Low-delay data transmission method in 5G network

Abstract

The invention discloses a 5G network low-delay data transmission method, and relates to the technical field of data transmission, and the method comprises the steps: carrying out the encryption compression of original data through a photon-quantum hybrid computing system, carrying out the quantum parallel computing, rapidly generating a secret key, and carrying out the high-speed compression of a photon integrated chip, thereby obtaining preprocessed data; the preprocessed data is subjected to space-time domain and frequency domain joint coding, the time domain adopts a non-orthogonal multiple access technology, the space domain dynamically adjusts the weight of an antenna array through beam forming, and the frequency domain adopts orthogonal frequency division multiplexing combined with sub-band aggregation to adapt the bandwidth; channel state information is predicted based on the long short-term memory network, the fuzzy logic system generates a transmission strategy, and a prediction result is optimized by using a symbol inference rule to reduce time delay; time-frequency resources are dynamically allocated according to a transmission strategy, an enhanced signal is pre-coded, and low-delay transmission is completed in combination with a fast hybrid automatic repeat request mechanism and a multi-connection transmission mode; the invention further relates to a related computer readable storage medium and electronic equipment, and low-delay and efficient transmission of 5G network data is achieved.

Description

Technical Field

[0001] This invention relates to the field of data transmission technology, and more specifically, to a low-latency data transmission method in a 5G network. Background Technology

[0002] With the widespread application of 5G networks, the demand for low-latency data transmission is increasing. In many scenarios such as industrial automation, autonomous driving, and telemedicine, low-latency transmission is crucial to the success and security of business operations. Traditional data transmission methods are gradually revealing many drawbacks when facing the massive data and stringent latency requirements of the 5G era. On the one hand, encryption and compression speeds cannot keep up with the rapid growth of data. If only conventional calculation methods are used, the encryption key generation speed is slow and the data compression efficiency is low, which increases data preprocessing time and thus affects the overall transmission latency. On the other hand, resource allocation and transmission strategy formulation are not precise and flexible enough, making it difficult to quickly optimize and adjust according to dynamic factors such as real-time channel conditions and user needs. This makes it impossible to effectively avoid resource waste and transmission delays during the transmission process. At the same time, in terms of channel state prediction, existing methods lack accuracy and real-time performance, and cannot accurately predict channel changes in advance. This results in the inability to make reasonable plans for transmission strategies in advance, making the transmission process susceptible to channel fluctuations and causing latency. In addition, the processing time and transmission time of each node in the transmission link also lack effective comprehensive consideration and optimization, further exacerbating the end-to-end latency problem.

[0003] Therefore, existing technologies have shortcomings in 5G low-latency data transmission, such as low encryption and compression efficiency, inflexible resource allocation and transmission strategies, inaccurate channel state prediction, and lack of transmission link latency management. Summary of the Invention

[0004] In order to overcome the problems of low encryption and compression efficiency, inflexible resource allocation and transmission strategies, inaccurate channel state prediction, and lack of transmission link latency management in existing technologies for 5G low-latency data transmission, this invention discloses a low-latency data transmission method in 5G networks that can effectively solve the above-mentioned technical problems.

[0005] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows: A low-latency data transmission method in a 5G network includes the following steps: The original data is encrypted and compressed using a photonic-quantum hybrid computing system. Encryption keys are generated quickly through the characteristics of quantum parallel computing, and high-speed data compression is achieved using a photonic integrated chip to obtain pre-processed data. The preprocessed data is jointly coded in the time-space-frequency domain to generate a three-dimensional coded data block containing time-domain symbols, spatial streams, and frequency-domain subcarriers. In the time domain, non-orthogonal multiple access technology is used to achieve multi-user resource reuse. In the spatial domain, beamforming technology is used to dynamically adjust the antenna array weights. In the frequency domain, orthogonal frequency division multiplexing combined with subband aggregation technology is used to adapt to different bandwidth requirements. Based on the prediction of channel state information for future time slots using long short-term memory networks, a fuzzy logic system is used to generate transmission strategies, including modulation and coding scheme selection, power allocation, and resource block scheduling. The neural network prediction results are dynamically optimized through symbolic inference rules to reduce end-to-end latency. Based on the transmission strategy generated by neural symbol hybrid control, time and frequency resources are dynamically allocated, signal quality is enhanced through precoding technology, and low-latency data transmission is achieved in 5G networks by combining a fast hybrid automatic repeat request mechanism and a multi-connection transmission mode.

