A dual-mode network communication method and system based on HPLC+HRF

Abstract

The present invention provides a dual-mode network communication method and system based on HPLC+HRF. The method includes: network initialization, initializing and configuring the HPLC module and the HRF module respectively; data acquisition and feature extraction; LSTM neural network training and prediction; communication mode selection and optimization; dynamic adjustment of the data transmission rate based on the LSTM neural network prediction results combined with current actual monitoring data; data transmission according to the selected communication mode and data transmission rate during the data transmission process; data fusion and optimization, fusing the data from the HPLC and HRF at the receiving end, error correction, and optimized sorting. By integrating LSTM neural network prediction technology with adaptive rate adjustment technology, the present invention achieves accurate prediction of network communication link quality and intelligent optimization of data transmission rate, effectively overcoming the shortcomings of traditional network communication technology in complex environments.

Description

Technical Field

[0001] The present invention relates to the field of wireless communication technology, and in particular to a dual-mode network communication method and system based on HPLC+HRF. Background Art

[0002] With the rapid development of the Internet of Things (IoT), fields such as smart homes and smart grids are placing increasingly stringent demands on the stability, reliability, and intelligence of network communications. Single network communication technologies, such as HPLC or HRF, often exhibit numerous limitations in complex and diverse practical application scenarios.

[0003] High-speed power line carrier communication (HPLC) is a broadband power line carrier technology that transmits data over low-voltage power lines. It offers advantages such as high data rate, real-time performance, strong anti-interference performance, high transmission speed, and inter-chip interconnection, meeting the current information collection needs of household and other electrical devices. However, with the widespread use of power electronic devices and inverters, the electromagnetic environment of the power grid has become increasingly complex, making the power line carrier communication environment increasingly harsh. This results in low fault reporting success rates, short single-hop communication distances, and the existence of "island / isolated points" in the network. For example, within large buildings, although power lines are extensive, they are susceptible to interference from electrical equipment, causing HPLC signal quality fluctuations. Furthermore, wireless signals can be affected by factors such as obstruction and multipath effects during propagation, resulting in coverage blind spots or rapid signal attenuation.

[0004] High-speed wireless communication (HRF) refers to long-distance transmission between multiple nodes without the use of conductors or cables. Due to its advantages such as low power consumption and flexible networking, it has gradually become a major information transmission mode in field area network construction. It can avoid various interference and noise in power lines, overcome the shortcomings of isolated islands / points, low success rate of power outage reporting, and short single-hop communication distance, and is an effective supplement to HPLC communication. However, HRF communication is also susceptible to interference from the external environment, especially in complex environments such as buildings and underground parking lots, where it suffers from severe signal attenuation and signal blind spots.

[0005] Furthermore, traditional dual-mode network communication switching mechanisms rely on passive switching based on real-time link quality detection results, lacking proactive prediction of link quality trends. Therefore, the development of a dual-mode network communication solution that integrates multiple advanced technologies and possesses intelligent predictive capabilities is urgent. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide a dual-mode network communication method and system based on HPLC+HRF. By integrating LSTM neural network prediction technology and adaptive rate adjustment technology, accurate prediction of network communication link quality and intelligent optimization of data transmission rate can be achieved, effectively overcoming the shortcomings of traditional network communication technology in complex environments, ensuring the efficiency, stability and reliability of network communication, and meeting the growing demand for network communication in modern Internet of Things applications.

[0007] To solve the above technical problems, the embodiments of the present invention provide the following technical solutions: a dual-mode network communication method based on HPLC+HRF, comprising the following steps:

[0008] Network initialization: initializing and configuring the HPLC module and HRF module respectively, including setting the communication baud rate, channel coding mode, transmission power, and performing channel scanning to determine available channel resources;

[0009] Data collection and feature extraction: Continuously collect multi-dimensional data related to the quality of the HPLC and HRF links at a specific sampling frequency. For the HPLC link, the voltage, current, signal frequency, phase and other parameters on the power line are collected for feature extraction. For the HRF link, the strength, frequency, bandwidth, bit error rate and other indicators of the wireless signal are collected for feature extraction.

[0010] LSTM neural network training and prediction: Build an LSTM neural network model with multiple hidden layers. The number of input layer nodes is determined by the dimension of the extracted feature vector, and the number of output layer nodes corresponds to the number of predicted link quality indicators. Training is performed using a stochastic gradient descent algorithm with adaptive learning rate adjustment, and early stopping is used to prevent overfitting. After training, real-time feature data is used to predict key indicators of dual-mode network communication link quality, such as signal strength change trends and bit error rate fluctuation range.

[0011] Communication mode selection and optimization: Select the appropriate communication mode based on the LSTM neural network prediction results and the current actual link quality detection data. When the HPLC link is predicted to meet the preset communication requirements in the short term, the HPLC communication mode is prioritized and the relevant communication parameters are adjusted in advance. When the HPLC link is predicted to fail and the HRF link quality is acceptable, the HRF communication mode is switched to and the transmission power and channel selection are adjusted.

[0012] Adaptive rate adjustment monitors dynamic changes in the network environment in real time during data transmission, including real-time fluctuations in link quality and changes in network load. It dynamically adjusts the data transmission rate based on the LSTM neural network prediction results combined with current actual monitoring data. When link quality improves and network load decreases, the data transmission rate is increased, and vice versa. A rate adjustment feedback mechanism is also established to optimize and modify the rate adjustment strategy based on the actual data transmission effect.

[0013] Data transmission and switching control: during the data transmission process, data is transmitted according to the selected communication mode and data transmission rate, the real-time link quality of the current communication mode is monitored, and the communication mode is automatically switched if the link quality deteriorates and the link quality of another communication mode is available; data is segmented, numbered, and error-corrected; relevant information, including but not limited to switching identifiers and rate adjustment information, is added to the data packet header when the communication mode is switched or the rate is adjusted; and data is reorganized and processed at the receiving end;

[0014] Data fusion and optimization: When dual-mode data transmission is used simultaneously, the data from HPLC and HRF are fused, error corrected, and optimized sorted at the receiving end. Data is reorganized according to the identification information and numbering sequence in the data packet header. A hybrid error correction scheme based on CRC and FEC is used for error detection and correction. A priority queue algorithm is used for sorting and optimized integration based on the importance level and real-time requirements of the data.

[0015] Furthermore, in the data collection and feature extraction steps, for the HPLC link, fast Fourier transform is used to calculate the signal frequency components and principal component analysis is used to extract the feature vectors, and for the HRF link, wavelet transform is used to extract the time-frequency domain features of the wireless signal.

[0016] Furthermore, in the LSTM neural network training and prediction steps, the number of hidden layers of the constructed LSTM neural network model is 3-5 layers, the number of input layer nodes is 10-20, the number of output layer nodes corresponds to the signal strength and bit error rate of 2, and the mean square error is used as the loss function to evaluate the model prediction performance.

[0017] Furthermore, in the communication mode selection and optimization step, the HPLC communication parameters adjusted in advance include optimizing the power line carrier frequency allocation and adjusting the encoding method, and the adjustment under the HRF communication mode includes dynamically adjusting the transmission power and channel selection according to the wireless signal propagation characteristics.

[0018] Furthermore, in the adaptive rate adjustment step, the rate adjustment is performed according to a preset rate adjustment strategy, and the strategy is optimized based on actual effects such as the bit error rate, data transmission accuracy and real-time performance fed back by the receiving end. The adjustment methods include adjusting the encoding method and adjusting the transmission time interval.

[0019] Furthermore, in the data transmission and switching control step, the length of the data segment processing is a preset fixed length, and the switching identifier includes the switching reason and the number range of the data segments transmitted in the original communication mode.

[0020] The present invention also proposes a dual-mode network communication system based on HPLC+HRF, comprising:

[0021] HPLC communication module, used to implement high-speed power line communication functions, including but not limited to power line signal modulation and demodulation, encoding and decoding, data transmission and reception operations;

[0022] HRF communication module, used to realize high-frequency wireless communication functions, with the ability to transmit and receive wireless signals, frequency modulation, and channel coding;

[0023] A data acquisition and feature extraction unit, for acquiring data related to link quality and extracting features, including a power line data acquisition and feature extraction module for acquiring data related to HPLC links and extracting features, and a wireless data acquisition and feature extraction module for acquiring data related to HRF links and extracting features;

[0024] The LSTM neural network prediction unit is used to predict link quality based on the LSTM model. It includes an LSTM model construction module, a training module, and a prediction calculation module, which are responsible for model construction, model training, and link quality prediction calculation, respectively.

