Artificial fish reef dynamic comprehensive monitoring platform

Abstract

The invention discloses a dynamic comprehensive monitoring platform for an artificial fish reef, which obtains ecological data such as fish varieties and water quality parameters in real time through a data acquisition module, ensures timeliness and continuity of the data, enables managers to master ecological dynamic states immediately, and overcomes the defect that the traditional method is difficult to know ecological changes in time. Meanwhile, a classification database construction module stores data in a multi-dimensional classification mode, regionalized reference data are formed, a dynamic monitoring and evaluation engine calculates an ecological benefit comprehensive index according to the data, multi-dimensional comprehensive evaluation can comprehensively and systematically reflect the ecological benefits of the artificial fish reef, and the problem that the evaluation index is single is solved; besides, the intelligent early warning and decision support module sets a safety threshold value, performs automatic early warning when indexes are abnormal, and generates a targeted and operable repair suggestion report, so that the platform can respond quickly, managers can take repair measures in time, ecological problem deterioration is avoided, and the problem that targeted repair cannot be performed in time is effectively solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of artificial reef monitoring, and particularly relates to a dynamic comprehensive monitoring platform for artificial reefs. BACKGROUND

[0002] With the gradual decline of marine fishery resources and the continuous improvement of ecological protection awareness, artificial reefs, as an effective means of marine ecological restoration and fishery resource proliferation, have been widely used in the global range. The construction of artificial reefs can provide habitats for marine organisms such as fish, and improve the marine ecological environment, thereby promoting the recovery and growth of fishery resources.

[0003] However, in the process of construction and management of artificial reefs, there are many challenges: on the one hand, the ecological environment of artificial reef area is complex and changeable, and factors such as fish species, water quality parameters, and proliferation and stocking conditions will affect the ecological benefits of the reef. Traditional monitoring methods often rely on manual periodic sampling and laboratory analysis, which is not only time-consuming and labor-intensive, but also cannot achieve real-time and dynamic monitoring, making it difficult to grasp the ecological changes in the artificial reef area in a timely manner.

[0004] On the other hand, there is a lack of scientific and systematic standards and methods for evaluating the ecological benefits of artificial reefs. The existing evaluation indicators are relatively single, and it is difficult to comprehensively and accurately reflect the comprehensive influence of artificial reefs on the marine ecosystem. At the same time, when the ecological benefits are abnormal, there is a lack of effective early warning mechanism and decision support, and it is difficult to take targeted remedial measures in a timely manner, which may further exacerbate the ecological problems. SUMMARY

[0005] Therefore, the present application provides a dynamic comprehensive monitoring platform for artificial reefs, which can effectively solve the defects of the prior art that it is difficult to grasp the ecological changes in the artificial reef area in a timely manner, the evaluation indicators are relatively single, and it is difficult to take targeted remedial measures in a timely manner.

[0006] The technical scheme of the present application is as follows:

[0007] A dynamic comprehensive monitoring platform for artificial reefs comprises:

[0008] A data acquisition module is configured to acquire fish species, water quality parameters, proliferation and stocking conditions, and ecological environment state data of the artificial reef area in real time.

[0009] A classification database construction module is configured to store historical data and regional ecological characteristics according to the dimensions of sea area, reef type, and ecological index, and obtain regional benchmark data.

[0010] A dynamic monitoring and evaluation engine is configured to call the regional benchmark data in the classification database, and calculate an ecological benefit comprehensive index.

[0011] An intelligent early warning and decision support module is configured to set a safety threshold range, automatically trigger an early warning mechanism when the ecological benefit comprehensive index exceeds the safety threshold range, and generate a decision report containing repair suggestions.

[0012] As a further optional solution of the artificial reef dynamic comprehensive monitoring platform, the data acquisition module comprises:

[0013] A water quality sensor is configured to acquire water quality parameters in the artificial reef area in real time.

[0014] A fish behavior monitor is configured to track fish activity trajectories, identify fish species, and analyze group behavior based on optical technology.

[0015] An ecological environment index monitoring device is configured to acquire ecological environment state data in the artificial reef area in real time.

[0016] A dynamic scene adaptation mechanism is configured to automatically adjust the sampling frequency of the water quality sensor, fish behavior monitor, and ecological environment index monitoring device according to the environmental characteristics of the sea area.

