A dual-mode communication method for low-power communication and power line carrier communication in HPLC

Abstract

This invention provides a method for low-power communication and power line carrier dual-mode communication in HPLC, belonging to the field of multiplexed communication technology. This method selects the communication line segment to be tested in a low-voltage power line carrier communication line, and determines the data verification section and data substitution section based on the carrier signal quality coefficient. Within the data verification section, low-power wireless communication is used to transmit redundant verification data to verify the carrier communication signal transmission data. Within the data substitution section, a dynamic programming method is used to determine the data transmission allocation scheme based on the signal attenuation of the carrier communication signal, dividing the carrier communication signal transmission data into a first data packet transmitted via the carrier communication signal and a second data packet transmitted via low-power wireless communication. The first and second data packets are segmented, numbered, and reassembled. The data transmission allocation scheme is adjusted based on the data transmission accuracy after reassembly, solving the problem of unreliable communication when the carrier communication signal attenuates severely.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of multiplex communication, and particularly relates to a micro-power communication and power line carrier dual-mode communication method of HPLC. BACKGROUND

[0002] Low-voltage power line carrier communication technology is widely used in smart grid, smart home and other fields. Traditional low-voltage power line carrier communication mainly relies on a single carrier signal transmission mode, which realizes data transmission by modulating a high-frequency carrier signal on a power line. This communication mode is relatively simple to implement and can achieve reliable data transmission in an ideal communication environment. The current mainstream carrier communication technology adopts orthogonal frequency division multiplexing modulation mode, which transmits data by distributing it to multiple orthogonal subcarriers to improve spectral efficiency.

[0003] However, in actual application, due to the complexity of the power line network, the carrier signal will be affected by various factors and attenuated during transmission. These influencing factors include line impedance variation, multipath effect, interference generated by various electrical equipment, etc. Especially when the power load changes dramatically or the line fails, the carrier signal may experience severe attenuation, leading to communication interruption or a sharp increase in data transmission error rate. The existing technology mainly deals with signal attenuation problems by increasing transmission power, using channel coding, etc., but these methods often increase system complexity and power consumption, and have limited effect when the signal is severely attenuated.

[0004] Current carrier communication technology cannot effectively solve the communication reliability problem caused by signal attenuation. Increasing the transmission power will increase energy consumption and electromagnetic interference, and in some application scenarios, it is limited by power; using complex channel coding technology can improve the anti-interference ability, but the increased encoding and decoding delay will affect the real-time requirement of high real-time applications; using signal relay technology can extend the communication distance, but will significantly increase the system cost and complexity. Therefore, a new technical solution is needed to solve the communication reliability problem when the carrier signal is attenuated. SUMMARY

[0005] Therefore, the application provides a micro-power communication and power line carrier dual-mode communication method of HPLC, which can solve the technical problem that the existing technology cannot guarantee communication reliability when the carrier communication signal is severely attenuated.

[0006] The application is implemented as follows: the application provides a micro-power communication and power line carrier dual-mode communication method of HPLC, comprising the following steps: selecting a communication line section to be detected in a low-voltage power line carrier communication line, collecting a carrier communication signal and performing signal quality analysis to obtain a carrier signal quality coefficient; determining a data check section and a data replacement section according to the carrier signal quality coefficient; transmitting redundant check data in the micro-power wireless communication in the data check section to check the carrier communication signal transmission data; in the data replacement section, determining a data transmission distribution scheme according to the signal attenuation degree of the carrier communication signal by using a dynamic programming method, and dividing the carrier communication signal transmission data into a first data packet transmitted through the carrier communication signal and a second data packet transmitted through the micro-power wireless communication; performing data segmentation numbering and reorganization on the first data packet and the second data packet; and adjusting the data transmission distribution scheme according to the data transmission accuracy after data reorganization.

[0007] In the step of collecting the carrier communication signal and performing signal quality analysis, the carrier communication signal is sampled by using a digital signal processing technology, the sampling frequency is set to 4 times the carrier signal frequency, the collected carrier communication signal is subjected to band-pass filtering processing and is converted to the frequency domain for analysis by Fourier transform, and the carrier signal quality coefficient is calculated by using a multi-index comprehensive evaluation method.

[0008] In the step of determining the data check section and the data replacement section according to the carrier signal quality coefficient, the spatial distribution characteristics of the carrier signal quality coefficient are calculated by using a K-means clustering algorithm, the communication line is divided into a signal attenuation section according to the threshold value of the carrier signal quality coefficient, and the data check section and the data replacement section are divided according to the signal attenuation degree of the signal attenuation section.

[0009] In the step of transmitting redundant check data in the micro-power wireless communication in the data check section, the carrier communication signal transmission data is encoded to generate the redundant check data by using a Reed-Solomon encoding method, and the redundant check data is transmitted by using a time division multiplexing mode.

[0010] In the step of determining the data transmission distribution scheme in the data replacement section, the signal attenuation degree of the carrier communication signal is calculated by using a channel attenuation model, a data transmission cost function containing transmission reliability and transmission delay is established, the carrier communication signal transmission data is divided into blocks by using a dynamic segmentation algorithm, and an optimal transmission scheme is solved by using a dynamic programming method.

[0011] Specifically, the step of segmenting and numbering the first data packet and the second data packet involves: generating data segment numbers using a globally unique identifier generation method; and establishing a data segment association table using a distributed hash table structure.

[0012] The method also includes a data transmission error rate calculation step: comparing the redundant check data with the carrier communication signal transmission data, and calculating the data transmission error rate using a bit error rate calculation method; and determining the data retransmission time based on the data transmission error rate.

[0013] The steps for adjusting the data transmission allocation scheme are as follows: calculating the data transmission accuracy using a hierarchical evaluation method; establishing a control model of the data transmission accuracy and allocation parameters using feedback control theory; and optimizing the data transmission allocation scheme based on the control model.

[0014] The process also includes a segment re-division step: collecting the carrier signal quality coefficients using a sliding window method; extracting feature vectors using principal component analysis; and determining segment boundaries using an edge detection algorithm.

