Hybrid time service method based on Beidou satellite navigation and TSN

Abstract

The invention relates to the technical field of time synchronization, and discloses a hybrid time service method based on Beidou satellite navigation and a TSN, which comprises the following steps: acquiring network topology structure information and historical time service error data of each synchronization node in a target distributed system, and constructing a dynamic time delay state set for jitter identification; based on the dynamic time delay state set, a current global time reference is extracted through a time calibration algorithm, a time difference reference vector sequence is generated, and initial synchronization adjustment is carried out on the TSN main node; constructing an inter-node clock convergence function curve according to the slave node response stability, and judging whether the synchronous network is in a low jitter interval or not based on the time offset rate and the time drift trend change; in combination with the node time state after directional offset suppression, a multi-channel redundant time synchronization mechanism in a time window is applied, a weight adjustment coefficient is extracted, and an unbalanced calibration factor is introduced; and according to a regression correction result, evaluating a time service stability index in real time. The method has the advantage of improving the time service stability.

Description

Technical Field

[0001] The present invention relates to the technical field of time synchronization, and in particular to a hybrid timing method based on Beidou satellite navigation and TSN. Background Art

[0002] In modern distributed systems, industrial control networks, and intelligent infrastructure, accurate time synchronization is crucial for ensuring stable system operation, data consistency, and event coordination. Traditional network timing methods primarily utilize synchronization mechanisms based on the Network Time Protocol (NTP) and the Precision Time Protocol (PTP). These methods typically achieve time alignment and time adjustment by calculating the round-trip message delay between a master and slave clock. While these methods can achieve a certain degree of synchronization in simple or local area network environments, they often suffer from unstable network latency in complex or large-scale networks. Specifically, during the timing process, transmission messages are susceptible to interference from factors such as network congestion, queuing delays, link jitter, and routing changes, resulting in unpredictable latency. This latency uncertainty directly introduces time drift errors, which in turn impacts the time consistency of multiple nodes within the system. When timing errors reach a certain level, they can easily lead to data sequence distortion, scheduling anomalies, and even failures in mission-critical systems. This problem is particularly prominent in high-reliability scenarios such as power dispatching, traffic control, high-speed communications, and industrial manufacturing. Therefore, it is imperative to design a hybrid timing method based on BeiDou satellite navigation and TSN to improve timing stability. Summary of the Invention

[0003] In response to the shortcomings of the existing technology, the present invention provides a hybrid timing method based on Beidou satellite navigation and TSN, which has the advantage of improving timing stability and solves the problems in the above-mentioned background technology.

[0004] To achieve the above-mentioned purpose of improving timing stability, the present invention provides the following technical solution: a hybrid timing method based on Beidou satellite navigation and TSN, comprising the following steps: Obtain network topology information and historical timing error data for each synchronization node in the target distributed system. Combined with inter-node link status fluctuation characteristics and message delay records, a dynamic delay state set for jitter identification is constructed. Based on the dynamic delay state set, the current global time reference is extracted through the time calibration algorithm, a time difference reference vector sequence is generated, the initial synchronization adjustment of the TSN master node is performed, and the response stability of each slave node is monitored; Based on the response stability of slave nodes, a clock convergence function curve is constructed between nodes. Based on the time offset rate and time drift trend changes, it is determined whether the synchronization network is in a low jitter range. If so, the clock consistency policy scheduling process is triggered to suppress the deviation of abnormal clock nodes in a targeted manner. Combined with the node time state after directional offset suppression, a multi-channel redundant time synchronization mechanism is applied within the time window. The weight adjustment coefficient is extracted and an unbalanced calibration factor is introduced to perform fitting regression correction on the clocks across nodes. Based on the regression correction results, the timing stability indicators are evaluated in real time and a synchronization consistency assessment report is output.

