Digital twinborn visual large screen building method

Abstract

The invention discloses a digital twin visual large screen building method, which realizes effective organization and management of data by extracting, classifying and storing family members in a BIM model, establishes association between a background marking configuration library and a family library, enhances the traceability and consistency of the data, realizes accurate association by combining the BIM model and IOT data and utilizing a GUID code and an equipment ID code, and improves the efficiency of building a twin visual large screen. The BIM model is converted into a digital twinborn model with high fidelity by using a UE engine, the user definition capability and the operation convenience of a user are enhanced through an interactive interface, real-time updating and dynamic display are realized through data interaction between an Internet of Things middle platform and a WEB visual front end, the system practicability and the user experience are improved, and the user experience is improved. The BIM model is subjected to lightweight conversion in the Internet of Things, the data processing efficiency and the response speed are improved, the Internet of Things and the BIM lightweight mobile terminal achieve automatic data collection and storage, the background IOT database integrates complete data, and support is provided for intelligent analysis and decision making.

Description

Technical Field

[0001] The present invention relates to the technical field of digital twin visualization, and in particular to a method for building a digital twin visualization large screen. Background Art

[0002] With the rapid development of information technology and the deepening of digital transformation, digital twin technology, as an important technical means, has been widely used in various fields. By establishing a virtual model of the entity object, digital twin technology realizes real-time monitoring, simulation and optimization of the operating status of the entity object, greatly improving management efficiency and decision-making accuracy. In the construction industry, BIM (Building Information Modeling), as an important foundation of digital twin technology, provides rich building information data and provides strong support for the construction of digital twin models.

[0003] However, there are some problems in the application of traditional BIM models. First, BIM models often contain a large amount of data and information, which tend to become complicated and cumbersome when processing and displaying, and are not conducive to quickly and accurately obtaining the required information. Secondly, the integration of BIM models with Internet of Things technology is not close enough, and there are obstacles to the association between data collection of smart devices and BIM models, resulting in serious data islands and the inability to give full play to the value of data. Finally, the visualization display method of BIM models is relatively single, lacking intuitive and interactive large-screen visualization display methods, which makes it difficult to meet the needs of modern management for information visualization. Summary of the invention

[0004] In view of this, the present invention proposes a method for building a digital twin visualization large screen, which can solve the defects of the prior art that it is not conducive to quickly and accurately obtaining the required information, the data island phenomenon is serious, and the visualization display means is relatively single.

[0005] The technical solution of the present invention is achieved in this way:

[0006] A method for building a digital twin visualization large screen, specifically comprising:

[0007] Get the BIM model;

[0008] Extract family components from the BIM model, and classify and store them according to classification standards;

[0009] Establish the association between the background tag configuration library and the background family library;

[0010] Acquire and organize the BIM model data through the Revit interactive terminal, and write them into the BIM model and the backend database respectively;

[0011] Import the BIM model into the IoT platform for lightweight conversion, and transfer the model data to the BIM lightweight model along with the GUID code;

[0012] Use the Internet of Things and BIM lightweight mobile terminal to collect IOT data of the family components of the BIM lightweight model corresponding to the smart device on site, and write the data into the background IOT database;

[0013] The backend IOT database uses GUID code and device ID code to associate model data with IOT data and integrate complete data;

[0014] The BIM model and the IoT platform obtain complete data through the backend IoT database;

[0015] Convert the BIM model into a digital twin model supported by the UE engine, and pass the complete data to the digital twin model along with the GUID code and equipment ID code;

[0016] Configure scenarios for the digital twin model through the digital twin model interactive interface;

[0017] The IoT middle platform interacts with the WEB visualization front end through GUID code and device ID code;

[0018] The digital twin model and the WEB visualization front-end are associated through the GUID code and the device ID code to realize the construction of the digital twin visualization large screen.

