A method for monitoring the health status of seagrass beds in ecological restoration areas

Abstract

The present invention provides a method for monitoring the health status of seagrass beds in ecological restoration areas, which relates to the technical field of seagrass bed ecological restoration. The method comprises the following steps: obtaining a seagrass bed ecological area, dividing the area, and determining a plurality of seagrass bed monitoring areas; performing seagrass status feature analysis based on an image sensor in combination with an image feature detection channel, and generating a plurality of seagrass status feature analysis results; performing light detection feature analysis based on a light sensor in combination with a light feature analysis channel, and generating a plurality of light detection feature analysis results; and performing abnormal management of the corresponding seagrass bed monitoring areas, while simultaneously performing abnormal linkage analysis and abnormal management. The present invention solves the technical problems that the prior art is limited by resolution and environmental factors, cannot accurately capture the microscopic changes in the seagrass bed, and has difficulty in comprehensively analyzing the health status of the seagrass bed from a combination of multiple monitoring dimensions, resulting in insufficient accuracy and comprehensiveness in seagrass bed status monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of seagrass bed ecological restoration, and in particular to a method for monitoring the health status of seagrass beds in an ecological restoration area. Background Art

[0002] Seagrass beds are fragile ecosystems that are easily affected by human activities, leading to restoration failures. Therefore, developing effective management and control measures is key to achieving good restoration results. However, existing monitoring equipment, such as remote sensing technology and ocean sensors, can provide some ocean data, but these data often have limitations. For example, remote sensing technology is limited by resolution and environmental factors and cannot accurately capture changes in the microscopic state of seagrass beds, resulting in limited accuracy and coverage. In addition, existing seagrass bed monitoring methods usually rely on traditional sampling and manual observation methods. These methods often rely on expert experience and are not suitable for large-scale or continuous monitoring. Manual monitoring is not only time-consuming and labor-intensive, and difficult to provide real-time data, but also lacks the ability to dynamically monitor health status and provide real-time early warning. During the growth cycle of seagrass beds and changes in environmental conditions, it is difficult to quickly detect problems and intervene, resulting in the inability to repair or adjust protection measures in a timely manner. Summary of the Invention

[0003] This application provides a method for monitoring the health status of seagrass beds in ecological restoration areas, aiming to solve the technical problems that existing technologies are limited by resolution and environmental factors, cannot accurately capture the changes in the microscopic state of seagrass beds, and find it difficult to comprehensively analyze the health status of seagrass beds from multiple monitoring dimensions, resulting in insufficient accuracy and comprehensiveness in seagrass bed status monitoring.

[0004] The present application discloses a method for monitoring the health status of seagrass beds in an ecological restoration area, the method comprising: obtaining the ecological area of ​​the seagrass beds in the target ecological restoration area, dividing the area based on the ecological characteristics of the seagrass beds, and determining a plurality of seagrass bed monitoring areas, wherein the plurality of seagrass bed monitoring areas correspond to a plurality of monitoring equipment groups; performing seagrass status characteristic analysis on the plurality of seagrass bed monitoring areas according to the image sensors in the plurality of monitoring equipment groups and in combination with image feature detection channels, and generating a plurality of seagrass status characteristic analysis results, wherein the plurality of seagrass status characteristic analysis results include a plurality of seagrass abnormal status warning information; and The light sensor in the detection equipment group, combined with the light feature analysis channel, performs light detection feature analysis on the multiple seagrass bed monitoring areas to generate multiple light detection feature analysis results, wherein the multiple light detection feature analysis results include a number of light abnormality point warning information; according to the several seagrass abnormal state warning information and the several light abnormality point warning information, abnormal management of the corresponding seagrass bed monitoring areas is performed; at the same time, in combination with the several seagrass abnormal state warning information and the several light abnormality point warning information, an abnormal linkage analysis is performed, and according to the linkage analysis results, abnormal management of the corresponding seagrass bed monitoring areas is performed.

[0005] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0006] According to the ecological characteristics of the seagrass bed, the target ecological restoration area is divided into multiple seagrass bed monitoring areas, and a corresponding monitoring equipment group is configured for each area. This process ensures that the health status of different areas can be accurately monitored separately, and each monitoring area is equipped with appropriate monitoring equipment, thereby improving the accuracy and comprehensiveness of the monitoring; through the image sensor combined with the image feature detection channel, the seagrass status characteristics of multiple seagrass bed monitoring areas are analyzed to generate multiple seagrass status characteristic analysis results, including seagrass abnormal status warning information. This process combines visual data and image processing technology, and can detect the health status of the seagrass bed in real time. If seagrass abnormalities are found, early warnings will be issued, which improves the sensitivity and accuracy of anomaly detection; through the light sensor combined with the light feature analysis function, the light conditions of the seagrass bed area are monitored. Light is a key factor affecting seagrass growth, so accurate monitoring of light is crucial. The light detection feature analysis results generated by this step include warning information on abnormal light points, which can issue a warning when the light conditions do not meet the health requirements of the seagrass bed, preventing poor light conditions from having an adverse effect on seagrass growth. By combining the abnormal seagrass status warning information and the abnormal light point warning information, an abnormal linkage analysis is performed, which can not only handle a single type of abnormality, but also integrate the mutual influence of multiple factors to conduct more comprehensive abnormality management. The linkage analysis results provide more accurate and comprehensive abnormality management measures for the seagrass bed monitoring area. This linkage analysis can integrate multiple monitoring data, identify the relationships and potential chain reactions between different factors, and enhance the comprehensiveness of abnormality detection and management. Overall, this method effectively improves the monitoring efficiency, prediction accuracy and emergency management capabilities of seagrass beds, and provides strong technical support for marine ecological restoration and seagrass bed protection.

