] Randomly extract a batch of real production workshop data and process card production plans from the initial production workshop data and the initial process card production plan, calculate the output and loss of the discriminator for the real data, collect a batch of noise vectors from the prior noise distribution, obtain generated data through the generator, and calculate the output and loss of the discriminator for the generated data;

[0098] The total loss of the discriminator is calculated based on the gradient penalty, and the discriminator weights are updated based on the gradient backpropagation. The discriminator weights are kept unchanged, and the generator loss is calculated again by sampling from the noise distribution, and the generator weights are updated by backpropagation.

[0099] Continuously adjust the learning rate, the ratio of discriminator and generator training times, and the noise distribution parameters, and optimize the hyperparameter settings by monitoring the loss curve and the quality of generated samples during training.

[0100] 106. The target process card production plan is managed and data shared in multiple threads through the Hadoop parallel computing framework, and the target process card production plan is pushed to the production equipment and operators for execution through the production control system.

[0101] It can be understood that a Q-table is established, where the rows correspond to different states in the state space S and the columns correspond to various actions in the action space A. The initial value of the Q-table is randomly set in the range of (-0.1, 0.1);

[0102] In each round of learning iteration, the agent is in the current state s t ; Select the greedy strategy according to the Q table and randomly select an action a with probability э t Explore and select the task with the largest Q value in the current state with probability 1-э, that is:

[0103]

[0104] Among them, the initial value of э is 0.5;

[0105] Execute action a t , if the process parameters are adjusted in the GAN generative adversarial network, the parameters of the generator generated process card are modified according to the set step size. If the generator structure adjustment is involved, the generator structure is changed;

[0106] Get new status s by simulating production or applying the newly generated process card in a small range in the actual production environment t+1 and instant rewards t+1 ;

[0107] Update the Q table according to the Q learning update formula,

[0108]

[0109] Where α is the learning rate, which determines how quickly new information overwrites old Q values, and its value range is between (0.01, 0.1). γ is the discount factor, which measures the importance of future rewards, and its value range is between (0, 1).

[0110] Through continuous cycles, the Q table gradually converges, and the optimal actions are taken for different production states, guiding the GAN to generate adversarial networks to generate results close to the target process card production plan, and thus obtain the target process card production plan.

[0111] Based on the method provided by this application, by obtaining equipment operation data, environmental data and production progress data in the production workshop, production workshop data is obtained, and the production workshop data is transmitted to the cloud platform via a wireless network; in the cloud platform, big data processing technology is used to clean the production workshop data, remove abnormal data in the production workshop data, and obtain initial production workshop data; based on the FEA finite element analysis model, a multi-level simulation model of the process card is established, and the production scene and process parameters are set according to the historical data of the production workshop. The production scene and process parameters are simulated and calculated using the multi-level simulation model of the process card to obtain the initial process card production plan; based on the improved GAN generation adversarial network, the initial production workshop data and the initial process card production plan are learned; the reinforcement algorithm Q learning is used to define the state, action and reward in the learning process of the GAN generation adversarial network to obtain the target process card production plan; the target process card production plan is multi-threaded managed and data shared through the Hadoop parallel computing framework, and the target process card production plan is pushed to the production equipment and operators for execution through the production control system. It can not only optimize the production process in real time, but also adaptively adjust the process card according to the production data, solving the problems of insufficient real-time, flexibility and intelligence in traditional methods, improving production efficiency, reducing costs and ensuring product quality.

[0112] See also Figure 2 , Figure 2 This is a flow chart of an embodiment of the third intelligent cloud simulation process card optimization method provided in the embodiments of this application, including:

[0113] 201. Obtain equipment operation data, environmental data, and production progress data in the production workshop, obtain production workshop data, and transmit the production workshop data to the cloud platform via a wireless network;

[0114] 202. Use big data processing technology on the cloud platform to clean the production workshop data, remove abnormal data in the production workshop data, and obtain initial production workshop data;

[0115] Among them, steps 201-202 are similar to the execution steps of the above steps 101-102, and the details are not repeated here.

[0116] 203. According to the product characteristics and process flow of the workshop, a multi-level simulation model of the process card is established based on the FEA finite element analysis principle in ANSYS simulation software;

[0117] It is understandable that the foundation of the FEA finite element analysis model is to build a multi-level simulation model of the process card based on the FEA finite element analysis principles in professional simulation software (such as ANSYS) according to the product characteristics and process flow of the workshop production. For example, in a machining workshop, factors such as workpiece material properties (elastic modulus, Poisson's ratio, etc.), tool cutting parameters (cutting speed, feed rate, cutting depth), and mechanical properties of the processing equipment are incorporated into the model, and key areas such as the contact area between the workpiece and the tool are gridded to improve simulation accuracy.

