Extreme environment trigger type intelligent monitoring system based on fiber grating sensor

Abstract

The invention discloses an extreme environment trigger type intelligent monitoring system based on a fiber grating sensor. The extreme environment trigger type intelligent monitoring system comprises a trigger sensing unit used for detecting environment meteorological data, environment vibration data and trace strain data of a to-be-monitored structure; the core control unit is used for receiving the detection data of the trigger sensing unit and preprocessing the detection data to identify the monitoring level; the fiber bragg grating monitoring unit is triggered to be started by a monitoring level instruction so as to monitor strain data and temperature data in the to-be-monitored structure; the data processing and transmitting unit is used for carrying out real-time preprocessing on the strain data and the temperature data, identifying abnormal data and normal data, uploading the abnormal data and storing the normal data; and the cloud platform is used for calculating the abnormal data so as to obtain an early warning value. According to the method and the system, the modes of wake-up detection, hierarchical monitoring and priority uploading of abnormal data are combined, so that the monitoring pertinence and the data accuracy are improved while the energy consumption and the data redundancy are reduced.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of structural health monitoring, and particularly relates to an extreme environment triggered intelligent monitoring system based on a fiber grating sensor. BACKGROUND

[0002] The structural strength and stability of buildings such as high-rise buildings, large-span bridges, wind power towers and ancient buildings need to be monitored for a long time to ensure the safety of the buildings, especially in extreme weather environments such as typhoons, heavy rain, snowstorms and environmental vibrations. The structural safety of such buildings needs to be monitored with high precision. The traditional monitoring method is to use a fiber grating monitoring system, which usually uses a "all-weather continuous sampling" mode, and real-time detection, data analysis and data uploading are performed through a sensor, a demodulator and a communication module. Such a detection system is in full-load operation state all the year round, and therefore faces the following problems: The existing fiber grating monitoring module (sensor, demodulator) demodulator and communication equipment continuously work, the overall power consumption of the system is large, the solar battery power supply scheme is frequently out of power in the rainy season, the sensor ages rapidly, and problems of high energy consumption and short service life occur.

[0003] The existing fiber grating monitoring module continuously samples without distinction, so that effective signals are submerged in a large amount of "normal data", the background storage and transmission flow surge, and data redundancy is serious.

[0004] The existing scheme uses a wind speed or rainfall single parameter threshold as a starting condition, cannot identify other dangerous sources such as surrounding vibration environments, causes frequent false starts, at the same time, short-time gusts or local heavy rain often make the system enter a high-power consumption mode too early, while the extreme section that really needs high-frequency sampling is submerged, the trigger source is single, false positives and false negatives coexist, and thus lead to missed monitoring. In addition, the traditional monitoring terminal directly transmits all raw data to the cloud, which not only occupies bandwidth, but also prolongs the dangerous response time. SUMMARY

[0005] The extreme environment triggered intelligent monitoring system based on the fiber grating sensor provided by the application solves the above-mentioned technical problems, and specifically adopts the following technical scheme: An extreme environment triggered intelligent monitoring system based on a fiber grating sensor, comprising: A trigger sensing unit is configured to detect environmental meteorological data, environmental vibration data and micro-strain data of a structure to be monitored. A core control unit is configured to receive detection data of the trigger sensing unit, pre-process the detection data, identify detection values in different dimensions, and generate a monitoring level instruction corresponding to the detection values according to a detection threshold corresponding to a monitoring level set in the core control unit. The fiber grating monitoring unit is triggered to start by the monitored level instruction to monitor strain data and temperature data in the structure to be monitored. The data processing and transmission unit is used to accept the strain data and the temperature data, to pre-process the strain data and the temperature data in real time through the edge calculation module, to identify abnormal data with strain values exceeding the safety strain threshold of the structure to be monitored and normal data with strain values within the safety strain threshold of the structure to be monitored, and to upload the abnormal data and save the normal data. The cloud platform is used to accept the abnormal data, to calculate the abnormal data through an abnormal algorithm model, to obtain a warning value, and to provide the staff with abnormal monitoring of the structure to be monitored. The power supply unit has a low-power supply mode and a high-power supply mode to provide low-power supply to the system when the fiber grating monitoring unit is not triggered, and to provide high-power supply to the system when the fiber grating monitoring unit is triggered.

[0006] Further, the core control unit comprises: The low-power MCU controller is used to execute instructions to control the system according to the received signals; The trigger logic processing chip is internally provided with multi-dimensional trigger threshold parameters, is used to compare and process the detection data of the trigger sensing unit in real time, to extract the trigger parameters of the corresponding dimensions, and to generate the corresponding trigger signals containing the dimension information according to the trigger threshold parameters of the dimension; The hierarchical response control module is used to generate a control instruction package for primary monitoring or a control instruction package for secondary monitoring according to the trigger parameters corresponding to the trigger signals; The monitoring frequency of the primary monitoring is greater than the monitoring frequency of the secondary monitoring.

[0007] Further, the monitoring data of the primary monitoring is uploaded in real time and real-time warning is provided; the detection data of the secondary monitoring is uploaded every interval of a preset time and warning is provided when abnormal data is identified.

