A method and related equipment for health monitoring of coral reef facilities

Abstract

This invention discloses a method and related equipment for monitoring the health of coral reef facilities. The invention constructs a coral reef health index through weighted fusion of multi-temporal remote sensing images, enabling rapid quantitative assessment of ecological health driven by the proportion of reclaimed land. Simultaneously, the invention extracts facility-level deformation feature values ​​using temporal deformation fields and combines them with statistical benchmarks for deformation of the same landform type to determine anomaly indices, achieving high-precision mapping of facility health levels. Furthermore, through the synergistic correlation determination between the coral reef health index and facility health levels, the invention can automatically identify "reclamation-driven structural risk" disaster patterns and output tiered early warnings, significantly improving the comprehensiveness, quantification, and reliability of island and reef monitoring, and can be widely applied in the field of image processing technology.

Description

Technical Field

[0001] This invention relates to the field of image processing technology, and in particular to a method and related equipment for health monitoring of coral reef facilities. Background Technology

[0002] Coral islands and reefs, as an important component of marine ecosystems, possess irreplaceable value in maintaining biodiversity, safeguarding national security, and protecting coastal zones. In recent years, with the continuous intensification of human engineering activities such as land reclamation and dredging in coral island and reef areas, the natural topography and ecological environment of these islands and reefs have been significantly disturbed. Therefore, monitoring the health status of coral islands and reefs has become a core issue in the fields of marine ecological protection and island and reef engineering management.

[0003] At the macro-ecological monitoring level, existing studies mainly rely on multi-temporal high-resolution optical remote sensing images (such as Sentinel-2, GF-1, WorldView, etc.) or existing coral reef geomorphology datasets. By extracting coral reef geomorphology classification maps, they statistically analyze the area of ​​artificial reclamation areas and their proportion of the total island and reef area. However, the above methods can only provide a single area proportion indicator and lack a quantitative evaluation system that can comprehensively reflect the ecological disturbance of coral reefs, making it difficult to achieve rapid and large-scale health assessments.

[0004] At the micro-engineering monitoring level, with the large-scale construction of artificial facilities such as airport runways, port terminals, breakwaters, and permanent buildings on islands and reefs, and given that these facilities are mostly located in areas with complex engineering geological conditions such as reclamation areas and reef flat modification areas, the stability of their foundations directly affects the operational safety of the facilities and the safety of personnel and property. Existing technologies for surface deformation monitoring mainly employ leveling, GNSS, and InSAR techniques. For example, some technologies have implemented UAV-based InSAR surface deformation information detection, using UAVs equipped with lidar to acquire topographic data and constructing a physical constraint field based on rock mass integrity coefficients and mining directions for deformation monitoring in scenarios such as mines. However, this physical constraint field is based on mining plans and rock mass parameters, making it unsuitable for long-term health diagnosis of artificial facilities on coral islands and reefs.

[0005] In summary, existing technologies lack methods for comprehensively monitoring and diagnosing the health status of coral reefs from both macro-ecological disturbance and micro-engineering deformation dimensions. Summary of the Invention

[0006] The main objective of this invention is to provide a method, device, electronic device, storage medium, and program product for monitoring the health of coral island and reef facilities, aiming to solve at least one problem of the prior art.

[0007] To achieve the above objectives, one aspect of the present invention provides a method for health monitoring of coral reef facilities, the method comprising: Acquire multi-temporal remote sensing images and synthetic aperture radar images of the target islands and reefs; among them, the remote sensing images are marked with the area of ​​each landform type of coral reef, and the synthetic aperture radar images are marked with the vector outline of each infrastructure and its corresponding landform type. The percentage of area of ​​each landform type in each time phase was obtained from remote sensing image statistics; Based on the area percentage of all landform types, the coral reef health index for each time phase is obtained by weighted fusion. Based on the vector profile of synthetic aperture radar imagery, the deformation rate field is obtained by interferometric evolution through spatiotemporal baseline. Based on the deformation rate field, the deformation characteristic value of each infrastructure is obtained by transforming discrete deformation points; Based on the deformation characteristic values ​​and the corresponding landform type, the deformation statistical benchmark for each landform type is statistically obtained. Based on deformation characteristic values ​​and deformation statistical benchmarks, quantitative assessment of the same group deviation of various infrastructures in the same landform type is carried out to obtain the anomaly index of each infrastructure, and then the health level is determined by mapping. By coordinating and determining the health index and health level of coral reefs, the early warning level of the target island / reef can be obtained for early warning diagnosis and push notification.

[0008] In some embodiments, the coral reef health index for each time phase is obtained by weighted fusion based on the area percentage of all landform types, including the following steps: All landform types are divided into multiple coral growth levels; each coral growth level includes at least one landform type, and each coral growth level has a preset level weight. Based on the area percentage, the grade score of each landform type in each time phase is determined by a preset grading standard mapping. Based on the grade weights, the grade scores of all coral growth grades corresponding to the landform types are weighted and merged to obtain the coral reef health index for each time phase.

[0009] In some embodiments, based on grade weights, the grade scores of all coral growth grades corresponding to landform types are weighted and fused to obtain the coral reef health index for each time phase, including the following steps: The total score for each coral growth level is obtained by statistically analyzing the grade scores of all landform types in a given time period. The total score is determined by the sum of the scores for all landform types included in the coral growth grade. Based on the grade weights, the sum of the aggregate scores of all coral growth grades is weighted and summed to obtain the coral reef health index for the corresponding time phase. The expression for the coral reef health index is as follows:

[0010] In the formula, Indicates the health index of coral reefs. Indicates the first Each coral growth level This represents the total number of coral growth grades. Indicates the first The weighting of each coral growth level. Indicates the first Each landform type Indicates the first The total number of landform types included in each coral growth level Indicates the first The first of the coral growth levels Each landform type has a grade score.

[0011] In some embodiments, the deformation characteristic values ​​include average deformation rate, internal variation, and range. Based on the deformation rate field, the deformation characteristic values ​​of each infrastructure are obtained through discrete deformation point transformation, including the following steps: The first infrastructure in the target island / reef will be used as the quantification facility; Extract the deformation rate of each sampling point of the quantization facility from the deformation rate field; The average deformation rate of the quantization facility is obtained by summing and averaging the deformation rates at each sampling point of the quantization facility. The standard deviation of deformation of the quantification facility is obtained by summing and averaging the squared differences between the deformation rate and the average deformation rate at each sampling point of the quantification facility and taking the square root. The range of the quantization facility is obtained by the difference between the maximum and minimum deformation rates among all sampling points of the quantization facility. Take the next infrastructure in the target island as the quantization facility, and return to perform the step of extracting the deformation rate of each sampling point of the quantization facility from the deformation rate field until the deformation characteristic value of each infrastructure is obtained.

[0012] In some embodiments, deformation characteristic values ​​include average deformation rate, deformation standard deviation, and range; deformation statistical benchmarks include mean benchmark, standard deviation benchmark, median standard deviation benchmark, and median range benchmark. Based on the deformation characteristic values ​​and the corresponding landform type, the deformation statistical benchmark for each landform type is statistically obtained, including the following steps: Based on the landform type, the infrastructure is divided into various landform types, resulting in landform groups corresponding to each landform type; The first geomorphic group is designated as the statistical geomorphic group; The average deformation rate of each infrastructure in the statistical geomorphological group is accumulated and averaged to obtain the average benchmark of the corresponding geomorphological type. The standard deviation benchmark for the corresponding landform type is obtained by summing and averaging the squared differences between the average deformation rate of each infrastructure in the statistical geomorphological group and the average benchmark. The benchmark of the median standard deviation of the corresponding landform type is determined based on the median of the standard deviation of deformation of all infrastructure in the statistical landform group. The baseline for the median range of the corresponding landform type is determined based on the median range of all infrastructure in the statistical landform group. Take the next landform group as the statistical landform group, and return to perform the step of accumulating and averaging the average deformation rate of each infrastructure in the statistical landform group until the deformation statistical baseline for each landform type is obtained.

