A system and method for monitoring the health of small islands in the deep sea

Abstract

The present invention relates to the technical field of health monitoring for small, deep-sea islands, and discloses a system and method for monitoring the health of small, deep-sea islands. A non-invasive data acquisition system consisting of a sensor network, shore-based cameras, and vibration sensors is used to achieve real-time monitoring of all elements, including offshore environmental parameters, reef flat images, and the vibration status of protective structures. Through the combined operation of a wave field distribution model and a structural damage prediction model, measured data is combined with numerical simulations to improve the spatiotemporal resolution of reef flat wave parameters and the accuracy of assessing the health status of protective structures. Through real-time correlation and deviation analysis between historical disaster data and current monitoring values, warning thresholds are dynamically adjusted to improve the timeliness and accuracy of extreme event warnings. Finally, through an automated triggering mechanism for multi-level warning signals, all-weather, highly reliable safety monitoring is provided for deep-sea islands, while reducing manual maintenance costs and achieving significant technical synergy.

Description

Technical Field

[0001] The present invention relates to the technical field of health monitoring of small islands in the deep and distant seas, and in particular to a system and method for monitoring the health of small islands in the deep and distant seas. Background Art

[0002] Deep-sea small islands generally refer to islands or reefs located more than 200 nautical miles from mainland coastlines, isolated in the open ocean. These islands are often composed of coral reefs, volcanic rocks, or sedimentary rocks and are relatively small in size.

[0003] Due to their unique geographical location, small, deep-sea islands are chronically exposed to high-intensity winds and waves, extreme weather events (such as super typhoons and tsunamis), and complex ocean currents. This leads to significant dynamic topography changes and shoreline erosion. Furthermore, the waters surrounding these islands are often hotspots of marine biodiversity, the health of which directly impacts the stability of coral reef ecosystems, fishery resources, and the habitats of endangered species. The deep waters, high waves, and complex marine environment of the deep sea place small islands and their structures at high risk of damage from sudden disasters such as earthquakes, typhoon waves, and storm surges.

[0004] However, the deep sea water is deep and the waves are strong, and the marine environment is very complex. At the same time, there is a lack of long-term marine observation data, and the coupling mechanism between island water-related buildings and strong hydrodynamic loads is unclear. Small islands and their buildings are at high risk of damage under sudden disasters such as earthquakes, typhoon waves, and storm surges. Once the protective projects become unstable, it will cause irreparable strategic losses.

[0005] Therefore, there is an urgent need for a health monitoring system and method for small islands in the deep sea to achieve rapid and accurate monitoring of the safety of small islands in the deep sea. Summary of the Invention

[0006] In order to solve the above technical problems, the present invention provides a deep-sea small island health monitoring system, which includes a data collection module, a data simulation module, a machine learning module and an early warning execution module;

[0007] The data collection module is used to collect real-time offshore marine environmental data of the island through a sensor network, obtain video image data of the reef flat area through a shore-based camera device, and obtain vibration data of the protective structure through a vibration sensor;

[0008] The data simulation module is connected to the data collection module and includes a wave field distribution model and a structural damage prediction model. The wave field distribution model is used to generate reef flat wave parameters based on offshore marine environment data. The structural damage prediction model is used to combine the reef flat wave parameters and protective building vibration data to output protective building health information.

[0009] The machine learning module is connected to the data simulation module and is used to dynamically generate a warning threshold range based on historical monitoring data and real-time monitoring data within a preset time window, wherein the warning threshold range includes a significant wave height threshold, a vibration main frequency deviation threshold, and a foundation settlement rate threshold;

[0010] An early warning execution module is connected to the machine learning module and is used to trigger a multi-level early warning signal when the reef flat wave parameters or the health information of the protective building exceed the early warning threshold range.

[0011] Furthermore, the data collection module includes:

[0012] Vibration sensor arrays are buried inside protective buildings to form a self-organizing network;

[0013] A shore-based panoramic camera system, deployed at the highest point on the island and equipped with a salt spray resistant protective cover;

[0014] The buoy array is deployed in the offshore area of the island and integrates Doppler current meters and temperature, salinity and depth sensors.

