A buoy-type cross-medium communication system and its algorithm

Abstract

The present invention relates to the field of communications technology and discloses a buoy-type cross-media communication system and its algorithm. The system comprises a surface control module and an underwater control module. The surface control module includes a TCP / IP protocol processor and simultaneously establishes a wireless connection with a remote terminal via WiFi communication. The underwater control module includes a visible light communication control unit, a visual positioning unit, a tracking power unit, and an environmental perception and adaptation unit. The algorithm comprises the following steps: Step 1: Initialization phase; Step 2: Communication establishment phase; Step 3: Dynamic tracking phase; Step 4: Energy efficiency management phase. This buoy-type cross-media communication system and its algorithm achieve seamless integration between surface WiFi and underwater visible light communication, while ensuring communication stability in complex marine environments through visual positioning and active tracking mechanisms.

Description

Technical Field

[0001] The present invention relates to the field of communication technology, and in particular to a buoy-type cross-media communication system and an algorithm thereof. Background Art

[0002] An underwater robot (UAR) is an intelligent device that can perform tasks in an underwater environment. It is widely used in marine scientific research, marine resource development, seabed exploration, environmental monitoring, emergency rescue and other fields. Currently, wireless communication between UARs and surface terminals or other equipment mainly relies on the following technical solutions: underwater acoustic communication, underwater radio frequency (RF) communication and underwater blue-green laser communication. Among them, acoustic communication uses sound waves to propagate in the water medium to achieve data transmission. It is currently the most mature underwater communication method. Its advantage is that it can achieve long-distance communication of kilometers, but it has significant defects: extremely low bandwidth (usually only kbps level), which makes it difficult to support large-capacity data transmission such as high-definition video and images; high latency (the speed of sound is about 1500m / s), which cannot meet real-time control requirements; it is easily interfered by multipath effects, and has poor communication stability in complex seabed terrain or dynamic water flow environments.

[0003] Underwater radio frequency (RF) communication uses low-frequency electromagnetic waves (such as 30-300 Hz) to penetrate water, but its limitations include: short transmission distance (usually <10 meters), requiring extremely high transmission power; limited spectrum resources, which can easily cause frequency band conflicts with marine life monitoring equipment; and large antenna size, making it difficult to integrate into small underwater robots.

[0004] Underwater blue-green laser communication uses 470-550nm wavelength lasers to penetrate water, enabling Mbps-level high-speed communication. However, there are technical bottlenecks: stringent alignment accuracy requirements require the use of precision mechanical servo mechanisms, increasing system complexity and cost; it is significantly affected by water turbidity, with performance dropping sharply in turbid waters or suspended particle environments; and the point-to-point communication mode cannot support dynamic networking of multiple devices.

[0005] Therefore, to achieve cross-media communication between underwater equipment such as underwater robots and surface / air terminals, existing technologies mainly use relay buoy solutions to solve the above problems. However, existing buoys usually use dual-mode relays of underwater acoustics + radio frequency (such as satellite). Due to the incompatibility of underwater acoustic and radio frequency protocols, multiple protocol conversions are required, resulting in increased end-to-end latency (usually >1 second). In addition, the communication protocol is easily fragmented, which can cause data packet loss due to protocol conversion errors. In addition, traditional buoys lack autonomous positioning and motion compensation capabilities and dynamic tracking capabilities. When the underwater robot or equipment moves due to mission requirements, the buoy cannot actively adjust its position to maintain the communication link, resulting in field of view angle deviation or optical communication interruption. This problem is particularly prominent in strong ocean currents. At the same time, the buoy needs to continuously turn on the underwater acoustic communication module to monitor underwater signals, and its power consumption is as high as 10-20W. The solar power supply system has difficulty supporting long-term operation in rainy weather, resulting in insufficient endurance and relatively low energy efficiency. Summary of the Invention

[0006] In response to the shortcomings of the existing technology, the present invention provides a buoy-type cross-medium communication system and its algorithm, which have the advantages of high bandwidth, low latency, and adaptability to dynamic environments, and realize seamless connection between surface WiFi and underwater visible light communication. At the same time, it ensures communication stability in complex marine environments through visual positioning and active tracking mechanisms. It solves the contradiction between the medium penetration ability and communication rate of traditional buoy underwater acoustic communication, such as long distance but low bandwidth; the optical communication rate is high but limited by the medium transmittance; the surface and underwater communication protocols are independently designed, and the cross-protocol collaboration efficiency is low due to large data conversion overhead; there is a lack of real-time tracking and compensation mechanism for the movement of underwater equipment; the system has poor adaptability to dynamic environments due to insufficient integration; the communication, positioning, and energy modules are designed in a decentralized manner, which makes it difficult to meet the requirements of miniaturization and low power consumption.

