Intelligent monitoring system and method for construction process of suspension bridge

Abstract

The invention discloses an intelligent monitoring system and method for the construction process of a suspension bridge. The system comprises a data acquisition module and a digital twinborn control platform which are connected with each other. The data acquisition module acquires bridge body structure data, construction equipment operation data and constructor safety data in real time through a multi-source sensor; in the digital twinborn control platform, a bridge body state monitoring unit constructs a three-dimensional dynamic twinborn model of the suspension bridge by fusing a BIM model and real-time structural data, and maps the state of each part of the bridge body in real time; the construction equipment monitoring unit adaptively adjusts equipment parameters according to a preset analysis model to guarantee normal operation of equipment; the operator safety monitoring unit is combined with the bridge body data and the safety data to ensure the construction safety; and the trend early warning analysis unit is used for early warning potential construction risks in advance according to the real-time data. Accurate quality control and management support is provided for suspension bridge construction, intelligent and efficient execution of construction projects can be promoted, and project quality and safety are ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of suspension bridge construction monitoring, and in particular to an intelligent monitoring system and method for a suspension bridge construction process. Background Art

[0002] Suspension bridges, as an important bridge structure, are widely used in long-span bridge construction due to their high span capacity and simple, aesthetically pleasing structure. However, the construction process is complex, involving the installation and adjustment of multiple key structural components (such as main cables, beam segments, and load-bearing cables), requiring extremely high construction precision. During construction, the stability of the bridge structure, the operating status of construction equipment, and the safety of workers all require strict monitoring to ensure construction quality and safety.

[0003] Currently, suspension bridge construction monitoring primarily relies on manual inspections, traditional sensor monitoring, and simple data recording systems. These methods have numerous shortcomings: First, manual inspections are inefficient, making it difficult to detect potential risks in a timely manner and subject to significant subjective factors. Second, traditional sensor monitoring systems are limited in functionality, collecting only limited local data and failing to fully monitor the overall condition of the bridge structure. Third, existing monitoring systems often only monitor the bridge deck structure and lack the ability to integrate with construction equipment operating parameters to dynamically optimize the overall bridge construction process. Finally, existing technologies have vulnerabilities in worker safety monitoring, failing to provide early warning of potential construction risks, resulting in high safety risks during construction.

[0004] Therefore, there is an urgent need to develop an intelligent monitoring system and method for the construction process of a suspension bridge, which can comprehensively monitor the bridge structure, construction equipment, and operation safety, and provide early warning of potential construction risks, thereby improving the safety and reliability of the construction process. Summary of the Invention

[0005] In view of this, the present invention provides an intelligent monitoring system and method for the construction process of a suspension bridge, which is used to solve the technical problems of low efficiency, incomplete monitoring, and lack of dynamic adjustment and early warning capabilities in existing monitoring methods for the construction process of a suspension bridge.

[0006] In order to achieve the above technical objectives, the present invention adopts the following technical solutions:

[0007] In a first aspect, the present invention provides an intelligent monitoring method for a suspension bridge construction process, comprising an interconnected data acquisition module and a digital twin control platform;

[0008] Data acquisition module, used to collect bridge structure data, construction equipment operation data and construction personnel safety data in real time through multi-source sensing modules;

[0009] The digital twin control platform includes a bridge status monitoring unit, a construction equipment monitoring unit and an operator safety monitoring unit, which are connected in sequence. The bridge status monitoring unit and the construction equipment monitoring unit are also connected to a trend analysis unit. Among them, the bridge status monitoring unit is used to construct a three-dimensional dynamic twin model of the suspension bridge by integrating the BIM model with the real-time bridge structure data, and dynamically map the real-time structural status of the main cables, beam sections and load-bearing cables of the suspension bridge; the construction equipment monitoring unit is used to adaptively adjust the operating parameters of the construction equipment according to the preset motion trajectory, thresholds and analysis models to ensure the normal operation of the construction equipment; the operator safety monitoring unit is used to judge the location information and physiological status of the operators based on the bridge structure data and the construction personnel safety data to ensure the safety of the operators' work; the trend analysis unit is used to provide early warning of potential construction risks based on the pre-trained analysis model and the bridge structure data and the construction equipment operation data.