[0013] In some embodiments, deformation characteristic values ​​include average deformation rate, deformation standard deviation, and range; deformation statistical benchmarks include mean benchmark, standard deviation benchmark, median standard deviation benchmark, and median range benchmark. Based on the deformation characteristic values ​​and deformation statistical benchmarks, a quantitative assessment of the group deviation of various infrastructures within the same landform type is performed to obtain an anomaly index for each infrastructure. This includes the following steps: The first landform type in the target islands and reefs is used as the statistical landform type; The first infrastructure of the corresponding statistical geomorphological type is used as the assessment facility; Based on the deformation characteristic values ​​of the assessment facility and the deformation statistical benchmark of the statistical landform type, the anomaly index of the assessment facility is calculated by combining the preset weight coefficients. The expression for the anomaly index is:

[0014] In the formula, This represents the anomaly index of the j-th infrastructure. , , These are the weighting coefficients. This represents the average deformation rate of the j-th infrastructure. This represents the baseline of the average value for the k-th landform type. The standard deviation of the k-th landform type is represented by the reference value. This represents a preset infinitesimal constant. This represents the standard deviation of the deformation of the j-th infrastructure element. This represents the median of the standard deviation for the k-th landform type. This represents the range of the j-th infrastructure. This represents the baseline of the range median for the k-th landform type; Take the next infrastructure of the corresponding statistical landform type as the assessment facility, return to the execution step of calculating the anomaly index of the assessment facility based on the deformation characteristic value of the assessment facility and the deformation statistical benchmark of the statistical landform type, combined with the preset weight coefficient, until all infrastructures of the corresponding statistical landform type have been traversed. Take the next landform type in the target island as the statistical landform type, return to execute the step of taking the first infrastructure of the corresponding statistical landform type as the evaluation facility, until all infrastructures of all landform types have been traversed.

[0015] In some embodiments, the warning level of a target island / reef is determined by synergistic correlation between the coral reef health index and health level, including the following steps: The decrease in value for each period is quantified based on the coral reef health index over continuous time phases. Based on the numerical decline and the health level of the infrastructure in the corresponding period, condition matching is performed, and the warning level of the target island / reef is determined by mapping the result of the condition matching.

[0016] To achieve the above objectives, another aspect of the present invention provides a health monitoring device for coral reef facilities, the device comprising: The first module is used to acquire multi-temporal remote sensing images and synthetic aperture radar images of the target islands and reefs. The remote sensing images are marked with the area of ​​each landform type of coral reef, and the synthetic aperture radar images are marked with the vector outline of each infrastructure and its corresponding landform type. The second module is used to statistically determine the area percentage of various landform types in each time phase based on remote sensing images; The third module is used to obtain the coral reef health index for each time phase by assigning weighted fusion based on the area percentage of all landform types. The fourth module is used to obtain the deformation rate field by interferometric evolution of the vector profile based on synthetic aperture radar imagery through spatiotemporal baseline. The fifth module is used to obtain the deformation characteristic values ​​of each infrastructure by transforming discrete deformation points based on the deformation rate field; The sixth module is used to statistically obtain the deformation statistical benchmark for each landform type based on the deformation characteristic values ​​and the landform type to which it belongs; The seventh module is used to quantitatively assess the deviation of each infrastructure in the same landform type based on deformation characteristic values ​​and deformation statistical benchmarks, obtain the anomaly index of each infrastructure, and then map and determine the health level. The eighth module is used to collaboratively determine the warning level of the target island / reef based on the coral reef health index and health level, and to push the warning diagnosis.

[0017] To achieve the above objectives, another aspect of the present invention provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the aforementioned method.

[0018] To achieve the above objectives, another aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the aforementioned method.

[0019] To achieve the above objectives, another aspect of the present invention provides a computer program product, including a computer program that, when executed by a processor, implements the aforementioned method.

[0020] The embodiments of this invention include at least the following beneficial effects: This invention provides a method, device, electronic device, storage medium, and program product for monitoring the health of coral reef facilities. This solution acquires multi-temporal remote sensing images and synthetic aperture radar (SAR) images of the target reef. The remote sensing images are labeled with the area of ​​each landform type of the coral reef, and the SAR images are labeled with the vector outline of each infrastructure and its corresponding landform type. The area percentage of each landform type in each temporal phase is statistically obtained from the remote sensing images. Based on the area percentages of all landform types, a coral reef health index for each temporal phase is obtained through weighted fusion. The vector profile of synthetic aperture radar imagery is interferometrically evolved through a spatiotemporal baseline to obtain a deformation rate field. Based on the deformation rate field, the deformation characteristic value of each infrastructure is obtained by transforming discrete deformation points. Based on the deformation characteristic value and the corresponding landform type, a deformation statistical benchmark is statistically obtained for each landform type. Based on the deformation characteristic value and the deformation statistical benchmark, a quantitative assessment of the deviation of each infrastructure in the same landform type is performed to obtain the anomaly index of each infrastructure, which is then mapped to determine the health level. Based on the coral reef health index and health level, a collaborative correlation determination is made to obtain the early warning level of the target island / reef for early warning diagnosis and push notification. This invention constructs a coral reef health index through weighted fusion of multi-temporal remote sensing images, enabling rapid quantitative assessment of ecological health driven by the proportion of reclaimed areas. Simultaneously, this invention extracts facility-level deformation feature values ​​using temporal deformation fields and combines them with statistical benchmarks for deformation of the same landform type to determine anomaly indices, achieving high-precision mapping of facility health levels. Furthermore, through the synergistic correlation determination between the coral reef health index and facility health levels, this invention can automatically identify "reclamation-driven structural risk" disaster patterns and output tiered early warnings, significantly improving the comprehensiveness, quantification, and reliability of island and reef monitoring and early warnings. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of an implementation environment for the method of monitoring the health of coral reef facilities provided in this embodiment of the invention; Figure 2 This is a flowchart illustrating the method for monitoring the health of coral reef facilities provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the overall process of the coral reef facility health monitoring method provided in the embodiments of the present invention; Figure 4 This is a schematic diagram of the structure of the coral reef facility health monitoring device provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of the structure of the electronic device provided in an embodiment of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In the following description, when referring to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of this invention; they are merely examples of apparatuses and methods consistent with some aspects of the embodiments of this invention as detailed in the appended claims.

[0023] It is understood that the terms "first," "second," etc., used in this invention may be used to describe various concepts, but unless specifically stated otherwise, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another. For example, without departing from the scope of embodiments of this invention, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the words "if" or "when" as used herein may be interpreted as "when," "in response to determination," or "in the event of a determination."

[0024] The terms “at least one,” “multiple,” “each,” “any,” etc., used in this invention, “at least one” includes one, two, or more than two; “multiple” includes two or more than two; “each” refers to each of the corresponding multiple; and “any” refers to any one of the multiple.

[0025] Unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this invention is for descriptive purposes only and is not intended to limit the invention.

[0026] In related technologies, there is currently no hierarchical collaborative early warning and correlation diagnosis rule model that can collaboratively analyze macro-level land reclamation evaluation indicators and micro-level facility deformation indicators, and therefore cannot automatically identify and output the typical disaster mode of "land reclamation-driven structural risk".

