A bim-based deep foundation pit support stress monitoring method and system

Abstract

The application relates to the technical field of building engineering safety monitoring, in particular to a deep foundation pit support stress monitoring method and system based on BIM (Building Information Modeling), which comprises a multi-source sensing acquisition module, a communication module and a processing terminal, the processing terminal is provided with a BIM model management module, a coupling analysis module, an intelligent early warning module and a visual interaction module, the coupling analysis module is taken as a core, a dynamic mapping relationship between sensor data and BIM components is established, real-time monitoring data is directly injected into a model attribute set, and a built-in structural mechanics calculation engine is driven to carry out real-time stress simulation. The three-dimensional building information model is changed from a static display platform into a digital twin body which can be autonomously updated, and the structure safety state can be dynamically reconstructed according to real-time data. Meanwhile, the intelligent early warning module actively judges based on simulation results and preset hierarchical early warning rules, so that the BIM system has real-time safety evaluation and early warning driving capabilities.

Description

Technical Field

[0001] This invention relates to the field of building engineering safety monitoring technology, and in particular to a BIM-based method and system for monitoring the stress of deep foundation pit supports. Background Technology

[0002] Traditional deep foundation pit support stress monitoring mainly relies on manual inspection and discrete sensor deployment. Data from key points is collected using vibrating wire stress gauges and strain gauges, and then manually recorded, calculated, and analyzed to assess the mechanical state of the support, including axial force and bending moment. While this method can reflect the structural stress state to some extent, it suffers from problems such as discontinuous data acquisition, information silos, and response lag, making it difficult to achieve real-time dynamic monitoring across all weather conditions and sections.

[0003] In recent years, the application of Building Information Modeling (BIM) technology in the field of civil engineering has gradually deepened, providing a new approach to three-dimensional visualization and information integration for deep foundation pit monitoring. BIM-based monitoring methods can initially integrate sensor placement, monitoring data, and structural models to achieve spatial positioning and static display of monitoring data.

[0004] Existing BIM-based monitoring systems mostly focus on post-event data display and lightweight model browsing, resulting in a lack of dynamic coupling and intelligent analysis functions between the BIM model and real-time monitoring data. This leads to the problem that the BIM model cannot autonomously update the structural safety status based on real-time data and proactively drive early warning decisions. Summary of the Invention

[0005] The purpose of this invention is to provide a BIM-based method and system for monitoring the stress of deep foundation pit supports, which solves the problem that existing BIM-based monitoring systems mostly focus on post-event data display and lightweight model browsing, and lack dynamic coupling and intelligent analysis functions between the BIM model and real-time monitoring data, resulting in the BIM model being unable to autonomously update the structural safety status and actively drive early warning decisions based on real-time data.

[0006] To achieve the above objectives, the present invention provides a BIM-based deep foundation pit support stress monitoring system. The BIM-based deep foundation pit support stress monitoring system includes a multi-source sensor acquisition module, a communication module, and a processing terminal. The processing terminal is equipped with a BIM model management module, a coupling analysis module, an intelligent early warning module, and a visualization interaction module. The multi-source sensor acquisition module is deployed at the monitoring points of the deep foundation pit support structure to collect stress, strain, and temperature sensing data of the support components in real time. The communication module is used to transmit the sensor data collected by the multi-source sensor acquisition module to the processing terminal; The BIM model management module is used to store and manage three-dimensional building information models containing information on the geometry, materials, and sensor layout of the support system. The coupling analysis module is used to receive sensor data and dynamically inject the data into the model according to the mapping relationship between the sensor and the BIM model components. At the same time, it drives the built-in structural mechanics calculation engine to perform simulation analysis and safety assessment of the real-time stress state of the support system. The intelligent early warning module is connected to the coupling analysis module and is used to generate multi-level early warning signals based on simulation analysis results and preset hierarchical early warning rules. The visualization and interaction module is used to dynamically display the BIM model, real-time stress status, early warning information and historical trends in a three-dimensional visualization form, and provides a human-computer interaction interface.

[0007] The multi-source sensing acquisition module includes a multi-source acquisition unit, a signal conditioning unit, and an analog-to-digital conversion unit. The multi-source acquisition unit is used to acquire raw frequencies or electrical signals from vibrating wire stress gauges, strain gauges, and temperature sensors. The signal conditioning unit is used to amplify, filter, and perform anti-interference processing on the original signal; The analog-to-digital conversion unit is used to convert the conditioned analog signal into a digital signal, and then package it according to a preset acquisition cycle through an embedded controller.