[0008] Further, the hierarchical response control module adjusts the control instruction package according to the real-time corresponding trigger signals, so as to adjust the monitoring level.

[0009] Further, the data processing and transmission unit further comprises a communication module for uploading data and a local storage module for temporarily storing normal data; The communication module comprises a 5G communication module and an NB-IoT communication module; The primary monitoring uses the 5G communication module to push the data exceeding the threshold in real time; The secondary monitoring uses the NB-IoT communication module to upload the critical data and the local storage module to cache the normal data.

[0010] Further, the abnormal data in the primary monitoring and the secondary monitoring includes: the primary abnormal data whose strain value exceeds the structural strain safety threshold, and the secondary abnormal data whose strain value is within the critical threshold between the structural strain safety threshold and the structural strain danger threshold; the primary abnormal data is uploaded through the 5G communication module; and the secondary abnormal data is uploaded through the NB-IoT communication module.

[0011] Further, when the monitoring instruction of the fiber grating monitoring unit is cancelled, the data processing and transmission unit uploads the stored normal data of this monitoring to the cloud platform for information tracing.

[0012] Further, the abnormal algorithm model includes: The data preprocessing module: removes environmental interference information through a built-in filtering algorithm; The feature parameter extraction module: extracts key feature indexes including the peak value / valley value of the structural strain, the duration of the vibration frequency, and the cumulative value of the meteorological parameters from the uploaded multi-dimensional detection data, and converts them into multi-dimensional parameters; The multi-source data fusion module: uses a weighted fusion algorithm to associate and match the strain value and the temperature value detected by the fiber grating monitoring unit with the multi-dimensional parameter data, and gives different dimensions of data with weights; The structural safety quantitative evaluation module: inputs the associated and matched feature parameters and the design parameters of the structure to be monitored, and outputs the safety factor of the structure to be monitored in real time.

[0013] Further, the cloud platform is also provided with a trend prediction calculation model for calculating the strain trend of the structure to be monitored; The trend prediction calculation model uses a time series prediction model, and calculates the change trend of the safety factor of the structural strain of the structure to be monitored based on historical monitoring data; When the predicted subsequent safety factor reaches the structural strain safety factor threshold critical point, an early warning signal is output.

[0014] Further, the trend prediction calculation model marks the safety factor corresponding to the fiber grating position that reaches the structural strain safety threshold critical point, and defines it as an abnormal position; when the multi-dimensional detection value of the trigger sensing unit is less than the detection threshold corresponding to the monitoring level, the core control unit controls the fiber grating monitoring unit to start at a preset time interval to monitor the marked abnormal position at the interval time until the safety factor that reaches the structural strain safety threshold critical point drops to within the safety threshold, and enters a complete dormant state.

[0015] The application has the advantages that the provided optical fiber grating sensor-based extreme environment trigger type intelligent monitoring system adopts a multi-dimensional trigger mechanism, environment meteorological data, environment vibration data and trace strain data of the structure to be monitored are used to build a double trigger system of extreme weather and environment vibration, and the problems of high energy consumption, data redundancy and shortened service life of sensors caused by the traditional monitoring system working all day long are solved.

[0016] The optical fiber grating sensor-based extreme environment trigger type intelligent monitoring system provided by the application adopts a hierarchical response monitoring logic, and according to the trigger signal type (typhoon / rainstorm / snowstorm / construction vibration) and intensity level (trigger parameter value), the corresponding monitoring frequency, data acquisition accuracy and transmission priority are automatically matched to realize on-demand monitoring and balance the monitoring effect and system energy consumption.

[0017] The optical fiber grating sensor-based extreme environment trigger type intelligent monitoring system provided by the application adopts a collaborative design of optical fiber grating sensors and trigger modules, the optical fiber grating monitoring unit is provided with a low-power sleep chip and is in a power-off sleep state in normal times, and is quickly woken up (response time ≤ 1s) after the trigger signal is activated, on-demand monitoring is realized, long-term continuous high-power monitoring is avoided, and in combination with the anti-electromagnetic interference and corrosion resistance of the optical fiber grating sensor, the service life of the sensor can be prolonged and the sensor is adapted to complex outdoor environments.

[0018] The optical fiber grating sensor-based extreme environment trigger type intelligent monitoring system provided by the application adopts an embedded edge computing module, and the collected strain, vibration and environment parameters are preprocessed (filtering, noise reduction and feature extraction) in real time, only the abnormal data or key indicators are uploaded to the cloud platform, the transmission bandwidth occupation is reduced, the timeliness and stability of uploading of the abnormal data or key indicators are ensured, and important data redundancy is avoided. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description only constitute some embodiments of the application, and for those skilled in the art, other drawings can also be obtained without creative labor.

