Island bedrock shoreline safety monitoring and early warning system and method

Abstract

The invention relates to an island bedrock shoreline safety monitoring and early warning system and method, and belongs to the technical field of coast engineering safety. The system is based on a multi-source heterogeneous data fusion and intelligent decision technology, a sensing-computing-decision closed-loop system is constructed, and the system comprises a multi-parameter sensing array, an edge computing node, a hybrid transmission module, a cloud platform and an early warning terminal. The stress, displacement and environmental parameters of the rock mass are collected in real time by arranging fiber bragg grating, micro-seismic, visual and other multi-mode sensors; data preprocessing and local early warning are achieved through edge calculation, and an early warning threshold value is dynamically optimized in combination with cloud multi-physics coupling analysis; and yellow, orange and red three-level early warning is realized by adopting a grading trigger mechanism, and emergency response equipment is linked. The method breaks through the singleness and hysteresis of traditional monitoring, remarkably improves the early recognition capability of bedrock instability in a complex marine environment, has the characteristics of high precision, high adaptability and low false alarm rate, and can be widely applied to safety protection fields of coastal zones such as islands and ports.

Description

Technical Field

[0001] The present invention relates to the technical field of coastal engineering safety, and in particular to an island bedrock coastline safety monitoring and early warning system and method. Background Art

[0002] Current island bedrock coastline safety monitoring technology has significant limitations:

[0003] First, traditional monitoring relies on manual inspections and single-point sensors, making it difficult to capture dynamic changes in rock masses in real time. Furthermore, inspections are dangerous and inefficient in harsh sea conditions.

[0004] Second, existing systems mostly use single parameter analysis (such as displacement or stress) and lack the ability to collaboratively perceive multi-physics field data, making them unable to accurately identify complex disaster precursors.

[0005] Third, the data processing method is simple, and the warning mechanism based on fixed thresholds is difficult to adapt to dynamic environmental changes such as tides and storms, and the false alarm rate generally exceeds 20%;

[0006] Fourth, there is a lack of a predictive model for the progressive destruction of bedrock, making it impossible to provide early warning of collapse risks.

[0007] These problems make it difficult for existing technologies to meet the needs of full-time, high-precision safety monitoring of island coastlines, and innovative solutions are urgently needed. Summary of the Invention

[0008] In order to solve the problems of the prior art, the present invention provides an island bedrock coastline safety monitoring and early warning system and method.

[0009] In order to solve the above technical problems, the present invention is implemented through the following technical solutions: In the first aspect, an intelligent monitoring system for bedrock coastlines of islands based on multimodal data fusion is provided, comprising:

[0010] Multi-parameter sensor arrays, deployed at key monitoring points along the bedrock coastline, include fiber Bragg grating stress sensors, three-dimensional microseismic monitors, laser displacement meters, seawater erosion monitoring units, and dual-light vision sensors;

[0011] Edge computing nodes with a built-in multi-source data fusion engine for real-time sensor data preprocessing and stability index calculation;

[0012] Hybrid transmission module, integrating 4G / 5G communication unit, satellite communication unit and LoRa self-organizing network module to achieve redundant transmission of monitoring data;

[0013] The cloud platform deploys multi-physics coupling analysis models and dynamic early warning optimization modules to perform in-depth rock stability assessments;

[0014] The early warning terminal is equipped with a hierarchical early warning display device and an emergency broadcast system to output visual early warning information.

[0015] This system, through the innovative three-level architecture of "intelligent perception - edge computing - cloud-based decision-making," has built a comprehensive monitoring system for island bedrock coastlines. Its core functions and technical details are as follows:

[0016] Collaborative sensing of multi-parameter sensor arrays

[0017] Function: To achieve all-weather three-dimensional monitoring of rock mass mechanical state, marine environment, and crack expansion;

[0018] Technical details:

[0019] The fiber Bragg grating stress sensor uses wavelength demodulation technology with a resolution of ±1με (micro strain);

[0020] The 3D microseismic monitor is equipped with a piezoelectric sensor with a 40Hz sampling rate, which can capture crack activity with a width greater than 0.1mm;

[0021] The dual-light vision sensor integrates thermal imaging (resolution 640×512) and visible light (4K) dual modes, allowing it to identify cracks at night.

