A method and device for analysis and transmission optimization based on ephemeris data

Abstract

The application discloses a kind of based on ephemeris data analysis and transmission optimization method and device, it is related to satellite navigation and data communication technical field.The method includes: the original ephemeris data obtained is input into ephemeris data analysis and verification model and is verified, until the operation result of ephemeris data modulo two division is zero;Ephemeris solution module is used to data conversion to clean data, and the frequency domain signal is obtained, and the frequency domain signal is corrected, and the reconstructed time domain signal of clean data is determined by inverse data conversion to the frequency domain signal after correction;CRC adjustment module is used to adjust CRC polynomial based on the real-time channel state of reconstructed time domain signal, and the CRC check bit length is determined;Based on the CRC remainder value of adjusted CRC polynomial and CRC check bit length, error detection is carried out according to the CRC remainder value.The problem of low ephemeris data processing efficiency and insufficient verification reliability in the prior art is solved.The reliability of data transmission process, verification accuracy and communication efficiency can be optimized.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of satellite navigation and data communication technology, in particular to a method and device for parsing and transmission optimization based on ephemeris data. BACKGROUND

[0002] With the rapid increase in the number of global satellites, ephemeris data has become the core support for space mission planning, disaster monitoring and real-time positioning services. In the aspect of real-time positioning services, high-precision ephemeris data can significantly enhance the satellite navigation and positioning capability. For example, by optimizing the ephemeris model, the single-point positioning error of the GPS system is reduced from meters to sub-meters, which is widely used in the fields of autonomous driving, agriculture, etc. As the core information of the navigation system, satellite ephemeris data contains satellite orbit parameters, clock correction values and other key contents. The improvement of its accuracy directly promotes the progress of space technology and provides convenience for daily life.

[0003] Traditional ephemeris data parsing methods mostly rely on time-domain interpolation algorithms such as Lagrange interpolation or least squares fitting, which have high computational complexity and are difficult to meet the needs of high real-time devices (such as airborne or high-speed mobile terminals). In addition, existing CRC (Cyclic Redundancy Check) techniques mostly use fixed polynomials (such as CRC-16-CCITT), which cannot dynamically adjust the check strength according to the channel conditions, resulting in a high failure rate of the check in the presence of burst noise or high BER (Bit Error Rate) scenarios.

[0004] Although FFT (Fast Fourier Transform) is introduced in the prior art to perform frequency domain parsing, it does not solve the frequency domain aliasing problem, and the parsing efficiency is not high. Even if an adaptive CRC algorithm is added to adjust the polynomial order through a static threshold, the dynamic response delay is large, and it cannot always respond to rapidly changing channel environments. SUMMARY

[0005] The embodiments of the present application provide a method and device for parsing and transmission optimization based on ephemeris data, which solves the problems of low ephemeris data processing efficiency and insufficient check reliability in the prior art. An optimized Fourier algorithm is constructed to perform frequency domain parsing to solve the frequency domain aliasing problem and improve the accuracy of ephemeris calculation. A CRC dynamic check is introduced to switch the CRC polynomial according to the channel state, reduce the dynamic response delay, and better respond to rapidly changing channel environments. The parsing speed of ephemeris data and the integrity of the transmission process are significantly improved, and the robustness of the ephemeris data parsing and check model in complex channel environments is enhanced.

[0006] In a first aspect, embodiments of this application provide a method for optimizing the parsing and transmission of ephemeris data, comprising: inputting the acquired raw ephemeris data into an ephemeris data parsing and verification model for verification until the result of modulo-2 division of the ephemeris data is zero; wherein, the ephemeris data parsing and verification model includes a preprocessing module, an ephemeris calculation module, a CRC adjustment module, a CRC error detection module, and a CRC verification and repair module connected in sequence for data transmission; the preprocessing module is used to preprocess the raw ephemeris data to obtain clean data; the ephemeris calculation module is used to perform data conversion on the clean data to obtain a frequency domain signal, and correct the... The frequency domain signal is modified, and an inverse data conversion is performed on the modified frequency domain signal to determine the reconstructed time domain signal of the clean data. The CRC adjustment module is used to adjust the CRC polynomial based on the real-time channel state of the reconstructed time domain signal and determine the CRC check bit length. The CRC error detection module is used to determine the CRC remainder value based on the adjusted CRC polynomial and the CRC check bit length, and perform error detection based on the CRC remainder value. The CRC check repair module is used to repair errors based on the error detection results to obtain the repaired ephemeris data. The repaired ephemeris data is then encapsulated and sent to the navigation terminal.

[0007] In one possible implementation, the preprocessing of the original ephemeris data to obtain clean data includes: defining a mask matrix and using the mask matrix to filter noise and redundant fields in the original ephemeris data to obtain the clean data; wherein, the filtering of noise and redundant fields in the original ephemeris data using the mask matrix is ​​as follows:

[0008] D clean =D raw M mask ;

[0009] In the formula, D clean D represents the cleanliness data. raw This refers to the original ephemeris data. M represents element-wise multiplication. mask This represents the mask matrix.

