Satellite navigation data synchronization system and method based on big data

Abstract

The application discloses a satellite navigation data synchronization system and method based on big data, relates to the technical field of satellite navigation data synchronization, and comprises the following steps: acquiring satellite navigation synchronization records of a satellite navigation platform in a current period, and analyzing data abnormal states of the satellite navigation synchronization data; analyzing the type matching degree between a synchronization abnormal type in the synchronization abnormal type data and a navigation error record set, obtaining a target synchronization abnormal type; acquiring synchronization abnormal root cause data corresponding to the target synchronization abnormal type in the satellite navigation platform, evaluating the data fitting degree between a historical contrast abnormal synchronization record corresponding to an abnormal root cause in the synchronization abnormal root cause data and abnormal satellite navigation synchronization data, and obtaining a target abnormal root cause; acquiring a preset abnormal solution of the target abnormal root cause from the satellite navigation platform, sending the abnormal solution to a staff member through the satellite navigation platform, and managing satellite navigation data synchronization.

Description

Technical Field

[0001] This invention relates to the field of satellite navigation data synchronization technology, specifically to a satellite navigation data synchronization system and method based on big data. Background Technology

[0002] Big data refers to a massive, diverse, rapidly generated dataset with high potential value. Satellite navigation systems, such as BeiDou, GPS, and Galileo, generate enormous amounts of data during continuous operation. This data includes satellite orbital parameters, time synchronization information, user terminal positioning data, and environmental interference signals. Big data technology is well-suited for processing big data. The application of big data technology in satellite navigation data synchronization systems offers several benefits, including but not limited to: 1. Real-time processing of massive amounts of data: Global navigation satellite systems generate terabytes of data per second, which traditional databases struggle to process in real time. Satellite signals need to be synchronized to ground stations and user terminals in real time, placing extremely high demands on data processing latency. Big data technology supports real-time data analysis and processing. 2. Effectively improving positioning accuracy and reliability: Big data technology can integrate multi-dimensional data (such as spatiotemporal data and sensor data) and optimize error correction models through machine learning, thereby improving positioning accuracy and reliability. 3. Effectively reducing costs and increasing efficiency: Utilizing technologies such as Hadoop and cloud storage allows for low-cost storage of historical data and analysis of satellite health status, reducing manual intervention and lowering maintenance costs.

[0003] In satellite navigation, the synchronization of satellite navigation data is fundamental to ensuring high accuracy and reliability of positioning, navigation, and timing services. If satellite navigation data synchronization fails during navigation, it can easily lead to delays and inaccuracies in positioning. Therefore, synchronizing satellite navigation data is crucial. However, in practice, various reasons can cause data asynchrony. Conventional methods for identifying the cause of this asynchrony are time-consuming and labor-intensive, resulting in significant waste of human and material resources. Furthermore, prolonged periods of inaccuracy can lead to substantial property damage and even potential safety accidents. Summary of the Invention

[0004] The purpose of this invention is to provide a satellite navigation data synchronization system and method based on big data, so as to solve the problems raised in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a satellite navigation data synchronization method based on big data, the method comprising:

[0006] Step S100: Obtain the satellite navigation synchronization record of the satellite navigation platform in the current period, obtain the satellite navigation synchronization data in different time periods from the satellite navigation synchronization record, analyze the abnormal data status of the satellite navigation synchronization data, and obtain abnormal satellite navigation synchronization data;

[0007] Step S200: Obtain the navigation error record set from the satellite navigation platform, obtain the synchronization anomaly type data from the satellite navigation platform, analyze the type matching degree between the synchronization anomaly type in the synchronization anomaly type data and the navigation error record set, and obtain the target synchronization anomaly type;

[0008] Step S300: Obtain the root cause data of the synchronization anomaly type corresponding to the target synchronization anomaly in the satellite navigation platform, evaluate the data fit between the historical comparison anomaly synchronization record and the abnormal satellite navigation synchronization data corresponding to the anomaly root cause in the synchronization anomaly root cause data, and obtain the root cause of the target anomaly.

[0009] Step S400: Obtain the preset anomaly solution from the satellite navigation platform, send the anomaly solution to the staff through the satellite navigation platform, and manage the synchronous satellite navigation data.

[0010] Furthermore, step S100 includes:

[0011] Step S101: Record the satellite navigation data synchronization behavior in the satellite navigation platform during the current period to obtain the satellite navigation synchronization record;

[0012] Step S102: Set the unit duration T and the feature unit duration T ′ The data in the satellite navigation synchronization record is divided into units of time to obtain satellite navigation synchronization data corresponding to each unit of time. This satellite navigation synchronization data includes data corresponding to various navigation synchronization parameters in the satellite navigation platform, where T = k × T. ′ k is a preset positive integer;

[0013] Step S103: Obtain the values ​​of various navigation synchronization parameters collected within a characteristic unit time period from the satellite navigation synchronization data, perform data preprocessing, and aggregate the values ​​of various navigation synchronization parameters after data preprocessing to obtain the multidimensional navigation vector f = {A1, A2, ..., A...} within the characteristic unit time period. n}, where A1, A2, ..., A n These are the values ​​of the 1st, 2nd, ..., nth navigation synchronization parameters within the characteristic unit of time, where n is the total number of navigation synchronization parameters.

