Micropower communication and power line carrier dual-mode communication method of HPLC (High Performance Liquid Chromatography)

Abstract

The invention provides an HPLC (High Performance Liquid Chromatography) micropower communication and power line carrier dual-mode communication method, which belongs to the technical field of multiplexing communication, and comprises the following steps: selecting a communication line section to be tested in a low-voltage power line carrier communication line, and determining a data verification section and a data replacement section according to a carrier signal quality coefficient; in the data verification section, micro-power wireless communication is adopted to transmit redundancy verification data for verifying carrier communication signal transmission data; determining a data transmission allocation scheme by adopting a dynamic planning method according to the signal attenuation degree of the carrier communication signal in the data replacement section, and dividing carrier communication signal transmission data into a first data packet transmitted through the carrier communication signal and a second data packet transmitted through micropower wireless communication; carrying out data segmentation numbering and recombination on the first data packet and the second data packet; and a data transmission allocation scheme is adjusted according to the data transmission accuracy after data recombination, so that the problem that the communication reliability cannot be ensured when the carrier communication signal attenuation is serious is solved.

Description

Technical Field

[0001] The invention belongs to the technical field of multiplexed communication, and in particular relates to a micro-power communication and power line carrier dual-mode communication method of HPLC. Background Art

[0002] Low-voltage power line carrier communication technology has been widely used in smart grids, smart homes and other fields. Traditional low-voltage power line carrier communication mainly relies on a single carrier signal transmission method, which realizes data transmission by modulating high-frequency carrier signals on power lines. This communication method is relatively simple to implement and can achieve reliable data transmission under ideal communication environments. The current mainstream carrier communication technology uses orthogonal frequency division multiplexing modulation, which distributes data to multiple orthogonal subcarriers for transmission to improve spectrum utilization efficiency.

[0003] However, in practical applications, due to the complexity of the power line network, the carrier signal will be affected by various factors during the transmission process and attenuate. These influencing factors include line impedance changes, multipath effects, interference from various electrical equipment, etc. Especially when the power load changes drastically or the line fails, the carrier signal may be severely attenuated, resulting in communication interruption or a sharp increase in the data transmission error rate. The existing technology mainly addresses the signal attenuation problem by increasing the transmission power and using channel coding, 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 lead to increased energy consumption and increased electromagnetic interference, and in some application scenarios, it is subject to power limitations. Although the use of complex channel coding technology can improve anti-interference capabilities, the increase in encoding and decoding delays will affect applications with high real-time requirements. The use of signal relay technology can extend the communication distance, but it will significantly increase system cost and complexity. Therefore, a new technical solution is needed to solve the communication reliability problem when the carrier signal is attenuated. Summary of the invention

[0005] In view of this, the present invention provides a micro-power communication and power line carrier dual-mode communication method for HPLC, which can solve the technical problem in the prior art that communication reliability cannot be guaranteed when the carrier communication signal is severely attenuated.

[0006] The present invention is implemented as follows: the present invention provides a micro-power communication and power line carrier dual-mode communication method of HPLC, comprising the following steps: selecting a communication line segment to be tested in a low-voltage power line carrier communication line, collecting a carrier communication signal and performing a signal quality analysis to obtain a carrier signal quality coefficient; determining a data verification section and a data replacement section according to the carrier signal quality coefficient; using micro-power wireless communication to transmit redundant verification data in the data verification section to verify the carrier communication signal transmission data; using a dynamic programming method to determine a data transmission allocation scheme in the data replacement section according to the signal attenuation of the carrier communication signal, dividing the carrier communication signal transmission data into a first data packet transmitted by the carrier communication signal and a second data packet transmitted by 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 allocation scheme according to the data transmission accuracy after the data reorganization.

[0007] Among them, the steps of collecting carrier communication signals and performing signal quality analysis are specifically: using digital signal processing technology to sample the carrier communication signal, and the sampling frequency is set to 4 times the carrier signal frequency; performing bandpass filtering on the collected carrier communication signal, and converting it to the frequency domain through Fourier transform for analysis; and using a multi-indicator comprehensive evaluation method to calculate the carrier signal quality coefficient.

[0008] Among them, the steps of determining the data verification section and the data replacement section according to the carrier signal quality coefficient are specifically: using the K-means clustering algorithm to calculate the spatial distribution characteristics of the carrier signal quality coefficient; dividing the communication line into signal attenuation sections according to the threshold of the carrier signal quality coefficient; and dividing the signal attenuation section into the data verification section and the data replacement section according to the signal attenuation degree of the signal attenuation section.

[0009] Among them, the step of using micro-power wireless communication to transmit redundant verification data in the data verification section is specifically: using the Reed-Solomon coding method to encode the carrier communication signal transmission data to generate the redundant verification data; and using time division multiplexing to transmit the redundant verification data.