[0010] In one possible implementation, the step of converting the clean data to obtain a frequency domain signal includes: using a fast Fourier transform method to convert the clean data into a time-frequency signal to obtain a frequency domain signal.

[0011] In one possible implementation, the step of performing inverse data transformation on the corrected frequency domain signal to determine the reconstructed time domain signal of the clean data includes: performing frequency domain correction on the frequency domain signal based on a window function to obtain the corrected frequency domain signal; and performing inverse data transformation on the corrected frequency domain signal according to the inverse Fourier transform method to obtain the reconstructed time domain signal of the clean data.

[0012] In one possible implementation, adjusting the CRC polynomial based on the real-time channel state of the reconstructed time-domain signal includes: determining the current channel state of the reconstructed time-domain signal; wherein the current channel state includes bit error rate and signal-to-noise ratio; defining the CRC polynomial; and dynamically adjusting the CRC polynomial according to a polynomial switching strategy; wherein the polynomial switching strategy includes: determining a channel threshold, comparing the current channel state with the channel threshold, and adjusting the polynomial state based on the comparison result; if the bit error rate is greater than or equal to a first channel threshold among the channel thresholds, and / or the signal-to-noise ratio is less than a second channel threshold among the channel thresholds, then switching the CRC polynomial to a higher-order polynomial; if the bit error rate is less than the first channel threshold among the channel thresholds, and / or the signal-to-noise ratio is greater than or equal to the second channel threshold among the channel thresholds, then switching the CRC polynomial to a lower-order polynomial.

[0013] In one possible implementation, determining the CRC checksum length includes: configuring a CRC generator according to the current channel state, sending data packets based on the CRC generator, and determining the checksum result; if the number of consecutive failures of the checksum result is greater than or equal to a preset maximum number of failures, then performing checksum extension and error correction processing until a normal checksum result is obtained; if the number of consecutive failures of the checksum result is less than the preset maximum number of failures, then determining the CRC checksum length; and transmitting the CRC polynomial and the CRC checksum length to the CRC error detection module.

[0014] In one possible implementation, determining the CRC remainder value based on the adjusted CRC polynomial and the CRC check bit length includes: performing a modulo-2 division between the reconstructed time-domain signal and the adjusted CRC polynomial to determine the CRC remainder value, as follows:

[0015] ;

[0016] In the formula, This represents the CRC remainder value. This represents the reconstructed time-domain signal. This represents the modulo-2 division operator. This represents the adjusted CRC polynomial.

[0017] In one possible implementation, the error detection based on the CRC remainder value includes: if the CRC remainder value is equal to zero, then the repaired ephemeris data is obtained; if the CRC remainder value is not equal to zero, then the reconstructed time-domain signal is detected and repaired using an error repair strategy to obtain the repaired ephemeris data; wherein the error repair strategy includes: if the current scenario is a non-real-time scenario, then a request is made to retransmit the reconstructed time-domain signal through a feedback channel; if the current scenario is a real-time scenario, then the transmission error location of the reconstructed time-domain signal is located and repaired to obtain the repaired ephemeris data.

[0018] In one possible implementation, locating and repairing the transmission error position of the reconstructed time-domain signal includes:

[0019] ;in, ;

[0020] In the formula, This indicates the repaired ephemeris data. E represents the reconstructed time-domain signal. mask Indicates the error mask. argmin represents the bitwise XOR operator. E Indicates from all possible China has determined to make The minimum E represents the optimal solution, where E represents the optimization variable of the error mask.

[0021] Secondly, embodiments of this application provide a device for parsing and optimizing the transmission of ephemeris data, comprising: a repair module, used to input the acquired raw ephemeris data into an ephemeris data parsing and verification model for verification until the result of modulo-2 division of the repaired ephemeris data is zero; wherein, the ephemeris data parsing and verification model includes a preprocessing module, an ephemeris calculation module, a CRC adjustment module, a CRC error detection module, and a CRC verification and repair module connected in sequence and used for data transmission; the preprocessing module is used to preprocess the raw ephemeris data to obtain clean data; the ephemeris calculation module is used to perform data conversion on the clean data to obtain a frequency domain signal and correct it. The frequency domain signal is subjected to inverse data conversion after correction to determine the reconstructed time domain signal of the clean data; the CRC adjustment module is used to adjust the CRC polynomial based on the real-time channel state of the reconstructed time domain signal and determine the CRC check bit length; the CRC error detection module is used to determine the CRC remainder value based on the adjusted CRC polynomial and the CRC check bit length, and perform error detection based on the CRC remainder value; the CRC check repair module is used to perform error repair based on the error detection result to obtain repaired ephemeris data; the encapsulation module is used to encapsulate the repaired ephemeris data and send it to the navigation terminal.

