Intelligent seabed energy detection system and method based on visible light communication

Abstract

The invention discloses an intelligent seabed energy detection system and method based on visible light communication, and belongs to the technical field of deep sea detection, the detection system comprises a data acquisition system (1) and a visible light communication system (2), the data acquisition system (1) structurally comprises a high-definition camera (11), an underwater sensor (12) and a first analog-to-digital converter ADC (13), the visible light communication system (2) structurally comprises a transmitting end and a receiving end; the detection method comprises the steps of front-end acquisition, signal conversion, signal processing, optical signal transmission and signal restoration. The characteristics of high frequency and large bandwidth of light waves are utilized, the intensity of light is modulated, binary code element signals are converted into optical signals, the optical signals are received through the photoelectric detector for underwater information transmission, efficient and real-time submarine energy monitoring and analysis are achieved, and comprehensive technical support is provided for development and utilization of submarine energy.

Description

Technical Field

[0001] The present invention belongs to the technical field of deep sea exploration and relates to an intelligent seabed energy detection system and method based on visible light communication. Background Art

[0002] With the ever-increasing demand for energy, submarine energy detection has become an area of significant interest. Traditional submarine energy detection communication methods primarily rely on acoustic and electromagnetic wave sensing technologies. However, these methods have limitations, such as being affected by environmental factors such as water depth and water quality, as well as high energy consumption and slow data transmission speeds. While underwater communication is possible to a certain extent, its transmission rate and quality are limited. For example, acoustic waves, due to the limitations of their carrier frequency, have a low transmission rate of only kbit / s, resulting in severe propagation delays in underwater environments. Radio communications are subject to significant attenuation when operating in underwater environments. The higher the frequency, the faster the attenuation. Even at low frequencies, the communication distance is limited to a few meters.

[0003] Visible light communication (VLC) is a new technology that uses visible light sources such as light emitting diodes (LEDs) to emit high-speed, dimming light signals that are difficult for the human eye to distinguish. Compared with sound waves and radio frequency, visible light has the advantages of high speed, low attenuation, long distance, and high confidentiality in underwater environments. Its speed can reach Gbit / s and the communication distance can reach hundreds of meters, making it a potential application for underwater wireless communication.

[0004] The intelligent seabed energy detection system based on visible light communication of the present invention solves the problems of low underwater communication transmission rate, susceptibility to interference, limited detection distance, and low resolution. It helps people better understand the geological structure of the seabed and the distribution of underwater resources, and provides an important scientific basis for the development and utilization of marine resources. Summary of the Invention

[0005] To overcome the shortcomings of the existing technology, the present invention provides an intelligent submarine energy detection system and method based on visible light communication. The main principle is to utilize the high frequency and large bandwidth of light waves, modulate the intensity of light, convert binary code element signals into optical signals, and receive the optical signals through photoelectric detectors to transmit underwater information.

[0006] The present invention is achieved through the following technical solutions:

[0007] An intelligent submarine energy detection system based on visible light communication includes the following three modules: a data acquisition system 1 and a visible light communication system 2, characterized in that:

[0008] The structure of the data acquisition system 1 includes a high-definition camera 11, an underwater sensor 12, and a first analog-to-digital converter ADC 13. The high-definition camera 11 is installed at the front end of the data acquisition system 1 and is used to capture high-definition images of the seabed environment in real time. The underwater sensor 12 is installed outside the data acquisition system 1 and is used to collect water temperature, salinity, pressure, flow rate, turbidity, and biochemical indicators of seawater. The first analog-to-digital converter ADC 13 is connected to the high-definition camera 11 and the underwater sensor 12 respectively, and is used to convert the analog image signal captured by the high-definition camera 11 and the analog signal of the seawater parameter collected by the underwater sensor 12 into digital signals. The collected digital signals are transmitted to the visible light communication system 2 via the IIC data bus.

