Thermal processing management and control method, system and equipment based on digital twinning and storage medium

Abstract

The invention relates to the technical field of hot working control, and discloses a hot working management and control method, system and device based on digital twinning and a storage medium. The method comprises the steps of obtaining technological parameters and quality data of smelting, casting, forging and pressing and heat treatment procedures through a multi-sensor fusion internet-of-things data acquisition system, constructing a hot working full-process data set and a multi-dimensional technological data platform, achieving parameter mapping and grid conversion through a self-adaptive interface algorithm, establishing a collaborative simulation platform, and obtaining a multi-dimensional technological data platform. And constructing a digital twinborn model and deploying the digital twinborn model to an edge computing gateway to generate a visual management and control system. And calculating a process parameter combination and a resource priority allocation scheme. According to the invention, on the basis of obtaining a large amount of process data in real time, cross-process collaborative optimization and intelligent decision can be realized, especially under the conditions of multi-physics field coupling and complex time-varying characteristics, how to effectively monitor and adjust process parameters in real time and ensure the optimization of product quality and production efficiency.

Description

Technical Field

[0001] This application relates to the technical field of hot processing control, and particularly to a hot processing management and control method, system, device, and storage medium based on digital twin. Background Art

[0002] With the continuous development of the manufacturing industry, hot processing technologies have been widely applied in multiple fields, such as casting, forging, heat treatment, etc. Especially when producing large components and high-strength products, hot processing technologies play a crucial role in product quality and production efficiency. In the existing hot processing process, the optimization of process parameters and quality control usually rely on experience and manual operations, and ensure product quality by monitoring the equipment status and adjusting the process conditions. However, with the increase in product complexity and the improvement of requirements for product accuracy and consistency, traditional control methods and technical means are difficult to meet the needs of high-efficiency and high-quality production. Most of the existing technologies lack real-time performance and adaptability, cannot process complex multi-physical field data in real time, and cannot perform automatic adjustment and optimization in a timely manner during the process.

[0003] Although many modern manufacturing systems have introduced data acquisition and monitoring systems, such as temperature sensors, pressure sensors, and on-line detection devices, etc., to collect process data, these systems are usually isolated and lack the ability to integrate and intelligently analyze data across processes and multiple process links. At the same time, the existing hot processing control systems cannot effectively cope with the time-varying characteristics and non-linear relationships of process parameters, and it is also difficult to perform dynamic optimization and adjustment through real-time data feedback and prediction. Due to the complexity of the system, the existing control methods still rely on a large amount of manual intervention, and it is difficult to achieve autonomous intelligent optimization and decision-making, resulting in waste of resources, low production efficiency, and unstable product quality. Summary of the Invention

[0004] This application provides a hot processing management and control method, system, device, and storage medium based on digital twin, which is used to achieve cross-process collaborative optimization and intelligent decision-making on the basis of real-time acquisition of a large amount of process data. Especially under the conditions of multi-physical field coupling and complex time-varying characteristics, how to effectively monitor and adjust process parameters in real time to ensure the optimization of product quality and production efficiency.

[0005] In a first aspect, the present application provides a digital-twin-based hot processing control method. The digital-twin-based hot processing control method includes: using a multi-sensor fusion Internet of Things data acquisition system to collect process parameters and quality data for smelting, casting, forging, and heat treatment processes, so as to obtain a hot processing full-process data set; constructing a multi-dimensional process data center for hot processing according to the hot processing full-process data set; based on the multi-dimensional process data center for hot processing, establishing a full-process collaborative simulation platform for hot processing by performing parameter mapping and grid conversion on multi-stage simulation results through an adaptive interface algorithm; performing hierarchical parametric modeling based on the full-process collaborative simulation platform for hot processing to construct a digital twin model; deploying the digital twin model to an edge computing gateway, and generating a visual control system through a virtual-real data interaction mechanism and multi-level situation awareness; inputting the real-time data of the manufacturing process collected by the visual control system into a multi-round distributed process optimization engine to calculate the target process parameter combination and the finite resource priority allocation scheme.

[0006] In a first implementation manner of the first aspect, the step of using a multi-sensor fusion Internet of Things data acquisition system to collect process parameters and quality data for smelting, casting, forging, and heat treatment processes, so as to obtain a hot processing full-process data set includes: extracting smelting temperature, pressure, and composition data from a configuration software and a PLC controller by using an OPC communication method, and collecting process parameters of charging, rough smelting, refining, vacuum, up-casting, and down-casting processes in the smelting process to obtain smelting process parameter data; accessing the on-site instrumentation equipment of the ingot casting through a Modbus protocol, collecting and digitally processing process requirement data, process execution data, and auxiliary material data to obtain casting process parameter data; transforming a large oil press and a press and supporting forging heating equipment for multi-sensor access, collecting temperature field, stress field, and deformation field data during the forging process to generate forging process parameter data; digitally processing the quality inspection samples of the heat treatment process based on image recognition and machine learning technologies, converting the microstructure characteristics and mechanical property characteristics into numerical representations, and constructing a heat treatment quality data set; performing time series marking and correlation analysis on the smelting process parameter data, the casting process parameter data, the forging process parameter data, and the heat treatment quality data set to construct a process chain data structure with a process inheritance relationship; performing outlier detection and data cleaning on the process chain data structure, and generating a hot processing full-process data set after standardization processing and feature extraction.

[0007] In the second implementation manner of the first aspect, constructing a multi-dimensional process data center for hot processing based on the hot processing full-process data set includes: performing data cleaning, trend item separation, and noise suppression processing on the hot processing full-process data set, extracting main process features, and obtaining preprocessed hot processing data; designing a hierarchical data structure including a metadata management layer, a task management layer, a data conversion layer, and a view management layer according to the preprocessed hot processing data, and constructing a multi-level data storage architecture; based on the multi-level data storage architecture, classifying and storing the preprocessed hot processing data according to smelting, casting, forging, and heat treatment process types, and generating a professional process database module; performing working condition segmentation and standardization processing on the data in the professional process database module, analyzing the mapping relationship between material-process-structure-property, and establishing a multi-dimensional data association model; using the multi-dimensional data association model, generating a process data service component through a standardized data service interface based on middleware; Combining the functions of the process data service component through service orchestration and a rule engine to construct a multi-dimensional process data center for hot processing with data management, process parameter retrieval, and process sequence correlation analysis functions.

[0008] In the third implementation manner of the first aspect, based on the multi-dimensional process data center for hot processing, establishing a full-process collaborative simulation platform for hot processing by performing parameter mapping and mesh conversion on the multi-stage simulation results through an adaptive interface algorithm, including: calling the material thermophysical property parameters and process condition parameters of each process of casting, forging, and heat treatment from the multi-dimensional process data center for hot processing to construct independent simulation models for each process; performing data extraction and structured processing on the calculation results of the independent simulation models for each process to generate a process simulation data set including temperature field, stress field, structure field, and defect distribution; based on the process simulation data set, using a spatial interpolation algorithm to convert the upstream process simulation grid point data into the grid form required for downstream process simulation to obtain a cross-process grid mapping model; performing error analysis and calibration processing on the cross-process grid mapping model to determine the error range of each parameter transfer and establish a data transfer error compensation mechanism; according to the data transfer error compensation mechanism, through simulation interface software, realizing the automatic conversion of the upstream process calculation results to the initial conditions of the downstream process, and generating a seamlessly coupled link system between processes; integrating the link system into the full-process simulation software of casting, forging, and heat treatment, and generating a full-process collaborative simulation platform for hot processing through hardware acceleration and computing resource scheduling integration.

[0009] In the fourth implementation manner of the first aspect, hierarchical parametric modeling is performed based on the full-process collaborative simulation platform for hot processing to construct a digital twin model, including: extracting the structural and multi-physical field characteristic data of forging equipment from the full-process collaborative simulation platform for hot processing, using geometric-physical field coupling-driven modeling to analyze the characteristic relationships, and obtaining a basic data set for the manufacturing process of large components; performing skin model processing on the large forging equipment structure in the basic data set, extracting the key feature points and associated parameters on the equipment surface, and constructing a digital model of the equipment; based on the digital model of the equipment, dividing the design parameters of large hot processing components into three levels: overall contour parameters, key component parameters, and process control parameters, and establishing a hierarchical parametric structure system; performing three-dimensional scanning and measurement on the hot processing components in the hierarchical parametric structure system, comparing and calibrating the obtained geometric dimensions and temperature field data with the simulation calculation results, and generating a time-varying feature database; inputting the time-varying feature database into the upsetting and drawing process analysis modules of the forging process of hot processing components, calculating the change laws of temperature, strain, and damage distribution, and generating process model data; applying a model accuracy evaluation index system to the process model data for accuracy, lightweight, and real-time evaluation and optimization, and constructing a digital twin model.