[0022] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0023] This application employs a method and apparatus for optimizing the parsing and transmission of ephemeris data. Through an ephemeris calculation module, the time-domain signal is converted into a frequency-domain signal. Frequency-domain correction effectively suppresses spectral leakage and reduces interference from sudden noise on the original ephemeris data, ensuring the integrity, accuracy, and real-time performance of the ephemeris data in highly dynamic scenarios, significantly improving ephemeris parsing efficiency. Based on real-time channel conditions, CRC dynamic adjustment is performed, switching the CRC polynomial and check bit length according to the real-time scenario to reduce computational overhead. Through CRC error detection and correction, the check strength and communication efficiency are optimized, enhancing the robustness of the ephemeris data parsing and verification model in complex channel environments. Attached Figure Description

[0024] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1A flowchart illustrating a method for optimizing the parsing and transmission of ephemeris data, provided in this application embodiment;

[0026] Figure 2 A comparative diagram illustrating the Fourier resolution efficiency provided in the embodiments of this application;

[0027] Figure 3 This is a schematic diagram of a device for parsing and optimizing the transmission of ephemeris data, provided in an embodiment of this application. Detailed Implementation

[0028] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0029] The following description of some technologies involved in the embodiments of this application is provided to aid understanding and should be considered merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this application. Similarly, for clarity and brevity, some descriptions of well-known functions and structures are omitted in the following description.

[0030] Figure 1 This is a flowchart of a method for parsing and optimizing the transmission of ephemeris data provided in an embodiment of this application, including steps 101 to 108. Figure 1 This is merely one execution order shown in the embodiments of this application and does not represent the only execution order of a method for optimizing the parsing and transmission of ephemeris data. Where the final result can be achieved, Figure 1 The steps shown can be performed in parallel or in reverse order, as detailed below.

[0031] Step 101: Obtain the raw ephemeris data.

[0032] In this embodiment, raw ephemeris data is received via a receiver. Raw ephemeris data describes satellite orbit and position information, typically including satellite orbital parameters, time information, etc., enabling the calculation of the satellite's specific position in space. Optionally, the receiver includes a software-defined radio (SDR) platform supporting the BeiDou B1I band. Those skilled in the art can also crawl data from the IGS (Global Navigation Satellite System Data Platform) and use it as raw ephemeris data.

[0033] Step 102: Preprocess the raw ephemeris data to obtain clean data.

[0034] In this embodiment, an ephemeris data parsing and verification model is first constructed, and the acquired raw ephemeris data is input into the ephemeris data parsing and verification model for verification. The ephemeris data parsing and verification model includes a preprocessing module, an ephemeris calculation module, a CRC adjustment module, a CRC error detection module, a CRC verification and repair module, and an encapsulation module.

[0035] In this embodiment, step 102 is implemented by a preprocessing module. Specifically, the original ephemeris data is preprocessed to obtain clean data, including: defining a mask matrix and using the mask matrix to filter noise and redundant fields in the original ephemeris data to obtain clean data. The filtering of noise and redundant fields in the original ephemeris data using the mask matrix is ​​as follows:

[0036] D clean =D raw M mask ;

[0037] In the formula, D clean Indicates cleanliness data, D raw This represents the original ephemeris data. M represents element-wise multiplication. mask This represents the mask matrix.

[0038] In one possible implementation, a mask matrix is ​​used to filter noise and irrelevant redundant fields in the raw ephemeris data. This mask matrix can be dynamically configured according to the corresponding satellite protocol. For example, for the GPSL1 signal (a frequency band of the GPS system), the mask matrix preserves the raw ephemeris parameters of subframes 1-3. The frequency is 1575.42MHz, and it contains pseudo-random noise code (C / A code) and navigation messages, including satellite orbit parameters, clock corrections, and satellite health status. For the B1I signal (a signal band of the BeiDou system), the frequency is 1561.098MHz, and the mask preserves the orbit parameters and clock correction values ​​of the D1 navigation message.

[0039] For example, the data format of the raw ephemeris data can be standardized to facilitate time-frequency conversion of clean data in the subsequent ephemeris calculation module. The data format can be represented as a one-dimensional array, such as [1.5,4.2,5.8,8.1,9.3,...]. The preprocessed clean data is then transmitted to the ephemeris calculation module.

[0040] Step 103: Perform data conversion on the clean data to obtain the frequency domain signal, correct the frequency domain signal, and perform inverse data conversion on the corrected frequency domain signal to determine the reconstructed time domain signal of the clean data.

[0041] In this embodiment, step 103 is implemented by the ephemeris calculation module. Specifically, an optimized Fourier algorithm is constructed using the Fast Fourier Transform (FFT), frequency domain correction, and inverse Fourier Transform (IFFT) methods to accelerate the parsing of the preprocessed clean data. For example, an FPGA (Field-Programmable Gate Array) is selected as the processor to accelerate the optimized Fourier algorithm, with a main frequency of 200MHz.