[0017] As a further optional solution of the artificial reef dynamic comprehensive monitoring platform, the fish activity trajectory tracking, species identification, and group behavior analysis based on optical technology specifically comprises:

[0018] An optical imaging device deployed in the artificial reef area is configured to acquire a sequence of fish movement images in real time.

[0019] The sequence of fish movement images is processed according to the inter-frame difference method combined with the Kalman filter algorithm to calculate the movement trajectory parameters of fish individuals in three-dimensional space.

[0020] The sequence of fish movement images is processed based on a convolutional neural network to extract fish morphological features, and a support vector machine classifier is used to calculate the species identification confidence.

[0021] Based on the sequence of fish movement images, a fish group aggregation index and a movement synchronization index are calculated to quantify group behavior characteristics.

[0022] As a further optional solution of the artificial reef dynamic comprehensive monitoring platform, the classification database construction module comprises:

[0023] A data acquisition unit is configured to acquire historical data and regional ecological characteristics of the artificial reef area.

[0024] An ecological baseline setting unit is configured to store the historical data and regional ecological characteristics of the artificial reef area according to the dimensions of sea area, reef type, and ecological index, and obtain regional baseline data.

[0025] As a further optional solution for the aforementioned artificial reef dynamic integrated monitoring platform, the specific formula for calculating the comprehensive ecological benefit index is as follows:

[0026] ;

[0027] in, and These are weighting coefficients, representing the relative importance of biodiversity indicators and water quality indicators in the comprehensive ecological benefit index, respectively. The weight of the i-th biological or ecological indicator; The value of the i-th species' quantity or ecological indicator as monitored in real time; This serves as the baseline value for the quantity or ecological indicator of the i-th organism in this sea area or artificial reef type within the classification database. These are the current water quality parameter values. is the optimal water quality parameter value for this sea area or artificial reef type, and n is the number of monitored biological or ecological indicators.

[0028] As a further optional solution to the aforementioned integrated dynamic monitoring platform for artificial reefs, the intelligent early warning and decision support module includes:

[0029] The threshold range setting unit is used to set the safe threshold range of the comprehensive ecological benefit index based on the ecological characteristics of the sea area, the type of artificial reef, and management objectives.

[0030] The dynamic early warning triggering unit is used to compare the comprehensive ecological benefit index with the set safety threshold range. When the comprehensive ecological benefit index exceeds the safety range, the early warning mechanism is automatically triggered.

[0031] The decision report generation unit is used to generate a decision report containing repair suggestions by calling regionalized benchmark data and historical repair case library in the classification database when an early warning is triggered.

[0032] A method for comprehensive dynamic monitoring of artificial reefs, specifically including:

[0033] Real-time data collection of fish species, water quality parameters, stocking and breeding status, and ecological environment status in the artificial reef area;

[0034] Historical data and regional ecological characteristics are classified and stored according to the dimensions of sea area, artificial reef type and ecological indicators to obtain regionalized benchmark data;

[0035] The comprehensive ecological benefit index is calculated by calling regional benchmark data from the classification database and combining it with real-time collected data.

[0036] The comprehensive ecological benefit index is compared with a preset safety threshold range. When the comprehensive ecological benefit index exceeds the safety threshold range, an early warning mechanism is automatically triggered, and a decision report containing restoration suggestions is generated.

[0037] As a further optional scheme of the aforementioned comprehensive dynamic monitoring method for artificial reefs, the specific formula for calculating the comprehensive ecological benefit index is as follows:

[0038] ;

[0039] in, and These are weighting coefficients, representing the relative importance of biodiversity indicators and water quality indicators in the comprehensive ecological benefit index, respectively. Let be the weight of the i-th biological or ecological indicator. The value of the i-th species' quantity or ecological indicator as monitored in real time; This serves as the baseline value for the quantity or ecological indicator of the i-th organism in this sea area or artificial reef type within the classification database. These are the current water quality parameter values. is the optimal water quality parameter value for this sea area or artificial reef type, and n is the number of monitored biological or ecological indicators.

[0040] A computing device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any of the above-described methods for dynamic integrated monitoring of artificial reefs.

[0041] A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of any of the above-described methods for dynamic integrated monitoring of artificial reefs.