[0020] Fig. 1 is a schematic diagram of the optical fiber grating sensor-based extreme environment trigger type intelligent monitoring system of the application; Fig. 2 is a flowchart of the optical fiber grating sensor-based extreme environment trigger type intelligent monitoring system of the application. DETAILED DESCRIPTION

[0021] Embodiments of the present invention are described in detail below. Examples of these embodiments are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0022] The accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0023] The flowchart shown in the attached diagram is merely an illustrative example and does not necessarily include all steps. For example, some steps may be broken down, while others may be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0024] In a specific embodiment of the present invention, the innovability and practicality of the invention are demonstrated by describing an exemplary embodiment in detail. This embodiment, in conjunction with the system architecture and flowcharts in the accompanying drawings, clearly illustrates the various key modules of the invention and their interactions. The embodiments of the present invention aim to provide those skilled in the art with a technical solution that is easy to understand and implement, while demonstrating the advantages and effects of the present invention in practical applications.

[0025] like Figs. 1-2 The diagram illustrates an extreme environment-triggered intelligent monitoring system based on a fiber Bragg grating sensor, comprising: a trigger sensing unit, a core control unit, a fiber Bragg grating monitoring unit, a data processing and transmission unit, a cloud platform, and a power supply unit. Wherein: The trigger sensing unit detects environmental meteorological data, environmental vibration data, and minute strain data of the structure to be monitored. This trigger sensing unit includes meteorological sensors (wind speed sensor, rain sensor, snow load sensor), vibration sensors, and strain gauges. The meteorological sensors detect environmental meteorological data in real time, the vibration sensors detect vibration data in the environment in real time, and the strain gauges directly detect strain changes in the structure to be monitored, thus enabling the acquisition of multi-dimensional signals from extreme weather (typhoons, heavy rain, heavy snow) and environmental vibrations (such as surrounding construction).

[0026] The core control unit receives the detection data from the trigger sensing unit, preprocesses the detection data, identifies detection values ​​in different dimensions, and generates a monitoring level instruction corresponding to the detection value based on the detection threshold corresponding to the built-in monitoring level.

[0027] The fiber grating monitoring unit is triggered to start by the monitoring level instruction to monitor strain data and temperature data in the structure to be monitored. The fiber grating monitoring unit comprises a fiber grating strain sensor and a fiber grating temperature sensor arranged in the component and a fiber demodulator arranged in the control room. The fiber grating strain sensor detects the strain signal in the structure to be monitored as a front detection component, the fiber grating temperature sensor detects the temperature in the structure to be monitored as a front detection component to calibrate the temperature compensation when calculating the strain value, and the fiber demodulator is used to demodulate the detected strain signal into a digital signal.

[0028] The data processing and transmission unit receives the strain data and temperature data, pre-processes the strain data and temperature data in real time through the edge calculation module, identifies abnormal data with a strain value exceeding the safety strain threshold of the structure to be monitored and normal data with a strain value within the safety strain threshold of the structure to be monitored, uploads the abnormal data, and saves the normal data. The abnormal data includes abnormal strain values and corresponding multi-dimensional trigger detection data, and the normal data includes normal strain values and corresponding multi-dimensional trigger detection data. Based on the built-in edge calculation module, a three-step extraction method of "filtering and denoising → data sampling → feature quantization" is adopted to adapt to the characteristics of multi-source data: first, environmental interference (such as sensor noise and instantaneous gust error) is removed through Kalman filtering and wavelet denoising; second, sampling is performed according to the monitoring level (first level 10Hz high-frequency sampling, second level 1Hz normal sampling); and finally, the original data is converted into quantized feature values through a feature extraction algorithm (such as peak detection and statistical analysis). The fiber grating monitoring unit feature values involved in the scheme include strain peak / valley, strain mean value and fluctuation amplitude, temperature mean value, and strain-temperature correlation coefficient (for temperature compensation correction verification). The trigger sensing unit feature values involved in the scheme include meteorological parameter peak value (maximum wind speed, 1-hour cumulative rainfall, and snow load peak value), vibration parameter (vibration frequency interval, acceleration peak value, and vibration duration).

[0029] The cloud platform receives the abnormal data and calculates the warning value through an abnormal algorithm model to provide abnormal monitoring of the structure to be monitored for the staff.

[0030] The power supply unit has a low-power supply mode and a high-power supply mode to provide low-power supply to the system when the fiber grating monitoring unit is not triggered, and to provide high-power supply to the system when the fiber grating monitoring unit is triggered, to ensure monitoring and transmission requirements and support a continuous running time of ≥72h without sunlight. The power supply unit adopts a low-power design of solar energy combined with a lithium battery pack to ensure long-term operation of the system.

[0031] That is, when the fiber grating monitoring unit is triggered to wake up, the structural strain and temperature data of the structure to be monitored are collected with the set precision. The structural strain collection precision is ±1με, and the temperature collection precision is ±0.1℃. In the whole monitoring process, the execution units of the trigger sensing unit are always in an open state. When the trigger signals in each dimension detected by the trigger sensing unit disappear, such as wind speed ≤8, vibration acceleration ≤0.05g, and the duration is 30min, the core control unit starts to close the fiber grating monitoring unit again, and then the data processing and transmission unit packs and uploads the data in the monitoring state to the cloud platform. The cloud platform discriminates whether to alarm according to the preset threshold.