[0019] As a further optional solution of the method for building a digital twin visualization large screen, the method also includes automatic updating of the digital twin model and data, specifically:

[0020] During the on-site IOT data collection process, if it is found that the on-site equipment does not match the model, call the tag configuration corresponding to the family component and place the tag at the corresponding position;

[0021] The IoT and BIM lightweight mobile terminals obtain the location data of the marked configuration on the lightweight model;

[0022] Add or delete information according to the on-site conditions of the equipment, collect IOT data, and write the tag data and IOT data into the background IOT database;

[0023] The BIM model side reads the location data and tag information of the tag configuration in the database and the device ID code in the IOT data through the Revit interactive terminal;

[0024] The Revit interactive terminal automatically calls the corresponding family component in the background family library according to the association of the tag configuration, and executes the add or delete command according to the configuration location data and tag information obtained from the database;

[0025] After completing the instructions for adding or deleting family components, the BIM model is used to obtain data and extract and sort the equipment IDs in the tag configuration, and write them into the BIM model and the background model database respectively;

[0026] The backend database once again associates the model data with the IOT data through the GUID code and the device ID code, integrating the complete data;

[0027] Update the digital twin model and data based on complete data.

[0028] As a further optional solution to the method for building a digital twin visualization large screen, the background family library is used for storage and classification management of BIM model component families, including standard family libraries and project family libraries. The standard family library sorts out equipment requirements according to the functional indicators required by the Internet of Things platform, and produces family components to form a stable standard library. The project family library is for a single project, and extracts family components in the project to form a project family library.

[0029] As a further optional scheme for the method of building the digital twin visualization large screen, the background tag configuration library is used for storage and classification management of tag configurations, including a standard tag configuration library and a custom tag configuration. The standard tag configuration library sets tag configurations of different forms according to the component classification requirements in the standard family library of the background family library to form a stable standard tag configuration library. The custom tag configuration is customized using custom tags when a family component classification that does not correspond to the standard tag configuration appears in the family component in the BIM model.

[0030] As a further optional solution to the method for building a digital twin visualization large screen, the background database is used to store relevant data of family components in the Internet of Things platform and the BIM model, including a model database and an IOT database. The model database stores relevant data of family components in the BIM model, and the IOT database stores relevant data of IOT device products.

[0031] As a further optional solution to the method for building a digital twin visualization large screen, convolutional neural network and natural language processing technology are used to perform semantic analysis on the extracted static features of the family components, and the semantic analysis results are input as a training set into the deep learning model for training.

[0032] As a further optional solution to the method for building a digital twin visualization large screen, the background tag configuration library and the family library are intelligently associated based on the organic combination of deep learning, reinforcement learning and distributed architecture.

[0033] As a further optional solution of the digital twin visualization large-screen construction method, the field data is continuously monitored, automatically judged, and automatically updated based on visual marker enhancement technology, multimodal data matching model, automatic repair and marker update technology.

[0034] A computing device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for building a digital twin visualization large screen are implemented.

[0035] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-mentioned method for building a digital twin visualization large screen.

[0036] The beneficial effects of the present invention are as follows: by extracting family components in the BIM model and classifying and storing them, effective organization and management of BIM model data is achieved, the association between the background tag configuration library and the background family library is established, the traceability and consistency of data are enhanced, the BIM model is combined with IOT data, and the precise association between model data and IOT data is achieved through GUID code and device ID code, ensuring the integrity and accuracy of the data, and using the UE engine to convert the BIM model into a digital twin model, thereby improving the fidelity and interactivity of the model, so that the visualization large screen can more intuitively display building information, and the digital twin model is configured for the scene through the digital twin model interaction interface, thereby enhancing the user's customization ability and ease of operation, and the data interaction between the Internet of Things middle platform and the WEB visualization front end, as well as the digital twin model and W The association of EB visualization front-end realizes real-time updating and dynamic display of data, improves the practicality and user experience of the system, imports BIM model into the IoT middle platform for lightweight conversion, reduces the complexity and data volume of the model, improves the efficiency of data processing and the response speed of the system, and transmits lightweight model data to the BIM lightweight model along with the GUID code, ensuring the integrity and consistency of data during transmission, while reducing the difficulty and cost of data processing. The IoT and BIM lightweight mobile terminals are used to collect data from smart devices, and the data is written into the background IOT database, realizing automatic data collection and storage. The background IOT database completes the association between model data and IOT data through GUID code and device ID code, integrates complete data, and provides strong support for intelligent analysis and decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0038] Figure 1A schematic diagram of the steps of a method for building a digital twin visualization large screen provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0039] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] refer to Figure 1 , a method for building a digital twin visualization large screen, specifically comprising:

