Coil current control method for water dropper electromagnetic brake

Abstract

The invention relates to the technical field of fishing gear control, in particular to a coil current control method for a water dropper electromagnetic brake. According to the method, current and voltage data generated in a coil when a water drip reel spool rotates are obtained in real time through a microcontroller, and the real-time rotating speed of the spool is calculated through current variation fitting based on a preset algorithm model so as to replace the function of a physical speed measurement module; and then a pulse width modulation signal is generated, coil current is adjusted through a control unit, and self-adaptive electromagnetic braking force is generated. Hardware is replaced by an algorithm, a physical speed measurement module and an independent monitoring circuit are omitted, the hardware cost and the system complexity are remarkably reduced, control parameters are automatically optimized according to user habits, and the throwing distance and the user experience are improved while line explosion is effectively prevented.

Description

Technical Field

[0001] The present invention relates to the technical field of fishing gear control, in particular to a coil current control method for an electromagnetic brake of a water-drip reel. Background Art

[0002] Lure fishing is a widely popular fishing method, and one of its core pieces of equipment is the drop-bar reel. With its advantages of precise casting and strong controllability, drop-bar reels occupy a crucial position in lure fishing. However, using a drop-bar reel presents certain technical barriers. The most prominent problem is line tangling, where the spool rotates too fast, exceeding the line delivery speed. This problem is particularly troublesome for novice users, but also affects the fishing experience of experienced anglers. To address line tangling, existing technologies generally use electromagnetic brake systems. Traditional electromagnetic brake systems employ magnets or conductive disks placed on the spool. These disks generate eddy currents generated by the rotating disks cutting through the magnetic flux lines, creating a reverse braking torque to control the spool's speed. More advanced electronically controlled electromagnetic brake systems incorporate an electronic control unit (ECU or MCU) to actively regulate the electromagnetic force. However, existing technologies still have several shortcomings. First, to precisely control the braking force, traditional solutions typically require a separate physical speed measurement module (such as a Hall effect sensor) to monitor the spool's speed in real time, as well as complex current and voltage monitoring circuits to provide feedback on the coil status. This additional hardware not only increases product manufacturing cost and size, but also increases the system's failure rate. Secondly, the control logic of existing systems often uses a preset fixed mode, requiring users to manually perform tedious adjustments based on varying lure weights, wind speeds, and other environmental factors. For novices, it's difficult to quickly find the right brake position, and improper adjustments can frequently lead to line breakage. For experienced players, however, the fixed control logic limits their ability to cast extreme long distances or perform precision fishing. Furthermore, the preset braking curve often fails to accommodate both heavy and light baits, often resulting in issues like "large baits don't cast far, and small baits are difficult to start." The system's adaptability to user force application habits and changing fishing environments is limited, making personalized and intelligent braking control impossible. Therefore, there is an urgent need for a reel electromagnetic brake method that simplifies hardware structure, reduces costs, and implements intelligent adaptive control to improve user experience and lower the barrier to entry. This invention aims to replace hardware with an algorithm, eliminating the need for a physical speed measurement module and independent monitoring circuitry, significantly reducing hardware cost and system complexity. Furthermore, it can automatically optimize based on user habits, effectively preventing line breakage and improving casting distance and user experience. Summary of the Invention

[0003] This invention addresses the technical shortcomings of existing electromagnetic brake systems for water-drop reels, such as high hardware complexity, high cost, and inflexible control logic. To this end, it provides a coil current control method for water-drop reel electromagnetic brakes. Through an innovative combination of hardware and software, this method effectively prevents line breakage while meeting user requirements for casting distance and precise control, significantly improving the performance and user experience of water-drop reels.

[0004] This invention provides a coil current control method for a water-drop reel electromagnetic brake. Its core is to use a microcontroller to acquire real-time current and voltage data generated in the coil as the reel spool rotates. This data is then calculated based on a pre-set algorithm model, replacing the functionality of a physical speed measurement module. The microcontroller generates a pulse-width modulated control signal based on the spool's real-time speed, and a control unit adjusts the coil current to generate the corresponding electromagnetic braking force or boosting force. This method addresses the high hardware cost, complex debugging, and poor adaptability inherent in traditional technologies by simplifying the hardware structure and optimizing the control logic.