[0042] The Fast Fourier Transform (FFT) method is used to convert cleanroom data into a time-frequency signal, obtaining a frequency-domain signal. Specifically, the FFT method converts cleanroom data from a time-domain signal to a frequency-domain signal. The FFT method is as follows:

[0043] ,

[0044] In the formula, Represents frequency domain signals, This represents performing a Fast Fourier Transform on the clean data, where N represents the total number of samples in the time domain, n represents the current sampling time point, and D... clean Indicates cleanliness data. denoted by , j represents the imaginary unit, i.e., the imaginary part of a complex number, and f represents the frequency component.

[0045] The total number of samples in the time domain signal is 1024. The frequency domain signal represents the amplitude and phase of the time domain signal at different frequency components. The frequency components are used to represent the frequency components in the current frequency domain signal.

[0046] In this embodiment, the inverse data transformation is performed on the corrected frequency domain signal to determine the reconstructed time domain signal of the clean data. This includes: performing frequency domain correction on the frequency domain signal based on a window function to obtain the corrected frequency domain signal; and performing inverse data transformation on the corrected frequency domain signal using the inverse Fourier transform method to obtain the reconstructed time domain signal of the clean data.

[0047] Specifically, in the ephemeris calculation process, the time-domain signal is converted into a frequency-domain signal. Due to the finite sampling of the signal and the periodic extension characteristics of the Fast Fourier Transform, spectral leakage may occur, causing signal aliasing between different frequency components and affecting the accuracy of the ephemeris calculation. Frequency domain correction, by applying window functions, can effectively suppress spectral leakage, thereby reducing the impact of frequency aliasing on the analytical results and obtaining a corrected frequency-domain signal. Simultaneously, it can also improve the accuracy of orbital parameter calculations. The window function is used to weight the frequency-domain signal; options include Hanning windows, Hamming windows, and cosine windows, as detailed below:

[0048] ,in, ,

[0049] In the formula, This represents the corrected frequency domain signal. Represents frequency domain signals, Represents the window function. Represents the cosine function. Indicates the current sampling time point. This represents the total number of samples in the time domain signal. Represents frequency components.

[0050] The corrected frequency domain signal is then subjected to inverse data transformation using the inverse Fourier transform method to obtain the reconstructed time domain signal of the clean data. The inverse Fourier transform method is as follows:

[0051] ,

[0052] In the formula, This represents the reconstructed time-domain signal. Indicates the inverse Fourier transform. This represents the corrected frequency domain signal.

[0053] The reconstructed time-domain signal is used to represent the time-domain signal corresponding to the analyzed orbital parameters. For example... Figure 2 The diagram illustrates a comparison of Fourier analysis efficiency. The horizontal axis represents the category of Fourier time-domain analysis methods, including traditional time-domain analysis methods and the optimized Fourier transform algorithm (FFT-Based Analysis) proposed in this application. The vertical axis represents the relative time consumption. Compared to traditional time-domain analysis, this application, by utilizing the optimized Fourier transform algorithm, can reduce the computational complexity of ephemeris calculation from... Down to The parsing speed is improved by more than 50%. The traditional Fourier time-domain parsing method takes T1 time, while the optimized Fourier transform method of this application takes T2 time, where T2 = 0.3T1. The traditional Fourier time-domain parsing method takes 12ms, while the optimized Fourier algorithm of this application takes 4ms.

[0054] In addition, the reconstructed time-domain signal is simultaneously input into the CRC adjustment module and the CRC error detection module to accelerate the verification efficiency of the reconstructed time-domain signal and enhance the robustness of the ephemeris data parsing and verification model.

[0055] Step 104: Adjust the CRC polynomial based on the real-time channel state of the reconstructed time-domain signal and determine the CRC check bit length.

[0056] In this embodiment, step 104 is implemented by the CRC adjustment module. Specifically, adjusting the CRC polynomial based on the real-time channel state of the reconstructed time-domain signal includes: determining the current channel state of the reconstructed time-domain signal. The current channel state includes the bit error rate (BER) and signal-to-noise ratio (SNR). A CRC polynomial is defined. The CRC polynomial is dynamically adjusted according to a polynomial switching strategy. The polynomial switching strategy includes: determining a channel threshold, comparing the current channel state with the channel threshold, and adjusting the polynomial state based on the comparison result. If the BER is greater than or equal to a first channel threshold, and / or the SNR is less than a second channel threshold, the CRC polynomial is switched to a higher-order polynomial. If the BER is less than the first channel threshold, and / or the SNR is greater than or equal to the second channel threshold, the CRC polynomial is switched to a lower-order polynomial.

[0057] For example, this application uses bit error rate (BER) and signal-to-noise ratio (SNR) as criteria for judging the current channel state. The specific formula for calculating the BER is as follows:

[0058] ,

[0059] In the formula, This indicates the bit error rate of the current channel. Indicates the number of error symbols. Indicates the total number of code elements.

