A navigation data checking method for a Beidou navigation receiver

Abstract

This invention discloses a navigation data verification method for BeiDou navigation receivers, relating to the field of BeiDou navigation technology, to address the problems of unstable navigation data transmission and limited data verification. This navigation data verification method for BeiDou navigation receivers effectively improves the matching degree between the number of transmitted datasets and the actual channel conditions by setting the number of datasets transmitted according to the data transmission module, thereby improving the stability of monitoring data during transmission. The classification data decision module ensures the comprehensiveness of classification data reception and storage, allowing data retrieval according to classification to prevent interference from different classifications, making data retrieval more accurate and operation simpler. Simultaneously, the data storage module ensures the security of classification data; the same monitoring data is verified using comparison verification, parity check, and neural network methods respectively.

Description

Technical Field

[0001] This invention relates to the field of BeiDou navigation technology, specifically to a navigation data verification method for BeiDou navigation receivers. Background Technology

[0002] The BeiDou Navigation Satellite System is China's independently developed satellite navigation system and is the third mature satellite navigation system after China and Russia.

[0003] Chinese patent CN105629264B discloses a navigation data verification method for GPS / BeiDou navigation receivers. This method performs a cyclic shift operation with XOR on the original data, compares the result with the received verification data, and determines the accuracy of the received original data based on the comparison result. This significantly reduces the computational load and is applicable to both GPS and BeiDou navigation systems. Furthermore, it eliminates the need for deinterleaving operations for non-first characters in the BeiDou navigation system, improving verification efficiency. While this patent solves the problem of single data computation, the following issues still exist in practical operation:

[0004] 1. When collecting navigation data from the receiver, the accuracy of the collected data is poor, and the lack of data unification during data collection leads to data matching errors.

[0005] 2. The navigation monitoring data in the receiver was not effectively classified, which made it impossible to classify, store and manage the data effectively, resulting in reduced data security.

[0006] 3. The data verification method is too simplistic, resulting in inaccurate verification results. Furthermore, the lack of data clustering analysis on the verified data reduces the security level of the verified data. Summary of the Invention

[0007] The purpose of this invention is to provide a navigation data verification method for BeiDou navigation receivers. Setting the number of data sets transmitted according to the data transmission module effectively improves the matching degree between the number of transmitted data sets and the actual channel conditions, thereby improving the stability of monitoring data during transmission. The classification data decision module ensures the comprehensiveness of classification data reception and storage, allowing data retrieval according to classification to prevent interference between different classifications, making data retrieval more accurate and operation simpler. Simultaneously, the data storage module ensures the security of classification data. Identical monitoring data is verified using comparison verification, parity verification, and neural network methods respectively. Different types of data are cached in corresponding sub-target cache spaces, ensuring the caching effect of monitoring data information and improving the security factor of monitoring data information, thus solving the problems in the prior art.

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] A method for verifying navigation data in a BeiDou navigation receiver, comprising the following steps:

[0010] S1: Navigation data acquisition: Based on the Beidou navigation terminal, acquire interface data, perform raw data analysis on the acquired data, generate corresponding positioning tags based on the analysis results, and store the tag data;

[0011] S2: Data Classification and Data Validation: Based on the collected monitoring data, the data is first classified according to the data type, and then the classified data is validated multiple times. The validation method adopts multiple validation methods, and each method is validated multiple times.

[0012] S3: Verification Data Cache: Based on the navigation monitoring data after verification, the data verified by different methods is classified. After classification, the different data are stored according to the space capacity. Among them, the key data information in the different data is extracted, and the information type of the verification data is determined based on the key data information.

[0013] Preferably, the collection of navigation data in S1 includes:

[0014] The navigation data monitoring module is used for:

[0015] Based on the wireless data received by the receiver, the data is collected in a unified manner, and the collected data is monitored in a unified manner.

[0016] The monitoring data detection module is used for:

[0017] The system generates monitoring data from the uniformly received data and performs delay detection on the monitoring data. Specifically, it adjusts the generated monitoring data based on the pre-detection data, aligning the generated monitoring data with the pre-detection data. The pre-detection data values ​​can be automatically adjusted on the receiver.

