A Beidou high-precision intelligent navigation method and system

Abstract

The invention discloses a Beidou high-precision intelligent navigation method and system, and specifically relates to the field of intelligent navigation technology. The method records the timestamps of a base station and a mobile terminal and calculates the time error for real-time correction, analyzes the error variation trend within a fixed time period, monitors the network transmission delay of differential correction data, evaluates its stability, conducts a comprehensive analysis in combination with the time error and the transmission delay fluctuation, evaluates the time synchronization accuracy of the system, and divides the synchronization state into precise synchronization, possible precise synchronization and failed synchronization according to the evaluation result. The system operates normally in the precise synchronization state, and automatically reduces the positioning accuracy and issues a warning signal in the case of failed synchronization. For the case of possible precise synchronization, the system conducts an in-depth analysis of the abnormal degree of time synchronization, and dynamically adjusts the operation strategy according to the analysis result to ensure the stability and adaptability of the system in different synchronization states.

Description

Technical Field

[0001] The present invention relates to the field of intelligent navigation technology, and in particular to a Beidou high-precision intelligent navigation method and system thereof. Background Art

[0002] Beidou high-precision intelligent navigation refers to the Beidou satellite navigation system (BDS) independently developed by China, combined with high-precision positioning technology and intelligent algorithms to achieve accurate navigation and location services. It can provide sub-meter or even centimeter-level positioning accuracy and is widely used in transportation, unmanned driving, agriculture, construction and other fields. Intelligent navigation not only relies on satellite signals, but also combines multi-source data (such as ground base stations, inertial navigation, etc.) and artificial intelligence technology, so that the navigation system can continuously provide stable and accurate location information in complex environments. This greatly improves the reliability and application scope of navigation.

[0003] In the existing technology, during Beidou high-precision intelligent navigation, first, Beidou satellites send positioning signals to ground user devices, and the devices determine the initial position by receiving these signals. Then, differential technology (such as RTK, i.e. real-time dynamic differential) or ground-based augmentation system is used to correct the error of satellite signals to achieve high-precision positioning. Next, intelligent algorithms fuse multi-source data, including satellite signals, ground base station data, inertial navigation information, etc., to improve the stability and continuity of positioning. Finally, after data processing and optimization, the system provides users with high-precision and intelligent navigation services.

[0004] The prior art has the following deficiencies:

[0005] RTK systems rely on precise time synchronization between the base station and the mobile terminal device. However, if the internal clock of the base station drifts or fails and cannot keep pace with the Global Positioning Satellite System (GNSS), incorrect correction data will be generated, which will affect the positioning calculation of the mobile terminal. And if the positioning data is inaccurate, the calculation and operation decisions of the positioning system will be based on wrong information, resulting in wrong action strategies, which will lead to a series of wrong decisions. Over time, the system will fall into a vicious cycle of ineffective self-correction, eventually leading to a complete system crash or major operational errors. Summary of the invention

[0006] The purpose of the present invention is to provide a Beidou high-precision intelligent navigation method and system thereof to solve the deficiencies in the background technology.

[0007] In order to achieve the above object, the present invention provides the following technical solution: a Beidou high-precision intelligent navigation method, comprising the following steps:

[0008] S1: The base station and the mobile terminal device receive BeiDou satellite signals and record accurate local timestamps based on the satellite's atomic clock; compare the timestamps of the base station and the mobile terminal, calculate the time error between the two, and make real-time corrections based on the time error, and record the changing trend of the time error within a fixed time period after correction;

[0009] S2: The base station sends the differential correction data to the mobile terminal, records the timestamp of the base station sending the correction data and the timestamp of the mobile terminal receiving the data, and calculates the network transmission delay; by analyzing the fluctuation state of the network transmission delay, the stability of time synchronization is evaluated;

[0010] S3: Comprehensively analyze the changing trend of the time error within a fixed time period after correction and the fluctuation state of the network transmission delay to evaluate the time synchronization accuracy between the base station and the mobile terminal device;

[0011] S4: according to the evaluation result, the time synchronization accuracy between the base station and the mobile terminal device is divided into accurate synchronization, possible accurate synchronization and failed synchronization;

[0012] S5: For the precise synchronous control system, the system operates normally; for the failed synchronous control system, the positioning accuracy is automatically reduced and a synchronization failure signal is issued;

[0013] S6: For possible precise synchronization, conduct an in-depth analysis of the time synchronization accuracy between the base station and the mobile terminal device, determine the degree of abnormality of the time synchronization between the base station and the mobile terminal device based on the analysis results, and dynamically adjust the operation decision based on the degree of abnormality.

[0014] Preferably, in S1, the time error anomaly index is generated after analyzing the change trend of the time error within the fixed time period after correction, and the method for obtaining the time error anomaly index is:

[0015] Obtain the historical data sequence of time error from the system, set a time window, and extract the time error data of the base station and the mobile terminal within this window. The historical time error sequence is: ,in Represents the error value at the nth time point. The current time error sequence is ,in Represents the error value at the mth time point; compare each point of the two time series and calculate the Euclidean distance between the two points. The expression is: ; Represents the Euclidean distance between the i-th point in sequence X and the j-th point in sequence Y; constructs an n×m cumulative distance matrix D, where each element D(i,j) in the matrix represents the minimum cumulative distance between the i-th point in sequence X and the j-th point in sequence Y. The specific calculation method is: ; In the formula, represents the optimal path from above, represents the optimal path from the left, represents the optimal path from the upper left, and the boundary conditions are: ; That is, the initial condition is the Euclidean distance of the first elements of the two sequences; In order to find the best match between two time series, it is necessary to calculate the optimal matching path P along the cumulative distance matrix D. Path P is a path from the upper left corner D(1,1) to the lower right corner D(n,m) of the matrix. Select the node with the smallest cumulative distance to pass through the path. The form of path P is: ; In the formula, Represents the matrix coordinate points that the path passes through. The optimal path is found by back-tracing the minimum value in the matrix ; By gradually selecting the smallest neighbor node, a path with the best matching degree is determined. The cumulative distance and the sum on the optimal path P represent the similarity of the two time series, and the expression is: ;in is the total cumulative distance on the optimal path, and the time error anomaly index is calculated. The expression is: ; In the formula, K is the length of the optimal path, represents the number of nodes passed on the path, and RT is the time error anomaly index.