[0007] To achieve the aforementioned high bandwidth, low latency, and adaptive dynamic environment, realize seamless connection between surface WiFi and underwater visible light communication, and simultaneously ensure communication stability in complex marine environments through visual positioning and active tracking mechanisms, the present invention provides the following technical solutions: a buoy-type cross-medium communication device, comprising a buoy, wherein the buoy includes a surface mechanism and an underwater mechanism;

[0008] The surface mechanism includes a surface WiFi communication antenna and a solar power supply module;

[0009] The underwater mechanism includes a pressure-resistant sealed cabin, an optical communication module, a visual positioning camera and a processing module are arranged inside the pressure-resistant sealed cabin, and micro-thruster groups are arranged on both sides of the pressure-resistant sealed cabin.

[0010] Preferably, the surface WiFi communication antenna specifically adopts a 2.4GHz / 5GHz dual-band antenna, and also includes a processor and an encryption chip inside; the solar power supply unit includes a solar panel and a storage battery.

[0011] Preferably, a cross-medium coupling mechanism is also included, specifically a waterproof light-transmitting window with an anti-reflection film on the surface, installed at a 30° angle to reduce interference from light reflected from the water surface, and an optical waveguide layer is integrated inside to reduce refractive loss.

[0012] Preferably, the optical communication module includes a transmitting LED, a photoelectric detection unit and a modulation and demodulation unit; the lens end of the visual positioning camera is provided with a wide-angle lens; and the processing module includes an MCU chip and a PHY chip.

[0013] A buoy-type cross-medium communication system includes a surface control module and an underwater control module;

[0014] The surface control module includes a TCP / IP protocol processor and establishes a wireless connection with a remote terminal through WiFi communication; the underwater control module includes a visible light communication control unit, a visual positioning unit, a tracking power unit and an environmental perception and adaptation unit.

[0015] Preferably, the visible light communication control unit includes a transmitting end using a high-brightness blue-green LED array, and a PIN photodiode array, and a receiving end integrating an optical filter, which is used to establish a bidirectional optical communication link with an underwater robot or sensor and support parallel access of multiple devices; the visual positioning unit includes an LED beacon recognition unit, and the LED beacon recognition unit adopts an underwater target detection model based on a convolutional neural network, which is used to track the position of the LED beacon carried by the underwater robot in real time and calculate the relative direction and distance; the tracking power unit includes a thruster drive control group and a fluid dynamics controller, which actively adjusts the buoy position according to the feedback from the visual positioning unit to ensure the coverage of the optical communication field of view; the thruster drive control group specifically adopts multiple groups of brushless motors to drive micro thrusters; the fluid dynamics controller dynamically adjusts the thruster output based on the PID algorithm and the ocean current prediction model.

[0016] Preferably, the environmental perception and adaptation unit includes a multi-parameter sensor and an adaptive adjustment controller; the multi-parameter sensor specifically adopts a water turbidity sensor, a depth sensor and a temperature sensor; the adaptive adjustment controller is used to dynamically optimize optical communication parameters according to environmental data.

[0017] Preferably, it also includes an energy collaborative management circuit module composed of a bidirectional DC-DC converter and an intelligent power distribution switch, which is used to dynamically allocate energy to the surface control module and the underwater control module according to task priority, and give priority to ensuring the power supply of the visible light communication control unit and the tracking power unit.

[0018] A buoy-type cross-medium communication algorithm includes the following steps:

[0019] Step 1: Initialization phase: After entering the water, the buoy automatically deploys its solar panels and enters standby mode. The buoy performs perturbative motion with small random displacements in the horizontal plane at a preset step size to detect signal quality changes. It also initiates GPS positioning and establishes a WiFi connection with the remote terminal. The underwater control module activates the visible light communication control unit and scans for LED beacons within its field of view.

[0020] Step 2: During the communication establishment phase, after detecting the underwater robot beacon, the visual positioning unit calculates the relative position, and the power system adjusts the buoy position to the optimal communication area. The protocol conversion module sends the surface WiFi command to the underwater robot using OFDM digital modulation technology.