[0007] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 A flow chart of a method for monitoring the health status of seagrass beds in an ecological restoration area is provided for the embodiment of the present application.

[0009] Figure 2 A schematic diagram of a process for generating several seagrass abnormal status warning information in a method for monitoring the health status of seagrass beds in an ecological restoration area is provided for an embodiment of the present application. DETAILED DESCRIPTION

[0010] The embodiments of the present application provide a method for monitoring the health status of seagrass beds in ecological restoration areas, thereby solving the technical problems that the existing technology is limited by resolution and environmental factors, cannot accurately capture the changes in the microscopic state of the seagrass beds, and has difficulty in comprehensively analyzing the health status of the seagrass beds from multiple monitoring dimensions, resulting in insufficient accuracy and comprehensiveness in seagrass bed status monitoring.

[0011] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically introduced in conjunction with the drawings in the specification.

[0012] like Figure 1 As shown, the embodiment of the present application provides a method for monitoring the health status of seagrass beds in an ecological restoration area, the method comprising:

[0013] The seagrass bed ecological area of ​​the target ecological restoration area is obtained, and the area is divided based on the ecological characteristics of the seagrass bed to determine multiple seagrass bed monitoring areas, wherein the multiple seagrass bed monitoring areas correspond to multiple sets of monitoring equipment groups.

[0014] By interacting with the ecological restoration system, or through remote sensing technology, such as satellite images or drone photography, and ground surveys, the specific location of the target ecological restoration area can be determined, and the seagrass bed ecological area can be obtained, which can be an artificial seagrass bed or a natural seagrass bed.

[0015] Regional divisions are made based on the ecological characteristics of seagrass beds, such as seagrass species, density, coverage, seagrass growth status, and water flow environment. For example, based on species differences, different regions may have different types of seagrass, such as red algae, green algae, and seagrass plants. Based on growth status differences, regions can be divided into healthy areas, risk areas, and degraded areas based on the growth conditions of seagrass, such as health, decline, and death. Based on differences in environmental conditions, different monitoring areas are divided based on the impact of environmental factors such as water depth, light intensity, and water flow rate on the growth of seagrass beds.

[0016] Based on the characteristics of the divided areas and combined with monitoring needs, it is determined that different areas require different types of monitoring equipment. For example, healthy areas only require basic light and water quality monitoring, while degraded areas may require higher-precision image recognition and depth monitoring. Each monitoring area is equipped with appropriate monitoring equipment to ensure that the ecological status of the seagrass bed can be fully monitored.

[0017] According to the image sensors in the multiple sets of monitoring equipment groups, combined with the image feature detection channel, the seagrass status feature analysis is performed on the multiple seagrass bed monitoring areas to generate multiple seagrass status feature analysis results, wherein the multiple seagrass status feature analysis results include a number of seagrass abnormal status warning information.

[0018] Image sensors, such as high-resolution cameras, drones, submarine robots or underwater cameras, are deployed according to the designated multiple seagrass bed monitoring areas to collect images of each monitoring area on a regular or real-time basis. The collected image data is input into the image feature detection channel for feature extraction. The image feature detection channel can identify and extract seagrass features in the image, such as seagrass morphological characteristics, seagrass color changes, seagrass coverage, seagrass growth patterns, etc.

[0019] Seagrass characteristics are compared against a seagrass feature recognition database. Any discrepancies between the results and those from normal seagrass are marked as abnormal seagrass. This includes both similar and non-similar anomalies. Similar anomalies indicate abnormalities in seagrass health, such as malnutrition, disease, or wilting. For example, if seagrass leaf color changes exceed a certain threshold, it may indicate health problems; if seagrass cover falls below a set threshold, it may signal degradation or environmental deterioration. Non-similar anomalies indicate invasive species, such as floating macroalgae. These not only compete with seagrass for nutrients but also block light, reducing the intensity of light reaching the seagrass canopy and leading to seagrass bed degradation. Based on the abnormal seagrass status, several corresponding seagrass abnormality warning messages are generated.

[0020] According to the light sensors in the multiple sets of monitoring equipment groups, combined with the light feature analysis channel, light detection feature analysis is performed on the multiple seagrass bed monitoring areas to generate multiple light detection feature analysis results, wherein the multiple light detection feature analysis results include warning information of several abnormal light points.