[0010] Furthermore, the data acquisition module includes: a bridge structure sensing unit, an equipment status monitoring unit and a personnel safety monitoring unit;

[0011] The bridge structure sensing unit includes at least a positioning module, a visual camera, a vibrating string sensor, a magnetic flux sensor and an inclinometer, which are used to monitor the bridge status data in real time;

[0012] The equipment status monitoring unit includes at least a current / voltage sensor, a tension sensor, and a porosity detector, which are used to monitor the operating status and construction effect of each construction equipment in real time;

[0013] The personnel safety monitoring unit is equipped with an RFID positioning bracelet that can monitor heart rate and is used to collect the construction workers' positioning information and physiological data in real time.

[0014] Furthermore, the bridge monitoring unit includes a main cable line shape analysis module;

[0015] The main cable linear analysis module obtains GNSS positioning data, IMU inertial data and visual image data through the positioning module and the visual camera;

[0016] The SfM algorithm is used to generate a point cloud model, and the bridge's 3D reconstruction technology and IMU inertial data are combined to eliminate point cloud jitter caused by wind loads or equipment vibrations.

[0017] The deviation of the main cable line shape is calculated based on the axis fitting, and the deviation value between the actual coordinates of the main cable axis and the designed line shape is output.

[0018] Furthermore, the bridge monitoring unit includes a beam segment status monitoring module;

[0019] The beam segment status monitoring module is used to monitor the strain changes and distribution of the beam segment structure during the lifting process in real time, judge the stress state of the structure, and detect overload.

[0020] The posture of the beam section is determined based on the stress distribution of the beam section and the measured inclination angle, and the working parameters of the lifting equipment are updated based on the posture detection results.

[0021] Furthermore, the bridge monitoring unit includes a load-bearing cable damage judgment unit;

[0022] The load-bearing rope damage judgment unit detects the damage state of the wire rope according to the magnetic flux sensor, and the judgment method includes:

[0023] Magnetic flux data is collected by magnetic flux sensors arranged at different positions of the load-bearing cable;

[0024] Calculate the rate of change of magnetic flux over time and identify potential fusion points based on the rate of change;

[0025] The time-frequency characteristic values ​​of potential fusion points are extracted. When the time-frequency characteristic value of any position of the load-bearing cable exceeds the set threshold, it is determined that the wire rope at the position is damaged.

[0026] Furthermore, the construction equipment monitoring unit includes a winch real-time monitoring module;

[0027] The winch real-time monitoring module establishes a dynamic relationship model between traction force and traction speed based on the physical parameters of the rope strands and the performance parameters of the winch;

[0028] The traction force and speed of the winch are coordinated and controlled according to the winch's real-time output power, design tension and real-time ambient wind force.

[0029] Furthermore, the dynamic relationship model between the traction force and the traction speed is:

[0030]

[0031] in, represents the traction friction, Indicates the ambient wind force, represents the load caused by gravity, P represents the real-time power of the winch, Indicates the pulling speed, Indicates the total mass of the winch and the traction load;

[0032] Based on the winch's real-time output power, design pulling force, and real-time ambient wind force, the winch's pulling force and speed are controlled in a coordinated manner, including:

[0033] The target traction speed and traction force are determined based on the dynamic relationship model between traction force and traction speed, and the PID control output and fuzzy control output are obtained using the PID controller and fuzzy inference mechanism respectively.

[0034] The comprehensive control output is obtained by weighted average based on the PID control output and the fuzzy control output to achieve a dynamic balance between the traction force and the traction speed.