[0015] The scheme also includes the following steps: monitoring the carrier signal quality coefficient using a real-time data stream processing framework; updating the data transmission allocation scheme using an incremental learning method; and dynamically adjusting the update cycle according to the system status.

[0016] Compared with existing technologies, this invention provides a dual-mode communication method for HPLC using low-power communication and power line carrier. This proposed method establishes an adaptive dual-mode communication mechanism by using low-power wireless communication as a supplementary means to carrier communication. The method dynamically divides communication segments based on the carrier signal quality coefficient and employs different communication strategies in different segments, thus achieving optimized allocation of communication resources.

[0017] In its implementation, this invention employs a multi-index comprehensive evaluation method to monitor carrier signal quality in real time and optimizes the data transmission allocation scheme through a dynamic programming algorithm. When carrier signal attenuation occurs, the system can automatically adjust the participation level of low-power communication, improving transmission reliability through data verification and ensuring communication continuity through partial data substitution. This adaptive adjustment mechanism based on signal quality avoids the drawbacks of blindly increasing transmission power or redundant coding in traditional technologies.

[0018] This invention solves the technical problem in existing technologies where communication reliability cannot be guaranteed when carrier communication signals suffer severe attenuation. The main principles are as follows: First, by monitoring and evaluating carrier signal quality in real time, signal attenuation can be detected promptly. Second, low-power communication is used as a supplementary channel, providing redundancy for data transmission. Finally, a dynamic optimization algorithm achieves efficient allocation of communication resources, ensuring stable system operation under various conditions. This solution not only improves communication reliability but also optimizes energy efficiency. Attached Figure Description

[0019] Figure 1 This is a flowchart of the method of the present invention.

[0020] Figure 2 This is a diagram showing the change in line impedance in Example 2.

[0021] Figure 3 This is a distribution diagram of signal quality parameters along the line distance in Example 2.

[0022] Figure 4 This is a logarithmic coordinate graph showing the change trend of data transmission performance indicators with distance in Example 2. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0024] like Figure 1 The diagram shown is a flowchart of a dual-mode communication method for low-power communication and power line carrier communication in HPLC provided by this invention. This method includes the following steps:

[0025] S01. Select a communication line segment to be tested in a low-voltage power line carrier communication line, and arrange a low-power wireless communication node and a low-voltage power line carrier communication node in the communication line segment to be tested.

[0026] S02. Collect the carrier communication signal of the low-voltage power line carrier communication node, perform signal quality analysis on the carrier communication signal, and obtain the carrier signal quality coefficient;

[0027] S03. Calculate the carrier signal attenuation section based on the carrier signal quality coefficient, and divide the carrier signal attenuation section into a data verification section and a data substitution section.

[0028] S04. Within the data verification section, the low-power wireless communication node is used to transmit redundant verification data, and the redundant verification data is synchronized with the carrier communication signal transmission data.

[0029] S05. Compare the redundant verification data with the carrier communication signal transmission data, and calculate the data transmission error rate;

[0030] S06. Determine the data retransmission time based on the data transmission error rate, and retransmit the carrier communication signal to transmit data at the data retransmission time.

[0031] S07. Within the data replacement section, calculate the signal attenuation of the carrier communication signal, establish a data transmission cost function based on the signal attenuation, and divide the carrier communication signal transmission data into blocks and number them.

[0032] S08. The data transmission allocation scheme is solved by dynamic programming. The data transmitted by the carrier communication signal is divided into a first data packet and a second data packet. The first data packet is transmitted through the carrier communication signal, and the second data packet is transmitted through the low-power wireless communication node.

[0033] S09. Number the data segments of the first data packet and the second data packet, and establish a data segment association table;

[0034] S10. Reassemble the received first data packet and the second data packet according to the data segmentation association table;

[0035] S11. Calculate the data transmission accuracy after data recombination, and adjust the data transmission allocation scheme according to the data transmission accuracy.

[0036] S12. The data verification section and the data substitution section are re-divided according to the carrier signal quality coefficient;

[0037] S13. Continuously monitor the carrier signal quality coefficient of the carrier communication signal, and update the data transmission allocation scheme according to the carrier signal quality coefficient.

[0038] The specific implementation methods of the above steps are described in detail below. The specific implementation method of step S01 is as follows: In practical applications, it is first necessary to determine the physical range of the communication line segment to be tested. By analyzing the topology of the low-voltage power line carrier communication line, a representative communication line segment is selected. This communication line segment should include multiple branch nodes and load access points. When arranging low-power wireless communication nodes, a signal coverage analysis method is used to determine the optimal arrangement position, so that the low-power wireless communication signal can effectively cover the entire communication line segment to be tested. Simultaneously, the distance between communication nodes should be controlled within the range of 50 meters to 200 meters to ensure the reliability of low-power wireless communication. The arrangement of low-voltage power line carrier communication nodes needs to consider the impedance characteristics and signal transmission attenuation characteristics of the power line. By measuring the characteristic impedance and signal transmission attenuation curve of the power line, a suitable installation position is selected. Specifically, a network topology optimization algorithm is used for the arrangement. This algorithm is based on the minimum spanning tree principle in graph theory. By calculating the connection weights between communication nodes, the optimal node arrangement scheme is obtained.

[0039] The specific implementation of step S02 is as follows: Digital signal processing technology is used to sample and analyze the carrier communication signal. The sampling frequency is set to four times the carrier signal frequency to meet the requirements of the Nyquist sampling theorem. The acquired carrier communication signal is first processed by bandpass filtering to remove power frequency interference and high-frequency noise. Then, the signal is converted to the frequency domain for analysis using Fourier transform. In the frequency domain analysis, the power spectral density and signal-to-noise ratio of the signal are calculated, and the amplitude and phase characteristics of the signal are extracted. The carrier signal quality coefficient is calculated using a multi-index comprehensive evaluation method. This method weights and combines multiple indicators such as signal-to-noise ratio, signal distortion, and signal stability. The weighting coefficients are determined using grey relational analysis, ultimately yielding a comprehensive evaluation index reflecting the quality of the carrier communication signal.

