System for monitoring and early warning highway infrastructure

Abstract

The invention discloses a highway infrastructure monitoring and early warning system, which comprises a Beidou displacement monitoring module used for monitoring the three-dimensional coordinate of a highway infrastructure in real time and comparing the three-dimensional coordinate with an initial value to obtain the displacement of the highway infrastructure on the three-dimensional coordinate; the three-axis acceleration sensor is used for acquiring the three-axis acceleration of the highway infrastructure; the data processing module is used for receiving displacement data sent by the Beidou displacement monitoring module and three-axis acceleration data sent by the three-axis MENS acceleration sensor; extracting a displacement characteristic value and a three-axis acceleration characteristic value; predicting the displacement characteristic value and the three-axis acceleration characteristic value to obtain corresponding characteristic prediction values; determining the alarm level of the road infrastructure according to the feature value and the feature prediction value; the communication module is used for sending corresponding alarm information according to the alarm level determined by the data processing module; and the battery module is used for supplying power to each module. And the perceptual ability of the highway infrastructure to disasters is improved.

Description

Technical Field

[0001] This invention relates to the field of risk monitoring technology, and more specifically to a system for monitoring and early warning of highway infrastructure. Background Technology

[0002] In recent years, my country's highway infrastructure has developed rapidly. According to the latest statistics from the Ministry of Transport, my country has built more than 1 million bridges, over 27,000 tunnels, and more than 8.7 million slopes. However, the initial design of highway infrastructure may not have fully considered the impact of extreme weather and natural disasters, resulting in structures that are unable to withstand the enormous impact forces when facing natural disasters such as floods, mudslides, and earthquakes. Some bridges also suffer from design flaws or aging issues, further reducing their ability to detect and respond to natural disasters.

[0003] The bottlenecks of weak perception capabilities, untimely alarm response, and low level of intelligent diagnosis and treatment in highway infrastructure during natural disasters remain prominent. In the face of highway infrastructure damage accidents, there is an urgent need to address how to quickly detect such incidents, how to immediately issue warning signals at the scene, and how to promptly notify drivers and passengers to slow down and pull over to avoid further accidents.

[0004] Traditional methods for monitoring highway infrastructure have the following problems:

[0005] (1) The system is complex, expensive, difficult to deploy in large quantities, and has high requirements for on-site power supply and communication.

[0006] (2) Monitoring equipment based on the measurement of a single physical quantity often experiences false alarms due to on-site interference and application environment issues.

[0007] (3) Data analysis based on current monitoring data does not predict the structural status of highway infrastructure, resulting in untimely alarms.

[0008] When a large-scale natural disaster occurs, there are usually problems such as power and network outages at the disaster site. The alarm signal cannot be transmitted through wired network or 4G network, and the alarm equipment on site cannot be activated because it cannot receive instructions from the data center. Summary of the Invention

[0009] To address the aforementioned technical problems, this invention provides a system for monitoring and early warning of highway infrastructure, comprising: a BeiDou displacement monitoring module, a triaxial accelerometer, a data processing module, a communication module, and a battery module;

[0010] The Beidou displacement monitoring module is used to monitor the three-dimensional coordinates of highway infrastructure in real time, compare them with initial values, obtain the displacement of the highway infrastructure in three-dimensional coordinates, and send the displacement data to the data processing module.

[0011] A triaxial accelerometer is used to acquire the triaxial acceleration of the highway infrastructure; the triaxial acceleration is then sent to the data processing module.

[0012] The data processing module is used to receive displacement data sent by the Beidou displacement monitoring module and triaxial acceleration data sent by the triaxial MENS accelerometer; extract displacement feature values ​​from the displacement data and triaxial acceleration feature values ​​from the triaxial acceleration data; predict the displacement feature values ​​and triaxial acceleration feature values ​​to obtain feature prediction values ​​of displacement and triaxial acceleration; and determine the alarm level of the highway infrastructure based on the displacement feature values ​​and feature prediction values, and the triaxial acceleration feature values ​​and feature prediction values.

[0013] The communication module is used to send corresponding alarm information according to the alarm level determined by the data processing module;

[0014] The battery module is used to provide power to the various modules of the system.

[0015] Furthermore, the BeiDou displacement monitoring module is used to monitor the three-dimensional coordinates of the highway infrastructure in real time and compare them with initial values ​​to obtain the displacement of the highway infrastructure in three-dimensional coordinates, including:

[0016] The longitude, latitude, and altitude of the monitored highway infrastructure are collected periodically and compared with the initial values ​​to obtain the displacement of the highway infrastructure in the three-dimensional coordinates of longitude, latitude, and altitude.

[0017] Furthermore, a triaxial accelerometer, used to acquire the triaxial acceleration of the highway infrastructure, includes:

[0018] Obtain the acceleration of the highway infrastructure in the three orthogonal directions of X, Y, and Z.