[0060] The specific formula for calculating the signal-to-noise ratio is as follows:

[0061] ,

[0062] In the formula, This represents the signal-to-noise ratio of the current channel. Indicates signal power. Indicates noise power.

[0063] For example, the CRC polynomial based on CRC-32 can be expressed as:

[0064] .

[0065] in, This represents the CRC polynomial generated by the CRC algorithm. Used to mark the position of the parity bit, determining the key bit for the XOR operation, where k represents the parity bit at the marked position k. " " indicates the symbol for bitwise XOR in actual calculations.

[0066] When the bit error rate is greater than or equal to the first channel threshold within a continuous time period (e.g., 100ms) (e.g.) When, such as bit error rate And / or, signal-to-noise ratio Choose a higher-order polynomial, such as CRC-32, to enhance detection capabilities. Once the bit error rate has stabilized for a period of time, the stabilization time can be set to 500ms, which is less than the first channel threshold, and / or the signal-to-noise ratio is greater than or equal to the second channel threshold. In this case, choose a lower-order polynomial, such as CRC-16.

[0067] In this embodiment, a CRC generator is configured according to the current channel state, data packets are sent based on the CRC generator, and the verification result is determined. If the number of consecutive failures in the verification result is greater than or equal to the preset maximum number of failures, the check bits are extended, and error correction is performed until a normal verification result is obtained. If the number of consecutive failures in the verification result is less than the preset maximum number of failures, the CRC check bit length is determined. The CRC polynomial and the CRC check bit length are transmitted to the CRC error detection module.

[0068] Specifically, the sending end feeds back the real-time channel status to the dynamic CRC generator, generates data packets, and sends these packets to the receiving end. Upon receiving the data packets, the receiving end performs CRC calculations. If the locally generated checksum does not match the received checksum, the data packet is marked as a failed packet. The preset maximum number of failures is 3. If three consecutive data packets are failed, the checksum length is dynamically extended to L+8 to enhance fault tolerance. Simultaneously, forward error correction is initiated to correct errors in the data packets.

[0069] Specifically, at the transmitting end, the FEC (Forward Error Correction) encoder processes the reconstructed frequency domain signal, adds redundant information, and generates codewords. The CRC check result is then determined based on these codewords. The codewords contain both the reconstructed frequency domain signal and the redundant information.

[0070] At the receiving end, the decoder detects and corrects errors that occur during transmission based on the redundancy information in the codewords. If an error is detected, the decoder uses the redundancy information to correct it and recover the reconstructed frequency domain signal. Assume the reconstructed frequency domain signal is divided into the following symbols: These correspond to "01001000", "01100101", "01101100", "01101100", and "01101111" respectively. Using Reed-Solomon coding, four redundant symbols are obtained: The final generated codeword is the reconstructed frequency domain signal. and redundant symbols The combination of symbols in the codeword. Assume that during transmission, the symbols in the codeword... and Errors occurred due to interference, and it became and After receiving the codeword, the decoder first checks for redundant information in the codeword. Using the Reed-Solomon decoding algorithm, it can then determine... and An error occurred, i.e., a failed data packet. Redundant symbols were used. And the correct The decoder can correct and The error.

[0071] The CRC adjustment module is designed to automatically switch polynomials in the corresponding application scenarios to balance the reliability and efficiency of the verification.

[0072] Step 105: Based on the adjusted CRC polynomial and CRC check bit length, determine the CRC remainder value and perform error detection based on the CRC remainder value.

[0073] In this embodiment, step 105 is implemented by the CRC error detection module. Specifically, the CRC remainder value is determined by performing a modulo-2 division between the reconstructed time-domain signal and the adjusted CRC polynomial, as follows:

[0074] ,

[0075] In the formula, This represents the CRC remainder value. This represents the reconstructed time-domain signal. This represents the modulo-2 division operator. This represents the adjusted CRC polynomial.

[0076] Step 106: Perform error repair based on the error detection results to obtain repaired ephemeris data.

[0077] In this embodiment, step 106 is implemented by the CRC check and repair module. Specifically, if the CRC remainder is equal to zero, the repaired ephemeris data is obtained. If the CRC remainder is not equal to zero, the reconstructed time-domain signal is detected and repaired using an error repair strategy to obtain the repaired ephemeris data. The error repair strategy includes: if the current scenario is a non-real-time scenario, requesting retransmission of the reconstructed time-domain signal through a feedback channel; if the current scenario is a real-time scenario, locating and repairing the transmission error position of the reconstructed time-domain signal to obtain the repaired ephemeris data.

[0078] Specifically, when the CRC remainder value If the transmission is successful, it is considered correct data and transmitted to the encapsulation module for encapsulation. When the CRC remainder value... In real-time scenarios, error correction strategies can be dynamically adjusted more accurately and efficiently based on the current context. For non-real-time scenarios, a retransmission of the reconstructed time-domain signal is requested via the feedback channel.