[0009] The structure of the visible light communication system 2 includes a transmitting end and a receiving end; wherein, the transmitting end includes a first FPGA processor 21, a digital-to-analog converter DAC 22, an optoelectronic isolation driving circuit 23 and a light-emitting diode LED 24. After the FPGA processor 21 performs data scrambling processing, 8b / 10b source coding and OFDM signal modulation on the collected digital signal, the modulated digital signal is converted into an analog signal by the digital-to-analog converter DAC 22, and the analog signal is converted into a light intensity modulated signal by the optoelectronic isolation driving circuit 23, and the light-emitting diode LED 24 is driven to generate a corresponding light intensity change to send the signal. The signal is transmitted to the receiving end in the underwater optical communication channel; the receiving end includes a PIN photodiode 25, an optoelectronic processing circuit 26, a second analog-to-digital converter ADC 27, and a second FPGA processor 28. The PIN photodiode 25 is responsible for receiving the optical signal, and the optical signal is converted into an analog electrical signal by the optoelectronic processing circuit 26, and the optical signal is converted into an analog electrical signal by the second analog-to-digital converter ADC 27 converts the analog electrical signal into a digital signal, and the second FPGA processor 28 performs OFDM signal demodulation, 8b / 10b source decoding and data descrambling on the digital signal to restore the original data for subsequent data analysis and processing.

[0010] Furthermore, an intelligent submarine energy detection system based on visible light communication of the present invention may also include a data analysis and processing module 3, wherein the data analysis and processing module 3 is a microcomputer or single-chip microcomputer system with a data analysis and processing system, and the structure of the data analysis and processing system includes a data input module, a data preprocessing module, a deep learning model analysis module and a result display module; the original image data received and restored by the receiving end of the visible light communication system 2 is transmitted to the data analysis and processing module 3 through the Ethernet data bus, and the data preprocessing module performs denoising, enhancement and normalization preprocessing on the image data, and then applies a deep learning model through the deep learning model analysis module, and uses the ResNet convolutional neural network to extract image features and analyze parameters to obtain relevant information about submarine energy. Finally, these analysis results are displayed by the result display module.

[0011] Preferably, the light emitting diode LED 24 at the transmitting end of the visible light communication system 2 is a blue or green light emitting diode, and the light emitting wavelength range is 450nm to 550nm.

[0012] An intelligent submarine energy detection method based on visible light communication has the following steps:

[0013] S1: Front-end acquisition: A data acquisition system 1 equipped with a high-definition camera 11 is deployed at a depth of 5 to 30 meters from the seabed to capture high-resolution images of the seabed environment in real time. Underwater sensors 12 are installed on the outside of the data acquisition system 1 to collect parameters such as seawater temperature, salinity, pressure, flow rate, turbidity, and biochemical parameters.

[0014] S2: Signal conversion, using the first analog-to-digital converter ADC 13 to convert the captured analog image signal and the analog signal of the seawater parameter collected by the underwater sensor 12 into a digital signal for subsequent processing;

[0015] S3: Signal processing: using the first FPGA processor 21 to perform scrambling, 8b / 10b source coding, and OFDM signal modulation on the converted digital signal for underwater optical channel transmission;

[0016] S4: Optical signal transmission. At the transmitting end of the visible light communication system 2, the digital signal processed by the first FPGA processor 21 is converted into an analog signal by the digital-to-analog converter DAC 22. The light intensity of the light-emitting diode LED 24 is modulated by the optical isolation drive circuit 23 to transmit the signal. A dedicated optical communication waveguide is then used to ensure a clear optical path and avoid signal attenuation or interference. At the receiving end of the visible light communication system 2, the optical signal is received by the PIN photodiode 25 and restored to an electrical signal by the optical processing circuit 26. Finally, the analog electrical signal is converted back into a digital signal by the second analog-to-digital converter ADC 27 for subsequent processing.

[0017] S5: Signal restoration: The second FPGA processor 28 performs OFDM signal demodulation, 8b / 10b signal source decoding and descrambling on the digital signal converted by the second analog-to-digital converter ADC 27 to restore the original data for subsequent data analysis.