[0006] Preferably, in the photon-quantum hybrid computing preprocessing step, a quantum key distribution protocol is used to generate an encryption key, and multiple data packets are processed in parallel using the principle of quantum superposition. The key generation formula is: K = QKD(ψ1,ψ2,…,ψ n ), where QKD is the quantum key distribution function, ψ n For particle states that participate in quantum computing.

[0007] Preferably, the spatiotemporal-frequency domain joint coding step includes: The input data is divided into multiple time-domain symbol blocks, and a time-domain index and data payload are added to each symbol block. Spatial domain coding is performed on time-domain symbol blocks using space-time block codes or space-frequency block codes; The encoded data is mapped to frequency domain subcarriers using inverse Fourier transform to generate a spatiotemporal-frequency domain coding matrix. , where t is the time domain index, s is the spatial domain flow index, and f is the frequency domain subcarrier index.

[0008] Preferably, the neural symbol mixing control step includes: A long short-term memory network is trained using historical channel state data to predict the channel state for the next k time slots, and a prediction vector is output. ; Based on fuzzy rule base and expert knowledge, the predicted channel state is mapped to transmission strategy parameters; The final transmission control command is generated by weighted fusion of neural network prediction results and symbolic reasoning conclusions.

[0009] Preferably, the adaptive transmission step includes: Construct a Markov decision process model, with the state space including the current channel state, user equipment queue length, service type, and quality of service requirements; The action space includes resource block allocation scheme, modulation and coding scheme selection, and transmit power adjustment; A deep Q-network algorithm is used to find the optimal strategy to achieve dynamic allocation of time-frequency resources.

[0010] Preferably, it further includes: The processing time of each distribution node for the encoded data block is obtained in real time, and the processing time includes data reception time, processing calculation time and transmission time; Based on the processing time of each distribution node, the actual transmission delay of data in the transmission link is calculated. When the actual transmission delay exceeds the preset threshold, the transmission strategy is regenerated through neural symbolic hybrid control, and the encoding method and resource allocation scheme are adjusted.

[0011] Preferably, the formula for the actual transmission delay of the calculated data in the transmission link is: ; in Let i be the data reception time of the i-th node. To handle computation time, For the sending time, The inter-node transmission time is specified. Preferably, in the low-latency transmission step, a flexible frame structure in time-division duplex mode is adopted, combined with the high-speed transmission characteristics of photonic crystal fiber, to reduce control signaling overhead and transmission waiting time, so that the end-to-end transmission delay is not less than or equal to 1 millisecond.

[0012] Preferably, a computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the data transmission method described above.

[0013] Preferably, an electronic device includes a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus, and when the machine-readable instructions are executed by the processor, the data transmission method described above is performed.

[0014] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention addresses many shortcomings in existing 5G low-latency data transmission. In the encryption and compression stage, it utilizes a photonic-quantum hybrid computing system, leveraging the efficient key generation capabilities of quantum parallel computing and the high-speed compression characteristics of photonic integrated chips to improve encryption and compression efficiency, fundamentally solving the problem of slow traditional encryption and compression speed. Regarding resource allocation and transmission strategies, it employs spatiotemporal-frequency domain joint coding, combined with non-orthogonal multiple access, beamforming, and orthogonal frequency division multiplexing technologies to comprehensively optimize resource utilization, making multi-user resource reuse more efficient and adaptable to different bandwidth requirements. More precise allocation makes resource allocation and transmission strategies less rigid; at the same time, based on the combination of long short-term memory networks and fuzzy logic systems, the channel state is accurately predicted and transmission strategies are generated. Then, symbolic reasoning rules are used to optimize the prediction results, improving the accuracy and real-time performance of channel state prediction and solving the problem of inaccurate channel state prediction in existing technologies; in addition, by monitoring the processing time of each node in real time to calculate the transmission delay, and adjusting the strategy in time when the delay exceeds the standard, fine management of transmission link delay is achieved, solving the shortcomings of existing technologies in delay management and ensuring low latency and high efficiency of data transmission in the entire 5G network. Attached Figure Description

[0015] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other embodiments can be derived from the provided drawings without creative effort.