[0018] The data synchronization module is used for:

[0019] The monitored data that has been detected is synchronized. Specifically, monitored data from the same time period are generated into data with the same sequence number, and the data with the same sequence number are packaged together to generate a monitoring data package.

[0020] Preferably, the collection of navigation data in S1 also includes:

[0021] Data transmission module, used for:

[0022] The generated monitoring data packets are transmitted to the next server for data processing via a communication channel. In this process, the data transmission speed of the monitoring data packets is obtained through the communication channel during the data transmission process. The communication channel with the fastest data transmission speed is selected, and the channel with the largest remaining capacity is extracted from the fastest channel as the target channel.

[0023] The data binding module is used for:

[0024] The monitoring data packets in the target channel are stored, and the stored data from different time periods are bound together.

[0025] Preferably, the classification of data types in S2 includes:

[0026] The data classification module is used for:

[0027] The received monitoring data is identified by data type and classified as data to be stored.

[0028] The classification data processing module is used for:

[0029] Obtain the capacity coefficient of the storage area for each category of data. The capacity coefficient of the storage area represents the space already used in the storage area. Find the available target storage area.

[0030] The classification data decision module is used for:

[0031] Configure the number of replicas and storage awareness strategy when storing categorized data;

[0032] The storage-aware strategy includes identifying data nodes in the categorized data storage area for storing categorized data.

[0033] Data storage module, used for:

[0034] Store the categorized data to be stored and the number of copies in the target storage area, and record the storage information of the operation behavior data to be stored and the number of copies.

[0035] Preferably, the method for data verification in S2 includes:

[0036] Comparison verification method, used for:

[0037] The categorized monitoring data and the data to be compared are directly compared numerically, and the same monitoring data is compared with the data to be compared at least once.

[0038] Parity checking is used for:

[0039] In the storage and transmission of monitoring data, an extra bit is added to each byte to check for errors. This check bit can be calculated by XORing the data bits. The check is performed based on whether the number of "1"s in the transmitted binary code is odd or even. Odd numbers are used for parity checking, and vice versa for even numbers.

[0040] Preferably, the data verification method further includes:

[0041] Neural network method, used for:

[0042] First, the monitoring data is propagated forward, from lower to higher levels.

[0043] When the data obtained from propagation does not match the expectations, backpropagation is performed. Backpropagation involves propagating the error from higher levels to lower levels for training.

[0044] The process of dissemination training is as follows:

[0045] First, the weights of the data are initialized. After the settings are complete, the parameter data is forward-propagated through the convolutional layer, the downsampling layer, and the fully connected layer to obtain the output value.

[0046] When the error is greater than the expected value, the error is propagated back into the network, and the errors of the fully connected layer, the downsampling layer and the convolutional layer are calculated in turn.

[0047] The error of each layer is the total error of the network; training is complete when the error is equal to or less than the expected value.

[0048] Preferably, the data verification method further includes:

[0049] Obtain base station positioning data from at least three fixed base stations in different directions around the BeiDou navigation terminal;

[0050] Construct a planar coordinate system based on base station positioning data;

[0051] Import the navigation data of the Beidou navigation terminal with the stored positioning tag into the plane coordinate system to form plane point cloud data with time-series changes;

[0052] Outlier data is removed through cluster analysis of planar point cloud data;

[0053] Temporal vector change analysis was performed on the planar point cloud data after removing outlier data to obtain the average vector change rate of navigation data from Beidou navigation terminals.

[0054] The latest BeiDou navigation terminal navigation data is imported into a plane coordinate system for time-series vector change analysis to obtain the current vector change rate;

[0055] The standard for verifying whether the latest BeiDou navigation terminal navigation data passes the test is that the deviation between the current vector change rate and the mean vector change rate is not greater than a preset change threshold.

