Urban land subsidence monitoring and early warning method and device

Abstract

The invention discloses an urban ground subsidence monitoring and early warning method which comprises the following steps: S1, a split type monitoring system is deployed, an underground probe module is physically isolated from a ground relay station, and the underground probe module comprises a three-mode sensing unit, an edge computing unit, a self-powered system and a wireless communication module; s2, establishing a three-dimensional coordinate system of a monitoring area, calibrating a spatial position relationship of each sensor, and converting data of a displacement meter into vector displacement under the area coordinate system; s3, calculating a settlement risk probability based on a D-S evidence theory, and generating an early warning grade; and S4, determining a final settlement rate threshold value Vth based on the basic threshold value Vbase and the seasonal correction factor kseason, and starting graded early warning response according to the accumulated settlement amount, the displacement sudden change and the inclination angle threshold value. According to the method, the monitoring precision is higher, the settlement recognition accuracy is higher, the threshold value can be dynamically adjusted, and graded early warning is achieved. The invention further discloses an urban land subsidence monitoring and early warning device.

Description

Technical Field

[0001] The present invention relates to the technical field of land subsidence monitoring, and in particular to a method and device for monitoring and early warning of urban land subsidence. Background Art

[0002] Traditional ground subsidence monitoring devices have defects such as isolated single-point sensor data, poor power supply continuity, and high false alarm rate; conventional equipment is mostly deployed in a centralized manner, which makes underground cables easy to damage and difficult to maintain, and the cost is high; existing early warning systems mostly use fixed thresholds and cannot adapt to the elastic deformation characteristics of soil caused by seasonal groundwater level fluctuations. Summary of the invention

[0003] In view of the shortcomings of the background technology, the technical problem to be solved by the present invention is to provide an urban land subsidence monitoring and early warning method with higher monitoring accuracy, higher settlement identification accuracy, dynamic adjustment of thresholds, and realization of graded early warning.

[0004] To this end, the present invention is implemented by adopting the following technical solutions: The urban land subsidence monitoring and early warning method is characterized by comprising the following steps: S1. Deploy a split monitoring system, physically isolate the underground probe module from the ground relay station, drill holes at intervals on the ground in the monitoring area and install the underground probe module, and install the ground relay station on the ground in the monitoring area; The underground probe module includes a tri-modal sensing unit, an edge computing unit, a self-powered system and a wireless communication module. The tri-modal sensing unit is composed of a distributed fiber grating sensor, a high-precision displacement meter and a capacitive inclination sensor. The data of the tri-modal sensing unit is locally pre-processed by the edge computing unit. The ground relay station includes an energy supply system, a data relay module and an alarm device; S2. Establish a three-dimensional coordinate system for the monitoring area, calibrate the spatial position relationship of each sensor, convert the displacement meter data into vector displacement in the regional coordinate system, and realize the time synchronization of all sensor data by building a GPS taming clock module into the underground probe module; S3, normalize and unify the dimensions of the FBG wavelength and displacement meter voltage, extract the statistical and morphological characteristics of each sensor data, calculate the subsidence risk probability based on the DS evidence theory, and generate the warning level; S4, through the LSTM network model, taking environmental parameters, historical settlement data and engineering parameters as input, outputs the seasonal correction factor k season ; Based on the basic threshold V base and seasonal correction factor k season Determine the final sedimentation rate threshold V th, and initiate graded early warning responses based on the accumulated settlement, displacement mutation and inclination threshold.

[0005] Furthermore, the edge computing unit in S1 adopts an STM32H743VIT6 microcontroller chip, which is connected to a Kalman filter. The Kalman filter fuses the optical fiber strain, displacement and inclination data to output the sedimentation rate, inclination angle and confidence index. The self-powered system includes a geothermal temperature difference generator and a lithium titanate battery. The wireless communication module includes a LoRa chip and an NB-IoT chip. The LoRa chip and the NB-IoT chip form dual-mode communication and automatically switch according to the signal strength. The distributed fiber grating sensor is arranged in a serpentine shape along the monitoring area, with a node spacing of 10m and a burial depth of 1.5-3m. The high-precision displacement meter is a magnetostrictive displacement meter, and the capacitive inclination sensor is a dual-axis MEMS inclinometer.

