Swivel bridge construction monitoring and early warning system and method based on digital twinning

Abstract

The invention belongs to the field of bridge construction monitoring, and particularly discloses a digital twinning-based swivel bridge construction monitoring and early warning system and method.The system comprises an acquisition unit and a data processing unit, and data transmission is achieved between the acquisition unit and the data processing unit in a wired communication and wireless communication cooperation mode; the acquisition unit comprises a stress meter, an inclinometer and an anemograph; the data processing unit comprises a data management library, a model loading program and a data processing server; by associating data acquired by a stress meter, an inclinometer and an anemograph with a BIM model, the swivel angle deviation, stress, swivel angle and wind speed are monitored in real time, so that the problems that an existing bridge swivel structure monitoring system is not timely enough in data acquisition and is difficult to make timely feedback when hidden dangers exist in local stress of the structure are solved; in addition, by adding a data processing and analysis algorithm and a threshold dynamic adjustment algorithm, the accuracy and reliability of data and the early warning precision and reliability of the system are improved.

Description

Technical Field

[0001] This invention relates to the field of bridge construction monitoring, and in particular to a digital twin-based monitoring and early warning system and method for swing bridge construction. Background Technology

[0002] With the continuous densification of urban transportation networks, construction in busy urban areas must be carried out while ensuring the uninterrupted operation of existing transportation lines such as railways and elevated highways. This places higher demands on construction efficiency, environmental impact, and economic benefits. Swing bridge construction, with its multiple advantages of high efficiency, environmental friendliness, economy, stability, and innovation, has become the preferred bridge construction solution for traversing complex terrain and busy traffic areas.

[0003] Construction monitoring of swing bridges typically relies on manual inspections and single sensors. This involves personnel going to the site to collect raw data using monitoring equipment, which is then processed and analyzed. This method depends on visual inspection and simple tools, making it difficult to accurately assess the structural condition. Furthermore, the inspection cycle is long and cannot cover the entire construction process.

[0004] Existing technologies struggle to provide real-time monitoring during construction and suffer from inadequate early warning mechanisms. Specifically, collected data is difficult to report promptly, and responses are often delayed when local stress exceeds design limits. Failure to address such situations in a timely manner can lead to irreversible structural damage.

[0005] Traditional early warning systems typically use fixed threshold values, which means that the thresholds cannot be adjusted in real time according to changes in the structural stress state and environmental factors during construction. This can easily lead to inaccurate warnings that fail to reflect the true safety status of the structure, potentially resulting in false alarms (over-intervention in construction) or missed alarms (missing danger signals).

[0006] Uploading collected data directly to the cloud can prevent effective organization and in-depth analysis of the monitoring data. It makes it difficult to quickly extract valuable information from massive amounts of data, and the data is susceptible to noise interference, making it impossible to accurately identify abnormal trends and potential risks. This will make it difficult to effectively control construction risks, thereby affecting construction progress and quality, and may even lead to safety hazards or accidents. Summary of the Invention

[0007] To address the aforementioned technical problems, this invention provides a construction monitoring and early warning system and method for a swing bridge based on digital twins.

[0008] To achieve the above objectives, the present invention is implemented according to the following technical solution:

[0009] The first technical solution provided by this invention is a construction monitoring and early warning system for a swing bridge based on digital twins, including a data acquisition unit and a data processing unit. The data acquisition unit and the data processing unit transmit data through a combination of wired and wireless communication.

[0010] The data acquisition unit includes a stress meter, an inclinometer, and an anemometer. The stress meter is installed at the ball joint of the swing bridge, key sections of the beam, piers, and anchor points of the traction system to collect data related to the stress of the steel structure and concrete. The anemometer is installed at the center and both ends of the upper surface of the swing bridge beam to collect environmental indices during construction. The inclinometer is installed in the four quadrants of the swing bridge's turntable to monitor the changes in inclination angle in the X and Y directions during the swing bridge's rotation in real time. Cameras are arranged on the swing bridge near the inclinometer to capture real-time images.