[0010] In the fifth implementation manner of the first aspect, deploying the digital twin model to an edge computing gateway and generating a visual management and control system through a virtual-real data interaction mechanism and multi-level situation awareness includes: deploying the digital twin model to the edge computing gateway between the device end and the digital system, constructing a data channel supporting 5G or wired transmission, and generating an edge computing service framework; performing distributed processing on the key equipment, production process, and workshop environment data collected by the edge computing service framework, establishing a real-time mapping matrix of virtual and physical data, and generating a virtual-real data interaction mechanism; based on the virtual-real data interaction mechanism, constructing a multi-level situation awareness model including equipment layer situation, process layer situation, and safety layer situation, and generating a real-time monitoring data stream of the production process; applying a fast scanning and multi-dimensional modeling analysis algorithm to the quality characteristics of the hot processing process in the real-time monitoring data stream of the production process, and generating a precise analysis data set for forging allowances; based on the precise analysis data set for forging allowances, constructing a fusion control system including a human-machine remote control module, an interactive operation control module, and a production safety management module, and generating a safety warning index for the manufacturing process; integrating the safety warning index for the manufacturing process and the real-time monitoring data stream of the production process into a unified interface framework through cloud configuration technology, and optimizing the parameters of the edge computing gateway according to the cloud strategy, and generating a visual management and control system.

[0011] In the sixth implementation manner of the first aspect, the input of the real-time manufacturing process data collected by the visual control system into the multi-round distributed process optimization engine to calculate the target process parameter combination and the limited resource priority allocation scheme includes: extracting the process parameters and quality inspection data of each process running in real time from the visual control system to construct a real-time process execution data set; applying a parameter evaluation algorithm based on knowledge reasoning and deep neural network to the real-time process execution data set to calculate the deviation matrix between the current process state and the target quality, and generating a multi-dimensional process risk assessment index; extracting historical optimization cases from the process database according to the multi-dimensional process risk assessment index, and applying the gradient boosting decision tree method to construct a non-linear mapping relationship between process parameters and quality characteristics to generate a multi-round distributed process optimization engine; setting multi-dimensional optimization goals such as optimal quality, minimum energy consumption, and shortest cycle in the multi-round distributed process optimization engine, and generating a candidate process parameter combination scheme set through parallel iterative optimization calculation; performing virtual testing on the candidate process parameter combination scheme set through a digital twin model, calculating the quality prediction score and resource consumption index of each scheme, and determining the target process parameter combination; based on the target process parameter combination, applying a multi-attribute analysis method to comprehensively evaluate the workpiece importance, process difficulty, and production urgency, calculating the priority allocation matrix of equipment and resources, and generating a limited resource priority allocation scheme.

[0012] In a second aspect, the present application provides a digital twin-based hot processing control system, and the digital twin-based hot processing control system includes: An acquisition module, configured to collect process parameters and quality data of smelting, casting, forging, and heat treatment processes by using an Internet of Things data acquisition system with multi-sensor fusion to obtain a hot processing full-process data set; A construction module, configured to construct a multi-dimensional process data center of hot processing according to the hot processing full-process data set; A conversion module, configured to perform parameter mapping and grid conversion on the multi-stage simulation results through an adaptive interface algorithm based on the multi-dimensional process data center of hot processing to establish a full-process collaborative simulation platform for hot processing; A modeling module, configured to perform hierarchical parametric modeling based on the full-process collaborative simulation platform for hot processing to construct a digital twin model; A perception module, configured to deploy the digital twin model to an edge computing gateway, and generate a visual control system through a virtual-real data interaction mechanism and multi-level situation awareness; An input module, configured to input the real-time manufacturing process data collected by the visual control system into a multi-round distributed process optimization engine to calculate the target process parameter combination and the limited resource priority allocation scheme.

[0013] In a third aspect, a hot processing control device based on digital twin is provided, including: 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 to enable the hot processing control device based on digital twin to execute the above-mentioned hot processing control method based on digital twin.

[0014] In a fourth aspect, a computer-readable storage medium is provided, in which instructions are stored. When it runs on a computer, it enables the computer to execute the above-mentioned hot processing control method based on digital twin.

[0015] In the technical solution provided by this application, through the multi-sensor fusion Internet of Things data acquisition system, the process parameters and quality data of smelting, casting, forging and heat treatment processes are collected in real time, and a full-process hot processing data set is constructed. It can realize the comprehensive monitoring of the entire production process. Through the adaptive interface algorithm, the system can perform accurate parameter mapping and grid conversion on the multi-stage simulation results to ensure seamless connection of data across processes, thereby realizing the collaborative simulation of the full process of hot processing. The cooperation efficiency between processes and data consistency are improved. The construction of hierarchical parametric modeling and digital twin models enables the process to reflect the actual production situation in real time. The deployment of the digital twin model to the edge computing gateway, combined with the virtual-real data interaction mechanism and multi-level situation awareness, not only realizes efficient data processing and real-time monitoring, but also enables each link in the production process to obtain accurate feedback. The intelligent level and automation ability of the hot processing process are improved, manual intervention is reduced, and production efficiency and quality are optimized. Using the multi-round distributed process optimization engine, the process parameters can be optimized and calculated, and a finite resource priority allocation scheme can be generated, so as to ensure the optimal balance of production efficiency and product quality under limited resources. Description of the Drawings

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0017] Figure 1 It is a schematic diagram of an embodiment of the hot processing control method based on digital twin in the embodiments of this application; Figure 2 It is a schematic diagram of an embodiment of the hot processing control system based on digital twin in the embodiments of this application; Figure 3 It is a structural schematic block diagram of the hot processing control device based on digital twin in the embodiments of the present invention. Detailed Embodiments

[0018] The embodiments of the present application provide a digital-twin-based hot processing control method, system, device, and storage medium. Terms such as "first", "second", "third", "fourth", etc. (if any) in the specification, claims, and the above-mentioned drawings of the present application are used to distinguish similar objects and do not necessarily need to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments described here can be implemented in an order other than that illustrated or described here. In addition, the term "including" or "having" and any of its variations are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products, or devices.

[0019] For ease of understanding, the specific process of the embodiments of the present application will be described below. Please refer to Figure 1 , an embodiment of the digital-twin-based hot processing control method in the embodiments of the present application includes: Step S101: Use an Internet of Things data acquisition system with multi-sensor fusion to collect process parameters and quality data for the smelting, casting, forging, and heat treatment processes, and obtain a full-process dataset of hot processing; Step S102: Construct a multi-dimensional process data center for hot processing based on the full-process dataset of hot processing; Step S103: Based on the multi-dimensional process data center for hot processing, perform parameter mapping and grid conversion on the multi-stage simulation results through an adaptive interface algorithm, and establish a full-process collaborative simulation platform for hot processing; Step S104: Perform hierarchical parametric modeling based on the full-process collaborative simulation platform for hot processing to construct a digital twin model; Step S105: Deploy the digital twin model to an edge computing gateway, and generate a visual control system through a virtual-real data interaction mechanism and multi-level situation awareness; Step S106: Input the real-time data of the manufacturing process collected by the visual control system into a multi-round distributed process optimization engine to calculate the target process parameter combination and the finite resource priority allocation plan.

[0020] It can be understood that the execution entity of the present application can be a digital-twin-based hot processing control system, or a terminal or a server. Specifically, it is not limited here. The embodiments of the present application will be described by taking the server as the execution entity as an example.

[0021] Specifically, data such as temperature, pressure, composition, and strain are collected through various sensors and transmitted to the system via OPC communication and Modbus protocol. In the smelting process, information such as the temperature, pressure, and composition of the smelting furnace is interacted with the PLC controller through OPC to obtain real-time data during raw material processing, refining, and other processes. In the casting process, the Modbus protocol is used to connect to on-site casting instruments to obtain process requirement data, execution data, and auxiliary material data. These data are converted into digital process parameter data through standardized processing. In the forging process, multiple sensors are connected to large hydraulic presses and presses to collect data on the temperature field, stress field, and deformation field during forging. These data are integrated into a dataset through a data acquisition system. At the same time, the quality data during the heat treatment process is transformed into numerical representations through image recognition and machine learning techniques, including organizational structure characteristics and mechanical property characteristics, thus constructing a heat treatment quality dataset. After being collected, these data are time-sequentially marked and subjected to correlation analysis to establish a process chain data structure. Through outlier detection, data cleaning, and standardized processing, a complete heat processing full-process dataset containing data from processes such as smelting, casting, forging, and heat treatment is finally generated.

[0022] The heat processing full-process dataset is used to construct a multi-dimensional process data middle platform for heat processing. Through data preprocessing techniques, such as removing noise, separating the trend components in the data, and standardizing various types of data, the consistency and comparability of the data are ensured. For example, for the temperature data in the smelting process, there may be slight fluctuations due to equipment errors. These fluctuations are removed through the trend separation method, and the long-term change trend is retained. The construction of the data middle platform adopts a hierarchical data structure, including a metadata management layer, a task management layer, a data conversion layer, a view management layer, etc. During this process, the flexible invocation and distribution of data are realized through middleware to ensure the accurate transfer of data between various process modules. At this stage, the process data is classified and stored according to different process types of smelting, casting, forging, and heat treatment to generate a professional process database. Through multi-dimensional correlation analysis of the data, a mapping relationship between materials, processes, organizations, and properties is established to generate a multi-dimensional data correlation model.

