A low-voltage power carrier time synchronization method, system, medium and electronic equipment based on distributed cooperation

Abstract

The application discloses a low-voltage power carrier time synchronization method and system based on distributed cooperation, a medium and an electronic device. The method comprises the following steps: establishing a clock error state model of a low-voltage power carrier network node; based on the clock error state model, a second-order linear state equation is used to represent the dynamic clock error of each node; a multi-source cooperative observation model is established based on the dynamic clock error, and multi-source observation data containing master-slave links, local nodes and neighbor nodes are collected based on the multi-source cooperative observation model; the multi-source observation data is preprocessed to obtain effective observation data after preprocessing; based on the effective observation data, an optimal time error of the current synchronization period of the node is obtained through the established weighted least square fusion objective function; based on the optimal time error, the dynamic clock error of the node is corrected through a distributed cooperative fusion mechanism, the time offset and frequency offset of the node are compensated, and the frequency random drift disturbance is inhibited.

Description

Technical Field

[0001] This invention relates to the field of clock synchronization technology, and more specifically, to a method, system, medium, and electronic device for low-voltage power line carrier time synchronization based on distributed collaboration. Background Technology

[0002] Low-voltage distribution areas, as a crucial link at the end of the smart grid, rely on time and frequency synchronization for accurate metering, efficient operation and maintenance, and reliable data. Power line carrier (PLC)-based synchronization technology has become the mainstream choice in the industry due to its low cost, lack of need for additional communication lines, and compatibility with existing distribution area topologies. Time and frequency synchronization within distribution areas based on carrier technology has been studied for over a decade, starting with narrowband carrier technology. With the widespread adoption of high-speed power line broadband (HPLC) carrier technology, synchronization accuracy has significantly improved through synchronization mechanisms such as OFDM modulation and beacon frames, forming a master-slave architecture of "concentrator → CCO → PCO → STA". However, traditional architectures suffer from bottlenecks such as multi-level error accumulation. Given the current distribution scale of distribution areas, multi-level topologies are prevalent in existing distribution area networks, affecting synchronization performance and stability, thus hindering the widespread adoption of this technology. Therefore, how to reduce multi-level synchronization errors is a pressing technical problem that needs to be solved in this field.

[0003] Prior art 1 (application number CN202210750508) provides a clock frequency synchronization method, device, and storage medium for a metering device. Based on power line carrier technology, it adopts a hierarchical, step-by-step synchronization method to synchronize the time and frequency of each node in the carrier network. However, as the number of carrier layers increases, the synchronization performance decreases linearly. For large-scale distribution areas, the time and frequency performance and stability of the end nodes are severely degraded, hindering the widespread adoption of the technology. Summary of the Invention

[0004] This invention proposes a low-voltage power line carrier time synchronization method and system based on distributed collaboration to solve the problems of low synchronization accuracy, weak anti-interference and significant error accumulation of traditional methods, and to achieve high-precision time and frequency synchronization across the entire network.

[0005] According to another aspect of the present invention, a low-voltage power line carrier time synchronization method based on distributed cooperation is provided, the method comprising: A clock error state model for nodes in a low-voltage power line carrier network is established; based on the clock error state model, a second-order linear state equation is used to characterize the dynamic clock error of each node. A multi-source collaborative observation model is established based on the dynamic clock error, and multi-source observation data including master-slave links, local nodes and neighboring nodes are collected based on the multi-source collaborative observation model. The multi-source observation data is preprocessed to obtain preprocessed effective observation data; Based on the effective observation data, the optimal time error of the node's current synchronization period is obtained through the established weighted least squares fusion objective function; Based on the optimal time error, the dynamic clock error of the nodes is corrected through a distributed collaborative fusion mechanism to compensate for the node time offset and frequency offset and suppress random frequency drift disturbances.

[0006] Preferably, the clock error state model includes an ideal clock model and an actual clock model; The expression for the ideal clock model is: in, For nodes Ideal clock phase; Nominal frequency; It is absolute physical time; The expression for the actual clock model is: in, For nodes The actual operating frequency; For nodes The initial phase shift; Introducing relative frequency offset , defined as the relative frequency deviation between the actual operating frequency and the nominal frequency: The actual operating frequency is expressed as ; Within the time-frequency synchronization period of the low-voltage distribution area, the frequency will be relatively offset. Treating it as a constant, the actual clock model simplifies to: .

[0007] Preferably, based on the clock error state model, a second-order linear state equation is used to characterize the dynamic clock error of each node; the dynamic clock error includes: time offset, frequency offset, and random frequency drift; The state equations for the second-order clock error state model are: in, For nodes Time offset, For nodes Frequency offset, The frequency is randomly drifted; the random frequency drift follows a zero-mean Gaussian white noise distribution.

[0008] Preferably, the multi-source collaborative observation model includes a local frequency offset observation model, a master-slave synchronous link observation model, and a multi-node distributed observation model of end neighbors; The local frequency offset observation model is used to collect observation data from local nodes, and its expression is: in, for node The relative offset of the true frequency ； To observe the noise, it follows a zero-mean Gaussian distribution; The master-slave synchronization link observation model includes the boundary clock PCO observation model and the slave clock STA observation model, which are used to collect observation data of the master-slave link; The expression for the boundary clock PCO observation model is: in, The relative offset of the actual frequency of the master clock CCO; The relative offset of the true frequency of the boundary clock PCO; The observation noise for the boundary clock PCO; The expression for the observation model from the clock STA is: in, The relative offset from the actual frequency of clock STA; The observation noise from the clock STA; The distributed observation model for multiple neighbor nodes is used to collect observation data from multiple neighbor nodes, and its expression is: in, Neighboring nodes The relative offset of the true frequency; For clock STA nodes and neighboring nodes The interactive observation noise follows a zero-mean Gaussian distribution.

[0009] Preferably, the expression for the weighted least squares fusion objective function is: in, This represents the measurement time error vector for all paths in the current period. For a fixed column vector of all 1s, It is a multi-dimensional adaptive weight matrix. The path error covariance matrix is... This represents the optimal time error for the final solution.

