Construction method of digital twinborn model of hot air roller fixation machine

Abstract

The invention discloses a construction method of a digital twinning model of a hot air roller fixation machine, and relates to the technical field of digital twinning. Comprising the following steps: establishing a three-dimensional model of a hot air roller fixation machine and tea particles; importing the three-dimensional model of the hot air roller fixation machine into software, setting boundary conditions, dividing grids, and exporting a mesh file; generating a tea particle model by automatically filling particles; establishing a particle factory of a hot air roller fixation machine; setting a flow model of fluid, and setting kinematics parameters of the moving wall surface; a coupling interface file is imported, loaded and compiled; utilizing simulation software to obtain a plurality of groups of three-dimensional temperature field simulation results; according to the established three-dimensional simulation model and the temperature field simulation result, generating a reduced-order model; determining whether a new reduced-order model needs to be obtained through retraining according to the error of the temperature distribution fields of the two; calculating the current water content of the tea particles through a formula. The method can quickly adapt to different tea types or market requirements, and personalized production is realized.

Description

Technical Field

[0001] The present invention relates to the field of digital twin technology, and more specifically to a method for constructing a digital twin model of a hot air drum fixing machine. Background Art

[0002] The moisture content of tea is crucial to its quality and preservation. The appropriate moisture content can effectively maintain the aroma and taste of tea, while too high or too low moisture content will cause flavor changes. High moisture content is prone to mildew and insect pests, shortening the shelf life; while low moisture content may cause the tea to dry out and lose its aroma. In the processing of certain types of tea, the appropriate moisture content can also promote withering and fermentation, thereby improving the overall quality. In addition, the moisture content of tea also affects the leaching rate during brewing, which in turn affects the concentration and taste of the tea soup. Therefore, controlling the moisture content of tea is the key to ensuring quality and extending the shelf life. As the first step in tea processing, the effect of withering is directly related to the quality of the finished tea. The application of hot air drum withering machine significantly improves the efficiency of withering and ensures the consistency of processing results.

[0003] At present, the detection of tea moisture content mainly relies on CFD-DEM coupling simulation technology to analyze the influence of different withering processes and drum speed on the withering temperature of fresh tea leaves, as well as the effect of guide leaf bar parameters on the movement form of fresh tea leaves. By optimizing the process, the tea leaves are ensured to be heated evenly during the withering process, and then the 120℃ drying method is used for weighing and measurement to further improve the processing quality.

[0004] The CFD-DEM method belongs to the finite element simulation technology. Although it can provide high-precision analysis, its calculation cost is very high when running a three-dimensional transient model, and the resources and time required are also huge. It is impossible to understand the moisture content of tea in real time during the production process of tea, and it is impossible to make further adjustments to the equipment after optimization.

[0005] Therefore, it is an urgent problem for technical personnel in this field to propose a method for constructing a digital twin model of a hot air drum withering machine to solve the difficulties existing in the prior art. Summary of the invention

[0006] In view of this, the present invention provides a method for constructing a digital twin model of a hot air drum withering machine, which is used to solve the technical problems existing in the prior art.

[0007] In order to achieve the above object, the present invention provides the following technical solutions:

[0008] A method for constructing a digital twin model of a hot air drum fixing machine comprises the following steps:

[0009] Establish the 3D model of the hot air drum withering machine and tea particles, convert the 3D model of the hot air drum withering machine into STEP format, and convert the 3D model of the tea particles into sldprt format;

[0010] Import the 3D model of the hot air drum fixing machine into ANSYS Workbench, set boundary conditions and divide the mesh using Fluent software, and export the mesh file;

[0011] Import the mesh file and the sldprt file of tea particles into the EDEM software, and generate the tea particle model by automatically filling the particles;

[0012] Establish the pellet factory of the hot air drum withering machine in EDEM software and adjust the position relationship with the hot air drum withering machine;

[0013] Set up the fluid flow model in Fluent software and set the kinematic parameters of the moving wall;

[0014] According to the position relationship and kinematic parameters, the Fluent-EDEM coupling interface file is imported into the Fluent software and loaded and compiled, and the simulation is started after initialization;

[0015] Using simulation software to perform multiple groups of working conditions on the established three-dimensional simulation model and obtain multiple groups of three-dimensional temperature field simulation results;

