A contactless intelligent transmission system and method for power data

Abstract

The present invention relates to the field of power data transmission, and discloses a contactless intelligent transmission system and method for power data. The system includes: an optical acquisition module, which is used to obtain display data of a smart meter and convert it into an optical signal; a multi-wavelength optical transmission module, which adopts a multi-wavelength encoding method to modulate and transmit data; an optical path dynamic adjustment module, which monitors the optical signal path through a laser adjustable lens and an AI visual recognition system, and dynamically adjusts the direction of the light beam; an intelligent light beam tracking system, which detects the light beam state in combination with a laser feedback control algorithm and adjusts the transmission path of the optical signal; a hybrid transmission module, which switches to a wireless communication method for data compensation transmission when the optical signal is interfered with; a data processing and error correction module, which adopts a deep learning signal enhancement algorithm to optimize signal quality, and performs data correction based on an adaptive error detection and correction algorithm to improve data accuracy.

Description

Technical Field

[0001] The present invention relates to the field of power data transmission, and in particular to a power data contactless intelligent transmission system and method. Background Art

[0002] With the development of smart grid technology, smart meters, as core equipment for power data collection and management, are widely used in power metering, load management, and remote meter reading systems. Traditional methods for reading meter data rely primarily on wired or short-range wireless communications. However, these methods suffer from complex wiring, limited transmission distances, and susceptibility to signal interference.

[0003] To address these issues, contactless data reading solutions based on optical transmission technology have been gaining attention in recent years. This solution uses optical signals to encode and decode data, enabling long-distance transmission of meter data while avoiding the wiring costs of traditional wired communications and improving system flexibility and reliability.

[0004] Shortcomings of existing technology

[0005] While optical data transmission technology offers numerous advantages in smart grids, existing technologies still have numerous shortcomings that limit their application over long distances and in complex environments. First, optical signal transmission is susceptible to atmospheric refraction interference. Over long distances or in harsh environments (such as fog, haze, rain, snow, and high temperatures), the light beam may deflect, attenuate, or scatter, leading to unstable data transmission and even bit errors. Second, existing optical transmission systems mostly use a fixed optical path and lack adaptive adjustment capabilities. When the light beam deviates from the receiving end due to external environmental influences, the system cannot adjust the optical path in real time, thereby reducing the transmission success rate. Summary of the Invention

[0006] The object of the present invention is to provide a contactless intelligent transmission system for electric power data, which has the advantages of improving the stability, anti-interference ability and data accuracy of optical data transmission.

[0007] The above technical objectives of the present invention are achieved through the following technical solutions:

[0008] A contactless intelligent transmission system for power data, comprising:

[0009] Optical acquisition module, used to obtain display data of smart meters and convert the data into optical signals;

[0010] The multi-wavelength optical transmission module is used to perform multi-wavelength encoding on the optical signal converted by the optical acquisition module and send the encoded optical signal to the optical path dynamic adjustment module;

[0011] Optical path dynamic adjustment module, including laser adjustable lens and AI visual recognition system,

[0012] The laser tunable lens is used to receive the optical signal from the multi-wavelength optical transmission module and adjust the propagation direction of the light beam;

[0013] The AI ​​visual recognition system is used to monitor the transmission path of the optical signal and provide adjustment parameters to the laser tunable lens;

[0014] Intelligent beam tracking system, which monitors the beam's transmission status and provides feedback to adjust the beam's direction. This system includes a laser feedback control algorithm and can be combined with other sensors and algorithms to enhance beam tracking capabilities.

[0015] Hybrid transmission module, including optical transmission unit and radio frequency wireless communication unit,

[0016] The optical transmission unit is used to receive the optical signal transmitted by the optical path dynamic adjustment module and perform data transmission when the optical signal meets the preset conditions;

[0017] The radio frequency wireless communication unit is used to receive data and perform data transmission when the optical signal does not meet the preset conditions;

[0018] The data processing and error correction module includes a deep learning signal enhancement algorithm and an adaptive error detection and correction algorithm. The adaptive error detection and correction algorithm can perform feature extraction, error detection and data correction based on the self-attention mechanism or other deep learning methods.