[0118] 204. Obtain historical production data from the production workshop, analyze the production scenario details and process parameters of different order products in the historical production data, and extract production scenario patterns;

[0119] It's understandable that when running simulation models, a wide range of possible production scenarios and process parameter combinations are fully considered. For example, diverse simulation scenarios are set up to account for factors such as different product models, raw material batches, and production environment conditions. Within each scenario, process parameters such as machining speed, cutting depth, and heating temperature are further adjusted, and large-scale, multiple simulation calculations are performed. Through a comprehensive evaluation of simulation results, including comprehensive consideration of multiple indicators such as production efficiency, product quality, equipment loss, and energy consumption, the optimal initial process card solution is selected.

[0120] 205. Set corresponding initial values ​​of process parameters according to the production scenario mode, and start the multi-level simulation model of the process card to perform simulation calculation according to the initial values ​​of the process parameters;

[0121] Understandably, historical data drives scenario and parameter settings: This involves deeply mining historical data accumulated in the production workshop to analyze detailed production scenarios for different order products, such as the impact of batch size and raw material batch differences on production, as well as the process parameter combinations that ensure stable operation of each process. Typical production scenarios are extracted from this data and, based on current order demand, set up production scenarios for this simulation, such as small-batch trial production of new products and large-scale production during peak season. At the same time, the corresponding initial values ​​of process parameters are precisely set to provide the basic conditions for simulation calculations.

[0122] 206. According to the mechanical equations and thermodynamic equations in the multi-level simulation model of the process card, numerical calculations are performed on the material deformation index, processing energy consumption index and production cycle index in the initial values ​​of the process parameters;

[0123] It is understandable that simulation calculations and initial plan generation: the process card multi-level simulation model is activated for simulation operations. The model uses built-in physical equations such as mechanics and thermodynamics, as well as production logic rules, to perform numerical calculations on key indicators such as material deformation, processing energy consumption, and production cycle time. After multiple iterative simulations (e.g., more than 100 times), the optimal process parameter combinations and production process arrangements are selected by comprehensively considering multi-dimensional goals such as production efficiency, product quality, and cost control, forming an initial process card production plan. The plan covers detailed operating procedures for each process, equipment scheduling plans, quality control nodes, and other content.

[0124] 207. Combined with production efficiency, product quality and cost control, the process parameter combination and production process arrangement are screened out to obtain the initial process card production plan.

[0125] 208. Based on the improved GAN generative adversarial network, the initial production workshop data and the initial process card production plan are learned;

[0126] 209. Use the reinforcement algorithm Q learning to define the state, action and reward in the GAN generative adversarial network learning process to obtain the target process card production plan;

[0127] 210. The target process card production plan is managed and data shared through multi-threading through the Hadoop parallel computing framework, and the target process card production plan is pushed to production equipment and operators for execution through the production control system.

[0128] Among them, steps 207-210 are similar to the execution steps of the above steps 104-106, and the details are not repeated here.

[0129] Based on the method provided in the embodiment of the present application, a multi-level simulation model of the process card is established based on the FEA finite element analysis model, the production scenario and process parameters are set according to the historical data of the production workshop, and the production scenario and process parameters are simulated and calculated using the multi-level simulation model of the process card. The FEA model can accurately predict the deformation of the equipment under different cutting forces, thereby ensuring that the equipment always maintains a stable operating state during the actual production process, avoiding processing errors or equipment failures caused by structural deformation, and providing a scientific basis for the optimization of equipment parameters in the process card.

[0130] See also Figure 3 , Figure 3 This is a flow chart of an embodiment of the third intelligent cloud simulation process card optimization method provided in the embodiments of this application, including:

[0131] 301. Acquire equipment operation data, environmental data, and production progress data in the production workshop to obtain production workshop data, and transmit the production workshop data to the cloud platform via a wireless network;

[0132] 302. Use big data processing technology on the cloud platform to clean the production workshop data, remove abnormal data in the production workshop data, and obtain initial production workshop data;

[0133] 303. Establish a multi-level simulation model of the process card based on the FEA finite element analysis model, set the production scenario and process parameters according to the historical data of the production workshop, and use the multi-level simulation model of the process card to simulate and calculate the production scenario and process parameters to obtain the initial process card production plan;

[0134] Among them, steps 301-303 are similar to the execution steps of the above steps 101-103, and the details are not repeated here.