[0019] Furthermore, the data processing module also includes the following functions:

[0020] The displacement data sent by the Beidou displacement monitoring module and the triaxial acceleration data sent by the triaxial accelerometer are preprocessed to remove abnormal and noisy data.

[0021] Furthermore, displacement feature values ​​are extracted from the displacement data, and triaxial acceleration feature values ​​are extracted from the triaxial acceleration data, wherein:

[0022] The triaxial acceleration characteristic values ​​in triaxial acceleration data include: tilt angle, vibration waveform, vibration frequency value, damping ratio, skewness index, and margin index;

[0023] Let the X-axis, Y-axis, and Z-axis be three mutually orthogonal directions, with the X-axis and Y-axis in the horizontal direction of the triaxial accelerometer and the Z-axis in the chip direction perpendicular to the triaxial accelerometer.

[0024] The acceleration value measured on the X-axis is a x Define the angle between the X-axis and the horizontal plane as the pitch angle α; the acceleration measured on the Y-axis is a. y The roll angle β is defined as the angle between the Y-axis and the horizontal plane; the acceleration value measured on the Z-axis is a. z Define the angle between the Z-axis and the horizontal plane as γ;

[0025] For the monitored highway infrastructure, the acceleration is equal to the vector sum of the three-axis accelerations, and its magnitude G is calculated from the magnitudes of the three vectors:

[0026]

[0027] Therefore, the tilt angle under motion can be calculated:

[0028]

[0029] The triaxial acceleration is integrated twice with the corresponding time to obtain the displacement value, and then the vibration discrete waveforms in three directions are obtained. The vibration waveforms are then fitted using a fitting algorithm.

[0030] Expressing the vibration frequency in terms of effective value, the vibration frequency in the X-axis direction is...

[0031] Vibration frequency value in the Y-axis direction

[0032] Vibration frequency value in the X-axis direction

[0033] The damping ratio is determined by analyzing the statistical characteristics of the random vibration response using the random decrement method.

[0034] Based on the discrete vibration waveforms along the X, Y, and Z axes, calculate the corresponding skewness index of the discrete vibration waveforms:

[0035] X-axis deviation index

[0036] Y-axis skewness index

[0037] Z-axis skewness index

[0038] Where u is the centrifugal value and σ is the standard deviation;

[0039] Based on the vibration discrete waveforms along the X, Y, and Z axes, calculate the corresponding margin index of the vibration discrete waveforms:

[0040] X-axis margin index

[0041] Y-axis margin indicator

[0042] Z-axis margin index

[0043] Calculate the displacement characteristic value based on the three-dimensional displacement value and preset characteristic parameters:

[0044] Displacement eigenvalue = A x *Displacement value x+A y *Displacement value y+A z *Displacement value z

[0045] Among them, A x Preset characteristic parameters for the X-axis, A y Preset characteristic parameters for the Y-axis, A z Preset characteristic parameters for the Z-axis.

[0046] Furthermore, the displacement characteristic values ​​and triaxial acceleration characteristic values ​​are predicted to obtain the predicted characteristic values ​​of displacement and triaxial acceleration, including:

[0047] Based on the displacement feature values ​​and triaxial acceleration feature values ​​within a preset time period, the predicted feature values ​​of displacement and triaxial acceleration within a future preset time period are obtained through a spatial extrapolation algorithm.

[0048] Furthermore, based on the displacement characteristic values ​​and predicted characteristic values, and the triaxial acceleration characteristic values ​​and predicted characteristic values, the alarm level of the highway infrastructure is determined, including:

[0049] Extract the maximum displacement characteristic value, maximum vibration frequency characteristic value, maximum damping characteristic value, maximum vibration skewness index characteristic value, and maximum vibration margin index characteristic value within a preset time period, and calculate the structural state characteristic value:

[0050] Structural state characteristic value = A* Maximum displacement characteristic value + B* Maximum vibration frequency characteristic value + C* Maximum damping ratio characteristic value + D* Maximum vibration skewness index characteristic value + E* Maximum vibration margin index characteristic value;

[0051] A, B, C, D, and E are weights, respectively.

[0052] Extract the predicted feature values ​​of maximum displacement, maximum vibration frequency, maximum damping, maximum vibration skewness, and maximum vibration margin within a preset future time period, and calculate the predicted feature value of structural state:

[0053] Structural state prediction characteristic value = A* Maximum displacement prediction characteristic value + B* Maximum vibration frequency value prediction characteristic value + C* Maximum damping ratio prediction characteristic value + D* Maximum vibration skewness index prediction characteristic value + E* Maximum vibration margin index prediction characteristic value;

[0054] The alarm level is calculated based on the structural state characteristic values ​​and the predicted structural state characteristic values.

[0055] m represents the number of past days, n represents the number of future days, Fi represents the structural state characteristic value on day i, and Ri represents the weight of the structural state characteristic value of each day in the alarm level.