[0016] Preferably, in S2, the delay jitter drift index is generated after analyzing the fluctuation state of the delay jitter data calculated in the S time period, and the method for obtaining the delay jitter drift index is:

[0017] Obtain the delay jitter data within a time period S from the time synchronization system and set it as a time series: ;in, Represents the delay jitter value at the nth time point, selects the attenuation factor α, and uses the initial value of the delay jitter as the initial exponentially weighted moving average ; That is, the first value of the EWMA sequence is equal to the first observed delay jitter value; according to the recursive formula, the subsequent exponentially weighted moving averages are calculated step by step : ;in: represents the smoothed value at the tth time point, Indicates the actual delay jitter value at the tth time point, is the EWMA value at the previous time point, indicating the smoothed value of historical data; the recursive formula indicates that the latest The current delay jitter value and the previous moment According to the weights α and Linear combination;

[0018] Calculate the actual delay jitter value at each time point t With smooth The difference between them is used to calculate the delay jitter drift index, and the expression is: ; Where DF is the delay jitter drift index.

[0019] Preferably, in S3, a comprehensive analysis is performed on the changing trend of the time error within the fixed time period after correction and the fluctuation state of the network transmission delay to evaluate the time synchronization accuracy between the reference station and the mobile terminal device, specifically:

[0020] The time error anomaly index and the delay jitter drift index are converted into the first eigenvector, and the first eigenvector is used as the input of the machine learning model. The machine learning model predicts the time synchronization accuracy value label of the reference station and the mobile terminal device for each group of first eigenvectors as the prediction target, and minimizes the sum of the prediction errors of the time synchronization accuracy value labels of all reference stations and mobile terminal devices as the training target. The machine learning model is trained until the sum of the prediction errors converges, and the model training is stopped. The time synchronization accuracy value of the reference station and the mobile terminal device is determined according to the model output results, wherein the machine learning model is a polynomial regression model.

[0021] Preferably, in S4, according to the evaluation result, the time synchronization accuracy between the reference station and the mobile terminal device is divided into precise synchronization, possible precise synchronization and failed synchronization, specifically:

[0022] The acquired time synchronization accuracy value between the reference station and the mobile terminal device is compared with a gradient standard threshold, where the gradient standard threshold includes a first standard threshold and a second standard threshold, and the first standard threshold is less than the second standard threshold, and the time synchronization accuracy value between the reference station and the mobile terminal device is compared with the first standard threshold and the second standard threshold respectively;

[0023] If the time synchronization accuracy value between the reference station and the mobile terminal device is greater than the second standard threshold, it means that the time synchronization accuracy between the reference station and the mobile terminal device is high, and a high-accuracy time synchronization signal is generated and classified as a precise synchronization level;

[0024] If the time synchronization accuracy value between the reference station and the mobile terminal device is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, it means that the time synchronization accuracy between the reference station and the mobile terminal device is average, and a medium accuracy time synchronization signal is generated at this time, and it is classified as a possible accurate synchronization level;

[0025] If the time synchronization accuracy value between the reference station and the mobile terminal device is less than the first standard threshold, it means that the time synchronization accuracy between the reference station and the mobile terminal device is low. At this time, a low-accuracy time synchronization signal is generated and classified as a failed synchronization level.

[0026] Preferably, in S6, for possible accurate synchronization, an in-depth analysis is performed on the time synchronization accuracy between the reference station and the mobile terminal device, specifically:

[0027] For possible precise synchronization, that is, the time synchronization accuracy value of the base station and the mobile terminal device generated is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, the time synchronization accuracy values ​​of the base station and the mobile terminal device that are subsequently generated that are greater than or equal to the first standard threshold and less than or equal to the second standard threshold are collected, and a corresponding data set is established, the mean and standard deviation of the data set are calculated, and after analyzing them, the degree of abnormality in the time synchronization between the base station and the mobile terminal device is determined based on the analysis results, and the operation decision is dynamically adjusted based on the degree of abnormality.

[0028] Preferably, if the mean of the time synchronization accuracy values ​​in the data set is greater than or equal to the reference threshold of the mean of the time synchronization accuracy values, and the standard deviation of the time synchronization accuracy values ​​is less than the reference threshold of the standard deviation of the time synchronization accuracy values, the accuracy of the time synchronization is high and the data fluctuation is small, then no warning signal is generated and the system maintains high-precision operation;

[0029] If the mean of the time synchronization accuracy values ​​is greater than or equal to the reference threshold of the mean of the time synchronization accuracy values, and the standard deviation of the time synchronization accuracy values ​​is greater than or equal to the reference threshold of the standard deviation of the time synchronization accuracy values, although the time synchronization accuracy is high, the data fluctuation is large. At this time, a third-level warning signal is generated, and the system should enable the error compensation mechanism to monitor the fluctuation and maintain high accuracy;

[0030] If the mean value of the time synchronization accuracy is less than the reference threshold of the mean value of the time synchronization accuracy, and the standard deviation of the time synchronization accuracy is greater than or equal to the reference threshold of the standard deviation of the time synchronization accuracy, the time synchronization accuracy is low and fluctuates greatly. At this time, a first-level warning signal is generated, and the system should be downgraded and switched to a low-precision mode to ensure system stability;

[0031] If the mean of the time synchronization accuracy values ​​is less than the reference threshold of the mean of the time synchronization accuracy values, and the standard deviation of the time synchronization accuracy values ​​is less than the reference threshold of the standard deviation of the time synchronization accuracy values, the time synchronization accuracy is low but the fluctuation is small. At this time, a secondary warning signal is generated, the system maintains low-precision operation, and monitors the synchronization status.

[0032] The present invention also provides a Beidou high-precision intelligent navigation system, including a time error correction module, a network transmission delay monitoring module, a time synchronization evaluation module, a time synchronization classification module, a synchronization failure processing module and a dynamic adjustment and decision module;

[0033] Time error correction module: The base station and mobile terminal devices receive Beidou satellite signals and record accurate local timestamps based on the satellite's atomic clock; compare the timestamps of the base station and the mobile terminal, calculate the time error between the two, and make real-time corrections based on the time error, and record the changing trend of the time error within a fixed time period after correction;

[0034] Network transmission delay monitoring module: The base station sends differential correction data to the mobile terminal, records the timestamp of the base station sending the correction data and the timestamp of the mobile terminal receiving the data, and calculates the network transmission delay; by analyzing the fluctuation state of the network transmission delay, the stability of time synchronization is evaluated;

[0035] Time synchronization evaluation module: Comprehensively analyzes the changing trend of time error and the fluctuation state of network transmission delay within a fixed time period after correction, and evaluates the time synchronization accuracy between the base station and the mobile terminal device;

[0036] Time synchronization classification module: according to the evaluation results, the time synchronization accuracy between the base station and the mobile terminal device is divided into precise synchronization, possible precise synchronization and failed synchronization;

[0037] Synchronous failure processing module: for the precise synchronous control system, the module operates normally; for the failed synchronous control system, the module automatically reduces the positioning accuracy and issues a synchronous failure signal;

[0038] Dynamic adjustment and decision-making module: For possible precise synchronization, the time synchronization accuracy of the base station and the mobile terminal device is deeply analyzed, the abnormality of the time synchronization between the base station and the mobile terminal device is determined based on the analysis results, and the operation decision is dynamically adjusted based on the abnormality.