[0027] In view of this, this invention provides a method and related equipment for monitoring the health of coral reef facilities. This method acquires multi-temporal remote sensing images and synthetic aperture radar (SAR) images of the target reef. The remote sensing images are labeled with the area of ​​each landform type of the coral reef, while the SAR images are labeled with the vector outline of each infrastructure and its corresponding landform type. The percentage of area for each landform type in each temporal phase is statistically obtained from the remote sensing images. Based on the percentage of area for all landform types, a weighted fusion is performed to obtain the coral reef health index for each temporal phase. The SAR images are then used to calculate the vector outline of each infrastructure and its corresponding landform type. The deformation rate field is obtained by interferometric evolution of the contour through spatiotemporal baseline. Based on the deformation rate field, the deformation characteristic value of each infrastructure is obtained by transforming discrete deformation points. Based on the deformation characteristic value and the corresponding landform type, the deformation statistical benchmark for each landform type is statistically obtained. Based on the deformation characteristic value and the deformation statistical benchmark, the deviation of each infrastructure in the same landform type is quantitatively assessed to obtain the anomaly index of each infrastructure, which is then mapped to determine the health level. Based on the coral reef health index and health level, a collaborative correlation judgment is made to obtain the early warning level of the target island reef for early warning diagnosis and push. This invention constructs a coral reef health index through weighted fusion of multi-temporal remote sensing images, enabling rapid quantitative assessment of ecological health driven by the proportion of reclaimed areas. Simultaneously, this invention extracts facility-level deformation feature values ​​using temporal deformation fields and combines them with statistical benchmarks for deformation of the same landform type to determine anomaly indices, achieving high-precision mapping of facility health levels. Furthermore, through the synergistic correlation determination between the coral reef health index and facility health levels, this invention can automatically identify "reclamation-driven structural risk" disaster patterns and output tiered early warnings, significantly improving the comprehensiveness, quantification, and reliability of island and reef monitoring and early warnings.

[0028] It is understood that the coral reef facility health monitoring method provided by this invention can be applied to any computer device with data processing and computing capabilities, and this computer device can be various types of terminals or servers. When the computer device in the embodiment is a server, the server is an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms. Optionally, the terminal can be a smartphone, tablet, laptop, or desktop computer, but it is not limited to these.

[0029] like Figure 1 The diagram shown is a schematic representation of an implementation environment provided by an embodiment of the present invention. (Refer to...) Figure 1 The implementation environment includes at least one terminal 102 and a server 101. The terminal 102 and the server 101 can be connected via a network, either wirelessly or via a wired connection, to complete data transmission and exchange.

[0030] Server 101 can be a standalone physical server, a server cluster or distributed system consisting of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.

[0031] Additionally, server 101 can also be a node server in a blockchain network. Blockchain is a novel application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms.

[0032] Terminal 102 can be a smartphone, tablet computer, laptop computer, desktop computer, smart speaker, smartwatch, etc., but is not limited to these. Terminal 102 and server 101 can be directly or indirectly connected via wired or wireless communication, and this embodiment of the invention does not impose any limitations.

[0033] For example, based on Figure 1 The implementation environment shown in this embodiment of the invention provides a method for monitoring the health of coral reef facilities. The following description uses the application of this method to server 101 as an example. It can be understood that this method can also be applied to terminal 102.

[0034] Reference Figure 2 , Figure 2 This is an optional flowchart of the coral reef facility health monitoring method provided in the embodiments of the present invention. The subject executing the coral reef facility health monitoring method can be any of the aforementioned computer devices (including servers or terminals). Figure 2 The method may include, but is not limited to, steps S100 to S800.

[0035] Step S100: Acquire multi-temporal remote sensing images and synthetic aperture radar images of the target islands and reefs; The remote sensing images are labeled with the area of ​​each type of coral reef landform, while the synthetic aperture radar images are labeled with the vector outline of each infrastructure and its corresponding landform type. For example, in some specific implementations, multi-temporal high-resolution remote sensing images of the target island reef (such as Sentinel-2, GF-1, WorldView, etc.) can be acquired. The annotation information of the remote sensing images can be obtained through the following preprocessing: interpreting remote sensing images from different periods separately to obtain the constructed areas of the artificially reclaimed area before, during, and after construction in multiple temporal phases. Coral reef geomorphological data comes from the Allen Coral Atlas, and the geomorphological types include: seaward reef slope, sheltered reef slope, reef top, outer reef flat, inner reef flat, platform, back reef slope, shallow lagoon, and deep lagoon. New geomorphological types are added: reclaimed area and sand island area, for a total of 11 geomorphological types. Furthermore, based on the pixels occupied by the pre-divided areas of each geomorphological type in the remote sensing images and the imaging ratio of the remote sensing images, the area of ​​each geomorphological type of coral reef in the target island reef can be obtained.

[0036] Furthermore, the names of infrastructure on and around the island are extracted according to the relevant content of nautical charts and topographic maps, standardizing the names and definitions of island and reef ancillary facilities. Island and reef infrastructure is categorized into: residential facilities and transportation facilities. Residential facilities include: temporary buildings and permanent buildings. Transportation facilities are divided into water transport facilities, shipping facilities, and land transport facilities. Water transport facilities are further divided into: 1. ship docks; 2. breakwaters; 3. ports. Shipping facilities are further divided into: 1. helicopter landing pads and airport runways. Land transport facilities include: 1. temporary roads; 2. paved roads; 3. railways. Vectorization is used to extract the infrastructure outlines, road surfaces, and building foundations. The main geometric type of the deformation monitoring objects for island and reef infrastructure is surface features. The annotation information of synthetic aperture radar (SAR) images can be obtained through the following preprocessing: Acquire multi-temporal SAR image datasets covering the target islands and reefs. Data sources include, but are not limited to: Sentinel-1 IW mode SLC data, TerraSAR-X SSC data, COSMO-SkyMed HDF5 data, RADARSAT-2 SLC data, or Gaofen-3 SLC data.

[0037] Simultaneously acquire three types of auxiliary data: (1) Precise orbit files: POD precise orbit ephemeris data corresponding to each satellite; (2) Digital Elevation Model (DEM): A high-precision DEM covering the study area. The steps for generating the island and reef DEM used for deformation monitoring are as follows: First, the island and reef DEM is generated using high-resolution remote sensing stereo image pairs; then, the vertical accuracy of the island and reef DEM is calibrated using ICESat-2 laser altimetry data passing through the island and reef to obtain a high-precision island and reef DEM. (3) Island and reef boundary vector: the study area range used for data cropping.

[0038] All raw SAR images are imported into a professional InSAR processing platform to perform data format conversion, radiometric calibration, and orbit correction preprocessing. They are also cropped according to the vector boundaries of islands and reefs to remove invalid marine data and reduce the amount of data processing.

[0039] Step S200: Calculate the area percentage of each landform type in each time phase based on remote sensing images. For example, in some specific implementations, the change in the area of ​​reclaimed land around coral reefs is determined based on images, for each time phase. Statistics on the area proportion of each landform type:

[0040] in, For the first Landforms in time phase area percentage, For the first Landforms in time phase area, For the first Landforms in time phase area, This represents the total number of landform types.

[0041] Step S300: Based on the area percentage of all landform types, the coral reef health index for each time phase is obtained by weighted fusion. It should be noted that in some embodiments, step S300 may include the following steps: dividing all landform types into multiple coral growth levels; each coral growth level includes at least one landform type, and each coral growth level has a preset level weight; based on the area percentage, determining the level score of each landform type in each time phase through a preset grading standard mapping; based on the level weight, weighting and fusing the level scores of all coral growth levels corresponding to the landform types to obtain the coral reef health index for each time phase.

