A multiphysics visualization rendering method, apparatus, electronic device and medium for digital twin of methanol-to-olefins process

Abstract

This invention relates to a multiphysics visualization rendering method, apparatus, electronic device, and medium for digital twins of methanol-to-olefins (MTO) processes. The method includes the following steps: S1, multiphysics data acquisition and preprocessing; S2, digital twin model construction; S3, multiphysics coupling mapping; S4, multi-scale dynamic visualization rendering parameter configuration; and S5, multiphysics dynamic visualization rendering. The method provided by this invention can achieve dynamic visualization rendering of the entire MTO process across multiple physics fields and scales, providing intuitive and accurate technical support for process monitoring, optimization, and fault diagnosis.

Description

Technical Field

[0001] This invention relates to the field of digital twin and chemical process visualization technology, specifically to a multiphysics visualization rendering method, device, electronic device and medium for a digital twin of a methanol-to-olefins process. Background Technology

[0002] The methanol-to-olefins (MTO) process is a key link between coal chemical industry and petrochemical industry. Its core is to convert methanol into low-carbon olefins such as ethylene and propylene under the action of molecular sieve catalysts. The entire process involves multiple core equipment such as reactors, regenerators, heat exchangers, and separators, and there are complex multi-physics coupling phenomena - including chemical reaction field, temperature field, pressure field, concentration field, flow field, etc. These physical fields influence and restrict each other, directly determining the process efficiency, product yield and equipment operation safety.

[0003] Digital twin technology, by constructing a virtual mirror of a physical entity, enables real-time mapping and co-simulation of physical and virtual processes, providing an effective means for monitoring, optimizing, and diagnosing faults in MTO processes. However, existing MTO process digital twin systems mostly focus on the simulation or static data display of a single physical field, making it difficult to achieve dynamic coupling rendering of multiple physical fields. Specifically, the following technical defects exist: (1) Disconnection of multiple physical field data: Existing technologies mostly process physical field data such as temperature, pressure, and concentration separately, failing to achieve deep coupling and correlation between various physical fields, and failing to intuitively present the interaction mechanism between physical fields, such as the coupling effect of catalyst carbon deposition with temperature and concentration fields, making it difficult to accurately grasp the essence of process operation; (2) Contradiction between visualization rendering accuracy and real-time performance: MTO process multi-physical field data is large in volume and changes rapidly. Existing rendering methods either pursue accuracy, resulting in excessively high rendering delays, and cannot meet the real-time monitoring requirements. Controlling demand either sacrifices accuracy to ensure real-time performance, resulting in the loss of physical field details and failing to accurately reflect the actual operating status of the process; (3) lack of multi-scale collaborative rendering capability: MTO process involves three scales: micro (catalyst particle reaction, mass and heat transfer), meso (internal flow field and reaction of equipment), and macro (full process flow). Existing systems cannot achieve seamless switching and collaborative rendering of multiple scales, making it difficult to balance overall process monitoring and local equipment detail observation; (4) insufficient interactivity: existing visualization systems are mostly passive displays, lacking interactive operation functions for multiple physical fields. Operators cannot customize rendering parameters and filter the physical fields of interest according to their needs, making it difficult to achieve personalized process analysis and problem location.

[0004] Furthermore, the application of existing digital twin visualization technology in the chemical industry often draws on general solutions from industries such as power and construction, without taking into account the specific characteristics of MTO processes (such as catalyst deactivation and regeneration synergy, multi-component reaction diffusion coupling, etc.) for targeted design. This results in rendering effects that are out of touch with the actual process requirements, and the guidance value of the digital twin system cannot be fully realized.

[0005] Therefore, providing a dynamic visualization rendering method and device that can achieve multi-physics coupling, high real-time performance, and multi-scale collaboration in MTO processes is a technical problem that urgently needs to be solved. Summary of the Invention

[0006] To address the above problems, the present invention aims to provide a multiphysics visualization rendering method, device, electronic device, and medium for digital twins of methanol-to-olefins (MTO) processes. Compared with existing technologies, the present invention can solve the problems of multiphysics disconnection, contradiction between rendering accuracy and real-time performance, insufficient multi-scale collaboration, and poor interactivity in existing MTO digital twin systems. It realizes dynamic visualization rendering of the entire MTO process across multiple physics fields and scales, providing intuitive and accurate technical support for process monitoring, optimization, and fault diagnosis.

[0007] To achieve this objective, the present invention adopts the following technical solution: In a first aspect, the present invention provides a multiphysics visualization rendering method for digital twins of methanol-to-olefins processes, the method comprising the following steps: S1, Multiphysics Data Acquisition and Preprocessing; S2, Digital Twin Model Construction; S3, Multiphysics Coupling Map; S4, multi-scale dynamic visualization rendering parameter configuration; S5, multiphysics dynamic visualization rendering.

[0008] This invention addresses the core characteristics of the MTO process, such as synergistic catalyst deactivation and regeneration, and multi-component reaction-diffusion coupling. By acquiring and preprocessing multi-physics data to construct a digital twin model, and performing multi-physics coupling mapping, followed by configuring rendering parameters and dynamic visualization rendering, the digital twin model and rendering method can accurately match process requirements. Compared with existing digital twin visualization rendering solutions, this invention is more suitable for the actual application scenarios of the MTO process and can be widely used for monitoring, optimization, and training of MTO industrial plants, demonstrating high industrial application value.

[0009] Preferably, the multiphysics data acquisition and preprocessing in step S1 includes: acquiring raw multiphysics data from the entire MTO process and preprocessing it to obtain standardized multiphysics data and establish a multiphysics database.

[0010] Preferably, step S1 specifically includes: S11, collect any one or at least two of the following: chemical reaction field data, temperature field data, pressure field data, concentration field data, flow field data, or equipment operating parameter data. The collected flow field data is corrected through computational fluid dynamics simulation to obtain multiphysics field raw data.

[0011] S12, preprocess the original multiphysics data obtained in step S11 to obtain standardized multiphysics data and establish a multiphysics database.

[0012] Preferably, the chemical reaction field data in step S11 includes any one or a combination of at least two of methanol conversion rate, olefin selectivity, catalyst carbon deposition, or chemical reaction rate.