[0010] Preferably, the weight factors of the multi-dimensional adaptive weight matrix satisfy: in, The weight is determined by the number of relay levels in the synchronous transmission path, and decreases accordingly. Channel quality weights ， It is positively correlated with the channel received signal strength indicator (RSSI) of the node; For frequency offset stability weights ， It is positively correlated with the inverse of the variance of the node's historical frequency offset; The neighbor association weight is positively correlated with the number of neighbors of the end clock STA and the neighbor synchronization accuracy.

[0011] Preferably, the path error covariance matrix This includes a path correlation coefficient, which is calculated based on the number of common nodes in the synchronous transmission path, using the following formula: Where a and b are the total number of nodes in the synchronization transmission path between the i-th neighbor node and the j-th neighbor node, respectively, and c is the number of common nodes in the two paths.

[0012] Preferably, the preprocessing of the multi-source observation data includes: performing anomaly removal processing on the multi-source observation data using the 3σ criterion, including: Calculate the initial mean and initial variance of the observation data of the local node, the observation data of the master-slave link, and the observation data of the end-slave clock STA neighbor node; Based on the initial mean and the initial variance, the 3σ anomaly discrimination condition is determined. If any observation data satisfies the 3σ anomaly discrimination condition, the observation data is determined to be an anomaly and is removed. Iteratively execute the initial mean and initial variance calculation, anomaly detection and removal operations until there are no abnormal observations left in the remaining observation data after removing abnormal observations.

[0013] According to another aspect of the present invention, the present invention provides a low-voltage power line carrier time synchronization system based on distributed cooperation, the system comprising: A clock error state model is established for the nodes of the low-voltage power line carrier network. Based on the clock error state model, a second-order linear state equation is used to characterize the dynamic clock error of each node. The observation unit is used to establish a multi-source collaborative observation model based on the dynamic clock error, and to collect multi-source observation data including master-slave links, local nodes and neighboring nodes based on the multi-source collaborative observation model. The processing unit is used to preprocess the multi-source observation data to obtain the preprocessed effective observation data; The fusion unit is used to obtain the optimal time error of the node's current synchronization period based on the effective observation data and through the established weighted least squares fusion objective function. The correction unit is used to correct the dynamic clock error of the node based on the optimal time error through a distributed collaborative fusion mechanism, to compensate for the node time offset and frequency offset and suppress random frequency drift disturbance.

[0014] According to another aspect of the present invention, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a power line carrier time synchronization method based on distributed cooperative methods.

[0015] According to another aspect of the present invention, the present invention provides an electronic device, comprising: The aforementioned computer-readable storage medium; and One or more processors for executing a program in the computer-readable storage medium.

[0016] This invention provides a distributed collaborative low-voltage power line carrier time synchronization method and system. The method includes: establishing a clock error state model for nodes in a low-voltage power line carrier network; characterizing the dynamic clock error of each node using a second-order linear state equation based on the clock error state model; establishing a multi-source collaborative observation model based on the dynamic clock error; collecting multi-source observation data including master-slave links, local nodes, and neighboring nodes based on the multi-source collaborative observation model; preprocessing the multi-source observation data to obtain preprocessed effective observation data; obtaining the optimal time error of the node's current synchronization period using an established weighted least squares fusion objective function based on the effective observation data; and correcting the node's dynamic clock error using a distributed collaborative fusion mechanism based on the optimal time error, compensating for node time offset and frequency offset, and suppressing random frequency drift disturbances. The technical solution of this invention constructs a second-order clock error state model to comprehensively characterize the dynamic error characteristics of node time offset, frequency offset, and random frequency drift. By combining local, master-slave link, and neighbor node multi-source collaborative observation and data preprocessing, and relying on weighted least squares fusion and distributed collaborative correction mechanism, it effectively solves the error accumulation problem of traditional unidirectional hierarchical synchronization, significantly improves the accuracy and anti-interference capability of power line carrier network clock synchronization, and achieves high-precision and high-stability time and frequency synchronization of the entire network. Attached Figure Description

[0017] Exemplary embodiments of the present invention can be more fully understood by referring to the following figures: Figure 1This is a flowchart of a low-voltage power line carrier time synchronization method based on distributed cooperation according to an embodiment of the present invention; Figure 2 This is a schematic diagram of a simulated environment communication topology according to an embodiment of the present invention; Figure 3 This is a structural diagram of a low-voltage power line carrier time synchronization system based on distributed cooperation according to an embodiment of the present invention. Detailed Implementation

[0018] Exemplary embodiments of the invention will now be described with reference to the accompanying drawings. However, the invention may be embodied in many different forms and is not limited to the embodiments described herein. These embodiments are provided to fully and completely disclose the invention and to fully convey its scope to those skilled in the art. The terminology used in the exemplary embodiments illustrated in the drawings is not intended to limit the invention. In the drawings, the same units / elements are referred to by the same reference numerals.

[0019] Unless otherwise stated, the terms used herein (including technical terms) have their common meaning as understood by one of ordinary skill in the art. Furthermore, it is understood that terms defined in commonly used dictionaries should be understood to have a meaning consistent with the context of their relevant field, and not to be interpreted as having an idealized or overly formal meaning.

[0020] Figure 1 This is a flowchart of a low-voltage power line carrier time synchronization method based on distributed cooperation according to an embodiment of the present invention.

[0021] Low-voltage power line carrier (HPLC) networks employ a master-slave multi-level synchronization architecture of "CCO→PCO→STA". The CCO acts as the Grandmaster (GM) to lead network-wide synchronization, the PCO acts as the Boundary Clock (BC) to relay time and frequency signals, and the STA acts as the slave clock to complete local synchronization. Traditional master-slave unidirectional single-node synchronization mechanisms suffer from significant hierarchical synchronization losses; with each additional relay level, synchronization errors accumulate, severely impacting network-wide synchronization accuracy. This invention improves synchronization accuracy through a distributed collaborative approach.