[0019] Further configuration: the optical acquisition module includes a high-precision CMOS imaging unit and a dynamic optical filter,

[0020] The high-precision CMOS imaging unit is used to receive the display data of the smart meter and convert it into an optical signal;

[0021] The dynamic optical filter acts on the high-precision CMOS imaging unit to filter the received light signal to reduce the impact of ambient light interference on data acquisition;

[0022] The high-precision CMOS imaging unit receives the signal processed by the dynamic optical filter and adjusts the exposure parameters according to the signal characteristics to make the collected data meet the requirements of optical signal conversion.

[0023] By adopting the above technical solution, the dynamic optical filter and the high-precision CMOS imaging unit form a signal optimization closed loop in the optical acquisition path to ensure that the input light signal enters the optical signal conversion process after filtering and optimization.

[0024] Furthermore, the optical acquisition module further includes an adaptive exposure control unit for dynamically adjusting the exposure parameters of the optical acquisition module when the ambient light intensity changes significantly. The adaptive exposure control unit includes:

[0025] An ambient light measurement module, including a light sensor, is used to detect the current ambient light intensity and calculate the rate of light change;

[0026] The exposure adjustment module is used to calculate the new exposure time according to the lighting changes and adjust the exposure parameters of the optical acquisition module;

[0027] The HDR imaging module is used to use a multiple exposure strategy in high-brightness or low-brightness environments to capture multiple images with different exposure times and generate high dynamic range images through an exposure fusion algorithm;

[0028] The data optimization processing module is used to perform an adaptive noise suppression algorithm on the HDR-processed image to reduce the impact of ambient light changes on data acquisition.

[0029] By adopting the above technical solutions, through ambient light measurement and exposure adjustment, the system can work stably in complex lighting environments such as daytime, nighttime, and haze; by using HDR multiple exposure technology, clear data collection results can be obtained in strong light or low light environments. Through the adaptive noise suppression algorithm, image noise caused by lighting changes is reduced, and the clarity and accuracy of optical data are improved.

[0030] Further configuration: the multi-wavelength optical transmission module uses a combination of infrared light, near-infrared light and visible light to encode data, and includes:

[0031] A data preprocessing unit, used to convert smart meter data into binary data streams and encode and distribute the data according to a preset strategy;

[0032] Multispectral coding unit, including:

[0033] Infrared light channel for encoding critical data bits in low-visibility environments;

[0034] Near-infrared optical channel, used as the main channel for long-distance optical transmission to reduce atmospheric refraction interference;

[0035] Visible light channel, used for data enhancement in short-range environments and to provide visual debugging information;

[0036] A synchronous modulation unit, used for performing time division multiplexing and orthogonal frequency division multiplexing modulation on infrared light, near infrared light and visible light signals;

[0037] an optical transmitting unit, comprising a multi-mode laser transmitter for modulating and transmitting infrared light, near-infrared light and visible light respectively;

[0038] Optical receiving unit, comprising:

[0039] Multi-spectral optical receiving module for receiving and decoding infrared, near-infrared and visible light signals;

[0040] The signal reconstruction unit is used to combine the redundant data of the three optical channels and perform data compensation when the signal is attenuated or lost.

[0041] By adopting the above technical solution, the system can transmit data under different visibility and environmental interference conditions through combined encoding of infrared light, near-infrared light and visible light; by using the near-infrared light channel as the main data-carrying channel, the impact of atmospheric refraction on the signal is reduced, ensuring the stability of long-distance transmission; by adopting orthogonal frequency division multiplexing technology, data of different frequencies are transmitted in different light wave channels, reducing interference between light waves; and by using a multi-spectral signal reconstruction algorithm, cross-checking is performed at the data receiving end, and error correction is performed in combination with multi-spectral information.