[0010] Among them, the step of determining the data transmission allocation plan within the data replacement section is specifically: using a channel attenuation model to calculate the signal attenuation of the carrier communication signal; establishing a data transmission cost function that includes transmission reliability and transmission delay; using a dynamic segmentation algorithm to divide the carrier communication signal transmission data into blocks; and using a dynamic programming method to solve the optimal transmission plan.

[0011] The step of performing data segment numbering on the first data packet and the second data packet specifically comprises: 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] Among them, it also includes a data transmission error rate calculation step: comparing the redundant check data with the carrier communication signal transmission data, using a bit error rate calculation method to calculate the data transmission error rate; determining the data retransmission time according to the data transmission error rate.

[0013] Among them, the steps of adjusting the data transmission allocation plan are specifically: using a hierarchical evaluation method to calculate the data transmission accuracy; using feedback control theory to establish a control model of the data transmission accuracy and allocation parameters; and optimizing the data transmission allocation plan according to the control model.

[0014] The method also includes the step of re-dividing the sections: using a sliding window method to collect the carrier signal quality coefficient; using a principal component analysis method to extract feature vectors; and using an edge detection algorithm to determine the section boundaries.

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

[0016] Compared with the prior art, the present invention provides a HPLC micro-power communication and power line carrier dual-mode communication method. The HPLC micro-power communication and power line carrier dual-mode communication method proposed by the present invention establishes an adaptive dual-mode communication mechanism by using micro-power wireless communication as a supplementary means of carrier communication. The method dynamically divides the communication segments according to the carrier signal quality coefficient, adopts different communication strategies in different segments, and realizes the optimal configuration of communication resources.

[0017] In the specific implementation, the present invention adopts a multi-index comprehensive evaluation method to monitor the quality of the carrier signal in real time, and optimizes the data transmission allocation scheme through a dynamic programming algorithm. When the carrier signal is attenuated, the system can automatically adjust the degree of participation in micro-power communication, which can improve the transmission reliability through data verification and ensure the continuity of communication through partial data replacement. This adaptive adjustment mechanism based on signal quality avoids the shortcomings of blindly increasing the transmission power or redundant coding in traditional technologies.

[0018] The present invention solves the technical problem in the prior art that the reliability of communication cannot be guaranteed when the carrier communication signal is severely attenuated. The main principles are: first, by real-time monitoring and evaluation of the carrier signal quality, the signal attenuation can be discovered in time; second, micro-power communication is used as a supplementary channel to provide redundant guarantee for data transmission; finally, the dynamic optimization algorithm is used to achieve efficient configuration of communication resources, ensuring the stable operation of the system under various working conditions. This solution not only improves communication reliability, but also optimizes energy efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 is a flow chart 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 of the data transmission performance index in Example 2 as a function of distance. DETAILED DESCRIPTION

[0023] In order to make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0024] like Figure 1 FIG. 1 is a flow chart of a micro-power communication and power line carrier dual-mode communication method of HPLC provided by the present invention, and the method comprises the following steps:

[0025] S01. Selecting a communication line segment to be tested in a low-voltage power line carrier communication line, and arranging a micro-power wireless communication node and a low-voltage power line carrier communication node in the communication line segment to be tested;

[0026] S02, collecting the carrier communication signal of the low-voltage power line carrier communication node, performing signal quality analysis on the carrier communication signal, and obtaining a carrier signal quality coefficient;

[0027] S03, calculating a carrier signal attenuation section according to the carrier signal quality coefficient, and dividing the carrier signal attenuation section into a data verification section and a data replacement section;

[0028] S04, in the data verification section, using the micro-power wireless communication node to transmit redundant verification data, wherein the redundant verification data is synchronized with the carrier communication signal transmission data;

[0029] S05, comparing the redundant check data with the carrier communication signal transmission data, and calculating the data transmission error rate;

[0030] S06, determining a data retransmission time according to the data transmission error rate, and retransmitting the carrier communication signal transmission data at the data retransmission time;

[0031] S07, in the data replacement section, calculating the signal attenuation of the carrier communication signal, establishing a data transmission cost function according to the signal attenuation, and numbering the carrier communication signal transmission data in blocks;

[0032] S08. Using a dynamic programming method to solve a data transmission allocation scheme, dividing the carrier communication signal transmission data into a first data packet and a second data packet, wherein the first data packet is transmitted through the carrier communication signal, and the second data packet is transmitted through the micro-power wireless communication node;

[0033] S09, performing data segment numbering on the first data packet and the second data packet, and establishing a data segment association table;

[0034] S10, reorganizing the received first data packet and the received second data packet according to the data segment association table;

[0035] S11, calculating the data transmission accuracy after the data is reorganized, and adjusting the data transmission allocation scheme according to the data transmission accuracy;

[0036] S12, re-dividing the data verification section and the data replacement section 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 plan according to the carrier signal quality coefficient.