[0079] For real-time scenarios, Hamming code error correction is used to locate errors in the reconstructed time-domain signal, and the errors are repaired using an XOR operation. The specific details of Hamming code error correction are as follows:

[0080] ,in, ,

[0081] In the formula, This indicates that the ephemeris data has been repaired. E represents the reconstructed time-domain signal. mask Indicates the error mask. argmin represents the bitwise XOR operator. E Indicates from all possible China has determined to make The minimum E represents the optimal solution, where E represents the optimization variable of the error mask.

[0082] The error mask indicates the location and type of the error, and is used together with the bitwise XOR operator for error location and repair. Experiments show that the success rate of traditional static CRC verification, such as CRC-16, is 92%, while the success rate of the dynamic CRC verification in this application is 98.5%.

[0083] Step 107: Determine if the result of the modulo-2 division of the repaired ephemeris data is zero. If the result of the modulo-2 division of the repaired ephemeris data (i.e., the CRC remainder) is zero, the transmission is error-free and can be encapsulated; proceed to step 108. If the result of the modulo-2 division of the repaired ephemeris data (i.e., the CRC remainder) is not zero, the transmission still contains errors; continue executing steps 103 to 106 until the result of the modulo-2 division of the CRC remainder is zero.

[0084] Step 108: Encapsulate the repaired ephemeris data and send it to the navigation terminal. Specifically, the verified repair data is encapsulated according to a protocol and output to the navigation terminal. This application exemplarily uses NMEA-0183 (a communication protocol in the field of navigation and positioning) to encapsulate the repaired ephemeris data, which uses ASCII character encoding. The data is initially transmitted in text format.

[0085] For example, one example of a packaging format is as follows: .

[0086] Interference and errors are inevitable during data transmission, which may lead to the corruption of one or more frames transmitted on the link (bit errors, such as 0 becoming 1 or 1 becoming 0), resulting in the receiver receiving incorrect data packets. This application utilizes an optimized Fourier algorithm to quickly parse the original ephemeris data, effectively suppressing spectral leakage and sudden noise interference, ensuring the integrity and real-time performance of the original ephemeris data in highly dynamic scenarios. Furthermore, CRC cyclic redundancy encoding is used to dynamically adjust the polynomial and check bit length, resulting in strong error detection capabilities, low overhead, and ease of implementation with encoders and detection circuits, optimizing the balance between check strength and communication efficiency. Simultaneously, it further enhances the robustness of the ephemeris data parsing and verification model in complex channel environments, providing technical support for the efficient and reliable operation of satellite navigation systems such as BeiDou and GPS, and is particularly suitable for fields with stringent requirements for data accuracy and timeliness, such as low-Earth orbit satellite communication and real-time positioning services.

[0087] While this application provides the method operation steps as described in the embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-inventive labor. The order of steps listed in this embodiment is merely one possible execution order among many and does not represent the only execution order. In actual device or client product execution, the methods shown in this embodiment or the accompanying drawings can be executed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment).

[0088] like Figure 3 As shown in the figure, this application embodiment also provides a parsing and transmission optimization device 300 based on ephemeris data. The device includes: a repair module 301 and an encapsulation module 307. The repair module 301 includes a preprocessing module 302, an ephemeris calculation module 303, a CRC adjustment module 304, a CRC error detection module 305, and a CRC verification and repair module 306, as detailed below.

[0089] The repair module 301 is used to input the acquired raw ephemeris data into the ephemeris data parsing and verification model for verification until the result of the modulo-2 division of the repaired ephemeris data is zero. The ephemeris data parsing and verification model includes a preprocessing module, an ephemeris calculation module, a CRC adjustment module, a CRC error detection module, and a CRC verification and repair module, which are connected and transmit data sequentially.

[0090] The preprocessing module 302 is used to preprocess the raw ephemeris data to obtain clean data.

[0091] The ephemeris calculation module 303 is used to perform data conversion on the clean data to obtain a frequency domain signal, correct the frequency domain signal, and perform inverse data conversion on the corrected frequency domain signal to determine the reconstructed time domain signal of the clean data.

[0092] The CRC adjustment module 304 is used to adjust the CRC polynomial based on the real-time channel state of the reconstructed time-domain signal and determine the CRC check bit length.

[0093] The CRC error detection module 305 is used to determine the CRC remainder value based on the adjusted CRC polynomial and the CRC check bit length, and to perform error detection based on the CRC remainder value.

[0094] The CRC check and repair module 306 is used to repair errors based on the error detection results and obtain repaired ephemeris data.

[0095] The encapsulation module 307 is used to encapsulate the repaired ephemeris data and send it to the navigation terminal.