[0015] Furthermore, the operation of the wave field distribution model includes:

[0016] Acquiring wind field data, inputting the wind field data into a wave numerical simulation model, and obtaining an offshore wave spectrum;

[0017] The offshore wave spectrum is used as a boundary condition and input into a nearshore wave-current coupling model to calculate reef flat wave parameters, which include wave height, period and flow velocity distribution in the reef flat area.

[0018] Furthermore, a wave-foundation coupled dynamic response model is integrated between the wave field distribution model and the structural damage prediction model. The wave-foundation coupled dynamic response model is used to:

[0019] According to the reef flat wave parameters and foundation geological parameters, the foundation stress distribution is obtained;

[0020] The foundation stress distribution is input into the structural damage prediction model, and the health information of the protective building is obtained by combining the reef flat wave parameters and the vibration data of the protective building.

[0021] Furthermore, the data simulation module also includes a GPU parallel computing cluster for performing the following operations:

[0022] Jointly compiling the wave numerical model, the nearshore wave-current coupling model, and the wave-foundation coupling dynamic response model;

[0023] When the typhoon path prediction data is updated, grid computing tasks within a preset radius area of the typhoon center are preferentially allocated to the GPU parallel computing cluster.

[0024] Furthermore, the specific steps of the machine learning module dynamically generating the warning threshold range include:

[0025] Acquire a historical monitoring data set within a preset time window, the data set including a significant wave height sequence, a vibration main frequency offset sequence, and a foundation settlement rate sequence;

[0026] Calculate the historical mean and standard deviation of each parameter, and determine the basic threshold based on the mean plus N times the standard deviation, where N is the preset safety factor;

[0027] Real-time collection of current effective wave height, vibration main frequency deviation and foundation settlement rate monitoring values. When any parameter deviates from the historical mean by more than a preset percentage, the corresponding threshold is increased or decreased in proportion to the deviation.

[0028] Compare the dynamically adjusted threshold with the design specification threshold, and take the smaller value as the final warning threshold;

[0029] When the real-time monitoring data exceeds the final warning threshold, the corresponding level of warning signal is activated.

[0030] Furthermore, the data collection module also includes:

[0031] The bionic drone unit adopts a flapping-wing silent structure and is activated when the shoreline is abnormal, and its flight trajectory avoids ecologically sensitive areas;

[0032] The bionic coral camera unit consists of a 3D-printed base and internal waterproof components. The surface texture of the base is consistent with the shape of the surrounding corals and has an integrated wireless charging module.

[0033] Furthermore, the system also includes a visualization module for integrating real-time monitoring data, simulation results and warning information for three-dimensional dynamic display.

[0034] Furthermore, the visualization module includes:

[0035] Digital twin engine, integrating multi-temporal remote sensing data with real-time monitoring results to construct a three-dimensional island model;

[0036] Disaster simulation interface, supporting interactive modification of typhoon paths and real-time prediction of coastline evolution trends;

[0037] The structural health monitoring dashboard displays the vibration spectrum and damage heat map of the protective building.

[0038] Another aspect of the present invention provides a method for monitoring the health of small deep-sea islands, which is implemented based on any of the above-mentioned systems for monitoring the health of small deep-sea islands. The method specifically includes:

[0039] Data collection: Real-time data on the offshore marine environment of the island is collected through a sensor network, video data of the reef area is obtained through shore-based cameras, and vibration data of protective structures is obtained through vibration sensors;

[0040] Data simulation: Generate reef flat wave parameters based on offshore marine environment data through a wave field distribution model. Output protective structure health information through a structural damage prediction model that combines reef flat wave parameters and protective structure vibration data.

[0041] Dynamically generate warning threshold ranges based on historical monitoring data and real-time monitoring data within a preset time window. The warning threshold ranges include significant wave height thresholds, vibration main frequency deviation thresholds, and foundation settlement rate thresholds.

[0042] Early warning triggering: when the reef flat wave parameters or the health information of the protective building exceed the early warning threshold range, a multi-level early warning signal is triggered.