[0014] Multidimensional navigation vectors within each characteristic unit time period are obtained from satellite navigation synchronization data and aggregated to obtain the navigation synchronization time series F = {f1, f2, ..., f...} corresponding to the satellite navigation synchronization data. k}, where f1, f2, ..., f k These are the multi-dimensional navigation vectors within the 1st, 2nd, ..., kth characteristic units of time in the maintenance satellite navigation synchronization data;

[0015] Step S104: From the navigation synchronization time series corresponding to the satellite navigation synchronization data within each unit of time in the current period, obtain the average value and mark difference of each navigation synchronization parameter, and analyze the data anomaly status of the satellite navigation synchronization data within each unit of time. Specifically, the data anomaly analysis process for the satellite navigation synchronization data within the b-th unit of time is as follows:

[0016] Obtain the navigation synchronization time series F of the satellite navigation synchronization data within the b-th unit of time. b Calculate the data mutation values ​​of each navigation synchronization parameter within the c-th characteristic unit of time in the b-th unit of time;

[0017] Obtain navigation synchronization time series F b The multidimensional navigation vector f corresponding to the c-th feature unit duration in the data. b c Among them, the data mutation value Q of the e-th navigation synchronization parameter within the c-th feature unit time period. (c,e) =(d (c,e) -μ e ) / σ e d (c,e) For the multidimensional navigation vector f b c The value of the e-th navigation synchronization parameter in the table, μ e and σ e , respectively, are the average and standard deviation of the navigation synchronization parameter e in the navigation synchronization time series of satellite navigation synchronization data within each unit of time;

[0018] When the data mutation value Q (c,e) If the data mutation exceeds the preset threshold, the e-th navigation synchronization parameter is determined to have an abnormal risk and is recorded as a navigation synchronization parameter abnormality within b units of time.

[0019] The total number of times α of navigation special parameter anomalies were obtained within the b-th unit of time. sum When α sum If the value exceeds a preset threshold, the data status of the satellite navigation synchronization data within the b-th unit of time is determined to be abnormal and recorded as abnormal satellite navigation synchronization data.

[0020] Furthermore, step S200 includes:

[0021] Step S201: Record the errors uploaded by users to the satellite navigation platform in the current period to obtain navigation error records, and collect all navigation error records in the current period to obtain a navigation error record set;

[0022] Extract the text content from the navigation error log to obtain the corresponding error document;

[0023] Step S202: Obtain synchronization anomaly type data from the satellite navigation platform. The synchronization anomaly type data includes the corresponding error reports for each synchronization anomaly type.

[0024] The analysis process involves determining the degree of type matching between various synchronization anomaly types and navigation error records.

[0025] By compiling the error documents in each navigation error record and the corresponding error documents for each synchronization anomaly type, a text database of the satellite navigation platform in the current period is obtained.

[0026] Retrieve error documents from navigation error logs, preprocess the error documents to obtain each keyword in the error documents, and aggregate them to obtain the keyword group γ corresponding to the error documents;

[0027] Calculate the word importance value of each keyword in the keyword group γ, where the word importance value of the v-th keyword in the keyword group γ is I. v =p v ×log / [β sum / (β v sum +1)],p v Let β be the frequency of the v-th keyword in keyword group γ. sum β is the total number of documents in the text database. v sum This represents the total number of documents in the text database that contain the v-th keyword.

[0028] The importance values ​​of each keyword in keyword group γ are aggregated to construct the word vector U corresponding to keyword group γ. γ Obtain the word vector corresponding to each error report document in each synchronization exception type;

[0029] Calculate the type match value R between navigation error records and synchronization exception types:

[0030]

[0031] Where i = 1, 2, 3, ..., m, and m is the total number of corresponding error reports for each synchronization exception type in the synchronization exception type data; W i This is the word vector corresponding to the i-th reference error document in the synchronization exception type;

[0032] Get the maximum value of the type matching value between the navigation error record and each synchronization exception type, get the synchronization exception type corresponding to the maximum value of the type matching value, and record it as the corresponding synchronization exception type of the navigation error record;

[0033] Step S203: Obtain the corresponding synchronization exception type for each navigation error record in the navigation error record set, obtain the total number of navigation error records in the navigation error record set when a certain synchronization exception type is the corresponding synchronization exception type, and record it as the total number of matches for a certain synchronization exception type.

[0034] Obtain the maximum value of the total number of matches for each synchronization exception type, determine the degree of type matching between the synchronization exception type corresponding to the maximum value of the total number of matches and the navigation error record set, and record the synchronization exception type corresponding to the maximum value of the total number of matches as the target synchronization exception type.

[0035] Furthermore, step S300 includes:

[0036] Step S301: Obtain the root cause data of the synchronization anomaly type corresponding to the target synchronization anomaly from the satellite navigation platform. The root cause data of the synchronization anomaly includes the historical comparison anomaly synchronization records corresponding to each anomaly root cause. Extract the historical navigation synchronization time series from the historical comparison anomaly synchronization records. The duration of the historical comparison anomaly synchronization records is equal to the unit duration.