[0135] 304. Establish a GAN generator using a fully connected neural network structure, including at least an input layer, an intermediate layer, and an output layer; set multiple hidden layers in the intermediate layer, use the Relu function as the activation function, introduce nonlinear transformation, and match the number of neurons in the output layer with the dimension of the process card production plan data;

[0136] It's understandable that the improved GAN generative adversarial network architecture utilizes the Wasserstein GAN (WGAN) as the basic framework and introduces a gradient penalty term to enhance the stability and convergence of network training. The network consists of a generator and a discriminator. The generator is responsible for generating new process card variants based on the input initial production workshop data, while the discriminator determines whether the input process card variant is a real initial solution or a fake solution generated by the generator.

[0137] 305. Use a fully connected neural network structure to establish the discriminator of the GAN generative adversarial network. The input layer is used to receive data with the same dimension as the generator output. The middle layer is set as a hidden layer. The LeakyRelu function is set as the activation function. The output layer uses the Sigmoid function to compress the output to between (0,1);

[0138] 306. Gradient penalty is introduced to improve the established GAN generative adversarial network. The gradient penalty term is added to the discriminator loss function to obtain the WGAN-GP generative adversarial network.

[0139] It's understandable that data input and learning training involve pairing initial production workshop data with initial process card production plans into the improved GAN network. The generator adjusts process parameters based on information such as the real-time status of workshop equipment and dynamic environmental changes, while the discriminator continuously improves its ability to distinguish true from false plans. The two are gradually optimized in a competitive game. The training process utilizes the Adam optimization algorithm, with a learning rate of 0.0002, β1 = 0.5, and β2 = 0.999. After thousands of rounds of iterative training (e.g., 5,000 rounds), the generator is able to accurately generate process card plans that are more suitable for actual production and more optimal.

[0140] 307. Randomly extract a batch of real production workshop data and process card production plans from the initial production workshop data and the initial process card production plan, calculate the output and loss of the discriminator for the real data, collect a batch of noise vectors from the prior noise distribution, obtain generated data through the generator, and calculate the output and loss of the discriminator for the generated data;

[0141] 308. The total loss of the discriminator is calculated based on the gradient penalty. The discriminator weights are updated based on the gradient backpropagation. The discriminator weights are kept unchanged. The generator loss is calculated again by sampling from the noise distribution. The generator weights are updated by backpropagation.

[0142] 309. Continuously adjust the learning rate, the ratio of discriminator and generator training times, and the noise distribution parameters, and optimize the hyperparameter settings by monitoring the loss curve and the quality of generated samples during training;

[0143] 310. Use the reinforcement algorithm Q learning to define the state, action and reward of the GAN generative adversarial network learning process to obtain the target process card production plan;

[0144] 311. The target process card production plan is managed and data shared through multi-threading through the Hadoop parallel computing framework, and the target process card production plan is pushed to production equipment and operators for execution through the production control system.

[0145] Among them, steps 310-311 are similar to the execution steps of the above steps 105-106, and the details are not repeated here.

[0146] Based on the method provided in the embodiments of the present application, the initial production workshop data and the initial process card production plan are learned based on the improved GAN generative adversarial network. The generative artificial intelligence module can immediately automatically adjust the equipment parameter settings in the process card based on the learned knowledge and real-time data, such as reducing the equipment operating speed or adjusting the cooling system parameters, to ensure that the equipment operates in a safe and stable state while ensuring that product quality is not affected.

[0147] The above describes the process card optimization method of intelligent cloud simulation in the embodiment of the present application. The following describes the process card optimization device of intelligent cloud simulation in the embodiment of the present application. Figure 4 , Figure 4 A schematic structural diagram of a process card optimization device for intelligent cloud simulation provided in an embodiment of the present application includes:

[0148] The data acquisition module 401 is used to obtain equipment operation data, environmental data and production progress data in the production workshop, obtain production workshop data, and transmit the production workshop data to the cloud platform via a wireless network;

[0149] The data processing module 402 is used to clean the production workshop data using big data processing technology in the cloud platform, remove abnormal data in the production workshop data, and obtain initial production workshop data;

[0150] The process card acquisition module 403 is used to establish a multi-level simulation model of the process card based on the FEA finite element analysis model, set the production scene and process parameters according to the historical data of the production workshop, and simulate and calculate the production scene and process parameters using the multi-level simulation model of the process card to obtain an initial process card production plan;

[0151] The process card optimization module 404 is used to learn the initial production workshop data and the initial process card production plan based on the improved GAN generative adversarial network;

[0152] The process card obtaining module 405 is used to define the state, action and reward in the GAN generative adversarial network learning process using the reinforcement algorithm Q learning to obtain the target process card production plan;

[0153] The process card execution module 406 is used to perform multi-threaded management and data sharing on the target process card production plan through the Hadoop parallel computing framework, and push the target process card production plan to the production equipment and operators for execution through the production control system.

