A Beidou data communication method and system based on the HarmonyOS

Abstract

The present invention discloses a Beidou data communication method and system based on the HarmonyOS. The method includes: obtaining Beidou data, encoding the Beidou data to generate encoded Beidou data, generating a check code for each of the encoded Beidou data, and simultaneously sending the encoded Beidou data and the check code to a Beidou terminal; setting a first error correction model to perform error correction on the encoded Beidou data to generate corrected first Beidou data, setting a second error correction model to perform error correction on the encoded Beidou data to generate corrected second Beidou data, wherein the first error correction model and the second error correction model include a plurality of error correction algorithms; weighted averaging the corrected first Beidou data and the corrected second Beidou data, and using the result as the corrected final Beidou data received by the Beidou terminal based on the HarmonyOS to complete Beidou data communication.

Description

Technical Field

[0001] The present invention belongs to the technical field of Beidou data communication, and more specifically, relates to a Beidou data communication method and system based on the HarmonyOS operating system. Background Art

[0002] Beidou positioning data usually includes satellite information received by a satellite signal receiver, such as the position, speed, time, etc. of the satellite, as well as the position, time, etc. of the receiver itself. This data can be used in application fields such as positioning, navigation, map making, navigation, etc.

[0003] The accuracy and reliability of Beidou positioning data depend on various factors, including the performance of the receiver, the surrounding environment, the satellite distribution, etc. Usually, Beidou positioning data can provide positioning accuracy from meters to centimeters, suitable for different application scenarios, such as vehicle navigation, ship positioning, logistics tracking, etc.

[0004] However, in the prior art, when Beidou data is communicated and transmitted, errors or data errors may occur due to some reasons of Beidou data, such as the complex types of data included in Beidou data, environmental factors, etc. Therefore, it is necessary to correct the errors of Beidou data during the Beidou data communication process, so as to improve the accuracy of Beidou data. Summary of the Invention

[0005] To solve the above technical problems, the present invention proposes a Beidou data communication method based on the HarmonyOS operating system, including:

[0006] Obtain Beidou data, encode the Beidou data to generate encoded Beidou data, generate a check code for each encoded Beidou data, and send the encoded Beidou data and the check code to a Beidou terminal simultaneously;

[0007] Set a first error correction model to correct the errors of the encoded Beidou data to generate a first corrected Beidou data, set a second error correction model to correct the errors of the encoded Beidou data to generate a second corrected Beidou data, wherein the first error correction model and the second error correction model include multiple error correction algorithms;

[0008] Perform weighted averaging on the first corrected Beidou data and the second corrected Beidou data, and use the result as the finally corrected Beidou data received by the Beidou terminal based on the HarmonyOS operating system to complete Beidou data communication.

[0009] Further, the first error correction model includes:

[0010] Ensemble(D′, C) = mode{h 1(D′, C), h 2 (D′, C),..., h N (D′, C)}

[0011] Among them, Ensemble(D′, C) is the first error correction model for correcting the encoded Beidou data C into the corrected first Beidou data D′, h N is the Nth error correction algorithm, and mode{} is the mode operation.

[0012] Furthermore, the second error correction model includes:

[0013]

[0014] Among them, Ensemble′(D′, C) is the second error correction model for correcting the encoded Beidou data C into the corrected Beidou data D′, C is the encoded Beidou data, and D″ is the corrected second Beidou data.

[0015] Furthermore, calculate the confidence of each of the error correction algorithms, and preferentially use the error correction algorithm with a high confidence, including:

[0016] T(h i ) = σ(W L ·σ(W L-1 ·...σ(W 1 ·[X i , Y i + b 1 )... + b L-1 ) + b L )

[0017] Among them, T(h i ) is the confidence of the ith error correction algorithm h i , σ is the activation function, and the activation function is the Sigmoid function or the ReLU function, W L is the weight of the Lth layer neural network, b L is the bias of the Lth layer neural network, X i is the historical correction accuracy of the ith error correction algorithm, and Y i is the current correction accuracy of the ith error correction algorithm.

