A fish tracking system for marine fisheries

Abstract

The invention discloses a fish school tracking system for marine fishery catching, and relates to the technical field of marine fishery. The invention comprises a satellite positioning module, an underwater sonar detection module, a data processing center, a route planning module and a user interface; the satellite positioning module is used to obtain the real-time position information of the fishing boat and the fish school; firstly, a satellite positioning receiver is selected; the invention realizes accurate monitoring of the real-time position of the fishing boat and the fish school by integrating the high-precision satellite positioning module and the advanced underwater sonar detection technology, and the system can predict the movement trend of the fish school and plan the optimal route in combination with the marine environment data and the performance parameters of the fishing boat, thereby significantly improving the fishing efficiency and accuracy. Compared with the traditional fishing method, the system reduces the waste of marine resources and improves the pertinence and success rate of the fishing operation.

Description

Technical Field

[0001] The present invention relates to the technical field of marine fishery, and in particular to a fish school tracking system for marine fishery. Background Art

[0002] As a traditional fishing industry, marine fisheries have long relied on the experience of fishermen and simple navigation equipment to carry out operations. However, with the increasing scarcity of marine resources and the enhancement of environmental protection awareness, traditional fishing methods are facing the problems of inefficiency and waste of resources. Existing technologies mainly rely on manual observation and limited marine environmental data to make fishing decisions, which limits the accuracy and efficiency of fishing. In addition, traditional fishing methods are difficult to monitor the dynamics of fish schools in real time, resulting in fishing operations often lacking in targeting, increasing the negative impact on non-target fish schools and the marine ecological environment;

[0003] With the development of satellite positioning technology, sonar detection technology and data processing technology, the marine fishing industry has the possibility of achieving technological upgrades. Modern fishery requires an integrated and intelligent system that can track and analyze fish dynamics in real time, optimize fishing routes, improve fishing efficiency, and reduce damage to the marine environment. In addition, the widespread use of satellite navigation systems such as the Global Positioning System (GPS), GLONASS, Galileo, and Beidou has provided fishing vessels with more accurate positioning services. However, how to effectively integrate these technologies and develop a comprehensive system that can respond to changes in the marine environment in real time and provide scientific fishing advice is still a technical challenge.

[0004] In order to solve the above defects, a technical solution is now provided. Summary of the invention

[0005] The purpose of the present invention is to solve the problems of low efficiency, serious waste of resources and great impact on the environment of traditional marine fishing methods, and to propose a fish tracking system for marine fishery fishing.

[0006] The purpose of the present invention can be achieved by the following technical solutions:

[0007] A fish tracking system for marine fishery, comprising:

[0008] Satellite positioning module, used to obtain real-time location information of fishing boats;

[0009] Underwater sonar detection module, used to detect the density and distribution of fish schools;

[0010] A data processing center to analyze data and predict fish movement trends;

[0011] The route planning module is used to combine the predicted results of the fish school movement trend and the real-time position of the fishing boat to provide the fishing boat with the best route to approach the fish school. The specific steps are as follows:

[0012] Collect forecasts of fish movement trends and real-time location information of fishing vessels, integrate ocean environment data, including currents, wind speed and direction, water depth and obstacles, and determine performance parameters of fishing vessels, including maximum speed, fuel efficiency and maneuverability;

[0013] Clarify the goal of route planning, generate multiple alternative routes based on the current position of the fishing vessel and the predicted position of the fish school, evaluate each alternative route, and obtain the optimal route by collecting the optimal parameters of the alternative routes for comprehensive analysis;

[0014] After determining the optimal route, update the navigation information based on real-time data. When the trend of fish movement changes, adjust the route to adapt to the new predicted results of fish movement.

[0015] Provide users with route fine-tuning function, so that users can fine-tune the optimal route according to experience or specific needs, and confirm the fine-tuned route with the fishing vessel operator;

[0016] After the finalization, the system will navigate the fish school according to the final route and provide real-time navigation support, including route tracking and deviation warning. It will also record the execution of the route, analyze the effect of route planning, provide data support for subsequent optimization, and use the feedback information of route execution to improve the route planning algorithm and model.