[0056] Preferably, the caching of verification data in S3 includes:

[0057] The data capacity determination module is used for:

[0058] The target cache space corresponding to the monitoring data information is determined based on the information type of the verification, and the capacity information of the target cache space is extracted. The first remaining available space capacity of the target cache space is determined based on the capacity information.

[0059] The spatial partitioning module is used for:

[0060] The target cache space is divided into first blocks according to the type identifier, and block identifiers are added to the sub-target cache spaces after the division. At the same time, each sub-target cache space is divided into second blocks to obtain the first storage entry and the second storage entry corresponding to each sub-target cache space. The block identifier corresponds to the type identifier.

[0061] The data caching module is used for:

[0062] Extract the target content of the monitoring data information corresponding to each data type according to the type identifier, and cache the type identifier and the target content to the first storage entry and the second storage entry respectively.

[0063] Preferably, the data capacity determination module is further configured to:

[0064] First, the length of the monitoring data is obtained. When the first remaining available space capacity is greater than the data length, the monitoring data is clustered to obtain a set of sub-data types corresponding to the SMS data information, and a type identifier is set for each sub-data type.

[0065] Preferably, the key data information extracted in S3 also includes:

[0066] Generate binary tree representations to be examined based on the key data information of different data, and normalize the binary tree representations to be examined.

[0067] Information types are represented using type binary trees, and the type binary tree representations are normalized.

[0068] The matching degree between the binary tree representation to be examined and the typed binary tree representation is calculated using the following formula:

[0069]

[0070] In the above formula, D i,jS represents the matching degree between the i-th binary tree representation to be examined and the j-th type binary tree representation; i L represents the sequence of node information in the binary tree representation of the i-th node to be examined; j N represents the sequence of node information in the j-th type of binary tree representation; mat (S i ,L j ) represents the number of nodes in the node information sequence of the i-th binary tree under investigation that are identical to the node information of the corresponding nodes starting from the root node in the node information sequence of the j-th type of binary tree; N Si N represents the total number of node information sequences in the node information sequence represented by the i-th binary tree to be examined; Lj This represents the total number of node information in the node information sequence represented by the j-th type of binary tree;

[0071] For the same binary tree representation to be examined and the matching degree of each corresponding item with the binary tree representations of various types, if there is no matching degree or only one matching degree not less than the preset threshold, then the data corresponding to the binary tree representation to be examined is classified as the information type with the highest matching degree; if there are multiple matching degrees not less than the preset threshold, then the following processing is performed:

[0072] Secondary data information other than key data information is extracted from the data corresponding to the binary tree representation to be examined, and a second binary tree representation to be examined is generated. The second matching degree between the second binary tree representation to be examined and the type binary tree representation is calculated, and the information type with the highest second matching degree is determined as the information type of the data corresponding to the binary tree representation to be examined.

[0073] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0074] 1. This invention provides a navigation data verification method for a BeiDou navigation receiver. It performs pre-inspection using a monitoring data detection module, allowing for adjustments as needed to ensure the synchronization accuracy of the main data acquisition. It also detects and modifies delays, improving ease of use. The data synchronization module enables synchronized data acquisition, adding a sequence number to the synchronization message to avoid data packet mismatch issues caused by transmission delay uncertainties. Setting the number of data sets transmitted via the data transmission module effectively improves the matching degree between the number of data sets transmitted and the actual channel conditions, thereby enhancing the stability of monitoring data transmission. This effectively prevents excessive data transmission volume from causing channel congestion and affecting data transmission efficiency, as the data cannot be specifically adjusted according to parameters such as actual channel saturation.

[0075] 2. This invention provides a navigation data verification method for a Beidou navigation receiver. Based on a data classification module, the monitoring data can be classified into different data types. A classification data decision module ensures the comprehensiveness of the received and stored classification data. During use, data can be retrieved according to classification, preventing interference from different classifications, making data retrieval more accurate and operation simpler. Simultaneously, a data storage module ensures the security of the classification data, preventing data loss and incompleteness, thus enhancing the system's reliability and practicality. The same monitoring data is verified using a comparison verification method, a parity check method, and a neural network method, with each method being tested multiple times. After multiple tests using these methods, the verification results are analyzed. These three verification methods make the verification results more accurate.