[0018] Furthermore, the error correction algorithm h i includes: cyclic redundancy check algorithm, convolutional code algorithm, Hamming code algorithm.

[0019] The present invention also proposes a Beidou data communication system based on the HarmonyOS, including:

[0020] A generation and encoding module, configured to obtain Beidou data, encode the Beidou data to generate encoded Beidou data, generate a check code for each of the encoded Beidou data, and send the encoded Beidou data and the check code to a Beidou terminal simultaneously;

[0021] A correction module, configured to set a first error correction model to correct the encoded Beidou data to generate a first corrected Beidou data, and set a second error correction model to correct the encoded Beidou data to generate a second corrected Beidou data, wherein the first error correction model and the second error correction model include multiple error correction algorithms;

[0022] A communication module, configured to perform weighted averaging on the first corrected Beidou data and the second corrected Beidou data, and use the result as the finally corrected Beidou data received by a Beidou terminal based on the HarmonyOS to complete Beidou data communication.

[0023] Further, the first error correction model includes:

[0024] Ensemble(D′, C) = mode{h 1 (D′, C), h 2 (D′, C),..., h N (D′, C)}

[0025] wherein, Ensemble(D′, C) is the first error correction model for correcting the encoded Beidou data C to the first corrected Beidou data D′, h N is the Nth error correction algorithm, and mode{} is the mode operation.

[0026] Further, the second error correction model includes:

[0027]

[0028] wherein, Ensemble′(D′, C) is the second error correction model for correcting the encoded Beidou data C to the corrected Beidou data D′, C is the encoded Beidou data, and D″ is the second corrected Beidou data.

[0029] Further, calculating the confidence of each of the error correction algorithms and preferentially using the error correction algorithm with a high confidence includes:

[0030] T(h i ) = σ(W L ·σ(W L-1 ·...σ(W 1 ·[X i , Y i +b1 )... + b L-1 ) + b L )

[0031] Among them, T(h i ) is the confidence level of the i-th error correction algorithm h i , σ is the activation function, and the activation function is the Sigmoid function or the ReLU function. W L is the weight of the L-th layer neural network, b L is the bias of the L-th layer neural network, X i is the historical correction accuracy of the i-th error correction algorithm, and Y i is the current correction accuracy of the i-th error correction algorithm.

[0032] Furthermore, the error correction algorithm h i includes: cyclic redundancy check algorithm, convolutional code algorithm, Hamming code algorithm.

[0033] Compared with the prior art by the above technical solution conceived by the present invention, the following beneficial effects are obtained:

[0034] The present invention obtains Beidou data, encodes the Beidou data to generate encoded Beidou data, generates a check code for each of the encoded Beidou data, and sends the encoded Beidou data and the check code to a Beidou terminal at the same time; sets a first error correction model to correct errors in the encoded Beidou data to generate corrected first Beidou data, sets a second error correction model to correct errors in the encoded Beidou data to generate corrected second Beidou data, wherein the first error correction model and the second error correction model include multiple error correction algorithms; performs weighted averaging on the corrected first Beidou data and the corrected second Beidou data, and uses the result as the corrected final Beidou data received by the Beidou terminal based on the HarmonyOS to complete Beidou data communication. Through the above technical solution, the present invention can correct errors in the encoded Beidou data during the Beidou data communication process, thereby improving the accuracy of the Beidou data received by the Beidou terminal. Description of the Drawings

[0035] Figure 1 is the flowchart of the method in Embodiment 1 of the present invention;

[0036] Figure 2 is the system structure diagram of Embodiment 2 of the present invention. Detailed Embodiments

[0037] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings of the specification and specific embodiments.

[0038] The method provided by the present invention can be implemented in the following terminal environment. The terminal may include one or more of the following components: a processor, a storage medium, and a display screen. Among them, at least one instruction is stored in the storage medium, and the instruction is loaded and executed by the processor to implement the method described in the following embodiments.

[0039] The processor may include one or more processing cores. The processor uses various interfaces and lines to connect various parts within the entire terminal, and by running or executing instructions, programs, code sets, or instruction sets stored in the storage medium, as well as calling data stored in the storage medium, it executes various functions of the terminal and processes data.