[0018] Furthermore, the intelligent submarine energy detection method based on visible light communication of the present invention further includes the following steps:

[0019] S6: Data analysis, performing data analysis and processing on the image data restored in step S5, and extracting relevant image parameter information by applying a deep learning model and using a ResNet convolutional neural network;

[0020] S7: Result display: summarize the analysis results of step S6 and display them through the upper computer interface.

[0021] The present invention is designed to achieve efficient, real-time monitoring and analysis of submarine energy, providing comprehensive technical support for the development and utilization of submarine energy. The effective combination of this structure ensures efficient data transmission and reliable analysis.

[0022] Compared with the existing technology, the present invention has the following advantages:

[0023] 1. This invention utilizes visible light communication technology, using high-power LEDs as light sources and visible light in the blue and green bands as information carriers. This technology effectively reduces light absorption and scattering by seawater, enabling high signal quality over extended underwater transmission distances. This enables high-speed, low-energy, secure, and reliable data transmission, thereby improving the overall performance of submarine energy detection systems. This technology enables timely and efficient transmission of data collected by submarine energy detection equipment to ground control centers, providing reliable data support for subsequent processing and analysis.

[0024] 2. This invention utilizes a photoelectric isolation drive circuit, using optical signals as the transmission medium to achieve electrical isolation between input and output circuits. This effectively prevents electrical interference between devices and suppresses common-mode noise, thereby ensuring pure signal transmission. Combined with a push-pull power amplifier circuit design, this structure not only provides high-power output signals but also achieves excellent matching with load impedance, maximizing signal transmission efficiency while significantly reducing signal waveform distortion.

[0025] 3. The present invention uses FPGA hardware to implement an intelligent submarine energy detection system. With its internal high-speed parallel logic processing architecture, it is beneficial to improve the data processing capability of the submarine energy detection system.

[0026] 4. The present invention realizes the intelligent detection and real-time monitoring functions of submarine energy. By using advanced data analysis technology, the system can monitor the status of submarine energy in real time, discover and identify abnormal situations in a timely manner, and provide important support for the safe and stable development of submarine energy.

[0027] 5. This invention significantly reduces the cost of traditional methods that rely on extensive human and financial resources by enabling remote control and monitoring of submarine energy detection equipment. This innovation significantly reduces labor costs and improves the efficiency and accuracy of submarine energy detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 This is a structural diagram of the intelligent submarine energy detection system based on visible light communication in the present invention.

[0029] Figure 2 This is an architecture diagram of the intelligent submarine energy detection system based on visible light communication in the present invention.

[0030] Figure 3 This is a schematic diagram of the visible light communication principle in the present invention.

[0031] Figure 4 This is a schematic diagram of the 8B / 10B encoding principle in the present invention.

[0032] Figure 5 This is a photoelectric isolation driving circuit based on visible light communication in the present invention.

[0033] Figure 6 This is a schematic diagram of the orthogonal frequency division multiplexing (OFDM) principle based on FPGA hardware in the present invention.

[0034] Figure 7 This is a diagram of the data analysis and processing principles based on convolutional neural networks in the present invention.

[0035] Figure 8 This is a diagram showing the actual application of the intelligent submarine energy detection system based on visible light communication in the present invention. DETAILED DESCRIPTION

[0036] The technical solution of the present invention is further described below with reference to the accompanying drawings, but is not limited thereto. Any modification or equivalent replacement of the solution of the present invention that does not depart from the technical scope of the present invention should be included in the protection scope of the present invention.

[0037] Example 1 Hardware composition of the present invention

[0038] This embodiment provides an intelligent submarine energy detection system based on visible light communication, the structural block diagram of which is as follows: Figure 1 As shown, it includes a data acquisition system 1 and a visible light communication system 2. Preferably, the structure of this embodiment also includes a data analysis and processing module 3.

[0039] The data acquisition system 1 is equipped with a high-definition camera 11 and an underwater sensor 12 , which can accurately capture images of the seabed environment and collect seawater parameters in an underwater environment, and send the acquired data to the visible light communication system 2 .

[0040] The visible light communication system 2 is used to establish data communication between the data acquisition system 1 and the data analysis and processing module 3. If there is no data analysis and processing module 3, the visible light communication system 2 will send the data collected by the data acquisition system 1 to any designated terminal via the Ethernet data bus for subsequent independent data analysis.