[0041] Get the BIM model;

[0042] Extract family components from the BIM model, and classify and store them according to classification standards;

[0043] Establish the association between the background tag configuration library and the background family library;

[0044] Acquire and organize the BIM model data through the Revit interactive terminal, and write them into the BIM model and the backend database respectively;

[0045] Import the BIM model into the IoT platform for lightweight conversion, and transfer the model data to the BIM lightweight model along with the GUID code;

[0046] Use the Internet of Things and BIM lightweight mobile terminal to collect IOT data of the family components of the BIM lightweight model corresponding to the smart device on site, and write the data into the background IOT database;

[0047] The backend IOT database uses GUID code and device ID code to associate model data with IOT data and integrate complete data;

[0048] The BIM model and the IoT platform obtain complete data through the backend IoT database;

[0049] Convert the BIM model into a digital twin model supported by the UE engine, and pass the complete data to the digital twin model along with the GUID code and equipment ID code;

[0050] Configure scenarios for the digital twin model through the digital twin model interactive interface;

[0051] The IoT middle platform interacts with the WEB visualization front end through GUID code and device ID code;

[0052] The digital twin model and the WEB visualization front-end are associated through the GUID code and the device ID code to realize the construction of the digital twin visualization large screen.

[0053] In this embodiment, by extracting family components from the BIM model and classifying and storing them, effective organization and management of BIM model data is achieved, an association between the background tag configuration library and the background family library is established, the traceability and consistency of data are enhanced, the BIM model is combined with IOT data, and the precise association between model data and IOT data is achieved through GUID codes and device ID codes, ensuring the integrity and accuracy of the data. The BIM model is converted into a digital twin model using the UE engine, which improves the realism and interactivity of the model, allowing the visualization large screen to display building information more intuitively, and the digital twin model is configured for scene through the digital twin model interaction interface, which enhances the user's customization ability and ease of operation. The data interaction between the IoT middle platform and the WEB visualization front end, as well as the digital twin model and WE B visualization front-end association realizes real-time update and dynamic display of data, improves the practicality and user experience of the system, imports BIM model into the Internet of Things middle platform for lightweight conversion, reduces the complexity and data volume of the model, improves the efficiency of data processing and the response speed of the system, and transmits lightweight model data to the BIM lightweight model along with the GUID code, ensuring the integrity and consistency of data during transmission, while reducing the difficulty and cost of data processing. The Internet of Things and BIM lightweight mobile terminals are used to collect data from smart devices, and the data is written into the background IOT database, realizing automatic data collection and storage. The background IOT database completes the association between model data and IOT data through GUID code and device ID code, integrates complete data, and provides strong support for intelligent analysis and decision-making.

[0054] Preferably, the method further includes automatic updating of the digital twin model and data, specifically:

[0055] During the on-site IOT data collection process, if it is found that the on-site equipment does not match the model, call the tag configuration corresponding to the family component and place the tag at the corresponding position;

[0056] The IoT and BIM lightweight mobile terminals obtain the location data of the marked configuration on the lightweight model;

[0057] Add or delete information according to the on-site conditions of the equipment, collect IOT data, and write the tag data and IOT data into the background IOT database;

[0058] The BIM model side reads the location data and tag information of the tag configuration in the database and the device ID code in the IOT data through the Revit interactive terminal;

[0059] The Revit interactive terminal automatically calls the corresponding family component in the background family library according to the association of the tag configuration, and executes the add or delete command according to the configuration location data and tag information obtained from the database;

[0060] After completing the instructions for adding or deleting family components, the BIM model is used to obtain data and extract and sort the equipment IDs in the tag configuration, and write them into the BIM model and the background model database respectively;

[0061] The backend database once again associates the model data with the IOT data through the GUID code and the device ID code, integrating the complete data;

[0062] Update the digital twin model and data based on complete data.