[0005] Furthermore, the system's hardware structure includes a coil, a rectifier unit, a microcontroller (MCU), and a control unit. The coil is fixed to the water dropper housing and is coaxial with the spool, but not physically in contact. When the spool rotates at high speed, the attached magnet or conductive metal part cuts the coil's magnetic flux lines, inducing current and voltage signals in the coil. The strength and frequency of these signals are directly related to the spool's rotational speed and provide basic data input for subsequent control. The rectifier unit is connected to the coil's output end and performs direction adjustment, filtering, and voltage stabilization on the raw AC signal induced by the coil, ensuring a relatively stable, low-noise DC signal input to the microcontroller (MCU). The MCU, serving as the "brain" of the system, directly collects and processes the rectified coil current and voltage data, eliminating the need for a separate current and voltage monitoring unit. A pre-set algorithm model is embedded within the MCU, which is used to fit and calculate the spool's rotational speed in real time and generate pulse-width modulation control signals. The control unit receives the PWM signal generated by the MCU and uses it to precisely control the current flowing through the coil, generating the corresponding electromagnetic braking force or thrust.

[0006] In particular, the core steps of the coil current control method are as follows:

[0007] S1: Start and collect initial data. When the system detects the start of the casting action, the MCU samples the coil current value from the rectifier unit at the current time point T, which is recorded as I(T).

[0008] S2: Calculate the current change ΔI. The MCU performs the next sampling within the T+1 period, obtaining the current value I(T+1). It then calculates the current difference within this extremely short period: ΔI = I(T+1) - I(T). ΔI is a key physical quantity reflecting the angular acceleration of the spool and serves as the core input for subsequent calculations.

[0009] S3: Invoke the algorithm model to calculate the rotational speed V_calc. The MCU uses ΔI as input and invokes the internally stored algorithm model to quickly calculate the current real-time rotational speed V_calc of the spool. This algorithm model, which includes a multivariate regression algorithm and a neural network model, accurately infers the complex relationship between ΔI and spool rotational speed.

[0010] S4: Compare with target speed curve and make a decision. The MCU stores one or more ideal "target speed curves," V_target. These curves define the ideal speed the spool should maintain during the various stages of casting (front, middle, and back) to achieve maximum distance without breaking the line. The MCU compares the real-time calculated speed, V_calc, with the target speed, V_target, corresponding to the current time point.

[0011] S5: Generate a PWM control signal. Based on the comparison between V_calc and V_target (for example, if V_calc > V_target, indicating excessive speed and a risk of wire breakage), the MCU generates a PWM signal with a specific duty cycle. The duty cycle is proportional to the required braking force.

[0012] S6: Adjust the coil current to apply control force. After receiving the PWM signal, the control unit interprets it as a specific current value and applies this control current to the coil, generating a corresponding electromagnetic braking force or boost force, thereby bringing the spool speed V_calc back to near the target speed V_target.

[0013] S7: Loop execution. As long as the casting process continues, the system will continue to repeat steps S2 to S6, forming a dynamic, real-time closed-loop control.

[0014] Furthermore, the working principle of the algorithm model is divided into two modules: a real-time speed inference module and an offline adaptive learning module. The real-time speed inference module includes the following specific implementation details:

[0015] Input: Real-time current change ΔI.

[0016] A multivariate regression algorithm, pre-trained based on fundamental physical formulas and extensive experimental data, quickly provides a rough estimate of spool speed, or a set of eigenvectors, based on ΔI and other possible auxiliary parameters. Through mathematical modeling, the multivariate regression algorithm maps the relationship between ΔI and spool speed into a set of equations, enabling preliminary speed estimation.

[0017] Neural Network Model: Leveraging nonlinear mapping capabilities, this model more accurately fits the complex relationship between ΔI and the actual spool speed (V_calc), corrects regression model errors, and outputs the final calculated speed (V_calc). This neural network model utilizes a multilayer perceptron architecture and uses a backpropagation algorithm to optimize weights and biases, thereby improving the accuracy of speed estimation.

[0018] The specific implementation details of the offline adaptive learning module are as follows:

[0019] Data accumulation: Records and stores key data from multiple casts, such as the complete ΔI sequence, cast duration, peak rotation speed, etc. This data accumulation process is completed through the MCU's built-in storage unit to ensure data integrity and reliability.