[0043] The embodiments of the present invention have the following technical effects:

[0044] The present invention provides a health monitoring system for small, deep-sea islands. It adopts a non-invasive data acquisition system consisting of a sensor network, shore-based cameras and vibration sensors, which realizes real-time monitoring of all elements of offshore environmental parameters, reef flat images and the vibration status of protective buildings, avoiding the interference of manual monitoring on the fragile island ecology. Secondly, through the joint calculation of the wave field distribution model and the structural damage prediction model, the measured data is combined with the numerical simulation, which significantly improves the spatiotemporal resolution of the reef flat wave parameters and the assessment accuracy of the health status of protective buildings. Furthermore, the warning threshold is dynamically adjusted based on the machine learning algorithm, breaking through the limitation that the traditional fixed threshold cannot adapt to the changes in the complex marine environment. Through real-time correlation of historical disaster data and deviation analysis of current monitoring values, intelligent optimization of warning indicators is realized, which greatly improves the timeliness and accuracy of extreme event warnings. Finally, through the automated triggering mechanism of multi-level warning signals, a closed-loop system from data acquisition, model deduction to risk decision-making is formed, providing all-weather, highly reliable safety monitoring guarantees for deep-sea islands, while reducing manual maintenance costs and having significant technical synergy effects. In short, by integrating multi-source data collection and model-based analysis and processing, the problems of low accuracy, ecological damage and high cost in traditional monitoring technologies have been effectively solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0046] Figure 1 This is a module diagram of a deep-sea small island health monitoring system provided by an embodiment of the present invention;

[0047] Figure 2 Schematic diagram of the relationship between the simulation models in the data simulation module provided by the embodiment of the present invention;

[0048] Figure 3 This is a flowchart of the steps of a method for monitoring the health of small islands in the deep sea provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0049] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention are described clearly and completely below. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are also within the scope of protection of the present invention.

[0050] In order to achieve rapid and accurate monitoring of the safety of small islands in the deep sea, the present invention provides a health monitoring system for small islands in the deep sea, such as Figure 1 As shown, it includes data collection module, data simulation module, machine learning module and early warning execution module;

[0051] The data collection module is used to collect real-time offshore marine environmental data of the island through a sensor network, obtain video image data of the reef flat area through shore-based cameras, and obtain vibration data of protective structures through vibration sensors;

[0052] The data collection module is responsible for integrating multi-type sensor networks and collecting environmental parameters of different dimensions using vibration sensors, shore-based cameras, etc.

[0053] In some implementations, the data collection module includes:

[0054] Vibration sensor arrays are buried inside protective buildings to form a self-organizing network;

[0055] A shore-based panoramic camera system, deployed at the highest point on the island and equipped with a salt spray resistant protective cover;

[0056] The buoy array is deployed in the offshore area of the island and integrates Doppler current meters and temperature, salinity and depth sensors.

[0057] Vibration sensors embedded within protective structures transmit the building's vibration status in real time via a self-organizing network. A distributed deployment strategy is employed, with micro-accelerometers embedded at key load-bearing nodes. Wireless ad hoc networking technology enables simultaneous data collection and redundant transmission from multiple nodes. Shore-based cameras are installed at a commanding height on the island, equipped with salt-fog resistant shields to withstand harsh marine conditions. This ensures long-term stable operation in highly corrosive environments and continuously captures video footage of the reef flat. High-resolution optical lenses are employed. An array of buoys, distributed offshore, integrate Doppler current meters and temperature, salinity, and depth sensors, periodically transmitting ocean environmental data. For example, they are radially deployed offshore, each buoy integrating a Doppler current meter, temperature, salinity, and depth sensor, along with a Beidou positioning module, transmitting real-time ocean parameters via satellite links. Buoys form a collaborative observation network through underwater acoustic communication. If a buoy shifts or malfunctions, neighboring buoys automatically adjust their observation frequency to ensure data continuity. This design achieves comprehensive, three-dimensional monitoring of ocean environmental parameters through the spatially optimized configuration of multi-source, heterogeneous sensors.