[0038] The specific implementation of the above steps is described in detail below. The specific implementation of step S01 is: in practical applications, it is first necessary to determine the physical range of the communication line segment to be tested, and select a representative communication line segment by analyzing the topological structure of the low-voltage power line carrier communication line. The communication line segment should contain multiple branch nodes and load access points. When arranging micro-power wireless communication nodes, the signal coverage analysis method is used to determine the optimal layout position so that the micro-power wireless communication signal can effectively cover the entire communication line segment to be tested. At the same time, the distance between the communication nodes should be controlled within the range of 50 meters to 200 meters to ensure the reliability of micro-power wireless communication. The layout 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 of the power line and the signal transmission attenuation curve, a suitable installation location is selected. The network topology optimization algorithm is used in the specific layout. The algorithm is based on the minimum spanning tree principle in graph theory. By calculating the connection weights between communication nodes, the optimal node layout scheme is obtained.

[0039] The specific implementation method of step S02 is: the carrier communication signal is sampled and analyzed by digital signal processing technology, and the sampling frequency is set to 4 times the carrier signal frequency to meet the requirements of Nyquist sampling theorem. The collected carrier communication signal is first processed by bandpass filtering to filter out power frequency interference and high-frequency noise, and then the signal is converted to the frequency domain for analysis by Fourier transform. In the frequency domain analysis, the power spectrum density and signal-to-noise ratio of the signal are calculated, and the amplitude characteristics and phase characteristics of the signal are extracted at the same time. The calculation of the carrier signal quality coefficient adopts a multi-index comprehensive evaluation method, which weights and combines multiple indicators such as signal-to-noise ratio, signal distortion, and signal stability. The weight coefficient is determined by the gray correlation analysis method, and finally a comprehensive evaluation index reflecting the quality of the carrier communication signal is obtained.

[0040] The specific implementation method of step S03 is: when segmenting based on the carrier signal quality coefficient, a cluster analysis method is used. The 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 sections. First, the carrier signal quality coefficient is sorted according to the spatial position to form a feature vector, and then the initial position of the cluster center is determined by calculating the local variance of the feature vector. In the clustering process, the threshold of the carrier signal quality coefficient is set to 0.75. When the signal quality coefficient of a certain section is lower than the threshold, it is divided into a signal attenuation section. For the signal attenuation section, it is further subdivided into a data verification section and a data replacement section according to the degree of signal attenuation, wherein the section with a signal quality coefficient between 0.5 and 0.75 is divided into a data verification section, and the section with a signal quality coefficient lower than 0.5 is divided into a data replacement section.

[0041] The specific implementation method of step S04 is: in the data verification section, forward error correction coding technology is used to generate redundant verification data, specifically using the Reed-Solomon coding method, which has a strong burst error correction capability. In the process of generating redundant verification data, the carrier communication signal transmission data is first divided into blocks according to a fixed length, and the length of each data block is 255 bytes, which contains 223 bytes of information data and 32 bytes of verification data. The micro-power wireless communication node uses time division multiplexing to transmit redundant verification data, and ensures that the redundant verification data is synchronized with the carrier communication signal transmission data through precise clock synchronization, and the clock synchronization accuracy is controlled at the microsecond level. The sliding time window technology is used in the data synchronization process, and the window length is dynamically adjusted according to the actual transmission delay, generally set to 1 millisecond to 10 milliseconds.

[0042] The specific implementation method of step S05 is: when comparing the redundant check data with the carrier communication signal transmission data, the bit error rate calculation method is adopted to calculate the difference between the two groups of data through the Hamming distance. First, the received data is synchronized at the bit level to eliminate the influence of the transmission delay, and then a bit-by-bit comparison is performed in units of data blocks. The data transmission error rate is calculated by the sliding average method, and the calculation time window is set to 1 second. The error rate is obtained by accumulating the ratio of the number of error bits in the time window to the total number of bits. In order to improve the calculation accuracy, the weighted average method is used to process the error rates of different time windows. The weight of the recent window is larger, the weight of the long-term window is smaller, and the weight attenuation coefficient is set to 0.8.

[0043] The specific implementation method of step S06 is: when determining the data retransmission time according to 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. The baseline threshold of the error rate is set to 0.001, and the retransmission mechanism is triggered when the error rate exceeds the threshold. The retransmission strategy adopts a selective retransmission scheme, and only retransmits the data blocks with errors. The retransmission priority is determined according to the importance of the data and the degree of error. The importance of the data block is evaluated by the information entropy calculation method, and the degree of error is calculated by the bit error rate. The determination of the retransmission time also needs to consider the channel conditions, and the appropriate retransmission time window is selected through the channel idle detection technology.

[0044] The specific implementation method of step S07 is: when calculating the signal attenuation of the carrier communication signal in the data replacement section, a channel attenuation model is used, which takes into account multiple factors such as transmission distance, dielectric loss, and multipath effect. The calculation of the signal attenuation adopts a piecewise linear fitting method to divide the transmission path into multiple sub-sections, and the attenuation characteristics of each sub-section are described by a linear equation. The establishment of the data transmission cost function adopts a multi-objective optimization method, which comprehensively considers multiple indicators such as transmission reliability, transmission delay, and energy consumption, and uses a hierarchical analysis method to determine the weight coefficient of each indicator. The data block process adopts a dynamic segmentation algorithm to determine the optimal block size based on the time correlation and spatial correlation of the data, and generally sets the block size between 64 bytes and 256 bytes.

