Monitoring method of food puffing and drying process based on digital twinning technology

Abstract

The invention relates to a monitoring method for a food puffing and drying process based on a digital twinning technology, and the method comprises the steps: constructing a virtual twinning model of equipment and a product; establishing an initial simulation model through an empirical model and a mechanism model to simulate and predict equipment operation states, material states, product quality indexes and the like under different input parameters; equipment process data and product quality data are collected and imported into a server; the three-dimensional model and the simulation model are integrated, and possible output of the next step is predicted and visually displayed; meanwhile, setting a machine learning model in the server, and correcting the model by comparing a simulation result of an initial model with actual operation monitoring data of a physical world so as to realize iterative improvement of the twin; on the basis, a large language model is introduced as an automatic, intelligent and real-time interaction means among the user, the entity equipment and the twin, so that the monitoring efficiency can be further improved.

Description

Technical Field

[0001] The present application relates to the technical field of puffing machine process monitoring, and in particular to a monitoring method for food puffing and drying processes based on digital twin technology. Background Art

[0002] At present, most of the models used for monitoring the food puffing process are empirical models (EM), which rely heavily on the experience of technicians and operators to design and improve. A large number of approximations and assumptions are required in the process, which have the following disadvantages:

[0003] 1. Low accuracy and unstable decision success rate: Since the existing process monitoring methods based on mechanism models and empirical models rely on the decisions of human operators based on experience and subjective judgment, the accuracy of parameter settings, quality problem warnings and fault warnings in the process of process monitoring is often low; and manual monitoring is easily affected by the operator's experience level and subjective factors. Different operators sometimes make different judgments and reactions to the same process indicators and process parameters. The same operator is prone to judgment or decision deviations when facing new working conditions, new products or failing to observe the changes in relevant parameters or indicators in a timely manner.

[0004] 2. Poor real-time warning: During manual monitoring, when process parameters or product indicators deviate, on-site operators and process technicians need to wait for a certain amount of time to collect data, make judgments and decisions. The response speed is slow, and sometimes it is easy to overlook some problems that are not easy to find, resulting in a large amount of material and product losses in the process.

[0005] 3. Human resource risks caused by reliance on specific employees in production management: In traditional methods, the effectiveness of process monitoring is highly dependent on whether the technicians have sufficient experience and knowledge, whether they have sufficient skills to correctly design key components (such as export molds and die plates) and quickly and correctly operate (such as startup, shutdown operations, and correct troubleshooting when encountering failures or quality problems) and parameter adjustments, etc. Moreover, due to the complexity and ambiguity of the model, it is difficult to accurately pass on knowledge and skills.

[0006] 4. Poor intuitiveness: In traditional management and control methods, it is impossible to simulate and display the material flow and change conditions, temperature distribution, pressure distribution, etc. in the equipment in real time and intuitively. There are also few real-time analysis and simulation prediction results of equipment configuration. As a result, relevant control personnel can only understand and estimate the process status based on abstract process parameter data combinations and products collected on site, which increases the difficulty of process monitoring and the difficulty of making correct judgments and actions to a certain extent.

[0007] 5. Unable to quickly collect and process large amounts of data: A large amount of data is generated during the pet food puffing process. Manually collecting, processing and analyzing data is time-consuming and costly, and important data is easily lost.

[0008] 6. Historical experience and data may not be applicable to process monitoring of specific equipment or specific products: The experience accumulated by technicians in other equipment over a long period of time may not be applicable to new equipment. Because each equipment has different degrees of wear and tear and different product designs, its process parameters and key equipment designs often need to be recalculated and improved. When monitoring the process of new equipment and new products based on past experience, deviations are prone to occur, resulting in waste or quality problems.

[0009] In summary, the existing food puffing process monitoring methods have poor accuracy, warning real-time and monitoring intuitiveness, and are more dependent on specific employees. It is difficult to quickly collect and process large amounts of data and has great limitations in usage scenarios, which need to be solved urgently. Summary of the invention

[0010] The present application provides a monitoring method for food puffing and drying processes based on digital twin technology to solve the problems of existing food puffing process monitoring methods, such as poor accuracy, early warning real-time and monitoring intuitiveness, and high reliance on specific employees, difficulty in quickly collecting and processing large amounts of data, and large limitations on usage scenarios.

[0011] The first aspect of the present application provides a monitoring method for food puffing and drying processes based on digital twin technology, comprising the following steps: constructing a virtual twin model of a target device, collecting equipment process data of the target device and product quality data of a target product, and preprocessing the equipment process data and the product quality data to obtain standard equipment process data and standard product quality data; constructing a mechanism model and an empirical model corresponding to the target device, so as to establish a simulation model of the target device through the mechanism model and the empirical model, and collecting expert technical information corresponding to the target device through a pre-constructed large language model, so as to input the expert technical information, the standard equipment process data and the standard product quality data into a target server; integrating the virtual twin model and the simulation model, and combining the standard equipment process data and the standard product quality data to predict the equipment operation status information of the target device; constructing a target machine learning model of the virtual twin in the target server, and collecting actual operation monitoring data of the target device, so as to correct the target machine learning model through the equipment operation status information and the actual operation monitoring data, so as to use the corrected target machine learning model to monitor the food puffing and drying processes of the target device and the target product.

[0012] Optionally, in one embodiment of the present application, while using the modified target machine learning model to monitor the food puffing and drying process of the target device and the target product, it also includes: generating a monitoring report corresponding to the target device and the target product, and constructing multiple target intelligent agents through the large language model, and establishing a two-way information interaction system based on the multiple target intelligent agents; sending the monitoring report to the target user through the two-way information interaction system, and the target user performs visual remote operation guidance on the puffing and drying process of the target device and the target product based on the monitoring report and through the two-way information interaction system and a preset AR strategy.

