Data transmission method, system, storage medium, device and product

Abstract

The present disclosure relates to a data transmission method, system, storage medium, device and product, the method comprising: determining a target compression parameter for communication between a first terminal and a second terminal, wherein the target compression parameter is determined according to respective computing resource data of the first terminal and the second terminal and network transmission data between the first terminal and the second terminal; encoding target data to be transmitted according to the target compression parameter, and transmitting the encoded target data to the second terminal. The above technical solution can be applied to the interaction scenario of intelligent networked vehicles and the cloud, can reduce the communication transmission delay between the vehicle end and the cloud, improve the network response real-time performance between the vehicle end and the cloud, and meet the intelligent networked low-latency and high-reliability transmission requirements.

Description

Technical Field

[0001] This disclosure relates to the field of electronic technology, and in particular to a data transmission method, system, storage medium, device, and product. Background Technology

[0002] In the context of the development of vehicle-to-everything (V2X) technology, vehicles need to transmit large amounts of business data to the cloud in real time. Transmission efficiency and latency directly affect the smoothness of vehicle-to-machine interaction. In related technologies, vehicles typically use fixed compression for data transmission. When network conditions are poor, fixed compression cannot effectively reduce data size, leading to increased transmission queuing delays. When computing resources are scarce, fixed compression exacerbates encoding time on the vehicle side, thus increasing the overall transmission latency from the vehicle to the cloud. Therefore, existing fixed compression transmission methods suffer from high communication latency between the vehicle and the cloud. Summary of the Invention

[0003] This disclosure provides a data transmission method, system, storage medium, device, and product that can be applied to the interaction scenarios between intelligent connected vehicles and the cloud. It can reduce the communication transmission latency between the vehicle and the cloud, improve the real-time network response between the vehicle and the cloud, and meet the low latency and high reliability transmission requirements of intelligent connected vehicles.

[0004] To achieve the above objectives, according to a first aspect of this disclosure, a data transmission method is provided, applied to a first terminal, the method comprising: The target compression parameters for communication between the first terminal and the second terminal are determined, wherein the target compression parameters are determined based on the computing resource data of the first terminal and the second terminal respectively, and the network transmission data between the first terminal and the second terminal; The target data to be sent is encoded according to the target compression parameters, and the encoded target data is sent to the second terminal.

[0005] In some embodiments, determining the target compression parameters for communication between the first terminal and the second terminal includes: Based on the network transmission data and the computing resource data, determine the target transmission delay model for communication between the first terminal and the second terminal; The optimal compression parameter corresponding to the target transmission delay model is determined as the target compression parameter, wherein the optimal compression parameter is the compression parameter with the minimum transmission delay corresponding to the target transmission delay model.

[0006] In some embodiments, the network transmission data includes uplink transmission data and downlink transmission data from the first terminal to the second terminal, and determining the target transmission delay model for communication between the first terminal and the second terminal based on the network transmission data and the computing resource data includes: Based on the uplink transmission data and the computing resource data, determine the uplink transmission delay model from the first terminal to the second terminal; Based on the downlink transmission data and the computing resource data, the downlink transmission delay model from the second terminal to the first terminal is determined; The target transmission delay model is determined based on the uplink transmission delay model and the downlink transmission delay model.

[0007] In some embodiments, the computing resource data includes a first available computing power of the first terminal and a second available computing power of the second terminal, and the uplink transmission data includes the uplink transmission rate from the first terminal to the second terminal and a first transmission delay of the uplink. Determining the uplink transmission delay model for communication between the first terminal and the second terminal based on the uplink transmission data and the computing resource data includes: Based on the uplink transmission rate, the first available computing power, and the second available computing power, determine the uplink encoding duration, uplink transmission duration, and uplink decoding duration from the first terminal to the second terminal; The uplink transmission delay model is determined based on the uplink encoding duration, the uplink transmission duration, the uplink decoding duration, and the first transmission delay.

[0008] In some embodiments, the computing resource data includes a first available computing power of the first terminal and a second available computing power of the second terminal, the downlink transmission data includes a downlink transmission rate from the second terminal to the first terminal and a second downlink transmission delay, and the step of determining a downlink transmission delay model for communication between the second terminal and the first terminal based on the downlink transmission data and the computing resource data includes: Based on the downlink transmission rate, the first available computing power, and the second available computing power, determine the downlink encoding duration, downlink transmission duration, and downlink decoding duration from the second terminal to the first terminal; The downlink transmission delay model is determined based on the downlink encoding duration, the downlink transmission duration, the downlink decoding duration, and the second transmission delay.

[0009] In some embodiments, the method further includes: Based on the first probe data sent by the first terminal to the second terminal and the first response data returned by the second terminal, the network transmission data and the first computing load data of the second terminal are determined, wherein the first response data corresponds to the first probe data; Based on the first computational load data, the second available computing power is determined.

[0010] In some embodiments, the method further includes: For each set of candidate compression parameters in the preset set of candidate compression parameters, the candidate transmission delay corresponding to the set of candidate compression parameters is determined based on the candidate probe data corresponding to the set of candidate compression parameters sent by the first terminal to the second terminal and the candidate response data returned by the second terminal. The candidate compression parameter corresponding to the minimum candidate transmission delay is determined as the optimal compression parameter.

[0011] In some embodiments, determining the candidate transmission delay corresponding to each set of candidate compression parameters from a preset set of candidate compression parameters includes: For each set of candidate compression parameters, the candidate transmission delay corresponding to the candidate probe data is determined based on the transmission time of the candidate probe data corresponding to the set of candidate compression parameters and the reception time of the candidate probe data corresponding to the set of candidate compression parameters.

[0012] In some embodiments, the method further includes: The system receives the target compression parameters sent by the second terminal, wherein the target compression parameters are determined by the second terminal based on the computing resource data and the network transmission data.

[0013] In some embodiments, the network transmission data includes uplink transmission data and downlink transmission data from the first terminal to the second terminal, the computing resource data includes a first available computing power of the first terminal and a second available computing power of the second terminal, and the first terminal and the second terminal are remotely connected.

[0014] According to a second aspect of this disclosure, a data transmission method is provided, applied to a second terminal, the method comprising: The system receives encoded target data sent by a first terminal, wherein the encoded target data is data encoded by the first terminal according to target compression parameters, and the target compression parameters are determined based on the computing resource data of the first terminal and the second terminal, and the network transmission data between the first terminal and the second terminal. The encoded target data is decoded to obtain the target data.