[0037] Step S302: Obtain abnormal satellite navigation synchronization data of the satellite navigation platform in the current period, and obtain the navigation synchronization time series F corresponding to the abnormal satellite navigation synchronization data. △ ;

[0038] Obtain abnormal satellite navigation synchronization data and compare it with historical abnormal synchronization records S for a specific root cause of the anomaly;

[0039] Obtain the historical navigation synchronization sequence F′ of the historical comparison abnormal synchronization record S. S Obtain the historical navigation synchronization sequence F′ S The multidimensional navigation vectors corresponding to each element in the vector are used to calculate the historical navigation synchronization sequence F′. S With, navigation synchronization time series F △ Cosine similarity between elements;

[0040] Calculate the fit value H between the historical control anomaly synchronization record S and the anomaly satellite navigation synchronization data for a certain root cause of an anomaly. s :

[0041]

[0042] Where z is the navigation synchronization time series F △ The total number of elements in L; (s,x) For historical navigation synchronization sequence F′ S With, navigation synchronization time series F △ The cosine similarity between the x-th elements in the interval;

[0043] Step S303: Obtain the maximum value of the matching value between the historical comparison abnormal synchronization record and the abnormal satellite navigation synchronization data corresponding to each abnormal root cause, and record the abnormal root cause corresponding to the maximum value of the matching value as the target abnormal root cause, and determine the data matching between the historical comparison abnormal synchronization record and the abnormal satellite navigation synchronization data corresponding to the target abnormal root cause.

[0044] Furthermore, step S400 includes:

[0045] Step S401: Obtain the root cause of the target anomaly in the current period from the satellite navigation platform, and obtain the preset anomaly solution from the root cause of the target anomaly in the satellite navigation platform;

[0046] Step S402: Send the anomaly solution and target anomaly root cause to the staff through the satellite navigation platform, prompting the staff to handle the satellite navigation data synchronization anomaly in the satellite navigation platform according to the anomaly solution, and to manage the satellite navigation data synchronization.

[0047] In the above steps, the pre-set anomaly solutions for the root causes of target anomalies are obtained from the satellite navigation platform, and the anomaly solutions are given to the staff in the satellite navigation platform. The staff only need to follow the anomaly solutions to quickly resolve the problems that occur in satellite navigation data synchronization, thereby realizing the management of satellite navigation data synchronization.

[0048] To better implement the above methods, a satellite navigation data synchronization system based on big data was also proposed. The system includes a data anomaly analysis module, a matching degree analysis module, a fit degree evaluation module, and a data synchronization management module.

[0049] The data anomaly analysis module is used to analyze the abnormal state of satellite navigation synchronization data and obtain abnormal satellite navigation synchronization data.

[0050] The matching degree analysis module is used to analyze the type matching degree between the synchronization anomaly type data and the navigation error record set to obtain the target synchronization anomaly type;

[0051] The matching degree assessment module is used to assess the data matching degree between the historical comparison abnormal synchronization records corresponding to the abnormal root causes in the synchronization abnormal root cause data and the abnormal satellite navigation synchronization data, so as to obtain the target abnormal root cause.

[0052] The data synchronization management module is used to acquire the preset abnormal solutions for the root causes of target anomalies, send the abnormal solutions to staff through the satellite navigation platform, and manage the synchronization of satellite navigation data.

[0053] Furthermore, the data anomaly analysis module includes a vector acquisition unit and a data anomaly analysis unit;

[0054] The vector acquisition unit is used to acquire satellite navigation synchronization records in the satellite navigation platform, acquire satellite navigation synchronization data from the satellite navigation synchronization records, acquire the navigation synchronization time series in the satellite navigation synchronization data, and acquire the multi-dimensional navigation vector of each element in the navigation synchronization sequence.

[0055] The data anomaly analysis unit is used to calculate the data mutation values ​​of various navigation synchronization parameters within a characteristic unit of time based on the multidimensional navigation vector, analyze the data anomaly status of satellite navigation synchronization data, and obtain abnormal satellite navigation synchronization data.

[0056] Furthermore, the matching degree analysis module includes a document acquisition unit and a matching degree analysis unit;

[0057] The document acquisition unit is used to acquire the error documents corresponding to the navigation error records and to acquire the corresponding error documents for each synchronization error type in the synchronization error type data.

[0058] The matching degree analysis unit is used to analyze the degree of type matching between various synchronization anomaly types and the navigation error record set based on the obtained comparison error documents, and to obtain the target synchronization anomaly type.

[0059] Furthermore, the fit assessment module includes a time series acquisition unit and a fit assessment unit;

[0060] The time series acquisition unit is used to acquire historical reference abnormal synchronization records corresponding to each abnormal root cause in the synchronization abnormal root cause data, acquire historical navigation synchronization time series from historical reference abnormal synchronization records, and acquire navigation synchronization time series corresponding to abnormal satellite navigation synchronization data.

[0061] The matching degree assessment unit is used to calculate the matching value between historical comparison abnormal synchronization records and abnormal satellite navigation synchronization data, and based on the matching value, to assess the degree of data matching between the abnormal root cause in the synchronization abnormal root cause data and the abnormal satellite navigation synchronization data, and to obtain the target abnormal root cause.

[0062] Furthermore, the data synchronization management module includes a data synchronization management unit;

[0063] The data synchronization management unit is used to obtain the root cause of the target anomaly from the satellite navigation platform and the preset anomaly solution, and to prompt the staff to handle the satellite navigation data synchronization anomaly in the satellite navigation platform according to the anomaly solution, and to manage the satellite navigation data synchronization.

