Bridge construction deformation monitoring system based on GNSS monitoring technology

Abstract

The invention relates to a bridge construction deformation monitoring system based on a GNSS monitoring technology, and the system comprises a main control module which is used for receiving data collected by a data collection module, is responsible for the unified scheduling and management of the system, and coordinates the data flow and operation among all modules; the data acquisition module comprises various sensors and is used for acquiring horizontal displacement, settlement, inclination angle, crack and vibration information of the bridge; the wireless communication control module transmits the data acquired by the data acquisition module to the main control module in real time; the data processing and analyzing module is used for processing, screening, correcting and analyzing the real-time data acquired by the data acquisition module to obtain signals without residual components; the threshold value judging module is used for judging the safety state of the bridge structure according to a preset safety threshold value; and the safety early warning module is used for sending out an early warning signal when abnormity is detected or a threshold value is exceeded.

Description

Technical Field

[0001] The invention belongs to the technical field of bridge construction, and relates to a bridge construction deformation monitoring system based on GNSS monitoring technology. Background Art

[0002] During the bridge construction process, the deformation of the bridge needs to be monitored, including horizontal displacement, settlement, inclination angle, cracks, vibration, etc., to ensure the safety and stability of the bridge.

[0003] Traditional bridge monitoring technology mostly uses total stations and levels for monitoring. Its advantages are low cost and relatively simple technology. Its disadvantages are that it relies on manual operation, cannot perform real-time monitoring, cannot reflect the deformation of the bridge in real time, and the measurement results will produce errors due to manual readings, and the measurement accuracy is low.

[0004] GNSS (Global Navigation Satellite System) monitoring is a technology that uses the carrier phase differential technology (RTK) in GNSS for measurement. When applied to bridge monitoring, it can reflect the deformation of the bridge in real time through radio signals, and can reduce the errors caused by manual measurement. However, in the actual measurement process, the results obtained will still have certain errors. The reason for this is mainly because the signal receiver not only receives the data collected normally by the sensor, but also receives the data information reflected by the surrounding obstructions, so that the information finally received by the receiver is the superposition of direct and reflected information, resulting in multipath errors, and cannot accurately reflect the actual deformation of the bridge. Summary of the invention

[0005] The purpose of the present invention is to solve the above problems and provide a bridge construction deformation monitoring system based on GNSS monitoring technology, which can reduce multipath errors and improve the accuracy of monitoring data.

[0006] The technical solution of the present invention is as follows:

[0007] A bridge construction deformation monitoring system based on GNSS monitoring technology, characterized by: comprising a main control module, a data acquisition module, a wireless communication module, a data processing and analysis module, a threshold judgment module and a safety warning module;

[0008] The main control module includes a reference station, which is used to receive data collected by the data acquisition module, is responsible for the unified scheduling and management of the system, and coordinates the data flow and operation between the modules;

[0009] The data acquisition module includes: a GNSS sensor for providing all-weather three-dimensional displacement monitoring; an acceleration sensor for capturing the vibration characteristics of the bridge; a strain sensor for monitoring the strain changes of key parts; a tilt sensor for obtaining the tilt degree of the bridge; and a meteorological sensor for collecting environmental conditions, including wind speed, temperature and humidity.

[0010] The wireless communication control module transmits the data collected by the data collection module to the main control module in real time by wireless means;

[0011] The data processing and analysis module processes, screens, corrects and analyzes the real-time data collected by the data collection module, including:

[0012] Use data processing algorithms to clean the collected data;

[0013] By adding adaptive auxiliary noise to improve modal aliasing, the data is corrected to eliminate the multipath error of the acquired information and obtain a signal without residual components;

[0014] Update and calculate the key indicators related to deformation in real time, generate a curve chart, with the vertical axis representing the monitoring value and the horizontal axis representing time. The curve form makes the changes in monitoring results clear at a glance, making it easy for staff to quickly discover bridge changes;

[0015] The threshold judgment module judges the safety status of the bridge structure according to a preset safety threshold;

[0016] The safety warning module sends out a warning signal when an abnormality is detected or a threshold is exceeded.

[0017] Furthermore, the main control module in the above-mentioned bridge construction deformation monitoring system based on GNSS monitoring technology adopts an embedded controller or an industrial computer as the main control unit, and provides a human-machine interface to monitor the real-time status.

[0018] Furthermore, various sensors of the data acquisition module in the above-mentioned bridge construction deformation monitoring system based on GNSS monitoring technology are respectively arranged at L / 4 of the main span of the bridge, the maximum deflection of the main span, 3L / 4 of the main span, the maximum deflection point of the side span, as well as piers and abutments where hidden dangers exist.