[0013] Optionally, in one embodiment of the present application, the virtual twin model of the target device is constructed, and the equipment process data of the target device and the product quality data of the target product are collected, and the equipment process data and the product quality data are pre-processed to obtain standard equipment process data and standard product quality data, including: constructing a virtual twin model of the target device through a preset Unity3D, and establishing a communication connection between a target PLC and a target host computer, so that the virtual twin model obtains the equipment process data and the product quality data through the target PLC; performing data cleaning and data normalization on the equipment process data and the product quality data to obtain the standard equipment process data and the standard product quality data; integrating the standard equipment process data and the standard product quality data through the large language model to obtain integrated natural language information, and displaying the integrated natural language information to the target user in the display interface of the virtual twin model, so that the target user can perform two-way interaction with multiple target agents constructed by the large language model according to the integrated natural language information.

[0014] Optionally, in one embodiment of the present application, constructing the mechanism model and the empirical model corresponding to the target device includes: performing physical and chemical process analysis on the target device to obtain analysis results, and constructing the mechanism model of the target device based on the analysis results; obtaining empirical information and actual production data of the target device, and constructing the empirical model based on the mechanism model, the empirical information and the actual production data.

[0015] Optionally, in one embodiment of the present application, the virtual twin model and the simulation model are integrated, and combined with the standard equipment process data and the standard product quality data, to predict the equipment operation status information of the target equipment, including: based on the standard equipment process data, the standard product quality data or the simulation parameters customized by the target user through the virtual twin model, and combining the virtual twin model and the simulation model to predict the equipment operation status information.

[0016] Optionally, in one embodiment of the present application, the target machine learning model of the virtual twin is constructed in the target server, and the actual operation monitoring data of the target device is collected to correct the target machine learning model through the device operation status information and the actual operation monitoring data, including: training the target machine learning model through the actual operation monitoring data, and using preset model evaluation indicators to perform performance evaluation on the trained target machine learning model to obtain a performance evaluation result; based on the performance evaluation result and a preset expert experience analysis model, correcting the target machine learning model.

[0017] The second aspect of the present application provides a monitoring device for food puffing and drying process based on digital twin technology, including: a first construction module, used to construct a virtual twin model of a target device, and collect equipment process data of the target device and product quality data of the target product, and pre-process the equipment process data and the product quality data to obtain standard equipment process data and standard product quality data; a second construction module, used to construct a mechanism model and an empirical model corresponding to the target device, so as to establish a simulation model of the target device through the mechanism model and the empirical model, and collect expert technical information corresponding to the target device through a pre-constructed large language model to convert the expert technical information into standard equipment process data and standard product quality data. The technical information, the standard equipment process data and the standard product quality data are input into the target server; an integration module is used to integrate the virtual twin model and the simulation model, and combine the standard equipment process data and the standard product quality data to predict the equipment operation status information of the target equipment; a correction module is used to construct a target machine learning model of the virtual twin in the target server, and collect the actual operation monitoring data of the target equipment, so as to correct the target machine learning model through the equipment operation status information and the actual operation monitoring data, so as to use the corrected target machine learning model to monitor the food puffing and drying process of the target equipment and the target product.

[0018] Optionally, in one embodiment of the present application, it also includes: a generation module, which is used to generate a monitoring report corresponding to the target device and the target product while monitoring the food puffing and drying process of the target device and the target product using the modified target machine learning model, and construct multiple target intelligent agents through the large language model, and establish a two-way information interaction system based on the multiple target intelligent agents; a large language model interaction module, which is used to send the monitoring report to the target user through the two-way information interaction system, and the target user performs visual remote operation guidance on the puffing and drying process of the target device and the target product based on the monitoring report and through the two-way information interaction system and a preset AR strategy.

[0019] Optionally, in one embodiment of the present application, the first construction module includes: a communication unit, used to construct a virtual twin model of the target device through a preset Unity3D, and establish a communication connection between the target PLC and the target host computer, so that the virtual twin model can obtain the equipment process data and the product quality data through the target PLC; a preprocessing unit, used to perform data cleaning and data normalization on the equipment process data and the product quality data to obtain the standard equipment process data and the standard product quality data; an interaction unit, used to integrate the standard equipment process data and the standard product quality data through the large language model to obtain integrated natural language information, and display the integrated natural language information to the target user in the display interface of the virtual twin model, so that the target user can perform two-way interaction with multiple target intelligent agents constructed by the large language model according to the integrated natural language information.

[0020] Optionally, in one embodiment of the present application, the second construction module includes: an analysis unit, used to perform physical and chemical process analysis on the target device to obtain analysis results, and construct a mechanism model of the target device based on the analysis results; an acquisition unit, used to acquire empirical information and actual production data of the target device, and construct the empirical model based on the mechanism model, the empirical information and the actual production data.

[0021] Optionally, in one embodiment of the present application, the integration module includes: a prediction unit, used to predict the equipment operation status information based on the standard equipment process data, the standard product quality data or the simulation parameters customized by the target user through the virtual twin model, and combining the virtual twin model and the simulation model.

[0022] Optionally, in one embodiment of the present application, the correction module includes: a training unit, used to train the target machine learning model through the actual operation monitoring data, and use preset model evaluation indicators to perform performance evaluation on the trained target machine learning model to obtain a performance evaluation result; an optimization unit, used to correct the target machine learning model based on the performance evaluation result and a preset expert experience analysis model.