[0045] The specific implementation method of step S08 is: when using the dynamic programming method to solve the data transmission allocation plan, the problem is converted into a deformation of the knapsack problem, and the goal is to minimize the overall transmission cost while meeting the transmission quality requirements. Construct a state transfer equation, the state variables include the current amount of allocated data and the cumulative transmission cost, and the decision variable is the transmission method selection of the data block. The forward dynamic programming algorithm is used to solve the optimal transmission plan, and the time complexity of the algorithm is controlled at the polynomial level. In practical applications, in order to improve the solution efficiency, a heuristic search strategy is adopted, the initial solution is obtained through a greedy algorithm, and then the solution is optimized through a local search method.

[0046] The specific implementation method of step S09 is: the data segment numbering adopts a globally unique identifier generation method to ensure that data packets of different transmission paths can be accurately identified and reassembled. The numbering rule includes information such as timestamp, data block sequence number, transmission path identifier, etc., and a fixed-length identifier is generated by hash coding. The data segment association table is stored in a distributed hash table structure, which has good query efficiency and scalability. The update of the association table adopts an incremental update mechanism, and only the changed data entries are modified, thereby reducing system overhead.

[0047] The specific implementation of step S10 is as follows: the data reorganization process adopts a parallel processing architecture, and simultaneously processes data packets from different transmission paths through multi-threading technology. The ordering of data packets adopts a merge sort algorithm, which can effectively process partially ordered data sequences. In the case of data packet loss or damage, a data recovery strategy is adopted to reconstruct through redundant data, and the threshold of data recovery success rate is set to 0.95.

[0048] The specific implementation method of step S11 is: the calculation of data transmission accuracy adopts a hierarchical evaluation method, and is evaluated at three levels: bit level, data packet level and service level. The bit level accuracy is calculated by the bit error rate, the data packet level accuracy is calculated by the packet loss rate, and the service level accuracy is calculated by the application layer data integrity check. The adjustment of the transmission allocation plan adopts feedback control theory, and the adaptive optimization of the allocation plan is achieved by establishing a control model between the accuracy and the allocation parameters.

[0049] The specific implementation of step S12 is: the segment re-division process adopts a sliding window method, the window size is set to 10 minutes, and the statistical characteristics of the carrier signal quality coefficient are collected in each window period, including mean, variance, skewness and kurtosis. The feature vector is extracted by the principal component analysis method, and then the segment type is judged by pattern recognition technology. The segment boundary is determined by an edge detection algorithm, which can accurately identify the mutation point of signal quality change.

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

[0051] 1. The calculation of carrier signal quality coefficient is 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 is the signal phase jitter standard deviation; α1, α2, α3 are weight 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 is the signal amplitude; n i is the noise amplitude; N is the number of sampling points.

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

[0058]

[0059] In the formula, A kis the amplitude of the kth harmonic; A1 is the amplitude of the fundamental wave; K is the number of harmonics considered, ranging from 2 to 10.

[0060] 2. The calculation of spatial distribution feature matrix is ​​expressed as follows:

[0061]

[0062] In the formula, Q ij is the carrier signal quality coefficient at the jth spatial position at the ith 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 expressed as follows:

[0064]

[0065] In the formula, k is the number of clusters; C i is the i-th cluster; x is the sample point; μ i is the i-th cluster center; ||x-μ i || 2 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] Where R is the transmission reliability index; T is the transmission delay; E is the energy consumption; Δt is the time jitter; β1, β2, β3, γ are weight coefficients.

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

[0070] R=e -λL (1-BER) N ;

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

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

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

[0074] Where f(i, j) is the maximum value when the capacity of the first i packets does not exceed j; w i is the bandwidth occupied by the ith data packet; v i is the transmission value of the i-th data packet.

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

[0076]

[0077] Where C is the covariance matrix; x i is the sample vector; is the sample mean; n is the sample size.

[0078] The eigenvalue decomposition is expressed as follows:

[0079] Cφ=λφ;

[0080] Where λ is the eigenvalue and φ is the eigenvector.

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

[0082] Th=μ+kσ;

[0083] Where Th is the detection threshold; μ is the signal mean; σ is the signal standard deviation; k is the 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 is the model parameter at time t; η is the learning rate; y t is the target value; f(x t ) is the model prediction value; g(x t ) is the gradient function.