[0023] Through the thermal processing multi-dimensional process data center, the adaptive interface algorithm is used to perform parameter mapping and mesh conversion on the multi-stage simulation results, thereby establishing a full-process collaborative simulation platform for thermal processing. During the process, the simulation models of different processes are connected to each other through the interface algorithm to ensure accurate data transfer between different processes. Taking the casting, forging, and heat treatment processes as examples, the simulation results of each process, such as the temperature field and stress field, will be extracted and structured. The mesh data of the upstream process is mapped to the mesh form required by the downstream process through the spatial interpolation algorithm. For example, the temperature field data in the casting process is converted into mesh data suitable for the forging process through the interpolation algorithm. After error analysis and calibration, these data ensure the accurate transfer of simulation results between different processes.

[0024] Based on the full-process collaborative simulation platform for thermal processing, hierarchical parametric modeling is carried out to construct a digital twin model. The construction of the digital twin model first extracts the basic data set in the thermal processing process, such as the structural and multi-physical field characteristic data of large forging equipment. Through the modeling method of geometric and physical field coupling, the basic data set of the equipment is obtained. After these data are processed by the skin model, the key feature points and associated parameters on the equipment surface are extracted to construct a digital model of the forging equipment. For the hot processing process of forgings, the design parameters are divided into three levels: overall contour parameters, key component parameters, and process control parameters. This hierarchical parametric structure enables precise control of the digital twin model at all levels. Through three-dimensional scanning and measurement, the geometric dimensions and temperature field data obtained are compared and calibrated with the simulation calculation results to generate a time-varying feature database.

[0025] The digital twin model is deployed to the edge computing gateway to achieve virtual-real data interaction and multi-level situation awareness, and generate a visual management and control system. After the digital twin model is deployed, data is transmitted between the device side and the digital system through a data channel that supports 5G or wired transmission. Through the edge computing service framework, various data of key equipment, production process, and workshop environment are collected and processed in real time. After these data are processed, they are interacted with virtual data to generate a virtual-real data mapping matrix. With the help of this matrix, a multi-level situation awareness model of equipment layer situation, process layer situation, and safety layer situation is constructed to generate a real-time monitoring data stream of the production process. Through the rapid scanning and multi-dimensional modeling analysis of these data streams, the forging allowance can be accurately analyzed, and based on these analysis results, the process control and production safety warning can be optimized.

[0026] The real-time data collected by the input visualization control system is used to calculate the deviation between the process state and the target quality through a parameter evaluation algorithm based on knowledge reasoning and deep neural networks. For example, assuming that there is a certain deviation between the temperature field data of a forging process and the target value, a deviation matrix can be obtained through the algorithm, and an optimization model can be generated by the gradient boosting decision tree method. During the optimization process, historical optimization cases in the process database are used to construct the non-linear mapping relationship between process parameters and quality characteristics. Through parallel iterative optimization, the target process parameter combination is obtained to ensure the best process quality, the minimum energy consumption, and the shortest production cycle. Combining with the resource allocation priority matrix, the optimal allocation of limited resources is ensured. Through these calculation results, the intelligent optimization and precise control of the entire hot processing process are realized.

[0027] In the embodiment of the present application, through the Internet of Things data acquisition system with multi-sensor fusion, the process parameters and quality data of the smelting, casting, forging, and heat treatment processes are collected in real time, and a full-process dataset of hot processing is constructed. It can realize the comprehensive monitoring of the entire production process. Through the adaptive interface algorithm, the system can perform precise parameter mapping and grid conversion on the multi-stage simulation results to ensure seamless data connection across processes, thereby realizing the collaborative simulation of the entire hot processing process. The cooperation efficiency and data consistency between processes are improved. The construction of the hierarchical parametric modeling and digital twin model enables the process to reflect the actual production situation in real time. The deployment of the digital twin model to the edge computing gateway, combined with the virtual-real data interaction mechanism and multi-level situation awareness, not only realizes efficient data processing and real-time monitoring, but also enables each link in the production process to obtain precise feedback. The intelligent level and automation ability of the hot processing process are improved, manual intervention is reduced, and the production efficiency and quality are optimized. Using the multi-round distributed process optimization engine, the process parameters can be optimized and calculated, and a limited resource priority allocation plan can be generated, so as to ensure the optimal balance of production efficiency and product quality under limited resources.

[0028] In a specific embodiment, the process of executing step S101 may specifically include the following steps: Extract the smelting temperature, pressure, and composition data from the configuration software and the PLC controller using the OPC communication method, and collect the process parameters of the stock preparation, rough smelting, refining, vacuum, top casting, and bottom casting processes in the smelting process to obtain the smelting process parameter data; Access the ingot casting on-site instrument equipment through the Modbus protocol, collect and digitally process the process requirement data, process execution data, and auxiliary material data to obtain the casting process parameter data; Perform multi-sensor access transformation on large hydraulic presses, presses, and supporting forging heating equipment, collect the temperature field, stress field, and deformation field data during the forging process, and generate the forging process parameter data; Digitally process the quality inspection samples of the heat treatment process based on image recognition and machine learning technologies, convert the organizational structure features and mechanical property features into numerical representations, and construct a heat treatment quality dataset; Perform time series marking and correlation analysis on the smelting process parameter data, casting process parameter data, forging process parameter data, and heat treatment quality dataset, and construct a process chain data structure with process inheritance relationships; Perform outlier detection and data cleaning on the process chain data structure, and generate a full-process dataset for hot processing after standardization processing and feature extraction.

[0029] Specifically, the smelting, casting, forging, and heat treatment processes perform precise real-time data collection through a variety of sensors and data protocols, and generate an optimization model through processing, analysis, thereby driving various decisions in the hot processing process. The data collection of the smelting process utilizes the combination of OPC communication mode and PLC controller to collect key data in the smelting process, such as temperature, pressure, and composition, etc. OPC (OLE for Process Control) is a widely used communication protocol in industrial automation, which can read real-time data from field devices through a standardized interface and transmit it to the control system. During the smelting process, devices such as temperature sensors, pressure sensors, and chemical composition analyzers transmit data to the PLC controller, and the PLC sends this data to the configuration software for monitoring and storage through the OPC protocol. At this time, real-time data such as various parameters of the smelting process, such as furnace temperature, pressure, and liquid level, are collected and generate a data stream, and these data provide key process parameters for the entire smelting process. In addition, parameters of links such as stock preparation, rough smelting, refining, vacuum, upcasting, and downcasting involved in the smelting process are also collected through corresponding devices. These data include the temperature, pressure, chemical composition, and flow rate of the materials, etc., and through the cooperation of the OPC protocol and the PLC, continuously collect and transmit them to the data processing platform to generate smelting process parameter data.

[0030] In the steps of the casting process, use the Modbus protocol to access the ingot casting on-site instrument equipment. Modbus is a serial communication protocol widely used in industrial automation, mainly used to read or write data from devices. Through the Modbus protocol, the system can interact with casting equipment (such as heating furnaces, mold temperature control systems, etc.) to collect process requirement data, process execution data, and auxiliary material data. Process requirement data may include parameters such as temperature, pressure, and time during the casting process, while process execution data reflects the operating status of the equipment in actual operation, and auxiliary material data is the auxiliary materials used in casting, such as the usage amount of alloy additives, etc. After the data is digitized through the Modbus protocol, it enters the data acquisition system.

[0031] In the forging process, data collection relies on equipment modified with multi-sensor access. For example, pressure, displacement, and temperature sensors on large hydraulic presses and presses can obtain data on the temperature field, stress field, and deformation field during the forging process in real time. Through these sensors, various parameters of the forging process can be comprehensively monitored, such as the temperature change, stress distribution, and deformation degree of forgings. The data is transmitted to the control system through the edge computing gateway and is fed back to the digital twin model in real time. Through the multi-sensor fusion, the forging process can be comprehensively monitored.

[0032] In the heat treatment process, data collection is achieved by digitizing quality inspection samples through image recognition and machine learning techniques. Image recognition technology is used to scan the surface of heat-treated workpieces to identify defects such as cracks and pores on the surface, while machine learning algorithms are used to analyze the relationship between these defects and process parameters. Through learning a large number of data samples, the system can convert the characteristics of the microstructure (such as grain size, phase transformation type, etc.) and mechanical property characteristics (such as hardness, tensile strength, etc.) into numerical representations, and these numerical data form the heat treatment quality data set.