[0056] Furthermore, the communication module includes: a 4G module and a LoRa module; among which,

[0057] The 4G module is used to send alarm information to the data center;

[0058] The Lora module is used to send alarm information to the Lora receiver on the local area network.

[0059] Furthermore, it also includes: a BeiDou short message module, a magnetic switch module, a configuration module, and a storage unit; among which,

[0060] The BeiDou short message module is used to send text messages to system operation and maintenance personnel;

[0061] Magnetic switch module, used to control the power on and off of the system using magnetic field signals;

[0062] The configuration module is used to configure relevant system parameters;

[0063] Storage unit, used to store initial values ​​and monitoring values ​​of relevant modules in the system.

[0064] Furthermore, alarm levels for highway infrastructure include: immediate alarms and predictive alarms.

[0065] The present invention provides a system for monitoring and early warning of highway infrastructure, which can monitor highway infrastructure for a long time and adapt to different environments. It can collect and analyze monitoring data in real time, and issue alarm signals in a timely manner when anomalies are detected, thereby improving the ability of highway infrastructure to perceive disasters. Attached Figure Description

[0066] Figure 1 This is a schematic diagram of a system for monitoring and early warning of highway infrastructure provided in an embodiment of the present invention;

[0067] Figure 2 This is the functional flow of highway infrastructure monitoring and early warning involved in the embodiments of the present invention;

[0068] Figure 3 This describes the workflow of the BeiDou displacement monitoring module involved in the embodiments of the present invention;

[0069] Figure 4 This is the working mode of the highway infrastructure monitoring and early warning system involved in the embodiments of the present invention. Detailed Implementation

[0070] Numerous specific details are set forth in the following description to provide a full understanding of the invention. However, the invention can be practiced in many other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0071] like Figure 1 As shown, the present invention provides a system for monitoring and early warning of highway infrastructure, including: a Beidou displacement monitoring module, a triaxial accelerometer, a data processing module, a communication module, and a battery module;

[0072] The Beidou displacement monitoring module is used to monitor the three-dimensional coordinates of highway infrastructure in real time, compare them with initial values, obtain the displacement of the highway infrastructure in three-dimensional coordinates, and send the displacement data to the data processing module.

[0073] A triaxial accelerometer is used to acquire the triaxial acceleration of the highway infrastructure; the triaxial acceleration is then sent to the data processing module.

[0074] The data processing module is used to receive displacement data sent by the Beidou displacement monitoring module and triaxial acceleration data sent by the triaxial MENS accelerometer; extract displacement feature values ​​from the displacement data and triaxial acceleration feature values ​​from the triaxial acceleration data; predict the displacement feature values ​​and triaxial acceleration feature values ​​to obtain feature prediction values ​​of displacement and triaxial acceleration; and determine the alarm level of the highway infrastructure based on the displacement feature values ​​and feature prediction values, and the triaxial acceleration feature values ​​and feature prediction values.

[0075] The communication module is used to send corresponding alarm information according to the alarm level determined by the data processing module;

[0076] The battery module is used to provide power to the various modules of the system.

[0077] Furthermore, this system also includes: a BeiDou short message module, a magnetic switch module, a configuration module, and a storage unit; among which,

[0078] The BeiDou short message module is used to send text messages to system operation and maintenance personnel. In the event of a large-scale disaster, the network may be interrupted. This method can promptly notify the operation and maintenance personnel of alarm information and trigger the operation and maintenance company's emergency event handling mechanism.

[0079] The magnetic switch module is used to control the power on and off of the system using magnetic field signals; the equipment is configured to be off when shipped from the factory, and enters the working mode after installation.

[0080] The configuration module is used to configure relevant system parameters; it configures threshold parameters, etc., in the monitoring device's storage unit via wired (serial communication) or wireless (LoRa communication).

[0081] The storage unit stores initial and monitored values ​​from relevant modules within the system. Initial values ​​include device longitude, latitude, altitude, background noise vibration frequency, background noise vibration amplitude, tilt angle, and three-dimensional acceleration zero-point calibration values. It also stores the acceleration wake-up threshold for the monitoring system, daily structural characteristic values ​​and alarm levels, and alarm condition thresholds for the monitoring device.

[0082] In this invention, relevant modules of the system, such as the BeiDou displacement monitoring module and the triaxial accelerometer, are deployed at fixed locations on the highway infrastructure to be monitored. These fixed locations facilitate the normal implementation of monitoring. The monitoring modules are also referred to as monitoring equipment in this invention. Furthermore, in this invention, a triaxial MEMS accelerometer is preferably used.

[0083] The monitoring equipment can be used in two ways:

[0084] 1. For normal monitoring function, the device is deployed at the monitoring locations on highway infrastructure. To ensure long standby time, the monitoring device has the following operating modes: power-off mode, low-power mode, data acquisition mode, and alarm mode. When used for normal monitoring function, the device generally selects the low-power mode, and its monitoring workflow is as follows: Figure 2 As shown.