[0023] The third aspect of the present application provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement a method for monitoring food puffing and drying processes based on digital twin technology as described in the above embodiment.

[0024] The fourth aspect embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the program is executed by a processor, it implements the above-mentioned method for monitoring the food puffing and drying process based on digital twin technology.

[0025] The fifth aspect embodiment of the present application provides a computer program product, including a computer program, which is executed to implement the above-mentioned food puffing and drying process monitoring method based on digital twin technology.

[0026] Therefore, the embodiments of the present application have the following beneficial effects:

[0027] The embodiments of the present application can construct a virtual twin model of the target equipment, collect the equipment process data of the target equipment and the product quality data of the target product, and pre-process the equipment process data and the product quality data to obtain standard equipment process data and standard product quality data; construct a mechanism model and an empirical model corresponding to the target equipment to establish a simulation model of the target equipment through the mechanism model and the empirical model, and collect the expert technical information corresponding to the target equipment through a pre-constructed large language model to input the expert technical information, standard equipment process data and standard product quality data into the target server; integrate the virtual twin model and the simulation model, and combine the standard equipment process data and the standard product quality data to predict the equipment operation status information of the target equipment; construct a target machine learning model of the virtual twin in the target server, and collect the actual operation monitoring data of the target equipment to correct the target machine learning model through the equipment operation status information and the actual operation monitoring data, so as to use the corrected target machine learning model to monitor the food puffing and drying process of the target equipment and the target product, thereby realizing accurate, real-time and visual monitoring, and can accurately and effectively predict, warn and intervene in specific equipment conditions during the process monitoring process. As a result, the problems of existing food puffing process monitoring methods, such as poor accuracy, real-time warning and monitoring intuitiveness, and being more dependent on specific employees, making it difficult to quickly collect and process large amounts of data, and having large limitations on usage scenarios, are solved.

[0028] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0030] Figure 1 A flowchart of a method for monitoring a food puffing and drying process based on digital twin technology provided according to an embodiment of the present application;

[0031] Figure 2 A conventional data collection and display schematic diagram provided for one embodiment of the present application;

[0032] Figure 3 A schematic diagram of the overall framework of a virtual twin model provided for one embodiment of the present application;

[0033] Figure 4 A schematic diagram of a decision support and simulation solution framework based on digital twin technology provided for one embodiment of the present application;

[0034] Figure 5A schematic diagram of iterative optimization of a virtual twin based on machine learning is provided for one embodiment of the present application;

[0035] Figure 6 A schematic diagram of a two-way interactive system architecture based on a large language model provided for one embodiment of the present application;

[0036] Figure 7 This is an example diagram of a monitoring device for food puffing and drying processes based on digital twin technology according to an embodiment of the present application;

[0037] Figure 8 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

[0038] Among them, 10-a monitoring device for food puffing and drying process based on digital twin technology; 100-a first building module, 200-a second building module, 300-an integration module, 400-a correction module; 801-a memory, 802-a processor, 803-a communication interface. DETAILED DESCRIPTION

[0039] Embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0040] The following describes the monitoring method of food puffing and drying process based on digital twin technology in an embodiment of the present application with reference to the accompanying drawings. In view of the problems mentioned in the above background technology that the input and output of the digital twin model heavily rely on the regular and complex input and output of ITOT experts, the iteration cost is high, the iteration speed is slow, and the output cannot specifically answer the questions of managers and employees in different positions, the present application provides a monitoring method of food puffing and drying process based on digital twin technology. In this method, a virtual twin model of the target equipment is constructed, and the equipment process data of the target equipment and the product quality data of the target product are collected, and the equipment process data and the product quality data are pre-processed to obtain standard equipment process data and standard product quality data; a mechanism model and an empirical model corresponding to the target equipment are constructed to establish a simulation model of the target equipment through the mechanism model and the empirical model, and the pre-constructed large language model is used to collect the equipment process data and the product quality data. Collect the expert technical information corresponding to the target equipment to input the expert technical information, standard equipment process data and standard product quality data into the target server; integrate the virtual twin model and the simulation model, and combine the standard equipment process data and standard product quality data to predict the equipment operation status information of the target equipment; build the target machine learning model of the virtual twin in the target server, and collect the actual operation monitoring data of the target equipment to correct the target machine learning model through the equipment operation status information and the actual operation monitoring data, so as to use the corrected target machine learning model to monitor the food puffing and drying process of the target equipment and target products, so as to achieve accurate, real-time and visual monitoring, and can accurately and effectively predict, warn and intervene in the specific equipment working conditions during the process monitoring process. In this way, the existing food puffing process monitoring methods have poor accuracy, real-time warning and monitoring intuitiveness, and are more dependent on specific employees, difficult to quickly collect and process large amounts of data, and have large usage scenario limitations.

[0041] Specifically, Figure 1 A flow chart of a method for monitoring food puffing and drying processes based on digital twin technology provided in an embodiment of the present application.

[0042] like Figure 1 As shown, the monitoring method of food puffing and drying process based on digital twin technology includes the following steps:

[0043] In step S101, a virtual twin model of the target equipment is constructed, and the equipment process data of the target equipment and the product quality data of the target product are collected, and the equipment process data and the product quality data are preprocessed to obtain standard equipment process data and standard product quality data.

[0044] As a feasible method, the embodiment of the present application takes the process monitoring of a single-screw extruder as an example to illustrate and introduce the monitoring method of the food puffing and drying process based on the digital twin technology in the embodiment of the present application.