[0005] Preferably, the process of constructing a dynamic delay state set for jitter identification is: Collect link data packet forwarding delay, queue length, routing path switching frequency and timing error statistics of each synchronization node in multiple time windows; Normalize link data and reorganize data according to the physical topology and logical connection relationship between nodes; A multi-dimensional delay fluctuation feature vector group is constructed based on the reorganized data, and the delay variation trend is extracted through the window sliding mechanism. An improved time series clustering algorithm is used to classify and model the delay feature vector groups and identify node clusters with common jitter behaviors. The node clusters and the corresponding delay fluctuation models are integrated with the historical error records to construct a set of quantitative instability indicators describing the current synchronization state as the dynamic delay state set.

[0006] Preferably, the process of extracting the current global time reference through the time calibration algorithm is: Receive multiple BeiDou satellite navigation signals and extract the high-precision timestamp information contained therein; According to the position of the timing receiving node, signal pseudorange and signal-to-noise ratio, the pseudorange combined with least squares algorithm is used to perform preliminary time solution. Combining the multipath offset correction model of the received signal with the atmospheric delay compensation function, the deviation of the solution result is adjusted; The Kalman filter is introduced to estimate and smooth the time deviation sequence within multiple solution cycles to obtain the global time reference.

[0007] Preferably, the process of generating a time difference reference vector sequence is: Select the TSN master node and several typical slave nodes as the synchronization control group; Using the current global time base as a reference, record the local timestamp difference between the master and slave nodes within the same time period; Perform error filtering and smoothing on the timestamp difference to form a basic time difference vector; The node weight factor is introduced to perform weighted processing on the time difference vector by combining the link transmission stability, historical error fluctuation level and topological location sensitivity. The final output is a time difference reference vector sequence.

[0008] Preferably, the process of constructing the inter-node clock convergence function curve is: Collect the response timestamps of each slave node to the master node synchronization signal within a continuous time period; Based on the time difference reference vector sequence, the evolution trajectory of the alignment error of each node is calculated; The error evolution trajectory is fitted into a time function curve, and the function trend factor is extracted by combining the convergence speed and error fluctuation range of each node; The sliding window averaging method is used to smooth the convergence trajectory, and a clock convergence function curve is constructed to reflect the trend of node synchronization state changes.

[0009] Preferably, the process of determining whether the synchronization network is in a low jitter range is: According to the constructed clock convergence function curve, the time offset rate and drift trend change value of each node within the preset time window are extracted; Calculate the synchronization standard deviation and maximum time deviation value between all nodes to form a set of network synchronization jitter indicators; Set the judgment threshold range, including the maximum allowed jitter amplitude and the minimum synchronization standard stability value; When the jitter indicators of all nodes in the network are lower than the set threshold and the synchronization standard deviation remains in the stable range for multiple consecutive cycles, it is determined to be in the low jitter range.

[0010] Preferably, the process of suppressing the directional offset of the abnormal clock node is as follows: Identify nodes whose offset rate deviates from the average value of the entire network from the clock convergence function curve; Combine the historical stability data of abnormal clock nodes with their topological locations in the network to calculate the offset suppression priority of abnormal clock nodes. According to the master node timing trajectory and the drift direction of the abnormal clock node, a unilateral or bilateral suppression strategy is constructed, and corresponding time correction instructions are generated; Inject suppression instructions into the TSN time synchronization scheduling path to ensure that the correction information reaches the target node within the precise time slot; The offset suppression effect is verified by reviewing the correction results and the changes in alignment errors before and after suppression.

[0011] Preferably, the process of fitting and regressing the clocks across nodes is as follows: Reconstruct the timing error matrix between the master and slave nodes based on the node time state after the offset has been suppressed; A multi-channel redundancy mechanism is introduced to perform parallel comparison between the Beidou time reference and the time difference within the TSN network. A weighted regression algorithm is used to establish a cross-node time fitting model, fit the error distribution and extract the fitting residual; A non-equilibrium calibration factor is introduced in the fitting process to give adaptive adjustment weights to nodes with poor stability.

[0012] Preferably, the process of outputting the synchronization consistency assessment report is: Summarize the alignment error, drift trend, jitter index, and clock stability score of all nodes after regression correction; Compare the above indicators with the set evaluation threshold library to generate the synchronization status label of each node; Construct a network synchronization consistency scoring matrix to mark abnormal node trajectories and potential synchronization risk areas; Extract the set of nodes with insufficient stability, form a target list for the next round of synchronization optimization and control, and output a synchronization consistency assessment report.

