Infrastructure monitoring and early warning method and system based on fusion of engineering structure and engineering environment

Abstract

The invention provides an infrastructure monitoring and early warning method and system based on engineering structure and engineering environment fusion. The infrastructure monitoring and early warning system based on the fusion of the engineering structure and the engineering environment comprises a data processing module, an early warning module, a maintenance and calibration module and sensor modules arranged on a mountain and a bridge structure, the sensor module comprises a tilt angle sensor, a vibration sensor, a GNSS displacement monitoring sensor, a distributed deformation sensor and a water level sensor; and the data processing module is used for performing analysis and time alignment, noise removal, abnormal value detection and missing value processing on the data acquired by the sensor module. According to the infrastructure monitoring and early warning method and system based on fusion of the engineering structure and the engineering environment, the engineering structure and the engineering environment are monitored by arranging various sensors, a multi-level early warning mechanism is established, accurate and efficient early warning of the infrastructure can be achieved, and safe operation of the infrastructure is guaranteed.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of infrastructure monitoring and early warning, and particularly relates to an infrastructure monitoring and early warning method and system based on fusion of engineering structures and engineering environments. BACKGROUND

[0002] Common infrastructure structure (house, bridge, etc.) monitoring and early warning is to measure the stress, deformation, vibration and other responses of the structure, set a threshold according to the limit degree of the structural response, and thus establish a monitoring and early warning system. This method is suitable for materials such as reinforced concrete and other physical and mechanical models, which have clear strength failure boundaries and are established on the basis of the probability limit state design method, and the external load is known and can be accurately quantified.

[0003] However, events such as the collapse of the Shangluo Zhashui Bridge in Shaanxi and the collapse of the Guizhou Houzihe Bridge in recent years show that the root cause of the collapse of infrastructure structures is not that the stress caused by the operating load is greater than the material strength and thus the structure is destroyed, but that the structure is destabilized by external environmental factors such as landslides, mudslides and water flow impacts. Therefore, the existing monitoring and early warning system has certain limitations and cannot fully guarantee the safe operation of infrastructure.

[0004] In order to provide timely and effective monitoring and early warning for infrastructure structures and guarantee the safe operation of infrastructure, the application provides an infrastructure monitoring and early warning method and system based on fusion of engineering structures and engineering environments to solve the above technical problems. SUMMARY

[0005] The technical problem solved by the application is to provide an infrastructure monitoring and early warning method and system based on fusion of engineering structures and engineering environments, which can realize accurate and efficient early warning of infrastructure by monitoring engineering structures and engineering environments through the arrangement of various sensors and establishing a multi-level early warning mechanism, and guarantee the safe operation of infrastructure.

[0006] To solve the above technical problems, the infrastructure monitoring and early warning system based on fusion of engineering structures and engineering environments provided by the application comprises a data processing module, an early warning module, a maintenance and calibration module and a sensor module arranged on a mountain and a bridge structure. The sensor module comprises an inclination sensor, a vibration sensor, a GNSS displacement monitoring sensor, a distributed deformation sensor and a water level sensor. The data processing module is used for analyzing and time-aligning the data collected by the sensor module, removing noise, detecting outliers and processing missing values. The pre-warning module establishes a multi-level pre-warning mechanism based on the processed data, in combination with engineering structure and engineering environment parameters, and issues environment pre-warning and structure pre-warning, such as constructing a multi-level pre-warning mechanism including three levels of general, serious and urgent, wherein the environment pre-warning covers risk prompts triggered by environmental factors such as sudden change of temperature and humidity, water level overrun and geological subsidence; the structure pre-warning is aimed at abnormal structure conditions such as stress concentration, displacement mutation and material fatigue, and through setting of differentiated threshold values and weight systems, precise grading response of environment and structure risks can be realized, and pre-warning information is issued in multiple forms such as sound and light alarm, short message push and platform pop-up window, and a risk traceability report and emergency disposal suggestion are generated at the same time. The maintenance calibration module is used for calibrating and maintaining the sensors and establishing an electronic calibration archive.

[0007] As a further scheme of the application, the measurement accuracy of the tilt sensor is ≤±0.01°, the range is ±10°, the sensitivity is 0.001° / Hz, the anti-interference ability meets EMI shielding ≥60dB, and the temperature drift is ≤0.005° / ℃.

[0008] As a further scheme of the application, the measurement accuracy of the vibration sensor is ±0.1m / s², the range is 0.01g~2g, the sensitivity is 100mV / g, the anti-interference ability meets IP66 protection, and the anti-radio frequency interference is, for example, EN61000.