[0013] In this invention, the aforementioned chemical reaction field data, temperature field data, pressure field data, concentration field data, flow field data, or equipment operating parameter data can be collected using methods commonly used in the art, such as using sensors. For example, online chromatographs, temperature sensors, pressure sensors, concentration sensors, flow rate sensors, and catalyst characterization agents can be used for data collection. The collected flow field data is corrected using computational fluid dynamics (CFD) simulation.

[0014] Preferably, the flow field data in step S11 includes any one or a combination of at least two of fluid velocity, flow direction, or turbulence intensity.

[0015] Preferably, the preprocessing in step S12 includes any one or a combination of at least two of the following: cleaning, noise reduction, normalization, or spatiotemporal alignment.

[0016] In this invention, the cleaning, noise reduction, and normalization methods are all conventional data processing methods in the field. For example, the noise reduction uses a mean filtering algorithm, and the cleaning uses linear interpolation to fill in missing values.

[0017] Preferably, the spatiotemporal alignment process in step S12 includes: using a timestamp-based alignment algorithm and setting a tolerance threshold to achieve spatiotemporal alignment of multiphysics data.

[0018] In this invention, the tolerance threshold can be selected according to actual needs, and is generally controlled to be 0.05~0.1s. In this invention, spatiotemporal alignment processing ensures the consistency of different physical field data in the temporal and spatial dimensions, laying the foundation for subsequent dynamic visualization rendering.

[0019] Preferably, the construction of the digital twin model in step S2 includes: constructing a digital twin model of the MTO process based on the physical entity of the MTO process and the standardized multiphysics data obtained in step S1.

[0020] Preferably, the physical entity includes any one or a combination of at least two of the following: a reactor, a regenerator, a heat exchanger, a separator, or connecting pipelines.

[0021] Preferably, the digital twin model includes any one or a combination of at least two of the following: a geometric model, a physical model, or a behavioral model.

[0022] Preferably, step S2 specifically includes: S21. Point cloud data of the physical entity is obtained through laser scanning. The iterative nearest point (ICP) registration algorithm is used to construct a geometric model so that the geometric model is proportionally reproduced from the design drawings of the physical entity.

[0023] In this invention, a geometric model is constructed to precisely align with the design drawings of the physical entity, achieving a 1:1 reproduction of the equipment's shape, internal structure, and pipeline connections. The accuracy of the geometric model is generally controlled to the millimeter level.

[0024] S22, a physical model is constructed by coupling chemical reaction kinetics model, mass transfer model, heat transfer model and fluid dynamics model to realize multi-physics coupled simulation.

[0025] S23 uses standardized multiphysics data as input and real-time operating parameters of physical entities as output to construct a behavior model. It uses machine learning algorithms to train the behavior model and dynamically updates the parameters of the behavior model to make the digital twin model consistent with the physical entity.

[0026] In this invention, by constructing a behavioral model, it is possible to dynamically map and predict the operating state of physical entities, update model parameters based on real-time data, and ensure the consistency between the digital twin model and the physical entity.

[0027] Preferably, the chemical reaction kinetic model in step S22 adopts a lumped reaction network, including the reaction of methanol to active cobalt species, the reaction of methanol to inactive cobalt species, and the reaction of methanol to olefin products.

[0028] Preferably, step S3 specifically includes: S31, clarify the coupling mechanism of multi-physics fields in the MTO process and establish a quantitative correlation model.

[0029] In this invention, the coupling mechanism of multiple physical fields is clearly defined, including but not limited to the influence of temperature field on chemical reaction rate, diffusion effect of flow field on concentration field, regulation effect of pressure field on reaction equilibrium, and influence of catalyst carbon deposition on heat and mass transfer.

[0030] S32, based on the quantization correlation model obtained in step S31, a spatiotemporal interpolation algorithm is used to map the standardized multiphysics data obtained in step S1 to the digital twin model obtained in step S2, thereby obtaining a digital twin model bound to multiphysics data.

[0031] In this invention, by mapping standardized multiphysics data to a digital twin model, a deep binding between multiphysics data and digital twin model can be achieved, enabling collaborative simulation of multiphysics.

[0032] Preferably, the multi-scale dynamic visualization rendering parameter configuration in step S4 includes: dividing the rendering layers according to the multi-scale characteristics of the MTO process, and configuring corresponding rendering parameters for each rendering layer.

[0033] Preferably, step S4 specifically includes: S41, based on the multi-scale characteristics of the MTO process, divides the rendering levels into microscale, mesoscale and macroscale, and clarifies the scope and display focus of each rendering level. The microscale focuses on the interior of catalyst particles, the mesoscale focuses on the interior of a single physical entity, and the macroscale focuses on the entire process flow.

[0034] S42, configure rendering parameters for the rendering layer obtained in step S41. The rendering parameters include any one or a combination of at least two of rendering precision, rendering frame rate, color mapping rules, or transparency.

[0035] In this invention, rendering parameters are configured according to different rendering levels. For example, for the macroscopic rendering level, a Level of Detail (LOD) optimization strategy is adopted, and the rendering accuracy is dynamically switched according to the observation distance. The rendering accuracy is reduced when observing at a long distance and increased when observing at a close distance to ensure the smoothness of the entire visualization process. For the mesoscopic rendering level, the focus is on the details of the multi-physics field inside the device to improve the rendering accuracy. The color mapping rule adopts a gradient color scale, with different colors corresponding to different ranges of physical field data. For example, the temperature field adopts a red-yellow-blue gradient, and the pressure field adopts a purple-pink-white gradient. For the microscopic rendering level, fluorescence imaging simulation technology is used to intuitively present the methanol diffusion, temperature conduction, and carbon deposition distribution within the catalyst particles.

[0036] In this invention, rendering parameters are configured at different rendering levels to balance rendering accuracy and real-time performance. Addressing the characteristics of large amounts of multiphysics data and rapid dynamic changes in MTO processes, the invention uses tiered matching of rendering parameters to ensure rendering accuracy at both the meso- and micro-scales. This resolves the contradiction between accuracy and real-time performance in existing methods, achieving a stable rendering frame rate of 20-30 fps, meeting the requirements for real-time process monitoring.