[0039] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0040] 1. The present invention receives Beidou satellite signals and corrects time errors in real time based on accurate timestamps, records and analyzes the error change trend within a fixed time period. At the same time, by calculating the network transmission delay and analyzing its fluctuation state, the time synchronization accuracy of the system is comprehensively evaluated. According to the synchronization accuracy evaluation result, the system can automatically classify the synchronization state into precise synchronization, possible precise synchronization or failed synchronization, and adopt different processing strategies respectively. The system runs normally under precise synchronization, and automatically downgrades the processing to maintain the basic functions of the system when the synchronization fails. For possible precise synchronization, the system further analyzes the abnormal degree of time synchronization, dynamically adjusts the operation strategy, and ensures the stability of the system under fluctuation conditions.

[0041] 2. The present invention uses the time error anomaly index and delay jitter drift index to evaluate the accuracy of time synchronization, and combines the machine learning model to improve the intelligence and accuracy of the system, and significantly improves the stability and adaptability of the navigation system. In addition, by dynamically adjusting the decision-making mechanism, the system can respond to different synchronization states in real time, achieve smooth switching from high-precision mode to low-precision mode, and ensure that the system maintains stable and reliable operation under various complex network environments and synchronization fluctuation conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0043] Figure 1 The figure is a flow chart of the method of the present invention.

[0044] Figure 2 It is a system module diagram of the present invention. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. 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 ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] Example 1, please refer to Figure 1 and Figure 2 As shown, the Beidou high-precision intelligent navigation method described in this embodiment includes the following steps:

[0047] S1: The base station and the mobile terminal device receive BeiDou satellite signals and record accurate local timestamps based on the satellite's atomic clock; compare the timestamps of the base station and the mobile terminal, calculate the time error between the two, and make real-time corrections based on the time error, and record the changing trend of the time error within a fixed time period after correction;

[0048] S2: The base station sends the differential correction data to the mobile terminal, records the timestamp of the base station sending the correction data and the timestamp of the mobile terminal receiving the data, and calculates the network transmission delay; by analyzing the fluctuation state of the network transmission delay, the stability of time synchronization is evaluated;

[0049] S3: Comprehensively analyze the changing trend of the time error within a fixed time period after correction and the fluctuation state of the network transmission delay to evaluate the time synchronization accuracy between the base station and the mobile terminal device;

[0050] S4: according to the evaluation result, the time synchronization accuracy between the base station and the mobile terminal device is divided into accurate synchronization, possible accurate synchronization and failed synchronization;

[0051] S5: For the precise synchronous control system, the system operates normally; for the failed synchronous control system, the positioning accuracy is automatically reduced and a synchronization failure signal is issued;

[0052] S6: For possible precise synchronization, conduct an in-depth analysis of the time synchronization accuracy between the base station and the mobile terminal device, determine the degree of abnormality of the time synchronization between the base station and the mobile terminal device based on the analysis results, and dynamically adjust the operation decision based on the degree of abnormality.

[0053] Among them, in S1, the base station and the mobile terminal device receive Beidou satellite signals and record accurate local timestamps based on the satellite's atomic clock; compare the timestamps of the base station and the mobile terminal, calculate the time error between the two, and perform real-time correction based on the time error, and record the change trend of the time error within a fixed time period after correction, specifically:

[0054] The base station and mobile terminal devices receive navigation signals from Beidou satellites at the same time. Usually, they need to receive signals from at least four satellites to ensure the accuracy of three-dimensional positioning (longitude, latitude, and altitude). Each Beidou satellite sends a signal containing satellite position (ephemeris) and precise time information. The propagation time of these signals is precisely timed by the satellite's atomic clock. The base station and mobile terminal devices must receive the same satellite signal at the same time to ensure that both work based on the same time reference.

[0055] The base station and mobile terminal devices record the local timestamp of each satellite signal received. This means that the device assigns a precise time stamp to each received satellite signal when the signal arrives. The accuracy of the timestamp is usually in the nanosecond level to ensure the minimum error during the time synchronization process. The satellite atomic clock serves as a global time reference to ensure that each timestamp is highly consistent with the real time. The base station and mobile terminal devices record their own local timestamps. The recording of these timestamps is crucial for subsequent error calculation and correction.

[0056] The timestamps of the base station and the mobile terminal are used to calculate the time difference between the two. That is, by comparing the timestamps of the two receiving the same satellite signal, the time error is calculated. This time error can be expressed by the formula: ;in, is the time error, Timestamp recorded for mobile terminal devices, Timestamp recorded for the base station.

[0057] Sources of error include: Clock drift: Slight inconsistency between the local clock of a base station or mobile terminal and the satellite atomic clock. Signal propagation delay: Satellite signals are affected by the ionosphere and troposphere as they propagate through the atmosphere, causing time delays.

[0058] The calculated time error will be used to adjust the local time reference of the mobile terminal device in real time to reduce the impact of time drift on positioning accuracy. Real-time correction is accomplished by dynamically adjusting the clock of the mobile terminal to keep it synchronized with the reference station. The correction process relies on continuous error calculation, and the error is recalculated and adjusted each time a new satellite signal is received.

[0059] When applying time error correction, the system will smooth short-term fluctuations through intelligent algorithms (such as Kalman filters) to avoid incorrect corrections caused by transient interference. The algorithm predicts future errors based on historical error trends and dynamically corrects the timestamp so that the time base of the mobile terminal is synchronized with the base station and the error is reduced to a minimum.

[0060] The system will continuously record the changes in the time error and generate a trend curve of the time error over time. By analyzing this trend, it can be determined whether there is long-term clock drift or other systemic problems. If the error fluctuation range is within a certain range, it can be considered that the system is working normally; but if the error continues to increase, there may be a clock problem in the base station or mobile terminal, which requires further processing.