[0085] 2. When used as a reference station for the BeiDou displacement monitoring module, it is deployed in an open area near highway infrastructure with no disaster risk. In this mode of use, the monitoring equipment only collects data from the BeiDou module and transmits data using the LoRa module. The 4G module, BeiDou short message module, and MEMS accelerometer acquisition function are turned off, and it is in normal working condition. Its workflow when used as a reference station for the BeiDou displacement monitoring module is as follows: Figure 3 As shown.

[0086] The monitoring device is powered by solar energy and batteries. To ensure normal operation during prolonged periods of cloudy or rainy weather, the device operates in four modes: power-off mode, low-power mode, data acquisition mode, and alarm mode. The switching process for each operating module is as follows: Figure 4 As shown:

[0087] The monitoring equipment enters the shutdown module:

[0088] The monitoring device is powered off during factory and transportation. After installation, it is powered on magnetically and enters low-power mode.

[0089] The monitoring device operates in low-power mode:

[0090] 1. After the monitoring equipment is powered on, enable the configuration module function to set various thresholds of the monitoring device. After a period of time, disable the configuration module function.

[0091] 2. Read the acceleration wake-up thresholds in three directions from the storage unit.

[0092] 3. Only perform data acquisition from the three-axis MEMS accelerometer and disable other functions, including turning off the power of the Beidou module and stopping the acquisition of data from the Beidou module, turning off the power of the 4G module, turning off the power of the LoRa module, etc.

[0093] 4. Acquire raw values ​​from the triaxial MEMS accelerometer at normal or reduced frequency, perform real-time sliding window filtering on the acquired raw values, and calculate the maximum acceleration value within the window range.

[0094] 5. If the acceleration value in a certain direction exceeds the wake-up threshold, or if the wake-up threshold is not reached within a specified time, the monitoring device will enter the acquisition mode.

[0095] When the monitoring device is operating in data acquisition mode:

[0096] 1. Read the initial installation values ​​of the monitoring device and the alarm condition thresholds from the storage unit.

[0097] 2. Normal operation of each data acquisition module: Power on the BeiDou module and begin acquiring data from it; the three-axis MEMS accelerometer begins acquiring data at its normal acquisition frequency. If configured to perform BeiDou positioning in conjunction with a BeiDou reference station, the LoRa module is activated and receives the offset values ​​of the three-dimensional coordinates from the reference station.

[0098] 3. The system calculates and extracts feature values ​​from the collected data, predicts these feature values, and fuses the feature values ​​and predicted feature values ​​to determine whether an alarm condition is met. If the alarm condition is not met, the system enters low-power mode; if the alarm condition is met, the system enters alarm mode.

[0099] When the monitoring device enters alarm mode:

[0100] 1. Package the alarm data.

[0101] 2. Classify alarms according to their alarm levels.

[0102] The BeiDou displacement monitoring module periodically collects the longitude, latitude, and altitude of the monitored highway infrastructure and compares them with initial values ​​to obtain the displacement of the highway infrastructure in three-dimensional coordinates of longitude, latitude, and altitude. A three-axis accelerometer acquires the acceleration of the highway infrastructure in the three orthogonal directions of X, Y, and Z.

[0103] The data processing module acquires longitude, latitude, altitude, and time information from the BeiDou displacement monitoring module via the RS232 communication interface. When the data processing module reads data from the BeiDou receiver for the first time, it records the time from the time correction monitoring device within the BeiDou receiver.

[0104] The data processing module processes the displacement data received from the BeiDou displacement monitoring module and the triaxial acceleration data received from the triaxial MENS accelerometer. The results include time information, which is used for feature-level fusion based on time. During installation, the monitoring device first reads and processes the data to establish the initial state of the monitored highway infrastructure.

[0105] When the monitoring device is used as a reference station, the offset of the three-dimensional coordinate values ​​is calculated based on the current three-dimensional coordinates and the initial installation coordinates.

[0106] When the monitoring equipment uses a base station, it receives the offset value of the three-dimensional coordinates of the base station through the Lora module and corrects the three-dimensional coordinates based on the offset value.

[0107] The monitoring equipment calculates the three-dimensional displacement value based on the three-dimensional coordinate value and the initial coordinate value.

[0108] The data processing module reads the acceleration and sensor temperature in three orthogonal directions (X, Y, Z axes) from the triaxial MEMS accelerometer via SPI / I2C, and adds the current monitoring time. The data processing module preprocesses the displacement data sent by the BeiDou displacement monitoring module and the triaxial acceleration data sent by the triaxial accelerometer to remove outlier and noisy data.

[0109] Furthermore, the data processing module preprocesses the triaxial acceleration data as follows: 1. Remove outliers from the original acceleration values: Set an appropriate acceleration range, and consider acceleration values ​​exceeding this range as outliers and remove them.

[0110] 2. Noise reduction processing of the original acceleration values: Using a low-pass filter algorithm to remove high-frequency noise helps smooth the data and reduce random fluctuations. Specifically, the Kalman filter algorithm is used.