[0009] As a further scheme of the application, the measurement accuracy of the GNSS displacement monitoring sensor is horizontal ±2mm+0.5ppm and vertical ±4mm+1ppm, the sensitivity is 0.1mm / 1mm, the anti-interference ability meets multi-frequency and anti-multipath, such as L1+L2+L5, and lightning protection, the sensitivity of 0.1mm is static, and the sensitivity of 1mm is dynamic.

[0010] As a further scheme of the application, the distributed deformation sensor adopts a low-power electric circuit device, and the terminal breaking elongation is <1%.

[0011] As a further scheme of the application, the measurement accuracy of the water level sensor is ±0.1%FS, the range is 0~20m, which can be set according to the historical water level of a drainage basin, the sensitivity is 1mm, and the anti-interference ability meets corrosion resistance, such as 316L stainless steel, and bio-attachment prevention.

[0012] An infrastructure monitoring and pre-warning method based on fusion of engineering structure and engineering environment, comprising the following steps: Step one. Each sensor in the sensor module is arranged according to a preset position on a mountain body and a bridge structure; Step two. The sensors are calibrated and maintained by the maintenance calibration module, an electronic calibration archive is established, and the calibration time, personnel, equipment number and correction coefficient are recorded; Step three. The sensor module collects the engineering structure and engineering environment data and transmits to the data processing module, the data transmission adopts the self-organizing network LORA technology, the edge end set perception, transmission, analysis and early warning function, and can independently operate under the condition of no network, and the cloud platform can automatically store and supervise under the condition of network; Step four. The data processing module analyzes and time aligns the data, removes noise, detects abnormal values and processes missing values. Step five. The early warning module judges whether to issue an environmental warning or a structure warning based on the processed data and engineering structure and engineering environment parameters.

[0013] As a further scheme of the application, the calibration period in step two is every six months, and the calibration method adopts on-site simple calibration, specifically including the following: S1. Inclination sensor: select a higher precision level device and place it on the same rigid platform to compare the consistency of the two; S2. Vibration sensor: adopt the free falling method of heavy object, and ensure the vibration source standard by knocking the same object with the heavy object; S3. GNSS displacement monitoring sensor and distributed deformation sensor: adopt portable high-precision micro-motion platform for calibration; S4. Water level sensor: calibrate by using a measuring cup or water pipe to inject water.

[0014] As a further scheme of the application, the data processing in step four specifically includes the following: A1. Data analysis and time alignment: analyze different protocol data, uniformly convert to structured data frame, realize mu s level synchronization by using IEEE1588-PTP protocol, and label the data with time difference greater than 100 ms for correction; A2. Noise removal: for vibration / strain data, adopt frequency domain filtering, calculate FFT spectrum, identify noise frequency band, design IIR band-pass filter to improve signal-to-noise ratio by ≥20dB; for other data, adopt wavelet threshold denoising, select "db4" wavelet basis, 5-layer decomposition, and adopt SUREShrink threshold; A3. Abnormal value detection: realize by using Gaussian distribution data-3σ criterion and non-Gaussian distribution-IQR method; A4. Missing value processing: select different processing methods according to the missing type, adopt linear / spline interpolation for random missing, adopt time series prediction such as ARIMA / LSTM for continuous missing, and mark block missing as invalid section, wherein random missing is less than 5%, continuous missing is 5%~30%, and block missing is more than 30%.

[0015] As a further scheme of the application, the early warning mechanism in step five is: (1). When the sensor data triggers an environmental warning, limit the personnel, vehicles and equipment in the structure, strengthen the environmental risk investigation and patrol, and remove the environmental warning after determining the environmental safety; (2). When the structure warning occurs after the environmental warning, suspend operation, close the structure, and prohibit all personnel, vehicles and equipment from entering.

[0016] Compared with the related art, the infrastructure monitoring and warning method and system based on the fusion of engineering structure and engineering environment provided by the present application have the following beneficial effects: The present application monitors the engineering structure and engineering environment by laying multiple sensors, establishes a multi-level warning mechanism, and can realize accurate and efficient warning of the infrastructure, thereby ensuring the safe operation of the infrastructure. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to facilitate understanding by those skilled in the art, the present application will be further described below with reference to the accompanying drawings.

[0018] Figure 1 is a flowchart of the present application; Figure 2 is a data processing flowchart in the present application; Figure 3 is an installation diagram of the sensor in the present application.

[0019] In the figure: 1, mountain; 2, bridge structure; 3, inclination sensor; 4, vibration sensor; 5, GNSS displacement monitoring sensor; 6, distributed deformation sensor; 7, water level sensor. DETAILED DESCRIPTION

[0020] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the specific embodiments, structures, features and effects according to the present application are described in detail as follows.