[0015] According to a third aspect of this disclosure, a data transmission system is provided, comprising: A first terminal is configured to determine target compression parameters for communication between the first terminal and a second terminal, wherein the target compression parameters are determined based on the computing resource data of the first terminal and the second terminal respectively, and the network transmission data between the first terminal and the second terminal; based on the target compression parameters, the target data to be sent is encoded, and the encoded target data is sent to the second terminal; The second terminal is remotely connected to the first terminal and is used to decode the encoded target data to obtain the target data.

[0016] According to a fourth aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps of the method as described in any of the first aspects.

[0017] According to a fifth aspect of this disclosure, a controller is provided, including a processor and a memory having a computer program stored thereon, which, when executed by the processor, implements the steps of the method as described in any of the first aspects.

[0018] According to a sixth aspect of this disclosure, a vehicle is provided, including a target controller as provided in the fourth aspect.

[0019] According to a seventh aspect of this disclosure, a computer program product is provided, comprising a computer program or instructions that, when executed by a processor, implement the steps of the method as provided in any of the first or second aspects.

[0020] In summary, in this embodiment, a target compression parameter is determined for communication between the first terminal and the second terminal. This target compression parameter is determined based on the computing resource data of the first terminal and the second terminal, and the network transmission data between them. Based on the target compression parameter, the target data to be sent is encoded, and the encoded target data is sent to the second terminal. By employing this technical solution, since the target compression parameter is determined based on network transmission data and computing resource data, it can be adjusted according to the actual computing power and real-time network transmission status of the first and second terminals. This ensures that the target compression parameter matches the real-time communication status between the first and second terminals, effectively reducing communication transmission latency and improving the real-time network response between them. Furthermore, this technical solution is applicable to the interaction scenarios between intelligent connected vehicles and the cloud, meeting the low-latency and high-reliability transmission requirements of intelligent connected vehicles.

[0021] Other features and advantages of this disclosure will be described in detail in the following detailed description section. Attached Figure Description To more clearly illustrate the technical solutions in the embodiments of this disclosure, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of this disclosure. Those skilled in the art can obtain other drawings based on these drawings without any creative effort.

[0022] To gain a more complete understanding of this disclosure and its beneficial effects, the following description will be made in conjunction with the accompanying drawings, wherein the same reference numerals denote the same parts in the following description.

[0023] Figure 1 This is a schematic diagram of a vehicle-to-everything (V2X) communication structure in an exemplary embodiment of this disclosure; Figure 2 This is a schematic diagram of a vehicle-to-everything (V2X) communication system with an intelligent codec module provided in an exemplary embodiment of this disclosure. Figure 3 This is a schematic flowchart of a data transmission method applied to a first terminal provided in an exemplary embodiment of this disclosure; Figure 4 This is a schematic diagram of a process for information interaction between a first terminal and a second terminal provided in an exemplary embodiment of this disclosure; Figure 5 This is a schematic flowchart of a data transmission method applied to a second terminal provided in an exemplary embodiment of this disclosure; Figure 6 This is a schematic diagram of a vehicle structure provided in an exemplary embodiment of this disclosure. Detailed Implementation

[0024] The technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort are within the protection scope of this disclosure.

[0025] The data transmission method in this embodiment can be applied to a first terminal or a second terminal. The first terminal can be, for example, a vehicle, including hybrid vehicles, electric vehicles, and gasoline vehicles. The vehicle terminal may include at least one of a telematics box (T-Box), an in-vehicle infotainment system, a smart cockpit domain controller, and an in-vehicle host module with cellular communication capabilities. The second terminal can be, for example, a cloud-based system, and the first terminal and the second terminal are remotely connected. The data transmission method in this embodiment is applicable to the interaction scenarios between intelligent connected vehicles and the cloud, and can meet the low latency and high reliability transmission requirements of intelligent connected vehicles. The following example specifically uses a vehicle as the first terminal and the cloud as the second terminal.

[0026] In the embodiments disclosed herein, Transmission Control Protocol (TCP) / Internet Protocol (IP) and User Datagram Protocol (UDP) are typically used.

[0027] Figure 1 This is a diagram of the vehicle-to-everything (V2X) communication architecture. The V2X communication nodes include the vehicle terminal 10, the vehicle-to-cloud network 20, and the cloud terminal 30. The vehicle terminal 10 includes a T-Box 101, a vehicle infotainment system 102, and a domain controller 103. The vehicle infotainment system 102 and the domain controller 103 are connected to the T-Box 101. The T-Box 101 enables wide-area cellular network access for the vehicle, providing an entry point for wide-area network communication, thereby enabling multimedia information transmission, driving data collection, remote status query and control, etc. The vehicle infotainment system 102 can be a smart cockpit host, in-vehicle infotainment system, in-vehicle navigation terminal, or in-vehicle communication terminal, etc.; the domain controller 103 can be a smart cockpit domain controller, an autonomous driving domain controller, a body domain controller, or a powertrain domain controller, etc. The vehicle-to-cloud network 20 includes a cellular access network 201 and a wide-area wired network 202. The cellular access network 201 provides a wireless access channel for vehicles and communicates with the T-Box 101. The wide-area wired network 202 consists of an aggregation network, a bearer network, and a backbone network, enabling the forwarding of vehicle-to-everything (V2X) information. The cloud platform 30 includes an Elastic Load Balancer (ELB) 301 and a Telematic Service Provider (TSP) 302. The ELB 301 is used to distribute traffic before the TSP 302, and the TSP 302 provides V2X cloud services, enabling real-time vehicle monitoring, status queries, and remote vehicle control in the cloud.

[0028] To enable communication between the vehicle and the cloud, taking the vehicle side, which includes the System-on-Chip (SoC) and T-Box, as an example, see [link to relevant documentation]. Figure 2 The vehicle infotainment SoC 104 includes an infotainment application 1041, an intelligent codec module 1042, a TCP / IP protocol stack 1043, and an Ethernet interface 1044. The T-Box 101 includes an Ethernet interface 1011 and a communication module 1012. The Ethernet interface 1011 and the Ethernet interface 1044 are connected for communication. The T-Box 101 communicates wirelessly with the vehicle cloud network 20 through the communication module 1012. The cloud 30 includes an ELB 301, a TSP 302, a TCP / IP protocol stack 303, and an intelligent codec module 304. The cloud 30 receives data to be transmitted from the vehicle cloud network 20 through the TCP / IP protocol stack 303. On the vehicle side 10, data to be transmitted can be sent to the cloud 30 via the vehicle application 1041. The data to be transmitted first passes through the intelligent codec module 1042, then through the TCP / IP protocol stack 1043 and the Ethernet interface 1044 to the Ethernet interface 1011, and then through the communication module 1012 to access the vehicle-cloud network 20, so that the data to be transmitted is transmitted to the cloud 30 through the vehicle-cloud network 20. The cloud 30 first receives the data to be transmitted through the TCP / IP protocol stack 303, then performs load balancing through the ELB 301, then recovers the data through the intelligent codec module 304 and forwards it to the TSP 302. Among them, the Ethernet interface 1011 and the Ethernet interface 1044 can be any of the Reduced Gigabit Media Independent Interface (RGMII), Serial Gigabit Media Independent Interface (SGMII), etc., and the communication module 1012 can be one or more of the 3G communication module, 4G communication module, and 5G communication module.