[0016] According to the established three-dimensional simulation model and the temperature field simulation results, the temperature information data of tea particles is associated by extracting the characteristic points, the three-dimensional simulation results of the temperature field are extracted as one-dimensional data of the temperature field, a database is constructed, and machine learning software is used for learning to generate a reduced-order model;

[0017] Select a set of data within the input range, use the reduced-order model to generate output data, and assign the results to the three-dimensional geometric model of the coupled simulation based on the extracted feature points. Determine whether it is necessary to retrain to obtain a new reduced-order model based on the error of the temperature distribution field of the two.

[0018] After the reduced-order model is completed, the current moisture content of the tea particles is calculated using the evaporation formula.

[0019] Optionally, the three-dimensional models of the hot air drum withering machine and tea particles are established, and the three-dimensional model of the hot air drum withering machine is converted into the STEP format, and the three-dimensional model of the tea particles is converted into the sldprt format. The specific contents are:

[0020] Simplify the three-dimensional structure of the hot air drum fixing machine;

[0021] Assemble the various components of the hot air drum fixing machine according to the actual assembly dimensions and export them to STEP format;

[0022] When simplifying the three-dimensional model of tea particles, the tea leaf size with the largest distribution of processed tea leaves is selected for modeling and exported as an sldprt file.

[0023] Optionally, the 3D model of the hot air drum fixing machine is imported into ANSYS Workbench, and the boundary conditions are set and the mesh is divided by Fluent software. The specific content of the exported mesh file is as follows:

[0024] Use ANSYS Workbench to open the 3D model of the hot air drum fixing machine, and use SpaceClaima software to confirm whether there are any errors in the 3D model;

[0025] Use the mesh module in Fluent to divide the tetrahedral mesh of the hot air drum fixing machine. After confirming the inlet and outlet of the fluid and closing the inlet and outlet of the fluid domain, export it as a mesh file.

[0026] Optionally, import the mesh file and the sldprt file of tea particles into the EDEM software, and generate the tea particle model by automatically filling the particles. The specific contents are as follows:

[0027] Import the three-dimensional model of tea particles into the EDEM software, and generate particle spheres using the particle generation method in the EDEM software;

[0028] Import the mesh file of the hot air drum withering machine into the EDEM software and generate a model of the hot air drum withering machine, and set the physical property parameters of the tea particles and the hot air drum withering machine.

[0029] Optionally, the specific contents of establishing a pellet factory for the hot air drum fixing machine in EDEM software are as follows:

[0030] Through the model of the hot air drum tea-killing machine, a particle factory is generated at the entrance of the fluid domain, and the number of tea particles, particle generation speed, particle distribution, and the initial temperature of the hot air drum tea-killing machine and tea particles are set;

[0031] Set the motion parameters and environmental parameters of the hot air drum withering machine, the friction model between tea particles and the friction model between tea particles and the hot air drum withering machine;

[0032] Set the simulation time and Rayleigh Time Step, select GPU as the solver, and open the coupling service interface of the EDEM software.

[0033] Optionally, the specific contents of setting the fluid flow model in Fluent software are:

[0034] Remove the inlet and outlet of the closed fluid domain in the Fluent software and check the meshing results;

[0035] Turn on transient calculation, set the initial temperature and velocity of the hot air inlet, and set the fluid flow model to turbulence model.

[0036] Optionally, import the Fluent-EDEM coupling interface file into the Fluent software and load and compile it. The specific contents of the simulation after initialization are as follows:

[0037] In Fluent software, compile and load the coupling file through user definition, and click connect to complete the coupling of Fluent-EDEM;

[0038] Select the dual Euler model for coupled solution, click Calculate Initialization and set the time step, number of time steps and maximum number of iterations of Fluent;

[0039] After the calculation parameters are set, the calculation is performed.

[0040] Optionally, the specific contents of using simulation software to perform multiple groups of working conditions on the established three-dimensional simulation model and obtain multiple groups of three-dimensional temperature field simulation results are as follows:

[0041] Analyze the factors affecting the withering of tea particles in the hot air drum withering machine;

[0042] Through the super Latin sampling method, multiple groups of different hot air temperatures and drum speeds are set for simulation, and the temperature field distribution of tea particles is output.