[0022] This invention establishes a node clock error state model and a carrier synchronization observation model, and proposes a fusion algorithm based on cluster frequency error distribution and a multi-node synchronization mechanism for end devices to improve the synchronization accuracy of the entire network.

[0023] like Figure 1 As shown, this invention provides a low-voltage power line carrier time synchronization method based on distributed cooperation, the method comprising: Step 101: Establish a clock error state model for nodes in a low-voltage power line carrier network; based on the clock error state model, use a second-order linear state equation to characterize the dynamic clock error of each node; Preferably, the clock error state model includes an ideal clock model and an actual clock model; The expression for the ideal clock model is: in, For nodes Ideal clock phase; Nominal frequency; It is absolute physical time; The expression for the actual clock model is: in, For nodes The actual operating frequency; For nodes The initial phase shift; Introducing relative frequency offset , defined as the relative frequency deviation between the actual operating frequency and the nominal frequency: The actual operating frequency is expressed as ; Within the time-frequency synchronization period of the low-voltage distribution area, the frequency will be relatively offset. Treating it as a constant, the actual clock model simplifies to: .

[0024] Preferably, the dynamic clock error of each node is characterized by a second-order linear state equation based on a clock error state model; the dynamic clock error includes: time offset, frequency offset, and random frequency drift; The state equations for the second-order clock error state model are: in, For nodes Time offset, For nodes Frequency offset, The frequency drift is random; the random frequency drift follows a zero-mean Gaussian white noise distribution.

[0025] This invention establishes a node clock ontology model and a carrier synchronization observation model, and proposes a fusion algorithm based on cluster frequency error distribution and a multi-node synchronization mechanism for end devices to improve the synchronization accuracy of the entire network.

[0026] The clock error state model of this invention is the basis of time-frequency synchronization. It is used to describe the deviation characteristics between the node local clock and absolute time. Combined with the short-term stability of the crystal oscillator of the low-voltage substation carrier node, a second-order clock model is used to characterize the time offset, frequency offset and random drift characteristics of the node clock.

[0027] The clock error state model of the present invention includes an ideal clock model.

[0028] An ideal clock's phase changes linearly with absolute time, unaffected by external disturbances or crystal characteristics; its phase expression is: ： In the formula, For nodes Ideal clock phase; The nominal carrier center frequency (25MHz is used as the standard nominal frequency for low-pressure HPLC in this invention); This is absolute physical time.

[0029] The clock error state model of the present invention includes an actual clock model.

[0030] Actual node clocks are affected by factors such as crystal oscillator accuracy, ambient temperature, and power supply fluctuations, resulting in time offsets, frequency offsets, and random drift, deviating from the ideal clock. (Define node) The actual local timescale is Its expression is: In the formula, For nodes The actual operating frequency; For nodes The initial phase shift.

[0031] Introducing relative frequency offset , defined as the relative deviation between the actual frequency and the nominal frequency: Therefore, the actual operating frequency can be expressed as: Within the time-frequency synchronization period (typically 1-20 seconds) in the low-voltage distribution area, the crystal oscillator frequency drift is approximately linear, and can be considered... Since it is a constant, the actual clock scale can be simplified to: The clock error state model of this invention accurately describes the dynamic changes in clock deviation. It uses a second-order linear state equation to characterize clock error and defines two core state variables: time offset and time deviation. (Deviation between node local timescale and absolute time) and frequency offset Its state equation is: In the formula, The frequency drifts randomly and follows a zero-mean Gaussian white noise distribution. , The drift noise variance characterizes the long-term stability of the crystal oscillator. This model accurately reflects the dynamic evolution of clock bias: the rate of change of time offset equals the rate of change of frequency offset, and the rate of change of frequency offset is random drift, which conforms to the actual working characteristics of low-voltage grid node crystal oscillators.

[0032] In traditional master-slave unidirectional single-node synchronization, with each additional relay level (e.g., CCO→PCO→STA), the errors between the two levels accumulate, and the more levels there are, the greater the error. By using multi-node synchronization between the end device and its neighboring nodes, the synchronization information from the neighboring nodes can be used to correct its own errors, breaking this unidirectional accumulation effect and improving synchronization accuracy. This improvement is not simply error cancellation, but rather a change in the error accumulation mechanism through distributed collaborative fusion.

[0033] Step 102: Establish a multi-source collaborative observation model based on dynamic clock error, and collect multi-source observation data including master-slave links, local nodes and neighboring nodes based on the multi-source collaborative observation model; Preferably, the multi-source collaborative observation model includes a local frequency offset observation model, a master-slave synchronous link observation model, and a multi-node distributed observation model of the end neighbor. The local frequency offset observation model is used to collect observation data from local nodes, and its expression is: in, for node The relative offset of the true frequency ； To observe the noise, it follows a zero-mean Gaussian distribution; The master-slave synchronization link observation model includes the boundary clock PCO observation model and the slave clock STA observation model, which are used to collect observation data of the master-slave link; The boundary clock PCO observation model expression is: in, The relative offset of the actual frequency of the master clock CCO; The relative offset of the true frequency of the boundary clock PCO; The observation noise for the boundary clock PCO; The expression for the observation model from the clock STA is: in, The relative offset from the actual frequency of clock STA; The observation noise from the clock STA; The distributed observation model with multiple nodes in the end-neighbor area is used to collect observation data from multiple neighboring nodes. The expression is: in, Neighboring nodes The relative offset of the true frequency; For clock STA nodes and neighboring nodes The interactive observation noise follows a zero-mean Gaussian distribution.

[0034] The multi-source collaborative observation model of this invention is used to establish the correlation between local node observations and the actual frequency offset. Nodes obtain local frequency offset observations through carrier frequency offset estimation (CFO). Combining the characteristics of master-slave synchronization links and the synchronization requirements of multiple nodes in the terminal neighbor, a master-slave observation model for PCO and STA, as well as a distributed observation model among STA neighbors, are constructed to provide data support for cluster frequency fusion and hierarchical synchronization loss optimization. The accuracy of the observation model directly affects the subsequent error fusion effect and is one of the core sources of error differences between traditional and distributed collaborative models.