[0033] The multi-dimensional process data platform for hot processing performs time series marking and correlation analysis on the data of each process to generate the process chain data structure. For the time series data of different process links, they will be marked to ensure that the data can be correctly associated according to the process sequence. For example, the temperature changes during the smelting process and the temperature changes during the casting process need to be marked according to the time points and process steps. Subsequently, based on these time series data, the system will perform correlation analysis to find out the inheritance relationship between each process. For example, the temperature change during the smelting process directly affects the cooling rate of the casting process, and the quality of the casting process in turn affects the forging accuracy. Through these correlation analyses, a complete process chain data structure is constructed.

[0034] After the data association is completed, the adaptive interface algorithm will be used to perform parameter mapping and grid conversion on the multi-stage simulation results. In the simulation results of processes such as smelting, casting, and forging, the system will automatically map the data generated by different processes to a common parameter space and grid. For example, the temperature field and pressure field during the smelting process need to be mapped to the simulation models of the casting and forging processes through grid conversion to ensure seamless data docking between different processes.

[0035] Through data cleaning and standardization processing, the smelting process parameter data, casting process parameter data, forging process parameter data, and heat treatment quality data are sorted out, noise is removed, missing values are filled, features are extracted, and a hot processing full-process data set is generated. This data set contains the complete data from smelting to heat treatment and serves as the basis for subsequent process optimization.

[0036] For example, during the hot working process, the temperature of the smelting furnace gradually increases from 1500°C to 1650°C, and the temperature control during the casting process is very sensitive. The temperature data collected through the Modbus protocol may contain certain fluctuations, such as the temperature changing approximately by ±5°C per minute. These data will undergo normalization processing to eliminate the influence of equipment errors and environmental factors, ensuring the accuracy of subsequent simulations and optimizations. These temperature data are mapped to the temperature field simulation in the forging process through grid transformation, enabling the forging process to be optimized based on more accurate smelting and casting process data.

[0037] In a specific embodiment, the process of executing step S102 may specifically include the following steps: Perform data cleaning, trend item separation, and noise suppression processing on the hot working full-process data set, extract the main process features, and obtain the preprocessed hot working data; According to the preprocessed hot working data, design a hierarchical data structure including a metadata management layer, a task management layer, a data conversion layer, and a view management layer, and construct a multi-level data storage architecture; Based on the multi-level data storage architecture, classify and store the preprocessed hot working data according to the process types of smelting, casting, forging, and heat treatment to generate a professional process database module; Perform working condition segmentation and normalization processing on the data in the professional process database module, analyze the mapping relationship between material-process-structure-property, and establish a multi-dimensional data association model; Utilize the multi-dimensional data association model to generate process data service components through a standardized data service interface based on middleware; Combine the functions of the process data service components through service orchestration and a rules engine to construct a multi-dimensional process data center for hot working with functions of data management, process parameter retrieval, and process correlation analysis.

[0038] Specifically, for the data collected from the smelting, casting, forging, and heat treatment processes, perform processing through data cleaning, trend item separation, and noise suppression to ensure the accuracy and availability of the data. During the smelting process, the collection of temperature, pressure, and composition data is often affected by factors such as equipment failures and environmental fluctuations, resulting in abnormal fluctuations in the data. The abnormal fluctuations may be caused by sensor failures, sudden changes in environmental temperature, or operation errors. By setting thresholds, these data that do not conform to the normal pattern can be quickly identified and eliminated. For example, when the temperature sensor reading of a certain smelting furnace jumps to an abnormal value at a certain moment, through the set abnormal value identification mechanism, the system will automatically identify and mark these data as invalid data and make corresponding corrections, such as filling them using the interpolation method of the front and back data, or repairing them using the historical data trend.

[0039] Trend term separation is to separate the long-term trend and short-term fluctuations in time series data. For example, during the smelting process, the temperature usually has a long-term upward trend over time, which is manifested as the gradual heating process of the smelting furnace. However, due to equipment adjustment or improper operation, short-term fluctuations in temperature may occur. By using filtering algorithms (such as low-pass filtering or moving average method), these short-term fluctuations can be effectively removed, and only the long-term temperature change trend is retained. This helps to analyze the temperature change law during the smelting process, reduce the interference of equipment periodic fluctuations, and ensure the accuracy of smelting process data.

[0040] During the forging process, temperature and stress field data are often affected by the external environment, such as factors like workshop temperature fluctuations and equipment vibrations, which will generate noise in the data. For example, the vibration of forging equipment may cause fluctuations in the data read by measurement sensors. Through techniques such as wavelet transform, the system can decompose the data into different frequency components, remove the high-frequency noise components, and only retain the low-frequency signals related to process parameters. This helps to significantly improve the signal-to-noise ratio of the data and ensure that the temperature field and stress field data can more accurately reflect the actual situation of the forging process.

[0041] After completing data cleaning, trend term separation, and noise suppression, it enters the stage of extracting the main process characteristics. In the smelting process, the core characteristics include temperature stability, pressure change range, composition change, etc.; in the casting process, they include casting speed, mold temperature change, etc.; in the forging process, the important characteristics may be the distribution of stress, temperature, and deformation degree. These characteristics are the key control variables in each process link. When constructing a multi-level data storage architecture, a hierarchical structure including a metadata management layer, a task management layer, a data conversion layer, and a view management layer is designed. The metadata management layer is used to manage the basic information of the data, such as data source, data collection time, equipment number, etc.; the task management layer is responsible for scheduling and managing data processing tasks to ensure that each processing task is carried out in the correct order and dependency relationship; the data conversion layer undertakes the task of converting different process data formats to ensure that data from different processes such as smelting, casting, forging, and heat treatment can be uniformly processed; the view management layer provides data query and visualization interfaces.

[0042] The data is classified and stored according to different process types to generate professional process database modules such as smelting, casting, forging, and heat treatment. For example, the temperature, pressure, and composition data during the smelting process are stored separately in the smelting process database, while the process parameters and auxiliary material data during the casting process are stored in the casting process database. This classification storage method helps to improve the data access efficiency, enabling engineers to quickly locate the relevant data of a specific process and conduct analysis.

[0043] Segment the operating conditions and standardize the data in these process database modules. Operating condition segmentation means distinguishing data according to different process conditions (such as different material types, different production batches, etc.). For example, different furnace charges in the smelting process will result in different temperature curves, so it is necessary to segment the data according to the types of furnace charges. Ensure that the performance of each process under different operating conditions can be analyzed separately. Standardization processing is used to eliminate the dimensional differences between different data sources and ensure that various types of data can be compared and analyzed on the same platform. In the smelting and casting processes, due to different equipment types, there may be inconsistent data units, and standardization processing can unify different process data to the same dimension.

[0044] Using the multi-dimensional data association model, the system quantitatively analyzes the relationships among materials, processes, microstructures, and properties. These association relationships can reveal the influence of each process on the material properties. For example, the influence of temperature and chemical composition changes in the smelting process on the mechanical properties such as strength and toughness of the final product. Through the model, a comprehensive analysis of the influence relationships between different process steps and different material characteristics can be achieved. Based on the data association model, process data service components are generated through a standardized data service interface. These service components can provide parameters, quality data, etc. of different processes such as smelting, casting, and forging for other modules to use. The process data service components combine functions through service orchestration and a rule engine, and finally build a multi-dimensional process data center for hot processing that can provide data management, process parameter retrieval, and process sequence association analysis functions. Through the data center, users can quickly query relevant data of different processes, monitor process parameters in real time, and make decisions based on data analysis results to ensure the efficiency and precision of the production process.

[0045] For example, in the casting process, through the process data center of this system, the temperature change trends of the casting temperature and the mold can be quickly obtained, and then the casting rate can be adjusted to ensure the quality of the castings. Through data association analysis, it is found that when the temperature in the smelting process is too high, the temperature control difficulty in the casting process increases, and the system can automatically adjust the temperature range in the smelting process according to these findings to ensure the quality and efficiency of the entire production chain.

[0046] In a specific embodiment, the process of executing step S103 may specifically include the following steps: Call the material thermal physical property parameters and process condition parameters of each process of casting, forging, and heat treatment from the multi-dimensional process data center for hot processing to construct independent simulation models for each process; Extract and structure the calculation results of the independent simulation models for each process to generate a process simulation data set including temperature field, stress field, microstructure field, and defect distribution; Based on the process simulation data set, the spatial interpolation algorithm is used to convert the upstream process simulation grid point data into the grid form required for the downstream process simulation, and a cross-process grid mapping model is obtained; Perform error analysis and calibration processing on the cross-process grid mapping model, determine the error range of each parameter transfer, and establish a data transfer error compensation mechanism; According to the data transfer error compensation mechanism, through the simulation interface software, realize the automatic conversion of the upstream process calculation results to the downstream process initial conditions, and generate a seamless coupling link system between processes; Integrate the link system into the full-process simulation software for casting, forging, and heat treatment, and generate a full-process collaborative simulation platform for hot processing through hardware acceleration and computing resource scheduling integration.

