Prestressed cable construction process dynamic monitoring system and method based on magnetic flux sensor

Abstract

The invention discloses a prestressed cable construction process dynamic monitoring system and method based on a magnetic flux sensor. The system comprises a self-adaptive clamping type magnetic flux sensor, an edge computing gateway, a central monitoring platform and a feedback execution module. The sensor is fixed on the surface of the inhaul cable in a clamping or adsorbing manner; the edge computing gateway is responsible for local data cleaning, fusion and preliminary diagnosis; the central monitoring platform integrates BIM and a simulation model, realizes data visualization and deep analysis, and generates a predictive regulation and control instruction; and the feedback execution module can be linked with the tensioning equipment to realize closed-loop control. The problems that in the prior art, sensor deployment is not flexible, dynamic construction process monitoring cannot be competent, and intelligent regulation and control capacity is lacked are solved, and safe, accurate and intelligent control over the whole prestress construction process is achieved.

Description

Technical Field

[0001] This invention belongs to the field of civil engineering construction monitoring technology, specifically relating to a dynamic monitoring system and method for the construction process of prestressed cables based on magnetic flux sensors. Background Technology

[0002] In prestressed steel structure construction, precise control of cable tension is crucial for ensuring structural integrity and construction safety. Magnetic flux sensors are used for cable tension monitoring due to their high accuracy and stability. For example, patent CN109668658A discloses a "smart cable" system that embeds a magnetic flux sensor within a parallel steel wire cable. While this technology provides long-term stable monitoring, it has significant limitations: First, the sensor must be embedded during factory prefabrication, making it unsuitable for already installed cables or scenarios requiring temporary monitoring points during construction, resulting in extremely poor flexibility. Second, the system primarily focuses on long-term health monitoring after bridge completion and is not designed for the dynamic nature of the construction process, the cross-influence of multiple processes, and the need for real-time control. It lacks deep integration with construction simulation and displacement monitoring, as well as proactive control capabilities.

[0003] Therefore, there is an urgent need for a dynamic monitoring system that is flexible in deployment, can deeply interact with the construction process, and has intelligent decision-making capabilities. Summary of the Invention

[0004] To address the aforementioned issues, this invention aims to provide a dynamic monitoring system and method for prestressed cable construction based on magnetic flux sensors. This system is flexible in deployment, deeply coupled with the construction process, and capable of intelligent early warning and proactive control, thereby resolving the problems existing in the prior art.

[0005] To achieve the above objectives, the technical solution of the present invention is as follows:

[0006] A dynamic monitoring system for the construction process of prestressed cables based on magnetic flux sensors includes:

[0007] The adaptive clamping magnetic flux sensor has a mechanical clamping mechanism or a magnetic adsorption mechanism for fixing itself to the outer surface of the cable.

[0008] The edge computing gateway is communicatively connected to an adaptive clamp-on magnetic flux sensor, whose built-in microprocessor is configured to execute the following program: perform real-time filtering on the received raw cable force data; fuse the filtered cable force data with synchronously acquired structural displacement data to form a force-shape coordinated data packet; diagnose the force-shape coordinated data packet based on preset rules, and generate a local early warning signal when an anomaly is diagnosed;

[0009] The central monitoring platform is communicatively connected to an edge computing gateway and is configured to: integrate the BIM model of the structure with the construction process simulation model; receive and store the force-form collaborative data packets; compare and display the measured data with the theoretical data of the simulation model in a visual interface; and run prediction algorithms to generate control suggestions for the tensioning process.

[0010] The feedback execution module is communicatively connected to the central monitoring platform and is configured to receive and execute the control recommendations, including triggering on-site alarms and / or sending control signals to the intelligent tensioning equipment to adjust the tensioning operation.

[0011] As an improvement of the present invention, the mechanical clamping mechanism is a U-shaped hinge structure with a flexible magnetic pad on the inner side to accommodate cables of different diameters and ensure magnetic circuit closure.

