A high-precision time and frequency synchronization optimization method based on BeiDou system

Abstract

The present invention relates to the field of control technology of synchronization signals, and in particular to a high-precision time and frequency synchronization optimization method based on the Beidou system. The method comprises the following steps: receiving initial time data, and constructing a satellite time matrix according to the initial time data to obtain Beidou satellite time matrix data; performing time offset calculation according to the Beidou satellite time matrix data to obtain Beidou satellite time offset matrix data; obtaining receiver position data, performing spatial path delay processing according to the receiver position data and Beidou satellite time offset matrix data to obtain time delay processing matrix data; obtaining time delay inversion matrix data according to the time delay processing matrix data; obtaining time delay correction data according to the time delay inversion matrix data, and performing frequency clock tuning on the time delay correction data to obtain time and frequency synchronization data. The present invention can still maintain a high time synchronization accuracy in a multi-satellite, multi-path signal environment.

Description

Technical Field

[0001] The present invention relates to the technical field of synchronization signal control, and in particular to a high-precision time and frequency synchronization optimization method based on the Beidou system. Background Art

[0002] With the widespread application of global satellite navigation systems (GNSS) such as the Global Positioning System (GPS), Beidou Satellite Navigation System (BDS), and Galileo, satellite timing technology has become an important means to achieve high-precision time synchronization on a global scale. Especially in the fields of communications, finance, power systems, aerospace, etc., high-precision time synchronization is an important basis for ensuring the normal operation of the system and improving efficiency and safety. However, since satellite signals are affected by various factors such as the atmosphere, ionosphere, multipath effects, and frequency drift during propagation, it is difficult to maintain the accuracy of satellite timing at an extremely high level, especially in complex environments, where the error is further increased.

[0003] Current time and frequency synchronization technology mainly relies on a single satellite or a single time data source for timing calibration. This method is prone to large time errors when the signal is interfered with, the signal path is blocked, or time drift occurs, and it is difficult to meet the high-precision requirements for time synchronization in certain key areas. Especially in complex geographical environments such as cities, high altitudes, and polar regions, satellite signals are easily interfered with by factors such as atmospheric changes and weather effects. Traditional time synchronization methods cannot respond to these interferences in real time, resulting in reduced synchronization accuracy. Summary of the invention

[0004] In order to solve the above technical problems, the present invention proposes a high-precision time and frequency synchronization optimization method based on the Beidou system to solve at least one of the above technical problems.

[0005] The present application provides a high-precision time and frequency synchronization optimization method based on the BeiDou system, comprising the following steps:

[0006] Step S1: receiving initial time data from two or more Beidou satellites, and constructing a satellite time matrix according to the initial time data to obtain Beidou satellite time matrix data;

[0007] Step S2: performing time offset calculation according to Beidou satellite time matrix data to obtain Beidou satellite time offset matrix data;

[0008] Step S3: Acquire the receiver position data, perform space path delay processing according to the receiver position data and Beidou satellite time offset matrix data, and obtain time delay processing matrix data;

[0009] Step S4: performing signal propagation error inversion processing according to the time delay processing matrix data to obtain time delay inversion matrix data;

[0010] Step S5: Perform frequency drift correction according to the time delay inversion matrix data to obtain time delay correction data, and perform frequency clock tuning on the time delay correction data to obtain time and frequency synchronization data, so as to perform high-precision time and frequency synchronization optimization operations of the Beidou system.

[0011] In the present invention, the time data of multiple satellites are matrixed and corrected to improve the accuracy of time synchronization from multiple dimensions, and nanosecond time synchronization accuracy can be achieved. By combining the precise position data of the receiver with the Beidou satellite time offset matrix for spatial path delay processing, the signal delay error introduced by the distance between the satellite and the receiving device, position change, and the atmosphere (ionosphere, troposphere) can be effectively eliminated. The spatial path delay processing optimizes the time delay correction of signal propagation through multiple corrections, and further improves the accuracy of time synchronization, especially in complex environments, such as urban areas or places with large terrain changes. Through the signal propagation error inversion processing of step S4, the error sources in propagation, such as multipath effects, ionospheric interference, etc., can be effectively identified and corrected, and the errors can be corrected in real time through the iterative optimization process, reducing the interference of complex environments on signal propagation. Through the frequency drift correction and frequency clock tuning in step S5, the frequency drift error in the satellite signal is dynamically corrected, which not only ensures the accuracy of time synchronization, but also ensures the consistency of frequency, avoiding synchronization failure or inaccuracy due to clock drift.

[0012] Preferably, the Beidou satellite time matrix data includes collaborative time matrix data and layered time matrix data, and step S1 is specifically as follows:

[0013] Step S11: receiving initial time data from two or more Beidou satellites;

[0014] Step S12: constructing a collaborative time matrix according to the initial time data to obtain collaborative time matrix data;

[0015] Step S13: construct a hierarchical time matrix according to the initial time data to obtain hierarchical time matrix data.

[0016] The collaborative time matrix in the present invention can integrate the time data between the satellites by collaboratively processing the time signals of multiple Beidou satellites, eliminate the unstable factors in a single satellite signal, and enhance the stability and consistency of the system timing. The layered time matrix layers the time data according to different processing levels, so that the data of each layer is corrected to different degrees, especially the multi-layer processing such as error correction, time and space delay correction, and frequency drift correction can ensure the high precision of time synchronization. With the combination of these two time matrices, the system can not only rely on multi-source data for optimization, but also reduce various errors in the data through layered processing, thereby obtaining a more accurate timing result than a single signal. Since the collaborative time matrix is ​​based on the time data of multiple satellites, the distortion or interference of any single satellite signal can be compensated by the data of other satellites, thereby enhancing the robustness and anti-interference ability of the time data. Through the collaborative time matrix, the time data from multiple Beidou satellites can be efficiently utilized, instead of relying solely on a single satellite signal. The multi-satellite collaborative timing method is adopted to improve the timing capability of the entire system, especially when the satellite signal is partially blocked and the signal quality fluctuates, it can still maintain efficient time synchronization.

[0017] Preferably, the collaborative time matrix is ​​constructed as follows:

[0018] Initialize the matrix according to the initial time data to obtain the initial time matrix data;

[0019] Perform time period comparison on the initial time matrix data to obtain initial time comparison matrix data;

[0020] Extracting the coordinated frequency features from the initial time comparison matrix data to obtain coordinated frequency feature data;

[0021] Obtain historical collaborative data corresponding to Beidou satellites, and extract collaborative output features based on the historical collaborative data to obtain collaborative output feature data;

[0022] The initial time comparison matrix data is weighted according to the collaborative frequency characteristic data and the collaborative output characteristic data to obtain the collaborative time matrix data.

[0023] The comparison-based processing method in the present invention can effectively detect small errors or offsets in satellite signals, so that the system can better correct time synchronization deviations at the data level. Collaborative frequency feature extraction can help the system capture subtle changes in satellite signals by extracting frequency features in satellite signals, making frequency features more stable, thereby improving the accuracy of timing. The two steps of collaborative frequency feature extraction and collaborative output feature extraction are combined by analyzing real-time signals and historical data to enable the system to have adaptive capabilities. The system can dynamically adapt to frequency changes of different satellite signals and make optimal correction decisions in combination with historical data. The use of historical collaborative data can provide a more robust correction basis. Historical data can reveal the long-term characteristics of satellite signals, such as periodic changes or long-term drift trends. By combining historical data for correction, the system can better predict and correct future time drift. The system eliminates random errors and local anomalies in a single satellite signal through the collaborative processing of multiple Beidou satellite signals. The time data of each satellite signal participates in the collaborative processing and weighted analysis of the matrix, and a more robust collaborative time matrix can be formed. Through collaborative frequency feature extraction, the system can extract more stable frequency features from time data. Frequency characteristics are relatively less sensitive to external environmental interference (such as atmosphere, electromagnetic interference, etc.), so the extracted feature data can effectively improve the anti-interference ability. The weighted processing step ensures the effective weight distribution of different satellite signal data in the time matrix by analyzing the coordinated frequency feature data and the coordinated output feature data.