[0041] The visible light communication system 2 includes a visible light communication transmitting end and a receiving end. The principle of the visible light communication used in the present invention is as follows Figure 3 As shown, the hardware structure is as follows Figure 1 As shown. The transmitting end includes: the digital signal enters the first FPGA processor 21 for processing, firstly, the data is scrambled by the scrambling module, 8B / 10B encoded by the source coding module, packaged through the serial port data, and enters the OFDM modulation module for digital modulation. After being modulated into a signal suitable for optical channel transmission, the digital signal is converted into an analog signal by the digital-to-analog converter DAC 22, and finally the photoelectric isolation drive circuit 23 drives the high-speed flashing light-emitting diode LED 24 to send the signal, and enter the optical channel for transmission. The specific circuit principle diagram of the photoelectric isolation drive circuit 23 is shown as follows. Figure 5The receiving end includes a PIN photodiode 25 that receives the optical signal. The optical signal is first converted into an electrical signal by the photoelectric processing circuit 26. The analog signal is then converted into a digital signal by the second analog-to-digital converter ADC 27. The signal then enters the second FPGA processor 28 for processing. The OFDM demodulation module performs digital demodulation, unpacks the signal through the serial port, and sends it to the source decoding module for 8B / 10B decoding. Finally, the descrambling module performs data descrambling and restores the original data. The data is then transmitted to the data analysis and processing system 3 or any other designated receiving terminal via the Ethernet bus for subsequent data analysis.

[0042] Example 2 Coding method used in the present invention

[0043] like Figure 4 As shown, an embodiment of the present invention uses 8B / 10B encoding to improve the reliability and anti-interference capabilities of transmitted data. Assuming that the original 8-bit data is represented from high to low as HGFEDCBA, the 8-bit data is divided into the upper 3 bits HGF and the lower 5 bits EDCBA. The lower 5 bits EDCBA are then 5B / 6B encoded and mapped to abcdei. The upper 3 bits HGF are then 3B / 4B encoded and mapped to fghj. Finally, the resulting 10-bit data is abcdeifghj. This encoding achieves two basic functions: first, it improves the efficiency of information transmission, and second, it ensures DC balance in the encoded output, ensuring that the number of 1s and 0s in the output sequence is approximately equal. This reduces the DC component of the transmitted signal and improves transmission reliability.

[0044] Example 3 Photoelectric isolation drive circuit used in the present invention

[0045] like Figure 5 As shown, this embodiment of the present invention employs an optoelectronic isolation driver circuit, using light as a medium to effectively isolate the input and output circuits, achieving signal isolation and conversion. Its core consists of a light-emitting diode (LED) and a light-receiving diode (PD). When a high-level signal is received at the input, the LED emits light, which is then received by the light-receiving diode (PD) and converted into an output signal. Using an optocoupler (ACPL-021L-000E) for isolation, this design not only effectively blocks electrical interference and common-mode noise between the input and output circuits, but also eliminates the transient effects of switching on the downstream operational amplifier circuit, thereby ensuring circuit stability and reliability. Furthermore, to enhance driving capability, the circuit utilizes a push-pull power amplifier circuit composed of two complementary transistors (12A02CH-TL-E). By alternating conduction, this circuit achieves bidirectional current output during the positive and negative half-cycles of the signal. This circuit offers advantages such as high output power, excellent impedance matching, and low crossover distortion, enabling efficient driving of high-power loads while maintaining signal integrity. This circuit exhibits excellent DC stability and operating efficiency, providing a stable output signal while effectively minimizing interference from the external environment.

[0046] Example 4 Modulation and demodulation method used in the present invention

[0047] like Figure 6 As shown, the embodiment of the present invention uses an OFDM modulation and demodulation method based on FPGA hardware. OFDM modulation divides the signal into multiple subcarriers for transmission. The bandwidth of each subcarrier is relatively narrow, thereby effectively utilizing spectrum resources and improving transmission efficiency. Each subcarrier can be modulated and demodulated independently. Therefore, when faced with interference, only the interfered subcarrier is affected, while other subcarriers can still transmit data normally, improving the system's anti-interference ability. At the same time, the different subcarriers are orthogonal, which can effectively reduce inter-symbol interference and improve system reliability and transmission performance.