[0035] The multi-source collaborative observation model of the present invention includes a local frequency offset observation model.

[0036] The low-pressure HPLC network employs OFDM modulation technology. Nodes estimate carrier frequency offset through periodic beacon frame broadcasting and preamble frame synchronization, obtaining local frequency offset observations. The observation model takes into account the effect of observation noise, and its expression is: In the formula, For nodes The relative offset of the true frequency; The observed noise follows a zero-mean Gaussian distribution. , To observe the noise variance and characterize the observation accuracy, this corresponds to the uncertainty of a single synchronization measurement in the traditional synchronization mode. .

[0037] The PCO serves as the boundary clock, communicating with both the upper-level CCO / PCO and the lower-level STA. Its observation accuracy is higher than that of the STA (less affected by relay link loss and channel interference). Therefore, the observation noise variance of the PCO is generally lower. Observation noise variance less than STA ,Right now .

[0038] The multi-source collaborative observation model of the present invention includes a master-slave synchronous link observation model.

[0039] Under normal operating conditions, the entire network adopts a master-slave synchronization mechanism. The CCO, acting as the master clock, sends time-frequency synchronization commands to the PCO. The PCO, acting as the boundary clock, forwards the synchronization commands to the PCO / STA, forming a synchronization link of "CCO→PCO→STA". The corresponding observation model is as follows: (1) PCO observation model: The PCO receives the time-frequency signal from the CCO / PCO and observes the frequency offset difference between itself and the CCO / PCO. The observed value is: In the formula, The relative offset of the true frequency of CCO; The relative offset of the true frequency of the PCO; This refers to the observation noise of the PCO. Under normal synchronization conditions, the PCO uses an appropriate algorithm to converge its frequency offset to the CCO, i.e. At this point, the synchronization error of PCO relative to CCO mainly comes from the uncertainty of preamble frame synchronization and software response, corresponding to the single synchronization measurement uncertainty in the traditional mode. .

[0040] (2) STA observation model: The STA receives the time-frequency signal of the corresponding PCO and observes the frequency offset difference between itself and the PCO. Its observed value is: In the formula, This represents the relative offset of the actual frequency of the STA. This refers to the observation noise of the STA. In traditional master-slave synchronization, the synchronization error of the STA relative to the PCO is superimposed on the error of the PCO relative to the CCO, and the error accumulates linearly with the increase of the level.

[0041] The multi-source collaborative observation model of the present invention includes a multi-node distributed observation model of end neighbors.

[0042] In addition to receiving master-slave synchronization information from the superior PCO, the terminal STA also exchanges synchronization information with neighboring STAs and neighboring PCOs to construct a distributed observation model with multiple neighboring nodes. This model utilizes the synchronization information from neighboring nodes to correct its own synchronization errors and combat hierarchical synchronization losses. This model is the core of the distributed collaborative synchronization mechanism, providing data support for error fusion through multi-source observations and changing the traditional error accumulation method of synchronization.

[0043] Define STA The set of neighbor nodes is (Including adjacent STAs and PCOs), STA with neighboring nodes The observed relative frequency shift values ​​are: In the formula, Neighboring nodes The relative offset of the true frequency; For STA with neighboring nodes The interactive observation noise follows a zero-mean Gaussian distribution. Its variance is negatively correlated with the channel quality among neighboring nodes; the better the channel, the smaller the variance. These multi-source observation data form the basis for collaborative fusion, corresponding to the number of independent time sources that the STA can utilize in the distributed collaborative synchronization mechanism. (Including 1 official main path and) (Neighbor collaboration path).

[0044] The terminal STA collects relative frequency offset observations from all neighboring nodes. Combined with master-slave observations obtained from the superior PCO This forms a multi-source observation system, providing data for subsequent distributed fusion and correction of synchronization errors.

[0045] Under normal synchronization conditions, the frequency offset of all nodes in the entire network converges to the frequency offset of the CCO, that is... At this point, the observations of all nodes form a set of... It is a narrowband distribution centered on the terminal STA; at the same time, the terminal STA obtains multi-source synchronization information through observation of multiple neighboring nodes.

[0046] Step 103: Preprocess the multi-source observation data to obtain the preprocessed valid observation data; Preferably, the multi-source observation data is preprocessed, including: anomaly removal processing of the multi-source observation data using the 3σ criterion, including: Calculate the initial mean and initial variance of the observation data of the local node, the observation data of the master-slave link, and the observation data of the end-slave clock STA neighbor node; The 3σ anomaly detection criteria are determined based on the initial mean and initial variance. If any observed data satisfies the 3σ anomaly detection criteria, the observed data is determined to be an anomaly and is removed. Iteratively execute the initial mean and initial variance calculation, anomaly detection and removal operations until there are no abnormal observations left in the remaining observation data after removing abnormal observations.

[0047] To avoid the influence of abnormal nodes and abnormal neighbor interaction data on the fusion results, this invention requires anomaly removal processing of observations before performing weighted least squares fusion. The 3σ criterion is used to identify and remove abnormal observations. 1. Calculate the master-slave observations for all nodes. and terminal STA neighbor interaction observations initial mean and initial variance ; 2. If a certain observed value satisfies If the observation is abnormal, it will be discarded. 3. Recalculate the mean and variance for the remaining valid observations, and repeat the above steps until no anomalies are found.

[0048] Step 104: Based on effective observation data, obtain the optimal time error of the node's current synchronization cycle through the established weighted least squares fusion objective function; Preferably, the expression for the weighted least squares fusion objective function is: in, This represents the measurement time error vector for all paths in the current period. For a fixed column vector of all 1s, It is a multi-dimensional adaptive weight matrix. The path error covariance matrix is... This represents the optimal time error for the final solution.