[0011] The data processing unit includes a data management library and a data processing server. The data management library is used to store the data transmitted from the acquisition unit in different tables, thereby increasing the speed and flexibility of data extraction. The data processing server has a built-in BIM model of the rotating bridge. The data processing server is used to process, analyze and compare the various data collected during the bridge rotation process with the limit values ​​of the construction design, and issue early warning prompts for data that exceed the limit values ​​of the construction design.

[0012] Furthermore, the stress gauge includes a vibrating wire strain gauge installed at the root of the cantilever section of the main beam to monitor the construction stress under maximum cantilever conditions, a smart bolt with strain gauges installed on both sides of the closure section to monitor the stress changes at the closure joint in real time, and a stress sensor installed at the top and bottom of the pier to monitor the combined compressive and bending stress caused by the rotation eccentricity.

[0013] Furthermore, the wired communication uses an RS-485 bus to connect each sensor to the data processing unit; the wireless communication uses a DTU wireless communication module, which enables wireless data transmission; by using the RS-485 bus communication module and the DTU wireless communication module in combination, the various data collected by the acquisition unit can be transmitted through a combination of wired and wireless communication.

[0014] Furthermore, the data processing server is used to clean, filter, reduce noise, extract features, and remove outliers from the raw data collected by stress gauges, inclinometers, and anemometers, thereby removing irrelevant noise and interference signals and extracting valuable information. Through coordinate mapping between device IDs and the BIM model, the processed real-time data is transmitted to the BIM model via the MQTT protocol, driving model attribute updates and 3D visualization. A video data interface is added to the BIM model, connecting the video streams from each camera via a network protocol. The data processing server includes a threshold dynamic adjustment algorithm that dynamically adjusts stress warning thresholds, wind speed warning thresholds, and tilt deviation warning thresholds by analyzing the trends, fluctuation ranges, and changes in construction conditions of the monitoring data. This adapts to complex construction environments and structural states at different stages, achieving real-time dynamic monitoring at different stages of construction, timely detection of rotation angle deviations and stress exceeding risks, and thus providing early warnings to reduce structural damage caused by untimely monitoring or data errors.

[0015] Furthermore, the formula for the threshold dynamic adjustment algorithm is as follows:

[0016] ;

[0017] In the formula: T base α is the base threshold, set according to design requirements and experience; α is the adjustment coefficient, used to control the magnitude of threshold adjustment; μ is the mean of historical data; σ is the standard deviation of historical data; k is the confidence coefficient, with a value of 1.96; β is the construction stage adjustment factor, used to adjust the threshold according to the construction stage.

[0018] Furthermore, the stress warning threshold is divided into three categories: Level 1 warning: less than 80% of the design value; Level 2 warning: reaching 80% to 100% of the design value; Level 3 warning: greater than or equal to 100% of the design value.

[0019] Furthermore, the wind speed warning thresholds are divided into three categories: Level 1 warning: normal construction can proceed when the wind speed is ≤10m / s; Level 2 warning: the rotation speed needs to be reduced when the wind speed is 10~15m / s; Level 3 warning: the rotation needs to be suspended when the wind speed is ≥15m / s.

[0020] Furthermore, the tilt angle deviation warning threshold is divided into three categories: Level 1 warning: tilt angle deviation between 0.03° and 0.05°; Level 2 warning: tilt angle deviation between 0.05° and 0.1°; Level 3 warning: tilt angle deviation > 0.1°.

[0021] The second technical solution provided by this invention is a construction monitoring and early warning method for a swing bridge based on digital twins. The method utilizes the aforementioned construction monitoring and early warning system for swing bridges based on digital twins for early warning purposes. The specific steps are as follows:

[0022] Step 1: Bridge Modeling: First, collect and organize data, including bridge design drawings, material parameters, geographical information, environmental conditions, etc.; then, create the bridge family framework in Revit, and gradually add details and other components; then set parameters and define constraints to ensure the model's realism and reliability; finally, perform analysis and optimization design.