[0029] In some embodiments, both the intelligent encoding / decoding module 1042 and the intelligent encoding / decoding module 304 employ encoding / decoding algorithms for encoding and decoding. These algorithms include, but are not limited to, data compression processing based on Huffman coding principles, dictionary compression algorithms, etc. They can adopt corresponding and appropriate encoding / decoding methods for different types of data such as text, video, and images. For example, text data compression can be achieved through the bzip algorithm, video data compression through the H.264 / H.265 algorithm, and image data compression through the JPEG algorithm.

[0030] Because network transmission status (such as available bandwidth and transmission latency) and computing resources (such as available computing power) on both the vehicle-mounted and cloud sides fluctuate dynamically in the vehicle-to-everything (V2X) environment, fixed compression strategies cannot adapt to real-time network and computing power changes. When network conditions are poor or computing resources are scarce, data encoding / decoding time and transmission time increase significantly, resulting in excessively high overall communication latency. Even with ample network and computing resources, fixed parameters cannot be optimized to effectively reduce transmission latency. Therefore, existing compression transmission methods generally suffer from high communication latency and poor real-time performance, failing to meet the low-latency transmission requirements of V2X.

[0031] To reduce communication latency in vehicle-to-everything (V2X) networks, this disclosure provides a data transmission method applied to a first terminal. Please refer to [link to relevant documentation]. Figure 3 The method includes steps S301-S302, as follows: Step S301: Determine the target compression parameters for communication between the first terminal and the second terminal. The target compression parameters are determined based on the computing resource data of the first terminal and the second terminal respectively, and the network transmission data between the first terminal and the second terminal. In some embodiments, network transmission data includes uplink transmission data and downlink transmission data from the first terminal to the second terminal, computing resource data includes the first available computing power of the first terminal and the second available computing power of the second terminal, and the first terminal and the second terminal are remotely connected.

[0032] Specifically, the uplink transmission data includes at least one of the uplink transmission rate from the first terminal to the second terminal and the first transmission delay of the uplink, and the downlink transmission data includes at least one of the downlink transmission rate from the second terminal to the first terminal and the second transmission delay of the downlink.

[0033] In some embodiments, the uplink transmission data may further include at least one of uplink packet loss rate, uplink bandwidth utilization, uplink data transmission jitter, uplink bit error rate, and uplink connection stability. Specifically, the uplink packet loss rate characterizes the proportion of lost data packets among the total transmitted data packets during the data transmission process from the first terminal to the second terminal; the uplink bandwidth utilization reflects the current resource occupancy of the uplink; excessively high bandwidth utilization can easily lead to transmission congestion, thereby increasing transmission latency; uplink data transmission jitter describes the fluctuation range of uplink transmission latency; the uplink bit error rate measures the probability of data signal distortion during uplink transmission; and uplink connection stability determines whether there are abnormal conditions such as uplink interruptions or stuttering.

[0034] In some embodiments, the downlink transmission data may further include at least one of downlink packet loss rate, downlink bandwidth utilization, downlink data transmission jitter, downlink bit error rate, and downlink connection stability.

[0035] It should be noted that the supplementary types of uplink and downlink transmission data mentioned above can be flexibly selected for collection according to the actual communication needs of the vehicle network, and it is not necessary to collect all of them. All types of transmission data collected are used to provide data support for the construction of the target transmission delay model and the determination of the target compression parameters, ensuring that the target compression parameters can accurately adapt to the real-time communication status of the first terminal and the second terminal, and further improve the stability and efficiency of communication between the second terminal and the first terminal.

[0036] In some embodiments, computing resource data and network transmission data can be acquired in advance. When acquiring computing resource data, the first terminal can detect its own computing resources to obtain a first available computing power. When acquiring network transmission data and a second available computing power, the network transmission data and the first computing load data of the second terminal can be determined based on the first probe data sent by the first terminal to the second terminal and the first response data returned by the second terminal, wherein the first response data corresponds to the first probe data. The second available computing power is determined based on the first computing load data. Thus, by using the first probe data and the first response data, the acquired network transmission data and the first computing load data can be made more accurate. Since the second encoding computing power is determined based on the first computing load data, the accuracy of the second encoding computing power will also be improved due to the higher accuracy of the first computing load data.

[0037] In some embodiments, based on the sending time of the first probe data and the receiving time of the first response data, as well as information such as data interaction delay and packet loss, the network transmission data between the first terminal and the second terminal is determined; at the same time, based on the processing delay of the first probe data on the second terminal, the first computing load data of the second terminal is parsed and obtained, and based on the first computing load data, the second available computing power of the second terminal for data encoding and decoding is further determined.

[0038] Specifically, the first terminal can detect currently available computing resources and thus determine the first available computing power by calling interfaces provided by the operating system or hardware drivers. For example, the first terminal's SoC typically includes a multi-core Central Processing Unit (CPU), Graphics Processing Unit (GPU), or Neural Network Processing Unit (NPU). The first terminal can periodically run a lightweight benchmark test to measure the number of times a standard encoding task (such as compressing 1MB of data) is completed per unit time, thereby obtaining the current available encoding computing power (unit: MB / s or operations / second). Alternatively, the first terminal can directly read the vehicle's computing load data (such as CPU utilization and available memory), combine it with the chip's nominal peak computing power, and calculate the current available computing power according to a preset formula, for example, the first available computing power can be peak computing power × (1 – current load rate). In this way, the first terminal can obtain the first available computing power in real time.

[0039] Specifically, taking the first probe data as the probe packet and the first response data as the response packet as an example, the first terminal constructs one or more UDP or TCP probe packets, which contain a timestamp and a data payload of a predetermined length. The first terminal records the sending time T_send. After receiving the probe packet, the second terminal processes the probe packet and returns a response packet. When the first terminal receives the response packet, it records the receiving time T_recv. Then, the total round-trip time RTT = T_recv – T_send. To separate network transmission delay from the second terminal processing delay, two probe packets of different lengths can be used: short packets (containing only a timestamp, with a very small data payload, such as 64 bytes) and long packets (containing a larger data payload, such as 1KB). Assuming that the round-trip time of the short packet mainly reflects network propagation delay and forwarding delay (the second terminal processing time is negligible), the round-trip time of the long packet includes network transmission time (proportional to the data volume) and the second terminal processing time. By comparing the difference between the two, the uplink transmission rate, downlink transmission rate, and the first computing load data of the second terminal can be estimated. Then, based on the first computing load data, the second available computing power can be determined. The process of determining the second available computing power can refer to the discussion on obtaining the first available computing power. For the sake of brevity, it will not be repeated here.