[0006] Furthermore, the energy supply system in S1 includes solar panels and lithium iron phosphate batteries, the data relay module includes a 4G / 5G communication module, a LoRa gateway for receiving data from the underground probe module and a storage card for caching data, and the alarm device includes an audible and visual alarm and a digital display screen.

[0007] Furthermore, the environmental parameters in S4 include real-time rainfall, groundwater level, and temperature from the municipal monitoring network; historical settlement data include settlement rate and soil moisture content in the same period of the past three years; and engineering parameters include allowable building settlement, tunnel convergence threshold, and basic threshold V base According to the building safety regulations: such as subway tunnel V base =2mm / month, seasonal correction factor k season Output from LSTM network: rainy season k season =1.2, dry season k season =0.8, the final sedimentation rate threshold V th =V base × k season .

[0008] Furthermore, the graded warning response in S4 includes: Level 1 warning: The trigger condition is that the sedimentation rate number is ≥1.2V th Lasting for three days, the response measures are that the data is marked yellow and transmitted to the ground relay station, which transmits the data to the cloud platform through the 4G / 5G communication module, and the cloud platform pushes the data to the inspection APP, and the sound and light alarm is always on yellow; Level 2 warning: The trigger condition is that the cumulative settlement is ≥ 80% of the design value. The response measure is to start the drone retest and the sound and light alarm is always blue. Level 3 warning: The triggering condition is an inclination angle ≥ 0.5° or a sudden displacement change ≥ 10mm. The response measures are an audible and visual alarm that sounds a buzzer and a red light flashes, automatically closing the associated road traffic.

[0009] Furthermore, the underground probe module in S1 includes a stainless steel shell, which has a sensor cabin for accommodating a tri-modal sensing unit, a computing cabin for accommodating an edge computing unit, a power cabin for accommodating a self-powered system and a wireless communication module, and a spiral guide fin is provided on the surface of the shell.

[0010] After adopting the above technical solution, this method isolates the underground probe module from the ground relay station through a split architecture, combines the multi-sensor data fusion and adaptive threshold algorithm of distributed fiber grating sensors, high-precision displacement meters and capacitive inclination sensors, breaks through the data islands, environmental fragility and high cost limitations of existing technologies, has higher monitoring accuracy, higher settlement identification accuracy, the threshold can be dynamically adjusted, and graded early warning can be achieved.

[0011] The present invention also provides a city ground subsidence monitoring and early warning device, which is characterized in that it includes an underground probe module arranged underground in the monitoring area and a ground relay station arranged on the ground in the monitoring area, the underground probe module includes an outer shell and a three-modal sensing unit, an edge computing unit, a self-power supply system and a wireless communication module arranged in the outer shell, and the ground relay station includes a column inserted into the ground and a power supply system, a data relay module and an alarm device arranged on the column.

[0012] Furthermore, the trimodal sensing unit is composed of a distributed fiber grating sensor, a high-precision displacement meter and a capacitive inclination sensor, the edge computing unit includes a circuit board and an STM32H743VIT6 microcontroller chip arranged on the circuit board, a Kalman filter is provided on the circuit board, the distributed fiber grating sensor, the high-precision displacement meter and the capacitive inclination sensor are connected to the STM32H743VIT6 microcontroller chip, the self-powered system includes a geothermal temperature difference generator and a lithium titanate battery connected to each other, the lithium titanate battery is connected to the circuit board, the wireless communication module includes a LoRa chip and a NB-IoT chip arranged on the circuit board and connected to the STM32H743VIT6 microcontroller chip, and the LoRa chip and the NB-IoT chip form dual-mode communication.

[0013] Furthermore, the energy supply system includes interconnected solar panels and lithium iron phosphate batteries, the data relay module includes a circuit board and a 4G / 5G communication module arranged on the circuit board, a LoRa gateway for receiving data from the underground probe module and a storage card for caching data, the alarm device includes an audible and visual alarm and a digital display connected to the circuit board, the audible and visual alarm and the digital display are powered by lithium iron phosphate batteries, the 4G / 5G communication module is connected to a cloud platform, and the cloud platform has a two-way communication inspection APP.