[0019] Furthermore, the data processing and analysis module in the bridge construction deformation monitoring system based on GNSS monitoring technology improves modal aliasing by adding adaptive auxiliary noise and corrects the data, including the following steps:

[0020] Step 1: Use the CEEMDAN algorithm to perform random noise perturbations on the original signal several times to obtain multiple noise perturbation data sets;

[0021] Step 2: Perform CEEMD decomposition on each noise disturbance data set to obtain multiple CEEMD decomposition sets;

[0022] Step 3: For each CEEMD decomposition set, an adaptive noise-assisted method is introduced to enhance the edge and high-frequency parts of the signal by adding noise signals to each local mode function;

[0023] Step 4: Average the corresponding components of each adaptive noise-assisted CEEMD decomposition set to obtain the final CEEMDAN decomposition result, that is, the signal without residual components.

[0024] Furthermore, in the above-mentioned bridge construction deformation monitoring system based on GNSS monitoring technology, the core algorithm of the CEEMDAN algorithm is as follows:

[0025] The original signal Y of data acquisition i (t) = y(t) + ε0ω i (t);

[0026] In the above formula, ω i (t)(i=1, 2, ...N) is Gaussian noise, N is the number of sample sequences of the signal set;

[0027] Perform empirical mode decomposition on y(t) to obtain Y i The first intrinsic mode function IMF in (t) is denoted as

[0028] Then the sum is used to get the mean as the first-order IMF of y(t), denoted as

[0029]

[0030] Calculate the first stage residual component: r1(t)=y(t)-F1(t)

[0031] in;

[0032] r1(t)——the residual component of the first stage;

[0033] y(t)——original signal;

[0034] F1(t)——the first IMF.

[0035] Decomposition r1(t)+ε1E1ω i (t) Obtaining the second stage IMF, namely:

[0036]

[0037] in;

[0038] E(ωi (t)——Gaussian noise added;

[0039] ε1——Parameter related to energy control.

[0040] Calculate the IMF of the J+1 stage in sequence. Finally, if the residual cannot be further decomposed and satisfies the following formula:

[0041]

[0042] Then the final signal after CEEMDAN decomposition can be expressed as:

[0043]

[0044] in;

[0045] ——the average value of the jth IMF;

[0046] R(t)——The residual signal of the final residual-free state;

[0047] y(t)——Signal without residual component.

[0048] Furthermore, the threshold judgment module in the above-mentioned bridge construction deformation monitoring system based on GNSS monitoring technology sets multi-level thresholds and corresponding safety strategies, and deploys an automated rule engine to realize the identification of excessive data.

[0049] Furthermore, in the safety warning module of the bridge construction deformation monitoring system based on GNSS monitoring technology, an alarm system is configured, including sound and light alarms and SMS notifications; a decision support system is integrated to provide warnings and response decision suggestions; and a warning interface is designed to display alarm information and suggested operations in real time.

[0050] Compared with the prior art, the present invention has the following advantages:

[0051] 1. Strong real-time performance: Through continuous data collection and real-time update, high-precision real-time deformation monitoring can be provided.

[0052] 2. Big data analysis: Ability to identify long-term trends and anomalies through in-depth analysis of historical data.

[0053] 3. High degree of automation: Reduce manual intervention, and have high work efficiency through automatic early warning and report generation functions.

[0054] 4. Comprehensiveness and flexibility: Sensor layout and data analysis strategies can be adjusted according to the bridge construction stage and structural characteristics.

[0055] 5. More accurate data analysis: The CEEMDAN algorithm is used to improve modal aliasing and eliminate multipath errors by adding adaptive auxiliary noise, making data monitoring more accurate.

[0056] Based on the above advantages, the present invention is expected to play a more important role in ensuring the safety of bridge construction and operation, while promoting the intelligent and digital transformation in the field of bridge engineering. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a system module composition diagram of the present invention;

[0058] Figure 2 It is a schematic diagram of the sensor layout of the present invention. DETAILED DESCRIPTION

[0059] Figure 1 The figure shows the system module composition diagram of the present invention, including a main control module, a data acquisition module, a wireless communication module, a data processing and analysis module, a threshold judgment module and a safety warning module;

[0060] The main control module includes a base station, which is used to receive data collected by the data collection module, is responsible for the unified scheduling and management of the system, and coordinates the data flow and operation between the modules.

[0061] The main control module can use an embedded controller or an industrial computer as the main control unit and provide a human-machine interface to monitor the real-time status.