[0035] Furthermore, the operator safety monitoring unit is used to monitor the stress conditions of the anchoring system and the transverse channel based on the real-time data of the vibrating wire sensor, monitor the tail rope force and the force changes of the catwalk in real time, and evaluate the slippage of the catwalk load-bearing cable based on the positioning module;

[0036] Determine the safe area of ​​the catwalk based on the stress conditions of the anchoring system and the transverse channel, as well as the tail rope force and the force changes of the catwalk;

[0037] The number and location of construction workers are determined based on the data from the RFID positioning bracelets, and real-time safety warnings are provided to construction workers based on the safety areas of the catwalk.

[0038] Furthermore, the pre-trained analysis model includes an input layer, a feature extraction layer, a fusion layer, a decision layer and an output layer;

[0039] Among them, the feature extraction layer includes a CNN network and an LSTM network. The CNN network is used to extract the spatial features of the bridge structure, and the LSTM is used to process time series data and capture the temporal dependency of construction equipment operation data. The attention mechanism is introduced into the fusion layer to improve the interpretability and accuracy of the model.

[0040] On the other hand, the present invention further provides a method for intelligently monitoring the construction process of a suspension bridge, which is implemented using the intelligent monitoring system for the construction process of a suspension bridge described in the above solution, comprising:

[0041] Use multi-source sensing modules to collect real-time data on bridge structure, construction equipment operation, and construction personnel safety.

[0042] A three-dimensional dynamic twin model of the suspension bridge is constructed based on real-time bridge structure data through the bridge status monitoring unit, dynamically mapping the real-time structural status of the main cables, beam segments, and load-bearing cables;

[0043] Adaptively adjust the operating parameters of construction equipment based on preset motion trajectories, thresholds, and analysis models to ensure normal operation of construction equipment;

[0044] Combine bridge structure data and construction worker safety data to determine workers' location information and physiological status, ensuring safety during operations.

[0045] Based on the pre-trained analysis model, early warning of potential construction risks is provided according to bridge structure data and construction equipment operation data.

[0046] Compared with the existing technology, the intelligent monitoring method for the suspension bridge construction process proposed in this invention has the following advantages:

[0047] (1) By integrating BIM models with real-time data, the system can accurately monitor each structural part of the bridge and dynamically adjust the operating parameters of construction equipment to ensure that the equipment works in the best condition, thereby improving construction efficiency, reducing equipment failure rate, and reducing unnecessary downtime. It also ensures that the construction quality is maintained at a high standard throughout the process and prevents any structural defects.

[0048] (2) By real-time monitoring of bridge structure data and construction personnel safety data, the system can promptly detect potential safety hazards, ensure the safety of workers and equipment, and avoid accidents;

[0049] (3) Based on the intelligent prediction of the trend warning analysis unit, the system can identify the risks that may occur during the construction process in advance, provide early warning information to the construction team, and help them take appropriate emergency measures to avoid serious consequences.

[0050] In summary, the present invention can remotely monitor and control various data on the construction site, allowing managers to monitor and track construction progress without having to visit the site in person, helping the project team to grasp the construction status in real time, ensuring the construction safety of the suspension bridge project, and improving the level of intelligent construction management. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 A structural diagram of the intelligent monitoring system for the suspension bridge construction process provided by the present invention;

[0052] Figure 2 A monitoring diagram of the main cable linear analysis module provided by the present invention;

[0053] Figure 3 This is a flow chart of the intelligent monitoring method for the suspension bridge construction process provided by the present invention. DETAILED DESCRIPTION

[0054] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, and are not used to limit the scope of the present invention.