[0024] Preferably, the initial time comparison matrix data includes first initial time comparison matrix data and second initial time comparison matrix data, and the time period comparison is specifically:

[0025] Sorting the timing event points of the initial time matrix data to obtain the timing event point sorting data;

[0026] Perform scan line traversal on the timed event point sorting data to obtain timed event traversal data;

[0027] Performing collaborative time calculation on the initial time matrix data according to the timed event traversal data to obtain first initial time comparison matrix data;

[0028] Performing interval tree construction on the initial time matrix data and the preset error time threshold to obtain initial time matrix interval tree data;

[0029] Perform overlapping matrix query on the initial time matrix interval tree data to obtain the initial time matrix overlapping data;

[0030] The initial time matrix data is updated with a coordinated time matrix according to the initial time matrix overlapping data to obtain second initial time comparison matrix data.

[0031] The steps of timing event point sorting and scan line traversal in the present invention can ensure the order and accuracy of time data by carefully sorting and traversing the events in the initial time matrix data, laying a good foundation for subsequent collaborative time calculation, and reducing the disorder or confusion in time data. Collaborative time calculation and interval tree construction further enhance the accuracy of time data, integrate and optimize multi-satellite time data through collaborative processing and calibration of error range, and improve the accuracy and stability of final time synchronization. Scan line traversal and interval tree construction steps process large-scale time data through efficient data structures and algorithms, can quickly traverse and analyze time data, especially when processing complex time matrices of multiple Beidou satellites, can significantly reduce computational complexity and time consumption. By analyzing and correcting the overlap of time data, potential deviations or conflicts in the data can be discovered and corrected, improving the system's ability to detect and handle abnormalities in time data, and ensuring that the system can maintain efficient time synchronization even when deviations or interference occur in the data. The setting of the error time threshold enables the system to more flexibly handle application scenarios with different accuracy requirements, calibrate within the error range, effectively reduce overcorrection and error accumulation, and improve the system's fault tolerance. Through precise sorting and traversal of event points, the system can quickly detect asynchronous phenomena in time data and make real-time corrections. Through efficient algorithms for interval tree construction and overlapping matrix query, the system can process large amounts of satellite time data in a shorter time, making it suitable for scenarios that require high real-time timing (such as mobile communications, financial transactions, power grid synchronization, etc.).

[0032] Preferably, the collaborative frequency feature extraction is specifically:

[0033] Perform collaborative event classification on the initial time comparison matrix data to obtain initial time collaborative event classification matrix data;

[0034] Perform cross-scale feature extraction on the initial time collaborative event classification matrix data to obtain cross-scale feature data of collaborative events;

[0035] Perform frequency domain conversion on the cross-scale feature data of collaborative events to obtain frequency domain data of collaborative events;

[0036] Agglomerative hierarchical clustering is performed on the frequency domain data of collaborative events to obtain collaborative frequency feature data.

[0037] In the present invention, by classifying the initial time comparison matrix data, it is helpful to accurately identify the key collaborative events in multiple satellite time data, effectively capture the association between different satellite time signals, and ensure that the system can quickly discover time anomalies or offsets during the synchronization process. It is possible to extract important features in collaborative events from different time scales. By analyzing the rapid changes in a short period of time and the trends in a long period of time, the system can understand the dynamic changes of time data more deeply. Multi-scale feature extraction enhances the system's perception of features such as frequency drift and periodic changes in time signals, thereby improving the accuracy of overall time synchronization and the system's response to different time frequency changes. Frequency domain conversion can more clearly display the frequency features in time data by converting the cross-scale feature data of collaborative events from the time domain to the frequency domain. Frequency domain data can help the system identify regular frequency changes in time data, such as periodic drift or harmonic interference. Agglomerative hierarchical clustering processing can group multiple frequency features by clustering the frequency domain data of collaborative events, ensuring that the time data frequencies of different satellites can be effectively coordinated, identifying similar frequency features, and eliminating frequency noise and irrelevant frequency changes.

[0038] Preferably, the hierarchical time matrix is ​​constructed as follows:

[0039] The original time matrix is ​​constructed according to the initial time data to obtain the first layered time matrix data;

[0040] Acquire Beidou satellite signal configuration data, and perform time weighting on the first layered time matrix data according to the Beidou satellite signal configuration data to obtain second layered time matrix data;

[0041] Obtain Beidou satellite signal transmission distance data;

[0042] Perform atmospheric path simulation based on Beidou satellite signal transmission distance data to obtain atmospheric path simulation data;

[0043] Performing path delay simulation according to the atmospheric path simulation data and the preset atmospheric layering data to obtain atmospheric path delay simulation data;

[0044] Performing path correction on the second layer time matrix data according to the atmospheric layer path delay simulation data to obtain the third layer time matrix data;

[0045] The first layered time matrix data, the second layered time matrix data and the third layered time matrix data are integrated to obtain layered time matrix data.

[0046] The present invention ensures the basic accuracy of time data and the reliability of satellite signals by processing original time data and weighted Beidou satellite signal configuration data. By simulating the transmission path of the signal in the atmosphere, the time delay caused by the atmosphere (especially the ionosphere and troposphere) is effectively corrected. The system can accurately simulate the propagation of each satellite signal in the atmosphere, thereby improving the system's ability to correct signal delays. The layered time matrix is ​​corrected using the transmission distance and the atmospheric simulation results. By simulating the real propagation path of the signal, the system can dynamically adapt to different satellite signal propagation conditions (such as signal transmission under different orbital altitudes and different environmental conditions). The multi-layer data integration step integrates the first layer, the second layer and the third layer time matrix data in a unified manner to ensure that the data of each layer complements each other and works in coordination. The multi-layer integration mechanism can reduce the error accumulation in the processing of data at different levels and improve the robustness of the overall time synchronization through multiple corrections.

[0047] Preferably, the Beidou satellite time offset matrix data includes Beidou satellite coordinated time offset matrix data and Beidou satellite layered time offset matrix data, and step S2 is specifically:

[0048] Step S21: performing multi-source time offset calculation on the collaborative time matrix data to obtain multi-source time offset data;

[0049] Step S22: performing coordinated time offset correction on the multi-source time offset data to obtain coordinated time offset corrected data;

[0050] Step S23: performing collaborative error compensation calculation according to the collaborative time offset correction data to obtain collaborative error compensation time data;

[0051] Step S24: generating Beidou satellite coordinated time offset matrix data according to the coordinated error compensation time data;

[0052] Step S25: performing layered time offset calculation according to the layered time matrix data to obtain layered time offset data;

[0053] Step S26: performing layered time offset correction according to the layered time offset data to obtain layered time offset correction data;

[0054] Step S27: performing layered error compensation calculation according to the layered time offset correction data to obtain layered error compensation time data;

[0055] Step S28: Perform voting calculation according to the layered error compensation time data to obtain Beidou satellite layered time offset matrix data.

[0056] The present invention can effectively detect and correct the error offset in multiple satellite signals. By performing error compensation for different satellite signals, the system can reduce the time difference between multiple satellites, thereby improving the accuracy of time synchronization. By analyzing the layered time matrix data and processing the time offset data layer by layer, the time offset in each layer of data can be more accurately identified and corrected. The layered processing can ensure that the data at each level is fully processed and optimized, reducing the error accumulation in a single processing process. The layered error compensation calculation can further enhance the accuracy of the layered data. By independently correcting the error at different levels, the error will not be amplified in subsequent calculations, thereby improving the final time synchronization accuracy. Voting calculation is a process of making a final decision through multiple time offset correction results. In this process, the system can identify and exclude abnormal time data, reduce the impact of abnormal data on the overall synchronization accuracy, improve the fault tolerance of the system, and avoid synchronization failure or error expansion caused by abnormal single satellite signals. Voting calculation can find the most reliable time offset value in multi-source data to ensure that the generated Beidou satellite layered time offset matrix data can reflect the optimal time offset result of the overall system.

[0057] Preferably, step S3 is specifically:

[0058] Step S31: Obtaining the receiver's location data;

[0059] Step S32: performing spatial distance calculation according to the receiving party position data and the Beidou satellite position data corresponding to the Beidou satellite time offset matrix data to obtain relative spatial distance data;

[0060] Step S33: Acquire the receiver's weather data corresponding to the receiver's location data;

[0061] Step S34: Calculate the signal propagation delay according to the relative spatial distance data and the weather data of the receiving party to obtain the signal propagation delay data;

[0062] Step S35: performing atmospheric delay correction according to the relative spatial distance data and the signal propagation delay data to obtain atmospheric delay correction data;

[0063] Step S36: Perform delay processing according to the atmospheric delay correction data and the Beidou satellite time offset matrix data to obtain time delay processing matrix data.