[0037] Meanwhile, by configuring rendering parameters at different rendering levels, this invention enables the rendering method to have multi-scale collaborative rendering capabilities. By dividing the rendering into three levels—microscopic, mesoscopic, and macroscopic—it achieves seamless switching and collaborative rendering across multiple scales. This allows it to display the overall operating status of the entire process, focus on the physical field details inside a single piece of equipment (physical entity), and present the microscopic reactions, mass transfer, and heat transfer processes inside catalyst particles, thus meeting the observation and analysis needs in different scenarios.

[0038] Preferably, the multiphysics dynamic visualization rendering in step S5 includes: performing dynamic visualization rendering on the digital twin model bound to the multiphysics data obtained in step S3 based on the rendering parameters described in step S4.

[0039] Preferably, step S5 specifically includes: S51 uses WebGL combined with a 3D engine for dynamic visualization rendering, and transforms standardized multiphysics data into visual signals through a custom GLSL shader.

[0040] In this invention, through dynamic visualization rendering, it is possible to achieve synchronous display, dynamic updating and seamless switching of multiple physics fields, while also realizing layered rendering of multiple physics fields, supporting individual rendering of a single physics field and overlay rendering of multiple physics fields, to meet different observation needs.

[0041] S52 employs an optimization strategy, including instantiation rendering, level of detail rendering, or occlusion culling, to optimize the obtained visual signal and obtain the optimized rendering effect.

[0042] In this invention, GPU drawing calls are reduced through optimization strategies to ensure real-time rendering.

[0043] S53 reads updated data from the multiphysics database in real time, driving the synchronous update of the digital twin model and rendering effects.

[0044] In this invention, the synchronous update of rendering effects can be, for example, temperature field data driving the color change of the model surface, flow field data driving the display of the motion trajectory of fluid particles, and chemical reaction field data driving the dynamic highlighting of the reaction area.

[0045] Preferably, the method further includes: S6, interactive control and feedback adjustment.

[0046] Preferably, the interactive control and feedback adjustment includes: multi-dimensional interaction and feedback adjustment with the user terminal, wherein the multi-dimensional interaction and feedback adjustment includes any one or a combination of at least two of the following: rendering level switching, physical field filtering, rendering parameter adjustment, local area magnification, or abnormal warning.

[0047] In this invention, the method, through interactive control and feedback adjustment, enables users to customize rendering parameters, filter physical fields of interest, and magnify local areas, thereby achieving personalized process analysis. At the same time, combined with the special characteristics of the MTO process, it realizes two-way linkage between anomaly warning and process control, which can quickly identify problems such as excessive temperature, abnormal pressure, and excessively rapid catalyst carbonization, providing intuitive and accurate support for process optimization and fault diagnosis, and improving the stability and efficiency of process operation.

[0048] Preferably, the method further includes: S7, data storage and management.

[0049] Preferably, the data storage and management includes: storing original multiphysics data, standardized multiphysics data, digital twin model data, rendering parameter data, and interaction log data, and classifying, backing up, and querying the data.

[0050] In a second aspect, the present invention provides a multiphysics visualization rendering apparatus for digital twins of methanol-to-olefins processes, the apparatus being used for the multiphysics visualization rendering method for digital twins of methanol-to-olefins processes as described in the first aspect of the present invention; the apparatus includes: Data acquisition and preprocessing module: used for multiphysics data acquisition and preprocessing; Digital Twin Model Building Module: Used to build digital twin models; Multiphysics coupling mapping module: used for performing multiphysics coupling mapping; Rendering parameter configuration module: used to configure multi-scale dynamic visualization rendering parameters; Dynamic Visualization Rendering Module: Used for dynamic visualization rendering of multiphysics fields.

[0051] The device provided by this invention is used to execute a multiphysics visualization rendering method for digital twins of methanol-to-olefins (MTO) processes. It can realize dynamic visualization rendering of the entire MTO process across multiple physics fields and scales, providing intuitive and accurate technical support for process monitoring, optimization, and fault diagnosis.

[0052] Preferably, the device further includes an interactive control and feedback module and / or a data storage and management module; the interactive control and feedback module is used for multi-dimensional interaction and feedback adjustments with the user terminal; the data storage and management module is used to store multi-physics raw data, standardized multi-physics data, digital twin model data, rendering parameter data and interactive log data, and to classify, back up and query the data.

[0053] Preferably, the data acquisition and preprocessing module includes a sensor acquisition unit, a CFD simulation unit, and a data processing unit.

[0054] Preferably, the digital twin model building module includes a geometric modeling unit, a physical modeling unit, and a behavioral modeling unit.

[0055] Preferably, the multiphysics coupling mapping module includes a coupling relationship definition unit and a data mapping unit.

[0056] Preferably, the rendering parameter configuration module includes a layer division unit and a parameter configuration unit.

[0057] Preferably, the dynamic visualization rendering module includes a rendering engine unit, an optimization unit, and a dynamic update unit.

[0058] Thirdly, the present invention provides an electronic device, the electronic device comprising: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to execute the multiphysics visualization rendering method for digital twins of methanol-to-olefins processes according to the first aspect of the present invention.

[0059] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the multiphysics visualization rendering method for digital twins of methanol-to-olefins processes described in the first aspect of the present invention.

[0060] Compared with the prior art, the present invention has the following beneficial effects: (1) The method provided by the present invention can realize the deep coupling rendering of multiple physical fields. By establishing a quantitative correlation model, the data of chemical reaction field, temperature field, pressure field, concentration field, flow field and other data in MTO process are deeply bound, and the interaction mechanism between various physical fields is presented intuitively. This solves the problem of data disconnection of multiple physical fields in the existing methods, enabling operators to accurately grasp the essence of process operation, such as clearly observing the coupling effect of catalyst carbon deposition with temperature field and concentration field.