[0008] The communication module supports at least one of 4G / 5G, LoRa, and Wi-Fi wireless communication methods, and uses MQTT or TCP / IP protocols to transmit data with the processing terminal.

[0009] The BIM model management module stores 3D building information models with an accuracy of no less than LOD300. Model components are associated and bound to sensors in the multi-source sensing acquisition module through unique identifiers to form a sensor-component mapping table.

[0010] The coupling analysis module further includes a data receiving submodule and a mapping submodule. The data receiving submodule is used to receive processed digital sensing data from the communication module. The mapping submodule dynamically injects the sensing data into the attribute set of the corresponding component based on the sensor-component mapping relationship table.

[0011] The structural mechanics calculation engine performs stress simulation analysis based on the injected data and the material and geometric properties of the components. The specific implementation of the structural mechanics calculation engine is as follows: The axial force of reinforced concrete supports is calculated using the following formula: ; In the formula, N1 represents the axial force of the reinforced concrete support. The stress is in the concrete, measured by a concrete strain gauge. A c The net cross-sectional area of ​​the concrete is calculated based on the geometric information of the components in the 3D building information model. σ si For the first i The stress in the reinforcing bar is directly measured using a reinforcing bar stress gauge. A si For the first i The cross-sectional area of ​​each reinforcing bar is obtained from the reinforcing bar layout information in the 3D building information model; The axial force of the steel support is calculated using the following formula: N 2 =EεA ; In the formula, N 2 represents the axial force of the steel support. E The elastic modulus of steel is obtained based on the material properties stored in the 3D building information model. ε To measure the strain directly, it was obtained using a strain gauge. A The cross-sectional area of ​​the steel support is calculated based on the geometric information of the component in the 3D building information model. In addition, a temperature compensation algorithm is introduced into the calculation process to correct the elastic modulus and strain measurements of concrete and steel using actual temperature sensor data, so as to eliminate measurement errors caused by changes in ambient temperature. Simultaneously, a multi-point data fusion strategy is adopted. When multiple sensors are arranged on the same component, the calculated axial force values ​​are weighted and averaged or outliers are removed to obtain a more reliable overall stress result of the component.

[0012] The intelligent early warning module includes an early warning rule base, a real-time decision unit, and an early warning output unit. The early warning rule base has preset multi-level thresholds and rate of change early warning rules. The real-time decision unit matches and makes decisions based on the simulation results output by the coupling analysis module with the early warning rules. The early warning output unit outputs early warning signals based on the decision results through at least one of the following methods: audio-visual equipment, visualization highlighting of a 3D building information model, and mobile terminal push.

[0013] This invention also provides a BIM-based method for monitoring the stress of deep foundation pit supports, applied to the BIM-based method for monitoring the stress of deep foundation pit supports as described above, comprising the following steps: In the BIM model management module, a three-dimensional building information model containing geometric information, material properties, and cross-sectional parameters of the deep foundation pit support system is constructed. The location information of each sensor arranged on the support structure is associated and bound with the corresponding component in the model, and a sensor-component mapping relationship table is established. The multi-source sensor acquisition module collects stress, strain, and temperature sensor data of key monitoring points of the support structure in real time according to a preset acquisition cycle, and transmits the collected sensor data to the processing terminal through the communication module. The coupling analysis module receives sensor data and dynamically injects the sensor data into the attribute set of the corresponding component in the 3D building information model according to the sensor-component mapping table. At the same time, it calls the built-in structural mechanics calculation engine to perform stress state simulation analysis and dynamic calculation of safety factor of the support system based on the injected real-time data and the material and geometric parameters in the 3D building information model. The intelligent early warning module matches and judges the simulation results output by the coupling analysis module with the preset hierarchical early warning rules. If the early warning triggering conditions are met, the corresponding early warning signal is generated. The visualization interaction module is based on the updated 3D building information model. It dynamically displays the real-time stress state of the support system in a 3D visualization form, and uses different colors to indicate the safety level of the components. It also displays early warning information, historical trend curves and key monitoring data, and provides a user interface for parameter configuration, view control and data export.

