Electric fishing reel control system based on intelligent fish signal detection

Abstract

The invention discloses an electric fishing vessel control system based on intelligent fish information detection, and particularly relates to the technical field of artificial intelligence, the electric fishing vessel control system comprises a fish information detection module, a data processing and analysis module, a control module, an intelligent decision and optimization module and a remote communication module, the data processing and analyzing module judges the position, the activity state and the environment change of the fish school through a data mining technology and transmits the position, the activity state and the environment change to the control module and the intelligent decision-making and optimizing module, and the control module is used for controlling the intelligent decision-making and optimizing module according to the feedback of the data processing and analyzing module. The intelligent decision-making and optimization module is used for predicting the behavior trend of a fish school based on fish school detection data, environment information and fishing ship feedback, and the remote communication module is used for communicating with external equipment, providing an operation interface for a user and checking fish school information in real time, adjusting fishing ship parameters and setting a fishing mode.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology. More specifically, the present invention relates to an electric fishing reel control system based on intelligent fish signal detection. Background Art

[0002] With the development of technology, modern fishing activities are gradually becoming more intelligent and automated. As one of the key devices, electric fishing reels have been widely used in various fishing occasions. Traditional fishing reel control systems rely on manual operation. Users need to manually adjust the line retrieval speed, force, and fishing tackle settings. Such an operation method is not only inefficient but also poses great operation difficulties and certain safety risks for beginners. With the continuous change of fishery resources, the demand for the adaptive ability and intelligent level of fishing reels in dealing with complex water environments has become increasingly prominent.

[0003] Traditional electric fishing reels are mainly driven by electric motors, and their control modes are usually mainly manual adjustment, lacking sufficient intelligent support. An electric fishing reel control system based on intelligent fish signal detection can intelligently adjust the operating state of the electric fishing reel based on real-time fish signal data. Through precise fish school detection and analysis, it can automatically optimize parameters such as the working mode, line retrieval speed, and torque output of the fishing reel. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides an electric fishing reel control system based on intelligent fish signal detection to solve the problems raised in the above background art.

[0005] To achieve the above object, the present invention provides the following technical solution. An electric fishing reel control system based on intelligent fish signal detection includes a fish signal detection module, a data processing and analysis module, a control module, an intelligent decision-making and optimization module, and a remote communication module; The fish signal detection module detects the activities of fish schools and the water environment underwater through integrated underwater sensing technology, obtains information about fish schools in the water area, including fish school distribution, quantity, size, and behavior data, and transmits it to the data processing and analysis module; The data processing and analysis module processes and analyzes the data collected by the fish signal detection module, judges the position, activity state, and environmental changes of the fish school through data mining technology, in order to determine the best fishing timing and fishing method in real time, and transmits it to the control module and the intelligent decision-making and optimization module; The control module automatically controls the working state of the electric fishing reel according to the feedback of the data processing and analysis module, including the line retrieval speed, force, line retrieval and release mode, optimizes the fishing process, and reduces manual intervention; The intelligent decision-making and optimization module is based on fish detection data, environmental information, and feedback from the fishing reel. It uses artificial intelligence algorithms to predict the behavioral trends of fish schools, make intelligent decisions and optimizations, and help users select the best fishing spots and the best fishing strategies. The remote communication module communicates with external devices, supports data synchronization and remote control, and provides an operation interface for users through Bluetooth wireless technology to view fish information in real time, adjust fishing reel parameters, and set fishing modes.