[0111] 3. Perform temperature correction on the acceleration values.

[0112] 4. Zero-point offset correction: Due to errors in the sensor manufacturing process, the sensor may output a non-zero value when stationary. The sensor reading in the stationary state is stored in the storage unit as the zero-point calibration value. Based on the zero-point calibration value stored in the storage unit, the acceleration value is corrected for zero-point offset.

[0113] 5. According to the specifications of the triaxial MEMS accelerometer, convert the acceleration value into the actual physical unit (g).

[0114] The triaxial acceleration characteristic values ​​in triaxial acceleration data include: tilt angle, vibration waveform, vibration frequency value, damping ratio, skewness index, and margin index;

[0115] Let the X-axis, Y-axis, and Z-axis be three mutually orthogonal directions, with the X-axis and Y-axis in the horizontal direction of the triaxial accelerometer and the Z-axis in the chip direction perpendicular to the triaxial accelerometer.

[0116] The acceleration value measured on the X-axis is a x Define the angle between the X-axis and the horizontal plane as the pitch angle α; the acceleration measured on the Y-axis is a. y The roll angle β is defined as the angle between the Y-axis and the horizontal plane; the acceleration value measured on the Z-axis is a. z Define the angle between the Z-axis and the horizontal plane as γ;

[0117] For the monitored highway infrastructure, the acceleration is equal to the vector sum of the three-axis accelerations, and its magnitude G is calculated from the magnitudes of the three vectors:

[0118]

[0119] Therefore, the tilt angle under motion can be calculated:

[0120]

[0121] Because a x G The three vectors form a right triangle, and the formula above can also be expressed using tan. -1 calculate:

[0122]

[0123] The triaxial acceleration is integrated twice with the corresponding time to obtain the displacement value, and then the vibration discrete waveforms in three directions are obtained. The vibration waveforms are then fitted using a fitting algorithm.

[0124] The vibration bandwidth is the sum of the original vibration signals without Fourier transform decomposition, including the superposition of all frequency components. The vibration bandwidth is used to quickly determine whether the vibration of highway infrastructure is abnormal, but it cannot directly pinpoint the cause of the fault. An abnormally large vibration bandwidth indicates that there may be some hidden danger in the highway infrastructure.

[0125] The passband value is usually expressed as peak-to-peak value or RMS value. This invention uses the RMS value, which is less affected by outliers.

[0126] Vibration frequency value in the X-axis direction

[0127] Vibration frequency value in the Y-axis direction

[0128] Vibration frequency value in the X-axis direction

[0129] Damping ratio is a physical quantity that describes the rate of vibration decay of a vibration system, representing the system's ability to dissipate energy during vibration.

[0130] Commonly used methods for calculating damping ratio include the half-power bandwidth method, the logarithmic decay method, and the stochastic reduction method. Since the vibration of highway infrastructure is caused by external excitations such as environmental factors and vehicle traffic, this invention uses the stochastic reduction method to calculate the damping ratio by analyzing the statistical characteristics of the random vibration response. This invention only calculates the damping ratio of the Z-axis vibration.

[0131] Skewness reflects the asymmetry of vibration signals. Normally, vibration signals are axisymmetric, and the skewness value should be close to 0. If friction or collision in one direction of the road infrastructure is greater, it will cause vibration asymmetry, making the skewness larger.

[0132] Based on the discrete vibration waveforms along the X, Y, and Z axes, calculate the corresponding skewness index of the discrete vibration waveforms:

[0133] X-axis deviation index

[0134] Y-axis skewness index

[0135] Z-axis skewness index

[0136] Where u is the centrifugal value and σ is the standard deviation;

[0137] Taking the X-axis as an example, the axis center value of the discrete waveform on the X-axis

[0138] X-axis standard deviation

[0139] Based on the above formula, the eccentricity and standard deviation of the Y-axis and Z-axis can be obtained;

[0140] The margin index is the ratio of the absolute value of the vibration peak to the root square amplitude, reflecting an impact characteristic of the vibration signal. When highway infrastructure suffers from wear, cracks, or surface damage, the maximum amplitude of the vibration signal increases significantly, while the change in the denominator is relatively small; therefore, the margin index increases accordingly.

[0141] Based on the vibration discrete waveforms along the X, Y, and Z axes, calculate the corresponding margin index of the vibration discrete waveforms:

[0142] X-axis margin index

[0143] Y-axis margin indicator

[0144] Z-axis margin index

[0145] Calculate the displacement characteristic value based on the three-dimensional displacement value and preset characteristic parameters:

[0146] Displacement eigenvalue = A x *Displacement value x+A y *Displacement value y+A z *Displacement value z

[0147] Among them, A x Preset characteristic parameters for the X-axis, A y Preset characteristic parameters for the Y-axis, A z Preset characteristic parameters for the Z-axis.