[0040] The storage medium may include a random access memory (RAM), or may also include a read-only memory (ROM). The storage medium can be used to store instructions, programs, code, code sets, or instructions.

[0041] The display screen is used to display the interaction cross-sections of various application programs.

[0042] In all subscripts in the formula of the present invention, they are only for distinguishing parameters and have no actual meaning.

[0043] In addition, those skilled in the art can understand that the structure of the above terminal does not constitute a limitation on the terminal. The terminal may include more or fewer components, or combine certain components, or have different component arrangements. For example, the terminal may also include components such as a radio frequency circuit, an input unit, a sensor, an audio circuit, and a power supply, which will not be elaborated here.

[0044] Embodiment 1

[0045] As Figure 1 shown, the embodiment of the present invention provides a Beidou data communication method based on the HarmonyOS, including:

[0046] Step 101, obtain Beidou data, encode the Beidou data to generate encoded Beidou data, generate a check code for each of the encoded Beidou data, and send the encoded Beidou data and the check code to the Beidou terminal at the same time;

[0047] Step 102, set a first error correction model to correct errors in the encoded Beidou data to generate corrected first Beidou data, set a second error correction model to correct errors in the encoded Beidou data to generate corrected second Beidou data, where the first error correction model and the second error correction model include multiple error correction algorithms;

[0048] Specifically, the first error correction model includes:

[0049] Ensemble(D′, C) = mode{h 1 (D′, C), h 2 (D′, C),..., h N (D′, C)}

[0050] where Ensemble(D′, C) is the first error correction model that corrects the encoded Beidou data c to the corrected first Beidou data D′, h N is the Nth error correction algorithm, and mode{} is the mode operation.

[0051] Specifically, the second error correction model includes:

[0052]

[0053] where Ensemble′(D′, C) is the second error correction model that corrects the encoded Beidou data C to the corrected Beidou data D′, C is the encoded Beidou data, and D″ is the corrected second Beidou data.

[0054] Specifically, calculating the confidence of each of the error correction algorithms and preferentially using the error correction algorithm with a high confidence includes:

[0055] T(h i ) = σ(W L ·σ(W L-1 ·...σ(W 1 ·[X i , Y i + b 1 )... + b L-1 ) + b L )

[0056] where T(h i ) is the confidence of the ith error correction algorithm h i , σ is the activation function, the activation function is the Sigmoid function or the ReLU function, W L is the weight of the Lth neural network layer, b L is the bias of the Lth neural network layer, X i is the historical correction accuracy of the ith error correction algorithm, and Y i is the current correction accuracy of the ith error correction algorithm.

[0057] Specifically, the error correction algorithm h i includes: cyclic redundancy check algorithm, convolutional code algorithm, Hamming code algorithm.

[0058] Step 103: Weight-average the corrected first Beidou data and the corrected second Beidou data, and use the result as the corrected final Beidou data received by the Beidou terminal based on the HarmonyOS to complete Beidou data communication.

[0059] Embodiment 2

[0060] As Figure 2 shown, an embodiment of the present invention further provides a Beidou data communication system based on the HarmonyOS, including:

[0061] A generation and encoding module, configured to obtain Beidou data, encode the Beidou data to generate encoded Beidou data, generate a check code for each of the encoded Beidou data, and send the encoded Beidou data and the check code to the Beidou terminal simultaneously;

[0062] A correction module, configured to set a first error correction model to correct the encoded Beidou data to generate corrected first Beidou data, set a second error correction model to correct the encoded Beidou data to generate corrected second Beidou data, wherein the first error correction model and the second error correction model include multiple error correction algorithms;

[0063] Specifically, the first error correction model includes:

[0064] Ensemble(D′, C) = mode{h 1 (D′, C), h 2 (D′, C),..., h N (D′, C)}

[0065] where Ensemble(D′, C) is the first error correction model that corrects the encoded Beidou data C to the corrected first Beidou data D′, h N is the Nth error correction algorithm, and mode{} is the mode operation.