[0055] Example 1

[0056] See Figure 1,This embodiment provides an intelligent monitoring system 100 for a suspension bridge construction process, comprising a data acquisition module 101 and a digital twin control platform 102 connected to each other;

[0057] The data acquisition module 101 is used to collect bridge structure data, construction equipment operation data and construction personnel safety data in real time through a multi-source sensing module;

[0058] The digital twin control platform 102 includes a bridge state monitoring unit 201, a construction equipment monitoring unit 202, a worker safety monitoring unit 203 and a trend warning analysis unit 204; wherein, the bridge state monitoring unit 201 is used to construct a three-dimensional dynamic twin model of the suspension bridge by integrating the BIM model with the real-time bridge structure data, and dynamically map the real-time structural status of the main cables, beam sections and load-bearing cables of the suspension bridge; the construction equipment monitoring unit 202 is used to adaptively adjust the operating parameters of the construction equipment according to the preset equipment motion trajectory, threshold and analysis model to ensure the normal operation of the construction equipment; the worker safety monitoring unit 203 is used to judge the location information and physiological status of the workers according to the bridge structure data and the construction workers' safety data to ensure the safety of the workers' work; the trend analysis unit 204 is used to provide early warning of potential construction risks based on the pre-trained analysis model and the bridge structure data and the construction equipment operation data.

[0059] The system of this embodiment dynamically adjusts the operating parameters of construction equipment to ensure that the equipment operates in optimal conditions, thereby improving construction efficiency, reducing equipment failure rates, and minimizing unnecessary downtime; by real-time monitoring of bridge structure data and construction personnel safety data, the system can promptly detect potential safety hazards, ensure the safety of workers and equipment, and avoid accidents; based on the intelligent prediction of the trend analysis unit, the system can identify risks that may arise during the construction process in advance, provide early warning information to the construction team, help them take appropriate emergency measures, and further ensure construction safety.

[0060] As a preferred embodiment, the data acquisition module includes: a bridge structure sensing unit, an equipment status monitoring unit and a personnel safety monitoring unit;

[0061] The bridge structure sensing unit includes at least a positioning module, a visual camera, a vibrating string sensor, a magnetic flux sensor and an inclinometer, which are used to monitor the bridge status data in real time;

[0062] The equipment status monitoring unit includes at least a current / voltage sensor, a tension sensor, and a porosity detector, which are used to monitor the operating status and construction effect of each construction equipment in real time;

[0063] The personnel safety monitoring unit is equipped with an RFID positioning bracelet that monitors heart rate and collects the construction workers' positioning information and physiological data in real time.

[0064] In some embodiments, the positioning module uses a combination of GNSS Beidou and an accelerometer, the visual camera uses an industrial-grade binocular camera + telephoto lens (focal length ≥ 200mm), and is equipped with an 850nm infrared fill light (installed on the tower top or gantry observation position). To ensure the synchronization of multi-camera data, the data is transmitted to the control platform using the PTP precision time protocol, and the real-time error is <0.1ms.

[0065] As a preferred embodiment, the bridge monitoring unit includes a main cable line shape analysis module;

[0066] The main cable linear analysis module obtains GNSS positioning data, IMU inertial data and visual image data through the positioning module and the visual camera respectively;

[0067] The SfM algorithm is used to generate a point cloud model, and the bridge's 3D reconstruction technology and IMU inertial data are combined to eliminate point cloud jitter caused by wind loads or equipment vibrations.

[0068] The deviation of the main cable line shape is calculated based on the axis fitting, and the deviation value between the actual coordinates of the main cable axis and the designed line shape is output.

[0069] like Figure 2 As shown, Figure 2 The monitoring diagram of the main cable linear analysis module is shown. Positioning data and visual data are obtained through an industrial-grade binocular camera and a telephoto lens installed at the tower top or gantry observation position, respectively. The SfM algorithm is used to generate a point cloud model, and the vibration error is eliminated by combining bridge 3D reconstruction technology and IMU inertial data. Finally, the deviation of the main cable axis coordinate (XYZ) from the designed linear value is output.

[0070] As a preferred embodiment, the bridge monitoring unit includes a beam segment status monitoring module;

[0071] The beam segment status monitoring module is used to monitor the strain changes and distribution of the beam segment structure during the lifting process in real time, judge the stress state of the structure, and detect overload.

