A multi-modal arrayed geologic body deformation monitoring sensor network system

Abstract

This application relates to the field of geological deformation monitoring technology and discloses a multimodal array-type geological deformation monitoring sensor network system, comprising: a surface monitoring array module: composed of M groups of surface monitoring units distributed in a preset spatial array, where M is a positive integer greater than or equal to 3. Each group of surface monitoring units includes one L-band synthetic aperture radar, and each group of L-band synthetic aperture radars has a corresponding surface detection area for acquiring geological surface deformation data within its detection area. This invention improves the monitoring accuracy and reliability in complex environments. The system, through a multimodal combination of L-band SAR surface monitoring and distributed fiber optic underground monitoring, combined with a dynamic weight calibration algorithm, can adjust the fusion weight of surface and underground data in real time according to surface environmental interference, avoiding the susceptibility of a single sensor to interference in complex scenarios, and achieving integrated high-precision deformation monitoring of both surface and underground data.

Description

Technical Field

[0001] This invention relates to the field of geological deformation monitoring technology, specifically a multimodal array-type geological deformation monitoring sensor network system. Background Technology

[0002] Geological deformation monitoring refers to the quantitative measurement and analysis of deformation characteristics such as displacement, strain, and settlement of geological structures such as mountains, landslides, and mineral layers. Multimodal refers to the integration of two or more monitoring technologies based on different physical principles, achieving comprehensive monitoring through the complementarity of heterogeneous data. Array-type refers to the large-scale deployment of sensors according to a preset spatial topology to form a three-dimensional sensing network covering the monitoring area.

[0003] In existing technologies, when using multimodal array sensor network systems to monitor geological deformation, two or more monitoring technologies based on different physical principles are integrated, such as combining satellite remote sensing and ground sensors. Sensors are deployed on a large scale according to a preset spatial topology to form a three-dimensional sensing network covering the monitoring area, so as to achieve monitoring through the complementarity of heterogeneous data.

[0004] In existing geological deformation monitoring technologies, relying on a single sensor for surface monitoring using SAR or solely on optical fiber for underground monitoring presents significant limitations in scenarios such as high vegetation cover, heavy rain, and terrain obstruction. Furthermore, existing technologies lack a mechanism to dynamically adjust data weights based on the environment, making it difficult to effectively integrate heterogeneous surface and underground data. This results in the inability to construct a high-precision deformation field that integrates surface and underground data, leading to insufficient reliability and comprehensiveness of monitoring results. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a multimodal array-type geological deformation monitoring sensor network system, which solves the problem of difficulty in coordinating and retrieving surface and underground monitoring data.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a multimodal array-type geological body deformation monitoring sensor network system, comprising:

[0007] Surface monitoring array module: It consists of M groups of surface monitoring units distributed in a preset spatial array, where M is a positive integer greater than or equal to 3. Each group of surface monitoring units contains one L-band synthetic aperture radar, and each group of L-band synthetic aperture radar has a corresponding surface detection area, which is used to acquire surface deformation data of geological bodies within its detection area, and to collaboratively acquire deformation data of different areas of the surface of geological bodies.

[0008] Underground monitoring array module: It consists of N groups of underground monitoring units buried underground in a spatial array corresponding to the surface monitoring array, where N is a positive integer greater than or equal to 3. Each group of underground monitoring units contains one distributed fiber optic sensor, which is used to collaboratively acquire deformation data of different regions inside the geological body.

[0009] The number of rows and columns of the preset spatial array satisfies M=N, and the i-th row and j-th column unit of the underground monitoring array is set directly below the i-th row and j-th column unit of the surface monitoring array.

[0010] Data aggregation unit: The data aggregation unit is communicatively connected to the surface monitoring array and the underground monitoring array, respectively, and is used to receive the raw data transmitted by the surface monitoring array and the underground monitoring array and perform preprocessing; the data aggregation unit is communicatively connected to the data processing unit and is used to transmit the preprocessed data to the data processing unit;

[0011] Data processing unit: It is communicatively connected to the surface monitoring array and the underground monitoring array respectively, and is used to receive and process the deformation data transmitted by the two arrays. The data processing unit has built-in penetration data fusion algorithm and dynamic weight calibration algorithm.

