System and method for automatically integrating iot devices into a digital twin platform based on an open building information model

Abstract

The present application provides a system that integrates BIM and IoT datasets into a digital twin platform, enabling real-time spatial monitoring, semantic referencing, and lifecycle management of building assets. The system includes a BIM interface module, an IFC conversion module, a preprocessing module, and a data linking module. The BIM interface module receives external data to generate or update a BIM model. The IFC conversion module converts the BIM model into an IFC-compatible format with a globally unique identifier. The preprocessing module performs model federation and data normalization, and includes subcomponents for performing geometric conversion to WebGL, semantic structuring, IoT spatial tagging, and floor tagging. The data linking module serializes the output information, uploads it to an object storage area, and registers access URLs and property mappings in the platform database.

Description

Technical Field

[0001] This invention relates to digital twin and building information modeling (BIM) technologies; more specifically, this invention relates to a system and method for automatically integrating Internet of Things (IoT) devices into a digital twin platform based on OpenBIM. Background Technology

[0002] In the field of building and facilities management, the increasing complexity of building operations and the growing need for real-time data-driven decision-making have driven the widespread adoption of Internet of Things (IoT) technology. IoT connects physical objects to the internet, enabling real-time monitoring and control of physical assets.

[0003] To fully realize the value of the Internet of Things (IoT) in the built environment, combining IoT with digital twin technology is crucial for optimizing energy efficiency, predictive maintenance, and overall operational performance. However, existing solutions often face challenges such as interoperability issues, inefficient data synchronization, and difficulties in achieving real-time monitoring. Many systems also lack standardized methods for integrating Building Information Modeling (BIM) data with IoT applications, leading to fragmented workflows and limited scalability.

[0004] Furthermore, the lack of automated mechanisms to link IoT devices with BIM elements hinders the large-scale deployment of digital twin systems, particularly in multi-building or multi-organizational scenarios. Therefore, a system and methodology are needed that can automatically integrate IoT devices into digital twins, while also supporting consistent data synchronization, semantic alignment, and real-time operability. Summary of the Invention

[0005] The purpose of this invention is to provide a system and method based on the Open Building Information Modeling (OpenBIM) standard, which can automatically integrate Internet of Things (IoT) devices into a digital twin platform.

[0006] This invention provides a solution to overcome the aforementioned limitations by automatically integrating IoT devices and digital twin technology using OpenBIM technology. This invention enables the automated integration of IoT devices and BIM models within a digital twin platform. By assigning unique IDs to BIM components and converting them to OpenBIM format, the proposed system ensures smooth interoperability and optimizes data storage within the digital twin platform through reprocessing steps. The automated process simplifies data synchronization, reduces manual workload, and improves the overall efficiency of facility management and operation.

[0007] According to a first aspect of the present invention, a system is provided for integrating BIM data with Internet of Things (IoT) data into a digital twin platform, thereby enabling real-time spatial monitoring, semantic referencing, and lifecycle management of building assets within the digital twin environment. The system includes a BIM interface module, an Industry Foundation Class (IFC) conversion module, a preprocessing module, and a data linking module. The BIM interface module receives external geometric and semantic data and generates or updates a BIM model, wherein the BIM model contains three-dimensional representations of building elements and related semantic specifications. The IFC conversion module converts the BIM model into a format conforming to the IFC schema to obtain a converted IFC model and assigns globally unique identifiers (GUIDs) to building elements. The preprocessing module receives the converted IFC model from the IFC conversion module and performs model federation and data normalization. The preprocessing module includes: a geometry transformation subcomponent, used to convert BIM geometry into a WebGL-compatible format based on the geometric representation contained in the transformed IFC model; a semantic structuring subcomponent, used to extract and construct non-geometric metadata based on the semantic attribute set contained in the transformed IFC model; an IoT tagging subcomponent, used to calculate centroid-based spatial tags for building elements that meet IoT conditions based on the mesh geometry derived from the joint IFC model; a floor tagging subcomponent, used to calculate the vertical position information of IFC floor elements based on the bounding box analysis of the floor-related geometry in the joint IFC model; and a data linking module, used to serialize the geometric and semantic outputs from the preprocessing module, upload the geometric and semantic outputs to the object storage area of ​​the digital twin platform, and register the access URL and attribute mapping to the database schema of the digital twin platform.

[0008] According to a second aspect of the present invention, a method is provided for integrating BIM data and IoT data into a digital twin platform, thereby enabling real-time spatial monitoring, semantic reference, and lifecycle management of building assets in a digital twin environment. The method includes the following steps: receiving external geometric data and semantic data through a BIM interface module; generating or updating a BIM model through the BIM interface module, which contains three-dimensional representations of building elements and related semantic specifications; converting the BIM model into a format conforming to the IFC model through an IFC conversion module to obtain a converted IFC model; assigning GUIDs to building elements through the IFC conversion module; receiving the converted IFC model from the IFC conversion module through a preprocessing module; and performing model federation and data normalization through the preprocessing module, including: (1) using the geometric conversion subcomponent of the preprocessing module to convert the BIM geometry into a WebGL-compatible format based on the geometric representation contained in the converted IFC model; (2) using the semantic structuring subcomponent of the preprocessing module, Based on the semantic attribute set contained in the transformed IFC model, non-geometric metadata is extracted and constructed; (3) using the IoT tag sub-component of the preprocessing module, based on the grid geometry derived from the joint IFC model, spatial tags based on centroids are calculated for building elements that meet the IoT conditions; (4) using the floor tag sub-component of the preprocessing module, based on the bounding box analysis of the geometry related to floors in the joint IFC model, the vertical position information of IFC floor elements is calculated; through the data linking module, the geometric and semantic output information of the preprocessing module is serialized; through the data linking module, the geometric and semantic output information is uploaded to the object storage area of ​​the digital twin platform; and through the data linking module, the access URL and attribute mapping are registered in the database schema of the digital twin platform. Attached Figure Description

[0009] The embodiments of the present invention will be described in more detail below with reference to the accompanying drawings, wherein:

[0010] Figure 1 A flowchart illustrating the automatic update and synchronization process of a BIM model according to an embodiment of the present invention is shown.