[0040] In some embodiments, the second terminal also carries the current load percentage or available computing power information of the second terminal directly in the response packet. In this case, the first terminal can obtain the second available computing power by parsing the response packet.

[0041] In some embodiments, when determining the target compression parameter, the target transmission delay model for communication between the first terminal and the second terminal can be determined based on network transmission data and computing resource data; the optimal compression parameter corresponding to the target transmission delay model is determined as the target compression parameter, wherein the optimal compression parameter is the compression parameter with the minimum transmission delay corresponding to the target transmission delay model.

[0042] Specifically, based on network transmission data and computing resource data, a target transmission delay model is constructed. Then, the optimal compression parameters corresponding to the target transmission delay model are selected and used as the target compression parameters. This ensures that the target compression parameters, dynamically adjusted according to changes in computing power and network conditions, maintain the minimum transmission delay corresponding to the target transmission delay model, thereby improving overall communication stability and reliability and adapting to transmission requirements under different operating conditions. Furthermore, when network conditions deteriorate (e.g., a decrease in transmission rate) or computing resources are strained (e.g., increased CPU load on the first terminal), the target transmission delay model automatically adjusts the compression parameters based on the actual situation (e.g., lowering the compression level to reduce encoding overhead, or increasing the compression level to reduce the amount of transmitted data), consistently maintaining a low transmission delay. Additionally, when computing resources are abundant, the target transmission delay model can choose lighter compression (or even no compression) to avoid unnecessary computational overhead and fully utilize bandwidth resources.

[0043] In some embodiments, when determining the target transmission delay model, the uplink transmission delay model from the first terminal to the second terminal can be determined based on the uplink transmission data and computing resource data; then, the downlink transmission delay model from the second terminal to the first terminal can be determined based on the downlink transmission data and computing resource data; finally, the target transmission delay model can be determined based on the uplink transmission delay model and the downlink transmission delay model.

[0044] In some embodiments, the uplink transmission data includes the uplink transmission rate from the first terminal to the second terminal and the first transmission delay of the uplink. In this case, the uplink encoding duration, uplink transmission duration, and uplink decoding duration from the first terminal to the second terminal can be determined based on the uplink transmission rate, the first available computing power, and the second available computing power. Then, the uplink transmission delay model is determined based on the uplink encoding duration, uplink transmission duration, uplink decoding duration, and the first transmission delay. In some embodiments, the uplink transmission delay model can also be determined based on the uplink encoding duration, uplink transmission duration, and uplink decoding duration.

[0045] Specifically, the uplink transmission duration can be determined based on the uplink transmission rate, preset compression parameters, and the first data from the first terminal to the second terminal. Specifically, the uplink transmission data volume can be determined first based on the data volume of the first data and the preset compression parameters, and then the uplink transmission duration can be determined based on the uplink transmission data volume and the uplink transmission rate. The uplink transmission duration can be determined based on the ratio of the uplink transmission data volume to the uplink transmission rate. The uplink transmission duration can be this ratio, or it can be the product of this ratio and a weight, or the difference or sum of this ratio and a fixed value. This specification does not impose specific limitations. Furthermore, the uplink encoding duration can be determined based on the first data, preset compression parameters, and first available computing power. Specifically, the first encoding computational overhead can be determined first based on the data volume of the first data and the preset compression parameters, and then the uplink encoding duration can be determined based on the first encoding computational overhead and the first available computing power. The uplink encoding duration can be determined based on the ratio of the first encoding computational overhead to the first available computing power. In some embodiments, the uplink encoding duration can be the ratio of the first encoding computational overhead to the first available computing power. In other embodiments, the uplink encoding duration can also be a weighted result of the above ratio, or with a fixed offset added. This disclosure does not impose specific limitations on this aspect.

[0046] Specifically, the uplink decoding duration can be determined based on the first data, preset compression parameters, and the second available computing power. Specifically, the uplink data transmission volume can be determined first based on the data volume of the first data and the preset compression parameters, and then the uplink decoding duration can be determined based on the uplink data transmission volume and the second available computing power. The uplink decoding duration can be determined based on the ratio of the uplink data transmission volume to the second available computing power. In some embodiments, the uplink decoding duration can be the ratio of the uplink data transmission volume to the second available computing power. In other embodiments, the uplink decoding duration can also be a weighted result of the above ratio, or with a fixed offset added. This disclosure does not impose specific limitations on this aspect.

[0047] In some embodiments, the downlink transmission data includes the downlink transmission rate from the second terminal to the first terminal and the second transmission delay of the downlink. In this case, the downlink encoding duration, downlink transmission duration, and downlink decoding duration from the second terminal to the first terminal can be determined based on the downlink transmission rate, the first available computing power, and the second available computing power. Then, the downlink transmission delay model is determined based on the downlink encoding duration, downlink transmission duration, downlink decoding duration, and the second transmission delay. In some embodiments, the downlink transmission delay model can also be determined based on the downlink encoding duration, downlink transmission duration, and downlink decoding duration.

[0048] Specifically, the downlink transmission duration can be determined based on the downlink transmission rate, preset compression parameters, and the second data from the second terminal to the first terminal. Specifically, the downlink transmission data volume can be determined first based on the data volume of the second data and the preset compression parameters, and then the downlink transmission duration can be determined based on the downlink transmission data volume and the downlink transmission rate. The downlink transmission duration can be determined based on the ratio of the downlink transmission data volume to the downlink transmission rate. The downlink transmission duration can be this ratio, or it can be the product of this ratio and a weight, or the difference or sum of this ratio and a fixed value. This specification does not impose specific limitations. Furthermore, the downlink encoding duration can also be determined based on the second data, preset compression parameters, and the second available computing power. Specifically, the second encoding computational overhead can be determined first based on the data volume of the second data and the preset compression parameters, and then the downlink encoding duration can be determined based on the second encoding computational overhead and the second available computing power. The downlink encoding duration can be determined based on the ratio of the second encoding computational overhead to the second available computing power. In some embodiments, the downlink encoding duration can be the ratio of the second encoding computational overhead to the second available computing power. In other embodiments, the downlink encoding duration may also be a weighted result of the above ratios, or with a fixed offset added. This disclosure does not impose specific limitations on this.