[0047] Specifically, call the material thermal physical property parameters and process condition parameters of each process of casting, forging, and heat treatment from the multi-dimensional process data platform for hot processing, and build independent simulation models for each process based on these data. The material thermal physical property parameters include properties such as specific heat capacity, thermal conductivity, and melting point, which describe the thermal response of the material at different temperatures, while the process conditions include operating variables such as temperature, pressure, and time. These data are collected from different sensors and devices and transmitted to the data platform in real time through the Internet of Things. Suppose in the casting process, the temperature of the molten steel and the thermal conductivity of the mold are the main influencing factors. By calling these data, the casting simulation model can simulate the temperature field and stress field during the solidification process based on the initial temperature of the molten steel and the mold characteristics. Similarly, in the forging process, by inputting data such as the yield strength and strain hardening index of the material, the forging model can simulate the stress and strain distribution of the material under different deformations.

[0048] After building the simulation model, extract and structure the calculation results of the independent simulation models for each process. The simulation model of each process will generate a series of process data sets, including temperature field, stress field, microstructure field, and defect distribution, etc. For example, during the casting process, the simulation model can generate a temperature field containing the temperature distribution of the molten steel, as well as distribution data of defects (such as pores, cracks) that may occur during the solidification process; during the forging process, the stress field distribution and possible hot crack areas of the material during deformation can be obtained; during the heat treatment process, the change of the microstructure field is obtained by simulating the phase transformation process. These data need to be extracted and organized into structured simulation data sets to ensure that the simulation results of each process can be analyzed and used in a consistent format for subsequent analysis.

[0049] Based on these process simulation data sets, a spatial interpolation algorithm is used to convert the upstream process simulation grid point data into the grid form required for downstream process simulation, and a cross-process grid mapping model is constructed. The simulation results generated by each process are converted through the spatial interpolation algorithm, so that the simulation data of the process can be compatible with the data format of the downstream process. For example, in the casting process, the temperature field may be calculated through a two-dimensional grid, while in the forging process, the temperature field may need to be converted into a three-dimensional grid for processing. Through the interpolation algorithm, the two-dimensional grid data in the casting process can be converted into the three-dimensional grid data required for the forging process. Interpolation algorithms (such as linear interpolation or bicubic interpolation) calculate the new position and value of each point according to the distance relationship between grid points, ensuring that the error is minimized during the spatial transformation of the data.

[0050] After the cross-process grid mapping is completed, error analysis and calibration processing need to be carried out on the mapping model. The goal of this stage is to ensure that the data transferred from one process to another is consistent in terms of space and physical meaning. The focus of error analysis is to identify any deviations that occur during the data transfer process and adjust them through calibration algorithms. For example, during the grid mapping process from casting to forging, temperature data transfer errors may occur in some areas due to grid differences. At this time, it is necessary to analyze the temperature field differences between the front and back processes and adjust the mapping relationship to ensure the transfer accuracy of the data. The data transfer error compensation mechanism automatically corrects these errors by analyzing the differences between the simulation results and the actual production data.

[0051] The simulation interface software can automatically convert the calculation results of the upstream process into the initial conditions required for the downstream process, thus generating a seamless coupling link system between processes. Achieve precise data transfer between smelting, casting, forging, and heat treatment processes. For example, during the heat treatment process, it is necessary to control the heating and cooling rates based on the temperature and stress data obtained during the casting and forging processes. Through the simulation interface software, the system will automatically convert the temperature data in the casting process and the stress field data in the forging process into the initial temperature and stress conditions of the heat treatment process, thus ensuring that the heat treatment process can be accurately optimized according to the state of the previous processes. Integrate the seamless coupling link system between processes into the full-process simulation software for casting, forging, and heat treatment to form a collaborative simulation platform for the full process of hot processing. On the platform, through hardware acceleration and computing resource scheduling, the efficiency of simulation calculation is greatly improved. The simulation results can be real-time fed back to the production line, thus realizing the optimization of each link in the production process. Through parallel computing and efficient resource scheduling, the system can quickly process complex multi-physical field problems under actual production conditions, ensuring that each process in the hot processing process can operate in the optimal state.

[0052] In a specific embodiment, the process of executing step S104 may specifically include the following steps: Extract the structural and multi - physical field characteristic data of forging equipment from the full - process collaborative simulation platform for hot processing. Use geometric - physical field coupling - driven modeling to analyze the characteristic relationships and obtain the basic data set for the manufacturing process of large components. Process the structure of large forging equipment in the basic data set with a skin model, extract the key feature points and associated parameters on the equipment surface, and construct a digital model of the equipment. Based on the digital model of the equipment, divide the design parameters of large hot - processed components into three levels: overall contour parameters, key component parameters, and process control parameters, and establish a hierarchical parametric structure system. Perform three - dimensional scanning and measurement on the hot - processed components in the hierarchical parametric structure system, compare and calibrate the obtained geometric dimensions and temperature field data with the simulation calculation results, and generate a time - varying feature database. Input the time - varying feature database into the upsetting and drawing process analysis modules for the forging process of hot - processed components, calculate the changing rules of temperature, strain, and damage distribution, and generate process model data. Apply the model accuracy evaluation index system to evaluate and optimize the accuracy, lightweight, and real - time performance of the process model data, and construct a digital twin model.

[0053] Specifically, extract the structural and multi - physical field characteristic data of forging equipment from the full - process collaborative simulation platform. The structure of forging equipment includes physical characteristics such as its geometric shape, material properties, and load distribution, while multi - physical field characteristics involve temperature field, stress field, mechanical properties, etc. These data are collected by different sensors and simulation models and transmitted to the data - processing platform. The geometric structure of forging equipment may include the geometric information of components such as the main frame, oil cylinders, and connecting rods of a hydraulic press, while physical field data such as temperature field and stress field are sourced from real - time monitoring devices and simulation simulations. By analyzing these multi - physical field data, the thermal and mechanical response characteristics of each process stage can be obtained. Process the structure of large forging equipment with a skin model. Skin model technology extracts the key feature points and associated parameters on the surface by analyzing the external characteristics of the equipment and the surface characteristics of key components. Taking a large hydraulic press as an example, the skin model can help identify key data points such as the positions of pressure sensors and heating areas on the outside of the hydraulic press. These data points can not only be used in simulation models but also help in real - time monitoring of the working state of forging equipment. The key to the skin model is to convert the geometric data on the equipment surface into digital parameters for accurate modeling of equipment surface temperature, stress distribution, etc. in the simulation.

[0054] Based on the above skin model, a digital model of the equipment is constructed. The design parameters of large hot - processed components are divided into three levels: overall contour parameters, key component parameters, and process control parameters. In the first stage, the overall design of the forging equipment should be clarified first, such as the size and shape of the frame and the cylinder (overall contour parameters). Secondly, the design requirements of key components should be focused on, such as the position of the pressure sensor and the temperature control range of the heating zone (key component parameters). Finally, the process control parameters should also be defined, such as the temperature, speed, and pressure used during the forging process. Through these hierarchical structures, the equipment can be systematically modeled, and it can be ensured that the parameters at each level can be effectively utilized in subsequent process optimization. The parameters at each level not only reflect the geometric form of the equipment but also involve the functionality and working environment of the equipment.

[0055] For the hot - processed components in the hierarchical parametric structure system, three - dimensional scanning and measurement are carried out. In the process, the geometric dimensions of the hot - processed components are obtained through scanning equipment, and the simulation calculation results are compared and calibrated in combination with the temperature field data. Three - dimensional scanning can accurately capture the geometric form of the components, and the original model can be optimized according to the scanned data. At the same time, the temperature field data can be used to monitor the temperature distribution on the surface of the components in real - time through temperature sensors and infrared measurement equipment. In this process, the geometric dimensions and temperature field data of the components are integrated and compared with the simulation calculation results to ensure that the simulation model can accurately reflect the actual process conditions. By calibrating the three - dimensional scanning data and the simulation calculation results, a time - varying feature database is generated. The database records the geometric changes, temperature changes, and possible stress, deformation, etc. of the hot - processed components at different time nodes. These time - varying feature data provide a dynamic basis for subsequent process control and optimization. For example, during the forging process, the changes in the geometric form and temperature distribution of the components at different stages will affect the final forging result, and the time - varying feature database helps to track these changes and make real - time adjustments.

[0056] The time - varying feature database will be input into the forging process analysis module of the hot - processed components. This module can calculate the change rules of temperature, strain, and damage distribution and generate process model data. In this process, the change rules of temperature, strain, and damage are not only obtained through numerical simulation calculations but also need to be optimized in combination with actual production data. For example, assume that during the forging process, the high temperature in a certain part causes thermal cracks, which may affect the quality of the final product. The process model data can help identify potential quality problems by calculating these change rules and provide correction strategies.