[0048] On the modulation side, 16QAM modulation is performed on the input binary data, and the real and imaginary parts of the data are mapped to different modulation symbols respectively. Different data is represented by changing the amplitude and phase. After serial-to-parallel conversion, the data is distributed to multiple subcarriers. The frequency domain symbols are converted into time domain signals using the inverse fast Fourier transform (IFFT). The signal is then converted from parallel to serial and a guard interval is added. Finally, the signal is moved to a high-frequency carrier for transmission through digital up-conversion.

[0049] Among them, the formula for QAM modulation is:

[0050] s(t)=A·cos(2πf c +θ)

[0051] Where A is the amplitude, fc is the carrier frequency, and θ is the phase. The modulated subcarriers are superimposed in parallel to form an OFDM symbol. The OFDM modulation formula is:

[0052]

[0053] Where x(t) is the OFDM signal, X[k] is the QAM modulated signal on the kth subcarrier, N is the total number of subcarriers, e is the base of the natural logarithm, j is the imaginary unit, and f is the sum of the two subcarriers. k is the frequency of the kth subcarrier.

[0054] On the demodulation side, digital down-conversion is first performed to move the received high-frequency signal back to the baseband. The guard interval is then removed to obtain the original OFDM symbols. After serial-to-parallel conversion, the OFDM symbols are converted into frequency domain signals using the fast discrete Fourier transform (FFT). The frequency domain signals are equalized and, after parallel-to-serial conversion, 16QAM demodulation is performed to map the signals on the complex plane back to data bits to obtain the data on each subcarrier. Finally, the demodulated subcarrier data is reassembled to obtain the original data.

[0055] Example 5 Data Analysis and Processing Module

[0056] In this embodiment, the data analysis and processing module 3 utilizes a microcomputer equipped with a data analysis and processing system. This system includes a pre-trained artificial intelligence convolutional neural network, which implements efficient image recognition and prediction based on a multi-layer neural network structure model. Compared to other traditional machine learning algorithms, convolutional neural networks can automatically acquire a large amount of feature information from the original image data. They possess the characteristics of local perception and parameter sharing, significantly reducing network parameters while increasing the depth of the convolutional layer, enabling the fitting of more complex nonlinear functions. Consequently, they possess powerful learning capabilities and highly efficient feature expression capabilities.

[0057] like Figure 7 As shown in Figure 1, the architecture of a convolutional neural network consists of an input layer, an activation layer, the first convolutional layer, the first pooling layer, the second convolutional layer, the second pooling layer, a fully connected layer, and an output layer. The convolutional layer is the core component of a convolutional neural network, primarily used to extract features from an image. The convolution operation generates a feature map by sliding a filter (convolution kernel) over the input image. Each filter can capture different features, such as edges and textures. The activation layer follows the convolutional layer and introduces a nonlinear activation function (ReLU) to enhance the network's nonlinear fitting capabilities. This helps the network learn more complex patterns and features. The pooling layer reduces the spatial size of the feature map, reducing computational complexity and increasing the network's translation invariance. Common pooling operations include max pooling and average pooling, which reduce the dimensionality of the feature map by retaining the maximum or average value. The fully connected layer connects all neurons in the previous layer with all neurons in the current layer, mapping the features extracted by the convolutional layer to the output layer for tasks such as classification or regression.

[0058] First, the collected image data undergoes preprocessing, including image denoising, augmentation, and normalization, to meet the network's input requirements. This preprocessed image data is then fed into a pre-built convolutional neural network. Through layers of convolution, activation, and pooling, the image features are gradually extracted. At the network's output layer, a softmax function is used to convert the network output into a probability distribution, representing the probabilities of different categories. The category with the highest probability is selected as the image's predicted result. Finally, the trained classifier matches and identifies targets based on the image's features, enabling effective analysis and processing of submarine energy detection.