[0040] The specific implementation of step S03 is as follows: When dividing the communication line into segments based on the carrier signal quality coefficient, a clustering analysis method is used. This method is based on an improved version of the K-means clustering algorithm. By calculating the spatial distribution characteristics of the carrier signal quality coefficient, the communication line is automatically divided into different segments. First, the carrier signal quality coefficients are sorted according to their spatial location to form a feature vector. Then, the initial position of the cluster center is determined by calculating the local variance of the feature vector. During the clustering process, a threshold of 0.75 is set for the carrier signal quality coefficient. When the signal quality coefficient of a certain segment is lower than this threshold, it is classified as a signal attenuation segment. For the signal attenuation segment, it is further subdivided into a data verification segment and a data substitution segment according to the degree of signal attenuation. The segment with a signal quality coefficient between 0.5 and 0.75 is classified as a data verification segment, and the segment with a coefficient lower than 0.5 is classified as a data substitution segment.

[0041] The specific implementation of step S04 is as follows: Within the data verification section, forward error correction coding technology is used to generate redundant verification data, specifically the Reed-Solomon coding method, which has strong burst error correction capabilities. During the generation of redundant verification data, the carrier communication signal transmission data is first divided into blocks of fixed length, each block being 255 bytes long, containing 223 bytes of information data and 32 bytes of verification data. The low-power wireless communication node uses time-division multiplexing to transmit the redundant verification data. Precise clock synchronization ensures that the redundant verification data remains synchronized with the carrier communication signal transmission data, with clock synchronization accuracy controlled at the microsecond level. A sliding time window technique is used during data synchronization, with the window length dynamically adjusted according to the actual transmission delay, typically set to 1 to 10 milliseconds.

[0042] The specific implementation of step S05 is as follows: When comparing the redundancy check data with the carrier communication signal transmission data, a bit error rate calculation method is used, and the difference between the two sets of data is calculated using Hamming distance. First, the received data is synchronized bit-by-bit to eliminate the impact of transmission delay, and then bit-by-bit comparison is performed on a data block basis. The data transmission error rate is calculated using a moving average method, with a calculation time window set to 1 second. The error rate is obtained by accumulating the ratio of the number of erroneous bits within this time window to the total number of bits. To improve the calculation accuracy, a weighted average method is used to process the error rate of different time windows, with a larger weight for recent windows and a smaller weight for distant windows. The weight attenuation coefficient is set to 0.8.

[0043] The specific implementation of step S06 is as follows: When determining the data retransmission time based on the data transmission error rate, an adaptive threshold decision method is adopted. This method is based on fuzzy control theory and makes decisions by establishing a mapping relationship between the error rate and the necessity of retransmission. A baseline threshold for the error rate is set at 0.001. When the error rate exceeds this threshold, the retransmission mechanism is triggered. The retransmission strategy adopts a selective retransmission scheme, retransmitting only data blocks with errors. The retransmission priority is determined based on the importance of the data and the degree of error. The importance of the data block is evaluated using the information entropy calculation method, while the degree of error is calculated using the bit error rate. Determining the retransmission time also needs to consider channel conditions, selecting a suitable retransmission time window using channel idle detection technology.

[0044] The specific implementation of step S07 is as follows: When calculating the signal attenuation of the carrier communication signal within the data substitution segment, a channel attenuation model is used. This model considers multiple factors such as transmission distance, medium loss, and multipath effects. The signal attenuation is calculated using a piecewise linear fitting method, dividing the transmission path into multiple sub-segments, with the attenuation characteristics of each sub-segment described by a linear equation. The data transmission cost function is established using a multi-objective optimization method, comprehensively considering multiple indicators such as transmission reliability, transmission delay, and energy consumption, and using the analytic hierarchy process (AHP) to determine the weight coefficients of each indicator. The data segmentation process uses a dynamic segmentation algorithm, determining the optimal segment size based on the temporal and spatial correlation of the data, generally setting the segment size between 64 bytes and 256 bytes.

[0045] The specific implementation of step S08 is as follows: When using dynamic programming to solve the data transmission allocation scheme, the problem is transformed into a variation of the knapsack problem, with the goal of minimizing the overall transmission cost while meeting transmission quality requirements. A state transition equation is constructed, where the state variables include the currently allocated data volume and the cumulative transmission cost, and the decision variable is the choice of data block transmission method. A forward dynamic programming algorithm is used to solve for the optimal transmission scheme, with the algorithm's time complexity controlled at the polynomial level. In practical applications, to improve solution efficiency, a heuristic search strategy is adopted. An initial solution is obtained through a greedy algorithm, and then the solution is optimized using a local search method.

[0046] The specific implementation of step S09 is as follows: Data segment numbering employs a globally unique identifier generation method to ensure that data packets from different transmission paths can be accurately identified and reassembled. The numbering rules include information such as timestamps, data block sequence numbers, and transmission path identifiers, and a fixed-length identifier is generated using hash encoding. The data segment association table is stored using a distributed hash table structure, which offers good query efficiency and scalability. Updates to the association table utilize an incremental update mechanism, modifying only changed data entries, thereby reducing system overhead.

[0047] The specific implementation of step S10 is as follows: The data reconstruction process adopts a parallel processing architecture, using multi-threading technology to process data packets from different transmission paths simultaneously. The data packets are sorted using a merge sort algorithm, which can effectively handle partially ordered data sequences. In the event of data packet loss or corruption, a data recovery strategy is employed, reconstructing the data using redundant data. The data recovery success rate threshold is set to 0.95.

[0048] The specific implementation of step S11 is as follows: Data transmission accuracy is calculated using a hierarchical evaluation method, evaluating at three levels: bit-level, data packet-level, and service-level. Bit-level accuracy is calculated using the bit error rate, data packet-level accuracy using the packet loss rate, and service-level accuracy using application layer data integrity checks. The adjustment of the transmission allocation scheme employs feedback control theory; by establishing a control model between accuracy and allocation parameters, adaptive optimization of the allocation scheme is achieved.