[0025] A communication mode selection and optimization unit is used to select and optimize the communication mode based on the prediction results and actual link quality, including a prediction result analysis module, a communication mode judgment module, and a parameter optimization module. The prediction result analysis module is used to analyze the LSTM neural network prediction results, the communication mode judgment module is used to judge the communication mode based on the analysis results and the actual link quality, and the parameter optimization module is used to optimize the parameters of the selected communication mode;

[0026] The adaptive rate adjustment unit is used to monitor dynamic changes in the network environment in real time and adjust the data transmission rate. It includes a rate monitoring module, a rate calculation module, and a feedback optimization module. The rate monitoring module is used to monitor link quality and network load. The rate calculation module calculates the rate based on LSTM prediction results and monitoring data. The feedback optimization module optimizes the rate adjustment strategy according to the actual data transmission effect.

[0027] A data processing and switching control unit, configured to perform segmentation processing, numbering, error correction coding, communication mode switching, and rate adjustment control on data, comprising a data segmentation module, a numbering module, an error correction coding module, a cache switching module, and a rate adjustment execution module. The data segmentation module is configured to segment data, the numbering module is configured to add numbers to segmented data, the error correction coding module is configured to add error correction coding information, the cache switching module is configured to cache untransmitted data when switching communication modes and initiate another communication mode for continued transmission, and the rate adjustment execution module is configured to perform rate adjustment operations.

[0028] The data fusion and optimization unit is used to perform fusion processing, error correction and optimized sorting of dual-mode transmitted data at the receiving end.

[0029] Furthermore, the rate monitoring module, rate calculation module and feedback optimization module are specifically:

[0030] (1) Rate monitoring module, including link quality monitoring submodule and network load monitoring submodule

[0031] The link quality monitoring submodule is used to obtain link quality related parameters in real time through interaction with the HPLC communication module and the HRF communication module;

[0032] The network load monitoring submodule is used to monitor the data traffic in the network, including but not limited to the number of currently connected devices, the frequency of data transmission requests from each device, the size of data packets transmitted, and the transmission rate;

[0033] (2) The rate calculation module includes the link quality rate adjustment submodule and the network load rate adjustment submodule

[0034] The link quality rate adjustment submodule calculates the data transmission rate using a pre-set rate adjustment algorithm based on the link quality parameters obtained by the link quality monitoring submodule;

[0035] The network load rate adjustment submodule optimizes the data transmission rate by combining the network load information provided by the network load monitoring submodule;

[0036] (3) Feedback optimization module, including transmission effect evaluation submodule and strategy adjustment submodule

[0037] The transmission effect evaluation submodule is used to collect the information fed back by the receiving end in real time during the data transmission process and to perform a multi-dimensional evaluation of the data transmission effect;

[0038] The policy adjustment submodule is used to optimize and modify the rate adjustment policy according to the evaluation results of the transmission effect evaluation submodule.

[0039] Furthermore, the data segmentation module, number adding module, error correction coding module, cache switching module and rate adjustment execution module include:

[0040] (1) Data segmentation module, including segmentation strategy formulation submodule and segmentation execution submodule

[0041] Segmentation strategy formulation submodule, used to formulate data segmentation strategy based on network communication protocol and data transmission characteristics;

[0042] The segmentation execution submodule is used to segment the data to be transmitted according to the segmentation rules determined by the segmentation strategy formulation submodule;

[0043] (2) Numbering addition module, including numbering rule setting submodule and numbering addition execution submodule

[0044] The numbering rule setting submodule is used to develop a numbering rule system to add numbers to the segmented data. The numbering rule should cover the important dimensions of the data source, transmission sequence information, communication session identifier, and data version information;

[0045] The numbering adding execution submodule adds corresponding numbering information to each piece of data according to the numbering rules set by the numbering rule setting submodule;

[0046] (3) Error correction coding module, including coding mode selection submodule and coding execution submodule

[0047] The coding mode selection submodule is used to dynamically select the appropriate error correction coding mode based on the current link quality and network load;

[0048] The encoding execution submodule is used to perform encoding operations on the segmented and numbered data according to the error correction encoding method determined by the encoding method selection submodule;

[0049] (4) Cache switching module, including cache management submodule and switching control submodule

[0050] The cache management submodule is used to store data that has not been fully transmitted during the communication mode switching or rate adjustment process;

[0051] The switching control submodule coordinates data caching and switching operations when communication mode switching or rate adjustment is required;

[0052] (5) Rate adjustment execution module, including rate adjustment instruction receiving submodule and rate adjustment operation execution submodule

[0053] The rate adjustment instruction receiving submodule is used to receive the rate adjustment instruction from the adaptive rate adjustment unit, the instruction including the target rate value, the adjustment reason and the adjustment time requirement;

[0054] The rate adjustment operation execution submodule is used to adjust the data transmission rate according to the instruction information parsed by the rate adjustment instruction receiving submodule.

[0055] Furthermore, the data fusion and optimization unit includes a data fusion processing module, an error correction module, and an optimization sorting module, wherein:

[0056] (1) Data fusion processing module, including data synchronization and alignment submodule, data merging and deduplication submodule

[0057] The data synchronization and alignment submodule uses the timestamp information carried in the data packet header to align data arriving at the receiving end at different times according to their original sending order;

[0058] Data merging and deduplication submodule, after data synchronization and alignment, the data merging and deduplication submodule merges the data from the two modes;

[0059] (2) Error correction module, including error detection submodule and error correction execution submodule

[0060] The error detection submodule is used to perform data integrity check based on the cyclic redundancy check mechanism and the CRC check code added by the data sender. It calculates the CRC value of the received data and compares it with the CRC check code sent by the sender. If the two do not match, it indicates that an error may have occurred during the data transmission process.

[0061] The error correction execution submodule is used to perform corresponding error correction operations according to the error correction coding method adopted by the data sending end when the error detection submodule detects that there is an error in the data;

[0062] (3) Optimize the sorting module, including the data evaluation submodule and the sorting execution submodule

[0063] The data importance assessment submodule determines the importance level of data based on factors including but not limited to the type and source of the data and the degree of impact on the upper-layer application system;

[0064] The sorting execution submodule is used to optimize the sorting of the data after error correction according to the importance level and weight determined by the data importance evaluation submodule.

[0065] The beneficial effects of the above technical solution of the present invention are as follows:

[0066] 1. The present invention uses an LSTM neural network to accurately predict the quality of dual-mode network communication links. The system can know the link quality change trend in advance and take proactive measures before the link quality deteriorates, such as switching the communication mode in advance or adjusting the data transmission rate, effectively avoiding data transmission interruptions or errors caused by sudden link failures.

[0067] 2. The adaptive rate adjustment technology of the present invention dynamically adjusts the data transmission rate according to the link quality and network load. When the link quality is poor or the network is congested, the rate is reduced to ensure the accuracy of data transmission, and when the link quality is good and the network is idle, the rate is increased to improve transmission efficiency, thereby maintaining reliable data transmission in various complex network environments.

[0068] 3. The error detection and correction mechanism in the data fusion processing of the present invention can identify and repair erroneous data generated during the dual-mode transmission process. Whether it is HPLC data errors caused by power line interference or HRF data errors caused by the multipath effect of wireless signals, they can be effectively processed, further enhancing the reliability of data transmission and ensuring the integrity and accuracy of the data obtained by the receiving end. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 Flow chart of the dual-mode network communication method based on HPLC+HRF of the present invention;

[0070] Figure 2 The figure is a functional block diagram of the dual-mode network communication system based on HPLC+HRF of the present invention;

[0071] Figure 3 Schematic diagram of the application of HPLC and HRF dual-mode communication in power data communication of the present invention;

[0072] Figure 4 This is a node topology diagram of HPLC and HRF dual-mode communications in the HPLC+HRF-based dual-mode network communication method and system of the present invention. DETAILED DESCRIPTION

[0073] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0074] Example 1

[0075] like Figure 1 As shown, the present invention proposes a dual-mode network communication method based on HPLC+HRF, comprising the following steps:

[0076] S1. Network initialization: Initialize and configure the HPLC module and HRF module respectively, including setting the communication baud rate, channel coding mode, transmission power, and performing channel scanning to determine available channel resources;

[0077] S2. Data collection and feature extraction: Continuously collect multi-dimensional data related to the quality of the HPLC and HRF links at a specific sampling frequency. For the HPLC link, the voltage, current, signal frequency, phase and other parameters on the power line are collected for feature extraction. For the HRF link, the strength, frequency, bandwidth, bit error rate and other indicators of the wireless signal are collected for feature extraction.