[0096] Some modules in the apparatus described in this application can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, classes, etc., that perform a specific task or implement a specific abstract data type. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0097] The apparatus or module described in the above embodiments can be implemented by a computer chip or physical entity, or by a product with a certain function. For ease of description, the above apparatus is described by dividing it into various modules according to their functions. When implementing the embodiments of this application, the functions of each module can be implemented in one or more software and / or hardware. Of course, a module that implements a certain function can also be implemented by combining multiple sub-modules or sub-units.

[0098] The methods, apparatus, or modules described in this application can be implemented in a computer-readable program code manner. The controller can be implemented in any suitable manner, such as a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicon Labs C8051F320. A memory controller can also be implemented as part of the control logic of a memory. Those skilled in the art will also recognize that, in addition to implementing the controller in purely computer-readable program code manner, the same functionality can be achieved by logically programming the method steps to make the controller take the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the means included within it for implementing various functions can also be considered as structures within the hardware component. Alternatively, the device used to implement various functions can be viewed as either a software module that implements the method or a structure within a hardware component.

[0099] This application also provides an apparatus for executing a method for parsing and transmitting ephemeris data, the apparatus comprising: a processor; a memory for storing processor-executable instructions; wherein, when the processor executes the executable instructions, it implements the method described in this application.

[0100] This application also provides a non-volatile computer-readable storage medium storing a computer program or instructions thereon, which, when executed, enables the method described in this application embodiment to be implemented.

[0101] Furthermore, in the various embodiments of this application, each functional module can be integrated into one processing module, or each module can exist independently, or two or more modules can be integrated into one module.

[0102] The aforementioned storage media include, but are not limited to, Random Access Memory (RAM), Read-Only Memory (ROM), Cache, Hard Disk Drive (HDD), or Memory Card. The memory can be used to store computer program instructions.

[0103] As can be seen from the above description of the embodiments, those skilled in the art can clearly understand that this application can be implemented by means of software plus necessary hardware. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product, or it can be embodied in the process of data migration. The computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, mobile terminal, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this application.

[0104] The various embodiments described in this specification are presented in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on its differences from other embodiments. All or part of this application can be used in numerous general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, mobile communication terminals, multiprocessor systems, microprocessor-based systems, programmable electronic devices, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices, etc.

[0105] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit this application. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of this application.

Claims

1. A method for optimizing the parsing and transmission of ephemeris data, characterized in that, include: The acquired raw ephemeris data is input into the ephemeris data parsing and verification model for verification until the result of the modulo-2 division of the ephemeris data is zero. The ephemeris data parsing and verification model includes a preprocessing module, an ephemeris calculation module, a CRC adjustment module, a CRC error detection module, and a CRC verification and repair module, which are connected in sequence and transmit data. The preprocessing module is used to preprocess the raw ephemeris data to obtain clean data; The ephemeris calculation module is used to perform data conversion on the cleanliness data to obtain a frequency domain signal, correct the frequency domain signal, and perform inverse data conversion on the corrected frequency domain signal to determine the reconstructed time domain signal of the cleanliness data. The CRC adjustment module is used to adjust the CRC polynomial based on the real-time channel state of the reconstructed time-domain signal and determine the CRC check bit length. The step of adjusting the CRC polynomial based on the real-time channel state of the reconstructed time-domain signal includes: determining the current channel state of the reconstructed time-domain signal; wherein the current channel state includes bit error rate and signal-to-noise ratio; defining the CRC polynomial; and dynamically adjusting the CRC polynomial according to a polynomial switching strategy; wherein the polynomial switching strategy includes: determining a channel threshold, comparing the current channel state with the channel threshold, and adjusting the polynomial state based on the comparison result; if the bit error rate is greater than or equal to a first channel threshold among the channel thresholds, and / or the signal-to-noise ratio is less than a second channel threshold among the channel thresholds, then switching the CRC polynomial to a higher-order polynomial; if the bit error rate is less than the first channel threshold among the channel thresholds, and / or the signal-to-noise ratio is greater than or equal to the second channel threshold among the channel thresholds, then switching the CRC polynomial to a lower-order polynomial. The process of determining the CRC checksum length includes: configuring a CRC generator according to the current channel state, sending data packets based on the CRC generator, and determining the checksum result; if the number of consecutive failures of the checksum result is greater than or equal to the preset maximum number of failures, then performing checksum extension and error correction processing until a normal checksum result is obtained; if the number of consecutive failures of the checksum result is less than the preset maximum number of failures, then determining the CRC checksum length; and transmitting the CRC polynomial and the CRC checksum length to the CRC error detection module. The CRC error detection module is used to determine the CRC remainder value based on the adjusted CRC polynomial and the CRC check bit length, and to perform error detection based on the CRC remainder value. The CRC check and repair module is used to repair errors based on the error detection results and obtain the repaired ephemeris data; The repaired ephemeris data is encapsulated and sent to the navigation terminal.