[0049] The specific implementation of step S12 is as follows: The segment re-division process adopts a sliding window method with a window size of 10 minutes. Statistical characteristics of the carrier signal quality coefficients, including mean, variance, skewness, and kurtosis, are collected within each window period. Feature vectors are extracted using principal component analysis, and then pattern recognition technology is used to determine the segment type. The segment boundaries are determined using an edge detection algorithm, which can accurately identify abrupt changes in signal quality.

[0050] The specific implementation of step S13 is as follows: The continuous monitoring process adopts a real-time data stream processing framework, and realizes online analysis of the carrier signal quality coefficient through streaming computing technology. The data transmission allocation scheme is updated using an incremental learning method, continuously optimizing the allocation strategy based on newly acquired data. The triggering conditions for scheme updates include multiple factors such as sudden changes in signal quality and degradation of transmission performance. The update cycle is dynamically adjusted according to the system status, generally set between 1 minute and 5 minutes.

[0051] 1. The carrier signal quality coefficient is calculated as follows:

[0052]

[0053] In the formula, Q is the carrier signal quality coefficient; S / N is the signal-to-noise ratio; THD is the total harmonic distortion of the signal; σ p Let α1, α2, and α3 be the standard deviation of signal phase jitter; α1, α2, and α3 are weighting coefficients, and satisfy α1 + α2 + α3 = 1.

[0054] The signal-to-noise ratio S / N is calculated as follows:

[0055]

[0056] In the formula, s i n is the signal amplitude. i denoted as noise amplitude; N represents the number of sampling points.

[0057] The total harmonic distortion (THD) is calculated as follows:

[0058]

[0059] In the formula, A kA1 is the amplitude of the kth harmonic; A1 is the amplitude of the fundamental frequency; K is the harmonic order considered, ranging from 2 to 10.

[0060] 2. The spatial distribution characteristic matrix is ​​calculated and represented as follows:

[0061]

[0062] In the formula, Q ij is the carrier signal quality coefficient at the j-th spatial location at the i-th time point; m is the number of time sampling points; n is the number of spatial sampling points.

[0063] 3. K-means clustering calculation is represented as follows:

[0064]

[0065] In the formula, k is the number of clusters; C i Let x be the i-th cluster; x be the sample point; μ i Let ||x-μ be the i-th cluster center; i || 2 It is the square of the Euclidean distance.

[0066] 4. The data transmission cost function is expressed as follows:

[0067] Cost=β1(1-R)+β2T+β3E+γΔt;

[0068] In the formula, R is the transmission reliability index; T is the transmission delay; E is the energy consumption; Δt is the time jitter; and β1, β2, β3, and γ are weighting coefficients.

[0069] The transmission reliability index R is calculated as follows:

[0070] R = e -λL (1-BER) N ;

[0071] In the formula, λ is the link attenuation coefficient; L is the transmission distance; BER is the bit error rate; and N is the data packet length.

[0072] 5. The dynamic programming state transition equation is expressed as follows:

[0073] f(i,j)=min{f(i-1,j),f(i-1,jw i )+v i};

[0074] In the formula, f(i, j) is the maximum value when the capacity of the first i data packets does not exceed j; w i The bandwidth usage of the i-th data packet; v i Let be the transmission value of the i-th data packet.

[0075] 6. The feature vector extraction calculation is represented as follows:

[0076]

[0077] In the formula, C is the covariance matrix; x i For sample vectors; is the sample mean; n is the sample size.

[0078] The eigenvalue decomposition is represented as follows:

[0079] Cφ=λφ;

[0080] In the formula, λ is the eigenvalue; φ is the eigenvector.

[0081] 7. The edge detection threshold is calculated as follows:

[0082] Th=μ+kσ;

[0083] In the formula, Th is the detection threshold; μ is the signal mean; σ is the signal standard deviation; and k is a coefficient, ranging from 2 to 3.

[0084] 8. The incremental learning update equation is expressed as follows:

[0085] w t+1 =w t +η(y t -f(x t ))g(x t );

[0086] In the formula, w t Let be the model parameters at time t; η be the learning rate; y t The target value; f(x) t g(x) represents the model's predicted value; t ) is the gradient function.

[0087] The principles and significance of choosing these equations are as follows:

[0088] 1. The carrier signal quality coefficient uses exponential and logarithmic functions because these functions can better describe the nonlinear characteristics of signal attenuation and degradation;

[0089] 2. The spatial distribution feature matrix adopts a two-dimensional matrix form, which can simultaneously express the changing characteristics of time and spatial dimensions;

[0090] 3. K-means clustering uses the squared Euclidean distance as a metric, which can amplify the differences between samples;

[0091] 4. The data transmission cost function adopts a linear combination form, which facilitates the adjustment of the weights of various influencing factors;

[0092] 5. The dynamic programming equation uses a recursive form, which guarantees that the global optimal solution can be obtained;

[0093] 6. The eigenvector extraction uses the covariance matrix, which can eliminate the influence of dimensions;

[0094] 7. The edge detection threshold is expressed as the mean plus the standard deviation, which conforms to the characteristics of a normal distribution.

[0095] 8. Incremental learning updates adopt gradient descent to ensure the convergence of the learning process.

[0096] The principle or derivation process of the above equation will be described in detail below:

[0097] 1. Derivation of the equation for carrier signal quality coefficient: First, consider the three main factors affecting carrier signal quality: signal-to-noise ratio (SNR), harmonic distortion, and phase jitter. The SNR is expressed logarithmically because the human ear's perception of sound intensity follows a logarithmic relationship, and the perception of signal quality also conforms to this rule. Harmonic distortion is expressed as 1 minus the distortion degree, aligning with the trend of SNR, where a higher value generally indicates better quality. Phase jitter is expressed as a negative exponential form because experimental data fitting revealed a negative exponential relationship between phase jitter and signal quality. The weighting coefficients α1, α2, and α3 are determined using grey relational analysis. The specific steps are: first, construct a reference sequence and a comparison sequence; then, calculate the correlation coefficient; finally, obtain the weight value by calculating the correlation degree. Experimental verification shows that when α1 = 0.5, α2 = 0.3, and α3 = 0.2, the equation can effectively reflect the signal quality level.