[0072] The posture of the beam section is determined based on the stress distribution of the beam section and the measured inclination angle, and the working parameters of the lifting equipment are updated based on the posture detection results.

[0073] As a specific example, steel beam hoisting is also an important step in the construction of a suspension bridge. Monitoring of beam sections usually includes stress detection of key structures of the beam section, posture level detection, and beam section load detection.

[0074] When stress testing is performed on key structures, strain gauges are attached to key structural parts to monitor the strain changes of the structure during the lifting process in real time, thereby calculating the stress state of the structure. When the set value is exceeded, the system automatically alarms and takes corresponding protective measures.

[0075] During attitude level detection, the inclination angle of the beam section is measured through an inclination sensor to ensure the horizontality of the beam section during the lifting process. At the same time, GNSS Beidou positioning and accelerometers are used to measure the height difference and acceleration of the beam section, and the attitude information of the beam section is calculated through a data fusion algorithm. Based on the attitude detection results, the lifting equipment is adjusted in real time through the control system to ensure that the beam section can remain horizontal.

[0076] In addition, weighing sensors are installed at key locations of the lifting equipment to monitor the size of the lifting load in real time. The actual load is judged to see whether it exceeds the set value based on the preset safety range of the lifting load, and corresponding protective measures are taken based on the judgment results.

[0077] As a preferred embodiment, the bridge monitoring unit includes a load-bearing cable damage judgment unit;

[0078] The load-bearing rope damage judgment unit detects the damage state of the wire rope according to the magnetic flux sensor, and the judgment method includes:

[0079] Magnetic flux data is collected by magnetic flux sensors arranged at different positions of the load-bearing cable;

[0080] Calculate the rate of change of magnetic flux over time and identify potential fusion points based on the rate of change;

[0081] The time-frequency characteristic values ​​of potential fusion points are extracted. When the time-frequency characteristic value of any position of the load-bearing cable exceeds the set threshold, it is determined that the wire rope at the position is damaged.

[0082] The magnetic flux sensor captures the magnetic field distortion of the wire rope to identify the damage location, and the damage identification accuracy reaches the millimeter level. In some embodiments, a wire rope damage evolution prediction model can also be configured in the trend analysis unit to predict the damage evolution trend based on the damage location and damage status.

[0083] As a preferred embodiment, the construction equipment monitoring unit includes a winch real-time monitoring module;

[0084] The winch real-time monitoring module establishes a dynamic relationship model between traction force and traction speed based on the physical parameters of the rope strands and the performance parameters of the winch;

[0085] The traction force and speed of the winch are coordinated and controlled according to the winch's real-time output power, design tension and real-time ambient wind force.

[0086] As a preferred embodiment, the dynamic relationship model between traction force and traction speed is:

[0087]

[0088] in, represents the traction friction, Indicates the ambient wind force, represents the load caused by gravity, P represents the real-time power of the winch, Indicates the pulling speed, Indicates the total mass of the winch and the traction load;

[0089] Based on the winch's real-time output power, design pulling force, and real-time ambient wind force, the winch's pulling force and speed are controlled in a coordinated manner, including:

[0090] The target traction speed and traction force are determined based on the dynamic relationship model between traction force and traction speed, and the PID control output and fuzzy control output are obtained using the PID controller and fuzzy inference mechanism respectively.

[0091] The comprehensive control output is obtained by weighted average based on the PID control output and the fuzzy control output to achieve a dynamic balance between the traction force and the traction speed.

[0092] Combining traction and speed control to achieve coordinated adjustment of the two can optimize the entire erection process and improve work efficiency.

[0093] In some embodiments, the control software of the construction equipment monitoring unit can coordinate the synchronization of multiple winches and various monitoring parameters, enabling one-click operation while also providing proactive safety protection. To gradually escalate responses to different risk levels and implement corresponding emergency measures in stages, improving the speed and accuracy of emergency response, some embodiments utilize real-time load status feedback from tension sensors, establishing a three-level early warning mechanism based on varying degrees of threshold exceeding.