[0042] Further configuration: the intelligent beam tracking system adopts a multi-sensor fusion tracking algorithm,

[0043] include:

[0044] Beam status data acquisition module, including:

[0045] Laser feedback sensor, used to detect the direction offset and intensity of the current light beam;

[0046] Infrared thermal imaging sensor, used to obtain the thermal imaging profile of the target and extract the target center coordinates;

[0047] AI visual recognition module, used to identify the visual features of the target point and calculate the target position coordinates;

[0048] Data fusion and error calculation module, used to calculate beam offset error and target position error based on weighted sensor fusion algorithm;

[0049] Beam adjustment module, including:

[0050] PID control unit, used to adjust the beam angle according to the error calculation results;

[0051] An optical deflection execution unit, configured to receive adjustment parameters calculated by the PID control unit and adjust the optical lens angle;

[0052] Adaptive optimization module, including:

[0053] A neural network regression optimization unit is used to optimize PID control parameters based on historical environmental data.

[0054] By adopting the above technical solutions, multi-sensor fusion is used to reduce the impact of single sensor errors on beam alignment, so that the beam can be more accurately aligned with the target; combined with laser feedback, infrared thermal imaging and AI visual recognition, high-precision beam tracking can be performed under different ambient light conditions, reducing the impact of external interference; a method combining PID control and neural network optimization is used to intelligently adjust the beam adjustment parameters, making the beam alignment smoother and reducing the errors caused by drastic adjustments; through the adaptive optimization module, the PID parameters are automatically adjusted under different environmental conditions to improve the system adaptability and make it suitable for complex environments with different weather, temperature and humidity.

[0055] Further configuration: the hybrid transmission module includes a signal status intelligent switching control unit, which automatically enables the radio frequency wireless communication unit for data transmission when the receiving intensity of the optical signal is lower than the set threshold; when the optical signal recovers to an available level, it switches back to the optical transmission unit for data transmission.

[0056] By adopting the above technical solution and controlling the signal status intelligently, the intelligent switching mechanism can be effectively enhanced and the transmission reliability can be improved.

[0057] It is further configured that the data processing and error correction module adopts a deep learning model based on a self-attention mechanism to perform feature extraction, error detection and correction on optical signal data, and the data processing and error correction module includes:

[0058] The optical signal acquisition module is used to obtain the optical signal data and wireless signal data transmitted by the hybrid transmission module and perform standardized preprocessing;

[0059] Feature extraction module, including:

[0060] Self-attention calculation unit, used to calculate the signal feature matrix based on the self-attention mechanism;

[0061] Signal feature matrix calculation unit, used to construct query matrix, key matrix and value matrix and perform feature weighting;

[0062] Error detection module, including:

[0063] an error calculation unit, configured to calculate an error matrix between the optical signal and the wireless signal;

[0064] Data correction module, including:

[0065] The neural network regression unit is used to calculate the error correction function based on the error matrix and perform error correction on the optical signal data.

[0066] By adopting the above technical solution and the self-attention mechanism, key features are extracted from optical signals to improve the accuracy of signal analysis; by combining the redundant data of optical signals and wireless signals, the error is accurately calculated to avoid the error influence of a single signal source; and the error distribution characteristics are learned through a neural network regression model to achieve automatic correction of signal data under different environmental conditions.

[0067] Another object of the present invention is to provide a method for contactless intelligent transmission of power data, the method comprising the following steps:

[0068] S1: Optical data acquisition:

[0069] Obtain the display data of the smart meter through the optical acquisition module and convert the data into optical signals;

[0070] S2: Optical signal multi-wavelength encoding and transmission:

[0071] The optical signal is multi-wavelength encoded through a multi-wavelength optical transmission module and sent to the target receiving end through a light beam transmitting device;

[0072] S3: Adaptive adjustment of beam transmission path:

[0073] Through the optical path dynamic adjustment module, using laser adjustable lens and AI visual recognition system, the optical signal transmission path is monitored in real time and the beam propagation direction is dynamically adjusted according to environmental changes;

[0074] S4: Beam tracking and feedback adjustment

[0075] Through the intelligent beam tracking system, laser feedback control algorithm is used to detect the propagation state of the optical signal and calculate the beam offset;

[0076] Generate beam adjustment parameters and feed them back to the optical path dynamic adjustment module to make the beam direction conform to the target path;

[0077] S5: Optical and wireless hybrid transmission

[0078] Through the hybrid transmission module, when the optical signal is interfered, it switches to the radio frequency wireless communication unit for data transmission to ensure data continuity;

[0079] S6: Data processing and error correction

[0080] Through the data processing and error correction module, the received optical signal is optimized using a deep learning signal enhancement algorithm;

[0081] Through the adaptive error detection and correction algorithm, the error of the optical signal is calculated and the signal data is error corrected to improve data accuracy.