[0098] 2. Construction process of the spatial distribution feature matrix: The number of rows m of the matrix is ​​determined by the sampling theorem, and the sampling frequency must be greater than twice the highest frequency of the signal; the number of columns n is determined by the spatial distribution of the communication line, and the distance between adjacent sampling points does not exceed the coherence length of the signal; matrix element Q ij The acquisition adopts the sliding window method, with a window length of 1 second and an overlap rate of 50%. This method can reflect the spatiotemporal evolution characteristics of signal quality.

[0099] 3. K-means clustering optimization process: Traditional K-means clustering is prone to getting trapped in local optima. Therefore, the following optimization steps are adopted: First, the density peak method is used to determine the initial cluster centers. This method selects the cluster centers by calculating the local density of each point and the minimum distance to points with high local density. Then, inertial weights are introduced to make the update of cluster centers take into account historical information and avoid drastic oscillations. Finally, an adaptive learning rate is adopted, which gradually decreases as the number of iterations increases to improve the convergence performance of the algorithm.

[0100] 4. Construction process of data transmission cost function: First, basic data on reliability, latency and energy consumption are obtained through transmission experiments; then, normalization is used to eliminate the influence of dimensions; the weight coefficients β1, β2, β3 and γ are determined by the analytic hierarchy process, by constructing a judgment matrix, calculating eigenvalues ​​and eigenvectors, and finally determining the weight values; the introduction of the time jitter term γΔt is to smooth the transmission process and avoid frequent switching of transmission modes.

[0101] 5. Optimization of dynamic programming state transition equations: To improve solution efficiency, the following optimization measures are adopted: First, the data packets are preprocessed and sorted according to the value per unit bandwidth; then, the memoization method is used to record the states that have been calculated to avoid repeated calculations; finally, a heuristic pruning strategy is introduced, and when the estimated value of a state is lower than the current optimal value, the subsequent calculation of that state is directly abandoned.

[0102] 6. Improvement of eigenvector extraction: Traditional eigenvalue decomposition is computationally intensive, so the power iteration method is used for optimization. The specific steps are as follows: select an initial vector, repeatedly perform matrix multiplication, and normalize after each iteration until convergence is obtained to obtain the eigenvector corresponding to the largest eigenvalue; then calculate other eigenvectors recursively; in order to improve numerical stability, the Gram-Schmidt orthogonalization method is used in the iteration process.

[0103] 7. Determination of edge detection threshold: The value of coefficient k is determined through ROC curve analysis. First, the true positive rate and false positive rate are calculated under different k values. Then, the ROC curve is plotted, and the k value corresponding to the inflection point of the curve is selected as the final value. The signal mean μ and standard deviation σ are calculated using the exponential weighted moving average method to enhance the response capability to recent data.

[0104] 8. Improvement of the incremental learning update equation: The learning rate η adopts an adaptive adjustment strategy, with an initial value of 0.1, which decreases as the training error decreases; to prevent overfitting, a regularization term is introduced, and the modified update equation is:

[0105] w t+1 =w t +η(y t -f(x t ))g(x t )-λw t ;

[0106] In the formula, λ is the regularization coefficient, which ranges from 0.01 to 0.1.

[0107] Specifically, the principle of this invention is as follows: The core principle of this invention is a dual-mode communication adaptive cooperative mechanism based on signal quality. By establishing a carrier signal quality assessment model, the system can acquire signal transmission status information in real time. This model comprehensively considers multiple parameters such as signal-to-noise ratio, harmonic distortion, and phase jitter, and obtains a comprehensive index reflecting signal quality through weighted combination. Based on this index, the system divides the communication line into a normal transmission segment, a data verification segment, and a data substitution segment, and adopts different communication strategies for each segment.

[0108] In optimizing data transmission schemes, this invention employs dynamic programming to transform the data packet allocation problem into a constrained optimization problem. By constructing a multi-objective cost function that includes transmission reliability, latency, and energy consumption, the system can find the optimal data allocation scheme under different constraints. Simultaneously, an incremental learning method is used to dynamically adjust the allocation strategy, enabling the system to adapt to changes in the communication environment.

[0109] This invention also employs a series of key technologies to ensure the reliability and practicality of the solution. For example, it provides a data verification mechanism through Reed-Solomon coding, uses a distributed hash table to manage the data segmentation relationships, and uses an adaptive threshold algorithm for edge detection. The organic combination of these technologies constitutes a complete dual-mode communication solution. Theoretically, this solution can effectively solve the carrier signal attenuation problem and has strong engineering practicality.

[0110] The following provides a specific embodiment 1 of the present invention, and the specific implementation of each step in this embodiment 1 is described in detail below.

[0111] The specific implementation of step S01 is as follows: When selecting the communication line segment to be tested in a low-voltage power line carrier communication line, it is first necessary to perform a characteristic analysis of the communication line. Line parameter measuring devices are used to obtain parameters such as line impedance, capacitance, and inductance. The line impedance measurement range is from 10 ohms to 1000 ohms, and the measurement frequency range is from 1 kHz to 500 kHz. After obtaining the line parameters, the optimal test line segment is determined using a line transmission characteristic evaluation method. The evaluation indicators include signal attenuation characteristics, noise interference level, and load distribution. The signal attenuation characteristics are calculated using the following formula:

[0112] A(f) = A0e -αf +A1e -βL ;

[0113] In the formula, A(f) is the signal attenuation value at frequency f; A0 and A1 are attenuation coefficients; α is the frequency-dependent attenuation factor; β is the distance-dependent attenuation factor; and L is the line length. Noise interference level is characterized by noise power spectral density.

[0114]

[0115] In the formula, N(f) is the noise power spectral density at frequency f; N0 is the background noise level; N k f is the noise intensity of the k-th type of interference source; k σ is the center frequency of the k-th type of interference source; k The spectrum broadening factor is used. Based on the measurement results, the line segment with the least signal attenuation and the lowest noise interference level is selected as the communication line segment to be tested, with the length of the line segment controlled within the range of 100 meters to 500 meters. The network topology optimization algorithm is used to arrange the communication nodes, and the node positions are determined by the minimum spanning tree method, with the distance between nodes not exceeding 200 meters.