[0076] 3. The present invention provides a navigation data verification method for a Beidou navigation receiver. By analyzing the monitoring data information through a data capacity determination module, the information type of SMS data information can be accurately and effectively confirmed. This facilitates the determination of the target cache space for caching SMS data information. Each sub-target storage space is further divided to ensure the caching effect and accuracy of each type of data content and type identifier. Caching different types of data in the corresponding sub-target cache space ensures the caching effect of monitoring data information and improves the security factor of monitoring data information. Attached Figure Description

[0077] Figure 1 This is a schematic diagram of the overall process of the present invention;

[0078] Figure 2 This is a schematic diagram of the navigation data acquisition module of the present invention;

[0079] Figure 3 This is a schematic diagram of the data type classification module of the present invention;

[0080] Figure 4 This is a schematic diagram of the cache module for verification data in this invention. Detailed Implementation

[0081] 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.

[0082] To address the issues of poor accuracy in acquiring navigation data from receivers and data matching errors caused by the lack of data unification during acquisition in existing technologies, please refer to [link to relevant documentation]. Figure 1 and Figure 2 This embodiment provides the following technical solution:

[0083] A method for verifying navigation data in a BeiDou navigation receiver, comprising the following steps:

[0084] S1: Navigation data acquisition: Based on the Beidou navigation terminal, acquire interface data, perform raw data analysis on the acquired data, generate corresponding positioning tags based on the analysis results, and store the tag data;

[0085] S2: Data Classification and Data Validation: Based on the collected monitoring data, the data is first classified according to the data type, and then the classified data is validated multiple times. The validation method adopts multiple validation methods, and each method is validated multiple times.

[0086] S3: Verification Data Cache: Based on the navigation monitoring data after verification, the data verified by different methods is classified. After classification, the different data are stored according to the space capacity. Among them, the key data information in the different data is extracted, and the information type of the verification data is determined based on the key data information.

[0087] The navigation data collection in S1 includes: a navigation data monitoring module, used to uniformly collect data from the wireless data received by the receiver and uniformly monitor the collected data; a monitoring data detection module, used to generate monitoring data from the uniformly received data and perform delay detection on the monitoring data, wherein, based on the pre-detection data and the generated monitoring data, pre-detection adjustment is performed to change the generated monitoring data to be consistent with the pre-detection data; wherein, the pre-detection data values ​​can be automatically adjusted on the receiver; and a data synchronization module, used to synchronize the detected monitoring data, wherein, monitoring data in the same time period are generated into data with the same sequence number, and the data with the same sequence number are packaged together to generate a monitoring data packet.

[0088] For the acquisition of navigation data in S1, the system also includes: a data transmission module, used to: transmit the generated monitoring data packets to the next server for data processing via a communication channel, wherein the module obtains the data transmission speed of the communication channel for the monitoring data packets during the monitoring data transmission process, obtains the communication channel with the fastest data transmission speed, and extracts the channel with the largest remaining capacity from the fastest channel as the target channel; and a data binding module, used to: store the monitoring data packets in the target channel, and bind the stored data from different time periods into data packets.

[0089] Specifically, the monitoring data detection module performs pre-inspection and can be adjusted as needed to ensure the synchronization accuracy of the main data acquisition. It can detect and modify the delay, improving ease of use. The data synchronization module realizes synchronized data acquisition and adds a sequence number to the synchronization message to avoid data packet matching errors that may be caused by the uncertainty of transmission delay. The data transmission module can set the number of datasets to be transmitted, which can effectively improve the matching degree between the number of datasets transmitted and the actual channel conditions. This can improve the stability of monitoring data during transmission and effectively prevent the data from being unable to be adjusted according to the actual channel saturation and other parameters, which could lead to excessive data transmission volume and channel congestion, affecting data transmission efficiency.