[0064] In the present invention, by obtaining the receiver's position data and calculating the spatial distance, the system can accurately calculate the signal propagation path according to the actual receiving device position and the Beidou satellite position. The signal propagation delay calculation is performed according to the real-time weather data, so that the system can dynamically adapt to different meteorological conditions and accurately correct the signal propagation speed changes caused by the weather. By combining the relative spatial distance data with the receiver's weather data to calculate the signal propagation delay, the propagation delay of the signal from the satellite to the receiving device can be carefully analyzed, including the influence of the ionosphere and troposphere. Atmospheric delay correction is to compensate for the delay caused by the influence of the ionosphere, troposphere, etc. when the signal propagates in the atmosphere. By combining weather data and relative spatial distance data, the system can more accurately calculate the interference of the atmosphere on the signal, thereby reducing the influence of the atmosphere on the accuracy of time synchronization.

[0065] Preferably, step S4 is specifically:

[0066] Step S41: performing multipath effect inversion processing according to the time delay processing matrix data to obtain multipath effect inversion data;

[0067] Step S42: performing ionospheric anomaly processing on the multipath effect inversion data to obtain ionospheric anomaly processing data;

[0068] Step S43: performing iterative error estimation on the ionospheric anomaly processing data to obtain signal propagation error iterative data;

[0069] Step S44: Obtaining time delay inversion matrix data according to the signal propagation error iteration data.

[0070] The multipath effect inversion processing in the present invention can identify the path delay caused by reflection, refraction, scattering and other factors during the transmission of the signal. By inverting the multipath effect of the time delay processing matrix data, the interference caused by the reflection of the satellite signal by ground buildings, mountains or other objects can be effectively corrected. By analyzing the ionospheric data, the nonlinear propagation error of the signal when passing through the ionosphere is corrected, thereby ensuring the stability of time synchronization. Error iteration estimation is a process of gradually optimizing error estimation, and the propagation error is corrected through multiple iterations to reduce error accumulation. The signal generates different types of errors under the interference of the atmosphere, ionosphere, multipath effect, etc. The iterative estimation process can repeatedly calculate these errors to obtain accurate propagation time data. The multipath effect inversion processing and ionospheric anomaly processing enable the system to cope with a variety of complex environments, such as urban high-rise buildings, mountain obstruction, solar activity or ionospheric disturbances. In these environments, the traditional time synchronization algorithm may produce large errors due to signal interference, and the present method can effectively cope with these interferences through inversion and correction.

[0071] Preferably, step S5 is specifically:

[0072] Step S51: performing frequency drift detection according to the time delay inversion matrix data to obtain frequency drift detection data;

[0073] Step S52: performing frequency drift correction and time delay inversion correction on the frequency drift detection data to obtain time delay correction data;

[0074] Step S53: Perform frequency clock tuning according to the time delay correction data to obtain time and frequency synchronization data to perform high-precision time and frequency synchronization optimization operations of the Beidou system.

[0075] The present invention can accurately detect the frequency drift in the satellite signal by analyzing the time delay inversion matrix data, reduce the errors caused by factors such as satellite clock instability and external environmental interference, and timely detect and correct these drifts, which is very important for high-precision time synchronization. By correcting the detected frequency drift data in real time, the frequency drift error introduced in the signal propagation process is effectively eliminated, ensuring that the frequency of the signal is more stable when it reaches the receiving end, so that the receiving device can obtain accurate time data. In the time synchronization system, cumulative error is a serious problem, especially in scenarios involving long-term signal transmission. Through the delay inversion correction generation, the system can dynamically correct the influence of frequency drift on time delay, reducing the occurrence of cumulative errors. Frequency drift and signal delay are the main factors affecting the stability of time synchronization. Through frequency drift detection, correction and delay inversion correction, the errors caused by these factors can be quickly and in real time identified and corrected, thereby enhancing the robustness and stability of the system in complex environments. The tuning mechanism enables the system to cope with time deviations caused by device clock drift, signal quality degradation, etc., ensure that the clock is synchronized with the signal of the Beidou system, and reduce the influence of external interference on the timing system.

[0076] The beneficial effect of the present invention is that based on the satellite time matrix and the receiver position data, the time offset can be corrected from multiple dimensions (time, space and frequency). Different from the traditional single time synchronization mechanism, this method uses the collaborative analysis of multi-source data (multiple satellite signals) to accurately model space, time and frequency, so that the system can handle complex time offset problems in a refined manner. The simulation and correction of atmospheric path delay accurately combines the path delay of satellite signals with atmospheric interference factors through inversion processing based on physical models. The system can automatically identify and correct the influence of complex environmental conditions (such as ionospheric interference, solar wind, thunderstorms, etc.) on signal propagation, ensuring that high-precision timing results can still be provided in complex external environments. Signal propagation error inversion combines multipath effects and atmospheric delays for comprehensive correction. Through iterative error estimation, multiple rounds of correction are performed on small errors in propagation, so that the generated time delay inversion matrix data has extremely high accuracy. Frequency drift correction combines time delay inversion matrix data to accurately detect and correct frequency errors caused by satellite clock drift or receiving device clock drift. Through refined detection and dynamic correction of frequency drift, the system ensures the stability of satellite signals at the frequency level. Through a multi-level correction method constructed by layered time matrix and collaborative time matrix, refined time data processing is performed at each step, from time offset calculation to spatial path delay processing, to error inversion and drift correction. Each level of processing provides support for the correction of the next level of data. The layer-by-layer correction design greatly reduces the possibility of error accumulation and avoids the inconsistency between corrections at different stages in traditional methods, making the final synchronization data more accurate and consistent. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting implementations made with reference to the following drawings:

[0078] FIG1 shows a flowchart of a method for optimizing high-precision time and frequency synchronization based on the Beidou system according to an embodiment of the present invention;

[0079] FIG2 shows a flowchart of a method for constructing a Beidou satellite time matrix according to an embodiment;

[0080] FIG3 shows a flowchart of a method for calculating a Beidou satellite time offset matrix according to an embodiment;

[0081] FIG4 shows a flowchart of a method for processing Beidou satellite space path delay according to an embodiment;

[0082] FIG5 shows a flowchart of a signal propagation error inversion processing method based on the Beidou system according to an embodiment;

[0083] FIG6 shows a flowchart of the steps of a Beidou system frequency clock tuning method according to an embodiment. DETAILED DESCRIPTION

[0084] The following is a clear and complete description of the technical method of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are 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 technicians in this field without creative work are within the scope of protection of the present invention.

[0085] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0086] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0087] The system is applied to a power dispatching center, which needs to provide high-precision time synchronization for multiple distributed power stations across the country to ensure the synchronization of power grids between power stations. The time synchronization accuracy required in this scenario reaches nanoseconds, and the system needs to be able to cope with adverse conditions such as complex signal environments, weather changes, and electromagnetic interference.

[0088] Timing accuracy requirements: time synchronization accuracy: ≤ 10 nanoseconds, frequency synchronization accuracy: ≤ (1 pHZ).

[0089] BeiDou Satellite System: Number of satellites: Receive signals from 4 BeiDou satellites. Orbital altitude: BeiDou medium earth orbit satellites (MEO) have an orbit altitude of approximately 21,528 km, and high earth orbit satellites (GEO) have an altitude of 35,786 km.

[0090] Receiving equipment: Positioning accuracy: ≤ 1 meter; Receiving frequency band: Beidou B1 and B2 bands, 1561.098 MHz and 1207.140 MHz respectively; Signal strength: ≥ -110 dBm (sensitivity); Clock source: high-precision rubidium atomic clock, frequency stability of 10 -12 .

[0091] Signal transmission conditions: Weather data source: Meteorological monitoring stations provide real-time weather data, including temperature, humidity, air pressure and ionospheric disturbance index. Signal propagation environment: Urban areas, high-rise buildings are densely populated, and there is an obvious multipath effect.

[0092] The dispatch center uses high-precision satellite receiving equipment to receive time data from four Beidou satellites, including satellite clock timestamps, satellite orbit data, signal strength, and frequency drift data. The time data of each satellite is sorted to construct a satellite time matrix. Each element in the time matrix represents the time data of the 𝑖th satellite at the jth moment. The above time matrix is ​​analyzed to calculate the offset between each satellite signal and the local rubidium atomic clock. By calculating the time offset of each satellite, the time offset matrix is ​​obtained. The time offset data of the four satellites are coordinated to reduce the error caused by the drift of a single satellite signal. A dynamic weighting algorithm is used to assign weights to the signals of each satellite, and the weights are based on signal strength and quality.