[0021] Step 3: During the dynamic tracking phase, the underwater robot's trajectory is monitored in real time. Combined with the ocean current prediction model, the micro-thruster group fine-tunes the buoy position to maintain a field of view deviation of ≤±5°; the environmental perception and adaptive unit adjusts the light intensity and divergence angle based on the turbidity data to ensure a bit error rate of ≤10⁻ 6 ;

[0022] Step 4: Energy efficiency management stage: During low-load periods, redundant sensors are turned off and the system switches to energy-saving mode, with energy storage batteries giving priority to powering the underwater mechanism.

[0023] Preferably, the communication establishment stage further includes collecting optical signals through an underwater visible light receiver, extracting modulation information, obtaining original data packets after demodulation, and then comparing the currently collected and demodulated optical signals to calculate the signal-to-noise ratio of the current signal, comparing the real-time calculated signal-to-noise ratio with a preset threshold to make an error judgment, and using the error value as an input error signal of the control system to perform signal-to-noise ratio feedback and differential control;

[0024] The displacement adjustment is calculated using a proportional-integral-derivative controller:

[0025]

[0026] in, is the signal-to-noise ratio deviation, is the dynamic adjustment coefficient;

[0027] Based on the differential output, the moving direction that improves the signal-to-noise ratio the fastest is selected. An initial step size of 0.2m is used. If the signal-to-noise ratio does not improve after three consecutive moves, the step size is increased to 0.5m. This adaptive step size strategy, which quickly approaches the optimal position, is used to determine the direction and optimize the step size.

[0028] The dynamic tracking phase in step 3 also includes switching to a steady-state tracking mode after the signal reaches the target signal-to-noise ratio, compensating for ocean current disturbances through periodic fine-tuning (±0.1m), and maintaining mobile tracking of the directivity of the optical communication link.

[0029] Compared with the existing technology, the present invention provides a buoy-type cross-medium communication system and its algorithm, which has the following beneficial effects:

[0030] 1. This buoy-type cross-medium communication device system adopts an underwater target detection model based on a convolutional neural network through the LED beacon recognition unit to track the position of the LED beacon carried by the underwater robot in real time, calculate the relative direction and distance, and then cooperate with the thruster drive control group and fluid dynamics controller in the tracking power unit to actively adjust the buoy position according to the feedback of the visual positioning unit to ensure the coverage of the optical communication field of view, so as to implement real-time tracking and compensation mechanism for the movement of underwater equipment, improve adaptability to dynamic environments, and also have the energy efficiency management of shutting down redundant sensors and switching to energy-saving mode during low-load periods, with the energy storage battery giving priority to powering the underwater control module.

[0031] 2. The buoy-type cross-medium communication device algorithm uses a visual positioning system based on the underwater feature point SLAM (Simultaneous Localization and Mapping) algorithm, cooperates with a camera to capture the underwater robot's LED beacon, and combines thruster group feedback control to achieve dynamic adjustment of the buoy position, ensuring that the optical communication field of view angle coverage deviation is ≤±5°. By integrating communication performance quality monitoring, it solves the stability problem of traditional optical positioning and tracking. At the same time, it also performs perturbation-like motion with small random displacements in the horizontal plane with a preset step size to detect the trend of signal quality changes. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 This is a schematic diagram of a buoy-type cross-medium communication device proposed by the present invention;

[0033] Figure 2 This is a schematic diagram of the surface mechanism of a buoy-type cross-medium communication device proposed by the present invention;

[0034] Figure 3 This is a schematic diagram of the underwater mechanism of a buoy-type cross-medium communication device proposed by the present invention;

[0035] Figure 4 This is a schematic diagram of a buoy-type cross-medium communication system proposed by the present invention;

[0036] Figure 5 This is a flow chart of a buoy-type cross-medium communication algorithm proposed by the present invention;

[0037] Figure 6 This is a schematic diagram of visual positioning and motion compensation tracking in a buoy-type cross-media communication algorithm proposed in the present invention;

[0038] Figure 7 This is a simplified schematic diagram of a buoy-type cross-media communication system scenario proposed by the present invention. DETAILED DESCRIPTION

[0039] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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.