[0014] Furthermore, the shell is a stainless steel shell having a sensor cabin for accommodating a tri-modal sensing unit, a computing cabin for accommodating an edge computing unit, a power cabin for accommodating a self-powered system and a wireless communication module, and a spiral guide fin is provided on the surface of the shell. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The present invention has the following accompanying drawings: Figure 1 It is a schematic block diagram of the circuit structure in the present invention. DETAILED DESCRIPTION

[0016] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation mode, structure, characteristics and effects of the present invention are described in detail below in combination with the accompanying drawings and preferred embodiments.

[0017] Referring to the above-mentioned figures, the urban land subsidence monitoring and early warning method provided by the present invention is characterized in that it comprises the following steps: S1. Deploy a split monitoring system, physically isolate the underground probe module from the ground relay station, drill holes at intervals on the ground in the monitoring area and install the underground probe module, and install the ground relay station on the ground in the monitoring area; The underground probe module includes a tri-modal sensing unit, an edge computing unit, a self-powered system and a wireless communication module. The tri-modal sensing unit is composed of a distributed fiber grating sensor, a high-precision displacement meter and a capacitive inclination sensor. The data of the tri-modal sensing unit is locally pre-processed by the edge computing unit. The ground relay station includes an energy supply system, a data relay module and an alarm device; S2. Establish a three-dimensional coordinate system for the monitoring area, calibrate the spatial position relationship of each sensor, convert the displacement meter data into vector displacement in the regional coordinate system, and realize the time synchronization of all sensor data by building a GPS taming clock module into the underground probe module; S3, normalize and unify the dimensions of the FBG wavelength and displacement meter voltage, extract the statistical and morphological characteristics of each sensor data, calculate the subsidence risk probability based on the DS evidence theory, and generate the warning level; S4, through the LSTM network model, taking environmental parameters, historical settlement data and engineering parameters as input, outputs the seasonal correction factor k season ; Based on the basic threshold V base and seasonal correction factor k season Determine the final sedimentation rate threshold V th , and initiate graded early warning responses based on the accumulated settlement, displacement mutation and inclination threshold.

[0018] The edge computing unit in S1 adopts the STM32H743VIT6 microcontroller chip, and the STM32H743VIT6 microcontroller chip is connected to a Kalman filter. The Kalman filter fuses the optical fiber strain, displacement and inclination data to output the sedimentation rate, inclination angle and confidence index. The self-powered system includes a geothermal temperature difference generator and a lithium titanate battery. The wireless communication module includes a LoRa chip and an NB-IoT chip. The LoRa chip and the NB-IoT chip form dual-mode communication and automatically switch according to the signal strength. The distributed fiber grating sensor is arranged in a serpentine shape along the monitoring area, with a node spacing of 10m and a burial depth of 1.5-3m. The high-precision displacement meter is a magnetostrictive displacement meter, and the capacitive inclination sensor is a dual-axis MEMS inclinometer.

[0019] The energy supply system in S1 includes solar panels and lithium iron phosphate batteries, the data relay module includes a 4G / 5G communication module, a LoRa gateway for receiving data from the underground probe module and a storage card for caching data, and the alarm device includes an audible and visual alarm and a digital display screen.

[0020] The environmental parameters in S4 include real-time rainfall, groundwater level, and temperature from the municipal monitoring network; historical settlement data include settlement rate and soil moisture content in the same period of the past three years; engineering parameters include allowable building settlement, tunnel convergence threshold, and foundation threshold V base According to the building safety regulations: such as subway tunnel V base =2mm / month, seasonal correction factor k season Output from LSTM network: rainy season k season =1.2 / month, dry season k season =0.8, the final sedimentation rate threshold V th =V base ×k season .