[0013] Compared with the existing technology, the present invention provides a hybrid timing method based on Beidou satellite navigation and TSN, which has the following beneficial effects: The present invention effectively overcomes the time drift problem caused by link fluctuations, asymmetric path transmission and delay jitter in traditional network timing by integrating the high-precision global time reference provided by Beidou satellites with the deterministic synchronization mechanism under the TSN architecture; adopts a dynamic delay state set construction mechanism and multi-cycle error modeling method to accurately identify node clusters with abnormal synchronization status in the network; integrates Beidou and TSN multi-source time references based on algorithms such as Kalman filtering and weighted regression, significantly improving the accuracy and robustness of clock alignment; by constructing a clock convergence function curve between nodes, dynamic quantification of node synchronization trends and fluctuation states is achieved, which helps to identify and suppress abnormal clock offset paths; finally, a synchronization consistency assessment report is used to provide system-level synchronization status diagnosis results and control basis, which has the advantages of high stability, high scalability and high adaptability, and is particularly suitable for key scenarios such as distributed industrial control systems, intelligent manufacturing platforms, wide-area sensor networks and vehicle networks that have strict requirements on time consistency. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a flow chart of the steps of the present invention. DETAILED DESCRIPTION

[0015] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0016] Example 1: Please refer to Figure 1As shown, a hybrid timing method based on BeiDou satellite navigation and TSN according to an embodiment of the present invention includes the following steps: S1: Obtain the network topology information and historical timing error data of each synchronization node in the target distributed system, combine the link status fluctuation characteristics between nodes and the message delay records, and construct a dynamic delay state set for jitter identification.

[0017] The process of constructing a dynamic delay state set for jitter identification in S1 is as follows: Collect link packet forwarding delay, queue length, routing path switching frequency and timing error statistics of each synchronization node in multiple time windows; set a unified time reference benchmark and divide the entire timing system into multiple sliding time windows, with each window period ranging from 1 second to 10 seconds, which is dynamically adjusted according to the network rate; in each time window, capture the timestamps of packet sending and receiving of each node in real time through the TSN synchronization communication protocol, and calculate the single-hop and multi-hop link forwarding delay; at the same time, collect the queue length and queue waiting time in the switching node queue, and extract them as features reflecting the link congestion status; monitor routing path switching events occurring in the network, record the number of path switching and triggering time, and use them to characterize the path fluctuation frequency; summarize the timing deviation records of each node in the time window to form an error statistical series, forming a basic link behavior data set; Link data is normalized and reorganized according to the physical topology and logical connection relationships between nodes. Collected numerical features, such as forwarding delay, queue length, and error deviation, are normalized using the Z-score method to achieve a mean of 0 and a standard deviation of 1 to eliminate dimensionality effects. Non-numerical features, such as path switching status, are converted into numerical inputs using one-hot encoding or event frequency statistics. Combined with the system's internal topology diagram and link connection table, the normalized features are rearranged according to the node pair dimension to form a multidimensional feature vector with each pair of adjacent nodes as the unit. The rearranged structure is uniformly organized into a three-dimensional tensor, with the dimensions being the time window sequence, the node pair identifier, and the feature dimension. A multidimensional delay fluctuation feature vector group is constructed based on the reorganized data, and delay variation trends are extracted through a window sliding mechanism. Within the aforementioned three-dimensional data tensor, multiple consecutive time windows are selected to form a sliding subsequence, and the rate of change of the eigenvalues ​​in each subsequence is calculated. For metrics such as forwarding delay and queue length, their mean, variance, maximum and minimum values, and first-order derivative within the window are calculated to capture variation trends. A delay fluctuation feature vector group is formed for each node pair within a given time period, and this vector group structure preserves time series characteristics. The window position and corresponding timestamp are also recorded to ensure that fluctuation trends can be tied to specific time periods. An improved time series clustering algorithm is used to classify and model delay feature vector groups, identifying node clusters with common jitter behavior. A dynamic time warping distance metric is used to calculate the similarity of feature change curves for different node pairs. An improved K-shape clustering algorithm is introduced to perform cluster analysis on all fluctuation feature vector groups. Clustering quality is evaluated using metrics such as the silhouette coefficient and the Davies-Bouldin index to determine the optimal number of clusters. After clustering, node pairs belonging to the same cluster are merged into a cluster with similar delay behavior and labeled as a node cluster with common jitter characteristics. The node clusters and the corresponding delay fluctuation models are integrated with the historical error records to construct a set of quantitative instability indicators describing the current synchronization state as the dynamic delay state set.