[0042] The beneficial effects of this invention are as follows: By using a data acquisition module to collect real-time data on fish species, water quality parameters, stocking and rearing status, and ecological environment status in the artificial reef area, compared to traditional methods relying on periodic manual sampling and laboratory analysis, this real-time acquisition method ensures the timeliness and continuity of data. By continuously acquiring the latest ecological data, managers can understand the ecological dynamics of the artificial reef area immediately and promptly detect trends and anomalies in ecological changes, thus overcoming the shortcomings of existing technologies in timely monitoring of ecological changes. Simultaneously, the classification database construction module categorizes and stores historical data and regional ecological characteristics according to sea area, reef type, and ecological indicators, obtaining regional benchmark data. The dynamic monitoring and evaluation engine calls upon this regional benchmark data to calculate a comprehensive ecological benefit index. This multi-dimensional classification storage and comprehensive evaluation method comprehensively considers the geographical differences of different sea areas and the different fish species... The characteristics of reef types and the interaction of various ecological indicators enable a comprehensive and systematic assessment of the ecological benefits of artificial reefs. Compared with existing technologies that rely on single assessment indicators, this platform can more accurately reflect the comprehensive impact of artificial reefs on the marine ecosystem. Furthermore, the intelligent early warning and decision support module sets a safety threshold range. When the comprehensive ecological benefit index exceeds this range, an early warning mechanism is automatically triggered, generating a decision report containing restoration recommendations. This function allows the platform to react quickly when ecological benefits are abnormal, promptly issuing early warning information to management personnel. Simultaneously, the restoration recommendations provided in the decision report are based on historical data in the classification database and regional ecological characteristics, possessing strong pertinence and operability. Management personnel can take timely restoration measures based on the recommendations in the report to prevent further deterioration of ecological problems, effectively solving the problem that existing technologies cannot promptly implement targeted restoration measures. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Fig. 1 This is a schematic diagram of the composition of an artificial reef dynamic integrated monitoring platform according to the present invention;

[0045] Fig. 2 This is a flowchart illustrating a dynamic integrated monitoring method for artificial reefs according to the present invention.

[0046] Fig. 3 This is a schematic diagram of the composition of a computing device according to the present invention. Detailed Implementation

[0047] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0048] refer to Figs. 1 to 3 A dynamic integrated monitoring platform for artificial reefs includes a data acquisition module, a classification database construction module, a dynamic monitoring and evaluation engine, and an intelligent early warning and decision support module, wherein:

[0049] A data acquisition module is used to collect data in real time on fish species, water quality parameters, stocking and rearing status, and ecological environment status in the artificial reef area; in some embodiments, the data acquisition module includes:

[0050] Water quality sensors are used to collect water quality parameters in the artificial reef area in real time.

[0051] Fish behavior monitoring devices are used for tracking fish activity trajectories, identifying species, and analyzing group behavior based on optical technology.

[0052] Ecological and environmental indicator monitoring equipment is used to collect real-time data on the ecological and environmental status of artificial reef areas.

[0053] A dynamic scene adaptation mechanism is used to automatically adjust the sampling frequency of water quality sensors, fish behavior monitors, and ecological environment indicator monitoring equipment according to the characteristics of the marine environment.

[0054] Specifically, water quality sensors, fish behavior monitors, and ecological environment indicator monitoring equipment are used to collect data on water quality parameters, fish activity, and ecological environment status in the artificial reef area. This achieves comprehensive coverage of multi-dimensional ecological information in the artificial reef area, which helps to obtain a complete ecological data profile of the artificial reef. The application of various professional monitoring equipment ensures the accuracy of data collection. For example, water quality sensors can accurately measure various water quality indicators, providing reliable data for assessing water quality. Fish behavior monitors, based on optical technology, can accurately track fish activity trajectories, identify species, and analyze group behavior, which helps to gain a deeper understanding of fish ecological habits and behavioral patterns.

[0055] The dynamic scene adaptation mechanism can automatically adjust the sampling frequency of water quality sensors, fish behavior monitors, and ecological environment indicator monitoring equipment according to the characteristics of the marine environment. Under different sea conditions, seasons, or ecological events, it can reasonably adjust the data collection frequency, ensuring that sufficiently detailed data is obtained during critical periods while reducing unnecessary data redundancy during relatively stable periods, thus improving the efficiency and relevance of data collection. The sampling frequency adjustment that adapts to changes in the marine environment makes the collected data more accurately reflect the ecological status of artificial reef areas under different environmental conditions, enhancing the reliability and practicality of the data.