[0032] The above scheme combines wake-up detection, hierarchical monitoring, and abnormal data priority uploading to reduce energy consumption and data redundancy while improving monitoring relevance and data accuracy, solving the problems of high energy consumption, data redundancy, and shortened sensor life caused by the traditional monitoring system working all day long.

[0033] As a specific implementation, the core control unit includes a low-power MCU controller, a trigger logic processing chip, and a hierarchical response control module. Among them: The trigger logic processing chip is provided with multi-dimensional trigger threshold parameters. The trigger logic processing chip performs real-time comparison processing on the detection data received by the trigger sensing unit, extracts the trigger parameters of the corresponding dimension, and generates the corresponding trigger signal containing the dimension information according to the trigger threshold parameters of the dimension. The real-time collected environmental parameters (wind speed ≥10 levels / rainfall ≥50mm / h / snow load ≥2kPa), vibration parameters (frequency 5-100Hz, acceleration ≥0.1g) and structural micro-strain parameters (≥5με) are used as trigger signal sources to trigger the start of the core control unit. That is, the trigger logic processing chip is provided with preset multi-dimensional trigger threshold parameters, such as wind speed ≥10 levels, vibration acceleration ≥0.1g, and micro-strain ≥5με. The trigger logic processing chip performs real-time comparison on the received original detection data, eliminates invalid interference signals (such as short-term gusts and instantaneous vibrations) through the built-in algorithm, judges whether the trigger signal meets the “duration standard” condition (such as vibration lasting ≥30s), and then converts the comparison result into an explicit trigger type signal (such as typhoon trigger, construction vibration trigger, and structure deformation trigger), i.e. the corresponding trigger signal containing the dimension information, and is transmitted to the hierarchical response control module. The threshold parameters here can be flexibly configured according to different buildings, so as to adapt to different monitoring scenes (such as reducing the vibration trigger threshold for ancient buildings and increasing the wind speed trigger threshold for wind power towers).

[0034] The hierarchical response control module confirms the generation of a control instruction package for first-level monitoring or a control instruction package for second-level monitoring according to a trigger parameter corresponding to a trigger signal. The monitoring frequency of the first-level monitoring is greater than the monitoring frequency of the second-level monitoring. That is, the hierarchical response control module is provided with two monitoring levels, i.e., the first-level monitoring and the second-level monitoring. The monitoring frequencies of the fiber grating monitoring units corresponding to different monitoring levels are different, wherein the first-level monitoring frequency is 10 Hz, and the second-level monitoring frequency is 1 Hz. The hierarchical response control module receives a corresponding trigger signal of the trigger logic processing chip, determines the monitoring level in combination with the signal strength, and also corresponds to the trigger signal, such as wind speed ≥ 12 levels corresponding to the first-level monitoring, and wind speed of 10-11 levels corresponding to the second-level monitoring. After confirming the monitoring level, a corresponding control instruction package is generated. The control instruction package includes a fiber grating monitoring unit wake-up instruction, a monitoring level instruction, a monitoring level corresponding data parameter, a local early warning instruction (when it is confirmed that the first-level monitoring, the sound-light early warning device provided in the monitored structure can accept the local early warning instruction to perform real-time local early warning), and the like.

[0035] The low-power MCU controller sends an execution instruction according to the received signal to control the system operation. The corresponding control instruction package is issued to each execution unit through the low-power MCU controller. During the monitoring process, the low-power MCU controller tracks the change of the trigger signal strength in real time, and generates a “monitoring degradation” or “hibernation recovery” instruction if the signal is weakened to a release threshold. The low-power MCU controller controls the ultra-low power operation in the hibernation state of the system (controls the total power consumption ≤ 50 mW): it manages the power on / off of the execution units such as the trigger sensing unit and the fiber grating monitoring unit; it receives the original trigger signals such as meteorological, vibration, and micro-strain signals from the trigger sensing unit and forwards them to the trigger logic processing chip; it executes the control instruction package output by the hierarchical response control module, issues the start / switch / hibernation command to the fiber grating monitoring unit, the data processing and transmission unit, and the local sound-light early warning device; and it feeds back the working state of each unit in real time (such as whether the sensor is awake, whether the power supply is stable), so as to ensure that the system operation failure can be traced back.

[0036] Further, the monitoring data of the first-level monitoring is uploaded in real time and real-time warning, and the detection data of the second-level monitoring is uploaded every interval preset time, such as 1 time per 5 minutes, and warning is given when abnormal data is identified. In this way, according to different monitoring requirements, the appropriate monitoring frequency is set, and according to the urgency of the data, the priority uploading and real-time uploading strategy is given, which can further reduce the energy consumption while ensuring the timeliness and accuracy of the monitoring.

[0037] Further, the hierarchical response control module adjusts the control instruction package according to the real-time corresponding trigger signal, so as to adjust the monitoring level. That is, according to the change of the multi-dimensional parameter value in the corresponding trigger signal, the monitoring level is adjusted in real time, which can more accurately reduce the energy consumption and ensure the timeliness and effectiveness of the data monitoring.