[0014] This invention discloses a BIM-based method and system for monitoring stress in deep foundation pit supports, comprising a multi-source sensor acquisition module, a communication module, and a processing terminal. The processing terminal is equipped with a BIM model management module, a coupling analysis module, an intelligent early warning module, and a visualization interaction module. With the coupling analysis module as the core, a dynamic mapping relationship is established between sensor data and BIM components. Real-time monitoring data is directly injected into the model attribute set, driving the built-in structural mechanics calculation engine to perform real-time stress simulation. Through a two-way coupling mechanism, the 3D building information model is transformed from a static display platform into a self-updating digital twin, capable of dynamically reconstructing the structural safety status based on real-time data. Simultaneously, the intelligent early warning module proactively judges based on simulation results and preset hierarchical early warning rules, achieving a leap from data display to analytical decision-making, enabling the BIM system to possess real-time safety assessment and early warning driving capabilities. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the BIM-based deep foundation pit support stress monitoring system provided by the present invention.

[0017] Figure 2 This is a schematic diagram of the processing terminal provided by the present invention.

[0018] Figure 3 This is a flowchart of the steps of the BIM-based deep foundation pit support stress monitoring method provided by the present invention.

[0019] 101-Multi-source sensor acquisition module, 102-Communication module, 103-Processing terminal, 104-BIM model management module, 105-Coupling analysis module, 106-Intelligent early warning module, 107-Visual interaction module, 108-Structural mechanics calculation engine, 109-Multi-source acquisition unit, 110-Signal conditioning unit, 111-Analog-to-digital conversion unit, 112-Data receiving submodule, 113-Mapping submodule, 114-Early warning rule base, 115-Real-time decision unit, 116-Early warning output unit. Detailed Implementation

[0020] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0021] Please see Figure 1 and Figure 2 This invention provides a BIM-based deep foundation pit support stress monitoring system. The BIM-based deep foundation pit support stress monitoring system includes a multi-source sensor acquisition module 101, a communication module 102, and a processing terminal 103. The processing terminal 103 is equipped with a BIM model management module 104, a coupling analysis module 105, an intelligent early warning module 106, and a visualization interaction module 107. The multi-source sensor acquisition module 101 is deployed at the monitoring points of the deep foundation pit support structure and is used to collect stress, strain, and temperature sensing data of the support components in real time. The communication module 102 is used to transmit the sensor data collected by the multi-source sensor acquisition module 101 to the processing terminal 103; The BIM model management module 104 is used to store and manage a three-dimensional building information model containing information on the geometry, materials and sensor layout of the support system. The coupling analysis module 105 is used to receive sensor data and dynamically inject the data into the model according to the mapping relationship between the sensor and the BIM model components. At the same time, it drives the built-in structural mechanics calculation engine 108 to perform simulation analysis and safety assessment of the real-time stress state of the support system. The intelligent early warning module 106 is connected to the coupling analysis module 105 and is used to generate multi-level early warning signals based on simulation analysis results and preset hierarchical early warning rules. The visualization interaction module 107 is used to dynamically display the BIM model, real-time stress status, early warning information and historical trends in a three-dimensional visualization form, and to provide a human-computer interaction interface.

[0022] In this embodiment, the coupling analysis module 105 serves as the core, establishing a dynamic mapping relationship between sensor data and BIM components. Real-time monitoring data is directly injected into the model attribute set, driving the built-in structural mechanics calculation engine 108 to perform real-time stress simulation. Through a bidirectional coupling mechanism, the 3D building information model transforms from a static display platform into a self-updating digital twin, capable of dynamically reconstructing the structural safety status based on real-time data. Simultaneously, the intelligent early warning module 106 proactively judges based on simulation results and preset hierarchical early warning rules, achieving a leap from data display to analysis and decision-making, enabling the BIM system to possess real-time safety assessment and early warning capabilities.

[0023] Furthermore, the multi-source sensing acquisition module 101 includes a multi-source acquisition unit 109, a signal conditioning unit 110, and an analog-to-digital conversion unit 111. The multi-source acquisition unit 109 is used to acquire raw frequency or electrical signals from vibrating wire stress gauges, strain gauges, and temperature sensors. The signal conditioning unit 110 is used to amplify, filter, and perform anti-interference processing on the original signal. The analog-to-digital conversion unit 111 is used to convert the conditioned analog signal into a digital signal and package it according to a preset acquisition cycle through an embedded controller.

[0024] In this embodiment, the embedded controller uses an STM32F407 series microprocessor, which achieves precise data acquisition at specific times through a timer interrupt mechanism, and encapsulates the packaged digital signals into JSON format data frames to improve the structured nature of data transmission and parsing efficiency.

[0025] Furthermore, the communication module 102 supports at least one of 4G / 5G, LoRa, and Wi-Fi wireless communication methods, and uses MQTT or TCP / IP protocols to transmit data with the processing terminal 103.