[0066] Specifically, the second error correction model includes:

[0067]

[0068] where Ensemble′(D′, C) is the second error correction model that corrects the encoded Beidou data C to the corrected Beidou data D′, C is the encoded Beidou data, and D″ is the corrected second Beidou data.

[0069] Specifically, calculate the confidence of each error correction algorithm, and preferentially use the error correction algorithm with a high confidence, including:

[0070] T(hi ) = σ(W L ·σ(W L-1 ·...σ(W 1 ·[X i , Y i + b 1 )... + b L-1 ) + b L )

[0071] where T(h i ) is the confidence level of the i-th error correction algorithm h i , σ is the activation function, and the activation function is the Sigmoid function or the ReLU function. W L is the weight of the L-th layer neural network, b L is the bias of the L-th layer neural network, X i is the historical correction accuracy of the i-th error correction algorithm, and Y i is the current correction accuracy of the i-th error correction algorithm.

[0072] Specifically, the error correction algorithm h i includes: cyclic redundancy check algorithm, convolutional code algorithm, Hamming code algorithm.

[0073] The communication module is used to weighted average the corrected first Beidou data and the corrected second Beidou data, and use the result as the corrected final Beidou data received by the Beidou terminal based on the HarmonyOS to complete Beidou data communication.

[0074] Embodiment 3

[0075] The embodiment of the present invention also proposes a storage medium storing multiple instructions, and the instructions are used to implement the described Beidou data communication method based on the HarmonyOS.

[0076] Optionally, in this embodiment, the above storage medium can be located in any computer terminal in the computer terminal group in the computer network, or in any mobile terminal in the mobile terminal group.

[0077] Optionally, in this embodiment, the storage medium is set to store program codes for performing the following steps: Step 101, obtain Beidou data, encode the Beidou data to generate encoded Beidou data, generate a check code for each encoded Beidou data, and send the encoded Beidou data and the check code to the Beidou terminal at the same time;

[0078] Step 102, set up a first error correction model to correct the encoded Beidou data, generating the corrected first Beidou data, and set up a second error correction model to correct the encoded Beidou data, generating the corrected second Beidou data, where the first error correction model and the second error correction model include multiple error correction algorithms;

[0079] Specifically, the first error correction model includes:

[0080] Ensemble(D′, C) = mode{h 1 (D′, C), h 2 (D′, C),..., h N (D′, C)}

[0081] where Ensemble(D′, C) is the first error correction model that corrects the encoded Beidou data C to the corrected first Beidou data D′, h N is the Nth error correction algorithm, and mode{} is the mode operation.

[0082] Specifically, the second error correction model includes:

[0083]

[0084] where Ensemble′(D′, C) is the second error correction model that corrects the encoded Beidou data C to the corrected Beidou data D′, C is the encoded Beidou data, and D″ is the corrected second Beidou data.

[0085] Specifically, calculate the confidence of each error correction algorithm, and preferentially use the error correction algorithm with a high confidence, including:

[0086] T(h i ) = σ(W L ·σ(W L-1 ·...σ(W 1 ·[X i , Y i + b 1 )... + b L-1 ) + b L )

[0087] where T(h i ) is the confidence of the ith error correction algorithm h i , σ is the activation function, the activation function is the Sigmoid function or the ReLU function, W L is the weight of the Lth layer neural network, b L is the bias of the Lth layer neural network, and X iis the historical correction accuracy rate of the i-th error correction algorithm, Y i is the current correction accuracy rate of the i-th error correction algorithm.

[0088] Specifically, the error correction algorithm h i includes: cyclic redundancy check algorithm, convolutional code algorithm, Hamming code algorithm.

[0089] Step 103: Weight-average the corrected first Beidou data and the corrected second Beidou data, and use the result as the corrected final Beidou data received by the Beidou terminal based on the HarmonyOS to complete Beidou data communication.

[0090] Embodiment 4

[0091] An embodiment of the present invention further provides an electronic device, including a processor and a storage medium connected to the processor. The storage medium stores multiple instructions, and the instructions can be loaded and executed by the processor so that the processor can execute a Beidou data communication method based on the HarmonyOS.