[0016] Figure 1 This is a flowchart of the 5G low-latency data transmission process of the present invention; Figure 2 This is a diagram of the photon-quantum hybrid preprocessing structure of the present invention; Figure 3 This is a diagram of the neural symbol hybrid control structure of the present invention. Detailed Implementation

[0017] The accompanying drawings are for illustrative purposes only and should not be construed as limiting the scope of this patent. To better illustrate this embodiment, some parts in the accompanying drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It will be understood by those skilled in the art that certain well-known structures and their descriptions may be omitted in the accompanying drawings.

[0018] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments. Example

[0019] A low-latency data transmission method in a 5G network includes the following steps: The original data is encrypted and compressed using a photonic-quantum hybrid computing system. Encryption keys are generated quickly through the characteristics of quantum parallel computing, and high-speed data compression is achieved using a photonic integrated chip to obtain pre-processed data. The preprocessed data is jointly coded in the time-space-frequency domain to generate a three-dimensional coded data block containing time-domain symbols, spatial streams, and frequency-domain subcarriers. In the time domain, non-orthogonal multiple access technology is used to achieve multi-user resource reuse. In the spatial domain, beamforming technology is used to dynamically adjust the antenna array weights. In the frequency domain, orthogonal frequency division multiplexing combined with subband aggregation technology is used to adapt to different bandwidth requirements. Based on the prediction of channel state information for future time slots using long short-term memory networks, a fuzzy logic system is used to generate transmission strategies, including modulation and coding scheme selection, power allocation, and resource block scheduling. The neural network prediction results are dynamically optimized through symbolic inference rules to reduce end-to-end latency. Based on the transmission strategy generated by neural symbol hybrid control, time and frequency resources are dynamically allocated, signal quality is enhanced through precoding technology, and low-latency data transmission is achieved in 5G networks by combining a fast hybrid automatic repeat request mechanism and a multi-connection transmission mode.

[0020] In the photon-quantum hybrid computing preprocessing step, a quantum key distribution protocol is used to generate an encryption key, and multiple data packets are processed in parallel using the principle of quantum superposition. The key generation formula is as follows: K = QKD(ψ1,ψ2,…,ψ n ), where QKD is the quantum key distribution function, ψ n For particle states that participate in quantum computing.

[0021] The spatiotemporal-frequency domain joint coding steps include: The input data is divided into multiple time-domain symbol blocks, and a time-domain index and data payload are added to each symbol block. Spatial domain coding is performed on time-domain symbol blocks using space-time block codes or space-frequency block codes; The encoded data is mapped to frequency domain subcarriers using inverse Fourier transform to generate a spatiotemporal-frequency domain coding matrix. , where t is the time domain index, s is the spatial domain flow index, and f is the frequency domain subcarrier index.

[0022] The neural symbol mixing control steps include: A long short-term memory network is trained using historical channel state data to predict the channel state for the next k time slots, and a prediction vector is output. ; Based on fuzzy rule base and expert knowledge, the predicted channel state is mapped to transmission strategy parameters; The final transmission control command is generated by weighted fusion of neural network prediction results and symbolic reasoning conclusions.

[0023] The adaptive transmission steps include: Construct a Markov decision process model, with the state space including the current channel state, user equipment queue length, service type, and quality of service requirements; The action space includes resource block allocation scheme, modulation and coding scheme selection, and transmit power adjustment; A deep Q-network algorithm is used to find the optimal strategy to achieve dynamic allocation of time-frequency resources.

[0024] It also includes: real-time acquisition of the processing time of each distribution node for the encoded data block, wherein the processing time includes data reception time, processing calculation time and transmission time; Based on the processing time of each distribution node, the actual transmission delay of data in the transmission link is calculated. When the actual transmission delay exceeds the preset threshold, the transmission strategy is regenerated through neural symbolic hybrid control, and the encoding method and resource allocation scheme are adjusted.