[0058] In some implementations, the data collection module further includes:

[0059] The bionic drone unit adopts a flapping-wing silent structure and is activated when the coastline is abnormal. The flight trajectory avoids ecologically sensitive areas. The body is equipped with a multi-spectral imager and lidar, which uses edge computing to identify coastline erosion characteristics in real time.

[0060] The bionic coral camera unit consists of a 3D-printed base and internal waterproof components. The base's surface texture matches the surrounding coral morphology and features an integrated wireless charging module. The 3D-printed base is made of biocompatible materials. When nearby organisms approach, the camera automatically enters sleep mode to minimize ecological disruption. This bionic monitoring device ensures data quality while maintaining the balance of the original ecosystem.

[0061] The data simulation module is connected to the data collection module and includes a wave field distribution model and a structural damage prediction model. The wave field distribution model is used to generate reef flat wave parameters based on offshore marine environment data. The structural damage prediction model is used to combine the reef flat wave parameters and the protective structure vibration data to output the protective structure health information.

[0062] In some embodiments, running the wave field distribution model includes:

[0063] Obtain wind field data, input the wind field data into the wave numerical simulation model, and obtain the offshore wave spectrum;

[0064] The offshore wave spectrum is used as the boundary condition and input into the nearshore wave-current coupling model to calculate the reef flat wave parameters, which include the wave height, period and velocity distribution in the reef flat area.

[0065] The present invention directly adopts the existing wave numerical simulation model without any improvement. The input parameters of wind field data include wind speed, wind direction, wind zone length, wind time, air pressure, etc., and the output is reef flat wave parameters.

[0066] In some embodiments, a wave-foundation coupled dynamic response model is integrated between the wave field distribution model and the structural damage prediction model, such as Figure 2 As shown in Figure 2, the wave-foundation coupled dynamic response model is used to:

[0067] According to the reef flat wave parameters and the foundation geological parameters, the foundation stress distribution is obtained; for example, the wave-foundation coupled dynamic response model can be realized by finite element simulation.

[0068] The foundation stress distribution is input into the structural damage prediction model, and the health information of the protective building is obtained by combining the reef flat wave parameters and the vibration data of the protective building.

[0069] Vibration data from protective structures provides a direct measurement of structural response, enabling validation of model predictions and real-time adjustments. For example, changes in the vibration spectrum may indicate a loss of structural stiffness or loosening of connection joints. However, the foundation stress distribution reflects the impact of wave loads on the foundation, a significant factor in structural damage. While vibration data alone may only detect the immediate response of the structure, combined with foundation stress, it can predict long-term cumulative damage. For example, stress concentration in the foundation can lead to uneven settlement of the structure's foundation, in turn causing structural damage. Incorporating foundation stress distribution into structural damage prediction models can improve the accuracy of damage detection, provide early warning capabilities, reduce false alarms and missed warnings, extend the service life of the structure, and reduce maintenance costs. For example, analysis of foundation stress can predict fatigue damage before visible cracks appear in the structure, allowing for proactive maintenance.

[0070] The data simulation module significantly reduces reliance on traditional intensive field monitoring by combining wave field distribution models with structural damage prediction models. The wave field model uses limited wind field data collected by offshore buoys to simulate and invert the distribution of wave parameters across the entire reef flat, replacing the traditional direct measurement method of deploying wave meters at key points on the reef flat, reducing the number of underwater sensor deployments and maintenance costs. The structural damage model combines reef flat wave parameters with vibration data from protective structures to avoid the need for dense installation of accelerometers on the entire surface of the building. While ensuring assessment accuracy, it greatly reduces the difficulty of equipment deployment and data collection costs in harsh deep-sea environments, extends the non-intervention operation period of the monitoring system, and reduces damage to the environment caused by human activities.