[0017] User interface for displaying analysis results and providing fishing recommendations.

[0018] Furthermore, the specific process of the route planning module obtaining the optimal route by collecting the optimal parameters of the alternative routes and performing comprehensive analysis is as follows:

[0019] The optimal parameters include:

[0020] Sailing time: assessing the time required to reach a destination;

[0021] Sailing distance: measure the total distance of the route;

[0022] Sailing cost: taking into account fuel cost, labor cost and other expenses, the total of all costs is calculated as the standard for measuring the sailing cost;

[0023] Safety: Evaluate potential risks on the route, including weather conditions, ocean currents, reefs and other navigation obstacles, and assign a score of 1-100 to the safety of the navigation, which is recorded as the safety score and used as the standard to measure the safety of the navigation;

[0024] Environmental impact: Consider the impact of the route on the marine environment, including avoiding sensitive ecological areas. The impact of the alternative route on the environment is measured according to the impact degree, which is set between 0 and 1. The greater the impact degree, the greater the impact of the alternative route on the environment.

[0025] Sailing efficiency: Evaluate the sailing efficiency of the route, such as taking into account the current and wind direction to optimize the sailing speed, assign a score of 1-10 to the sailing efficiency score, and use this score as a standard to measure sailing efficiency;

[0026] The obtained voyage time, voyage distance, voyage cost, safety score, impact and voyage efficiency score are normalized and input into the following formula:

[0027] To obtain the optimal value QYZ, and use this optimal value as the standard for measuring the priority of alternative routes, where They are navigation time, navigation distance, navigation cost, safety score, impact and navigation efficiency score. They are the preset weight coefficients for navigation time, navigation distance, navigation cost, safety score, impact and navigation efficiency score;

[0028] The calculated optimal values ​​of all the alternative routes are sorted in order, and the alternative route with the largest optimal value is selected as the optimal route.

[0029] Furthermore, the specific operation steps of the satellite positioning module to obtain the real-time location information of the fishing boat are as follows:

[0030] First, a satellite positioning receiver is selected and installed on a fishing vessel. During installation, the installation position of the fishing vessel is screened to ensure that the satellite positioning receiver has a sky view for receiving satellite signals;

[0031] The receiver automatically searches for and locks onto signals from GPS, GLONASS, Galileo or BeiDou satellite navigation systems, decodes the received signals, and extracts position information, including latitude, longitude and altitude data;

[0032] Install a high-precision clock in the satellite positioning receiver to synchronize with the satellite navigation system to ensure the accuracy of the time tag;

[0033] When the satellite positioning receiver supports multi-system signal reception, data fusion is used to improve positioning accuracy and reliability;

[0034] Based on the received satellite signals, the precise position of the fishing boat is calculated through trilateral measurement or multi-variable measurement methods, and then the calculated position of the fishing boat is converted into a digital signal and sent to the data processing center through the communication system on the fishing boat. Error detection and verification are performed during the data transmission process to ensure the integrity and accuracy of the transmitted data.

[0035] Furthermore, the specific operation steps of data fusion of multi-system signal reception in the satellite positioning module are as follows:

[0036] Receive signals from different satellite navigation systems through satellite positioning receivers, decode the received signals, extract the position angle, carrier phase and timestamp of each satellite signal, and correct the errors generated during signal propagation, including ionospheric delay, tropospheric delay and multipath effect;

[0037] Correct the differences in time systems and coordinate systems among different satellite systems to ensure that data received from different systems are synchronized in time;

[0038] Then, a weight is assigned to each satellite signal based on the quality of the signal, which includes the signal-to-noise ratio and the observation geometry;

[0039] Utilizing all satellite signals, the position of the fishing vessel is calculated by a positioning algorithm, wherein the positioning algorithm includes a least square method or a Kalman filter;

[0040] Apply data fusion algorithms to combine observation data from multiple systems to improve the accuracy and reliability of positioning results. Based on the fusion results, optimize positioning accuracy by using filtering technology to reduce random errors.