[0049] Specifically, the downlink decoding duration can be determined based on the second data, preset compression parameters, and the first available computing power. Specifically, the downlink transmission data volume can first be determined based on the data volume of the second data and the preset compression parameters, and then the downlink decoding duration can be determined based on the downlink transmission data volume and the first available computing power. The downlink decoding duration can be determined based on the ratio of the downlink transmission data volume to the first available computing power. In some embodiments, the downlink decoding duration can be the ratio of the downlink transmission data volume to the first available computing power. In other embodiments, the downlink decoding duration can also be a weighted result of the above ratio, or with a fixed offset added. This disclosure does not impose specific limitations on this aspect.

[0050] This disclosure describes an end-to-end communication scenario between a first terminal and a second terminal, such as... Figure 4 As shown, the data interaction between the vehicle terminal 10 and the cloud 30 includes the uplink direction (the vehicle SoC 104 sends data to the TSP 302) and the downlink direction (the TSP 302 sends data to the vehicle SoC 104).

[0051] In some embodiments, before sending uplink data to the TSP302, the vehicle infotainment SoC104 first performs step S41 to determine the available computing power and encoding computation overhead, then encodes the uplink data, and sends the encoded uplink data to the TSP302. Specifically, at time t1, the amount of uplink data to be sent by the vehicle infotainment SoC104 to the cloud is denoted as [data missing]. The preset compression parameters are denoted as x, and x is used to... The amount of encoded uplink data is denoted as The encoding computation overhead of the vehicle infotainment SoC104 is denoted as The available computing power of the vehicle's SoC104 at time t1 is denoted as And, the uplink transmission rate is denoted as Therefore, the uplink encoding duration is... Uplink transmission time is .in, It is positively correlated with the preset compression parameters, which are used to control the degree of data compression, such as compression ratio, compression level or encoding bitrate. The size of the encoded data is related to the size of the original data and the compression parameters. Generally, the larger x is, the higher the degree of compression. The smaller the value, the better. After encoding the uplink data, the vehicle's SoC 104 forwards the encoded uplink data sequentially through T-Box 101 and ELB 301 before transmitting it to TSP 302.

[0052] In some embodiments, before, after, or simultaneously with receiving the encoded uplink data, the TSP302 executes step S42, determines the available computing power and encoding computation overhead, and then decodes the encoded uplink data. Specifically, the TSP302 receives the encoded uplink data sent by the vehicle SoC104 at time t2, and the data volume of the encoded uplink data... At time t2, the decoding computation cost of TSP302 is The available computing power of TSP302 is denoted as Therefore, the uplink decoding time is... ,in, It is positively correlated with the preset compression parameters.

[0053] In some embodiments, before sending downlink data to the vehicle infotainment SoC 104, the TSP302 first performs step S43 to determine the available computing power and encoding computation overhead, then encodes the downlink data, and sends the encoded downlink data to the vehicle infotainment SoC 104. Specifically, the amount of downlink data to be sent by the TSP302 to the vehicle infotainment SoC 104 is denoted as [data missing]. The computational overhead of encoding in the cloud is The available computing power of TSP302 at time t3 is denoted as The amount of data after encoding the downlink data is denoted as and the downlink transmission rate is still Therefore, the downlink encoding duration is... Downlink transmission time is After encoding the downlink data, the TSP302 forwards the encoded downlink data sequentially through the ELB301 and T-Box101 before transmitting it to the vehicle's SoC104.

[0054] In some embodiments, before, after, or simultaneously with receiving the encoded downlink data, the vehicle infotainment SoC 104 executes step S44, determines the available computing power and encoding computation overhead, and then decodes the encoded downlink data. Specifically, at time t4, the vehicle infotainment SoC 104 receives the encoded downlink data sent by the TSP302, and the data volume of the encoded downlink data... The decoding computation overhead of the vehicle's SoC104 at time t4 is... The available computing power of the vehicle's SoC104 is denoted as Therefore, the downlink decoding time is ,in, It is positively correlated with the preset compression parameters.

[0055] Furthermore, if the average link propagation and processing delay (first transmission delay) from the vehicle SoC104 to the TSP302 is denoted as... The average link propagation and processing delay (second transmission delay) from TSP302 to the vehicle SoC104 is denoted as... The target transmission delay model can be represented by the following formula (1), as follows: (1) In the above formula (1), Represents the target transmission delay model. These are the transmission delay coefficients from the first terminal to the second terminal. This represents the transmission delay coefficient from the second terminal to the first terminal.

[0056] In some embodiments, after determining the target transmission delay model, the optimal compression parameter corresponding to the target transmission delay model can also be determined as the target compression parameter, so that the target compression parameter is better matched with the network transmission status and the computing resources on both sides of the first terminal and the second terminal in the vehicle network environment, thereby reducing the communication transmission delay between the first terminal and the second terminal and ensuring the real-time communication between the first terminal and the second terminal.

[0057] In some embodiments, when determining the target transmission delay model, an optimization model for determining the optimal compression parameters can be established based on the target transmission delay model. The optimization model can be expressed by the following formula (2), as follows: (2) Among them, due to and Since it is a constant independent of the compression parameter x, it can be expressed in formula (2) as follows: and omitted, This represents the optimal compression parameters. To determine the optimal compression parameters, formula (2) can be split into two parts, denoted as follows: and ,in, and The formulas (3) and (4) are used to express this, as follows: (3) (4) in, The sum of encoding and decoding processing delays, and There is a positive correlation (the higher the compression level, the greater the computational overhead of encoding and decoding, and the longer the processing latency). The sum of network transmission delays, and There is a negative correlation (the higher the compression level, the smaller the amount of data transmitted, and the shorter the transmission latency). Therefore... = + There must exist a unique optimal solution. , making .at the same time, It is a quasi-convex function, satisfying the definition of quasi-convexity, as shown in formula (5), specifically as follows: (5) In formula (5), Indicates and Different compression parameters, This represents the weighting coefficient.

[0058] because It is a quasi-convex function, and the quasi-convexity of the quasi-convex function guarantees that the target transmission delay model Delay(x) does not have multiple local optima, but only a unique global optimum. This allows for the rapid and stable solution of optimal compression parameters using conventional one-dimensional search algorithms (such as the golden section method, bisection method, Newton's method, etc.), avoiding getting trapped in local optima and ensuring the global optimality of compression parameter selection.