[0090] To address the problem in existing technologies where navigation monitoring data in the receiver is not effectively classified, leading to ineffective data classification, storage, and management, and consequently reduced data security, please refer to [link to relevant documentation]. Figure 3 This embodiment provides the following technical solution:

[0091] The classification of data types in S2 includes: a data classification module, used to identify the data types of received monitoring data and classify them as data to be stored; a classification data processing module, used to obtain the capacity coefficient of the storage area for each type of data, where the capacity coefficient represents the used space of the storage area, and to find available target storage areas; a classification data decision module, used to set the number of replicas and storage awareness strategy when storing classification data; wherein, the storage awareness strategy includes determining the data nodes in the classification data storage area for storing classification data; and a data storage module, used to store the classification data to be stored and the number of replicas in the target storage area, and record the storage information of the operation behavior data to be stored and the number of replicas.

[0092] Specifically, the data classification module can identify the data type of the monitoring data, and the classification data decision module ensures the comprehensiveness of the reception and storage of the classification data. When using the data, it can retrieve the data according to the classification, preventing interference from different classification data, making the data retrieval more accurate and the operation more convenient. At the same time, the data storage module ensures the security of the classification data, preventing the loss of classification data and thus preventing data incompleteness, thereby enhancing the reliability and practicality of the system.

[0093] To address the problems of limited verification methods and poor accuracy in existing navigation data verification technologies, this embodiment provides the following technical solution:

[0094] The data verification methods in S2 include: comparison verification, which involves directly comparing the classified monitoring data with the data to be compared, and comparing the same monitoring data with the data to be compared at least once; and parity verification, which involves adding an extra bit to the bytes in the stored monitoring data and transmission to check for errors. The parity bit can be calculated by XORing the data bits, and the verification is performed based on whether the number of "1"s in the transmitted binary code is odd or even. Using an odd number of values ​​is called odd parity, and vice versa, it's called even parity. The neural network method is used to: first, propagate the monitoring data forward, from lower to higher levels; when the propagated data does not match the expectation, propagate backward, where the error is propagated from higher to lower levels for training; the training process is as follows: first, initialize the weights of the data; after initialization, the parameter data is propagated forward through convolutional layers, downsampling layers, and fully connected layers to obtain the output value; when the error is greater than the expected value, the error is propagated back into the network, and the errors of the fully connected layer, downsampling layer, and convolutional layer are calculated sequentially; the error of each layer is the total error of the network; when the error is equal to or less than the expected value, training is complete.

[0095] Specifically, the same monitoring data is verified using the comparison verification method, the parity check method, and the neural network method, respectively. Each of these methods is tested multiple times, and the verification results are analyzed after multiple tests. The comparison verification method has the best accuracy, but the verification efficiency is relatively low. The parity check method usually sets a parity check bit to ensure that the number of "1"s in the code is odd or even. If odd parity is used, when the receiver receives this code, it checks whether the number of "1"s is odd to determine the correctness of the transmitted code. The neural network method obtains the output value by forward propagating the parameter data through convolutional layers, lower layers, and fully connected layers. Forward propagation propagates the parameter data from lower levels to higher levels. When the data obtained from the propagation does not match the expectation, backpropagation is performed. Backpropagation propagates the error from higher levels to lower levels for training. After training, when the error is greater than the expected value, the error is propagated back into the network to calculate the errors of the downsampling layer and the convolutional layer in sequence. When the error is equal to or less than the expected value, the training is complete. When the parameter data is trained through forward propagation, it can pass through each hidden layer, and the final loss data is obtained when passing through the hidden layer. When the parameter data is backpropagated, according to the gradient decrease formula, it is fed forward layer by layer to form a backpropagation mechanism, which can optimize the parameters and improve the quality of the verification.

[0096] The methods for data validation in S2 also include:

[0097] Obtain base station positioning data from at least three fixed base stations in different directions around the BeiDou navigation terminal;

[0098] Construct a planar coordinate system based on base station positioning data;

[0099] Import the navigation data of the Beidou navigation terminal with the stored positioning tag into the plane coordinate system to form plane point cloud data with time-series changes;

[0100] Outlier data is removed through cluster analysis of planar point cloud data;

[0101] Temporal vector change analysis was performed on the planar point cloud data after removing outlier data to obtain the average vector change rate of navigation data from Beidou navigation terminals.