[0045] The embodiments of the present application can first use modeling software such as Unity3D or Blender to build a virtual twin model of the target device, i.e., a single-screw extruder, and establish a three-dimensional dynamic virtual model of the equipment and products as a visualization interface and a carrier and interface of the virtual world twin.

[0046] Afterwards, the embodiments of the present application also need to perform pre-processing operations such as data cleaning on the equipment process data and product quality data, so as to obtain standard equipment process data and standard product quality data.

[0047] Optionally, in one embodiment of the present application, a virtual twin model of the target device is constructed, and the equipment process data of the target device and the product quality data of the target product are collected, and the equipment process data and the product quality data are preprocessed to obtain standard equipment process data and standard product quality data, including: constructing a virtual twin model of the target device through the preset Unity3D, and establishing a communication connection between the target PLC and the target host computer, so that the virtual twin model obtains the equipment process data and the product quality data through the target PLC; performing data cleaning and data normalization on the equipment process data and the product quality data to obtain standard equipment process data and standard product quality data; integrating and processing the standard equipment process data and the standard product quality data through a large language model to obtain integrated natural language information, and displaying the integrated natural language information to the target user in the display interface of the virtual twin model, so that the target user can conduct two-way interaction with multiple target intelligent agents constructed by the large language model based on the integrated natural language information.

[0048] Specifically, the embodiments of the present application can first use engineering design software such as Solidworks / Solid Edge / ProE / UG or 3D Max / Maya to establish a high-precision three-dimensional model, and use modeling software such as Unity3D or Blender to build a high-precision virtual model of the single-screw extruder, that is, a virtual twin model; furthermore, the embodiments of the present application also need to integrate sensor data and realize real-time data synchronization between the virtual model and the actual equipment through protocols such as OPC UA and MQTT.

[0049] It should be noted that, in the modeling process, the embodiments of the present application may adopt a real 3D design model of the equipment (such as a model of software such as Solidworks / Solid Edge / UG) or use 3D scanning technology to obtain the actual size and details of the equipment to ensure the accuracy of the virtual model; in addition, the embodiments of the present application may embed animation and interactive functions in the virtual model to simulate the actual operation process of the extruder, and display the operating status, key process parameters, real-time data and other information of the extruder through a visual interface.

[0050] Afterwards, the embodiments of the present application monitor the key parameters of the puffing process in real time by connecting to temperature sensors, flow meter pressure sensors, speed sensors, etc., and can use data acquisition cards or data acquisition systems (such as NI cDAQ, LabVIEW, etc.) to realize automatic recording and storage of data, and can also be connected to PLC.

[0051] In the specific implementation process, in order to enable the virtual twin model constructed by Unity3D to obtain the parameters of the temperature sensor and the like from the PLC, the embodiment of the present application needs to first establish communication between the PLC and the host computer (usually the computer running Unity3D) through various protocols such as Modbus, OPC UA, and Profibus, so that the host computer can read the data in the PLC, such as Figure 2 As shown, including the values ​​of temperature sensors, etc.; at the same time, the embodiment of the present application needs to develop a software or script on the host computer for communicating with the PLC and reading data. The software can be written in programming languages ​​such as C# and Python to achieve the function of regularly refreshing the values ​​of each sensor and passing these values ​​to the Unity3D model; then, the embodiment of the present application can write a script in Unity3D to receive sensor data from the host computer software. The script can be a simple data receiver to achieve the function of monitoring data from the host computer and updating these data to the corresponding temperature and other display components in the virtual model.

[0052] Furthermore, the embodiments of the present application can clean, standardize or normalize the collected raw data (i.e., equipment process data and product quality data) to remove outliers, noise and duplicate data, and eliminate the dimensional differences between different parameters to obtain standard equipment process data and standard product quality data; for example, the embodiments of the present application can perform simple singular point removal on data such as temperature, and use sliding windows, Fourier transforms and other technologies to extract data features of data such as motor vibration or noise to facilitate subsequent analysis.

[0053] In the actual execution process, the cleaned data can be directly displayed in the twin interface, or it can be visualized through the Unity3D program. As a feasible method, the embodiment of the present application can use numbers or charts to call the basic components such as Text, Image, Slider, Progress Bar in the UI system of Unity3D to realize the dynamic display of the cleaned data; secondly, the embodiment of the present application can also use third-party icon libraries such as UnityPlot and Plotly.Unity to display in more chart forms; in more demanding scenarios, data can also be intuitively displayed on the virtual model of the twin in a more intuitive and flexible way through custom rendering, such as the speed of the fluid is displayed in real time through the particle flow rate, the density of the material is displayed as particle density, and the temperature and pressure of the material are displayed at different temperatures.

[0054] Therefore, the embodiments of the present application construct a virtual twin model and collect equipment process data and product quality data, thereby providing reliable data guidance and basis for the monitoring of subsequent food puffing processes.

[0055] In step S102, a mechanism model and an empirical model corresponding to the target device are constructed to establish a simulation model of the target device through the mechanism model and the empirical model, and expert technical information corresponding to the target device is collected through a pre-constructed large language model to input the expert technical information, standard equipment process data and standard product quality data into the target server.

[0056] Furthermore, the embodiments of the present application also need to collect and parse through a large language model the oral information of experts and front-line operating technicians, text information on design and operation (such as technical parameters and historical reports of equipment, information on related raw materials and products, design descriptions, maintenance guides, risk assessments and FMEA), and other expert technical information, and establish an initial simulation model through empirical models and mechanism models.