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

[0088] 1. The carrier signal quality coefficient uses exponential function and logarithmic function 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 space dimensions;

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

[0091] 4. The data transmission cost function adopts the form of linear combination, which is convenient for adjusting the weight of each influencing factor;

[0092] 5. The dynamic programming equation adopts a recursive form, which can ensure the global optimal solution;

[0093] 6. The covariance matrix is ​​used to extract the feature vector, which can eliminate the dimension effect;

[0094] 7. The edge detection threshold is in the form of mean plus standard deviation, which conforms to the normal distribution characteristics;

[0095] 8. Incremental learning updates are performed in the form of gradient descent to ensure the convergence of the learning process.

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

[0097] 1. Derivation of the equation for the carrier signal quality coefficient: First, consider the three main factors that affect the quality of the carrier signal: signal-to-noise ratio, harmonic distortion and phase jitter; the signal-to-noise ratio is in logarithmic form because the human ear perceives sound intensity in a logarithmic relationship, and the perception of signal quality also conforms to this law; harmonic distortion is in the form of 1 minus the distortion degree, so that it is consistent with the change trend of the signal-to-noise ratio, and the larger the value, the better the quality; phase jitter is in the form of a negative exponential because it is found that the phase jitter and signal quality show a negative exponential relationship based on experimental data fitting; weight coefficients α1, α2, and α3 are determined by the gray correlation analysis method, and the specific steps are: first construct a reference series and a comparison series, then calculate the correlation coefficient, and finally obtain the correlation degree to obtain the weight value; through experimental verification, when α1=0.5, α2=0.3, and α3=0.2, the equation can better reflect the signal quality level.

[0098] 2. Construction process of 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; the matrix element Q ij The acquisition adopts the sliding window method with a window length of 1 second and an overlap rate of 50%. In this way, the temporal and spatial evolution characteristics of the signal quality can be reflected.

[0099] 3. K-means clustering optimization process: Traditional K-means clustering is prone to fall into local optimality, so the following optimization steps are adopted: first, the density peak method is used to determine the initial cluster center. This method selects the cluster center by calculating the local density of each point and the minimum distance to the high local density point; then, the inertia weight is introduced to make the update of the cluster center take into account historical information to avoid violent fluctuations; finally, an adaptive learning rate is used to gradually reduce the learning rate as the number of iterations increases to improve the convergence performance of the algorithm.

[0100] 4. The construction process of the data transmission cost function: First, the basic data of reliability, delay and energy consumption are obtained through transmission experiments; then normalization processing is used to eliminate the dimensionality effect; the weight coefficients β1, β2, β3, γ are determined by the hierarchical analysis method, and the weight value is finally determined by constructing a judgment matrix, calculating the eigenvalues ​​and eigenvectors; 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 equation: In order to improve the solution efficiency, the following optimization measures are adopted: first, the data packets are preprocessed and sorted according to the unit bandwidth value; then the memo method is used to record the calculated states to avoid repeated calculations; finally, the heuristic pruning strategy is introduced to directly abandon the subsequent calculation of a state when the estimated value of a state is lower than the current optimal value.

[0102] 6. Improvement of eigenvector extraction: The traditional eigenvalue decomposition has a large amount of calculation, so the power iteration method is used for optimization. The specific steps are: select the initial vector, repeatedly perform matrix multiplication operations, and normalize after each iteration until convergence to obtain the eigenvector corresponding to the maximum eigenvalue; then use a recursive method to calculate other eigenvectors; in order to improve numerical stability, the Gram-Schmidt orthogonalization method is used in the iterative process.

[0103] 7. The process of determining the edge detection threshold: The value of the coefficient k is determined by ROC curve analysis. First, the true positive rate and false positive rate are calculated under different k values, and then the ROC curve is drawn, 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 exponentially weighted moving average method to enhance the responsiveness to recent data.

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

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

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

[0107] Specifically, the principle of the present invention is: the core principle of the present invention is a dual-mode communication adaptive coordination mechanism based on signal quality. By establishing a carrier signal quality evaluation model, the system can obtain signal transmission status information in real time. The 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 section, a data verification section and a data replacement section, and adopts different communication strategies respectively.

[0108] In the optimization of data transmission scheme, the present invention adopts dynamic programming method to transform the data packet allocation problem into a constrained optimization problem. By constructing a multi-objective cost function including transmission reliability, delay and energy consumption, the system can find the optimal data allocation scheme under different constraints. At the same time, the incremental learning method is used to dynamically adjust the allocation strategy so that the system can adapt to changes in the communication environment.

[0109] The present invention also adopts a series of key technologies to ensure the reliability and practicality of the solution. For example, a data verification mechanism is provided through Reed-Solomon coding, a distributed hash table is used to manage the association relationship of data segments, and an adaptive threshold algorithm is used for edge detection. The organic combination of these technologies constitutes a complete dual-mode communication solution. The solution can effectively solve the problem of carrier signal attenuation in theory and has strong engineering practicality.

[0110] A specific embodiment 1 of the present invention is provided below, and the specific implementation method of each step in this embodiment 1 is described in detail as follows.