[0094] As a specific embodiment, the construction equipment monitoring unit also includes an intelligent traction control unit, which uses the Beidou positioning module to realize automatic deceleration of the tractor when passing through the portal frame and precise stopping at the end point; when the tractor enters the deceleration area, the system automatically adjusts the speed of the tractor; when approaching the end point, the system automatically adjusts the speed and stops precisely, significantly improving the safety and efficiency of the tractor during the construction process.

[0095] As a specific embodiment, the construction equipment monitoring unit also includes a cable tensioning machine monitoring system, which extracts contour information from image data of beam sections or wire ropes taken at different angles, calculates the out-of-roundness based on the cross-sectional profile of the wire rope, and detects the integrity and performance of the steel structure during the construction process to ensure its reliability and safety in actual applications.

[0096] In some embodiments, the cable traction status can also be monitored through real-time images fed back by an anti-shake AI camera installed on the grabber. The host computer analyzes the cable status in real time. When loose wires or twisting occur, the system promptly outputs warning information and replaces manual follow-up observation with an AI image tracking system, realizing automated monitoring of the construction process.

[0097] As a preferred embodiment, the operator safety monitoring unit is used to monitor the stress conditions of the anchoring system and the transverse channel based on the real-time data of the vibrating wire sensor, monitor the tail rope force and the force changes of the catwalk in real time, and evaluate the slippage of the catwalk load-bearing cable based on the positioning module;

[0098] Determine the safe area of ​​the catwalk based on the stress conditions of the anchoring system and the transverse channel, as well as the tail rope force and the force changes of the catwalk;

[0099] The number and location of people on the upstream and downstream catwalks are monitored based on the data from the RFID positioning bracelets, and real-time safety warnings are provided to construction workers based on the safety areas of the catwalks.

[0100] In some embodiments, combined with the heart rate monitoring function on the positioning bracelet, it is possible to understand the health status of construction workers on the catwalk and prevent construction accidents caused by physical discomfort.

[0101] As a preferred embodiment, the pre-trained analysis model includes an input layer, a feature extraction layer, a fusion layer, a decision layer and an output layer;

[0102] Among them, the feature extraction layer includes a CNN network and an LSTM network. The CNN network is used to extract the spatial features of the bridge structure, and the LSTM is used to process time series data and capture the temporal dependency of construction equipment operation data. The attention mechanism is introduced into the fusion layer to improve the interpretability and accuracy of the model.

[0103] In a specific embodiment, the trend analysis unit further includes a construction work efficiency and equipment utilization statistics module, which uses work efficiency and equipment utilization data to optimize construction plans and resource allocation strategies that are more in line with actual construction practices.

[0104] Example 2

[0105] like Figure 3 As shown, an embodiment of the present invention further provides a method for intelligently monitoring the construction process of a suspension bridge, which is implemented using the intelligent monitoring device for the construction process of a suspension bridge described in Example 1, and includes:

[0106] Step S101: constructing a three-dimensional dynamic twin model of the suspension bridge based on real-time bridge structure data through a bridge state monitoring unit, and dynamically mapping the real-time structural states of the main cables, beam segments, and load-bearing cables;

[0107] Step S102: Adaptively adjust the operating parameters of the construction equipment according to the preset motion trajectory, threshold value and analysis model to ensure the normal operation of the construction equipment;

[0108] Step S103: combining the bridge structure data and the construction personnel safety data to determine the location information and physiological status of the workers to ensure the safety of the workers' work;

[0109] Step S104: Based on the pre-trained analysis model, according to the bridge structure data and construction equipment operation data, an early warning of potential construction risks is issued.