[0021] Light sensors are deployed across multiple seagrass bed monitoring areas to collect real-time light data at different depths. Commonly used light sensors include light intensity sensors, spectral sensors, and radiometers. Based on historical data and ecological research, a normal light intensity range is established. For example, multiple light detection thresholds are preset based on parameters such as light intensity and wavelength. Points with abnormal light levels are identified by comparing them with these thresholds. When the light value collected by the light sensor exceeds the set normal range, the corresponding collection point is automatically marked as an abnormal light level point. The location and time of the abnormality are recorded, and several warning messages for abnormal light levels are generated.

[0022] Abnormal management of the corresponding seagrass bed monitoring area is performed based on the several seagrass abnormal status warning information and the several abnormal light point warning information. At the same time, abnormal linkage analysis is performed in combination with the several seagrass abnormal status warning information and the several abnormal light point warning information, and abnormal management of the corresponding seagrass bed monitoring area is performed based on the linkage analysis results.

[0023] The seagrass abnormal status warning information reflects the abnormalities in the growth status and health status of the seagrass bed, such as seagrass withering, yellowing, disease, etc., as well as biological invasions of the seagrass bed, such as the invasion of large floating seaweed. The warning information includes information such as the abnormal type, abnormal area and location, and the severity of the abnormality. The abnormal light point warning information reflects that light problems directly affect the health status of the seagrass bed, such as excessive light, insufficient light, light fluctuations, etc. The warning information includes the abnormal light type, light intensity deviation value, abnormal point location, and abnormal time period.

[0024] Conduct a preliminary diagnosis of each warning message to assess the severity, frequency of occurrence, and potential threat to seagrass beds of the anomaly. Prioritize warning points based on factors such as the severity and frequency of the anomaly information. Severe anomalies, such as large-scale insufficient light or seagrass withering, should be dealt with as a priority. Depending on the type of anomaly, take appropriate response measures, such as adjusting light intensity, removing invasive species, strengthening water quality management, and adding beneficial substances.

[0025] Abnormal linkage analysis reveals the potential correlation between different types of abnormalities through the integration of cross-data sources, helping to determine whether certain abnormal phenomena are causally related to each other, or whether one abnormality may aggravate the occurrence of another abnormality. Specifically, by analyzing the relationship between the abnormal state of seagrass and the abnormal light points, it is determined whether the change in light is the root cause of the abnormal seagrass health. For example, if a certain area has excessive light and is accompanied by yellowing of seagrass, this may mean that the excessive light directly affects the photosynthesis of seagrass. The seagrass abnormal state data and the abnormal light data are integrated for joint analysis. The analysis methods include multivariate regression analysis, neural network models, support vector machines, etc., to identify and classify different types of abnormal patterns. For example, when the abnormal light continues to exceed a certain threshold, it may trigger a seagrass withering warning; excessive light fluctuations may lead to unstable seagrass growth.

[0026] Combining historical data and real-time monitoring results, we can predict future development trends of abnormal situations. For example, through linkage analysis, we can predict that insufficient light in a certain area may lead to long-term seagrass health problems. Based on the linkage analysis results, we can develop more precise abnormal management strategies. For example, if the linkage analysis shows that excessive or weak light is causing seagrass health problems, we can adjust the light, such as adjusting underwater lights, installing shading nets, etc.

[0027] Through intelligent data analysis and decision-making, timely and accurate management of seagrass bed anomalies can be achieved, the negative impact of environmental disturbances on seagrass health can be minimized, and effective restoration measures can be taken to ensure the stability and sustainability of ecological restoration areas.

[0028] Furthermore, if Figure 2As shown, the method includes performing seagrass status characteristic analysis on the multiple seagrass bed monitoring areas based on the image sensors in the multiple monitoring equipment groups in combination with the image feature detection channels to generate multiple seagrass status characteristic analysis results, wherein the multiple seagrass status characteristic analysis results include a number of seagrass abnormal status warning information, and the method includes:

[0029] Through the image sensor in the first monitoring equipment group, continuous depth image acquisition is performed in the first seagrass bed monitoring area to obtain a first continuous image data set, wherein the first seagrass bed monitoring area is any area of ​​the multiple seagrass bed monitoring areas, and the first continuous image data set has an image depth identifier; the seagrass type and seagrass growth status of the first seagrass bed monitoring area are obtained, and a seagrass feature recognition library is established; an initial pre-anchor frame is set according to the seagrass growth status, and the initial pre-anchor frame is adjusted based on the image depth identifier to establish a pyramid anchor frame; the first continuous image data set is framed and compared step by step with the pyramid anchor frame, and based on the frame selection and comparison results, the seagrass feature recognition library is called to perform feature recognition, and the plurality of seagrass abnormal status warning information is generated based on the feature recognition results.