[0043] Optionally, according to the established three-dimensional simulation model and the temperature field simulation results, the temperature information data of the tea particles is associated by extracting the characteristic points, the three-dimensional simulation results of the temperature field are extracted as one-dimensional data of the temperature field, a database is constructed, and machine learning software is used for learning to generate the reduced-order model. The specific contents are as follows:

[0044] Through the position of tea particles and simulation analysis results, the characteristic points of tea particles are used as an intermediary to extract the three-dimensional simulation analysis results into a one-dimensional data set;

[0045] The simulation is performed by changing different input conditions, and the corresponding relationship between the output one-dimensional data group and the temperature field one-dimensional data group is established to form a set of training data;

[0046] Generate a reduced-order model of the temperature field based on machine learning.

[0047] Optionally, the specific content of determining whether it is necessary to retrain to obtain a new reduced-order model based on the error of the temperature distribution fields of the two is:

[0048] The errors of the temperature fields of the two are compared. If the error exceeds 2%, the training is repeated, and a new reduced-order model is obtained and verified again until the verification error is within the range.

[0049] It can be seen from the above technical solutions that, compared with the prior art, the present invention discloses a method for constructing a digital twin model of a hot air drum fixing machine, which has the following beneficial effects:

[0050] 1) The construction of the digital twin model of the hot air drum withering machine brings many advantages: First, it can realize real-time monitoring and prediction, helping production personnel to grasp the equipment status and tea processing process in a timely manner, thereby improving production efficiency. This real-time feedback mechanism makes the effect prediction under different working conditions more accurate, ensuring the quality of tea;

[0051] 2) Through simulation analysis, the processing parameters, such as temperature and time, are optimized to achieve the best withering effect, which not only improves the quality of tea, but also reduces the waste of resources caused by improper parameters; at the same time, testing in a virtual environment can significantly reduce experimental costs, saving time and manpower;

[0052] 3) Enhanced decision-making support capabilities; through data analysis and machine learning, producers can obtain scientific basis and formulate more effective processing strategies; they can quickly adapt to different tea types or market demands and realize personalized production. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0054] Figure 1 A flow chart of a method for constructing a digital twin model of a hot air drum fixing machine provided by the present invention. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] See also Figure 1 As shown, the present invention discloses a method for constructing a digital twin model of a hot air drum fixing machine, comprising the following steps:

[0057] Establish the 3D model of the hot air drum withering machine and tea particles, convert the 3D model of the hot air drum withering machine into STEP format, and convert the 3D model of the tea particles into sldprt format;

[0058] Import the 3D model of the hot air drum fixing machine into ANSYS Workbench, set boundary conditions and divide the mesh using Fluent software, and export the mesh file;

[0059] Import the mesh file and the sldprt file of tea particles into the EDEM software, and generate the tea particle model by automatically filling the particles;

[0060] Establish the pellet factory of the hot air drum withering machine in EDEM software and adjust the position relationship with the hot air drum withering machine;

[0061] Set up the fluid flow model in Fluent software and set the kinematic parameters of the moving wall;

[0062] According to the position relationship and kinematic parameters, the Fluent-EDEM coupling interface file is imported into the Fluent software and loaded and compiled, and the simulation is started after initialization;

[0063] Using simulation software to perform multiple groups of working conditions on the established three-dimensional simulation model and obtain multiple groups of three-dimensional temperature field simulation results;

[0064] According to the established three-dimensional simulation model and the temperature field simulation results, the temperature information data of tea particles is associated by extracting the characteristic points, the three-dimensional simulation results of the temperature field are extracted as one-dimensional data of the temperature field, a database is constructed, and machine learning software is used for learning to generate a reduced-order model;

[0065] Select a set of data within the input range, use the reduced-order model to generate output data, and assign the results to the three-dimensional geometric model of the coupled simulation based on the extracted feature points. Determine whether it is necessary to retrain to obtain a new reduced-order model based on the error of the temperature distribution field of the two.

[0066] After the reduced-order model is completed, the current moisture content of the tea particles is calculated using the evaporation formula.

[0067] Specifically, the water content of tea leaves is calculated using the Langmuir evaporation formula.