[0061] (2) The method provided by the present invention can be specifically designed for the core characteristics of MTO process, such as catalyst deactivation and regeneration synergy and multi-component reaction diffusion coupling. The rendering method and the constructed digital twin model can accurately match the process requirements. Compared with the existing digital twin visualization scheme, it is more in line with the actual application scenario of MTO process and can be widely used in the monitoring, optimization and training of MTO industrial plants. It has high engineering application value. Attached Figure Description

[0062] Figure 1This is a flowchart of the method provided in Embodiments 1 and 2 of the present invention; Figure 2 This is a schematic diagram of the device provided in Embodiment 3 of the present invention; Figure 3 This is a schematic diagram of the structure of the electronic device provided in Embodiment 4 of the present invention. Detailed Implementation

[0063] The technical solution of the present invention will be further illustrated below through specific embodiments. Those skilled in the art should understand that the embodiments described are merely illustrative of the present invention and should not be construed as limiting the invention in any way.

[0064] The numerical range described in this invention includes not only the point values ​​listed above, but also any point values ​​within the numerical ranges not listed above. Due to space limitations and for the sake of brevity, this invention will not exhaustively list all the specific point values ​​included in the range.

[0065] Example 1 This embodiment provides a multiphysics visualization rendering method for digital twins of methanol-to-olefins processes, such as... Figure 1 As shown, the method includes the following steps: S1, Multiphysics Data Acquisition and Preprocessing: Acquire raw multiphysics data from the entire MTO process and preprocess it to obtain standardized multiphysics data and establish a multiphysics database.

[0066] Furthermore, step S1 specifically includes: S11, collect any one or at least two of the following: chemical reaction field data, temperature field data, pressure field data, concentration field data, flow field data, or equipment operating parameter data. The collected flow field data is corrected through computational fluid dynamics simulation to obtain multiphysics field raw data.

[0067] Specifically, the chemical reaction field data includes any one or a combination of at least two of methanol conversion rate, olefin selectivity, catalyst carbon deposition, or chemical reaction rate; the flow field data includes any one or a combination of at least two of fluid velocity, flow direction, or turbulence intensity.

[0068] S12, preprocess the original multiphysics data obtained in step S11 to obtain standardized multiphysics data and establish a multiphysics database.

[0069] Specifically, the preprocessing includes any one or a combination of at least two of the following: cleaning, noise reduction, normalization, or spatiotemporal alignment; the spatiotemporal alignment includes: using a timestamp-based alignment algorithm and setting a tolerance threshold to achieve spatiotemporal alignment of multiphysics data.

[0070] In this embodiment, the aforementioned chemical reaction field data, temperature field data, pressure field data, concentration field data, flow field data, or equipment operating parameter data can be collected using methods commonly used in the art, such as using sensors. For example, online chromatographs, temperature sensors, pressure sensors, concentration sensors, flow rate sensors, and catalyst characterization agents can be used for data collection. The collected flow field data is corrected using computational fluid dynamics (CFD) simulation.

[0071] S2, Digital Twin Model Construction: Based on the physical entity of the MTO process, and combined with the standardized multiphysics data obtained in step S1, a digital twin model of the MTO process is constructed.

[0072] Specifically, the physical entity includes any one or a combination of at least two of the following: reactor, regenerator, heat exchanger, separator, or connecting pipeline; the digital twin model includes any one or a combination of at least two of the following: geometric model, physical model, or behavioral model.

[0073] Furthermore, step S2 specifically includes: S21: Point cloud data of the physical entity is obtained through laser scanning, and a geometric model is constructed using an iterative nearest point registration algorithm, so that the geometric model is proportionally restored to the design drawings of the physical entity.

[0074] S22, a physical model is constructed by coupling chemical reaction kinetics model, mass transfer model, heat transfer model and fluid dynamics model to realize multi-physics coupled simulation.

[0075] Specifically, the chemical reaction kinetic model described in step S22 adopts a lumped reaction network, including the reaction of methanol to active coke species, the reaction of methanol to inactive coke species, and the reaction of methanol to olefin products.

[0076] S23 uses standardized multiphysics data as input and real-time operating parameters of physical entities as output to construct a behavior model. It uses machine learning algorithms to train the behavior model and dynamically updates the parameters of the behavior model to make the digital twin model consistent with the physical entity.

[0077] S3, Multiphysics Coupling Mapping.

[0078] Furthermore, step S3 specifically includes: S31, clarify the coupling mechanism of multi-physics fields in the MTO process and establish a quantitative correlation model.

[0079] S32, based on the quantized correlation model obtained in step S31, the standardized multiphysics data obtained in step S1 is mapped to the digital twin model obtained in step S2 to obtain a digital twin model bound to multiphysics data.

[0080] In this embodiment, the coupling mechanism of multiple physics fields is clarified, including but not limited to the influence of temperature field on chemical reaction rate, the diffusion effect of flow field on concentration field, the regulation effect of pressure field on reaction equilibrium, and the influence of catalyst carbon deposition on heat and mass transfer.

[0081] S4, Multi-scale dynamic visualization rendering parameter configuration: Based on the multi-scale characteristics of the MTO process, the rendering layers are divided, and the corresponding rendering parameters are configured for each rendering layer.

[0082] Furthermore, step S4 specifically includes: S41, based on the multi-scale characteristics of the MTO process, divides the rendering levels into microscale, mesoscale and macroscale, and clarifies the scope and display focus of each rendering level. The microscale focuses on the interior of catalyst particles, the mesoscale focuses on the interior of a single physical entity, and the macroscale focuses on the entire process flow.

[0083] S42, configure rendering parameters for the rendering layer obtained in step S41. The rendering parameters include any one or a combination of at least two of rendering precision, rendering frame rate, color mapping rules, or transparency.

[0084] In this embodiment, rendering parameters are configured according to different rendering levels. For example, for the macroscopic rendering level, a Level of Detail (LOD) optimization strategy is adopted, and the rendering accuracy is dynamically switched according to the observation distance. The rendering accuracy is reduced when observing at a long distance and increased when observing at a close distance to ensure the smoothness of the entire visualization process. For the mesoscopic rendering level, the focus is on the details of the multi-physics field inside the device to improve the rendering accuracy. The color mapping rule adopts a gradient color scale, with different colors corresponding to different ranges of physical field data. For example, the temperature field adopts a red-yellow-blue gradient, and the pressure field adopts a purple-pink-white gradient. For the microscopic rendering level, fluorescence imaging simulation technology is used to intuitively present the methanol diffusion, temperature conduction, and carbon deposition distribution within the catalyst particles.

