Electric power data non-contact intelligent transmission system and method

Abstract

The invention relates to the field of electric power data transmission, and discloses an electric power data non-contact intelligent transmission system and method, and the system comprises an optical collection module which is used for obtaining the display data of an intelligent electric meter and converting the display data into an optical signal; the multi-wavelength optical sending module is used for carrying out data modulation and transmission by adopting a multi-wavelength coding mode; the light path dynamic adjustment module monitors a light signal path through a laser adjustable lens and an AI visual identification system and dynamically adjusts the light beam direction; the intelligent light beam tracking system is combined with a laser feedback control algorithm to detect a light beam state and adjust a transmission path of an optical signal; the hybrid transmission module is switched to a wireless communication mode to carry out data compensation transmission when the optical signal is interfered; and the data processing and error correction module adopts a deep learning signal enhancement algorithm to optimize the signal quality, and performs data correction based on an adaptive error detection and correction algorithm to improve the data accuracy.

Description

Technical Field

[0001] The present invention relates to the field of power data transmission, and particularly to a non-contact intelligent power data transmission system and method. Background Art

[0002] With the development of smart grid technology, smart meters, as the core devices for power data acquisition and management, are widely used in power metering, load management, and remote meter reading systems. The traditional methods for reading meter data mainly rely on wired communication or short-distance wireless communication. However, these methods have problems such as complex wiring, limited transmission distance, and susceptibility to signal interference.

[0003] To solve these problems, in recent years, non-contact data reading solutions based on optical transmission technology have gradually received attention. This solution encodes and decodes data through optical signals to achieve long-distance transmission of meter data, while avoiding the wiring costs of traditional wired communication and improving the flexibility and reliability of the system.

[0004] Deficiencies of the Prior Art

[0005] Although optical data transmission technology has many advantages in smart grids, the prior art still has many deficiencies, which limit its application in long-distance and complex environments. First, optical signal transmission is susceptible to atmospheric refraction interference. In long-distance or harsh environments (such as haze, rain, snow, high temperature), the light beam may be deflected, attenuated, or scattered, resulting in unstable data transmission and even error codes. Second, most existing optical transmission systems adopt a fixed optical path method and lack the ability of adaptive adjustment. When the light beam deviates from the receiving end due to external environmental influences, the system cannot adjust the optical path in real time, thus reducing the transmission success rate. Summary of the Invention

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

[0007] The above technical purpose of the present invention is achieved through the following technical solutions:

[0008] A non-contact intelligent power data transmission system includes:

[0009] An optical acquisition module, configured to acquire the display data of a smart meter and convert the data into optical signals;

[0010] A multi-wavelength optical transmission module, configured to perform multi-wavelength encoding on the optical signals converted by the optical acquisition module and send the encoded optical signals to the optical path dynamic adjustment module;

[0011] An optical path dynamic adjustment module, including a laser adjustable lens and an AI vision recognition system,

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

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

[0014] The intelligent beam tracking system is used to monitor the transmission state of the beam and provide feedback to adjust the beam direction, including a laser feedback control algorithm, and can combine other sensors and algorithms to enhance the beam tracking ability;

[0015] The hybrid transmission module includes an optical transmission unit and a 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 when the optical signal does not meet the preset conditions and perform data transmission;

[0018] The data processing and error correction module includes a deep learning signal enhancement algorithm and an adaptive error detection and correction algorithm, where 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 setting: 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 and is used to filter the received optical signal to reduce the influence 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 acquired 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 optical signal enters the optical signal conversion process after filtering and optimization.

[0024] Further setting, the optical acquisition module further includes an adaptive exposure control unit, which is used to dynamically adjust the exposure parameters of the optical acquisition module when the ambient light intensity changes greatly. 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 light change rate;

[0026] An exposure adjustment module is used to calculate a new exposure time based on the light change and adjust the exposure parameters of the optical acquisition module;

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

[0028] A data optimization and processing module is used to execute an adaptive noise suppression algorithm on the image after HDR processing to reduce the influence of ambient light change 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, night, and haze; by adopting the HDR multiple exposure technology, clear data acquisition results can be obtained in strong light or weak light environments. Through the adaptive noise suppression algorithm, the image noise caused by light change is reduced, and the clarity and accuracy of optical data are improved.