[0049] Preferably, the weight factors of the multi-dimensional adaptive weight matrix satisfy: in, The weight is determined by the number of relay levels in the synchronous transmission path, and decreases accordingly. Channel quality weights ， It is positively correlated with the channel received signal strength indicator (RSSI) of the node; For frequency offset stability weights ， It is positively correlated with the inverse of the variance of the node's historical frequency offset; The neighbor association weight is positively correlated with the number of neighbors of the end clock STA and the neighbor synchronization accuracy.

[0050] Preferably, the path error covariance matrix This includes the path correlation coefficient, which is calculated based on the number of common nodes in the synchronous transmission path. The formula is: Where a and b are the total number of nodes in the synchronization transmission path between the i-th neighbor node and the j-th neighbor node, respectively, and c is the number of common nodes in the two paths.

[0051] This invention designs a distributed fusion algorithm that combines synchronization information from multiple neighboring nodes of the end device, integrates master-slave observations with distributed neighbor observation data, combats hierarchical synchronization loss, breaks through the cumulative limitation of levels, and improves the synchronization accuracy of the entire network.

[0052] The core of the distributed collaborative synchronization mechanism of this invention is the introduction of multi-source observations and data fusion at the end (STA layer), which changes the way errors accumulate. It couples real-time measurement errors, path correlation coefficients, adaptive weights, and observation variance into the same objective function, achieving integrated solution of correlation calculation, weight allocation, and residual optimization. The fused weighted least squares objective function is as follows: , The meanings of the relevant variables are as follows: The method ultimately solves for the optimal time error of the current synchronization cycle of the node.

[0053] : The real-time measurement time error vector of all valid paths in the current synchronization cycle.

[0054] The number of independent time sources available for data fusion at the end STA, including 1 official main path (from the parent PCO) and Each cooperative path originates from a neighboring STA, corresponding to the set of neighboring nodes of that STA. The number of elements is increased by 1.

[0055] : No. With the The correlation coefficient between path errors ( This characterizes the correlation between errors in different synchronization paths; the lower the correlation, the better the fusion effect. At this time, the path errors are completely independent, resulting in optimal fusion performance.

[0056] : The path error covariance matrix, where (Error variance of a single path) (Error covariance of the two paths) This matrix fully characterizes the error characteristics and correlation of each synchronization path.

[0057] In data fusion algorithms, the data is assigned to the first... Optimal weights for each path ( The weight design of this invention is determined by factors such as node observation accuracy and channel quality.

[0058] Multi-dimensional adaptive weight matrix Let w be a diagonal weight matrix, where the diagonal element wi is the final weight of the i-th path after weighting and normalization by each factor.

[0059] H The observation coefficient matrix, H matrix is ​​fixed as an M-row, 1-column vector with all elements always being 1.

[0060] Based on the above objective function, a weighted least squares solution is performed to obtain the optimal time error of the current synchronization period of the node.

[0061] This invention considers the topological characteristics of low-voltage power line carrier multi-level networks and introduces hierarchical weights, channel quality weights, frequency offset stability weights, and neighbor association weights. The total weight relationship satisfies the following... ： The specific definitions of each weight are as follows: (1) Hierarchical weight Different weights are assigned based on the node level, with the weight multiplied by 0.9 for each additional level. This ensures that higher-level nodes have a greater impact on the fusion results.

[0062] (2) Channel quality weight It is positively correlated with the Received Signal Strength Indication (RSSI) of the node. The higher the RSSI value, the better the channel quality, the lower the observation noise, and the greater the weight. The expression is: ,in and These are the maximum and minimum values ​​of the node's RSSI, respectively. and They are not equal.

[0063] (3) Frequency offset stability weight The frequency offset is positively correlated with the inverse of the variance of the node's historical frequency offset. The smaller the variance, the better the frequency offset stability, and the larger the weight. The expression is: ,in The historical frequency offset variance of the node. The denominator should be a very small positive number to avoid being zero.

[0064] (4) Neighbor association weight This applies only to the terminal STA (Stationary Target). The weight is positively correlated with the number of neighboring nodes and their synchronization accuracy. A larger number of neighboring nodes and higher synchronization accuracy result in a greater weight. The expression is as follows: ,in For STA The number of neighboring nodes, For STA with neighboring nodes The variance of interactive observation noise.

[0065] The correlation coefficient of this invention is related to the overlap of the synchronous transmission paths of two nodes. The higher the overlap, the higher the correlation. Assuming the total number of nodes in the synchronous transmission path of the i-th neighbor node is 'a', the total number of nodes in the synchronous transmission path of the j-th neighbor node is 'b', and the number of nodes in the common path of i and j is 'c', then the correlation coefficient is: .

[0066] Step 105: Based on the optimal time error, the dynamic clock error of the node is corrected through a distributed collaborative fusion mechanism to compensate for the node time offset and frequency offset and suppress random frequency drift disturbance.

[0067] The time-frequency distributed cooperative synchronization mechanism based on power line carrier communication proposed in this invention, by introducing multi-source information fusion, not only reduces the variance of synchronization errors at the node level through statistical averaging, but more importantly, changes the error propagation and accumulation mechanism in multi-level synchronization networks. It transforms the traditional, deterministic linear error accumulation process into a more robust accumulation process with filtering and redundancy characteristics. Essentially, this method utilizes distributed signal processing and the network's spatial diversity gain to combat the time-varying and uncertainties of the power line channel. This is fundamentally significant for constructing highly reliable and high-precision power line carrier time-frequency synchronization networks.

[0068] The following is a time error model for a traditional multi-level master-slave synchronization mode.

[0069] In the traditional tree-structured master-slave architecture (CCO→PCO→STA), the time error accumulates linearly along the synchronization path, and its error model can be characterized by the following definition and formula: The total number of stages in the synchronization path (from root to end STA), for example, in the path "CCO→PCO→STA", (CCO is) PCO is STA is ).

[0070] : indicates the first Level node ( As the root cause, (For terminal STA).