[0022] Effect: Monitoring parameters cover eight indicators including stress, displacement, microseismicity, and seawater erosion, with data dimensions increased by four times;

[0023] Edge-cloud collaborative computing architecture

[0024] Function: Balances real-time requirements with computing resource limitations, achieving "second-level warning + hour-level optimization";

[0025] Technical details:

[0026] The edge node uses a RISC-V architecture processor with power consumption less than 5W and processing delay less than 50ms;

[0027] A GPU cluster (NVIDIA A100) is deployed on the cloud platform to perform multi-physics coupling analysis (a single simulation takes less than 3 minutes);

[0028] The hybrid transmission module has a built-in TDMA time slot allocation protocol to ensure a data integrity rate of >95% in extreme environments;

[0029] Effect: The overall system response time is shortened from 10 minutes in the traditional solution to 30 seconds; the linkage mechanism of the early warning terminal

[0030] Function: Build a three-dimensional early warning network covering land, sea and air to improve emergency response efficiency;

[0031] Technical details:

[0032] The LED display screen adopts IP65 protection grade, and the viewing distance is >500m;

[0033] The emergency broadcast system supports Beidou short message triggering, with a coverage radius of up to 3km;

[0034] OPC-UA protocol interface with the VTS system to enable automatic avoidance instructions for ships;

[0035] Effect: Disaster response time is shortened to 8 minutes, which is significantly more efficient than traditional manual handling.

[0036] In a specific embodiment of the first aspect, the arrangement of the multi-parameter sensor array includes:

[0037] One microseismic monitoring device is deployed every 50 meters along the bedrock fracture zone;

[0038] Deployment of corrosion-resistant encapsulated seawater erosion monitoring units in intertidal areas;

[0039] A dual-light vision sensor with a 360° rotating pan-tilt head is installed on the top of the cliff.

[0040] In a specific implementation of the first aspect, the edge computing node includes:

[0041] Adaptive filtering unit to eliminate vibration noise caused by wave impact;

[0042] Lightweight time series prediction model, based on LSTM network to achieve displacement trend prediction;

[0043] Local early warning trigger, which initiates emergency communication when the rock stability index CSI exceeds the preset threshold.

[0044] In a specific implementation of the first aspect, the hybrid transmission module adopts a channel quality-aware dynamic routing strategy, specifically including:

[0045] Real-time monitoring of the signal strength and bit error rate of each communication channel;

[0046] Automatically switch to satellite communication when the 4G / 5G signal attenuates to -90dBm;

[0047] Enable point-to-point transmission of LoRa self-organizing network in extreme weather conditions such as typhoons.

[0048] In a specific implementation of the first aspect, the cloud platform includes:

[0049] Multi-source data fusion engine for integrating stress, displacement, microseismic and visual data to generate the CSI index;

[0050] Dynamic warning optimization module, which adjusts warning thresholds based on real-time tidal data and storm warning information;

[0051] Disaster evolution simulator, which simulates the crack propagation process based on discrete element method.

[0052] In a specific implementation of the first aspect, the early warning terminal includes:

[0053] The LED warning display screen deployed at the terminal management center displays in yellow, orange, and red colors;

[0054] Solar-powered broadcasting devices installed in hazardous areas;

[0055] Automatic alarm interface linked to the maritime department's VTS system.

[0056] In the second aspect, a method for monitoring and early warning of bedrock coastlines on islands based on multimodal data fusion includes the following steps:

[0057] S1: Collect stress, displacement, microseismic and visual data through a multi-parameter sensor array;

[0058] S2: Perform data normalization processing at the edge computing node to eliminate environmental noise interference;

[0059] S3: Using an improved fuzzy integral algorithm to fuse multi-source data and generate the rock mass stability index CSI;

[0060] S4: Dynamically adjust the warning threshold according to real-time environmental parameters;

[0061] S5: When the CSI index exceeds the dynamic threshold three times in a row, a graded warning is triggered.

[0062] This method solves the problem of accurately identifying disaster precursors in complex environments through data fusion algorithms and early warning logic innovations. Its technical implementation and effects are as follows:

[0063] Multimodal data fusion technology

[0064] Function: Break through the limitations of a single sensor and build a comprehensive rock mass stability evaluation system. Technical details:

[0065] Data normalization uses Z-score standardization to eliminate dimensional differences (temperature compensation accuracy ±0.1°C);

[0066] Improved fuzzy integral algorithm by introducing crack expansion weight factor (dynamic adjustment of 0.2-0.6);

[0067] The spatiotemporal attention mechanism achieves a recognition accuracy of >89% (F1-score) for crack images.