[0068]

[0069] Among them, m is the mass evaporated per unit area and per unit time, p is v is the saturated vapor pressure of the liquid at temperature, M is the molar mass of the evaporating substance, R is the ideal gas constant, and T is the absolute temperature.

[0070] Specifically, a simplified three-dimensional model of the hot air drum withering machine and a three-dimensional model of tea particles were established through NXUG software.

[0071] Specifically, on the basis of the above technical scheme, the physical property parameters of the hot air drum withering machine and tea particles are set according to the actual physical properties, and the adhesion properties between various particles should also be consistent with the actual mechanical properties, thereby greatly improving the accuracy of the test results.

[0072] Furthermore, the three-dimensional models of the hot air drum withering machine and tea particles are established, and the three-dimensional model of the hot air drum withering machine is converted into the STEP format, and the three-dimensional model of the tea particles is converted into the sldprt format. The specific contents are as follows:

[0073] Simplify the three-dimensional structure of the hot air drum fixing machine;

[0074] Specifically, structures such as punching, chamfering, and welds that do not have a significant impact on the simulation results can be ignored.

[0075] Assemble the various components of the hot air drum fixing machine according to the actual assembly dimensions and export them to STEP format;

[0076] When simplifying the three-dimensional model of tea particles, the tea leaf size with the largest distribution of processed tea leaves is selected for modeling and exported as an sldprt file.

[0077] Furthermore, the 3D model of the hot air drum fixing machine was imported into ANSYS Workbench, and the boundary conditions were set and the mesh was divided using Fluent software. The specific content of the exported mesh file was as follows:

[0078] Use ANSYS Workbench to open the 3D model of the hot air drum fixing machine, and use SpaceClaima software to confirm whether there are any errors in the 3D model;

[0079] Use the mesh module in Fluent to divide the tetrahedral mesh of the hot air drum fixing machine. After confirming the inlet and outlet of the fluid and closing the inlet and outlet of the fluid domain, export it as a mesh file.

[0080] Furthermore, the mesh file and the sldprt file of tea particles are imported into the EDEM software, and the specific contents of the tea particle model are generated by automatically filling the particles as follows:

[0081] Import the three-dimensional model of tea particles into the EDEM software, and generate particle spheres using the particle generation method in the EDEM software;

[0082] Specifically, the particle generation method in the EDEM software is used to generate particle spheres as few as possible but satisfying the characteristics.

[0083] Import the mesh file of the hot air drum withering machine into the EDEM software and generate a model of the hot air drum withering machine, and set the physical property parameters of the tea particles and the hot air drum withering machine.

[0084] Furthermore, the specific contents of establishing a pellet factory for the hot air drum fixing machine in EDEM software are as follows:

[0085] Through the model of the hot air drum tea-killing machine, a particle factory is generated at the entrance of the fluid domain, and the number of tea particles, particle generation speed, particle distribution, and the initial temperature of the hot air drum tea-killing machine and tea particles are set;

[0086] Set the motion parameters and environmental parameters of the hot air drum withering machine, the friction model between tea particles and the friction model between tea particles and the hot air drum withering machine;

[0087] Specifically, the motion parameters and environmental parameters of the hot air drum withering machine are set, and the friction model between the tea particles and the friction model between the tea particles and the hot air drum withering machine are Hertz-Mindlin (no slip).

[0088] Set the simulation time and Rayleigh Time Step, select GPU as the solver, and open the coupling service interface of the EDEM software.

[0089] Furthermore, the specific contents of setting the fluid flow model in Fluent software are as follows:

[0090] Remove the inlet and outlet of the closed fluid domain in the Fluent software and check the meshing results;

[0091] Turn on transient calculation, set the initial temperature and velocity of the hot air inlet, and set the fluid flow model to turbulence model.

[0092] Furthermore, the Fluent-EDEM coupling interface file is imported into the Fluent software and loaded and compiled. The specific contents of the simulation after initialization are as follows:

[0093] In Fluent software, compile and load the coupling file through user definition, and click connect to complete the coupling of Fluent-EDEM;

[0094] Select the dual Euler model for coupled solution, click Calculate Initialization and set the time step, number of time steps and maximum number of iterations of Fluent;

[0095] Specifically, the time step in Fluengt should be an integer multiple of the Rayleigh Time Step in EDEM.