[0026] In this embodiment, the system prioritizes 4G / 5G modules as a remote transmission solution based on the network coverage conditions and transmission distance requirements at the construction site. It also utilizes the lightweight and low-power characteristics of the MQTT protocol to achieve reliable and real-time push of monitoring data to the processing terminal 103, while supporting disconnection reconnection and data caching mechanisms.

[0027] Furthermore, the accuracy of the three-dimensional building information model stored in the BIM model management module 104 is not less than LOD300, and the model components are associated and bound with the sensors in the multi-source sensing acquisition module 101 through unique identifiers to form a sensor-component mapping relationship table.

[0028] In this embodiment, the sensor-component mapping table is stored in the model attribute database in XML format, including sensor ID, component ID, installation location, acquisition type and calibration parameters. The system automatically loads the mapping table during the initialization phase and uses the table to quickly match monitoring data with model components and update their attributes during the data injection phase.

[0029] Furthermore, the coupling analysis module 105 also includes a data receiving submodule 112 and a mapping submodule 113. The data receiving submodule 112 is used to receive processed digital sensing data from the communication module 102; the mapping submodule 113 dynamically injects the sensing data into the attribute set of the corresponding component based on the sensor-component mapping relationship table. The structural mechanics calculation engine 108 performs stress simulation analysis based on the injected data and the material and geometric properties of the components. The specific implementation of the structural mechanics calculation engine 108 is as follows: The axial force of reinforced concrete supports is calculated using the following formula: ; In the formula, N 1 represents the axial force of the reinforced concrete support. The stress is in the concrete, measured by a concrete strain gauge. A c The net cross-sectional area of ​​the concrete is calculated based on the geometric information of the components in the 3D building information model. σ si For the first i The stress in the reinforcing bar is directly measured using a reinforcing bar stress gauge. A si For the first i The cross-sectional area of ​​each reinforcing bar is obtained from the reinforcing bar layout information in the 3D building information model; The axial force of the steel support is calculated using the following formula: N 2 =EεA ; In the formula, N 2 represents the axial force of the steel support. E The elastic modulus of steel is obtained based on the material properties stored in the 3D building information model. ε To measure the strain directly, it was obtained using a strain gauge. A The cross-sectional area of ​​the steel support is calculated based on the geometric information of the component in the 3D building information model. In addition, a temperature compensation algorithm is introduced into the calculation process to correct the elastic modulus and strain measurements of concrete and steel using actual temperature sensor data, so as to eliminate measurement errors caused by changes in ambient temperature. Simultaneously, a multi-point data fusion strategy is adopted. When multiple sensors are arranged on the same component, the calculated axial force values ​​are weighted and averaged or outliers are removed to obtain a more reliable overall stress result of the component.

[0030] In this embodiment, after receiving the injected sensor data, the structural mechanics calculation engine 108 automatically identifies the type of supporting component and calls the corresponding formula to calculate the axial force. During the calculation process, the system simultaneously reads temperature sensor data and compensates for the elastic modulus and strain values ​​in real time based on the material's thermal expansion coefficient, thereby significantly improving the accuracy of monitoring data under complex temperature environments. For the same component with multiple sensors, the structural mechanics calculation engine 108 employs an outlier elimination algorithm based on statistical principles (such as the Laida criterion) and a weighted average strategy to further improve the reliability and engineering applicability of the stress assessment results.

[0031] Furthermore, the intelligent early warning module 106 includes an early warning rule base 114, a real-time decision unit 115, and an early warning output unit 116. The early warning rule base 114 has preset multi-level thresholds and rate of change early warning rules. The real-time decision unit 115 matches and makes decisions based on the simulation results output by the coupling analysis module 105 with the early warning rules. The early warning output unit 116 outputs early warning signals based on the decision results through at least one of the following methods: audio-visual equipment, visualization highlighting of a 3D building information model, and mobile terminal push.

[0032] In this embodiment, the multi-level thresholds in the early warning rule base 114 are set according to design specifications and engineering experience, including 70% (prompt level), 85% (early warning level), and 100% (alarm level) of the design value. The change rate threshold is dynamically adjustable according to the engineering risk level. The real-time decision unit 115 executes the decision logic once at regular intervals. When the monitoring data meets any level of early warning conditions, the early warning output unit 116 simultaneously triggers the on-site audible and visual alarms to flash and sound, highlights the corresponding component in the three-dimensional model with the corresponding color, and pushes a structured early warning message to the responsible personnel through WeChat Enterprise Account or SMS platform, realizing multi-channel and three-dimensional early warning information transmission.