[0071] : No. The measurement uncertainty of a single synchronization at a level node (such as leader synchronization error, processing jitter, etc.) corresponds to the observation noise variance. The square root of.

[0072] : No. Local crystal oscillators at the level node during synchronization period Ts The Allen bias (characterizing its inherent frequency noise) corresponds to the frequency random drift noise. Quantitative indicators.

[0073] : No. Total time synchronization uncertainty (standard deviation) of the level nodes.

[0074] No. Independent error of level nodes: during synchronization period Ts Internally, the time uncertainty it generates To measure the root sum of the sum of squares of noise and crystal oscillator noise, the effects of single synchronization error and inherent crystal oscillator noise were combined: Formula: Because traditional master-slave synchronization is a serial architecture, errors are propagated entirely along the path. The total uncertainty of a level node is the direct accumulation of the total uncertainty of its parent node and its own independent uncertainty (assuming that the errors at each level are uncorrelated): The initial conditions are: (The total uncertainty of the root node is equal to its independent uncertainty.)

[0075] end ( Total uncertainty of STA (level): Expanding the above cumulative formula, we can obtain that the total simultaneous uncertainty of the final STA is the root sum of the squares of the independent errors of each level, i.e. ： In the traditional model, the total error and the series It grows in direct proportion to the square root ( With each additional relay level, the accuracy at the end point deteriorates deterministically.

[0076] The error model and performance improvement of the distributed cooperative synchronization mechanism in this invention include: The local synchronization error variance of a STA employing collaborative fusion It is no longer the variance of a single official path, but all The minimum variance of the path errors after optimal weighted fusion, combined with the weighted least squares fusion algorithm, can be expressed as: Ideal case (errors along each path are independent and identically distributed): When (Path errors are completely independent) When the independent errors of each path are the same, the optimal weight is: ,at this time: Local synchronization uncertainty is reduced to that of the traditional mode. For example, when When, the uncertainty is reduced to half of its original value; when At that time, the uncertainty was reduced to 1 / 3 of the original. This improvement is essentially achieved by statistical averaging of multi-source observations, which effectively suppresses random errors and realizes a systematic improvement in the single-point synchronization accuracy.

[0077] Actual situation (path error related): When When path errors are correlated, the performance improvement will diminish, but it will still be significant. The equivalent number of independent sources at this point... ( The quantification fusion effect (including the impact of path correlation) and the local synchronization uncertainty can be expressed as: In practical applications, neighbor node selection can be optimized through carrier-based networking (prioritizing the allocation of neighbor nodes to different time-frequency synchronization transmission paths), maximizing... Improve fusion performance.

[0078] This invention constructs, in the laboratory, such as Figure 2 The communication topology shown is used to perform effect analysis and measurement comparison of the method described in this invention on node S2, which contains neighbor nodes.

[0079] Topology link description: 1) Traditional master-slave synchronous transmission path (blue layer link): R→B1→S1, R→B2→S2, R→B2→S3 2) Neighbor-coordinated multipath links (purple redundant links): The neighbor node pairs include: [S1, S2] and [S2, S3]. 3) The synchronization time error of a single layer within the current topology is approximately 25 μs.

[0080] 4) Hierarchical Definition: R is the primary reference node, B1 / B2 are secondary relay nodes, and S1 / S2 / S3 are tertiary end nodes. Analysis of Error Theory in Traditional Master-Slave Synchronization Mode: Traditional methods rely solely on a single upper-level link for synchronization, lacking neighbor multipath redundancy correction. Time errors follow a multi-level independent error root-sum-of-squares accumulation formula. The synchronization link for node S2 is: R→B2→S2, with a total synchronization level L=2, including two levels of independent error accumulation. The theoretical error variance is: Based on the error theory analysis of this invention: Step 1: Determine the set of available synchronization paths S2 is a third-level end node. In distributed coordination mode, the available synchronization paths include: Main path (path 1): R→B2→S2, providing reference time and frequency data transmission; Neighbor collaboration path: Indirect synchronization path obtained through neighbor nodes S1 and S3: Path 2: R→B1→S1→S2 Path 3: R→B2→S3→S2 The total number of independent time sources participating in the fusion is M=3. Step 2: Calculate the variance of the single-path error. Setting a single-level standard synchronization error The variance of the error for each path is estimated using the traditional method: Where L is the error variance of each path node at that node level: Step 3: Determine the path relevance coefficient The path relevance coefficient formula defined in this invention is used: in, Let be the number of common nodes in the two paths. These represent the total number of nodes in the two paths, excluding the solution nodes.

[0081] Calculate the correlation coefficient between any two paths using the following formula: Path 1 (nodes: R, B2) and Path 2 (nodes: R, B1, S1) public nodes (Node R), number of path nodes .

[0082] Common nodes of path 1 (nodes: R, B2) and path 3 (nodes: R, B2, S3) (Nodes R, B2), number of path nodes .

[0083] Common nodes of path 2 (nodes: R, B1, S1) and path 3 (nodes: R, B2, S3) (Node R), number of path nodes .

[0084] Step 4: Construct the path error covariance matrix The elements of the covariance matrix satisfy , Substituting the values, we get: Step 5: Calculate the weight matrix Considering the characteristics of S2 as the terminal STA, according to the weighting formula Perform the calculation: Hierarchical weights: Path 1 is a level 2 path. .

[0085] Paths 2 and 3 are level 3 paths. .

[0086] Channel quality weight: Assuming that the RSSI of each path is similar, therefore .

[0087] Frequency offset stability weight: Assuming the historical frequency offset variance is consistent, therefore .

[0088] Neighbor association weight: Assumption .

[0089] Weight normalization: Preliminary weights: , .

[0090] Normalized sum: .