[0096] After the calculation parameters are set, the calculation is performed.

[0097] Furthermore, the specific contents of using simulation software to perform multiple groups of working conditions on the established three-dimensional simulation model and obtain multiple groups of three-dimensional temperature field simulation results are as follows:

[0098] Analyze the factors affecting the withering of tea particles in the hot air drum withering machine;

[0099] Through the super Latin sampling method, multiple groups of different hot air temperatures and drum speeds are set for simulation, and the temperature field distribution of tea particles is output.

[0100] Furthermore, according to the established three-dimensional simulation model and the temperature field simulation results, the temperature information data of the tea particles are associated by extracting the characteristic points, the three-dimensional simulation results of the temperature field are extracted as one-dimensional data of the temperature field, a database is constructed, and machine learning software is used for learning to generate the specific content of the reduced-order model:

[0101] Through the position of tea particles and simulation analysis results, the characteristic points of tea particles are used as an intermediary to extract the three-dimensional simulation analysis results into a one-dimensional data set;

[0102] The simulation is performed by changing different input conditions, and the corresponding relationship between the output one-dimensional data group and the temperature field one-dimensional data group is established to form a set of training data;

[0103] Generate a reduced-order model of the temperature field based on machine learning.

[0104] Furthermore, the specific contents of determining whether it is necessary to retrain to obtain a new reduced-order model based on the error of the temperature distribution fields of the two are as follows:

[0105] The errors of the temperature fields of the two are compared. If the error exceeds 2%, the training is repeated, and a new reduced-order model is obtained and verified again until the verification error is within the range.

[0106] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0107] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for constructing a digital twin model of a hot air drum fixing machine, characterized in that: The following steps are involved: Establish the 3D model of the hot air drum withering machine and tea particles, convert the 3D model of the hot air drum withering machine into STEP format, and convert the 3D model of the tea particles into sldprt format; Import the 3D model of the hot air drum fixing machine into ANSYS Workbench, set boundary conditions and divide the mesh using Fluent software, and export the mesh file; Import the mesh file and the sldprt file of tea particles into the EDEM software, and generate the tea particle model by automatically filling the particles; Establish the pellet factory of the hot air drum withering machine in EDEM software and adjust the position relationship with the hot air drum withering machine; Set up the fluid flow model in Fluent software and set the kinematic parameters of the moving wall; According to the position relationship and kinematic parameters, the Fluent-EDEM coupling interface file is imported into the Fluent software and loaded and compiled, and the simulation starts after initialization; Using simulation software to perform multiple groups of working conditions on the established three-dimensional simulation model and obtain multiple groups of three-dimensional temperature field simulation results; According to the established three-dimensional simulation model and the temperature field simulation results, the temperature information data of tea particles is associated by extracting the characteristic points, the three-dimensional simulation results of the temperature field are extracted as one-dimensional data of the temperature field, a database is constructed, and machine learning software is used for learning to generate a reduced-order model; Select a set of data within the input range, use the reduced-order model to generate output data, and assign the results to the three-dimensional geometric model of the coupled simulation based on the extracted feature points. Determine whether it is necessary to retrain to obtain a new reduced-order model based on the error of the temperature distribution field of the two. After the reduced-order model is completed, the current moisture content of the tea particles is calculated using the evaporation formula.

2. The method for constructing a digital twin model of a hot air drum fixing machine according to claim 1, characterized in that: The three-dimensional models of the hot air drum withering machine and tea particles are established, the three-dimensional model of the hot air drum withering machine is converted into the STEP format, and the three-dimensional model of the tea particles is converted into the sldprt format. The specific contents are as follows: Simplify the three-dimensional structure of the hot air drum fixing machine; Assemble the various components of the hot air drum fixing machine according to the actual assembly dimensions and export them into STEP format; When simplifying the three-dimensional model of tea particles, the tea leaf size with the largest distribution of processed tea leaves is selected for modeling and exported as an sldprt file.