[0040] See also Figures 1 to 3 A buoy-type cross-medium communication device includes a buoy, which includes a surface mechanism and an underwater mechanism with a split modular design, and also includes a cross-medium coupling mechanism. Specifically, a waterproof and light-transmitting window with an anti-reflection film (transmittance ≥ 95%) is used, which is installed at a 30° angle to reduce interference from light reflected from the water surface. An optical waveguide layer is integrated inside to reduce refraction loss. Lossless transmission of cross-medium signals is achieved through a waterproof and light-transmitting coupling structure. By adopting a split integrated design that divides the buoy into two parts, namely a surface mechanism and an underwater mechanism, and by adopting a physical separation design, independent optimization of the surface (air medium) and underwater (water medium) communication modules can be achieved, effectively avoiding signal crosstalk.

[0041] The surface mechanism includes a surface WiFi communication antenna and a solar power supply module. The surface WiFi communication antenna specifically uses a 2.4GHz / 5GHz dual-band antenna and also includes a processor and encryption chip. The solar power supply unit includes a solar panel and a storage battery. Through the setting of the solar power supply unit, the device can be solar-charged, effectively extending the operating time of the device.

[0042] The underwater mechanism includes a pressure-resistant sealed cabin to enhance the protection of internal components. The pressure-resistant sealed cabin is equipped with an optical communication module, a visual positioning camera and a processing module. The optical communication module includes a transmitting LED, a photoelectric detection unit and a modulation and demodulation unit. The lens end of the visual positioning camera is provided with a wide-angle lens. The visual positioning camera can specifically use a 2-megapixel CMOS sensor with a frame rate of 30fps (horizontal field of view angle >= 120°). The specific processing modules used include an MCU chip and a PHY chip. Micro-thruster groups are provided on both sides of the pressure-resistant sealed cabin. The micro-thruster groups specifically use 4 groups of brushless motors to drive ducted thrusters, making the device miniaturized.

[0043] See also Figure 4 A buoy-type cross-media communication system includes a surface control module and an underwater control module, as well as an energy collaborative management circuit module composed of a bidirectional DC-DC converter (efficiency ≥ 92%) and an intelligent power distribution switch. The system is used to dynamically allocate energy between the surface control module and the underwater control module based on task priority, giving priority to powering the visible light communication control unit and the tracking power unit, greatly reducing the power consumption requirements of the device and effectively extending the device's operating time.

[0044] The surface control module includes a TCP / IP protocol processor and establishes a wireless connection with the remote terminal via WiFi communication, with a maximum communication distance of ≥10 meters;

[0045] The underwater control module includes a visible light communication control unit, a visual positioning unit, a tracking power unit, and an environmental perception and adaptation unit. This system integrates communication, positioning, and energy module designs. The visible light communication control unit includes a transmitter using a high-brightness blue-green LED array (wavelength 470-530nm) with adjustable peak power (0.1-5W), a PIN photodiode array, and a receiver with an integrated optical filter to suppress background light interference. It is used to establish a bidirectional optical communication link with underwater robots or sensors and supports parallel access of multiple devices.

[0046] The visual positioning unit includes an internal LED beacon recognition unit. The LED beacon recognition unit uses an underwater target detection model based on a convolutional neural network (CNN) to track the position of the LED beacon carried by the underwater robot in real time. The recognition delay is ≤100ms, and the relative direction and distance are calculated, greatly improving the adaptability to dynamic environments.

[0047] The tracking power unit includes a thruster drive control group and a fluid dynamics controller. Based on the feedback from the visual positioning unit, it actively adjusts the buoy position to ensure the coverage of the optical communication field of view, and implements real-time tracking and compensation mechanisms for the movement of underwater equipment such as underwater robots. The thruster drive control group uses multiple brushless motors to drive micro-thrusters. The micro-thrusters use ducted thrusters with a single thrust of ≥20N. The fluid dynamics controller dynamically adjusts the thruster output based on the PID algorithm and ocean current prediction model.

[0048] The environmental perception and adaptation unit includes a multi-parameter sensor and an adaptive adjustment controller; the multi-parameter sensor specifically adopts a water turbidity sensor, a depth sensor and a temperature sensor; the adaptive adjustment controller is used to dynamically optimize optical communication parameters (such as transmission power and modulation frequency) according to environmental data.

[0049] See also Figures 4 to 6 , a buoy-type cross-medium communication algorithm, comprising the following steps:

[0050] Step 1: During the initialization phase, the buoy (mobile station) automatically deploys its solar panels upon entering the water and enters standby mode. The buoy then performs perturbative motion with small, random displacements in the horizontal plane at a preset step size (e.g., ±0.2m) to detect trends in signal quality changes. It also initiates GPS positioning and establishes a Wi-Fi connection with the remote terminal. The underwater control module activates the visible light communication control unit and scans for LED beacons within its field of view.