[0148] Next, based on the displacement feature values ​​and triaxial acceleration feature values ​​within a preset time period, a spatial extrapolation algorithm is used to obtain the predicted feature values ​​of displacement and triaxial acceleration within a future preset time period.

[0149] The process of obtaining feature prediction values ​​is based on monitoring values ​​over a previous period (e.g., 3 months). Through spatial extrapolation algorithms, prediction values ​​for the next period (e.g., 5 days) are obtained and sorted by time.

[0150] 1. Based on the three-dimensional displacement values ​​over a previous period, the predicted three-dimensional displacement values ​​for the next period are obtained through a spatial extrapolation algorithm and sorted by time.

[0151] 2. Based on the three-dimensional vibration frequency values ​​over a previous period, the predicted three-dimensional vibration frequency values ​​for the next period are obtained through a spatial extrapolation algorithm and sorted by time.

[0152] 3. Based on the damping ratio over a previous period, the predicted damping ratio for the next period is obtained through a spatial extrapolation algorithm and sorted by time.

[0153] 4. Based on the three-dimensional skewness index over a previous period, the predicted value of the three-dimensional skewness index for the next period is obtained through spatial extrapolation algorithm and sorted by time.

[0154] 5. Based on the three-dimensional margin indices over a previous period, the predicted values ​​of the three-dimensional margin indices for the next period are obtained through spatial extrapolation algorithm and sorted by time.

[0155] Based on the characteristics of monitoring data over a period of time, different spatial extrapolation algorithms are selected. If recent data changes show a clear linear relationship, linear extrapolation is used; if recent data fluctuates significantly but shows an overall trend, exponential smoothing is used.

[0156] Next, the feature values ​​and the predicted feature values ​​will be fused, which includes the following steps:

[0157] Calculate the displacement characteristic value based on the three-dimensional displacement value and preset characteristic parameters:

[0158] Displacement eigenvalue = A x *Displacement value x+A y *Displacement value y+A z *Displacement value z

[0159] In addition to setting weights for the three-dimensional directions, the preset parameters also normalize the displacement characteristic values ​​so that the displacement, vibration frequency value, damping ratio, vibration skewness index, and vibration margin index can be uniformly integrated and calculated in the future.

[0160] Calculate the maximum displacement characteristic value for the day. The maximum displacement characteristic value for the day is the maximum value of all displacement characteristic values ​​for the day.

[0161] Based on the daily three-dimensional displacement prediction values ​​over a future period, calculate the displacement prediction characteristic value and the maximum daily displacement prediction characteristic value over the future period.

[0162] Using the same method, calculate the current day and a period of time to come:

[0163] 1. Characteristic value of the maximum vibration frequency on the current day, and predicted characteristic value of the maximum vibration frequency for each day in the future.

[0164] 2. The characteristic value of the maximum damping ratio on the current day, and the predicted characteristic value of the maximum damping ratio for each day in the future.

[0165] 3. The characteristic value of the maximum vibration distortion index on the current day, and the predicted characteristic value of the maximum vibration distortion index for each day in the future.

[0166] 4. The characteristic value of the maximum vibration margin index for the day, and the predicted characteristic value of the maximum vibration margin index for each day in the future.

[0167] Extract characteristic values ​​such as maximum displacement, maximum vibration frequency, maximum damping, maximum vibration skewness, and maximum vibration margin within a preset time period, and calculate the structural state characteristic values:

[0168] Structural state characteristic value = A* Maximum displacement characteristic value + B* Maximum vibration frequency characteristic value + C* Maximum damping ratio characteristic value + D* Maximum vibration skewness index characteristic value + E* Maximum vibration margin index characteristic value;

[0169] A, B, C, D, and E are weights, respectively.

[0170] Save the structural state characteristic values ​​of the day to the storage unit.

[0171] Extract the predicted feature values ​​of maximum displacement, maximum vibration frequency, maximum damping, maximum vibration skewness, and maximum vibration margin within a preset future time period, and calculate the predicted feature value of structural state:

[0172] Structural state prediction characteristic value = A* Maximum displacement prediction characteristic value + B* Maximum vibration frequency value prediction characteristic value + C* Maximum damping ratio prediction characteristic value + D* Maximum vibration skewness index prediction characteristic value + E* Maximum vibration margin index prediction characteristic value;

[0173] The weighting values ​​used to calculate structural state characteristic values ​​vary depending on the type of highway infrastructure. For slopes or tunnel inclines, displacement characteristic values ​​have a higher weighting; for bridges, damping ratio, vibration frequency, vibration skewness index, and vibration margin index have a higher weighting.

[0174] Calculate the alarm level based on the structural state characteristic values ​​and the predicted structural state characteristic values:

[0175] 1. Obtain the structural state feature values ​​of each day for a previous period from the storage unit.