[0006] In a preferred embodiment, the fish signal detection module detects the activities of fish schools and the water area environment underwater through integrated underwater sensing technology, obtains fish school information in the water area, including fish school distribution, quantity, size, and behavior data, and transmits it to the data processing and analysis module. The specific steps are as follows: Step A1, data collection: Use sonar sensors, temperature sensors, and depth sensors to obtain fish school information in the water area, including fish school distribution, quantity, size, and behavior data. The temperature sensor is used to detect changes in water temperature, the depth sensor is used to obtain the depth data of the water area, and the sonar sensor uses the data reflected by sonar. Combining the position of the detection area, the distribution of fish schools in the water area is obtained, and the size and quantity of fish schools are judged according to the number of detected echoes. The specific formula is: , where K is the number of fish schools, n is the total number of detected echoes, is the intensity of the i-th echo, is the indicator function, and the specific formula is: , is the set echo intensity threshold; Step A2, data transmission: Clean the collected data, remove irrelevant signals and noises, filter out echoes unrelated to fish schools, and transmit the cleaned data to the data processing and analysis module to ensure data quality through denoising and signal optimization.

[0007] In a preferred embodiment, the data processing and analysis module processes and analyzes the data collected by the fish signal detection module, judges the position, activity status, and environmental changes of fish schools through data mining technology to determine the best fishing time and fishing method in real time, and transmits it to the control module and the intelligent decision-making and optimization module. The specific steps are as follows: Step B1, according to the echo data, temperature, and depth data returned by the sonar sensor, use the clustering algorithm to identify the position and density of fish schools, and find the position of fish schools by minimizing the distance from each point to the cluster center. The specific formula is: , where, is the position of the j-th data point in the k-th class, is the center point of the k-th class, J is the objective function, K is the number of clusters, representing the number of fish schools, is the number of data points in the k-th class, and M represents the total number of all data points detected by the sonar; Step B2: Based on the position and distribution of the fish schools, combined with water temperature and water depth data, establish a model based on the regression analysis algorithm to predict the activity state of the fish schools , which further includes the following steps: Step B201: Extract the position density and distance change features from the clustering results of the fish schools, and combine them with water temperature and water depth data as the input features for regression analysis; Step B202: Use the linear regression algorithm to establish a model. The output of the regression model is to predict the activity state of the fish schools. The specific formula is , where is the predicted activity state of the fish school, is the error term, is the intercept term, are the extracted input features, are the coefficients in the regression model, obtained by fitting the training data; Step B203: Use the training set data to fit the regression model, aiming to minimize the sum of squared residuals. The specific formula is: , where is the actually observed activity state, is the activity state predicted by the model, RSS represents the sum of squared residuals, represents the number of samples; Step B3: According to the clustering results of the fish schools and the input of environmental data, use the trained regression model to predict the activity state of the fish schools, judge the best fishing time and fishing method. When , it means the fish school is in an active state, judged as the best fishing time, and use active fishing gear; when , it means the fish school is not active, and use static fishing gear.

[0008] In a preferred embodiment, the control module automatically controls the working state of the electric fishing reel according to the feedback of the data processing and analysis module, including the line winding speed, strength, and line winding and unwinding mode. The specific steps are as follows: Step C1: Set working parameters: Receive the feedback from the data processing and analysis module, including the activity state of the fish school, the best fishing time, and fishing method information. According to the received feedback information, set the working parameters of the electric fishing reel, including the line winding speed V, line winding strength L, and line winding and unwinding mode M. It further includes the following steps: Step C101: Line winding speed: According to the predicted activity state , set the line winding speed. The specific formula is , where V is the wire take-up speed, , , represent high speed, medium speed and low speed values respectively, is the predicted activity state of the fish school; Step C102, wire take-up force: Set the wire take-up force according to the size and species of the fish, combined with the activity level. The specific formula is , where L is the wire take-up force, , , represent high, medium and low force values respectively, is the predicted activity state of the fish school; Step C103, wire take-up and pay-out mode: Select the mode according to the activity state of the fish school and preset conditions. When , M = automatic wire take-up mode. When , M = automatic wire pay-out mode; Step C2, send control signal: Convert the set parameters V, L and M into control signals and send them to the electric fishing reel. According to the real-time feedback data, continuously adjust the wire take-up speed and force to ensure the best fishing effect. The specific formula is: , where is the currently observed activity state of the fish school, is the adjusted wire take-up speed, V is the initially set wire take-up speed, and k is the adjustment coefficient used to control the response sensitivity.