[0051] Step 2: During the communication establishment phase, after detecting the underwater robot beacon, the visual positioning unit calculates the relative position and the power system adjusts the buoy's position to the optimal communication area. The protocol conversion module sends the surface WiFi command to the underwater robot using OFDM digital modulation technology. The communication establishment phase also includes collecting optical signals through underwater visible light receivers, extracting modulation information (such as OOK / PPM encoding), demodulating to obtain the original data packet, and then comparing the currently collected and demodulated optical signal to calculate the current signal-to-noise ratio (SNR). This real-time calculated SNR is compared with a preset threshold (such as ≥20dB) for error judgment. The error value is used as the input error signal of the control system for SNR feedback and differential control.

[0052] The displacement adjustment is calculated using a proportional-integral-derivative (PID) controller:

[0053]

[0054] in, is the signal-to-noise ratio deviation, Dynamic adjustment coefficient

[0055] (like );

[0056] Based on the differential output, the moving direction (for example, forward, backward, left, or right) with the fastest improvement in signal-to-noise ratio (SNR) is selected. An initial step size of 0.2m is used. If the SNR does not improve after three consecutive moves, the step size is increased to 0.5m. This adaptive step size strategy, which quickly approaches the optimal position, is used to determine the direction and optimize the step size.

[0057] Step 3: During the dynamic tracking phase, the underwater robot's trajectory is monitored in real time. Combined with the ocean current prediction model, the micro-thruster group fine-tunes the buoy position to maintain a field of view deviation of ≤±5°; the environmental perception and adaptive unit adjusts the light intensity and divergence angle based on the turbidity data to ensure a bit error rate of ≤10⁻ 6 ,The dynamic tracking phase also includes switching to the steady-state tracking mode after the signal reaches the target ,SNR, and periodically fine-tuning (±0.1m) is used to compensate for the ocean current disturbance and ,maintain the mobile tracking of the optical communication link directivity;

[0058] Step 4: Energy efficiency management stage: During low-load periods, redundant sensors are turned off and the system switches to energy-saving mode, with energy storage batteries giving priority to powering the underwater mechanism.

[0059] This buoy-type cross-medium communication device algorithm uses a visual positioning system based on the underwater feature point SLAM (Simultaneous Localization and Mapping) algorithm, cooperates with a camera to capture the underwater robot's LED beacon, and combines thruster group feedback control to achieve dynamic adjustment of the buoy position, ensuring that the optical communication field of view angle coverage deviation is ≤±5°. By integrating communication performance quality monitoring, it solves the stability problem of traditional optical positioning and tracking, ensuring communication stability in complex marine environments.

[0060] The electrical components mentioned in this article are all connected to an external main controller and 220V / 380V mains electricity, and the main controller can be a conventional known device that performs control such as a computer.

[0061] In summary, combined Figure 7This buoy-type cross-media communication system and its algorithm, when in use, floats on the water surface with its surface mechanism exposed. On the surface, it establishes a wireless connection with a remote terminal (mobile phone, computer, ship-borne base station) via WiFi communication, allowing the surface operator to control the remote terminal and issue instructions through user interaction. Meanwhile, the underwater mechanism emits visible light through the transmitting end of the visible light communication control unit in the system. A bidirectional optical communication link is then established through the receiving end of the visible light communication control unit connected to the communication cable on the underwater robot. This system supports the parallel access of multiple devices and controls the operation of the underwater robot for marine environmental monitoring and scientific research, intelligent aquaculture management, underwater equipment collaborative operations, underwater rescue and salvage, and entertainment and marine tourism. Visible light communication can replace traditional cables, preventing users from being restricted by traditional communication cables. This invention can also be applied to underwater swimming pool robots. The buoy acts as a surface communication relay station, connected to shore-based / ship-based terminals (mobile phone, computer) via WiFi. The underwater mechanism interacts with the robot in real time using visible light communication, transmitting sensor data (such as temperature, depth, and images) back to the mobile phone remote terminal.

[0062] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a" does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0063] In this application, terms such as "upper," "lower," "inner," "middle," "outer," "front," and "back" indicate positions or locations based on the positions or locations shown in the accompanying drawings. These terms are primarily intended to better describe this application and its embodiments and are not intended to limit the devices, elements, or components indicated to having a specific orientation, or to being constructed or operated in a specific orientation.