[0038] Different monitoring levels correspond to different communication modes (level 1 5G / level 2 NB-IoT) of data; As a specific embodiment, the data processing and transmission unit further comprises a communication module for uploading data and a local storage module for temporarily storing normal data. The communication module comprises a 5G communication module and an NB-IoT communication module. In this scheme, different monitoring levels correspond to different communication modes, i.e., level 1 monitoring uses the 5G communication module to push real-time data over the threshold, level 2 monitoring uses the NB-IoT communication module to upload critical data, and the local storage module caches normal data.

[0039] Further, the abnormal data in level 1 monitoring and level 2 monitoring includes level 1 abnormal data whose strain value exceeds the structural strain safety threshold and level 2 abnormal data whose strain value is within the critical threshold of the structural strain safety threshold and the structural strain danger threshold. The level 1 abnormal data in this scheme is uploaded through the 5G communication module, and the level 2 abnormal data in this scheme is uploaded through the NB-IoT communication module. In this way, on the basis of level 1 monitoring using the 5G communication module to push real-time data over the threshold and level 2 monitoring using the NB-IoT communication module to upload critical data, the monitoring abnormal data of level 1 monitoring and level 2 monitoring are further refined into level 1 abnormal data and level 2 abnormal data, which further optimizes the priority uploading path of abnormal data, realizes the priority uploading of emergency data with the highest efficiency, and ensures the timeliness of abnormal data transmission and the efficiency of data processing of the cloud platform, i.e., the dangerous signal can be quickly identified according to the priority without complex and multiple information interference.

[0040] As an embodiment of this scheme, when the monitoring instruction of the fiber grating monitoring unit is released, the data processing and transmission unit uploads the stored normal data of this monitoring to the cloud platform for information tracing. In this scheme, the stored normal data of this monitoring is uploaded to the cloud platform after triggering is released, because the system returns to the low-power mode, batch uploading can reduce the number of communication module start-ups and reduce power consumption. In addition, normal data has no real-time warning requirement, and batch uploading can avoid occupying too much 5G / NB-IoT bandwidth and reduce operation and maintenance costs. At the same time, the normal data during the triggering of the fiber grating monitoring unit is temporarily stored in the local storage module, and batch uploading can ensure the complete archiving of historical data, which is convenient for the cloud platform to trace and analyze the trend of structure changes.

[0041] In the above scheme, the abnormal algorithm model of the cloud platform comprises: Data preprocessing module: environmental interference information is removed through built-in filtering algorithm; The feature parameter extraction module extracts key feature indexes including the peak value / valley value of structural strain, the duration of vibration frequency, and the cumulative value of meteorological parameters from the uploaded multi-dimensional detection data, and converts them into multi-dimensional parameters. The multi-source data fusion module uses a weighted fusion algorithm to associate and match the strain values and temperature values detected by the fiber grating monitoring unit with the multi-dimensional parameter data, and gives different dimensions of data different weights. The structural safety quantitative evaluation module inputs the associated and matched feature parameters and the design parameters of the structure to be monitored, and outputs the safety factor of the structure to be monitored in real time.

[0042] That is, the data preprocessing module eliminates environmental interference noise such as strain instantaneous fluctuations caused by typhoons and gusts, and temperature data deviations caused by rain erosion of the sensor through Kalman filtering or wavelet denoising, to ensure the purity of the input data and lay a foundation for subsequent analysis. The feature parameter extraction module extracts key feature indexes such as the peak value / valley value of structural strain, the duration of vibration frequency, and the cumulative value of meteorological parameters (such as cumulative rainfall) from the uploaded multi-dimensional data, converts massive raw data into quantifiable core parameters, and reduces the complexity of analysis. The multi-source data fusion module uses a weighted fusion method to associate and match the strain data and temperature data of the fiber grating monitoring unit with the meteorological data and vibration data of the trigger sensing unit, and gives different dimensions of data different weights (such as a structural strain weight of 0.5, a wind speed weight of 0.3, and a vibration weight of 0.2), thereby solving the one-sidedness problem of single-dimensional data evaluation. The structural safety quantitative evaluation module inputs the fused feature parameters and the structural design parameters (such as concrete strength grade and steel structure fatigue limit), and calculates the safety factor of the structure to be monitored in real time. The calculation formula is simplified as: safety factor = actual bearing capacity of structure / current load (calculated from meteorological, vibration, etc. data). The preset safety factor threshold in the present scheme is 0.8-0.9. When the safety factor of the structure to be monitored is lower than the threshold, a warning signal is sent.

[0043] Further, the cloud platform is also provided with a trend prediction calculation model for calculating the strain trend of the structure to be monitored. The trend prediction calculation model adopts a time series prediction model, and based on historical monitoring data, calculates the change trend of the safety factor of the structural strain of the structure to be monitored. When the predicted subsequent safety factor reaches the critical point of the structural strain safety threshold, an early warning signal is output. That is, the trend prediction calculation model takes the time series data of the structural strain monitoring as the core input, after preprocessing such as noise reduction and outlier elimination, adopts a time series prediction model (such as an ARIMA model), based on the uploaded historical monitoring data, fits the historical strain evolution law, modifies the prediction parameters in combination with the structural mechanics constraint conditions, and outputs the strain trend prediction value in the future period, so as to calculate the change trend of the structural strain and the safety factor, such as predicting whether the safety factor threshold will be broken through in the subsequent 3 hours through the strain growth data in the subsequent 3 hours, that is, by comparing with the preset safety threshold, the early warning of the trend anomaly is realized.

