A high-speed rail station building full-factor health monitoring and early warning system integrating BIM and IoT

Abstract

This invention relates to a comprehensive health monitoring and early warning system for high-speed railway stations integrating BIM and IoT, comprising a perception layer, a network layer, a platform layer, and an application layer. The perception layer collects real-time monitoring data of all elements of the high-speed railway station. The perception layer transmits data through a communication connection with the platform layer via the network layer. The platform layer establishes a BIM model and maps the BIM model in the data space to the physical perception layer, reflecting the comprehensive monitoring data of the perception layer in the BIM model. The platform layer sets early warning thresholds based on three types of logical entry points: standard requirements, calculation requirements, and custom requirements. The platform layer analyzes and issues early warnings based on the different set early warning thresholds collected by the perception layer. The application layer serves as the system's user interaction terminal, used for system deployment in different environments. The advantages of this invention are: significantly improved monitoring comprehensiveness; outstanding data fusion and visualization capabilities; improved early warning response and handling efficiency; full-cycle support for operation and maintenance management; and strong system stability and scalability.

Description

Technical Field

[0001] This invention relates to the field of building safety monitoring and intelligent operation and maintenance technology, and in particular to a full-element health monitoring and early warning system for high-speed railway stations that integrates BIM and IoT. It is applicable to the full-cycle, multi-dimensional monitoring and early warning of the structural safety, environmental quality and security status of large and complex buildings such as high-speed railway stations and railway hub complexes. Background Technology

[0002] As a core hub of the railway transportation network, high-speed railway station buildings are characterized by complex structural systems (including multi-level components such as the main roof structure and track layer structure), heavy operational loads, dense passenger flow, and long service life. Their safe and stable operation is directly related to public travel safety and the smooth operation of the national transportation network. Currently, the monitoring of high-speed railway station buildings faces the following prominent problems: 1) Fragmented monitoring dimensions: Traditional monitoring often focuses on a single dimension (such as monitoring only structural strain or ambient temperature), lacking integrated coverage of all elements such as structure, environment, security, water leakage and seepage, making it difficult to reflect the overall health status of the building.

[0003] 2) Low data integration: Structural sensor data, environmental monitoring data, and video surveillance data are stored and managed in a scattered manner, failing to achieve cross-dimensional data correlation analysis, and thus unable to provide comprehensive data support for risk assessment.

[0004] 3) Insufficient visualization and interactivity: There is a lack of a 3D visualization carrier that accurately maps to the building entity, the monitoring data is presented in a single form, and it is difficult for maintenance personnel to quickly locate abnormal locations and related components.

[0005] 4) Delayed early warning response: The early warning mechanism lacks a hierarchical and categorized design, the handling procedures for different levels of risks are unclear, and equipment failures (such as hardware damage and communication interruptions) do not form a closed-loop alarm, affecting the continuity of monitoring.

[0006] 5) Lack of full-cycle support for operation and maintenance management: A complete system from basic information management and in-depth data analysis to automated assessment report output has not been built, making it difficult to adapt to the operation and maintenance needs of high-speed railway station buildings that have been in service for a long time.

[0007] While BIM technology has been applied to the architectural design and construction phases, its application in operational health monitoring is insufficient. Although IoT technology can collect multi-source data, it lacks precise correlation with BIM models and intelligent cross-dimensional data analysis capabilities. Therefore, there is an urgent need to construct a health monitoring and early warning system that deeply integrates the three-dimensional visualization advantages of BIM with the real-time data acquisition capabilities of IoT, covering all elements and the entire lifecycle, to address the many shortcomings of traditional monitoring methods. Summary of the Invention

[0008] The purpose of this invention is to address the shortcomings of the existing technology by providing a comprehensive health monitoring and early warning system for high-speed railway stations that integrates BIM and IoT.