[0009] In a preferred embodiment, the intelligent decision-making and optimization module is based on fish school detection data, environmental information and fishing reel feedback, uses artificial intelligence algorithms to predict the behavior trend of the fish school, makes intelligent decisions and optimizations, and helps users select the best fishing spots and the best fishing strategies. The specific steps are as follows: Step D1, fish school behavior prediction: Obtain the distribution, size, quantity, activity state, and environmental parameters of the fish school from the data processing and analysis module and the control module, and use them as input features , and use the classification model to predict the fish school behavior category. The prediction target is the discrete fish school behavior pattern. The specific formula is: , where to are the features representing the fish school distribution, size, quantity, activity state and environmental parameters, is the predicted fish school behavior category, including aggregation, dispersion, is the probability that the fish school belongs to category when the given input feature is A, is the decision function for each category , is for the Class 's decision function 's exponential operation, is the sum of the exponential values of the decision functions for all classes, used to normalize the output probability; Step D2, for the newly input feature A, make a prediction and output the probability that the fish school belongs to each class , calculate the potential benefits of different fishing spots according to the prediction results, and formulate the best strategy by selecting the fishing spot with the maximum potential benefit. The formula for the potential benefits of different fishing spots is , and the formula for the fishing spot with the maximum potential benefit is: , where, is the index of the optimal fishing spot, indicating the fishing spot with the maximum potential benefit, represents the fishing spot 's potential benefit, is the probability that the fish school is predicted to belong to class , is the fishing spot under the fish school behavior category 's expected benefit. L is the number of all fish school behavior categories. When the predicted potential benefit , adopt an active reeling-in strategy; when the predicted potential benefit , adopt a static line-casting strategy.

[0010] In a preferred embodiment, the remote communication module communicates with external devices, supports data synchronization and remote control, uses Bluetooth wireless technology and provides a user operation interface for real-time viewing of fish school information, adjustment of fishing reel parameters, and setting of fishing modes. The specific steps are as follows: Step E1, establish a connection with an external device through the Bluetooth protocol, confirm the connection status to ensure a successful connection, obtain real-time fish school information and environmental parameters from the intelligent decision-making and optimization module, and transmit the obtained data to the user device via Bluetooth; Step E2, display the fish school information and environmental parameters in real time on the user operation interface. The update frequency is set according to requirements to update the fish school information every 5 seconds and the fishing reel status every 10 seconds. Provide a user-friendly interface, allow the user to manually adjust the fishing reel parameters and fishing mode settings, and send the adjusted parameters to the control module via Bluetooth. The control module confirms the receipt of the adjusted parameters and applies them to ensure that the fishing reel is in the correct working state.

[0011] The beneficial effects of the present invention are as follows: By integrating underwater sensing technology to detect the activities of fish schools and the water environment underwater, fish school information in the water area is obtained, including fish school distribution, quantity, size, and behavior data. The collected data is processed and analyzed, and through data mining technology, the position, activity status, and environmental changes of the fish school are judged to determine the best fishing timing and fishing method in real time. According to the feedback of the data processing and analysis module, the working state of the electric fishing reel is automatically controlled, including the line winding speed, strength, and line winding and unwinding mode, optimizing the fishing process and reducing manual intervention. Based on the fish school detection data, environmental information, and fishing reel feedback, artificial intelligence algorithms are used to predict the behavior trend of the fish school, perform intelligent decision-making and optimization, help users select the best fishing spots and the best fishing strategies, and communicate with external devices to support data synchronization and remote control. Through Bluetooth wireless technology, an operation interface is provided to the user for real-time viewing of fish school information, adjustment of fishing reel parameters, and setting of fishing modes. The present invention helps users quickly find the best fishing spots, improve the fishing success rate by detecting and predicting the behavior of fish schools in real time, reduce the user's dependence on complex operations by automatically adjusting the performance of the fishing reel, and enhance the convenience of use. Using Bluetooth wireless technology, users can view fish school information and environmental parameters in real time, facilitating quick responses. Brief Description of the Drawings

[0012] Figure 1 It is a system flowchart of the present invention. Detailed Embodiments

[0013] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts fall within the protection scope of the present application.