[0057] In addition, if Figure 3 As shown, the embodiment of the present application also needs to import the equipment process data collected by relevant sensors at the physical equipment end (i.e., physical space) from the PLC / SCADA system to the server where the digital twin is located (either a local server or a cloud server), such as Figure 4 As shown, the product quality data is imported into the same server through LIMS.

[0058] Optionally, in one embodiment of the present application, a mechanism model and an empirical model corresponding to the target device are constructed, including: performing physical and chemical process analysis on the target device to obtain analysis results, and constructing a mechanism model of the target device based on the analysis results; obtaining empirical information and actual production data of the target device, and constructing an empirical model based on the mechanism model, empirical information and actual production data.

[0059] It should be noted that the embodiments of the present application establish a mechanism model based on physical principles by analyzing the physical and chemical processes of a single-screw extruder, and use finite element analysis, computational fluid dynamics and other methods to solve the mechanism model, and construct the mechanism model based on existing experience and the conditions of specific equipment; the mechanism model can be visualized and embodied in real time by being transformed in Unity3D, and used as an initial model.

[0060] Secondly, the embodiment of the present application can also construct a preliminary model / approximate algorithm based on the historical information of the same model, the same formula, or similar models and formulas of the equipment, form a preliminary empirical calculation model, and present it in a simple form such as Excel; then, by collecting actual production data, including the operation data of the extruder, product quality data, etc., the empirical model can be appropriately improved by being visualized and reflected in real time in Unity3D. As an empirical model, PLC control programs, etc. can usually be designed accordingly based on the model.

[0061] In step S103, the virtual twin model and the simulation model are integrated, and combined with standard equipment process data and standard product quality data, the equipment operation status information of the target equipment is predicted.

[0062] Furthermore, the embodiments of the present application can also integrate the virtual twin model and the simulation model so that they can display relevant parameters in real time, and input relevant parameters such as process parameters and material information into the virtual twin model and the simulation model in real time for computational simulation, so as to simulate and predict the equipment operation status, material status and product quality indicators under different input parameters in the next step and display them visually.

[0063] Optionally, in one embodiment of the present application, the virtual twin model and the simulation model are integrated, and combined with standard equipment process data and standard product quality data to predict the equipment operation status information of the target equipment, including: based on standard equipment process data, standard product quality data or simulation parameters customized by the target user through the virtual twin model, and combining the virtual twin model and the simulation model to predict the equipment operation status information.

[0064] In the actual implementation process, after constructing the above-mentioned mechanism model and empirical model, the embodiments of the present application further need to combine them with digital twins constructed by Unity3D, etc. to realize real-time display of model prediction results; at the same time, technical personnel in this field can also input different parameters in the digital twin interface for simulation, and view the simulation results in real time, which can be used to restore the causes of process or quality problems, pre-optimize parameters in the process of new product development and process improvement, and select and design key components in various scenarios, thereby improving the efficiency and reliability of process monitoring.

[0065] In step S104, a target machine learning model of a virtual twin is constructed in the target server, and actual operation monitoring data of the target device is collected, so as to correct the target machine learning model through the device operation status information and the actual operation monitoring data, so as to use the corrected target machine learning model to monitor the food puffing and drying process of the target device and the target product.

[0066] Afterwards, the embodiment of the present application further needs to set up a machine learning model in the server, and correct the machine learning model by comparing the simulation results of the initial model (i.e., the equipment operation status information) with the actual operation monitoring data of the physical world, so as to achieve continuous iteration and improvement of the virtual twin, such as Figure 5 As shown, the iteratively improved machine learning model is used to monitor the puffing and drying processes of the target equipment and target products.

[0067] Therefore, the embodiments of the present application greatly improve the efficiency of process monitoring based on digital twin technology and data-driven model fault diagnosis, high visualization, and iterative improvement of virtual twins.

[0068] Optionally, in one embodiment of the present application, a target machine learning model of a virtual twin is constructed in the target server, and actual operation monitoring data of the target device is collected to correct the target machine learning model through the device operation status information and the actual operation monitoring data, including: training the target machine learning model through the actual operation monitoring data, and using preset model evaluation indicators to perform performance evaluation on the trained target machine learning model to obtain a performance evaluation result; based on the performance evaluation result and the preset expert experience analysis model, correcting the target machine learning model.

[0069] Specifically, based on the mechanism model and the empirical model, the embodiments of the present application can also use machine learning algorithms such as multivariate linear regression, support vector machine, neural network, random forest, gradient boosting tree, deep learning, etc. to build a model improvement system, and continuously collect input and output parameters such as process parameters, formula design, actual working conditions and product quality during testing and daily production, train the machine learning model, and optimize the model's hyperparameters through cross-validation, grid search and other techniques.

[0070] On this basis, the model is evaluated and optimized after iteration, and the prediction performance of the model is evaluated using indicators such as mean square error and root mean square error to obtain performance evaluation results; the error sources of the machine learning model are analyzed in combination with the experience of relevant experts and the performance evaluation results, and model optimization or data enhancement is performed for the parts with large errors. After a period of time, an optimized machine learning model is formed, and a basic model is locked as a basic model for long-term use to guide long-term production and continuous improvement.

[0071] Afterwards, with the continuous collection of new data, changes in the production environment, and continuous improvements in equipment, processes or operating procedures, the machine learning model is updated and iterated regularly, and online learning, incremental learning and other technologies are used to achieve continuous learning and adaptive adjustment of the machine learning model.