[0116] The specific implementation of step S02 is as follows: When acquiring carrier communication signals and performing signal quality analysis, digital signal processing technology is first used to sample the carrier communication signals. The sampling frequency is set to four times the carrier signal frequency, and the sampled data is quantized through a 16-bit analog-to-digital converter. The sampled signal is first processed by bandpass filtering. An elliptic finite impulse response filter is used, with a passband range of 3 kHz to 500 kHz and a stopband attenuation greater than 60 dB. The filtered signal is then converted to the frequency domain using Fourier transform. The Fast Fourier Transform algorithm is used, with 4096 transformation points. The carrier signal quality coefficient is calculated using a multi-index comprehensive evaluation method. The specific calculation formula is as follows:

[0117]

[0118] The parameters in the formula are obtained as follows: The signal-to-noise ratio is calculated using the following formula:

[0119]

[0120] Total harmonic distortion is calculated using the following formula:

[0121]

[0122] Phase jitter σ p Calculated using the standard deviation of the signal phase difference:

[0123]

[0124] In the formula, Δφ i The phase difference of the i-th sampling point; The average phase difference is denoted as α1. The weighting coefficients are determined using grey relational analysis, specifically by constructing a reference and comparison series, calculating the correlation coefficients, and determining the correlation degree. Experiments have shown that when α1 = 0.5, α2 = 0.3, and α3 = 0.2, this equation effectively reflects the signal quality level.

[0125] The specific implementation of step S03 is as follows: When determining the data verification section and the data substitution section based on the carrier signal quality coefficient, a spatial distribution feature matrix is ​​first constructed:

[0126]

[0127] Based on this matrix, an improved K-means clustering algorithm is used for segmentation. The clustering objective function is:

[0128] Cluster optimization uses the density peak method to determine the initial cluster centers, and calculates the local density ρ. i and distance factor δ i Select cluster center:

[0129]

[0130] In the formula, d ij d is the distance between sample points i and j; c χ(x) is the cutoff distance; x(x) is the indicator function. The distance factor calculation formula is:

[0131]

[0132] Choose ρ i and δ i Points with larger values ​​are used as initial cluster centers. An inertia weight w is introduced during the clustering process, and the cluster center update formula is:

[0133]

[0134] In the formula, w is the inertia weight, with a value ranging from 0.1 to 0.3. According to the clustering results, when the carrier signal quality coefficient is greater than 0.75, it is a normal transmission segment; when it is between 0.5 and 0.75, it is a data verification segment; and when it is less than 0.5, it is a data substitution segment.

[0135] The specific implementation of step S04 is as follows: Redundant check data is generated within the data check section using the Reed-Solomon coding method. The coding process first divides the data into fixed-length data blocks, each 255 bytes long, containing 223 bytes of information data and 32 bytes of check data. The Reed-Solomon coding uses a generator polynomial:

[0136]

[0137] In the formula, a is the Galois domain GF(2 8 The primitive element in the sequence is t, which represents the error correction capability and has a value of 16. The encoded data is transmitted using time-division multiplexing. The time-division multiplexing frame structure includes a synchronization header, data segments, and a check segment. The frame length is 1 millisecond, and the synchronization header uses a Barker code sequence.

[0138] B(x)=x 7 +x 6 +x 4 +1.

[0139] Clock synchronization employs an improved delay-locked loop method, and phase error detection uses an advance-delay phase detector.

[0140] e(n)=sgn[y(n)][y(n-τ / 2)-y(n+τ / 2)];

[0141] In the formula, y(n) is the input signal; τ is the symbol period; and sgn is the symbol function. The loop filter adopts a proportional-integral structure:

[0142]

[0143] In the formula, K p K is the proportionality coefficient. i K is the integral coefficient. In practical applications, K p The value range is from 0.1 to 0.5, K i The value ranges from 0.01 to 0.05.

[0144] The specific implementation of step S05 is as follows: When calculating the signal attenuation of the carrier communication signal within the data substitution segment, a piecewise linear channel attenuation model is adopted:

[0145]

[0146] In the formula, L(f, d) is the path loss at frequency f and distance d; a i (f) and b i (f) represents the attenuation coefficient; M represents the number of segments; ε(f, d) represents the random fading term. The data transmission cost function is constructed using a multi-objective optimization method:

[0147] Cost=β1(1-R)+β2T+β3E+γΔt;

[0148] The formula for calculating the transmission reliability index is:

[0149] R = e -λL (1-BER) N .

[0150] The weight coefficients are determined using the analytic hierarchy process (AHP), and a judgment matrix is ​​constructed.

[0151]

[0152] The weight values ​​are obtained by calculating the eigenvalues ​​and eigenvectors. Data partitioning employs a dynamic segmentation algorithm, determining the optimal partition size based on the temporal and spatial correlations of the data.

[0153] B opt =argmin B [H(B)+λC(B)];

[0154] In the formula, H(B) is the block entropy; C(B) is the block cost; and λ is the balance factor.

[0155] The specific implementation of step S06 is as follows: When segmenting and numbering the first data packet and the second data packet, a globally unique identifier generation method is used. The identifier consists of a timestamp, a data block sequence number, and a transmission path identifier, and its mathematical expression is:

[0156] ID=t stamp ×2 32 +s num ×2 16 +p id ;

[0157] In the formula, t stamp For timestamps; s num p is the data block number; id This serves as the transmission path identifier. The data segmentation association table is stored using a distributed hash table structure, with the hash function selected as follows:

[0158]

[0159] In the formula, x[i] is the i-th byte of the input data; p is a prime number with a value of 31; and m is the size of the hash table. To resolve hash collisions, a double hashing method is used:

[0160] h2(x) = R - (h(x) mod R);

[0161] In the formula, R is the largest prime number less than m.