[0009] The objective of this invention is achieved through the following technical solutions: A comprehensive health monitoring and early warning system for high-speed railway station buildings integrating BIM and IoT is characterized by comprising a perception layer, a network layer, a platform layer, and an application layer; wherein, The perception layer includes several data acquisition terminals, which are used to collect real-time monitoring data of all elements of the high-speed railway station building. The monitoring data of all elements includes at least structural safety data, environmental quality data, security status data, and water leakage and seepage data. The perception layer communicates with the platform layer through the network layer to transmit data. The platform layer establishes a BIM model and simultaneously establishes a mapping between the BIM model in the data space and the perception layer in the physical space, so that the full-element monitoring data of the perception layer is reflected on the BIM model; the platform layer sets early warning thresholds based on three types of logical entry points, including standard requirements, calculation requirements and custom requirements, and the platform layer analyzes and issues early warnings based on the different early warning thresholds collected by the perception layer. The application layer is the system user interaction terminal, used for system deployment in different environments.

[0010] The data acquisition terminal includes a structural monitoring sensor group, an environmental monitoring sensor group, a leakage and seepage monitoring sensor group, and a video monitoring equipment group. The structural monitoring sensor group is used to monitor the structural response data of the high-speed railway station building. The environmental monitoring sensor group is used to collect meteorological parameters at key points of the high-speed railway station building. The leakage and seepage monitoring sensor group is used to collect leakage and seepage status data of the high-speed railway station building. The video monitoring equipment group is used to realize video image acquisition and abnormal behavior recognition.

[0011] The network layer adopts a hybrid transmission architecture of 5G, LoRa and Ethernet.

[0012] The platform layer includes a data storage and preprocessing module, a BIM model integration and mapping module, a core function module, and a system management module. The data storage and preprocessing module is used to store and preprocess the full-element monitoring data. The BIM model integration and mapping module realizes the parameterized association of the BIM model through IFC standard format parsing and binds the full-element monitoring data to the BIM model. The core function module is equipped with a hierarchical closed-loop early warning mechanism. The system management module is used to manage the system's configuration information.

[0013] The core functional modules include an engineering overview module, a monitoring equipment management module, a structural information management module, a multi-dimensional data analysis module, a hierarchical and classified early warning processing module, and a video surveillance and intelligent linkage module.

[0014] The system management module includes a user management unit, a role management unit, a menu management unit, and a file management unit.

[0015] The application layer is the system user interaction terminal, including a web-based management platform, a mobile app, and an on-site monitoring terminal.

[0016] The specifications require that the strain value of the steel components in the high-speed railway station building be used as the warning value, and the warning level be determined according to the magnitude of the strain value of the steel components; the calculation requirements require that the finite element simulation calculation result of the structure of the high-speed railway station building be used as the warning value, and the warning level be determined according to the magnitude of the maximum vertical displacement warning value of the roof; the custom requirements are based on temperature.

[0017] The advantages of this invention are: 1) Significantly improved monitoring comprehensiveness: This invention covers all monitoring dimensions such as structure, environment, security, water leakage and seepage, integrates multiple types of sensors and video monitoring equipment, breaks through the dimensional barriers of traditional monitoring, and realizes all-round, blind-angle perception of the health status of high-speed railway station buildings.

[0018] 2) Outstanding data fusion and visualization capabilities: Through the deep integration of BIM and IoT, a precise mapping between physical space and data space is constructed. Combined with multi-dimensional data visualization charts and BIM model interaction functions, monitoring data becomes more intuitive and positioning becomes more accurate, significantly reducing the operational difficulty for maintenance personnel.

[0019] 3) Improved early warning response and handling efficiency: A three-level graded early warning mechanism and differentiated notification strategies ensure rapid response to risks of different levels; closed-loop management of equipment failures ensures the continuity of the monitoring network, and the early warning and handling response speed is improved by more than 60% compared with traditional monitoring methods.