[0102] The latest BeiDou navigation terminal navigation data is imported into a plane coordinate system for time-series vector change analysis to obtain the current vector change rate;

[0103] The standard for verifying whether the latest BeiDou navigation terminal navigation data passes the test is that the deviation between the current vector change rate and the mean vector change rate is not greater than a preset change threshold.

[0104] By introducing base station positioning data from fixed base stations, a planar coordinate system is constructed. Navigation data is then incorporated into this planar coordinate system to form an intuitive coordinate graph. Based on this, change analysis can be performed, which can effectively verify the correctness of the data and improve the accuracy and reliability of the verification. Furthermore, by linking the data into the planar coordinate system with the time information of the data acquisition, time-series vector change analysis can be achieved.

[0105] To address the issue in existing technologies where data clustering analysis is not performed on verified data, leading to a decrease in the security level of the verified data, please refer to [link / reference needed]. Figure 4 This embodiment provides the following technical solution:

[0106] The caching of verification data in S3 includes: a data capacity determination module, used to: determine the target cache space corresponding to the monitoring data information according to the verification information type, extract the capacity information of the target cache space, and determine the first remaining available space capacity of the target cache space according to the capacity information; a space partitioning module, used to: divide the target cache space into first blocks according to the type identifier, add block identifiers to the partitioned sub-target cache spaces, and simultaneously divide each sub-target cache space into a second partition to obtain the first storage entry and the second storage entry corresponding to each sub-target cache space, wherein the block identifier corresponds to the type identifier; and a data caching module, used to: extract the target content of the monitoring data information corresponding to each data type according to the type identifier, and cache the type identifier and the target content to the first storage entry and the second storage entry respectively. The data capacity determination module is further used to: first obtain the data length of the monitoring data information, and when the first remaining available space capacity is greater than the data length, cluster the monitoring data information to obtain the sub-data type set corresponding to the SMS data information, and set a type identifier for each sub-data type.

[0107] Specifically, the data capacity determination module analyzes the monitoring data to accurately and effectively confirm the information type of SMS data, thereby facilitating the determination of the target cache space for caching SMS data. Each sub-target storage space is further divided to ensure the caching effect and accuracy of each type of data content and type identifier. Caching different types of data in the corresponding sub-target cache space ensures the caching effect of monitoring data and also improves the security of monitoring data.

[0108] The key data information extracted from S3 also includes:

[0109] Generate binary tree representations to be examined based on the key data information of different data, and normalize the binary tree representations to be examined.

[0110] Information types are represented using type binary trees, and the type binary tree representations are normalized.

[0111] The matching degree between the binary tree representation to be examined and the typed binary tree representation is calculated using the following formula:

[0112]

[0113] In the above formula, D i,j S represents the matching degree between the i-th binary tree representation to be examined and the j-th type binary tree representation; i L represents the sequence of node information in the binary tree representation of the i-th node to be examined; j N represents the sequence of node information in the j-th type of binary tree representation; mat (S i ,L j ) represents the number of nodes in the node information sequence of the i-th binary tree under investigation that are identical to the node information of the corresponding nodes starting from the root node in the node information sequence of the j-th type of binary tree; N Si N represents the total number of node information sequences in the node information sequence represented by the i-th binary tree to be examined; Lj This represents the total number of node information in the node information sequence represented by the j-th type of binary tree;

[0114] For the same binary tree representation to be examined and the matching degree of each corresponding item with the binary tree representations of various types, if there is no matching degree or only one matching degree not less than the preset threshold, then the data corresponding to the binary tree representation to be examined is classified as the information type with the highest matching degree; if there are multiple matching degrees not less than the preset threshold, then the following processing is performed:

[0115] Secondary data information other than key data information is extracted from the data corresponding to the binary tree representation to be examined, and a second binary tree representation to be examined is generated. The second matching degree between the second binary tree representation to be examined and the type binary tree representation is calculated, and the information type with the highest second matching degree is determined as the information type of the data corresponding to the binary tree representation to be examined.

