Method, device, equipment, medium and product for constructing digital twin model

Abstract

The application provides a digital twin model construction method, device, equipment, medium and product, and relates to the technical field of digital twin. Including: in response to the modeling operation initiated by the user through the graphical interaction interface, importing the three-dimensional model data corresponding to the target physical entity, and generating the initial digital twin model according to the user-defined parameters; continuously acquiring the real-time running data of the target physical entity through the real-time data integration engine, and dynamically updating the state of the initial digital twin model; acquiring the access sub-model from the external heterogeneous model data source through the compatibility adapter; performing format conversion operation on the access sub-model to obtain the target access sub-model consistent with the format of the main digital twin model; embedding the target access sub-model into the specified position of the main digital twin model to generate the target digital twin model with unified structure. Through the method of the application, the real-time state of the target physical entity can be accurately reflected, and efficient fusion of multi-source models is realized.

Description

Technical Field

[0001] This application relates to the field of digital twin technology, and in particular to a method, apparatus, device, medium and product for constructing a digital twin model. Background Technology

[0002] With the rapid development of industry and intelligent manufacturing, digital twin technology, as a core technology for realizing real-time mapping between physical entities and virtual models, has been widely used in many fields such as intelligent manufacturing, smart buildings, energy and power, smart cities and healthcare.

[0003] In existing technologies, the construction of digital twin models typically relies on a single modeling tool to import static 3D models and access limited sensor data through a preset interface to achieve basic state synchronization. When faced with heterogeneous models from multiple sources such as Computer-Aided Design (CAD) or Building Information Modeling (BIM), manual intervention is required for format conversion, coordinate alignment, and semantic annotation in order to achieve fusion.

[0004] However, existing methods for constructing digital twin models lack a unified model adaptation and fusion mechanism, which limits the collaborative application of multi-source models and results in lagging model updates, failing to accurately reflect the real-time state of the target physical entity. Summary of the Invention

[0005] This application provides a method, apparatus, device, medium, and product for constructing a digital twin model. The method is used to obtain a master digital twin model through graphical interactive modeling and real-time data-driven updates, obtain an access sub-model through a compatibility adapter, and automatically perform format conversion operations on the access sub-model to generate a target access sub-model with the same format as the master digital twin model. Then, the target access sub-model is embedded into the master digital twin model according to a spatial coordinate system or logical mounting relationship to obtain a target digital twin model with a unified structure that accurately reflects the real-time state of the target physical entity.

[0006] Firstly, this application provides a method for constructing a digital twin model, the method comprising:

[0007] In response to the modeling operation initiated by the user through the graphical interface, the system imports the 3D model data corresponding to the target physical entity, and performs geometric transformations, mesh editing, and material texture adjustments on the 3D model data according to the user-defined parameters to generate an initial digital twin model.

[0008] The real-time data integration engine continuously acquires real-time operational data of the target physical entity.

[0009] Based on real-time operational data, the state of the initial digital twin model is dynamically updated to obtain a master digital twin model that is consistent with the state of the target physical entity.

[0010] Access sub-models are obtained from external heterogeneous model data sources via a compatibility adapter;

[0011] According to the preset mapping rules, the access sub-model is converted to a target access sub-model with the same format as the master digital twin model.

[0012] Based on a preset spatial coordinate system or logical attachment relationship, the target access sub-model is embedded into a specified position in the main digital twin model to generate a target digital twin model with a unified structure.

[0013] In one possible implementation, the state of the initial digital twin model is dynamically updated based on real-time operational data to obtain a master digital twin model consistent with the state of the target physical entity, including:

[0014] Configure the association between the components in the initial digital twin model and the corresponding sensors on the target physical entity;

[0015] Based on the correlation, the real-time running data is mapped to the corresponding components of the initial digital twin model to obtain the mapped real-time running data;

[0016] Based on the real-time running data after mapping, the state of the initial digital twin model is dynamically updated to obtain a master digital twin model that is consistent with the state of the target physical entity.

[0017] In one possible implementation, according to a preset mapping rule, the access sub-model undergoes a format conversion operation to obtain a target access sub-model consistent with the format of the master digital twin model, including:

[0018] Parse the basic format information of the access sub-model;

[0019] According to the preset mapping rules, the basic format information is transformed to obtain the transformed basic format information;

[0020] Perform a semantic alignment operation between the transformed basic format information and the semantic system of the master digital twin model to obtain the semantically aligned basic format information;

[0021] Based on the semantically aligned basic format information, a target access sub-model is generated.

[0022] In one possible implementation, the basic format information includes geometric data, topology, and material properties;

[0023] According to preset mapping rules, the basic format information is transformed to obtain the transformed basic format information, including:

[0024] The geometric data is transformed into the coordinate system used by the master digital twin model to obtain the transformed geometric data;

[0025] The topology is converted into a hierarchical node format supported by the master digital twin model to obtain the converted topology.

[0026] The material properties are mapped to uniform shading parameters to obtain the transformed material properties;

[0027] The transformed geometric data, transformed topology, and transformed material properties are used as the basic format information after transformation.

[0028] In one possible implementation, a semantic alignment operation is performed on the transformed basic format information and the semantic system of the master digital twin model to obtain semantically aligned basic format information, including:

[0029] Extract component identifiers, device types, or function tags from the converted base format information;

[0030] Based on the preset semantic mapping table, the component identifier, device type or function label are mapped to the corresponding semantic unit in the preset standard semantic classification system adopted by the main digital twin model, and the corresponding mapping results are obtained.

[0031] Based on the mapping results, a unified set of semantic attributes is bound to the corresponding model components. The set of semantic attributes includes at least one of the following: device category, running role, maintenance cycle, or interaction interface.

[0032] The transformed basic format information is associated with the semantic attribute set to obtain semantically enhanced model data, which is then used as the basic format information after semantic alignment.

[0033] In one possible implementation, after generating a structurally unified target digital twin model, the process further includes:

[0034] Obtain the current state data of each component in the target digital twin model. The state data includes at least one of geometric pose, operating parameters, alarm status, or attribute labels.

[0035] According to the preset visualization rules, the state data is mapped to the corresponding visual performance parameters, including color, transparency, highlighting effect, animation or annotation text.

[0036] The visualization rendering engine is invoked to perform 3D rendering of the target digital twin model based on visual performance parameters, generating dynamic visualization images;

[0037] The system sends dynamic visualizations to the graphical user interface in real time and responds to user view operation commands, enabling multi-angle, interactive model browsing and status monitoring.

[0038] Secondly, this application provides an apparatus for constructing a digital twin model, the apparatus comprising:

[0039] The generation module is used to respond to the modeling operation initiated by the user through the graphical interface, import the 3D model data corresponding to the target physical entity, and perform geometric transformation, mesh editing and material texture adjustment on the 3D model data according to the user-defined parameters to generate the initial digital twin model.