[0020] 4) Full-cycle support for operation and maintenance management: From basic information management to automated assessment report generation, and then to multi-terminal interaction, a full-cycle operation and maintenance management system has been built to achieve standardization, intelligence and efficiency of operation and maintenance work, and reduce operation and maintenance costs by more than 30%.

[0021] 5) Strong system stability and scalability: The system adopts a hybrid transmission architecture of "5G+LoRa+Ethernet" and a distributed storage solution to ensure the stability of data transmission and storage; the modular design supports the addition of monitoring dimensions (such as air quality and equipment energy consumption) and functional modules to adapt to the personalized needs of different high-speed rail stations and future business expansion. Attached Figure Description

[0022] Figure 1 This is a block diagram of the overall architecture of the present invention; Figure 2 This is a functional architecture block diagram of the present invention. Detailed Implementation

[0023] The features and other related features of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments, so as to facilitate understanding by those skilled in the art: Example: Figure 1 and Figure 2 As shown, the high-speed railway station building full-element health monitoring and early warning system integrating BIM and IoT in this embodiment is mainly divided into a perception layer, a network layer, a platform layer, and an application layer. Each layer is sequentially connected to form a complete technical link of "data acquisition-transmission-processing-application". Specifically, the system in this embodiment is as follows: 1) Perception layer: This perception layer serves as the system's data acquisition terminal, enabling real-time acquisition of comprehensive monitoring data for high-speed railway station buildings. It covers all aspects of monitoring, including structural safety, environmental quality, security status, and water leakage / seepage, breaking down data silos and achieving integrated multi-dimensional data management. Structural monitoring sensor group: Deployed on key components such as the main roof structure and track layer structure of high-speed railway station buildings, it consists of 6 types of sensors, including surface strain gauges, static levels, tilt meters, and triaxial vibration meters, to collect structural response data such as stress, strain, and tilt angle of key components.

[0024] Environmental monitoring sensor array: Distributed at key locations in the monitoring area, including temperature and humidity sensors, wind direction and speed sensors, air pressure sensors, and rainfall sensors, synchronously collecting meteorological parameters such as temperature, humidity, wind direction, wind speed, air pressure, and precipitation.

[0025] Leakage and seepage monitoring sensor group: Deployed in areas prone to water seepage, such as roofs, walls, and track layers, to collect data on leakage and seepage status.

[0026] Video surveillance equipment group: including high-definition network cameras and pan-tilt controllers, covering the station's public areas, equipment rooms, and surrounding security areas, to achieve video image acquisition and abnormal behavior recognition.

[0027] In this embodiment, taking a high-speed railway station as an example, the specific deployment of the perception layer includes: Structural monitoring: Twelve surface strain gauges, four embedded strain gauges, six tilt meters, six static levels, six machine vision intelligent measuring instruments, and two triaxial vibration meters are deployed at key stress-bearing parts such as critical nodes of the roof truss and crossbeams of the track layer, totaling six types of structural sensors, to collect data on structural stress, strain, tilt angle, displacement, and vibration.

[0028] Environmental monitoring: One ultrasonic anemometer and wind direction sensor will be deployed in an open area outside the station building, and one temperature and humidity sensor will be deployed in the main indoor areas. In addition, one single temperature transmitter, one air pressure sensor, and one rainfall sensor will be deployed to monitor the ambient temperature, humidity, wind speed and direction, air pressure, and rainfall.

[0029] Leakage and seepage monitoring: Ten leakage rope sensors are deployed in areas prone to leakage, such as roof joints, curtain wall edges, and below the track layer, to detect the occurrence of leakage in real time.

[0030] Video surveillance: 24 high-speed PTZ cameras are deployed in public areas of the station, equipment rooms, entrances and exits, etc., supporting 360-degree rotation, optical zoom and infrared night vision functions, covering the main monitoring areas.