[0041] Continuously receive new satellite signals, update positioning results in real time, and perform quality control on the fused positioning results to ensure that the predetermined accuracy requirements are met. The fused positioning results are fed back to the user through the user interface for navigation or other decision-making.

[0042] Furthermore, the specific operation steps of the underwater sonar detection module to detect the density and distribution of fish schools are as follows:

[0043] The sonar equipment is deployed underwater as an independent towed body on the fishing boat. The sonar equipment emits sound waves of a specific frequency. When the sound waves propagate in the water and encounter fish or other objects, echoes are generated. The receiver captures the reflected sound waves and converts them into electrical signals. The received sound wave signals are then filtered, amplified and digitized.

[0044] The time delay and intensity of sonar signals are collected to analyze the location and density of fish schools. The collected data are used to generate sonar images, which are two-dimensional or three-dimensional and are used to visualize the distribution of fish schools. The characteristics of fish schools in sonar images are identified through image analysis algorithms, and fish schools are distinguished from other echo sources.

[0045] Analyze the density of fish schools based on the echo intensity and acoustic characteristics of fish schools, and determine the precise underwater location of fish schools by combining the depth and angle information of sonar equipment. Synchronize the fish school data detected by sonar with the real-time location data of the fishing boat to ensure data consistency;

[0046] The sonar data is integrated with the satellite positioning data to improve the accuracy of the fish location information, update the sonar detection data in real time, and transmit it to the data processing center for analysis.

[0047] Furthermore, the specific operation steps of the data processing center to analyze the data and predict the movement trend of the fish school are as follows:

[0048] Collect the required data from various sensors and data sources, including:

[0049] Sonar detection data: including the location, density and distribution of fish schools;

[0050] Satellite positioning data: real-time geographic location of fishing vessels and fish schools;

[0051] Marine environmental data: water temperature, salinity, currents and tides;

[0052] Meteorological data: wind speed, wind direction, air pressure and temperature;

[0053] Historical fishing data: including past fishing locations, times and catches;

[0054] Biological characteristics data: fish species, habits and migration patterns;

[0055] Clean the collected data, remove outliers and noise, standardize the data format for processing and analysis, integrate data from different sources to form a unified data set, extract key features for prediction from the raw data, including fish density and movement speed, and select the corresponding prediction model based on the data characteristics, including machine learning models or time series analysis;

[0056] Use historical data to train the prediction model, adjust the model parameters to obtain the best performance of the model, and evaluate the accuracy and generalization ability of the model through cross-validation method;

[0057] The data collected in real time is then analyzed to extract the characteristics of the current fish school status. The trained model is used to process the real-time data to generate a prediction of the fish school's movement trend. The prediction results are evaluated to ensure that the fish school's movement trend is consistent with actual observations.

[0058] Furthermore, the process of displaying the analysis results and providing fishing suggestions on the user interface is as follows:

[0059] Design a user interface that integrates and displays data from satellite positioning, sonar detection, and data processing centers to display the real-time location of fishing vessels and the predicted location of fish schools, represented by icons or markers on the map;

[0060] Use charts, heat maps or 3D views to show the density and distribution of fish schools, show recommended routes, including alternative routes and the final optimal route, and show the predicted results of fish movement trends, including animated or simulated paths;

[0061] Provide fishing recommendations based on the analysis results, including the best fishing areas and times, provide interactive controls that allow users to query and adjust the displayed data and recommendations, and design an alarm unit to notify when a fishing boat approaches a school of fish or encounters special circumstances;

[0062] Provide query and analysis functions for historical fishing data to help users understand fishing patterns and trends, display environmental factors that affect fishing, including ocean currents and wind speed and direction, design a feedback mechanism to collect user feedback on the interface and suggestions for improving the system.