[0012] As an improvement of the present invention, the preset rules executed by the edge computing gateway include: determining whether the difference in cable force between adjacent cables exceeds a first threshold, whether the rate of change of cable force of a single cable exceeds a second threshold, and whether there is a logical conflict between the trend of cable force change and the trend of displacement change.

[0013] As an improvement of the present invention, the prediction algorithm running on the central monitoring platform is a machine learning model. This model is trained with historical construction data and is used to predict the impact of subsequent construction steps on the overall cable force distribution based on the current construction status, and generate the control recommendations accordingly.

[0014] As an improvement of the present invention, the control signal sent by the feedback execution module to the intelligent tensioning device is specifically a sequence of instructions to adjust the target hydraulic pressure value of a specific tensioning jack or to change the tensioning sequence.

[0015] This invention also provides a method for dynamic monitoring of the prestressed cable construction process, comprising the following steps:

[0016] The adaptive clamp-on magnetic flux sensor was fixed at the predetermined monitoring point of the cable on site.

[0017] During construction, cable force data and structural displacement data are collected simultaneously.

[0018] The edge computing gateway filters the cable force data and fuses it with displacement data for local diagnosis.

[0019] The central monitoring platform is used to conduct in-depth comparative analysis of the fused measured data and the expected data of the construction process simulation model, and to run prediction algorithms.

[0020] Based on the analysis results and prediction algorithm output, the feedback execution module triggers an early warning or performs control over the tensioning equipment.

[0021] As an improvement of the present invention, the steps of the running prediction algorithm include: using a machine learning model to analyze the impact of the current tensioning operation on the subsequent untensioned or tensioned cable forces, predicting the risk of cable force imbalance, and outputting optimized tensioning force or tensioning sequence as control suggestions.

[0022] As an improvement of the present invention, the method further includes the step of associating all data collected throughout the construction process, the generated early warnings, the executed control instructions with the corresponding construction procedure information and timestamps to form a digital twin archive of the construction process that can be traced back and queried.

[0023] The beneficial effects of this invention are as follows:

[0024] The sensors no longer rely on factory pre-installation and can be quickly and directly installed on any existing cable in place on-site. At the same time, the sensors become reusable construction tools, significantly reducing the monitoring cost per project and improving equipment utilization efficiency.

[0025] This invention employs a layered approach to computational tasks. At the edge, the gateway performs real-time data cleaning and preliminary diagnostics, significantly reducing network dependence and ensuring critical local early warning functions are maintained even in the event of cloud communication disruptions. In the cloud (or on a central server), the platform performs deep data mining and predictive analytics.

[0026] This invention deeply integrates monitoring data, BIM models, and construction simulation, and introduces machine learning algorithms, enabling the system not only to identify current problems but also to predict future trends. The system can anticipate potential uneven cable tension caused by current tensioning operations and proactively provide or implement control measures. This transforms construction control from reactive remediation to proactive prevention, effectively avoiding repeated adjustments and rework, ensuring the structure is formed in one go, significantly improving construction accuracy and efficiency, and creating a complete and traceable digital twin archive. Attached Figure Description

[0027] Figure 1 This is a framework diagram of the present invention;

[0028] Figure 2 This is a flowchart of the method of the present invention. Detailed Implementation

[0029] The present invention will be further illustrated below with reference to the accompanying drawings and specific embodiments. It should be understood that the following specific embodiments are for illustrative purposes only and are not intended to limit the scope of the invention.

[0030] Example 1

[0031] This embodiment provides a dynamic monitoring system for the construction process of prestressed cables based on a magnetic flux sensor, such as... Figure 1 As shown, the system includes an adaptive clamp-on magnetic flux sensor, an edge computing gateway, a central monitoring platform, and a feedback execution module. These modules work together to achieve real-time monitoring, diagnosis, and control of the cable-stayed bridge construction process.