2. The method according to claim 1, characterized in that, The preprocessing of the original ephemeris data to obtain clean data includes: Define a mask matrix and use the mask matrix to filter out noise and redundant fields in the original ephemeris data to obtain the clean data; The step of using the mask matrix to filter noise and redundant fields in the original ephemeris data is as follows: D clean =D raw M mask ; In the formula, D clean D represents the cleanliness data. raw This refers to the original ephemeris data. M represents element-wise multiplication. mask This represents the mask matrix.

3. The method according to claim 1, characterized in that, The step of converting the clean data to obtain a frequency domain signal includes: The clean data is converted into a time-frequency signal using the Fast Fourier Transform method to obtain a frequency domain signal.

4. The method according to claim 1, characterized in that, The step of performing inverse data transformation on the corrected frequency domain signal to determine the reconstructed time domain signal of the clean data includes: The frequency domain signal is corrected by performing a window function to obtain the corrected frequency domain signal. The modified frequency domain signal is subjected to inverse data transformation using the inverse Fourier transform method to obtain the reconstructed time domain signal of the clean data.

5. The method according to claim 1, characterized in that, The determination of the CRC remainder value based on the adjusted CRC polynomial and the CRC check bit length includes: Based on the reconstructed time-domain signal and the adjusted CRC polynomial, a modulo-2 division is performed to determine the CRC remainder value, as follows: ； In the formula, This represents the CRC remainder value. This represents the reconstructed time-domain signal. This represents the modulo-2 division operator. This represents the adjusted CRC polynomial.

6. The method according to claim 1, characterized in that, The error detection based on the CRC remainder value includes: If the CRC remainder is equal to zero, then the repaired ephemeris data is obtained; If the CRC remainder is not equal to zero, the reconstructed time-domain signal is detected and repaired using an error repair strategy to obtain the repaired ephemeris data; wherein, the error repair strategy includes: If the current scenario is a non-real-time scenario, then a request to retransmit the reconstructed time-domain signal is made through the feedback channel; If the current scenario is a real-time scenario, the transmission error location of the reconstructed time-domain signal is located and repaired to obtain the repaired ephemeris data.

7. The method according to claim 6, characterized in that, The step of locating and repairing the transmission error position of the reconstructed time-domain signal includes: ;in, ; In the formula, This indicates the repaired ephemeris data. E represents the reconstructed time-domain signal. mask Indicates the error mask. argmin represents the bitwise XOR operator. E Indicates from all possible China has determined to make The minimum E represents the optimal solution, where E represents the optimization variable of the error mask.

8. A device for parsing and optimizing the transmission of ephemeris data, characterized in that, include: The repair module is used to input the acquired raw ephemeris data into the ephemeris data parsing and verification model for verification until the result of the modulo-2 division operation of the repaired ephemeris data is zero; wherein, the ephemeris data parsing and verification model includes a preprocessing module, an ephemeris calculation module, a CRC adjustment module, a CRC error detection module and a CRC verification and repair module that are connected in sequence and transmit data. The preprocessing module is used to preprocess the raw ephemeris data to obtain clean data; The ephemeris calculation module is used to perform data conversion on the cleanliness data to obtain a frequency domain signal, correct the frequency domain signal, and perform inverse data conversion on the corrected frequency domain signal to determine the reconstructed time domain signal of the cleanliness data. The CRC adjustment module is used to adjust the CRC polynomial based on the real-time channel state of the reconstructed time-domain signal and determine the CRC check bit length. The adjustment of the CRC polynomial based on the real-time channel state of the reconstructed time-domain signal includes: determining the current channel state of the reconstructed time-domain signal; wherein the current channel state includes bit error rate (BER) and signal-to-noise ratio (SNR); defining the CRC polynomial; and dynamically adjusting the CRC polynomial according to a polynomial switching strategy. The polynomial switching strategy includes: determining a channel threshold; comparing the current channel state with the channel threshold; and adjusting the polynomial state based on the comparison result; if the BER is greater than or equal to a first channel threshold and / or the SNR is less than a second channel threshold, then the CRC polynomial is switched to a higher-order polynomial; if the BER is less than the first channel threshold and / or the SNR is greater than or equal to the second channel threshold, then the CRC polynomial is switched to a lower-order polynomial. The process of determining the CRC checksum length includes: configuring a CRC generator according to the current channel state, sending data packets based on the CRC generator, and determining the checksum result; if the number of consecutive failures of the checksum result is greater than or equal to the preset maximum number of failures, then performing checksum extension and error correction processing until a normal checksum result is obtained; if the number of consecutive failures of the checksum result is less than the preset maximum number of failures, then determining the CRC checksum length; and transmitting the CRC polynomial and the CRC checksum length to the CRC error detection module. The CRC error detection module is used to determine the CRC remainder value based on the adjusted CRC polynomial and the CRC check bit length, and to perform error detection based on the CRC remainder value. The CRC check and repair module is used to repair errors based on the error detection results and obtain repaired ephemeris data; The encapsulation module is used to encapsulate the repaired ephemeris data and send it to the navigation terminal.

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