[0031] 2) Network layer: The network layer is used to implement data transmission between the perception layer and the platform layer, and adopts a hybrid transmission architecture of "5G+LoRa+Ethernet". For sensor data with small data volume and low latency requirements, such as structural monitoring and environmental monitoring, LoRa gateways are used for aggregation and transmission to reduce energy consumption. For data streams with large data volumes and high bandwidth requirements, such as video surveillance, transmission is carried out through 5G networks or Ethernet to ensure transmission speed and stability. The built-in data encryption module uses the AES encryption algorithm to encrypt transmitted data, ensuring data security and privacy.

[0032] In this embodiment, a hybrid "5G+LoRa" network can be adopted. All sensors are connected to the nearest LoRa gateway via a LoRa wireless communication module, and the gateway uploads data via 5G / Ethernet; video surveillance cameras are directly connected to the platform layer via 5G or wired network to ensure video stream transmission bandwidth.

[0033] 3) Platform layer: The platform layer is the core processing unit of the system, and it constructs a multi-dimensional data fusion processing and operation and maintenance management system, including: (1) Data storage and preprocessing module: A hybrid storage architecture is built using a distributed database (HBase + MySQL): HBase is used to store massive amounts of real-time and historical sensor data, while MySQL is used to store structured data such as device information, structural parameters, and user permissions.

[0034] Data preprocessing unit: Cleans the collected data by using outlier removal algorithms (based on the 3σ principle) and data smoothing algorithms (moving average method) to remove noisy data and ensure data quality; at the same time, it supports standardized data format conversion to achieve unified storage and retrieval of cross-type data.

[0035] (2) BIM model integration and mapping module: It supports importing BIM models built with mainstream software such as Revit and Bentley, and achieves parametric association of models through IFC standard format parsing.

[0036] Establish a precise mapping relationship between sensors and BIM model components: Bind the installation location, device number, monitoring parameters, and other information of the sensors in the sensing layer to the corresponding components and points in the BIM model, achieving a one-to-one correspondence between physical devices and the virtual model. In the BIM model integration and mapping module, bind the installation location, device number, and other information of all sensors and cameras to the corresponding components in the BIM model, completing the precise mapping between physical devices and the virtual model. For example, associate the surface strain gauge at the roof truss node with the corresponding member in the model; clicking on the member in the model allows you to view the real-time data and historical curves of that strain gauge.

[0037] It supports lightweight processing of BIM models, ensures smooth interaction of 3D scenes, and adapts to the access needs of web and mobile devices.

[0038] (3) Core functional modules: a. Project Overview Module: As the platform's default display page, it presents a standardized overview of the project, including core information such as project background, monitoring objectives, monitoring scope, structural system overview, and equipment deployment overview, providing maintenance personnel with a holistic understanding.

[0039] b. Monitoring Equipment Management Module: Divided into four categories according to the monitoring object: structural monitoring equipment, environmental monitoring equipment, video surveillance equipment, and water leakage and seepage monitoring equipment. It constructs a full life cycle management file for the equipment, displaying detailed information such as equipment model, installation location, installation time, operating status, and fault history. It supports adding, deleting, modifying, querying, and batch exporting of equipment information.

[0040] c. Structural Information Management Module: Based on a three-level architecture of "building level - structural system - component unit", it manages information on key structures such as the main roof structure and track layer structure, and establishes component information archives, including detailed data such as component material, cross-sectional dimensions, design load, stress characteristics, and construction technology, providing basic parameter support for structural health assessment.

[0041] d. Multi-dimensional Data Analysis Module: Divided into structural data analysis sub-modules, environmental data analysis sub-modules, video surveillance data analysis sub-modules, and water leakage / seepage data analysis sub-modules according to the monitored object. This multi-dimensional data analysis module includes the following functions: Data query function: Supports precise query by time range (real-time, today, yesterday, custom period), device number, monitoring parameters and other conditions, realizes real-time dynamic refresh of sensor data (refresh frequency is configurable, minimum 1Hz) and historical data backtracking query (supports data storage and export of the past 5 years).