[0061] The time error anomaly index is generated by analyzing the changing trend of the time error within a fixed time period after correction. The method for obtaining the time error anomaly index is as follows:

[0062] Obtain the historical data sequence of time error from the system, set a time window, extract the time error data of the base station and the mobile terminal within this window, and set the historical time error sequence as ,in Represents the error value at the nth time point. Let the current time error sequence be ,in Represents the error value at the mth time point; compare each point of the two time series and calculate the Euclidean distance between the two points. The expression is: ; Represents the Euclidean distance between the i-th point in sequence X and the j-th point in sequence Y; constructs an n×m cumulative distance matrix D, where each element D(i,j) in the matrix represents the minimum cumulative distance between the i-th point in sequence X and the j-th point in sequence Y. The specific calculation method is: ; In the formula, represents the optimal path from above, represents the optimal path from the left, represents the optimal path from the upper left (diagonal), with the boundary conditions: ; That is, the initial condition is the Euclidean distance of the first elements of the two sequences; In order to find the best match between the two time series, it is necessary to calculate the optimal matching path P along the cumulative distance matrix D. Path P is a path from the upper left corner D(1,1) to the lower right corner D(n,m) of the matrix. Select the node with the smallest cumulative distance to pass through the path. The form of path P is: ; In the formula, Represents the matrix coordinate points that the path passes through. The optimal path is found by back-tracing the minimum value in the matrix ; By gradually selecting the smallest neighbor node, a path with the best matching degree is determined. The cumulative distance sum on the optimal path P represents the similarity of the two time series. The smaller the cumulative distance, the more consistent the time error change and the smaller the anomaly. The expression is: ;in is the total cumulative distance on the optimal path, and the time error anomaly index is calculated. The expression is: ; In the formula, K is the length of the optimal path, represents the number of nodes passed on the path, and RT is the time error anomaly index.

[0063] The larger the time error anomaly index is, the more unstable the time error change trend is within a fixed time period after correction, indicating that the time error fluctuates greatly and deviates from the normal state. A large anomaly index may be due to the increased clock drift of the base station or mobile terminal, or the influence of environmental interference (such as abnormal changes in the ionosphere and troposphere) or fluctuations in signal transmission delay, resulting in the system being unable to effectively synchronize time. A large anomaly index also indicates that the system's error compensation mechanism fails to correct this unstable error in a timely manner, and further correction or troubleshooting measures may be required.

[0064] The smaller the time error anomaly index is, the more stable the time error change trend is within a fixed time period after correction, and the error is basically maintained within a reasonable range. This shows that the time synchronization status of the base station and the mobile terminal device is good, the error in the system is small and consistent, and the clock drift and signal delay fluctuations are within a controllable range. The smaller anomaly index reflects that the system is in normal operation, the error correction mechanism works effectively, the time synchronization accuracy remains at the expected level, and the high-precision positioning service can be maintained.

[0065] S2: The base station sends the differential correction data to the mobile terminal, records the timestamp of the base station sending the correction data and the timestamp of the mobile terminal receiving the data, and calculates the network transmission delay; by analyzing the fluctuation state of the network transmission delay, the stability of time synchronization is evaluated.

[0066] The base station receives positioning signals from Beidou satellites and calculates error correction data based on the known precise position. These correction data are used to correct the positioning error of the mobile terminal and improve its accuracy. After the base station generates and prepares the differential correction data, it marks the time when these data are sent.

[0067] Before the base station sends the differential correction data, a sending timestamp is recorded, indicating the exact time when the base station sends the correction data. This timestamp is usually high-precision, generated using the internal clock of the base station, and preferably synchronized with the satellite time. Let the sending timestamp be Tsend.

[0068] The base station sends the differential correction data to the mobile terminal through the network (e.g., radio signal, cellular network, Wi-Fi, etc.). Network transmission may be affected by different factors, such as bandwidth, delay, jitter, packet loss rate, etc.

[0069] The mobile terminal receives the differential correction data from the base station through the communication module. When the data arrives at the mobile terminal, the time of receiving the data is recorded. The time when the mobile terminal receives the data may fluctuate due to the quality of network transmission, so it is necessary to accurately record the receiving time.

[0070] When the mobile terminal receives the differential correction data, it records a receiving timestamp, which indicates the specific time when the data packet arrives at the mobile terminal. Let the receiving timestamp be Trecv. The clock of the mobile terminal should be synchronized with the clock of the reference station as much as possible to ensure the accuracy of the timestamp.

[0071] Calculate the network transmission delay, the delay calculation formula is: ΔTdelay = Trecv-Tsend; where ΔTdelay is the network transmission delay, which means the time difference from the base station sending the differential correction data to the mobile terminal receiving the data. The delay value reflects the real-time performance of network transmission. If the delay is large, there may be network transmission problems (such as congestion, signal interference, etc.).

[0072] Record the transmission delay values ​​in multiple time periods and analyze the fluctuation trend of the delay. If the delay is stable, it means that the network transmission quality is good; if the delay fluctuates greatly, it means that the network transmission may be unstable and needs further optimization.

[0073] Calculate the average of all transmission delay values ​​to measure the overall performance of the transmission. The expression is: ; In the formula, n is the total number of network transmission delay data, and the delay jitter is calculated as follows: ; If the network transmission delay value at a certain moment is found to be beyond the normal range (for example, the delay is too long or fluctuates violently), the system can handle the exception. For example, an alarm may be issued or redundant communication paths may be adopted to ensure the stability of data transmission. Based on the delay monitoring data, the system can choose to optimize the network transmission method. For example, switching to a faster or more stable communication channel, reducing signal interference, etc., to ensure that the differential correction data can be quickly and reliably transmitted to the mobile terminal.

[0074] After analyzing the fluctuation state of the delay jitter data calculated in the S time period, a delay jitter drift index is generated to evaluate the stability of time synchronization. The delay jitter drift index is obtained as follows:

[0075] Obtain the delay jitter data within a time period S from the time synchronization system and set it as a time series: ;in, Indicates the delay jitter value at the nth time point. This jitter value usually indicates the difference between the network delay calculated each time and its average value.

[0076] Select the decay factor α, which is a parameter between 0 and 1, and determines the weight ratio of the latest data and historical data. A common choice is to set α according to the desired smoothness. For example, a larger α (such as 0.8) means more attention to the latest data and faster weight decay. A smaller α (such as 0.2) means a longer impact on historical data and a more obvious smoothing effect. The formula for α can be dynamically determined based on the time window T (the length of the data or the length of the sliding window): ; where T is the desired smoothing time period length.

[0077] Take the initial value of the delay jitter as the initial exponentially weighted moving average ; That is, the first value of the EWMA sequence is equal to the first observed delay jitter value.