[0012] Data feedback unit: It communicates with the data processing unit to receive integrated deformation field data output by the data processing unit, generate early warning information based on preset thresholds and send it to the remote monitoring platform. At the same time, it feeds back adjustment instructions to the surface monitoring array and underground monitoring array according to the spatial distribution characteristics of the deformation field data, so as to realize the closed-loop adaptive control of the monitoring system.

[0013] Preferably, the dynamic weight calibration algorithm is used for real-time calculation. and The calculation formula is:

[0014]

[0015] In the formula:

[0016] Let be the quality coefficient of the surface SAR data at time t. Where k is a calibration constant (ranging from 1 to 10), used to adjust the baseline level of the quality coefficient. Let be the signal-to-noise ratio of the SAR data at time t. The surface environment interference index (range 0-1) is the value of the L-band synthetic aperture radar detection area at time t, and is calculated based on the vegetation coverage, rainfall intensity and terrain occlusion angle in the detection area.

[0017] Let be the quality coefficient of the underground fiber optic data at time t. ,in Let be the signal-to-noise ratio of the fiber optic data at time t. The attenuation rate of the fiber optic signal at time t (range 0-1);

[0018] Dynamic weight values ​​of surface SAR data at time t Dynamic weight values ​​of underground fiber optic data And both satisfy (The values ​​are all in the range of 0-1);

[0019] and The numerical value directly reflects the ratio between surface and underground data; the higher the quality coefficient of the data source, the greater its corresponding weight.

[0020] Preferably, the penetration data fusion algorithm is used to fuse deformation data acquired by the surface monitoring array and the underground monitoring array, and the calculation formula is as follows:

[0021] .

[0022] In the formula:

[0023] Let t be the fused deformation value of the array node in the i-th row and j-th column of the monitoring area at time t;

[0024] Let be the weight value of the surface SAR data at time t. Let be the weight value of the underground fiber optic data at time t, and ;

[0025] The deformation data acquired by the i-th row and j-th column unit of the surface monitoring array at time t;

[0026] The deformation data acquired by the i-th row and j-th column unit of the underground monitoring array at time t;

[0027] Deformation data acquired by underground monitoring arrays The output according to the dynamic weight calibration algorithm and The result is obtained by weighted summation.

[0028] Preferably, the dynamic weight calibration algorithm generates and As input parameters for penetrating data fusion algorithms;

[0029] when Enlargement leads to During the descent, the output of the dynamic weight correction algorithm is... Synchronous reduction, Simultaneous improvement increases the proportion of underground fiber optic data in the penetration data fusion algorithm;

[0030] when Reduced During the rise, the output of the dynamic weight calibration algorithm is Simultaneous improvement The synchronous reduction increases the proportion of surface SAR data in the penetrating data fusion algorithm.

[0031] Preferably, the spatial array of the surface monitoring array is a rectangular grid, the distance between two adjacent groups of surface monitoring units is 5-10 meters, and the SAR detection range of each group of units overlaps with the adjacent units by 10%-15%.

[0032] Preferably, the distributed fiber optic sensors of the underground monitoring array employ BOTDR technology, are buried 50-100cm below the surface, and the fiber optic cables run parallel to the edges of the rectangular grid of the surface monitoring array. The quality coefficient is calculated based on the measurement signals from the distributed optical fiber sensor, and the attenuation rate of the optical fiber signal monitored by the distributed optical fiber sensor is used to calculate the quality coefficient. .