[0014] In the description of the present application, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of the described features. In the description of the present application, "a plurality" means two or more, unless otherwise specifically defined.

[0015] In the description of the present application, the term "for example" is used to mean "serving as an example, illustration, or explanation". Any embodiment described in the present application as "for example" is not necessarily construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to implement and use the present invention. In the following description, details are set forth for purposes of explanation. It should be understood that those of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other instances, well-known structures and processes are not elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed in the present application.

[0016] Embodiment 1 This embodiment provides an electric fishing reel control system based on intelligent fish signal detection as shown in Figure 1 Figure [not provided], which specifically includes a fish signal detection module, a data processing and analysis module, a control module, an intelligent decision-making and optimization module, and a remote communication module; The fish signal detection module detects the activities of fish schools and the water area environment underwater through integrated underwater sensing technology, obtains fish school information in the water area, including fish school distribution, quantity, size, and behavior data, and transmits it to the data processing and analysis module; The data processing and analysis module processes and analyzes the data collected by the fish signal detection module, judges the position, activity state, and environmental changes of the fish school through data mining technology, in order to judge the best fishing timing and fishing method in real time, and transmits it to the control module and the intelligent decision-making and optimization module; The control module automatically controls the working state of the electric fishing reel according to the feedback of the data processing and analysis module, including the line winding speed, strength, line winding and unwinding mode, optimizes the fishing process, and reduces manual intervention; The intelligent decision-making and optimization module is based on fish school detection data, environmental information, and fishing reel feedback, uses artificial intelligence algorithms to predict the behavior trend of the fish school, conducts intelligent decision-making and optimization, and helps users select the best fishing spots and the best fishing strategies; The remote communication module communicates with external devices, supports data synchronization and remote control, provides an operation interface for users through Bluetooth wireless technology, and is used to view fish school information in real time, adjust fishing reel parameters, and set fishing modes.

[0017] In this embodiment, specifically, it should be noted that for the fish signal detection module, the fish signal detection module detects the activities of fish schools and the water area environment underwater through integrated underwater sensing technology, obtains fish school information in the water area, including fish school distribution, quantity, size, and behavior data, and transmits it to the data processing and analysis module. The specific steps are as follows: Step A1, Data Acquisition: Use sonar sensors, temperature sensors, and depth sensors to obtain information about fish populations in the water area, including fish population distribution, quantity, size, and behavior data. The temperature sensor is used to detect changes in water temperature, and the depth sensor is used to obtain the depth data of the water area. By using the data reflected by the sonar sensor and combining it with the position of the detection area, the distribution of the fish population in the water area is obtained, and the size and quantity of the fish population are judged according to the number of detected echoes. The specific formula is: , where K is the number of the fish population, n is the total number of detected echoes, is the intensity of the i-th echo, is the indicator function, and the specific formula is: , is the set echo intensity threshold; Step A2, Data Transmission: Clean the collected data, remove irrelevant signals and noises, filter out echoes unrelated to the fish population, and transmit the cleaned data to the data processing and analysis module to ensure data quality through denoising and signal optimization.