[0011] Figure 2 A flowchart illustrating the preprocessing process of a BIM model according to an embodiment of the present invention is provided.

[0012] Figure 3 A flowchart illustrating the automatic tagging process of the Internet of Things according to an embodiment of the present invention is shown;

[0013] Figure 4 A flowchart illustrating the automatic floor marking process according to an embodiment of the present invention is shown;

[0014] Figure 5 A schematic diagram illustrating how BIM model information is associated with a digital twin platform according to an embodiment of the present invention;

[0015] Figure 6 The process of multi-organization, multi-building, and multi-protocol data integration using a digital twin platform according to embodiments of the present invention is demonstrated.

[0016] Figure 7 A block diagram illustrating a system according to an embodiment of the present invention and its interaction with a digital twin platform is shown. Detailed Implementation

[0017] In the following description, preferred examples are presented, including systems and methods for automatically integrating Internet of Things (IoT) devices into digital twin platforms based on the OpenBIM standard. Those skilled in the art will understand that modifications, including additions and / or substitutions, can be made without departing from the scope and spirit of the invention. Specific details may be omitted to avoid obscuring the invention; however, this disclosure is intended to enable those skilled in the art to practice the teachings herein without extensive experimentation.

[0018] This invention provides a solution for automatically integrating IoT devices into a digital twin platform based on the OpenBIM standard. The proposed solution, called AutoSync, achieves semantic alignment, spatial referencing, and real-time data synchronization between the BIM model and IoT data sources. AutoSync includes BIM model preprocessing, automatic labeling of elements and floors, and structured linking of geometric and semantic data with the digital twin platform. The proposed method supports scalable integration across different buildings and systems, resolving interoperability issues present in existing solutions.

[0019] The technical architecture and working principle of the AutoSync solution of this invention will be introduced first, followed by the hardware configuration that can support the AutoSync solution.

[0020] Figure 1 A flowchart illustrating the automatic update and synchronization process of a BIM model according to an embodiment of the present invention is provided. Figure 1 The process includes steps S110, S120, S130, S140, and S142. First, in step S110, a BIM model is created or updated. The BIM model can be a three-dimensional representation of a building or infrastructure project, containing detailed information about its components and material specifications. Each component and element in the BIM model is assigned a unique identifier (ID).

[0021] In step S120, the BIM model can be converted to Industry Foundation Classes (IFC) format. IFC is an open standard file format for sharing building and construction project information, and this conversion method ensures interoperability between different software tools and platforms. In step S130, the converted IFC model can be preprocessed. The preprocessing steps are as follows: Figure 2 As shown, where Figure 2 A flowchart illustrating the preprocessing process of a BIM model according to an embodiment of the present invention is provided. Preprocessing includes the conversion of WebGL-compatible graphics, the conversion of semantic information, and the automatic labeling of IoT devices and floors. In step S140, the preprocessed BIM model data can be stored in the object storage area of ​​the digital twin platform. In step S142, a unique ID is used as an attribute classification and stored in the IoT database of the digital twin platform. The structure of the digital twin platform can be as follows... Figure 6 As shown, it illustrates the multi-organization, multi-building, and multi-protocol data integration process of a digital twin platform according to an embodiment of the present invention. The digital twin platform can serve as a central repository for facility-related data and support downstream applications such as monitoring, analysis, and maintenance planning.

[0022] Figure 2 A flowchart illustrating the preprocessing procedure for a BIM model according to an embodiment of the present invention includes steps S210, S220, S222, S224, S226, and S230. The proposed flowchart can correspond to... Figure 1 Step S130 involves preprocessing the converted BIM model before integration with the digital twin platform. In step S210, various BIM models from different sources (referred to as syndicated models) are merged into a unified representation. This merging step ensures consistency and compatibility for subsequent processing. In step S220, since BIM models typically contain complex 3D geometry, the geometry is converted to a WebGL-compatible format. This web-based graphics format allows for efficient visualization and interoperability in a browser environment. In step S222, semantic information about building elements (including material type, attributes, and functions) is converted into a structured format suitable for integration with the digital twin platform. In step S224, to support real-time data collection and integration, objects in the BIM model that can be associated with IoT devices are automatically identified and tagged. In step S226, building floors in the BIM model are automatically located in a manner similar to IoT tags. This step drives spatial references and provides contextual meaning to associated data in the digital twin platform. In step S230, the processed BIM data (including geometric and semantic information) is linked to the digital twin platform. Figure 5A schematic diagram illustrating how BIM model information is associated with a digital twin platform according to an embodiment of the present invention is provided to further illustrate this association method. This integration method allows the platform to use BIM data for various purposes, such as visualization, analysis, and facility operation.