[0030] Further settings: The multi-wavelength optical transmission module uses a combination of infrared light, near-infrared light, and visible light for data encoding, and includes:

[0031] A data preprocessing unit is used to convert smart meter data into a binary data stream and encode and allocate the data according to a preset strategy;

[0032] A multi-spectral encoding unit includes:

[0033] An infrared light channel is used to encode key data bits in low visibility environments;

[0034] A near-infrared light channel is used as the main channel for long-distance optical transmission to reduce atmospheric refraction interference;

[0035] A visible light channel is used for data enhancement in short-distance environments and provides visual debugging information;

[0036] A synchronous modulation unit is used to perform time-division multiplexing and orthogonal frequency-division multiplexing modulation on infrared light, near-infrared light, and visible light signals;

[0037] An optical emission unit includes a multi-mode laser transmitter, which is used to modulate and emit infrared light, near-infrared light, and visible light respectively;

[0038] An optical receiving unit includes:

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

[0040] A signal reconstruction unit for combining redundant data from three optical channels and performing data compensation when signals are attenuated or lost.

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

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

[0043] including:

[0044] A beam state data acquisition module, including:

[0045] A laser feedback sensor for detecting the direction offset and intensity of the current beam;

[0046] An infrared thermal imaging sensor for obtaining the thermal imaging contour of the target and extracting the target center coordinates;

[0047] An AI vision recognition module for recognizing the visual features of the target point and calculating the target position coordinates;

[0048] A data fusion and error calculation module for calculating the beam offset error and target position error based on the weighted sensor fusion algorithm;

[0049] A beam adjustment module, including:

[0050] A PID control unit for adjusting the beam angle according to the error calculation result;

[0051] An optical deflection execution unit for receiving the adjustment parameters calculated by the PID control unit and adjusting the optical lens angle;

[0052] An adaptive optimization module, including:

[0053] A neural network regression optimization unit for optimizing the PID control parameters based on historical environmental data.

[0054] By adopting the above technical solutions, through multi-sensor fusion, the influence of single-sensor error on beam alignment is reduced, enabling the beam to be more accurately aligned with the target; combining laser feedback, infrared thermal imaging, and AI visual recognition, high-precision beam tracking can be performed under different ambient light conditions, reducing the influence of external interference; adopting a method combining PID control and neural network optimization to intelligently adjust the beam adjustment parameters, making the beam alignment more stable 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, which is applicable to complex environments with different weather, temperatures, and humidities.

[0055] Further setting: The hybrid transmission module includes a signal status intelligent switching control unit. When the received intensity of the optical signal is lower than the set threshold, the radio frequency wireless communication unit is automatically enabled for data transmission; when the optical signal resumes to an available level, the optical transmission unit is switched back for data transmission.

[0056] By adopting the above technical solutions, through the signal status intelligent switching control unit, the intelligent switching mechanism can be effectively enhanced, improving the transmission reliability.

[0057] Further setting: The data processing and error correction module adopts a deep learning model based on the self-attention mechanism for feature extraction, error detection, and correction of optical signal data. The data processing and error correction module includes:

[0058] An optical signal acquisition module for acquiring optical signal data and wireless signal data transmitted by the hybrid transmission module and performing standardized preprocessing;

[0059] A feature extraction module, including:

[0060] A self-attention calculation unit for calculating the signal feature matrix based on the self-attention mechanism;

[0061] A signal feature matrix calculation unit for constructing query matrix, key matrix, and value matrix and performing feature weighting;

[0062] An error detection module, including:

[0063] An error calculation unit for calculating the error matrix between the optical signal and the wireless signal;

[0064] A data correction module, including:

[0065] A neural network regression unit for calculating the error correction function based on the error matrix and correcting the optical signal data for errors.