[0018] In this embodiment, specifically, it should be noted that for the data processing and analysis module, the data processing and analysis module processes and analyzes the data collected by the fish signal detection module, judges the position, activity status, and environmental changes of the fish population through data mining technology, in order to judge the best fishing timing and fishing method in real time, and transmits them to the control module and the intelligent decision-making and optimization module. The specific steps are as follows: Step B1, According to the echo data, temperature, and depth data returned by the sonar sensor, use the clustering algorithm to identify the position and density of the fish population, and find the position of the fish population by minimizing the distance from each point to the clustering center. The specific formula is: , where, is the position of the j-th data point in the k-th class, is the center point of the k-th class, J is the objective function, K is the number of clusters, representing the number of the fish population, is the number of data points in the k-th class, and M represents the total number of all data points detected by the sonar; Step B2, According to the position and distribution of the fish population, combined with water temperature and water depth data, establish a model based on the regression analysis algorithm to predict the activity status of the fish population , which further includes the following steps: Step B201, Extract the position density and distance change characteristics from the clustering results of the fish population, and combine them with water temperature and water depth data as the input features of the regression analysis; Step B202, Use the linear regression algorithm to establish a model. The output of the regression model is to predict the activity status of the fish population. The specific formula is , where, is the predicted fish school activity state, is the error term, is the intercept term, are the extracted input features, are the coefficients in the regression model, obtained by fitting with the training data; Step B203: Use the training set data to fit the regression model, with the aim of minimizing the sum of squared residuals. The specific formula is: , where, is the actually observed activity state, is the activity state predicted by the model, RSS represents the sum of squared residuals, represents the number of samples; Step B3: According to the clustering result of the fish school and the input of environmental data, use the trained regression model to predict the activity state of the fish school, judge the best fishing timing and fishing method. When , it indicates that the fish school is in an active state, judged as the best fishing timing, and use active fishing gear; when , it indicates that the fish school is inactive, and use static fishing gear.

[0019] In this embodiment, specifically, the control module needs to be described. The control module automatically controls the working state of the electric fishing reel according to the feedback of the data processing and analysis module, including the line winding speed, strength, and line winding and unwinding mode, optimizes the fishing process, reduces manual intervention, reduces the interference to the water ecological environment and the risk of fish school escape. The specific steps are as follows: Step C1: Set working parameters: Receive the feedback from the data processing and analysis module, including the activity state of the fish school, the best fishing timing, and the fishing method information. According to the received feedback information, set the working parameters of the electric fishing reel, including the line winding speed V, the line winding strength L, and the line winding and unwinding mode M. The line winding speed adjusts the line winding speed according to the fish school activity, the line winding strength adjusts the strength according to the type and activity state of the fish school, and the line winding and unwinding mode selects different modes according to the behavior characteristics of the fish school, including the automatic line winding mode and the free line unwinding mode. Further include the following steps: Step C101: Line winding speed: Set the line winding speed according to the predicted activity state , and the specific formula is , where V is the line winding speed, , , respectively represent high speed, medium speed and low speed values, is the predicted fish school activity state; Step C102: Line winding strength: Set the line winding strength according to the size and variety of the fish, combined with the activity. The specific formula is , where L is the line winding strength, , , Represents high, medium and low force values, respectively. is the predicted fish activity status; Step C103, line retracting and releasing mode: select the mode according to the activity status of the fish school and the preset conditions. , M = automatic reeling mode, when , M = automatic pay-off mode; Step C2, sending control signals: convert the set parameters V, L and M into control signals, send them to the electric fishing reel, and continuously adjust the reeling speed and strength according to the real-time feedback data to ensure the best fishing effect. The specific formula is: ,in, is the currently observed activity status of the fish school, is the adjusted take-up speed, V is the initially set take-up speed, and k is the adjustment coefficient used to control the sensitivity of the response.