[0042] For example, in some specific implementations, a weighting coefficient is assigned to each landform type based on its ecological sensitivity. (Weight range 0-1, with higher weights for landforms with better coral diversity and growth, such as seaward reef slopes and sheltered reef slopes, and lower weights for reclaimed areas, with inverse weights assigned.) A Coral Reef Health Index (ACI) suitable for artificialization was constructed. Evaluation levels were assigned based on the coral coverage of each landform type, and corresponding weights were assigned. Based on the area proportion of each type, they were divided into three levels: "I", "II", and "III". The grading criteria for each level vary depending on the characteristics of the landform type, as detailed in Table 1 below.

[0043] Table 1

[0044] It should be noted that, in some embodiments, the reef health index for each time phase is obtained by weighted fusion of the grade scores of all coral growth grades corresponding to the landform types based on grade weights. This may include the following steps: calculating the total score of each coral growth grade based on the grade scores of all landform types in a time phase; wherein, the total score is determined based on the sum of the grade scores of all landform types included in the coral growth grade; and weighted summing of the total scores of all coral growth grades based on grade weights to obtain the reef health index for the corresponding time phase.

[0045] For example, in some specific implementations, the expression for the Aquatic Reef Health Index (ACI) is:

[0046] In the formula, Indicates the health index of coral reefs. Indicates the first Each coral growth level This represents the total number of coral growth grades. Indicates the first The weighting of each coral growth level. Indicates the first Each landform type Indicates the first The total number of landform types included in each coral growth level Indicates the first The first of the coral growth levels Each landform type has a grade score.

[0047] Step S400: Based on the vector profile of the synthetic aperture radar image, interferometric evolution is performed through the spatiotemporal baseline to obtain the deformation rate field; In some specific implementations, interferometric pairs are generated based on synthetic aperture radar (SAR) imagery, with spatial baseline thresholds (45%–50% of the critical baseline) and temporal baseline thresholds (100–500 days). For each interferometric pair, image registration, interferogram generation, terrain phase removal, adaptive filtering, and phase unwrapping (coherence coefficient threshold 0.2–0.25) are performed sequentially. The deformation time series is inverted using singular value decomposition (SVD), and atmospheric delay phase is separated through spatiotemporal filtering. The results are geocoded into the WGS84 coordinate system to generate an annual average deformation rate raster map. (i.e., SBAS-InSAR deformation rate field, in mm / year) and deformation time series files. This deformation rate field will serve as input data for subsequent infrastructure deformation feature extraction.

[0048] Step S500: Based on the deformation rate field, the deformation characteristic value of each infrastructure is obtained by transforming discrete deformation points; It should be noted that the deformation characteristic values ​​include the average deformation rate, internal variation, and range. In some embodiments, step S500 may include the following steps: taking the first infrastructure in the target island as the quantification facility; extracting the deformation rate of each sampling point of the quantification facility from the deformation rate field; performing a cumulative average calculation on the deformation rate of each sampling point of the quantification facility to obtain the average deformation rate of the quantification facility; performing a cumulative average calculation and square root calculation on the square difference between the deformation rate of each sampling point of the quantification facility and the average deformation rate to obtain the deformation standard deviation of the quantification facility; obtaining the range of the quantification facility based on the difference between the largest and smallest deformation rates among all sampling points of the quantification facility; taking the next infrastructure in the target island as the quantification facility, and returning to the step of extracting the deformation rate of each sampling point of the quantification facility from the deformation rate field until the deformation characteristic value of each infrastructure is obtained.

[0049] For example, in some specific implementations, the extraction of infrastructure deformation feature values ​​can be achieved as follows: Objective: To transform discrete deformation points into representative values ​​for each individual infrastructure unit; For the j-th infrastructure unit, the set of deformation monitoring points it covers is:

[0050] in, For the first The planar coordinates of each monitoring point For the first The deformation rate (unit: mm / year) at each monitoring point, with positive values ​​indicating uplift and negative values ​​indicating settlement.

[0051] The number of monitoring points contained within facility j; Define the deformation characteristic value of this facility:

[0052]

[0053]

[0054] Average deformation rate (reflecting the overall settlement / uplift trend), unit: mm / year; Internal variation (standard deviation of deformation, reflecting uneven settlement), unit: mm / year; Range (reflects the maximum deformation difference), unit: mm / year.

[0055] Step S600: Based on the deformation characteristic values ​​and the landform type, statistical benchmarks for deformation of each landform type are obtained. It should be noted that the deformation characteristic values ​​include the average deformation rate, deformation standard deviation, and range. The deformation statistical benchmark includes the mean benchmark, standard deviation benchmark, median standard deviation benchmark, and median range benchmark. In some embodiments, step S600 may include the following steps: classifying the infrastructure into various landform types based on their respective landform types to obtain a landform group corresponding to each landform type; taking the first landform group as a statistical landform group; performing a cumulative average calculation on the average deformation rate of each infrastructure in the statistical landform group to obtain the mean benchmark for the corresponding landform type; and performing a cumulative average calculation on the average deformation rate of each infrastructure in the statistical landform group to obtain the mean benchmark for the corresponding landform type. The average deformation rate is summed and averaged, and the square root is taken to obtain the standard deviation benchmark for the corresponding landform type. The median standard deviation benchmark for the corresponding landform type is determined based on the median of the deformation standard deviation of all infrastructure in the statistical landform group. The median range benchmark for the corresponding landform type is determined based on the median of the range of all infrastructure in the statistical landform group. The next landform group is taken as the statistical landform group, and the step of summing and averaging the average deformation rate of each infrastructure in the statistical landform group is repeated until the deformation statistical benchmark for each landform type is obtained.

[0056] For example, in some specific implementations, establishing a deformation statistical benchmark for each landform type can be achieved as follows: The input is the average deformation rate of each facility. The geomorphological classification data G determines the geomorphological type to which each facility belongs.

[0057] Based on geomorphological classification data, all facilities are divided into K geomorphological groups. , ,..., .

[0058] For the Group( Calculate the statistical parameters of the group (e.g., 1, 2, ..., K).

[0059]

[0060]

[0061]

[0062] in: : No. The arithmetic mean of the average deformation rate of facilities in the group of landforms; : No. Group of landforms; : No. The total number of facilities contained within the landform group; : No. The standard deviation of the average deformation rate of facilities on a group of landforms; Standard deviation of deformation of all facilities within the k-th geomorphic group The median is used to characterize the normal level of uneven settlement within the facilities of this landform group; The deformation range of all facilities within the kth geomorphic group The median is used to characterize the normal level of maximum variation within facilities in this landform group.

[0063] Step S700: Based on deformation characteristic values ​​and deformation statistical benchmarks, quantitative assessment of the same group deviation of each infrastructure in the same landform type is carried out to obtain the anomaly index of each infrastructure, and then the health level is determined by mapping. It should be noted that deformation characteristic values ​​include average deformation rate, deformation standard deviation, and range. Deformation statistical benchmarks include mean benchmark, standard deviation benchmark, median standard deviation benchmark, and median range benchmark. In some embodiments, quantitative assessment of the same group deviation of various infrastructures in the same landform type is performed based on deformation characteristic values ​​and deformation statistical benchmarks to obtain an anomaly index for each infrastructure. This may include the following steps: taking the first landform type in the target island as the statistical landform type; taking the first infrastructure of the statistical landform type as the assessment facility; calculating the anomaly index of the assessment facility based on the deformation characteristic value of the assessment facility and the deformation statistical benchmark of the statistical landform type, combined with a preset weighting coefficient; taking the next infrastructure of the statistical landform type as the assessment facility, and returning to execute the step of calculating the anomaly index of the assessment facility based on the deformation characteristic value of the assessment facility and the deformation statistical benchmark of the statistical landform type, combined with a preset weighting coefficient, until all infrastructures of the statistical landform type have been traversed; taking the next landform type in the target island as the statistical landform type, and returning to execute the step of taking the first infrastructure of the statistical landform type as the assessment facility, until all infrastructures of all landform types have been traversed.