[0033] Preferably, the preset threshold includes multi-level warning thresholds, specifically:

[0034] Level 1 warning threshold: triggered when the deformation rate of the local geological body exceeds 3 mm / h, or the cumulative horizontal displacement exceeds 20 mm, or the cumulative vertical displacement exceeds 15 mm;

[0035] Level 2 warning threshold: triggered when the deformation rate of the local geological body exceeds 5 mm / h, or the cumulative horizontal displacement exceeds 50 mm, or the cumulative vertical displacement exceeds 40 mm;

[0036] Level 3 warning threshold: triggered when the deformation rate of the local geological body exceeds 10 mm / h, or the cumulative horizontal displacement exceeds 100 mm, or the cumulative vertical displacement exceeds 80 mm, or the underground strain data exceeds 1000 με.

[0037] The values ​​of the multi-level early warning thresholds are determined based on the geological stability level of the monitored area, engineering safety specifications, and historical deformation database, and can be dynamically adjusted through the remote configuration interface of the data feedback unit, with an adjustment range of ±30% of the first-level early warning threshold.

[0038] Preferably, the data aggregation unit performs noise filtering on the received raw data to reduce environmental interference, and performs format standardization to unify the spatiotemporal reference and data structure of surface SAR data and underground optical fiber data, and sends the preprocessed data to the data processing unit.

[0039] Preferably, the feedback adjustment command includes a sampling frequency adjustment command and an anti-interference mode activation command, specifically:

[0040] Sampling frequency adjustment command: When the deformation rate of a certain region (i,j) in the integrated deformation field data output by the data processing unit exceeds the first-level warning threshold, the data feedback unit sends an adjustment command to the surface monitoring unit and underground monitoring unit corresponding to that region, increasing their sampling frequency from 1 time / minute to 5 times / minute in high-frequency mode; when the deformation rate is below 0.5mm / h for 30 consecutive minutes, the command is restored to the reference frequency;

[0041] Anti-interference mode activation command: When the data processing unit detects that the signal-to-noise ratio (SNR) of a certain monitoring unit is lower than the preset threshold, the data feedback unit sends an anti-interference command to the unit: For surface L-band synthetic aperture radar, it commands it to enable pulse compression technology with a compression ratio of 8:1 and increase the transmission power; for underground distributed fiber optic sensors, it commands them to enable the weighted average filtering algorithm and extend the signal integration time.

[0042] This invention provides a multimodal array-type geological body deformation monitoring sensor network system. It has the following advantages:

[0043] 1. This invention improves the monitoring accuracy and reliability in complex environments. The system combines L-band SAR surface monitoring and distributed fiber optic underground monitoring in a multi-modal manner with a dynamic weight calibration algorithm. It can adjust the fusion weight of surface and underground data in real time according to surface environmental interference, avoiding the vulnerability of a single sensor to interference in complex scenarios and realizing high-precision integrated deformation monitoring of surface and underground.

[0044] 2. This invention realizes closed-loop adaptive control of the monitoring system: the data feedback unit generates early warning information based on multi-level early warning thresholds, and dynamically adjusts the sensor sampling frequency and anti-interference mode according to the deformation field distribution characteristics. This ensures the monitoring density of the deformation active area and optimizes the system resource consumption, solving the problems of traditional monitoring systems lacking dynamic adaptability and being unable to cope with environmental changes and deformation differences. Attached Figure Description

[0045] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0046] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0047] Please see the appendix Figure 1 This invention provides a multimodal array-type geological body deformation monitoring sensor network system, comprising:

[0048] Surface monitoring array module: It consists of M groups of surface monitoring units distributed in a preset spatial array, where M is a positive integer greater than or equal to 3. Each group of surface monitoring units contains one L-band synthetic aperture radar, and each group of L-band synthetic aperture radar has a corresponding surface detection area, which is used to acquire surface deformation data of geological bodies within its detection area, and to collaboratively acquire deformation data of different areas of the surface of geological bodies.

[0049] Composition and parameters: M=4 groups of surface monitoring units are used, distributed in a 2×2 rectangular grid array. Each group of units contains one L-band synthetic aperture radar, model X-LSAR-02, with an operating frequency of 1.2GHz, a detection range of 5-30m, and a spatial resolution of 0.5m×0.5m.