[0078] According to the recursive formula, the subsequent exponentially weighted moving average is calculated step by step : ;in: represents the smoothed value (exponentially weighted moving average) at the t-th time point, Indicates the actual delay jitter value at the tth time point, It is the EWMA value at the previous time point, indicating the smoothed value of historical data.

[0079] This recursive formula shows that the latest The current delay jitter value and the previous moment According to the weights α and Linear combination.

[0080] Calculate the actual delay jitter value at each time point t With smooth The difference between them is used to calculate the delay jitter drift index, and the expression is: ; Where DF is the delay jitter drift index.

[0081] The larger the delay jitter drift index, the greater the fluctuation of delay jitter in network transmission, and the system's time synchronization becomes unstable. A larger drift index indicates that delay jitter is frequent and of high amplitude, and the actual delay deviates significantly from the expected trend. This situation may be caused by problems such as jitter in network transmission, signal interference or congestion, which leads to aggravated time synchronization errors. If the drift index continues to increase, the system's time synchronization accuracy will be seriously affected, which may lead to increased data transmission delays or the system's inability to maintain high-precision time synchronization, thereby affecting the reliability of the overall positioning or communication system.

[0082] The smaller the delay jitter drift index, the smaller the delay jitter in network transmission and the better the stability of time synchronization. A smaller drift index indicates that the delay jitter is within a reasonable range and the system's time synchronization remains stable. A small deviation of the delay from the predicted value means that the transmission delay is relatively consistent without significant fluctuations or interference. A smaller drift index reflects that the system's time synchronization accuracy is high, the network transmission is smoother, and the time error is effectively controlled, ensuring the high accuracy and reliability of the system.

[0083] S3: Comprehensively analyze the changing trend of the time error within a fixed time period after correction and the fluctuation state of the network transmission delay to evaluate the time synchronization accuracy between the base station and the mobile terminal device.

[0084] The time error anomaly index and the delay jitter drift index are converted into the first eigenvector, and the first eigenvector is used as the input of the machine learning model. The machine learning model predicts the time synchronization accuracy value label of the reference station and the mobile terminal device for each group of first eigenvectors as the prediction target, and minimizes the sum of the prediction errors of the time synchronization accuracy value labels of all reference stations and mobile terminal devices as the training target. The machine learning model is trained until the sum of the prediction errors converges, and the model training is stopped. The time synchronization accuracy value of the reference station and the mobile terminal device is determined according to the model output results, wherein the machine learning model is a polynomial regression model.

[0085] The method for obtaining the time synchronization accuracy value between the base station and the mobile terminal device is: from the first feature vector training data of the trained machine learning model, obtain the corresponding function expression: ; Where F is the output function of the model, RT is the time error anomaly index, DF is the delay jitter drift index, and ER is the time synchronization accuracy value between the base station and the mobile terminal device.

[0086] S4: According to the evaluation result, the time synchronization accuracy between the base station and the mobile terminal device is divided into precise synchronization, possible precise synchronization and failed synchronization.

[0087] The acquired time synchronization accuracy value between the reference station and the mobile terminal device is compared with a gradient standard threshold, where the gradient standard threshold includes a first standard threshold and a second standard threshold, and the first standard threshold is less than the second standard threshold, and the time synchronization accuracy value between the reference station and the mobile terminal device is compared with the first standard threshold and the second standard threshold respectively;

[0088] If the time synchronization accuracy value between the reference station and the mobile terminal device is greater than the second standard threshold, it means that the time synchronization accuracy between the reference station and the mobile terminal device is high, and a high-accuracy time synchronization signal is generated and classified as a precise synchronization level;

[0089] If the time synchronization accuracy value between the reference station and the mobile terminal device is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, it means that the time synchronization accuracy between the reference station and the mobile terminal device is average, and a medium accuracy time synchronization signal is generated at this time, and it is classified as a possible accurate synchronization level;

[0090] If the time synchronization accuracy value between the reference station and the mobile terminal device is less than the first standard threshold, it means that the time synchronization accuracy between the reference station and the mobile terminal device is low. At this time, a low-accuracy time synchronization signal is generated and classified as a failed synchronization level.

[0091] S5: For the precise synchronous control system, it operates normally. For the failed synchronous control system, it automatically reduces the positioning accuracy and sends a synchronization failure signal.

[0092] In the precise synchronization state, the time synchronization between the base station and the mobile terminal is very accurate, the time error is small, and the network transmission is stable. This means that the system can maintain high-precision positioning services and ensure the normal operation of the time synchronization control system.

[0093] The system can provide centimeter-level or millimeter-level positioning accuracy because the time synchronization error between the base station and the mobile terminal is extremely low, and the delay and jitter of the network transmission are also within a reasonable range. The differential correction data in the system can be transmitted to the mobile terminal in a timely manner, so that the error is corrected in real time, ensuring the high accuracy and real-time performance of the positioning data. Even in the precise synchronization state, the system will still monitor parameters such as time synchronization error and network delay in real time to ensure the stability of the synchronization state. If the synchronization error begins to increase, the system will issue an early warning and make adaptive adjustments. In this state, all modules of the system (such as unmanned driving, precise navigation, automatic control, etc.) can operate normally without downgrading. Equipment such as autonomous vehicles and drones can perform high-precision operations and are suitable for mission scenarios with extremely high precision requirements. The system continuously adjusts the time synchronization strategy based on the real-time changes in sensor data and positioning accuracy to ensure high-precision synchronization and positioning even in dynamic environments.

[0094] In the failed synchronization state, the time synchronization error between the base station and the mobile terminal is too large, and the network transmission may experience serious delay jitter or instability, causing the system to be unable to maintain normal high-precision positioning services. In this case, the system will automatically enter the degraded mode to prevent serious positioning errors from affecting operational safety and send out a synchronization failure signal.

[0095] Degraded mode start: When the system detects that the time synchronization error or network transmission delay exceeds the preset threshold, the system will automatically reduce the positioning accuracy and reduce the high-precision mode to meter or sub-meter level to reduce the impact of the error on actual applications. In this mode, the system may abandon the use of differential correction data and use standard positioning based on a single GNSS signal, or enable auxiliary positioning systems (such as inertial navigation system, INS) to provide basic positioning capabilities to ensure that the device can continue to operate. When time synchronization fails, the system will send a synchronization failure signal to the user or the background server, indicating that the current time synchronization state can no longer maintain high-precision positioning and the system may enter degraded mode. The system can notify operators through the device interface, warning lights, or sound and light signals to remind them that the current positioning accuracy has been reduced and is not suitable for delicate operations or tasks. Synchronization failure information will be recorded in the system log to facilitate troubleshooting and subsequent maintenance work. In the event of time synchronization failure, the system may automatically switch to the inertial navigation system (INS), using sensors such as accelerometers and gyroscopes to provide short-term positioning and navigation support. Although the accuracy of the inertial navigation system will decay over time, it can still provide relatively reliable location information in the short term.