[0020] In this embodiment, it is specifically necessary to explain the intelligent decision-making and optimization module, which is based on fish detection data, environmental information and fishing reel feedback, uses artificial intelligence algorithms to predict the behavior trend of fish schools, makes intelligent decisions and optimizations, and helps users choose the best fishing spots and the best fishing strategies, which can reduce blindness and improve capture efficiency. The specific steps are as follows: Step D1, fish school behavior prediction: Obtain the distribution, size, quantity, activity status, and environmental parameters of the fish school from the data processing and analysis module and the control module, and use them as input features , the fish school behavior category predicted by the classification model, the prediction target is the discrete fish school behavior pattern, the specific formula is: ,in, arrive It is a characteristic of fish distribution, size, number, activity status and environmental parameters. The predicted fish school behavior categories include aggregation and dispersion. Given input feature A, the fish school belongs to the category The probability of For each category The decision function of It is for kind The decision function The exponential operation of is the sum of the exponential values ​​of the decision functions of all categories, used to normalize the output probability; Step D2: For the newly input feature A, make a prediction and output the probability that the fish school belongs to each category , calculate the potential benefits of different fishing spots according to the prediction results, and formulate the best strategy by selecting the fishing spot with the maximum potential benefit. The formula for the potential benefits of different fishing spots is , and the formula for the fishing spot with the maximum potential benefit is: , where is the index of the optimal fishing spot, representing the fishing spot with the maximum potential benefit, represents the potential benefit of fishing spot . is the probability that the fish school prediction belongs to category , is the expected benefit of fishing spot under the fish school behavior category . L is the number of all fish school behavior categories. When the predicted potential benefit , adopt an active reeling strategy; when the predicted potential benefit , adopt a static casting strategy.

[0021] In this embodiment, specifically, the remote communication module needs to be described. The remote communication module communicates with external devices, supports data synchronization and remote control, provides a user operation interface through Bluetooth wireless technology, and is used to view fish school information in real time, adjust fishing reel parameters, and set fishing modes. The specific steps are as follows: Step E1: Establish a connection with an external device through the Bluetooth protocol, confirm the connection status to ensure a successful connection, obtain real-time fish school information and environmental parameters from the intelligent decision-making and optimization module, and transmit the obtained data to the user device through Bluetooth; Step E2: Display fish school information and environmental parameters on the user operation interface in real time. The update frequency is set according to requirements, with the fish school information updated every 5 seconds and the fishing reel status updated every 10 seconds. Provide a user-friendly interface, allow the user to manually adjust fishing reel parameters and fishing mode settings, and send the adjusted parameters to the control module through Bluetooth. The control module confirms the receipt of the adjusted parameters and applies them to ensure that the fishing reel is in the correct working state.

[0022] It should be noted that in the above embodiments, the descriptions of each embodiment have their own focuses. For the parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0023] Those skilled in the art should understand that the embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0024] The present invention is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present invention. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded computers, or other programmable data processing devices to generate a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices produce means for implementing the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 block or multiple blocks.

[0025] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including instruction means that implement the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 block or multiple blocks.

[0026] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one Figure 1 flow or multiple flows and / or blocks Figure 1 block or multiple blocks.

[0027] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they learn the basic creative concepts. Therefore, the appended claims are intended to be construed to include the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0028] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. An electric fishing reel control system based on intelligent fish signal detection, characterized in that: It includes fish signal detection module, data processing and analysis module, control module, intelligent decision-making and optimization module, and remote communication module; The fish information detection module detects underwater fish activities and water environment through integrated underwater sensing technology, obtains fish information in the water area, including fish distribution, quantity, size and behavior data, and transmits it to the data processing and analysis module; The data processing and analysis module processes and analyzes the data collected by the fish information detection module, determines the location, activity status and environmental changes of the fish school through data mining technology, and determines the best fishing time and method in real time, and transmits it to the control module and the intelligent decision and optimization module; The control module automatically controls the working state of the electric fishing reel, including the reeling speed, force, and reeling and releasing mode, according to the feedback from the data processing and analysis module; The intelligent decision-making and optimization module uses artificial intelligence algorithms to predict the behavior trends of fish schools based on fish school detection data, environmental information and fishing reel feedback, and makes intelligent decisions and optimizations to help users choose the best fishing spots and the best fishing strategies; The remote communication module communicates with external devices, supports data synchronization and remote control, and provides users with an operation interface through Bluetooth wireless technology for real-time viewing of fish information, adjustment of fishing reel parameters, and setting of fishing modes.