[0057] Based on process model data, a model accuracy evaluation index system is used to evaluate and optimize accuracy, lightweight and real-time performance, so as to ensure that the generated digital twin model can operate efficiently and accurately in the actual production environment. In the process, the model accuracy evaluation index system helps to evaluate the deviation between the simulation results and the actual data, so as to adjust the model parameters and ensure the accuracy of the model. At the same time, the lightweight and real-time performance evaluation helps to ensure that the model can operate efficiently in the production process on the premise of ensuring accuracy, avoiding waste of computing resources and excessive computing delay.

[0058] In a specific embodiment, the process of executing step S105 may specifically include the following steps: Deploy the digital twin model to the edge computing gateway between the device side and the digital system, construct a data channel supporting 5G or wired transmission, and generate an edge computing service framework; Distributively process the key equipment, production process and workshop environment data collected by the edge computing service framework, establish a real-time mapping matrix of virtual and physical data, and generate a virtual-real data interaction mechanism; Based on the virtual-real data interaction mechanism, construct a multi-level situation awareness model including equipment layer situation, process layer situation and security layer situation, and generate a real-time monitoring data stream of the production process; Apply the fast scanning and multi-dimensional modeling analysis algorithm to the quality characteristics of the hot working process in the real-time monitoring data stream of the production process, and generate an accurate analysis data set of forging allowance; Based on the accurate analysis data set of forging allowance, construct a fusion control system including a human-machine remote control module, an interactive operation control module and a production safety management module, and generate a safety warning index for the manufacturing process; Integrate the safety warning index of the manufacturing process and the real-time monitoring data stream of the production process into a unified interface framework through cloud configuration technology, optimize the parameters of the edge computing gateway according to the cloud strategy, and generate a visual management and control system.

[0059] Specifically, through the deployment of the edge computing gateway, the digital twin model enables data collection and processing to be carried out close to the data source, thus greatly improving the response speed and computing efficiency. This process is to enable the system to have fast real-time data processing capabilities and enhance the intelligent decision-making capabilities in the production process. A reliable data transmission channel is established between the device side and the digital system, supporting the real-time transmission of a large amount of production data through 5G or wired networks. The introduction of 5G technology ensures low-latency and high-bandwidth data transmission, and can quickly respond to the data provided by various sensors and devices in the production process. The device side obtains the data of key equipment, production process and workshop environment in real time through the edge computing gateway, such as equipment status, production parameters and environmental conditions. These data are distributively processed through the edge computing service framework to ensure the fast storage and analysis of data.

[0060] The edge computing service framework processes the collected key data and establishes a real-time mapping matrix between virtual and physical data. This mapping matrix docks the production data in the physical world with the data in the virtual model, realizing the interaction of virtual and real data. For example, data such as temperature and pressure collected in real time by sensors will be compared with the physical fields (such as stress field, temperature field, etc.) in the digital twin model through the edge computing platform, enabling the virtual model to be updated in real time and reflecting the status of each link in the production process. The interaction mechanism ensures that all data in the production process can be accurately fed back into the virtual model.

[0061] Based on the virtual-real data interaction mechanism, a multi-level situation awareness model is further constructed. This model includes the device layer situation, the process layer situation, and the safety layer situation. The device layer situation focuses on the operating status of key devices, including temperature, pressure, load, etc.; the process layer situation focuses on various process parameters and actual performance in the production process, such as temperature field, stress field, deformation, etc.; the safety layer situation focuses on the safety of the workshop environment and operations, including personnel contact, equipment overload, environmental changes, etc. By integrating the situation awareness of these three layers, the system can monitor the status changes of each link in the production process in real time and promptly feedback to the process adjustment or safety management module. The real-time monitoring data stream in the production process uses data stream processing and analysis algorithms to deeply analyze the quality characteristics of the hot processing process. During the process, fast scanning technology and multi-dimensional modeling analysis algorithms are used to extract and analyze the quality data in the forging process. These technologies can efficiently identify the key quality characteristics in the hot processing process, such as surface defects and dimensional deviations of forgings. Through the processing of the data stream, the system can generate a precise analysis dataset of forging allowance in real time, analyze the actual allowance and forming error of the forging, and provide data support for subsequent process optimization and adjustment.

[0062] Based on the precise analysis dataset of forging allowance, the system further constructs a fusion control system including a human-machine remote control module, an interactive operation control module, and a production safety management module. The core of the control system is to improve the operation flexibility and safety in the production process through intelligent algorithms and real-time data analysis. The human-machine remote control module allows operators to remotely monitor and control equipment for real-time adjustment and emergency handling; the interactive operation control module automatically adjusts according to the data collected in real time during the production process to optimize process parameters; the production safety management module prevents potential safety hazards, such as overheating, equipment failure, or operation errors, through real-time monitoring of the workshop environment and equipment status. Through the close cooperation of these modules, the smooth progress of the production process can be ensured and safety can be guaranteed.

[0063] To improve the level of intelligence, safety warning indicators for the manufacturing process are generated and combined with the real-time monitoring data stream of the production process. Through cloud configuration technology, the system can integrate these warning indicators and real-time data streams into a unified interface framework. This enables operators to quickly obtain safety warning information during the production process and make timely decisions based on this information. The cloud-based strategy optimizes the parameters of the edge computing gateway, enabling the system to make dynamic adjustments according to different production requirements and production states, thereby realizing the intelligent scheduling of process parameters and resources during the production process.

[0064] In this way, the constructed visual control system can display all key data in real time and provide decision support. The data and warning information of each link are integrally displayed through cloud configuration technology, enabling operators and managers to view, analyze, and make decisions on a unified platform. This integrated visual management not only improves production efficiency but also reduces risks during the production process, ensuring product quality and production safety.

[0065] For example, during the forging production process, the system collects data such as the temperature and stress of forgings in real time through the edge computing gateway and compares these data with the temperature field and stress field in the digital twin model in real time. Suppose the surface temperature of a certain forging shows an abnormality during the processing, exceeding the preset safety range. The system can immediately detect the abnormality through the virtual-real data interaction mechanism and issue an alarm in the visual control system. The operator can adjust the heating process of the equipment through the remote control module to ensure that the temperature of the forging does not rise further and avoid affecting product quality. At the same time, the system will automatically adjust the equipment parameters during the production process according to the data in the safety layer situation to ensure that the entire production process can proceed safely and efficiently.

[0066] In a specific embodiment, the process of executing step S106 may specifically include the following steps: Extract the process parameters and quality inspection data of each process running in real time from the visual control system to construct a real-time process execution data set; Apply a parameter evaluation algorithm based on knowledge reasoning and deep neural network to the real-time process execution data set, calculate the deviation matrix between the current process state and the target quality, and generate multi-dimensional process risk assessment indicators; According to the multi-dimensional process risk assessment indicators, extract historical optimization cases from the process database, and apply the gradient boosting decision tree method to construct a non-linear mapping relationship between process parameters and quality characteristics to generate a multi-round distributed process optimization engine; Set multi-dimensional optimization goals such as optimal quality, minimum energy consumption, and shortest cycle in the multi-round distributed process optimization engine, and generate a candidate process parameter combination scheme set through parallel iterative optimization calculation; The candidate process parameter combination set is virtually tested through a digital twin model, the quality prediction scores and resource consumption indicators of each scheme are calculated, and the target process parameter combination is determined. Based on the target process parameter combination, a multi-attribute analysis method is applied to comprehensively evaluate the workpiece importance, process difficulty, and production urgency, calculate the priority allocation matrix of equipment and resources, and generate a limited resource priority allocation scheme.

[0067] Specifically, the process parameters and quality inspection data of each process running in real time are extracted from the visual control system. The data includes process parameters such as real-time temperature, pressure, speed, displacement, etc. measured by various sensors, as well as quality inspection data such as the dimensions, surface quality, hardness, etc. of forgings. These data are collected in real time and converted into a structured format to form a real-time process execution data set. The real-time process execution data set is a complete record of all key parameters and quality characteristics in the production process. Through the data collection and real-time monitoring system, the accuracy and timeliness of the data are ensured.

[0068] For the real-time process execution data set, a parameter evaluation algorithm based on knowledge reasoning and deep neural network is applied to calculate the deviation matrix between the current process state and the target quality. Evaluate the gap between the current process state (e.g., current temperature, pressure, etc.) and the target quality (such as the expected dimensions, surface quality, etc. of forgings). The deep neural network (DNN) algorithm establishes a relationship model between process parameters and quality characteristics by training a large amount of historical data, and helps to evaluate the deviation between the current state and the ideal target. For example, if the hardness requirement of the target forging is X, but the current production data shows that the actual hardness is Y, DNN will calculate the deviation between X and Y and generate a deviation matrix based on this. The deviation matrix not only reflects the gap between the current state and the target.

