Fishery resource evaluation system based on multi-source data fusion

Abstract

The invention relates to the technical field of fishery resource evaluation, in particular to a fishery resource evaluation system based on multi-source data fusion. The system comprises a data acquisition module, a data processing module, a multi-source data fusion module, a resource evaluation module, an early warning and notification module, a log archiving and updating module, a system management and maintenance module and a user interface module. According to the method, fishery resources are monitored in real time through sonar, radar and satellite remote sensing, and the fishery resources are evaluated through a Bayesian classifier algorithm of multi-source data fusion; the early warning and notification module can ensure that potential risks can be coped with in time; and the log archiving and updating module is responsible for recording important log information and supporting checking through a fishing log APP so as to ensure the traceability of data and the maintainability of the system. According to the method, the accuracy and efficiency of fishery resource evaluation are improved, powerful support is provided for fishery sustainable development, and the intelligence and safety level of fishery management can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of fishery resource assessment, and in particular to a fishery resource assessment system integrating multi-source data. Background Art

[0002] With the continuous development and utilization of global fishery resources, the sustainable management of fishery resources has become an important challenge facing the current fishery sector. Traditional fishery resource assessment methods mainly rely on a single data source through manual surveys, which is not only time-consuming and labor-intensive, but also difficult to accurately reflect the dynamic changes and spatial distribution of fishery resources. Especially in the face of complex and changing marine environments, the limitations of traditional assessment methods are particularly prominent.

[0003] With the advancement of science and technology, modern monitoring technologies such as sonar, radar, and satellite remote sensing are increasingly used in the field of fisheries. These technologies make it possible to monitor fishery resources in real time, but they also bring new challenges such as massive data and multi-source heterogeneity. How to effectively integrate these multi-source data and improve the accuracy and efficiency of fishery resource assessment has become an urgent problem to be solved in the current field of fishery resource assessment.

[0004] As an advanced data processing technology, multi-source data fusion technology can eliminate redundancy and contradictions between data while retaining the effective information of each data source, thereby improving the overall quality and reliability of data. However, in the field of fishery resource assessment, the application of multi-source data fusion technology is still in the exploratory stage, and a mature system and method has not yet been formed.

[0005] Therefore, there is an urgent need for a fishery resource assessment system that integrates multi-source data to monitor the status of fishery resources in real time and accurately, and to integrate multi-source data through advanced data processing algorithms to achieve accurate and comprehensive assessment of fishery resources. At the same time, the system should also have early warning and notification functions to ensure that relevant personnel can be notified in a timely manner when abnormalities or potential risks are found. It should also have log archiving and updating functions, which can be responsible for recording all important log information during the operation of the system, facilitating the development and calling of fishing log APP, and realizing seamless docking and sharing of data. Summary of the invention

[0006] The present invention aims to provide a fishery resource assessment system with multi-source data fusion, which realizes real-time monitoring and comprehensive assessment of fishery resources by integrating sonar, radar, and satellite remote sensing data collection methods and combining the data fusion algorithm of Bayesian classifier. The system can not only provide a scientific basis for fishery management and decision-making, but also has early warning and notification functions to ensure timely response to potential risks. At the same time, the log archiving and update module ensures the traceability of data and the maintainability of the system.

[0007] The present invention provides a fishery resource assessment system for multi-source data fusion, the system comprising:

[0008] Data acquisition module: responsible for collecting relevant data on fishery resources in real time through sonar, radar, and satellite remote sensing technology, including fish distribution, quantity, size, and movement trajectory, and outputting it to the data processing module.

[0009] Data processing module: pre-process the collected raw data, remove outliers, duplicate values ​​and incomplete data through data cleaning, and then perform denoising and data standardization.

[0010] Multi-source data fusion module: uses the Bayesian classifier algorithm to fuse information from different data sources such as sonar, radar, and satellite remote sensing.

[0011] Resource assessment module: Based on the integrated data, statistical methods are used to conduct a comprehensive assessment of fishery resources, including key indicators such as resource quantity, distribution status, and change trends, and a detailed assessment report is automatically generated.

[0012] Log archiving and updating module: responsible for recording all important log information during the operation of the system, including the data fusion processing results stored in time series and the evaluation report generated by the resource evaluation module. At the same time, this module receives log information and updates the display information on the fishing log APP in real time.