[0063] In this embodiment, during the on-site IOT data collection process, once it is found that the on-site equipment is inconsistent with the model, the system can immediately call the marking configuration corresponding to the family component and place a mark at the corresponding position. This mechanism ensures the timely discovery and marking of problems, and provides accurate positioning information for subsequent data updates; by obtaining the position data of the marking configuration on the lightweight model through the Internet of Things and BIM lightweight mobile terminal, the system can accurately know the location and range that need to be updated, thereby improving the accuracy and efficiency of data updates; it can automatically add or delete information according to the on-site conditions of the equipment, and perform IOT data collection, and write the marking data and IOT data into the background IOT database. This automated process reduces manual intervention and improves the efficiency and accuracy of data updates. The Revit interactive terminal can automatically call the corresponding family component in the background family library according to the association of the marking configuration, and according to the group obtained from the database This intelligent operation greatly simplifies the model update process and reduces the difficulty of operation. After completing the addition or deletion instructions of family components, the system will extract and organize the device ID in the BIM model data acquisition and tag configuration, and write them into the BIM model and background model database respectively. This step ensures the data consistency between the BIM model and the background database, avoids data conflicts and redundancy. The background database once again completes the association between model data and IOT data through GUID code and device ID code, and integrates complete data. This mechanism ensures the integrity and accuracy of the data and provides reliable data support for the update of the digital twin model. The digital twin model and data are updated according to the complete data, so that the digital twin model can reflect the actual situation of the on-site equipment in real time. This dynamic update mechanism improves the practicality and accuracy of the digital twin model.

[0064] Preferably, the background family library is used for storage and classification management of BIM model component families, including a standard family library and a project family library. The standard family library sorts out equipment requirements according to functional indicators required by the Internet of Things platform and produces family components to form a stable standard library. The project family library is for a single project and extracts family components in the project to form a project family library.

[0065] In this embodiment, the standard family library sorts out the equipment requirements according to the functional indicators required by the Internet of Things platform, produces family components, and forms a stable standard library. This design ensures the standardization and normalization of family components, and avoids the inconsistency of family components caused by different production personnel or project requirements. Through the standard family library, the expression of equipment information can be unified to improve the consistency and comparability of data; the project family library is formed by extracting family components from a single project. This design enables the project family library to flexibly adapt to the needs of different projects, avoiding the problem of inapplicability of family components due to project differences. At the same time, the project family library can be used with the changes of the project. The family library can be expanded and updated according to the progress and changes in demand, ensuring the timeliness and accuracy of the family library; the background family library stores and classifies the family components, allowing users to quickly retrieve the required family components, improving work efficiency. Through classification management, users can clearly understand the types, properties and uses of family components, avoiding misuse or omissions caused by information confusion; the design of standard family libraries and project family libraries makes the data integration between BIM models and IoT platforms smoother. The standard family library provides a unified data interface and expression method for the IoT platform, enabling the IoT platform to accurately identify and parse equipment information in the BIM model.

[0066] Preferably, the background tag configuration library is used for storage and classification management of tag configurations, including a standard tag configuration library and a custom tag configuration. The standard tag configuration library sets tag configurations of different forms according to the component classification requirements in the standard family library of the background family library to form a stable standard tag configuration library. The custom tag configuration is customized using custom tags when family components in the BIM model have family component classifications that do not correspond to the standard tag configuration.