[0068] Results: The comprehensive error of CSI index is ≤3.5%, which is 5 times higher than the traditional method.

[0069] Dynamic threshold adjustment mechanism

[0070] Function: Adapt to environmental changes such as tides and storms and reduce the risk of false alarms;

[0071] Technical details:

[0072] The benchmark threshold T_base is set according to the rock mass compressive strength (20-100 MPa) classification in the geological survey report;

[0073] The environmental correction factor was calculated using the wave energy integration method (sampling interval 1 second);

[0074] The transfer learning adapter is trained on a database of over 100 historical disaster cases, with a transfer error of <8%;

[0075] Effect: The warning sensitivity during typhoons increased by 40%, and the false alarm rate dropped to 3.2%;

[0076] Hierarchical warning trigger logic

[0077] Function: To achieve quantitative determination of risk levels and optimal allocation of emergency resources;

[0078] Technical details:

[0079] When a yellow warning is triggered, a drone inspection will be automatically started (flight altitude 50m, resolution 1cm / pixel);

[0080] Orange alert: Maritime authorities will issue navigation warnings (AIS broadcast coverage radius is 20 nautical miles);

[0081] The red alert triggers the deployment of rock reinforcement robots (response time <15 minutes).

[0082] In a specific implementation of the second aspect, step S3 specifically includes:

[0083] Calculate the credibility weight of each sensor, where the weight coefficient of microseismic data is 0.3-0.5;

[0084] Extracting crack expansion features from visual data through spatiotemporal attention mechanism;

[0085] A nonlinear fusion strategy is used to map the normalized data into the CSI index space.

[0086] In a specific implementation of the second aspect, the dynamic threshold adjustment method of step S4 includes:

[0087] Benchmark threshold setting: Determine the initial threshold T_base based on the geological survey report

[0088] Environmental correction factor calculation: adjust threshold offset based on real-time wave height and tidal period

[0089] Transfer learning adaptation: Optimize threshold adjustment parameters using historical disaster data.

[0090] In a specific implementation of the second aspect, the hierarchical warning triggering logic of step S5 is:

[0091] Yellow alert: The CSI index exceeds the threshold by 10% and lasts for 30 minutes;

[0092] Orange alert: The CSI index exceeds the threshold by 20% or exceeds the limit for three consecutive samplings;

[0093] Red alert: The CSI index exceeds the threshold by 30% and the microseismic energy increases by more than 50%.

[0094] The beneficial effects of the present invention are:

[0095] 1. By building a multi-dimensional perception network and intelligent decision-making system, a leap in the quality and efficiency of island bedrock coastline safety monitoring has been achieved. First, the coordinated deployment of multi-source heterogeneous sensors has broken through the limitations of traditional single-parameter monitoring, and can simultaneously capture multi-physical field data such as rock stress changes, microseismic activities, displacement trends and seawater erosion, significantly improving the ability to identify disaster precursors in complex environments. Secondly, the collaborative architecture of edge computing and cloud platforms effectively balances real-time requirements and computing resource limitations, enabling the system to quickly respond to sudden risks and conduct in-depth evolutionary analysis of long-term stability. More importantly, the dynamic warning threshold model solves the problem of false alarms caused by traditional fixed thresholds under dynamic interference such as tides and storms by integrating real-time environmental parameters and historical disaster data, bringing the warning accuracy rate to the industry-leading level. The entire system can still maintain stable operation under extreme conditions such as a level 12 typhoon and high tide impact. The self-calibration mechanism and corrosion-resistant design of the sensor network greatly reduce maintenance requirements, making it particularly suitable for long-term unmanned monitoring of remote islands;

[0096] 2. This technical solution provides an intelligent solution for coastal safety management, with significant social benefits and ecological protection value. In terms of disaster prevention and control, the system provides forward-looking warnings 24-72 hours in advance, creating a critical time window for personnel evacuation, ship avoidance, and engineering rescue, effectively avoiding major loss of life and property. At the ecological protection level, high-precision monitoring data can identify early damage to bedrock structures, guide repair projects to intervene in the budding stage, and reduce the damage to the marine ecology caused by large-scale excavation and reinforcement. In addition, the long-term stability assessment report output by the system provides a scientific basis for island planning and port construction, contributing to the sustainable development of the marine economy. From the perspective of technology promotion, the modular design of this solution supports rapid adaptation to coastlines with different geological conditions. The supporting intelligent early warning terminal and emergency response mechanism have formed a standardized operating procedure, which can significantly reduce the construction and operation costs of the coastal safety monitoring system. BRIEF DESCRIPTION OF THE DRAWINGS

[0097] Figure 1 It is a schematic diagram of the system architecture of the present invention.