[0078] S3, LSTM neural network training and prediction: Build an LSTM neural network model with multiple hidden layers. The number of input layer nodes is determined by the dimension of the extracted feature vector, and the number of output layer nodes corresponds to the number of predicted link quality indicators. Training is performed using a stochastic gradient descent algorithm with adaptive learning rate adjustment, and early stopping is used to prevent overfitting. After training, real-time feature data is used to predict key indicators of dual-mode network communication link quality, such as signal strength change trends and bit error rate fluctuation ranges.

[0079] S4. Communication mode selection and optimization: Select an appropriate communication mode based on the LSTM neural network prediction results and the current actual link quality detection data. When it is predicted that the HPLC link will meet the preset communication requirements in the short term, the HPLC communication mode is preferred and the relevant communication parameters are adjusted in advance. When it is predicted that the HPLC link is about to fail and the HRF link quality is acceptable, switch to the HRF communication mode and adjust the transmission power and channel selection;

[0080] S5, Adaptive Rate Adjustment: During data transmission, the system monitors dynamic changes in the network environment in real time, including fluctuations in link quality and changes in network load. It dynamically adjusts the data transmission rate based on the LSTM neural network prediction results combined with the current actual monitoring data. When link quality improves and network load decreases, the data transmission rate is increased; otherwise, it is decreased. A rate adjustment feedback mechanism is established to optimize and modify the rate adjustment strategy based on the actual data transmission effect.

[0081] S6. Data transmission and switching control: During the data transmission process, data is transmitted according to the selected communication mode and data transmission rate, the real-time link quality in the current communication mode is monitored, and the communication mode is automatically switched if the link quality deteriorates and the link quality of another communication mode is available. The data is segmented, numbered, and error-corrected. When switching communication modes or adjusting the rate, relevant information, including but not limited to switching flags and rate adjustment information, is added to the data packet header. The data is reassembled and processed at the receiving end.

[0082] S7. Data fusion and optimization. When dual-mode data transmission is used, the data from HPLC and HRF are fused, error-corrected, and optimized at the receiving end. Data is reorganized based on the identification information and numbering sequence in the packet header. A hybrid error correction scheme based on CRC and FEC is used for error detection and correction. A priority queue algorithm is used for sorting and optimization integration based on the importance level and real-time requirements of the data.

[0083] In this embodiment, in the data collection and feature extraction steps, for the HPLC link, fast Fourier transform is used to calculate the signal frequency components and principal component analysis is used to extract feature vectors. For the HRF link, wavelet transform is used to extract the time-frequency domain features of the wireless signal.

[0084] In this embodiment, in the LSTM neural network training and prediction steps, the number of hidden layers of the constructed LSTM neural network model is 3-5 layers, the number of input layer nodes is 10-20, the number of output layer nodes corresponds to the signal strength and bit error rate of 2, and the mean square error is used as the loss function to evaluate the model prediction performance.

[0085] In this embodiment, in the communication mode selection and optimization step, the HPLC communication parameters adjusted in advance include optimizing the power line carrier frequency allocation and adjusting the encoding method. The adjustment in the HRF communication mode includes dynamically adjusting the transmission power and channel selection according to the wireless signal propagation characteristics.

[0086] In this embodiment, in the adaptive rate adjustment step, the rate adjustment is performed according to a preset rate adjustment strategy, and the strategy is optimized based on actual effects such as the bit error rate, data transmission accuracy and real-time performance fed back by the receiving end. The adjustment methods include adjusting the encoding method and adjusting the transmission time interval.

[0087] In this embodiment, in the data transmission and switching control steps, the length of the data segment processing is a preset fixed length, and the switching identifier includes the switching reason and the number range of the data segments transmitted in the original communication mode.

[0088] The present invention is based on the dual-mode network communication method of HPLC+HRF, and the specific implementation principle is:

[0089] Network initialization: When the dual-mode network communication system is activated, the HPLC and HRF modules are fully and carefully initialized and configured. This includes the precise setting of key communication parameters, such as the HPLC module's power line carrier frequency range (e.g., 2-12MHz), modulation method (e.g., Orthogonal Frequency Division Multiplexing (OFDM), and initial transmit power (e.g., 10dBm), as well as the HRF module's wireless frequency band (e.g., 2.4GHz or 5GHz), modulation method (e.g., QPSK), and initial transmit power (e.g., 15dBm). Simultaneously, a channel scan is initiated to comprehensively detect the power line network and wireless environment, identifying and recording available channel resources and their associated characteristic parameters, laying a solid foundation for subsequent data transmission.

[0090] Data Collection and Feature Extraction: The system continuously collects multi-dimensional data closely related to the quality of the HPLC and HRF links at a specific sampling frequency (e.g., every 100 milliseconds). For the HPLC link, the system collects instantaneous voltage and current values ​​on the power line. The Fast Fourier Transform (FFT) algorithm is used to accurately calculate the signal frequency components. Phase detection circuits are then used to precisely obtain signal phase information. Advanced feature extraction algorithms such as Principal Component Analysis (PCA) are then used to extract feature vectors that are critical to link quality, such as the rate of change of voltage amplitude, current harmonic content, and signal frequency offset. For the HRF link, a Radio Signal Strength Indicator (RSSI) circuit is used to obtain received signal strength in real time. A spectrum analyzer is used to accurately measure signal bandwidth, generate bit error rate statistics, and apply methods such as wavelet transforms to deeply extract time-frequency domain features of the wireless signal, such as feature vectors such as the signal's multipath delay spread and Doppler shift. This rich and accurate feature data provides high-quality input for subsequent LSTM neural network predictions.

[0091] LSTM neural network training and prediction: A carefully constructed LSTM neural network model specifically for predicting dual-mode network communication link quality is constructed. This model consists of multiple hidden layers (e.g., 3-5 layers). The number of input layer nodes is determined by the dimensions of the extracted feature vector (e.g., 10-20 nodes), and the number of output layer nodes corresponds to the number of predicted link quality indicators (e.g., 2 nodes for signal strength and bit error rate). Historically collected and feature-extracted link quality data is scientifically divided into training and test sets in an 80:20 ratio. The LSTM model is deeply trained using the stochastic gradient descent (SGD) algorithm with adaptive learning rate adjustment. During training, the model's prediction performance is rigorously evaluated using loss functions such as the mean squared error (MSE), and early stopping is used to effectively prevent overfitting. After training is completed, the latest link quality feature data collected in real time is input into the trained LSTM model to achieve accurate prediction of the HPLC link and HRF link quality in the future (such as the next 5 seconds, 10 seconds, etc.), including key indicators such as signal strength change trends and bit error rate fluctuation range, providing a forward-looking decision-making basis for subsequent communication mode selection and rate adjustment.