2. The electric fishing reel control system based on intelligent fish signal detection according to claim 1, characterized in that: The fish information detection module detects underwater fish activities and water environment by integrating underwater sensing technology, obtains fish information in the water area, including fish distribution, quantity, size and behavior data, and transmits it to the data processing and analysis module. The specific steps are as follows: Step A1, data collection: Use sonar sensors, temperature sensors and depth sensors to obtain information about fish schools in the water area, including fish distribution, quantity, size and behavior data. The temperature sensor is used to detect changes in water temperature, and the depth sensor is used to obtain depth data of the water area. The data reflected by the sonar sensor is combined with the location of the detection area to obtain the distribution of fish schools in the water area, and the size and quantity of the fish school are determined based on the number of detected echoes. The specific formula is: , where K is the number of fish schools, n is the total number of detected echoes, is the intensity of the ith echo, is the indicator function, and the specific formula is: , is the set echo intensity threshold; Step A2, data transmission: clean the collected data, filter out the echoes irrelevant to the fish school, and transmit the cleaned data to the data processing and analysis module to ensure data quality through denoising and signal optimization.

3. The electric fishing reel control system based on intelligent fish signal detection according to claim 1, characterized in that: The data processing and analysis module processes and analyzes the data collected by the fish detection module, determines the location, activity status and environmental changes of the fish school through data mining technology, and determines the best fishing time and method in real time, and transmits it to the control module and the intelligent decision and optimization module. The specific steps are as follows: Step B1: Based on the echo data, temperature and depth data returned by the sonar sensor, a clustering algorithm is used to identify the location and density of the fish school, and the location of the fish school is found by minimizing the distance from each point to the cluster center. The specific formula is: ,in, is the position of the jth data point in the kth class, is the center point of the kth class, J is the objective function, K is the number of clusters, indicating the number of fish schools, is the number of data points in the kth class, and M represents the total number of all data points detected by the sonar; Step B2: Based on the location and distribution of the fish school, combined with the water temperature and depth data, a model is established based on the regression analysis algorithm to predict the activity status of the fish school. ; Step B3: Based on the clustering results of the fish school and the input of environmental data, the trained regression model is used to predict the activity status of the fish school and determine the best time and method for fishing. , indicating that the fish school is active, it is the best time to fish and use active fishing gear; when , which means the fish are not active, so use static tackle.

4. The electric fishing reel control system based on intelligent fish signal detection according to claim 3 is characterized in that: In step B2, according to the location and distribution of the fish school, combined with the water temperature and depth data, a model is established based on a regression analysis algorithm to predict the activity status of the fish school. , further comprising the following steps: Step B201, extracting location density and distance change features from the clustering results of the fish school, and combining them with water temperature and water depth data as input features for regression analysis; Step B202: Use linear regression algorithm to build a model. The output of the regression model is to predict the activity status of the fish school. The specific formula is: ,in, is the predicted fish activity state, is the error term, is the intercept term, is the extracted input feature, are the coefficients in the regression model, obtained by fitting the training data; Step B203: Use the training set data to fit the regression model, the purpose is to minimize the residual sum of squares, the specific formula is: ,in, is the actual observed activity state, is the activity state predicted by the model, RSS represents the residual sum of squares, Indicates the sample size.

5. The electric fishing reel control system based on intelligent fish signal detection according to claim 1, characterized in that: The control module automatically controls the working state of the electric fishing reel, including the reeling speed, force, and reeling mode, according to the feedback from the data processing and analysis module. The specific steps are as follows: Step C1, setting working parameters: receiving feedback from the data processing and analysis module, including the activity status of the fish school, the best fishing time, and the fishing method information, and setting the working parameters of the electric fishing reel according to the received feedback information, including the line reeling speed V, the line reeling force L, and the line reeling mode M; Step C2, sending control signals: convert the set parameters V, L and M into control signals, send them to the electric fishing reel, and continuously adjust the reeling speed and strength according to the real-time feedback data to ensure the best fishing effect. The specific formula is: ,in, is the currently observed activity status of the fish school, is the adjusted take-up speed, V is the initially set take-up speed, and k is the adjustment coefficient used to control the sensitivity of the response.