[0085] S5, Multiphysics Dynamic Visualization Rendering: Based on the rendering parameters described in step S4, perform dynamic visualization rendering on the digital twin model bound to the multiphysics data obtained in step S3.

[0086] Furthermore, step S5 specifically includes: S51 uses WebGL combined with a 3D engine for dynamic visualization rendering, and transforms standardized multiphysics data into visual signals through a custom GLSL shader.

[0087] S52 employs an optimization strategy, including instantiation rendering, level of detail rendering, or occlusion culling, to optimize the obtained visual signal and obtain the optimized rendering effect.

[0088] S53 reads updated data from the multiphysics database in real time, driving the synchronous update of the digital twin model and rendering effects.

[0089] S6, Interactive Control and Feedback Adjustment: Perform multi-dimensional interaction and feedback adjustment with the user terminal. The multi-dimensional interaction and feedback adjustment includes any one or a combination of at least two of the following: rendering level switching, physical field filtering, rendering parameter adjustment, local area magnification, or abnormal warning.

[0090] S7, Data Storage and Management: Stores raw multiphysics data, standardized multiphysics data, digital twin model data, rendering parameter data, and interaction log data; classifies, manages, backs up, and queries the data.

[0091] Example 2 This embodiment takes a 500,000-ton / year MTO industrial plant as an example to provide a multiphysics visualization rendering method for digital twins of methanol-to-olefins processes, so as to further explain the composition of the specific technical solution.

[0092] like Figure 1 As shown, the method provided in this embodiment includes: S1, Multiphysics Data Acquisition and Preprocessing: Acquire raw multiphysics data from the entire MTO process and preprocess it to obtain standardized multiphysics data and establish a multiphysics database.

[0093] S11 collects data on chemical reaction field, temperature field, pressure field, concentration field, flow field, and equipment operating parameters. The collected flow field data is corrected through computational fluid dynamics simulation to obtain the original multiphysics field data.

[0094] Specifically, chemical reaction field data such as methanol conversion rate and ethylene / propylene selectivity are collected using an online chromatograph at a sampling frequency of 1 time / min; temperature field data (range 0~800℃) and pressure field data (range 0.1~1.0MPa) of various regions of the reactor and regenerator are collected using temperature and pressure sensors at a sampling frequency of 1 time / 10s; concentration field data of methanol, ethylene, propylene, and water at the reactor inlet and outlet are collected using concentration sensors at a sampling frequency of 1 time / min; flow field data (flow velocity range 0~10m / s) inside the reactor and regenerator are obtained through CFD simulation combined with flow rate sensor calibration; catalyst carbon deposition data are collected using a catalyst characterization intelligent agent at a sampling frequency of 1 time / h; and operating parameters (speed and power) of equipment such as pumps and compressors are collected simultaneously.

[0095] S12, preprocess the original multiphysics data obtained in step S11 to obtain standardized multiphysics data and establish a multiphysics database.

[0096] Specifically, in the preprocessing process, a mean filtering algorithm is used to remove noise from the temperature and pressure data; a linear interpolation method is used to fill missing values ​​in the concentration and carbon deposition data; all data are normalized to map the data to the range [0, 1]; a timestamp-based alignment algorithm is used, with a tolerance threshold of 0.05s, to perform spatiotemporal alignment of multiphysics data from different sources and with different sampling frequencies to obtain standardized multiphysics data, which is then established and stored in a distributed multiphysics database.

[0097] S2, Digital Twin Model Construction: Based on the physical entity of the MTO process, and combined with the standardized multiphysics data obtained in step S1, a digital twin model of the MTO process is constructed.

[0098] S21. Point cloud data of physical entities (reactor, regenerator, heat exchanger, separator and connecting pipelines) are obtained by laser scanning. The iterative nearest point registration algorithm is used to construct a geometric model so that the geometric model can restore the design drawings of the physical entities on a proportional scale and clearly restore the external shape, internal structure (such as the catalyst bed in the reactor and the cyclone separator in the regenerator) and pipeline connection relationship of the physical entities.

[0099] S22, a physical model is constructed by coupling chemical reaction kinetics model, mass transfer model, heat transfer model and fluid dynamics model.

[0100] Specifically, the chemical reaction kinetics model adopts a lumped reaction network, covering three key reactions: methanol reacts with acidic sites to form active cobalt species, methanol reacts with acidic sites to form inactive cobalt species, and methanol reacts with active cobalt species to form olefins. The reaction rate constants are set with reference to existing literature. The mass transfer model adopts a dilute mass transfer model suitable for porous media, distinguishing the diffusion characteristics of the catalyst binder matrix and molecular sieve crystals, and setting different diffusion coefficients. The heat transfer model considers thermal conductivity, convection, and reaction heat effects, and the parameters are configured in combination with the equipment material parameters.

[0101] S23 uses standardized multiphysics data as input and real-time operating parameters of physical entities as output to construct a behavior model. It uses machine learning algorithms to train the behavior model and dynamically updates the parameters of the behavior model to make the digital twin model consistent with the physical entity.

[0102] Specifically, deep learning algorithms are used to train the behavioral model. After training, the model parameters can be dynamically updated based on real-time data to ensure that the digital twin model and the physical entity maintain consistent operating status.

[0103] S3, Multiphysics Coupling Mapping.

[0104] S31, clarify the coupling mechanism of multi-physics fields in the MTO process and establish a quantitative correlation model.

[0105] Specifically, the interaction relationships of various physical fields are clarified. For example, an increase in temperature will accelerate the methanol conversion reaction rate and accelerate catalyst carbonization; the flow rate affects the diffusion rate of reactants and products, and thus affects the concentration field distribution; an increase in pressure will promote the forward progress of the methanol conversion reaction, but will increase the catalyst carbonization rate; catalyst carbonization will reduce its activity and change the heat transfer and mass transfer characteristics of the catalyst, thus affecting the temperature field and concentration field distribution.

[0106] S32, based on the quantization correlation model obtained in step S31, a spatiotemporal interpolation algorithm is used to map the standardized multiphysics data obtained in step S1 to the digital twin model obtained in step S2, thereby obtaining a digital twin model bound to multiphysics data.