[0018] S2: Based on the dynamic delay state set, the current global time reference is extracted through the time calibration algorithm, a time difference reference vector sequence is generated, the initial synchronization adjustment of the TSN master node is performed, and the response stability of each slave node is monitored.

[0019] The process of extracting the current global time reference by the time calibration algorithm in S2 is: Receive multiple Beidou satellite navigation signals and extract the high-precision timestamp information contained therein; configure a GNSS receiving module on the target timing node to support simultaneous reception of Beidou system B1I / B3I and other multi-frequency signals; each Beidou satellite signal frame contains UTC reference time, high-precision week time, and millisecond-level timing tags; the receiving module extracts the navigation message field containing the timestamp by synchronously detecting the satellite signal main carrier and navigation message; calculate the current visible satellite list based on ephemeris data, and eliminate low-quality satellite signals with low elevation angles and signal-to-noise ratios; and cache the timing information of high-quality satellites in the timing data buffer; According to the position of the timing receiving node, signal pseudorange and signal-to-noise ratio, the pseudorange combined with least squares algorithm is used to perform preliminary time solution; the pseudorange calculation formula is: ; Where, is the pseudorange between the receiving node and the i-th satellite, c is the speed of light, The time of receipt; is the launch time of the i-th satellite, is the path offset item, is the error term; Using signals from at least four BeiDou satellites, the receiver's position (x, y, z) and reception time offset are jointly estimated. Weighting coefficients are adjusted based on the SNR of each satellite, giving higher SNR values ​​greater weight to reduce interference from weak signals. The solution output includes an estimated time offset of the current node relative to UTC. The solution is adjusted for deviations by combining the multipath offset correction model of the received signal with the atmospheric delay compensation function. A local scene terrain model is introduced to determine the reflection conditions of buildings and terrain, and the relative delay between the reflection path and the main path is calculated. A dual-frequency receiver is used to measure the carrier phase difference and construct a code phase difference model to eliminate pseudorange delays caused by buildings. A pseudorange residual model based on maximum expectation is applied to correct errors in satellite signals affected by multipath. The Kalman filter is introduced to estimate and smooth the time deviation sequence within multiple solution cycles to obtain the global time reference.

[0020] The process of generating the time difference reference vector sequence in S2 is: Select a TSN master node and several typical slave nodes as a synchronization control group; in a distributed TSN timing network, identify the current master clock node through the network management module; based on the network topology and node communication frequency, prioritize and select several slave nodes that are representative of the master node; apply synchronization monitoring marks to the selected nodes, and establish a master-slave node comparison relationship table containing the unique identifier, physical connection path, and historical error range of each pair of synchronization nodes; the number of nodes in the synchronization control group should be no less than 3 to ensure that the reference value has a certain degree of statistical representativeness and anti-interference ability; Using the current global time base as a reference, record the local timestamp difference between the master and slave nodes in the same time period; set a unified sampling window and alignment period; in each synchronization period, based on the global time base extracted by the BeiDou system, trigger the timing request between the master node and all the control slave nodes; collect the current local system clock value of each node to form the master node time With the slave node time Calculate the original time difference between the master and the slave using the following formula: , where i is the slave node number; record The sequence and sampling timestamp constitute a set of original time difference samples; Perform error filtering and smoothing on the timestamp difference to form a basic time difference vector; The node weight factor is introduced to weight the time difference vector by combining the link transmission stability, historical error fluctuation level, and topological location sensitivity. A weight factor is assigned to each slave node, considering the following indicators: Link stability coefficient: calculated based on the fluctuation range of link transmission delay in recent cycles; Historical error stability coefficient: evaluated by the standard deviation of historical timing deviations; Topological sensitivity coefficient: depends on the hierarchical depth and path dependency of the node in the topological structure; The final output is a time difference reference vector sequence.