[0162] The specific implementation of step S07 is as follows: When calculating the data transmission error rate, the bit error rate is calculated using a sliding time window method with a window size of 1 second and an overlap rate of 50%. The bit error rate calculation formula is:

[0163]

[0164] In the formula, b i For sending bits; For received bits, N is the total number of bits. The packet error rate is calculated using the following formula:

[0165]

[0166] In the formula, N error N represents the number of error packets. total denoted as the total number of packets; λ is the time decay factor. The data retransmission strategy employs a selective retransmission mechanism, with the retransmission trigger condition being:

[0167]

[0168] In the formula, Th1 is the bit error rate threshold, with a value of 0.001; Th2 is the packet error rate threshold, with a value of 0.01.

[0169] The specific implementation of step S08 is as follows: When using dynamic programming to solve the data transmission allocation scheme, the state transition equation is:

[0170] f(i,j)=min{f(i-1,j),f(i-1,jw i )+v i};

[0171] To improve solution efficiency, a memoization method is used to construct the state cache table:

[0172]

[0173] Heuristic pruning strategies are based on estimation functions:

[0174]

[0175] In the formula, W represents the total bandwidth constraint; n represents the total number of data packets. When f(i,j)+h(i,j) is greater than the current optimal value, the state is pruned.

[0176] The specific implementation methods for steps S09 to S13 are as follows: Feature vector extraction adopts the power iteration method, and the iteration formula is:

[0177]

[0178] In the formula, A is the covariance matrix; x (k) Let be the eigenvector of the k-th iteration. The convergence criterion is:

[0179] ||x (k+1) -x (k) ||<ε;

[0180] In the formula, ε is the convergence threshold, with a value of 0.0001. Edge detection uses an adaptive thresholding method:

[0181] Th=μ+kσ;

[0182] The coefficient k is determined by the Otsu's method:

[0183] k opt =argmaxk [ω1(k)ω2(k)(μ1(k)-μ2(k)) 2 ];

[0184] In the formula, ω1(k) and ω2(k) are the proportions of the two classes of samples; μ1(k) and μ2(k) are the means of the two classes of samples. Incremental learning uses regularized stochastic gradient descent:

[0185] w t+1 =w t +η(y t -f(x t ))g(x t )-λw t ;

[0186] The learning rate employs an exponential decay strategy:

[0187] η t =η0e -γt ;

[0188] In the formula, η0 is the initial learning rate, with a value of 0.1; γ is the decay coefficient, with a value of 0.01. The regularization coefficient λ is determined through cross-validation, with a value ranging from 0.01 to 0.1. Real-time data stream processing employs a sliding window technique, with the window update strategy as follows:

[0189] W t =αW t-1 +(1-α)D t ;

[0190] In the formula, W t D represents the t-th time window; t This is new data; α is the smoothing coefficient, with a value of 0.8. The scheme update cycle is dynamically adjusted:

[0191] T update =T0×(1+βΔQ);

[0192] In the formula, T0 is the reference update period; ΔQ is the signal quality change rate; and β is the adjustment coefficient.

[0193] To better understand and implement this invention, a specific application scenario is provided below as Example 2: During the upgrade and renovation of a power distribution automation system, a unit implemented the dual-mode communication scheme of this invention on a typical urban residential area's power distribution network. This residential area covers approximately 2 square kilometers and includes 3,000 households. The power distribution network adopts a 10 kV ring network structure and has 25 distribution transformers. The engineering team selected a 300-meter section of low-voltage power distribution line as the communication line segment to be tested. This line connects 150 households, whose electrical load mainly consists of residential lighting and household appliances. To verify the feasibility of this invention, the research team conducted a three-month experimental study.

[0194] During the testing phase, basic parameters of the carrier communication signal were first collected, with a sampling frequency of 500 kHz and a sampling precision of 16 bits. Measurements revealed that the characteristic impedance of this line segment varied between 100 ohms and 800 ohms, the distributed capacitance was approximately 70 picofarads / meter, and the distributed inductance was approximately 0.5 microhenries / meter. The impedance variation data over a 24-hour period is shown in Table 1 below.

[0195] Table 1. Data on line impedance variation

[0196]

[0197]

[0198] Figure 2 It displays the changes in line impedance over 24 hours, including the upper and lower limits of the impedance range and the average value, visually showing the characteristics of impedance changes at different times.

[0199] Based on the measurement data, the weighting coefficients were set as follows when calculating the carrier signal quality coefficients: α1 = 0.5, α2 = 0.3, α3 = 0.2. The measured signal quality parameters are shown in Table 2 below:

[0200] Table 2 Signal Quality Parameters

[0201] Parameter type Minimum value Maximum value Average value Signal-to-noise ratio (dB) 15 35 25 Harmonic distortion 0.02 0.15 0.08 Phase jitter (°) 2 12 6

[0202] Based on the above parameters, the research team used the K-means clustering algorithm to divide the communication line into segments. During the clustering process, an inertia weight w = 0.2 was set, and the number of iterations was 50. Ultimately, the 300-meter line was divided into the following segments: 0 to 120 meters for normal transmission, 120 to 220 meters for data verification, and 220 to 300 meters for data replacement.

[0203] Figure 3 The distribution of signal quality parameters along the line distance is shown, including three key parameters: signal-to-noise ratio, harmonic distortion, and phase jitter, reflecting the variation of signal quality with distance.

[0204] Within the data verification section, Reed-Solomon coding is used to generate redundant check data. The data block length is set to 255 bytes, including 223 bytes of information data and 32 bytes of check data. The time-division multiplexing frame length is set to 1 millisecond, with the synchronization header occupying 0.1 milliseconds, the data segment occupying 0.8 milliseconds, and the check segment occupying 0.1 milliseconds. The measured data transmission parameters are shown in Table 3 below:

[0205] Table 3 Transmission Parameter Table

[0206] Transmission parameter Numerical range Average value Bit error rate 1E-6 to 1E-4 5E-5 Packet error rate 1E-5 to 1E-3 2E-4 Transmission delay (ms) 10 to 50 25

[0207] Within the data substitution segment, a dynamic programming algorithm is used to optimize the data transmission allocation scheme. The weighting coefficients of the cost function are set as follows: β1 = 0.5, β2 = 0.3, β3 = 0.15, γ = 0.05. The data block size is set to 128 bytes, and the data is divided into two parts according to the signal attenuation. The configuration parameters are shown in Table 4 below.