[0110] The intelligent monitoring system and method for the suspension bridge construction process disclosed in the present invention ensure that the equipment works in the optimal state by dynamically adjusting the operating parameters of the construction equipment, thereby improving construction efficiency, reducing equipment failure rate, and shortening unnecessary downtime; by real-time monitoring of bridge structure data and construction personnel safety data, the system can promptly detect potential safety hazards, ensure the safety of workers and equipment, and avoid accidents; based on the intelligent prediction of the trend analysis unit, the system can identify risks that may arise during the construction process in advance, and through remote monitoring and control of various data on the construction site, managers can monitor and track construction progress without having to visit the site in person, helping the project team to grasp the construction status in real time, ensuring the construction safety of the suspension bridge project, and improving the level of intelligent construction management.

[0111] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with this technical field within the technical scope disclosed by the present invention should be covered by the scope of protection of the present invention.

Claims

1. An intelligent monitoring system for the construction process of a suspension bridge, characterized in that: Includes interconnected data acquisition modules and digital twin control platform; Data acquisition module, used to collect bridge structure data, construction equipment operation data and construction personnel safety data in real time through multi-source sensing modules; The digital twin control platform includes a bridge status monitoring unit, a construction equipment monitoring unit and an operator safety monitoring unit, which are connected in sequence. The bridge status monitoring unit and the construction equipment monitoring unit are also connected to a trend analysis unit. Among them, the bridge status monitoring unit is used to construct a three-dimensional dynamic twin model of the suspension bridge by integrating the BIM model with the real-time bridge structure data, and dynamically map the real-time structural status of the main cables, beam sections and load-bearing cables of the suspension bridge; the construction equipment monitoring unit is used to adaptively adjust the operating parameters of the construction equipment according to the preset equipment motion trajectory, thresholds and analysis models to ensure the normal operation of the construction equipment; the operator safety monitoring unit is used to judge the location information and physiological status of the operators based on the bridge structure data and the construction personnel safety data to ensure the safety of the operators' work; the trend analysis unit is used to provide early warning of potential construction risks based on the pre-trained analysis model and the bridge structure data and the construction equipment operation data.

2. The intelligent monitoring system for the suspension bridge construction process according to claim 1 is characterized in that: The data acquisition module includes: a bridge structure sensing unit, an equipment status monitoring unit and a personnel safety monitoring unit; The bridge structure sensing unit includes at least a positioning module, a visual camera, a vibrating string sensor, a magnetic flux sensor and an inclinometer, which are used to monitor the bridge status data in real time; The equipment status monitoring unit includes at least a current / voltage sensor, a tension sensor, and a porosity detector, which are used to monitor the operating status and construction effect of each construction equipment in real time; The personnel safety monitoring unit is equipped with an RFID positioning bracelet that can monitor heart rate and is used to collect the construction workers' positioning information and physiological data in real time.

3. The intelligent monitoring system for the construction process of a suspension bridge according to claim 2 is characterized in that: The bridge monitoring unit includes a main cable line shape analysis module; The main cable linear analysis module obtains GNSS positioning data, IMU inertial data and visual image data through the positioning module and the visual camera; The SfM algorithm is used to generate a point cloud model, and the bridge's 3D reconstruction technology and IMU inertial data are combined to eliminate point cloud jitter caused by wind loads or equipment vibrations. The deviation of the main cable line shape is calculated based on the axis fitting, and the deviation value between the actual coordinates of the main cable axis and the designed line shape is output.

4. The intelligent monitoring system for the construction process of a suspension bridge according to claim 2 is characterized in that: The bridge monitoring unit includes a beam segment status monitoring module; The beam segment status monitoring module is used to monitor the strain changes and distribution of the beam segment structure during the lifting process in real time, judge the stress state of the structure, and detect overload. The posture of the beam section is determined based on the stress distribution of the beam section and the measured inclination angle, and the working parameters of the lifting equipment are updated based on the posture detection results.