[0062] The data acquisition module is provided with an equipment control module, a positioning module and an attitude measurement module, including multiple monitoring stations, each of which is provided with multiple sensors, including: a GNSS sensor for providing all-weather three-dimensional displacement monitoring; an acceleration sensor for capturing the vibration characteristics of the bridge; a strain sensor for monitoring the strain changes in key parts; a tilt sensor for obtaining the tilt degree of the bridge; and a meteorological sensor for collecting environmental conditions, including wind speed, temperature and humidity.

[0063] like Figure 2 As shown, as a specific implementation method of data collection, for a continuous beam bridge, various sensors of the data collection module can be respectively arranged at L / 4 of the main span of the bridge, the point of maximum deflection of the main span, 3L / 4 of the main span, the point of maximum deflection of the side span, and on piers and abutments where hidden dangers exist.

[0064] The wireless communication control module transmits the data collected by the data collection module to the main control module in real time by wireless means;

[0065] During specific implementation, the wireless communication control module should select appropriate wireless communication technologies such as LoRa, 4G / 5G, and configure redundant communication paths to prevent signal interruption, as well as verify the accuracy and integrity of data transmission.

[0066] The data processing and analysis module is responsible for processing, screening, correcting and analyzing the real-time data collected by the data acquisition module, including:

[0067] (1) Use data processing algorithms (such as filtering, signal processing, etc.) to clean the collected data;

[0068] (2) A data correction module is also provided in the data processing and analysis module, which improves modal aliasing by adding adaptive auxiliary noise, corrects the data, eliminates multipath errors of the collected information, and obtains a signal without residual components;

[0069] (3) Update and calculate key indicators related to deformation in real time and generate a curve graph with the vertical axis representing the monitoring value and the horizontal axis representing time. The curve makes the changes in the monitoring results clear at a glance, making it easier for staff to quickly discover changes in the bridge.

[0070] The data correction module uses the CEEMDAN algorithm (Complete Ensemble Empirical Mode Decomposition of Adaptive Noise) to eliminate the data information reflected by the surrounding obstructions contained in the collected signal, and specifically includes the following steps:

[0071] Step 1: Use the CEEMDAN algorithm to perform random noise perturbations on the original signal several times to obtain multiple noise perturbation data sets;

[0072] Step 2: Perform CEEMD decomposition on each noise disturbance data set to obtain multiple CEEMD decomposition sets;

[0073] Step 3: For each CEEMD decomposition set, an adaptive noise-assisted method is introduced to enhance the edge and high-frequency parts of the signal by adding noise signals to each local mode function;

[0074] Step 4: Average the corresponding components of each adaptive noise-assisted CEEMD decomposition set to obtain the final CEEMDAN decomposition result, that is, the signal without residual components.

[0075] The CEEMDAN core algorithm is as follows:

[0076] The original signal Y of data acquisition i (t) = y(t) + ε0ω i (t);

[0077] In the above formula, ω i(t)(i=1, 2, ...N) is Gaussian noise, N is the number of sample sequences of the signal set;

[0078] Perform empirical mode decomposition on y(t) to obtain Y i The first intrinsic mode function IMF in (t) is denoted as

[0079] Then the sum is used to get the mean as the first-order IMF of y(t), denoted as

[0080]

[0081] Calculate the first stage residual component: r1(t)=y(t)-F1(t)

[0082] in;

[0083] r1(t)——the residual component of the first stage;

[0084] y(t)——original signal;

[0085] F1(t)——the first IMF.

[0086] Decompose r1 ( t)+ε1E1ω i (t) Obtaining the second stage IMF, namely:

[0087]

[0088] in;

[0089] E(ω i (t)——Gaussian noise added;

[0090] ε1——Parameter related to energy control.

[0091] Calculate the IMF of the J+1 stage in sequence. Finally, if the residual cannot be further decomposed and satisfies the following formula:

[0092]

[0093] Then the final signal after CEEMDAN decomposition can be expressed as:

[0094]

[0095] in;

[0096] ——the average value of the jth IMF;

[0097] R(t)——The residual signal of the final residual-free state;

[0098] y(t)——Signal without residual component.

[0099] The threshold judgment module judges the safety status of the bridge structure according to the preset safety threshold. In specific implementation, multi-level thresholds and corresponding safety policies can be set, and an automated rule engine can be deployed to realize the identification of over-limit data.

[0100] The safety warning module sends out a warning signal when an abnormality is detected or a threshold is exceeded.

[0101] The safety warning module is equipped with an alarm system, including sound and light alarms and SMS notifications. It is integrated with a decision support system to provide warnings and response decision suggestions. An early warning interface is set up to display alarm information and recommended operations in real time.