[0071] In some implementations, the data simulation module further includes a GPU parallel computing cluster configured to perform the following operations:

[0072] Joint compilation of wave numerical models, nearshore wave-current coupling models, and wave-foundation coupled dynamic response models;

[0073] When typhoon path forecast data is updated, grid computing tasks within a preset radius from the typhoon center are prioritized for allocation to the GPU parallel computing cluster. As the typhoon path changes, the GPU cluster rapidly recalculates wave parameters to ensure timely warnings. Furthermore, jointly compiling multiple models allows them to work efficiently together on the same cluster, reducing data transmission latency and improving overall computing efficiency.

[0074] A GPU parallel computing cluster is a hardware architecture that uses multiple GPUs for parallel processing to accelerate computing tasks. It jointly compiles and optimizes wave numerical models, nearshore wave-current coupling models, and wave-foundation coupling dynamic response models to achieve dynamic load balancing of computing tasks. When typhoon path forecast data is updated, the system automatically identifies the computing grids within the influence range of the typhoon center and prioritizes high-priority tasks to the GPU cluster for accelerated computing. The parallel computing framework adopts an unstructured grid partitioning strategy, combined with a dynamic memory allocation algorithm, to significantly improve the computational efficiency of large-scale wave field simulations. This design ensures real-time computing needs in extreme weather conditions such as typhoons through intelligent task scheduling and hardware acceleration.

[0075] A machine learning module, connected to the data simulation module, is used to dynamically generate warning threshold ranges based on historical monitoring data and real-time monitoring data within a preset time window. The warning threshold ranges include significant wave height thresholds, vibration main frequency deviation thresholds, and foundation settlement rate thresholds;

[0076] In some implementations, the specific steps of the machine learning module dynamically generating the warning threshold range include:

[0077] Obtain historical monitoring data sets within a preset time window, including significant wave height series, vibration main frequency offset series, and foundation settlement rate series;

[0078] Calculate the historical mean and standard deviation of each parameter, and determine the basic threshold based on the mean plus N times the standard deviation, where N is the preset safety factor;

[0079] Real-time collection of current effective wave height, vibration main frequency deviation and foundation settlement rate monitoring values. When any parameter deviates from the historical mean by more than a preset percentage, the corresponding threshold is increased or decreased in proportion to the deviation.

[0080] Compare the dynamically adjusted threshold with the design specification threshold, and take the smaller value as the final warning threshold;

[0081] When the real-time monitoring data exceeds the final warning threshold, the corresponding level of warning signal is activated.

[0082] The dynamic warning threshold generation mechanism uses sliding time window technology to continuously update the historical monitoring database. It performs time series analysis on parameters such as significant wave height, vibration main frequency offset, and foundation settlement rate. When a trend shift occurs in the real-time monitoring value, the system automatically adjusts the threshold sensitivity, effectively improving the environmental adaptability of the warning system and enhancing the accuracy of warnings to adapt to the complex and changing marine environment of small islands in the deep sea, reducing maintenance costs, and extending the system's autonomous operation time. For example, during typhoon season, significant wave heights may be higher than normal, and static thresholds may not be applicable. Therefore, it is necessary to dynamically adjust the warning threshold and optimize the warning sensitivity based on the current situation.

[0083] The early warning execution module is connected to the machine learning module and is used to trigger multi-level early warning signals when the reef flat wave parameters or the health information of the protective building exceed the early warning threshold range.

[0084] In some embodiments, the system further includes a visualization module for integrating real-time monitoring data, simulation results, and warning information for three-dimensional dynamic display.

[0085] In some implementations, the visualization module includes:

[0086] Digital twin engine, integrating multi-temporal remote sensing data with real-time monitoring results to construct a three-dimensional island model;

[0087] Disaster simulation interface, supporting interactive modification of typhoon paths and real-time prediction of coastline evolution trends;

[0088] The structural health monitoring dashboard displays the vibration spectrum and damage heat map of the protective building.

[0089] For example, the visualization module can also utilize a 3D geographic information system engine to integrate multi-temporal remote sensing imagery with real-time monitoring data to construct a digital twin. Disaster simulations support interactive modification of typhoon paths, providing real-time predictions of shoreline evolution trends under different scenarios. The data dashboard provides multi-dimensional statistical analysis charts and supports timeline playback of historical disaster events. A virtual reality interface creates an immersive observation environment, assisting managers in intuitively assessing the status of protective engineering structures.