[0069] Generate multi-dimensional process risk assessment indicators based on the deviation matrix. The indicators comprehensively consider the impact of process parameters such as temperature, pressure, and time on quality, as well as risks brought by factors such as equipment and personnel. The multi-dimensional process risk assessment indicators provide a comprehensive risk assessment framework for process optimization through a comprehensive analysis of the process state, helping to identify links that may lead to unqualified product quality or low production efficiency. The process not only focuses on the current status of the process but also gives early warnings of potential risks in the production process, providing decision-making support for subsequent optimization. Extract historical optimization cases from the process database and apply the Gradient Boosting Decision Tree (GBDT) method to construct a non-linear mapping relationship between process parameters and quality characteristics. Historical optimization cases refer to successful cases recorded in previous production processes, including various process parameters and corresponding quality output results. By extracting this historical data, the GBDT model can establish a decision-making model reflecting the non-linear relationship between complex processes and quality. For example, in the casting process, GBDT can help determine the complex relationship between temperature and the final casting quality and provide decision-making support for new production tasks. The GBDT model will evaluate the impact of different process parameters in the current production process based on the experience of historical data and generate an optimization model.

[0070] The multi-round distributed process optimization engine performs optimization calculations based on multi-dimensional objectives such as target quality, energy consumption, and production cycle through parallel computing and iterative optimization. The multi-round optimization algorithm can continuously approach the optimal solution through multiple iterative calculations according to predefined objectives (such as optimal quality, minimum energy consumption, shortest cycle, etc.). For example, in the forging process, the process optimization engine can perform a series of simulation calculations based on historical optimization data to generate multiple candidate process parameter combinations, such as combinations of different temperatures, pressures, and deformation rates. Finally, the system selects the optimal process parameter combination to ensure the quality and efficiency of the production process.

[0071] Conduct virtual tests on the candidate process parameter combination scheme set. Through the digital twin model, the system simulates each candidate scheme and calculates the quality prediction score and resource consumption indicators of each scheme. The digital twin model reproduces the actual production process through simulation technology, calculates indicators such as the quality, dimensions, and surface quality of the product under each candidate process scheme, and at the same time evaluates the resource consumption such as energy and time required for each scheme. The purpose of this test step is to compare the theoretical results of each process scheme with the actual production results to ensure that the selected process parameter combination can not only achieve the quality target but also minimize resource waste and production cycle.

[0072] Based on the target process parameter combination, apply the multi-attribute analysis method to comprehensively evaluate the importance, process difficulty, and production urgency of workpieces, calculate the priority allocation matrix of equipment and resources, and generate a priority allocation plan for limited resources. During the production process, resources (such as equipment, raw materials, time, etc.) are limited, so it is necessary to reasonably allocate resources according to the needs of different workpieces. Through multi-attribute analysis, the system can evaluate the priority of each workpiece. For example, for a batch of forgings, perhaps its quality requirements are particularly high, or its production urgency is relatively large, and the system will allocate more production resources to these high-priority workpieces. Optimizing the allocation plan ensures the maximum utilization of limited resources and also guarantees that critical workpieces can be completed on time and meet quality standards.

[0073] For example, assume that in a certain production batch, through the deep neural network algorithm, it is found that there are significant deviations in the hardness and size of the current forgings (the deviation matrix shows high risk). Based on this, the process optimization engine proposes three different combinations of process parameters, which are adjusted for temperature, pressure, and deformation rate respectively. Through virtual testing of the digital twin model, it is found that the forgings of the second process combination have the best quality but higher energy consumption. After multi-attribute analysis, considering the production urgency and equipment load, the system finally selects the third process combination. This combination ensures quality while having lower energy consumption and meeting the production cycle requirements. Through multi-dimensional optimization and decision support, the system realizes the reasonable allocation of resources and ensures the dual optimization of production efficiency and product quality.

[0074] The above describes the digital twin-based hot processing control method in the embodiments of the present application. Next, the digital twin-based hot processing control system in the embodiments of the present application will be described. Please refer to Figure 2 , an embodiment of the digital twin-based hot processing control system in the embodiments of the present application includes: The acquisition module 201 is used to collect process parameters and quality data for the smelting, casting, forging, and heat treatment processes using a multi-sensor fusion Internet of Things data acquisition system to obtain a full-process hot processing data set; The construction module 202 is used to construct a multi-dimensional process data center for hot processing according to the full-process hot processing data set; The conversion module 203 is used to, based on the multi-dimensional process data center for hot processing, perform parameter mapping and grid conversion on the multi-stage simulation results through an adaptive interface algorithm to establish a full-process collaborative simulation platform for hot processing; The modeling module 204 is used to perform hierarchical parametric modeling based on the full-process collaborative simulation platform for hot processing to construct a digital twin model; The perception module 205 is used to deploy the digital twin model to the edge computing gateway, and generate a visual control system through a virtual-real data interaction mechanism and multi-level situation awareness; An input module 206 for inputting the real-time data of the manufacturing process collected by the visual control system into a multi-round distributed process optimization engine to calculate the target process parameter combinations and the preferential allocation scheme of limited resources.

[0075] Through the collaborative cooperation of the above-mentioned various components, a real-time data acquisition system for the Internet of Things with multi-sensor fusion is used to collect the process parameters and quality data of the smelting, casting, forging, and heat treatment processes in real time, and a full-process dataset for hot processing is constructed. It can realize the comprehensive monitoring of the entire production process. Through the adaptive interface algorithm, the system can perform accurate parameter mapping and grid conversion on the multi-stage simulation results to ensure seamless data connection across processes, thereby realizing the collaborative simulation of the full process of hot processing. The collaboration efficiency and data consistency between processes are improved. The construction of the hierarchical parametric modeling and digital twin model enables the process to reflect the actual production situation in real time. The deployment of the digital twin model to the edge computing gateway, combined with the virtual-real data interaction mechanism and multi-level situation awareness, not only realizes efficient data processing and real-time monitoring, but also enables each link in the production process to obtain accurate feedback. The intelligent level and automation ability of the hot processing process are improved, manual intervention is reduced, and the production efficiency and quality are optimized. Using the multi-round distributed process optimization engine, the process parameters can be optimized and calculated, and a preferential allocation scheme for limited resources can be generated, so as to ensure the optimal balance of production efficiency and product quality under limited resources.

[0076] Above Figure 2 From the perspective of modular functional entities, the digital twin-based hot processing control system in the embodiments of the present invention is described in detail. Next, the digital twin-based hot processing control equipment in the embodiments of the present invention is described in detail from the perspective of hardware processing.

[0077] Figure 3FIG. 0 is a schematic structural diagram of a digital-twin-based hot processing control device provided by an embodiment of the present invention. The digital-twin-based hot processing control device 300 may vary greatly due to different configurations or performances, and may include one or more processors (central processing units, CPUs) 310 (for example, one or more processors) and a memory 320, and one or more storage media 330 (for example, one or more mass storage device terminals) storing application programs 333 or data 332. Among them, the memory 320 and the storage media 330 may be transient storage or persistent storage. The program stored in the storage media 330 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations on the digital-twin-based hot processing control device 300. Further, the processor 310 may be configured to communicate with the storage media 330 and execute a series of instruction operations in the storage media 330 on the digital-twin-based hot processing control device 300 to implement the steps of the above-mentioned digital-twin-based hot processing control method.

[0078] The digital-twin-based hot processing control device 300 may further include one or more power supplies 340, one or more wired or wireless network interfaces 350, one or more input / output interfaces 360, and / or one or more operating systems 331, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, and so on. Those skilled in the art can understand that Figure 3 the shown structure of the digital-twin-based hot processing control device does not limit the digital-twin-based hot processing control device provided by the present invention, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0079] The present invention 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 is caused to execute the steps of the digital-twin-based hot processing control method.

[0080] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described systems, systems, and units may refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0081] When 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 invention, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a digital-twin-based hot processing control device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs.

[0082] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A thermal processing control method based on digital twin, characterized in that: The method comprises: The multi-sensor fusion IoT data acquisition system is used to collect process parameters and quality data for smelting, casting, forging and heat treatment processes, and obtain a full-process data set for hot processing; Construct a multi-dimensional process data platform for hot working according to the hot working full process data set; Based on the multi-dimensional process data platform for hot working, the multi-stage simulation results are parameter mapped and meshed through an adaptive interface algorithm to establish a collaborative simulation platform for the entire hot working process. Based on the thermal processing full-process collaborative simulation platform, hierarchical parametric modeling is performed to build a digital twin model; Deploy the digital twin model to the edge computing gateway, and generate a visual management and control system through virtual-real data interaction mechanism and multi-level situation awareness; The real-time data of the manufacturing process collected by the visual management and control system is input into a multi-round distributed process optimization engine to calculate the target process parameter combination and the limited resource priority allocation plan.