[0030] Each monitoring equipment group is used to monitor a designated area in real time and is composed of multiple sensors. An analysis object is randomly selected from multiple seagrass bed monitoring areas as the first seagrass bed monitoring area, and the first monitoring equipment group corresponding to the first seagrass bed monitoring area is obtained. The image sensor in the first monitoring equipment group, which can be a high-resolution camera, a drone, a seabed robot or an underwater camera, is used to perform continuous depth photography of the target area to capture image changes in the seagrass growth area. In addition to conventional two-dimensional images, the first continuous image data set obtained also includes depth information of each pixel point, and the depth mark provides the underwater depth of the photography.

[0031] Obtaining seagrass types, such as directly interacting with the ecological restoration system to obtain the seagrass types in the first seagrass bed monitoring area, or matching the seagrass in the image with an existing species library to determine the seagrass type in the image. Common seagrass types include brittle grass, ocean grass, and cowgrass; obtaining the growth status of seagrass, such as identifying its different growth stages, such as seedling stage, growth stage, and aging stage, based on its morphology, color and other characteristics.

[0032] The seagrass feature recognition library is a database built based on a large amount of annotated data. It contains typical image features and corresponding growth status information for each seagrass type. Each seagrass species or growth status corresponds to an entry, which includes image features, type information, and growth status annotations. The seagrass feature recognition library is used to compare new image data, quickly identify seagrass types, and determine their growth status.

[0033] Anchor frames are rectangular boxes used in object detection algorithms to predict target locations. Initial pre-anchor frames are set in the image based on the seagrass's growth status, such as healthy, damaged, overcrowded, or scarce. For example, healthy seagrass, typically characterized by dense leaves and intact morphology, may require a larger anchor frame; damaged or diseased seagrass, on the other hand, may require a smaller or irregular frame. Seagrass in the image is located at varying depths, and depth information can be used to correct the spatial position of the anchor frames. For example, seagrass farther from the camera will appear smaller, while closer seagrass will appear larger. Based on the depth signature, the initial pre-anchor frames are resized to better match the actual size and position of the seagrass. The depth signature is calculated to determine the true scale of each anchor frame relative to the actual seagrass, thereby adjusting the aspect ratio of the initial pre-anchor frames. Based on this adjustment, a pyramid of anchor frames is constructed to better cover the seagrass area. Pyramid anchor frames are used to handle objects of varying scales in the image. By constructing multiple layers of anchor frames at different scales, seagrass targets of varying sizes and depths can be more effectively processed.

[0034] Using pyramid anchor frames, the first continuous image dataset is compared in a step-by-step frame selection manner. The anchor frames of each layer are matched with the seagrass areas in the image dataset to find the seagrass areas that best match the characteristics. Specifically, starting from the bottom layer of the pyramid, the image is initially framed using larger anchor frames. As the frame is passed upward step by step, the scale of the frame is gradually reduced to ensure accurate frame selection from global to local.

[0035] The established seagrass feature recognition library is called to match the selected area through the feature recognition library to identify the type, health status and growth status of the seagrass. Based on the comparison results, seagrass areas that may have abnormalities are identified. For example, seagrass invasion, seagrass disease, absence, over-reproduction, etc. are all marked as abnormal states. Based on the identified abnormal states, several seagrass abnormal state warning messages are generated. Each abnormal state warning message includes the specific location of the seagrass area, the type of abnormality detected, the severity of the abnormality, etc.

[0036] Furthermore, the method of adjusting the initial pre-anchor frame based on the image depth identifier to establish a pyramid anchor frame includes:

[0037] Acquire sensor control parameters of the image sensor, wherein the sensor control parameters include focal length, resolution, and field of view angle; adjust the size and position of the initial pre-anchor frame based on the focal length, resolution, and field of view angle in combination with the image depth identifier, and establish the pyramid anchor frame based on the adjustment results.

[0038] Obtain sensor control parameters, including focal length, resolution, and field of view. Focal length is a parameter that affects the field of view of the image sensor. It determines the camera's viewing angle and the projection size of objects in the image. The focal length directly affects the size and near-far distribution of the seagrass area captured by the sensor. When the focal length is long, the distant seagrass in the seagrass bed will appear smaller in the image, and the size of the pre-anchor frame needs to be increased accordingly. Conversely, when the focal length is short, the distant seagrass area will be smaller and the nearby area will appear larger, so the pre-anchor frame needs to be adjusted to a smaller size.

[0039] Resolution refers to the clarity of an image, usually expressed in pixels. Higher resolutions allow for more accurate identification and location of seagrass locations and morphological features. Among the image sensor control parameters, resolution determines the small range of variations that can be distinguished. The size of the anchor frame needs to be adjusted to suit the image clarity based on the resolution.

[0040] The field of view angle determines the range of the image captured by the sensor and affects the distribution of objects in the image and their relative positions. When the field of view angle is large, the pre-anchor frame needs to cover more seagrass areas; when the field of view angle is small, the pre-anchor frame will be more concentrated in a small area.