[0042] Data visualization function: Through professional charts such as line charts, bar charts, pie charts, and heat maps, the dynamic trend of data changes can be presented intuitively; the structural data analysis submodule additionally supports the generation of component stress cloud maps and displacement distribution cloud maps, realizing the visual analysis of structural response.

[0043] Early warning type classification function: set early warning thresholds based on three types of logical entry points, including standard requirements (based on the "Technical Standard for Monitoring Building Structures" GB 50981), calculation requirements (based on structural mechanics simulation calculation results), and owner-defined requirements (adapting to special needs in the operation phase), to meet the risk assessment needs in different scenarios.

[0044] In this embodiment, the following preferred warning threshold setting mode can be adopted: The standard requires that, in accordance with the Technical Standard for Monitoring Building Structures GB 50981, the strain warning values ​​for Q345 steel components are set as ±2000με (Level 1 warning), ±1500με (Level 2 warning), and ±1000με (Level 3 warning).

[0045] Calculation requirements: Based on the structural finite element simulation results, the maximum vertical displacement warning values ​​for the roof are set to 20mm (Level 1), 15mm (Level 2), and 10mm (Level 3).

[0046] Owner-defined: Based on operational needs, the maximum indoor temperature warning thresholds for summer are set at 32℃ (Level 1), 30℃ (Level 2), and 28℃ (Level 3).

[0047] e. Hierarchical and Classified Early Warning Processing Module: Early warning level handling units: Divided into Level 1 (major risk), Level 2 (relatively high risk), and Level 3 (general risk) according to risk level, with different handling procedures and notification strategies corresponding to different levels: Level 1 alerts trigger triple notifications via SMS, phone call, and platform pop-up, requiring a response within 30 minutes and handling within 2 hours; Level 2 alerts trigger SMS and platform pop-up notifications, requiring a response within 1 hour and handling within 4 hours; Level 3 alerts trigger platform pop-up notifications, requiring a response within 2 hours and handling within 8 hours; dynamic adjustment of alert thresholds and access control are also supported.

[0048] Equipment early warning processing unit: Automatically triggers operation and maintenance alarms for abnormal equipment events such as sensor hardware failure, communication failure, and video equipment offline, records the fault type, occurrence time, and scope of impact, supports closed-loop management of fault dispatch, processing tracking, and result feedback, and ensures the continuity of the monitoring network.

[0049] f. Video surveillance and intelligent linkage module: Visualization of monitoring points: Mark the location of all cameras in the BIM model, support clicking on the model points to quickly retrieve the monitoring video of the corresponding area, and support multi-screen (1 / 4 / 9 / 16 split screen) linkage switching and full-screen display.

[0050] Intelligent event recognition: Integrating YOLO target detection algorithm and behavior recognition algorithm, it automatically identifies abnormal behaviors such as intrusion, loitering, climbing, and leaving objects, as well as safety hazards such as smoke and flames, with an accuracy rate of ≥95%. It also displays alarm content, location of occurrence, and event screenshots on the platform in real time.

[0051] Video control and playback: Supports remote control of camera horizontal / vertical rotation via joystick button, adjustment of pan / tilt rotation speed (0.1° / s-10° / s) via slider button, support for video playback at multiple speeds (0.5-16x) and keyframe marking, and enables precise event location and rapid playback by combining with alarm timeline.

[0052] g. Full-cycle assessment report module: Supports automatic generation of standardized assessment reports on a monthly, quarterly, and annual basis. The report content includes: data integrity statistics, monitoring parameter trend analysis, summary and handling analysis of early warning events, structural health status assessment, environmental quality assessment, equipment operation status assessment, operation and maintenance optimization suggestions, and other professional content; supports online report preview, PDF export and batch archiving, and also supports custom report template configuration.

[0053] In this embodiment, a server cluster is deployed in the station monitoring center, and data storage and preprocessing modules, BIM model integration and mapping modules, core function modules and system management modules are installed.