[0044] Further, the trend prediction calculation model marks the safety factor corresponding to the position of the fiber grating that reaches the critical point of the structural strain safety threshold, which is defined as an abnormal position. When the multi-dimensional detection value of the trigger sensing unit is less than the detection threshold corresponding to the monitoring level, the core control unit controls the fiber grating monitoring unit to start at a preset time interval to monitor the marked abnormal position at an interval, until the safety factor that reaches the critical point of the structural strain safety threshold decreases to within the safety threshold, and enters a completely dormant state. In this way, a closed loop from monitoring problems to solving the problems monitored can be realized, avoiding the risk of structure risk handling, so as to continuously exist, and thus forming a structure safety hidden danger.

[0045] The above-mentioned extreme environment trigger type intelligent monitoring system based on fiber grating sensor has the following closed loop linkage process when executed: First step: signal acquisition and transmission The trigger sensing unit continuously detects and collects the signals of the environment and the structure. The low-power MCU controller receives these original signals at a low frequency (1 time / 10 min). At this time, the trigger logic processing chip and the hierarchical response control module are in a low-load standby state, and only the low-power MCU controller maintains basic operation to ensure low power consumption of the system.

[0046] Second step: trigger signal analysis and judgment When the original signal reaches the preset threshold, the low-power MCU controller immediately activates the trigger logic processing chip, and synchronously transmits the signal to the trigger logic processing chip. Then, the trigger logic processing chip quickly completes signal filtering, threshold comparison and data validity verification, determines the type (such as heavy rain trigger) and trigger parameters of the trigger fiber grating monitoring unit, sets the corresponding trigger level according to the trigger parameters, and then sends the identification result (such as “heavy rain secondary trigger”) to the hierarchical response control module.

[0047] Third step: hierarchical monitoring matching and instruction generation The hierarchical response control module retrieves the corresponding monitoring scheme (first-level monitoring or second-level monitoring) from the preset strategy library according to the received identification result, generates a control instruction package containing instructions such as "wake up the fiber grating monitoring unit", "set the monitoring frequency to 1 Hz", "start NB-IoT communication", and the like, and feeds back to the low-power MCU controller.

[0048] Fourth step: instruction execution and state feedback The low-power MCU controller disassembles the control instruction package into specific instructions and issues them to the fiber grating monitoring unit, data processing and transmission unit, power supply unit, etc. After each execution unit executes the instructions, the low-power MCU controller feeds back the working state signal (such as "monitoring unit has woken up" and "communication module has switched"), and the low-power MCU controller monitors the execution in real time. If an abnormality occurs (such as the sensor not responding), a backup control scheme (such as manual start) is started.

[0049] Fifth step: monitoring release and hibernation recovery When the trigger parameter (trigger signal) of the trigger sensing unit disappears and the disappearance state lasts for 30 minutes, the trigger logic processing chip sends a "trigger release" signal to the hierarchical response control module, and the hierarchical response control module generates a "monitoring degradation -> hibernation recovery" instruction to the low-power MCU controller. After receiving the instruction, the low-power MCU controller controls each execution unit to gradually reduce the running load, and after the batch of normal data of this monitoring is stored and uploaded, the system returns to the initial low-power hibernation state.

[0050] The above system forms a rigorous control closed loop through the sequential linkage of "signal reception -> logical judgment -> strategy generation -> instruction execution -> state feedback".

[0051] Specifically, the system of the present scheme is in a low-power hibernation mode in normal times, and only the core components (meteorological sensors, vibration sensors, and strain gauges) of the trigger sensing unit collect data at the lowest frequency (1 time / 10 minutes). The fiber grating monitoring unit is completely powered off, and the power supply unit charges the lithium battery through the solar panel to maintain the basic power consumption. When the trigger sensing unit collects any of the following signals, the core control unit sends a control instruction package to trigger the fiber grating monitoring unit: Meteorological parameters meet the standards: when the wind speed is ≥10 (24.5 m / s), the 1-hour rainfall is ≥50 mm (heavy rain), and the snow load is ≥2 kPa (heavy snow), the detection value of this dimension is the trigger signal; Environmental vibration meets the standards: when the vibration sensor detects an acceleration of ≥0.1g and the duration is ≥30s (surrounding construction vibration), the detection value of this dimension is the trigger signal; Structural micro-strain pre-monitoring: when the detection value of the strain gauge is greater than or equal to 5με, it means that the structure to be monitored has a precursor of abnormal deformation, and the detection value of this dimension is the trigger signal.

[0052] The core control unit of the scheme starts the specific mode of the corresponding monitoring level according to the trigger signal type and intensity as described above, as follows: The first level of monitoring corresponds to extreme environments such as strong typhoons, heavy rain, heavy snow, strong vibration, etc. The monitoring frequency of the fiber Bragg grating monitoring unit of this monitoring level is 10Hz, the data accuracy is the highest, and the data is uploaded to the cloud in real time, and the local sound-light warning is synchronized. The second level of monitoring corresponds to extreme environments such as heavy rain, heavy snow, weak vibration, etc. The monitoring frequency of the fiber Bragg grating monitoring unit of this monitoring level is 1Hz, and the data is uploaded once every 5min after preprocessing, and only when there is an anomaly will a warning be given.