[0023] Figure 3 A flowchart illustrating an automatic tagging process for the Internet of Things (IoT) according to an embodiment of the present invention is provided. This process corresponds to... Figure 2 Step S224 involves automatically identifying elements with IoT tags in the BIM model and includes steps S310, S320, S330, and S340. In step S310, the mesh representation of the IFC element is extracted. This step involves converting the geometric information of the building element into a mesh composed of vertices and faces. In step S320, after mesh extraction, the bounding box is calculated. The bounding box is a rectangular body that tightly surrounds the entire mesh and defines its spatial boundaries. In step S330, the centroid of the bounding box is calculated. The centroid represents the geometric center of the box and corresponds to the average position of all spatial points within the bounding box. In step S340, the three-dimensional coordinates of the centroid are extracted. The three-dimensional coordinates define the x, y, and z positions of the centroid in a three-dimensional coordinate system and are used to represent the spatial position of the associated IoT device within the digital twin platform.

[0024] Figure 4 A flowchart illustrating an automatic floor marking process according to an embodiment of the present invention is provided. This process corresponds to... Figure 2 Step S226 involves automatically identifying building floors in the BIM model and includes steps S410, S420, S430, and S440. In step S410, the process extracts mesh data for all IFC floor elements (e.g., elements on the same floor of a building) located on the same floor. The mesh data represents the three-dimensional geometry of each floor element. In step S420, for each extracted mesh, a bounding box is calculated. The bounding box is a rectangle that tightly encloses the entire geometry of the mesh. In step S430, the process determines the point with the lowest z-coordinate within each bounding box. Here, the z-axis refers to the vertical height in a standard Cartesian coordinate system. The identified point can be considered the lowest point of the corresponding floor element. In step S440, the three-dimensional coordinates of the lowest point are extracted. These coordinates, represented by x, y, and z values, indicate the lowest position of the IFC floor element and are used to establish a floor-based spatial reference within the digital twin platform.

[0025] Figure 5 A schematic diagram illustrating the link between BIM model information and the digital twin platform is shown. Figure 5 The illustrations in the diagram further illustrate Figure 1Steps S140 and S142 in the diagram. The attributes shown include the IFC GUID, geometric model URL, semantic information URL, IFC category name, and floor name, which can represent structured metadata and access metrics. This content can be stored in the object store (i.e., step S140) or as database attributes in the digital twin platform (i.e., step S142). These data elements support model versioning, semantic referencing, and multi-level spatial organization for downstream digital twin applications.

[0026] The IFC GUID (Industry Base Class Globally Unique Identifier) ​​is a unique identifier assigned to each BIM (Building Information Modeling) object. This attribute is extremely important for version control, ensuring that the correct version of the model is used in the digital twin. The geometry model URL refers to the location that leads to the building or target geometry model (3D representation). The digital twin platform can use these URLs to retrieve the precise geometric information needed for visualization and analysis. Similar to the geometry model URL, the semantic information URL provides access to semantic information related to the building or object. Semantic information includes data such as materials, dimensions, fixture types, and other relevant attributes. This information is crucial for understanding the characteristics and behavior of the object. IFC categories define the types of building elements (e.g., IFCWALL, IFCSLAB, IFCBUILDINGELEMENTPROXY, etc.). This attribute informs the digital twin platform of the specific categories included in the building model, allowing for effective data filtering and analysis. Floor names are used to indicate different levels or floors within the building.

[0027] The aforementioned information will help the digital twin platform accurately organize and visualize building structures. For example, these attributes are generated and stored by the data linking module of the system (e.g., system 700 described later). During step S140, the geometric and semantic output files generated from the BIM model preprocessing can be uploaded to the object storage area, generating their corresponding access URLs. These URLs are then written to the metadata table within the digital twin platform. In step S142, the IFC GUIDs of building elements and their associated category and floor information are stored as attributes in the platform's IoT database. This process is performed by a schema mapping operation that binds each unique identifier to its semantic and spatial context.

[0028] Figure 6 This diagram illustrates the data organization and system architecture of a digital twin platform, showcasing the platform's ability to receive and utilize data. Figures 1 to 5 The processing results are as described. Figure 6The layered structure shown reflects how preprocessed BIM data, semantic tags, geometric models, and IoT connections are organized and accessed in various subsystems within the platform.

[0029] exist Figure 6 The diagram illustrates a multi-layered structure that allows for the integration of data from various sources, including different organizations, buildings, and systems within the digital twin platform. This data can then be used to create a comprehensive digital twin, providing observable sources and optimizing building operations and services. An organization represents the overall entity or organization that owns and operates the buildings and systems within the digital twin. Users include information about the people interacting with the buildings and systems, such as their roles, permissions, and preferences. Buildings represent building information such as name, address, and building type. Floors represent the different floors within a building, each with its own independent set of data and systems. BMS refers to the devices within a building management system, a crucial component of facility management and maintenance. Sensors include various types of sensors used to collect environmental data, such as temperature, humidity, occupancy, noise, and air quality. Robots represent autonomous robots or drones operating within the building, collecting data, or performing tasks.

[0030] Figure 6 The multi-layered architecture shown is designed to utilize and operationalize the data generated by previous steps, including BIM model preprocessing (i.e. Figure 2 ), IoT tags (i.e.) Figure 3 ), floor markers (i.e. Figure 4 ) and attribute links (i.e. Figure 5 ). Figure 6 This represents a platform-level achievement that enables data-driven operations, monitoring, and optimization processes across multiple organizations and buildings.