[0041] By calculating the focal length and the field of view of the image, the mapping relationship of the anchor frame in space is determined, and its size is adjusted as needed. For example, the anchor frame needs to be larger for seaweed in the distance, while a smaller frame can be used for seaweed nearby. The resolution adaptability of the anchor frame is adjusted according to the resolution. If the image resolution is high, the size of the anchor frame can be smaller and the target can be located more accurately. If the resolution is low, the anchor frame needs to be enlarged to ensure the visibility of the target. The field of view angle determines the range of images that the camera can capture, and therefore affects the spatial positioning of the anchor frame. If the field of view angle is large, the seaweed targets appearing in the image will be more scattered, and the anchor frame needs to be enlarged to adapt to a wider area. If the field of view angle is small, the anchor frame can be relatively small and concentrate on covering the target area.

[0042] The image depth identifier is the actual distance from each pixel to the camera. It provides depth data for each pixel. The combination of the depth identifier and the sensor control parameters can further adjust the position and size of the anchor frame in the image. For example, depth information can help determine the location of the target object. If the target seaweed is farther away, the depth information will provide additional adjustment basis, allowing the anchor frame to adapt to seaweed targets at different depths.

[0043] According to different focal lengths, field of view angles, resolutions and depth identification information, the size and position of the anchor frame are adjusted layer by layer, and finally a multi-level anchor frame structure that adapts to different scales is formed to obtain a pyramid anchor frame. The creation of the pyramid anchor frame ensures that the system can simultaneously process seagrass targets of different distances and sizes. Through multi-level frame selection, it can capture all targets from large-scale seagrass beds to small-scale details.

[0044] Furthermore, the method for generating the plurality of seaweed abnormal status warning information based on the feature recognition results includes:

[0045] Establish seaweed similarity constraints based on the seaweed type; for the first seaweed abnormality status warning information, perform warning evaluation according to the similarity constraints; when the warning evaluation result is a similar abnormality, perform seaweed abnormality warning feedback; when the warning evaluation result is not a similar abnormality, perform invasion warning feedback.

[0046] Similar constraints refer to setting a set of rules based on the characteristics of seagrass types. These rules are used to constrain the growth or state change patterns that seagrasses of the same type should follow during monitoring. For example, the normal growth rate and health status of seagrass of the same type should be within a certain range, and growth or degradation beyond this range can be considered abnormal.

[0047] When an abnormal seaweed status warning is generated, the warning information is evaluated according to the same type of constraints to determine whether it is outside the normal growth variation range of the same type of seaweed. For example, for the abnormal seaweed detected, it can be determined whether it is an abnormality of the same type of seaweed by comparing the historical data of the abnormal seaweed. If so, it is a similar abnormality. If not, there may be an invasion.

[0048] When the warning evaluation result is the same type of abnormality, it means that the abnormality is an abnormal change of the seagrass type within its ecological range, such as seagrass disease, absence, over-reproduction, etc., so as to provide abnormal warning feedback for seagrass.

[0049] When the early warning evaluation result is not a similar abnormality, it means that the detected seagrass does not belong to the same type of seagrass in the area, that is, there is an invasion of alien species, such as floating large seaweed. Floating large seaweed not only competes with seagrass for nutrients, but also blocks light, resulting in a decrease in the light intensity reaching the seagrass canopy, which may lead to the degradation of the seagrass bed. This is used to provide early warning feedback for the invasion of alien species, such as further inspection of the floating large seaweed in the seagrass bed. When the distribution amount and distribution area increase to the point of affecting the survival of the seagrass, it should be manually removed in a timely manner. If necessary, a protective net should be added around the seagrass bed, and the large seaweed on the protective net should be removed regularly.

[0050] Through the above process, it is possible to effectively distinguish between abnormal changes caused by abnormal seagrass growth status and those caused by external invasion, and provide appropriate early warning feedback according to different situations.

[0051] Furthermore, when the warning evaluation result is a similar abnormality, a seaweed abnormality warning feedback is performed, and the method includes:

[0052] When the warning evaluation result is a similar abnormality, the proportion of abnormal individuals is obtained to generate a first abnormality coefficient; the abnormal individual status of the same abnormality is collected to obtain a data set of abnormal individual status; based on a preset abnormal individual status evaluation function, the abnormal individual status data set is analyzed to generate an abnormal individual status evaluation coefficient and a second abnormality coefficient; based on the first abnormality coefficient and the second abnormality coefficient, seagrass abnormality warning feedback is performed.

[0053] If the abnormal state of the seagrass bed is assessed as a similar abnormality, the proportion of abnormal seagrass individuals in the monitoring area is analyzed. By analyzing the collected image data, it can be determined which seagrass individuals show abnormal characteristics. By calculating the proportion of these individuals, the proportion of abnormal individuals is obtained. For example, if 20 of the 100 seagrass samples in the monitoring area are abnormal, then the proportion of abnormal individuals is 20%. Based on the proportion of abnormal individuals, the first abnormality coefficient is generated. This coefficient can reflect the prevalence of the current seagrass bed abnormality. If the proportion of abnormal individuals is high, it means that the impact of the abnormality is more widespread, and higher intervention is required.