[0092] Specifically, the electronic device in this embodiment may be a computer terminal, and the computer terminal may include: one or more processors and a storage medium.

[0093] Among them, the storage medium can be used to store software programs and modules, such as a Beidou data communication method based on the HarmonyOS in the embodiment of the present invention, corresponding program instructions / modules. The processor runs the software programs and modules stored in the storage medium to execute various functional applications and data processing, that is, to implement the above-mentioned Beidou data communication method based on the HarmonyOS. The storage medium may include a high-speed random storage medium, and may also include a non-volatile storage medium, such as one or more magnetic storage systems, flash memories, or other non-volatile solid-state storage media. In some instances, the storage medium may further include a storage medium remotely set relative to the processor, and these remote storage media can be connected to the terminal through a network. Examples of the above network include but are not limited to the Internet, enterprise intranet, local area network, mobile communication network and their combinations.

[0094] The processor can call the information and application programs stored in the storage medium through the transmission system to execute the steps: Step 101, obtain Beidou data, encode the Beidou data to generate encoded Beidou data, generate a check code for each encoded Beidou data, and send the encoded Beidou data and the check code to the Beidou terminal at the same time;

[0095] Step 102, set the first error correction model to correct the encoded Beidou data, generating the corrected first Beidou data, and set the second error correction model to correct the encoded Beidou data, generating the corrected second Beidou data, where the first error correction model and the second error correction model include multiple error correction algorithms;

[0096] Specifically, the first error correction model includes:

[0097] Ensemble(D′, C) = mode{h 1 (D′, C), h 2 (D′, C),..., h N (D′, C)}

[0098] Among them, Ensemble(D′, C) is the first error correction model that corrects the encoded Beidou data c into the corrected first Beidou data D′, h N is the Nth error correction algorithm, and mode{} is the mode operation.

[0099] Specifically, the second error correction model includes:

[0100]

[0101] Among them, Ensemble′(D′, C) is the second error correction model that corrects the encoded Beidou data C into the corrected Beidou data D′, C is the encoded Beidou data, and D″ is the corrected second Beidou data.

[0102] Specifically, calculate the confidence of each error correction algorithm, and preferentially use the error correction algorithm with a high confidence, including:

[0103] T(h i ) = σ(W L ·σ(W L-1 ·...σ(W 1 ·[X i , Y i + b 1 )... + b L-1 ) + b L )

[0104] Among them, T(h i ) is the confidence of the i-th error correction algorithm h i , σ is the activation function, the activation function is the Sigmoid function or the ReLU function, WL is the weight of the L-th layer neural network, b L is the bias of the L-th layer neural network, X iis the historical correction accuracy of the i-th error correction algorithm, Y i is the current correction accuracy of the i-th error correction algorithm.

[0105] Specifically, the error correction algorithm h i includes: cyclic redundancy check algorithm, convolutional code algorithm, Hamming code algorithm.

[0106] Step 103: Weight-average the corrected first Beidou data and the corrected second Beidou data, and use the result as the corrected final Beidou data received by the Beidou terminal based on the HarmonyOS to complete Beidou data communication.

[0107] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages and disadvantages of the embodiments.

[0108] In the above embodiments of the present invention, the descriptions of each embodiment have their own emphases. For the parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0109] In the several embodiments provided by the present invention, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the system embodiments described above are only illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point, the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of the units or modules can be in an electrical or other form.

[0110] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0111] In addition, the functional units in each embodiment of the present invention can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0112] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The foregoing storage medium includes: various media that can store program codes, such as USB flash drives, read-only storage media (ROM, Read-Only Memory), random access storage media (RAM, Random Access Memory), mobile hard disks, magnetic disks, or optical discs.

[0113] Obviously, the above embodiments are merely examples given for clear illustration and are not limitations on the implementation manners. For those of ordinary skill in the art, other different forms of changes or variations can be made based on the above description. It is not necessary and impossible to enumerate all the implementation manners here. The obvious changes or variations derived therefrom still fall within the protection scope of the present invention.