[0023] Step 2: Install stress gauges, inclinometers, anemometers, and cameras on site;

[0024] Step 3: Assign BIM element IDs to each stress meter, inclinometer, anemometer, and camera to ensure a one-to-one correspondence;

[0025] Step 4: The data collected by the stress gauge, inclinometer, anemometer, and camera is wirelessly transmitted to the data processing unit for cleaning, filtering, noise reduction, feature extraction, and outlier removal. After compensation, the data is imported into the BIM cloud platform for 3D dynamic visualization: stress distribution is displayed using color gradients (green = safe, red = exceeding limits); wind direction and speed are displayed in the BIM model (arrow size represents wind speed); data points exceeding limits flash or pop up warning boxes.

[0026] Step 5: The data processing unit uses a data fusion algorithm to match the timestamps of the video data collected by the camera with the timestamps of the sensor data, so that the video footage and sensor monitoring data at the same moment can be viewed simultaneously in the BIM model.

[0027] Step 6: By analyzing the trends, fluctuation ranges, and changes in construction conditions of the monitoring data, dynamically adjust the stress warning threshold, wind speed warning threshold, and tilt angle deviation warning threshold to adapt to the complex construction environment and structural state at different stages. This will enable real-time dynamic monitoring at different stages of the construction process, timely detection of rotation angle deviation and stress over-limit risks, and thus provide early warnings to reduce structural damage caused by untimely monitoring or data errors.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] 1. The various sensors of this invention capture dynamic data of the construction site in real time and transmit the data to the data processing unit through a wireless communication module. After processing, the data is associated with the BIM model and displayed together through a 3D animation module. This allows construction personnel to monitor the bridge rotation construction more comprehensively and in a timely manner, observe whether any accidents occur around the construction site, and ensure that in the event of a safety problem, the set camera video can detect the problem and respond immediately, suspending the bridge rotation until the fault is eliminated, thereby further improving the safety of construction.

[0030] 2. This invention, by setting a threshold, enables the system to immediately issue an early warning upon detecting anomalies or potential risks, allowing construction personnel to take timely measures to prevent accidents. Furthermore, a dynamic adjustment algorithm for the early warning threshold is incorporated, adaptively adjusting the threshold based on real-time and historical data to adapt to different construction stages and environmental conditions. This dynamic adjustment mechanism effectively avoids false alarms or missed alarms caused by fixed thresholds. For example, when the stress at a certain part of the bridge exceeds a preset threshold, the system will issue an early warning, and the threshold will be adjusted according to the set algorithm at different construction stages, prompting construction personnel to inspect and reinforce the structure, thereby achieving the effect of risk early warning.

[0031] 3. This invention uses LoRa sensors to collect data, which is then shared through a cloud platform. This allows all project stakeholders (such as design, construction, and supervision) to obtain the necessary information in real time, improving collaborative management efficiency and achieving data sharing.

[0032] 4. This invention uses the BIM model as a data carrier, enabling the association between virtual data and on-site construction entities, thereby improving the overall level and efficiency of construction management. This achieves the effects of collaborative management and real-time monitoring.

[0033] 5. The data processing algorithm incorporated in this invention significantly improves the performance and reliability of the swing bridge construction monitoring system. By cleaning, filtering, denoising, and extracting features from the massive amounts of raw data collected, it effectively removes irrelevant noise and outliers, thereby improving the accuracy and usability of the data. This not only makes the monitoring results more realistically reflect the structural state during construction but also provides a more scientific and accurate basis for construction decisions. Furthermore, the data processing algorithm can quickly filter out valuable information, improve the system's response speed and processing efficiency, thereby optimizing the construction management process, reducing construction risks, and ensuring the safety and efficiency of swing bridge construction. Attached Figure Description

[0034] Figure 1 This is a structural flowchart of the construction monitoring and early warning system for a swing bridge based on digital twins, as described in this invention.

[0035] Figure 2 This is a flowchart of the stress gauge monitoring process for the construction monitoring and early warning system of the rotating bridge based on digital twins, as described in this invention.