[0091] Final weights: The weight matrix is ​​a diagonal matrix: Step 6: Use weighted least squares fusion to solve for the optimal error The optimal error variance after fusion is solved using the quadratic error variance formula: Final synchronization uncertainty: Based on the actual measurement results of this invention: In the laboratory, using an SR620 time interval measuring device, the output second pulses of S2 and R in the above-mentioned simulated topology were measured in both conventional and cooperative modes. The measurement duration was 10 minutes. The measurement results were obtained in conventional mode. Collaborative mode , The actual measurement results are in good agreement with the theoretical analysis. The collaborative fusion algorithm of this invention reduces the error from 36.12μs to 30.45μs, improving the synchronization accuracy by 15.7%. This effectively verifies the ability of the multi-path correlation decoupling weighted fusion mechanism to suppress multi-level cumulative errors.

[0092] Based on another aspect of the present invention, the present invention provides a low-voltage power line carrier time synchronization system based on distributed cooperation, the system comprising: Unit 301 is established to establish a clock error state model for nodes in a low-voltage power line carrier network. Based on the clock error state model, a second-order linear state equation is used to characterize the dynamic clock error of each node. Preferably, the clock error state model includes an ideal clock model and an actual clock model; The expression for the ideal clock model is: in, For nodes Ideal clock phase; Nominal frequency; It is absolute physical time; The expression for the actual clock model is: in, For nodes The actual operating frequency; For nodes The initial phase shift; Introducing relative frequency offset , defined as the relative frequency deviation between the actual operating frequency and the nominal frequency: The actual operating frequency is expressed as ; Within the time-frequency synchronization period of the low-voltage distribution area, the frequency will be relatively offset. Treating it as a constant, the actual clock model simplifies to: .

[0093] Preferably, the dynamic clock error of each node is characterized by a second-order linear state equation based on a clock error state model; the dynamic clock error includes: time offset, frequency offset, and random frequency drift; The state equations for the second-order clock error state model are: in, For nodes Time offset, For nodes Frequency offset, The frequency drift is random; the random frequency drift follows a zero-mean Gaussian white noise distribution.

[0094] Observation unit 302 is used to establish a multi-source collaborative observation model based on dynamic clock error, and to collect multi-source observation data including master-slave links, local nodes and neighboring nodes based on the multi-source collaborative observation model; Preferably, the multi-source collaborative observation model includes a local frequency offset observation model, a master-slave synchronous link observation model, and a multi-node distributed observation model of the end neighbor. The local frequency offset observation model is used to collect observation data from local nodes, and its expression is: in, for node The relative offset of the true frequency ； To observe the noise, it follows a zero-mean Gaussian distribution; The master-slave synchronization link observation model includes the boundary clock PCO observation model and the slave clock STA observation model, which are used to collect observation data of the master-slave link; The boundary clock PCO observation model expression is: in, The relative offset of the actual frequency of the master clock CCO; The relative offset of the true frequency of the boundary clock PCO; The observation noise for the boundary clock PCO; The expression for the observation model from the clock STA is: in, The relative offset from the actual frequency of clock STA; The observation noise from the clock STA; The distributed observation model with multiple nodes in the end-neighbor area is used to collect observation data from multiple neighboring nodes. The expression is: in, Neighboring nodes The relative offset of the true frequency; For clock STA nodes and neighboring nodes The interactive observation noise follows a zero-mean Gaussian distribution.

[0095] Processing unit 303 is used to preprocess multi-source observation data and obtain preprocessed effective observation data; Preferably, the multi-source observation data is preprocessed, including: anomaly removal processing of the multi-source observation data using the 3σ criterion, including: Calculate the initial mean and initial variance of the observation data of the local node, the observation data of the master-slave link, and the observation data of the end-slave clock STA neighbor node; The 3σ anomaly detection criteria are determined based on the initial mean and initial variance. If any observed data satisfies the 3σ anomaly detection criteria, the observed data is determined to be an anomaly and is removed. Iteratively execute the initial mean and initial variance calculation, anomaly detection and removal operations until there are no abnormal observations left in the remaining observation data after removing abnormal observations.

[0096] The fusion unit 304 is used to obtain the optimal time error of the node's current synchronization period based on effective observation data and through the established weighted least squares fusion objective function; Preferably, the expression for the weighted least squares fusion objective function is: in, This represents the measurement time error vector for all paths in the current period. For a fixed column vector of all 1s, It is a multi-dimensional adaptive weight matrix. The path error covariance matrix is... This represents the optimal time error for the final solution.

[0097] Preferably, the weight factors of the multi-dimensional adaptive weight matrix satisfy: in, The weight is determined by the number of relay levels in the synchronous transmission path, and decreases accordingly. Channel quality weights ， It is positively correlated with the channel received signal strength indicator (RSSI) of the node; For frequency offset stability weights ， It is positively correlated with the inverse of the variance of the node's historical frequency offset; The neighbor association weight is positively correlated with the number of neighbors of the end clock STA and the neighbor synchronization accuracy.

[0098] Preferably, the path error covariance matrix This includes the path correlation coefficient, which is calculated based on the number of common nodes in the synchronous transmission path. The formula is: Where a and b are the total number of nodes in the synchronization transmission path between the i-th neighbor node and the j-th neighbor node, respectively, and c is the number of common nodes in the two paths.

[0099] The correction unit 305 is used to correct the dynamic clock error of the node based on the optimal time error through a distributed collaborative fusion mechanism, to compensate for the node time offset and frequency offset and suppress the random frequency drift disturbance.

[0100] According to another aspect of the present invention, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a power line carrier time synchronization method based on distributed cooperative methods.

[0101] According to another aspect of the present invention, the present invention provides an electronic device, comprising: The computer-readable storage medium mentioned above; and One or more processors for executing a program in a computer-readable storage medium.

[0102] The present invention has been described with reference to a few embodiments. However, it will be apparent to those skilled in the art that other embodiments besides those disclosed above fall equivalently within the scope of the present invention.

[0103] Generally, all terms used in this invention are interpreted according to their ordinary meaning in the art, unless otherwise expressly defined herein. All references to "a / / the [device, component, etc.]" are openly interpreted as at least one instance of a device, component, etc., unless otherwise expressly stated. The steps of any method disclosed herein need not be performed in the exact order disclosed, unless explicitly stated otherwise.