[0082] In summary, the present invention has the following beneficial effects:

[0083] 1. Improve anti-interference capability: Adopt multi-wavelength optical encoding combined with intelligent polarization compensation to significantly reduce the impact of atmospheric refraction and ensure data stability.

[0084] 2. Dynamic optical path adjustment: Through AI visual tracking + laser adjustable lens, the beam direction is corrected in real time to reduce signal deviation.

[0085] 3. Enhanced signal stability: The optical + wireless hybrid transmission mechanism ensures that optical signals can still be transmitted stably even when interfered with, improving system reliability.

[0086] 4. Reduce bit error rate: Combined with deep learning signal enhancement algorithm, it can realize intelligent data correction and improve transmission accuracy.

[0087] 5. Adaptive adjustment of the beam transmission path, beam tracking and feedback adjustment, optical and wireless hybrid transmission, as well as data processing and error correction form a closed-loop optimization mechanism for signal transmission, ensuring the stability and accuracy of optical data transmission. BRIEF DESCRIPTION OF THE DRAWINGS

[0088] Figure 1 It is a schematic diagram of the system architecture of the present invention;

[0089] Figure 2 It is a schematic flow chart of the method steps of the present invention. DETAILED DESCRIPTION

[0090] The present invention will be further described in detail below with reference to the accompanying drawings.

[0091] Example 1:

[0092] A contactless intelligent transmission system for power data, such as Figure 1 Shown, including:

[0093] Optical acquisition module, used to obtain display data of smart meters and convert the data into optical signals;

[0094] The multi-wavelength optical transmission module is used to perform multi-wavelength encoding on the optical signal converted by the optical acquisition module and send the encoded optical signal to the optical path dynamic adjustment module;

[0095] Optical path dynamic adjustment module, including laser adjustable lens and AI visual recognition system,

[0096] The laser tunable lens is used to receive the optical signal from the multi-wavelength optical transmission module and adjust the propagation direction of the light beam;

[0097] The AI ​​visual recognition system is used to monitor the transmission path of the optical signal and provide adjustment parameters to the laser tunable lens;

[0098] Intelligent beam tracking system, which monitors the beam's transmission status and provides feedback to adjust the beam's direction. This system includes a laser feedback control algorithm and can be combined with other sensors and algorithms to enhance beam tracking capabilities.

[0099] Hybrid transmission module, including optical transmission unit and radio frequency wireless communication unit,

[0100] The optical transmission unit is used to receive the optical signal transmitted by the optical path dynamic adjustment module and perform data transmission when the optical signal meets the preset conditions;

[0101] The radio frequency wireless communication unit is used to receive data and perform data transmission when the optical signal does not meet the preset conditions;

[0102] The data processing and error correction module includes a deep learning signal enhancement algorithm and an adaptive error detection and correction algorithm. The adaptive error detection and correction algorithm can perform feature extraction, error detection and data correction based on the self-attention mechanism or other deep learning methods.

[0103] The optical acquisition module includes a high-precision CMOS imaging unit and a dynamic optical filter.

[0104] The high-precision CMOS imaging unit is used to receive the display data of the smart meter and convert it into an optical signal;

[0105] The dynamic optical filter acts on the high-precision CMOS imaging unit to filter the received light signal to reduce the impact of ambient light interference on data acquisition;

[0106] The high-precision CMOS imaging unit receives the signal processed by the dynamic optical filter and adjusts the exposure parameters according to the signal characteristics to ensure that the collected data meets the requirements of optical signal conversion;

[0107] The optical acquisition module further includes an adaptive exposure control unit for dynamically adjusting exposure parameters of the optical acquisition module when the ambient light intensity changes significantly. The adaptive exposure control unit includes:

[0108] An ambient light measurement module, including a light sensor, is used to detect the current ambient light intensity and calculate the rate of light change;

[0109] The exposure adjustment module is used to calculate the new exposure time according to the lighting changes and adjust the exposure parameters of the optical acquisition module;

[0110] The HDR imaging module is used to use a multiple exposure strategy in high-brightness or low-brightness environments to capture multiple images with different exposure times and generate high dynamic range images through an exposure fusion algorithm;

[0111] The data optimization processing module is used to perform an adaptive noise suppression algorithm on the HDR-processed image to reduce the impact of ambient light changes on data acquisition.