[0050] Spatial distribution: The spacing between adjacent units is 8 meters, and the diameter of the surface detection area of ​​each SAR group is 10 meters, overlapping with the detection areas of adjacent units by 12% (i.e., an overlap width of 1.2 meters) to ensure that there are no blind spots in the monitoring area. For example, the detection area of ​​the unit in the first row and first column covers coordinates (0,0) to (10,10), and the unit in the first row and second column covers coordinates (8,0) to (18,10), with an overlap area of ​​(8,0) to (10,10).

[0051] Data content: The surface deformation data of the geological body acquired in each unit includes:

[0052] Spatial location information (latitude and longitude based on the WGS84 coordinate system, such as (116.3°E, 39.9°N));

[0053] Timestamp (accurate to the second, e.g., 2024-06-01 08:00:00);

[0054] Horizontal displacement (components along the slope direction and vertical direction, in mm), vertical displacement (lift and fall, in mm).

[0055] Deformation rate (unit: mm / h, calculated from three consecutive sampling data);

[0056] Underground monitoring array module: It consists of N groups of underground monitoring units buried underground in a spatial array corresponding to the surface monitoring array, where N is a positive integer greater than or equal to 3. Each group of underground monitoring units contains one distributed fiber optic sensor, which is used to collaboratively acquire deformation data of different regions inside the geological body.

[0057] The number of rows and columns of the preset spatial array satisfies M=N, and the i-th row and j-th column unit of the underground monitoring array is set directly below the i-th row and j-th column unit of the surface monitoring array.

[0058] Composition and parameters: N=4 groups of underground monitoring units are used (since M=N, it is a 2×2 grid), each group contains 1 distributed optical fiber sensor (using BOTDR technology, model: F-BOTDR-100, optical fiber type is G.652D single-mode fiber, diameter 0.2mm, measurement accuracy ±5με).

[0059] Deployment method: Buried 70cm below the ground surface, with the fiber optic cable running parallel to the rectangular grid edge of the ground array (i.e., along the east-west and north-south directions).

[0060] Spatial correspondence: The fiber optic starting point of the i-th row and j-th column unit on the surface (vertical deviation ≤ 5cm) is located directly below the i-th row and j-th column unit underground. Each underground unit corresponds to a 2-meter-long fiber optic segment (gauge length).

[0061] Data content: The deformation data of the geological body obtained for each unit includes:

[0062] Spatial positioning information (array index (i,j) and burial depth 70cm);

[0063] Timestamp (synchronized with surface data, via GPS timing, error ≤10ms);

[0064] Strain data (axial strain, unit με, e.g., 500με means 0.5mm elongation per meter of fiber);

[0065] The displacement is converted (= strain value × gauge length, e.g., 500με × 2m = 1mm).

[0066] Deformation dynamic parameters (strain rate με / h, displacement rate mm / h);

[0067] Data aggregation unit: The data aggregation unit is communicatively connected to the surface monitoring array and the underground monitoring array, respectively, and is used to receive the raw data transmitted by the surface monitoring array and the underground monitoring array and perform preprocessing; the data aggregation unit is communicatively connected to the data processing unit and is used to transmit the preprocessed data to the data processing unit;

[0068] Hardware configuration: An industrial-grade data gateway (model: GW-900, supporting LoRa wireless communication (transmission distance 1km) and Ethernet, protection level IP67) is deployed in a protective box at the edge of the monitoring area;

[0069] The data aggregation unit preprocesses the received raw data, including:

[0070] (1) Noise filtering: Multi-view processing and Leesigma filtering are used to reduce speckle noise for surface SAR data; a moving average filter with a length of 5 is used to suppress random fluctuations for underground fiber optic BOTDR data.