[0063] Compared with the prior art, the present invention has the following beneficial effects:

[0064] The present invention realizes accurate monitoring of the real-time position of fishing boats and fish schools by integrating high-precision satellite positioning modules and advanced underwater sonar detection technology. Combining marine environmental data and fishing boat performance parameters, the system can predict the movement trend of fish schools and plan the optimal route, thereby significantly improving fishing efficiency and accuracy. Compared with traditional fishing methods, the system reduces the waste of marine resources and improves the pertinence and success rate of fishing operations.

[0065] In the present invention, the system comprehensively considers safety and environmental impact in route planning, and ensures the safety of navigation and the protection of the marine environment by evaluating the impact of potential risks and sensitive ecological areas. In addition, the user interface provides real-time navigation support and deviation warning, which further enhances the safety of navigation. The implementation of the present invention helps to reduce the fishing of non-target fish and reduce the negative impact on the marine ecology, reflecting the environmentally friendly design concept;

[0066] In the present invention, the user interface not only displays the analysis results and fishing suggestions, but also provides historical fishing data query and analysis functions to help users understand fishing patterns and trends. The system collects feedback information on route execution to provide data support for subsequent optimization and realize data-driven decision support. The fine-tuning function designed for the system allows users to adjust the route based on experience, thereby enhancing the flexibility and adaptability of the system. Through continuous learning and optimization, the system of the present invention can adapt to the ever-changing marine environment and fishing needs, providing an intelligent and efficient solution for marine fishery. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0068] Figure 1 This is the overall system block diagram of the present invention. DETAILED DESCRIPTION

[0069] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0070] It should be understood that the terms "include" and "comprising" used in the specification and claims of the present disclosure indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0071] It should also be understood that the terms used in this disclosure are only for the purpose of describing specific embodiments and are not intended to limit the disclosure. As used in this disclosure and claims, the singular forms of "a", "an", and "the" are intended to include the plural forms unless the context clearly indicates otherwise. It should also be further understood that the term "and / or" used in this disclosure and claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations.

[0072] like Figure 1 As shown, a fish tracking system for marine fishery includes a satellite positioning module, an underwater sonar detection module, a data processing center, a route planning module and a user interface;

[0073] The satellite positioning module is used to obtain the real-time location information of fishing boats and fish schools;

[0074] First, the satellite positioning receiver is selected, ensuring that the selected satellite positioning receiver has waterproof and corrosion-resistant properties, and the selected satellite positioning receiver is installed on the fishing boat. The location of the fishing boat is screened during installation to ensure that the satellite positioning receiver has a view of the sky for receiving satellite signals; the receiver automatically searches and locks on signals from the GPS, GLONASS, Galileo or Beidou satellite navigation system, and decodes the received signals to extract location information, including latitude, longitude and altitude data; a high-precision clock is installed in the satellite positioning receiver and synchronized with the time of the satellite navigation system to ensure the accuracy of the time tag;

[0075] When the satellite positioning receiver supports multi-system signal reception, data fusion is used to improve positioning accuracy and reliability. Specifically: the satellite positioning receiver receives signals from different satellite navigation systems, decodes the received signals, extracts the position angle, carrier phase and timestamp of each satellite signal, and corrects the errors generated during signal propagation, including ionospheric delay, tropospheric delay and multipath effect; makes consistent corrections to the differences in time systems and coordinate systems of different satellite systems to ensure that data received from different systems are synchronized in time; and assigns weights to each satellite signal based on signal quality, where signal quality includes signal-to-noise ratio and observation geometry; uses all available satellite signals to calculate the position of the fishing vessel through a positioning algorithm, where the positioning algorithm includes a least squares method or a Kalman filter;

[0076] Apply data fusion algorithms and combine observation data from multiple systems to improve the accuracy and reliability of positioning results. Based on the fusion results, further optimize positioning accuracy by using filtering technology to reduce random errors. Continuously receive new satellite signals, update positioning results in real time, and perform quality control on the fused positioning results to ensure that they meet the predetermined accuracy requirements. Feedback the fused positioning results to the user through the user interface for navigation or other decision-making. Based on the received satellite signals, calculate the precise position of the fishing vessel through trilateral measurement or multi-variable measurement methods, convert the calculated position of the fishing vessel into a digital signal, send it to the data processing center through the communication system on the fishing vessel, and perform error detection and verification during data transmission to ensure the integrity and accuracy of the transmitted data.