[0025] The formula for the actual transmission delay of the calculated data in the transmission link is:

[0026] in Let i be the data reception time of the i-th node. To handle computation time, For the sending time, This refers to the transmission time between nodes.

[0027] In the low-latency transmission step, a flexible frame structure in time-division duplex mode is adopted, combined with the high-speed transmission characteristics of photonic crystal fiber, to reduce control signaling overhead and transmission waiting time, so that the end-to-end transmission delay is not less than or equal to 1 millisecond.

[0028] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the data transmission method described above.

[0029] An electronic device includes a processor, a memory, and a bus. The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via the bus, and when the machine-readable instructions are executed by the processor, the data transmission method described above is performed.

[0030] This solution is applicable to latency-sensitive data transmission scenarios in 5G network environments, such as real-time control command transmission for autonomous vehicles and real-time data transmission for remote medical surgery.

[0031] Please see Figure 1-3The system utilizes a photon-quantum hybrid computing system to encrypt and compress the raw data. A quantum key distribution protocol, such as BB84, is used to generate the encryption key. Multiple data packets are processed in parallel using the principle of quantum superposition. For example, user data to be transmitted, such as sensor data from an autonomous vehicle, is divided into multiple qubit sequences, each corresponding to a data packet. The principle of quantum superposition is then used to process these data packets simultaneously, rapidly generating the encryption key K = QKD(ψ1, ψ2, ..., ψ...). n ), where QKD is the quantum key distribution function, ψ1, ψ2, ..., ψ n For particle states that participate in quantum computing.

[0032] Simultaneously, a photonic integrated chip is used to achieve high-speed data compression. The original data is input into the silicon-based integrated photonic chip, and the photonic compression algorithm inside the chip is used to compress the data to less than 1 / 10 of the original size, ensuring a compression ratio greater than 10:1, and obtaining pre-processed encrypted and compressed data.

[0033] The preprocessed data is jointly encoded in the spatiotemporal and frequency domains to generate a three-dimensional encoded data block containing time-domain symbols, spatial streams, and frequency-domain subcarriers.

[0034] In the time domain, non-orthogonal multiple access (NOMA) technology is used to achieve multi-user resource multiplexing. The input data is divided into multiple time domain symbol blocks. Each symbol block is given a time domain index, such as sequence number 1, 2, 3, etc., and a data payload, such as a specific service data segment. For data from multiple users, power multiplexing is performed in the same time domain symbol block through NOMA (orthogonal multiple access) technology. The signals of different users are allocated to the same time domain resources according to a certain power ratio.

[0035] In the spatial domain, beamforming technology is used to dynamically adjust the weights of the antenna array. Space-time block code (STBC) or space-frequency block code (SFBC) is applied to the time-domain symbol blocks for spatial coding. The encoded data is then distributed to different antenna ports. By adjusting the weights of the antenna array, the signal can be beamed in a specific direction, thereby enhancing signal coverage and anti-interference capabilities.

[0036] In the frequency domain, orthogonal frequency division multiplexing (OFDM) combined with subband aggregation technology is used to adapt to different bandwidth requirements. The encoded data is mapped to frequency domain subcarriers through inverse Fourier transform to generate a spatiotemporal-frequency domain coding matrix. Where t is the time domain index, s is the spatial domain stream index, and f is the frequency domain subcarrier index. Subcarriers are aggregated or split according to different user and service requirements to form transmission resources with different bandwidths.

[0037] The Long Short-Term Memory (LSTM) network is trained using historical channel state data, such as channel gain and noise power in past time slots, to predict the channel state in the next k time slots and output a prediction vector (containing predicted parameters such as channel gain and delay spread).

[0038] Based on a fuzzy rule base and expert knowledge, the predicted channel state is mapped to transmission strategy parameters. For example, when the predicted channel state is good, such as high channel gain and low noise power, a high-order modulation and coding scheme, such as 256QAM and high transmit power, is selected; when the channel state is poor, a low-order modulation and coding scheme, such as QPSK and low transmit power, is selected, while the redundancy coding ratio is increased. The rules in the fuzzy rule base can be that if the channel gain is high and the noise power is low, then a high-order modulation scheme and high power allocation are selected. The fuzzy inference mechanism transforms the fuzzy information of the channel state into specific transmission strategy parameters.