[0072] Therefore, the embodiments of the present application can achieve visual process monitoring, automatic iteration and improvement of the model of the pet food puffing process, intelligent early warning and decision-making suggestions under limited human intervention, thereby solving problems in actual production.

[0073] Optionally, in one embodiment of the present application, while using the modified target machine learning model to monitor the food puffing and drying processes of the target device and the target product, it also includes: generating monitoring reports corresponding to the target device and the target product, and constructing multiple target intelligent agents through a large language model, and establishing a two-way information interaction system based on the multiple target intelligent agents; sending the monitoring report to the target user through the two-way information interaction system, and the target user performs visual remote operation guidance on the puffing and drying processes of the target device and the target product based on the monitoring report and through the two-way information interaction system and a preset AR strategy.

[0074] It should be noted that the embodiments of the present application may also be equipped with an intelligent visual feedback and control system, so that the digital twin can simulate and confirm whether the equipment is ready for startup, process flow or shutdown through real-time acquired process and equipment data, or predict possible operation or quality problems during operation, and display the warning information on the large screen of the central control room and the remote terminal interface of relevant technical personnel (such as PC web APP, tablet computer, mobile phone APP, etc.).

[0075] At the same time, if Figure 6As shown, the embodiment of the present application can also construct a two-way information interaction system based on a large language model, and set up a number of different personalized customized Agents based on LLMs (Large Language Models) as a medium for real-time input and output information and a long-term iterative information input entry, and conduct real-time two-way communication with relevant experts, managers, operating staff, etc., further improve the digital twin model and generate personalized customized reports. In the process of setting up a number of different personalized customized Agents, different permissions need to be granted according to different departments and personnel.

[0076] In addition, the embodiments of the present application can also use AR tools to help on-site personnel observe the internal operation of the digital twin of the single-screw extruder equipment through AR glasses in real time when observing the equipment, and accurately and intuitively judge possible problems or faults, and conduct real-time voice communication with remote experts and the virtual expert Agency set up in the previous step, and guide the entire debugging or maintenance operation process. When necessary, the digital twin model can be combined to conduct advance rehearsal and simulation of the operation to obtain corresponding operation optimization feedback. After the operation or debugging method is verified, the operation is performed on the physical equipment, and the operation is reflected in real time through the digital twin model to generate further operation suggestions.

[0077] Therefore, the embodiments of the present application realize real-time personalized interaction between the digital twin model and production management and technical personnel by using a large language model.

[0078] In summary, the embodiment of the present application is based on digital twin technology, with a large language model as an efficient and personalized real-time interactive means, a VR / AR dynamic model as a visualization means, and a theoretical model as a basis, through the experience of production practice and correction; then through long-term big data accumulation (such as collecting information through MES system and large language model interaction information), more key data are collected, and weak supervision machine learning algorithms are supported to continue training to achieve continuous iterative optimization of the model, thereby effectively solving the problem of inaccurate modeling, high reliance on experience, and high reliance on manual monitoring of front-line technicians in process monitoring of complex process represented by food puffing process. In addition, the embodiment of the present application can significantly improve the advance and accuracy of early warning, and continuously provide suggestions for improvement of the process, so as to better cope with the problems of greatly accelerated industry innovation speed, high personnel mobility, and difficulty in recruiting, training and stabilizing production front-line technicians, and continuously improve quality and production efficiency.

[0079] According to the monitoring method of food puffing and drying process based on digital twin technology proposed in the embodiment of the present application, a virtual twin model of the target equipment is constructed, and the equipment process data of the target equipment and the product quality data of the target product are collected, and the equipment process data and the product quality data are preprocessed to obtain standard equipment process data and standard product quality data; a mechanism model and an empirical model corresponding to the target equipment are constructed to establish a simulation model of the target equipment through the mechanism model and the empirical model, and the expert technical information corresponding to the target equipment is collected through the pre-constructed large language model, so as to input the expert technical information, standard equipment process data and standard product quality data into the target server; the virtual twin model and the simulation model are integrated, and the equipment operation status information of the target equipment is predicted in combination with the standard equipment process data and the standard product quality data; a target machine learning model of the virtual twin is constructed in the target server, and the actual operation monitoring data of the target equipment is collected, so as to correct the target machine learning model through the equipment operation status information and the actual operation monitoring data, so as to use the corrected target machine learning model to monitor the food puffing and drying process of the target equipment and the target product, thereby realizing accurate, real-time and visual monitoring, and can accurately and effectively predict, warn and intervene in specific equipment conditions during the process monitoring process.

[0080] Secondly, a monitoring device for food puffing and drying process based on digital twin technology proposed in an embodiment of the present application is described with reference to the accompanying drawings.

[0081] Figure 7 It is a block diagram of a monitoring device for food puffing and drying process based on digital twin technology in an embodiment of the present application.

[0082] like Figure 7 As shown, the monitoring device 10 for food puffing and drying process based on digital twin technology includes: a first building module 100, a second building module 200, an integration module 300 and a correction module 400.

[0083] Among them, the first construction module 100 is used to construct a virtual twin model of the target equipment, collect equipment process data of the target equipment and product quality data of the target product, and pre-process the equipment process data and product quality data to obtain standard equipment process data and standard product quality data.

[0084] The second construction module 200 is used to construct a mechanism model and an empirical model corresponding to the target device, so as to establish a simulation model of the target device through the mechanism model and the empirical model, and to collect expert technical information corresponding to the target device through a pre-constructed large language model, so as to input the expert technical information, standard equipment process data and standard product quality data into the target server.