[0092] Communication Mode Selection and Optimization: Based on the LSTM neural network prediction results and combined with current actual link quality measurement data, an intelligent communication mode selection and optimization strategy is developed. When the HPLC link is predicted to maintain good quality and meet preset communication requirements in the short term, the HPLC communication mode is prioritized for data transmission. Communication parameters are adjusted in advance based on this prediction, such as optimizing power line carrier frequency allocation and adjusting the encoding method to improve transmission efficiency. If the prediction indicates that the HPLC link is about to fail or experience a significant quality degradation, while the HRF link quality is expected to remain acceptable within the forecast period, the system proactively switches to the HRF communication mode. Transmit power and channel selection are dynamically adjusted based on wireless signal propagation characteristics to ensure communication continuity and stability. In special scenarios, such as those with extremely high data rate requirements and predictions indicating that both dual-mode links can provide stable service for a certain period of time, data traffic can be rationally allocated to both the HPLC and HRF channels based on the prediction results, achieving coordinated high-speed transmission and fully leveraging the advantages of the dual-mode network. Furthermore, when determining the communication mode, adaptive rate adjustment technology is incorporated to intelligently determine the initial data rate based on the link quality prediction results and current network load. For example, when the link quality is predicted to be good and the network load is low, a higher data transmission rate is selected (such as up to 100Mbps in HPLC mode and up to 50Mbps in HRF mode); when there are certain fluctuations in link quality or the network load is high, the data transmission rate is appropriately reduced to ensure the reliability and stability of data transmission.

[0093] Adaptive Rate Adjustment: During data transmission, the system monitors dynamic changes in the network environment in real time, including real-time fluctuations in link quality (such as changes in signal strength and bit error rate) and changes in network load (such as the number of connected devices and data traffic volume). Leveraging the predictive capabilities of the LSTM neural network, the system anticipates changing trends in link quality and dynamically adjusts the data transmission rate based on current monitoring data. As link quality improves and network load decreases, the system gradually increases the data transmission rate. Conversely, as link quality degrades or network load increases, the system promptly reduces the data transmission rate to maintain data transmission stability and reliability. Furthermore, a rate adjustment feedback mechanism is established to optimize and modify the rate adjustment strategy based on the actual performance of data transmission (such as data transmission accuracy and real-time performance, as well as feedback from the receiving end), ensuring that the adaptive rate adjustment mechanism accurately adapts to changes in the network environment.

[0094] Data transmission and switching control: During the data transmission process, efficient data transmission is carried out strictly in accordance with the selected communication mode and the adaptively determined data transmission rate. On the one hand, the real-time link quality of the current communication mode is closely monitored. If the link quality of the current communication mode suddenly deteriorates to the point where it cannot meet normal communication needs, and based on the LSTM prediction results, the link quality of another communication mode can be restored to a usable state in a short period of time, the system will quickly and smoothly switch to the other communication mode to continue data transmission. Detailed information about the communication mode switch, including the switch time, switch reason, and link quality parameters before and after, is recorded to facilitate subsequent in-depth system performance analysis and optimization. On the other hand, data is scientifically segmented and uniquely numbered before transmission. Combined with error correction coding technology, this ensures that data can be accurately reassembled, verified, and transmitted during the switching between different communication modes and rate adjustments, effectively preventing data loss or errors.

[0095] Data fusion and optimization (receiving end): When dual-mode data is transmitted simultaneously, an intelligent data fusion and optimization processing mechanism is established at the receiving end. First, based on the identification information and numbering sequence in the packet header, the data from HPLC and HRF are accurately reassembled to restore the original data sequence. Next, advanced error correction algorithms, such as a hybrid error correction scheme based on a combination of cyclic redundancy check (CRC) and forward error correction (FEC), are used to perform comprehensive error detection and correction on the reassembled data, effectively eliminating all types of errors introduced during the data transmission process. Finally, based on the data's importance level (e.g., control instruction data has the highest priority, followed by real-time monitoring data, and then general business data) and real-time requirements (e.g., time-sensitive video stream data is prioritized), the data is intelligently sorted and optimized, ensuring that the receiving end can quickly, accurately, and orderly obtain complete and high-quality data information, providing reliable data support for upper-level application systems.

[0096] Example 2

[0097] like Figure 2 As shown, the present invention also proposes a dual-mode network communication system based on HPLC+HRF, comprising:

[0098] HPLC communication module 101, used to implement high-speed power line communication functions, including but not limited to power line signal modulation and demodulation, encoding and decoding, data transmission and reception operations;

[0099] HRF communication module 102, used to implement high-frequency wireless communication functions, with the ability to transmit and receive wireless signals, frequency modulation, and channel coding;

[0100] A data acquisition and feature extraction unit 103 is used to acquire data related to link quality and extract features, including a power line data acquisition and feature extraction module for acquiring data related to the HPLC link and extracting features, and a wireless data acquisition and feature extraction module for acquiring data related to the HRF link and extracting features;

[0101] The LSTM neural network prediction unit 104 is used to predict link quality based on the LSTM model. It includes an LSTM model construction module, a training module, and a prediction calculation module, which are responsible for model construction, model training, and link quality prediction calculation respectively.

[0102] The communication mode selection and optimization unit 105 is used to select and optimize the communication mode based on the prediction results and the actual link quality. It includes a prediction result analysis module, a communication mode judgment module, and a parameter optimization module. The prediction result analysis module is used to analyze the LSTM neural network prediction results. The communication mode judgment module is used to judge the communication mode based on the analysis results and the actual link quality. The parameter optimization module is used to optimize the parameters of the selected communication mode.

[0103] Adaptive rate adjustment unit 106, which is used to monitor dynamic changes in the network environment in real time and adjust the data transmission rate. It includes a rate monitoring module, a rate calculation module, and a feedback optimization module. The rate monitoring module is used to monitor link quality and network load. The rate calculation module calculates the rate based on LSTM prediction results and monitoring data. The feedback optimization module optimizes the rate adjustment strategy based on the actual data transmission effect.

[0104] The data processing and switching control unit 107 is used to segment, number, and error-correct the data, as well as switch communication modes and adjust the rate. The control unit includes a data segmentation module, a numbering module, an error-correction coding module, a cache switching module, and a rate adjustment execution module. The data segmentation module is used to segment the data, the numbering module is used to add numbers to the segmented data, the error-correction coding module is used to add error-correction coding information, the cache switching module is used to cache untransmitted data when switching communication modes and start another communication mode for continued transmission, and the rate adjustment execution module is used to perform rate adjustment operations.

[0105] The data fusion and optimization unit 108 is used to perform fusion processing, error correction and optimization sorting on the dual-mode transmitted data at the receiving end.

[0106] In this embodiment, the rate monitoring module, the rate calculation module and the feedback optimization module are specifically:

[0107] (1) Rate monitoring module, including link quality monitoring submodule and network load monitoring submodule

[0108] The Link Quality Monitoring submodule interacts deeply with the HPLC and HRF communication modules to acquire link quality parameters in real time. For the HPLC link, it continuously monitors indicators such as signal strength, bit error rate, and noise level on the power line. For the HRF link, it closely monitors wireless signal reception strength, signal-to-noise ratio, and signal fluctuations caused by multipath. High-precision sensors and signal detection circuits ensure the accuracy and real-time nature of the collected data. For example, signal strength monitoring is accurate to 0.1dBm resolution, and the statistical error of the bit error rate is kept to a very low level.

[0109] Network load monitoring submodule: This module comprehensively monitors data traffic in the network, including the number of currently connected devices, the frequency of data transmission requests from each device, the size of data packets sent, and the transmission rate. By deploying traffic monitoring software or hardware probes at key network nodes, network traffic data can be captured in real time and analyzed in depth. For example, it can distinguish the proportions and changing trends of different types of data traffic (such as control instruction traffic, real-time monitoring data traffic, and general business data traffic), providing comprehensive network load information for rate calculation and adjustment.

[0110] (2) Rate calculation module, including link quality rate adjustment submodule and network load rate adjustment submodule

[0111] Link quality-based rate adjustment submodule: Based on the link quality parameters obtained by the link quality monitoring submodule, a pre-set rate adjustment algorithm is used to calculate the data transmission rate. When the HPLC link signal strength is high and the bit error rate is low, a basic rate increase ratio is determined based on the comparison between the signal strength and the preset signal strength threshold, and the bit error rate and the bit error rate threshold. For example, if the signal strength is 10dBm higher than the good threshold and the bit error rate is lower than 0.1%, the basic rate can be increased by 50%. For HRF links, a similar calculation method is used based on parameters such as reception strength and signal-to-noise ratio, while considering the propagation characteristics of the wireless signal and channel state information, such as the degree of multipath fading, channel busyness and other factors that affect the rate. For example, when the channel is relatively idle and the reception strength is stable at a high level, the modulation order of the data transmission is appropriately increased to increase the transmission rate.