[0053] When the trigger signal disappears, such as wind speed less than or equal to 8 levels, vibration acceleration less than or equal to 0.05g, and the disappearance state lasts for 30min, the system automatically reduces the monitoring level, that is, reduces the monitoring of the fiber Bragg grating monitoring unit, maintains a low-power operation process for 10min, and after uploading the monitoring data to the cloud platform for archiving, the system returns to the sleep state.

[0054] The extreme environment trigger type intelligent monitoring system based on fiber Bragg grating sensors provided by the scheme adopts a multi-dimensional trigger mechanism, integrates meteorological sensors, vibration sensors and strain gauges, and constructs a "extreme weather + environmental vibration" double trigger system, solving the problems of high energy consumption, data redundancy, and shortened sensor life caused by the "all-weather continuous work" of traditional monitoring systems.

[0055] The extreme environment trigger type intelligent monitoring system based on fiber Bragg grating sensors provided by the scheme adopts a hierarchical response monitoring logic, which automatically matches the corresponding monitoring frequency, data acquisition accuracy and transmission priority according to the trigger signal type (typhoon / rain / snow / construction vibration, etc.) and intensity level (trigger parameter value), realizes on-demand monitoring, and balances the monitoring effect and system energy consumption.

[0056] The extreme environment trigger type intelligent monitoring system based on fiber Bragg grating sensors provided by the scheme adopts a collaborative design of fiber Bragg grating sensors and trigger modules, and the fiber Bragg grating monitoring unit is provided with a low-power sleep chip, which is in a power-off sleep state at ordinary times, and is quickly awakened (response time less than or equal to 1s) after the trigger signal is activated, realizing on-demand monitoring and avoiding long-term continuous high-power monitoring. Combined with the anti-electromagnetic interference and corrosion resistance of the fiber Bragg grating sensor, the service life of the sensor can be prolonged, and it is suitable for complex outdoor environments.

[0057] The extreme environment trigger type intelligent monitoring system based on the fiber grating sensor provided in the scheme adopts a built-in edge computing module, and performs real-time preprocessing (filtering, noise reduction and feature extraction) on collected strain, vibration and environmental parameters, only uploads abnormal data or key indicators to a cloud platform, reduces transmission bandwidth occupation, guarantees timeliness and stability of uploading of abnormal data or key indicators, and avoids important data redundancy.

[0058] In summary, compared with the existing all-weather monitoring system, the extreme environment trigger type intelligent monitoring system based on the fiber grating sensor provided in the scheme adopts a trigger type working mode, the sleep power consumption is less than or equal to 50 mW, the service life of the fiber grating sensor is effectively prolonged, the data transmission amount per unit time is reduced, and the operation and maintenance cost is reduced; meanwhile, the multi-dimensional trigger signal covers extreme weather and environmental vibration, and the multi-level monitoring logic of hierarchical response improves the pertinence and effectiveness of monitoring, effectively avoiding missed measurement and false measurement; in addition, the fiber grating sensor has the characteristics of anti-interference and high precision, and the collaborative design of the fiber grating sensor and the low-power trigger module, in combination with the sleep wake-up mechanism, can adapt to the complex outdoor environment. Therefore, the system of the scheme can be applied to structure monitoring in multiple scenes, such as: (1) adaptive to structure health monitoring of high-rise buildings and super high-rise buildings (resisting structure deformation caused by typhoon and heavy rain); (2) adaptive to monitoring of traffic infrastructure such as bridges, tunnels and slopes (resisting landslides caused by heavy rain and structure damage caused by construction vibration); (3) adaptive to monitoring of traditional wood structure buildings and ancient buildings (avoiding damage to cultural relics buildings caused by extreme weather and surrounding construction, and reducing intervention on ancient buildings by low-power design); (4) adaptive to monitoring of outdoor high-rise structures such as wind power towers and communication base stations (adapted to extreme environments such as typhoon and blizzard to ensure equipment safety).

[0059] The basic principles, main features and advantages of the present application are shown and described above. It should be understood by those skilled in the art that the above examples do not limit the present application in any form, and any technical solution obtained by equivalent replacement or equivalent transformation falls within the protection scope of the present application.