[0093] The precise location of the dispatch center (1 meter accuracy) is obtained through the positioning function of the Beidou system. The spatial distance from each satellite to the receiving device is calculated using the receiver's location data and satellite location data. The dispatch center obtains meteorological data from the local meteorological station in real time to monitor the current temperature, humidity, air pressure, and ionospheric disturbances. Signal propagation delay correction: Calculate the signal propagation delay based on the relative spatial distance and weather data. Apply the ionosphere and troposphere models to correct the delay and obtain the time delay processing matrix.

[0094] Since the dispatch center is located in an area with densely populated high-rise buildings in the city, the multipath effect has a great impact. The system identifies and corrects the interference of the multipath effect through the inversion model to obtain the multipath effect inversion data. Using real-time ionospheric data, the abnormal delay caused by the ionosphere in signal propagation is corrected to generate ionospheric anomaly processing data. Based on the multipath effect inversion data and ionospheric anomaly processing data, multiple rounds of iterative estimation are performed to generate signal propagation error iteration data. All error corrections are summarized to generate a time delay inversion matrix that reflects the actual propagation delay of the signal.

[0095] Frequency drift detection is performed on the time delay inversion matrix data to detect the frequency offset in the satellite signal. The received satellite signal is corrected for frequency drift using a rubidium atomic clock. The corrected signal accurately matches the clock frequency of the receiving end to eliminate the time error caused by frequency drift. Based on the corrected time delay data, the atomic clock of the dispatch center is further fine-tuned to ensure that the local clock is synchronized with the Beidou satellite signal and to generate time and frequency synchronization data.

[0096] Please refer to Figures 1 to 6. This application provides a high-precision time and frequency synchronization optimization method based on the Beidou system, including the following steps:

[0097] Step S1: receiving initial time data from two or more Beidou satellites, and constructing a satellite time matrix according to the initial time data to obtain Beidou satellite time matrix data;

[0098] In one embodiment, initial time data is received from multiple BeiDou satellites, and each satellite transmits its current time. These time data are organized into a matrix, where the rows of the matrix represent different satellites and the columns represent the time signals transmitted by the satellites. In this way, a BeiDou satellite time matrix is ​​constructed.

[0099] Step S2: performing time offset calculation according to Beidou satellite time matrix data to obtain Beidou satellite time offset matrix data;

[0100] In one embodiment, the time data in the satellite time matrix are compared to calculate the time offsets between different satellites. The received time data is compared with the reference time in the system to calculate the time offset of each satellite relative to the reference time. These time offsets are reconstructed into a time offset matrix, and each element in the matrix represents the time offset value of the satellite relative to the reference time.

[0101] Step S3: Acquire the receiver position data, perform space path delay processing according to the receiver position data and Beidou satellite time offset matrix data, and obtain time delay processing matrix data;

[0102] In one embodiment, the delay caused by different paths during signal propagation is calculated based on the geographical location information of the receiver and the position of the satellite. The spatial distance of the signal from each satellite to the receiver is calculated using the three-dimensional position coordinates of the receiver and the spatial position coordinates of each satellite. By considering the signal propagation speed (such as the speed of light) and the spatial distance, the propagation delay of each satellite signal to the receiver is calculated, and a time delay processing matrix is ​​constructed.

[0103] Step S4: performing signal propagation error inversion processing according to the time delay processing matrix data to obtain time delay inversion matrix data;

[0104] In one embodiment, various error factors, such as atmospheric refraction, multipath effect, ionospheric delay, etc., are considered to introduce errors in the propagation process. Through a certain inversion calculation method, these errors are modeled and estimated based on known delay matrix data, and more accurate signal propagation time is gradually inverted. A time delay inversion matrix is ​​generated, which contains the corrected time delay data.

[0105] Step S5: Perform frequency drift correction according to the time delay inversion matrix data to obtain time delay correction data, and perform frequency clock tuning on the time delay correction data to obtain time and frequency synchronization data, so as to perform high-precision time and frequency synchronization optimization operations of the Beidou system.

[0106] In one embodiment, the received time signal is corrected for frequency drift using the data in the time delay inversion matrix to compensate for the time accumulation error caused by the frequency offset in the system. On the basis of frequency drift correction, further clock tuning is performed to ensure that the time synchronization accuracy between the receiver clock and the Beidou satellite system meets high-precision requirements. Time and frequency synchronization data is generated, which is used for high-precision time and frequency synchronization optimization operations.

[0107] Preferably, the Beidou satellite time matrix data includes collaborative time matrix data and layered time matrix data, and step S1 is specifically as follows:

[0108] Step S11: receiving initial time data from two or more Beidou satellites;

[0109] In one embodiment, the receiver receives respective initial time data from multiple BeiDou satellites. The time signal sent by each satellite is generated by its internal clock and includes a timestamp and other necessary information.

[0110] Step S12: constructing a collaborative time matrix according to the initial time data to obtain collaborative time matrix data;

[0111] In one embodiment, the initial time data of all satellites are processed according to the time synchronization mechanism to form a coordinated time matrix. The core goal of the coordinated time matrix is ​​to ensure that the time signal of each satellite can be coordinated under the same reference time frame, even if the initial time of the satellite has errors or deviations.

[0112] By aligning the timestamps of each satellite, the difference between them and the system reference time is found. This involves calculating the time offset of each satellite relative to a reference time (such as the standard time of a ground base station). According to the time offset of each satellite, a preliminary error correction is performed so that the time signals of each satellite are theoretically at the same reference time. Through a specific coordination mechanism, the time signals of multiple satellites are jointly processed to ensure that these signals can be consistent at the same reference time and construct a coordinated time matrix. After the above steps, the coordinated time matrix data obtained can reflect the coordinated state of the time signals of multiple satellites under a unified benchmark. Each element in the matrix represents the relative time relationship between a satellite and other satellites and the time offset result after alignment.

[0113] Step S13: construct a hierarchical time matrix according to the initial time data to obtain hierarchical time matrix data.

[0114] In one embodiment, similarly, the receiver receives respective initial time data from multiple BeiDou satellites, and these data are used to construct a hierarchical time matrix.

[0115] The construction process of the hierarchical time matrix is ​​to group and process the satellite's time signals according to certain levels or weights. Satellites at different levels play different roles in the time synchronization process. Satellites are divided into different levels according to factors such as the satellite's orbital altitude, signal quality, and time accuracy. Usually, high-orbit satellites are in the upper level and low-orbit satellites are in the lower level. For each layer of satellites, their time offset values ​​are calculated separately. The time of the upper satellite is used as a reference time with a higher weight, and the lower satellite is further corrected based on these reference times. The time of the lower satellite will rely on the time signal of the upper satellite for correction, thereby constructing a hierarchical time matrix. The generated hierarchical time matrix data reflects the role and hierarchical relationship of satellites at different levels in time correction. The structure of the matrix represents the time synchronization status between satellites at each level, especially the dependence and correction of the lower satellite on the time of the higher satellite.

[0116] Preferably, the collaborative time matrix is ​​constructed as follows:

[0117] Initialize the matrix according to the initial time data to obtain the initial time matrix data;

[0118] In one embodiment, initial time data is received from multiple BeiDou satellites. The received time data is organized according to satellite numbers and timestamps to generate initial time matrix data. The rows of the matrix represent different satellites, and the columns represent timestamps of different time nodes.

[0119] Perform time period comparison on the initial time matrix data to obtain initial time comparison matrix data;

[0120] In one embodiment, the received initial time data is divided into different time periods. The common time period division method can be set according to the transmission interval or the observation period. In each time period, the time difference between each satellite in the same time period is calculated to generate a time comparison matrix. The elements of the matrix represent the time difference of different satellites in the same time period. By comparing these time differences, an initial time comparison matrix is ​​obtained to reflect the time synchronization of different satellites.

[0121] Extracting the coordinated frequency features from the initial time comparison matrix data to obtain coordinated frequency feature data;

[0122] In one embodiment, the changing rules of the time difference are analyzed based on the initial time comparison matrix data, and the frequency characteristics thereof are extracted. This includes: capturing the synchronization characteristics between the satellites by observing the changing frequency of the time difference in different time periods. Determining the stability of the time signal of each satellite in multiple time periods, and extracting the high-frequency or low-frequency synergistic characteristics.