Claims

1. A Beidou data communication method based on Hongmeng operating system, characterized in that: include: Acquire Beidou data, encode the Beidou data to generate encoded Beidou data, generate a check code for each of the encoded Beidou data, and send the encoded Beidou data and the check code to a Beidou terminal at the same time; Setting a first error correction model, performing error correction on the encoded Beidou data to generate corrected first Beidou data, setting a second error correction model, performing error correction on the encoded Beidou data to generate corrected second Beidou data, wherein the first error correction model and the second error correction model include multiple error correction algorithms; The first error correction model includes: Ensemble(D′,C)=mode{h1(D′,C),h2(D′,C),...,h N (D′,C)} Ensemble(D′, C) is the first error correction model that corrects the encoded Beidou data C to the corrected first Beidou data D′, h N is the Nth error correction algorithm, mode{} is the majority operation; The second error correction model includes: Wherein, Ensemble′(D′, C) is a second error correction model that corrects the encoded Beidou data C to the corrected Beidou data D′, C is the encoded Beidou data, and D″ is the corrected second Beidou data; Calculating the confidence of each of the error correction algorithms and preferentially using the error correction algorithm with a high confidence, including: T(h i )=σ(W L ·σ(W L-1 ...σ(W1 [X i ,Y i ]+b1)...+b L-1 )+b L ) Among them, T(h i ) is the i-th error correction algorithm h i , σ is the activation function, the activation function is Sigmoid function or ReLU function, W L is the weight of the L-th layer neural network, b L is the bias of the L-th layer neural network, X i is the historical correction accuracy of the i-th error correction algorithm, Y i is the current correction accuracy of the i-th error correction algorithm; The corrected first Beidou data and the corrected second Beidou data are weighted averaged, and the result is used as the corrected final Beidou data received by the Beidou terminal based on the Hongmeng operating system to complete the Beidou data communication.

2. A Beidou data communication method based on Hongmeng operating system as claimed in claim 1, characterized in that: The error correction algorithm h i Including: cyclic redundancy check algorithm, convolutional code algorithm, and Hamming code algorithm.

3. A Beidou data communication system based on Hongmeng operating system, characterized in that: include: Generate a coding module, used for acquiring Beidou data, encoding the Beidou data, generating encoded Beidou data, generating a check code for each of the encoded Beidou data, and sending the encoded Beidou data and the check code to a Beidou terminal at the same time; A correction module, used for setting a first error correction model, performing error correction on the encoded Beidou data, generating corrected first Beidou data, setting a second error correction model, performing error correction on the encoded Beidou data, generating corrected second Beidou data, wherein the first error correction model and the second error correction model include a plurality of error correction algorithms; The first error correction model includes: Ensemble(D′,C)=mode{h1(D′,C),h2(D′,C),...,h N (D′,C)} Ensemble(D′, C) is the first error correction model that corrects the encoded Beidou data C to the corrected first Beidou data D′, h N is the Nth error correction algorithm, mode{} is the majority operation; The second error correction model includes: Wherein, Ensemble′(D′, C) is a second error correction model for correcting the encoded Beidou data C to the corrected Beidou data D′, C is the encoded Beidou data, and D″ is the corrected second Beidou data; Calculating the confidence of each of the error correction algorithms and preferentially using the error correction algorithm with a high confidence, including: T(h i )=σ(W L ·σ(W L-1 ...σ(W1 [X i ,Y i ]+b1)...+b L-1 )+b L ) Among them, T(h i ) is the i-th error correction algorithm h i , σ is the activation function, the activation function is Sigmoid function or ReLU function, W L is the weight of the L-th layer neural network, b L is the bias of the L-th layer neural network, X i is the historical correction accuracy of the i-th error correction algorithm, Y i is the current correction accuracy of the i-th error correction algorithm; A communication module is used to perform weighted averaging of the corrected first Beidou data and the corrected second Beidou data, and use the result as the corrected final Beidou data received by the Beidou terminal based on the Hongmeng operating system to complete Beidou data communication.

4. A Beidou data communication system based on Hongmeng operating system as claimed in claim 3, characterized in that: The error correction algorithm h i Including: cyclic redundancy check algorithm, convolutional code algorithm, and Hamming code algorithm.

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