6. The electric fishing reel control system based on intelligent fish signal detection according to claim 5, characterized in that: In the step C1, the working parameters of the electric fishing reel are set according to the received feedback information, including the line reeling speed V, the line reeling force L, and the line reeling and releasing mode M, and further include the following steps: Step C101, take-up speed: according to the predicted activity state , set the take-up speed, the specific formula is: , where V is the take-up speed, , , Represent high speed, medium speed and low speed values ​​respectively. is the predicted fish activity status; Step C102, reeling force: set the reeling force according to the size and species of the fish and its activity. The specific formula is: , where L is the winding force, , , Represents high, medium and low force values, respectively. is the predicted fish activity status; Step C103, line retracting and releasing mode: select the mode according to the activity status of the fish school and the preset conditions. , M = automatic reeling mode, when , M = automatic pay-off mode.

7. The electric fishing reel control system based on intelligent fish signal detection according to claim 1, characterized in that: The intelligent decision-making and optimization module is based on fish detection data, environmental information and fishing reel feedback, uses artificial intelligence algorithms to predict the behavior trends of fish schools, makes intelligent decisions and optimizations, and helps users choose the best fishing spots and the best fishing strategies. The specific steps are as follows: Step D1, fish school behavior prediction: Obtain the distribution, size, quantity, activity status, and environmental parameters of the fish school from the data processing and analysis module and the control module, and use them as input features , the fish school behavior category predicted by the classification model, the prediction target is the discrete fish school behavior pattern, the specific formula is: ,in, arrive It is a characteristic of fish distribution, size, number, activity status and environmental parameters. The predicted fish school behavior categories include aggregation and dispersion. Given input feature A, the fish school belongs to the category The probability of For each category The decision function of It is for kind The decision function The exponential operation of is the sum of the exponential values ​​of the decision functions of all categories, used to normalize the output probability; Step D2: For the newly input feature A, make a prediction and output the probability that the fish school belongs to each category , calculate the potential benefits of different fishing spots based on the prediction results, and formulate the best strategy by selecting the fishing spot with the maximum potential benefit. The potential benefit formula of different fishing spots is: , the formula for the fishing point with the maximum potential profit is: ,in, is the index of the optimal fishing spot, indicating the fishing spot with the greatest potential profit. Indicates fishing spot potential benefits, Is the fish school prediction belonging to the category The probability of It's a fishing spot In the fish school behavior category The expected return under this condition is L, which is the number of all fish school behavior categories. , adopt an active closing strategy; when the predicted potential profit , adopt a static line-release strategy.

8. The electric fishing reel control system based on intelligent fish signal detection according to claim 1, characterized in that: The remote communication module communicates with external devices, supports data synchronization and remote control, and provides users with an operation interface through Bluetooth wireless technology for real-time viewing of fish information, adjustment of fishing reel parameters, and setting of fishing modes. The specific steps are as follows: Step E1, establishing a connection with an external device through the Bluetooth protocol and confirming the connection status, obtaining real-time fish school information and environmental parameters from the intelligent decision-making and optimization module, and transmitting the obtained data to the user device through Bluetooth; Step E2: Display fish school information and environmental parameters in real time on the user operation interface. The update frequency is set as needed: fish school information is updated every 5 seconds, and fishing reel status is updated every 10 seconds. A user-friendly interface is provided to allow users to manually adjust fishing reel parameters and fishing mode settings, and the adjusted parameters are sent to the control module via Bluetooth. The control module confirms the receipt of the adjusted parameters and applies them to ensure that the fishing reel is in the correct working state.

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