[0013] Early warning and notification module: Based on the results of resource assessment, the changes in fishery resources are monitored in real time. Once an abnormal decrease in resource volume, illegal fishing activities or other potential risks are found, the early warning mechanism is immediately triggered and the fishery management department and relevant personnel are notified through the APP push.

[0014] System management and maintenance module: provides comprehensive user management, permission settings and data security functions to ensure stable operation and continuous optimization of the system, while also providing system logs, performance monitoring and troubleshooting tools.

[0015] User interface module: used to design an intuitive and easy-to-use user interface, allowing users to easily view fishery resource assessment results, receive early warning notifications, and manage system configuration functions, thereby improving user experience and ease of operation.

[0016] Furthermore, the data acquisition module is specifically used for:

[0017] Sonar uses underwater sound wave transmission and reception to detect the distribution, quantity and size of fish. Radar uses the propagation and reflection principle of electromagnetic waves in the air to monitor the movement trajectory and group behavior of fishery resources on the water surface and nearshore areas. Satellite remote sensing uses sensors carried by satellites to periodically scan large areas of sea to obtain the macro distribution and change trends of fishery resources.

[0018] Furthermore, the data processing module is specifically used for:

[0019] The raw data is identified and outliers, duplicate values, and incomplete data are removed. For outliers, the IQR (interquartile range) method is used to eliminate or correct them. Then, the noise interference in the data is removed through low-pass filtering technology. The data is standardized through Z-score, and all data are converted to the same scale or dimensionless range, so that data from different sources can be directly compared and integrated.

[0020] Furthermore, the multi-source data fusion module is specifically used for:

[0021] Extract effective features from the pre-processed data of sonar, radar and satellite remote sensing according to their respective physical meanings and characteristics. For sonar data, extract echo intensity and frequency features; for radar data, extract target speed, direction and shape; for satellite remote sensing data, extract spectral features and species index.

[0022] Based on historical data and expert knowledge, a priori probabilities are set for different feature categories of each data source, denoted as P(A). For the observation data provided by each data source, its likelihood function P(B|A) under different categories is calculated. Using the Bayesian formula, the posterior probability P(A|B) of the observation data under a given category is calculated by combining the prior probability P(A) and the likelihood function P(B|A). The basic form of the Bayesian formula is:

[0023]

[0024] Where P(B) is the marginal probability, that is, the total probability of event B occurring, which is calculated by considering all possible events A using the total probability formula:

[0025]

[0026] Finally, decision fusion is performed based on the size of the posterior probability P(A|B). The maximum posterior probability criterion is adopted, that is, the category with the largest posterior probability is selected as the final fusion result. The output fusion results include target recognition, position estimation, and state monitoring information.

[0027] Furthermore, the resource evaluation module is specifically used to:

[0028] By analyzing the fused data, the size, weight or biomass of different types of fish can be estimated through statistical methods, and the total amount of fishery resources in a specific area can be recorded. The spatial distribution of fishery resources, including horizontal and vertical distribution, can be used to record the gradient distribution of fish in the sea area and the density of fish in different water layers. The trend of fishery resources over time, including long-term trends and seasonal fluctuations, can be monitored, and future resource conditions can be predicted to provide data reports for the formulation and adjustment of fishery policies.

[0029] Furthermore, the log archiving and updating module is specifically used for:

[0030] Collect the log information fed back by each part of the system and arrange it in chronological order. Store the log information in the database in chronological order and create an index for subsequent query.

[0031] By updating the push interface, the latest log information and assessment reports are pushed to the fishing log APP in real time, ensuring that relevant personnel can view the latest information at any time.

[0032] Archive and back up log information to prevent data loss or damage. Archived log information can still be used for subsequent query and analysis.

[0033] Furthermore, the warning and notification module is specifically used for:

[0034] According to historical data, expert opinions and fishery policies, reasonable warning thresholds are set for different resource indicators. The warning thresholds are divided into three levels: primary warning, intermediate warning and advanced warning. Statistical methods are used to detect anomalies in real-time data, and abnormal high-frequency fishing and exceeding fishing limits are used to automatically identify abnormal resource reduction and illegal fishing activities.

[0035] When an abnormal situation is detected, an early warning signal is generated and the abnormal information is recorded. The early warning signal includes the abnormal type, time of occurrence, location, and scope of impact. After the system generates the early warning information, it is pushed to the fishing log APP for relevant personnel to view and handle.