[0112] The multi-wavelength optical transmission module uses a combination of infrared light, near-infrared light and visible light to encode data, and includes:

[0113] A data preprocessing unit, used to convert smart meter data into binary data streams and encode and distribute the data according to a preset strategy;

[0114] Multispectral coding unit, including:

[0115] Infrared light channel for encoding critical data bits in low-visibility environments;

[0116] Near-infrared optical channel, used as the main channel for long-distance optical transmission to reduce atmospheric refraction interference;

[0117] Visible light channel, used for data enhancement in short-range environments and to provide visual debugging information;

[0118] A synchronous modulation unit, used for performing time division multiplexing and orthogonal frequency division multiplexing modulation on infrared light, near infrared light and visible light signals;

[0119] an optical transmitting unit, comprising a multi-mode laser transmitter for modulating and transmitting infrared light, near-infrared light and visible light respectively;

[0120] Optical receiving unit, comprising:

[0121] Multi-spectral optical receiving module for receiving and decoding infrared, near-infrared and visible light signals;

[0122] The signal reconstruction unit is used to combine the redundant data of the three optical channels and perform data compensation when the signal is attenuated or lost.

[0123] To ensure the stability and adaptability of optical signals in different environments, the combined data encoding process of infrared, near-infrared, and visible light can be divided into the following steps:

[0124] Data preprocessing:

[0125] The optical acquisition module acquires the display data of the smart meter, formats it, and converts it into a binary data stream suitable for optical encoding.

[0126] Multispectral Code Allocation:

[0127] The infrared light channel is responsible for data transmission in low-visibility environments (such as at night or in foggy weather) and encodes key data bits.

[0128] The near-infrared light channel is mainly used to reduce the impact of atmospheric refraction on the signal and serves as the main data-carrying channel during long-distance transmission.

[0129] The visible light channel is an auxiliary channel, mainly used for data enhancement in short-distance environments and can provide visual debugging information.

[0130] Code synchronization and modulation:

[0131] Time division multiplexing is used to modulate the data carriers of the three optical waves at different time segments to ensure that the data carried by each optical signal is synchronized.

[0132] Orthogonal frequency division multiplexing is used to load different data streams into different optical wave channels, thereby improving transmission bandwidth and anti-interference capabilities.

[0133] Data transmission and beam shaping:

[0134] Through a multi-wavelength optical transmission module, a multi-mode laser transmitter is used to modulate three light waves separately, and combined with intelligent beam shaping technology, the coherence of the light waves is optimized before transmission to reduce mutual interference.

[0135] Optical reception and decoding:

[0136] The multi-spectral optical receiving unit in the optical + wireless hybrid transmission module collects three optical signals and decodes them according to the time-division multiplexing information to extract the data carried by infrared light, near-infrared light and visible light respectively.

[0137] A signal reconstruction algorithm is used to combine the redundant information of the three optical channels to improve data integrity and compensate for data loss or attenuation.

[0138] The intelligent beam tracking system adopts a multi-sensor fusion tracking algorithm.

[0139] include:

[0140] Beam status data acquisition module, including:

[0141] Laser feedback sensor, used to detect the direction offset and intensity of the current light beam;

[0142] Infrared thermal imaging sensor, used to obtain the thermal imaging profile of the target and extract the target center coordinates;

[0143] AI visual recognition module, used to identify the visual features of the target point and calculate the target position coordinates;

[0144] Data fusion and error calculation module, used to calculate beam offset error and target position error based on weighted sensor fusion algorithm;

[0145] Beam adjustment module, including:

[0146] PID control unit, used to adjust the beam angle according to the error calculation results;

[0147] An optical deflection execution unit, configured to receive adjustment parameters calculated by the PID control unit and adjust the optical lens angle;

[0148] Adaptive optimization module, including:

[0149] A neural network regression optimization unit is used to optimize PID control parameters based on historical environmental data.