[0111] The specific implementation method of step S01 is: when selecting a 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, and use a line parameter measurement device to obtain line impedance, capacitance, inductance and other parameters. The line impedance measurement value range is 10 ohms to 1000 ohms, and the measurement frequency range is 1 kHz to 500 kHz. After the line parameters are obtained, the line transmission characteristic evaluation method is used to determine the best test line segment. The evaluation indicators include signal attenuation characteristics, noise interference level and load distribution. The signal attenuation characteristics are calculated by 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-related attenuation factor; β is the distance-related attenuation factor; and L is the line length. The noise interference level is characterized by the noise power spectral density:

[0114]

[0115] Where N(f) is the noise power spectral density at frequency f; N0 is the background noise level; N k is the noise intensity of the kth interference source; f k is the center frequency of the kth interference source; σ k is the spectrum broadening factor. According to the measurement results, the line segment with the minimum signal attenuation and the low noise interference level is selected as the communication line segment to be tested, and the line segment length is controlled within the range of 100 meters to 500 meters. The communication node layout adopts the network topology optimization algorithm, and the node position is determined by the minimum spanning tree method. The distance between nodes does not exceed 200 meters.

[0116] The specific implementation method of step S02 is: when collecting the carrier communication signal and performing signal quality analysis, the carrier communication signal is first sampled using digital signal processing technology, the sampling frequency is set to 4 times the carrier signal frequency, and the sampled data is quantized by a 16-bit analog-to-digital converter. The sampled signal is first bandpass filtered, and the filter adopts an elliptical finite impulse response filter with a passband range of 3 kHz to 500 kHz and a stopband attenuation greater than 60 decibels. The filtered signal is converted to the frequency domain by Fourier transform for analysis. The Fourier transform adopts the fast Fourier transform algorithm, and the number of transformation points is 4096 points. The calculation of the carrier signal quality coefficient adopts a multi-index comprehensive evaluation method, and the specific calculation formula is:

[0117]

[0118] The method for obtaining each parameter in the formula is: The signal-to-noise ratio is calculated by the following formula:

[0119]

[0120] The total harmonic distortion is calculated by the following formula:

[0121]

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

[0123]

[0124] In the formula, Δφ i is the phase difference of the i-th sampling point; is the mean phase difference. The weight coefficient is determined by the grey correlation analysis method, and the specific steps include: constructing reference series and comparison series, calculating the correlation coefficient, and obtaining the correlation degree. After experimental verification, when α1=0.5, α2=0.3, α3=0.2, the equation can better reflect the signal quality level.

[0125] The specific implementation of step S03 is: when determining the data verification section and the data replacement section according to the carrier signal quality coefficient, firstly construct a spatial distribution feature matrix:

[0126]

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

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

[0129]

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

[0131]

[0132] Choose ρ i and δ i The points with the largest values ​​are taken as the initial cluster centers. In the clustering process, the inertia weight w is introduced, and the cluster center update formula is:

[0133]

[0134] Where w is the inertia weight, which ranges 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 section, when it is between 0.5 and 0.75, it is a data verification section, and when it is less than 0.5, it is a data replacement section.

[0135] The specific implementation of step S04 is: Reed-Solomon coding method is used to generate redundant check data in the data check section. The coding process first divides the data into fixed-length data blocks, each of which is 255 bytes long and contains 223 bytes of information data and 32 bytes of check data. Reed-Solomon coding uses the generating polynomial:

[0136]

[0137] Where a is the Galois Field GF(2 8 ) in the primitive element; t is the error correction capability, which is 16. The encoded data is transmitted through time division multiplexing. The time division multiplexing frame structure includes a synchronization header, a data segment, 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] The clock synchronization adopts the improved delay locked loop method, and the phase error detection adopts the advance delay type phase detector:

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

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

[0142]

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

[0144] The specific implementation of step S05 is: when calculating the signal attenuation of the carrier communication signal in the data replacement section, a piecewise linear channel attenuation model is used:

[0145]

[0146] Where L(f, d) is the path loss when the frequency is f and the distance is d; i (f) and b i (f) is the attenuation coefficient; M is the number of segments; ε(f, d) is 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 transmission reliability index calculation formula is:

[0149] R=e -λL (1-BER) N .

[0150] The weight coefficients are determined by the hierarchical analysis method and the judgment matrix is ​​constructed:

[0151]

[0152] Calculate the eigenvalue and eigenvector to get the weight value. The data block adopts the dynamic segmentation algorithm to determine the optimal block size based on the time correlation and spatial correlation of the data:

[0153] B opt =argmin B [H(B)+λC(B)];

[0154] Where H(B) is the block entropy; C(B) is the block cost; λ is the balance factor.

[0155] The specific implementation of step S06 is: when the first data packet and the second data packet are numbered by data segments, a globally unique identifier generation method is adopted, and the identifier is composed 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] Where, t stamp is the timestamp; s num is the data block number; p id It is the transmission path identifier. The data segment association table is stored in a distributed hash table structure, and the hash function is 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. In order to resolve hash conflicts, a double hashing method is used:

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

[0161] Where R is the largest prime number less than m.