[0020] Unsupervised learning algorithm: Analyzes accumulated user data to automatically identify the user's throwing pattern (for example, explosive or smooth). This unsupervised learning algorithm uses clustering algorithms and autoencoder technology to accurately identify user habits by extracting and classifying features from user data.

[0021] Model Parameter Optimization: Based on identified user habits, the system automatically adjusts the weights and biases of the neural network in the real-time speed inference module and optimizes the target speed curve (V_target). For example, for users who tend to use explosive power, the system automatically increases the target speed tolerance at the beginning of the cast, while applying a smoother, more linear braking curve in the middle and later stages to maximize casting distance while preventing line breakage.

[0022] Compared with the existing technology, the present invention achieves a significant reduction in hardware costs through an innovative combination of software and hardware, while providing an intelligent and personalized electromagnetic brake control solution. Specifically, the beneficial effects of the present invention include the following aspects:

[0023] By replacing hardware with algorithms, the traditional physical speed measurement module (such as Hall sensor) and independent current and voltage monitoring unit are completely eliminated, which greatly simplifies the hardware design of the control circuit, reduces material costs and production assembly complexity, and improves the cost competitiveness and production efficiency of the product.

[0024] It uses an algorithm model based on unsupervised machine learning, which can learn and remember the user's throwing force habits during use, automatically iterate and optimize control parameters, so that the braking system can adapt to the operational needs of different users without the need for tedious manual debugging, significantly improving the product's intelligence level and user-friendliness.

[0025] The braking curve optimized by the deep learning model makes the intervention of braking force more linear and smooth, and can perform real-time and precise intervention in the entire time domain at the front, middle and end stages of the casting process, effectively avoiding the frustration caused by sudden changes in braking force. While ensuring that the line does not explode, unnecessary energy loss is minimized, thereby significantly improving the casting distance and stability.

[0026] Adaptive control logic can better take into account the casting requirements of baits of different weights, solving the pain points of traditional systems such as "large baits cannot be cast far and small baits are difficult to start." At the same time, the simplified hardware structure reduces potential failure points, making the entire control system simpler, more reliable, and more adaptable to changing fishing scenarios and needs.

[0027] In summary, the present invention, through an innovative combination of software and hardware, not only helps novice users effectively prevent line blowouts, but also meets the pursuit of casting distance and fine control by experienced players, thereby comprehensively improving the performance and user experience of water drop reel products. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0029] Figure 1 This is a hardware structure block diagram of a water drop reel electromagnetic brake system provided by an embodiment of the present invention.

[0030] Figure 2 This is a flow chart of a coil current control method provided by an embodiment of the present invention.

[0031] Figure 3 It is a principle block diagram of the speed fitting calculation and adaptive learning of the algorithm model in the embodiment of the present invention. DETAILED DESCRIPTION

[0032] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0033] The present invention provides a coil current control method for a water-drip reel electromagnetic brake. The core of the method is to obtain the current and voltage data generated in the coil when the water-drip reel spool rotates in real time through a microcontroller, and calculate the real-time rotation speed of the spool based on a preset algorithm model, thereby replacing the function of the physical speed measurement module. Figure 1 , Attachment Figure 2 and attached Figure 3 Specific embodiments of the present invention are described in detail.

[0034] As attached Figure 1 As shown, the hardware system of the present invention primarily comprises a coil, a rectifier unit, a microcontroller (MCU), and a control unit. The coil is fixed to the water reel housing, coaxially arranged with the spool but not in direct contact. When the user casts, the spool rotates at high speed, and the attached magnet or conductive metal part cuts the coil's magnetic flux lines, inducing current and voltage signals in the coil. The strength and frequency of these signals are directly related to the spool's rotational speed and provide basic data input for subsequent control. The rectifier unit, connected to the coil's output, steers, filters, and stabilizes the raw AC signal induced by the coil, ensuring a relatively stable and low-noise DC signal input to the microcontroller (MCU). The MCU, serving as the "brain" of the system, directly collects and processes the rectified coil current and voltage data, eliminating the need for a separate current and voltage monitoring unit. A pre-set algorithm model is embedded within the MCU, which calculates the spool's rotational speed in real time and generates a pulse-width modulation (PWM) control signal. The control unit receives the PWM signal generated by the MCU and uses it to precisely control the current flowing through the coil, generating the corresponding electromagnetic braking force or boost force, thereby achieving closed-loop control of the spool's rotational speed.