[0112] Rate adjustment submodule based on network load: Combined with the network load information provided by the network load monitoring submodule, the data transmission rate is further optimized. When the number of connected devices in the network is small and the data traffic is low, the rate is appropriately increased to make full use of network bandwidth resources; conversely, when the network load is high, such as when the number of connected devices exceeds the preset device number threshold and the total data traffic is close to the network bandwidth upper limit, the data transmission rate is proportionally reduced according to the load level. For example, if the network load reaches 80% bandwidth utilization, the rate will be reduced by 30% to avoid network congestion causing data transmission errors or delays. At the same time, this submodule also takes into account the priority of different types of data traffic to ensure that high-priority data (such as control instructions) can still maintain a relatively stable transmission rate when the network load is high, while low-priority data is subject to more rate adjustments.

[0113] (3) Feedback optimization module, including transmission effect evaluation submodule, strategy adjustment submodule,

[0114] Transmission effect evaluation submodule: During the data transmission process, the information fed back by the receiving end is collected in real time, and a multi-dimensional evaluation of the data transmission effect is performed. By analyzing factors such as the change in the bit error rate of the data received by the receiving end, the delay time of data transmission, the integrity of the data, and the processing feedback of the upper-layer application system on the data (such as whether the data triggers the corresponding control operation or business processing in a timely and accurate manner), the effectiveness of the current rate adjustment strategy is comprehensively evaluated. For example, if the bit error rate fed back by the receiving end is continuously higher than the preset acceptable bit error rate upper limit, and the data transmission delay causes abnormal operation of the upper-layer application system, it is determined that there is a problem with the current rate adjustment strategy.

[0115] The policy adjustment submodule optimizes and modifies the rate adjustment policy based on the evaluation results of the transmission performance evaluation submodule. If a particular rate adjustment policy is found to result in poor data transmission performance, different adjustment methods are tried. For example, if a rate increase based on link quality results in an increase in the bit error rate, the system might consider adjusting the encoding scheme, switching from a more efficient but less error-correcting encoding scheme to one with stronger error correction capabilities but slightly lower efficiency, and adjusting the rate calculation parameters in the rate calculation module accordingly. Alternatively, the transmission interval can be adjusted to reduce the bit error rate by extending the data transmission interval, but this will also slightly reduce the transmission rate. Furthermore, the policy adjustment submodule can automatically adjust the rate adjustment policy parameters and thresholds based on long-term trends in the network environment (such as periodic changes in network load throughout the day and differences in link quality stability over different time periods). This allows the entire adaptive rate adjustment mechanism to better adapt to dynamic network changes and continuously improve data transmission performance and reliability.

[0116] In this embodiment, the data segmentation module, the numbering module, the error correction coding module, the cache switching module, and the rate adjustment execution module are specifically:

[0117] (1) Data segmentation module

[0118] Segmentation strategy formulation submodule: formulates a scientific and reasonable data segmentation strategy based on the network communication protocol and the characteristics of data transmission. Taking into account the transmission requirements and processing efficiency of different types of data (such as control instruction data, real-time monitoring data, large-capacity file data, etc.), different segment length ranges are set for each type of data. For example, for control instruction data, since its data volume is usually small and the real-time requirements are extremely high, its segment length is set to a shorter fixed value, such as 64 bytes, to ensure fast transmission and processing; for real-time monitoring data, the segment length is set between 256-512 bytes based on the frequency and data volume of its data generation; and for large-capacity file data, a dynamic segmentation method is adopted. According to the current bandwidth status and transmission load of the network, the segment length is adjusted between 1024-4096 bytes to improve transmission efficiency while ensuring data integrity.

[0119] Segmentation execution submodule: According to the segmentation rules determined by the segmentation strategy formulation submodule, the data to be transmitted is accurately segmented. During the segmentation process, the logical structure and integrity of the data are fully considered to avoid dividing closely related data into different segments. For example, for a data packet containing multiple data fields with dependencies between the fields, by analyzing the structure and protocol specifications of the data packet, it is ensured that the related fields are completely divided into the same segment during segmentation. At the same time, after the segmentation operation is completed, corresponding segment identification information is generated for each segment of data. This identification information contains key information such as the segment number, the data packet identifier, and the data type, so that each segment of data can be accurately identified and processed during the subsequent data transmission, switching, and reorganization process.

[0120] (2) Numbering module

[0121] Numbering rule setting submodule: Develop a comprehensive and unique numbering rule system for adding numbers to segmented data. The numbering rule should cover important dimensions such as the source of the data (i.e., from the HPLC or HRF communication module), transmission sequence information, the communication session identifier, and data version information. For example, a hierarchical numbering structure is adopted, in which the high-order part indicates the source of the data (such as 0 for HPLC, 1 for HRF), the middle part indicates the transmission sequence number, and the numbering is incremented in the order in which the data is generated or enters the transmission queue. The low-order part indicates the unique identifier of the communication session and the data version number, so as to ensure that in a complex network communication environment, data from different sources, different orders, and different versions can be accurately distinguished.

[0122] The Numbering Execution submodule adds corresponding numbering information to each data segment based on the numbering rules established by the Numbering Rule Setting submodule. During the numbering process, the accuracy and completeness of the numbering information are ensured, and the numbering information is tightly bound to the data segment. This ensures that the receiving end can quickly and accurately reassemble and process the data based on the numbering information during data transmission, whether within the same communication mode or when switching between communication modes. Furthermore, the numbering information is verified and encrypted to prevent tampering or corruption during transmission, thereby ensuring the reliability and security of data transmission.

[0123] (3) Error Correction Coding Module

[0124] Coding method selection submodule: Dynamically selects the appropriate error correction coding method based on the reliability requirements of network communication, the current link quality, and the network load. When the link quality is good and the network load is low, a coding method with high coding efficiency but relatively weak error correction capability, such as convolutional coding, is preferred to increase the data transmission rate. When there are certain fluctuations in link quality or the network load is high, the method switches to a coding method with strong error correction capability but slightly lower coding efficiency, such as Turbo coding or low-density parity-check code (LDPC). For example, by monitoring the link's bit error rate and the network's bandwidth utilization in real time, when the bit error rate is less than 0.1% and the bandwidth utilization is less than 50%, convolutional coding is selected; when the bit error rate is higher than 0.5% or the bandwidth utilization is higher than 80%, the method switches to Turbo coding or LDPC coding.

[0125] Coding execution submodule: This module encodes the segmented and numbered data according to the error correction coding method determined by the coding method selection submodule. During the encoding process, the data is encoded according to the algorithm requirements of the selected coding method to generate a coded data block containing error correction information. For example, with Turbo coding, the data is interleaved according to specific interleaving rules and then encoded using two recursive systematic convolutional encoders. The encoded results are then combined and processed to generate the final coded data block. Coding method identification information is also added to the coded data block so that the receiving end can accurately identify the coding method used during decoding, thereby performing correct decoding operations.

[0126] (4) Cache switching module

[0127] Cache management submodule: Build an efficient cache management system to store data that has not been fully transmitted during communication mode switching or rate adjustment. The cache management system adopts a dynamic memory allocation strategy to reasonably allocate cache space based on the type, size, and expected cache time of the data. For example, a smaller but extremely fast-responding cache area is allocated for control instruction data and real-time monitoring data to ensure that these data with high real-time requirements can be quickly read and written in the cache; for large-capacity file data, a larger cache space is allocated, and a hierarchical cache structure is adopted to store data that may be transmitted in the near future in the first-level cache close to the processor, and data that will be transmitted in the distant future is stored in the second or third-level cache to improve cache utilization and data reading and writing efficiency.

[0128] Switching control submodule: When communication mode switching or rate adjustment is required, it is responsible for coordinating data caching and switching operations. Before the communication mode is switched, the switching control submodule first suspends data transmission in the current communication mode, quickly transfers the untransmitted data to the corresponding cache area of ​​the cache management submodule, and records the current transmission location information (such as the range of transmitted data segment numbers, the storage location of the data in the cache, etc.). Then, according to the switched communication mode or the adjusted rate requirements, the data is read from the cache and the new communication mode or rate is started for data transmission. During the switching process, the continuity and integrity of the data are ensured to avoid data loss or repeated transmission. For example, when switching from HPLC communication mode to HRF communication mode, the switching control submodule converts and processes the data in the HPLC cache according to the transmission requirements of the HRF communication module, and then transmits it to the HRF communication module for transmission, while updating the relevant transmission status information and cache management information.