[0154] Based on the device provided in the embodiment of the present application, the equipment operation data, environmental data and production progress data in the production workshop are received, and the cloud platform receives the production workshop data through a wireless network; the big data processing technology is used in the cloud platform to clean the production workshop data, remove the abnormal data in the production workshop data, establish a multi-level simulation model of the process card based on the FEA finite element analysis model, set the production scene and process parameters according to the historical data of the production workshop, use the multi-level simulation model of the process card to simulate and calculate the production scene and process parameters, and learn the initial production workshop data and the initial process card production plan based on the improved GAN generation adversarial network; use the reinforcement algorithm Q learning to define the state, action and reward in the learning process of the GAN generation adversarial network to obtain the target process card production plan; the target process card production plan is multi-threaded managed and data shared through the Hadoop parallel computing framework, and the target process card production plan is pushed to the production equipment and operators for execution through the production control system. It can not only optimize the production process in real time, but also adaptively adjust the process card according to the production data, solving the problems of insufficient real-time, flexibility and intelligence in traditional methods, improving production efficiency, reducing costs and ensuring product quality.

[0155] See also Figure 5 , Figure 5 : It is a structural diagram of a process card optimization device for intelligent cloud simulation provided by an embodiment of the present application. The process card optimization device 500 for intelligent cloud simulation may have relatively large differences due to different configurations or performances, and may include one or more processors 510 (for example, one or more processors) and a memory 520, and one or more storage media 530 for storing application programs 533 or data 532. Among them, the memory 520 and the storage medium 530 may be temporary storage or persistent storage. The program stored in the storage medium 530 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the process card optimization device 500 for intelligent cloud simulation. Furthermore, the processor 510 may be configured to communicate with the storage medium 5630 to execute a series of instruction operations in the storage medium 530 on the process card optimization device 500 for intelligent cloud simulation.

[0156] The process card optimization device 500 of intelligent cloud simulation may further include one or more power supplies 540, one or more wired or wireless network interfaces 550, one or more input and output interfaces 560, and / or one or more operating systems 531, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. It will be understood by those skilled in the art that Figure 5The structure of the process card optimization device for intelligent cloud simulation shown does not constitute a limitation of the process card optimization device for intelligent cloud simulation, and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0157] The present application also provides a process card optimization device for intelligent cloud simulation, wherein the computer device includes a memory and a processor, wherein the memory stores computer-readable instructions. When the computer-readable instructions are executed by the processor, the processor executes each step of the process card optimization method for intelligent cloud simulation in the above-mentioned embodiments.

[0158] The present application also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are run on a computer, the computer executes the various steps of the process card optimization method of intelligent cloud simulation.

[0159] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0160] 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 technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which 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 various embodiments of the present application. 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.

[0161] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments in which tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0162] As described above, the above embodiments 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 above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some of the technical features therein. However, these 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.

Claims

1. A process card optimization method for intelligent cloud simulation, characterized in that: The process card optimization method comprises the following steps: Acquire equipment operation data, environmental data, and production progress data in the production workshop to obtain production workshop data, and transmit the production workshop data to the cloud platform via a wireless network; Using big data processing technology in the cloud platform to clean the production workshop data, remove abnormal data in the production workshop data, and obtain initial production workshop data; Establish a multi-level simulation model of the process card based on the FEA finite element analysis model, set the production scene and process parameters according to the historical data of the production workshop, and use the multi-level simulation model of the process card to simulate and calculate the production scene and process parameters to obtain an initial process card production plan; Learning the initial production workshop data and the initial process card production plan based on an improved GAN generative adversarial network; The reinforcement algorithm Q learning is used to define the state, action and reward of the GAN generative adversarial network learning process, and the target process card production plan is obtained; The target process card production plan is managed and data shared by multiple threads through the Hadoop parallel computing framework, and the target process card production plan is pushed to the production equipment and operators for execution through the production control system; Wherein, the obtaining of the initial process card production plan includes: Numerical calculations are performed on the material deformation index, processing energy consumption index, and production cycle index in the initial values ​​of the process parameters according to the mechanical equations and thermodynamic equations in the multi-level simulation model of the process card; Combining production efficiency, product quality and cost control, the process parameter combination and production process arrangement are screened out to obtain the initial process card production plan.