[0036] Figure 3 This is a flowchart of the tiltmeter monitoring process for the digital twin-based construction monitoring and early warning system for a rotating bridge, as described in this invention.

[0037] Figure 4 This is a flowchart of the anemometer monitoring process for the construction monitoring and early warning system of the rotating bridge based on digital twins, as described in this invention.

[0038] Figure 5The arrow points to a schematic diagram of the stress gauge arrangement in the construction monitoring and early warning method for rotating bridges based on digital twins according to this invention.

[0039] Figure 6 This is a schematic diagram of the inclinometer arrangement for the digital twin-based construction monitoring and early warning method for rotating bridges according to the present invention.

[0040] Figure 7 This is a schematic diagram of the wind measurement instrument layout for the construction monitoring and early warning method for a swing bridge based on digital twins, as described in this invention.

[0041] Figure 8 This is a flowchart of the data processing and analysis algorithm for the construction monitoring and early warning method for rotating bridges based on digital twins, as described in this invention.

[0042] Figure 9 The flowchart of the dynamic threshold algorithm of the construction monitoring and early warning method for swing bridge based on digital twins in this invention. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments. The specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention.

[0045] Example 1

[0046] like Figure 1 As shown in the figure, this embodiment exemplarily demonstrates a construction monitoring and early warning system for a swing bridge based on digital twins, including a data acquisition unit 1 and a data processing unit 2. The data acquisition unit 1 and the data processing unit 2 transmit data through a combination of wired and wireless communication.

[0047] Data acquisition unit 1 includes a stress gauge 11, an inclinometer 12, and an anemometer 13. The stress gauge 11 is installed at the ball joint of the swing bridge, key sections of the beam, piers, and anchor points of the traction system. Figure 5 As shown, the stress gauge 11 includes a vibrating wire strain gauge installed at the root of the cantilever section of the main beam to monitor the construction stress under maximum cantilever conditions; smart bolts with strain gauges installed on both sides of the closure section to monitor stress changes at the closure joint in real time; and stress sensors installed at the top and bottom of the piers to monitor the combined compressive and bending stress caused by the rotational eccentricity. First, the most unfavorable stress location on the bridge is calculated using the Midas model. Then, an externally mounted stress-strain sensor is fixed at each of these locations to collect data related to the stress in the steel structure and concrete. Figure 7 As shown, the anemometer 13 is installed at the center and both ends of the upper surface of the swing bridge beam to collect environmental indices during construction; such as Figure 6As shown, the inclinometer 12 is set in the four quadrants of the turntable of the swing bridge to monitor the changes in inclinometer angle in the X and Y directions in real time during the swing bridge's rotation; a camera is arranged on the swing bridge near the inclinometer 12 to collect real-time images.

[0048] Data processing unit 2 includes a data management library 21 and a data processing server 22. The data management library 21 stores the data transmitted from acquisition unit 1 in different tables, thereby increasing the speed and flexibility of data extraction. The data processing server 22 has a built-in BIM model of the rotating bridge. It processes, analyzes, and compares the collected data with the limits set in the construction design, and provides early warnings for data exceeding these limits. In practical use, the data can also be transmitted to PCs and mobile devices via data processing unit 2 to intuitively and accurately display various monitoring data and basic project information during the bridge rotation process, highlighting information requiring early warning.

[0049] Wired communication uses an RS-485 bus to connect each sensor to the data processing unit 2; wireless communication uses a DTU wireless communication module, which enables wireless data transmission; by using the RS-485 bus communication module and the DTU wireless communication module in combination, the data collected in the acquisition unit 1 can be transmitted through a combination of wired and wireless communication.