[0162] The specific implementation of step S07 is: when calculating the data transmission error rate, the bit error rate calculation adopts the sliding time window method, the window size is 1 second, and the overlap rate is 50%. The bit error rate calculation formula is:

[0163]

[0164] Where b i To send bits; is the received bit; N is the total number of bits. The packet error rate calculation formula is:

[0165]

[0166] Where N error is the number of error packets; N total is the total number of packets; λ is the time decay factor. The data retransmission strategy adopts the selective retransmission mechanism, and the retransmission triggering condition is:

[0167]

[0168] Where Th1 is the bit error rate threshold, which is 0.001; Th2 is the packet error rate threshold, which is 0.01.

[0169] The specific implementation of step S08 is: when the dynamic programming method is used to solve the data transmission allocation scheme, the state transfer equation is:

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

[0171] In order to improve the solving efficiency, the memo method is used to construct the state cache table:

[0172]

[0173] The heuristic pruning strategy is based on the estimation function:

[0174]

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

[0176] The specific implementation of step S09 to step S13 is: the feature vector is extracted using the power iteration method, and the iteration formula is:

[0177]

[0178] Where A is the covariance matrix; x (k) is the eigenvector of the kth iteration. The convergence criterion is:

[0179] ||x (k+1) -x (k) ||<ε;

[0180] Where ε is the convergence threshold, which is 0.0001. Edge detection uses the adaptive threshold method:

[0181] Th=μ+kσ;

[0182] The coefficient k is selected by the maximum inter-class variance 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 ratios of the two types of samples; μ1(k) and μ2(k) are the means of the two types of samples. Incremental learning uses the stochastic gradient descent method with regularization:

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

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

[0187] η t =η0e -γt ;

[0188] In the formula, η0 is the initial learning rate, which is 0.1; γ is the decay coefficient, which is 0.01. The regularization coefficient λ is determined by cross-validation and ranges from 0.01 to 0.1. The real-time data stream processing uses sliding window technology, and the window update strategy is:

[0189] W t =αW t-1 +(1-α)D t ;

[0190] Where W t is the tth time window; D t is the new data; α is the smoothing coefficient, which is set to 0.8. Dynamic adjustment of the scheme update cycle:

[0191] T update =T0×(1+βΔQ);

[0192] Where T0 is the reference update period; ΔQ is the signal quality change rate; β is the adjustment coefficient.

[0193] In order to better understand and implement the present invention, Example 2 of a specific application scenario of the present invention is provided below: When a certain unit was upgrading the distribution automation system, it implemented the dual-mode communication solution of the present invention for a typical urban residential distribution network. The residential area covers an area of ​​about 2 square kilometers and includes 3,000 residential users. The distribution network adopts a 10 kV ring network structure and is equipped with 25 distribution transformers. The engineering team selected a low-voltage distribution line with a distance of 300 meters as the communication line segment to be tested. There are 150 users connected to the line, and the power load is mainly civil lighting and household appliances. In order to verify the feasibility of the present invention, the research team conducted a three-month experimental study.

[0194] During the test phase, the basic parameters of the carrier communication signal were first collected, with the sampling frequency set to 500 kHz and the sampling accuracy set to 16 bits. Through measurement, it was found that the characteristic impedance of the line segment varied between 100 ohms and 800 ohms, the distributed capacitance of the line was about 70 pF / m, and the distributed inductance was about 0.5 μH / m. Within 24 hours a day, the line impedance change data is shown in Table 1 below:

[0195] Table 1 Line impedance change data table

[0196]

[0197]

[0198] Figure 2 The changes in line impedance within 24 hours are displayed, including the upper and lower limits and average value of the impedance range, intuitively showing the impedance change characteristics in different time periods.

[0199] According to the measured data, when calculating the carrier signal quality coefficient, the weight coefficient is set to: 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 parameter table

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

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

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

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

[0205] Table 3 Transmission parameters

[0206] Transmission parameters 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 (milliseconds) 10 to 50 25

[0207] In the data replacement section, the data transmission allocation scheme is optimized by dynamic programming algorithm. The weight coefficients of the cost function are set to: 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 Logarithmic coordinates are used to show the changing trends of data transmission performance indicators with distance, including bit error rate, packet error rate and transmission delay.

[0212] During system operation, the incremental learning method is used to dynamically adjust the transmission strategy. The initial value of the learning rate is set to 0.1, and it decays once every 1,000 iterations, with a decay coefficient of 0.01. During real-time monitoring, the window size is set to 10 minutes, and the data update cycle is 1 minute. After 3 months of operation testing, the system performance indicators are shown in Table 5 below:

[0213] Table 5 System performance table

[0214] Performance Indicators Target value Measured 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 mainly adopt the following measures to deal with the problem of signal attenuation: first, increase the transmission power, generally increase the power to 2 to 3 times the original; second, use forward error correction coding, the redundancy is usually 20% to 30%; third, set up signal repeaters, and set up a relay point every 100 meters. These measures have problems such as high energy consumption, complex system, and high cost. The present invention adopts micro-power communication as a supplementary means, and through an adaptive dual-mode communication mechanism, it significantly reduces the system energy consumption while ensuring communication reliability. Measured data show that, under the condition of achieving the same communication reliability index, the solution of the present invention saves 30% of energy consumption, reduces 40% of system complexity, and reduces 50% of hardware costs compared with the traditional solution. At the same time, the adaptive mechanism of the present invention enables the system to have stronger environmental adaptability, can quickly respond to changes in communication quality, and ensure the stability of communication.