[0059] Specifically, based on quasi-convexity, the compression parameters can be determined first. The feasible region is defined as [xmin, xmax], where xmin represents the lowest compression level and xmax represents the highest compression level. The golden section method is used to iteratively search within the feasible region, calculating two trial points in each iteration. to obtain a value, and narrow the search interval after comparison; stopping iteration when the precision of the search interval satisfies a preset threshold example, and the midpoint of the interval at this time is the optimal compression parameter ; taking as the target compression parameter, which is used for encoding, decoding and transmission of subsequent data.

[0060] In addition, quasi-convexity also ensures the robustness of the target transmission delay model: when the network transmission rate in the networking environment and the computing power of the first terminal and the second terminal fluctuate dynamically, the quasi-convexity of the model will not be destroyed, and the optimal compression parameter can be quickly updated only by re-substituting real-time parameters, realizing dynamic optimization of communication between the first terminal and the second terminal, and can adapt to dynamic environment changes in the Internet of Vehicles scenario.

[0061] In some embodiments, when determining the optimal compression parameter, for each group of candidate compression parameters in a plurality of preset groups of candidate compression parameters, the candidate transmission delay corresponding to the group of candidate compression parameters can be determined according to candidate detection data corresponding to the group of candidate compression parameters sent by the first terminal to the second terminal and candidate response data returned by the second terminal; and the candidate compression parameter corresponding to the minimum candidate transmission delay is determined as the optimal compression parameter.

[0062] Specifically, multiple groups of different candidate compression parameters may be preset, then candidate detection data corresponding to each group of candidate compression parameters is sent to the second terminal, and candidate response data corresponding to each group of candidate compression parameters returned by the second terminal is received. In this way, for each group of candidate compression parameters, the candidate transmission delay corresponding to the group of candidate compression parameters can be determined according to the sending time of the candidate detection data corresponding to the group of candidate compression parameters and the receiving time of the candidate detection data corresponding to the group of candidate compression parameters.

[0063] In some embodiments, the first terminal presets a set of candidate compression parameters, for example {x1, x2, …, xn}, where 0≤x1<x2<…<xn≤xmax. For each group of candidate compression parameters, the first terminal actively sends candidate detection data corresponding to the candidate compression parameter to the second terminal, and receives candidate response data returned by the second terminal. By measuring the time difference between the sending time of the detection data and the receiving time of the response data, the candidate transmission delay corresponding to the group of candidate compression parameters can be obtained.

[0064] Specifically, the first terminal can construct a series of probe data packets, each carrying the following information: candidate compression parameters corresponding to the current probe packet; a timestamp of the sending time; and a data payload of a certain length (used to simulate real business data). After receiving the probe data packets, the second terminal decodes them, records the decoding time and data reception time, generates candidate response data (carrying key information such as decoding time and reception time), and feeds the candidate response data back to the first terminal. The first terminal records the reception time of each set of response data. Combining the encoding time of the first terminal, the network transmission time, and the decoding time of the second terminal, the total transmission delay corresponding to each set of candidate compression parameters is accurately calculated. The specific calculation logic is as follows: Candidate transmission delay = encoding time of the first terminal + network transmission time + decoding time of the second terminal + basic link delay. Among them, the encoding time is determined by the candidate compression parameters, the amount of probe data, and the available computing power of the first terminal; the transmission time is determined by the amount of probe data after encoding and the uplink transmission rate; the decoding time is determined by the candidate compression parameters, the amount of probe data, and the available computing power of the second terminal; and the basic link delay is a fixed value. After receiving all candidate response data, the first terminal obtains the candidate transmission delays corresponding to all candidate compression parameters, and then compares and analyzes the candidate transmission delays corresponding to all candidate compression parameters to select the set of candidate compression parameters with the smallest candidate transmission delay, and determines it as the final optimal compression parameter; if there are two or more sets of candidate compression parameters with the same candidate transmission delay and both are the minimum value, further filtering can be carried out in combination with data transmission quality (such as video clarity and text integrity). This embodiment of the disclosure does not impose specific restrictions on this, which meets the core requirement of optimal latency calculation.

[0065] In some embodiments, the optimal compression parameters can be expressed by the following formula (6), as follows: (6) In formula (6) This represents the optimal compression parameters measured in practice. At this point, we can... As the optimal compression parameter.

[0066] By using the above methods, random interference such as sudden network jitter and instantaneous congestion can be effectively filtered out, ensuring that the candidate transmission delay measurement results truly reflect the actual state of vehicle-to-cloud communication, thereby guaranteeing the reliability of the optimal compression parameters, adapting to the complex and ever-changing network environment of the Internet of Vehicles, and minimizing end-to-end transmission latency.

[0067] Step S302: Encode the target data to be sent according to the target compression parameters, and send the encoded target data to the second terminal.

[0068] In some embodiments, after obtaining the target transmission delay model, the first terminal can obtain target compression parameters by solving the target transmission delay model or by screening candidate parameters. Then, for the target data to be sent to the second terminal (i.e., actual business data, such as vehicle driving data, multimedia data, remote control commands, etc.), the first terminal encodes and compresses the target data according to the compression level, compression ratio, or encoding bitrate corresponding to the target compression parameters, calling a preset encoding / decoding algorithm (such as H.264 / H.265, bzip, JPEG, etc.). The first terminal intelligently encodes the target data. Specifically, during the encoding process, the first terminal dynamically adjusts the encoding strategy according to the target compression parameters: if the target compression parameters correspond to a high compression level, a more complex encoding algorithm is used to maximize the compressed data volume; if they correspond to a low compression level, a low-complexity encoding algorithm is used to reduce computing power consumption, ensuring that the encoding process adapts to the current computing power state of the first terminal. Specifically, after encoding, the first terminal encapsulates the encoded target data, adds a data header (carrying information such as target compression parameter identifiers, data type, and data length), and generates a transmission data packet conforming to the vehicle-to-cloud communication protocol. Subsequently, through vehicle communication modules such as T-Box, the encapsulated data packet is sent to the second terminal via the vehicle-to-cloud network. During transmission, the first terminal monitors the network transmission status in real time. If network congestion, packet loss, or other anomalies occur, the retransmission strategy can be dynamically adjusted based on the target compression parameters to ensure the integrity of data transmission.

[0069] In some embodiments, after receiving the encoded target data, the second terminal calls the corresponding encoding and decoding algorithm to decode and restore the data according to the target compression parameter identifier carried in the data header, thereby restoring the original format of the target data and completing the data interaction between the second terminal and the first terminal.