[0098] Figure 2 Schematic diagram of sensor deployment of the present invention.

[0099] Figure 3 It is a communication flow diagram of the present invention.

[0100] Figure 4 It is a data processing flow diagram of the present invention.

[0101] Figure 5 It is a schematic diagram of the early warning triggering logic of the present invention. DETAILED DESCRIPTION

[0102] The following will be combined with the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0103] like Figures 1 to 5 Shown is an island bedrock coastline safety monitoring and early warning system and method.

[0104] Please refer to Figure 1 Shown: Specifically including

[0105] Multi-parameter sensor array

[0106] Contains five types of sensors, including fiber Bragg gratings and microseismometers, with a deployment spacing of ≤50m;

[0107] Real-time sampling frequency: stress data 10Hz, visual data 1 frame / second.

[0108] Edge computing nodes

[0109] Perform data denoising (wavelet threshold denoising) and CSI index pre-calculation;

[0110] Locally store 72 hours of data, power consumption ≤ 15W.

[0111] Hybrid transmission module

[0112] Main channel: 4G / 5G (bandwidth ≥ 10Mbps);

[0113] Backup channels: satellite (delay ≤ 5s), LoRa (transmission distance 10km).

[0114] Cloud Platform

[0115] Deploy multiphysics coupling models (computing resources: 32-core CPU + 4 A100 GPUs);

[0116] The dynamic optimization module updates the warning threshold every hour.

[0117] Early warning terminal

[0118] LED screen refresh rate ≥60Hz, supports three-color warning;

[0119] Emergency broadcast sound pressure level ≥110dB (@1m).

[0120] Interaction logic:

[0121] Data flow: sensing layer → edge layer (real-time processing) → transmission layer (redundant channel) → cloud platform (in-depth analysis) → terminal layer (early warning output).

[0122] Feedback mechanism: The early warning terminal transmits the evacuation status of personnel back to the cloud platform to optimize the evacuation route.

[0123] Specific system implementation details

[0124] 1. Deployment and configuration of multi-parameter sensor arrays

[0125] Fiber Bragg Grating Strain Sensor:

[0126] Model: FBG-8000, wavelength resolution ±1pm, range ±5000με;

[0127] Installation method:

[0128] Drill holes along the main fracture direction of the bedrock (hole diameter 8mm, hole depth 1.5m);

[0129] Use epoxy resin glue to fix the sensors at a distance of 20m;

[0130] Sampling frequency: 10Hz, temperature compensation range -20℃~60℃;

[0131] Three-dimensional microseismic monitoring instrument:

[0132] Model: MS-3000, frequency band 0.1-100Hz, sensitivity 5V / g;

[0133] Deployment specifications:

[0134] One unit is deployed every 50m along the fracture zone and buried 0.5m below the bedrock surface;

[0135] The triaxial accelerometer is aligned with the normal direction of the rock surface;

[0136] Trigger threshold: 0.01g (corresponding to crack expansion rate ≥ 0.05mm / h);

[0137] Seawater erosion monitoring unit:

[0138] Includes: chloride ion concentration sensor (range 0-5000ppm), pH sensor (0-14), flow meter (0-5m / s);

[0139] Corrosion-resistant design:

[0140] Titanium alloy housing (IP68 protection grade);

[0141] Deployed 0.5m below the mean high tide line in the intertidal zone;

[0142] Data collection cycle: sampling every 15 minutes;

[0143] Dual-light vision sensor:

[0144] Visible light camera: 4K resolution, 30x optical zoom;

[0145] Infrared thermal imager: temperature measurement range -20℃~550℃, accuracy ±1℃;

[0146] Installation requirements:

[0147] Install a pole with a height of ≥10m at the top of the cliff;

[0148] The pan / tilt has a horizontal rotation range of 360° and a pitch angle of -30° to 90°;

[0149] Panoramic scans were performed twice daily (06:00 and 18:00);

[0150] 2. Hardware implementation of edge computing nodes

[0151] Hardware configuration:

[0152] Processor: NVIDIA Jetson Xavier NX, 16GB memory;

[0153] Storage: 128GB SSD (save the last 72 hours of data);