[0033] Please see Figure 3 The present invention also provides a BIM-based method for monitoring the stress of deep foundation pit supports, which is applied to the BIM-based method for monitoring the stress of deep foundation pit supports as described above, and includes the following steps: S1: Construct a three-dimensional building information model in the BIM model management module 104, which includes the geometric information, material properties, and cross-sectional parameters of the deep foundation pit support system, and associate and bind the location information of each sensor arranged on the support structure with the corresponding component in the model to establish a sensor-component mapping relationship table. S2: The multi-source sensor acquisition module 101 collects stress, strain and temperature sensor data of key monitoring points of the support structure in real time according to a preset acquisition cycle, and transmits the collected sensor data to the processing terminal 103 through the communication module 102. S3: The coupling analysis module 105 receives the sensing data and dynamically injects the sensing data into the attribute set of the corresponding component in the three-dimensional building information model according to the sensor-component mapping relationship table. At the same time, it calls the built-in structural mechanics calculation engine 108 to perform stress state simulation analysis and dynamic calculation of safety factor on the support system based on the injected real-time data and the material and geometric parameters in the three-dimensional building information model. S4: The intelligent early warning module 106 matches and judges the simulation results output by the coupling analysis module 105 with the preset hierarchical early warning rules. If the early warning triggering conditions are met, the corresponding early warning signal is generated. S5: The visualization interaction module 107 is based on the updated three-dimensional building information model. It dynamically displays the real-time stress state of the support system in a three-dimensional visualization form, and uses different colors to indicate the safety level of the components. At the same time, it displays early warning information, historical trend curves and key monitoring data, and provides a user interface for parameter configuration, view control and data export.

[0034] In this embodiment, the real-time monitoring data is deeply coupled with the 3D building information model, driving the model to dynamically reconstruct the stress state of the supporting structure and calculate the safety factor in real time. This achieves a fundamental transformation from traditional static visualization to dynamic simulation and intelligent decision-making, enabling proactive risk identification, graded early warning, and intuitive presentation of the structural safety status in a 3D visualization manner. This significantly improves the real-time performance, accuracy, and decision support capabilities of safety monitoring for deep foundation pit projects.

[0035] The above description discloses only one preferred embodiment of the present invention, and should not be construed as limiting the scope of the present invention. Those skilled in the art will understand that all or part of the processes of the above embodiments can be implemented, and equivalent changes made in accordance with the claims of the present invention are still within the scope of the invention.

Claims

1. A BIM-based deep foundation pit support stress monitoring system, characterized in that, It includes a multi-source sensor acquisition module, a communication module, and a processing terminal. The processing terminal is equipped with a BIM model management module, a coupling analysis module, an intelligent early warning module, and a visualization interaction module. The multi-source sensor acquisition module is deployed on the monitoring points of the deep foundation pit support structure to collect stress, strain, and temperature sensing data of the support components in real time. The communication module is used to transmit the sensor data collected by the multi-source sensor acquisition module to the processing terminal; The BIM model management module is used to store and manage three-dimensional building information models containing information on the geometry, materials, and sensor layout of the support system. The coupling analysis module is used to receive sensor data and dynamically inject the data into the model according to the mapping relationship between the sensor and the BIM model components. At the same time, it drives the built-in structural mechanics calculation engine to perform simulation analysis and safety assessment of the real-time stress state of the support system. The intelligent early warning module is connected to the coupling analysis module and is used to generate multi-level early warning signals based on simulation analysis results and preset hierarchical early warning rules. The visualization and interaction module is used to dynamically display the BIM model, real-time stress status, early warning information and historical trends in a three-dimensional visualization form, and provides a human-computer interaction interface.

2. The BIM-based deep foundation pit support stress monitoring system as described in claim 1, characterized in that, The multi-source sensing acquisition module includes a multi-source acquisition unit, a signal conditioning unit, and an analog-to-digital conversion unit. The multi-source acquisition unit is used to acquire raw frequency or electrical signals from vibrating wire stress gauges, strain gauges, and temperature sensors. The signal conditioning unit is used to amplify, filter, and perform anti-interference processing on the original signal; The analog-to-digital conversion unit is used to convert the conditioned analog signal into a digital signal, and then package it according to a preset acquisition cycle through an embedded controller.