[0129] (5) Rate adjustment execution module

[0130] The rate adjustment command receiving submodule receives rate adjustment commands from the adaptive rate adjustment unit. These commands contain key information such as the target rate value, the reason for the adjustment (such as changes in link quality or network load), and the required time for the adjustment. The rate adjustment command receiving submodule parses and verifies the received commands to ensure their integrity and accuracy. For example, it checks whether the target rate value in the command is within the system's allowable rate range and whether the reason for the adjustment is reasonable and consistent with the current network status. If the command is found to be erroneous or incomplete, it promptly sends an error message to the adaptive rate adjustment unit and requests that the command be resent.

[0131] The rate adjustment execution submodule actually adjusts the data transmission rate based on the command information parsed by the rate adjustment command receiving submodule. During rate adjustment, it collaborates with related modules such as the communication mode selection and optimization unit and the data processing and switching control unit. For example, when the rate needs to be reduced, the rate adjustment execution submodule may adjust the data transmission interval, lower the modulation order, or reduce the number of parallel transmission data channels. When the rate needs to be increased, the opposite action is taken, such as shortening the transmission interval, increasing the modulation order, or increasing the number of parallel transmission channels. Furthermore, during the rate adjustment process, the data transmission status and performance indicators (such as bit error rate and transmission delay) are monitored in real time to ensure that the rate adjustment operation does not negatively impact the stability and reliability of data transmission. If any data transmission anomalies are detected during the adjustment process, the rate adjustment operation is promptly suspended and the anomaly is reported to the adaptive rate adjustment unit for further analysis and resolution.

[0132] In this embodiment, the data fusion and optimization unit includes a data fusion processing module, an error correction module, and an optimization sorting module, wherein:

[0133] (1) Data fusion processing module

[0134] Data synchronization and alignment submodule: Since dual-mode transmitted data may differ in transmission time and transmission order, the data synchronization and alignment submodule first performs time synchronization processing on the data from HPLC and HRF. Using the timestamp information carried in the packet header, data arriving at the receiving end at different times are aligned according to their original transmission order. For example, if it is found that the timestamp of a certain data segment from HPLC is earlier than that of the adjacent data segment from HRF, but it arrives at the receiving end later due to transmission delays, it will be adjusted to the correct sequential position to ensure the consistency of the data sequence on the timeline. At the same time, based on the numbering information added to each data segment by the data segmentation module at the sending end, the data is further numbered and aligned to ensure the logical coherence and integrity of the data, laying the foundation for subsequent fusion processing.

[0135] Data merging and deduplication submodule: After data synchronization and alignment, the data merging and deduplication submodule merges the data from the two modes. For repeatedly transmitted data segments (which may be due to redundant transmission or network jitter during the communication mode switching process, resulting in repeated reception of some data), duplicate data is identified and removed by comparing the content, number, and related feature information of the data segments. For example, a hash algorithm is used to perform a fast hash calculation on the data segment, and the calculated hash value is used as the unique identifier of the data segment. The hash value is compared to determine whether the data segment is duplicated. After removing duplicate data, the data from different modes are merged according to the data packets or data sequences to which they originally belonged to form a complete, non-duplicate data set, ready for error correction processing.

[0136] (2) Error Correction Module

[0137] Error detection submodule: Use a variety of error detection technologies to perform comprehensive detection on the fused data. First, based on the cyclic redundancy check (CRC) mechanism, the data integrity is checked according to the CRC check code added by the data sender. The CRC value of the received data is calculated and compared with the CRC check code sent by the sender. If the two do not match, it indicates that an error may have occurred in the data transmission process. At the same time, combined with other error detection methods, such as parity check, checksum and other methods, multi-dimensional error detection is performed on the data. For example, for some control instruction data with extremely high data accuracy requirements, in addition to CRC check, multiple parity checks are also used to detect whether there are error bits in the data from different angles, thereby improving the accuracy and reliability of error detection.

[0138] Error Correction Execution Submodule: When the Error Detection Submodule detects data errors, the Error Correction Execution Submodule performs appropriate error correction operations based on the error correction coding scheme used by the data transmitter. If the transmitter uses forward error correction (FEC) coding, such as Turbo coding or low-density parity-check (LDPC) coding, the erroneous data is corrected using the code's redundant information and an error correction algorithm. For example, for LDPC-coded data, an iterative decoding algorithm uses the parity check matrix and the received coded data to gradually repair erroneous bits and recover the original data. Depending on the severity of the error and the performance of the error correction algorithm, multiple iterations may be required to ensure complete error correction. If uncorrectable errors persist after the error correction algorithm, the erroneous data is marked and the relevant error information is recorded. An error notification is also sent to the upper-layer application system for further action, such as requesting data retransmission.

[0139] (3) Optimize sorting module

[0140] Data importance assessment submodule: Evaluate and classify the importance of data. Determine the importance level of data based on factors such as the type and source of the data and the degree of impact on the upper-level application system. For example, control instruction data (such as device switching instructions in smart home systems, power dispatch instructions in smart grids, etc.) is set to the highest importance level because this data directly affects the operating status and control functions of the system; real-time monitoring data (such as environmental sensor data, equipment operating status data, etc.) is set to a higher importance level. These data have important reference value for the real-time monitoring and decision-making of the system; and ordinary business data (such as user entertainment video data, file transfer data, etc.) is set to a lower importance level. By assigning different importance weights to different types of data, a basis is provided for subsequent optimization sorting.

[0141] The sorting and execution submodule optimizes the sorting of error-corrected data based on the importance levels and weights determined by the data importance assessment submodule. Using data structures such as priority queues, data with higher importance levels is prioritized at the front of the queue, ensuring it is processed first by higher-level applications. For example, at the receiving end of a smart home system, when both device control command data and general entertainment data are received simultaneously, the control command data is prioritized in the processing queue for immediate execution, while the entertainment data is placed at the back of the queue, awaiting processing during the system's idle time. Furthermore, the sorting process considers the real-time requirements of the data. For data with higher real-time requirements (such as keyframes in real-time video streams), even if its importance level is relatively low, its position in the queue is appropriately advanced to ensure timely processing and meet the real-time requirements of higher-level applications. This improves the data processing efficiency and quality at the receiving end of the entire dual-mode network communication system, providing more reliable and efficient data services to higher-level applications.

[0142] The specific implementation principle of the dual-mode network communication system based on HPLC+HRF of the present invention is:

[0143] HPLC communication module: Assume the core functions of high-speed power line communication, including efficient modulation and demodulation of power line signals, precise encoding and decoding, stable data transmission and reception, etc. Through close connection with the power line network, it realizes reliable transmission and interaction of data on the power line medium.

[0144] HRF communication module: Responsible for the implementation of high-frequency wireless communication functions, it has the capabilities of powerful transmission and high-sensitivity reception of wireless signals, flexible frequency modulation, and efficient channel coding. It uses wireless antennas and peripheral devices to build wireless data communication links to ensure smooth data transmission in wireless channels.

[0145] Data acquisition and feature extraction unit: Deeply connected with the HPLC communication module and the HRF communication module, it collects various raw data reflecting the link quality in real time, and uses advanced data processing algorithms and feature engineering techniques to extract key feature vectors closely related to changes in link quality, providing a rich and accurate data foundation for subsequent LSTM neural network predictions.

[0146] LSTM neural network prediction unit: Based on the constructed LSTM neural network model, it receives real-time feature data from the data acquisition and feature extraction unit, uses the trained model to perform link quality prediction calculations, and outputs the predicted results of the dual-mode network communication link quality in the future, including the changing trends of key indicators such as signal strength and bit error rate. This provides a forward-looking decision-making basis for the communication mode selection and optimization unit and the adaptive rate adjustment unit.