[0036] Furthermore, the system management and maintenance module is specifically used for:

[0037] Responsible for the registration and login operations of system users, and divide them into administrators and ordinary users according to different user roles to ensure that different users have appropriate access rights. Set detailed access control policies according to user roles and business needs. Implement data encryption and access control to protect system data from illegal access and tampering. Record all important events and operation logs during system operation, including user login, data operation, system anomalies, and provide log query and analysis functions to facilitate operation and maintenance personnel to track problems, troubleshoot and optimize system performance.

[0038] Furthermore, the user interface module is specifically used for:

[0039] Provides graphical display, choose to use maps and charts to intuitively display the distribution of fishery resources and assessment results. Users can view detailed assessment reports of fishery resources, including resource volume, fishing intensity, and ecological index through the user interface module. When the system detects an abnormal situation and triggers the early warning mechanism, the user interface module will push the early warning notification to the user in real time. The notification content includes the type of abnormality, time of occurrence, location, scope of impact, and recommended measures.

[0040] Beneficial effects:

[0041] The invention provides a fishery resource assessment system for multi-source data fusion, which includes data collection, data processing, multi-source data fusion, resource assessment, log archiving and updating, early warning and notification, system management and maintenance, and user interface modules. The system realizes comprehensive real-time monitoring of the distribution, quantity, size and movement trajectory of fishery resources by adopting sonar, radar and satellite remote sensing technology. The data processing module effectively cleans and standardizes data to ensure data quality. The multi-source data fusion module uses the Bayesian classifier algorithm to effectively integrate information from different data sources and improve the consistency and accuracy of data. The resource assessment module uses statistical methods based on the fused data to comprehensively assess fishery resources and provide a scientific basis for management. The early warning and notification module can monitor resource changes in real time, trigger an early warning immediately once an abnormal situation is found, and notify relevant personnel through APP push notification. The system management and maintenance module ensures the stable operation and continuous optimization of the system. The user interface module is intuitively designed and easy to use, supporting users to easily view assessment results, receive early warning notifications and manage system configuration, thereby improving user experience and operation convenience. The system provides strong technical support for the scientific management of fishery resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained according to the structures shown in these drawings without creative work.

[0043] Figure 1 A flow chart of a fishery resource assessment system for multi-source data fusion in an embodiment of the present invention;

[0044] Figure 2 The figure is a block diagram of a fishery resource assessment system for multi-source data fusion in an embodiment of the present invention. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical scheme and advantages of the present application clearer, some embodiments of the present application are further described in detail below in conjunction with the accompanying drawings and Examples. It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application. However, it will be appreciated by those skilled in the art that in the various embodiments of the present application, many technical details are proposed in order to enable the reader to better understand the present application. However, even without these technical details and various changes and modifications based on the following embodiments, the technical scheme claimed in the present application can also be implemented.

[0046] Example 1: See Figure 1 , a fishery resource assessment system based on multi-source data fusion, including:

[0047] Data collection module: Real-time collection of fishery resource data through sonar, radar and satellite remote sensing technology. Sonar uses underwater sound wave transmission and reception to detect the distribution, quantity and size of fish. Radar monitors the movement trajectory and group behavior of fishery resources on the water surface and nearshore areas through the propagation and reflection of electromagnetic waves. Satellite remote sensing uses the sensors on board to periodically scan a large area of ​​sea to obtain the macro distribution and change trend of fishery resources.

[0048] Data processing module: pre-process the data, including identifying and removing outliers, duplicate values, and incomplete data. For outliers, the IQR (interquartile range) method is used to eliminate or correct them to ensure data quality. At the same time, low-pass filtering technology is used to remove noise interference in the data to improve the signal-to-noise ratio and reliability of the data. Finally, the Z-score is used to standardize the data and convert all data to the same scale or dimensionless range to facilitate direct comparison and fusion of data from different sources.

[0049] Multi-source data fusion module: extract effective features from the pre-processed data of sonar, radar, and satellite remote sensing according to their respective physical meanings and characteristics. For example, sonar data extracts echo intensity and frequency features; radar data extracts target speed, direction, and shape; and satellite remote sensing data extracts spectral features and species index.