[0040] The acquisition module is used to continuously acquire real-time operational data of the target physical entity through the real-time data integration engine;

[0041] The update module is used to dynamically update the state of the initial digital twin model based on real-time running data, so as to obtain a master digital twin model that is consistent with the state of the target physical entity.

[0042] The acquisition module is also used to acquire access sub-models from external heterogeneous model data sources through a compatibility adapter;

[0043] The format conversion module is used to perform format conversion operations on the access sub-model according to the preset mapping rules, so as to obtain the target access sub-model with the same format as the master digital twin model.

[0044] The fusion module is used to embed the target access sub-model into a specified position of the main digital twin model according to a preset spatial coordinate system or logical attachment relationship, thereby generating a target digital twin model with a unified structure.

[0045] In one possible implementation, the update module is also used to configure the association between the components in the initial digital twin model and the corresponding sensors on the target physical entity;

[0046] The update module is also used to map real-time running data to the corresponding components of the initial digital twin model based on the association relationship, so as to obtain the mapped real-time running data;

[0047] The update module is also used to dynamically update the state of the initial digital twin model based on the mapped real-time running data, so as to obtain a master digital twin model that is consistent with the state of the target physical entity.

[0048] In one possible implementation, the format conversion module is also used to parse the basic format information of the access sub-model;

[0049] The format conversion module is also used to convert the basic format information according to the preset mapping rules to obtain the converted basic format information;

[0050] The format conversion module is also used to perform semantic alignment operations on the converted basic format information and the semantic system of the master digital twin model to obtain the semantically aligned basic format information;

[0051] The format conversion module is also used to generate the target access sub-model based on the semantically aligned basic format information.

[0052] In one possible implementation, the basic format information includes geometric data, topology, and material properties.

[0053] The format conversion module is also used to convert geometric data to the coordinate system used by the master digital twin model, so as to obtain the converted geometric data;

[0054] The format conversion module is also used to convert the topology into a hierarchical node format supported by the master digital twin model, so as to obtain the converted topology.

[0055] The format conversion module is also used to map material properties to uniform shading parameters to obtain the converted material properties;

[0056] The format conversion module is also used to use the converted geometric data, converted topology, and converted material properties as the basic format information after conversion.

[0057] In one possible implementation, the format conversion module is also used to extract component identifiers, device types, or function tags from the converted base format information.

[0058] The format conversion module is also used to map component identifiers, device types or function tags to corresponding semantic units in the preset standard semantic classification system adopted by the main digital twin model according to the preset semantic mapping table, so as to obtain the corresponding mapping results.

[0059] The format conversion module is also used to bind a unified set of semantic attributes to the corresponding model components based on the mapping results. The set of semantic attributes includes at least one of device category, running role, maintenance cycle or interaction interface.

[0060] The format conversion module is also used to associate the converted basic format information with the semantic attribute set to obtain semantically enhanced model data, and use the semantically enhanced model data as the basic format information after semantic alignment.

[0061] In one possible implementation, the device further includes an interaction module;

[0062] The interaction module is used to obtain the current status data of each component in the target digital twin model. The status data includes at least one of geometric pose, running parameters, alarm status or attribute labels.

[0063] The interaction module is also used to map state data to corresponding visual performance parameters according to preset visualization rules. The visual performance parameters include color, transparency, highlighting effect, animation or annotation text.

[0064] The interaction module is also used to call the visualization rendering engine to perform 3D rendering of the target digital twin model based on visual performance parameters, and generate dynamic visualization images.

[0065] The interaction module is also used to send dynamic visualizations to the graphical user interface in real time and respond to user view operation commands, enabling multi-angle, interactive model browsing and status monitoring.

[0066] Thirdly, this application provides an electronic device, including: a processor, and a memory communicatively connected to the processor.

[0067] The memory stores the instructions that the computer executes.

[0068] The processor executes computer execution instructions stored in memory to implement a method for constructing a digital twin model according to the first aspect of the invention.

[0069] Fourthly, this application provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement a method for constructing a digital twin model according to the first aspect of the invention.

[0070] Fifthly, this application provides a computer program product, including a computer program, which, when executed by a processor, is used to implement a method for constructing a digital twin model according to the first aspect of the invention.

[0071] Based on the implementation methods provided in the above aspects, this application can be further combined to provide more implementation methods.

[0072] This application provides a method, apparatus, device, medium, and product for constructing a digital twin model, comprising: First, in response to a modeling operation initiated by a user through a graphical user interface, importing 3D model data corresponding to a target physical entity, and performing geometric transformations, mesh editing, and material texture adjustments on the 3D model data according to user-defined parameters to generate an initial digital twin model; Next, continuously acquiring real-time operating data of the target physical entity through a real-time data integration engine; Then, dynamically updating the state of the initial digital twin model based on the real-time operating data to obtain a master digital twin model consistent with the state of the target physical entity; Subsequently, obtaining an access sub-model from an external heterogeneous model data source through a compatibility adapter; Then, performing a format conversion operation on the access sub-model according to preset mapping rules to obtain a target access sub-model with a format consistent with the master digital twin model; Finally, embedding the target access sub-model into a specified position of the master digital twin model according to a preset spatial coordinate system or logical mounting relationship to generate a structurally unified target digital twin model. The following technical effects were achieved: A graphical interactive modeling approach provides users with an intuitive and convenient interface, enabling them to more flexibly build and edit digital twin models and adjust 3D model data, thus improving modeling efficiency and accuracy. A real-time data-driven update mechanism ensures that the master digital twin model can promptly acquire the latest state information of the target physical entity. Real-time connection with various sensors continuously collects operational data of the target physical entity and feeds this data back to the initial digital twin model, resulting in a master digital twin model consistent with the state of the target physical entity. This ensures that the master digital twin model remains synchronized with the target physical entity, accurately reflecting its real-time state. By introducing a compatibility adapter, access sub-models can be obtained from different data sources. Even if these access sub-models come from different systems and have different formats, automatic format conversion transforms them into target access sub-models consistent with the master digital twin model, solving the problem of multi-source heterogeneous model fusion and achieving efficient compatibility and collaborative work between different models. By accurately embedding the target, after format conversion, into a sub-model according to spatial coordinates or logical attachment relationships, a target digital twin model with a unified structure that can accurately reflect the real-time state of the target physical entity is obtained, achieving efficient fusion of multi-source models. Attached Figure Description

[0073] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0074] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0075] Figure 1 A flowchart illustrating a method for constructing a digital twin model provided in this application embodiment. Figure 1 ;