[0054] (4) System Management Module: User Management Unit: Supports adding, modifying, and deleting user accounts, includes password reset and role assignment functions, and adopts an RBAC (Role-Based Access Control)-based permission management mechanism to ensure platform access security; Role Management Unit: Supports adding, modifying, and deleting roles. It allows you to configure the functional permissions and data access scope of different roles (such as system administrator, operation and maintenance engineer, monitoring specialist, and read-only user) according to business needs. Menu Management Unit: Supports dynamic configuration of platform function menus, including adding, deleting, and sorting menus to adapt to business process iterations and upgrades; The file management unit supports uploading, storing, and managing resources such as title images, logos, BIM model files (IFC / Revit format), and assessment report templates. It also supports online file preview and batch download.

[0055] In this embodiment, a server cluster is deployed in the station monitoring center, and the platform layer software of this embodiment is installed, including a data storage and preprocessing module, a BIM model integration and mapping module, a core function module, and a system management module.

[0056] 4) Application layer: The application layer consists of the system's user interaction terminals, including a web-based management platform, a mobile app, and on-site monitoring terminals. Web-based management platform: Designed for operations and maintenance personnel, it provides visualized operation and data display of all functional modules, supports large-screen display mode, and is suitable for monitoring center scenarios; Mobile App: Supports iOS and Android systems, providing convenient access to core functions (real-time data viewing, alarm notification reception, fault reporting, video preview) to meet the mobile office needs of on-site maintenance personnel; On-site monitoring terminal: Deployed in the station equipment room and monitoring room, it provides local display of key data and early warning information, and ensures the availability of basic monitoring functions in the event of network outage.

[0057] In this embodiment, the monitoring center is equipped with a large-screen display system and runs a web-based management platform; mobile terminals are provided to maintenance personnel, and a mobile app is installed; on-site monitoring terminals are deployed in the equipment room for local data backup and emergency display.

[0058] In practical applications, this embodiment has the following characteristics: After the system is put into operation, sensors and cameras in the perception layer collect data in real time and transmit it to the platform layer via LoRa / 5G network. After data preprocessing, it is stored in a distributed database. Maintenance personnel can view the real-time data associated with the BIM model through a web-based platform.

[0059] One day, the strain value at a certain node of the roof exceeded the level 2 warning threshold (-1600με). The system automatically triggered a level 2 warning, notifying the maintenance engineer via SMS and a pop-up window on the platform. The engineer clicked on the warning information on the platform, and the system automatically located the corresponding component in the BIM model, displaying the component's strain history curve and data from adjacent sensors. Simultaneously, it retrieved video playback from nearby cameras, confirming no external impacts or other anomalies, and initially determined the cause to be stress fluctuations caused by temperature changes. The engineer recorded the handling results, forming a closed loop.

[0060] Leakage Detection: After a rainfall event, the leak detection rope sensor detected a leak in a certain area of ​​the track layer. The system triggered a level three alert, a pop-up notification appeared on the platform, and nearby cameras automatically zoomed in on the leaking area, displaying the water accumulation on video. Maintenance personnel immediately dispatched a work order to handle the situation, effectively preventing further water damage.

[0061] At the end of each month, the system automatically generates a monthly assessment report, including structural health status assessment, environmental quality evaluation, early warning event statistics, and equipment operation analysis, providing data support for station operation and maintenance decisions.

[0062] Through actual operation testing, this embodiment has realized intelligent, visualized and closed-loop management of the health monitoring of all elements of high-speed railway station buildings. The early warning response time is shortened by more than 60% compared with the traditional method, the operation and maintenance cost is reduced by about 35%, and the system operates stably and reliably.

[0063] In specific implementation of this embodiment: This embodiment can adopt a modular design and can further expand the monitoring dimensions as needed, such as adding functional modules for air quality monitoring (PM2.5, CO2, etc.) and equipment energy consumption monitoring. It is only necessary to add corresponding sensors to the sensing layer and add corresponding data analysis sub-modules to the core functional modules of the platform layer, without changing the overall architecture.