[0123] The analyzed frequency features are extracted to form coordinated frequency feature data, which represent the time and frequency synchronization of each satellite with other satellites.

[0124] Obtain historical collaborative data corresponding to Beidou satellites, and extract collaborative output features based on the historical collaborative data to obtain collaborative output feature data;

[0125] In one embodiment, historical coordination data in the BeiDou system is called, which includes the time synchronization records between each satellite and other satellites at different times in the past. The historical data is analyzed to identify the coordination mode related to the current time matrix data. By analyzing the historical data, the coordination law of a specific satellite can be identified. By analyzing the time offset, frequency change and other features in the historical coordination data, the coordination output features are extracted.

[0126] The initial time comparison matrix data is weighted according to the collaborative frequency characteristic data and the collaborative output characteristic data to obtain the collaborative time matrix data.

[0127] In one embodiment, a weighted processing model is constructed based on the collaborative frequency characteristic data and the collaborative output characteristic data. The data of different satellites will be assigned different weights according to their collaborative performance and historical data. According to the frequency synchronization characteristics of each satellite, a higher weight is given to satellites with more stable frequencies. According to the satellites that perform well in the historical collaborative data, they are given higher historical weights to ensure the accuracy of time synchronization. The initial time comparison matrix is ​​weighted, and the current frequency characteristics and historical collaborative data of the satellite are comprehensively considered. After weighted processing, the collaborative time matrix data is obtained. This matrix reflects the time synchronization status of each satellite after weighted correction, which can improve the accuracy of time and frequency synchronization.

[0128] Preferably, the initial time comparison matrix data includes first initial time comparison matrix data and second initial time comparison matrix data, and the time period comparison is specifically:

[0129] Sorting the timing event points of the initial time matrix data to obtain the timing event point sorting data;

[0130] In one embodiment, the initial time matrix data is time information received from multiple Beidou satellites, and each row represents a time series of a satellite, which is listed as different time points. Representative timing event points are extracted from these time data, and these event points are usually key timestamps, such as the start and end time of each time period. These timing event points are sorted in chronological order to ensure that the timing event points of all satellites can be arranged on a unified time base, and the timing event point sorting data is obtained. The sorting can be performed according to time from small to large or a certain time reference.

[0131] Perform scan line traversal on the timed event point sorting data to obtain timed event traversal data;

[0132] In one embodiment, the scan line technology is used to traverse the sorted data of the timing event points, that is, a scan line is used to start from the earliest time point and process each time point one by one. The core of the scan line traversal is to capture the time data of each satellite at the time point by sequentially traversing the time event points. During the traversal process, each time point is passed, the timestamps and time differences of all satellites at the time point are recorded to form the timing event traversal data, which reflects the time synchronization of different satellites in the time period.

[0133] Performing collaborative time calculation on the initial time matrix data according to the timed event traversal data to obtain first initial time comparison matrix data;

[0134] In one embodiment, according to the time information in the timed event traversal data, the time difference of different satellites at each time point is calculated to perform coordinated time calculation. The coordinated time calculation is based on the time deviation and error of different satellites to correct time synchronization. Through the calculation of these time differences, the first initial time comparison matrix data is obtained. Each element in the matrix represents the time difference between two satellites at a specific time point.

[0135] Performing interval tree construction on the initial time matrix data and the preset error time threshold to obtain initial time matrix interval tree data;

[0136] In one embodiment, the initial time matrix data and the preset error time threshold are input. The error time threshold is the allowable time error range, which is used to determine whether the time difference needs to be corrected. The time data of each satellite is taken as an interval, and the upper and lower limits of the interval depend on the timestamp of the satellite and the error time threshold. Then, an interval tree data structure is constructed to effectively process the overlapping query of satellite time. Through the interval tree construction, the time data of different satellites can be quickly searched and compared to obtain the initial time matrix interval tree data, which is convenient for subsequent overlapping query and correction.

[0137] Perform overlapping matrix query on the initial time matrix interval tree data to obtain the initial time matrix overlapping data;

[0138] In one embodiment, the initial time matrix is ​​queried using an interval tree to find the overlapping parts between the time data intervals of different satellites. The purpose of the overlap query is to find the overlapping areas in the satellite time synchronization process, that is, the synchronization errors that may occur in each satellite time period. The overlap information between different satellites in the time interval is obtained by querying, and the initial time matrix overlap data is generated to identify which satellites have intersections or overlaps in their time intervals.

[0139] The initial time matrix data is updated with a coordinated time matrix according to the initial time matrix overlapping data to obtain second initial time comparison matrix data.

[0140] In one embodiment, based on the initial time matrix overlapping data, the time data of the overlapping area is corrected. The basis for the correction is to uniformly adjust the overlapping satellite time intervals to ensure that the time data of each satellite remains consistent within the overlapping interval. The time of these overlapping areas is processed by weighted average or minimum deviation method, the error is adjusted and the time matrix is ​​updated. After the overlapping process, the second initial time comparison matrix data is generated. Compared with the first initial time comparison matrix data, this matrix includes the correction results for the overlapping area and better reflects the time synchronization status of each satellite.

[0141] Preferably, the collaborative frequency feature extraction is specifically:

[0142] Perform collaborative event classification on the initial time comparison matrix data to obtain initial time collaborative event classification matrix data;

[0143] In one embodiment, the initial time comparison matrix data includes time difference information between different satellites. According to the time difference information, the time difference data are classified into different coordination events, and the coordination events include: frequency deviation, time offset, signal interference and other phenomena.

[0144] Set some classification criteria, such as the size of the time difference, the duration, and the trend of the error change. According to the set classification rules, classify the time difference data into events, and classify each time difference into different types of collaborative events. You can use thresholds, change trends and other criteria to preliminarily classify events. Organize the classified collaborative events into a matrix to form the initial time collaborative event classification matrix data. The rows of the matrix represent the time differences between different satellites, and the columns represent the categories of collaborative events in different time periods.

[0145] Perform cross-scale feature extraction on the initial time collaborative event classification matrix data to obtain cross-scale feature data of collaborative events;

[0146] In one embodiment, by analyzing the data in the collaborative event classification matrix, the characteristics of collaborative events at different time scales are extracted. Time is divided into different time ranges, such as short-term, medium-term, long-term, etc., and the characteristics of collaborative events at each time scale are analyzed. In each time scale, the characteristics of collaborative events are extracted. The characteristics include: the frequency of event occurrence, the distribution of event duration, the periodicity of event occurrence, etc. Collaborative events are compared at different time scales to find out which events have cross-scale characteristics (i.e., they show consistent behavioral characteristics at different time scales). The extracted feature data are organized into a new matrix, namely the cross-scale feature data of collaborative events, which represents the characteristic performance of each collaborative event at different time scales.

[0147] Perform frequency domain conversion on the cross-scale feature data of collaborative events to obtain frequency domain data of collaborative events;

[0148] In one embodiment, the cross-scale feature data is converted from the time domain to the frequency domain to analyze the frequency performance of the event features. The cross-scale feature data of collaborative events contains information about collaborative events at different time scales, and the data is essentially represented in the time domain. The cross-scale feature data is converted from the time domain to the frequency domain using frequency domain analysis techniques (such as Fourier transform). The purpose of the frequency domain conversion is to identify the performance of event features in frequency, such as whether certain events have specific frequency patterns. After conversion, the frequency domain performance of the collaborative event is obtained, that is, the collaborative event frequency domain data. The collaborative event frequency domain data shows the distribution and intensity of collaborative events in different frequency ranges, which can help identify periodic or random characteristics in satellite time synchronization.

[0149] Agglomerative hierarchical clustering is performed on the frequency domain data of collaborative events to obtain collaborative frequency feature data.

[0150] In one embodiment, collaborative events with similar frequency characteristics are clustered according to frequency domain data to generate collaborative frequency characteristic data. The collaborative event frequency domain data contains the representations of different events in the frequency domain. First, the similarity between collaborative events is defined (for example, using Euclidean distance or other similarity metrics), and based on these similarity metrics, it is determined which events are more similar in frequency domain representation. Using the agglomerative hierarchical clustering method, starting from each collaborative event, similar events are gradually merged into a cluster. This process is performed layer by layer until all events are classified into specific categories. By gradually agglomerating similar collaborative events, clustering results are obtained, and finally collaborative frequency characteristics of different categories are obtained. The collaborative frequency characteristics represent a set of collaborative events with similar frequency characteristics, which helps to understand the time synchronization behavior patterns between satellites.