5. The intelligent monitoring system for the construction process of a suspension bridge according to claim 2 is characterized in that: The bridge monitoring unit includes a load-bearing cable damage judgment unit; The load-bearing rope damage judgment unit detects the damage state of the wire rope according to the magnetic flux sensor, and the judgment method includes: Magnetic flux data is collected by magnetic flux sensors arranged at different positions of the load-bearing cable; Calculate the rate of change of magnetic flux over time and identify potential fusion points based on the rate of change; The time-frequency characteristic values ​​of potential fusion points are extracted. When the time-frequency characteristic value of any position of the load-bearing cable exceeds the set threshold, it is determined that the wire rope at the position is damaged.

6. The intelligent monitoring system for the construction process of a suspension bridge according to claim 2 is characterized in that: The construction equipment monitoring unit includes a winch real-time monitoring module; The winch real-time monitoring module establishes a dynamic relationship model between traction force and traction speed based on the physical parameters of the rope strands and the performance parameters of the winch; The traction force and speed of the winch are coordinated and controlled according to the winch's real-time output power, design tension and real-time ambient wind force.

7. The intelligent monitoring system for the construction process of a suspension bridge according to claim 6 is characterized in that: The dynamic relationship model between traction force and traction speed is: in, represents the traction friction, Indicates the ambient wind force, represents the load caused by gravity, P represents the real-time power of the winch, Indicates the pulling speed, Indicates the total mass of the winch and the traction load; Based on the winch's real-time output power, design pulling force, and real-time ambient wind force, the winch's pulling force and speed are controlled in a coordinated manner, including: The target traction speed and traction force are determined based on the dynamic relationship model between traction force and traction speed, and the PID control output and fuzzy control output are obtained using the PID controller and fuzzy inference mechanism respectively. The comprehensive control output is obtained by weighted average based on the PID control output and the fuzzy control output to achieve a dynamic balance between the traction force and the traction speed.

8. The intelligent monitoring system for the construction process of a suspension bridge according to claim 2 is characterized in that: The operator safety monitoring unit is used to monitor the stress conditions of the anchoring system and the transverse channel based on the real-time data of the vibrating wire sensor, monitor the tail rope force and the force changes of the catwalk in real time, and evaluate the slippage of the catwalk load-bearing cable based on the positioning module; Determine the safe area of ​​the catwalk based on the stress conditions of the anchoring system and the transverse channel, as well as the tail rope force and the force changes of the catwalk; The number and location of construction workers are determined based on the data from the RFID positioning bracelets, and real-time safety warnings are provided to construction workers based on the safety areas of the catwalk.

9. The intelligent monitoring system for the construction process of a suspension bridge according to claim 1, characterized in that: The pre-trained analysis model includes an input layer, a feature extraction layer, a fusion layer, a decision layer and an output layer; Among them, the feature extraction layer includes a CNN network and an LSTM network. The CNN network is used to extract the spatial features of the bridge structure, and the LSTM is used to process time series data and capture the temporal dependency of construction equipment operation data. The attention mechanism is introduced into the fusion layer to improve the interpretability and accuracy of the model.

10. An intelligent monitoring method for the construction process of a suspension bridge, characterized in that: The intelligent monitoring system for the construction process of a suspension bridge according to any one of claims 1 to 9 is used for implementation, comprising: Use multi-source sensing modules to collect real-time data on bridge structure, construction equipment operation, and construction personnel safety. A three-dimensional dynamic twin model of the suspension bridge is constructed based on real-time bridge structure data through the bridge status monitoring unit, dynamically mapping the real-time structural status of the main cables, beam segments, and load-bearing cables; Adaptively adjust the operating parameters of construction equipment based on preset motion trajectories, thresholds, and analysis models to ensure normal operation of construction equipment; Combine bridge structure data and construction worker safety data to determine workers' location information and physiological status, ensuring safety during operations. Based on the pre-trained analysis model, early warning of potential construction risks is provided according to bridge structure data and construction equipment operation data.

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