[0021] Example 1

[0022] The sensors are arranged at appropriate positions of the bridge, the inclination sensor 3 is arranged on the pier and the beam of the bridge, for monitoring the inclination angle change of the bridge; the vibration sensor 4 is arranged at the key structure part of the bridge, for monitoring the vibration of the bridge; the GNSS displacement monitoring sensor 5 is arranged on the bridge deck and the top of the pier, for monitoring the displacement change of the bridge; the distributed deformation sensor 6 is arranged in the soil under the bridge or at the structure connection, for measuring whether the soil is deformed greatly and whether the structure falls off; the water level sensor 7 is arranged in the river under the bridge, for monitoring the water level change.

[0023] During the system operation, the maintenance calibration module calibrates each sensor every half year, and in a calibration, it is found that the measurement data of a certain vibration sensor 4 deviates from the standard seismic source, and after inspection, it is found that it is caused by the aging of the internal components of the sensor, so the sensor is replaced and recorded in the electronic calibration file.

[0024] During the flood season, the water level sensor 7 monitors that the water level of the river continues to rise and reaches the environmental warning threshold, and the system issues an environmental warning, at this time, the relevant departments limit the speed and flow of the bridge and strengthen the patrol of the surrounding environment of the bridge, with the continuous rise of the water level, the GNSS displacement monitoring sensor 5 monitors that the bridge pier appears a slight displacement, the distributed deformation sensor 6 monitors that the beam body has a slight deformation, and the vibration sensor 4 also monitors the abnormal vibration, the system issues a structure warning, the relevant departments immediately suspend the operation of the bridge, close the bridge and prohibit personnel and vehicles from entering, which avoids the possible safety accidents.

[0025] Embodiment 2

[0026] The inclination sensor 3, the vibration sensor 4 and the distributed deformation sensor 6 are arranged at the foundation, the wall and the floor of the building, and the GNSS displacement monitoring sensor 5 is arranged at the top of the building for monitoring the overall displacement of the building.

[0027] If in strong typhoon weather, the vibration sensor 4 can monitor that the vibration amplitude of the building exceeds the environmental warning threshold, the system issues an environmental warning, and the relevant departments timely inform the residents to reduce going out and avoid staying around the building, during the typhoon process, the inclination sensor 3 monitors that the building has a slight inclination, the system issues a structure warning, and the relevant departments organize the residents to evacuate, which ensures the safety of the personnel.

[0028] The above only describes the preferred embodiments of the present application, but the protection scope of the present application is not limited to this, any skilled person in the art can make equivalent replacement or change according to the technical scheme and the inventive concept of the present application within the technical range disclosed by the present application, which should be covered in the protection scope of the present application.

Claims

1. An infrastructure monitoring and early warning system based on the integration of engineering structure and engineering environment, characterized in that, include: Data processing module, early warning module, maintenance and calibration module, and sensor modules installed on mountains and bridge structures; The sensor module includes a tilt sensor, a vibration sensor, a GNSS displacement monitoring sensor, a distributed deformation sensor, and a water level sensor; The data processing module is used to parse and time-align the data collected by the sensor module, remove noise, detect outliers, and handle missing values. Based on the processed data and combined with engineering structure and environmental parameters, the early warning module establishes a multi-level early warning mechanism to issue environmental and structural early warnings. For example, it can construct a multi-level early warning mechanism with three levels: general, severe, and emergency. The environmental early warning covers risk alerts triggered by environmental factors such as sudden changes in temperature and humidity, exceeding water level limits, and geological subsidence. The structural early warning targets structural anomalies such as stress concentration, sudden displacement, and material fatigue. By setting differentiated thresholds and weighting systems, it can achieve precise graded response to environmental and structural risks and issue early warning information in multiple forms such as audible and visual alarms, SMS push notifications, and platform pop-ups. At the same time, it generates risk tracing reports and emergency response suggestions. The maintenance and calibration module is used to calibrate and maintain the sensors and establish electronic calibration records.

2. The infrastructure monitoring and early warning system based on the integration of engineering structure and engineering environment as described in claim 1, characterized in that: The tilt sensor has a measurement accuracy of ≤ ±0.01°, a measurement range of ±10°, a sensitivity of 0.001° / Hz, an anti-interference capability that meets EMI shielding requirements of ≥60dB, and a temperature drift of ≤0.005° / ℃.

3. The infrastructure monitoring and early warning system based on the integration of engineering structure and engineering environment as described in claim 1, characterized in that: The vibration sensor has a measurement accuracy of ±0.1m / s², a measurement range of 0.01g~2g, a sensitivity of 100mV / g, and meets IP66 protection and radio frequency interference resistance, such as EN61000.