[0147] Communication mode selection and optimization unit: This unit comprehensively considers the prediction results provided by the LSTM neural network prediction unit and the current actual link quality information fed back by the link quality detection unit. Based on the preset intelligent communication mode selection strategy, it determines the most suitable communication mode for data transmission and dynamically optimizes and adjusts the communication parameters under the selected mode. At the same time, it controls the precise transmission path of data on the corresponding communication module to achieve intelligent switching and optimized configuration of communication modes.

[0148] Adaptive Rate Adjustment Unit: This unit monitors dynamic changes in the network environment in real time, including factors such as link quality and network load. Based on the predictions of the LSTM neural network and current monitoring data, it dynamically calculates and adjusts the data transmission rate. Furthermore, a rate adjustment feedback mechanism is established to optimize and modify the rate adjustment strategy based on the actual data transmission results, ensuring the effectiveness and accuracy of the adaptive rate adjustment mechanism.

[0149] Data Processing and Switching Control Unit: During data transmission, this unit is responsible for scientifically segmenting and uniquely numbering data, as well as fine-tuning control operations during communication mode switching and rate adjustments. Through carefully designed data segmentation strategies and numbering rules, combined with efficient cache management mechanisms, this unit ensures data integrity, accuracy, and continuity during communication mode switching and rate adjustments, effectively preventing data interruptions and confusion.

[0150] Data Fusion and Optimization Unit (Receiver): An intelligent data fusion and optimization processing engine is built on the receiver side to accurately restructure data from dual-mode transmissions, perform in-depth error detection and correction, and intelligently sort and optimize data. By integrating multiple advanced data processing technologies and algorithms, the receiver side can quickly and accurately obtain complete and high-quality data information, providing solid data support for the stable operation of upper-layer application systems.

[0151] In summary, the present invention has the following technical effects:

[0152] (1) Improve communication reliability:

[0153] By accurately predicting the quality of dual-mode network communication links using an LSTM neural network, the system can proactively identify changing trends in link quality and take proactive measures before link quality deteriorates, such as switching communication modes or adjusting data transmission rates. This effectively avoids data transmission interruptions or errors caused by sudden link failures. For example, in a smart factory environment, this reliability guarantee for the transmission of control commands for production equipment can prevent production accidents caused by communication failures and ensure the continuity and stability of the production process.

[0154] Adaptive rate adjustment technology dynamically adjusts the data transmission rate based on link quality and network load. When the link quality is poor or the network is congested, the rate is reduced to ensure data transmission accuracy. When the link quality is good and the network is idle, the rate is increased to improve transmission efficiency, thereby maintaining reliable data transmission in various complex network environments.

[0155] The error detection and correction mechanism in data fusion processing can identify and repair erroneous data generated during dual-mode transmission. Whether it is HPLC data errors caused by power line interference or HRF data errors caused by wireless signal multipath effects, they can be effectively handled, further enhancing the reliability of data transmission and ensuring the integrity and accuracy of data obtained by the receiving end.

[0156] (2) Enhance network adaptability:

[0157] Combining HPLC and HRF dual-mode communications, it fully leverages the advantages of wide coverage of power line networks and flexible deployment of wireless networks, and can adapt to communication needs in different scenarios, such as Figure 4 As shown in Figure 1, it is the node topology diagram of HPLC and HRF dual-mode communication. Figure 3 As shown, this is a schematic diagram of the application of HPLC and HRF dual-mode communication in the present invention for electric energy data communication. In the figure, except for the water / heat / gas meter and the intelligent leakage protection switch which are HRF single-mode communication, the rest are HPLC+HRF dual-mode communication.

[0158] LSTM neural network prediction combined with adaptive rate adjustment enables the system to automatically optimize communication parameters based on network environment changes at different times and in different areas. For example, during peak office network usage, the system automatically reduces data transmission rates based on increased network load and allocates dual-mode traffic appropriately. At night, when the network is idle, the system increases the rate to expedite tasks like data backups and system updates. This significantly improves the network's adaptability to diverse usage scenarios and load fluctuations.

[0159] The data acquisition and feature extraction unit collects and analyzes multi-dimensional data, and can perceive subtle changes in the network environment in real time, including changes in power line noise and the dynamics of wireless signal interference sources. This provides rich information basis for the system to adjust communication strategies in a timely manner, enabling the network to quickly adapt to complex and changing actual application environments.

[0160] (3) Optimize data transmission efficiency:

[0161] The communication mode selection and optimization strategy based on the prediction results of the LSTM neural network can select the optimal communication mode or enable dual-mode collaborative transmission at the appropriate time. For example, when the HPLC link quality is predicted to be good for a period of time and large data files are transmitted, the HPLC mode is prioritized and its parameters are optimized to fully utilize the high bandwidth characteristics of the power line network and increase the data transmission rate. When mobility is required or the wireless signal is good in a local area, the HRF mode is switched to avoid the performance bottlenecks of a single mode in specific scenarios and maximize data transmission efficiency.

[0162] Adaptive rate adjustment technology ensures data transmission reliability while flexibly adjusting the rate based on link quality and network load, avoiding resource waste or transmission delays caused by fixed rate settings. For example, when transmitting high-definition video streams, dynamically adjusting the rate based on network conditions ensures smooth video playback, preventing video freezes caused by network congestion due to excessively high rates, and preventing severe video quality degradation due to excessively low rates. This effectively improves user experience and actual data transmission efficiency.

[0163] (4) Intelligent decision-making and management:

[0164] The introduction of the LSTM neural network enables the system to intelligently predict changes in link quality. Decisions for operations such as communication mode selection and optimization, and adaptive rate adjustment are based on these intelligent predictions, reducing the limitations of human intervention and traditional empirical decision-making. For example, in various IoT application scenarios in smart cities, the system can automatically make optimal communication decisions based on network conditions in different regions and time periods, eliminating the need for manual configuration and adjustment, significantly improving the intelligence and efficiency of network management.

[0165] The data fusion and optimization unit intelligently processes data at the receiving end, including error correction and prioritization, and also dynamically manages data based on its importance and real-time nature. For example, urgent security alert data can bypass the regular data queue and trigger the corresponding security response mechanism. This demonstrates the system's intelligent priority management capabilities in data processing, enhancing the overall network communication system's emergency response capabilities and overall intelligence.

[0166] The entire dual-mode network communication system achieves intelligent monitoring and management of the entire network communication process through collaborative work and data exchange between modules. From data collection and feature extraction to link quality prediction, communication mode selection, data transmission control, and data processing at the receiving end, each link is interconnected and optimized based on intelligent algorithms, forming a closed-loop intelligent communication management system. This effectively improves the stability, reliability, and overall performance of network communications, meeting the high demands of modern IoT applications for intelligent network communications.

[0167] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A dual-mode network communication method based on HPLC+HRF, characterized in that, The following steps are involved: Network initialization: initializing and configuring the HPLC module and HRF module respectively, and performing channel scanning to determine available channel resources; Data collection and feature extraction: continuously collect multi-dimensional data related to the quality of HPLC and HRF links at a specific sampling frequency, and perform data feature extraction; LSTM neural network training and prediction: Build and train an LSTM neural network model with multiple hidden layers. After training, use real-time feature data to predict the quality indicators of the dual-mode network communication link. Communication mode selection and optimization: switching communication modes based on LSTM neural network prediction results and current actual link quality detection data; Adaptive rate adjustment: monitors the dynamic changes of the network environment in real time during data transmission, dynamically adjusts the data transmission rate based on the LSTM neural network prediction results and the current actual monitoring data, establishes a rate adjustment feedback mechanism, and optimizes and corrects the rate adjustment strategy based on the actual data transmission effect; Data transmission and switching control: during the data transmission process, data is transmitted according to the selected communication mode and data transmission rate, and the real-time link quality in the current communication mode is monitored. If the link quality deteriorates and the link quality of another communication mode is available, the communication mode is automatically switched; Data fusion and optimization: When dual-mode data transmission is used, the data from HPLC and HRF are fused, error corrected, and optimized at the receiving end. Data is reorganized according to the identification information and number sequence in the data packet header. A hybrid error correction scheme based on CRC and FEC is used for error detection and correction. A priority queue algorithm is used for sorting and optimization integration.