[0107] Specifically, a spatiotemporal interpolation algorithm is used to map the standardized multiphysics data obtained in step S1 to the corresponding positions in the digital twin model. For example, the temperature data of different regions of the reactor is mapped to the corresponding regions of the geometric model, and the flow field data is mapped to the fluid simulation region inside the equipment. This achieves deep binding between the multiphysics data and the digital twin model, completes the collaborative simulation of the multiphysics, and controls the simulation error within 5%.

[0108] S4, Multi-scale dynamic visualization rendering parameter configuration: Based on the multi-scale characteristics of the MTO process, the rendering layers are divided, and the corresponding rendering parameters are configured for each rendering layer.

[0109] S41, based on the multi-scale characteristics of the MTO process, divides the rendering levels into microscale, mesoscale and macroscale, and clarifies the scope and display focus of each rendering level. The microscale focuses on the interior of catalyst particles, the mesoscale focuses on the interior of a single physical entity, and the macroscale focuses on the entire process flow.

[0110] S42, configure rendering parameters for the rendering layer obtained in step S41. The rendering parameters include any one or a combination of at least two of rendering precision, rendering frame rate, color mapping rules, or transparency.

[0111] Specifically, the macro-scale rendering hierarchy covers the entire MTO process, including all units such as raw material pretreatment, reaction, regeneration, and separation. It adopts a LOD optimization strategy, using lower rendering precision when the observation distance is >5m and switching to higher rendering precision when the observation distance is ≤5m. Color mapping rules are used, such as: the temperature field uses a red-yellow-blue gradient, the pressure field uses a purple-pink-white gradient, and the concentration field uses a green (high concentration)-light green (medium concentration)-white (low concentration) gradient. Different colors correspond to different ranges of physical field data.

[0112] Specifically, the rendering level at the mesoscale focuses on the interior of core equipment (physical entities) such as reactors and regenerators. The rendering accuracy is set to a high level, the color mapping rules are consistent with the macroscale, the visualization of flow field particles is increased, and different particle colors are set to correspond to different flow velocities, such as red for higher flow velocities and blue for lower flow velocities. Particle sizes correspond to different turbulence intensities.

[0113] Specifically, the microscale rendering level focuses on the interior of the catalyst particles, with the rendering precision set to a high level. Fluorescence imaging simulation technology is used, with green fluorescence representing methanol concentration, red fluorescence representing carbon deposition concentration, and yellow fluorescence representing active acid sites, to intuitively present the reaction, mass transfer, and heat transfer processes within the catalyst particles.

[0114] S5, Multiphysics Dynamic Visualization Rendering: Based on the rendering parameters described in step S4, perform dynamic visualization rendering on the digital twin model bound to the multiphysics data obtained in step S3.

[0115] S51 uses WebGL combined with a 3D engine for dynamic visualization rendering, and transforms standardized multiphysics data into visual signals through a custom GLSL shader.

[0116] S52 employs an optimization strategy, including instantiation rendering, level of detail rendering, or occlusion culling, to optimize the obtained visual signal and obtain the optimized rendering effect.

[0117] S53 reads updated data from the multiphysics database in real time, driving the synchronous update of the digital twin model and rendering effects.

[0118] Specifically, temperature field data changes the surface color of the device in real time, flow field data drives the movement trajectory of fluid particles, and chemical reaction field data drives the dynamic highlighting of reaction areas (highlighting and flashing effects are displayed in areas of intense reaction). Instantiated rendering is used to batch draw the same type of device parts, and occlusion culling technology is used to skip occluded parts of the model, reducing GPU rendering calls and ensuring that the rendering frame rate is stable at 20~30fps. It supports rendering a single physics field separately (such as rendering only the temperature field) and rendering multiple physics fields in superposition (such as rendering the temperature field and the concentration field at the same time), realizing the synchronous display and dynamic update of multiple physics fields.

[0119] S6, Interactive Control and Feedback Adjustment: Perform multi-dimensional interaction and feedback adjustment with the user terminal. The multi-dimensional interaction and feedback adjustment includes any one or a combination of at least two of the following: rendering level switching, physical field filtering, rendering parameter adjustment, local area magnification, or abnormal warning.

[0120] Specifically, the rendering level can be switched using the mouse wheel (scrolling up switches to the microscopic scale, and scrolling down switches to the macroscopic scale); the physical fields to be rendered can be selected using the checkboxes in the sidebar (temperature field and pressure field can be selected individually, or multiple fields can be selected and overlaid); the rendering transparency, color gradient, and particle size can be adjusted using the slider; the model can be rotated and translated by dragging the mouse, and double-clicking the device can zoom in on a local area to view detailed physical field data.

[0121] Simultaneously, it receives user interaction commands in real time and adjusts the rendering state; it presets safety thresholds for each physical field, and when multiple physical field data exceed the safety thresholds, it generates visual warning prompts such as displaying a red flashing border in abnormal areas, while simultaneously popping up text prompts such as "Reactor temperature exceeds the standard, it is recommended to reduce the feed temperature" and issuing an audible warning to remind operators to adjust process parameters in a timely manner; operators can view detailed physical field data and control suggestions for abnormal areas through interactive commands, realizing two-way linkage between rendering results and process control.

[0122] S7, Data Storage and Management: Stores raw multiphysics data, standardized multiphysics data, digital twin model data, rendering parameter data, and interaction log data; classifies, manages, backs up, and queries the data.

[0123] Example 3 This embodiment provides a multiphysics visualization rendering device for digital twins of methanol-to-olefins processes, such as... Figure 2 As shown, the device includes: a data acquisition and preprocessing module 110, a digital twin model construction module 120, a multiphysics coupling mapping module 130, a rendering parameter configuration module 140, a dynamic visualization rendering module 150, an interactive control and feedback module 160, and a data storage and management module 170.

[0124] The data acquisition and preprocessing module 110 is used for multi-physics data acquisition and preprocessing, including: acquiring raw multi-physics data of the entire MTO process and preprocessing it to obtain standardized multi-physics data and establish a multi-physics database.