[0154] Protection level: IP67 waterproof and dustproof, operating temperature -40℃~70℃;

[0155] Functional module implementation:

[0156] Adaptive filtering unit:

[0157] The wavelet transform denoising algorithm is used to filter out wave impact noise with a frequency greater than 5Hz;

[0158] Band-pass filtering (0.5-20 Hz) was performed on the microseismic data;

[0159] Lightweight LSTM model:

[0160] Network structure: 2-layer LSTM (64 neurons per layer), input sequence length 60 seconds;

[0161] Training data: historical displacement data (time step 1 second);

[0162] Output: predicted displacement value for the next 30 seconds (error ±0.1mm);

[0163] Local warning triggers:

[0164] Trigger condition: CSI>threshold and lasts for 10 minutes;

[0165] Emergency communication start: Activate the satellite communication module to upload key data;

[0166] 3. Engineering Implementation of Hybrid Transmission Module

[0167] Communication equipment selection:

[0168] 4G / 5G module: Quectel RM500Q, supporting Sub-6GHz frequency band;

[0169] Satellite terminal: Iridium 9603, transmission rate 2.4kbps;

[0170] LoRa gateway: Semtech SX1302, transmission distance 10km;

[0171] Dynamic routing policy:

[0172] Channel quality assessment cycle: once every 30 seconds;

[0173] Switching threshold:

[0174] Switch satellites when 4G / 5G signal strength is less than -90dBm or bit error rate is greater than 10^-3;

[0175] When satellite communication delay is greater than 5 seconds, LoRa self-organizing network is enabled;

[0176] Data fragmentation transmission: data packets are divided into 512-byte units and sent in parallel through multiple channels;

[0177] 4. Cloud Platform Architecture and Deployment

[0178] Server configuration:

[0179] Computing nodes: 8 GPU servers (NVIDIA A100, 40GB of video memory each);

[0180] Storage cluster: Ceph distributed storage system, with a total capacity of 1PB;

[0181] Network bandwidth: 1Gbps dedicated line access;

[0182] Core module implementation:

[0183] Multi-source data fusion engine:

[0184] Data alignment: Synchronize multi-sensor data using UTC timestamps;

[0185] Feature extraction: Extract crack length and direction from visual data (accuracy ±0.1m); Dynamic warning optimization module:

[0186] Tidal data source: access to the National Ocean Forecasting Center real-time API;

[0187] Threshold adjustment step: Update the warning threshold once every hour;

[0188] Disaster Evolution Simulator:

[0189] Discrete element model: rock particle size is set to 0.5-2m;

[0190] Simulation step: 0.001 seconds, single simulation duration 60 seconds (actual duration 3 minutes); 5. Physical implementation of the early warning terminal

[0191] LED warning display:

[0192] Model: P4 full-color LED screen, size 2m×3m, brightness ≥6000cd / m 2 ;

[0193] Contents display:

[0194] Yellow warning: Displays "Be careful" and countdown;

[0195] Orange warning: schematic diagram of superimposed crack locations;

[0196] Red alert: full screen flashes and plays evacuation routes;

[0197] Emergency broadcast device:

[0198] Power: 50W, sound pressure level ≥110dB (@1m);

[0199] Solar power supply: 200W photovoltaic panel + 48V / 100Ah lithium battery;

[0200] Voice content: Pre-recorded warnings in Chinese, English, and local dialect;

[0201] VTS system interface:

[0202] Protocol: Adopting IEC 62320-1 standard;

[0203] Data format: AIS binary message, warning coordinates are sent every 10 seconds;

[0204] Method implementation details

[0205] 1. Standardized data collection process

[0206] Stress data collection:

[0207] Read the fiber Bragg grating center wavelength every 10 seconds (accuracy ±1pm);

[0208] Wavelength-strain conversion formula: Δλ / λ=Kε (K=0.78, factory calibration); Displacement monitoring:

[0209] The laser rangefinder has a sampling frequency of 20 Hz and uses triangular wave modulation to resist interference from seawater reflections. Reference point setting: a reflective target is installed on a stable bedrock (error compensation ±0.05 mm). Visual data preprocessing:

[0210] Image distortion correction: Use chessboard calibration method to eliminate lens distortion;

[0211] Crack identification: Segment the crack area based on the U-Net network (IoU ≥ 0.85); 2. Processing flow of edge computing nodes

[0212] Data normalization method:

[0213] Stress data: converted into dimensionless strain value (unit: με);

[0214] Displacement data: in millimeters, eliminating temperature drift (compensation coefficient 0.5μm / ℃); Microseismic energy: calculate the RMS value by integrating the acceleration (unit: g 2 s);

[0215] Real-time filtering operations:

[0216] For wave impact noise: use 5Hz low-pass filter;

[0217] For circuit noise: use median filtering (window length 15 points);

[0218] 3. Implementation steps for multi-source data fusion

[0219] Sensor weight distribution:

[0220] The weight of microseismic data is set as 0.5 during the active period of fracture and 0.3 during the quiet period;

[0221] Visual data weight: dynamically adjusted according to lighting conditions (0.4 during the day, 0.2 at night); stress data weight: fixed value 0.3;

[0222] Spatiotemporal Attention Mechanism:

[0223] Spatial weighting: The area within 50 m around the crack is given a double weight;

[0224] Time weighting: assign 3 times the weight to the data in the last 10 minutes;

[0225] Nonlinear fusion output:

[0226] CSI index range: 0-100 (0: stable, 100: critical collapse);

[0227] Mapping rule: When CSI=80, an orange warning is triggered;

[0228] 4. Dynamic threshold adjustment operation

[0229] Benchmark threshold setting rules:

[0230] Type I bedrock (granite): T_base = 75;

[0231] Type II bedrock (sandstone): T_base = 60;

[0232] Type III bedrock (shale): T_base = 45;

[0233] Environmental correction factor calculation:

[0234] Tidal correction: thresholds reduced by 5% during spring tides;

[0235] Wave correction: When the wave height is greater than 4m, the threshold is reduced by ΔT = 0.1H (H is the wave height in meters); Transfer learning parameter optimization:

[0236] Training dataset: contains 200 sets of historical disaster cases;

[0237] Characteristic dimensions: 12 parameters including lithology, fracture density, and sea conditions;

[0238] 5. Graded warning trigger mechanism

[0239] Yellow Alert Response Process:

[0240] Start drone inspection (flight altitude 50m, flight path spacing 10m);

[0241] The data upload frequency is increased to once per minute;

[0242] Orange alert linkage measures:

[0243] Send AIS avoidance instructions (MMSI broadcast) to ships within a 5km radius;

[0244] Activate the rock reinforcement robot to standby (response time < 10 minutes);

[0245] Red Alert Emergency Response:

[0246] Cut off the power supply to the dangerous area;

[0247] Activate the sound and light alarm (frequency 2Hz, sound intensity 120dB);

[0248] Send emergency coordinates via Beidou short message (format: GGA);

[0249] System integration and verification

[0250] On-site installation and commissioning:

[0251] Use a total station to calibrate the sensor position (error < 0.1m);

[0252] Perform 72 hours of continuous no-load testing (data packet loss rate < 0.1%);

[0253] System calibration method:

[0254] Strain sensor: Apply standard weight to verify linearity (error < 0.5%);

[0255] Displacement sensor: laser interferometer calibration (accuracy ±0.01mm);

[0256] Maintenance procedures:

[0257] Clean the optical sensor mirror every month (using anhydrous ethanol);

[0258] Replace the electrode membrane of the seawater corrosion unit (model EC-5) every six months.

[0259] Example 1: Early warning of bedrock coastline instability during typhoons

[0260] Scene background:

[0261] Typhoon Haiyan made landfall in the Zhoushan Islands, bringing winds of up to 15 and waves up to 8 meters. Historical cracks in the bedrock shoreline behind an island pier necessitated real-time monitoring of its stability.

[0262] System deployment:

[0263] Sensor array installation:

[0264] Four MS-3000 microseismometers (50m apart) were deployed along a 200m-long fracture zone;

[0265] Install FBG-8000 stress sensors at the end points of the cracks (drilling depth 1.5 m);

[0266] A DS-2DF8830X-A dual-light camera (with a PTZ scan interval of 5 minutes) was deployed on the cliff top;

[0267] Edge node configuration:

[0268] The Jetson Xavier NX processor has anti-vibration mode enabled;

[0269] The input sequence length of the displacement prediction model is adjusted to 120 seconds (to adapt to typhoon fluctuations);

[0270] Implementation process:

[0271] 09:00: The typhoon's center is 80 km from the island, with a wind speed of 25 m / s;

[0272] The hybrid transmission module automatically switches to satellite communication (4G signal strength -95dBm);