Claims

1. A fiber grating sensor based extreme environment triggered intelligent monitoring system, characterized in that, The system comprises: a trigger sensing unit for detecting environmental meteorological data, environmental vibration data and micro-strain data of a structure to be monitored; a core control unit for receiving detection data from the trigger sensing unit, pre-processing the detection data, identifying detection values of different dimensions, and generating a monitoring level instruction corresponding to the detection values according to a detection threshold corresponding to a monitoring level set in advance; a fiber grating monitoring unit triggered by the monitoring level instruction to monitor strain data and temperature data in the structure to be monitored; a data processing and transmission unit for receiving the strain data and the temperature data, pre-processing the strain data and the temperature data in real time through an edge calculation module, identifying abnormal data with a strain value exceeding a safe strain threshold of the structure to be monitored and normal data with a strain value within the safe strain threshold of the structure to be monitored, uploading the abnormal data, and saving the normal data; a cloud platform for receiving the abnormal data, calculating the abnormal data through an abnormal algorithm model, and obtaining a warning value for abnormal monitoring of the structure to be monitored by a worker; a power supply unit having a low-power supply mode and a high-power supply mode to provide low-power supply to the system when the fiber grating monitoring unit is not triggered, and to provide high-power supply to the system when the fiber grating monitoring unit is triggered.

2. The extreme environment trigger type intelligent monitoring system based on the fiber grating sensor according to claim 1, wherein the core control unit comprises: a low-power MCU controller for executing an instruction to control system operation according to a received signal; a trigger logic processing chip having a plurality of trigger threshold parameters of different dimensions for real-time comparison and processing of the detection data received from the trigger sensing unit, extraction of trigger parameters of corresponding dimensions, and generation of a corresponding trigger signal containing the dimension information according to the trigger threshold parameters of the dimension; a hierarchical response control module for generating a control instruction package for primary monitoring or a control instruction package for secondary monitoring according to the trigger parameters corresponding to the trigger signal; the monitoring frequency of the primary monitoring is greater than the monitoring frequency of the secondary monitoring.

3. The extreme environment trigger type intelligent monitoring system based on the fiber grating sensor according to claim 2, wherein the monitoring data of the primary monitoring is uploaded in real time and a real-time warning is given; the detection data of the secondary monitoring is uploaded every interval of a preset time, and a warning is given when abnormal data is identified.

4. The extreme environment trigger type intelligent monitoring system based on the fiber grating sensor according to claim 3, wherein the hierarchical response control module adjusts the control instruction package according to the corresponding trigger signal in real time, thereby adjusting the monitoring level.

5. The extreme environment trigger type intelligent monitoring system based on the fiber grating sensor according to claim 2, wherein the data processing and transmission unit further comprises a communication module for uploading data and a local storage module for temporarily storing the normal data; the communication module comprises a 5G communication module and an NB-IoT communication module. ​ ​ ​ ​ The first-level monitoring adopts a 5G communication module to push the data exceeding the threshold value in real time. The second-level monitoring adopts an NB-IoT communication module to upload the critical data, and the normal data is cached through the local storage module.

6. The intelligent monitoring system of extreme environment triggered by fiber grating sensor according to claim 5, wherein the abnormal data in the first-level monitoring and the second-level monitoring comprises: first-level abnormal data of strain value exceeding a structure strain safety threshold value, and second-level abnormal data of strain value being within a critical threshold value between the structure strain safety threshold value and a structure strain danger threshold value. The first-level abnormal data is uploaded through the 5G communication module. The second-level abnormal data is uploaded through the NB-IoT communication module.

7. The intelligent monitoring system of extreme environment triggered by fiber grating sensor according to claim 1, wherein when the monitoring instruction of the fiber grating monitoring unit is released, the data processing and transmission unit uploads the normal data of the current monitoring to the cloud platform for information tracing.

8. The intelligent monitoring system of extreme environment triggered by fiber grating sensor according to claim 1, wherein the abnormal algorithm model comprises: a data preprocessing module for eliminating environmental interference information through an internal filtering algorithm; a feature parameter extraction module for extracting key feature indexes including peak / valley of structure strain, duration of vibration frequency, and cumulative value of meteorological parameters from the uploaded multi-dimensional detection data, and converting the multi-dimensional parameter data; a multi-source data fusion module for associating and matching the strain value and temperature value detected by the fiber grating monitoring unit with the multi-dimensional parameter data by using a weighted fusion algorithm, and giving different dimensions of data with weights; a structure safety quantitative evaluation module for inputting the associated and matched feature parameters and design parameters of the structure to be monitored, and outputting the safety coefficient of the structure to be monitored in real time.

9. The intelligent monitoring system of extreme environment triggered by fiber grating sensor according to claim 1, wherein the cloud platform is further provided with a trend prediction calculation model for calculating the strain trend of the structure to be monitored. The trend prediction calculation model adopts a time series prediction model, and calculates the change trend of the safety coefficient of the structure strain of the structure to be monitored based on historical monitoring data. When the predicted subsequent safety coefficient reaches the critical point of the structure strain safety coefficient threshold value, an early warning signal is output.

10. The intelligent monitoring system of extreme environment triggered by fiber grating sensor according to claim 9, wherein the trend prediction calculation model marks the fiber grating position corresponding to the safety coefficient reaching the critical point of the structure strain safety threshold value as an abnormal position. When the multi-dimensional detection value of the trigger sensing unit is less than the detection threshold value corresponding to the monitoring level, the core control unit controls the fiber grating monitoring unit to start at a preset time interval to monitor the marked abnormal position at an interval until the safety coefficient reaching the critical point of the structure strain safety threshold value decreases to within the safety threshold value, and enters a complete dormant state. ​ ​ ​ ​ ​

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