[0176] 2. Calculate the alarm level based on the structural state characteristic values ​​for each day over a past period, the structural state characteristic values ​​for the current day, and the predicted structural state characteristic values ​​for each day over a future period:

[0177] m represents the number of past days, n represents the number of future days, Fi represents the structural state characteristic value on day i, and Ri represents the weight of the structural state characteristic value of each day in the alarm level.

[0178] If the alarm level calculated above reaches the alarm threshold, the device will send an alarm:

[0179] 1. Package the alarm data, including: structural state characteristic values ​​(including predicted values) for the past period, the current day, and the future period, as well as the alarm level.

[0180] 2. Alarms are categorized into immediate alarms and predictive alarms. For immediate alarms, an alarm message is sent to the local area network (LAN) LoRa display via the LoRa module, to the data center via the 4G module, and an SMS message is sent to the mobile phone via BeiDou short message mode. For predictive alarms, an alarm message is sent to the data center via the 4G module, and an SMS message is sent to the mobile phone via BeiDou short message mode. Maintenance personnel then handle the alarms accordingly.

[0181] The present invention provides a system for monitoring and early warning of highway infrastructure, which has the following advantages:

[0182] 1. It highly integrates functions such as data acquisition, power supply, and transmission, and is easy to install. The monitoring device does not need to be connected to power supply and communication lines, reducing dependence on on-site power supply and communication.

[0183] 2. Low power consumption design: The monitoring device is divided into low power consumption mode, data acquisition mode and alarm mode according to different operating power consumption. It is normally in low power consumption mode to achieve ultra-long standby time.

[0184] 3. Integrate multiple parameters such as displacement and vibration into one, perform feature extraction and fusion of multi-source data, and make comprehensive judgments to avoid false alarms and missed alarms.

[0185] 4. Supports solar power supply and can be online for a long time, meeting the needs of long-term trend monitoring of structures and abnormal alarms in case of emergencies.

[0186] 5. Multiple alarm methods: 4G to the data center, LoRa to the local area network LoRa warning signs, and BeiDou messaging to mobile phones to send short messages, so as to ensure that in the event of a large-scale disaster that causes power and network outages, the damage status of highway infrastructure can be promptly released to vehicles in motion and maintenance units.

[0187] In summary, the present invention provides a system for monitoring and early warning of highway infrastructure, which can monitor highway infrastructure for a long time, adapt to different environments, collect and analyze monitoring data in real time, and issue alarm signals in a timely manner when anomalies are detected, thereby improving the ability of highway infrastructure to perceive disasters.

[0188] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The present invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, produce implementations of the flowchart illustrations and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0189] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0190] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0191] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of the claims of the present invention.

Claims

1. A system for monitoring and early warning of highway infrastructure, characterized in that, include: The system includes a BeiDou displacement monitoring module, a three-axis accelerometer, a data processing module, a communication module, and a battery module. The Beidou displacement monitoring module is used to monitor the three-dimensional coordinates of highway infrastructure in real time, compare them with initial values, obtain the displacement of the highway infrastructure in three-dimensional coordinates, and send the displacement data to the data processing module. A triaxial accelerometer is used to acquire the triaxial acceleration of the highway infrastructure; the triaxial acceleration is then sent to the data processing module. The data processing module is used to receive displacement data sent by the Beidou displacement monitoring module and triaxial acceleration data sent by the triaxial MENS accelerometer; and to extract displacement feature values ​​from the displacement data and triaxial acceleration feature values ​​from the triaxial acceleration data. The displacement feature value and triaxial acceleration feature value are predicted to obtain the feature prediction value of displacement and triaxial acceleration. Based on the displacement characteristic values ​​and characteristic prediction values, and the triaxial acceleration characteristic values ​​and characteristic prediction values, the alarm level of the highway infrastructure is determined; The communication module is used to send corresponding alarm information according to the alarm level determined by the data processing module; The battery module is used to provide power to the various modules of the system.

2. The system according to claim 1, characterized in that, The BeiDou displacement monitoring module is used to monitor the three-dimensional coordinates of highway infrastructure in real time and compare them with initial values ​​to obtain the displacement of the highway infrastructure in three-dimensional coordinates, including: The longitude, latitude, and altitude of the monitored highway infrastructure are collected periodically and compared with the initial values ​​to obtain the displacement of the highway infrastructure in the three-dimensional coordinates of longitude, latitude, and altitude.

3. The system according to claim 1, characterized in that, A triaxial accelerometer sensor is used to acquire the triaxial acceleration of the highway infrastructure, including: Obtain the acceleration of the highway infrastructure in the three orthogonal directions of X, Y, and Z.

4. The system according to claim 1, characterized in that, The data processing module also includes the following functions: The displacement data sent by the Beidou displacement monitoring module and the triaxial acceleration data sent by the triaxial accelerometer are preprocessed to remove abnormal and noisy data.