[0054] Detailed status information of these abnormal individuals is further collected. This status information can reflect the health status of the seagrass, such as shrinkage, yellowing or rotting. All the collected abnormal individual status data form an abnormal individual status data set, which includes information in multiple dimensions.

[0055] To further assess the status of abnormal individuals, an abnormal individual status evaluation function is introduced. This function scores the various statuses of each abnormal individual based on the growth characteristics of the seaweed. The function then outputs a comprehensive abnormal individual status evaluation coefficient, generating a second abnormality coefficient. This second abnormality coefficient reflects the severity of the abnormal individual. This coefficient can be a standardized value between 0 and 1, used to measure the status of each abnormal individual, with higher values ​​indicating more severe abnormality. Furthermore, the status evaluation results of all abnormal individuals are aggregated to obtain a comprehensive second abnormality coefficient for the region. For example, if the majority of abnormal individuals exhibit severe slow growth or wilting, the second abnormality coefficient will be high.

[0056] The first anomaly coefficient and the second anomaly coefficient are combined, for example, a weighted sum of the two is performed to generate a comprehensive anomaly feedback result. This comprehensive coefficient can reflect the extensiveness and severity of the anomaly. If the comprehensive anomaly coefficient is high, it means that the anomaly is serious, which may be caused by pollution, invasion or major environmental changes. In this case, an emergency feedback is issued, requiring rapid intervention measures, such as clearing pollution and repairing the environment.

[0057] Furthermore, the method includes performing light detection feature analysis on the plurality of seagrass bed monitoring areas based on the light sensors in the plurality of monitoring equipment groups in combination with the light feature analysis channel to generate a plurality of light detection feature analysis results, wherein the plurality of light detection feature analysis results include a plurality of light abnormality point warning information, and the method includes:

[0058] Through the light sensor in the first monitoring equipment group, continuous depth light monitoring is performed in the first seagrass bed monitoring area to obtain a first continuous light data set, wherein the first continuous light data set has a light depth identifier; based on the light depth identifier, a predetermined light intensity data set of the first seagrass bed monitoring area is loaded; according to the predetermined light intensity data set, a light intensity deviation analysis is performed on the first continuous light data set, and points where the light intensity deviation value exceeds the preset light intensity deviation value are marked as light anomaly points, and first light anomaly point warning information is generated; and so on, the multiple seagrass bed monitoring areas are traversed to generate the several light anomaly point warning information.

[0059] In the first seagrass bed monitoring area, continuous depth light monitoring is carried out through light sensors to collect light intensity data. This process records the light changes in the area in real time or periodically. Continuous depth monitoring means that data collection will cover different depth positions and capture the changing trends of light. The collected light data is marked according to the collection time and depth to form a data set with light depth identifiers. These identifiers help distinguish light values ​​at different times and depths.

[0060] Based on the light depth identifier in the data, a predetermined light intensity dataset corresponding to each depth position is loaded. This predetermined dataset is preset according to the light intensity range under normal seagrass growth conditions, including the ideal light intensity values ​​of seagrass beds in different depth areas. These data are usually derived from historical monitoring, scientific research or expert settings and are used to represent ideal or normal light conditions.

[0061] The first continuous light dataset is compared with the predetermined light intensity dataset to perform a light intensity deviation analysis. This analysis primarily analyzes the difference between the actual light intensity and the predetermined value. Points where the deviation exceeds a preset deviation threshold are marked. This light intensity deviation threshold can be adjusted based on the seaweed's growth requirements and environmental conditions. For example, seaweed growth varies in its adaptability to light intensity; some areas may be more sensitive to light deviations, while others may be more tolerant. If the deviation value at a point exceeds a preset threshold, such as ±20%, the point is marked as a light anomaly point, and a corresponding first light anomaly point warning message is generated, notifying relevant personnel to further analyze the cause of the anomaly and take appropriate intervention measures.

[0062] After completing the analysis of the abnormal light points in the first seagrass bed monitoring area, continue to traverse other seagrass bed monitoring areas and perform the same light deviation analysis to obtain abnormal light points, and finally generate the aforementioned abnormal light point warning information. Through this traversal, the abnormal light points in all seagrass bed monitoring areas can be fully identified.

[0063] Furthermore, the method of combining the plurality of seaweed abnormal state warning information and the plurality of abnormal light point warning information to perform abnormal linkage analysis includes:

[0064] When the second seagrass abnormal status warning information is triggered, a continuous tracking window is established; continuous light tracking is performed within the continuous tracking window to obtain the second light abnormality point warning information; when the second light abnormality point warning information is not empty, the abnormal linkage analysis of the second seagrass bed monitoring area is triggered.

[0065] When the second seagrass abnormal status warning information in the second seagrass bed monitoring area is triggered, a continuous tracking window is started. The duration of this window can be set according to actual needs. For example, the tracking time length is determined according to the nature of the abnormality, and can be set to ten minutes, thirty minutes, etc. This window is used for subsequent abnormality monitoring to ensure that the abnormal status can be continuously tracked.