[0125] Specifically, the data acquisition and preprocessing module 110 includes a sensor acquisition unit, a CFD simulation unit, and a data processing unit.

[0126] The sensor acquisition unit is used to collect data on chemical reaction fields, temperature fields, pressure fields, concentration fields, flow fields, or equipment operating parameters. This unit integrates an online chromatograph, temperature sensor, pressure sensor, concentration sensor, flow rate sensor, and a catalyst characterization agent to collect raw physical field data during process operation in real time. The CFD simulation unit is used to correct the acquired flow field data through computational fluid dynamics simulation. The data processing unit preprocesses the raw multiphysics data to obtain standardized multiphysics data, establishes a multiphysics database, and ensures data accuracy.

[0127] The digital twin model construction module 120 is used to construct a digital twin model, including a physical entity based on the MTO process, and to construct a digital twin model of the MTO process by combining the standardized multiphysics data.

[0128] Specifically, the digital twin model construction module 120 includes a geometric modeling unit, a physical modeling unit, and a behavioral modeling unit.

[0129] The geometric modeling unit acquires point cloud data of the physical entity through laser scanning and constructs a geometric model using the Iterative Closest Point (ICP) registration algorithm, ensuring that the geometric model proportionally replicates the design drawings of the physical entity. The physical modeling unit couples chemical reaction kinetics, mass transfer, heat transfer, and fluid dynamics models to construct a physical model, enabling coupled simulation of multiple physics. The behavioral modeling unit uses standardized multiphysics data as input and the real-time operating parameters of the physical entity as output to construct a behavioral model. It trains the behavioral model using machine learning algorithms and dynamically updates its parameters, ensuring consistency between the digital twin model and the physical entity.

[0130] The multiphysics coupling mapping module 130 is used to perform multiphysics coupling mapping.

[0131] Specifically, the multiphysics coupling mapping module 130 includes a coupling relationship definition unit and a data mapping unit.

[0132] The coupling relationship definition unit is used to clarify the coupling mechanism of multiphysics in the MTO process and establish a quantitative correlation model. The data mapping unit is used to map standardized multiphysics data to a digital twin model based on the quantitative correlation model and using a spatiotemporal interpolation algorithm, thereby obtaining a digital twin model of multiphysics data binding.

[0133] The rendering parameter configuration module 140 is used to configure multi-scale dynamic visualization rendering parameters, including: dividing the rendering layers according to the multi-scale characteristics of the MTO process, and configuring the corresponding rendering parameters for each rendering layer.

[0134] Specifically, the rendering parameter configuration module 140 includes a hierarchy division unit and a parameter configuration unit.

[0135] The layer division unit is used to divide the rendering layers into micro-scale, meso-scale, and macro-scale based on the multi-scale characteristics of the MTO process, clarifying the scope and display focus of each rendering layer. The micro-scale focuses on the interior of catalyst particles, the meso-scale focuses on the interior of a single physical entity, and the macro-scale focuses on the entire process flow. The parameter configuration unit is used to configure rendering parameters for each rendering layer. These rendering parameters include any one or a combination of at least two of the following: rendering precision, rendering frame rate, color mapping rules, or transparency.

[0136] The dynamic visualization rendering module 150 is used for multi-physics dynamic visualization rendering, including: dynamic visualization rendering of digital twin models bound to multi-physics data based on rendering parameters.

[0137] Specifically, the dynamic visualization rendering module 150 includes a rendering engine unit, an optimization unit, and a dynamic update unit.

[0138] The rendering engine unit uses WebGL combined with a 3D engine for dynamic visualization rendering, converting standardized multiphysics data into visual signals through a custom GLSL shader. The optimization unit uses optimization strategies, including instantiation rendering, level of detail rendering, or occlusion culling, to optimize the obtained visual signals and achieve the optimized rendering effect. The dynamic update unit reads updated data from the multiphysics database in real time, driving the synchronous update of the digital twin model and the rendering effect.

[0139] The interactive control and feedback module 160 is used for multi-dimensional interaction and feedback adjustments with the user terminal. Specifically, it includes multi-dimensional interaction and feedback adjustments with the user terminal, wherein the multi-dimensional interaction and feedback adjustments include any one or a combination of at least two of the following: rendering level switching, physics field filtering, rendering parameter adjustment, local area magnification, or anomaly warning.

[0140] The data storage and management module 170 is used to store multiphysics raw data, standardized multiphysics data, digital twin model data, rendering parameter data, and interaction log data, and to classify, back up, and query the data.

[0141] Example 3 This embodiment provides an electronic device for a multiphysics visualization rendering method for digital twins of methanol-to-olefins processes, such as... Figure 3 As shown, this electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. This electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0142] like Figure 3 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 can also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0143] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as secondary storage area, optical disc, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0144] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, central processing unit (CPU), graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as multiphysics visualization rendering methods for digital twins of methanol-to-olefins processes.

[0145] In some embodiments, the multiphysics visualization rendering method for a digital twin of a methanol-to-olefins (MTO) process can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the multiphysics visualization rendering method for a digital twin of a MTO process described above can be performed. Alternatively, in other embodiments, processor 11 can be configured to execute the multiphysics visualization rendering method for a digital twin of a MTO process by any other suitable means (e.g., by means of firmware).

[0146] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0147] Computer programs used to implement the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable target determination device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer programs can be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0148] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0149] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0150] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0151] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0152] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired information of the technical solution of this invention can be achieved, and this is not limited herein.

[0153] The above description is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Those skilled in the art should understand that any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention fall within the protection and disclosure scope of the present invention.

Claims

1. A multiphysics visualization rendering method for digital twins of methanol-to-olefins processes, characterized in that, The method includes the following steps: S1, Multiphysics Data Acquisition and Preprocessing; S2, Digital Twin Model Construction; S3, Multiphysics Coupling Map; S4, multi-scale dynamic visualization rendering parameter configuration; S5, multiphysics dynamic visualization rendering.