[0151] Preferably, the hierarchical time matrix is ​​constructed as follows:

[0152] The original time matrix is ​​constructed according to the initial time data to obtain the first layered time matrix data;

[0153] In one embodiment, initial time data is received from multiple BeiDou satellites, and the time signal of each satellite is synchronized according to its internal clock. The initial time data is arranged according to the satellite number and the time point to form an original time matrix. The rows represent different satellites, and the columns represent the time data at different time points. The constructed original time matrix is ​​used as the first hierarchical time matrix data, which represents the original time information of each satellite at each time point.

[0154] Acquire Beidou satellite signal configuration data, and perform time weighting on the first layered time matrix data according to the Beidou satellite signal configuration data to obtain second layered time matrix data;

[0155] In one embodiment, the Beidou satellite signal configuration data includes information such as satellite transmission power, signal quality, orbital altitude, etc. These data are used to assign different weights to the time signal of each satellite. According to the signal configuration data of the satellite, each time signal in the first layered time matrix is ​​weighted. The weight is set according to the signal quality or other configuration parameters of the satellite, and the satellite time signal with better signal quality will be given a higher weight. In the specific implementation process, weighted calculation is performed by multiplying the satellite time value by the weight factor. After weighted processing, the second layered time matrix data is obtained, which reflects the time weight adjustment based on the signal configuration.

[0156] Obtain Beidou satellite signal transmission distance data;

[0157] In one embodiment, the transmission distance of the signal is calculated based on the positions of the satellite and the receiver. This distance data includes the orbital height of the satellite in space and the path length of the signal from the satellite to the ground receiving end. The calculation of the transmission distance is usually based on the orbital parameters of the satellite and the geographical location of the receiver, combined with known physical formulas.

[0158] Perform atmospheric path simulation based on Beidou satellite signal transmission distance data to obtain atmospheric path simulation data;

[0159] In one embodiment, the propagation path of the signal through the atmosphere is simulated to evaluate the delay and attenuation of the signal in the atmosphere. A known atmospheric signal propagation model is used to simulate the propagation path of the signal through the atmosphere when it is transmitted from the satellite to the ground. This involves considering the refraction and delay effects of the ionosphere, troposphere and other atmospheric layers. Based on the transmission distance data of the satellite signal, the signal propagation paths at different levels in the atmosphere are calculated to generate atmospheric path simulation data, which reflects the propagation trajectory of the signal in the atmosphere and the delay it suffers.

[0160] Performing path delay simulation according to the atmospheric path simulation data and the preset atmospheric layering data to obtain atmospheric path delay simulation data;

[0161] In one embodiment, the delay caused by different layers of the atmosphere to signal transmission is simulated, and the atmospheric delay of each layer is calculated. Based on the layered data of the atmosphere, the atmosphere is divided into different layers (such as the ionosphere, troposphere, etc.), and each layer has a different impact on signal propagation. Using the atmospheric path simulation data and combining the layered characteristics of the atmosphere, the delay of each layer to signal propagation is simulated. The path delay of the signal in each layer is calculated to obtain the atmospheric path delay simulation data.

[0162] Performing path correction on the second layer time matrix data according to the atmospheric layer path delay simulation data to obtain the third layer time matrix data;

[0163] In one embodiment, the second layered time matrix data is corrected according to the atmospheric path delay simulation data to compensate for the delay caused by the signal propagating in the atmosphere. The time data in the second layered time matrix is ​​corrected using the atmospheric path delay simulation data, and the specific correction method is to add the simulated delay time back to the corresponding time signal. The time matrix after the path correction is called the third layered time matrix, which reflects the time synchronization state after the atmospheric delay effect is eliminated.

[0164] The first layered time matrix data, the second layered time matrix data and the third layered time matrix data are integrated to obtain layered time matrix data.

[0165] In one embodiment, the layered time matrix data generated in different steps are integrated to obtain comprehensive layered time matrix data. The first layered time matrix (original time data), the second layered time matrix (weighted time data) and the third layered time matrix (corrected time data) are integrated. The integration can be done by weighted averaging or other combined methods to ensure data consistency. The integrated layered time matrix data represents more accurate time synchronization data after signal configuration weighting and atmospheric path correction.

[0166] Preferably, the Beidou satellite time offset matrix data includes Beidou satellite coordinated time offset matrix data and Beidou satellite layered time offset matrix data, and step S2 is specifically:

[0167] Step S21: performing multi-source time offset calculation on the collaborative time matrix data to obtain multi-source time offset data;

[0168] In one embodiment, the coordinated time matrix data reflects the time synchronization of multiple Beidou satellites at different time points. Based on multiple satellite time signals, the time offset relative to a reference time source (such as a ground reference clock or a time reference of a primary satellite) is calculated. The time data of different satellites are compared to calculate the offset of each satellite relative to other satellites or a reference time source. Using a known time reference as a reference, the time offset of each satellite is calculated to obtain multi-source time offset data, and the offset data reflects the difference of each satellite relative to the reference time.

[0169] Step S22: performing coordinated time offset correction on the multi-source time offset data to obtain coordinated time offset corrected data;

[0170] In one embodiment, the multi-source time offset data is corrected to eliminate the time offset caused by system inconsistency or error. According to the multi-source time offset data, the main source of the time offset is analyzed, such as system error, signal propagation delay, etc. The offset data is adjusted, for example, by averaging the offset or minimizing the offset correction method to synchronize the time of each satellite to a coordinated reference. After correction, the coordinated time offset correction data is generated, which represents the corrected multi-source time offset result.

[0171] Step S23: performing collaborative error compensation calculation according to the collaborative time offset correction data to obtain collaborative error compensation time data;

[0172] In one embodiment, the coordinated time error caused by the atmosphere, ionosphere, multipath effect, etc. is further compensated. An error compensation model is constructed based on known error sources (such as atmospheric delay, path delay, etc.). Based on the coordinated time offset correction data, the time signal of each satellite is compensated using the error model, and the time offset value after error compensation is calculated. The error-compensated time data is obtained, which reflects a more accurate coordinated time offset.

[0173] Step S24: generating Beidou satellite coordinated time offset matrix data according to the coordinated error compensation time data;

[0174] In one embodiment, based on the error-compensated time data, a coordinated time offset matrix of Beidou satellites is generated. The coordinated error-compensated time data is integrated according to the satellite number and time period to construct a coordinated time offset matrix. The rows of the matrix represent different satellites, the columns represent different time points, and each element in the matrix represents the offset of each satellite at that time point.

[0175] Step S25: performing layered time offset calculation according to the layered time matrix data to obtain layered time offset data;

[0176] In one embodiment, the layered time matrix data is layered based on the satellite's orbital altitude, signal quality, etc. According to the satellites at different levels, their respective time offsets are calculated. According to the layered information of the satellites (such as high orbit, medium orbit, low orbit, etc.), the time offset calculation is performed for the satellites at different levels. Similar to the multi-source time offset, the time offset of each layer of satellites is calculated according to the reference benchmark to obtain the layered time offset data.

[0177] Step S26: performing layered time offset correction according to the layered time offset data to obtain layered time offset correction data;

[0178] In one embodiment, the layered time offset data is corrected to ensure that the time offset of each layer of satellites is consistent. The time offsets of different layers are compared, and the differences between the layers are corrected to ensure that each layer of satellites is synchronized on the same time reference. The layered time offset data is corrected by a minimum offset method or a weighted average method. Corrected layered time offset correction data is generated.

[0179] Step S27: performing layered error compensation calculation according to the layered time offset correction data to obtain layered error compensation time data;

[0180] In one embodiment, time error compensation is performed on satellites at different levels according to the layered characteristics. Different error models are used for compensation of satellites at different levels. For example, low-orbit satellites need to consider more significant atmospheric effects, while high-orbit satellites need to consider orbital disturbances. According to the characteristics of each layer, the time data after error compensation is calculated. The layered time offset data after error compensation is obtained, which reflects the time synchronization of satellites at different levels.

[0181] Step S28: Perform voting calculation according to the layered error compensation time data to obtain Beidou satellite layered time offset matrix data.