[0050] Based on historical data and expert knowledge, a priori probabilities are set for different feature categories of each data source, denoted as P(A). For the observation data provided by each data source, its likelihood function P(B|A) under different categories is calculated. Using the Bayesian formula, the posterior probability P(A|B) of the observation data under a given category is calculated by combining the prior probability P(A) and the likelihood function P(B|A). The basic form of the Bayesian formula is:

[0051]

[0052] Where P(B) is the marginal probability, that is, the total probability of event B occurring, which is calculated by considering all possible events A using the total probability formula:

[0053]

[0054] Finally, decision fusion is performed based on the size of the posterior probability P(A|B). The maximum posterior probability criterion is adopted, that is, the category with the largest posterior probability is selected as the final fusion result.

[0055] Resource assessment module: Use statistical methods to estimate the size, weight or biomass of different types of fish, record the total amount of fishery resources in a specific area, and analyze the spatial distribution of fishery resources, including horizontal and vertical distribution, as well as the gradient distribution of fish in the sea area and the density of fish in different water layers. Monitor the trend of fishery resources over time, including long-term trends and seasonal fluctuations, to predict future resource conditions.

[0056] Log archiving and updating module: collects log information fed back by various parts of the system and stores it in the database in chronological order. At the same time, an index is established to facilitate subsequent queries. By updating the push interface, the module can also push the latest log information and evaluation reports to the fishing log APP in real time, ensuring that relevant personnel can view the latest information at any time. In addition, the module will archive and back up the log information to prevent data loss or damage.

[0057] Early warning and notification module: According to historical data, expert opinions and fishery policies, reasonable early warning thresholds are set for different resource indicators, and divided into three levels: primary warning, intermediate warning and advanced warning. Statistical methods are used to detect anomalies in real-time data, and automatically identify abnormal resource reduction and illegal fishing activities. When an abnormal situation is detected, the module will generate an early warning signal and record the abnormal information, which will then be pushed to the fishing log APP for relevant personnel to view and handle.

[0058] System management and maintenance module: provides user registration and login operations, and divides different user roles into administrators and ordinary users to ensure that different users have appropriate access rights. At the same time, data encryption and access control are implemented to protect system data from illegal access and tampering. At the same time, all important events and operation logs during system operation are recorded, and log query and analysis functions are provided to facilitate operation and maintenance personnel to track problems, troubleshoot faults and optimize system performance.

[0059] User interface module: Provides graphical display function, and chooses to display the distribution of fishery resources and assessment results intuitively through maps and charts. Users can view detailed assessment reports of fishery resources through this module, including resource volume, fishing intensity, and ecological index. When the system detects an abnormal situation and triggers the early warning mechanism, the user interface module will also push early warning notifications to users in real time so that users can take timely countermeasures.

[0060] Example 2: See Figure 2 , based on a fishery resource assessment system with multi-source data fusion described in Example 1, taking fish farming in a certain inner pond as an example, the growth status of fish is monitored through this system, and multiple key indicators of fish farming in the inner pond are evaluated, so as to achieve refined management and optimization of the fish farming process. This system uses sonar and radar to continuously track the growth rate, body shape changes and behavior patterns of fish, and provide fish farmers with intuitive growth curves and group dynamics information. It can estimate the fish biomass and potential yield of the inner pond based on real-time monitoring data, providing a scientific basis for fish farming planning and market forecasting. Fish farmers can reasonably adjust the fish farming density, feeding strategy and disease prevention and control measures according to the evaluation report given by the system to achieve sustainable development of inner pond fish farming. The specific process includes:

[0061] Data collection module: Using sonar and radar technology, real-time data from the inner pond fish farming base is collected. Sonar technology accurately measures the length, weight and growth rate of fish, providing an intuitive growth curve. Radar technology monitors the group dynamics of fish, including swimming trajectories, aggregation levels and activity habits.

[0062] Data processing module: pre-process the collected raw data, including denoising, outlier removal and standardization. Then, use statistical methods to estimate the fish biomass and potential yield in the pond, providing a scientific basis for fish farming planning and market forecasting.

[0063] Multi-source data fusion module: Combines sonar, radar and fish behavior pattern data, integrates information from different data sources, evaluates the suitability of the pond environment and the health status of fish, tracks the growth rate and body shape changes of fish in real time, and provides accurate growth data for fish farmers.