[0064] For example, in some specific implementations, the degree to which an individual facility deviates from the normal level of its group is quantitatively assessed:

[0065] Abnormal index, facilities To standardize the statistical scale of the abnormality index and determine the degree of deviation from the normal level of the same group, this invention performs normalization processing on the weighted sum. :facility Average deformation rate (unit: mm / year); : Mean deformation within the group, the deformation benchmark (average benchmark) of geomorphic group k to which facility j belongs; Within-group standard deviation of deformation, facilities Belonging to the geomorphological group Normal band range (standard deviation baseline); : A tiny constant to prevent division by zero (value 0.0001 mm / year); Standard deviation of the internal deformation rate of facility j (unit: mm / year); All facilities within terrain group k The median (standard deviation median benchmark); : Range of internal deformation rate of facility j (mm / year); All facilities within terrain group k The median (baseline of range median); , , Weighting coefficients, satisfying + + =1, which can be adjusted according to the type of facility.

[0066] In addition, facility health level settings can achieve the following: Infrastructure health level mapping function:

[0067] Facility anomaly level, status classification of facility j.

[0068] Step S800: Based on the coral reef health index and health level, a collaborative correlation determination is made to obtain the warning level of the target island / reef for early warning diagnosis and push. It should be noted that in some embodiments, the early warning level of the target island / reef is determined by coordinating the coral reef health index and health level. This may include the following steps: quantifying the numerical decline of the coral reef health index for each period based on continuous time phases; performing condition matching based on the numerical decline and the health level of the infrastructure in the corresponding period; and determining the early warning level of the target island / reef based on the result of the condition matching.

[0069] For example, in some specific implementations, the hierarchical collaborative early warning and associated diagnosis rules can be implemented as follows, as shown in Table 2 below: The warning levels are set from strongest to weakest as follows: red, orange, yellow, and blue. Table 2

[0070] To explain in detail the principle of the technical solution of the present invention, the overall process of the present invention will be described below with reference to some specific embodiments. It is easy to understand that the following is an explanation of the technical principle of the present invention and should not be regarded as a limitation of the present invention.

[0071] First, it should be noted that the artificial construction of coral reefs typically uses dry reefs and sand islands as a foundation. There are two main approaches: one is to upgrade existing facilities directly on the existing reefs; the other is to create artificial islands through land reclamation projects, constructing a larger land platform. Based on this, various infrastructures are systematically deployed, including navigational aids such as lighthouses and beacons, transportation hubs such as ports, docks, and airport runways, as well as supporting production and living facilities such as buildings and parks.

[0072] However, current research on the health of coral reefs under artificial construction still faces significant technological gaps, specifically a lack of systematic assessment of the overall health status of the reefs. On the one hand, existing studies often focus on single-dimensional monitoring, such as using remote sensing images to extract the proportion of reclaimed land area to reflect the degree of ecological disturbance, or monitoring the deformation of artificial islands to assess their local stability. On the other hand, these methods often separate ecological disturbance from engineering safety, failing to establish the intrinsic link between the two. In fact, large-scale land reclamation, reef flat modification, and other artificial construction not only directly alter the natural topography and ecological structure of reefs (macro level), but also trigger engineering geological problems such as foundation settlement and slope instability, thereby threatening the long-term operational safety of artificial facilities (micro level). However, current technologies lack a unified health diagnostic framework that can analyze macro-level land reclamation evaluation indicators (such as the proportion of reclaimed land) and micro-level facility deformation monitoring data, thus failing to reveal the overall impact of artificial construction on the co-evolution of coral reef ecosystems and engineering systems. Therefore, it is urgent to construct a technical solution that integrates macro-level land reclamation evaluation indicators with micro-level facility deformation monitoring under the background of artificial construction, and to establish a corresponding hierarchical collaborative early warning and correlation diagnosis rule model, so as to achieve accurate identification and early warning of land reclamation-driven structural risks, thereby improving the collaborative management capabilities of coral island and reef ecological security and engineering safety.

[0073] To address the shortcomings of existing technologies, this invention primarily focuses on two aspects of artificial coral reef construction to periodically assess the health of coral reefs: the impact of reclamation on the reef's condition and the deformation monitoring of infrastructure on the reef. The overall process of the coral reef facility health monitoring method is as follows: Figure 3 As shown, in some specific application scenarios, the technical solution of this invention can be implemented through the following process steps: Step 1: Obtain coral reef geomorphological classification data; High-resolution remote sensing images of the target islands and reefs (such as Sentinel-2, GF-1, WorldView, etc.) were acquired over multiple time periods. These images were then interpreted to obtain the constructed areas before, during, and after the artificial reclamation of the sea area. Coral reef geomorphology data were obtained from the Allen Coral Atlas, including: seaward reef slope, sheltered reef slope, reef top, outer reef flat, inner reef flat, platform, back reef slope, shallow lagoon, and deep lagoon. New geomorphologies were added: reclamation area and sand island area, for a total of 11 geomorphic types.

[0074] Step 2: Calculate the proportion of different landform types; Determining changes in the area of ​​reclaimed land around coral reefs based on imagery, for each time phase. Statistics on the area proportion of each landform type:

[0075] in, For the first Landforms in time phase area percentage, For its area, The total number of landform types is 11.

[0076] Step 3: Construct a coral reef health index; Based on the ecological sensitivity of landform types, a weighting coefficient is assigned to each landform type. (Weight range 0-1, with higher weights for landforms with better coral diversity and growth, such as seaward reef slopes and sheltered reef slopes, and lower weights for reclaimed areas, with inverse weights applied.) A Coral Reef Health Index (ACI, Coral Reef Health Index in the Context of Artificialization) was constructed to suit the context of artificialization. Evaluation levels were assigned based on the coral coverage of each landform type, and corresponding weights were assigned. Based on the area proportion of each type, they were divided into three levels: "I", "II", and "III". The grading criteria for each level vary depending on the characteristics of the landform type, as detailed in Table 1. The ACI calculation formula is as follows:

[0077] In the formula, The coral growth level (values ​​range from 1 to 7). For grade weight, For the first The number of landform types in the grade, The grade scores for each landform type are as follows (Level I: 100; Level II: 50; Level III: 10). The score ranges from 0 to 100, with higher scores indicating better coral reef health.

[0078] Step 4: Construct infrastructure classification and vectorization; The names of infrastructure on and around the island are extracted based on relevant information from nautical charts and topographic maps, standardizing the names and definitions of island and reef ancillary facilities. Island and reef infrastructure is categorized into: residential facilities and transportation facilities. Residential facilities include temporary and permanent buildings. Transportation facilities are divided into water transport facilities, shipping facilities, and land transport facilities. Water transport facilities include: 1. wharves; 2. breakwaters; 3. ports. Shipping facilities include: 1. helicopter landing pads and airport runways. Land transport facilities include: 1. temporary roads; 2. paved roads; 3. railways.

[0079] Vectorization extracts infrastructure outlines, road surfaces, and building foundations. The main geometric type of the objects used for deformation monitoring of island and reef infrastructure is surface features.

[0080] Step 5: Preparation of multi-source SAR image data; Acquire multi-temporal SAR image datasets covering the target islands and reefs. Data sources include, but are not limited to: Sentinel-1 IW mode SLC data, TerraSAR-X SSC data, COSMO-SkyMed HDF5 data, RADARSAT-2 SLC data, or Gaofen-3 SLC data.