[0050] The data processing server performs cleaning, filtering, noise reduction, feature extraction, and outlier removal operations on the raw data collected by the stress gauge 11, inclinometer 12, and anemometer 13, thereby removing irrelevant noise and interference signals and extracting valuable information. Through coordinate mapping between the device ID and the BIM model, the processed real-time data is transmitted to the BIM model via the MQTT protocol, driving model attribute updates and 3D visualization. A video data interface is added to the BIM model, connecting the video streams from each camera via a network protocol. The data processing server includes a dynamic threshold adjustment algorithm, the formula of which is:

[0051] ;

[0052] In the formula: T base α is the base threshold, set according to design requirements and experience; μ is the adjustment coefficient, used to control the magnitude of threshold adjustment; μ is the mean of historical data; σ is the standard deviation of historical data; k is the confidence coefficient, with a value of 1.96; β is the construction stage adjustment factor, used to adjust the threshold according to the construction stage.

[0053] By analyzing the trends, fluctuation ranges, and changes in construction conditions of monitoring data, the stress warning threshold, wind speed warning threshold, and tilt deviation warning threshold are dynamically adjusted to adapt to complex construction environments and structural states at different stages. This enables real-time dynamic monitoring at different stages of construction, timely detection of rotation angle deviations and stress over-limit risks, and early warning of risks, thereby reducing structural damage caused by untimely monitoring or data errors.

[0054] The stress warning thresholds are divided into three categories: Level 1 warning: less than 80% of the design value; Level 2 warning: 80% to 100% of the design value; Level 3 warning: greater than or equal to 100% of the design value.

[0055] The wind speed warning thresholds are divided into three categories: Level 1 warning: normal construction can proceed when the wind speed is ≤10m / s; Level 2 warning: the rotation speed needs to be reduced when the wind speed is 10~15m / s; Level 3 warning: the rotation needs to be suspended when the wind speed is ≥15m / s.

[0056] The tilt deviation warning thresholds are divided into three categories: Level 1 warning: tilt deviation between 0.03° and 0.05°; Level 2 warning: tilt deviation between 0.05° and 0.1°; Level 3 warning: tilt deviation > 0.1°.

[0057] The specific steps for using the aforementioned digital twin-based construction monitoring and early warning system for swing bridges are as follows:

[0058] Step 1: Bridge Modeling: First, collect and organize data, including bridge design drawings, material parameters, geographical information, environmental conditions, etc.; then, create the bridge family framework in Revit, and gradually add details and other components; then set parameters and define constraints to ensure the model's realism and reliability; finally, perform analysis and optimization design.

[0059] Step 2: Install stress gauge 11, inclinometer 12, anemometer 13 and camera on site;

[0060] Step 3: Assign BIM element IDs to each stress gauge 11, inclinometer 12, anemometer 13, and camera to ensure a one-to-one correspondence;

[0061] Step 4: The data collected by the stress gauge 11, inclinometer 12, anemometer 13, and camera are wirelessly transmitted to the data processing unit 2 for cleaning, filtering, noise reduction, feature extraction, and outlier removal. High-frequency noise is removed using a low-pass filter. The formula for the low-pass filter is as follows:

[0062] ;

[0063] Where M[n] is the filtered data, y[n] is the original data, and x is the filtering coefficient (0 < x < 1).

[0064] Furthermore, outliers are detected using the following formula.

[0065] ;

[0066] Where Y is a data point, It is the mean. That is the standard deviation. If... If the value is greater than 3, then the data point is considered an outlier.

[0067] Furthermore, the formula is updated through filtering:

[0068] ;

[0069] Data from multiple sensors is processed together to improve data accuracy; It is the state estimate at the current moment. It is a priori estimation. It is the Kalman gain, z k is the Kalman gain, and A is the observation matrix.