[0064] Resource assessment module: Use statistical methods to estimate the size, weight or biomass of different types of fish, and record the total amount of fishery resources in a specific pond. Analyze the distribution of fishery resources in the pond, including horizontal and vertical distribution, as well as the density of fish in different water layers. Provide scientific fish farming decision-making suggestions based on the fused data, and give measures to adjust fish farming density, feeding strategies and disease prevention and control.

[0065] Log archiving and updating module: collects log information fed back by various parts of the system, including data collection time, collection source, fish growth rate, body shape change data, behavior pattern recognition results, biomass estimation model input and output, and potential yield forecast. It is stored in the database in chronological order and indexed for subsequent queries. Fish farmers can easily query historical data, compare fish growth conditions and breeding environments in different time periods, and provide more comprehensive information support for breeding decisions. At the same time, this module can push the latest log information and evaluation reports to the fishing log APP in real time.

[0066] Early warning and notification module: According to historical data, expert opinions and fishery policies, reasonable early warning thresholds are set for different resource indicators, and divided into three levels: primary warning, intermediate warning and advanced warning. Once an abnormality or key event is detected, such as water quality deterioration, abnormal fish growth, and disease outbreak, the system will immediately trigger the early warning mechanism, generate early warning information, and pass it to fish farmers and related personnel in a timely and accurate manner through APP push.

[0067] System management and maintenance module: provides user registration and login operations, and divides different user roles into administrators and ordinary users to ensure that different users have appropriate access rights. This module is responsible for monitoring the real-time operating status of the system, ensuring the smooth progress of data collection, processing, transmission and storage, and regularly evaluating the system's performance to ensure that the system can maintain stable operation under high load.

[0068] User interface module: provides graphical display function, intuitively displaying the distribution of fishery resources and evaluation results of fish farming in ponds through maps and charts. Fish farmers can view detailed fish farming evaluation reports through this module, including resource quantity, feeding suggestions, and ecological indexes, which can provide a scientific basis for fish farming planning and market forecasting.

[0069] Example 3: Based on the multi-source data fusion fishery resource assessment system described in Example 1, taking a certain nearshore aquaculture as an example, this system monitors the growth status of fish and evaluates multiple key indicators of aquaculture, thereby achieving refined management and optimization of the breeding process. This system uses sonar and nearshore radar technology to continuously track the growth rate, body shape changes and behavior patterns of fish, providing breeders with intuitive growth curves and group dynamics information. It can estimate the biomass and potential yield of fish in the breeding waters based on real-time monitoring data, providing a scientific basis for breeding planning and market forecasting. Breeders can reasonably adjust the breeding density, feeding strategy and disease prevention and control measures based on the evaluation report provided by the system to achieve sustainable aquaculture development. The specific process includes:

[0070] Data collection module: Real-time data collection of aquaculture bases is carried out through sonar, radar and satellite remote sensing technologies. Sonar technology accurately measures the length, weight and growth rate of fish, providing an intuitive growth curve. Nearshore radar technology monitors the group dynamics of fish, including swimming trajectories, aggregation levels and activity habits. Satellite remote sensing uses sensors to periodically scan large areas of sea, monitor the environmental conditions of aquaculture waters, and obtain the macro distribution and change trends of nearshore aquatic resources.

[0071] Data processing module: pre-process the collected raw data, including denoising, outlier removal and standardization. Then, use statistical methods to estimate the fishery biomass and potential yield in the aquaculture waters, providing a scientific basis for aquaculture planning and market forecasting.

[0072] Multi-source data fusion module: Combines sonar, radar, satellite remote sensing data and fish behavior pattern data, integrates information from different data sources, evaluates the suitability of the breeding environment and the health status of fish, tracks the growth rate and body shape changes of fish in real time, and provides accurate growth data for breeders.

[0073] Resource assessment module: Use statistical methods to estimate the size, weight or biomass of different types of fish, record the total amount of fishery resources in a specific area, and analyze the spatial distribution of fishery resources, including horizontal and vertical distribution, as well as the gradient distribution of fish in the sea area and the density of fish in different water layers. Provide scientific breeding decision-making suggestions based on the fused data, and give measures to adjust breeding density, feeding strategy and disease prevention and control.