[0021] S3: Based on the response stability of the slave nodes, a clock convergence function curve is constructed between nodes. Based on the changes in the time offset rate and time drift trend, it is determined whether the synchronization network is in a low jitter range. If so, the clock consistency policy scheduling process is triggered to suppress the deviation of abnormal clock nodes in a targeted manner.

[0022] The process of constructing the inter-node clock convergence function curve in S3 is as follows: Collect the response timestamps of each slave node to the master node's synchronization signal within a continuous time period; establish a synchronous communication mechanism based on IEEE 802.1AS between the master node and each slave node. The master node regularly broadcasts synchronization frames. During each synchronization frame transmission, the slave node records the receiving timestamp and the master node records the sending timestamp. Each slave node calculates the deviation between its local time and the master node time by comparing the received and sent frames. The deviation value sequence of each slave node within multiple continuous synchronization cycles is recorded to form a timestamp time series; Based on the time difference reference vector sequence, the evolution trajectory of the alignment error of each node is calculated; the time difference sequence between the master and slave nodes at the corresponding moment is obtained from the generated time difference reference vector sequence. For each node, the actual response timestamp of the node is compared with the node weighted reference time difference to obtain the instantaneous alignment error. The instantaneous alignment error is sequenced according to the time dimension to form the node error evolution trajectory sequence; The error evolution trajectory is fitted into a time function curve, and the function trend factor is extracted by combining the convergence speed and error fluctuation range of each node; The sliding window averaging method is used to smooth the convergence trajectory, and a clock convergence function curve is constructed to reflect the trend of node synchronization state changes.

[0023] The process of determining whether the synchronization network is in the low jitter range in S3 is as follows: Based on the constructed clock convergence function curve, the time offset rate and drift trend change value of each node within the preset time window are extracted. The sliding time window size is set, for example, 5 consecutive time periods, to capture the dynamic trend of clock offset changes. For each synchronization node, the time alignment error at each moment within the window is extracted based on its constructed clock convergence function curve. The time offset rate of the node within the window is calculated, that is, the average growth rate of the time offset, which is used to assess the speed of the node's deviation from the master node. At the same time, the drift trend change value is calculated, that is, the first-order difference average of the time offset, which is used to quantify the fluctuation intensity of the offset value within the window and reflect the synchronization fluctuation behavior of the node. The synchronization standard deviation and maximum time deviation between all nodes are calculated to form a set of network synchronization jitter indicators. In each time period, the current offsets of all nodes are combined into an offset data set. The standard deviation of the offset data set is calculated to reflect the dispersion of the alignment errors of each node in the entire network, which serves as a quantitative indicator of synchronization consistency. At the same time, the difference between the maximum offset and the minimum offset is extracted to obtain the maximum time deviation value, which indicates the most extreme timing error phenomenon in the network. The time deviation rate, drift trend, synchronization standard deviation and maximum deviation value are combined to form a set of network synchronization jitter indicators, which are used to comprehensively evaluate the stability of the synchronization network. Set the judgment threshold range, including the maximum allowed jitter amplitude and the minimum synchronization standard stability value; preset a set of standard thresholds for judging the synchronization status, including: A maximum permissible excursion rate threshold, for example, no more than 50 nanoseconds per second; Maximum drift trend change threshold, for example, the average fluctuation amplitude does not exceed 20 nanoseconds; The upper limit of the synchronization standard deviation is allowed, for example, no more than 100 nanoseconds; The maximum time deviation limit, for example, the time difference between any two nodes does not exceed 200 nanoseconds; When the jitter indicators of all nodes in the network are lower than the set threshold and the synchronization standard deviation remains in the stable range for multiple consecutive cycles, it is determined to be in the low jitter range.