[0031] The above technical solutions or technical services can be implemented through the following hardware configurations.

[0032] Figure 7 A block diagram illustrating a system 700 according to an embodiment of the present invention and its interaction with a digital twin platform 750 is shown. The system 700 is configured to automatically synchronize BIM data and IoT device data to the digital twin platform 750, thereby enabling real-time spatial monitoring, semantic referencing, and lifecycle management of building assets in the digital twin environment. The system 700 includes a BIM interface module 710, an IFC conversion module 720, a preprocessing module 730, and a data link module 740. In some embodiments, the system 700 may be implemented from one or more computing environments selected from a group of cloud servers, edge computing units, or virtualization containers, and each computing environment may further include one or more processors for executing software instructions associated with the modules owned by the system 700.

[0033] BIM interface module 710 is used to: (i) receive external geometric and semantic data streams and generate a building information model based on the received data; or (ii) receive an existing BIM model written externally and convert it into a format compatible with the internal processing architecture of system 700. In this respect, the operating mechanism of BIM interface module 710 may correspond to... Figure 1 Step S110 involves the creation or updating of a BIM model. The generated or converted BIM model includes a three-dimensional representation of building or infrastructure components and their associated semantic specifications, including but not limited to: material properties, element types, and structural roles. The BIM interface module 710 is also used to assign or store a unique ID for each building element to support traceability and cross-module data integration. For example, the unique ID assigned to a building element can be represented as a structured alphanumeric string, such as a string conforming to the IFC schema. Each ID can be permanently associated with the corresponding geometric node and semantic object in the system database and is machine-readable and unambiguous within the model scope. These identifiers can support cross-referencing between geometric representations, metadata, and real-time IoT data binding. In one embodiment, the BIM interface module 710 is also used to store pre-existing element identifiers or generate temporary alphanumeric tags for building elements before subsequent IFC conversions to maintain the traceability of geometric and semantic data between processing stages.

[0034] It is worth noting that the operations performed by the BIM interface module 710 involve structured data processing steps that cannot be performed manually, such as schema-based parsing, machine-generated unique identifiers, and memory-based object linking. These steps require the use of executable logical components and interaction with computing resources.

[0035] The IFC conversion module 720 is used to convert the received building information model into a format compliant with the IFC model, thereby enabling interoperability between different BIM authoring tools and the digital twin platform (e.g., digital twin platform 750). The operating mechanism of the IFC conversion module 720 corresponds to... Figure 1Step S120 involves converting the BIM model into IFC format to support downstream processing. The IFC conversion module 720 parses the model content, extracting the geometric representation and semantic metadata of each building element, and re-encodes it into structured IFC entities according to OpenBIM-based standards. In some embodiments, the IFC conversion module 720 can perform type mapping, attribute normalization, and model reconstruction to ensure the input data conforms to the requirements of the IFC schema. The IFC conversion module 720 can also assign or validate a GUID for each building element in the converted model. These GUIDs can serve as persistent references for cross-platform traceability, version control, and links to external datasets (such as IoT device mappings or facility databases). The resulting IFC model can be a machine-readable and schema-compliant representation, preserving the spatial structure and semantic richness of the original BIM data.

[0036] The conversion process of the IFC conversion module 720 involves executable parsing logic, structured pattern mapping, and data serialization operations, all of which rely on computing resources and are therefore executed by machine-executable components (such as the IFC conversion module 720).

[0037] Preprocessing module 730 is used to perform, for example Figure 2 The BIM model preprocessing shown includes the model fusion procedure in step S210 and the data integration procedure with the digital twin platform in step S230. The preprocessing module 730 also includes several sub-components that can be used to perform intermediate steps, including geometric transformation in step S220, semantic structuring in step S222, IoT candidate tagging in step S224, and floor tagging in step S226.

[0038] In some implementations, the preprocessing module 730 is used to perform, for example... Figure 2 Step S210 involves merging various BIM models (often referred to as syndicated models) from different sources into a unified IFC representation. This mechanism includes parsing and interpreting geometric and semantic data from each source model, resolving inconsistencies in format, and merging overlapping or nested structures into a consistent, machine-readable schema.

[0039] The preprocessing module 730 includes a geometric transformation subcomponent 732, a semantic structuring subcomponent 734, an IoT tagging subcomponent 736, and a floor tagging subcomponent 738. The generated joint model serves as the normalized input source for subsequent preprocessing operations, with these subcomponents responsible for geometric transformation, semantic structuring, IoT candidate tagging, and floor tagging, respectively. Through this stage, data consistency and structural alignment rules are established, enabling the preprocessing module 730 to allow downstream modules to operate based on a unified and context-valid model representation.

[0040] For example, the geometry transformation subcomponent 732 is used to convert complex BIM geometry into a WebGL-compatible format for efficient web-based visualization (as described in step S220). The semantic structuring subcomponent 734 is used to extract and construct semantic information (e.g., materials, functions, and attributes) for downstream integration (as described in step S222). The IoT tagging subcomponent 736 is used to identify BIM objects that can be associated with IoT devices (as described in step S224). The floor tagging subcomponent 738 is used to detect and tag building floors in the BIM model (as described in step S226).