2. The method according to claim 1, characterized in that, Step S1, the multiphysics data acquisition and preprocessing, includes: acquiring raw multiphysics data from the entire MTO process and preprocessing it to obtain standardized multiphysics data and establish a multiphysics database; Preferably, step S1 specifically includes: S11, collect any one or at least two of the following: chemical reaction field data, temperature field data, pressure field data, concentration field data, flow field data, or equipment operating parameter data. The collected flow field data is corrected by computational fluid dynamics simulation to obtain multiphysics field raw data. S12, preprocess the original multiphysics data obtained in step S11 to obtain standardized multiphysics data and establish a multiphysics database; Preferably, the chemical reaction field data in step S11 includes any one or a combination of at least two of methanol conversion rate, olefin selectivity, catalyst coking amount, or chemical reaction rate; Preferably, the flow field data in step S11 includes any one or a combination of at least two of fluid velocity, flow direction, or turbulence intensity; Preferably, the preprocessing in step S12 includes any one or a combination of at least two of the following: cleaning, noise reduction, normalization, or spatiotemporal alignment. Preferably, the spatiotemporal alignment process in step S12 includes: using a timestamp-based alignment algorithm and setting a tolerance threshold to achieve spatiotemporal alignment of multiphysics data.

3. The method according to claim 2, characterized in that, The digital twin model construction in step S2 includes: constructing a digital twin model of the MTO process based on the physical entity of the MTO process and the standardized multiphysics data obtained in step S1. Preferably, the physical entity includes any one or a combination of at least two of the following: a reactor, a regenerator, a heat exchanger, a separator, or connecting pipelines; Preferably, the digital twin model includes any one or a combination of at least two of the following: a geometric model, a physical model, or a behavioral model; Preferably, step S2 specifically includes: S21. Point cloud data of physical entities are obtained through laser scanning, and a geometric model is constructed using an iterative nearest point registration algorithm, so that the geometric model can be proportionally restored to the design drawings of the physical entities. S22, a physical model is constructed by coupling chemical reaction kinetics model, mass transfer model, heat transfer model and fluid dynamics model to realize multi-physics coupled simulation; S23 uses standardized multiphysics data as input and real-time operating parameters of physical entities as output to construct a behavior model. It uses machine learning algorithms to train the behavior model and dynamically updates the parameters of the behavior model to make the digital twin model consistent with the physical entity. Preferably, the chemical reaction kinetic model in step S22 adopts a lumped reaction network, including the reaction of methanol to active cobalt species, the reaction of methanol to inactive cobalt species, and the reaction of methanol to olefin products.

4. The method according to claim 3, characterized in that, Step S3 specifically includes: S31, clarify the coupling mechanism of multi-physics fields in the MTO process and establish a quantitative correlation model; S32, based on the quantization correlation model obtained in step S31, a spatiotemporal interpolation algorithm is used to map the standardized multiphysics data obtained in step S1 to the digital twin model obtained in step S2, thereby obtaining a digital twin model bound to multiphysics data.

5. The method according to claim 4, characterized in that, Step S4, the configuration of multi-scale dynamic visualization rendering parameters, includes: dividing the rendering layers according to the multi-scale characteristics of the MTO process, and configuring corresponding rendering parameters for each rendering layer. Preferably, step S4 specifically includes: S41, based on the multi-scale characteristics of the MTO process, divides the rendering levels into microscale, mesoscale and macroscale, and clarifies the scope and display focus of each rendering level. The microscale focuses on the interior of catalyst particles, the mesoscale focuses on the interior of a single physical entity, and the macroscale focuses on the entire process flow. S42, Configure rendering parameters for the rendering layer obtained in step S41. The rendering parameters include any one or a combination of at least two of rendering precision, rendering frame rate, color mapping rules or transparency. Preferably, the multiphysics dynamic visualization rendering in step S5 includes: performing dynamic visualization rendering on the digital twin model bound to the multiphysics data obtained in step S3 based on the rendering parameters in step S4. Preferably, step S5 specifically includes: S51 uses WebGL combined with a 3D engine for dynamic visualization rendering, and converts standardized multiphysics data into visual signals through a custom GLSL shader. S52, the obtained visual signal is optimized by using any one or at least two of the optimization strategies, including instantiation rendering, level of detail or occlusion culling, to obtain the optimized rendering effect; S53 reads updated data from the multiphysics database in real time, driving the synchronous update of the digital twin model and rendering effects.

6. The method according to any one of claims 1 to 5, characterized in that, The method further includes: S6, interactive control and feedback adjustment; Preferably, the interactive control and feedback adjustment includes: multi-dimensional interaction and feedback adjustment with the user terminal, wherein the multi-dimensional interaction and feedback adjustment includes any one or a combination of at least two of the following: rendering level switching, physical field filtering, rendering parameter adjustment, local area magnification or abnormal warning; Preferably, the method further includes: S7, data storage and management; Preferably, the data storage and management includes: storing original multiphysics data, standardized multiphysics data, digital twin model data, rendering parameter data, and interaction log data, and classifying, backing up, and querying the data.

7. A multiphysics visualization and rendering device for digital twins of methanol-to-olefins processes, characterized in that, The apparatus is used in the multiphysics visualization rendering method for digital twins of methanol-to-olefins processes as described in any one of claims 1 to 6; the apparatus comprises: Data acquisition and preprocessing module: used for multiphysics data acquisition and preprocessing; Digital Twin Model Building Module: Used to build digital twin models; Multiphysics coupling mapping module: used for performing multiphysics coupling mapping; Rendering parameter configuration module: used to configure multi-scale dynamic visualization rendering parameters; Dynamic Visualization Rendering Module: Used for dynamic visualization rendering of multiphysics fields.

8. The apparatus according to claim 7, characterized in that, The device also includes an interactive control and feedback module and / or a data storage and management module; The interactive control and feedback module is used for multi-dimensional interaction and feedback adjustments with the user terminal; The data storage and management module is used to store raw multiphysics data, standardized multiphysics data, digital twin model data, rendering parameter data, and interaction log data, and to classify, back up, and query the data.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores a computer program that can be executed by the at least one processor, which is then executed by the at least one processor to enable the at least one processor to perform the multiphysics visualization rendering method for digital twins of methanol-to-olefins processes as described in any one of claims 1 to 6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the multiphysics visualization rendering method for digital twins of methanol-to-olefins processes as described in any one of claims 1 to 6.

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