[0039] By weighted fusion of neural network prediction results and symbolic reasoning conclusions, the final transmission control instructions are generated. For example, the channel state probability distribution predicted by LSTM is weighted and summed with the transmission strategy parameters output by the fuzzy logic system to obtain the final modulation and coding scheme selection, power allocation and resource block scheduling instructions.

[0040] Based on the transmission strategy generated by neural symbolic hybrid control, time-frequency resources are dynamically allocated, and a Markov decision process model is constructed. The state space includes the current channel state, such as channel gain and noise power; user equipment queue length, such as the number of data packets waiting to be transmitted; service type, such as video stream and voice call; and service quality requirements, such as latency requirements and bit error rate requirements.

[0041] The action space includes resource block allocation schemes, such as allocating different frequency domain resource blocks to different users, modulation and coding scheme selection, such as selecting different MCS indices, and transmit power adjustment, such as adjusting the transmit power of the base station. The Deep Q Network (DQN) algorithm is used to solve the optimal strategy to realize the dynamic allocation of time and frequency resources. For example, for latency-sensitive services, priority is given to allocating continuous resource blocks in the frequency domain and selecting high-order modulation and coding schemes to maximize the transmission rate and reduce latency while ensuring transmission quality.

[0042] Precoding techniques enhance signal quality. At the transmitting end, a precoding matrix is ​​calculated based on channel estimation results and transmission strategy. The transmitted signal is then precoded to improve signal recovery at the receiving end, thereby reducing the bit error rate.

[0043] By combining the Fast Hybrid Automatic Repeat Request (HARQ) mechanism and the multi-connection transmission mode, low-latency data transmission is achieved in the 5G network. When the receiver detects a transmission error, it immediately sends a retransmission request to the transmitter. The transmitter quickly retransmits the relevant data blocks according to the HARQ mechanism. At the same time, by utilizing the multi-connection characteristics of the 5G network, data can be transmitted simultaneously through multiple cells or multiple frequency bands, thereby improving the reliability and efficiency of transmission.

[0044] Real-time acquisition of the processing time of each distribution node for encoded data blocks, including data reception time (such as the time to receive data from the previous node), processing and calculation time (such as the time spent on encryption, encoding, scheduling, etc. at the current node), and transmission time (such as the time to send data to the next node).

[0045] Based on the processing time of each distribution node, the actual transmission delay of the data in the transmission link is calculated using the following formula:

[0046] in Let i be the data reception time of the i-th node. To handle computation time, For the sending time, This refers to the transmission time between nodes.

[0047] When the actual transmission delay exceeds the preset threshold, the transmission strategy is regenerated through neural symbolic hybrid control, and the encoding method is adjusted, such as reducing the encoding rate or changing the encoding scheme, and the resource allocation scheme, such as increasing the number of resource blocks or adjusting the position of resource blocks, to ensure that the end-to-end transmission delay is not less than or equal to 1 millisecond.

[0048] In the low-latency transmission step, a flexible frame structure in Time Division Duplex (TDD) mode is adopted. Combined with the high-speed transmission characteristics of photonic crystal fiber, control signaling overhead and transmission waiting time are reduced. For example, in the TDD frame structure, the uplink and downlink time slot ratio can be flexibly configured, and the uplink and downlink transmission time can be dynamically adjusted according to service requirements. At the same time, the low-loss and high-bandwidth characteristics of photonic crystal fiber are utilized to achieve high-speed data transmission and further reduce transmission latency.

[0049] The same or similar labels correspond to the same or similar parts; The terms used to describe positional relationships in the accompanying drawings are for illustrative purposes only and should not be construed as limiting this patent. Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation of the present invention. For those skilled in the art, other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all implementation methods here. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the claims of the present invention.