[0096] The system can also enhance positioning accuracy through other external data sources (such as ground base station signals, visual navigation, etc.) to ensure that the device can still obtain basic positioning information in the event of synchronization failure. The system will continuously monitor the time synchronization status and network transmission status and try to restore synchronization. If the time error or network delay is detected to return to the normal range, the system will automatically switch back to high-precision mode and resume full functional operation. In some cases, the user may need to manually intervene or restart the device to re-establish the synchronization status. Synchronization failure signals can help users take timely action to avoid more serious system failures or safety issues.

[0097] In summary, in the precise synchronization state, the system operates normally, provides high-precision positioning, and all functions are in the best state, suitable for tasks with high-precision requirements. In the failed synchronization state, the system automatically reduces the positioning accuracy and sends a synchronization failure signal to remind users to pay attention to the degradation of system performance, and ensures the basic operation of the device through a degraded mode (such as inertial navigation or low-precision positioning) until synchronization is restored.

[0098] S6: For possible precise synchronization, conduct an in-depth analysis of the time synchronization accuracy between the base station and the mobile terminal device, determine the degree of abnormality of the time synchronization between the base station and the mobile terminal device based on the analysis results, and dynamically adjust the operation decision based on the degree of abnormality.

[0099] For possible precise synchronization, that is, the time synchronization accuracy value of the base station and the mobile terminal device generated is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, the time synchronization accuracy values ​​of the base station and the mobile terminal device that are subsequently generated that are greater than or equal to the first standard threshold and less than or equal to the second standard threshold are collected, and a corresponding data set is established, the mean and standard deviation of the data set are calculated, and after analyzing them, the degree of abnormality in the time synchronization between the base station and the mobile terminal device is determined based on the analysis results, and the operation decision is dynamically adjusted based on the degree of abnormality.

[0100] If the mean of the time synchronization accuracy values ​​in the data set is greater than or equal to the reference threshold of the mean of the time synchronization accuracy values, and the standard deviation of the time synchronization accuracy values ​​is less than the reference threshold of the standard deviation of the time synchronization accuracy values, the time synchronization accuracy is high and the data fluctuation is small, indicating that the system synchronization stability is good and the time error is stable within an acceptable range. At this time, no warning signal is generated, and the system can maintain the current high-precision mode operation without major adjustments, because the synchronization status is good and the system can continue to provide high-precision positioning services; Operational decision: Maintain high-precision operation, such as autonomous driving, precision agriculture and other scenarios continue to operate with high precision. The real-time monitoring frequency can be appropriately relaxed to reduce system resource consumption.

[0101] If the mean of the time synchronization accuracy value is greater than or equal to the reference threshold of the mean of the time synchronization accuracy value, and the standard deviation of the time synchronization accuracy value is greater than or equal to the reference threshold of the standard deviation of the time synchronization accuracy value, although the time synchronization accuracy is high, the data fluctuation is large, indicating that the system has a certain degree of instability and may have large synchronization error fluctuations. Attention should be paid to network transmission or environmental impact. At this time, a third-level warning signal is generated, and the system can continue to provide high-precision positioning, but vigilance should be increased. In order to cope with unstable fluctuations, the error monitoring frequency should be increased or the fault tolerance mechanism should be enabled to ensure that fluctuations will not have a significant impact on positioning accuracy.

[0102] Action decision: Continue to operate with high precision, but enable real-time error compensation, such as Kalman filtering, to cope with synchronization fluctuations. Strengthen network transmission link monitoring, or temporarily adjust network parameters to reduce fluctuations. If the environment continues to be unstable, it may be necessary to warn users or system administrators of potential synchronization fluctuation risks.

[0103] If the mean value of the time synchronization accuracy value is less than the reference threshold of the mean value of the time synchronization accuracy value, and the standard deviation of the time synchronization accuracy value is greater than or equal to the reference threshold of the standard deviation of the time synchronization accuracy value, the time synchronization accuracy is low and fluctuates greatly, indicating that the system synchronization error is large and unstable. The system is in an unreliable state, the positioning accuracy is reduced, and it may be seriously affected by network delays, interference, etc. At this time, a first-level warning signal is generated, and the system should immediately take downgrade processing, reduce the positioning accuracy, switch to low-precision mode, or start the backup system (such as the inertial navigation system) to ensure that basic functions can continue to operate.

[0104] Operational decision: Switch to low-precision mode, for example, switch high-precision positioning to meter-level positioning. Enable backup navigation systems, such as inertial navigation (INS), to temporarily provide position support until synchronization is restored. Trigger synchronization failure warning to notify users or system administrators that time synchronization is inaccurate and may affect system safety or reliability.

[0105] If the mean value of the time synchronization accuracy value is less than the reference threshold of the mean value of the time synchronization accuracy value, and the standard deviation of the time synchronization accuracy value is less than the reference threshold of the standard deviation of the time synchronization accuracy value, the time synchronization accuracy is low but the fluctuation is small, which means that the system synchronization error is stable, but the accuracy is not enough, and the error is at a low but stable level. This usually means that the system is running in low-precision mode or is affected by the external environment and cannot provide high-precision services. At this time, a secondary warning signal is generated, and the system can maintain the current low-precision mode operation, but the synchronization error should continue to be monitored and try to restore to high-precision synchronization. If the high-precision state cannot be restored for a long time, the environment or equipment needs to be checked and adjusted.

[0106] Operational decision: Maintain low-precision operation to ensure that the system provides basic services in a stable state, such as maintaining positioning accuracy at the meter level, which is suitable for low-precision application scenarios. Continue to monitor synchronization errors and try to gradually restore to high-precision mode. If synchronization cannot be restored, it is recommended to check the base station or network equipment to check for external factors that may cause inaccurate time synchronization (such as signal interference, equipment failure, etc.).

[0107] It should be noted here that the importance of the first-level warning signal is greater than that of the second-level warning signal, and the importance of the second-level warning signal is greater than that of the third-level warning signal. Relevant personnel can take corresponding handling measures according to different warning signal levels.