[0064] Compared with the prior art, the beneficial effects of the present invention are: the present invention enables the analysis of anomalies in the satellite navigation data synchronization process, thereby quickly finding the cause of satellite navigation data asynchrony, avoiding the time and workload consumed by conventional methods in finding the cause of satellite navigation data asynchrony, and also has a high accuracy rate, ensuring satellite navigation data synchronization, ensuring high precision and high reliability of satellite navigation positioning, navigation and timing services, and at the same time, avoiding property losses and safety accidents caused by problems in satellite navigation positioning. Attached Figure Description

[0065] Figure 1 This is a flowchart of the satellite navigation data synchronization method based on big data according to the present invention;

[0066] Figure 2 This is a schematic diagram of the modules of the satellite navigation data synchronization system based on big data according to the present invention. Detailed Implementation

[0067] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0068] Example: Figures 1-2 As shown, this invention provides a technical solution: a satellite navigation data synchronization method based on big data, the method comprising:

[0069] Step S100: Obtain the satellite navigation synchronization record of the satellite navigation platform in the current period, obtain the satellite navigation synchronization data in different time periods from the satellite navigation synchronization record, analyze the abnormal data status of the satellite navigation synchronization data, and obtain abnormal satellite navigation synchronization data;

[0070] Step S100 includes:

[0071] Step S101: Record the satellite navigation data synchronization behavior in the satellite navigation platform during the current period to obtain the satellite navigation synchronization record;

[0072] Step S102: Set the unit duration T and the feature unit duration T ′ The data in the satellite navigation synchronization record is divided into units of time to obtain satellite navigation synchronization data corresponding to each unit of time. This satellite navigation synchronization data includes data corresponding to various navigation synchronization parameters in the satellite navigation platform, where T = k × T. ′ k is a preset positive integer;

[0073] For example, various navigation synchronization parameters include GPS error, satellite clock error, etc.

[0074] For example, the least squares method or the extended Kalman filter (EKF) can be used to estimate satellite clock bias;

[0075] Step S103: Obtain the values ​​of various navigation synchronization parameters collected within a characteristic unit time period from the satellite navigation synchronization data, perform data preprocessing, and aggregate the values ​​of various navigation synchronization parameters after data preprocessing to obtain the multidimensional navigation vector f = {A1, A2, ..., A...} within the characteristic unit time period. n}, where A1, A2, ..., A n These are the values ​​of the 1st, 2nd, ..., nth navigation synchronization parameters within the characteristic unit of time, where n is the total number of navigation synchronization parameters.

[0076] For example, data preprocessing includes data alignment and cleaning;

[0077] Time alignment is performed based on GPS (GPST), aligning all data timestamps with an interpolation frequency of 1Hz;

[0078] Missing value imputation uses linear interpolation to fill in missing clock error or pseudorange data;

[0079] Noise filtering: Apply a Hampel filter to remove transient noise;

[0080] Multidimensional navigation vectors within each characteristic unit time period are obtained from satellite navigation synchronization data and aggregated to obtain the navigation synchronization time series F = {f1, f2, ..., f...} corresponding to the satellite navigation synchronization data. k}, where f1, f2, ..., f k These are the multi-dimensional navigation vectors within the 1st, 2nd, ..., kth characteristic units of time in the maintenance satellite navigation synchronization data;

[0081] Step S104: From the navigation synchronization time series corresponding to the satellite navigation synchronization data within each unit of time in the current period, obtain the average value and mark difference of each navigation synchronization parameter, and analyze the data anomaly status of the satellite navigation synchronization data within each unit of time. Specifically, the data anomaly analysis process for the satellite navigation synchronization data within the b-th unit of time is as follows:

[0082] Obtain the navigation synchronization time series F of the satellite navigation synchronization data within the b-th unit of time. b Calculate the data mutation values ​​of each navigation synchronization parameter within the c-th characteristic unit of time in the b-th unit of time;

[0083] Obtain navigation synchronization time series F b The multidimensional navigation vector f corresponding to the c-th feature unit duration in the data. b c Among them, the data mutation value Q of the e-th navigation synchronization parameter within the c-th feature unit time period. (c,e) =(d (c,e) -μ e ) / σ e d (c,e) For the multidimensional navigation vector f b c The value of the e-th navigation synchronization parameter in the table, μ e and σ e , respectively, are the average and standard deviation of the navigation synchronization parameter e in the navigation synchronization time series of satellite navigation synchronization data within each unit of time;

[0084] When the data mutation value Q (c,e) If the data mutation exceeds the preset threshold, the e-th navigation synchronization parameter is determined to have an abnormal risk and is recorded as a navigation synchronization parameter abnormality within b units of time.

[0085] The total number of times α of navigation special parameter anomalies were obtained within the b-th unit of time. sum When α sum If the value exceeds a preset threshold, the data status of the satellite navigation synchronization data within the b-th unit of time is determined to be abnormal and recorded as abnormal satellite navigation synchronization data.