[0032] The adaptive clamping magnetic flux sensor is fixed to the outer surface of the cable via a mechanical clamping mechanism or a magnetic adsorption mechanism. Preferably, the mechanical clamping mechanism is a U-shaped hinge structure with a flexible magnetically conductive pad on the inner side to accommodate cables of different diameters and ensure magnetic circuit closure. The raw cable force data collected by the sensor is transmitted to the edge computing gateway wirelessly or via a wired connection.

[0033] The edge computing gateway has a built-in microprocessor that executes the following program steps:

[0034] The received raw cable force data is filtered in real time using a first-order low-pass filtering algorithm, as shown in the following formula:

[0035]

[0036] in, This represents the filtered cable force value at the current moment. This represents the filtered cable force value from the previous time step. This represents the original cable force value at the current moment. These are the filter coefficients (values ​​ranging from 0.9 to 0.99).

[0037] The filtered cable force data is fused with the synchronously acquired structural displacement data to form a force-shape coordinated data package. The fusion formula is as follows:

[0038]

[0039] in, This represents the fused force-form collaborative data. This represents the displacement data at the current moment. and Weighting coefficients (usually taken as...) ).

[0040] The force-shape coordinated data packet is diagnosed based on preset rules to determine whether it is abnormal. The preset rules include:

[0041] Determine whether the difference in cable tension between adjacent cables exceeds a first threshold. ;

[0042] Determine whether the rate of change of cable force in a single cable exceeds the second threshold. ;

[0043] Determine whether there is a logical conflict between the trend of cable force change and the trend of displacement change (for example, an increase in cable force while a decrease in displacement is considered a conflict).

[0044] If an abnormality is diagnosed, a local early warning signal is generated and uploaded to the central monitoring platform.

[0045] The central monitoring platform integrates the BIM model of the structure and the construction process simulation model, and receives and stores the force-form coordination data package. The platform compares and displays the measured data with the theoretical data from the simulation model in a visual interface, and runs a prediction algorithm to generate control suggestions for the tensioning process. The prediction algorithm is a machine learning model (e.g., a gradient boosting decision tree model trained on historical data), whose inputs are the current cable force distribution, displacement state, and construction stage information, and whose output is a predicted value of the impact on subsequent cable force distribution, thereby generating control suggestions (such as adjusting the tension force or tensioning sequence).

[0046] The feedback execution module receives and executes the control suggestions, including triggering a field alarm and / or sending control signals to the intelligent tensioning equipment. Specifically, the control signals are a sequence of instructions to adjust the target hydraulic pressure value of a specific tensioning jack or to change the tensioning sequence.

[0047] Example 2

[0048] This embodiment provides a method for dynamic monitoring of the prestressed cable construction process based on the above system, such as... Figure 2 As shown, it includes the following steps:

[0049] S1. Fix the adaptive clamping magnetic flux sensor to the predetermined monitoring point of the cable on site.

[0050] S2. During construction, cable force data and structural displacement data are collected simultaneously.

[0051] S3. The cable force data is filtered through the edge computing gateway and fused with the displacement data for local diagnosis. The diagnosis rules are as described in Example 1.

[0052] S4. Through the central monitoring platform, the fused measured data and the expected data from the construction process simulation model are subjected to in-depth comparative analysis, and a prediction algorithm is run. The steps of running the prediction algorithm include: using a machine learning model to analyze the impact of the current tensioning operation on the subsequent untensioned or tensioned cable forces, predicting the risk of cable force imbalance, and outputting optimized tensioning force or tensioning sequence as control suggestions.

[0053] S5. Based on the analysis results and prediction algorithm output, trigger an early warning or perform control of the tensioning equipment through the feedback execution module.

[0054] S6. Link all data collected throughout the construction process, the early warnings generated, the control instructions executed, and the corresponding construction procedure information and timestamps to form a digital twin archive of the construction process that can be traced back and queried.

[0055] It should be noted that the above are merely preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. For those skilled in the art, several improvements and modifications can be made on the basis of the above embodiments without departing from the principle of the present invention, and all such improvements and modifications fall within the scope of protection of the claims of the present invention.