[0064] Although the above embodiments have described the concept and embodiments of the present invention in detail with reference to the accompanying drawings, those skilled in the art will recognize that various improvements and modifications can still be made to the present invention without departing from the scope of the claims, and therefore will not be elaborated here.

Claims

1. A comprehensive health monitoring and early warning system for high-speed railway station buildings integrating BIM and IoT, characterized in that: It includes the perception layer, network layer, platform layer, and application layer; among which, The perception layer includes several data acquisition terminals, which are used to collect real-time monitoring data of all elements of the high-speed railway station building. The monitoring data of all elements includes at least structural safety data, environmental quality data, security status data, and water leakage and seepage data. The perception layer communicates with the platform layer through the network layer to transmit data. The platform layer establishes a BIM model and simultaneously establishes a mapping between the BIM model in the data space and the perception layer in the physical space, so that the full-element monitoring data of the perception layer is reflected on the BIM model; the platform layer sets early warning thresholds based on three types of logical entry points, including standard requirements, calculation requirements and custom requirements, and the platform layer analyzes and issues early warnings based on the different early warning thresholds collected by the perception layer. The application layer is the system user interaction terminal, used for system deployment in different environments.

2. The high-speed railway station building full-element health monitoring and early warning system integrating BIM and IoT as described in claim 1, characterized in that: The data acquisition terminal includes a structural monitoring sensor group, an environmental monitoring sensor group, a leakage and seepage monitoring sensor group, and a video monitoring equipment group. The structural monitoring sensor group is used to monitor the structural response data of the high-speed railway station building. The environmental monitoring sensor group is used to collect meteorological parameters at key points of the high-speed railway station building. The leakage and seepage monitoring sensor group is used to collect leakage and seepage status data of the high-speed railway station building. The video monitoring equipment group is used to realize video image acquisition and abnormal behavior recognition.

3. The high-speed railway station building full-element health monitoring and early warning system integrating BIM and IoT as described in claim 1, characterized in that: The network layer adopts a hybrid transmission architecture of 5G, LoRa and Ethernet.

4. The high-speed railway station building full-element health monitoring and early warning system integrating BIM and IoT as described in claim 1, characterized in that: The platform layer includes a data storage and preprocessing module, a BIM model integration and mapping module, a core function module, and a system management module. The data storage and preprocessing module is used to store and preprocess the full-element monitoring data. The BIM model integration and mapping module realizes the parameterized association of the BIM model through IFC standard format parsing and binds the full-element monitoring data to the BIM model. The core function module is equipped with a hierarchical closed-loop early warning mechanism. The system management module is used to manage the system's configuration information.

5. The high-speed railway station building full-element health monitoring and early warning system integrating BIM and IoT as described in claim 4, characterized in that: The core functional modules include an engineering overview module, a monitoring equipment management module, a structural information management module, a multi-dimensional data analysis module, a hierarchical and classified early warning processing module, and a video surveillance and intelligent linkage module.

6. The high-speed railway station building full-element health monitoring and early warning system integrating BIM and IoT as described in claim 4, characterized in that: The system management module includes a user management unit, a role management unit, a menu management unit, and a file management unit.

7. The high-speed railway station building full-element health monitoring and early warning system integrating BIM and IoT as described in claim 1, characterized in that: The application layer is the system user interaction terminal, including a web-based management platform, a mobile app, and an on-site monitoring terminal.

8. The high-speed railway station building full-element health monitoring and early warning system integrating BIM and IoT as described in claim 1, characterized in that: The specifications require that the strain value of the steel components in the high-speed railway station building be used as the warning value, and the warning level be determined according to the magnitude of the strain value of the steel components; the calculation requirements require that the finite element simulation calculation result of the structure of the high-speed railway station building be used as the warning value, and the warning level be determined according to the magnitude of the maximum vertical displacement warning value of the roof; the custom requirements are based on temperature.

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