[0086] Step S200: Obtain the navigation error record set from the satellite navigation platform, obtain the synchronization anomaly type data from the satellite navigation platform, analyze the type matching degree between the synchronization anomaly type in the synchronization anomaly type data and the navigation error record set, and obtain the target synchronization anomaly type;

[0087] Step S300: Obtain the synchronization anomaly root cause data corresponding to the target synchronization anomaly type in the satellite navigation platform, evaluate the data matching degree between the historical control abnormal synchronization records corresponding to the anomaly root cause in the synchronization anomaly root cause data and the abnormal satellite navigation synchronization data, and obtain the target anomaly root cause;

[0088] Step S400: Obtain the preset anomaly solution for the target anomaly root cause from the satellite navigation platform, send the anomaly solution to the staff through the satellite navigation platform, and manage the satellite navigation data synchronization;

[0089] Among them, step S200 includes:

[0090] Step S201: Record the error reports uploaded by users in the satellite navigation platform during the current period to obtain navigation error reports, and collect each navigation error report in the current period to obtain a navigation error report set;

[0091] Obtain the text content in the navigation error report to obtain the error report document corresponding to the navigation error report;

[0092] Step S202: Obtain the synchronization anomaly type data from the satellite navigation platform. The synchronization anomaly type data includes each control error report document corresponding to each synchronization anomaly type;

[0093] For example, each synchronization anomaly type includes time out-of-sync, orbit out-of-sync, etc.;

[0094] Analyze the type matching degree between each synchronization anomaly type and the navigation error report. The specific analysis process is as follows:

[0095] Collect the error report documents in each navigation error report and each control error report document corresponding to each synchronization anomaly type to obtain the text database of the satellite navigation platform in the current period;

[0096] Obtain the error report document in the navigation error report, preprocess the error report document to obtain each keyword in the error report document, and collect them to obtain the keyword group γ corresponding to the error report document;

[0097] For example, the preprocessing process includes text cleaning:

[0098] Word segmentation processing: Use a word segmentation tool to split the text;

[0099] Stop word removal: Filter out meaningless words (such as "of", "in");

[0100] Calculate the word importance value of each keyword in the keyword group γ. Among them, the word importance value I of the vth keyword in the keyword group γ v =p v ×log / [βsum / (β v sum +1)],p v Let β be the frequency of the v-th keyword in keyword group γ. sum β is the total number of documents in the text database. v sum This represents the total number of documents in the text database that contain the v-th keyword.

[0101] Calculate the frequency of the second keyword in the keyword group γ, p2 is 20, and the total number of documents β in the text database. sum The total number of documents β containing the second keyword in the text database is 50. 2 sum It is 9;

[0102] Calculate the word importance value I2 of the second keyword in the keyword group γ: I2 = p2 × log / [β] sum / (β 2 sum +1)]=20×log / [50 / (9+1)]=20log5;

[0103] The importance values ​​of each keyword in keyword group γ are aggregated to construct the word vector U corresponding to keyword group γ. γ Obtain the word vector corresponding to each error report document in each synchronization exception type;

[0104] Calculate the type match value R between navigation error records and synchronization exception types:

[0105]

[0106] Where i = 1, 2, 3, ..., m, and m is the total number of corresponding error reports for each synchronization exception type in the synchronization exception type data; W i This is the word vector corresponding to the i-th reference error document in the synchronization exception type;

[0107] Get the maximum value of the type matching value between the navigation error record and each synchronization exception type, get the synchronization exception type corresponding to the maximum value of the type matching value, and record it as the corresponding synchronization exception type of the navigation error record;

[0108] Step S203: Obtain the corresponding synchronization exception type for each navigation error record in the navigation error record set, obtain the total number of navigation error records in the navigation error record set when a certain synchronization exception type is the corresponding synchronization exception type, and record it as the total number of matches for a certain synchronization exception type.

[0109] Get the maximum value of the total number of matches for each synchronization exception type, determine the degree of type matching between the synchronization exception type corresponding to the maximum value of the total number of matches and the navigation error record set, and record the synchronization exception type corresponding to the maximum value of the total number of matches as the target synchronization exception type.

[0110] Step S300 includes:

[0111] Step S301: Obtain the root cause data of the synchronization anomaly type corresponding to the target synchronization anomaly from the satellite navigation platform. The root cause data of the synchronization anomaly includes the historical comparison anomaly synchronization records corresponding to each anomaly root cause. Extract the historical navigation synchronization time series from the historical comparison anomaly synchronization records. The duration of the historical comparison anomaly synchronization records is equal to the unit duration.

[0112] For example, the root causes of various anomalies include: atomic clock frequency drift not being corrected in time, ephemeris not being updated after satellite maneuvering, and ionospheric TEC surge causing delay model failure.

[0113] Step S302: Obtain abnormal satellite navigation synchronization data of the satellite navigation platform in the current period, and obtain the navigation synchronization time series F corresponding to the abnormal satellite navigation synchronization data. △ ;

[0114] Obtain abnormal satellite navigation synchronization data and compare it with historical abnormal synchronization records S for a specific root cause of the anomaly;

[0115] Obtain the historical navigation synchronization sequence F′ of the historical comparison abnormal synchronization record S. S Obtain the historical navigation synchronization sequence F′ S The multidimensional navigation vectors corresponding to each element in the vector are used to calculate the historical navigation synchronization sequence F′. S With, navigation synchronization time series F △ Cosine similarity between elements;

[0116] Calculate the fit value H between the historical control anomaly synchronization record S and the anomaly satellite navigation synchronization data for a certain root cause of an anomaly. s :

[0117]

[0118] Where z is the navigation synchronization time series F △ The total number of elements in L; (s,x) For historical navigation synchronization sequence F′ S With, navigation synchronization time series F △ The cosine similarity between the x-th elements in the interval;

[0119] Step S303: Obtain the maximum value of the matching value between the historical comparison abnormal synchronization record and the abnormal satellite navigation synchronization data corresponding to each abnormal root cause, and record the abnormal root cause corresponding to the maximum value of the matching value as the target abnormal root cause, and determine the data matching between the historical comparison abnormal synchronization record and the abnormal satellite navigation synchronization data corresponding to the target abnormal root cause.