Claims

1. A low-latency data transmission method in a 5G network, characterized in that, Includes the following steps: The original data is encrypted and compressed using a photonic-quantum hybrid computing system. Encryption keys are generated quickly through the characteristics of quantum parallel computing, and high-speed data compression is achieved using a photonic integrated chip to obtain pre-processed data. The preprocessed data is jointly coded in the time-space-frequency domain to generate a three-dimensional coded data block containing time-domain symbols, spatial streams, and frequency-domain subcarriers. In the time domain, non-orthogonal multiple access technology is used to achieve multi-user resource reuse. In the spatial domain, beamforming technology is used to dynamically adjust the antenna array weights. In the frequency domain, orthogonal frequency division multiplexing combined with subband aggregation technology is used to adapt to different bandwidth requirements. Based on the prediction of channel state information for future time slots using long short-term memory networks, a fuzzy logic system is used to generate transmission strategies, including modulation and coding scheme selection, power allocation, and resource block scheduling. The neural network prediction results are dynamically optimized through symbolic inference rules to reduce end-to-end latency. Based on the transmission strategy generated by neural symbol hybrid control, time and frequency resources are dynamically allocated, signal quality is enhanced through precoding technology, and low-latency data transmission is achieved in 5G networks by combining a fast hybrid automatic repeat request mechanism and a multi-connection transmission mode.

2. The data transmission method according to claim 1, characterized in that, In the photon-quantum hybrid computing preprocessing step, a quantum key distribution protocol is used to generate an encryption key, and multiple data packets are processed in parallel using the principle of quantum superposition. The key generation formula is as follows: K = QKD(ψ1,ψ2,…,ψ n ), where QKD is the quantum key distribution function, ψ n For particle states that participate in quantum computing.

3. The data transmission method according to claim 1, characterized in that, The spatiotemporal-frequency joint coding step includes: dividing the input data into multiple time-domain symbol blocks, adding a time-domain index and data payload to each symbol block; applying space-time block codes or space-frequency block codes to the time-domain symbol blocks for spatial coding; and mapping the encoded data to frequency-domain subcarriers through inverse Fourier transform to generate a spatiotemporal-frequency coding matrix. , where t is the time domain index, s is the spatial domain flow index, and f is the frequency domain subcarrier index.

4. The data transmission method according to claim 1, characterized in that, The neural symbolic hybrid control step includes: training a long short-term memory network using historical channel state data, predicting the channel state for the next k time slots, and outputting a prediction vector. Based on a fuzzy rule base and expert knowledge, the predicted channel state is mapped to transmission strategy parameters; the final transmission control command is generated by weighted fusion of neural network prediction results and symbolic reasoning conclusions.

5. The data transmission method according to claim 1, characterized in that, The adaptive transmission steps include: constructing a Markov decision process model, with the state space including the current channel state, user equipment queue length, service type, and quality of service requirements; the action space including resource block allocation scheme, modulation and coding scheme selection, and transmit power adjustment; and using a deep Q-network algorithm to solve for the optimal strategy to achieve dynamic allocation of time and frequency resources.

6. The data transmission method according to claim 1, characterized in that, Also includes: The processing time of each distribution node for the encoded data block is obtained in real time, and the processing time includes data reception time, processing calculation time and transmission time; Based on the processing time of each distribution node, the actual transmission delay of data in the transmission link is calculated. When the actual transmission delay exceeds the preset threshold, the transmission strategy is regenerated through neural symbolic hybrid control, and the encoding method and resource allocation scheme are adjusted.

7. The data transmission method according to claim 6, characterized in that, The formula for the actual transmission delay of the calculated data in the transmission link is: ;in Let i be the data reception time of the i-th node. To handle computation time, For the sending time, This refers to the transmission time between nodes.

8. The method according to claim 1, characterized in that, In the low-latency transmission step, a flexible frame structure in time-division duplex mode is adopted, combined with the high-speed transmission characteristics of photonic crystal fiber, to reduce control signaling overhead and transmission waiting time, so that the end-to-end transmission delay is not less than or equal to 1 millisecond.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the data transmission method according to any one of claims 1-8.

10. An electronic device comprising a processor, a memory, and a bus, characterized in that, The memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory via a bus, and when the machine-readable instructions are executed by the processor, the data transmission method as described in any one of claims 1-8 is performed.

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