[0035] The core steps of the present invention are as follows Figure 2The specific implementation is as follows. First, the system starts and collects initial data. When the system detects the start of casting, the microcontroller (MCU) samples the coil current from the rectifier unit at the current time T, denoted as I(T). Next, the current change, ΔI, is calculated. The MCU performs the next sampling within the T+1 period, obtaining the current value I(T+1). The current difference within this extremely short period, ΔI, is calculated as ΔI = I(T+1) - I(T). ΔI is a key physical quantity reflecting the angular acceleration of the spool and serves as the core input for subsequent calculations. Subsequently, an algorithm model is called to fit and calculate the speed V_calc. The MCU uses ΔI as input and calls an internally stored algorithm model to quickly fit and calculate the current real-time speed V_calc of the spool. The algorithm model, which includes a multivariate regression algorithm and a neural network model, enables accurate calculations based on the complex relationship between ΔI and the spool speed. Next, the target speed curve is compared with the target speed curve and a decision is made. The MCU internally stores one or more ideal "target speed curves" V_target. This curve defines the ideal spool speed to maintain during the various stages of casting (front, middle, and back) to achieve maximum distance without breaking the line. The microcontroller (MCU) compares the real-time calculated speed V_calc with the target speed V_target corresponding to the current time point. It then generates a PWM control signal. Based on the comparison between V_calc and V_target (for example, if V_calc > V_target, indicating excessive speed and a risk of line breaking), the MCU generates a PWM signal with a specific duty cycle. The duty cycle is proportional to the required braking force. Finally, the coil current is adjusted to apply the control force. After receiving the PWM signal, the control unit interprets it as a specific current value and applies this control current to the coil, generating the corresponding electromagnetic braking force or boost force, thereby bringing the spool speed V_calc back to near the target speed V_target. As long as the casting process continues, the system repeats these steps, forming a dynamic, real-time closed-loop control system.

[0036] Attachment Figure 3This article details the working principles of the algorithm model within the microcontroller (MCU). The model consists of two modules: a real-time speed inference module and an offline adaptive learning module. The implementation details of the real-time speed inference module are as follows. The input is the real-time current change, ΔI. A multivariate regression algorithm, pre-trained based on basic physical formulas and extensive experimental data, can quickly provide a rough estimate of the spool speed or a set of feature vectors based on ΔI and other possible auxiliary parameters. Through mathematical modeling, the multivariate regression algorithm maps the relationship between ΔI and spool speed into a set of equations, thereby completing preliminary speed estimation. The neural network model, leveraging its nonlinear mapping capabilities, more accurately fits the complex relationship between ΔI and the actual spool speed, V_calc, corrects for errors in the regression model, and outputs the final calculated speed, V_calc. The neural network model utilizes a multilayer perceptron architecture and uses a backpropagation algorithm to optimize weights and biases, significantly improving the accuracy of speed estimation. The implementation details of the offline adaptive learning module are as follows. Data accumulation records and stores key data from multiple user casts, such as the complete ΔI sequence, cast duration, and peak speed for each cast. The data accumulation process is completed through the built-in storage unit of the microcontroller MCU to ensure the integrity and reliability of the data. The unsupervised learning algorithm analyzes the accumulated user data and automatically identifies the user's casting pattern (for example, explosive force or smooth force). The unsupervised learning algorithm uses clustering algorithms and autoencoder technology to accurately identify user habits by extracting and classifying the features of user data. Model parameter optimization automatically adjusts the weights and biases of the neural network model in the real-time speed inference module based on the identified user habits, and optimizes the target speed curve V_target. For example, for users who are accustomed to explosive force, the system automatically increases the target speed tolerance in the early stage of casting, while applying a smoother, more linear braking curve in the middle and late stages to maximize the casting distance while preventing line explosion.