[0076] Figure 2 A flowchart illustrating a method for constructing a digital twin model provided in this application embodiment. Figure 2 ;

[0077] Figure 3 A flowchart illustrating a method for constructing a digital twin model provided in this application embodiment. Figure 3 ;

[0078] Figure 4 A flowchart illustrating a method for constructing a digital twin model provided in this application embodiment. Figure 4 ;

[0079] Figure 5 A schematic diagram of a digital twin model construction device provided in an embodiment of this application;

[0080] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0081] Figure label:

[0082] 510 - Generation module; 520 - Acquisition module; 530 - Update module; 540 - Format conversion module; 550 - Fusion module; 610 - Processor; 620 - Memory; 630 - Communication component; 640 - Bus. Detailed Implementation

[0083] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0084] In the embodiments of this application, the terms "first" and "second" are used to distinguish identical or similar items with substantially the same function and effect. Those skilled in the art will understand that the terms "first" and "second" do not limit the quantity or execution order, and that "first" and "second" do not necessarily imply difference. It should be noted that in the embodiments of this application, the words "exemplary" or "for example" are used to indicate that something is being used as an example, illustration, or description. Any embodiment or design scheme described as "exemplary" or "for example" in this application should not be construed as being better or more advantageous than other embodiments or design schemes. Specifically, the use of "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner. In the embodiments of this application, "at least one" refers to one or more, and "more than one" refers to two or more.

[0085] It should be noted that the phrase "at...time" in the embodiments of this application can refer to the instant at which a certain situation occurs, or to a period of time after the occurrence of a certain situation; the embodiments of this application do not specifically limit this. Furthermore, the method for constructing a digital twin model provided in the embodiments of this application is merely an example; a method for constructing a digital twin model may include more or fewer elements.

[0086] With the rapid development of industry and intelligent manufacturing, digital twin technology, with its significant advantages such as real-time mapping between physical entities and virtual models, and providing intelligent monitoring and decision support, has become a key technological means to promote the digital transformation of various industries. Currently, this technology is widely used in many fields such as manufacturing, construction, healthcare, urban management, and energy, playing a vital role in improving production efficiency, optimizing resource allocation, and ensuring stable system operation.

[0087] In existing technologies, the construction of digital twin models mainly relies on importing static 3D models using a single modeling tool and connecting a limited number of sensor data points through a pre-defined interface to achieve basic state synchronization. However, in practical applications, there are often situations involving heterogeneous models from multiple sources such as CAD and BIM. For these models from different sources and with varying formats, existing methods require manual intervention to fuse them through manual conversion.

[0088] More importantly, existing methods for constructing digital twin models have numerous limitations. On the one hand, the lack of a unified model adaptation mechanism makes it difficult for multi-source models to achieve efficient collaborative applications, resulting in poor compatibility and interoperability between different models, which greatly limits the application scope and effectiveness of digital twin models. On the other hand, model updates are lagging. Due to limitations in data access and processing methods, models cannot obtain real-time status information of the target physical entity in a timely and accurate manner, leading to discrepancies between the digital twin model and the actual physical entity. This results in the model failing to accurately and comprehensively reflect the operational status of the physical entity, thus affecting the accuracy and effectiveness of decision-making based on digital twin models.

[0089] Based on this, this application proposes a method, apparatus, device, medium, and product for constructing a digital twin model, which can be used in the field of digital twin technology and aims to solve the above-mentioned technical problems of the prior art. A graphical user interface allows users to flexibly create initial digital twin models, and a real-time data integration engine continuously acquires the operational data of the target physical entity, dynamically driving the initial digital twin model's state update. Simultaneously, a compatibility adapter is introduced to automatically connect to external heterogeneous model data sources, identify their formats, and complete a unified structural and semantic conversion according to preset mapping rules, generating a target access sub-model with a format consistent with the main digital twin model. Finally, based on a preset spatial coordinate system or logical mounting relationship, the target access sub-model is precisely embedded into a specified position of the main digital twin model, constructing a target digital twin model with a unified structure that accurately reflects the real-time state of the target physical entity, thereby achieving efficient fusion of multi-source models.

[0090] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0091] Figure 1 A flowchart illustrating a method for constructing a digital twin model provided in this application embodiment. Figure 1 The execution entity in this embodiment can be a data processing server or other devices with data processing capabilities, such as laptops, personal computers, tablets, and smartphones. The data processing server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server, etc., without specific limitations. For ease of description, this application embodiment uniformly describes the execution entity of a digital twin model construction method as a server. Figure 1 As shown, the method includes:

[0092] S101. In response to the modeling operation initiated by the user through the graphical interactive interface, import the three-dimensional model data corresponding to the target physical entity, and perform geometric transformation, mesh editing and material texture adjustment on the three-dimensional model data according to the user-defined parameters to generate an initial digital twin model.

[0093] Specifically, the server can respond to modeling operations initiated by users through a graphical user interface by importing 3D model data corresponding to the target physical entity (such as an industrial robot, a building, or a production line) from local storage or a cloud model library. This 3D model data can come from various modeling tools, such as 3D design software (SolidWorks), building information modeling software (Autodesk Revit), computer-aided design software (AutoCAD), etc.

[0094] Subsequently, the server can perform geometric transformations, mesh topology optimizations, and material texture adjustments on the 3D model data based on parameters defined by the user on the graphical interface (such as size scaling ratio, component rotation angle, surface roughness, or color mapping), thereby generating an interactive and customizable initial digital twin model.

[0095] The graphical user interface (GUI) refers to a visual operating environment deployed on computing devices such as servers, computers, tablets, or industrial terminals. It supports users in intuitively and efficiently participating in the construction and editing of digital twin models. This GUI provides a 3D view window, toolbar, property panel, and operation menus, allowing users to rotate, scale, and translate the digital twin model through mouse dragging, touch gestures, or voice commands. It also supports user input of parameters (such as size, material type, and mesh density) and real-time preview of geometric transformations, mesh optimization, and texture mapping adjustments. Furthermore, the GUI can integrate modeling wizards, template libraries, and status feedback prompts to lower the modeling threshold and improve human-computer collaboration efficiency.

[0096] S102. Continuously acquire real-time operational data of the target physical entity through the real-time data integration engine.

[0097] Specifically, the server can continuously collect real-time operating data of the target physical entity from various sensors deployed on the target physical entity, such as temperature sensors, pressure transmitters, vibration monitors, and programmable logic controllers (PLCs), through its built-in real-time data integration engine.

[0098] The real-time data integration engine is a data acquisition and processing module responsible for communicating with sensors, controllers, or edge devices on the target physical entity side. This engine supports various industrial communication protocols, such as OPC Unified Architecture (OPC UA), Message Queuing Telemetry Transport (MQTT), and Modbus Transmission Control Protocol (Modbus TCP) based on TCP / IP. It can periodically or event-drivenly acquire operational data (including but not limited to temperature, pressure, vibration, position, current, and switch status) from the target physical entity to ensure high timeliness and reliability of the data.