[0021] The graded warning response in S4 includes: Level 1 warning: The trigger condition is that the sedimentation rate number is ≥1.2V th Lasting for three days, the response measures are that the data is marked yellow and transmitted to the ground relay station, which transmits the data to the cloud platform through the 4G / 5G communication module, and the cloud platform pushes the data to the inspection APP, and the sound and light alarm is always on yellow; Level 2 warning: The trigger condition is that the cumulative settlement is ≥ 80% of the design value. The response measure is to start the drone retest and the sound and light alarm is always blue. Level 3 warning: The triggering condition is an inclination angle ≥ 0.5° or a sudden displacement change ≥ 10mm. The response measures are an audible and visual alarm that sounds a buzzer and a red light flashes, automatically closing the associated road traffic.

[0022] The underground probe module in S1 includes a stainless steel shell, which has a sensor cabin for placing a tri-modal sensing unit, a computing cabin for placing an edge computing unit, and a power cabin for placing a self-powered system and a wireless communication module, and a spiral guide fin is provided on the surface of the shell.

[0023] The present invention also provides an urban ground subsidence monitoring and early warning device, comprising an underground probe module arranged underground in a monitoring area and a ground relay station arranged on the ground in the monitoring area, wherein the underground probe module comprises a shell and a trimodal sensing unit, an edge computing unit, a self-powered system and a wireless communication module arranged in the shell, the ground relay station comprises a column inserted into the ground and an energy supply system, a data relay module and an alarm device arranged on the column, the trimodal sensing unit is composed of a distributed fiber grating sensor, a high-precision displacement meter and a capacitive inclination sensor, the edge computing unit comprises a circuit board and an STM32H743VIT6 microcontroller chip arranged on the circuit board, a Kalman filter connected to the STM32H743VIT6 microcontroller chip is arranged on the circuit board, the distributed fiber grating sensor, the high-precision displacement meter and the capacitive inclination sensor are connected to the STM32H743VIT6 microcontroller chip, the self-powered system comprises a geothermal temperature difference generator and a titanic acid generator connected to each other A lithium battery, a lithium titanate battery and a circuit board are connected, the wireless communication module includes a LoRa chip and an NB-IoT chip which are arranged on the circuit board and connected to the STM32H743VIT6 microcontroller chip, the LoRa chip and the NB-IoT chip form dual-mode communication, the energy supply system includes solar panels and lithium iron phosphate batteries which are connected to each other, the data relay module includes a circuit board and a 4G / 5G communication module arranged on the circuit board, a LoRa gateway for receiving data from an underground probe module and a storage card for caching data, the 4G / 5G communication module is connected to a cloud platform, and the cloud platform has a two-way communication inspection APP, the alarm device includes an audible and visual alarm and a digital display connected to the circuit board, the audible and visual alarm and the digital display are powered by a lithium iron phosphate battery, the shell is a shell, the stainless steel shell has a sensor cabin for accommodating a tri-modal sensing unit, a computing cabin for accommodating an edge computing unit, a power cabin for accommodating a self-powered system and a wireless communication module, and a spiral guide fin is provided on the surface of the shell.