[0116] This scheme enables rapid and accurate classification of different data types, facilitating data storage and use. Using key data as the primary consideration reduces computational load and saves resources. Adding secondary data as consideration when necessary improves the rationality and accuracy of matching and classification. Transforming the data into a tree structure and combining it with matching degree calculation refines the evaluation content, further enhancing accuracy and improving the objectivity and reliability of the results. Normalization makes the binary tree representation of the data to be examined and the binary tree representation of the types more standardized, facilitating matching degree comparison and calculation.

[0117] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0118] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A navigation data verification method for a Beidou navigation receiver, characterized in that: Includes the following steps: S1: Navigation data acquisition: Based on the Beidou navigation terminal, acquire interface data, perform raw data analysis on the acquired data, generate corresponding positioning tags based on the analysis results, and store the tag data; S2: Data Classification and Data Validation: Based on the collected monitoring data, the data is first classified according to the data type, and then the classified data is validated multiple times. The validation method adopts multiple validation methods, and each method is validated multiple times. The data validation methods include: Comparison verification method, used for: The categorized monitoring data and the data to be compared are directly compared numerically, and the same monitoring data is compared with the data to be compared at least once; Parity checking is used for: In the storage and transmission of monitoring data, an extra bit is added to the byte to check for errors. The check bit is calculated by XORing the data bits. The check is performed based on whether the number of "1"s in the transmitted binary code is odd or even. If the number of "1"s is odd, it is called odd check; otherwise, it is called even check. Neural network method, used for: First, the monitoring data is propagated forward, from lower to higher levels. When the data obtained from propagation does not match the expectations, backpropagation is performed. Backpropagation involves propagating the error from higher levels to lower levels for training. The process of dissemination training is as follows: First, the weights of the data are initialized. After the settings are complete, the parameter data is forward-propagated through the convolutional layer, the downsampling layer, and the fully connected layer to obtain the output value. When the error is greater than the expected value, the error is propagated back into the network, and the errors of the fully connected layer, the downsampling layer and the convolutional layer are calculated in turn. The error of each layer is the total error of the network; training is complete when the error is equal to or less than the expected value. Data validation methods also include: Obtain base station positioning data from at least three fixed base stations in different directions around the BeiDou navigation terminal; Construct a planar coordinate system based on base station positioning data; Import the navigation data of the Beidou navigation terminal with the stored positioning tag into the plane coordinate system to form plane point cloud data with time-series changes; Outlier data is removed through cluster analysis of planar point cloud data; Temporal vector change analysis was performed on the planar point cloud data after removing outlier data to obtain the average vector change rate of navigation data from Beidou navigation terminals. The latest BeiDou navigation terminal navigation data is imported into a plane coordinate system for time-series vector change analysis to obtain the current vector change rate; The standard for whether the latest Beidou navigation terminal navigation data passes the verification is that the deviation between the current vector change rate and the mean vector change rate is no greater than a preset change threshold. S3: Verification Data Cache: Based on the navigation monitoring data after verification, the data verified by different methods is classified. After classification, different data are stored according to the space capacity. Among them, key data information is extracted from different data, and the information type of the verification data is determined based on the key data information.

2. The navigation data verification method for a Beidou navigation receiver according to claim 1, characterized in that: The collection of navigation data in S1 includes: The navigation data monitoring module is used for: Based on the wireless data received by the receiver, the data is collected in a unified manner, and the collected data is monitored in a unified manner. The monitoring data detection module is used for: The system generates monitoring data from the uniformly received data and performs delay detection on the monitoring data. Specifically, it adjusts the generated monitoring data based on the pre-detection data, aligning the generated monitoring data with the pre-detection data. The pre-detection data values ​​can be automatically adjusted on the receiver. The data synchronization module is used for: The monitoring data that has been detected is synchronized. Specifically, monitoring data from the same time period are generated into data with the same sequence number, and the data with the same sequence number are packaged together to generate a monitoring data package.