[0099] After the real-time running data of the target physical entity is accessed, the real-time data integration engine can also perform preprocessing operations such as filtering, noise reduction, normalization, timestamp alignment and outlier detection, and push the preprocessed structured data stream to the corresponding component of the initial digital twin model to ensure that the virtual model state and the physical entity are synchronized at the millisecond level.

[0100] S103. Based on real-time operating data, dynamically update the state of the initial digital twin model to obtain a master digital twin model that is consistent with the state of the target physical entity.

[0101] Specifically, the server can map the acquired real-time operational data to the corresponding components in the initial digital twin model, and dynamically update the state of the initial digital twin model accordingly (e.g., changing the color of the equipment to indicate the running / stopping state, driving the robotic arm to move at the actual angle, displaying the fluid pressure value in the pipeline, etc.), thereby generating a master digital twin model that is highly consistent with the target physical entity in terms of operational state.

[0102] S104. Obtain the access sub-model from the external heterogeneous model data source through the compatibility adapter.

[0103] Specifically, to further enrich the completeness and detail accuracy of the master digital twin model, the server can also connect to external heterogeneous model data sources (such as CAD models, BIM files, laser point cloud reconstruction models, or third-party simulation outputs from different manufacturers) through a compatibility adapter to obtain access sub-models.

[0104] Among them, the compatibility adapter refers to an extensible model access middleware used to interface with external heterogeneous models from different modeling platforms or data sources, such as Standard for the Exchange of Product model data (STEP) files or 3D model exchange format (Filmbox, FBX) files output by CAD systems, Industry Foundation Classes (IFC) models exported by BIM platforms, Polygon File Format (PLY) data or Wavefront Object Format (OBJ) data generated by point cloud scanning, or Graphics Transmission Format (GLTF) and its binary form GLB files output by third-party simulation software.

[0105] S105. According to the preset mapping rules, perform a format conversion operation on the access sub-model to obtain a target access sub-model with the same format as the master digital twin model.

[0106] Specifically, the server can automatically identify the file format (such as FBX, OBJ, IFC, STEP, GLTF, etc.) or internal data structure of the access sub-model through the compatibility adapter, and perform format conversion and data reconstruction operations on the access sub-model according to the preset mapping rules (including coordinate system transformation rules, hierarchical structure mapping table, material parameter normalization strategy, etc.) to generate a target access sub-model that is consistent with the format of the main digital twin model in terms of geometric expression, topology and rendering attributes.

[0107] Specifically, the compatibility adapter can automatically recognize formats, automatically parsing the file header, metadata, or internal structure of the input access sub-model to determine its original format. Subsequently, according to preset mapping rules, the geometry, topology, and materials of the access sub-model are converted into a representation format unified with the main digital twin model, thereby generating a target access sub-model that is compatible with the main digital twin model in terms of structure, rendering, and semantics, thus achieving plug-and-play multi-source model fusion.

[0108] S106. Based on the preset spatial coordinate system or logical mounting relationship, embed the target access sub-model into the specified position of the main digital twin model to generate a target digital twin model with a unified structure.

[0109] Specifically, the server can precisely embed the target access sub-model into a designated location in the main digital twin model based on a preset spatial coordinate system (such as the world coordinate system or a marker-based aligned coordinate system) or logical attachment relationship (such as semantic associations like pump and pipe interfaces, cameras and monitoring areas), thus completing the fusion of multi-source models. The resulting target digital twin model is not only geometrically unified and complete but also semantically consistent, capable of realistically, dynamically, and comprehensively reflecting the current state of the target physical entity, providing a reliable foundation for subsequent visualization monitoring, fault diagnosis, simulation, and intelligent decision-making.

[0110] This embodiment provides a method for constructing a digital twin model. First, in response to a modeling operation initiated by a user through a graphical user interface, 3D model data corresponding to the target physical entity is imported. Based on user-defined parameters, the 3D model data undergoes geometric transformation, mesh editing, and material texture adjustment to generate an initial digital twin model. Next, a real-time data integration engine continuously acquires real-time operational data of the target physical entity. Then, based on the real-time operational data, the state of the initial digital twin model is dynamically updated to obtain a master digital twin model consistent with the state of the target physical entity. Subsequently, an access sub-model is obtained from an external heterogeneous model data source through a compatibility adapter. Then, according to preset mapping rules, the access sub-model undergoes format conversion to obtain a target access sub-model with a format consistent with the master digital twin model. Finally, based on a preset spatial coordinate system or logical mounting relationship, the target access sub-model is embedded into a specified position of the master digital twin model to generate a structurally unified target digital twin model.

[0111] The following technical effects were achieved: A graphical interactive modeling approach provides users with an intuitive and convenient interface, enabling them to more flexibly build and edit digital twin models and adjust 3D model data, thus improving modeling efficiency and accuracy. A real-time data-driven update mechanism ensures that the master digital twin model can promptly acquire the latest state information of the target physical entity. By connecting to various sensors in real time, the system continuously collects operational data of the target physical entity and feeds this data back to the initial digital twin model, resulting in a master digital twin model consistent with the state of the target physical entity. This ensures that the master digital twin model remains synchronized with the target physical entity, accurately reflecting its real-time state. Furthermore, a compatibility adapter with powerful data acquisition and conversion capabilities is introduced. This adapter can acquire access sub-models from different data sources, even those from different systems or with different formats. By automatically converting the access sub-models to a target access sub-model with a format consistent with the master digital twin model, the system solves the problem of multi-source heterogeneous model fusion and achieves efficient compatibility and collaborative work between different models. By accurately embedding the target, after format conversion, into a sub-model according to spatial coordinates or logical attachment relationships, into a specified position in the main digital twin model, a target digital twin model with a unified structure that can accurately reflect the real-time state of the target physical entity is obtained. This achieves efficient fusion of multi-source models and provides more reliable and efficient technical support for the intelligent application of digital twin technology in various industries.

[0112] Figure 2 A flowchart illustrating a method for constructing a digital twin model provided in this application embodiment. Figure 2 In one possible implementation, such as Figure 2 As shown, S103, based on real-time running data, dynamically update the state of the initial digital twin model to obtain a master digital twin model consistent with the state of the target physical entity, including:

[0113] S201. Configure the association between the components in the initial digital twin model and the corresponding sensors on the target physical entity.

[0114] S202. Based on the correlation, map the real-time running data to the corresponding components of the initial digital twin model to obtain the mapped real-time running data.