[0150] The intelligent beam tracking system uses a multi-sensor fusion tracking algorithm, combining laser feedback sensors, infrared thermal imaging sensors and AI visual recognition modules to perform dynamic beam tracking. The specific steps are as follows:

[0151] Step 1: Beam status data collection

[0152] The laser feedback sensor collects the direction offset and intensity of the current light beam;

[0153] The infrared thermal imaging sensor obtains the thermal imaging profile of the beam target and extracts the target center coordinates;

[0154] The AI ​​visual recognition module uses image processing algorithms to identify the location of the target point and calculate the offset.

[0155] Step 2: Offset calculation and fusion

[0156] A weighted sensor fusion algorithm is used to calculate the beam offset error Δθ and target position errors Δx, Δy:

[0157] Δθ=w1·θ laser +w2·θ infrared +w3·θ vision

[0158] Δx=w1·x laser +w2·x infrared +w3·x vision

[0159] Δy=w1·y laser +w2·y infrared +w3·y vision

[0160] Parameter Description:

[0161] θ laser , x laser ,y laser : Angle and target coordinate offset obtained by the laser feedback sensor;

[0162] θ infrared , xinfrared ,y infrared : Angle and target coordinate offset obtained by infrared thermal imaging sensor;

[0163] θ vision , x vision ,y vision : Angle and target coordinate offset obtained by the AI ​​visual recognition module;

[0164] w1, w2, and w3 are sensor data weights, and their sum is 1.

[0165] Step 3: Beam Adjustment

[0166] The PID control algorithm is used to adjust the direction of the light beam so that it gradually approaches the target point:

[0167]

[0168] Parameter Description:

[0169] θ new : adjusted beam angle;

[0170] K p , K i , K d : PID control parameters;

[0171] θ t : Angle error at time t;

[0172] The rate of change of angular error.

[0173] Step 4: Adaptive Adjustment

[0174] Combined with the neural network regression model, the beam tracking data under different ambient light conditions is trained to optimize the PID parameters.

[0175] The hybrid transmission module includes a signal status intelligent switching control unit, which automatically enables the radio frequency wireless communication unit for data transmission when the receiving intensity of the optical signal is lower than the set threshold; when the optical signal recovers to a usable level, it switches back to the optical transmission unit for data transmission.

[0176] The data processing and error correction module uses a deep learning model based on the self-attention mechanism to perform feature extraction, error detection and correction on optical signal data. The data processing and error correction module includes:

[0177] The optical signal acquisition module is used to obtain the optical signal data and wireless signal data transmitted by the hybrid transmission module and perform standardized preprocessing;

[0178] Feature extraction module, including:

[0179] Self-attention calculation unit, used to calculate the signal feature matrix based on the self-attention mechanism;

[0180] Signal feature matrix calculation unit, used to construct query matrix, key matrix and value matrix and perform feature weighting;

[0181] Error detection module, including:

[0182] an error calculation unit, configured to calculate an error matrix between the optical signal and the wireless signal;

[0183] Data correction module, including:

[0184] The neural network regression unit is used to calculate the error correction function based on the error matrix and perform error correction on the optical signal data.

[0185] The specific implementation method of this solution is realized through a non-contact intelligent transmission method of power data, such as Figure 2 As shown:

[0186] The method comprises the following steps:

[0187] S1: Optical data acquisition:

[0188] Obtain the display data of the smart meter through the optical acquisition module and convert the data into optical signals;

[0189] S2: Optical signal multi-wavelength encoding and transmission:

[0190] The optical signal is multi-wavelength encoded through a multi-wavelength optical transmission module and sent to the target receiving end through a light beam transmitting device;

[0191] S3: Adaptive adjustment of beam transmission path:

[0192] Through the optical path dynamic adjustment module, using laser adjustable lens and AI visual recognition system, the optical signal transmission path is monitored in real time and the beam propagation direction is dynamically adjusted according to environmental changes;

[0193] S4: Beam tracking and feedback adjustment

[0194] Through the intelligent beam tracking system, laser feedback control algorithm is used to detect the propagation state of the optical signal and calculate the beam offset;