2. The process card optimization method for intelligent cloud simulation according to claim 1, characterized in that: The acquiring of equipment operation data, environmental data, and production progress data in the production workshop to obtain production workshop data, and transmitting the production workshop data to the cloud platform via a wireless network, includes: Temperature sensors, pressure sensors, and vibration sensors are deployed in the production workshop. Every 5 seconds, the sensors acquire the motor speed and temperature change parameters of equipment components. The collected data is organized according to equipment number and collection time information to obtain equipment operation data. The temperature and humidity sensors, light sensors, and air quality sensors are used to collect data from different locations in the workshop. The collected data is marked with spatial locations to obtain environmental data. Production progress data is obtained by reading the task completion mark of each production process node, the product flow counting device, and the manually entered time node information of material preparation and product quality inspection links; Aligning and merging the equipment operation data, environmental data, and production progress data according to a unified timestamp to obtain production workshop data; The production workshop data is encrypted using the AES encryption algorithm, and after encryption, the production workshop data is transmitted to the cloud platform via a wireless network.

3. The process card optimization method for intelligent cloud simulation according to claim 1, characterized in that: The method of using big data processing technology in the cloud platform to clean the production workshop data, remove abnormal data in the production workshop data, and obtain initial production workshop data includes: Acquire production workshop data, and use linear interpolation to supplement missing values ​​in the production workshop data to obtain first production workshop data; The K-means clustering algorithm is used to cluster the first production workshop data. Where E is the total clustering error sum of squares, k is the number of clusters, and C i represents the i-th cluster, x is cluster C i The data points in u i The centroid of the cluster; The data points with similar characteristics in the production workshop data are divided into the same category, and the centroid positions are continuously updated iteratively to redistribute the data points to the cluster with the nearest centroid until convergence to the minimum value, thereby obtaining normal data clustering. The data points far away from the centroids of each cluster are removed to obtain the second production workshop data; The equipment operation data, environmental data and production progress data in the second production workshop data are stored in separate tables to obtain initial production workshop data.

4. The process card optimization method for intelligent cloud simulation according to claim 1, characterized in that: The process card multi-level simulation model is established based on the FEA finite element analysis model, the production scene and process parameters are set according to the historical data of the production workshop, and the production scene and process parameters are simulated and calculated using the process card multi-level simulation model, including: According to the product characteristics and process flow of the workshop production, a multi-level simulation model of the process card is established based on the FEA finite element analysis principle in ANSYS simulation software; Obtain historical production data from the production workshop, analyze the production scenario details and process parameters of different order products in the historical production data, and extract production scenario patterns; The corresponding initial values ​​of the process parameters are set according to the production scenario mode, and the multi-level simulation model of the process card is started to perform simulation calculations according to the initial values ​​of the process parameters.

5. The process card optimization method for intelligent cloud simulation according to claim 1, characterized in that: The improved GAN generative adversarial network is used to learn the initial production workshop data and the initial process card production plan, including: A fully connected neural network structure is used to establish a GAN generator, which includes at least an input layer, an intermediate layer, and an output layer. Multiple hidden layers are set in the intermediate layer, and the Relu function is used as the activation function. Nonlinear transformation is introduced, and the number of neurons in the output layer matches the dimension of the process card production plan data. A fully connected neural network structure is used to establish the discriminator of the GAN generative adversarial network. The input layer is used to receive data with the same dimension as the generator output. The middle layer is set as a hidden layer, the LeakyRelu function is set as the activation function, and the output layer uses the Sigmoid function to compress the output to between (0,1); The gradient penalty is introduced to improve the established GAN generative adversarial network. The gradient penalty term is added to the discriminator loss function to obtain the WGAN-GP generative adversarial network. Randomly extract a batch of real production workshop data and process card production plans from the initial production workshop data and the initial process card production plan, calculate the output and loss of the discriminator for the real data, collect a batch of noise vectors from the prior noise distribution, obtain generated data through the generator, and calculate the output and loss of the discriminator for the generated data; The total loss of the discriminator is calculated based on the gradient penalty, and the discriminator weights are updated based on the gradient backpropagation. The discriminator weights are kept unchanged, and the generator loss is calculated again by sampling from the noise distribution, and the generator weights are updated by backpropagation. Continuously adjust the learning rate, the ratio of discriminator and generator training times, and the noise distribution parameters, and optimize the hyperparameter settings by monitoring the loss curve and the quality of generated samples during training.