4. The infrastructure monitoring and early warning system based on the integration of engineering structure and engineering environment as described in claim 1, characterized in that: The GNSS displacement monitoring sensor has a measurement accuracy of ±2mm+0.5ppm horizontally and ±4mm+1ppm vertically, a sensitivity of 0.1mm / 1mm, and anti-interference capabilities that meet multi-frequency and multipath requirements, such as L1+L2+L5, and lightning protection. The sensitivity of 0.1mm is for static conditions, and the sensitivity of 1mm is for dynamic conditions.

5. The infrastructure monitoring and early warning system based on the integration of engineering structure and engineering environment as described in claim 1, characterized in that: The distributed deformation sensor employs a low-power electrical circuit device with an end-fracture elongation of less than 1%.

6. The infrastructure monitoring and early warning system based on the integration of engineering structure and engineering environment as described in claim 1, characterized in that: The water level sensor has a measurement accuracy of ±0.1%FS, a measurement range of 0~20m, and can be set according to the historical water level of the basin. It has a sensitivity of 1mm, and its anti-interference capability meets the requirements of corrosion resistance, such as 316L stainless steel, and anti-biofouling.

7. An infrastructure monitoring and early warning method based on the integration of engineering structure and engineering environment as described in claims 1-6, characterized in that, Includes the following steps: Step 1. Deploy the sensors in the sensor module according to the preset locations on the mountain and bridge structures; Step 2. Calibrate and maintain the sensor through the maintenance and calibration module, establish an electronic calibration file, and record the calibration time, personnel, equipment number, and correction factor; Step 3. The sensor module collects engineering structure and environmental data and transmits it to the data processing module. The data transmission adopts LoRa self-organizing network technology. The edge device integrates sensing, transmission, analysis and early warning functions. It can operate independently without network conditions, and the cloud platform automatically stores and monitors the data when there is a network. Step 4. The data processing module performs data parsing and time alignment, noise removal, outlier detection, and missing value handling. Step 5. Based on the processed data and combined with engineering structure and environmental parameters, the early warning module determines whether to issue an environmental or structural early warning.

8. The infrastructure monitoring and early warning method based on the integration of engineering structure and engineering environment according to claim 7, characterized in that: In step two, the calibration cycle is every six months, and the calibration method adopts a simplified on-site calibration, specifically including the following: S1. Tilt sensor: Select a device with a higher accuracy level and install it on the same rigid platform to compare the consistency between the two. S2. Vibration sensor: The vibration source standard is ensured by using the free fall method of heavy objects to strike the same object. S3.GNSS displacement monitoring sensor and distributed deformation sensor: calibrated using a portable high-precision micro-motion platform; S4. Water level sensor: Calibrated by filling a measuring cup or water pipe with water.

9. The infrastructure monitoring and early warning method based on the integration of engineering structure and engineering environment according to claim 7, characterized in that: The data processing in step four specifically includes the following: A1. Data parsing and time alignment: Parse data from different protocols, convert them into structured data frames, and use the IEEE1588-PTP protocol to achieve μs-level synchronization. Data with a time difference > 100ms is tagged for correction. A2. Noise Removal: Frequency domain filtering is applied to vibration / strain data, FFT spectrum is calculated, noise frequency bands are identified, and IIR band-stop filters are designed to improve the signal-to-noise ratio by ≥20dB; For other data, wavelet thresholding was used for denoising. The "db4" wavelet basis was selected, and the data was decomposed into 5 levels using the SUREShrink threshold. A3. Outlier detection: Implemented using Gaussian distribution data - 3σ criterion and non-Gaussian distribution data - IQR method; A4. Missing value handling: Different handling methods are selected according to the missing type. For random missing values, linear / spline interpolation is used, and for continuous missing values, time series prediction such as ARIMA / LSTM is used. Block missing values ​​are marked as invalid segments, with random missing values ​​<5%, continuous missing values ​​5%~30%, and block missing values ​​>30%.

10. The infrastructure monitoring and early warning method based on the integration of engineering structure and engineering environment according to claim 7, characterized in that: The early warning mechanism in step five is as follows: (1) When abnormal sensor data triggers an environmental warning, restrictions are placed on personnel, vehicles and equipment used for structural services, environmental risk investigation and inspection are strengthened, and the environmental warning is lifted after environmental safety is confirmed. (2) When a structural warning is issued after an environmental warning, operations shall be suspended, the structure shall be closed, and all personnel, vehicles and equipment shall be prohibited from entering.

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