[0195] Generate beam adjustment parameters and feed them back to the optical path dynamic adjustment module to make the beam direction conform to the target path;

[0196] S5: Optical and wireless hybrid transmission

[0197] Through the hybrid transmission module, when the optical signal is interfered, it switches to the radio frequency wireless communication unit for data transmission to ensure data continuity;

[0198] S6: Data processing and error correction

[0199] Through the data processing and error correction module, the received optical signal is optimized using a deep learning signal enhancement algorithm;

[0200] Through the adaptive error detection and correction algorithm, the error of the optical signal is calculated and the signal data is error corrected to improve data accuracy.

[0201] The above-described embodiments do not constitute a limitation on the scope of protection of this technical solution. Any modifications, equivalent replacements, and improvements made within the spirit and principles of the above-described embodiments shall be included in the scope of protection of this technical solution.

Claims

1. A contactless intelligent transmission system for power data, characterized in that: include: Optical acquisition module, used to obtain display data of smart meters and convert the data into optical signals; The multi-wavelength optical transmission module is used to perform multi-wavelength encoding on the optical signal converted by the optical acquisition module and send the encoded optical signal to the optical path dynamic adjustment module; Optical path dynamic adjustment module, including laser adjustable lens and AI visual recognition system, The laser tunable lens is used to receive the optical signal from the multi-wavelength optical sending module and adjust the propagation direction of the light beam. The AI ​​visual recognition system is used to monitor the transmission path of the optical signal and provide adjustment parameters to the laser tunable lens; Intelligent beam tracking system, which monitors the beam's transmission status and provides feedback to adjust the beam's direction. This system includes a laser feedback control algorithm and can be combined with other sensors and algorithms to enhance beam tracking capabilities. Hybrid transmission module, including optical transmission unit and radio frequency wireless communication unit, The optical transmission unit is used to receive the optical signal transmitted by the optical path dynamic adjustment module and perform data transmission when the optical signal meets the preset conditions; The radio frequency wireless communication unit is used to receive data and perform data transmission when the optical signal does not meet the preset conditions; The data processing and error correction module includes a deep learning signal enhancement algorithm and an adaptive error detection and correction algorithm. The adaptive error detection and correction algorithm can perform feature extraction, error detection and data correction based on the self-attention mechanism or other deep learning methods.

2. The power data contactless intelligent transmission system according to claim 1, characterized in that: The optical acquisition module includes a high-precision CMOS imaging unit and a dynamic optical filter. The high-precision CMOS imaging unit is used to receive the display data of the smart meter and convert it into an optical signal; The dynamic optical filter acts on the high-precision CMOS imaging unit to filter the received light signal to reduce the impact of ambient light interference on data acquisition; The high-precision CMOS imaging unit receives the signal processed by the dynamic optical filter and adjusts the exposure parameters according to the signal characteristics to make the collected data meet the requirements of optical signal conversion.

3. The power data contactless intelligent transmission system according to claim 2, characterized in that: The optical acquisition module further includes an adaptive exposure control unit for dynamically adjusting exposure parameters of the optical acquisition module when the ambient light intensity changes significantly. The adaptive exposure control unit includes: An ambient light measurement module, including a light sensor, is used to detect the current ambient light intensity and calculate the rate of light change; The exposure adjustment module is used to calculate the new exposure time according to the lighting changes and adjust the exposure parameters of the optical acquisition module; The HDR imaging module is used to use a multiple exposure strategy in high-brightness or low-brightness environments to capture multiple images with different exposure times and generate high dynamic range images through an exposure fusion algorithm; The data optimization processing module is used to perform an adaptive noise suppression algorithm on the HDR-processed image to reduce the impact of ambient light changes on data acquisition.