[0090] The present invention provides a health monitoring system for small, deep-sea islands. It adopts a non-invasive data acquisition system consisting of a sensor network, shore-based cameras and vibration sensors, which realizes real-time monitoring of all elements of offshore environmental parameters, reef flat images and the vibration status of protective buildings, avoiding the interference of manual monitoring on the fragile island ecology. Secondly, through the joint calculation of the wave field distribution model and the structural damage prediction model, the measured data is combined with the numerical simulation, which significantly improves the spatiotemporal resolution of the reef flat wave parameters and the assessment accuracy of the health status of protective buildings. Furthermore, the warning threshold is dynamically adjusted based on the machine learning algorithm, breaking through the limitation that the traditional fixed threshold cannot adapt to the changes in the complex marine environment. Through real-time correlation of historical disaster data and deviation analysis of current monitoring values, intelligent optimization of warning indicators is realized, which greatly improves the timeliness and accuracy of extreme event warnings. Finally, through the automated triggering mechanism of multi-level warning signals, a closed-loop system from data acquisition, model deduction to risk decision-making is formed, providing all-weather, highly reliable safety monitoring guarantees for deep-sea islands, while reducing manual maintenance costs and having significant technical synergy effects. In short, by integrating multi-source data collection and model-based analysis and processing, the problems of low accuracy, ecological damage and high cost in traditional monitoring technologies have been effectively solved.

[0091] Another aspect of the present invention further provides a method for monitoring the health of small islands in the deep sea, which is implemented based on a system for monitoring the health of small islands in the deep sea, such as Figure 3 As shown, the method specifically includes:

[0092] S1: Data collection: collects real-time offshore marine environmental data from the island through a sensor network, obtains video image data of the reef flat area through shore-based cameras, and obtains vibration data of protective structures through vibration sensors;

[0093] S2: Data simulation: Generate reef flat wave parameters based on offshore marine environment data through the wave field distribution model, and output protective building health information through the structural damage prediction model combined with reef flat wave parameters and protective building vibration data;

[0094] S3: Dynamically generate warning threshold ranges based on historical monitoring data and real-time monitoring data within a preset time window. The warning threshold ranges include significant wave height thresholds, vibration main frequency deviation thresholds, and foundation settlement rate thresholds.

[0095] S4: Early warning triggering: when the reef flat wave parameters or the health information of the protective buildings exceed the early warning threshold range, a multi-level early warning signal is triggered.

[0096] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.

Claims

1. A deep sea small island health monitoring system, characterized in that: It includes data collection module, data simulation module, machine learning module and early warning execution module; The data collection module is used to collect real-time offshore marine environmental data of the island through a sensor network, obtain video image data of the reef flat area through a shore-based camera device, and obtain vibration data of the protective structure through a vibration sensor; The data simulation module is connected to the data collection module and includes a wave field distribution model and a structural damage prediction model. The wave field distribution model is used to generate reef flat wave parameters based on offshore marine environment data. The structural damage prediction model is used to combine the reef flat wave parameters and protective building vibration data to output protective building health information. The machine learning module is connected to the data simulation module and is used to dynamically generate a warning threshold range based on historical monitoring data and real-time monitoring data within a preset time window, wherein the warning threshold range includes a significant wave height threshold, a vibration main frequency deviation threshold, and a foundation settlement rate threshold; An early warning execution module, connected to the machine learning module, is used to trigger a multi-level early warning signal when the reef flat wave parameters or the protective building health information exceeds the early warning threshold range; The specific steps of the machine learning module dynamically generating the warning threshold range include: Acquire a historical monitoring data set within a preset time window, the data set including a significant wave height sequence, a vibration main frequency offset sequence, and a foundation settlement rate sequence; Calculate the historical mean and standard deviation of each parameter, and determine the basic threshold based on the mean plus N times the standard deviation, where N is the preset safety factor; Real-time collection of current effective wave height, vibration main frequency deviation and foundation settlement rate monitoring values. When any parameter deviates from the historical mean by more than a preset percentage, the corresponding threshold is increased or decreased in proportion to the deviation. Compare the dynamically adjusted threshold with the design specification threshold, and take the smaller value as the final warning threshold; When the real-time monitoring data exceeds the final warning threshold, the corresponding level of warning signal is activated.