[0085] The integration module 300 is used to integrate the virtual twin model and the simulation model, and combine the standard equipment process data and the standard product quality data to predict the equipment operation status information of the target equipment.

[0086] The correction module 400 is used to build a target machine learning model of a virtual twin in the target server and collect actual operation monitoring data of the target device, so as to correct the target machine learning model through the device operation status information and the actual operation monitoring data, so as to use the corrected target machine learning model to monitor the food puffing and drying process of the target device and the target product.

[0087] Optionally, in one embodiment of the present application, the monitoring device 10 for food puffing and drying process based on digital twin technology of the embodiment of the present application further includes: a generation module and a large language model interaction module.

[0088] Among them, the generation module is used to generate monitoring reports corresponding to the target equipment and target products while monitoring the food puffing and drying processes of the target equipment and target products using the modified target machine learning model, and to build multiple target intelligent agents through a large language model, and to establish a two-way information interaction system based on multiple target intelligent agents.

[0089] The large language model interaction module is used to send monitoring reports to target users through a two-way information interaction system, and the target users can provide visual remote operation guidance on the puffing and drying processes of target equipment and target products based on the monitoring reports through the two-way information interaction system and preset AR strategies.

[0090] Optionally, in one embodiment of the present application, the first building module 100 includes: a communication unit, a preprocessing unit and an interaction unit.

[0091] Among them, the communication unit is used to build a virtual twin model of the target device through the preset Unity3D, and establish a communication connection between the target PLC and the target host computer, so that the virtual twin model can obtain equipment process data and product quality data through the target PLC.

[0092] The preprocessing unit is used to perform data cleaning and data normalization on the equipment process data and product quality data to obtain standard equipment process data and standard product quality data.

[0093] The interaction unit is used to integrate and process standard equipment process data and standard product quality data through a large language model to obtain integrated natural language information, and display the integrated natural language information to the target user in the display interface of the virtual twin model, so that the target user can conduct two-way interaction with multiple target intelligent agents constructed by the large language model based on the integrated natural language information.

[0094] Optionally, in one embodiment of the present application, the second building module 200 includes: an analyzing unit and an acquiring unit.

[0095] The analysis unit is used to perform physical and chemical process analysis on the target device to obtain analysis results, and to construct a mechanism model of the target device based on the analysis results.

[0096] The acquisition unit is used to acquire the empirical information and actual production data of the target device, and to construct an empirical model based on the mechanism model, the empirical information and the actual production data.

[0097] Optionally, in one embodiment of the present application, the integration module 300 includes: a prediction unit for predicting equipment operating status information based on standard equipment process data, standard product quality data, or simulation parameters customized by the target user through a virtual twin model, and combining the virtual twin model and the simulation model.

[0098] Optionally, in one embodiment of the present application, the correction module 400 includes: a training unit and an optimization unit.

[0099] Among them, the training unit is used to train the target machine learning model through actual operation monitoring data, and use preset model evaluation indicators to perform performance evaluation on the trained target machine learning model to obtain performance evaluation results.

[0100] The optimization unit is used to correct the target machine learning model based on the performance evaluation results and the preset expert experience analysis model.

[0101] It should be noted that the aforementioned explanation of the embodiment of the monitoring method for food puffing and drying process based on digital twin technology is also applicable to the monitoring device for food puffing and drying process based on digital twin technology of this embodiment, and will not be repeated here.

[0102] According to the monitoring device of the food puffing and drying process based on the digital twin technology proposed in the embodiment of the present application, it includes a first construction module, which is used to construct a virtual twin model of the target equipment, and collect the equipment process data of the target equipment and the product quality data of the target product, and pre-process the equipment process data and the product quality data to obtain standard equipment process data and standard product quality data; a second construction module, which is used to construct a mechanism model and an empirical model corresponding to the target equipment, so as to establish a simulation model of the target equipment through the mechanism model and the empirical model, and collect the expert technical information corresponding to the target equipment through the pre-constructed large language model, so as to integrate the expert technical information, the standard equipment process data and the standard product quality data. The data is input into the target server; an integration module is used to integrate the virtual twin model and the simulation model, and combine the standard equipment process data and the standard product quality data to predict the equipment operation status information of the target equipment; a correction module is used to build a target machine learning model of the virtual twin in the target server, and collect the actual operation monitoring data of the target equipment, so as to correct the target machine learning model through the equipment operation status information and the actual operation monitoring data, so as to use the corrected target machine learning model to monitor the food puffing and drying process of the target equipment and target product, so as to realize accurate, real-time and visual monitoring, and can accurately and effectively predict, warn and intervene in the specific equipment operating conditions during the process monitoring process.

[0103] Figure 8 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0104] A memory 801 , a processor 802 , and a computer program stored in the memory 801 and executable on the processor 802 .

[0105] When the processor 802 executes the program, the monitoring method of the food puffing and drying process based on the digital twin technology provided in the above embodiment is implemented.

[0106] Furthermore, the electronic device further comprises:

[0107] The communication interface 803 is used for communication between the memory 801 and the processor 802 .

[0108] The memory 801 is used to store computer programs that can be executed on the processor 802 .

[0109] The memory 801 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0110] If the memory 801, the processor 802 and the communication interface 803 are implemented independently, the communication interface 803, the memory 801 and the processor 802 can be connected to each other through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 8 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0111] Optionally, in a specific implementation, if the memory 801, the processor 802 and the communication interface 803 are integrated on a chip, the memory 801, the processor 802 and the communication interface 803 can communicate with each other through an internal interface.

[0112] The processor 802 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0113] An embodiment of the present application also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method for monitoring the food puffing and drying process based on the digital twin technology is implemented as described above.