[0081] Simultaneously acquire three types of auxiliary data: (1) Precise orbit files: POD precise orbit ephemeris data corresponding to each satellite; (2) Digital Elevation Model (DEM): A high-precision DEM covering the study area. The steps for generating the island and reef DEM used for deformation monitoring are as follows: First, the island and reef DEM is generated using high-resolution remote sensing stereo image pairs; then, the vertical accuracy of the island and reef DEM is calibrated using ICESat-2 laser altimetry data passing through the island and reef to obtain a high-precision island and reef DEM. (3) Island and reef boundary vector: the study area range used for data cropping.

[0082] All raw SAR images are imported into a professional InSAR processing platform to perform data format conversion, radiometric calibration, and orbit correction preprocessing. They are also cropped according to the vector boundaries of islands and reefs to remove invalid marine data and reduce the amount of data processing.

[0083] Step 6: SBAS-InSAR core processing and deformation field generation; Based on preprocessed image data, interferometric pairs were generated by setting spatial baseline thresholds (45%–50% of the critical baseline) and temporal baseline thresholds (100–500 days). For each interferometric pair, image registration, interferogram generation, topographic phase removal, adaptive filtering, and phase unwrapping (coherence coefficient threshold 0.2–0.25) were performed sequentially. Singular value decomposition (SVD) was used to invert the deformation time series, and atmospheric delayed phase was separated through spatiotemporal filtering. The results were geocoded into the WGS84 coordinate system to generate an annual average deformation rate raster map. (i.e., SBAS-InSAR deformation rate field, in mm / year) and deformation time series files. This deformation rate field will serve as input data for subsequent infrastructure deformation feature extraction.

[0084] Step 7: Extraction of infrastructure deformation feature values; Objective: To transform discrete deformation points into representative values ​​for each individual infrastructure unit; For the j-th infrastructure unit, the set of deformation monitoring points it covers is:

[0085] in, For the first The planar coordinates of each monitoring point For the first The deformation rate (unit: mm / year) at each monitoring point, with positive values ​​indicating uplift and negative values ​​indicating settlement.

[0086] The number of monitoring points contained within facility j; Define the deformation characteristic value of this facility:

[0087]

[0088]

[0089] Average deformation rate (reflecting the overall settlement / uplift trend), unit: mm / year; Internal variation (standard deviation of deformation, reflecting uneven settlement), unit: mm / year; Range (reflects the maximum deformation difference), unit: mm / year.

[0090] Select different features based on facility type: For extended facilities such as runways and roads: Special attention should be paid to... (Very poor), because uneven settlement has the greatest impact on operational safety; For buildings, docks, and other facilities: Special attention should be paid to... (Average value), reflecting the overall settlement trend; For breakwaters, revetments, and other similar facilities: (in conjunction with...) (Standard deviation) assesses the consistency of settlement along the route.

[0091] Step 8: Statistical reference system for geomorphological grouping; Establish deformation statistical benchmarks for each landform type enter: Step 7: Calculation of the average deformation rate of each facility The geomorphological classification data G determines the geomorphological type to which each facility belongs.

[0092] Based on geomorphological classification data, all facilities are divided into K geomorphological groups. , ,..., .

[0093] For the Group( Calculate the statistical parameters of the group (e.g., 1, 2, ..., K).

[0094]

[0095]

[0096]

[0097] in: : No. The arithmetic mean of the average deformation rate of facilities in the group of landforms; : No. Group of landforms; : No. The total number of facilities contained within the landform group; : No. The standard deviation of the average deformation rate of facilities on a group of landforms; Standard deviation of deformation of all facilities within the k-th geomorphic group The median is used to characterize the normal level of uneven settlement within the facilities of this landform group; The deformation range of all facilities within the kth geomorphic group The median is used to characterize the normal level of maximum variation within facilities in this landform group.

[0098] List of landform types (11 categories in total): 1. Seaward reef slope; 2. Sheltering reef slope; 3. Reef top; 4. Outer reef flat; 5. Inner reef flat; 6. Terrace; 7. Back reef slope; 8. Shallow lagoon; 9. Deep lagoon; 10. Reclaimed area (artificially reclaimed area); 11. Sand island area (natural sandy island).

[0099] Step 9: Facility Anomaly Index; Quantitatively assess the degree to which an individual facility deviates from the normal level of its group:

[0100] Abnormal index, facilities To standardize the statistical scale of the abnormality index and determine the degree of deviation from the normal level of the same group, this invention performs normalization processing on the weighted sum. :facility Average deformation rate (unit: mm / year); Mean deformation within the group, the deformation benchmark of geomorphic group k to which facility j belongs; Within-group standard deviation of deformation, facilities Belonging to the geomorphological group The normal band range; : A tiny constant to prevent division by zero (value 0.0001 mm / year); Standard deviation of the internal deformation rate of facility j (unit: mm / year); All facilities within terrain group k the median; : Range of internal deformation rate of facility j (mm / year); All facilities within terrain group k the median; , , Weighting coefficients, satisfying + + =1, which can be adjusted according to the type of facility.

[0101] Table 3 below shows examples of weight settings for some facility types: Table 3

[0102] Step 10, Facility health level setting; Infrastructure health level mapping function:

[0103] Facility anomaly level, status classification of facility j.

[0104] Step 11: Hierarchical collaborative early warning and correlation diagnosis rule model; Referring to Table 2 above, the warning levels are set from strongest to weakest as follows: red, orange, yellow, and blue. In some specific application scenarios, let's assume that the calculation example of the ACI index for coral reef A is shown in Table 4 below: Table 4

[0105] Assume the following table 5 shows an example of calculating the facility anomaly index in island A: Table 5

[0106] The early warning and diagnosis results for island A are as follows: A red alert has been triggered. The diagnosis includes: a 12.84% quarterly decline in the ACI index; abnormalities in runway A, pier C, and breakwater D facilities, with the proportion of abnormal facilities ≥100%. This indicates a severe structural risk driven by land reclamation, and immediate intervention is recommended.

[0107] In summary, this invention performs a correlation analysis between dynamic monitoring of reclamation areas and deformation monitoring of artificial facilities. First, a geomorphic health assessment index is calculated based on changes in the reclamation area. Simultaneously, InSAR deformation monitoring is conducted on key facilities (wharves, runways, etc.) in different geomorphic units to obtain facility deformation anomaly indices. By establishing a hierarchical collaborative early warning and correlation diagnosis rule model, a mapping correlation rule is established between the state combination of macro-health indicators and micro-deformation indices and the early warning level and diagnostic conclusion, achieving diagnostic output for reclamation-driven structural risks. Specifically, existing ecological assessment methods largely rely on field surveys, which are costly, time-consuming, and difficult to cover remote islands and reefs. Geomorphic classification data alone cannot quantify the impact of artificial construction on reefs, and there is a lack of quantitative evaluation indicators (such as the geomorphic health assessment ACI of this invention) from "reclamation disturbance" to "geomorphic health." Furthermore, traditional deformation monitoring methods struggle to achieve long-term, high-precision diagnosis at the facility level, and there is a lack of methods to correlate InSAR deformation monitoring results with infrastructure vector data and coral reef geomorphic classification data. To this end, this invention calculates the anomaly index of each facility through a geomorphological grouping statistical reference system to achieve facility-level health diagnosis. Furthermore, it establishes a hierarchical collaborative early warning and correlation diagnosis rule model, maps the macroscopic ACI with the microscopic facility deformation in a state combination, and outputs four-level early warning (blue, yellow, orange, and red) and differentiated diagnostic conclusions such as "reclamation-driven structural risk".