[0066] In the continuous tracking window, continuous light tracking is performed, data is obtained in real time through the light sensor, and the light intensity is analyzed to identify abnormal light points and generate relevant second abnormal light point warning information. The generation process of the abnormal light point warning information has been described in detail in the previous description and will not be repeated here for the sake of brevity.

[0067] When the second abnormal light point warning information is not empty, it means that an abnormal light point has been detected within the continuous tracking window, which further triggers the abnormal linkage analysis, that is, a comprehensive analysis of the relationship between the abnormal seagrass state and the abnormal light point, an assessment of whether there is a correlation, and the adoption of appropriate management measures. For example, abnormal light may be one of the direct causes of the abnormal seagrass state, or it may aggravate the existing abnormal state. Through linkage analysis, the most reasonable management or response strategy can be comprehensively judged.

[0068] Furthermore, the method further comprises:

[0069] Based on the abnormal linkage analysis results, an abnormal situation analysis is performed to obtain abnormal situation information, wherein the abnormal situation information has an abnormal development direction; based on the abnormal development direction, an abnormal reminder is issued to the adjacent areas of the second seagrass bed monitoring area.

[0070] Abnormal situation refers to the possible evolution trend of abnormal events within a specific time, including the expansion, slowdown, and intensification of the abnormality. Specifically, by deeply mining abnormal data and environmental information, the potential development direction of the abnormality can be identified, including whether the abnormal event may further expand and the direction of the abnormal spread after expansion, which may spread to adjacent areas.

[0071] If the abnormal development direction indicates that the seagrass beds in a neighboring area may be affected, an abnormal alert will be automatically issued. This alert can cover multiple neighboring areas to ensure that environmental managers in neighboring areas can take necessary actions in a timely manner, such as strengthening monitoring, adjusting environmental conditions, and carrying out restoration work, to ensure that the health of the entire seagrass bed is not affected.

[0072] In summary, the method for monitoring the health status of seagrass beds in ecological restoration areas provided by the embodiments of the present application has the following technical effects:

[0073] According to the ecological characteristics of the seagrass bed, the target ecological restoration area is divided into multiple seagrass bed monitoring areas, and a corresponding monitoring equipment group is configured for each area. This process ensures that the health status of different areas can be accurately monitored separately, and each monitoring area is equipped with appropriate monitoring equipment, thereby improving the accuracy and comprehensiveness of the monitoring; through the image sensor combined with the image feature detection channel, the seagrass status characteristics of multiple seagrass bed monitoring areas are analyzed to generate multiple seagrass status characteristic analysis results, including seagrass abnormal status warning information. This process combines visual data and image processing technology, and can detect the health status of the seagrass bed in real time. If seagrass abnormalities are found, early warnings will be issued, which improves the sensitivity and accuracy of anomaly detection; through the light sensor combined with the light feature analysis function, the light conditions of the seagrass bed area are monitored. Light is a key factor affecting seagrass growth, so accurate monitoring of light is crucial. The light detection feature analysis results generated by this step include warning information on abnormal light points, which can issue a warning when the light conditions do not meet the health requirements of the seagrass bed, preventing poor light conditions from having an adverse effect on seagrass growth. By combining the abnormal seagrass status warning information and the abnormal light point warning information, an abnormal linkage analysis is performed, which can not only handle a single type of abnormality, but also integrate the mutual influence of multiple factors to conduct more comprehensive abnormality management. The linkage analysis results provide more accurate and comprehensive abnormality management measures for the seagrass bed monitoring area. This linkage analysis can integrate multiple monitoring data, identify the relationships and potential chain reactions between different factors, and enhance the comprehensiveness of abnormality detection and management. Overall, this method effectively improves the monitoring efficiency, prediction accuracy and emergency management capabilities of seagrass beds, and provides strong technical support for marine ecological restoration and seagrass bed protection.

[0074] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present application. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for monitoring the health status of seagrass beds in ecological restoration areas, characterized in that: The method comprises: Obtaining seagrass bed ecological regions in the target ecological restoration area, dividing the regions based on the ecological characteristics of the seagrass beds, and determining multiple seagrass bed monitoring areas, wherein the multiple seagrass bed monitoring areas correspond to multiple sets of monitoring equipment; performing seagrass status characteristic analysis on the plurality of seagrass bed monitoring areas according to the image sensors in the plurality of monitoring equipment sets in combination with image feature detection channels, and generating a plurality of seagrass status characteristic analysis results, wherein the plurality of seagrass status characteristic analysis results include a plurality of seagrass abnormal status warning information; Performing a light detection feature analysis on the plurality of seagrass bed monitoring areas based on the light sensors in the plurality of monitoring equipment sets in combination with a light feature analysis channel to generate a plurality of light detection feature analysis results, wherein the plurality of light detection feature analysis results include warning information of a plurality of light abnormality points; performing abnormal management of the corresponding seagrass bed monitoring area based on the plurality of seagrass abnormal state warning information and the plurality of abnormal light point warning information, and at the same time, performing abnormal linkage analysis in combination with the plurality of seagrass abnormal state warning information and the plurality of abnormal light point warning information, and performing abnormal management of the corresponding seagrass bed monitoring area based on the linkage analysis results; Combining the plurality of seaweed abnormal state warning information and the plurality of abnormal light point warning information to perform abnormal linkage analysis, the method includes: When the second seagrass abnormal status warning information is triggered, a continuous tracking window is established; Continuous illumination tracking is performed within the continuous tracking window to obtain warning information of a second illumination abnormality point; When the second abnormal light point warning information is not empty, the abnormal linkage analysis of the second seagrass bed monitoring area is triggered.