[0059] Example 6 Specific steps of the intelligent submarine energy detection method based on visible light communication of the present invention:

[0060] S1: Front-end acquisition: Deploy a data acquisition system 1 equipped with a high-definition camera 11 at the front end 5 to 30 meters below the seabed to capture high-resolution images of the seabed environment in real time. Underwater sensors 12 are installed on the outside of the data acquisition system 1 to collect parameters such as seawater temperature, salinity, pressure, flow rate, turbidity, and biochemical parameters.

[0061] S2: Signal conversion, using the first analog-to-digital converter ADC 13 to convert the captured analog image signal and the analog signal of the seawater parameter collected by the underwater sensor 12 into a digital signal for subsequent processing;

[0062] S3: Signal processing: using the first FPGA processor 21 to perform scrambling, 8b / 10b source coding, and OFDM signal modulation on the converted digital signal for underwater optical channel transmission;

[0063] S4: Optical signal transmission. At the transmitting end of VLC system 2, the digital signal processed by the first FPGA processor 21 is converted into an analog signal via a digital-to-analog converter (DAC) 22. The optical isolation driver circuit 23 modulates the light intensity of the light-emitting diode (LED) 24 to transmit the signal. A dedicated optical communication waveguide is then used to ensure a clear optical path and avoid signal attenuation or interference. At the receiving end of VLC system 2, the optical signal is received by a PIN photodiode 25, and converted back into an electrical signal by an optoelectronic processing circuit 26. Finally, a second analog-to-digital converter (ADC) 27 converts the analog electrical signal back into a digital signal for subsequent processing.

[0064] S5: Signal restoration: The second FPGA processor 28 performs OFDM signal demodulation, 8b / 10b signal source decoding and descrambling on the digital signal converted by the second analog-to-digital converter ADC 27 to restore the original data for subsequent data analysis.

[0065] S6: Data analysis, performing data analysis and processing on the image data restored in step S5, and extracting relevant image parameter information by applying a deep learning model and using a ResNet convolutional neural network;

[0066] S7: Result display: summarize the analysis results of step S6 and display them through the upper computer interface.

[0067] Example 7 Practical application of the present invention

[0068] like Figure 8As shown, the transmitter front-end uses a high-resolution camera to capture image information. This camera is waterproof, pressure-resistant, and corrosion-resistant to withstand the complex and harsh working environment of the seabed. After data processing, the captured image information is transmitted using an LED light source. The intermediate pipe simulates underwater optical transmission. Taking into account the special characteristics of the underwater environment (such as the refractive index of water and the light attenuation coefficient), optical lenses and reflectors are designed inside the pipe to ensure stable transmission of the optical signal. The receiver uses a photodetector to receive the optical signal and, after data processing, restore the original image information.

Claims

1. An intelligent submarine energy detection system based on visible light communication, comprising the following modules: a data acquisition system (1), a visible light communication system (2), characterized in that: The structure of the data acquisition system (1) includes a high-definition camera (11), an underwater sensor (12) and a first analog-to-digital converter ADC (13); wherein the high-definition camera (11) is installed at the front end of the data acquisition system (1) and is used to capture high-definition images of the seabed environment in real time; the underwater sensor (12) is installed outside the data acquisition system (1) and is used to collect water temperature, salinity, pressure, flow rate, turbidity and biochemical indicators of seawater; the first analog-to-digital converter ADC (13) is connected to the high-definition camera (11) and the underwater sensor (12) respectively, and is used to convert the analog image signal captured by the high-definition camera (11) and the analog signal of the seawater parameter collected by the underwater sensor (12) into a digital signal, and the collected digital signal is transmitted to the visible light communication system (2) via the IIC data bus; The structure of the visible light communication system (2) includes a transmitting end and a receiving end; wherein the transmitting end includes a first FPGA processor (21), a digital-to-analog converter DAC (22), an optoelectronic isolation driving circuit (23) and a light emitting diode (LED) (24); the FPGA processor (21) performs data scrambling processing, 8b / 10b source coding and OFDM signal modulation on the collected digital signal, and then converts the modulated digital signal into an analog signal through the digital-to-analog converter DAC (22); the analog signal is converted into a light intensity modulation signal through the optoelectronic isolation driving circuit (23), and the light emitting diode (LED) (24) is driven to generate a corresponding light intensity change. The optical signal is transmitted through the optical fiber transmission channel, and the signal is transmitted to the receiving end in the underwater optical communication channel; the receiving end includes a PIN photodiode (25), a photoelectric processing circuit (26), a second analog-to-digital converter ADC (27), and a second FPGA processor (28); the PIN photodiode (25) is responsible for receiving the optical signal, converting the optical signal into an analog electrical signal through the photoelectric processing circuit (26), converting the analog electrical signal into a digital signal through the second analog-to-digital converter ADC (27), and the second FPGA processor (28) performs OFDM signal demodulation, 8b / 10b source decoding and data descrambling processing on the digital signal to restore the original data for subsequent data analysis and processing.