[0070] The data processing method of this disclosure is applied to the interaction scenario between intelligent connected vehicles and the cloud. Taking an electric vehicle as the first terminal and the cloud as the second terminal as an example, if the electric vehicle needs to send real-time driving data (target data) to the cloud, including vehicle location, battery status, driving speed, etc., the original data size is 10MB. The first terminal solves the target transmission delay model mentioned above and obtains the target compression parameter of 60% under the current network (uplink transmission rate 50Mbps), computing power (the electric vehicle's first available computing power 2GHz), and the cloud's second available computing power (4GHz). At this time, the electric vehicle calls the bzip text compression algorithm to encode the 10MB of driving data at a compression ratio of 60%. After encoding, the data size is compressed to 4MB. The encoding time is controlled within 20ms by the first available computing power to avoid occupying too much vehicle computing power and affecting other services. The electric vehicle encapsulates the encoded 4MB data, adds a data header, and sends it to the cloud via a T-Box through the 5G cellular network. The transmission time is 4MB / 50Mbps=640ms, which is much lower than the 1600ms transmission time corresponding to the original 10MB data. After receiving the data, the cloud calls the corresponding decoding algorithm based on the target compression parameters (60%) to quickly reconstruct the original 10MB of driving data. The decoding time is controlled within 10ms, completing the data interaction. Ultimately, the total latency of this data transmission (encoding + transmission + decoding) is only about 670ms, which is nearly 50% lower than that of fixed compression parameters (e.g., a total latency of about 1300ms with a 30% compression ratio). By dynamically matching the target compression parameters based on the computing resources and network transmission data at both ends, the overall time for data encoding, transmission, and decoding can be significantly reduced, effectively improving the real-time network response between the electric vehicle and the cloud. This adapts to the characteristics of intelligent connected vehicle cloud interaction services and meets the low-latency, high-reliability transmission requirements of intelligent connected vehicles.

[0071] In some embodiments, the target compression parameters may be determined by a first terminal or by a second terminal. In this case, the first terminal receives the target compression parameters sent by the second terminal, wherein the target compression parameters are determined by the second terminal based on computing resource data and network transmission data.

[0072] In some embodiments, when the target compression parameters are determined by the second terminal, the second terminal can determine the target compression parameters for communication between the second terminal and the first terminal based on the respective computing resource data of the second terminal and the first terminal, as well as the network transmission data between the second terminal and the first terminal, and send the target compression parameters to the first terminal so that the first terminal can receive and determine the target compression parameters.

[0073] In some embodiments, network transmission data includes uplink transmission data and downlink transmission data from the first terminal to the second terminal, and computing resource data includes the first available computing power of the first terminal and the second available computing power of the second terminal.

[0074] Specifically, the uplink transmission data includes at least one of the uplink transmission rate from the first terminal to the second terminal and the first transmission delay of the uplink, and the downlink transmission data includes at least one of the downlink transmission rate from the second terminal to the first terminal and the second transmission delay of the downlink.

[0075] In some embodiments, computing resource data and network transmission data can be acquired in advance. When acquiring computing resource data, the second terminal can detect its own computing resources to obtain the second encoding computing power. When acquiring network transmission data and the first available computing power, the network transmission data and the second computing load data of the first terminal can be determined based on the second probe data sent by the second terminal to the first terminal and the second response data returned by the first terminal, wherein the second response data corresponds to the second probe data. The first available computing power is then determined based on the second computing load data. Thus, the second probe data and the second response data make the acquired network transmission data and second computing load data more accurate. Since the first encoding computing power is determined based on the second computing load data, the higher accuracy of the second computing load data also improves the accuracy of the first encoding computing power.

[0076] In some embodiments, the target compression parameters can be determined by the second terminal and then sent to the first terminal. Specifically, the second terminal determines the target compression parameters adapted to the current communication environment based on the network transmission data it collects (e.g., uplink / downlink rates and link latency monitored through the vehicle-to-cloud network) and the computing resource data reported by the first terminal (e.g., the first terminal's first available computing power). Then, the second terminal sends the target compression parameters to the first terminal via downlink signaling or a data channel. After receiving the target compression parameters, the first terminal encodes the target data to be sent according to the target compression parameters and sends the encoded target data to the second terminal. In this way, by determining the target compression parameters through the second terminal, the stronger computing power and more global network view of the second terminal (e.g., information reported by multiple vehicles and core network status) can be fully utilized to achieve a better selection of compression parameters. At the same time, the method of sending the parameters by the second terminal is suitable for scenarios where the first terminal has limited computing power or requires unified scheduling and management (e.g., fleet collaboration), which can reduce the computing burden of the first terminal and ensure the consistency of the compression strategy.

[0077] This disclosure also provides a data transmission method, applied to a second terminal, such as... Figure 5 As shown, the method includes: Step S501: Receive the encoded target data sent by the first terminal. The encoded target data is the data after the first terminal encodes the target data to be sent according to the target compression parameters. The target compression parameters are determined based on the computing resource data of the first terminal and the second terminal, and the network transmission data between the first terminal and the second terminal.

[0078] In some embodiments, the second terminal is remotely connected to the first terminal, enabling the second terminal to receive encoded target data sent by the first terminal.

[0079] Step S502: Decode the encoded target data to obtain the target data.

[0080] In some embodiments, after receiving the encoded target data sent by the first terminal, the second terminal decodes the encoded target data through an intelligent codec module to obtain the target data.

[0081] Specifically, taking the cloud as an example of a second terminal, see [link to relevant documentation]. Figure 2 The cloud-based 30 can decompress the received data using the encoding / decoding module 304, following the same decoding algorithm and compression parameters (i.e., target compression parameters) as the vehicle-side 10, to recover the original target data. The decoded data is then forwarded to the cloud-based 30's business processing modules (such as TSP, data analysis platform, etc.) for further processing, such as storage, analysis, or issuing instructions. Thus, through cloud-based decoding, the target data uploaded by the vehicle-side 10 can be completely restored, ensuring data availability and integrity. The encoding / decoding module 304 can dynamically adjust the decoding strategy based on the compression parameters, avoiding decoding failures or inefficiencies due to parameter mismatches. Furthermore, the cloud-based 30's decoding and the vehicle-side 10's encoding work together to form an end-to-end compressed transmission closed loop, effectively reducing transmission latency while ensuring high-fidelity data recovery.

[0082] This disclosure also provides a data transmission system, the system comprising: The first terminal is used to determine the target compression parameters, which are determined based on the computing resource data of the first terminal and the second terminal respectively, and the network transmission data between the first terminal and the second terminal; based on the target compression parameters, the target data to be sent is encoded, and the encoded target data is sent to the second terminal.

[0083] The second terminal is remotely connected to the first terminal and is used to decode the encoded target data to obtain the target data.

[0084] In some embodiments, the first terminal is further configured to determine a target transmission delay model for communication between the first terminal and the second terminal based on network transmission data and computing resource data; and to determine the optimal compression parameter corresponding to the target transmission delay model as the target compression parameter, wherein the optimal compression parameter is the compression parameter with the minimum transmission delay corresponding to the target transmission delay model.