[0064] Furthermore, some of the above terms may be used to express other meanings besides indicating a position or location. For example, the term "on" may also be used to indicate a dependency or connection in certain circumstances. Those skilled in the art will understand the specific meanings of these terms in this application based on the specific circumstances.

[0065] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A buoy-type cross-medium communication system, characterized by: Including surface control module and underwater control module; The surface control module includes a TCP / IP protocol processor and simultaneously establishes a wireless connection with a remote terminal via WiFi communication; The underwater control module includes a visible light communication control unit, a visual positioning unit, a tracking power unit, and an environmental perception and adaptation unit. The visible light communication control unit includes a transmitter using a high-brightness blue-green LED array and a PIN photodiode array. The receiving end also integrates an optical filter and is used to establish a bidirectional optical communication link with an underwater robot or sensor, supporting parallel access of multiple devices. The visual positioning unit includes an LED beacon recognition unit. The LED beacon recognition unit uses an underwater target detection model based on a convolutional neural network to track the position of the LED beacon carried by the underwater robot in real time and calculate the relative direction and distance. The tracking power unit includes a thruster drive control group and a fluid dynamics controller. According to the feedback from the visual positioning unit, the buoy position is actively adjusted to ensure the coverage of the optical communication field of view. The propeller drive control group specifically uses multiple groups of brushless motors to drive micro propellers; The fluid dynamics controller dynamically adjusts the thruster output based on the PID algorithm and the ocean current prediction model; It also includes an energy collaborative management circuit module composed of a bidirectional DC-DC converter and an intelligent power distribution switch, which is used to dynamically allocate energy to the surface control module and the underwater control module according to task priority, and give priority to ensuring the power supply of the visible light communication control unit and the tracking power unit.

2. The buoy-type cross-medium communication system according to claim 1, characterized in that: The environmental perception and adaptive unit includes a multi-parameter sensor and an adaptive adjustment controller; The multi-parameter sensor specifically adopts a water turbidity sensor, a depth sensor and a temperature sensor; The adaptive adjustment controller is used to dynamically optimize optical communication parameters according to environmental data.

3. A buoy-type cross-medium communication algorithm, characterized by: The following steps are involved: Step 1: Initialization phase: After entering the water, the buoy automatically deploys its solar panels and enters standby mode. The buoy performs perturbative motion with small random displacements in the horizontal plane at a preset step size to detect signal quality changes. It also initiates GPS positioning and establishes a WiFi connection with the remote terminal. The underwater control module activates the visible light communication control unit and scans for LED beacons within its field of view. Step 2: During the communication establishment phase, after detecting the underwater robot beacon, the visual positioning unit calculates the relative position and the power system adjusts the buoy position to the optimal communication area. The protocol conversion module sends the surface WiFi command to the underwater robot using OFDM digital modulation technology. The communication establishment phase also includes collecting optical signals through underwater visible light receivers, extracting modulation information, and demodulating to obtain the original data packet. The collected and demodulated optical signals are then compared to calculate the signal-to-noise ratio of the current optical signal. This real-time calculated signal-to-noise ratio is compared with a preset threshold for error judgment. The error value is used as the input error signal of the control system for signal-to-noise ratio feedback and differential control. The displacement adjustment is calculated using a proportional-integral-derivative controller: in, is the signal-to-noise ratio deviation, is the dynamic adjustment coefficient; Based on the differential output, the moving direction that improves the signal-to-noise ratio the fastest is selected. An initial step size of 0.2m is used. If the signal-to-noise ratio does not improve after three consecutive moves, the step size is increased to 0.5m. This adaptive step size strategy, which quickly approaches the optimal position, is used to determine the direction and optimize the step size. Step 3: During the dynamic tracking phase, the underwater robot's trajectory is monitored in real time. Combined with the ocean current prediction model, the micro-thruster group fine-tunes the buoy's position to maintain a field of view deviation of ≤±5°. The environmental perception and adaptive unit adjusts the light intensity and divergence angle based on turbidity data. The dynamic tracking phase also includes switching to steady-state tracking mode after the signal reaches the target signal-to-noise ratio. Periodic fine-tuning is used to compensate for ocean current disturbances and maintain the directivity of the optical communication link for mobile tracking. Step 4: Energy efficiency management stage: During low-load periods, redundant sensors are turned off and the system switches to energy-saving mode, with energy storage batteries giving priority to powering the underwater mechanism.

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