Claims

1. A bridge construction deformation monitoring system based on GNSS monitoring technology, characterized by: It includes a main control module, a data acquisition module, a wireless communication module, a data processing and analysis module, a threshold judgment module and a safety warning module; The main control module includes a reference station, which is used to receive data collected by the data acquisition module, is responsible for the unified scheduling and management of the system, and coordinates the data flow and operation between the modules; The data acquisition module includes: a GNSS sensor for providing all-weather three-dimensional displacement monitoring; an acceleration sensor for capturing the vibration characteristics of the bridge; a strain sensor for monitoring the strain changes of key parts; a tilt sensor for obtaining the tilt degree of the bridge; and a meteorological sensor for collecting environmental conditions, including wind speed, temperature and humidity. The wireless communication control module transmits the data collected by the data collection module to the main control module in real time by wireless means; The data processing and analysis module processes, screens, corrects and analyzes the real-time data collected by the data collection module, including: Use data processing algorithms to clean the collected data; By adding adaptive auxiliary noise to improve modal aliasing, the data is corrected to eliminate the multipath error of the acquired information and obtain a signal without residual components; Update and calculate the key indicators related to deformation in real time, generate a curve chart, with the vertical axis representing the monitoring value and the horizontal axis representing time. The curve form makes the changes in monitoring results clear at a glance, making it easy for staff to quickly discover bridge changes; The threshold judgment module judges the safety status of the bridge structure according to a preset safety threshold; The safety warning module sends out a warning signal when an abnormality is detected or a threshold is exceeded.

2. The bridge construction deformation monitoring system based on GNSS monitoring technology according to claim 1 is characterized by: The main control module adopts an embedded controller or an industrial computer as the main control unit and provides a human-machine interface to monitor the real-time status.

3. The bridge construction deformation monitoring system based on GNSS monitoring technology according to claim 1 is characterized by: Various sensors of the data acquisition module are respectively arranged at L / 4 of the main span of the bridge, the main span with maximum deflection, 3L / 4 of the main span, the maximum deflection point of the side span, and piers and abutments where hidden dangers exist.

4. The bridge construction deformation monitoring system based on GNSS monitoring technology according to claim 1 is characterized by: In the data processing and analysis module, modal aliasing is improved by adding adaptive auxiliary noise and the data is corrected, including the following steps: Step 1: Use the CEEMDAN algorithm to perform random noise perturbations on the original signal several times to obtain multiple noise perturbation data sets; Step 2: Perform CEEMD decomposition on each noise disturbance data set to obtain multiple CEEMD decomposition sets; Step 3: For each CEEMD decomposition set, an adaptive noise-assisted method is introduced to enhance the edge and high-frequency parts of the signal by adding noise signals to each local mode function; Step 4: Average the corresponding components of each adaptive noise-assisted CEEMD decomposition set to obtain the final CEEMDAN decomposition result, that is, the signal without residual components.

5. The bridge construction deformation monitoring system based on GNSS monitoring technology according to claim 4 is characterized by: The CEEMDAN core algorithm is as follows: The original signal Y of data acquisition i (t) = y(t) + ε0ω i (t); In the above formula, ω i (t)(i=1, 2, ...N) is Gaussian noise, N is the number of sample sequences of the signal set; Perform empirical mode decomposition on y(t) to obtain Y i The first intrinsic mode function IMF in (t) is denoted as Then the sum is used to get the mean as the first-order IMF of y(t), denoted as Calculate the first stage residual component: r1(t)=y(t)-F1(t) in; r1(t)——residual component of the first stage; y(t)——original signal; F1(t)——the first IMF. Decomposition r1(t)+ε1E1ω i (t) Obtaining the second stage IMF, namely: in; E(ω i (t)——Gaussian noise added; ε1——Parameter related to energy control. Calculate the IMF of the J+1 stage in sequence. Finally, if the residual cannot be further decomposed and satisfies the following formula: Then the final signal after CEEMDAN decomposition can be expressed as: in; ——the average value of the jth IMF; R(t)——The residual signal of the final residual-free state; y(t)——Signal without residual component.

6. The bridge construction deformation monitoring system based on GNSS monitoring technology according to claim 1 is characterized by: In the threshold judgment module, multi-level thresholds and corresponding security policies are set, and an automated rule engine is deployed to identify excessive data.

7. The bridge construction deformation monitoring system based on GNSS monitoring technology according to claim 1 is characterized by: In the safety warning module, an alarm system is configured, including sound and light alarms and SMS notifications; an integrated decision support system is used to provide warnings and response decision suggestions; Design an early warning interface to display alarm information and suggested operations in real time.

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