2. The intelligent submarine energy detection system based on visible light communication according to claim 1 is characterized in that: The structure also includes a data analysis and processing module (3), which is a microcomputer or single-chip microcomputer system with a data analysis and processing system. The structure of the data analysis and processing system includes a data input module, a data preprocessing module, a deep learning model analysis module and a result display module. The original image data received and restored by the receiving end of the visible light communication system (2) is transmitted to the data analysis and processing module (3) through the Ethernet data bus. The data preprocessing module performs denoising, enhancement and normalization preprocessing on the image data. The deep learning model analysis module then applies a deep learning model, uses a ResNet convolutional neural network to extract image features and analyze parameters, and obtains relevant information about submarine energy. Finally, these analysis results are displayed by the result display module.

3. The intelligent submarine energy detection system based on visible light communication according to claim 1 is characterized in that: The light emitting diode LED (24) at the transmitting end in the visible light communication system (2) is a blue or green light emitting diode with a light emitting wavelength of 450nm to 550nm.

4. An intelligent submarine energy detection method based on visible light communication, comprising the following steps: S1: Front-end acquisition: a data acquisition system (1) equipped with a high-definition camera (11) is deployed at a depth of 5 to 30 meters below the seabed to capture high-resolution images of the seabed environment in real time. An underwater sensor (12) is installed outside the data acquisition system (1) to collect parameters such as seawater temperature, salinity, pressure, flow rate, turbidity, and biochemical parameters. S2: signal conversion, using a first analog-to-digital converter ADC (13) to convert the captured analog image signal and the analog signal of the seawater parameter collected by the underwater sensor (12) into a digital signal for subsequent processing; S3: signal processing, using the first FPGA processor (21) to perform scrambling processing, 8b / 10b source coding and OFDM signal modulation on the converted digital signal so as to perform underwater optical channel transmission; S4: Optical signal transmission. At the transmitting end of the visible light communication system (2), the digital signal processed by the first FPGA processor (21) is converted into an analog signal through a digital-to-analog converter DAC (22). The light intensity of the light emitting diode LED (24) is modulated by the photoelectric isolation driving circuit (23) to send the signal. Then, a dedicated optical communication waveguide is used to ensure a clear optical path to avoid signal attenuation or interference. At the receiving end of the visible light communication system (2), the optical signal is received by a PIN photodiode (25), and is restored to an electrical signal by a photoelectric processing circuit (26). Finally, the analog electrical signal is converted back into a digital signal by a second analog-to-digital converter ADC (27) for subsequent processing. S5: Signal restoration: the second FPGA processor (28) performs OFDM signal demodulation, 8b / 10b signal source decoding and descrambling processing on the digital signal converted by the second analog-to-digital converter ADC (27), and restores the original data for subsequent data analysis.

5. The intelligent submarine energy detection method based on visible light communication according to claim 4 is characterized in that: After step S5, there are the following steps: S6: Data analysis, performing data analysis and processing on the image data restored in step S5, and extracting relevant image parameter information by applying a deep learning model and using a ResNet convolutional neural network; S7: Result display: summarize the analysis results of step S6 and display them through the upper computer interface.

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