[0041] The geometry transformation sub-component 732 is used to perform, for example Figure 2 Step S220 involves converting the geometric representation of BIM elements into a WebGL-compatible format. The geometry transformation subcomponent 732 receives the IFC-based geometry of each building element (e.g., the transformed IFC model from the IFC transformation module 720) as input, parses its structural components (e.g., faces, edges, and vertices), and also converts it into a mesh format suitable for real-time Web rendering. The output is a set of optimized, WebGL-compliant geometry files that support lightweight visualization on the digital twin platform 750.

[0042] Semantic structured subcomponent 734 is used to perform, for example Figure 2 Step S222 involves extracting and organizing semantic information embedded in BIM elements. The semantic structure subcomponent 734 receives IFC elements containing non-geometric attributes as input (e.g., a converted IFC model from the IFC conversion module 720), where non-geometric attributes include, for example, material specifications, functional classifications, and attribute sets. The semantic structure subcomponent 734 parses these attributes, normalizes them into a structured metadata format, and generates output information compatible with the semantic data model of the digital twin platform. The generated semantic records enable downstream modules to perform data filtering, querying, and interpretation based on building-specific metadata.

[0043] The IoT tag sub-component 736 is used to perform Figure 2 The operation procedure of step S224 and Figure 3 The detailed process shown includes steps S310, S320, S330, and S340. The IoT tagging sub-component 736 is used to generate spatial tags for building elements in the BIM model that meet the conditions for IoT integration.

[0044] The IoT tagging sub-component 736 performs the following steps: It receives IFC elements (e.g., converted IFC models from IFC conversion module 720) that have been identified as IoT candidate elements as input sources. Figure 3Step S310: The IoT tag subcomponent 736 accesses the geometric definition of the element and converts it into a mesh representation consisting of vertices and faces. The output of this step is a discrete mesh model suitable for geometric analysis.

[0045] The IoT tag sub-component 736 can perform operations by calculating the mesh bounding box of the discrete mesh model generated in the previous step. Figure 3 Step S320. This step takes the mesh as input and calculates the minimum volume bounding box that tightly encloses the mesh geometry. The bounding box can be represented by its angular coordinates and size range.

[0046] The IoT tag sub-component 736 is used to perform, for example Figure 3 In step S330, the bounding box can be used as an input source to calculate the centroid. The IoT tag sub-component 736 can calculate the centroid as the geometric center of the bounding box and represent the average position of all closed points. The output information after performing this step is a three-dimensional point, represented by x, y, and z coordinates in the BIM space reference system.

[0047] The IoT tag sub-component 736 is also used to perform Figure 3 In step S340, the centroid coordinates are extracted and packaged into a structured spatial tag. The final output of the IoT tag subcomponent 736 is a data object that maps the identifier of the IFC element to its corresponding centroid coordinates. This spatial tag can be stored, indexed, and associated with IoT device data in subsequent stages of the application.

[0048] The IoT tagging sub-component 736 can perform these steps using a geometry resolution engine, a mesh processor, spatial computing routines, and data structuring logic. These processes can manipulate structured BIM data formats and generate machine-readable output information for integration into the digital twin platform 750.

[0049] Floor marker subcomponent 738 is used to perform Figure 2 The operation of step S226 and Figure 4 The process shown includes steps S410, S420, S430, and S440. The floor marker sub-component 738 can be used to determine the vertical position of building elements by identifying the lowest point of each IFC floor element, thereby enabling floor marking and spatial reference within the BIM model.

[0050] The floor marker sub-component 738 can perform [operation] by receiving a set of IFC elements classified as floor-related (e.g., a converted IFC model from the IFC conversion module 720) as input. Figure 4In step S410, these IFC elements can be classified as being related to floors, such as floor slabs or structural platforms. The floor tagging subcomponent 738 can extract the mesh geometry of each element, which consists of polygonal faces representing the floor surface. The output information after performing this step is the mesh dataset for each floor element.

[0051] The floor marker sub-component 738 can calculate the bounding box by processing the mesh geometry of each floor element, thereby performing actions such as... Figure 4 Step S420. The bounding box is a rectangular body used to tightly enclose the grid, and its extent is defined by the minimum and maximum coordinates along each spatial axis. The output information after performing this step is a bounding box object, which represents the spatial boundary extent of each floor element.

[0052] The floor marker sub-component 738 can perform actions such as analyzing the bounding box and identifying the point with the lowest z-coordinate. Figure 4 Step S430 in the calculation uses a Cartesian coordinate system, where the z-axis represents vertical height and the lowest point represents the reference elevation of the floor element. The output is a single three-dimensional coordinate (x, y, z) that represents the lowest vertical position of the element.

[0053] The floor marker subcomponent 738 can perform the following by assigning the extracted lowest point as the floor marker of the corresponding IFC floor element: Figure 4 Step S440. The final output information of the floor marker subcomponent 738 is a data record that maps the identifier of each floor element to its calculated vertical position. This data record can be used to support floor-aware organization and visualization in the digital twin platform 750.

[0054] The floor marker sub-component 738 can perform these operations using a mesh analyzer, bounding box calculation routines, coordinate extraction logic, and structured marker algorithms. The output information can be read by a machine and can serve as a spatial reference for classifying building elements within a floor level.