[0070] Furthermore, principal component analysis is performed using the following formula:

[0071] ;

[0072] Where Y is the original data matrix, V is the eigenvector matrix, and Y new It is the data matrix after dimensionality reduction;

[0073] After compensation, the data is imported into the BIM cloud platform for 3D dynamic visualization: stress distribution is displayed using color gradients (green = safe, red = exceeding limits); wind direction and speed are displayed in the BIM model (arrow size represents wind speed); exceeding-limit data points flash or pop up warning boxes; for example... Figure 2 The diagram illustrates the stress gauge monitoring process. The stress warning threshold settings should follow these principles: Level 1 Warning (Green): Stress is less than 80% of the design value; normal construction is permitted. If three or more measuring points on the same cross-section simultaneously exceed 80%, it automatically escalates to Level 2 Warning. Level 2 Warning (Yellow): 80% ≤ Stress < 100% of the design allowable value; at this point, reduce the rotation speed and perform manual verification. Level 3 Warning (Red): Stress ≥ 100% of the design allowable value or sudden change > 10% / min. Immediately halt rotation and initiate a reinforcement plan. Risk warnings are synchronized to the BIM model. When stress exceeds the limit, a red pulsed halo appears at the component boundary, with the frequency increasing with the excess percentage. Construction personnel should promptly address the alarm location based on the model. Figure 3The diagram illustrates the inclinometer monitoring process. The inclinometer employs a dual-axis MEMS inclinometer with a built-in LoRa module, acquiring inclinometer (X / Y axis) data every 3 seconds. This data is encapsulated into LoRaWAN protocol packets, received via a gateway, and demodulated using the LoRaWAN protocol. During this process, a series of anti-interference measures are implemented, and the gateway's built-in system performs preliminary data verification, discarding weak signal packets. The device ID is matched to the ball joint component in the BIM model, updating dynamic parameters. The ball joint in the BIM model is colored according to the inclinometer gradient (blue → yellow → red), overlaying the designed rotation path (green dashed line) and the actual trajectory (red solid line). When the inclinometer deviation is between 0.03° and 0.05°, the component flashes yellow, and a pop-up window prompts for manual measurement verification; between 0.05° and 0.1°, the model flashes red with an alarm, then automatically reduces speed to 50%; when the deviation > 0.1°, the entire model flashes red, operation permissions are locked, and the system is shut down immediately. Figure 4 The diagram illustrates the anemometer monitoring process. The anemometer is an ultrasonic anemometer with a range of 0-60 m / s and an accuracy of ±0.1 m / s. It integrates a LoRa wireless module, collecting wind speed and direction data every 2 seconds and encapsulating it into LoRaWAN protocol data packets. An internal heating module is incorporated to ensure normal operation even in low-temperature environments. The gateway uploads MQTT protocol data to the cloud via 4G. Real-time wind direction and speed are displayed in the BIM model using arrow flows. When the wind speed exceeds the limit, the affected area automatically flashes red and displays a numerical label. Wind speed thresholds are set to three levels: ≤10 m / s, component flashes yellow, status bar prompts to check equipment fixation; 10~15 m / s, model flashes red + pop-up warning box, automatic speed reduction to 50%, wind-resistant cables tightened; ≥15 m / s, full-screen red coverage + alarm sound for emergency shutdown, personnel evacuation. Afterwards, engineers manually monitor and review the wind vibration simulation results in the BIM model to manually confirm resumption of construction.

[0074] Step 5: Data Processing Unit 2 uses a data fusion algorithm to match the timestamps of the video data collected by the camera with the timestamps of the sensor data, allowing simultaneous viewing of video footage and sensor monitoring data at the same moment in the BIM model. A video data interface is added to the BIM model, accessing the camera video stream via the RTSP protocol. The data fusion algorithm matches the timestamps of the video data with those of the sensor data, achieving synchronized display of video footage and monitoring data. Real-time meteorological data is obtained from the weather station and accessed to the BIM model via an API interface. The meteorological data is then correlated with the monitoring data to analyze the impact of environmental factors on the structure. Construction progress data is obtained from the construction management software and correlated with the monitoring data in the BIM model, synchronously displaying progress and monitoring status through construction progress bars and other methods. Simultaneously, big data analytics is used to deeply mine historical monitoring data. Appropriate time series analysis or machine learning algorithms are selected to train the data, establishing a predictive model to analyze the changing trends of data such as stress, tilt angle, and wind speed, identifying potential risks in advance. The prediction results are displayed through a visualization module in the BIM model using color markings or animation effects. The model is dynamically updated based on real-time monitoring data. When the prediction results indicate a risk, an early warning mechanism is automatically triggered to notify construction personnel to take measures.