[0056] In some embodiments, the fish activity trajectory tracking, species identification, and group behavior analysis based on optical technology specifically includes:

[0057] Real-time acquisition of fish movement image sequences using optical imaging devices deployed in the artificial reef area;

[0058] The fish motion image sequence is processed by combining the inter-frame difference method with the Kalman filter algorithm to calculate the motion trajectory parameters of individual fish in three-dimensional space. Specifically, the inter-frame difference method is used to extract the pixel displacement between adjacent frames, and the Kalman filter algorithm is used to dynamically correct the noise. The coordinate sequence of individual fish in three-dimensional space is calculated, and a continuous motion path is generated through trajectory smoothing (such as moving average or spline interpolation). The motion trajectory parameters are output to quantify the tracking reliability.

[0059] This paper describes a method for processing fish motion image sequences using convolutional neural networks (CNNs) to extract fish morphological features. The method is then combined with a support vector machine (SVM) classifier to calculate the species identification confidence score. Specifically, the CNNs are used to perform depth extraction of fish morphological features (such as body length, fin shape, and markings) from the image sequences to generate feature vectors. These feature vectors are then input into an SVM classifier, which compares the results with a pre-trained fish database to output the species identification confidence score and classification results.

[0060] Fish group aggregation index and movement synchronization index are calculated based on fish motion image sequences to quantify group behavior characteristics.

[0061] Specifically, optical imaging devices deployed in the artificial reef area are used to acquire real-time sequences of fish movement images. These images are then processed using a combination of inter-frame difference and Kalman filtering algorithms. Inter-frame difference effectively captures changes between adjacent frames in the image sequence, quickly locating the fish's movement position. The Kalman filtering algorithm optimizes the measurement data, reducing noise interference, thereby calculating more accurate movement trajectory parameters of individual fish in three-dimensional space. This enables precise tracking of fish activity trajectories and helps to gain a deeper understanding of the activity range and patterns of fish in the artificial reef area.

[0062] This paper utilizes convolutional neural networks (CNNs) to process fish motion image sequences. CNNs possess powerful feature extraction capabilities, automatically learning and extracting morphological features of fish, such as shape, color, and texture. Compared to traditional feature extraction methods, CNNs can more comprehensively and deeply mine features in fish images, improving the efficiency and accuracy of feature extraction. Combined with a support vector machine (SVM) classifier, the extracted fish morphological features are used to calculate species identification confidence. SVM is an effective classification algorithm that can find the optimal classification hyperplane in a high-dimensional feature space, accurately classifying different fish species. By calculating the confidence score, the reliability of the classification results can be intuitively understood, providing accurate fish species information for subsequent ecological research and management.

[0063] Based on fish motion image sequences, fish group aggregation index and motion synchronicity index are calculated to quantify group behavior characteristics. The group aggregation index reflects the density of fish groups, helping to understand the distribution of fish in artificial reef areas. The motion synchronicity index measures the coordination of individual movements within a group, revealing the group behavior patterns and social interaction characteristics of fish. By quantifying group behavior characteristics, we can study the ecological behavior of fish groups in greater depth, such as foraging, reproduction, and predator avoidance. This information is of great significance for assessing the attractiveness and protective effect of artificial reefs on fish groups, and helps to optimize the design and management strategies of artificial reefs, thereby improving their ecological benefits.

[0064] It should be noted that the formula for calculating the trajectory parameters of an individual fish in three-dimensional space is as follows:

[0065] ;

[0066] in, For motion trajectory parameters, Let be the actual detected coordinates at time t. To predict coordinates, , , N represents the total number of frames, which is the length of the monitoring area boundary.

[0067] The confidence score for category identification is calculated using the following formula:

[0068] ;

[0069] in, To determine the confidence level for species identification, Let K be the classification score of the m-th fish category, K be the total number of categories, and M be the number of validation samples.

[0070] The formula for calculating the fish community aggregation index is as follows:

[0071] ;

[0072] in, This is a fish community aggregation index. Let G be the number of individuals in the i-th group, G be the total number of groups, and N be the total number of individuals.

[0073] The motion synchronization index is calculated using the following formula:

[0074] ;

[0075] in, The motion synchronization index, Let be the standard deviation of the velocity of an individual within the i-th group. This represents the average speed.