[0104] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0105] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0106] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0107] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0108] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the protection scope of the present invention.

Claims

1. A low-voltage power line carrier time synchronization method based on distributed cooperation, characterized in that, The method includes: A clock error state model for nodes in a low-voltage power line carrier network is established; based on the clock error state model, a second-order linear state equation is used to characterize the dynamic clock error of each node. A multi-source collaborative observation model is established based on the dynamic clock error, and multi-source observation data including master-slave links, local nodes and neighboring nodes are collected based on the multi-source collaborative observation model. The multi-source observation data is preprocessed to obtain preprocessed effective observation data; Based on the effective observation data, the optimal time error of the node's current synchronization period is obtained through the established weighted least squares fusion objective function; Based on the optimal time error, the dynamic clock error of the nodes is corrected through a distributed collaborative fusion mechanism to compensate for the node time offset and frequency offset and suppress random frequency drift disturbances.

2. The method according to claim 1, characterized in that, The clock error state model includes an ideal clock model and an actual clock model; The expression for the ideal clock model is: in, For nodes Ideal clock phase; Nominal frequency; It is absolute physical time; The expression for the actual clock model is: in, For nodes The actual operating frequency; For nodes The initial phase shift; Introducing relative frequency offset , defined as the relative frequency deviation between the actual operating frequency and the nominal frequency: The actual operating frequency is expressed as ; Within the time-frequency synchronization period of the low-voltage distribution area, the frequency will be relatively offset. Treating it as a constant, the actual clock model simplifies to: 。 3. The method according to claim 2, characterized in that, The clock error state model uses a second-order linear state equation to characterize the dynamic clock error of each node; the dynamic clock error includes: time offset, frequency offset, and random frequency drift. The state equations for the second-order clock error state model are: in, For nodes Time offset, For nodes Frequency shift, The frequency is randomly drifted; the random frequency drift follows a zero-mean Gaussian white noise distribution.

4. The method according to claim 1, characterized in that, The multi-source collaborative observation model includes a local frequency offset observation model, a master-slave synchronous link observation model, and a multi-node distributed observation model of end neighbors. The local frequency offset observation model is used to collect observation data from local nodes, and its expression is: in, for node The relative offset of the true frequency ； To observe the noise, it follows a zero-mean Gaussian distribution; The master-slave synchronization link observation model includes the boundary clock PCO observation model and the slave clock STA observation model, which are used to collect observation data of the master-slave link; The expression for the boundary clock PCO observation model is: in, The relative offset of the actual frequency of the master clock CCO; The relative offset of the true frequency of the boundary clock PCO; The observation noise for the boundary clock PCO; The expression for the observation model from the clock STA is: in, The relative offset from the actual frequency of clock STA; The observation noise from the clock STA; The distributed observation model for multiple neighbor nodes is used to collect observation data from multiple neighbor nodes, and its expression is: in, Neighboring nodes The relative offset of the true frequency; For clock STA nodes and neighboring nodes The interactive observation noise follows a zero-mean Gaussian distribution.

5. The method according to claim 1, characterized in that, The expression for the weighted least squares fusion objective function is: in, This represents the measurement time error vector for all paths in the current period. For a fixed column vector of all 1s, It is a multi-dimensional adaptive weight matrix. The path error covariance matrix is... This represents the optimal time error for the final solution.

6. The method according to claim 5, characterized in that, The weight factors of the multidimensional adaptive weight matrix satisfy: in, The weight is determined by the number of relay levels in the synchronous transmission path, and decreases accordingly. Channel quality weights ， It is positively correlated with the channel received signal strength indicator (RSSI) of the node; For frequency offset stability weights ， It is positively correlated with the inverse of the variance of the node's historical frequency offset; The neighbor association weight is positively correlated with the number of neighbors of the end clock STA and the neighbor synchronization accuracy.

7. The method according to claim 5, characterized in that, The path error covariance matrix This includes a path correlation coefficient, which is calculated based on the number of common nodes in the synchronous transmission path, using the following formula: Where a and b are the total number of nodes in the synchronization transmission path between the i-th neighbor node and the j-th neighbor node, respectively, and c is the number of common nodes in the two paths.

8. The method according to claim 1, characterized in that, The preprocessing of the multi-source observation data includes: using the 3σ criterion to perform anomaly removal processing on the multi-source observation data.

9. The method according to claim 8, characterized in that, The anomaly removal process for the multi-source observation data using the 3σ criterion includes: Calculate the initial mean and initial variance of the observation data of the local node, the observation data of the master-slave link, and the observation data of the end-slave clock STA neighbor node; Based on the initial mean and the initial variance, the 3σ anomaly detection criteria are determined. If any observation data satisfies the 3σ anomaly detection criteria, the observation data is determined to be abnormal observation data and is removed. Iteratively execute the initial mean and initial variance calculation, anomaly detection and removal operations until there are no abnormal observations left in the remaining observation data after removing abnormal observations.

10. A low-voltage power line carrier time synchronization system based on distributed cooperation, characterized in that, The system includes: A clock error state model is established for the nodes of the low-voltage power line carrier network. Based on the clock error state model, a second-order linear state equation is used to characterize the dynamic clock error of each node. The observation unit is used to establish a multi-source collaborative observation model based on the dynamic clock error, and to collect multi-source observation data including master-slave links, local nodes and neighboring nodes based on the multi-source collaborative observation model. The processing unit is used to preprocess the multi-source observation data to obtain the preprocessed effective observation data; The fusion unit is used to obtain the optimal time error of the node's current synchronization period based on the effective observation data and through the established weighted least squares fusion objective function. The correction unit is used to correct the dynamic clock error of the node based on the optimal time error through a distributed collaborative fusion mechanism, to compensate for the node time offset and frequency offset and suppress random frequency drift disturbance.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method as described in any one of claims 1-9.

12. An electronic device, characterized in that, include: The computer-readable storage medium as described in claim 11; as well as One or more processors for executing a program in the computer-readable storage medium.

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