[0066] By adopting the above technical solution, the self-attention mechanism is used to extract key features from optical signals, improving 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; by learning the error distribution characteristics through the neural network regression model, the signal data is automatically corrected under different environmental conditions.

[0067] Another object of the present invention is to provide a non-contact intelligent transmission method for power data, and the method includes the following steps:

[0068] S1: Optical data acquisition:

[0069] The display data of the smart meter is obtained through the optical acquisition module, and the data is converted into optical signals;

[0070] S2: Multi-wavelength encoding and transmission of optical signals:

[0071] Through the multi-wavelength optical transmission module, the optical signals are multi-wavelength encoded and sent to the target receiving end through the beam emission device;

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

[0073] Through the optical path dynamic adjustment module, using a laser adjustable lens and an AI vision 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, using the laser feedback control algorithm, the propagation state of the optical signal is detected, and the beam offset is calculated;

[0076] Beam adjustment parameters are generated and fed back to the optical path dynamic adjustment module to make the beam direction conform to the target path;

[0077] S5: Hybrid optical and wireless transmission

[0078] Through the hybrid transmission module, when the optical signal is interfered, it is switched 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, using the deep learning signal enhancement algorithm, the received optical signals are optimized;

[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 ability: By adopting multi-wavelength optical coding combined with intelligent polarization compensation, the influence of atmospheric refraction is significantly reduced, ensuring data stability.

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

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

[0086] 4. Reduce bit error rate: By combining deep learning signal enhancement algorithms, intelligent data correction is achieved, improving transmission accuracy.

[0087] 5. The adaptive adjustment of the beam transmission path, beam tracking and feedback adjustment, optical and wireless hybrid transmission, and 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 is a schematic diagram of the system architecture of the present invention;

[0089] Figure 2 is a schematic diagram of the method step flow of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

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

[0091] Embodiment 1:

[0092] A non-contact intelligent transmission system for power data, as Figure 1 shown, includes:

[0093] An optical acquisition module, configured to acquire the display data of the intelligent electric meter and convert the data into optical signals;

[0094] A multi-wavelength optical transmission module, configured to perform multi-wavelength coding on the optical signals converted by the optical acquisition module and send the encoded optical signals to the optical path dynamic adjustment module;

[0095] The optical path dynamic adjustment module includes a laser adjustable lens and an AI vision recognition system,

[0096] The laser adjustable lens is configured to receive the optical signals from the multi-wavelength optical transmission module and adjust the beam propagation direction;

[0097] The AI vision recognition system is configured to monitor the transmission path of the optical signals and provide adjustment parameters to the laser adjustable lens;

[0098] An intelligent beam tracking system for monitoring the transmission status of a beam and providing feedback to adjust the beam direction, including a laser feedback control algorithm, and can be combined with other sensors and algorithms to enhance the beam tracking ability;

[0099] A hybrid transmission module, including an optical transmission unit and a 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 when the optical signal does not meet the preset conditions and perform data transmission;

[0102] A data processing and error correction module, including a deep learning signal enhancement algorithm and an adaptive error detection and correction algorithm, where the adaptive error detection and correction algorithm can perform feature extraction, error detection, and data correction based on a 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 and is used to filter the received optical signal to reduce the influence 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 make the acquired data meet the requirements of optical signal conversion;

[0107] 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 greatly. The adaptive exposure control unit includes:

[0108] An ambient light measurement module, including a light sensor, for detecting the current ambient light intensity and calculating the light change rate;

[0109] An exposure adjustment module for calculating a new exposure time according to the light change and adjusting the exposure parameters of the optical acquisition module;

[0110] An HDR imaging module for adopting a multiple exposure strategy in high-brightness or low-brightness environments, acquiring images with multiple different exposure times, and generating a high-dynamic range image through an exposure fusion algorithm;

[0111] A data optimization processing module, which is used to execute an adaptive noise suppression algorithm on the image after HDR processing to reduce the influence 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 for data encoding, and includes:

[0113] A data preprocessing unit, which is used to convert the smart meter data into a binary data stream and perform encoding and allocation on the data according to a preset strategy;

[0114] A multi-spectral encoding unit, including:

[0115] An infrared light channel, which is used to encode key data bits in a low visibility environment;

[0116] A near-infrared light channel, which is used as the main channel for long-distance optical transmission to reduce atmospheric refraction interference;

[0117] A visible light channel, which is used for data enhancement in a short-distance environment and provides visual debugging information;

[0118] A synchronous modulation unit, which is used to perform time-division multiplexing and orthogonal frequency-division multiplexing modulation on infrared light, near-infrared light, and visible light signals;

[0119] An optical emission unit, including a multi-mode laser transmitter, which is used to modulate and emit infrared light, near-infrared light, and visible light respectively;

[0120] An optical receiving unit, including:

[0121] A multi-spectral optical receiving module, which is used to receive and decode infrared light, near-infrared light, and visible light signals;

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

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

[0124] Data preprocessing:

[0125] The optical acquisition module obtains the display data of the smart meter and performs formatting processing to convert the data into a binary data stream suitable for optical encoding.

[0126] Multi-spectral encoding allocation:

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

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

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

[0130] Coding synchronization and modulation:

[0131] Time-division multiplexing is adopted to perform data carrier modulation on the three light waves in different time segments to ensure the synchronization of the data carried by each optical signal.

[0132] Orthogonal frequency-division multiplexing is used to load different data streams onto different optical wave channels respectively, improving the transmission bandwidth and anti-interference ability.

[0133] Data transmission and beam shaping:

[0134] Through a multi-wavelength optical transmission module, multi-mode laser transmitters are used to modulate the three light waves respectively, 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 reception unit in the optical + wireless hybrid transmission module collects the three optical signals and decodes them according to the time-division multiplexing information to extract the data carried by the infrared light, near-infrared light, and visible light respectively.

[0137] A signal reconstruction algorithm is adopted to combine the redundant information of the three optical channels to improve data integrity and perform data compensation when the signal attenuates or is lost.

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

[0139] including:

[0140] The beam state data acquisition module includes:

[0141] A laser feedback sensor for detecting the direction offset and intensity of the current beam;

[0142] An infrared thermal imaging sensor for obtaining the thermal imaging contour of the target and extracting the target center coordinates;

[0143] An AI vision recognition module for identifying the visual features of the target point and calculating the target position coordinates;

[0144] The data fusion and error calculation module for calculating the beam offset error and target position error based on the weighted sensor fusion algorithm;

[0145] The beam adjustment module includes:

[0146] A PID control unit for adjusting the beam angle according to the error calculation result;

[0147] An optical deflection execution unit for receiving the adjustment parameters calculated by the PID control unit and adjusting the angle of the optical lens;

[0148] An adaptive optimization module, including:

[0149] A neural network regression optimization unit for optimizing the PID control parameters based on historical environmental data.

[0150] The intelligent beam tracking system adopts a multi-sensor fusion tracking algorithm, combines a laser feedback sensor, an infrared thermal imaging sensor and an AI vision recognition module for dynamic beam tracking. The specific steps are as follows:

[0151] Step 1: Beam state data acquisition

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

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

[0154] The AI vision recognition module identifies the position of the target point through image processing algorithms and calculates the offset.

[0155] Step 2: Offset calculation and fusion

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

[0157] Δθ = w 1 ·θ laser + w 2 ·θ infrared + w 3 ·θ vision

[0158] Δx = w 1 ·x laser + w 2 ·x infrared + w 3 ·x vision

[0159] Δy = w 1 ·y laser + w 2 ·y infrared + w 3 ·y vision

[0160] Parameter description:

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

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

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

[0164] w 1 , w 2 , w 3 are the sensor data weights, and the sum of the three is 1.