[0114] An embodiment of the present application also provides a computer program product, including a computer program, which, when executed, is used to implement the above-mentioned method for monitoring the food puffing and drying process based on the digital twin technology.

[0115] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or N embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0116] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of the features. In the description of this application, "N" means at least two, such as two, three, etc., unless otherwise clearly and specifically defined.

[0117] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in reverse order depending on the functions involved, which should be understood by technicians in the technical field to which the embodiments of the present application belong.

[0118] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purpose of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or N wirings (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways as necessary and then storing it in a computer memory.

[0119] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above embodiment, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0120] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0121] In addition, each functional unit in each embodiment of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0122] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present application. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A method for monitoring food puffing and drying process based on digital twin technology, characterized in that: The following steps are involved: Constructing a virtual twin model of the target device, collecting equipment process data of the target device and product quality data of the target product, and preprocessing the equipment process data and the product quality data to obtain standard equipment process data and standard product quality data; Constructing a mechanism model and an empirical model corresponding to the target device, so as to establish a simulation model of the target device through the mechanism model and the empirical model, and collecting expert technical information corresponding to the target device through a pre-constructed large language model, so as to input the expert technical information, the standard equipment process data and the standard product quality data into a target server; Integrating the virtual twin model and the simulation model, and combining the standard equipment process data and the standard product quality data, to predict equipment operation status information of the target equipment; A target machine learning model of the virtual twin is constructed in the target server, and actual operation monitoring data of the target device is collected, so as to correct the target machine learning model through the device operation status information and the actual operation monitoring data, so as to monitor the food puffing and drying process of the target device and the target product using the corrected target machine learning model.

2. The method according to claim 1, characterized in that While monitoring the food puffing and drying process of the target equipment and the target product using the modified target machine learning model, it also includes: Generate monitoring reports corresponding to the target device and the target product, build multiple target agents through the large language model, and establish a two-way information interaction system based on the multiple target agents; The monitoring report is sent to the target user through the two-way information interaction system, and the target user performs visual remote operation guidance on the puffing and drying processes of the target equipment and the target product based on the monitoring report and through the two-way information interaction system and a preset AR strategy.

3. The method according to claim 1, characterized in that The step of constructing a virtual twin model of the target device, collecting device process data of the target device and product quality data of the target product, and preprocessing the device process data and the product quality data to obtain standard device process data and standard product quality data includes: Construct a virtual twin model of the target device through the preset Unity3D, and establish a communication connection between the target PLC and the target host computer, so that the virtual twin model obtains the equipment process data and the product quality data through the target PLC; Performing data cleaning and data normalization processing on the equipment process data and the product quality data to obtain the standard equipment process data and the standard product quality data; The standard equipment process data and the standard product quality data are integrated and processed through the large language model to obtain integrated natural language information, and the integrated natural language information is displayed to the target user in the display interface of the virtual twin model, so that the target user can perform two-way interaction with multiple target intelligent agents constructed by the large language model based on the integrated natural language information.

4. The method according to claim 3, characterized in that The constructing of the mechanism model and the empirical model corresponding to the target device includes: Performing physical and chemical process analysis on the target device to obtain analysis results, and constructing a mechanism model of the target device according to the analysis results; The empirical information and actual production data of the target device are obtained, and the empirical model is constructed based on the mechanism model, the empirical information and the actual production data.

5. The method according to claim 4, characterized in that The integrating the virtual twin model and the simulation model, and combining the standard equipment process data and the standard product quality data to predict the equipment operation status information of the target equipment, includes: Based on the standard equipment process data, the standard product quality data or the simulation parameters customized by the target user through the virtual twin model, and combining the virtual twin model and the simulation model, the equipment operation status information is predicted.

6. The method according to claim 5, characterized in that The target machine learning model of the virtual twin is constructed in the target server, and actual operation monitoring data of the target device is collected to correct the target machine learning model through the device operation status information and the actual operation monitoring data, including: The target machine learning model is trained by using the actual operation monitoring data, and the performance of the trained target machine learning model is evaluated by using a preset model evaluation indicator to obtain a performance evaluation result; Based on the performance evaluation results and the preset expert experience analysis model, the target machine learning model is modified.

7. A monitoring device for food puffing and drying process based on digital twin technology, characterized in that: include: The first construction module is used to construct a virtual twin model of the target device, collect equipment process data of the target device and product quality data of the target product, and pre-process the equipment process data and the product quality data to obtain standard equipment process data and standard product quality data; A second construction module is used to construct a mechanism model and an empirical model corresponding to the target device, so as to establish a simulation model of the target device through the mechanism model and the empirical model, and to collect expert technical information corresponding to the target device through a pre-constructed large language model, so as to input the expert technical information, the standard equipment process data and the standard product quality data into a target server; An integration module, used to integrate the virtual twin model and the simulation model, and combine the standard equipment process data and the standard product quality data to predict the equipment operation status information of the target equipment; A correction module is used to build a target machine learning model of the virtual twin in the target server and collect actual operation monitoring data of the target device, so as to correct the target machine learning model through the device operation status information and the actual operation monitoring data, so as to use the corrected target machine learning model to monitor the food puffing and drying process of the target device and the target product.

8. An electronic device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method for monitoring the food puffing and drying process based on the digital twin technology as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement a method for monitoring food puffing and drying processes based on digital twin technology as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that The computer program is executed to implement the method for monitoring the food puffing and drying process based on the digital twin technology as described in any one of claims 1 to 6.

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