[0075] Step 6: As Figure 8 , Figure 9 As shown, the threshold dynamic adjustment algorithm of data processing unit 2 dynamically adjusts the stress warning threshold, wind speed warning threshold, and tilt angle deviation warning threshold by analyzing the trend, fluctuation range, and changes in construction conditions of the monitoring data. The formula for the threshold dynamic adjustment algorithm is:

[0076] ;

[0077] In the formula: T base α is the basic threshold, set according to design requirements and experience; μ is the adjustment coefficient, used to control the magnitude of threshold adjustment; μ is the mean of historical data; σ is the standard deviation of historical data; k is the confidence coefficient, with a value of 1.96; β is the construction stage adjustment factor, used to adjust the threshold according to the construction stage. This is to adapt to the complex construction environment and the structural state at different stages, to achieve real-time dynamic monitoring at different stages of the construction process, to promptly detect rotation angle deviations and stress over-limit risks, and to provide early warnings of risks, so as to reduce structural damage caused by untimely monitoring or data errors.

[0078] The technical solutions of the present invention are not limited to the specific embodiments described above. Any technical modifications made in accordance with the technical solutions of the present invention fall within the protection scope of the present invention.

Claims

1. A construction monitoring and early warning system for a swing bridge based on digital twins, comprising a data acquisition unit 1 and a data processing unit, characterized in that: The acquisition unit and the data processing unit transmit data through a combination of wired and wireless communication. The data acquisition unit includes a stress meter, an inclinometer, and an anemometer. The stress meter is installed at the ball joint of the swing bridge, key sections of the beam, piers, and anchor points of the traction system to collect data related to the stress of the steel structure and concrete. The anemometer is installed at the center and both ends of the upper surface of the swing bridge beam to collect environmental indices during construction. The inclinometer is installed in the four quadrants of the swing bridge's turntable to monitor the changes in inclination angle in the X and Y directions during the swing bridge's rotation in real time. Cameras are arranged on the swing bridge near the inclinometer to capture real-time images. The data processing unit includes a data management library and a data processing server; The data management library is used to store the data transmitted from the acquisition unit in different tables, thereby increasing the speed and flexibility of data extraction; the data processing server has a built-in BIM model of the rotating bridge. The data processing server is used to process, analyze and compare the various data collected during the bridge rotation process with the limit values ​​of the construction design, and issue early warning prompts for data that exceed the limit values ​​of the construction design.

2. The construction monitoring and early warning system for a swing bridge based on digital twins as described in claim 1, characterized in that: The stress gauges include a vibrating wire strain gauge installed at the root of the cantilever section of the main beam to monitor the construction stress under maximum cantilever conditions, a smart bolt with strain gauges installed on both sides of the closure section to monitor stress changes at the closure joint in real time, and a stress sensor installed at the top and bottom of the pier to monitor the combined compressive and bending stress caused by the rotation eccentricity.

3. The construction monitoring and early warning system for a swing bridge based on digital twins according to claim 1, characterized in that: The wired communication uses an RS-485 bus to connect each sensor to the data processing unit; the wireless communication uses a DTU wireless communication module, which enables wireless data transmission; by using the RS-485 bus communication module and the DTU wireless communication module in combination, the various data collected by the acquisition unit can be transmitted through a combination of wired and wireless communication.