[0074] Log archiving and updating module: collects log information fed back by various parts of the system, including data collection time, collection source, fish growth rate, body size change data, behavior pattern recognition results, biomass estimation model input and output, and potential yield forecast. Then it is stored in chronological order in the database and indexed for subsequent queries. Through the log archiving and updating module, farmers can easily query historical data, compare the growth status of fish and the breeding environment in different time periods, and provide more comprehensive information support for breeding decisions. By updating the push interface, the module can also push the latest log information and evaluation reports to the fishing log APP in real time, ensuring that relevant personnel can view the latest information at any time.

[0075] Early warning and notification module: According to historical data, expert opinions and fishery policies, reasonable early warning thresholds are set for different resource indicators, and divided into three levels: primary warning, intermediate warning and advanced warning. Once an abnormality or key event is detected, such as water quality deterioration, abnormal fish growth, and disease outbreak, the system will immediately trigger the early warning mechanism and generate early warning information. The notification function is responsible for delivering the early warning information to farmers and related personnel in a timely and accurate manner. The notification method uses APP push to ensure the complete transmission of information.

[0076] System management and maintenance module: provides user registration and login operations, and divides different user roles into administrators and ordinary users to ensure that different users have appropriate access rights. In a nearshore aquaculture example, this module is not only responsible for monitoring the real-time operating status of the system to ensure the smooth progress of data collection, processing, transmission and storage, but also regularly evaluates the system's performance to ensure that the system can maintain stable operation under high load.

[0077] User interface module: Provides graphical display functions, using maps and charts to intuitively display the distribution of fishery resources and assessment results of nearshore aquaculture. Through this module, you can view detailed assessment reports on aquaculture, including resource volume, fishing intensity, and ecological index. Based on the resource assessment report, it can provide a scientific basis for aquaculture planning and market forecasting.

[0078] The embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the present invention is limited by the appended claims rather than the foregoing description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0079] It should be understood that those skilled in the art can make improvements or changes based on the above description, and all such improvements and changes should fall within the scope of protection of the appended claims of the present invention. Parts not described in detail in this specification belong to the prior art known to those skilled in the art.

Claims

1. A fishery resource assessment system based on multi-source data fusion, characterized in that: The system comprises: Data collection module: responsible for collecting relevant data on fishery resources in real time through sonar, radar, and satellite remote sensing technology, including fish distribution, quantity, size, and movement trajectory, and outputting it to the data processing module; Data processing module: pre-process the collected raw data, remove outliers, duplicate values ​​and incomplete data through data cleaning, and then perform denoising and data standardization; Multi-source data fusion module: uses Bayesian classifier algorithm to fuse information from different data sources such as sonar, radar, and satellite remote sensing; Resource assessment module: Based on the integrated data, the module uses statistical methods to conduct a comprehensive assessment of fishery resources, including key indicators such as resource quantity, distribution status, and change trends, and automatically generates a detailed assessment report; Log archiving and updating module: responsible for recording all important log information during the operation of the system, including the data fusion processing results stored in time series and the evaluation report generated by the resource evaluation module; at the same time, the module receives log information and updates the display information on the fishing log APP in real time; Early warning and notification module: Based on the results of resource assessment, the changes in fishery resources are monitored in real time; once an abnormal decrease in resource volume, illegal fishing activities or other potential risks are found, the early warning mechanism is immediately triggered and the fishery management department and relevant personnel are notified through the APP push; System management and maintenance module: provides comprehensive user management, permission settings and data security functions to ensure stable operation and continuous optimization of the system, while also providing system logs, performance monitoring and troubleshooting tools; User interface module: used to design an intuitive and easy-to-use user interface, allowing users to easily view fishery resource assessment results, receive early warning notifications, and manage system configuration functions, thereby improving user experience and ease of operation.

2. A fishery resource assessment system for multi-source data fusion according to claim 1, characterized in that: The data acquisition module is specifically used for: Through sonar, underwater sound wave transmission and reception are used to detect the distribution, number and size of fish. Through radar, the propagation and reflection principles of electromagnetic waves in the air are used to monitor the movement trajectory and group behavior of fishery resources on the water surface and nearshore areas. Through satellite remote sensing, sensors carried by satellites are used to periodically scan large areas of sea to obtain the macro-distribution and changing trends of fishery resources.