[0055] To support the integration of BIM data into the digital twin platform, the preprocessing module 730 is designed to selectively identify key information categories that are critical to real-time visualization, semantic interpretation, and contextual analysis. It can select geometric transformation, semantic structuring, IoT candidate tags, and floor tagging as the primary preprocessing operations, reflecting the technical requirements of downstream modules that rely on accurate geometric representation, domain-specific metadata, spatial device mapping, and floor hierarchical organization. These four data domains were chosen because they collectively represent the minimum feasible set of information required to support core digital twin functionalities, such as web-based 3D rendering, sensor data overlay, and cross-building floor sensing operations. By focusing on these dimensions, the preprocessing module 730 can efficiently and scalably transform static BIM models into dynamically operable digital twin environments.

[0056] The preprocessing module 730 uses machine-executable logic, such as geometric analysis, attribute mapping, or spatial inference, to operate each sub-component and prepare the processed data, enabling it to link with the digital twin platform 750 according to the process described in step S230. The preprocessing module 730 aggregates the outputs generated by the geometric transformation sub-component 732, the semantic structuring sub-component 734, the IoT tagging sub-component 736, and the floor tagging sub-component 738. This output information includes a WebGL-compliant geometric representation, structured semantic metadata, spatial coordinates of IoT mapping elements, and floor annotations. The preprocessing module 730 can organize this information into a set of data objects or metadata records compatible with the storage and retrieval structure of the digital twin platform 750, enabling the platform to access, render, and semantically interpret the building model and its associated device mappings.

[0057] Due to the structural complexity and heterogeneity of the input BIM model, the fusion process requires executable logic for model parsing, conflict resolution, schema mapping, and data serialization. These procedures involve large-volume and highly complex data transformations, and therefore can be executed by the preprocessing module 730 using system-level computing resources.

[0058] Data link module 740 is used to perform, for example Figure 1 Steps S140 and S142 involve storing the preprocessed BIM data and linking it to the digital twin platform 750. The data linking module 740 receives the output information generated by the preprocessing module 730 as an input source, including WebGL-compliant geometry files, structured semantic metadata, spatially labeled IoT elements, and floor annotations.

[0059] In step S140, the primary objective is to store the preprocessed geometric and semantic data in the object storage area of ​​the digital twin platform 750, so that other components within the digital twin platform 750 can access, visualize, and analyze it. To this end, the data linking module 740 serializes the geometric and semantic output into structured data objects. These structured data objects may include 3D mesh files in glTF, JSON, or other WebGL-compatible geometric formats, as well as semantic metadata formatted in JSON, XML, or any other machine-readable structure / document. The data linking module 740 organizes these data objects by associating them with their respective building elements and uploads them to the object storage area of ​​the digital twin platform 750 using predefined storage interfaces or API endpoints. After uploading, the data linking module 740 generates an access URL for each stored object; these generated access URLs can be registered within the digital twin platform 750 for use across the entire platform.

[0060] In step S142, the goal is to establish searchable and traceable associations between building element identifiers and their corresponding metadata in the database of the digital twin platform 750. To this end, the data linking module 740 constructs a set of attribute mappings that associates the unique identifier of each building element (e.g., an IFC GUID) with its corresponding metadata. The metadata may include geometry file URLs, semantic tags, IFC category names, and floor identifiers. The data linking module 740 can format this association information to match the schema used by the internal database of the digital twin platform 750 and write the generated records to appropriate tables or sets for external querying and retrieval.

[0061] The data linking module 740 can be operated using schema mapping routines, metadata indexers, data object packaging logic, and API-based storage connectors. These components enable the structured transformation of BIM-derived data from the preprocessing stage into the system architecture of the digital twin platform 750, thereby supporting consistent retrieval, rendering, and semantic referencing across multiple platform layers.

[0062] In some embodiments, system 700 may further include a digital twin synchronization module 780 for maintaining real-time operational consistency between the BIM data generated by the system and the dynamic state of the digital twin platform 750. The digital twin synchronization module 780 is operatively coupled between the data link module 740 and the digital twin platform 750. While the data link module 740 performs the initial data upload and attribute mapping during steps S140 and S142, the digital twin synchronization module 780 can monitor for updates, changes, or new inputs within the digital twin platform 750 that may affect the existing model representation. The digital twin synchronization module 780 can retrieve updated content, such as revised geometric models, modified semantic data, or newly labeled IoT elements, and perform incremental updates to maintain consistency between the data state of the digital twin platform 750 and system 700. When significant structural or semantic changes are detected, the digital twin synchronization module 780 can further trigger downstream processes, such as reindexing or remapping. The Digital Twin Synchronization Module 780 can operate using platform-oriented APIs, difference comparison routines, and real-time event monitors to support continuous consistency across the entire platform architecture.

[0063] After receiving and registering data transmitted from system 700, digital twin platform 750 can store and organize various structured content, including WebGL-compliant geometric models, semantic metadata, IoT device associations, and spatial floor markers. These datasets are labeled, indexed, and cross-linked in the internal schema of digital twin platform 750, enabling dynamic querying, visualization, and integration with real-time operational data.

[0064] For example, in one use case, real-time facility monitoring could be involved, where spatially tagged IoT data, such as temperature, occupancy, or air quality, can be presented in three dimensions via geometric files uploaded by data link module 740. Building managers can visually inspect sensor conditions floor by floor and identify rooms with abnormal readings using floor information and IFC categories stored in the digital twin platform database. In other use cases, digital twin platform 750 can support maintenance planning and asset tracking. Because each BIM element is linked to a persistent identifier (e.g., IFC GUID) and semantic description, the system can be used to locate specific installations (e.g., HVAC units or fire dampers), retrieve their service history, and flag them for inspection based on sensor data trends. These platform-level capabilities can directly rely on and utilize the structured data pipeline established through the modules of system 700.