[0182] In one embodiment, the time offset data of each layer of satellites are determined by a voting mechanism. According to the error compensation data of each layer of satellites, a voting mechanism is used to determine the final time offset value at each time point. The voting mechanism can be based on a variety of rules, such as majority voting, weighted voting, etc. The time offset data of each layer are counted, and the result with a relatively consistent offset value is selected as the final time offset data. According to the voting results, the final hierarchical time offset matrix data is generated, which represents the time offset of satellites at different layers at each time point.

[0183] Preferably, step S3 specifically includes:

[0184] Step S31: Obtaining the receiver's location data;

[0185] In one embodiment, the location data of the receiver includes its geographic coordinates (longitude, latitude and altitude). The real-time location data of the receiver is obtained from the receiving device or other positioning system. These data will serve as the basis for the subsequent calculation of the signal propagation path.

[0186] Step S32: performing spatial distance calculation according to the receiving party position data and the Beidou satellite position data corresponding to the Beidou satellite time offset matrix data to obtain relative spatial distance data;

[0187] In one embodiment, the orbital information of each Beidou satellite includes its three-dimensional position coordinates (X, Y, Z) at different time points. The relative spatial distance between the receiver and each Beidou satellite is calculated. The spatial relationship between the two parties is determined based on the real-time position of the receiver and the orbital position of the Beidou satellite at the same time. The straight-line distance between the receiver and each Beidou satellite is calculated using the Euclidean distance formula. The result of the calculation is relative spatial distance data, which indicates the relative distance from the receiver to each satellite.

[0188] Step S33: Acquire the receiver's weather data corresponding to the receiver's location data;

[0189] In one embodiment, real-time weather data corresponding to the location of the receiver is obtained, mainly including environmental factors that affect signal propagation, such as temperature, humidity, and air pressure. Weather data can be obtained from local weather stations, weather satellites, or other real-time weather monitoring systems. These data are input into the signal propagation delay calculation model to consider the impact of weather factors on signal propagation.

[0190] Step S34: Calculate the signal propagation delay according to the relative spatial distance data and the weather data of the receiving party to obtain the signal propagation delay data;

[0191] In one embodiment, the delay time of the Beidou satellite signal propagating to the receiver is calculated based on the relative spatial distance and the receiver's weather data. An atmospheric refraction model (such as the International Reference Ionosphere Model or the Tropospheric Delay Model) is used to evaluate the delay of the signal when passing through the atmosphere. The signal propagation speed is corrected in combination with the receiver's weather data, such as humidity, temperature, air pressure, etc. Higher humidity and low air pressure will increase the delay of the signal, while lower temperature will have a slight effect on the propagation speed. In combination with the relative spatial distance data, the total delay time of the signal reaching the receiver is calculated by calculating the propagation speed of the signal in the air (close to the speed of light) and correcting the influence of environmental factors. The signal propagation delay data is obtained, reflecting the actual time delay of the Beidou satellite signal from the satellite to the receiver.

[0192] Step S35: performing atmospheric delay correction according to the relative spatial distance data and the signal propagation delay data to obtain atmospheric delay correction data;

[0193] In one embodiment, the signal propagation delay caused by the atmosphere (such as the ionosphere and troposphere) is corrected. The preset models of the ionosphere and troposphere are used to simulate and correct the atmospheric delay in combination with the signal propagation delay data. The time error in the signal propagation path is adjusted according to the refraction, scattering and delay effects of the signal when passing through different layers of the atmosphere. By compensating for the delay of the signal in the ionosphere and troposphere, the original delay data is adjusted to ensure that the signal propagation delay is more accurate. After correction, atmospheric delay correction data is generated to eliminate the signal delay error caused by the atmospheric effect.

[0194] Step S36: Perform delay processing according to the atmospheric delay correction data and the Beidou satellite time offset matrix data to obtain time delay processing matrix data.

[0195] In one embodiment, the final time delay of each satellite signal is calculated and a time delay processing matrix is ​​generated based on the atmospheric delay correction data and the Beidou satellite time offset matrix data. The atmospheric delay correction data is combined with the Beidou satellite time offset matrix data, and the atmospheric effect and the satellite time offset are comprehensively considered. The time offset of each satellite is corrected, and the time delay value is generated taking into account the path delay and propagation characteristics of the signal. The corrected delay data of all satellites are organized into a matrix, with rows representing different satellites and columns representing different time points, to obtain the time delay processing matrix data.

[0196] Preferably, step S4 is specifically:

[0197] Step S41: performing multipath effect inversion processing according to the time delay processing matrix data to obtain multipath effect inversion data;

[0198] In one embodiment, during the signal propagation process, the satellite signal may reach the receiver through multiple paths, causing a multipath effect (for example, the signal is reflected on objects such as buildings and the ground). The interference of the multipath effect on the signal propagation is analyzed and eliminated through inversion calculation. The multipath effect model is used in combination with the time delay processing matrix data to identify the multipath interference signal in the signal propagation. By analyzing the received time delay signal, the delay characteristics of different paths are detected, and the direct path signal and the reflected path signal are separated. According to the delay characteristics, the reflected signal is inverted and calculated to estimate the real signal propagation time and eliminate the interference of the multipath effect. The generated multipath effect inversion data includes the signal propagation time after the multipath effect is eliminated.

[0199] Step S42: performing ionospheric anomaly processing on the multipath effect inversion data to obtain ionospheric anomaly processing data;

[0200] In one embodiment, changes in the electron density of the ionosphere may cause abnormal refraction or delay during signal propagation, especially when solar activity is strong. Identify and correct the abnormal delay caused by the ionosphere to signal propagation to ensure the accuracy of the signal propagation time. Detect anomalies caused by the ionosphere during signal propagation through an ionosphere model. Use multipath effect inversion data as input, combined with ionosphere electron density data, to analyze ionosphere anomalies in signal propagation. Correct the detected ionosphere anomaly, for example, by adjusting the electron density or refractive index to compensate for the delay caused by the ionosphere anomaly. After correction, generate ionosphere anomaly processing data, which reflects the signal propagation time after ionosphere anomaly processing.

[0201] Step S43: performing iterative error estimation on the ionospheric anomaly processing data to obtain signal propagation error iterative data;

[0202] In one embodiment, the residual error in the signal propagation process is estimated through multiple iterations, and is continuously adjusted to improve the time synchronization accuracy. The ionospheric anomaly processing data is used to perform preliminary error estimation. The initial error comes from multipath effects, ionospheric delays, and other factors that may affect signal propagation. Based on the error model, multiple iterations are estimated. At each iteration, the error estimation parameters are adjusted to reduce the residual error. The results after each iteration will be used for the next round of error correction. An error convergence condition is set (for example, the error value is less than a certain threshold), and the iteration is stopped when the error is reduced to a certain range. Through the iterative process, signal propagation error estimation data is obtained, which reflects the accurate signal propagation time after error correction.

[0203] Step S44: Obtaining time delay inversion matrix data according to the signal propagation error iteration data.

[0204] In one embodiment, an inversion matrix reflecting the precise time delay is generated based on the error iteration data. The final correction of the delay time is performed in combination with the ionospheric anomaly processing data and the error iteration data obtained previously. The corrected time delay data is organized into a matrix, with rows representing different satellites and columns representing delay data at different time points. The generated time delay inversion matrix data reflects the precise time delay after multipath effects, ionospheric anomaly processing and error iteration correction.

[0205] Preferably, step S5 is specifically:

[0206] Step S51: performing frequency drift detection according to the time delay inversion matrix data to obtain frequency drift detection data;

[0207] In one embodiment, the frequency drift of the satellite signal is detected by analyzing the time delay inversion matrix data, and the trend of the signal frequency changing over time is identified. This data provides the time delay information of each satellite at different time points, which is the basis for frequency drift detection. By differentiating or differencing the time delays at different time points, the frequency change rate of each satellite signal is calculated to identify whether there is a frequency drift. The drift is manifested as a continuous increase or decrease in frequency. The frequency change rate is compared with the normal frequency range to detect abnormal frequency drift. Signals with frequency changes beyond the normal range are marked as drift anomalies. The detected frequency drift data is organized into a new data set to represent the frequency drift of each satellite at different time points.