[0024] The process of suppressing the directional offset of abnormal clock nodes in S3 is as follows: Identify nodes whose drift rates deviate from the network-wide average from the clock convergence function curve; analyze the clock convergence function curves of all synchronized nodes to extract the time drift rate of each node; calculate the average and standard deviation of the network-wide time drift rate to construct a reference alignment benchmark interval; compare the drift rate of each node with the network-wide average; if the absolute drift amplitude exceeds a set multiple threshold, mark the node as an abnormally drifted node; and use the identified set of nodes as candidate targets for subsequent drift suppression. The deviation suppression priority of abnormal clock nodes is calculated by combining the historical stability data of abnormal clock nodes with their topological position in the network. For each abnormal node, its historical timing deviation records are extracted, and its synchronization standard deviation, maximum offset, and average drift direction over the past several cycles are statistically analyzed to calculate its historical stability score. The criticality of the node in the network topology is assessed, such as whether it is at the edge, whether it is a bridge forwarding node, and whether it has a multi-hop forwarding path, to generate a topological sensitivity coefficient. Combining the historical stability score and the topological sensitivity coefficient, a weighted ranking model is used to calculate the suppression priority index of each abnormal node, which is used to determine the priority processing order and policy strength. Based on the master node timing trajectory and the drift direction of the abnormal clock node, a unilateral or bilateral suppression strategy is constructed, and corresponding time correction instructions are generated; the relative change trend of the abnormal node's current offset direction and the master node timing trajectory is compared; if the node is offset in only one direction and has a stable drift slope, a unilateral suppression strategy is adopted, that is, a one-time correction timestamp is sent to the node; if the node offset direction fluctuates greatly or the offset oscillates periodically, a bilateral suppression strategy is adopted, that is, upper and lower limits are set within the synchronization window, and the node convergence interval is adjusted through continuous suppression; based on the suppression strategy, a time correction instruction is constructed, which includes the suppression target node, correction value, correction direction and execution timestamp; Inject suppression instructions into the TSN time synchronization scheduling path to ensure that the correction information reaches the target node within the precise time slot; analyze the scheduling path and time slot configuration of the TSN network and select an idle time slot or management time slot within the appropriate GCL cycle to inject the suppression instruction; encapsulate the time correction command using the management frame format supported by the IEEE 802.1AS or 802.1Qbv protocol to ensure that it can be identified as a high-priority synchronization control message; in the network control plane, the controller or scheduler sends the correction frame to ensure that it arrives at the target node along the optimal path within the specified synchronization period; The offset suppression effect is verified by reviewing the correction results and the changes in alignment errors before and after suppression.

[0025] Example 2: Figure 1 As shown, a hybrid timing method based on BeiDou satellite navigation and TSN also includes the following steps: S4: Combined with the node time state after directional offset suppression, the multi-channel redundant timing mechanism within the time window is applied, the weight adjustment coefficient is extracted and the non-balanced calibration factor is introduced to perform fitting regression correction on the clocks across nodes.

[0026] The process of fitting and regressing the clocks across nodes in S4 is as follows: Based on the node time state after the offset has been suppressed, the timing error matrix between the master and slave nodes is reconstructed. After the abnormal node offset is suppressed, the time alignment difference between all master and slave nodes at the current moment is collected. The master node is used as the benchmark, and the local timestamp difference between it and each slave node is recorded to construct a preliminary error matrix. The error matrix is ​​then processed to remove abnormal data points such as signal packet loss and timeouts. A multi-channel redundancy mechanism is introduced to perform a parallel comparison between the Beidou time reference and the internal time difference of the TSN network. While obtaining the internal error matrix of the TSN network, the Beidou timing module is used to obtain the high-precision UTC timestamp corresponding to the current moment of the master node. The current Beidou timestamp is also bound to all slave nodes. A dual-channel time difference list is constructed using the TSN time and Beidou time corresponding to the master and slave nodes respectively. The TSN timing results are compared with the Beidou timing data to identify the consistency and deviation, forming a cross-channel time difference reference comparison table. A weighted regression algorithm is used to establish a cross-node time fitting model, fit the error distribution and extract the fitting residual; During the fitting process, a non-equilibrium calibration factor is introduced to give adaptive adjustment weights to nodes with poor stability. For each node, the error fluctuation range, offset rate change trend and historical error stability coefficient are statistically analyzed over multiple cycles to form a comprehensive stability score. The non-equilibrium calibration factor is introduced. The worse the stability, the larger the calibration factor.