[0120] Step S400 includes:

[0121] Step S401: Obtain the root cause of the target anomaly in the current period from the satellite navigation platform, and obtain the preset anomaly solution from the root cause of the target anomaly in the satellite navigation platform;

[0122] Step S402: Send the anomaly solution and target anomaly root cause to the staff through the satellite navigation platform, prompting the staff to handle the satellite navigation data synchronization anomaly in the satellite navigation platform according to the anomaly solution, and to manage the satellite navigation data synchronization.

[0123] To better implement the above methods, a satellite navigation data synchronization system based on big data was also proposed. The system includes a data anomaly analysis module, a matching degree analysis module, a fit degree evaluation module, and a data synchronization management module.

[0124] The data anomaly analysis module is used to analyze the abnormal state of satellite navigation synchronization data and obtain abnormal satellite navigation synchronization data.

[0125] The matching degree analysis module is used to analyze the type matching degree between the synchronization anomaly type data and the navigation error record set to obtain the target synchronization anomaly type;

[0126] The matching degree assessment module is used to assess the data matching degree between the historical comparison abnormal synchronization records corresponding to the abnormal root causes in the synchronization abnormal root cause data and the abnormal satellite navigation synchronization data, so as to obtain the target abnormal root cause.

[0127] The data synchronization management module is used to acquire the preset abnormal solutions for the root causes of target anomalies, send the abnormal solutions to staff through the satellite navigation platform, and manage the synchronization of satellite navigation data.

[0128] The data anomaly analysis module includes a vector acquisition unit and a data anomaly analysis unit.

[0129] The vector acquisition unit is used to acquire satellite navigation synchronization records in the satellite navigation platform, acquire satellite navigation synchronization data from the satellite navigation synchronization records, acquire the navigation synchronization time series in the satellite navigation synchronization data, and acquire the multi-dimensional navigation vector of each element in the navigation synchronization sequence.

[0130] The data anomaly analysis unit is used to calculate the data mutation values ​​of various navigation synchronization parameters within a characteristic unit of time based on the multidimensional navigation vector, analyze the data anomaly status of satellite navigation synchronization data, and obtain abnormal satellite navigation synchronization data.

[0131] The matching degree analysis module includes a document acquisition unit and a matching degree analysis unit.

[0132] The document acquisition unit is used to acquire the error documents corresponding to the navigation error records and to acquire the corresponding error documents for each synchronization error type in the synchronization error type data.

[0133] The matching degree analysis unit is used to analyze the type matching degree between various synchronization anomaly types and the navigation error record set based on the obtained comparison error documents, and to obtain the target synchronization anomaly type.

[0134] The fit assessment module includes a time series acquisition unit and a fit assessment unit.

[0135] The time series acquisition unit is used to acquire historical reference abnormal synchronization records corresponding to each abnormal root cause in the synchronization abnormal root cause data, acquire historical navigation synchronization time series from historical reference abnormal synchronization records, and acquire navigation synchronization time series corresponding to abnormal satellite navigation synchronization data.

[0136] The matching degree assessment unit is used to calculate the matching value between historical comparison abnormal synchronization records and abnormal satellite navigation synchronization data, and based on the matching value, to assess the degree of data matching between the abnormal root cause in the synchronization abnormal root cause data and the abnormal satellite navigation synchronization data, and to obtain the target abnormal root cause.

[0137] The data synchronization management module includes a data synchronization management unit;

[0138] The data synchronization management unit is used to obtain the root cause of the target anomaly from the satellite navigation platform and the preset anomaly solution, and to prompt the staff to handle the satellite navigation data synchronization anomaly in the satellite navigation platform according to the anomaly solution, and to manage the satellite navigation data synchronization.

[0139] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A method for synchronizing satellite navigation data based on big data, characterized in that, The method comprises: Step S100: acquiring satellite navigation synchronization records of a satellite navigation platform in a current period, acquiring satellite navigation synchronization data in different time periods from the satellite navigation synchronization records, analyzing data abnormal states of the satellite navigation synchronization data, and obtaining abnormal satellite navigation synchronization data; Step S200: acquiring a navigation error record set in the satellite navigation platform, acquiring synchronization abnormal type data in the satellite navigation platform, analyzing a type matching degree between a synchronization abnormal type in the synchronization abnormal type data and the navigation error record set, and obtaining a target synchronization abnormal type; Step S300: acquiring synchronization abnormal root cause data corresponding to the target synchronization abnormal type in the satellite navigation platform, evaluating a data fitting degree between a historical comparison abnormal synchronization record corresponding to an abnormal root cause in the synchronization abnormal root cause data and the abnormal satellite navigation synchronization data, and obtaining a target abnormal root cause; Step S400: acquiring a preset abnormal solution of the target abnormal root cause from the satellite navigation platform, sending the abnormal solution to a staff member through the satellite navigation platform, and managing satellite navigation data synchronization; The step S300 comprises: Step S301: acquiring synchronization abnormal root cause data corresponding to the target synchronization abnormal type from the satellite navigation platform, the synchronization abnormal root cause data comprising historical comparison abnormal synchronization records corresponding to each abnormal root cause, and extracting a historical navigation synchronization time sequence from the historical comparison abnormal synchronization records, wherein a time length of the historical comparison abnormal synchronization records is equal to a unit time length; Step S302: acquiring abnormal satellite navigation synchronization data of the satellite navigation platform in the current period, acquiring a navigation synchronization time sequence F corresponding to the abnormal satellite navigation synchronization data △ ; acquiring the abnormal satellite navigation synchronization data and a historical comparison abnormal synchronization record of a certain abnormal root cause S; obtaining a history navigation synchronization sequence F´ of the history control abnormal synchronization record S S , obtaining a multi-dimensional navigation vector corresponding to each element in the history navigation synchronization sequence F´ S , calculating the cosine similarity between each element in the history navigation synchronization sequence F´ S and the navigation synchronization time sequence F △ ; calculating a fit value H between the historical control anomaly synchronization record S of the certain abnormal root cause and the abnormal satellite navigation synchronization data s : ； wherein z is the total number of elements in the navigation synchronization time sequence F △ L (s,x) is the cosine similarity between the xth element in the history navigation synchronization sequence F´ S and the navigation synchronization time sequence F △ ​ Step S303: acquiring a maximum value of fitting values between the historical comparison abnormal synchronization records corresponding to each abnormal root cause and the abnormal satellite navigation synchronization data, recording an abnormal root cause corresponding to the maximum value of the fitting values as a target abnormal root cause, and determining a data fitting between the historical comparison abnormal synchronization record corresponding to the target abnormal root cause and the abnormal satellite navigation synchronization data.