[0115] S203. Based on the real-time running data after mapping, dynamically update the state of the initial digital twin model to obtain a master digital twin model that is consistent with the state of the target physical entity.

[0116] Specifically, the server can first configure the association between each virtual component (such as the equipment body, valve, motor, pipeline section, etc.) in the initial digital twin model and the corresponding physical sensors deployed on the target physical entity. This association can be established through a unique identifier; for example, associating the pump component in the initial digital twin model with pressure sensors and vibration sensors installed in the field.

[0117] The unique identifier can be a device identifier (ID), a sensor serial number, or a custom label, etc.

[0118] Subsequently, the server can map the raw operational data (such as temperature, speed, current, and switch status) obtained from the real-time data integration engine to the corresponding virtual components in the initial digital twin model based on the configured associations, generating mapped real-time operational data with spatial and semantic context. For example, when receiving a value uploaded by a specific pressure sensor, the server can automatically assign it to the pipe node in the initial digital twin model that is bound to that sensor.

[0119] Finally, based on the mapped real-time operational data, the server can dynamically drive state changes of corresponding components in the initial digital twin model, including but not limited to geometric pose adjustments, color / transparency changes, animation triggers, numerical label updates, or alarm highlighting. This generates a master digital twin model that is highly synchronized with the target physical entity in behavior, state, and appearance. This master digital twin model can realistically reflect the current operating status of the target physical entity, providing a reliable basis for subsequent monitoring, analysis, and decision-making.

[0120] Figure 3 A flowchart illustrating a method for constructing a digital twin model provided in this application embodiment. Figure 3 In one possible implementation, such as Figure 3 As shown, S105, according to the preset mapping rules, the access sub-model is subjected to a format conversion operation to obtain a target access sub-model with the same format as the master digital twin model, including:

[0121] S301. Parse the basic format information of the access sub-model.

[0122] S302. According to the preset mapping rules, the basic format information is transformed to obtain the transformed basic format information.

[0123] S303. Perform a semantic alignment operation on the semantic system of the transformed basic format information and the master digital twin model to obtain the semantically aligned basic format information.

[0124] S304. Generate the target access sub-model based on the semantically aligned basic format information.

[0125] Specifically, after obtaining the access sub-model, the server can further perform in-depth analysis to extract its basic format information. This basic format information includes, but is not limited to: geometric data (such as vertex coordinates, patch indices, and normal vectors), topological structure (such as parent-child hierarchy relationships, assembly trees, and primitive groupings), and material properties (such as diffuse color, roughness, metallicity, and texture mapping paths). This access sub-model can originate from various heterogeneous data sources, such as STEP or IGES files exported from CAD systems, IFC models generated by BIM platforms, OBJ / PLY point cloud meshes reconstructed from 3D scanning, or FBX / GLTF format files output by third-party simulation tools.

[0126] Subsequently, the server can perform a structured transformation operation on the aforementioned basic format information according to preset mapping rules. These mapping rules can be pre-configured in the compatibility adapter and include: uniformly transforming the original coordinate system (such as a local coordinate system or a device-defined coordinate system) to the world coordinate system used by the main digital twin model using a coordinate transformation matrix; reconstructing non-standard assembly structures or layer organizations into a hierarchical node format compatible with the main digital twin model, such as a tree structure based on a scene graph, where each node corresponds to an independently transformable and renderable component; and normalizing material parameters from different rendering engines to unified physically based rendering (PBR) standard shading parameters. After this processing, the transformed basic format information, which is formatted correctly and structurally compatible, is obtained.

[0127] Furthermore, the server can perform semantic alignment between the transformed basic format information and the semantic system adopted by the main digital twin model. Specifically, component identifiers, device types, or functional keywords (such as motor A, cooling valve, camera) are extracted from the transformed basic format information, and mapped to corresponding semantic units in the semantic classification system of the main digital twin model, such as rotating equipment, control valve, and surveillance device, according to a preset semantic mapping table (such as a mapping dictionary built based on industry ontology or enterprise knowledge graph), to achieve semantic consistency across models. On this basis, a unified set of semantic attributes is bound to each aligned component. This set of semantic attributes can include structured information such as device category, operating role, maintenance cycle, communication interface protocol, and security level, thereby generating semantically aligned basic format information.

[0128] Finally, the server can integrate the semantically aligned basic format information to construct a complete model object, endowing it with the same data structure, rendering interface, and interaction capabilities as the main digital twin model. This ultimately generates a target access sub-model that can be seamlessly loaded, manipulated, and analyzed on the same platform. This target access sub-model not only maintains geometric and visual consistency with the main digital twin model but also achieves semantic interoperability, laying the foundation for unified management and intelligent application of multi-source models.

[0129] Figure 4 A flowchart illustrating a method for constructing a digital twin model provided in this application embodiment. Figure 4 In one possible implementation, the basic format information includes geometric data, topology, and material properties. For example... Figure 4 As shown, S302, according to the preset mapping rules, the basic format information is transformed to obtain the transformed basic format information, including:

[0130] S401. Transform the geometric data to the coordinate system used by the master digital twin model to obtain the transformed geometric data.

[0131] S402. Convert the topology into a hierarchical node format supported by the master digital twin model to obtain the converted topology.

[0132] S403. Map the material properties to uniform shading parameters to obtain the converted material properties.

[0133] S404. Use the converted geometric data, converted topology, and converted material properties as the basic format information after conversion.

[0134] Specifically, the basic format information of the sub-model includes its core 3D representation elements, specifically covering geometric data (such as vertex coordinates, patch indices, normal vectors, and boundary representations), topological structure (such as parent-child assembly relationships between components, layer grouping, or part hierarchy organization), and material properties (such as diffuse color, specular intensity, transparency, and texture mapping paths). This basic format information is usually stored in the original model file (e.g., FBX, OBJ, STEP, or IFC formats) in embedded or external form, forming the basis for model visualization and structural analysis.

[0135] The server can perform structured transformation operations on the above basic format information according to preset mapping rules to generate a data representation compatible with the master digital twin model.

[0136] Specifically, firstly, the geometric data is uniformly transformed from its original coordinate system (such as the device local coordinate system, the default coordinate system of the modeling tool, or the custom coordinate system of the scanning device) to the target coordinate system (usually the global world coordinate system) used by the master digital twin model through coordinate transformation matrices (including translation, rotation, and scaling), thereby ensuring the consistency of spatial position and obtaining the transformed geometric data.

[0137] Secondly, the topology is reconstructed into a hierarchical node format supported by the main digital twin model platform. This hierarchical node format is typically implemented based on a tree structure of a scene graph, where each node represents a logical component that can be independently transformed, rendered, or interacted with, and spatial relationships between parent and child nodes are passed through transformation matrices. Through this reconstruction, the original non-standard assembly tree or layer structure is normalized into a node hierarchy recognizable by the main digital twin model platform, resulting in the transformed topology.