3. The method for constructing a digital twin model of a hot air drum fixing machine according to claim 1, characterized in that: The 3D model of the hot air drum fixing machine was imported into ANSYS Workbench. The boundary conditions were set and the mesh was divided using Fluent software. The specific content of the exported mesh file is as follows: Use ANSYS Workbench to open the 3D model of the hot air drum fixing machine, and use SpaceClaima software to confirm whether there are any errors in the 3D model; Use the mesh module in Fluent to divide the tetrahedral mesh of the hot air drum fixing machine. After confirming the inlet and outlet of the fluid and closing the inlet and outlet of the fluid domain, export it as a mesh file.

4. The method for constructing a digital twin model of a hot air drum fixing machine according to claim 1, characterized in that: Import the mesh file and the sldprt file of tea particles into the EDEM software, and generate the tea particle model by automatically filling the particles. The specific contents are as follows: Import the three-dimensional model of tea particles into the EDEM software, and generate particle spheres using the particle generation method in the EDEM software; Import the mesh file of the hot air drum withering machine into the EDEM software and generate a model of the hot air drum withering machine, and set the physical property parameters of the tea particles and the hot air drum withering machine.

5. The method for constructing a digital twin model of a hot air drum fixing machine according to claim 1, characterized in that: The specific contents of establishing a pellet factory with hot air drum fixing machine in EDEM software are as follows: Through the model of the hot air drum tea-killing machine, a particle factory is generated at the entrance of the fluid domain, and the number of tea particles, particle generation speed, particle distribution, and the initial temperature of the hot air drum tea-killing machine and tea particles are set; Set the motion parameters and environmental parameters of the hot air drum withering machine, the friction model between tea particles and the friction model between tea particles and the hot air drum withering machine; Set the simulation time and Rayleigh Time Step, select GPU as the solver, and open the coupling service interface of the EDEM software.

6. The method for constructing a digital twin model of a hot air drum fixing machine according to claim 1, characterized in that: The specific contents of setting the fluid flow model in Fluent software are as follows: In Fluent software, remove the inlet and outlet of the closed fluid domain and check the meshing results; Turn on transient calculation, set the initial temperature and velocity of the hot air inlet, and set the fluid flow model to turbulence model.

7. The method for constructing a digital twin model of a hot air drum fixing machine according to claim 1, characterized in that: Import the Fluent-EDEM coupling interface file into the Fluent software and load and compile it. The specific contents of the simulation after initialization are as follows: In Fluent software, compile and load the coupling file through user definition, and click connect to complete the coupling of Fluent-EDEM; Select the dual Euler model for coupled solution, click Calculate Initialize and set the time step, number of time steps and maximum number of iterations of Fluent; After the calculation parameters are set, the calculation is performed.

8. The method for constructing a digital twin model of a hot air drum fixing machine according to claim 1, characterized in that: The specific contents of using simulation software to perform multiple groups of working conditions on the established three-dimensional simulation model and obtain multiple groups of three-dimensional temperature field simulation results are as follows: Analyze the factors affecting the withering of tea particles in the hot air drum withering machine; Through the super Latin sampling method, multiple groups of different hot air temperatures and drum speeds are set for simulation, and the temperature field distribution of tea particles is output.

9. The method for constructing a digital twin model of a hot air drum fixing machine according to claim 1, characterized in that: According to the established three-dimensional simulation model and the temperature field simulation results, the temperature information data of tea particles is associated by extracting the characteristic points, and the three-dimensional simulation results of the temperature field are extracted as one-dimensional data of the temperature field. The database is constructed and the machine learning software is used for learning. The specific contents of the reduced-order model are as follows: Through the position of tea particles and simulation analysis results, the characteristic points of tea particles are used as an intermediary to extract the three-dimensional simulation analysis results into a one-dimensional data set; The simulation is performed by changing different input conditions, and the corresponding relationship between the output one-dimensional data group and the temperature field one-dimensional data group is established to form a set of training data; Generate a reduced-order model of the temperature field based on machine learning.

10. The method for constructing a digital twin model of a hot air drum fixing machine according to claim 1, characterized in that: The specific content of determining whether it is necessary to retrain and obtain a new reduced-order model based on the error of the temperature distribution fields of the two is: The errors of the temperature fields of the two are compared. If the error exceeds 2%, the training is repeated, and a new reduced-order model is obtained and verified again until the verification error is within the range.

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