[0165] Step 3: Beam adjustment

[0166] The PID control algorithm is used to adjust the beam direction to gradually approach 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] Rate of change of the angle 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. When the received intensity of the optical signal is lower than the set threshold, the radio frequency wireless communication unit is automatically enabled for data transmission; when the optical signal resumes to an available level, it switches back to the optical transmission unit for data transmission.

[0176] The data processing and error correction module adopts a deep learning model based on the self-attention mechanism, which is used for feature extraction, error detection and correction of optical signal data. The data processing and error correction module includes:

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

[0178] A feature extraction module, including:

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

[0180] A signal feature matrix calculation unit, which is used to construct a query matrix, a key matrix and a value matrix and perform feature weighting;

[0181] An error detection module, including:

[0182] An error calculation unit, which is used to calculate the error matrix between the optical signal and the wireless signal;

[0183] A data correction module, including:

[0184] A neural network regression unit, which 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 of this solution is realized through a non-contact intelligent transmission method for power data, as Figure 2 shown:

[0186] This method includes 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: Multi-wavelength encoding and transmission of optical signals:

[0190] Through the multi-wavelength optical transmission module, perform multi-wavelength encoding on the optical signal and send it to the target receiving end through the beam emission device;

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

[0192] Through the optical path dynamic adjustment module, use the laser tunable lens and the AI vision recognition system to monitor the optical signal transmission path in real time and dynamically adjust the beam propagation direction according to environmental changes;

[0193] S4: Beam tracking and feedback adjustment

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

[0195] Generate beam adjustment parameters and feedback them 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, switch 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, using the deep learning signal enhancement algorithm, optimize the received optical signal;

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

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

Claims

1. A non-contact intelligent transmission system for power data, characterized in that: include: An optical acquisition module is used to obtain display data of the smart meter and convert the data into optical signals; A multi-wavelength optical sending 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 transmission status of the beam and provides feedback to adjust the beam direction, including laser feedback control algorithms, and can be combined with other sensors and algorithms to enhance beam tracking capabilities; A hybrid transmission module, including an optical transmission unit and a 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 a 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 so that the collected data meets 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 also includes an adaptive exposure control unit, which is used to dynamically adjust the exposure parameters of the optical acquisition module when the ambient light intensity changes greatly. The adaptive exposure control unit includes: An ambient light measurement module, including a light sensor, for detecting the current ambient light intensity and calculating the rate of light change; An exposure adjustment module is used to calculate a new exposure time according to changes in illumination and adjust exposure parameters of the optical acquisition module; HDR imaging module, which is used to adopt multiple exposure strategies in high-brightness or low-brightness environments, collect multiple images with different exposure times, and generate high dynamic range images through exposure fusion algorithms; 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 a binary data stream and encode and distribute the data according to a preset strategy; Multispectral encoding unit, comprising: Infrared optical 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 respectively modulating and transmitting infrared light, near-infrared light and visible light; An 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 state data acquisition module, including: A laser feedback sensor for detecting 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: A PID control unit, used to adjust the beam angle according to the error calculation result; An optical deflection execution unit, used to receive the adjustment parameters calculated by the PID control unit and adjust the optical lens angle; Adaptive optimization module, including: 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 an available level, 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 adopts a deep learning model based on a self-attention mechanism to perform feature extraction, error detection and correction on optical signal data. The data processing and error correction module includes: An optical signal acquisition module is used to acquire the optical signal data and wireless signal data transmitted by the hybrid transmission module and perform standardized preprocessing; Feature extraction module, including: A self-attention calculation unit, used to calculate the signal feature matrix based on the self-attention mechanism; A signal feature matrix calculation unit, used to construct a query matrix, a key matrix and a value matrix and perform feature weighting; Error detection module, including: An error calculation unit, used for calculating 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 non-contact intelligent transmission of electric power data, applied to a non-contact 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: The display data of the smart meter is obtained through the optical acquisition module and converted into an optical signal; 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 a target receiving end through a light beam transmitting device; S3: Adaptive adjustment of beam transmission path: Through the optical path dynamic adjustment module, 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, the 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 the data accuracy.

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