[0138] Secondly, material properties are mapped to uniform shading parameters. Specifically, the server can normalize non-physical or engine-specific rendering parameters in material properties, such as specular coefficients and emissivity in the Phong lighting model, into a set of universal parameters conforming to the PBR standard, including metallicity, roughness, base color, and normal map, to ensure consistent visual performance under different lighting conditions and obtain the transformed material properties.

[0139] Finally, the transformed geometric data, transformed topology, and transformed material properties are integrated to form a complete and uniform intermediate data representation, which is used as the basic format information after transformation for subsequent semantic alignment and model fusion operations.

[0140] In one possible implementation, a semantic alignment operation is performed on the transformed basic format information and the semantic system of the master digital twin model to obtain semantically aligned basic format information. This includes: extracting component identifiers, device types, or function tags from the transformed basic format information; mapping the component identifiers, device types, or function tags to corresponding semantic units in the preset standard semantic classification system adopted by the master digital twin model according to a preset semantic mapping table, to obtain corresponding mapping results; binding a unified set of semantic attributes to the corresponding model components according to the mapping results, the set of semantic attributes including at least one of device category, operating role, maintenance cycle, or interaction interface; associating the transformed basic format information with the set of semantic attributes to obtain semantically enhanced model data, and using the semantically enhanced model data as the semantically aligned basic format information.

[0141] Specifically, the server can automatically extract key metadata fields that can be used for semantic recognition from the converted basic format information, including but not limited to component identifiers (such as names defined by the modeling tool or equipment manufacturer, such as motor A, pump 01, valve B, etc.), equipment types (such as descriptive categories such as centrifugal pumps, servo motors, electric regulating valves, etc.), and function labels (such as usage descriptions such as cooling loop control, main drive unit, safety monitoring node, etc.). This information usually exists in the model data in the form of embedded attributes, user-defined fields, or external relational tables.

[0142] Subsequently, the server can map the extracted component identifiers, device types, or function tags to the corresponding semantic units in the preset standard semantic classification system adopted by the main digital twin model, according to the preset semantic mapping table.

[0143] The semantic mapping table can be pre-configured by the main digital twin model platform or customized by the user, supporting fuzzy matching, regular expressions, or ontology-based knowledge reasoning. For example, a non-standardized identifier like motor A can be mapped to the standard semantic unit rotating equipment, cooling valve X to a control valve, and a forward-looking camera to a monitoring device. This standard semantic classification system can be built based on industry standards (such as IFC equipment classification) or an enterprise's internal knowledge graph, ensuring semantic consistency and scalability. This process yields a structured mapping result.

[0144] Furthermore, the server can dynamically bind a unified set of semantic attributes to the corresponding model components based on the mapping results.

[0145] The semantic attribute set is a structured, machine-readable collection of metadata used to describe the behavior, role, and management strategies of components within the digital twin system. Its content includes, but is not limited to: equipment category (e.g., power equipment, fluid control devices), operating role (e.g., main control unit, backup redundancy, monitoring terminal), maintenance cycle (e.g., lubrication every 500 hours, annual calibration), and interaction interface (e.g., support for OPC UA communication, Modbus TCP control interface, and ability to trigger alarm events). This attribute set can originate from the equipment manufacturer's Digital Product Passport, Computerized Maintenance Management System (CMMS) database, or be automatically populated from a default template by the main digital twin model platform based on semantic units.

[0146] Finally, the server can deeply associate the transformed base format information (including standardized geometry, topology, and material data) with the semantic attribute set. For example, it can establish bidirectional references through unique component IDs or embed attribute dictionaries in scene graph nodes to generate semantically enhanced model data. This semantically enhanced model data not only retains visual and structural consistency but also possesses clear semantic identity and business attributes, enabling it to be directly understood and invoked by upper-layer applications (such as fault diagnosis engines, energy efficiency analysis modules, and AR inspection systems). Ultimately, the server can use this semantically enhanced model data as the semantically aligned base format information for subsequent model embedding, visualization rendering, or intelligent service invocation.

[0147] In one possible implementation, after generating a structurally unified target digital twin model, the method further includes: acquiring the current state data of each component in the target digital twin model, the state data including at least one of geometric pose, operating parameters, alarm status, or attribute labels; mapping the state data to corresponding visual performance parameters according to preset visualization rules, the visual performance parameters including color, transparency, highlighting effect, animation, or labeled text; calling a visualization rendering engine to perform 3D rendering of the target digital twin model based on the visual performance parameters to generate a dynamic visualization screen; and sending the dynamic visualization screen to a graphical interactive interface in real time, responding to the user's view operation commands to achieve multi-angle, interactive model browsing and status monitoring.

[0148] Specifically, after generating a structurally unified target digital twin model, the server can further perform dynamic visualization and interactive monitoring functions.

[0149] Specifically, firstly, the server can continuously acquire the current state data of each virtual component in the target digital twin model. This current state data originates from multiple real-time inputs, including: geometric poses, such as position coordinates, rotation angles, and scaling ratios, used to reflect robotic arm movement, valve opening / closing status, etc.; operating parameters, such as motor speed, pipeline pressure, temperature, and current intensity, from industrial data from sensors or PLCs; alarm states, such as high-temperature warnings, vibration exceeding limits, and communication interruptions, generated by edge computing nodes or diagnostic engines; and attribute tags, such as device ID, maintenance personnel, last maintenance time, and security level, etc., representing static or semi-static metadata.

[0150] The aforementioned current status data can be continuously synchronized through a real-time data integration engine using protocols such as MQTT and OPC UA to ensure a high degree of consistency between the virtual model (i.e., the target digital twin model) and the target physical entity.

[0151] Subsequently, the server can map the current state data to corresponding visual performance parameters according to preset visualization rules. These preset visualization rules can be pre-defined by the main digital twin model platform or customized by the user through a graphical configuration interface, supporting conditional triggering and threshold grading. For example: when the temperature exceeds 80 degrees Celsius, the component color is gradually changed from green to red; when the device is in a stopped state, the transparency is set to 50% and a gray mask is overlaid; for high-priority alarms, pulsed highlighting effects (such as flashing borders) or pop-up annotation text (such as "Motor A: Bearing vibration exceeds standard") are enabled; for a running conveyor belt, its surface texture is driven to scroll along the direction of movement, creating an animation effect.

[0152] Visual representation parameters include, but are not limited to: color, opacity, highlight / outline, animation (such as rotation, translation, material flow), and label / tooltip text, which are used to intuitively convey the device status.