[0076] A classification database construction module is used to classify and store historical data and regional ecological characteristics according to the dimensions of sea area, artificial reef type, and ecological indicators to obtain regionalized benchmark data; in some embodiments, the classification database construction module includes:

[0077] The data acquisition unit is used to acquire historical data and regional ecological characteristics of the artificial reef area.

[0078] The ecological baseline setting unit is used to classify and store historical data and regional ecological characteristics of artificial reef areas according to the dimensions of sea area, reef type and ecological indicators, so as to obtain regionalized baseline data.

[0079] Specifically, by classifying and storing historical data and regional ecological characteristics according to the dimensions of sea area, artificial reef type, and ecological indicators, the previously chaotic data becomes well-organized. This multi-dimensional classification method meets the actual needs of artificial reef ecological research. Different sea areas have different geographical environments and ecological conditions, and different artificial reef types have different impacts on the surrounding ecology. Ecological indicators directly reflect various characteristics of the ecosystem. Through this classification and storage, relevant ecological data for specific sea areas and specific artificial reef types can be quickly located and obtained, improving data management efficiency and query speed. Storing historical data in combination with regional ecological characteristics helps to discover the inherent connections between data. For example, it is possible to analyze the evolution of ecological indicators in a certain sea area over different time periods as artificial reef types change. This data integration and correlation facilitates in-depth research on the ecological effects of artificial reefs, enabling data analysis from multiple perspectives, both holistic and local.

[0080] The ecological baseline setting unit generates regionalized baseline data based on the classified and stored data. Because it takes into account factors such as sea area and artificial reef type, these baseline data are more targeted and accurate. Different sea areas have different ecological background values, and different artificial reef types have different effects on ecological improvement. Regionalized baseline data can accurately reflect the normal ecological state under specific areas and artificial reef types.

[0081] It should be noted that the historical data includes, but is not limited to, fish-related data (e.g., species information, quantity changes, growth and reproduction data), water quality parameter data (e.g., conventional indicators, nutrient concentration, pollutant content), and stocking data (e.g., released species and quantity, release time and location); the regional ecological characteristics include, but are not limited to, marine geographical characteristics (e.g., geographical location and extent, seabed topography, coastline characteristics), artificial reef characteristics (e.g., artificial reef type, artificial reef distribution, artificial reef material and scale), and ecological community characteristics (e.g., biodiversity, food chain structure).

[0082] A dynamic monitoring and evaluation engine is used to call regional benchmark data from a classification database to calculate a comprehensive ecological benefit index; in some embodiments, the specific formula for calculating the comprehensive ecological benefit index is as follows:

[0083] ;

[0084] in, and These are weighting coefficients, representing the relative importance of biodiversity indicators and water quality indicators in the comprehensive ecological benefit index, respectively. The weight of the i-th biological or ecological indicator; The value of the i-th species' quantity or ecological indicator as monitored in real time; This serves as the baseline value for the quantity or ecological indicator of the i-th organism in this sea area or artificial reef type within the classification database. These are the current water quality parameter values. is the optimal water quality parameter value for this sea area or artificial reef type, and n is the number of monitored biological or ecological indicators.

[0085] Specifically, the formula comprehensively considers multiple factors, including biodiversity indicators and water quality indicators, and uses weighting coefficients. and By assigning different indicators relative importance in the ecological benefit assessment, this comprehensive assessment method avoids the limitations of single-indicator assessment and can more comprehensively and scientifically reflect the ecological benefits of artificial reef areas. For example, it considers both the quantity and diversity of organisms such as fish and the impact of water quality parameters on the ecosystem, making the assessment results more objective and accurate.

[0086] The formula uses real-time monitored biological populations or ecological indicators (such as...) ) and benchmark values ​​in the classification database ( The comprehensive ecological benefit index is derived by calculating the relationship between the two. As time goes by, real-time data is continuously updated, and the comprehensive ecological benefit index can dynamically reflect the changes in the ecological benefits of the artificial reef area. Managers can understand the health status and development trend of the ecosystem in a timely manner based on the changing trend of the comprehensive ecological benefit index.