[0077] The underwater sonar detection module is used to detect the density and distribution of fish schools;

[0078] The sonar equipment is deployed underwater as an independent towed body on a fishing boat. The sonar equipment emits sound waves of a specific frequency. When the sound waves propagate in the water and encounter a school of fish or other objects, an echo is generated. The receiver captures the reflected sound waves and converts them into electrical signals. The received sound wave signals are then filtered, amplified and digitized. The time delay and intensity of the sonar signal are collected to analyze the location and density of the school of fish. The collected data is used to generate a sonar image, which can be two-dimensional or three-dimensional to visualize the distribution of the school of fish. The characteristics of the school of fish in the sonar image are identified through image analysis algorithms to distinguish between the school of fish and other echo sources.

[0079] The density of fish schools is analyzed based on the echo intensity and the acoustic characteristics of fish schools. The precise underwater position of fish schools is determined in combination with the depth and angle information of the sonar equipment. The fish school data detected by sonar is synchronized with the real-time position data of the fishing vessel to ensure data consistency. The sonar data is integrated with satellite positioning data to improve the accuracy of fish school location information. The sonar detection data is updated in real time and transmitted to the data processing center for further analysis.

[0080] The data processing center is used to analyze data and predict the movement trends of fish schools;

[0081] Collect the required data from various sensors and data sources, including:

[0082] Sonar detection data: including the location, density and distribution of fish schools; satellite positioning data: providing the real-time geographic location of fishing boats and fish schools; marine environment data: such as water temperature, salinity, ocean currents, tides, etc.;

[0083] Meteorological data: such as wind speed, wind direction, air pressure, temperature, etc.; Historical fishing data: including past fishing locations, times and harvests; Biological characteristics data: fish species, habits, migration patterns, etc.;

[0084] The required collected data is cleaned to remove outliers and noise, and the data format is standardized for processing and analysis. Data from different sources are integrated to form a unified data set, and then the key features for prediction are extracted from the original data, including fish density and movement speed. The corresponding prediction model is selected according to the data characteristics, including machine learning model or time series analysis; the prediction model is trained using historical data, and the model parameters are adjusted to obtain the best performance of the model, and the accuracy and generalization ability of the model are evaluated through cross-validation methods; the data collected in real time is then analyzed to extract the characteristics of the current fish state, and the trained model is used to process the real-time data to generate a prediction of the fish movement trend, and the prediction results are evaluated to ensure that the fish movement trend is consistent with actual observations.

[0085] The route planning module is used to combine the predicted results of the fish school's movement trend with the real-time position of the fishing boat to provide the fishing boat with the best route to approach the fish school;

[0086] Collect the prediction results of fish movement trends and the real-time location information of fishing boats, integrate marine environmental data, including ocean currents, wind speed and direction, water depth, obstacles, etc., determine the performance parameters of fishing boats, such as maximum speed, fuel efficiency, maneuverability, etc.; clarify the goals of route planning, such as shortest arrival time, lowest cost, maximum catch, etc., generate multiple alternative routes based on the current position of the fishing boat and the predicted position of the fish school, evaluate each alternative route, and conduct a comprehensive analysis by collecting the optimal parameters of the alternative routes, where the optimal parameters include:

[0087] Sailing time: Evaluate the time required to reach the destination. Usually, shorter sailing time is preferred. Sailing distance: Measure the total distance of the route. A shorter distance can reduce fuel consumption and cost. Sailing cost: Take into account fuel cost, labor cost and other possible expenses, calculate the total of all expenses, and use this as a standard to measure the sailing cost. Safety: Evaluate the potential risks on the route, such as weather conditions, currents, reefs and other navigation obstacles, and assign a score of 1-100 to the safety of navigation, which is recorded as the safety score, and use this safety score as a standard to measure the safety of navigation. Environmental impact: Consider the impact of the route on the marine environment, such as avoiding sensitive ecological areas, and measure the impact of the alternative route on the environment according to the impact degree, which is set between 0 and 1. The greater the impact degree, the greater the impact of the alternative route on the environment. Navigation efficiency: Evaluate the navigation efficiency of the route, such as considering the current and wind direction to optimize the navigation speed, and assign a score of 1-10 to the navigation efficiency, which is recorded as the navigation efficiency score, and use this navigation efficiency score as a standard to measure the navigation efficiency.

[0088] The obtained voyage time, voyage distance, voyage cost, safety score, impact and voyage efficiency score are normalized and input into the following formula:

[0089] To obtain the optimal value QYZ, and use this optimal value as the standard for measuring the priority of alternative routes, where They are navigation time, navigation distance, navigation cost, safety score, impact and navigation efficiency score. The preset weight coefficients are respectively the sailing time, sailing distance, sailing cost, safety score, impact and sailing efficiency score; the calculated optimal values ​​of all alternative routes are sorted in order of size, and the alternative route with the largest optimal value is selected as the optimal route;

[0090] After determining the optimal route, update the navigation information based on real-time data to ensure the accuracy and timeliness of route planning. When the trend of fish movement changes, adjust the route in time to adapt to the new predicted results of fish movement. Provide users with route fine-tuning function, which is used to fine-tune the optimal route based on experience or specific needs, and confirm the fine-tuned route with the fishing vessel operators. After the determination is completed, navigate to the fish school according to the final route, and provide real-time navigation support, including route tracking and deviation warning. Record the execution of the route, analyze the effect of route planning, provide data support for subsequent optimization, and use the feedback information of route execution to improve route planning algorithms and models.

[0091] The user interface is used to display analysis results and provide fishing recommendations;

[0092] Design an intuitive and easy-to-use user interface to ensure that all functions and data are clearly displayed and easy to understand. Integrate and display data from satellite positioning, sonar detection, and data processing centers on the interface to display the real-time position of fishing vessels and the predicted position of fish schools, represented by icons or markers on the map; use charts, heat maps or three-dimensional views to show the density and distribution of fish schools, show recommended routes, including alternative routes and the final optimal route, and show the predicted results of fish movement trends, including animation or simulated paths;

[0093] Provide fishing recommendations based on the analysis results, including the best fishing areas and times; provide interactive controls to allow users to query and adjust the displayed data and recommendations; design an alarm unit to issue notifications when a fishing boat approaches a school of fish or encounters special circumstances; provide query and analysis functions for historical fishing data to help users understand fishing patterns and trends; display environmental factors that affect fishing, such as ocean currents and wind speed and direction; design a feedback mechanism to collect user feedback on the interface and recommendations for improving the system.