[0153] Next, the server can invoke a built-in 3D visualization rendering engine, such as Three.js based on the Web Graphics Library (WebGL), the Unity engine, the Unreal Engine, or a self-developed renderer, to perform real-time 3D rendering of the target digital twin model based on the aforementioned visual performance parameters, generating high-frame-rate, low-latency dynamic visualizations. The rendering process supports PBR materials, dynamic lighting, shadow casting, and Level of Detail (LOD) optimization to ensure a smooth interactive experience even in complex industrial scenarios.

[0154] Finally, the server can push dynamic visualizations to the user's graphical user interface in real time via network or local interface, such as a World Wide Web (Web) browser, mobile application (App), or augmented reality / virtual reality (AR / VR) head-mounted display. Simultaneously, the server can monitor and respond to user view operation commands, including but not limited to: viewpoint control (e.g., mouse drag rotation, scroll wheel zoom, two-finger swipe pan); component focusing (e.g., user clicking on a device automatically centers and highlights it); state drill-down (e.g., user clicking on labeled text expands a detailed operating parameter panel); time rewind (e.g., user dragging a timeline to view historical states); and AR overlay (e.g., activating AR mode on a mobile device to spatially register the target digital twin model with the real device, aligning the target digital twin model in real time based on the spatial pose of the real device).

[0155] Through the above mechanism, users can achieve multi-angle, immersive, and interactive browsing and real-time status monitoring of target physical entities, significantly improving operation and maintenance efficiency, fault response speed, and decision-making accuracy.

[0156] This application embodiment can divide an electronic device or main control device into functional modules according to the above method examples. For example, each function can be divided into its own functional modules, or two or more functions can be integrated into one processing unit. The integrated unit can be implemented in hardware or as a software functional module. It should be noted that the module division in this embodiment is illustrative and only represents one logical functional division; in actual implementation, there may be other division methods.

[0157] Figure 5 This is a schematic diagram of a device for constructing a digital twin model, provided in an embodiment of this application. Figure 5 As shown, the device includes: a generation module 510, an acquisition module 520, an update module 530, a format conversion module 540, and a fusion module 550.

[0158] The generation module 510 is used to respond to the modeling operation initiated by the user through the graphical interactive interface, import the three-dimensional model data corresponding to the target physical entity, and perform geometric transformation, mesh editing and material texture adjustment on the three-dimensional model data according to the user-defined parameters to generate an initial digital twin model.

[0159] The acquisition module 520 is used to continuously acquire real-time operational data of the target physical entity through the real-time data integration engine;

[0160] The update module 530 is used to dynamically update the state of the initial digital twin model based on real-time running data, so as to obtain a master digital twin model that is consistent with the state of the target physical entity.

[0161] The acquisition module 520 is also used to acquire access sub-models from external heterogeneous model data sources through a compatibility adapter;

[0162] The format conversion module 540 is used to perform format conversion operations on the access sub-model according to the preset mapping rules to obtain a target access sub-model with the same format as the master digital twin model.

[0163] The fusion module 550 is used to embed the target access sub-model into a specified position of the main digital twin model according to a preset spatial coordinate system or logical attachment relationship, thereby generating a target digital twin model with a unified structure.

[0164] In one possible implementation, the update module 530 is also used to configure the association between the components in the initial digital twin model and the corresponding sensors on the target physical entity;

[0165] The update module 530 is also used to map real-time running data to the corresponding components of the initial digital twin model according to the association relationship, so as to obtain the mapped real-time running data;

[0166] The update module 530 is also used to dynamically update the state of the initial digital twin model based on the mapped real-time running data, so as to obtain a master digital twin model that is consistent with the state of the target physical entity.

[0167] In one possible implementation, the format conversion module 540 is also used to parse the basic format information of the access sub-model;

[0168] The format conversion module 540 is also used to perform conversion operations on the basic format information according to the preset mapping rules to obtain the converted basic format information;

[0169] The format conversion module 540 is also used to perform a semantic alignment operation on the converted basic format information and the semantic system of the master digital twin model to obtain the semantically aligned basic format information;

[0170] The format conversion module 540 is also used to generate the target access sub-model based on the semantically aligned basic format information.

[0171] In one possible implementation, the basic format information includes geometric data, topology, and material properties.

[0172] The format conversion module 540 is also used to convert geometric data to the coordinate system used by the master digital twin model to obtain the converted geometric data;

[0173] The format conversion module 540 is also used to convert the topology into a hierarchical node format supported by the master digital twin model, so as to obtain the converted topology.

[0174] The format conversion module 540 is also used to map material properties to uniform shading parameters to obtain the converted material properties;

[0175] The format conversion module 540 is also used to use the converted geometric data, the converted topology, and the converted material properties as the converted basic format information.

[0176] In one possible implementation, the format conversion module 540 is further configured to extract component identifiers, device types, or function tags from the converted base format information.

[0177] The format conversion module 540 is also used to map the component identifier, device type or function label to the corresponding semantic unit in the preset standard semantic classification system adopted by the main digital twin model according to the preset semantic mapping table, so as to obtain the corresponding mapping result.

[0178] The format conversion module 540 is also used to bind a unified set of semantic attributes to the corresponding model components according to the mapping result. The set of semantic attributes includes at least one of device category, running role, maintenance cycle or interaction interface.

[0179] The format conversion module 540 is also used to associate the converted basic format information with the semantic attribute set to obtain semantically enhanced model data, and use the semantically enhanced model data as the basic format information after semantic alignment.

[0180] In one possible implementation, the device further includes an interaction module;

[0181] The interaction module is used to obtain the current status data of each component in the target digital twin model. The status data includes at least one of geometric pose, running parameters, alarm status or attribute labels.

[0182] The interaction module is also used to map state data to corresponding visual performance parameters according to preset visualization rules. The visual performance parameters include color, transparency, highlighting effect, animation or annotation text.

[0183] The interaction module is also used to call the visualization rendering engine to perform 3D rendering of the target digital twin model based on visual performance parameters, and generate dynamic visualization images.

[0184] The interaction module is also used to send dynamic visualizations to the graphical user interface in real time and respond to user view operation commands, enabling multi-angle, interactive model browsing and status monitoring.

[0185] This embodiment provides a digital twin model construction apparatus that can execute a digital twin model construction method described in the above embodiment. Its implementation principle and technical effects are similar, and will not be repeated here.

[0186] In a specific implementation of the aforementioned digital twin model construction device, each module can be implemented as a processor. The processor can execute computer execution instructions stored in the memory, thereby enabling the processor to execute the aforementioned digital twin model construction method.

[0187] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 6As shown, the electronic device includes at least one processor 610 and a memory 620. The electronic device also includes a communication component 630. The processor 610, memory 620, and communication component 630 are connected via a bus 640.