2. The deep-sea small island health monitoring system according to claim 1, characterized in that: The data collection module includes: Vibration sensor arrays are buried inside protective buildings to form a self-organizing network; A shore-based panoramic camera system, deployed at the highest point on the island and equipped with a salt spray resistant protective cover; The buoy array is deployed in the offshore area of the island and integrates Doppler current meters and temperature, salinity and depth sensors.

3. The deep-sea small island health monitoring system according to claim 1, characterized in that: The operation of the wave field distribution model includes: Acquiring wind field data, inputting the wind field data into a wave numerical simulation model, and obtaining an offshore wave spectrum; The offshore wave spectrum is used as a boundary condition and input into a nearshore wave-current coupling model to calculate reef flat wave parameters, which include wave height, period and flow velocity distribution in the reef flat area.

4. The deep-sea small island health monitoring system according to claim 3, characterized in that: A wave-foundation coupled dynamic response model is integrated between the wave field distribution model and the structural damage prediction model. The wave-foundation coupled dynamic response model is used to: According to the reef flat wave parameters and foundation geological parameters, the foundation stress distribution is obtained; The foundation stress distribution is input into the structural damage prediction model, and the health information of the protective building is obtained by combining the reef flat wave parameters and the vibration data of the protective building.

5. The deep-sea small island health monitoring system according to claim 3 is characterized in that: The data simulation module also includes a GPU parallel computing cluster for performing the following operations: Jointly compiling the wave numerical model, the nearshore wave-current coupling model, and the wave-foundation coupling dynamic response model; When the typhoon path prediction data is updated, grid computing tasks within a preset radius area of the typhoon center are preferentially allocated to the GPU parallel computing cluster.

6. The deep-sea small island health monitoring system according to claim 1, characterized in that: The data collection module also includes: The bionic drone unit adopts a flapping-wing silent structure and is activated when the shoreline is abnormal, and its flight trajectory avoids ecologically sensitive areas; The bionic coral camera unit consists of a 3D-printed base and internal waterproof components. The surface texture of the base is consistent with the shape of the surrounding corals and has an integrated wireless charging module.

7. The deep-sea small island health monitoring system according to claim 1, characterized in that: The system also includes a visualization module for integrating real-time monitoring data, simulation results and early warning information for three-dimensional dynamic display.

8. The deep-sea small island health monitoring system according to claim 7, characterized in that: The visualization module includes: Digital twin engine, integrating multi-temporal remote sensing data with real-time monitoring results to construct a three-dimensional island model; Disaster simulation interface, supporting interactive modification of typhoon paths and real-time prediction of coastline evolution trends; The structural health monitoring dashboard displays the vibration spectrum and damage heat map of the protective building.

9. A method for monitoring the health of small islands in the deep sea, implemented based on the health monitoring system for small islands in the deep sea according to any one of claims 1 to 8, characterized in that: The method specifically includes: Data collection: Real-time data on the offshore marine environment of the island is collected through a sensor network, video data of the reef area is obtained through shore-based cameras, and vibration data of protective structures is obtained through vibration sensors; Data simulation: Generate reef flat wave parameters based on offshore marine environment data through a wave field distribution model. Output protective structure health information through a structural damage prediction model that combines reef flat wave parameters and protective structure vibration data. Dynamically generate warning threshold ranges based on historical monitoring data and real-time monitoring data within a preset time window. The warning threshold ranges include significant wave height thresholds, vibration main frequency deviation thresholds, and foundation settlement rate thresholds. Early warning triggering: when the reef flat wave parameters or the health information of the protective building exceed the early warning threshold range, a multi-level early warning signal is triggered.

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