[0071] (2) Format standardization: Add high-precision GPS time stamps to all data; convert the deformation data of each monitoring unit and its corresponding original position coordinates to the local coordinate system of the project; encapsulate the processed data into a unified structured data packet and transmit it to the data processing unit via Ethernet;

[0072] Data processing unit: It is communicatively connected to the surface monitoring array and the underground monitoring array respectively, and is used to receive and process the deformation data transmitted by the two arrays. The data processing unit has built-in penetration data fusion algorithm and dynamic weight calibration algorithm.

[0073] Hardware configuration: It adopts an edge computing server (CPU: Intel Xeon E3, memory 16GB) and is connected to the data aggregation unit via Ethernet (transmission rate 100Mbps).

[0074] The dynamic weight calibration algorithm is used for real-time calculation. and The calculation formula is:

[0075]

[0076] In the formula:

[0077] Let be the quality coefficient of the surface SAR data at time t. Where k is a calibration constant (ranging from 1 to 10), used to adjust the baseline level of the quality coefficient. Let be the signal-to-noise ratio of the SAR data at time t. The surface environment interference index (range 0-1) is the value of the L-band synthetic aperture radar detection area at time t, and is calculated based on the vegetation coverage, rainfall intensity and terrain occlusion angle in the detection area.

[0078] Let be the quality coefficient of the underground fiber optic data at time t. ,in Let be the signal-to-noise ratio of the fiber optic data at time t. The attenuation rate of the fiber optic signal at time t (range 0-1);

[0079] Dynamic weight values ​​of surface SAR data at time t Dynamic weight values ​​of underground fiber optic data And both satisfy (The values ​​are all in the range of 0-1);

[0080] and The numerical value directly reflects the ratio between surface and underground data; the higher the quality coefficient of the data source, the greater the corresponding weight.

[0081] Input parameters: SAR data signal-to-noise ratio SNR_S(t) = 20dB, environmental interference index E_S(t) = 0.3 (vegetation coverage 0.4, rainfall intensity 0.2, terrain occlusion angle 0.3, calculated as E_S = 0.4 × 0.4 + 0.3 × 0.2 + 0.3 × 0.3 = 0.3); fiber optic data signal-to-noise ratio SNR_F(t) = 18dB, signal attenuation rate L_F(t) = 0.1;

[0082] Calculate the quality coefficient: Take k=5,

[0083] Q_S(t) = 5 × 20 / (1 + 0.3) ≈ 76.9,

[0084] Q_F(t) = 5 × 18 / (1 + 0.1) ≈ 81.8;

[0085] Calculate the weights: w_S(t) = 76.9 / (76.9+81.8) ≈ 0.48, w_F(t) = 1 - 0.48 = 0.52;

[0086] The penetration data fusion algorithm is used to fuse deformation data acquired by surface monitoring arrays and underground monitoring arrays. The calculation formula is as follows:

[0087] .

[0088] In the formula:

[0089] Let t be the fused deformation value of the array node in the i-th row and j-th column of the monitoring area at time t;

[0090] Let be the weight value of the surface SAR data at time t. Let be the weight value of the underground fiber optic data at time t, and ;

[0091] The deformation data acquired by the i-th row and j-th column unit of the surface monitoring array at time t;

[0092] The deformation data acquired by the i-th row and j-th column unit of the underground monitoring array at time t;

[0093] Deformation data acquired by underground monitoring arrays The output according to the dynamic weight calibration algorithm and The result is obtained by weighted summation;

[0094] Input data: Surface deformation D_S(i,j,t)=2.5mm, subsurface transformation displacement D_F(i,j,t)=0.6mm, weights w_S=0.48, w_F=0.52.

[0095] Fusion result: D_total = 0.48 × 2.5 + 0.52 × 0.6 ≈ 1.2 + 0.31 = 1.51 mm;

[0096] The dynamic weight calibration algorithm generates and As input parameters for penetrating data fusion algorithms;

[0097] when Enlargement leads to During the descent, the output of the dynamic weight correction algorithm is... Synchronous reduction, Simultaneous improvement increases the proportion of underground fiber optic data in the penetration data fusion algorithm;

[0098] when Reduced During the rise, the output of the dynamic weight calibration algorithm is Simultaneous improvement The synchronous reduction increases the proportion of surface SAR data in the penetrating data fusion algorithm;

[0099] The surface monitoring array is spatially distributed in a rectangular grid pattern, with a spacing of 5-10 meters between adjacent groups of surface monitoring units, and the SAR detection range of each group of units overlaps with that of the adjacent units by 10%-15%.