5. The system according to claim 1, characterized in that, Extract displacement feature values ​​from the displacement data and triaxial acceleration feature values ​​from the triaxial acceleration data, wherein: The triaxial acceleration characteristic values ​​in triaxial acceleration data include: tilt angle, vibration waveform, vibration frequency value, damping ratio, skewness index, and margin index; Let the X-axis, Y-axis, and Z-axis be three mutually orthogonal directions, with the X-axis and Y-axis in the horizontal direction of the triaxial accelerometer and the Z-axis in the chip direction perpendicular to the triaxial accelerometer. The acceleration value measured on the X-axis is a x Define the angle between the X-axis and the horizontal plane as the pitch angle α; the acceleration measured on the Y-axis is a. y The roll angle β is defined as the angle between the Y-axis and the horizontal plane; the acceleration value measured on the Z-axis is a. z Define the angle between the Z-axis and the horizontal plane as γ; For the monitored highway infrastructure, the acceleration is equal to the vector sum of the three-axis accelerations, and its magnitude G is calculated from the magnitudes of the three vectors: Therefore, the tilt angle under motion can be calculated as follows: The triaxial acceleration is integrated twice with the corresponding time to obtain the displacement value, and then the vibration discrete waveforms in three directions are obtained. The vibration waveforms are then fitted using a fitting algorithm. Expressing the vibration frequency in terms of effective value, the vibration frequency in the X-axis direction is... Vibration frequency value in the Y-axis direction Vibration frequency value in the X-axis direction The damping ratio is determined by analyzing the statistical characteristics of the random vibration response using the random reduction method. Based on the discrete vibration waveforms along the X, Y, and Z axes, calculate the corresponding skewness index of the discrete vibration waveforms: X-axis deviation index Y-axis skewness index Z-axis skewness index Where u is the centrifugal value and σ is the standard deviation; Based on the vibration discrete waveforms along the X, Y, and Z axes, calculate the corresponding margin indices for the vibration discrete waveforms: X-axis margin index Y-axis margin indicator Z-axis margin index Calculate the displacement characteristic value based on the three-dimensional displacement value and preset characteristic parameters: Displacement eigenvalue = A x *Displacement value x+A y *Displacement value y+A z *Displacement value z Among them, A x Preset characteristic parameters for the X-axis, A y Preset characteristic parameters for the Y-axis, A z Preset characteristic parameters for the Z-axis.

6. The system according to claim 1, characterized in that, Predicting the displacement feature values ​​and triaxial acceleration feature values ​​to obtain predicted feature values ​​of displacement and triaxial acceleration includes: Based on the displacement feature values ​​and triaxial acceleration feature values ​​within a preset time period, the predicted feature values ​​of displacement and triaxial acceleration within a future preset time period are obtained through a spatial extrapolation algorithm.

7. The system according to claim 1, characterized in that, Based on the displacement characteristic values ​​and predicted characteristic values, and the triaxial acceleration characteristic values ​​and predicted characteristic values, the alarm level of highway infrastructure is determined, including: Extract the maximum displacement characteristic value, maximum vibration frequency characteristic value, maximum damping characteristic value, maximum vibration skewness index characteristic value, and maximum vibration margin index characteristic value within a preset time period, and calculate the structural state characteristic value: Structural state characteristic value = A* Maximum displacement characteristic value + B* Maximum vibration frequency characteristic value + C* Maximum damping ratio characteristic value + D* Maximum vibration skewness index characteristic value + E* Maximum vibration margin index characteristic value; A, B, C, D, and E are weights, respectively. Extract the predicted feature values ​​of maximum displacement, maximum vibration frequency, maximum damping, maximum vibration skewness, and maximum vibration margin within a preset future time period, and calculate the predicted feature value of structural state: Structural state prediction characteristic value = A* Maximum displacement prediction characteristic value + B* Maximum vibration frequency value prediction characteristic value + C* Maximum damping ratio prediction characteristic value + D* Maximum vibration skewness index prediction characteristic value + E* Maximum vibration margin index prediction characteristic value; The alarm level is calculated based on the structural state characteristic values ​​and the predicted structural state characteristic values. m represents the number of past days, n represents the number of future days, Fi represents the structural state characteristic value on day i, and Ri represents the weight of the structural state characteristic value of each day in the alarm level.

8. The system according to claim 1, characterized in that, The communication module includes: a 4G module and a LoRa module; among which, The 4G module is used to send alarm information to the data center; The Lora module is used to send alarm information to the Lora receiver on the local area network.

9. The system according to claim 1, characterized in that, Also includes: The system comprises a BeiDou short message module, a magnetic switch module, a configuration module, and a storage unit; among which, The BeiDou short message module is used to send text messages to system operation and maintenance personnel; Magnetic switch module, used to control the power on and off of the system using magnetic field signals; The configuration module is used to configure relevant system parameters; Storage unit, used to store initial values ​​and monitoring values ​​of relevant modules in the system.

10. The system according to claim 1, characterized in that, Alarm levels for highway infrastructure include: immediate alarms and predictive alarms.

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