[0024] The urban ground subsidence monitoring and early warning method and device provided by the present invention isolate the underground probe module and the ground relay station through a split architecture, and combine the multi-sensor data fusion and adaptive threshold algorithm of distributed fiber grating sensors, high-precision displacement meters and capacitive inclinometers, so as to break through the data island, environmental vulnerability and high cost limitations of the prior art, and have higher monitoring accuracy, higher settlement identification accuracy, dynamic adjustment of thresholds, and hierarchical early warning. The distributed fiber grating sensors are arranged in a serpentine shape along the monitoring area, with a node spacing of 10m and a burial depth of 1.5-3m, avoiding the shallow backfill soil disturbance layer. The magnetostrictive displacement meter has a range of ±50mm, a resolution of 0.001mm, a sampling frequency of 10Hz, a dual-axis MEMS inclinometer range of ±30°, and a built-in temperature compensation chip (- 40℃~85℃), output RS485 digital signal, and can suppress high-frequency vibration noise through built-in FIR filter under typhoon conditions; STM32H743VIT6 microcontroller chip performs wavelet threshold denoising on fiber wavelength drift data (using sym8 wavelet basis, 5-layer decomposition), calculates the standard deviation and kurtosis coefficient of displacement meter data, identifies mutation events, and fuses fiber strain, displacement and inclination data through Kalman filter to output sedimentation rate, inclination angle and confidence index; LSTM network model includes 1. Input layer: 30-dimensional features (including time series environmental data and sedimentation), 2. Output layer: Hidden layer: 2 layers of LSTM (128 neurons per layer), Dropout rate 0.2, 3. Output layer: Sedimentation rate threshold V for the current season th ,4. Training data: 10,000 sets of historical monitoring data (covering extreme conditions such as typhoons and droughts); using the temperature difference between the underground probe and the surface (ΔT ≥ 10 ° C), Bi2Te3 thermoelectric materials are used to generate electricity, with an output power of ≥ 2W, to charge and store lithium titanate batteries for self-powered use. The cycle life of lithium titanate batteries is > 10,000 times, and they support low-temperature discharge at -30 ° C; LoRa chips and NB-IoT chips form dual-mode communication, which automatically switches according to signal strength to ensure the success rate of data transmission under complex terrain. LoRa mode: transmission distance 3km (urban area), 20km (suburban area), rate 5kbps, NB-IoT mode: support mobile / telecom Band5 / Band8 frequency bands, average monthly traffic consumption < 50MB; solar panels are used to collect solar energy to charge and store lithium iron phosphate batteries. Lithium iron phosphate batteries support a wide temperature range of -20 ° C ~ 60 ° C, and a cycle life of > 3,000 times; digital display screens are used to display the settlement rate, historical curves and warning levels of each monitoring point in real time.

Claims

1. The urban land subsidence monitoring and early warning method is characterized by: The following steps are involved: S1. Deploy a split monitoring system, physically isolate the underground probe module from the ground relay station, drill holes at intervals on the ground in the monitoring area and install the underground probe module, and install the ground relay station on the ground in the monitoring area; The underground probe module includes a tri-modal sensing unit, an edge computing unit, a self-powered system and a wireless communication module. The tri-modal sensing unit is composed of a distributed fiber grating sensor, a high-precision displacement meter and a capacitive inclination sensor. The data of the tri-modal sensing unit is locally pre-processed by the edge computing unit. The ground relay station includes an energy supply system, a data relay module and an alarm device; S2. Establish a three-dimensional coordinate system for the monitoring area, calibrate the spatial position relationship of each sensor, convert the displacement meter data into vector displacement in the regional coordinate system, and realize the time synchronization of all sensor data by building a GPS taming clock module into the underground probe module; S3, normalize and unify the dimensions of the FBG wavelength and displacement meter voltage, extract the statistical and morphological characteristics of each sensor data, calculate the subsidence risk probability based on the DS evidence theory, and generate the warning level; S4, through the LSTM network model, taking environmental parameters, historical settlement data and engineering parameters as input, outputs the seasonal correction factor k season ; Based on the basic threshold V base and seasonal correction factor k season Determine the final sedimentation rate threshold V th , and initiate graded early warning responses based on the accumulated settlement, displacement mutation and inclination threshold.

2. The urban land subsidence monitoring and early warning method according to claim 1 is characterized in that: The edge computing unit in S1 adopts the STM32H743VIT6 microcontroller chip, and the STM32H743VIT6 microcontroller chip is connected to a Kalman filter. The Kalman filter fuses the optical fiber strain, displacement and inclination data to output the sedimentation rate, inclination angle and confidence index. The self-powered system includes a geothermal temperature difference generator and a lithium titanate battery. The wireless communication module includes a LoRa chip and an NB-IoT chip. The LoRa chip and the NB-IoT chip form dual-mode communication and automatically switch according to the signal strength. The distributed fiber grating sensor is arranged in a serpentine shape along the monitoring area, with a node spacing of 10m and a burial depth of 1.5-3m. The high-precision displacement meter is a magnetostrictive displacement meter, and the capacitive inclination sensor is a dual-axis MEMS inclinometer.

3. The urban land subsidence monitoring and early warning method according to claim 1 is characterized in that: The energy supply system in S1 includes solar panels and lithium iron phosphate batteries, the data relay module includes a 4G / 5G communication module, a LoRa gateway for receiving data from the underground probe module and a storage card for caching data, and the alarm device includes an audible and visual alarm and a digital display screen.