[0100] The distributed fiber optic sensors of the underground monitoring array employ BOTDR technology, are buried 50-100cm below the surface, and the fiber optic cables run parallel to the edges of the rectangular grid of the surface monitoring array. The quality coefficient is calculated based on the measurement signals from the distributed optical fiber sensor, and the attenuation rate of the optical fiber signal monitored by the distributed optical fiber sensor is used to calculate the quality coefficient. ;

[0101] The preset thresholds include multi-level warning thresholds, specifically:

[0102] Level 1 warning threshold: triggered when the deformation rate of the local geological body exceeds 3 mm / h, or the cumulative horizontal displacement exceeds 20 mm, or the cumulative vertical displacement exceeds 15 mm;

[0103] Level 2 warning threshold: triggered when the deformation rate of the local geological body exceeds 5 mm / h, or the cumulative horizontal displacement exceeds 50 mm, or the cumulative vertical displacement exceeds 40 mm;

[0104] Level 3 warning threshold: triggered when the deformation rate of the local geological body exceeds 10 mm / h, or the cumulative horizontal displacement exceeds 100 mm, or the cumulative vertical displacement exceeds 80 mm, or the underground strain data exceeds 1000 με.

[0105] The values ​​of the multi-level early warning thresholds are determined based on the geological stability level of the monitored area, engineering safety specifications, and historical deformation database, and can be dynamically adjusted through the remote configuration interface of the data feedback unit, with an adjustment range of ±30% of the first-level early warning threshold.

[0106] Warning threshold triggered:

[0107] When the deformation rate of a certain area reaches 3.2 mm / h (exceeding the first-level warning threshold of 3 mm / h), a first-level warning is triggered, and an SMS message and platform alarm are sent to the remote monitoring platform.

[0108] When the cumulative horizontal displacement reaches 55mm (exceeding the level 2 warning threshold of 50mm), the level 2 warning is triggered, and the on-site audible and visual alarms are activated.

[0109] The feedback adjustment command includes a sampling frequency adjustment command and an anti-interference mode activation command, specifically:

[0110] Sampling frequency adjustment command: When the deformation rate of a certain region (i,j) in the integrated deformation field data output by the data processing unit exceeds the first-level warning threshold, the data feedback unit sends an adjustment command to the surface monitoring unit and underground monitoring unit corresponding to that region, increasing their sampling frequency from 1 time / minute to 5 times / minute in high-frequency mode; when the deformation rate is below 0.5mm / h for 30 consecutive minutes, the command is restored to the reference frequency;

[0111] Anti-interference mode activation command: When the data processing unit detects that the signal-to-noise ratio (SNR) of a certain monitoring unit is lower than the preset threshold, the data feedback unit sends an anti-interference command to the unit: For the surface L-band synthetic aperture radar, it is instructed to enable pulse compression technology with a compression ratio of 8:1 and increase the transmission power; for the underground distributed fiber optic sensor, it is instructed to enable the weighted average filtering algorithm and extend the signal integration time.

[0112] Feedback adjustment instructions:

[0113] Sampling frequency adjustment: For areas with a deformation rate of 3.2 mm / h, the surface SAR and underground optical fiber are instructed to increase the sampling frequency from 1 time / minute to 5 times / minute; after the rate is below 0.5 mm / h for 30 consecutive minutes, the reference frequency is restored.

[0114] Anti-interference mode: When the SAR signal-to-noise ratio drops to 9dB (below the preset threshold of 10dB), it is instructed to enable pulse compression technology (compression ratio 8:1) and the transmit power is increased from 10W to 15W; when the fiber optic signal-to-noise ratio drops to 14dB (below the preset threshold of 15dB), it is instructed to enable weighted average filtering (5 points in the window) and the integration time is extended from 1 second to 3 seconds.