Claims

1. A dynamic monitoring system for prestressed cable construction process based on a magnetic flux sensor, comprising an adaptive clamp-on magnetic flux sensor, an edge computing gateway, a central monitoring platform, and a feedback execution module, characterized in that: The adaptive clamping magnetic flux sensor has a mechanical clamping mechanism or a magnetic adsorption mechanism for fixing itself to the outer surface of the cable. The edge computing gateway is communicatively connected to an adaptive clamp-on magnetic flux sensor, whose built-in microprocessor is configured to execute the following program: real-time filtering of the received raw cable force data; The filtered cable force data is fused with the synchronously acquired structural displacement data to form a force-shape collaborative data package. The force-form collaborative data packet is diagnosed based on preset rules, and a local early warning signal is generated when an anomaly is detected. The central monitoring platform is communicatively connected to an edge computing gateway and is configured to: integrate the BIM model of the structure with the construction process simulation model; receive and store the force-form collaborative data packets; compare and display the measured data with the theoretical data of the simulation model in a visual interface; and run prediction algorithms to generate control suggestions for the tensioning process. The feedback execution module is communicatively connected to the central monitoring platform and is configured to receive and execute the control recommendations, including triggering on-site alarms and / or sending control signals to the intelligent tensioning equipment to adjust the tensioning operation.

2. The dynamic monitoring system for prestressed cable construction process based on a magnetic flux sensor according to claim 1, characterized in that: The mechanical clamping mechanism is a U-shaped hinge structure with a flexible magnetic pad on the inner side to accommodate cables of different diameters and ensure magnetic circuit closure.

3. The dynamic monitoring system for prestressed cable construction process based on a magnetic flux sensor according to claim 1, characterized in that: The preset rules executed by the edge computing gateway include: determining whether the difference in cable force between adjacent cables exceeds a first threshold, whether the rate of change of cable force of a single cable exceeds a second threshold, and whether there is a logical conflict between the trend of cable force change and the trend of displacement change.

4. The dynamic monitoring system for prestressed cable construction process based on a magnetic flux sensor according to claim 1, characterized in that: The prediction algorithm running on the central monitoring platform is a machine learning model. This model is trained using historical construction data to predict the impact of subsequent construction steps on the overall cable force distribution based on the current construction status, and generates the control recommendations accordingly.

5. The dynamic monitoring system for prestressed cable construction process based on a magnetic flux sensor according to claim 1, characterized in that: The control signals sent by the feedback execution module to the intelligent tensioning device are specifically a sequence of instructions to adjust the target hydraulic pressure value of a specific tensioning jack or to change the tensioning sequence.

6. A method for dynamic monitoring of the prestressed cable construction process based on the system described in any one of claims 1-5, characterized in that, Includes the following steps: The adaptive clamp-on magnetic flux sensor was fixed at the predetermined monitoring point of the cable on site. During construction, cable force data and structural displacement data are collected simultaneously. The edge computing gateway filters the cable force data and fuses it with displacement data for local diagnosis. The central monitoring platform is used to conduct in-depth comparative analysis of the fused measured data and the expected data of the construction process simulation model, and to run prediction algorithms. Based on the analysis results and prediction algorithm output, the feedback execution module triggers an early warning or performs control over the tensioning equipment.

7. The method for dynamic monitoring of prestressed cable construction process based on magnetic flux sensor according to claim 6, characterized in that: The steps of the prediction algorithm include: using a machine learning model to analyze the impact of the current tensioning operation on the tension of subsequent untensioned or tensioned cables, predicting the risk of cable tension imbalance, and outputting optimized tensioning force or tensioning sequence as control suggestions.

8. The method for dynamic monitoring of prestressed cable construction process based on magnetic flux sensor according to claim 6, characterized in that: It also includes the step of associating all data collected throughout the construction process, the early warnings generated, the control instructions executed, and the corresponding construction procedure information and timestamps to form a digital twin archive of the construction process that can be traced back and queried.

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