[0108] In this embodiment, a series of steps are used to implement the time synchronization evaluation and dynamic adjustment of the base station and the mobile terminal device. First, the base station and the mobile terminal receive Beidou satellite signals and record timestamps, and calculate and correct the time error in real time. The base station sends differential correction data to the mobile terminal, records the sending and receiving timestamps to calculate the network transmission delay, and analyzes the delay fluctuation to evaluate the stability of time synchronization. According to the trend of the corrected time error change and the network delay fluctuation, the accuracy of time synchronization is comprehensively evaluated, and it is divided into precise synchronization, possible precise synchronization, and failed synchronization. For the precise synchronization system, it operates normally, while for the failed synchronization system, the positioning accuracy is automatically reduced and a synchronization failure signal is issued. For the possible precise synchronization system, the abnormality of the time synchronization is further analyzed, and the operation decision is dynamically adjusted according to the results to ensure the adaptive operation of the system.

[0109] Embodiment 2, a Beidou high-precision intelligent navigation system described in this embodiment includes a time error correction module, a network transmission delay monitoring module, a time synchronization evaluation module, a time synchronization classification module, a synchronization failure processing module and a dynamic adjustment and decision module;

[0110] Time error correction module: The base station and mobile terminal devices receive Beidou satellite signals and record accurate local timestamps based on the satellite's atomic clock; compare the timestamps of the base station and the mobile terminal, calculate the time error between the two, and make real-time corrections based on the time error, and record the changing trend of the time error within a fixed time period after correction;

[0111] Network transmission delay monitoring module: The base station sends differential correction data to the mobile terminal, records the timestamp of the base station sending the correction data and the timestamp of the mobile terminal receiving the data, and calculates the network transmission delay; by analyzing the fluctuation state of the network transmission delay, the stability of time synchronization is evaluated;

[0112] Time synchronization evaluation module: Comprehensively analyzes the changing trend of time error and the fluctuation state of network transmission delay within a fixed time period after correction, and evaluates the time synchronization accuracy between the base station and the mobile terminal device;

[0113] Time synchronization classification module: according to the evaluation results, the time synchronization accuracy between the base station and the mobile terminal device is divided into precise synchronization, possible precise synchronization and failed synchronization;

[0114] Synchronous failure processing module: for the precise synchronous control system, the module operates normally; for the failed synchronous control system, the module automatically reduces the positioning accuracy and issues a synchronous failure signal;

[0115] Dynamic adjustment and decision-making module: For possible precise synchronization, the time synchronization accuracy of the base station and the mobile terminal device is deeply analyzed, the abnormality of the time synchronization between the base station and the mobile terminal device is determined based on the analysis results, and the operation decision is dynamically adjusted based on the abnormality.

[0116] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0117] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0118] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0119] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. A Beidou high-precision intelligent navigation method, characterized in that: The steps include: S1: The base station and the mobile terminal device receive BeiDou satellite signals and record accurate local timestamps based on the satellite's atomic clock; compare the timestamps of the base station and the mobile terminal, calculate the time error between the two, and make real-time corrections based on the time error, and record the changing trend of the time error within a fixed time period after correction; S2: The base station sends the differential correction data to the mobile terminal, records the timestamp of the base station sending the correction data and the timestamp of the mobile terminal receiving the data, and calculates the network transmission delay; by analyzing the fluctuation state of the network transmission delay, the stability of time synchronization is evaluated; S3: Comprehensively analyze the changing trend of the time error within a fixed time period after correction and the fluctuation state of the network transmission delay to evaluate the time synchronization accuracy between the base station and the mobile terminal device; S4: according to the evaluation result, the time synchronization accuracy between the base station and the mobile terminal device is divided into accurate synchronization, possible accurate synchronization and failed synchronization; S5: For the precise synchronous control system, the system operates normally; for the failed synchronous control system, the positioning accuracy is automatically reduced and a synchronization failure signal is issued; S6: For possible accurate synchronization, conduct an in-depth analysis of the time synchronization accuracy between the base station and the mobile terminal device, determine the degree of abnormality of the time synchronization between the base station and the mobile terminal device based on the analysis results, and dynamically adjust the operation decision based on the degree of abnormality; In S1, the time error anomaly index is generated by analyzing the changing trend of the time error within a fixed time period after correction. The method for obtaining the time error anomaly index is: Obtain the historical data sequence of time error from the system, set a time window, and extract the time error data of the base station and the mobile terminal within this window. The historical time error sequence is: ,in Represents the error value at the nth time point. The current time error sequence is ,in Represents the error value at the mth time point; Compare each point of the two time series and calculate the Euclidean distance between the two points. The expression is: ; Represents the Euclidean distance between the i-th point in sequence X and the j-th point in sequence Y; constructs an n×m cumulative distance matrix D, where each element D(i,j) in the matrix represents the minimum cumulative distance between the i-th point in sequence X and the j-th point in sequence Y. The specific calculation method is: ; In the formula, represents the optimal path from above, represents the optimal path from the left, represents the optimal path from the upper left, and the boundary conditions are: ; That is, the initial condition is the Euclidean distance of the first elements of the two sequences; In order to find the best match between two time series, it is necessary to calculate the optimal matching path P along the cumulative distance matrix D. Path P is a path from the upper left corner D(1,1) to the lower right corner D(n,m) of the matrix. Select the node with the smallest cumulative distance to pass through the path. The form of path P is: ; In the formula, Represents the matrix coordinate points that the path passes through. The optimal path is found by back-tracing the minimum value in the matrix ; By gradually selecting the smallest neighbor node, a path with the best matching degree is determined. The cumulative distance and the sum on the optimal path P represent the similarity of the two time series, and the expression is: ;in is the total cumulative distance on the optimal path, and the time error anomaly index is calculated. The expression is: ; In the formula, K is the length of the optimal path, which indicates the number of nodes passed on the path, and RT is the time error anomaly index; In S2, the delay jitter drift index is generated after analyzing the fluctuation state of the delay jitter data calculated in the S time period. The method for obtaining the delay jitter drift index is: Obtain the delay jitter data within a time period S from the time synchronization system and set it as a time series: ;in, Represents the delay jitter value at the nth time point, selects the attenuation factor α, and uses the initial value of the delay jitter as the initial exponentially weighted moving average ; That is, the first value of the EWMA sequence is equal to the first observed delay jitter value; according to the recursive formula, the subsequent exponentially weighted moving averages are calculated step by step : ;in: represents the smoothed value at the tth time point, Indicates the actual delay jitter value at the tth time point, is the EWMA value at the previous time point, indicating the smoothed value of historical data; the recursive formula indicates that the latest The current delay jitter value and the previous moment According to the weights α and Linear combination; Calculate the actual delay jitter value at each time point t With smooth The difference between them is used to calculate the delay jitter drift index, and the expression is: ; Where DF is the delay jitter drift index; In S3, a comprehensive analysis is performed on the changing trend of the time error within a fixed time period after correction and the fluctuation state of the network transmission delay to evaluate the time synchronization accuracy between the base station and the mobile terminal device, specifically: The time error anomaly index and the delay jitter drift index are converted into the first eigenvector, and the first eigenvector is used as the input of the machine learning model. The machine learning model predicts the time synchronization accuracy value label of the reference station and the mobile terminal device for each group of first eigenvectors as the prediction target, and minimizes the sum of the prediction errors of the time synchronization accuracy value labels of all reference stations and mobile terminal devices as the training target. The machine learning model is trained until the sum of the prediction errors converges, and the model training is stopped. The time synchronization accuracy value of the reference station and the mobile terminal device is determined according to the model output results, wherein the machine learning model is a polynomial regression model.