3. The navigation data verification method for a Beidou navigation receiver according to claim 2, characterized in that: The collection of navigation data in S1 also includes: Data transmission module, used for: The generated monitoring data packets are transmitted to the next server for data processing via a communication channel. In this process, the data transmission speed of the monitoring data packets is obtained through the communication channel during the data transmission process. The communication channel with the fastest data transmission speed is selected, and the channel with the largest remaining capacity is extracted from the fastest channel as the target channel. The data binding module is used for: The monitoring data packets in the target channel are stored, and the stored data from different time periods are bound together.

4. The navigation data verification method for a Beidou navigation receiver according to claim 1, characterized in that: The classification of data types in S2 includes: The data classification module is used for: The received monitoring data is identified by data type and classified as data to be stored. The classification data processing module is used for: Obtain the capacity coefficient of the storage area for each category of data. The capacity coefficient of the storage area represents the space already used in the storage area. Find the available target storage area. The classification data decision module is used for: Configure the number of replicas and storage awareness strategy when storing categorized data; The storage-aware strategy includes identifying data nodes in the categorized data storage area for storing categorized data; Data storage module, used for: Store the categorized data to be stored and the number of copies in the target storage area, and record the storage information of the operation behavior data to be stored and the number of copies.

5. A navigation data verification method for a Beidou navigation receiver according to claim 1, characterized in that: The caching of verification data in S3 includes: The data capacity determination module is used for: The target cache space corresponding to the monitoring data information is determined based on the information type of the verification, and the capacity information of the target cache space is extracted. The first remaining available space capacity of the target cache space is determined based on the capacity information. The space partitioning module is used for: The target cache space is divided into first blocks according to the type identifier, and block identifiers are added to the sub-target cache spaces after the division. At the same time, each sub-target cache space is divided into second blocks to obtain the first storage entry and the second storage entry corresponding to each sub-target cache space. The block identifier corresponds to the type identifier. The data caching module is used for: Extract the target content of the monitoring data information corresponding to each data type according to the type identifier, and cache the type identifier and the target content to the first storage entry and the second storage entry respectively.

6. A navigation data verification method for a Beidou navigation receiver according to claim 5, characterized in that: The data capacity determination module is further configured to: First, the length of the monitoring data is obtained. When the first remaining available space capacity is greater than the data length, the monitoring data is clustered to obtain a set of sub-data types corresponding to the SMS data information, and a type identifier is set for each sub-data type.

7. A navigation data verification method for a Beidou navigation receiver according to claim 1, characterized in that: The key data information extracted from S3 also includes: Generate binary tree representations to be examined based on the key data information of different data, and normalize the binary tree representations to be examined. Information types are represented using type binary trees, and the type binary tree representations are normalized. The matching degree between the binary tree representation to be examined and the typed binary tree representation is calculated using the following formula: In the above formula, Indicates the first The binary tree to be examined represents the first... The matching degree is represented by a binary tree of each type; Indicates the first A sequence of node information represented by a binary tree to be examined; Indicates the first A sequence of node information represented by a binary tree of various types; Indicates the first The sequence of node information represented by the binary tree to be examined and the sequence of nodes to be examined. The number of nodes in a binary tree representation of a given type whose node information is identical starting from the root node. Indicates the first The total number of node information sequences represented by the binary tree to be examined; Indicates the first The total number of node information sequences represented by each type of binary tree; For the same binary tree representation to be examined and the matching degree of each corresponding item with the binary tree representations of various types, if there is no matching degree or only one matching degree not less than the preset threshold, then the data corresponding to the binary tree representation to be examined is classified as the information type with the highest matching degree; if there are multiple matching degrees not less than the preset threshold, then the following processing is performed: Secondary data information other than key data information is extracted from the data corresponding to the binary tree representation to be examined, and a second binary tree representation to be examined is generated. The second matching degree between the second binary tree representation to be examined and the type binary tree representation is calculated, and the information type with the highest second matching degree is determined as the information type of the data corresponding to the binary tree representation to be examined.

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