2. The big data based satellite navigation data synchronization method of claim 1, wherein, The step S100 comprises: Step S101: recording satellite navigation data synchronization behaviors in the satellite navigation platform in a current period, and obtaining satellite navigation synchronization records; Step S102: set a unit time length T and a characteristic unit time length T ´ divide the data in the satellite navigation synchronization record every unit time length to obtain satellite navigation synchronization data corresponding to each unit time length, wherein the satellite navigation synchronization data includes data corresponding to each navigation synchronization parameter in the satellite navigation platform, T=k×T ´ k is a preset positive integer; Step S103: Obtain the values ​​of the navigation synchronization parameters collected within the characteristic unit time period from the satellite navigation synchronization data, perform data preprocessing, and aggregate the values ​​of the navigation synchronization parameters after data preprocessing to obtain the multidimensional navigation vector f={A1, A2, ..., A...} within the characteristic unit time period. n }, where A1, A2, ..., A n These are the values ​​of the 1st, 2nd, ..., nth navigation synchronization parameters within the specified characteristic unit of time, where n is the total number of navigation synchronization parameters. The multi-dimensional navigation vectors in each characteristic unit time length are obtained from the satellite navigation synchronization data, and are collected to obtain a navigation synchronization time sequence F={f1, f2,..., f k} corresponding to the satellite navigation synchronization data, wherein f1, f2,..., f k are respectively multi-dimensional navigation vectors in the 1st, 2nd,..., kth characteristic unit time length in the satellite navigation synchronization data. Step S104: acquiring average values and marked differences of the navigation synchronization parameters from navigation synchronization time sequences corresponding to satellite navigation synchronization data in each unit time length in the current period, and analyzing data abnormal states of the satellite navigation synchronization data in each unit time length, wherein a data abnormal state analysis process of satellite navigation synchronization data in a bth unit time length is as follows: obtain a navigation synchronization time sequence F of satellite navigation synchronization data in the bth unit time length b , calculate the data mutation value of the navigation synchronization parameter in the cth characteristic unit time length in the bth unit time length; obtaining the navigation synchronization time sequence F b corresponding to the cth characteristic unit length in the sequence F b c , wherein the data mutation value Q （c,e） of the e th navigation synchronization parameter in the cth characteristic unit length is （c,e） = (d e - μ e ) / σ （c,e） , d b is the value of the e th navigation synchronization parameter in the sequence f c , μ e and σ e are the average value and the standard deviation of the e th navigation synchronization parameter in the navigation synchronization time sequence of the satellite navigation synchronization data in each unit length, respectively. When the data mutation value Q （c,e） is greater than the preset data mutation threshold, it is determined that the e-th navigation synchronization parameter has an abnormal risk, and is recorded as a navigation synchronization parameter abnormality in the b unit time length. acquiring a total number of navigation special parameter exceptions α in the bth unit time length sum When the α sum is greater than a preset threshold, determining that the data state of the satellite navigation synchronization data in the bth unit time length is abnormal, and recording as abnormal satellite navigation synchronization data. 3.The big data based satellite navigation data synchronization method according to claim 1, characterized in that, The step S200 comprises: Step S201: recording errors uploaded by a user on the satellite navigation platform in a current period, obtaining navigation error records, collecting each navigation error record in the current period, and obtaining a navigation error record set; acquiring text content in the navigation error records, and obtaining error documents corresponding to the navigation error records; Step S202: acquiring synchronization exception type data from the satellite navigation platform, the synchronization exception type data including respective reference error documents corresponding to respective synchronization exception types; analyzing the type matching degree between the synchronization exception types and the navigation error records, and the specific process is as follows: collecting the error documents in the navigation error records and the respective reference error documents corresponding to the respective synchronization exception types to obtain a text database of the satellite navigation platform in a current period; acquiring error documents in the navigation error records, pre-processing the error documents to obtain respective keywords in the error documents, and collecting the respective keywords to obtain a keyword group γ corresponding to the error documents; Calculate the word importance value of each keyword in the keyword group γ, where the word importance value of the v-th keyword in the keyword group γ is I. v =p v ×log / [β sum / (β v sum +1)],p v β is the frequency of the v-th keyword in the keyword group γ. sum β is the total number of documents in the text database. v sum The total number of documents in the text database that contain the v-th keyword; The word importance values of each keyword in the keyword group γ are collected to construct a keyword group vector U corresponding to the keyword group γ γ The keyword group vector corresponding to each contrast error report document in the each item synchronization exception type is obtained. calculating the type matching value R between the navigation error records and the synchronization exception types: ； wherein i = 1, 2, 3, …, m, wherein m is the total number of the control error reporting documents corresponding to the synchronization exception type in the synchronization exception type data; W i is the word vector corresponding to the i-th control error reporting document in the synchronization exception type. acquiring the maximum value of the type matching values between the navigation error records and the respective synchronization exception types, acquiring the synchronization exception type corresponding to the maximum value of the type matching values, and recording the synchronization exception type as a reference synchronization exception type of the navigation error records; Step S203: acquiring the reference synchronization exception types of the respective navigation error records in the navigation error record set, acquiring the total number of navigation error records corresponding to a certain synchronization exception type in the navigation error record set when the certain synchronization exception type is the reference synchronization exception type, and recording the total number as a matching total number of the certain synchronization exception type; acquiring the maximum value of the matching total numbers of the respective synchronization exception types, determining that the synchronization exception type corresponding to the maximum value of the matching total numbers has a type matching degree with the navigation error record set, and recording the synchronization exception type corresponding to the maximum value of the matching total numbers as a target synchronization exception type.