3. The BIM-based deep foundation pit support stress monitoring system as described in claim 2, characterized in that, The communication module supports at least one of 4G / 5G, LoRa, and Wi-Fi wireless communication methods, and uses MQTT or TCP / IP protocols to transmit data with the processing terminal.

4. The BIM-based deep foundation pit support stress monitoring system as described in claim 3, characterized in that, The BIM model management module stores 3D building information models with an accuracy of no less than LOD300. Model components are associated and bound to sensors in the multi-source sensing acquisition module through unique identifiers to form a sensor-component mapping table.

5. The BIM-based deep foundation pit support stress monitoring system as described in claim 4, characterized in that, The coupling analysis module further includes a data receiving submodule and a mapping submodule. The data receiving submodule is used to receive processed digital sensing data from the communication module. The mapping submodule dynamically injects sensor data into the attribute set of the corresponding component based on the sensor-component mapping table.

6. The BIM-based deep foundation pit support stress monitoring system as described in claim 5, characterized in that, The structural mechanics calculation engine performs stress simulation analysis based on the injected data and the material and geometric properties of the components. The specific implementation of the structural mechanics calculation engine is as follows: The axial force of reinforced concrete supports is calculated using the following formula: ； In the formula, N 1 represents the axial force of the reinforced concrete support. The stress is in the concrete, measured by a concrete strain gauge. A c This is the net cross-sectional area of ​​the concrete, calculated based on the component's geometric information in the 3D building information model. σ si For the first i The stress in the reinforcing bar is directly measured using a reinforcing bar stress gauge. A si For the first i The cross-sectional area of ​​each reinforcing bar is obtained from the reinforcing bar layout information in the 3D building information model; The axial force of the steel support is calculated using the following formula: N 2 =EεA ； In the formula, N 2 represents the axial force of the steel support. E The elastic modulus of steel is obtained based on the material properties stored in the 3D building information model. ε To measure the strain directly, it was obtained using a strain gauge. A The cross-sectional area of ​​the steel support is calculated based on the geometric information of the component in the 3D building information model. A temperature compensation algorithm is introduced into the calculation process to correct the elastic modulus and strain measurements of concrete and steel using actual temperature sensor data, so as to eliminate measurement errors caused by changes in ambient temperature. Simultaneously, a multi-point data fusion strategy is adopted. When multiple sensors are arranged on the same component, the calculated axial force values ​​are weighted and averaged or outliers are removed to obtain a more reliable overall stress result of the component.

7. The BIM-based deep foundation pit support stress monitoring system as described in claim 6, characterized in that, The intelligent early warning module includes an early warning rule base, a real-time decision unit, and an early warning output unit. The early warning rule base has preset multi-level thresholds and rate of change early warning rules. The real-time decision unit matches and makes decisions based on the simulation results output by the coupling analysis module with the early warning rules. The early warning output unit outputs early warning signals based on the decision results through at least one of the following methods: audio-visual equipment, visualization highlighting of a 3D building information model, and mobile terminal push.

8. A BIM-based method for monitoring the stress of deep foundation pit supports, applied to the BIM-based method for monitoring the stress of deep foundation pit supports as described in claim 1, characterized in that, Includes the following steps: In the BIM model management module, a three-dimensional building information model containing geometric information, material properties, and cross-sectional parameters of the deep foundation pit support system is constructed. The location information of each sensor arranged on the support structure is associated and bound with the corresponding component in the model, and a sensor-component mapping relationship table is established. The multi-source sensor acquisition module collects stress, strain, and temperature sensor data of key monitoring points of the support structure in real time according to a preset acquisition cycle, and transmits the collected sensor data to the processing terminal through the communication module. The coupling analysis module receives sensor data and dynamically injects the sensor data into the attribute set of the corresponding component in the 3D building information model according to the sensor-component mapping table. At the same time, it calls the built-in structural mechanics calculation engine to perform stress state simulation analysis and dynamic calculation of safety factor of the support system based on the injected real-time data and the material and geometric parameters in the 3D building information model. The intelligent early warning module matches and judges the simulation results output by the coupling analysis module with the preset hierarchical early warning rules. If the early warning triggering conditions are met, the corresponding early warning signal is generated. The visualization interaction module is based on the updated 3D building information model. It dynamically displays the real-time stress state of the support system in a 3D visualization form, and uses different colors to indicate the safety level of the components. It also displays early warning information, historical trend curves and key monitoring data, and provides a user interface for parameter configuration, view control and data export.

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