[0027] S5: Based on the regression correction results, the timing stability indicators are evaluated in real time and a synchronization consistency assessment report is output.

[0028] The process of outputting the synchronization consistency assessment report in S5 is as follows: Summarize the alignment error, drift trend, jitter index, and clock stability score of all nodes after regression correction. After completing the regression correction step, extract the residual error sequence of each synchronization node at the latest moment, that is, the difference between the fitted value and the actual alignment time. Statistically analyze the residual error and calculate the average alignment error, maximum offset, and error fluctuation range of each node. At the same time, based on the error change slope within a continuous time period, extract the drift trend parameters of the node, such as the offset direction and change speed. Use data such as the standard deviation of the error and the drift rate to generate a synchronization jitter index to reflect the stability of the node's time synchronization. Summarize the above items, combine the node's historical performance and current response behavior, and calculate the node's clock stability score using a preset weight model. Compare the above indicators with the set evaluation threshold library to generate the synchronization status label of each node; A network synchronization consistency scoring matrix is ​​constructed to mark abnormal node trajectories and potential synchronization risk areas. A node-level synchronization scoring table is constructed based on each node's synchronization status label and stability score. Based on the network topology relationships between nodes, the node scores are combined into a two-dimensional matrix, with rows and columns representing node numbers and key synchronization attributes, respectively. Nodes with significantly low scores are visually marked in the matrix, and their time series score changes are tracked to identify their error evolution paths. If multiple problem nodes have regional clustering or link correlation, they are automatically marked as potential synchronization risk areas, indicating the presence of topological structural risks. Extract the set of nodes with insufficient stability, form a target list for the next round of synchronization optimization and control, and output a synchronization consistency assessment report.

[0029] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0030] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A hybrid timing method based on BeiDou satellite navigation and TSN, characterized in that: The following steps are involved: Obtain network topology information and historical timing error data for each synchronization node in the target distributed system. Combined with inter-node link status fluctuation characteristics and message delay records, a dynamic delay state set for jitter identification is constructed. Based on the dynamic delay state set, the current global time reference is extracted through the time calibration algorithm, a time difference reference vector sequence is generated, the initial synchronization adjustment of the TSN master node is performed, and the response stability of each slave node is monitored; Based on the response stability of slave nodes, a clock convergence function curve is constructed between nodes. Based on the time offset rate and time drift trend changes, it is determined whether the synchronization network is in a low jitter range. If so, the clock consistency policy scheduling process is triggered to suppress the deviation of abnormal clock nodes in a targeted manner. Combined with the node time state after directional offset suppression, a multi-channel redundant time synchronization mechanism is applied within the time window. The weight adjustment coefficient is extracted and an unbalanced calibration factor is introduced to perform fitting regression correction on the clocks across nodes. Based on the regression correction results, the timing stability indicators are evaluated in real time and a synchronization consistency assessment report is output.

2. A hybrid timing method based on BeiDou satellite navigation and TSN according to claim 1, characterized in that: The process of constructing a dynamic delay state set for jitter identification is as follows: Collect link data packet forwarding delay, queue length, routing path switching frequency and timing error statistics of each synchronization node in multiple time windows; Normalize link data and reorganize data according to the physical topology and logical connection relationship between nodes; A multi-dimensional delay fluctuation feature vector group is constructed based on the reorganized data, and the delay variation trend is extracted through the window sliding mechanism. An improved time series clustering algorithm is used to classify and model the delay feature vector groups and identify node clusters with common jitter behaviors. The node clusters and the corresponding delay fluctuation models are integrated with the historical error records to construct a set of quantitative instability indicators describing the current synchronization state as the dynamic delay state set.