[0094] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. A fish tracking system for marine fishing, characterized in that: include: Satellite positioning module, used to obtain real-time location information of fishing boats; Underwater sonar detection module, used to detect the density and distribution of fish schools; A data processing center to analyze data and predict fish movement trends; The route planning module is used to combine the predicted results of the fish school movement trend and the real-time position of the fishing boat to provide the fishing boat with the best route to approach the fish school. The specific steps are as follows: Collect forecasts of fish movement trends and real-time location information of fishing vessels, integrate ocean environment data, including currents, wind speed and direction, water depth and obstacles, and determine performance parameters of fishing vessels, including maximum speed, fuel efficiency and maneuverability; Clarify the goal of route planning, generate multiple alternative routes based on the current position of the fishing vessel and the predicted position of the fish school, evaluate each alternative route, and obtain the optimal route by collecting the optimal parameters of the alternative routes for comprehensive analysis; After determining the optimal route, update the navigation information based on real-time data. When the trend of fish movement changes, adjust the route to adapt to the new predicted results of fish movement. Provide users with route fine-tuning function, so that users can fine-tune the optimal route according to experience or different needs, and confirm the fine-tuned route with the fishing vessel operator; After the determination is completed, the system will navigate to the school of fish according to the final route and provide real-time navigation support, including route tracking and deviation warning; Record the execution of routes, analyze the effects of route planning, provide data support for subsequent optimization, and use the feedback information of route execution to improve route planning algorithms and models; A user interface to display analysis results and provide fishing recommendations; The specific process of the route planning module obtaining the optimal route by collecting the optimal parameters of the alternative routes and conducting a comprehensive analysis is as follows: The optimal parameters include: Sailing time: assesses the time required to reach a destination; Sailing distance: measures the total distance of a route; Sailing cost: taking into account fuel cost, labor cost and other expenses, the total of all costs is calculated as the standard for measuring the sailing cost; Safety: Evaluate potential risks on the route, including weather conditions, ocean currents, reefs and other navigation obstacles, and assign a score of 1-100 to the safety of the navigation, which is recorded as the safety score and used as the standard to measure the safety of the navigation; Environmental impact: Consider the impact of the route on the marine environment, including avoiding sensitive ecological areas. The impact of the alternative route on the environment is measured according to the impact degree, which is set between 0 and 1. The greater the impact degree, the greater the impact of the alternative route on the environment. Sailing efficiency: Evaluate the sailing efficiency of the route, including currents and wind direction to optimize sailing speed, assign a score of 1-10 to the sailing efficiency score, and use this score as a measure of sailing efficiency; The obtained voyage time, voyage distance, voyage cost, safety score, impact and voyage efficiency score are normalized and input into the following formula: In order to obtain the optimal value QYZ, this optimal value is used as the standard for measuring the priority of alternative route selection, where st, hj, hb, ap, yd, hx are respectively the navigation time, navigation distance, navigation cost, safety score, impact and flight efficiency score, ε1, ε2, ε3, ε4, ε5, ε6 They are the preset weight coefficients for navigation time, navigation distance, navigation cost, safety score, impact and navigation efficiency score; The calculated optimal values ​​of all the alternative routes are sorted in order, and the alternative route with the largest optimal value is selected as the optimal route.

2. A fish school tracking system for marine fishing according to claim 1, characterized in that: The specific operation steps of the satellite positioning module to obtain the real-time location information of the fishing boat are as follows: First, a satellite positioning receiver is selected and installed on a fishing vessel. During installation, the installation position of the fishing vessel is screened to ensure that the satellite positioning receiver has a sky view for receiving satellite signals; The receiver automatically searches for and locks onto signals from GPS, GLONASS, Galileo or BeiDou satellite navigation systems, decodes the received signals, and extracts position information, including latitude, longitude and altitude data; Install a high-precision clock in the satellite positioning receiver to synchronize with the satellite navigation system to ensure the accuracy of the time tag; When the satellite positioning receiver supports multi-system signal reception, data fusion is used to improve positioning accuracy and reliability; Based on the received satellite signals, the precise position of the fishing boat is calculated through trilateral measurement or multi-variable measurement methods, and then the calculated position of the fishing boat is converted into a digital signal and sent to the data processing center through the communication system on the fishing boat. Error detection and verification are performed during the data transmission process to ensure the integrity and accuracy of the transmitted data.

3. A fish tracking system for marine fishing according to claim 2, characterized in that: The specific operation steps for data fusion of multi-system signal reception in the satellite positioning module are as follows: Receive signals from different satellite navigation systems through satellite positioning receivers, decode the received signals, extract the position angle, carrier phase and timestamp of each satellite signal, and correct the errors generated during signal propagation, including ionospheric delay, tropospheric delay and multipath effect; Correct the differences in time systems and coordinate systems among different satellite systems to ensure that data received from different systems are synchronized in time; Then, a weight is assigned to each satellite signal based on the quality of the signal, which includes the signal-to-noise ratio and the observation geometry; Utilizing all satellite signals, the position of the fishing vessel is calculated by a positioning algorithm, wherein the positioning algorithm includes a least square method or a Kalman filter; Apply data fusion algorithms to combine observation data from multiple systems to improve the accuracy and reliability of positioning results. Based on the fusion results, optimize positioning accuracy by using filtering technology to reduce random errors. Continuously receive new satellite signals, update positioning results in real time, and perform quality control on the fused positioning results to ensure that the predetermined accuracy requirements are met. The fused positioning results are fed back to the user through the user interface for navigation or other decision-making.