4. The power data contactless intelligent transmission system according to claim 1, characterized in that: The multi-wavelength optical transmission module uses a combination of infrared light, near-infrared light and visible light to encode data, and includes: A data preprocessing unit, used to convert smart meter data into binary data streams and encode and distribute the data according to a preset strategy; Multispectral coding unit, including: Infrared light channel for encoding critical data bits in low-visibility environments; Near-infrared optical channel, used as the main channel for long-distance optical transmission to reduce atmospheric refraction interference; Visible light channel, used for data enhancement in short-range environments and to provide visual debugging information; A synchronous modulation unit, used for performing time division multiplexing and orthogonal frequency division multiplexing modulation on infrared light, near infrared light and visible light signals; an optical transmitting unit, comprising a multi-mode laser transmitter for modulating and transmitting infrared light, near-infrared light and visible light respectively; Optical receiving unit, comprising: Multi-spectral optical receiving module for receiving and decoding infrared, near-infrared and visible light signals; The signal reconstruction unit is used to combine the redundant data of the three optical channels and perform data compensation when the signal is attenuated or lost.

5. The power data contactless intelligent transmission system according to claim 1, characterized in that: The intelligent beam tracking system adopts a multi-sensor fusion tracking algorithm, including: Beam status data acquisition module, including: Laser feedback sensor, used to detect the direction offset and intensity of the current light beam; Infrared thermal imaging sensor, used to obtain the thermal imaging profile of the target and extract the target center coordinates; AI visual recognition module, used to identify the visual features of the target point and calculate the target position coordinates; Data fusion and error calculation module, used to calculate beam offset error and target position error based on weighted sensor fusion algorithm; Beam adjustment module, including: PID control unit, used to adjust the beam angle according to the error calculation results; An optical deflection execution unit, configured to receive adjustment parameters calculated by the PID control unit and adjust the optical lens angle; Adaptive optimization module, including: A neural network regression optimization unit is used to optimize PID control parameters based on historical environmental data.

6. The power data contactless intelligent transmission system according to claim 1, characterized in that: The hybrid transmission module includes a signal status intelligent switching control unit, which automatically enables the radio frequency wireless communication unit for data transmission when the receiving intensity of the optical signal is lower than the set threshold; when the optical signal recovers to a usable level, it switches back to the optical transmission unit for data transmission.

7. The power data contactless intelligent transmission system according to claim 1, characterized in that: The data processing and error correction module uses a deep learning model based on the self-attention mechanism to perform feature extraction, error detection and correction on optical signal data. The data processing and error correction module includes: The optical signal acquisition module is used to obtain the optical signal data and wireless signal data transmitted by the hybrid transmission module and perform standardized preprocessing; Feature extraction module, including: Self-attention calculation unit, used to calculate the signal feature matrix based on the self-attention mechanism; Signal feature matrix calculation unit, used to construct query matrix, key matrix and value matrix and perform feature weighting; Error detection module, including: an error calculation unit, configured to calculate an error matrix between the optical signal and the wireless signal; Data correction module, including: The neural network regression unit is used to calculate the error correction function based on the error matrix and perform error correction on the optical signal data.

8. A method for contactless intelligent transmission of electric power data, applied to a contactless intelligent transmission system for electric power data according to any one of claims 1 to 7, characterized in that: The method comprises the following steps: S1: Optical data acquisition: Obtain the display data of the smart meter through the optical acquisition module and convert the data into optical signals; S2: Optical signal multi-wavelength encoding and transmission: The optical signal is multi-wavelength encoded through a multi-wavelength optical transmission module and sent to the target receiving end through a light beam transmitting device; S3: Adaptive adjustment of beam transmission path: Through the optical path dynamic adjustment module, using laser adjustable lens and AI visual recognition system, the optical signal transmission path is monitored in real time and the beam propagation direction is dynamically adjusted according to environmental changes; S4: Beam tracking and feedback adjustment Through the intelligent beam tracking system, laser feedback control algorithm is used to detect the propagation state of the optical signal and calculate the beam offset; Generate beam adjustment parameters and feed them back to the optical path dynamic adjustment module to make the beam direction conform to the target path; S5: Optical and wireless hybrid transmission Through the hybrid transmission module, when the optical signal is interfered, it switches to the radio frequency wireless communication unit for data transmission to ensure data continuity; S6: Data processing and error correction Through the data processing and error correction module, the received optical signal is optimized using a deep learning signal enhancement algorithm; Through the adaptive error detection and correction algorithm, the error of the optical signal is calculated and the signal data is error corrected to improve data accuracy.

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