[0065] Compared to traditional methods that process IoT data and BIM models in standalone systems or require extensive manual configuration, the solution described in this paper offers significant technical improvements in automation, scalability, and semantic integration. By automatically associating IoT devices with spatially and semantically structured BIM elements, System 700 eliminates the manual labeling, device binding, and geometric interpretation tasks that are error-prone and labor-intensive in traditional workflows. In this invention, the use of unique identifiers (e.g., IFC GUIDs), floor marking, and structured geometric output enables precise location and cross-referencing within the digital twin platform 750, thereby improving the accuracy and responsiveness of building operations. Consequently, operations such as fault detection, maintenance routing, and environmental monitoring can be performed more efficiently, reducing latency and improving reliability in multi-building or multi-story deployments.

[0066] The functional units and modules of the systems and methods according to the embodiments disclosed herein can be implemented using computing devices, computer processors, or electronic circuits, including but not limited to application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), microcontrollers, and other programmable logic devices configured or programmed in accordance with the teachings of this disclosure. Those skilled in the art of software or electronics can readily write computer instructions or software code that execute in computing devices, computer processors, or programmable logic devices based on the teachings of this disclosure.

[0067] All or part of the methods according to the embodiments can be performed in one or more computing devices, including server computers, personal computers, laptops, and mobile computing devices (such as smartphones and tablets).

[0068] The given embodiments may include computer storage media, transient and non-transient memory devices storing computer instructions or software code, which can be used to program or configure computing devices, computer processors, or electronic circuits to perform any of the processes of the present invention. Storage media, transient and non-transient memory devices may include, but are not limited to, floppy disks, optical disks, Blu-ray discs, DVDs, CD-ROMs, magneto-optical disks, ROMs, RAMs, flash memory devices, or any type of medium or device suitable for storing instructions, code, and / or data.

[0069] Each functional unit and module according to the various embodiments can also be implemented in a distributed computing environment and / or cloud computing environment, wherein all or part of the machine instructions are executed in a distributed manner by one or more processing devices interconnected by a communication network, such as an intranet, a wide area network (WAN), a local area network (LAN), the Internet, and other forms of data transmission media.

[0070] The above description of the present invention is provided for illustrative purposes. It is not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many modifications and variations will be apparent to those skilled in the art.

[0071] These embodiments were chosen and described in order to best explain the principles of the invention and its practical application, thereby enabling those skilled in the art to understand the various embodiments of the invention and the various modifications suitable for the intended particular use.

Claims

1. A system for integrating Building Information Modeling (BIM) data and Internet of Things (IoT) data into a digital twin platform, thereby enabling real-time spatial monitoring, semantic referencing, and lifecycle management of building assets within a digital twin environment, characterized in that, The system includes: The BIM interface module is used to receive geometric and semantic data from external sources and generate or update BIM models, which contain three-dimensional representations of building elements and related semantic specifications. The Industrial Foundation Class (IFC) conversion module is used to convert the BIM model into a format that conforms to the IFC model to obtain the converted IFC model, and to assign a globally unique identifier (GUID) to the building elements. A preprocessing module is used to receive the converted IFC model from the IFC conversion module, and perform model federation and data normalization. The preprocessing module includes: The geometry transformation subcomponent is used to transform the BIM model based on the geometric representation contained in the transformed IFC model. Geometry is converted to a WebGL-compatible format; The semantic structuring sub-component is used to extract and construct non-geometric metadata based on the set of semantic attributes contained in the transformed IFC model. An IoT tagging sub-component is used to calculate centroid-based spatial tags for building elements that meet IoT conditions, based on the mesh geometry derived from the joint IFC model; and A floor marker subcomponent is used to calculate the vertical position information of IFC floor elements based on bounding box analysis of the floor-related geometry in the joint IFC model; and The data linking module is used to serialize the geometric and semantic output information from the preprocessing module, upload the geometric and semantic output information to the object storage area of ​​the digital twin platform, and register the access URL and attribute mapping to the database schema of the digital twin platform.

2. The system according to claim 1, wherein the BIM interface module is further configured to retain pre-existing element identifiers or generate temporary alphanumeric tags for the building elements before IFC conversion, so as to maintain traceability between geometric and semantic data between processing stages.

3. In the system according to claim 2, the IFC conversion module is further configured to perform pattern-compliant type mapping, attribute normalization, and data serialization operations to ensure that the BIM model is consistent with the standard based on Open Building Information Modeling (OpenBIM).

4. The system of claim 1, wherein the geometry transformation subcomponent is further configured to parse mesh elements composed of vertices, edges and faces, and output a WebGL-compatible file format selected from geometry in glTF, JSON or other WebGL-compatible formats.

5. In the system according to claim 1, the semantic structuring subcomponent is further used to parse functional metadata, including material type, element role and related attributes, and format the data into structured JSON, XML or other types of machine-readable documents.

6. The system according to claim 1, wherein the IoT tag sub-component is further configured to: Generate a grid representation from BIM elements identified as conforming to IoT requirements; Calculate the bounding box that tightly encloses the mesh representation; Calculate the centroid coordinates of the bounding box; and The centroid coordinates are packaged into a spatial tag associated with the identifier of the BIM element.