3. A fishery resource assessment system for multi-source data fusion according to claim 1, characterized in that: The data processing module is specifically used for: The original data is identified and outliers, duplicate values ​​and incomplete data are removed; outliers are eliminated or corrected using the IQR (interquartile range) method; noise interference in the data is then removed through low-pass filtering technology; the data is standardized through the Z-score, and all data are converted to the same scale or dimensionless range, allowing direct comparison and fusion of data from different sources.

4. A fishery resource assessment system for multi-source data fusion according to claim 1, characterized in that: The multi-source data fusion module is specifically used for: Extract effective features from the pre-processed data of sonar, radar and satellite remote sensing according to their respective physical meanings and characteristics; extract echo intensity and frequency features from sonar data; Radar data extracts the speed, direction and shape of the target; satellite remote sensing data extracts spectral characteristics and species index; According to historical data and expert knowledge, a priori probability is set for different feature categories of each data source, denoted as P(A); for the observation data provided by each data source, its likelihood function P(B|A) under different categories is calculated; using the Bayesian formula, the Bayesian formula is used to combine the prior probability P(A) and the likelihood function P(B|A) to calculate the posterior probability P(A|B) of the observation data under a given category; the basic form of the Bayesian formula is: Where P(B) is the marginal probability, that is, the total probability of event B occurring, which is calculated by considering all possible events A using the total probability formula: Finally, according to the size of the posterior probability P(A|B), decision fusion is performed; the maximum posterior probability criterion is adopted, that is, the category with the largest posterior probability is selected as the final fusion result; Output fusion results, including target recognition, position estimation, and status monitoring information.

5. The fishery resource assessment system of multi-source data fusion according to claim 1 is characterized in that: The resource assessment module is specifically used for: By analyzing the fused data, the size, weight or biomass of different types of fish can be estimated through statistical methods, and the total amount of fishery resources in a specific area can be recorded; through the spatial distribution of fishery resources, including horizontal and vertical distribution, the gradient distribution of fish in the sea area and the density of fish in different water layers can be recorded; Monitor the changing trends of fishery resources over time, including long-term trends and seasonal fluctuations, predict future resource conditions, and provide data reports for the formulation and adjustment of fishery policies.

6. A fishery resource assessment system for multi-source data fusion according to claim 1, characterized in that: The log archiving and updating module is specifically used for: Collect the log information fed back by each part of the system and arrange it in chronological order; store the log information in the database according to the time series and create an index; By updating the push interface, the latest log information and assessment reports are pushed to the Fishing Log APP in real time, ensuring that relevant personnel can view the latest information at any time; Archive and back up log information to prevent data loss or damage; The archived log information can still be used for subsequent query and analysis.

7. The fishery resource assessment system of multi-source data fusion according to claim 1 is characterized in that: The warning and notification module is specifically used for: According to historical data, expert opinions and fishery policies, reasonable warning thresholds are set for different resource indicators. The warning thresholds are divided into three levels: primary warning, intermediate warning and advanced warning. Statistical methods are used to detect anomalies in real-time data, and abnormal high-frequency fishing and exceeding fishing quota judgment methods are used to automatically identify abnormal resource reduction and illegal fishing activities. When an abnormal situation is detected, an early warning signal is generated and abnormal information is recorded; The warning signal includes the abnormality type, occurrence time, location, and impact range; after the system generates the warning information, it is pushed to the fishing log APP for relevant personnel to view and handle.

8. The fishery resource assessment system of multi-source data fusion according to claim 1 is characterized in that: The system management and maintenance module is specifically used for: Responsible for the registration and login operations of system users, and divide them into administrators and ordinary users according to different user roles to ensure that different users have appropriate access rights; set detailed access control policies according to user roles and business needs; implement data encryption and access control to protect system data from illegal access and tampering; Record all important events and operation logs during system operation, including user login, data operation, system abnormalities, and provide log query and analysis functions.

9. The fishery resource assessment system of multi-source data fusion according to claim 1 is characterized in that: The user interface module is specifically used for: Provides graphical display, intuitively showing the distribution of fishery resources and assessment results through maps and charts; users can view detailed assessment reports of fishery resources, including resource quantity, fishing intensity, and ecological index through the user interface module; when the system detects an abnormal situation and triggers the early warning mechanism, the user interface module will push early warning notifications to users in real time; the notification content includes the type of abnormality, time and location of occurrence, scope of impact, and recommended measures.

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