3. A hybrid timing method based on BeiDou satellite navigation and TSN according to claim 2, characterized in that: The process of extracting the current global time base through the time calibration algorithm is: Receive multiple BeiDou satellite navigation signals and extract the high-precision timestamp information contained therein; According to the position of the timing receiving node, signal pseudorange and signal-to-noise ratio, the pseudorange combined with least squares algorithm is used to perform preliminary time solution. Combining the multipath offset correction model of the received signal with the atmospheric delay compensation function, the deviation of the solution result is adjusted; The Kalman filter is introduced to estimate and smooth the time deviation sequence within multiple solution cycles to obtain the global time reference.

4. A hybrid timing method based on BeiDou satellite navigation and TSN according to claim 3, characterized in that: The process of generating a time difference reference vector sequence is: Select the TSN master node and several typical slave nodes as the synchronization control group; Using the current global time base as a reference, record the local timestamp difference between the master and slave nodes within the same time period; Perform error filtering and smoothing on the timestamp difference to form a basic time difference vector; The node weight factor is introduced to perform weighted processing on the time difference vector by combining the link transmission stability, historical error fluctuation level and topological location sensitivity. The final output is a time difference reference vector sequence.

5. A hybrid timing method based on BeiDou satellite navigation and TSN according to claim 4, characterized in that: The process of constructing the clock convergence function curve between nodes is as follows: Collect the response timestamps of each slave node to the master node synchronization signal within a continuous time period; Based on the time difference reference vector sequence, the evolution trajectory of the alignment error of each node is calculated; The error evolution trajectory is fitted into a time function curve, and the function trend factor is extracted by combining the convergence speed and error fluctuation range of each node; The sliding window averaging method is used to smooth the convergence trajectory, and a clock convergence function curve is constructed to reflect the trend of node synchronization state changes.

6. A hybrid timing method based on BeiDou satellite navigation and TSN according to claim 5, characterized in that: The process of determining whether the synchronization network is in the low jitter range is as follows: According to the constructed clock convergence function curve, the time offset rate and drift trend change value of each node within the preset time window are extracted; Calculate the synchronization standard deviation and maximum time deviation value between all nodes to form a set of network synchronization jitter indicators; Set the judgment threshold range, including the maximum allowed jitter amplitude and the minimum synchronization standard stability value; When the jitter indicators of all nodes in the network are lower than the set threshold and the synchronization standard deviation remains in the stable range for multiple consecutive cycles, it is determined to be in the low jitter range.

7. A hybrid timing method based on BeiDou satellite navigation and TSN according to claim 6, characterized in that: The process of suppressing directional offset of abnormal clock nodes is as follows: Identify nodes whose offset rate deviates from the average value of the entire network from the clock convergence function curve; Combine the historical stability data of abnormal clock nodes with their topological locations in the network to calculate the offset suppression priority of abnormal clock nodes. According to the master node timing trajectory and the drift direction of the abnormal clock node, a unilateral or bilateral suppression strategy is constructed, and corresponding time correction instructions are generated; Inject suppression instructions into the TSN time synchronization scheduling path to ensure that the correction information reaches the target node within the precise time slot; The offset suppression effect is verified by reviewing the correction results and the changes in alignment errors before and after suppression.

8. A hybrid timing method based on BeiDou satellite navigation and TSN according to claim 7, characterized in that: The process of fitting and regressing the clocks across nodes is as follows: Reconstruct the timing error matrix between the master and slave nodes based on the node time state after the offset has been suppressed; A multi-channel redundancy mechanism is introduced to perform parallel comparison between the Beidou time reference and the time difference within the TSN network. A weighted regression algorithm is used to establish a cross-node time fitting model, fit the error distribution and extract the fitting residual; A non-equilibrium calibration factor is introduced in the fitting process to give adaptive adjustment weights to nodes with poor stability.

9. A hybrid timing method based on BeiDou satellite navigation and TSN according to claim 8, characterized in that: The process of outputting a synchronization consistency assessment report is as follows: Summarize the alignment error, drift trend, jitter index, and clock stability score of all nodes after regression correction; Compare the above indicators with the set evaluation threshold library to generate the synchronization status label of each node; Construct a network synchronization consistency scoring matrix to mark abnormal node trajectories and potential synchronization risk areas; Extract the set of nodes with insufficient stability, form a target list for the next round of synchronization optimization and control, and output a synchronization consistency assessment report.

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