2. A method for monitoring the health status of seagrass beds in an ecological restoration area according to claim 1, characterized in that: The method includes performing seagrass status characteristic analysis on the plurality of seagrass bed monitoring areas based on the image sensors in the plurality of monitoring equipment groups in combination with image feature detection channels to generate a plurality of seagrass status characteristic analysis results, wherein the plurality of seagrass status characteristic analysis results include a plurality of seagrass abnormal status warning information, and comprising: Performing continuous depth image acquisition in a first seagrass bed monitoring area using an image sensor in the first monitoring equipment group to obtain a first continuous image data set, wherein the first seagrass bed monitoring area is any one of the plurality of seagrass bed monitoring areas, and the first continuous image data set has an image depth identifier; Acquire the seagrass type and seagrass growth status in the first seagrass bed monitoring area, and establish a seagrass feature recognition library; An initial pre-anchor frame is set according to the seaweed growth state, and the initial pre-anchor frame is adjusted based on the image depth identifier to establish a pyramid anchor frame; The first continuous image data set is framed and compared step by step using the pyramid anchor frame. Based on the framed and compared results, the seaweed feature recognition library is called to perform feature recognition. Based on the feature recognition results, the plurality of seaweed abnormal status warning information is generated.

3. A method for monitoring the health status of seagrass beds in an ecological restoration area according to claim 2, characterized in that: The method of adjusting the initial pre-anchor frame based on the image depth identifier to establish a pyramid anchor frame includes: Acquiring sensor control parameters of the image sensor, wherein the sensor control parameters include focal length, resolution, and field of view angle; According to the focal length, resolution, and field of view, combined with the image depth identifier, the size and position of the initial pre-anchor frame are adjusted, and the pyramid anchor frame is established according to the adjustment result.

4. A method for monitoring the health status of seagrass beds in an ecological restoration area according to claim 2, characterized in that: The method of generating the plurality of seaweed abnormal state warning information based on the feature recognition results includes: establishing a seagrass category constraint based on the seagrass type; For the first seaweed abnormal state warning information, performing a warning evaluation based on the same type constraints; When the early warning evaluation result is the same type of abnormality, a seagrass abnormality early warning feedback is carried out; When the warning evaluation result is not the same type of abnormality, an intrusion warning feedback is performed.

5. A method for monitoring the health status of seagrass beds in an ecological restoration area according to claim 4, characterized in that: When the warning evaluation result is a similar abnormality, a seaweed abnormality warning feedback is performed, and the method includes: When the early warning evaluation result is the same type of anomaly, the proportion of abnormal individuals is obtained to generate the first anomaly coefficient; Collect the abnormal individual states of the same abnormality to obtain the abnormal individual state data set; Based on a preset abnormal individual state evaluation function, the abnormal individual state data set is analyzed to generate an abnormal individual state evaluation coefficient and a second abnormal coefficient; Seaweed abnormality warning feedback is performed based on the first abnormality coefficient and the second abnormality coefficient.

6. A method for monitoring the health status of seagrass beds in an ecological restoration area according to claim 1, characterized in that: The method includes performing light detection feature analysis on the plurality of seagrass bed monitoring areas based on the light sensors in the plurality of monitoring equipment groups in combination with the light feature analysis channels to generate a plurality of light detection feature analysis results, wherein the plurality of light detection feature analysis results include a plurality of light abnormality point warning information, and comprising: Performing continuous depth light monitoring in a first seagrass bed monitoring area using a light sensor in a first monitoring device group to obtain a first continuous light data set, wherein the first continuous light data set has a light depth identifier; Based on the light depth identifier, loading a predetermined light intensity dataset of the first seagrass bed monitoring area; performing a light intensity deviation analysis on the first continuous light data set according to the predetermined light intensity data set, marking points where the light intensity deviation value exceeds the preset light intensity deviation value as light abnormality points, and generating first light abnormality point warning information; By analogy, the plurality of seagrass bed monitoring areas are traversed to generate the plurality of abnormal light point warning information.

7. A method for monitoring the health status of seagrass beds in an ecological restoration area according to claim 1, characterized in that: The method further comprises: Perform abnormal situation analysis based on the abnormal linkage analysis results to obtain abnormal situation information, wherein the abnormal situation information has an abnormal development direction; Based on the abnormal development direction, abnormal reminders are issued to adjacent areas of the second seagrass bed monitoring area.

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