[0087] An intelligent early warning and decision support module is used to set a safety threshold range. When the comprehensive ecological benefit index exceeds the safety threshold range, an early warning mechanism is automatically triggered, and a decision report containing remediation suggestions is generated. In some embodiments, the intelligent early warning and decision support module includes:

[0088] The threshold range setting unit is used to set the safe threshold range of the comprehensive ecological benefit index based on the ecological characteristics of the sea area, the type of artificial reef, and management objectives, including the lower threshold and the upper threshold.

[0089] The dynamic early warning triggering unit is used to compare the comprehensive ecological benefit index with the set safety threshold range. When the comprehensive ecological benefit index is less than the lower threshold or greater than the upper threshold, the early warning mechanism is automatically triggered.

[0090] The decision report generation unit is used to generate a decision report containing repair suggestions by calling regionalized benchmark data and historical repair case library in the classification database when an early warning is triggered.

[0091] It should be noted that the multi-level early warning mechanism includes:

[0092] Level 1 Warning (EBI exceeds the threshold by less than 10%): Notify the administrator via SMS and email;

[0093] Level 2 warning (EBI exceeds the threshold by 10%~30%): Warning information is pushed to mobile terminals simultaneously, and high-definition video surveillance of the local area is initiated for review;

[0094] Level 3 warning (EBI exceeds the threshold by more than 30%): Triggers the audible and visual alarm device and automatically reports to the marine ecological supervision department.

[0095] Repair suggestions include:

[0096] Water quality issues: It is recommended to increase the species and quantity of filter-feeding fish (such as silver carp and bighead carp) for stock enhancement, or to initiate an artificial wetland purification program;

[0097] Biological issues: Suggestions for adjusting the stock enhancement and release strategy (such as increasing the proportion of native species and controlling the release density) or deploying artificial reef-attached habitats;

[0098] Structural issues: Generate a priority list for artificial reef restoration, including the location of damaged artificial reefs, selection of restoration materials (such as concrete and eco-ceramics), and recommendations for construction windows.

[0099] A method for comprehensive dynamic monitoring of artificial reefs, specifically including:

[0100] Real-time data collection of fish species, water quality parameters, stocking and breeding status, and ecological environment status in the artificial reef area;

[0101] Historical data and regional ecological characteristics are classified and stored according to the dimensions of sea area, artificial reef type and ecological indicators to obtain regionalized benchmark data;

[0102] The comprehensive ecological benefit index is calculated by calling regional benchmark data from the classification database and combining it with real-time collected data.

[0103] The comprehensive ecological benefit index is compared with a preset safety threshold range. When the comprehensive ecological benefit index exceeds the safety threshold range, an early warning mechanism is automatically triggered, and a decision report containing restoration suggestions is generated.

[0104] In some embodiments, the specific formula for calculating the comprehensive ecological benefit index is as follows:

[0105] ;

[0106] in, and These are weighting coefficients, representing the relative importance of biodiversity indicators and water quality indicators in the comprehensive ecological benefit index, respectively. Let be the weight of the i-th biological or ecological indicator. The value of the i-th species' quantity or ecological indicator as monitored in real time; This serves as the baseline value for the quantity or ecological indicator of the i-th organism in this sea area or artificial reef type within the classification database. These are the current water quality parameter values. is the optimal water quality parameter value for this sea area or artificial reef type, and n is the number of monitored biological or ecological indicators.

[0107] A computing device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any of the above-described methods for dynamic integrated monitoring of artificial reefs.

[0108] A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of any of the above-described methods for dynamic integrated monitoring of artificial reefs.

[0109] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A dynamic integrated monitoring platform for artificial reefs, characterized in that, include: The data acquisition module is used to collect data on fish species, water quality parameters, stocking and breeding status, and ecological environment status in the artificial reef area in real time. The classification database construction module is used to classify and store historical data and regional ecological characteristics according to the dimensions of sea area, artificial reef type and ecological indicators to obtain regionalized benchmark data. The dynamic monitoring and evaluation engine is used to call regional benchmark data from the classification database to calculate the comprehensive ecological benefit index. The intelligent early warning and decision support module is used to set a safety threshold range. When the comprehensive ecological benefit index exceeds the safety threshold range, an early warning mechanism is automatically triggered, and a decision report containing remediation suggestions is generated.