[0108] Compared with the prior art, the present invention has at least the following beneficial effects: 1. A macro-micro dual-dimensional collaborative monitoring framework for coral island and reef health was constructed: In existing technologies, coral reef monitoring either focuses on ecology / geomorphology (macro) or on engineering facility deformation (micro), with the two being disconnected. This invention is the first to incorporate geomorphic health assessment indicators based on multi-temporal high-resolution optical remote sensing and multi-facility deformation anomaly indices based on InSAR into a unified analytical framework, realizing full-chain monitoring from land reclamation disturbance to facility structural response, and filling the technological gap in overall island and reef health analysis under the background of artificial construction.

[0109] 2. A hierarchical collaborative early warning and correlation diagnosis rule model was proposed: This invention establishes a hierarchical collaborative early warning and correlation diagnosis rule model. This model defines state combination rules between macro and micro indicators, classifies early warning levels into four levels—blue, yellow, orange, and red—from weak to strong, and establishes a precise mapping relationship from indicator anomalies to early warning levels and then to differentiated diagnostic conclusions. This model overcomes the shortcomings of traditional single-indicator early warning systems, which are prone to false alarms, and multi-indicator early warning rules, which are often ambiguous, achieving a hierarchical and refined expression of island and reef risks.

[0110] 3. A composite early warning trigger condition based on the proportion of facility anomalies and the level of danger was designed: To address the comprehensive assessment of multiple deformed facilities, this invention employs a dual judgment standard combining facility anomaly index grading (normal, watchful, abnormal, hazardous) with the proportion of abnormal facilities. By setting mutually exclusive conditions such as "at least one hazardous facility exists" and "the proportion of abnormal facilities is ≥30%", it achieves differentiation of different levels of risk diffusion (e.g., individual hazards vs. group anomalies), making the early warning logic more scientific and avoiding overlapping conditions.

[0111] 4. Achieved spatiotemporal collaborative analysis of optical remote sensing and InSAR: This invention employs a multi-scale nested strategy of quarterly macro-assessment and micro-monitoring in terms of time, and utilizes geomorphological zoning to link the land reclamation disturbance area with the geomorphological unit where the facility is located. This spatiotemporal collaborative mechanism provides a data foundation for the correlation model, enabling early warning not only to be based on the current state but also to implicitly include causal temporal relationships.

[0112] like Figure 4 As shown, this embodiment of the invention also provides a coral reef facility health monitoring device 900, which can implement the above-described method. This device may include: The first module 901 is used to acquire multi-temporal remote sensing images and synthetic aperture radar images of the target islands and reefs; wherein, the remote sensing images are marked with the area of ​​each landform type of coral reef, and the synthetic aperture radar images are marked with the vector outline of each infrastructure and its corresponding landform type. The second module 902 is used to obtain the area percentage of various landform types in each time phase based on remote sensing images. The third module 903 is used to obtain the coral reef health index for each time phase by assigning weighted fusion based on the area percentage of all landform types. The fourth module 904 is used to obtain the deformation rate field by interferometric evolution of the vector profile based on synthetic aperture radar imagery through spatiotemporal baseline. The fifth module 905 is used to obtain the deformation characteristic value of each infrastructure by transforming discrete deformation points based on the deformation rate field; Module 6, 906, is used to statistically obtain the deformation statistical benchmark for each landform type based on deformation characteristic values ​​and the landform type to which it belongs; Module 7, 907, is used to quantitatively assess the deviation of each infrastructure in the same landform type based on deformation characteristic values ​​and deformation statistical benchmarks, obtain the anomaly index of each infrastructure, and then map and determine the health level. Module 8, 908, is used to make collaborative judgments based on the coral reef health index and health level to obtain the early warning level of the target island reef for early warning diagnosis and push notification.

[0113] It is understood that the content of the above method embodiments is applicable to the present device embodiments. The specific functions implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0114] This invention also provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the method described above. This electronic device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0115] It is understood that the content of the above method embodiments is applicable to this device embodiment. The specific functions implemented by this device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0116] like Figure 5 As shown, Figure 5 The hardware structure of an electronic device 1000 according to another embodiment is illustrated. The electronic device 1000 includes: The processor 1001 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (aSIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present invention. The memory 1002 can be implemented as a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RaM). The memory 1002 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1002 and is called and executed by the processor 1001. Input / output interface 1003 is used to implement information input and output; The communication interface 1004 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.). Bus 1005 transmits information between various components of the device (e.g., processor 1001, memory 1002, input / output interface 1003, and communication interface 1004); The processor 1001, memory 1002, input / output interface 1003 and communication interface 1004 are connected to each other within the device via bus 1005.

[0117] The electronic device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0118] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the above-described method.

[0119] It is understood that the content of the above method embodiments is applicable to this storage medium embodiment. The specific functions implemented in this storage medium embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0120] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method.

[0121] It is understood that the content of the above method embodiments is applicable to the embodiments of this program product. The specific functions implemented by the embodiments of this program product are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0122] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0123] The coral reef facility health monitoring method, device, electronic device, storage medium, and program product provided in this invention acquire multi-temporal remote sensing images and synthetic aperture radar (SAR) images of the target reef. The remote sensing images are labeled with the area of ​​each landform type of the coral reef, while the SAR images are labeled with the vector outline of each infrastructure and its corresponding landform type. The percentage of area for each landform type in each temporal phase is statistically obtained from the remote sensing images. Based on the percentage of area for all landform types, a coral reef health index for each temporal phase is obtained through weighted fusion. The SAR images are then used to... The vector profile is obtained by interferometric evolution through a spatiotemporal baseline to obtain the deformation rate field. Based on the deformation rate field, the deformation characteristic value of each infrastructure is obtained by transforming discrete deformation points. Based on the deformation characteristic value and the corresponding landform type, the deformation statistical benchmark for each landform type is statistically obtained. Based on the deformation characteristic value and the deformation statistical benchmark, the deviation of each infrastructure in the same landform type is quantitatively assessed to obtain the anomaly index of each infrastructure, which is then mapped to determine the health level. Based on the coral reef health index and health level, a collaborative correlation judgment is made to obtain the early warning level of the target island reef for early warning diagnosis and push. This invention constructs a coral reef health index through weighted fusion of multi-temporal remote sensing images, enabling rapid quantitative assessment of ecological health driven by the proportion of reclaimed areas. Simultaneously, this invention extracts facility-level deformation feature values ​​using temporal deformation fields and combines them with statistical benchmarks for deformation of the same landform type to determine anomaly indices, achieving high-precision mapping of facility health levels. Furthermore, through the synergistic correlation determination between the coral reef health index and facility health levels, this invention can automatically identify "reclamation-driven structural risk" disaster patterns and output tiered early warnings, significantly improving the comprehensiveness, quantification, and reliability of island and reef monitoring and early warnings.

[0124] The embodiments described in this invention are for the purpose of more clearly illustrating the technical solutions of the embodiments of this invention, and do not constitute a limitation on the technical solutions provided by the embodiments of this invention. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this invention are also applicable to similar technical problems.

[0125] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present invention, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0126] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0127] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0128] The preferred embodiments of the present invention have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and spirit of the present invention should be within the scope of the claims of the present invention.