[0188] In the specific implementation process, at least one processor 610 executes computer execution instructions stored in memory 620, causing at least one processor 610 to execute a method for constructing a digital twin model as executed on the electronic device side.

[0189] The specific implementation process of processor 610 can be found in the above method embodiments, and its implementation principle and technical effect are similar. It will not be repeated here.

[0190] In the above embodiments, it should be understood that the processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0191] The memory may include high-speed RAM, and may also include non-volatile storage (NVM), such as at least one disk storage.

[0192] The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of illustration, the buses shown in the accompanying drawings are not limited to a single bus or a single type of bus.

[0193] The above description of the functions implemented by electronic devices and main control devices has introduced the solutions provided by the embodiments of the present invention. It is understood that, in order to implement the above functions, the electronic device or main control device includes hardware structures and / or software modules corresponding to the execution of each function. By combining the units and algorithm steps of the various examples described in the embodiments of the present invention, the embodiments of the present invention can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed by hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the technical solutions of the embodiments of the present invention.

[0194] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the above-described method for constructing a digital twin model.

[0195] The aforementioned readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0196] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in an electronic device or a host device.

[0197] This application also provides a computer program product, which includes a computer program stored in a readable storage medium. At least one processor of an electronic device can read the computer program from the readable storage medium, and the at least one processor executes the computer program to cause the electronic device to perform the solution provided in the above embodiments.

[0198] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disk, or optical disk.

[0199] The technical solutions of this application have been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it is readily understood by those skilled in the art that the scope of protection of this application is obviously not limited to these specific embodiments. The above embodiments are only used to illustrate the technical solutions of this application and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. These modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for constructing a digital twin model, characterized in that, include: In response to the modeling operation initiated by the user through the graphical user interface, the system imports the 3D model data corresponding to the target physical entity, and performs geometric transformations, mesh editing, and material texture adjustments on the 3D model data according to the user-defined parameters to generate an initial digital twin model. The real-time data integration engine continuously acquires the real-time operational data of the target physical entity; Based on the real-time operating data, the state of the initial digital twin model is dynamically updated to obtain a master digital twin model that is consistent with the state of the target physical entity; Access sub-models are obtained from external heterogeneous model data sources via a compatibility adapter; According to the preset mapping rules, the access sub-model is subjected to a format conversion operation to obtain a target access sub-model with the same format as the master digital twin model. Based on a preset spatial coordinate system or logical attachment relationship, the target access sub-model is embedded into a specified position of the main digital twin model to generate a target digital twin model with a unified structure.

2. The method according to claim 1, characterized in that, The step of dynamically updating the state of the initial digital twin model based on the real-time operating data to obtain a master digital twin model consistent with the state of the target physical entity includes: Configure the association between the components in the initial digital twin model and the corresponding sensors on the target physical entity; Based on the aforementioned association, the real-time operational data is mapped to the corresponding components of the initial digital twin model to obtain the mapped real-time operational data; Based on the mapped real-time running data, the state of the initial digital twin model is dynamically updated to obtain a master digital twin model that is consistent with the state of the target physical entity.

3. The method according to claim 1, characterized in that, The step of performing a format conversion operation on the access sub-model according to a preset mapping rule to obtain a target access sub-model with a format consistent with the master digital twin model includes: Parse the basic format information of the access sub-model; According to the preset mapping rules, the basic format information is transformed to obtain the transformed basic format information; Perform a semantic alignment operation between the transformed basic format information and the semantic system of the master digital twin model to obtain semantically aligned basic format information; The target access sub-model is generated based on the semantically aligned basic format information.

4. The method according to claim 3, characterized in that, The basic format information includes geometric data, topological structure, and material properties; The step of converting the basic format information according to the preset mapping rules to obtain the converted basic format information includes: The geometric data is converted to the coordinate system used by the master digital twin model to obtain the converted geometric data; The topology is converted into a hierarchical node format supported by the master digital twin model to obtain the converted topology. The material properties are mapped to uniform shading parameters to obtain the transformed material properties; The transformed geometric data, the transformed topology, and the transformed material properties are used as the basic format information of the transformation.

5. The method according to claim 3, characterized in that, The step of performing a semantic alignment operation between the transformed basic format information and the semantic system of the master digital twin model to obtain semantically aligned basic format information includes: Extract component identifiers, device types, or function tags from the converted base format information; According to the preset semantic mapping table, the component identifier, device type or function label are respectively mapped to the corresponding semantic unit in the preset standard semantic classification system adopted by the main digital twin model, and the corresponding mapping result is obtained. Based on the mapping result, a unified set of semantic attributes is bound to the corresponding model component. The set of semantic attributes includes at least one of device category, running role, maintenance cycle, or interaction interface. The transformed basic format information is associated with the semantic attribute set to obtain semantically enhanced model data, and the semantically enhanced model data is used as the basic format information after semantic alignment.

6. The method according to any one of claims 1 to 5, characterized in that, Following the generation of a structurally unified target digital twin model, the following is also included: Obtain the current state data of each component in the target digital twin model, wherein the state data includes at least one of geometric pose, operating parameters, alarm status or attribute label; According to preset visualization rules, the state data is mapped to corresponding visual performance parameters, including color, transparency, highlighting effect, animation or annotation text. The visualization rendering engine is invoked to perform three-dimensional rendering of the target digital twin model based on the visual performance parameters, generating a dynamic visualization image; The dynamic visualization is sent to the graphical user interface in real time and responds to the user's view operation commands, enabling multi-angle, interactive model browsing and status monitoring.

7. A device for constructing a digital twin model, characterized in that, include: The generation module is used to respond to the modeling operation initiated by the user through the graphical interactive interface, import the three-dimensional model data corresponding to the target physical entity, and perform geometric transformation, mesh editing and material texture adjustment on the three-dimensional model data according to the user-defined parameters to generate an initial digital twin model. The acquisition module is used to continuously acquire the real-time operating data of the target physical entity through the real-time data integration engine; The update module is used to dynamically update the state of the initial digital twin model based on the real-time running data, so as to obtain a master digital twin model that is consistent with the state of the target physical entity. The acquisition module is also used to acquire access sub-models from external heterogeneous model data sources through a compatibility adapter; The format conversion module is used to perform format conversion operations on the access sub-model according to preset mapping rules to obtain a target access sub-model with the same format as the master digital twin model. The fusion module is used to embed the target access sub-model into a specified position of the main digital twin model according to a preset spatial coordinate system or logical attachment relationship, thereby generating a target digital twin model with a unified structure.

8. An electronic device, characterized in that, include: Memory, processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory, causing the processor to perform the method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the method as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, Includes a computer program that, when executed by a processor, implements the method as described in any one of claims 1 to 6.

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