2. The dual-mode network communication method based on HPLC+HRF according to claim 1, wherein In the data collection and feature extraction steps, for the HPLC link, fast Fourier transform is used to calculate the signal frequency components and principal component analysis is used to extract the feature vectors. For the HRF link, wavelet transform is used to extract the time-frequency domain features of the wireless signal.

3. The dual-mode network communication method based on HPLC+HRF according to claim 1, wherein In the LSTM neural network training and prediction steps, the number of hidden layers of the constructed LSTM neural network model is 3-5 layers, the number of input layer nodes is 10-20, the number of output layer nodes corresponds to the signal strength and bit error rate of 2, and the mean square error is used as the loss function to evaluate the model prediction performance.

4. The dual-mode network communication method based on HPLC+HRF according to claim 1, wherein In the communication mode selection and optimization step, the HPLC communication parameters adjusted in advance include optimizing the power line carrier frequency allocation and adjusting the encoding method. The adjustment in the HRF communication mode includes dynamically adjusting the transmission power and channel selection according to the wireless signal propagation characteristics.

5. The dual-mode network communication method based on HPLC+HRF according to claim 1, wherein In the adaptive rate adjustment step, the rate adjustment is performed according to a preset rate adjustment strategy, and the strategy is optimized. The adjustment methods include adjusting the encoding method and adjusting the transmission time interval.

6. The dual-mode network communication method based on HPLC+HRF according to claim 1, wherein In the data transmission and switching control steps, the length of the data segment processing is a preset fixed length, and the switching identifier includes the switching reason and the number range of the data segments transmitted in the original communication mode.

7. The dual-mode network communication system based on HPLC+HRF is characterized by: include: HPLC communication module, used to implement high-speed power line communication functions, including but not limited to power line signal modulation and demodulation, encoding and decoding, data transmission and reception operations; HRF communication module, used to realize high-frequency wireless communication functions, with the ability to transmit and receive wireless signals, frequency modulation, and channel coding; A data acquisition and feature extraction unit, for acquiring data related to link quality and extracting features, including a power line data acquisition and feature extraction module for acquiring data related to HPLC links and extracting features, and a wireless data acquisition and feature extraction module for acquiring data related to HRF links and extracting features; The LSTM neural network prediction unit is used to predict link quality based on the LSTM model. It includes an LSTM model construction module, a training module, and a prediction calculation module, which are responsible for model construction, model training, and link quality prediction calculation, respectively. A communication mode selection and optimization unit is used to select and optimize the communication mode based on the prediction results and actual link quality, including a prediction result analysis module, a communication mode judgment module, and a parameter optimization module. The prediction result analysis module is used to analyze the LSTM neural network prediction results, the communication mode judgment module is used to judge the communication mode based on the analysis results and the actual link quality, and the parameter optimization module is used to optimize the parameters of the selected communication mode; The adaptive rate adjustment unit is used to monitor dynamic changes in the network environment in real time and adjust the data transmission rate. It includes a rate monitoring module, a rate calculation module, and a feedback optimization module. The rate monitoring module is used to monitor link quality and network load. The rate calculation module calculates the rate based on LSTM prediction results and monitoring data. The feedback optimization module optimizes the rate adjustment strategy based on the actual data transmission effect. A data processing and switching control unit, configured to segment, number, and error-correct data, and to switch communication modes and adjust rates, comprises a data segmentation module, a numbering module, an error-correction coding module, a cache switching module, and a rate adjustment execution module; the data segmentation module is configured to segment data, the numbering module is configured to add numbers to segmented data, the error-correction coding module is configured to add error-correction coding information, the cache switching module is configured to cache untransmitted data when switching communication modes and to initiate another communication mode for continued transmission, and the rate adjustment execution module is configured to perform rate adjustment operations; The data fusion and optimization unit is used to perform fusion processing, error correction and optimized sorting of dual-mode transmitted data at the receiving end.

8. The dual-mode network communication system based on HPLC+HRF according to claim 7, characterized in that, The rate monitoring module, rate calculation module and feedback optimization module are specifically: (1) Rate monitoring module, including link quality monitoring submodule and network load monitoring submodule The link quality monitoring submodule is used to obtain link quality related parameters in real time through interaction with the HPLC communication module and the HRF communication module; The network load monitoring submodule is used to monitor the data traffic in the network, including but not limited to the number of currently connected devices, the frequency of data transmission requests from each device, the size of data packets transmitted, and the transmission rate; (2) The rate calculation module includes the link quality rate adjustment submodule and the network load rate adjustment submodule The link quality rate adjustment submodule calculates the data transmission rate using a pre-set rate adjustment algorithm based on the link quality parameters obtained by the link quality monitoring submodule; The network load rate adjustment submodule optimizes the data transmission rate by combining the network load information provided by the network load monitoring submodule; (3) Feedback optimization module, including transmission effect evaluation submodule and strategy adjustment submodule The transmission effect evaluation submodule is used to collect the information fed back by the receiving end in real time during the data transmission process and to perform a multi-dimensional evaluation of the data transmission effect; The policy adjustment submodule is used to optimize and modify the rate adjustment policy according to the evaluation results of the transmission effect evaluation submodule.

9. The dual-mode network communication system based on HPLC+HRF according to claim 7, characterized in that, The data segmentation module, number adding module, error correction coding module, cache switching module and rate adjustment execution module include: (1) Data segmentation module, including segmentation strategy formulation submodule and segmentation execution submodule Segmentation strategy formulation submodule, used to formulate data segmentation strategy based on network communication protocol and data transmission characteristics; The segmentation execution submodule is used to segment the data to be transmitted according to the segmentation rules determined by the segmentation strategy formulation submodule; (2) Numbering addition module, including numbering rule setting submodule and numbering addition execution submodule The numbering rule setting submodule is used to develop a numbering rule system to add numbers to the segmented data. The numbering rule should cover the important dimensions of the data source, transmission sequence information, communication session identifier, and data version information; The numbering adding execution submodule adds corresponding numbering information to each piece of data according to the numbering rules set by the numbering rule setting submodule; (3) Error correction coding module, including coding mode selection submodule and coding execution submodule The coding mode selection submodule is used to dynamically select the appropriate error correction coding mode based on the current link quality and network load; The encoding execution submodule is used to perform encoding operations on the segmented and numbered data according to the error correction encoding method determined by the encoding method selection submodule; (4) Cache switching module, including cache management submodule and switching control submodule The cache management submodule is used to store data that has not been fully transmitted during the communication mode switching or rate adjustment process; The switching control submodule coordinates data caching and switching operations when communication mode switching or rate adjustment is required; (5) Rate adjustment execution module, including rate adjustment instruction receiving submodule and rate adjustment operation execution submodule The rate adjustment instruction receiving submodule is used to receive the rate adjustment instruction from the adaptive rate adjustment unit, the instruction including the target rate value, the adjustment reason and the adjustment time requirement; The rate adjustment operation execution submodule is used to adjust the data transmission rate according to the instruction information parsed by the rate adjustment instruction receiving submodule.

10. The dual-mode network communication system based on HPLC+HRF according to claim 7, characterized in that: The data fusion and optimization unit includes a data fusion processing module, an error correction module, and an optimization sorting module, wherein: (1) Data fusion processing module, including data synchronization and alignment submodule, data merging and deduplication submodule The data synchronization and alignment submodule uses the timestamp information carried in the data packet header to align data arriving at the receiving end at different times according to their original sending order; Data merging and deduplication submodule, after data synchronization and alignment, the data merging and deduplication submodule merges the data from the two modes; (2) Error correction module, including error detection submodule and error correction execution submodule The error detection submodule is used to perform data integrity check based on the cyclic redundancy check mechanism and the CRC check code added by the data sender. It calculates the CRC value of the received data and compares it with the CRC check code sent by the sender. If the two do not match, it indicates that an error may have occurred during the data transmission process. The error correction execution submodule is used to perform corresponding error correction operations according to the error correction coding method adopted by the data sending end when the error detection submodule detects that there is an error in the data; (3) Optimize the sorting module, including the data evaluation submodule and the sorting execution submodule The data importance assessment submodule determines the importance level of data based on factors including but not limited to the type and source of the data and the degree of impact on the upper-layer application system; The sorting execution submodule is used to optimize the sorting of the data after error correction according to the importance level and weight determined by the data importance evaluation submodule.

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