Claims

1. A method for monitoring the health of coral island and reef facilities, characterized in that, The method includes the following steps: Acquire multi-temporal remote sensing images and synthetic aperture radar images of the target islands and reefs; wherein, the remote sensing images are marked with the area of ​​each landform type of coral reef, and the synthetic aperture radar images are marked with the vector outline of each infrastructure and its corresponding landform type. The area percentage of each landform type in each time phase was obtained statistically from the remote sensing images. Based on the area percentage of all the aforementioned landform types, the coral reef health index for each time phase is obtained through weighted fusion. Based on the vector profile of the synthetic aperture radar image, the deformation rate field is obtained by interferometric evolution through the spatiotemporal baseline. Based on the deformation rate field, the deformation characteristic value of each of the infrastructures is obtained by transforming discrete deformation points; Based on the deformation characteristic values ​​and the corresponding landform type, a deformation statistical benchmark is obtained for each of the landform types. Based on the deformation characteristic values ​​and the deformation statistical benchmark, a quantitative assessment of the deviation of each of the infrastructures in the same landform type is performed to obtain the anomaly index of each infrastructure, and then the health level is determined by mapping. Based on the coral reef health index and the health level, a collaborative correlation determination is made to obtain the warning level of the target island reef for early warning diagnosis and push notification. The deformation characteristic values ​​include the average deformation rate, deformation standard deviation, and range. The process of obtaining the deformation characteristic values ​​of each infrastructure element based on the deformation rate field through discrete deformation point transformation includes the following steps: The first infrastructure in the target island / reef is used as a quantified facility; Extract the deformation rate of each sampling point of the quantization facility from the deformation rate field; The average deformation rate of the quantization facility is obtained by summing and averaging the deformation rates at each sampling point of the quantization facility. The standard deviation of the deformation of the quantification facility is obtained by summing and averaging the squared difference between the deformation rate and the average deformation rate at each sampling point of the quantification facility and taking the square root. The range of the quantization facility is obtained by the difference between the maximum and minimum deformation rates among all sampling points of the quantization facility. Take the next infrastructure in the target island as the quantification facility, and return to the step of extracting the deformation rate of each sampling point of the quantification facility from the deformation rate field until the deformation characteristic value of each infrastructure is obtained; The process of obtaining the coral reef health index for each time phase by weighted fusion based on the area percentages of all the landform types includes the following steps: All the aforementioned landform types are divided into multiple coral growth levels; each coral growth level includes at least one of the aforementioned landform types, and each coral growth level has a preset level weight. Based on the area percentage, the grade score of each landform type in each time phase is determined by a preset grading standard mapping. Based on the grade weights, the grade scores of all the coral growth grades corresponding to the landform types are weighted and fused to obtain the coral reef health index for each time phase; The step of weightedly fusing the grade scores of all coral growth grades corresponding to the landform type to obtain the coral reef health index for each time phase, based on the grade weights, includes the following steps: The total score for each coral growth level is obtained by statistically analyzing the grade scores of all the landform types in a given time period; The total score is determined based on the sum of the score scores for all the landform types included in the coral growth grade; Based on the grade weights, the sum of the aggregate scores for all the coral growth grades is weighted and summed to obtain the coral reef health index for the corresponding time phase. The expression for the coral reef health index is as follows: In the formula, Indicates the health index of coral reefs. Indicates the first Each coral growth level This represents the total number of coral growth grades. Indicates the first The weighting of each coral growth level. Indicates the first Each landform type Indicates the first The total number of landform types included in each coral growth level Indicates the first The first of the coral growth levels Each landform type has a grade score.

2. The method according to claim 1, characterized in that, The deformation characteristic values ​​include the average deformation rate, deformation standard deviation, and range. The deformation statistical benchmarks include the mean benchmark, standard deviation benchmark, median standard deviation benchmark, and median range benchmark. The step of quantitatively assessing the group deviation of each infrastructure within the same landform type based on the deformation characteristic values ​​and the deformation statistical benchmarks to obtain the anomaly index for each infrastructure includes the following steps: The first type of landform in the target islands and reefs is taken as the statistical landform type; The first infrastructure belonging to the statistical geomorphological type is used as the assessment facility; Based on the deformation characteristic value of the assessment facility and the deformation statistical benchmark of the statistical landform type, the anomaly index of the assessment facility is calculated by combining the preset weighting coefficients. The expression for the anomaly index is as follows: In the formula, This represents the anomaly index of the j-th infrastructure. , , These are the weighting coefficients. This represents the average deformation rate of the j-th infrastructure. This represents the baseline of the average value for the k-th landform type. The standard deviation of the k-th landform type is represented by the reference value. This represents a preset infinitesimal constant. This represents the standard deviation of the deformation of the j-th infrastructure element. This represents the median of the standard deviation for the k-th landform type. This represents the range of the j-th infrastructure. This represents the baseline of the range median for the k-th landform type; The next infrastructure belonging to the statistical landform type is taken as the evaluation facility. The process of calculating the anomaly index of the evaluation facility based on the deformation characteristic value of the evaluation facility and the deformation statistical benchmark of the statistical landform type, combined with the preset weight coefficient, is repeated until all infrastructure belonging to the statistical landform type has been traversed. Take the next landform type in the target island as the statistical landform type, and return to the step of taking the first infrastructure belonging to the statistical landform type as the evaluation facility, until all infrastructures of all landform types have been traversed.

3. The method according to claim 1, characterized in that, The step of determining the warning level of the target island / reef by synergistic correlation based on the coral reef health index and the health level includes the following steps: The decrease in value for each period is quantified based on the coral reef health index over continuous time phases. Based on the numerical decrease and the health level of the infrastructure in the corresponding period, a conditional matching is performed, and the warning level of the target island / reef is determined by mapping the result of the conditional matching.

4. A health monitoring device for coral island and reef facilities, characterized in that, The apparatus for implementing the method of claim 1 includes: The first module is used to acquire multi-temporal remote sensing images and synthetic aperture radar images of the target islands and reefs; wherein, the remote sensing images are marked with the area of ​​each landform type of coral reef, and the synthetic aperture radar images are marked with the vector outline of each infrastructure and its corresponding landform type. The second module is used to statistically obtain the area percentage of each landform type in each time phase based on the remote sensing images; The third module is used to obtain the coral reef health index for each time phase by assigning weighted fusion based on the area percentage of all the landform types. The fourth module is used to obtain the deformation rate field by interferometric evolution through a spatiotemporal baseline based on the vector profile of the synthetic aperture radar image. The fifth module is used to obtain the deformation characteristic value of each of the infrastructures by transforming discrete deformation points based on the deformation rate field; The sixth module is used to statistically obtain the deformation statistical benchmark for each landform type based on the deformation characteristic values ​​and the corresponding landform type; The seventh module is used to perform a quantitative assessment of the deviation of each of the infrastructures in the same landform type based on the deformation characteristic value and the deformation statistical benchmark, to obtain the anomaly index of each infrastructure, and then map and determine the health level. The eighth module is used to make a collaborative correlation judgment based on the coral reef health index and the health level to obtain the early warning level of the target island reef for early warning diagnosis and push. The deformation characteristic values ​​include the average deformation rate, deformation standard deviation, and range. The process of obtaining the deformation characteristic values ​​of each infrastructure element based on the deformation rate field through discrete deformation point transformation includes the following steps: The first infrastructure in the target island / reef is used as a quantified facility; Extract the deformation rate of each sampling point of the quantization facility from the deformation rate field; The average deformation rate of the quantization facility is obtained by summing and averaging the deformation rates at each sampling point of the quantization facility. The standard deviation of the deformation of the quantification facility is obtained by summing and averaging the squared difference between the deformation rate and the average deformation rate at each sampling point of the quantification facility and taking the square root. The range of the quantization facility is obtained by the difference between the maximum and minimum deformation rates among all sampling points of the quantization facility. Take the next infrastructure in the target island as the quantization facility, and return to the step of extracting the deformation rate of each sampling point of the quantization facility from the deformation rate field until the deformation characteristic value of each infrastructure is obtained.

5. An electronic device, characterized in that, The electronic device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method according to any one of claims 1 to 3.

6. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method described in any one of claims 1 to 3.

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