4. The construction monitoring and early warning system for a swing bridge based on digital twins according to claim 1, characterized in that: The data processing server is used to clean, filter, reduce noise, extract features, and remove outliers from the raw data collected by stress gauges, inclinometers, and anemometers, thereby removing irrelevant noise and interference signals and extracting valuable information. Through coordinate mapping between device IDs and the BIM model, the processed real-time data is transmitted to the BIM model via the MQTT protocol, driving model attribute updates and 3D visualization. A video data interface is added to the BIM model, connecting the video streams from each camera via a network protocol. The data processing server includes a dynamic threshold adjustment algorithm that dynamically adjusts stress warning thresholds, wind speed warning thresholds, and tilt deviation warning thresholds by analyzing the trends, fluctuation ranges, and changes in construction conditions of the monitoring data. This adapts to complex construction environments and structural states at different stages, achieving real-time dynamic monitoring at different stages of construction, timely detection of rotation angle deviations and stress exceeding risks, and thus providing early warnings to reduce structural damage caused by untimely monitoring or data errors.

5. The construction monitoring and early warning system for a swing bridge based on digital twins according to claim 4, characterized in that: The formula for the dynamic threshold adjustment algorithm is: ； In the formula: T base α is the base threshold, set according to design requirements and experience; α is the adjustment coefficient, used to control the magnitude of threshold adjustment. μ is the mean of the historical data; σ is the standard deviation of the historical data; k is the confidence coefficient, with a value of 1.96; β is the construction stage adjustment factor, used to adjust the threshold according to the construction stage.

6. The construction monitoring and early warning system for a swing bridge based on digital twins according to claim 1, characterized in that: The stress warning thresholds are divided into three categories: Level 1 warning: less than 80% of the design value; Level 2 warning: 80% to 100% of the design value; Level 3 warning: greater than or equal to 100% of the design value.

7. The construction monitoring and early warning system for a swing bridge based on digital twins according to claim 1, characterized in that: The wind speed warning thresholds are divided into three categories: Level 1 warning: normal construction can proceed when the wind speed is ≤10m / s; Level 2 warning: the wind speed warning is 10~15m / s, at which point the rotation speed needs to be reduced. Level 3 warning: If the wind speed is ≥15m / s, the rotation must be suspended.

8. The construction monitoring and early warning system for a swing bridge based on digital twins according to claim 1, characterized in that: The tilt angle deviation warning thresholds are divided into three categories: Level 1 warning: tilt angle deviation between 0.03° and 0.05°; Level 2 warning: tilt angle deviation between 0.05° and 0.1°; Level 3 warning: Tilt angle deviation > 0.1°.

9. A construction monitoring and early warning method for a swing bridge based on digital twins, characterized in that: The early warning system for construction monitoring and early warning of a swing bridge based on digital twins as described in any one of claims 1-8 is used, and the specific steps are as follows: Step 1: Bridge Modeling: First, collect and organize data, including bridge design drawings, material parameters, geographical information, environmental conditions, etc.; then, create the bridge family framework in Revit, and gradually add details and other components; then set parameters and define constraints to ensure the model's realism and reliability; finally, perform analysis and optimization design. Step 2: Install stress gauges, inclinometers, anemometers, and cameras on site; Step 3: Assign BIM element IDs to each stress meter, inclinometer, anemometer, and camera to ensure a one-to-one correspondence; Step 4: The data collected by the stress gauge, inclinometer, anemometer, and camera is wirelessly transmitted to the data processing unit for cleaning, filtering, noise reduction, feature extraction, and outlier removal. After compensation, the data is imported into the BIM cloud platform for 3D dynamic visualization: stress distribution is displayed using color gradients (green = safe, red = exceeding limits); wind direction and speed are displayed in the BIM model (arrow size represents wind speed); data points exceeding limits flash or pop up warning boxes. Step 5: The data processing unit uses a data fusion algorithm to match the timestamps of the video data collected by the camera with the timestamps of the sensor data, so that the video footage and sensor monitoring data at the same moment can be viewed simultaneously in the BIM model. Step 6: By analyzing the trends, fluctuation ranges, and changes in construction conditions of the monitoring data, dynamically adjust the stress warning threshold, wind speed warning threshold, and tilt angle deviation warning threshold to adapt to the complex construction environment and structural state at different stages. This will enable real-time dynamic monitoring at different stages of the construction process, timely detection of rotation angle deviation and stress over-limit risks, and thus provide early warnings to reduce structural damage caused by untimely monitoring or data errors.

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