[0208] Table 4 Attenuation Data Table

[0209]

[0210]

[0211] Figure 4 The logarithmic coordinates are used to illustrate how data transmission performance metrics change with distance, including bit error rate, packet error rate, and transmission delay.

[0212] During system operation, an incremental learning method was used to dynamically adjust the transmission strategy. The initial learning rate was set to 0.1, and it decreased every 1000 iterations with a decay coefficient of 0.01. During real-time monitoring, the window size was set to 10 minutes, and the data update cycle was 1 minute. After three months of operation and testing, the system performance indicators are shown in Table 5 below:

[0213] Table 5 System Performance Table

[0214] Performance index Target value Actual value Compliance rate (%) Communication reliability (%) 99.9 99.92 100 Average delay (ms) ≤50 35 100 System stability (%) 99.99 99.995 100

[0215] Traditional carrier communication solutions primarily address signal attenuation by employing the following measures: First, increasing transmission power, typically by 2 to 3 times; second, using forward error correction coding with redundancy usually between 20% and 30%; and third, setting up signal repeaters, with a repeater point every 100 meters. These measures suffer from high energy consumption, system complexity, and high cost. This invention, however, uses low-power communication as a supplementary means. Through an adaptive dual-mode communication mechanism, it significantly reduces system energy consumption while ensuring communication reliability. Real-world testing data shows that, while achieving the same communication reliability, this invention's solution saves 30% of energy, reduces system complexity by 40%, and lowers hardware costs by 50% compared to traditional solutions. Furthermore, the adaptive mechanism of this invention gives the system stronger environmental adaptability, enabling rapid response to changes in communication quality and ensuring communication stability.

[0216] It should be noted that the variables involved in this invention are explained in detail in Table 5 below.

[0217] Table 5. Variable Explanation Table

[0218]

[0219]

[0220] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method of HPLC micro-power communication and power line carrier dual-mode communication, characterized in that, The method comprises the following steps: The method comprises the following steps: According to the threshold value of the carrier signal quality coefficient, the communication line is divided into a signal attenuation section; according to the signal attenuation degree of the signal attenuation section, the data checking section and the data replacement section are divided; in the data checking section, redundant checking data is transmitted by using micro-power wireless communication to check the carrier communication signal transmission data; in the data replacement section, a data transmission allocation scheme is determined by using a dynamic programming method according to the signal attenuation degree of the carrier communication signal, specifically: the signal attenuation degree of the carrier communication signal is calculated by using a channel attenuation model; a data transmission cost function containing transmission reliability and transmission time delay is established; the carrier communication signal transmission data is divided into blocks by using a dynamic segmentation algorithm; and an optimal transmission scheme is solved by using a dynamic programming method; The carrier communication signal transmission data is divided into a first data packet transmitted by a carrier communication signal and a second data packet transmitted by micro-power wireless communication; the first data packet and the second data packet are numbered and recombined; the data transmission allocation scheme is adjusted according to the data transmission accuracy after data recombination, specifically: the data transmission accuracy is calculated by using a hierarchical evaluation method; a control model of the data transmission accuracy and allocation parameters is established by using feedback control theory; and the data transmission allocation scheme is optimized according to the control model; wherein the establishment of the data transmission cost function adopts a multi-objective optimization method, and multiple indexes of transmission reliability, transmission time delay and energy consumption are comprehensively considered, and the weight coefficients of the indexes are determined by using an analytic hierarchy process; The optimal transmission scheme is solved by using a dynamic programming method, specifically: the problem is transformed into a variant of the knapsack problem, the target is to minimize the overall transmission cost on the premise of meeting the transmission quality requirements; a state transition equation is constructed, the state variables include the currently allocated data amount and the cumulative transmission cost, and the decision variable is the transmission mode selection of the data block, and the optimal transmission scheme is solved by using a forward dynamic programming algorithm; The channel attenuation model is expressed as: ； wherein is the path loss at a frequency of , a distance of ; and is the attenuation coefficient; is the number of segments; is the random fading term; The dynamic segmentation algorithm is used to segment the carrier communication signal transmission data, specifically represented as: ; wherein is a block entropy; is a block cost; is a balance factor; When the data transmission allocation scheme is solved by using a dynamic programming method, the state transition equation is: ； In the formula, is the maximum value when the first data packet capacity does not exceed ; is the bandwidth occupation of the first data packet; is the transmission value of the first data packet.

2. The HPLC micro-power communication and power line carrier dual-mode communication method of claim 1, wherein, The step of collecting the carrier communication signal and performing signal quality analysis is specifically: the carrier communication signal is sampled by using a digital signal processing technology, the sampling frequency is set to 4 times the carrier signal frequency; the collected carrier communication signal is subjected to band-pass filtering processing, and is converted to the frequency domain for analysis by Fourier transform; and the carrier signal quality coefficient is calculated by using a multi-index comprehensive evaluation method.

3. The HPLC micro-power communication and power line carrier dual-mode communication method of claim 2, wherein, The step of transmitting redundant checking data by using micro-power wireless communication in the data checking section is specifically: the carrier communication signal transmission data is encoded by using a Reed Solomon encoding method to generate the redundant checking data; and the redundant checking data is transmitted by using a time division multiplexing mode.

4. The HPLC micro-power communication and power line carrier dual-mode communication method of claim 3, wherein, The step of performing data segment numbering on the first data packet and the second data packet comprises: generating a data segment number by using a globally unique identifier generation method; and establishing a data segment association table by using a distributed hash table structure.

5. The HPLC micro-power communication and power line carrier dual-mode communication method of claim 4, wherein, The method further comprises a data transmission error rate calculation step of: comparing the redundant check data and the carrier communication signal transmission data, calculating the data transmission error rate by using a bit error rate calculation method, and determining a data retransmission time according to the data transmission error rate.

6. The HPLC micro-power communication and power line carrier dual-mode communication method of claim 5, wherein, The method further comprises a section redivision step of: collecting the carrier signal quality coefficients by using a sliding window method, extracting a feature vector by using a principal component analysis method, and determining a section boundary by using an edge detection algorithm.

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