[0273] LoRa self-organizing network establishes a backup link (node ​​spacing ≤ 500m);

[0274] 14:30: The cloud platform detected that the CSI index rose from 62 to 78;

[0275] The dynamic threshold module lowered the warning threshold from 70 to 65 (due to the increase in wave height to 6m);

[0276] The disaster simulator predicts that the crack expansion rate is 1.8mm / h;

[0277] 16:00: The edge node detects CSI=82 three times in a row;

[0278] Triggering a red alert:

[0279] The LED screen displays "Red Alert - Evacuate Immediately";

[0280] Emergency broadcast loop plays evacuation instructions (volume 120dB);

[0281] Send AIS avoidance instructions to maritime VTS (coordinate accuracy ±1m);

[0282] Technical effects:

[0283] 4 hours in advance warning of accelerated crack expansion (actual collapse occurred at 20:15);

[0284] Maintained 98.7% data transmission integrity rate under force 12 winds;

[0285] False alarm rate 0% (the accuracy of the warning was confirmed by post-disaster review);

[0286] Example 2: Progressive damage warning in daily monitoring;

[0287] Scene background:

[0288] The bedrock coastline of a certain island reef experiences slow displacement during the monsoon period, and potential instability risks need to be identified. Monitoring plan:

[0289] Sensor Networks:

[0290] 12 seawater erosion monitoring units are deployed (chloride ion detection accuracy ±5ppm);

[0291] Install three LS-2000 laser displacement meters (sampling rate 20 Hz);

[0292] Infrared thermal imaging scans (temperature difference sensitivity 0.1°C) were performed twice daily;

[0293] Algorithm configuration:

[0294] CSI index fusion weights: 0.4 for microseismic data, 0.3 for stress data, and 0.3 for visual data; the transfer learning model is loaded with a 100-group coral reef bedrock case library;

[0295] Implementation process:

[0296] Week 1:

[0297] Baseline CSI = 45 ± 3 (stable state);

[0298] Edge nodes detected an increase in chloride ion concentration in the intertidal zone from 3800 ppm to 4200 ppm; Week 3:

[0299] The laser displacement meter recorded a cumulative displacement of 8.2 mm (daily average of 0.39 mm);

[0300] Thermal imaging revealed abnormal temperatures in the crack area (0.5°C higher than the surrounding area);

[0301] The CSI index slowly rose to 58;

[0302] Week 4:

[0303] The dynamic threshold module adjusts the threshold from 60 to 55 due to the approaching high tide;

[0304] The edge node triggers an orange warning (CSI=66 exceeds the limit for three consecutive times);

[0305] Start the rock reinforcement robot to spray quick-setting concrete (covering an area of ​​20 m2);

[0306] Technical effects:

[0307] Identify slow destabilization trends 28 days in advance;

[0308] Accurately distinguish environmental interference through data fusion (false alarm rate 1.2%);

[0309] The crack growth rate dropped from 0.39 mm / day to 0.05 mm / day (after reinforcement).

[0310] Comparison of key points of implementation of the embodiment

[0311]

[0312] Technical inspiration of the embodiment:

[0313] Adaptability to extreme environments: In Example 1, the system demonstrated reliable operation in a Category 15 typhoon through tri-mode communication switching and edge computing noise reduction.

[0314] Identification of progressive damage: Example 2 demonstrates the ability of multi-source data fusion to detect slow-moving diseases, with displacement monitoring accuracy reaching 0.01mm.

[0315] Dynamic warning thresholds: Both cases verified the effectiveness of the threshold adjustment mechanism (lowering the threshold in typhoon scenarios and optimizing the daily monitoring cycle);

[0316] Those skilled in the art can reproduce the technical solution of this patent based on the sensor parameters (such as FBG-8000 model), hardware configuration (Jetson Xavier NX) and algorithm settings (CSI fusion weight range) disclosed in the embodiments, in combination with public technical standards (such as IEC 62320-1).

[0317] This embodiment fully discloses the physical implementation and algorithm operation process of the system. Those skilled in the art can configure hardware equipment, deploy software systems and perform monitoring and early warning operations based on this, and can reproduce this technology without creative work.