[0115] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A multimodal array-type geological body deformation monitoring sensor network system, characterized in that, include: Surface monitoring array module: It consists of M groups of surface monitoring units distributed in a preset spatial array, where M is a positive integer greater than or equal to 3. Each group of surface monitoring units contains one L-band synthetic aperture radar, and each group of L-band synthetic aperture radar has a corresponding surface detection area, which is used to acquire surface deformation data of geological bodies within its detection area, and to collaboratively acquire deformation data of different areas of the surface of geological bodies. Underground monitoring array module: It consists of N groups of underground monitoring units buried underground in a spatial array corresponding to the surface monitoring array, where N is a positive integer greater than or equal to 3. Each group of underground monitoring units contains one distributed fiber optic sensor, which is used to collaboratively acquire deformation data of different regions inside the geological body. The number of rows and columns of the preset spatial array satisfies M=N, and the i-th row and j-th column unit of the underground monitoring array is set directly below the i-th row and j-th column unit of the surface monitoring array. Data aggregation unit: The data aggregation unit is communicatively connected to the surface monitoring array and the underground monitoring array, respectively, and is used to receive the raw data transmitted by the surface monitoring array and the underground monitoring array and perform preprocessing; the data aggregation unit is communicatively connected to the data processing unit and is used to transmit the preprocessed data to the data processing unit; Data processing unit: It is communicatively connected to the surface monitoring array and the underground monitoring array respectively, and is used to receive and process the deformation data transmitted by the two arrays. The data processing unit has built-in penetration data fusion algorithm and dynamic weight calibration algorithm. Data feedback unit: It communicates with the data processing unit to receive integrated deformation field data output by the data processing unit, generate early warning information based on preset thresholds and send it to the remote monitoring platform. At the same time, it feeds back adjustment instructions to the surface monitoring array and underground monitoring array according to the spatial distribution characteristics of the deformation field data, so as to realize the closed-loop adaptive control of the monitoring system.

2. The multimodal array-type geological body deformation monitoring sensor network system according to claim 1, characterized in that, The dynamic weight calibration algorithm is used for real-time calculation. and The calculation formula is: In the formula: Let be the quality coefficient of the surface SAR data at time t. Where k is a calibration constant (ranging from 1 to 10), used to adjust the baseline level of the quality coefficient. Let be the signal-to-noise ratio of the SAR data at time t. The surface environment interference index (range 0-1) is the value of the L-band synthetic aperture radar detection area at time t, and is calculated based on the vegetation coverage, rainfall intensity and terrain occlusion angle in the detection area. Let be the quality coefficient of the underground fiber optic data at time t. ,in Let be the signal-to-noise ratio of the fiber optic data at time t. The attenuation rate of the fiber optic signal at time t (range 0-1); Dynamic weight values ​​of surface SAR data at time t Dynamic weight values ​​of underground fiber optic data And both satisfy (The values ​​are all in the range of 0-1); and The numerical value directly reflects the ratio between surface and underground data; the higher the quality coefficient of the data source, the greater its corresponding weight.

3. The multimodal array-type geological body deformation monitoring sensor network system according to claim 2, characterized in that, The penetration data fusion algorithm is used to fuse deformation data acquired by surface monitoring arrays and underground monitoring arrays. The calculation formula is as follows: In the formula: Let t be the fused deformation value of the array node in the i-th row and j-th column of the monitoring area at time t; Let be the weight value of the surface SAR data at time t. Let be the weight value of the underground fiber optic data at time t, and ; The deformation data acquired by the i-th row and j-th column unit of the surface monitoring array at time t; The deformation data acquired by the i-th row and j-th column unit of the underground monitoring array at time t; Deformation data acquired by underground monitoring arrays The output according to the dynamic weight calibration algorithm and The result is obtained by weighted summation.