2. The artificial reef dynamic integrated monitoring platform according to claim 1, characterized in that, The data acquisition module includes: Water quality sensors are used to collect water quality parameters in the artificial reef area in real time. Fish behavior monitoring devices are used for tracking fish activity trajectories, identifying species, and analyzing group behavior based on optical technology. Ecological and environmental indicator monitoring equipment is used to collect real-time data on the ecological and environmental status of artificial reef areas. A dynamic scene adaptation mechanism is used to automatically adjust the sampling frequency of water quality sensors, fish behavior monitors, and ecological environment indicator monitoring equipment according to the characteristics of the marine environment.

3. The artificial reef dynamic integrated monitoring platform according to claim 2, characterized in that, The aforementioned optical technology-based fish activity tracking, species identification, and group behavior analysis specifically includes: Real-time acquisition of fish movement image sequences using optical imaging devices deployed in the artificial reef area; The fish motion image sequence was processed by combining the inter-frame difference method with the Kalman filter algorithm to calculate the motion trajectory parameters of individual fish in three-dimensional space; The fish motion image sequence is processed based on convolutional neural network to extract fish morphological features, and combined with support vector machine classifier to calculate species identification confidence. Fish group aggregation index and movement synchronization index are calculated based on fish motion image sequences to quantify group behavior characteristics.

4. The artificial reef dynamic integrated monitoring platform according to claim 3, characterized in that, The classification database construction module includes: The data acquisition unit is used to acquire historical data and regional ecological characteristics of the artificial reef area. The ecological baseline setting unit is used to classify and store historical data and regional ecological characteristics of artificial reef areas according to the dimensions of sea area, reef type and ecological indicators, so as to obtain regionalized baseline data.

5. The artificial reef dynamic integrated monitoring platform according to claim 4, characterized in that, The specific formula for calculating the comprehensive ecological benefit index is as follows: ； in, and These are weighting coefficients, representing the relative importance of biodiversity indicators and water quality indicators in the comprehensive ecological benefit index, respectively. The weight of the i-th biological or ecological indicator; The value of the i-th species' quantity or ecological indicator as monitored in real time; This serves as the baseline value for the quantity or ecological indicator of the i-th organism in this sea area or artificial reef type within the classification database. These are the current water quality parameter values. is the optimal water quality parameter value for this sea area or artificial reef type, and n is the number of monitored biological or ecological indicators.

6. The artificial reef dynamic integrated monitoring platform according to claim 5, characterized in that, The intelligent early warning and decision support module includes: The threshold range setting unit is used to set the safe threshold range of the comprehensive ecological benefit index based on the ecological characteristics of the sea area, the type of artificial reef, and management objectives. The dynamic early warning triggering unit is used to compare the comprehensive ecological benefit index with the set safety threshold range. When the comprehensive ecological benefit index exceeds the safety range, the early warning mechanism is automatically triggered. The decision report generation unit is used to generate a decision report containing repair suggestions by calling regionalized benchmark data and historical repair case library in the classification database when an early warning is triggered.

7. A method for comprehensive dynamic monitoring of artificial reefs, characterized in that, Specifically, it includes: Real-time data collection of fish species, water quality parameters, stocking and breeding status, and ecological environment status in the artificial reef area; Historical data and regional ecological characteristics are classified and stored according to the dimensions of sea area, artificial reef type and ecological indicators to obtain regionalized benchmark data; The comprehensive ecological benefit index is calculated by calling regional benchmark data from the classification database and combining it with real-time collected data. The comprehensive ecological benefit index is compared with a preset safety threshold range. When the comprehensive ecological benefit index exceeds the safety threshold range, an early warning mechanism is automatically triggered, and a decision report containing restoration suggestions is generated.

8. The method for dynamic integrated monitoring of artificial reefs according to claim 7, characterized in that, The specific formula for calculating the comprehensive ecological benefit index is as follows: ； in, and These are weighting coefficients, representing the relative importance of biodiversity indicators and water quality indicators in the comprehensive ecological benefit index, respectively. Let be the weight of the i-th biological or ecological indicator. The value of the i-th species' quantity or ecological indicator as monitored in real time; This serves as the baseline value for the quantity or ecological indicator of the i-th organism in this sea area or artificial reef type within the classification database. These are the current water quality parameter values. is the optimal water quality parameter value for this sea area or artificial reef type, and n is the number of monitored biological or ecological indicators.

9. A computing device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the integrated dynamic monitoring method for artificial reefs according to any one of claims 7-8.

10. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the steps of the dynamic integrated monitoring method for artificial reefs according to any one of claims 7-8.

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