7. The system according to claim 1, wherein the floor marker sub-component is further configured to: Extract the grid representation from the BIM elements that are classified as floor elements; Calculate the bounding box that tightly encloses the extracted mesh representation; Identify the lowest z-coordinate point of the calculated bounding box; as well as Assign a floor label to the floor element based on the identified lowest z-coordinate point.

8. The system according to claim 1, wherein the data link module is further configured to: Generate and register the access URLs for the geometry and semantic files uploaded to the object storage area of ​​the digital twin platform; and Format and store the mapping between building element identifiers and related metadata, including geometry file URLs, IFC categories, semantic tags, and floor tags.

9. The system according to claim 1, further comprising: The digital twin synchronization module is used to retrieve updated content from the digital twin platform and update the stored BIM representation accordingly. The digital twin synchronization module is also used to monitor changes in geometric, semantic attributes or Internet of Things (IoT) associations in the digital twin platform.

10. The system of claim 1, wherein the system is implemented in a computing environment of one or more units selected from a group of cloud servers, edge computing units, or virtualization containers.

11. A method for integrating Building Information Modeling (BIM) data and Internet of Things (IoT) data into a digital twin platform to enable real-time spatial monitoring, semantic referencing, and lifecycle management of building assets within a digital twin environment, characterized in that, The method includes: Receive external geometric and semantic data through the BIM interface module; The BIM interface module is used to generate or update BIM models, which include three-dimensional representations of building elements and related semantic specifications. The BIM model is converted into a format that conforms to the IFC model using the Industrial Foundation Class (IFC) conversion module to obtain the converted IFC model. The industrial base class conversion module assigns a globally unique identifier (GUID) to the building element. The preprocessing module receives the converted IFC model from the industrial basic class conversion module. The preprocessing module is used to perform model federation and data normalization, including: Using the geometry transformation subcomponent of the preprocessing module, the BIM geometry is converted into a WebGL-compatible format based on the geometric representation contained in the converted IFC model; Using the semantic structuring sub-component of the preprocessing module, non-geometric metadata is extracted and constructed based on the set of semantic attributes contained in the transformed IFC model. Using the IoT tagging sub-component of the preprocessing module, centroid-based spatial tags are calculated for building elements that meet IoT conditions, based on the mesh geometry derived from the joint IFC model; and Using the floor marker sub-component of the preprocessing module, the vertical position information of the IFC floor elements is calculated based on the bounding box analysis of the geometry related to the floor in the joint IFC model. The geometric and semantic output information of the preprocessing module is serialized through the data link module; The geometric and semantic output information is uploaded to the object storage area of ​​the digital twin platform via the data link module; and The data linking module registers the access URL and attribute mappings into the database schema of the digital twin platform.

12. The method of claim 11, further comprising: Prior to IFC conversion, the BIM interface module is used to retain pre-existing element identifiers or generate temporary alphanumeric tags for building elements to maintain traceability between geometric and semantic data between processing phases.

13. The method of claim 12, further comprising: The IFC conversion module performs pattern-compliant type mapping, attribute normalization, and data serialization operations to ensure that the BIM model is consistent with the Open Building Information Modeling (OpenBIM) standard.

14. The method of claim 11, further comprising: The geometry transformation subcomponent is used to parse mesh elements composed of vertices, edges, and faces. as well as The geometry transformation subcomponent outputs a WebGL-compatible file format selected from geometry in glTF, JSON, or other WebGL-compatible formats.

15. The method of claim 11, further comprising: The semantically structured sub-component is used to parse functional metadata, including material type, element role, and related attributes. as well as The semantic structuring subcomponent formats the data into structured JSON, XML, or other machine-readable documents.

16. The method of claim 11, further comprising: The IoT tagging sub-component generates a grid representation from BIM elements identified as conforming to IoT requirements. The IoT tag sub-component is used to calculate the bounding box that tightly surrounds the grid representation. The centroid coordinates of the bounding box are calculated using the IoT tag sub-component. as well as The centroid coordinates are packaged into a spatial tag associated with the identifier of the BIM element using the IoT tag sub-component.

17. The method of claim 11, further comprising: The grid representation is extracted from BIM elements that are classified as floor elements using the floor marker sub-component. The floor marker sub-component is used to calculate a bounding box that tightly encloses the extracted grid representation. The lowest z-coordinate point of the calculated bounding box is identified using the floor marker sub-component. as well as The floor marker sub-component assigns a floor marker to the floor element based on the identified lowest z-coordinate point.

18. The method of claim 11, further comprising: The data link module generates and registers access URLs for the geometric and semantic files uploaded to the object storage area of ​​the digital twin platform. as well as The data linking module formats and stores the mapping between building element identifiers and related metadata. It includes geometry file URLs, IFC categories, semantic tags, and floor tags.

19. The method of claim 11, further comprising: The updated content is retrieved from the digital twin platform via the digital twin synchronization module; The stored BIM representation is updated accordingly through the digital twin synchronization module. as well as The digital twin synchronization module monitors changes in geometric and semantic attributes or IoT associations within the digital twin platform.

20. The method of claim 11, wherein the method is performed on a computing environment of one or more units selected from a group of cloud servers, edge computing units, or virtualization containers.

Images