4. The multimodal array-type geological body deformation monitoring sensor network system according to claim 3, characterized in that, The dynamic weight calibration algorithm generates and As input parameters for penetrating data fusion algorithms; when Enlargement leads to During the descent, the output of the dynamic weight correction algorithm is... Synchronous reduction, Simultaneous improvement increases the proportion of underground fiber optic data in the penetration data fusion algorithm; when Reduced During the rise, the output of the dynamic weight calibration algorithm is Simultaneous improvement The synchronous reduction increases the proportion of surface SAR data in the penetrating data fusion algorithm.

5. The multimodal array-type geological body deformation monitoring sensor network system according to claim 1, characterized in that, The surface monitoring array is spatially distributed in a rectangular grid pattern, with a spacing of 5-10 meters between adjacent groups of surface monitoring units, and the SAR detection range of each group of units overlaps with that of the adjacent units by 10%-15%.

6. The multimodal array-type geological body deformation monitoring sensor network system according to claim 1, characterized in that, The distributed fiber optic sensors of the underground monitoring array employ BOTDR technology, are buried 50-100cm below the surface, and the fiber optic cables run parallel to the edges of the rectangular grid of the surface monitoring array. The quality coefficient is calculated based on the measurement signals from the distributed optical fiber sensor, and the attenuation rate of the optical fiber signal monitored by the distributed optical fiber sensor is used to calculate the quality coefficient. .

7. The multimodal array-type geological body deformation monitoring sensor network system according to claim 1, characterized in that, The preset thresholds include multi-level warning thresholds, specifically: Level 1 warning threshold: triggered when the deformation rate of the local geological body exceeds 3 mm / h, or the cumulative horizontal displacement exceeds 20 mm, or the cumulative vertical displacement exceeds 15 mm; Level 2 warning threshold: triggered when the deformation rate of the local geological body exceeds 5 mm / h, or the cumulative horizontal displacement exceeds 50 mm, or the cumulative vertical displacement exceeds 40 mm; Level 3 warning threshold: triggered when the deformation rate of the local geological body exceeds 10 mm / h, or the cumulative horizontal displacement exceeds 100 mm, or the cumulative vertical displacement exceeds 80 mm, or the underground strain data exceeds 1000 με. The values ​​of the multi-level early warning thresholds are determined based on the geological stability level of the monitored area, engineering safety specifications, and historical deformation database, and can be dynamically adjusted through the remote configuration interface of the data feedback unit, with an adjustment range of ±30% of the first-level early warning threshold.

8. The multimodal array-type geological body deformation monitoring sensor network system according to claim 1, characterized in that, The data aggregation unit performs noise filtering on the received raw data to reduce environmental interference and performs format standardization to unify the spatiotemporal reference and data structure of surface SAR data and underground optical fiber data. After preprocessing, the data is sent to the data processing unit.

9. A multimodal array-type geological body deformation monitoring sensor network system according to claim 1, characterized in that, The feedback adjustment command includes a sampling frequency adjustment command and an anti-interference mode activation command, specifically: Sampling frequency adjustment command: When the deformation rate of a certain region (i,j) in the integrated deformation field data output by the data processing unit exceeds the first-level warning threshold, the data feedback unit sends an adjustment command to the surface monitoring unit and underground monitoring unit corresponding to that region, increasing their sampling frequency from 1 time / minute to 5 times / minute in high-frequency mode; when the deformation rate is below 0.5mm / h for 30 consecutive minutes, the command is restored to the reference frequency; Anti-interference mode activation command: When the data processing unit detects that the signal-to-noise ratio (SNR) of a certain monitoring unit is lower than the preset threshold, the data feedback unit sends an anti-interference command to the unit: For surface L-band synthetic aperture radar, it commands it to enable pulse compression technology with a compression ratio of 8:1 and increase the transmission power; for underground distributed fiber optic sensors, it commands them to enable the weighted average filtering algorithm and extend the signal integration time.

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