6. The process card optimization method for intelligent cloud simulation according to claim 1, characterized in that: The reinforcement algorithm Q learning is used to define the state, action and reward of the GAN generative adversarial network learning process to obtain the target process card production plan, including: Create a Q-table, where rows correspond to different states in the state space S and columns correspond to various actions in the action space A. The initial value of the Q-table is randomly set in the range of (-0.1, 0.1); In each round of learning iteration, the agent is in the current state s t ; Select the greedy strategy according to the Q table and randomly select an action a with probability э t Explore and select the task with the largest Q value in the current state with probability 1-э, that is: Among them, the initial value of э is 0.5; Execute action a t , if the process parameters are adjusted in the GAN generative adversarial network, the parameters of the generator generating the process card are modified according to the set step size. If the generator structure adjustment is involved, the generator structure is changed; Get new status s by simulating production or applying the newly generated process card in a small range in the actual production environment t+1 and instant rewards t+1 ; Update the Q table according to the Q learning update formula, Where α is the learning rate, which determines how quickly new information overwrites old Q values, and its value range is between (0.01, 0.1). γ is the discount factor, which measures the importance of future rewards, and its value range is between (0, 1). Through continuous cycles, the Q table gradually converges, and the optimal actions are taken for different production states, guiding the GAN to generate adversarial networks to generate results close to the target process card production plan, and thus obtain the target process card production plan.

7. The process card optimization method for intelligent cloud simulation according to claim 1, characterized in that: The target process card production plan is managed and shared by multiple threads through the Hadoop parallel computing framework, and the target process card production plan is pushed to the production equipment and operators for execution through the production control system, including: Determine the number and specifications of computing nodes and storage nodes of the Hadoop cluster based on production scale and data processing volume; Organizing various data in the target process card production plan, the target process card production plan at least including numerical sequences of cutting speed and feed rate for machining, timestamp records of temperature and pressure setting values ​​for chemical processes, product design specification documents, and equipment adaptation information, into structured data; Analyze the data processing logic of the target process card production plan and determine the MapReduce task structure. The YARN resource manager allocates the Map and Reduce subtasks of the MapReduce task to multiple threads on different computing nodes for execution based on the idle resources of the cluster nodes and the task priority. Through the communication module built into the production control system, the Kafka message queue mechanism is used to push the process card plan data to the production equipment controller and operator terminal.

8. A process card optimization device for intelligent cloud simulation, characterized in that: The process card optimization device includes the following modules: A data acquisition module is used to acquire equipment operation data, environmental data, and production progress data in the production workshop, obtain production workshop data, and transmit the production workshop data to the cloud platform via a wireless network; A data processing module is used to clean the production workshop data using big data processing technology in the cloud platform, remove abnormal data in the production workshop data, and obtain initial production workshop data; A process card acquisition module is used to establish a multi-level simulation model of the process card based on the FEA finite element analysis model, set production scenarios and process parameters according to historical data of the production workshop, and use the multi-level simulation model of the process card to simulate and calculate the production scenarios and process parameters to obtain an initial process card production plan; perform numerical calculations on the material deformation index, processing energy consumption index, and production cycle index in the initial values ​​of the process parameters based on the mechanical equations and thermodynamic equations in the multi-level simulation model of the process card; and screen out process parameter combinations and production process arrangements in combination with production efficiency, product quality, and cost control to obtain an initial process card production plan; A process card optimization module, configured to learn the initial production workshop data and the initial process card production plan based on an improved GAN generative adversarial network; A process card acquisition module is used to define the state, action and reward of the GAN generative adversarial network learning process using the reinforcement algorithm Q learning to obtain the target process card production plan; The process card execution module is used to perform multi-threaded management and data sharing on the target process card production plan through the Hadoop parallel computing framework, and push the target process card production plan to production equipment and operators for execution through the production control system.

9. An intelligent cloud simulation process card optimization device, characterized in that: The process card optimization device of the intelligent cloud simulation includes: a memory and at least one processor, wherein the memory stores instructions; The at least one processor calls the instructions in the memory so that the process card optimization device of the intelligent cloud simulation executes each step of the process card optimization method of the intelligent cloud simulation as described in any one of claims 1-7.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instructions are executed by the processor, the various steps of the process card optimization method for intelligent cloud simulation as described in any one of claims 1 to 7 are implemented.

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