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

[0217] Table 5 Variable explanation table

[0218]

[0219]

[0220] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A HPLC micro-power communication and power line carrier dual-mode communication method, characterized in that: The following steps are involved: Select a communication line section to be tested in a low-voltage power line carrier communication line, collect a carrier communication signal and perform signal quality analysis to obtain a carrier signal quality coefficient; A data verification section and a data replacement section are determined according to the carrier signal quality coefficient; redundant verification data is transmitted in the data verification section using micro-power wireless communication to verify the carrier communication signal transmission data; a data transmission allocation scheme is determined in the data replacement section according to the signal attenuation of the carrier communication signal using a dynamic programming method, and the carrier communication signal transmission data is divided into a first data packet transmitted via the carrier communication signal and a second data packet transmitted via micro-power wireless communication; the first data packet and the second data packet are segmented and numbered and reorganized; and the data transmission allocation scheme is adjusted according to the data transmission accuracy after the data is reorganized.

2. The HPLC micro-power communication and power line carrier dual-mode communication method according to claim 1, characterized in that: The steps of collecting carrier communication signals and performing signal quality analysis are specifically: using digital signal processing technology to sample the carrier communication signal, and setting the sampling frequency to 4 times the carrier signal frequency; performing bandpass filtering on the collected carrier communication signal, and converting it to the frequency domain through Fourier transform for analysis; and using a multi-index comprehensive evaluation method to calculate the carrier signal quality coefficient.

3. The HPLC micro-power communication and power line carrier dual-mode communication method according to claim 1, characterized in that: The steps of determining the data verification section and the data replacement section according to the carrier signal quality coefficient are specifically: using the K-means clustering algorithm to calculate the spatial distribution characteristics of the carrier signal quality coefficient; dividing the communication line into signal attenuation sections according to the threshold of the carrier signal quality coefficient; and dividing the signal attenuation section into the data verification section and the data replacement section according to the signal attenuation degree of the signal attenuation section.

4. The HPLC micro-power communication and power line carrier dual-mode communication method according to claim 1, characterized in that: The step of using micro-power wireless communication to transmit redundant check data in the data check section is specifically: using the Reed-Solomon coding method to encode the carrier communication signal transmission data to generate the redundant check data; and using time division multiplexing to transmit the redundant check data.

5. The HPLC micro-power communication and power line carrier dual-mode communication method according to claim 1, characterized in that: The steps of determining the data transmission allocation plan within the data replacement section are specifically: using a channel attenuation model to calculate the signal attenuation of the carrier communication signal; establishing a data transmission cost function that includes transmission reliability and transmission delay; using a dynamic segmentation algorithm to divide the carrier communication signal transmission data into blocks; and using a dynamic programming method to solve the optimal transmission plan.

6. The HPLC micro-power communication and power line carrier dual-mode communication method according to claim 1, characterized in that: The step of performing data segment numbering on the first data packet and the second data packet specifically comprises: generating data segment numbers using a globally unique identifier generation method; and establishing a data segment association table using a distributed hash table structure.

7. The HPLC micro-power communication and power line carrier dual-mode communication method according to claim 1, characterized in that: It also includes a data transmission error rate calculation step: comparing the redundant check data with the carrier communication signal transmission data, using a bit error rate calculation method to calculate the data transmission error rate; determining the data retransmission time according to the data transmission error rate.

8. The HPLC micro-power communication and power line carrier dual-mode communication method according to claim 1, characterized in that: The steps of adjusting the data transmission allocation scheme are specifically: using a hierarchical evaluation method to calculate the data transmission accuracy; using feedback control theory to establish a control model of the data transmission accuracy and allocation parameters; and optimizing the data transmission allocation scheme according to the control model.

9. The HPLC micro-power communication and power line carrier dual-mode communication method according to claim 1, characterized in that: The method also includes the step of re-dividing the sections: using a sliding window method to collect the carrier signal quality coefficient; using a principal component analysis method to extract feature vectors; and using an edge detection algorithm to determine the section boundary.

10. The HPLC micro-power communication and power line carrier dual-mode communication method according to claim 1, characterized in that: The method also includes a scheme updating step: using a real-time data stream processing framework to monitor the carrier signal quality coefficient; using an incremental learning method to update the data transmission allocation scheme; and dynamically adjusting the update cycle according to the system status.

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