4. The urban land subsidence monitoring and early warning method according to claim 1 is characterized in that: The environmental parameters in S4 include real-time rainfall, groundwater level, and temperature from the municipal monitoring network; historical settlement data include settlement rate and soil moisture content in the same period of the past three years; engineering parameters include allowable building settlement, tunnel convergence threshold, and foundation threshold V base According to the building safety regulations: such as subway tunnel V base =2mm / month, seasonal correction factor k season Output from LSTM network: rainy season k season =1.2, dry season k season =0.8, the final sedimentation rate threshold V th =V base ×k season .

5. The urban land subsidence monitoring and early warning method according to claim 4 is characterized in that: The graded warning response in S4 includes: Level 1 warning: The trigger condition is that the sedimentation rate number is ≥1.2V th Lasting for three days, the response measures are that the data is marked yellow and transmitted to the ground relay station, which transmits the data to the cloud platform through the 4G / 5G communication module, and the cloud platform pushes the data to the inspection APP, and the sound and light alarm is always on yellow; Level 2 warning: The trigger condition is that the cumulative settlement is ≥ 80% of the design value. The response measure is to start the drone retest and the sound and light alarm is always blue. Level 3 warning: The triggering condition is an inclination angle ≥ 0.5° or a sudden displacement change ≥ 10mm. The response measures are an audible and visual alarm that sounds a buzzer and a red light flashes, automatically closing the associated road traffic.

6. The urban land subsidence monitoring and early warning method according to claim 1 is characterized by: The underground probe module in S1 includes a stainless steel shell, which has a sensor cabin for placing a tri-modal sensing unit, a computing cabin for placing an edge computing unit, and a power cabin for placing a self-powered system and a wireless communication module, and spiral guide fins are provided on the surface of the shell.

7. An urban land subsidence monitoring and early warning device, characterized by: It includes an underground probe module arranged underground in the monitoring area and a ground relay station arranged on the ground in the monitoring area. The underground probe module includes an outer shell and a three-modal sensing unit, an edge computing unit, a self-power supply system and a wireless communication module arranged in the outer shell. The ground relay station includes a column inserted into the ground and a power supply system, a data relay module and an alarm device arranged on the column.

8. The urban land subsidence monitoring and early warning device according to claim 7 is characterized in that: The trimodal sensing unit is composed of a distributed fiber grating sensor, a high-precision displacement meter and a capacitive inclination sensor. The edge computing unit includes a circuit board and an STM32H743VIT6 microcontroller chip arranged on the circuit board. A Kalman filter is arranged on the circuit board. The distributed fiber grating sensor, the high-precision displacement meter and the capacitive inclination sensor are connected to the STM32H743VIT6 microcontroller chip. The high-precision displacement meter is a magnetostrictive displacement meter. The capacitive inclination sensor is a dual-axis MEMS inclinometer. The self-powered system includes a geothermal temperature difference generator and a lithium titanate battery connected to each other. The lithium titanate battery is connected to the circuit board. The wireless communication module includes a LoRa chip and an NB-IoT chip which are arranged on the circuit board and connected to the STM32H743VIT6 microcontroller chip. The LoRa chip and the NB-IoT chip form dual-mode communication.

9. The urban land subsidence monitoring and early warning device according to claim 7 is characterized in that: The energy supply system includes interconnected solar panels and lithium iron phosphate batteries, the data relay module includes a circuit board and a 4G / 5G communication module arranged on the circuit board, a LoRa gateway for receiving data from the underground probe module and a storage card for caching data, the alarm device includes an audible and visual alarm and a digital display connected to the circuit board, the audible and visual alarm and the digital display are powered by lithium iron phosphate batteries, the 4G / 5G communication module is connected to a cloud platform, and the cloud platform has a two-way communication inspection APP.

10. The urban land subsidence monitoring and early warning device according to claim 7 is characterized in that: The shell is a stainless steel shell having a sensor cabin for accommodating a tri-modal sensing unit, a computing cabin for accommodating an edge computing unit, a power cabin for accommodating a self-powered system and a wireless communication module, and a spiral guide fin is provided on the surface of the shell.

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