[0037] The present invention achieves a significant reduction in hardware costs through an innovative combination of software and hardware, while providing an intelligent and personalized electromagnetic brake control solution. Specifically, the technical effects of the present invention are embodied in the following aspects. By replacing hardware with an algorithm, the traditional physical speed measurement module (such as a Hall sensor) and an independent current and voltage monitoring unit are completely eliminated, which greatly simplifies the hardware design of the control circuit, reduces material costs and production assembly complexity, and improves the cost competitiveness and production efficiency of the product. The algorithm model based on unsupervised machine learning can learn and memorize the user's casting force habits during use, automatically iterate and optimize control parameters, so that the brake system can adapt to the operational needs of different users without the need for tedious manual debugging, significantly improving the intelligence level and user-friendliness of the product. The brake curve optimized by the deep learning model makes the intervention of the brake force more linear and smooth, and can perform real-time and precise intervention in the entire time domain in the front, middle and back sections of the casting process, effectively avoiding the frustration caused by sudden changes in the brake force. On the premise of ensuring that the line is not blown, unnecessary energy loss is minimized, thereby significantly improving the casting distance and stability. Adaptive control logic can better take into account the casting requirements of baits of different weights, solving the pain points of traditional systems such as "large baits cannot be cast far and small baits are difficult to start." At the same time, the simplified hardware structure reduces potential failure points, making the entire control system simpler, more reliable, and more adaptable to changing fishing scenarios and needs.

[0038] In summary, the present invention not only helps novice users effectively prevent line blowouts through an innovative combination of software and hardware, but also meets the pursuit of casting distance and fine control by experienced players, thereby comprehensively improving the performance and user experience of water drop reel products.

[0039] The above description is only a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included in the scope of protection of the present invention.

Claims

1. A coil current control method for a water drop wheel electromagnetic brake, characterized in that: include: Start and collect initial data. The microcontroller samples the coil current value I(T) from the rectifier unit at the current time point T. Calculate the current change ΔI. The microcontroller performs the next sampling in the T+1 period to obtain the coil current value I(T+1) from the rectifier unit and calculates the current difference ΔI in this time period = I(T+1) - I(T). The algorithm model is called to fit and calculate the speed V_calc. The microcontroller takes ΔI as input, calls the internally solidified algorithm model, and fits and calculates the current real-time speed V_calc of the spool. Comparison with the target speed curve and decision making, the microcontroller compares the real-time calculated speed V_calc with the target speed V_target corresponding to the current time point; Generate PWM control signal. Based on the comparison result of V_calc and V_target, the microcontroller generates a PWM signal with a specific duty cycle. Adjust the coil current to apply control force. After receiving the PWM signal, the control unit interprets it into a specific current value and applies the control current to the coil to generate corresponding electromagnetic braking force or thrust.

2. The method according to claim 1, characterized in that The algorithm model includes a multiple regression algorithm and a neural network model.

3. The method according to claim 2, characterized in that The multivariate regression algorithm is pre-trained based on basic physical formulas and experimental data, and is used to provide a rough estimate of the spool rotation speed based on ΔI and auxiliary parameters.

4. The method according to claim 2, characterized in that The neural network model adopts a multi-layer perceptron structure and optimizes weights and biases through a back-propagation algorithm to correct errors in the multivariate regression algorithm and output the final calculated speed V_calc.

5. The method according to claim 1, characterized in that The microcontroller internally stores one or more target speed curves V_target, which are used to define the ideal speed that the spool should maintain at different stages of casting.

6. The method according to claim 1, characterized in that The rectifier unit performs direction adjustment, filtering and voltage stabilization on the original AC signal induced by the coil, ensuring that a relatively stable DC signal with less noise is input to the microcontroller.

7. The method according to claim 1, characterized in that It also includes an offline adaptive learning module for recording and storing key data from multiple user casts, and optimizing the weights and biases of the neural network model in the real-time speed inference module.

8. The method according to claim 7, characterized in that The offline adaptive learning module adopts an unsupervised learning algorithm to identify the user's casting pattern and optimize the target speed curve V_target by extracting and classifying the features of the user data.

9. The method according to claim 1, characterized in that The coil is fixed on the water drop wheel housing and is arranged coaxially with the spool but not in direct contact with it. The current and voltage signal strength and frequency induced by the coil are directly related to the rotation speed of the spool.

Images