2. The thermal processing control method based on digital twin according to claim 1 is characterized in that: The multi-sensor fusion IoT data acquisition system is used to collect process parameters and quality data for the smelting, casting, forging and heat treatment processes, and a full-process data set of hot processing is obtained, including: The OPC communication method is used to extract the smelting temperature, pressure and composition data from the configuration software and PLC controller, collect the material preparation, rough refining, refining, vacuum, upper casting and lower casting process parameters in the smelting process, and obtain the smelting process parameter data; Access the ingot casting field instrument equipment through the Modbus protocol to collect and digitally process the process requirement data, process execution data and auxiliary material data to obtain the casting process parameter data; Carry out multi-sensor access transformation on large hydraulic presses and presses as well as supporting forging heating equipment to collect temperature field, stress field and deformation field data during the forging process and generate forging process parameter data; Based on image recognition and machine learning technology, the heat treatment process quality inspection samples are digitized, the organizational structure characteristics and mechanical properties characteristics are converted into numerical representations, and the heat treatment quality data set is constructed; Performing time series marking and association analysis on the smelting process parameter data, the casting process parameter data, the forging process parameter data and the heat treatment quality data set to construct a process chain data structure with a process inheritance relationship; The process chain data structure is subjected to outlier detection and data cleaning, and is subjected to standardization and feature extraction to generate a full-process data set for hot working.

3. The thermal processing control method based on digital twin according to claim 1 is characterized in that: The method of constructing a multi-dimensional process data platform for hot working according to the hot working full process data set includes: Performing data cleaning, trend item separation and noise suppression processing on the full-process data set of the hot working process, extracting the main process features, and obtaining the pre-processed hot working data; According to the pre-processed thermal processing data, a hierarchical data structure including a metadata management layer, a task management layer, a data conversion layer and a view management layer is designed to construct a multi-level data storage architecture; Based on the multi-level data storage architecture, the pre-processed thermal processing data is classified and stored according to the smelting, casting, forging and heat treatment process types to generate a professional process database module; Performing working condition segmentation and standardization processing on the data in the professional process database module, analyzing the mapping relationship between material-process-structure-performance, and establishing a multi-dimensional data association model; Using the multi-dimensional data association model, a process data service component is generated through a standardized data service interface based on middleware; The process data service components are functionally combined through service orchestration and rule engine to build a hot processing multi-dimensional process data middle platform with data management, process parameter retrieval and process correlation analysis functions.

4. The thermal processing control method based on digital twin according to claim 1 is characterized in that: Based on the multi-dimensional process data platform of hot working, the multi-stage simulation results are parameter mapped and meshed through an adaptive interface algorithm to establish a hot working full-process collaborative simulation platform, including: The material thermal property parameters and process condition parameters of each process of casting, forging, and heat treatment are called from the multi-dimensional process data of the hot processing platform to build an independent simulation model for each process; Performing data extraction and structural processing on the calculation results of the independent simulation models of each process to generate a process simulation data set including temperature field, stress field, organization field and defect distribution; Based on the process simulation data set, a spatial interpolation algorithm is used to convert the upstream process simulation grid point data into a grid form required for downstream process simulation, so as to obtain a cross-process grid mapping model; Performing error analysis and calibration on the cross-process grid mapping model, determining the error range of each parameter transmission, and establishing a data transmission error compensation mechanism; According to the data transmission error compensation mechanism, the automatic conversion of the calculation results of the upstream process to the initial conditions of the downstream process is realized through the simulation interface software, and a link system with seamless coupling between the processes is generated; The link system is integrated into the full-process simulation software of casting, forging and heat treatment, and a full-process collaborative simulation platform for hot processing is generated through hardware acceleration and computing resource scheduling.

5. The thermal processing control method based on digital twin according to claim 1 is characterized in that: The hierarchical parameterized modeling is performed based on the thermal processing full-process collaborative simulation platform to construct a digital twin model, including: Extracting forging equipment structure and multi-physical field characteristic data from the hot working full-process collaborative simulation platform, using geometry-physical field coupling driven modeling to analyze characteristic relationships, and obtaining a basic data set for large-scale component manufacturing process; Perform skin model processing on the large forging equipment structure in the basic data set, extract key feature points and associated parameters on the equipment surface, and build a digital model of the equipment; Based on the digital model of the equipment, the design parameters of the hot-processed large-scale components are divided into three levels: overall profile parameters, key component parameters and process control parameters, and a hierarchical parameterized structure system is established; Performing three-dimensional scanning and measurement on the hot-processed components in the hierarchical parameterized structural system, comparing and calibrating the acquired geometric dimensions and temperature field data with the simulation calculation results, and generating a time-varying feature database; Inputting the time-varying characteristic database into the upsetting and drawing process analysis module of the hot working component forging process, calculating the temperature, strain and damage distribution change law, and generating process model data; The model accuracy evaluation index system is applied to the process model data to evaluate and optimize the accuracy, lightweight and real-time performance to build a digital twin model.

6. The thermal processing control method based on digital twin according to claim 1 is characterized in that: The digital twin model is deployed to the edge computing gateway, and a visual management and control system is generated through a virtual-real data interaction mechanism and multi-level situation awareness, including: Deploy the digital twin model to the edge computing gateway between the device end and the digital system, build a data channel supporting 5G or wired transmission, and generate an edge computing service framework; Distribute the key equipment, production process and workshop environment data collected by the edge computing service framework, establish a real-time mapping matrix between virtual and physical data, and generate a virtual-real data interaction mechanism; Based on the virtual-real data interaction mechanism, a multi-level situation awareness model including equipment layer situation, process layer situation and safety layer situation is constructed to generate a real-time monitoring data stream for the production process; The quality characteristics of the hot working process in the real-time monitoring data stream of the production process are applied to generate a data set for accurate analysis of forging allowance by using fast scanning and multi-dimensional modeling analysis algorithms; Based on the forging allowance accurate analysis data set, a fusion control system including a human-machine remote control module, an interactive operation control module and a production safety management module is constructed to generate manufacturing process safety early warning indicators; The manufacturing process safety early warning indicators and the real-time monitoring data flow of the production process are integrated into a unified interface framework through cloud configuration technology, and the edge computing gateway parameters are optimized according to the cloud strategy to generate a visual management and control system.

7. The thermal processing control method based on digital twin according to claim 1 is characterized in that: The real-time data of the manufacturing process collected by the visual management and control system is input into a multi-round distributed process optimization engine to calculate the target process parameter combination and the limited resource priority allocation plan, including: Extracting the process parameters and quality inspection data of each process in real time from the visual control system to build a real-time process execution data set; Applying a parameter evaluation algorithm based on knowledge reasoning and deep neural network to the real-time process execution data set, calculating a deviation matrix between the current process state and the target quality, and generating a multi-dimensional process risk assessment index; According to the multi-dimensional process risk assessment indicators, historical optimization cases are extracted from the process database, a nonlinear mapping relationship between process parameters and quality characteristics is constructed by applying a gradient boosting decision tree method, and a multi-round distributed process optimization engine is generated; In the multi-round distributed process optimization engine, multi-dimensional optimization goals such as optimal quality, minimum energy consumption and shortest cycle are set, and a set of candidate process parameter combination solutions is generated through parallel iterative optimization calculation; The candidate process parameter combination scheme set is virtually tested through a digital twin model, the quality prediction score and resource consumption index of each scheme are calculated, and the target process parameter combination is determined; Based on the target process parameter combination, a multi-attribute analysis method is applied to comprehensively evaluate the workpiece importance, process difficulty and production urgency, calculate the priority allocation matrix of equipment and resources, and generate a limited resource priority allocation plan.

8. A thermal processing control system based on digital twin, characterized in that: Used to implement the hot working control method based on digital twin according to any one of claims 1 to 7, the hot working control system based on digital twin includes: The acquisition module is used to collect process parameters and quality data of smelting, casting, forging and heat treatment processes using a multi-sensor fusion IoT data acquisition system to obtain a data set for the entire hot processing process; A construction module is used to construct a multi-dimensional process data platform for hot working according to the hot working full process data set; A conversion module is used to perform parameter mapping and mesh conversion on multi-stage simulation results based on the multi-dimensional process data platform of hot working through an adaptive interface algorithm to establish a collaborative simulation platform for the entire hot working process; A modeling module, used for performing hierarchical parameterized modeling based on the thermal processing full-process collaborative simulation platform to construct a digital twin model; A perception module is used to deploy the digital twin model to the edge computing gateway and generate a visual management and control system through a virtual-real data interaction mechanism and multi-level situational awareness; The input module is used to input the real-time data of the manufacturing process collected by the visual management and control system into the multi-round distributed process optimization engine to calculate the target process parameter combination and the limited resource priority allocation plan.

9. A thermal processing control equipment based on digital twin, characterized in that: It includes a memory and a processor, the memory stores a computer program that can be run on the processor, and the processor implements the digital twin-based thermal processing control method described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the processor executes the thermal processing control method based on digital twin according to any one of claims 1 to 7.

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