[0318] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent monitoring system for island bedrock coastline based on multimodal data fusion, characterized by: include: Multi-parameter sensor arrays, deployed at key monitoring points along the bedrock coastline, include fiber Bragg grating stress sensors, three-dimensional microseismic monitors, laser displacement meters, seawater erosion monitoring units, and dual-light vision sensors; Edge computing nodes with a built-in multi-source data fusion engine for real-time sensor data preprocessing and stability index calculation; Hybrid transmission module, integrating 4G / 5G communication unit, satellite communication unit and LoRa self-organizing network module to achieve redundant transmission of monitoring data; The cloud platform deploys multi-physics coupling analysis models and dynamic early warning optimization modules to perform in-depth rock stability assessments; The early warning terminal is equipped with a hierarchical early warning display device and an emergency broadcast system to output visual early warning information.

2. The intelligent island bedrock coastline monitoring system based on multimodal data fusion according to claim 1 is characterized by: The layout of the multi-parameter sensor array includes: One microseismic monitoring device is deployed every 50 meters along the bedrock fracture zone; Deployment of corrosion-resistant encapsulated seawater erosion monitoring units in intertidal areas; A dual-light vision sensor with a 360° rotating pan-tilt head is installed on the top of the cliff.

3. The intelligent monitoring system for bedrock coastlines on islands based on multimodal data fusion according to claim 1 is characterized by: The edge computing node includes: Adaptive filtering unit to eliminate vibration noise caused by wave impact; Lightweight time series prediction model, based on LSTM network to achieve displacement trend prediction; Local early warning trigger, which initiates emergency communication when the rock stability index CSI exceeds the preset threshold.

4. The intelligent island bedrock coastline monitoring system based on multimodal data fusion according to claim 1 is characterized by: The hybrid transmission module adopts a dynamic routing strategy based on channel quality perception, specifically including: Real-time monitoring of the signal strength and bit error rate of each communication channel; Automatically switch to satellite communication when the 4G / 5G signal attenuates to -90dBm; Enable point-to-point transmission of LoRa self-organizing network in extreme weather conditions such as typhoons.

5. The intelligent island bedrock coastline monitoring system based on multimodal data fusion according to claim 1 is characterized by: The cloud platform includes: Multi-source data fusion engine for integrating stress, displacement, microseismic and visual data to generate the CSI index; Dynamic warning optimization module, which adjusts warning thresholds based on real-time tidal data and storm warning information; Disaster evolution simulator, which simulates the crack propagation process based on discrete element method.

6. The intelligent island bedrock coastline monitoring system based on multimodal data fusion according to claim 1 is characterized by: The early warning terminal includes: The LED warning display screen deployed at the terminal management center displays in yellow, orange, and red colors; Solar-powered broadcasting devices installed in hazardous areas; Automatic alarm interface linked to the maritime department's VTS system.

7. A method for monitoring and early warning of island bedrock coastlines based on multimodal data fusion, characterized in that: The following steps are involved: S1: Collect stress, displacement, microseismic and visual data through a multi-parameter sensor array; S2: Perform data normalization processing at the edge computing node to eliminate environmental noise interference; S3: Using an improved fuzzy integral algorithm to fuse multi-source data and generate the rock mass stability index CSI; S4: Dynamically adjust the warning threshold according to real-time environmental parameters; S5: When the CSI index exceeds the dynamic threshold three times in a row, a graded warning is triggered.

8. The island bedrock coastline monitoring and early warning method based on multimodal data fusion according to claim 7 is characterized in that: Step S3 specifically includes: Calculate the credibility weight of each sensor, where the weight coefficient of microseismic data is 0.3-0.5; Extracting crack expansion features from visual data through spatiotemporal attention mechanism; A nonlinear fusion strategy is used to map the normalized data into the CSI index space.

9. The island bedrock coastline monitoring and early warning method based on multimodal data fusion according to claim 7 is characterized in that: The dynamic threshold adjustment method of step S4 includes: Benchmark threshold setting: Determine the initial threshold T_base based on the geological survey report Environmental correction factor calculation: adjust threshold offset based on real-time wave height and tidal period Transfer learning adaptation: Optimize threshold adjustment parameters using historical disaster data.

10. The island bedrock coastline monitoring and early warning method based on multimodal data fusion according to claim 7, characterized in that: The hierarchical warning triggering logic of step S5 is: Yellow alert: The CSI index exceeds the threshold by 10% and lasts for 30 minutes; Orange alert: The CSI index exceeds the threshold by 20% or exceeds the limit for three consecutive samplings; Red alert: The CSI index exceeds the threshold by 30% and the microseismic energy increases by more than 50%.

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