4. The big data based satellite navigation data synchronization method of claim 3, wherein, The step S400 includes: Step S401: acquiring a target exception root cause of the satellite navigation platform in a current period from the satellite navigation platform, and acquiring a preset exception solution of the target exception root cause from the satellite navigation platform; Step S402: sending the exception solution and the target exception root cause to a staff member through the satellite navigation platform, prompting the staff member to process satellite navigation data synchronization exceptions in the satellite navigation platform according to the exception solution, and managing the satellite navigation data synchronization.

5. A satellite navigation data synchronization system based on big data for performing the satellite navigation data synchronization method based on big data according to any one of claims 1 to 4, characterized in that, The system includes a data exception analysis module, a matching degree analysis module, a matching degree evaluation module, and a data synchronization management module; The data exception analysis module is configured to analyze the data exception state of the satellite navigation synchronization data to obtain exception satellite navigation synchronization data. The matching degree analysis module is configured to analyze the type matching degree between the synchronization exception types in the synchronization exception type data and the navigation error record set to obtain a target synchronization exception type. The matching degree evaluation module is configured to evaluate the data matching degree between the historical reference exception synchronization records corresponding to the exception root causes in the synchronization exception root cause data and the exception satellite navigation synchronization data to obtain a target exception root cause. The data synchronization management module is configured to acquire a preset abnormal solution of the target abnormal root cause, send the abnormal solution to the staff through the satellite navigation platform, and manage the satellite navigation data synchronization.

6. The big data based satellite navigation data synchronization system of claim 5, wherein, The data anomaly analysis module includes a vector acquisition unit and a data anomaly analysis unit. The vector acquisition unit is configured to acquire satellite navigation synchronization records in the satellite navigation platform, acquire satellite navigation synchronization data from the satellite navigation synchronization records, acquire a navigation synchronization time sequence in the satellite navigation synchronization data, and acquire a multi-dimensional navigation vector of each element in the navigation synchronization sequence. The data anomaly analysis unit is configured to calculate data mutation values of navigation synchronization parameters in a characteristic unit time according to the multi-dimensional navigation vector, analyze a data anomaly state of the satellite navigation synchronization data, and obtain abnormal satellite navigation synchronization data.

7. The big data based satellite navigation data synchronization system of claim 5, wherein, The matching degree analysis module includes a document acquisition unit and a matching degree analysis unit. The document acquisition unit is configured to acquire error reporting documents corresponding to navigation error reporting records, and acquire each comparison error reporting document corresponding to each synchronization abnormal type in synchronization abnormal type data. The matching degree analysis unit is configured to analyze a type matching degree between the synchronization abnormal types and the navigation error reporting record set according to the comparison error reporting documents, and obtain a target synchronization abnormal type.

8. The big data based satellite navigation data synchronization system of claim 5, wherein, The matching degree analysis module includes a time sequence acquisition unit and a matching degree analysis unit. The time sequence acquisition unit is configured to acquire historical comparison abnormal synchronization records corresponding to each abnormal root cause in the synchronization abnormal root cause data, acquire a historical navigation synchronization time sequence from the historical comparison abnormal synchronization records, and acquire a navigation synchronization time sequence corresponding to the abnormal satellite navigation synchronization data. The matching degree analysis unit is configured to calculate a matching value between the historical comparison abnormal synchronization records and the abnormal satellite navigation synchronization data, and evaluate a data matching degree between the abnormal root cause in the synchronization abnormal root cause data and the abnormal satellite navigation synchronization data according to the matching value, and obtain a target abnormal root cause.

9. The big data based satellite navigation data synchronization system of claim 5, wherein, The data synchronization management module includes a data synchronization management unit. The data synchronization management unit is configured to acquire the preset abnormal solution of the target abnormal root cause from the satellite navigation platform, prompt the staff to process satellite navigation data synchronization abnormalities in the satellite navigation platform according to the abnormal solution, and manage the satellite navigation data synchronization.

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