2. A Beidou high-precision intelligent navigation method according to claim 1, characterized in that: In S4, according to the evaluation result, the time synchronization accuracy between the base station and the mobile terminal device is divided into precise synchronization, possible precise synchronization and failed synchronization, specifically: The acquired time synchronization accuracy value between the reference station and the mobile terminal device is compared with a gradient standard threshold, where the gradient standard threshold includes a first standard threshold and a second standard threshold, and the first standard threshold is less than the second standard threshold, and the time synchronization accuracy value between the reference station and the mobile terminal device is compared with the first standard threshold and the second standard threshold respectively; If the time synchronization accuracy value between the reference station and the mobile terminal device is greater than the second standard threshold, it means that the time synchronization accuracy between the reference station and the mobile terminal device is high, and a high-accuracy time synchronization signal is generated and classified as a precise synchronization level; If the time synchronization accuracy value between the reference station and the mobile terminal device is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, it means that the time synchronization accuracy between the reference station and the mobile terminal device is average, and a medium accuracy time synchronization signal is generated at this time, and it is classified as a possible accurate synchronization level; If the time synchronization accuracy value between the reference station and the mobile terminal device is less than the first standard threshold, it means that the time synchronization accuracy between the reference station and the mobile terminal device is low. At this time, a low-accuracy time synchronization signal is generated and classified as a failed synchronization level.

3. A Beidou high-precision intelligent navigation method according to claim 1, characterized in that: In S6, for possible accurate synchronization, the time synchronization accuracy between the base station and the mobile terminal device is analyzed in depth, specifically: For possible precise synchronization, that is, the time synchronization accuracy value of the base station and the mobile terminal device generated is greater than or equal to the first standard threshold and less than or equal to the second standard threshold, the time synchronization accuracy values ​​of the base station and the mobile terminal device that are subsequently generated that are greater than or equal to the first standard threshold and less than or equal to the second standard threshold are collected, and a corresponding data set is established, the mean and standard deviation of the data set are calculated, and after analyzing them, the degree of abnormality in the time synchronization between the base station and the mobile terminal device is determined based on the analysis results, and the operation decision is dynamically adjusted based on the degree of abnormality.

4. A Beidou high-precision intelligent navigation method according to claim 3, characterized in that: If the mean of the time synchronization accuracy values ​​in the data set is greater than or equal to the reference threshold of the mean of the time synchronization accuracy values, and the standard deviation of the time synchronization accuracy values ​​is less than the reference threshold of the standard deviation of the time synchronization accuracy values, the accuracy of time synchronization is high and the data fluctuation is small. At this time, no warning signal is generated and the system maintains high-precision operation; If the mean of the time synchronization accuracy values ​​is greater than or equal to the reference threshold of the mean of the time synchronization accuracy values, and the standard deviation of the time synchronization accuracy values ​​is greater than or equal to the reference threshold of the standard deviation of the time synchronization accuracy values, although the time synchronization accuracy is high, the data fluctuation is large. At this time, a third-level warning signal is generated, and the system should enable the error compensation mechanism to monitor the fluctuation and maintain high accuracy; If the mean value of the time synchronization accuracy is less than the reference threshold of the mean value of the time synchronization accuracy, and the standard deviation of the time synchronization accuracy is greater than or equal to the reference threshold of the standard deviation of the time synchronization accuracy, the time synchronization accuracy is low and fluctuates greatly. At this time, a first-level warning signal is generated, and the system should be downgraded and switched to a low-precision mode to ensure system stability; If the mean of the time synchronization accuracy values ​​is less than the reference threshold of the mean of the time synchronization accuracy values, and the standard deviation of the time synchronization accuracy values ​​is less than the reference threshold of the standard deviation of the time synchronization accuracy values, the time synchronization accuracy is low but the fluctuation is small. At this time, a secondary warning signal is generated, the system maintains low-precision operation, and monitors the synchronization status.

5. A Beidou high-precision intelligent navigation system, used to implement a Beidou high-precision intelligent navigation method according to any one of claims 1 to 4, characterized in that: It includes a time error correction module, a network transmission delay monitoring module, a time synchronization evaluation module, a time synchronization classification module, a synchronization failure processing module, and a dynamic adjustment and decision-making module; Time error correction module: The base station and mobile terminal devices receive Beidou satellite signals and record accurate local timestamps based on the satellite's atomic clock; compare the timestamps of the base station and the mobile terminal, calculate the time error between the two, and make real-time corrections based on the time error, and record the changing trend of the time error within a fixed time period after correction; Network transmission delay monitoring module: The base station sends differential correction data to the mobile terminal, records the timestamp of the base station sending the correction data and the timestamp of the mobile terminal receiving the data, and calculates the network transmission delay; by analyzing the fluctuation state of the network transmission delay, the stability of time synchronization is evaluated; Time synchronization evaluation module: Comprehensively analyzes the changing trend of time error and the fluctuation state of network transmission delay within a fixed time period after correction, and evaluates the time synchronization accuracy between the base station and the mobile terminal device; Time synchronization classification module: according to the evaluation results, the time synchronization accuracy between the base station and the mobile terminal device is divided into precise synchronization, possible precise synchronization and failed synchronization; Synchronous failure processing module: for the precise synchronous control system, the module operates normally; for the failed synchronous control system, the module automatically reduces the positioning accuracy and issues a synchronous failure signal; Dynamic adjustment and decision-making module: For possible precise synchronization, the time synchronization accuracy of the base station and the mobile terminal device is deeply analyzed, the abnormality of the time synchronization between the base station and the mobile terminal device is determined based on the analysis results, and the operation decision is dynamically adjusted based on the abnormality.

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