4. A fish tracking system for marine fishing according to claim 1, characterized in that: The specific operation steps of the underwater sonar detection module to detect the density and distribution of fish schools are as follows: The sonar equipment is deployed underwater as an independent towed body on the fishing boat. The sonar equipment emits sound waves of a specific frequency. When the sound waves propagate in the water and encounter fish or other objects, echoes are generated. The receiver captures the reflected sound waves and converts them into electrical signals. The received sound wave signals are then filtered, amplified and digitized. The time delay and intensity of sonar signals are collected to analyze the location and density of fish schools. The collected data are used to generate sonar images, which are two-dimensional or three-dimensional and are used to visualize the distribution of fish schools. The characteristics of fish schools in sonar images are identified through image analysis algorithms, and fish schools are distinguished from other echo sources. Analyze the density of fish schools based on the echo intensity and acoustic characteristics of fish schools, and determine the precise underwater location of fish schools by combining the depth and angle information of sonar equipment. Synchronize the fish school data detected by sonar with the real-time location data of the fishing boat to ensure data consistency; The sonar data is integrated with the satellite positioning data to improve the accuracy of the fish location information, update the sonar detection data in real time, and transmit it to the data processing center for analysis.

5. A fish tracking system for marine fishing according to claim 1, characterized in that: The specific operation steps of the data processing center to analyze data and predict the movement trend of fish schools are as follows: Collect the required data from various sensors and data sources, including: Sonar detection data: including the location, density and distribution of fish schools; Satellite positioning data: real-time geographic location of fishing vessels and fish schools; Marine environmental data: water temperature, salinity, currents and tides; meteorological data: wind speed, wind direction, air pressure and temperature; Historical fishing data: including past fishing locations, times and harvests; biological characteristics data: fish species, habits and migration patterns; Clean the collected data, remove outliers and noise, standardize the data format for processing and analysis, integrate data from different sources to form a unified data set, extract key features for prediction from the raw data, including fish density and movement speed, and select the corresponding prediction model based on the data characteristics, including machine learning models or time series analysis; Use historical data to train the prediction model, adjust the model parameters to obtain the best performance of the model, and evaluate the accuracy and generalization ability of the model through cross-validation method; The data collected in real time is then analyzed to extract the characteristics of the current fish school status. The trained model is used to process the real-time data to generate a prediction of the fish school's movement trend. The prediction results are evaluated to ensure that the fish school's movement trend is consistent with actual observations.

6. A fish tracking system for marine fishing according to claim 1, characterized in that: The process of the user interface displaying the analysis results and providing fishing suggestions is as follows: Design a user interface that integrates and displays data from satellite positioning, sonar detection, and data processing centers to display the real-time location of fishing vessels and the predicted location of fish schools, represented by icons or markers on the map; Use charts, heat maps or 3D views to show the density and distribution of fish schools, show recommended routes, including alternative routes and the final optimal route, and show the predicted results of fish movement trends, including animated or simulated paths; Provide fishing recommendations based on the analysis results, including the best fishing areas and times; provide interactive controls to allow users to query and adjust the displayed data and recommendations; design an alarm unit to issue notifications when a fishing boat approaches a school of fish or encounters special circumstances; provide query and analysis functions for historical fishing data to help users understand fishing patterns and trends; display environmental factors that affect fishing, including ocean currents and wind speed and direction; design a feedback mechanism to collect user feedback on the interface and recommendations for improving the system.

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