[0067] In this embodiment, the standard tag configuration library sets different forms of tag configurations according to the component classification requirements in the standard family library of the background family library, forming a stable standard tag configuration library. This design ensures the standardization and consistency of the tag configuration, so that different projects or different personnel can follow the same specifications and standards when using it. Through the standard tag configuration library, the expression and use of tags can be unified to improve the consistency and comparability of data; the introduction of custom tag configuration makes it possible to use custom tags to mark the family component classification that does not correspond to the standard tag configuration in the BIM model. This design improves the flexibility and scalability of the tag configuration, so that the tag configuration can adapt to different projects or different structures. To meet the special needs of components, custom tag configurations can be created and modified according to actual needs, ensuring the timeliness and accuracy of tag configurations; the background tag configuration library stores and classifies tag configurations, allowing users to quickly retrieve the required tag configurations, improving work efficiency. Through classification management, users can clearly understand the types, forms and uses of tag configurations, avoiding misuse or omissions caused by information confusion; the design of the standard tag configuration library and custom tag configurations makes the data integration between the BIM model and the background database smoother. The standard tag configuration library provides a unified data interface and expression method for the background database, enabling the background database to accurately identify and parse the tag information in the BIM model.

[0068] Preferably, the background database is used to store relevant data of family components in the Internet of Things platform and the BIM model, including a model database and an IOT database. The model database stores relevant data of family components in the BIM model, and the IOT database stores relevant data of IOT device products.

[0069] In this embodiment, by dividing the background database into a model database and an IOT database, separate storage of data is achieved. The model database focuses on storing relevant data of family components in the BIM model, while the IOT database focuses on storing relevant data of IOT equipment products. This separation makes the data clearer and easier to manage and maintain; the separate design of the model database and the IOT database ensures the consistency and integrity of their respective data. The family component data in the BIM model and the data of IOT equipment products are stored separately, avoiding data intersection and confusion, thereby ensuring data accuracy and reliability; the separate database design makes data access and processing more efficient, and According to the different needs of BIM products, the storage structure and query performance of the model database and IOT database can be optimized respectively to improve the speed and efficiency of data access; by storing data separately, data access permissions and security policies can be set more flexibly. For sensitive data in the BIM model, more stringent security measures can be taken to protect it. For the data of IOT device products, corresponding access permissions can be set according to actual needs to ensure data security and privacy protection; although the data is stored separately, the model database and IOT database can be integrated and collaborated through data interfaces or middleware. This design allows the data between BIM models and IOT device products to be interconnected and interactive.

[0070] Preferably, convolutional neural network and natural language processing technology are used to perform semantic analysis on the extracted static features of family components, and the semantic analysis results are input as training sets into the deep learning model for training.

[0071] In this embodiment, by adopting the fusion of convolutional neural network (CNN) and natural language processing (NLP) technology, the static features of family components are extracted, and the text data such as device name and model description are extracted for semantic analysis. The semantic analysis results are input into the deep learning model as a training set for training, so as to improve the matching efficiency and matching accuracy of the system. The specific steps are as follows:

[0072] Collect and import data sets: collect pictures related to smart device family components based on the background standard family library; collect pictures related to various tag configurations based on the standard tag configuration library; collect text data such as device names and model descriptions of family components based on classification standards; divide them into picture training sets and text training sets for import;

[0073] Data preprocessing: Standardize the pixel values ​​of the image training set data, and perform data preprocessing such as removing punctuation, segmenting words, and removing stop words on the text training set data;

[0074] Build the model: extract features from the data, implement classification and compile the model to build the model;

[0075] Training and evaluating the model: Input the prepared training set data (including images and corresponding labels) and the test set data (including images and corresponding labels) to train the model. The smaller the difference between the model prediction value and the true value, the higher the model accuracy.

[0076] In addition, the specific steps of building the model are:

[0077] Feature extraction: Use convolutional neural networks to extract static features of equipment from the geometric features, location information, installation methods, etc. of equipment family components and images of tagged configurations in the BIM model. Use natural language processing technology to perform semantic analysis on text data such as equipment names and model descriptions, and use embedded vectorization methods (BERT model) to uniformly map them to a standardized data space.