[0208] Step S52: performing frequency drift correction and time delay inversion correction on the frequency drift detection data to obtain time delay correction data;

[0209] In one embodiment, the frequency of the signal is corrected for the detected frequency drift so that it returns to the expected frequency range, and the time delay is further corrected. According to the frequency drift detection data, the drift trend of each satellite is analyzed, and the frequency is adjusted using a certain correction coefficient or model. The correction method includes linear regression or prediction correction based on historical data. On the basis of frequency drift correction, the time delay deviation caused by frequency drift is corrected. This correction process is achieved by correcting the influence of frequency drift on signal propagation time, ensuring the time accuracy of the signal arriving at the receiver. After frequency drift correction and time delay inversion correction, the generated time delay correction data reflects the precise time information after the frequency drift is eliminated.

[0210] Step S53: Perform frequency clock tuning according to the time delay correction data to obtain time and frequency synchronization data to perform high-precision time and frequency synchronization optimization operations of the Beidou system.

[0211] In one embodiment, the frequencies of the receiver and satellite clocks are tuned according to the corrected time delay data to ensure the accuracy and stability of time synchronization. The relative drift of the receiver clock and the satellite clock is analyzed, and the receiver clock is judged whether there is an error according to the time delay correction data. This process combines the difference between the receiver and satellite time signals to determine the deviation of the clock frequency. According to the clock drift, the receiver clock frequency is adjusted. The adjustment process can keep the receiver clock consistent with the satellite clock by increasing or decreasing the clock frequency deviation. During the tuning process, a feedback mechanism is used to evaluate the effect of the clock frequency adjustment. If there is still a deviation in the adjusted frequency, continue to tune until the predetermined synchronization accuracy standard is reached. The generated time and frequency synchronization data represents the precise synchronization state of the receiver clock and the satellite clock, ensuring that the receiver can achieve high-precision time and frequency synchronization with the Beidou system.

[0212] Therefore, from any point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is limited by the attached application documents rather than the above description, and it is intended that all changes falling within the meaning and scope of equivalent elements of the application documents are included in the present invention.

[0213] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A high-precision time and frequency synchronization optimization method based on the Beidou system, characterized in that: The following steps are involved: Step S1: receiving initial time data from two or more Beidou satellites, and constructing a satellite time matrix according to the initial time data to obtain Beidou satellite time matrix data; Step S2: performing time offset calculation according to Beidou satellite time matrix data to obtain Beidou satellite time offset matrix data; Step S3: Acquire the receiver position data, perform space path delay processing according to the receiver position data and Beidou satellite time offset matrix data, and obtain time delay processing matrix data; Step S4: performing signal propagation error inversion processing according to the time delay processing matrix data to obtain time delay inversion matrix data; Step S5: Perform frequency drift correction according to the time delay inversion matrix data to obtain time delay correction data, and perform frequency clock tuning on the time delay correction data to obtain time and frequency synchronization data, so as to perform high-precision time and frequency synchronization optimization operations of the Beidou system.

2. The method according to claim 1, characterized in that The Beidou satellite time matrix data includes collaborative time matrix data and layered time matrix data. Step S1 is specifically as follows: Receive initial time data from two or more BeiDou satellites; Construct a collaborative time matrix according to the initial time data to obtain collaborative time matrix data; A hierarchical time matrix is ​​constructed according to the initial time data to obtain hierarchical time matrix data.

3. The method according to claim 2, characterized in that The collaborative time matrix is ​​constructed as follows: Initialize the matrix according to the initial time data to obtain the initial time matrix data; Perform time period comparison on the initial time matrix data to obtain initial time comparison matrix data; Extracting the coordinated frequency features from the initial time comparison matrix data to obtain coordinated frequency feature data; Obtain historical collaborative data corresponding to Beidou satellites, and extract collaborative output features based on the historical collaborative data to obtain collaborative output feature data; The initial time comparison matrix data is weighted according to the collaborative frequency characteristic data and the collaborative output characteristic data to obtain the collaborative time matrix data.

4. The method according to claim 3, characterized in that The initial time comparison matrix data includes the first initial time comparison matrix data and the second initial time comparison matrix data, and the time periods are more specifically: Sorting the timing event points of the initial time matrix data to obtain the timing event point sorting data; Perform scan line traversal on the sorted data of the timing event points to obtain the timing event traversal data; Performing collaborative time calculation on the initial time matrix data according to the timed event traversal data to obtain first initial time comparison matrix data; Performing interval tree construction on the initial time matrix data and the preset error time threshold to obtain the initial time matrix interval tree data; Perform overlapping matrix query on the initial time matrix interval tree data to obtain the initial time matrix overlapping data; The initial time matrix data is updated with a coordinated time matrix according to the initial time matrix overlapping data to obtain second initial time comparison matrix data.

5. The method according to claim 3, characterized in that: The specific extraction of collaborative frequency features is as follows: Perform collaborative event classification on the initial time comparison matrix data to obtain initial time collaborative event classification matrix data; Perform cross-scale feature extraction on the initial time collaborative event classification matrix data to obtain cross-scale feature data of collaborative events; Perform frequency domain conversion on the cross-scale feature data of collaborative events to obtain frequency domain data of collaborative events; Agglomerative hierarchical clustering is performed on the frequency domain data of collaborative events to obtain collaborative frequency feature data.

6. The method according to claim 2, characterized in that The hierarchical time matrix is ​​constructed as follows: The original time matrix is ​​constructed according to the initial time data to obtain the first layered time matrix data; Acquire Beidou satellite signal configuration data, and perform time weighting on the first layered time matrix data according to the Beidou satellite signal configuration data to obtain second layered time matrix data; Obtain Beidou satellite signal transmission distance data; Perform atmospheric path simulation based on Beidou satellite signal transmission distance data to obtain atmospheric path simulation data; Performing path delay simulation according to the atmospheric path simulation data and the preset atmospheric layering data to obtain atmospheric path delay simulation data; Performing path correction on the second layer time matrix data according to the atmospheric layer path delay simulation data to obtain the third layer time matrix data; The first layered time matrix data, the second layered time matrix data and the third layered time matrix data are integrated to obtain layered time matrix data.

7. The method according to claim 2, characterized in that The Beidou satellite time offset matrix data includes Beidou satellite coordinated time offset matrix data and Beidou satellite layered time offset matrix data. Step S2 is specifically as follows: Perform multi-source time offset calculation on the collaborative time matrix data to obtain multi-source time offset data; Performing coordinated time offset correction on multi-source time offset data to obtain coordinated time offset correction data; Perform collaborative error compensation calculation based on collaborative time offset correction data to obtain collaborative error compensation time data; Generate Beidou satellite coordinated time offset matrix data based on coordinated error compensation time data; Performing layered time offset calculation according to layered time matrix data to obtain layered time offset data; Performing layered time offset correction according to the layered time offset data to obtain layered time offset correction data; Performing layer error compensation calculation according to the layer time offset correction data to obtain layer error compensation time data; Voting calculation is performed based on the layered error compensation time data to obtain the Beidou satellite layered time offset matrix data.

8. The method according to claim 1, characterized in that: Step S3 is specifically as follows: Obtaining the receiver's location data; The spatial distance is calculated according to the receiving party's position data and the Beidou satellite position data corresponding to the Beidou satellite time offset matrix data to obtain the relative spatial distance data; Obtain the receiver's weather data corresponding to the receiver's location data; Calculate signal propagation delay according to relative spatial distance data and receiver weather data to obtain signal propagation delay data; Performing atmospheric delay correction according to the relative space distance data and the signal propagation delay data to obtain atmospheric delay correction data; Delay processing is performed based on the atmospheric delay correction data and Beidou satellite time offset matrix data to obtain time delay processing matrix data.

9. The method according to claim 1, characterized in that: Step S4 is specifically as follows: Performing multipath effect inversion processing according to the time delay processing matrix data to obtain multipath effect inversion data; Perform ionospheric anomaly processing on the multipath effect inversion data to obtain ionospheric anomaly processing data; Perform iterative error estimation on the ionospheric anomaly processing data to obtain iterative data of signal propagation error; The time delay inversion matrix data is obtained according to the signal propagation error iteration data.

10. The method according to claim 1, characterized in that Step S5 is specifically as follows: Perform frequency drift detection according to the time delay inversion matrix data to obtain frequency drift detection data; Performing frequency drift correction on the frequency drift detection data and performing time delay inversion correction to generate time delay correction data; Frequency clock tuning is performed based on the time delay correction data to obtain time and frequency synchronization data for high-precision time and frequency synchronization optimization operations of the Beidou system.

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