[0078] Implementation of classification: Use softmax logistic regression for classification;

[0079] Compile the model: IT compiles the model, selects the loss function, optimizer, metrics, etc., and analyzes and evaluates the model.

[0080] Preferably, the background tag configuration library and the family library are intelligently associated based on the organic combination of deep learning, reinforcement learning and distributed architecture.

[0081] In this embodiment, through the organic combination of deep learning, reinforcement learning and distributed architecture, a technical innovation from traditional static matching to dynamic adaptive association is achieved in the intelligent association between the tag configuration library and the family library, avoiding manual intervention and improving data matching and updating efficiency. The specific steps are:

[0082] Extract project model family data: extract family components from the project model, and extract project model family data such as family images and text data according to model requirements;

[0083] Data preprocessing: Combined with automated data cleaning algorithms, the project model family data is denoised, outliers are processed, and the format is unified. If the data missing rate exceeds the set threshold, an exception report is triggered and a request for re-collection is requested. If the data is detected to be beyond a reasonable range (by setting upper and lower limits), it is marked as invalid data and replaced or removed to ensure the integrity and consistency of the input data.

[0084] Data import into CNN+NLP model: Import the preprocessed data into the model through the program interface;

[0085] Setting the error threshold: The accuracy of the output results can be ensured by designing the error threshold for the system. However, sometimes if the error threshold is too high, it will affect the operating efficiency of the system. Therefore, it is necessary to select a reasonable error threshold to ensure the accuracy within a reasonable range and maximize the operating efficiency of the system.

[0086] Error determination: Using the reinforcement learning algorithm Deep Q-Learning, the AI ​​system is trained to learn the characteristics of the equipment and historical matching rules. If the matching confidence is lower than the threshold (less than 80%), it triggers manual review and optimizes the model parameters. If the family meets the error threshold requirements, it will be automatically archived according to the classification standard. If the family does not meet the requirements, it will be manually modified and archived. Finally, the family is identified, corrected, classified and stored in the project family library;

[0087] Matching judgment: The CNN+NLP model performs tag configuration matching judgment on the project family library organized according to the classification standards. If the judgment conditions are met, the system obtains the tag configuration corresponding to the configuration library for automatic association. If the judgment conditions are not met, it means that the device type does not exist in the existing standard tag library, triggering the custom tag generation process. The system combines image recognition and pattern recognition technology to generate personalized tags for device family components, automatically stores them in the configuration library, and automatically associates them at the same time.

[0088] Preferably, the field data is continuously monitored, automatically determined, and automatically updated based on visual marker enhancement technology, multimodal data matching model, and automatic repair and marker update technology.

[0089] In this embodiment, the visual tag enhancement technology: uses the on-site images collected by the device camera and the AR device to identify the device through the deep learning image processing algorithms YOLO and Mask R-CNN. If the detected device does not match the BIM model tag, the tag repair process between the image and the model is triggered to dynamically associate the detected device tag with the tag in the BIM model;

[0090] Multimodal data matching model: By integrating IoT real-time data (device ID, operating status, etc.) with BIM model static data (location, geometric features, etc.), a multimodal association model is constructed. If there is a conflict between the matched static and dynamic data (such as different locations of the same device), the conflict resolution mechanism is triggered to achieve accurate matching of the tag configuration and the family library;

[0091] Automatic repair and tag update: The system can automatically detect inconsistencies between on-site equipment and BIM models, predict equipment status through AI and dynamically generate or correct tags. If the inconsistency rate exceeds the normal range (such as more than 10%), a large-scale tag repair process is triggered. It also supports tag extension and custom tags in complex scenarios.

[0092] A computing device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for building a digital twin visualization large screen are implemented.

[0093] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the above-mentioned method for building a digital twin visualization large screen.

[0094] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for building a digital twin visualization large screen, characterized in that: Specifically include: Get the BIM model; Extract family components from the BIM model, and classify and store them according to classification standards; Establish the association between the background tag configuration library and the background family library; Acquire and organize the BIM model data through the Revit interactive terminal, and write them into the BIM model and the backend database respectively; Import the BIM model into the IoT platform for lightweight conversion, and transfer the model data to the BIM lightweight model along with the GUID code; Use the Internet of Things and BIM lightweight mobile terminal to collect IOT data of the family components of the BIM lightweight model corresponding to the smart device on site, and write the data into the background IOT database; The backend IOT database uses GUID code and device ID code to associate model data with IOT data and integrate complete data; The BIM model and the IoT platform obtain complete data through the backend IoT database; Convert the BIM model into a digital twin model supported by the UE engine, and pass the complete data to the digital twin model along with the GUID code and equipment ID code; Configure scenarios for the digital twin model through the digital twin model interactive interface; The IoT middle platform interacts with the WEB visualization front end through GUID code and device ID code; The digital twin model and the WEB visualization front-end are associated through the GUID code and the device ID code to realize the construction of the digital twin visualization large screen.

2. A method for building a digital twin visualization large screen according to claim 1, characterized in that: The method also includes automatic updating of the digital twin model and data, specifically: During the on-site IOT data collection process, if it is found that the on-site equipment does not match the model, call the tag configuration corresponding to the family component and place the tag at the corresponding position; The IoT and BIM lightweight mobile terminals obtain the location data of the marked configuration on the lightweight model; Add or delete information according to the on-site conditions of the equipment, collect IOT data, and write the tag data and IOT data into the background IOT database; The BIM model side reads the location data and tag information of the tag configuration in the database and the device ID code in the IOT data through the Revit interactive terminal; The Revit interactive terminal automatically calls the corresponding family component in the background family library according to the association of the tag configuration, and executes the add or delete command according to the configuration location data and tag information obtained from the database; After completing the instructions for adding or deleting family components, the BIM model is used to obtain data and extract and sort the equipment IDs in the tag configuration, and write them into the BIM model and the background model database respectively; The backend database once again associates the model data with the IOT data through the GUID code and the device ID code, integrating the complete data; Update the digital twin model and data based on complete data.

3. A method for building a digital twin visualization large screen according to claim 2, characterized in that: The background family library is used for storage and classification management of BIM model component families, including standard family libraries and project family libraries. The standard family library sorts out equipment requirements according to the functional indicators required by the Internet of Things platform and produces family components to form a stable standard library. The project family library is for a single project and extracts family components in the project to form a project family library.

4. A method for building a digital twin visualization large screen according to claim 3, characterized in that: The background tag configuration library is used for storage and classification management of tag configurations, including a standard tag configuration library and a custom tag configuration. The standard tag configuration library sets tag configurations of different forms according to the component classification requirements in the standard family library of the background family library to form a stable standard tag configuration library. The custom tag configuration is customized using custom tags when a family component classification that does not correspond to the standard tag configuration appears in the family component in the BIM model.

5. A method for building a digital twin visualization large screen according to claim 4, characterized in that: The background database is used to store relevant data of the IoT platform and family components in the BIM model, including a model database and an IOT database. The model database stores relevant data of family components in the BIM model, and the IOT database stores relevant data of IOT device products.

6. A method for building a digital twin visualization large screen according to claim 5, characterized in that: Convolutional neural network and natural language processing technology are used to perform semantic analysis on the extracted static features of family components, and the semantic analysis results are used as training sets to input into the deep learning model for training.

7. A method for building a digital twin visualization large screen according to claim 6, characterized in that: Based on the organic combination of deep learning, reinforcement learning and distributed architecture, the background tag configuration library and the family library are intelligently associated.

8. A method for building a digital twin visualization large screen according to claim 7, characterized in that: Based on visual marker enhancement technology, multimodal data matching model, automatic repair and marker update technology, field data is continuously monitored, automatically judged and automatically updated.

9. A computing device, characterized in that It includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the method for building a digital twin visualization large screen described in any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, which, when executed by a processor, implements the steps of the method for building a digital twin visualization large screen as described in any one of claims 1 to 8.

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