[0085] In some embodiments, the first terminal is further configured to determine an uplink transmission delay model from the first terminal to the second terminal based on uplink transmission data and computing resource data; determine a downlink transmission delay model from the second terminal to the first terminal based on downlink transmission data and computing resource data; and determine a target transmission delay model based on the uplink transmission delay model and the downlink transmission delay model, wherein the network transmission data includes uplink transmission data and downlink transmission data from the first terminal to the second terminal.

[0086] In some embodiments, the first terminal is further configured to determine the uplink encoding duration, uplink transmission duration, and uplink decoding duration from the first terminal to the second terminal based on the uplink transmission rate, the first available computing power, and the second available computing power; and to determine an uplink transmission delay model based on the uplink encoding duration, the uplink transmission duration, the uplink decoding duration, and the first transmission delay, wherein the computing resource data includes the first available computing power of the first terminal and the second available computing power of the second terminal, and the uplink transmission data includes the uplink transmission rate from the first terminal to the second terminal and the first transmission delay of the uplink.

[0087] In some embodiments, the first terminal is further configured to determine the downlink encoding duration, downlink transmission duration, and downlink decoding duration from the second terminal to the first terminal based on the downlink transmission rate, the first available computing power, and the second available computing power; and to determine a downlink transmission delay model based on the downlink encoding duration, the downlink transmission duration, the downlink decoding duration, and the second transmission delay, wherein the computing resource data includes the first available computing power of the first terminal and the second available computing power of the second terminal, and the downlink transmission data includes the downlink transmission rate from the second terminal to the first terminal and the second transmission delay of the downlink.

[0088] In some embodiments, the first terminal is further configured to determine network transmission data and first computing load data of the second terminal based on first probe data sent by the first terminal to the second terminal and first response data returned by the second terminal, wherein the first response data corresponds to the first probe data; and to determine second available computing power based on the first computing load data.

[0089] In some embodiments, the first terminal is further configured to, for each of the preset multiple sets of candidate compression parameters, determine the candidate transmission delay corresponding to the set of candidate compression parameters based on the candidate probe data corresponding to the set of candidate compression parameters sent by the first terminal to the second terminal and the candidate response data returned by the second terminal; and determine the candidate compression parameter corresponding to the minimum candidate transmission delay as the optimal compression parameter.

[0090] In some embodiments, the first terminal is further configured to determine the candidate transmission delay corresponding to each set of candidate compression parameters based on the transmission time of the candidate probe data corresponding to the set of candidate compression parameters and the reception time of the candidate probe data corresponding to the set of candidate compression parameters.

[0091] In some embodiments, the target compression parameters may be sent from the second terminal to the first terminal. In this case, the second terminal is used to determine the target compression parameters for communication between the second terminal and the first terminal based on the computing resource data of the second terminal and the first terminal, and the network transmission data between the second terminal and the first terminal, and send the target compression parameters to the first terminal. The first terminal is used to encode the target data to be sent according to the target compression parameters, and send the encoded target data to the second terminal.

[0092] Specifically, the computing resource data includes the first available computing power of the first terminal and the second terminal, which is used to determine the network transmission data and the second computing load data of the first terminal based on the second probe data sent by the second terminal to the first terminal and the second response data returned by the first terminal, wherein the second response data corresponds to the second probe data; and to determine the first available computing power based on the second computing load data.

[0093] In some embodiments, the second terminal is further configured to determine the target compression parameters based on computing resource data and network transmission data, and send the target compression parameters to the first terminal; the first terminal is configured to receive and determine the target compression parameters.

[0094] In some embodiments, network transmission data includes uplink transmission data and downlink transmission data from the first terminal to the second terminal, and computing resource data includes the first available computing power of the first terminal and the second available computing power of the second terminal.

[0095] This disclosure also provides a computer-readable storage medium storing instructions that, when executed by a processor, configure the processor to perform the data transmission method described above.

[0096] Those skilled in the art will understand that embodiments of this disclosure can be provided as methods, systems, or computer program products. Therefore, this disclosure can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this disclosure can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0097] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0098] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0099] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0100] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0101] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0102] Computer-readable media include both permanent and non-permanent, removable and non-removable media, which can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient media, such as modulated communication signals and carrier waves.

[0103] This disclosure also provides an electrical device, including a processor and a memory for storing processor-executable instructions, wherein the processor is configured to execute the instructions to implement the data transmission method described above.

[0104] It should be noted that the aforementioned electrical equipment can be any conventionally needed electronic device, such as, but not limited to, controllers or vehicles. The following example uses a vehicle as the electrical equipment.

[0105] like Figure 6 The diagram shown is a schematic representation of a vehicle architecture provided in an embodiment of this disclosure. In this embodiment, vehicle 60 includes the aforementioned controller. In this embodiment, vehicle 60 can be a gasoline-powered vehicle, a plug-in hybrid electric vehicle, or a new energy vehicle, etc., and this disclosure does not specifically limit it in this regard.

[0106] In the description of this disclosure, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more features. In the description of this disclosure, "a plurality of" means two or more, unless otherwise explicitly specified.

[0107] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0108] The embodiments, implementation methods and related technical features disclosed herein can be combined and substituted for each other without conflict.

[0109] The above are merely preferred embodiments of this disclosure and are not intended to limit this disclosure in any way. Any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of this disclosure without departing from the scope of the technical solution of this disclosure shall still fall within the scope of the technical solution of this disclosure.

Claims

1. A data transmission method, applied to a first terminal, characterized in that, The method includes: The target compression parameters for communication between the first terminal and the second terminal are determined, wherein the target compression parameters are determined based on the computing resource data of the first terminal and the second terminal respectively, and the network transmission data between the first terminal and the second terminal; The target data to be sent is encoded according to the target compression parameters, and the encoded target data is sent to the second terminal.

2. The method as described in claim 1, characterized in that, Determining the target compression parameters for communication between the first terminal and the second terminal includes: Based on the network transmission data and the computing resource data, determine the target transmission delay model for communication between the first terminal and the second terminal; The optimal compression parameter corresponding to the target transmission delay model is determined as the target compression parameter, wherein the optimal compression parameter is the compression parameter with the minimum transmission delay corresponding to the target transmission delay model.

3. The method as described in claim 2, characterized in that, The network transmission data includes uplink transmission data and downlink transmission data from the first terminal to the second terminal. Determining the target transmission delay model for communication between the first terminal and the second terminal based on the network transmission data and the computing resource data includes: Based on the uplink transmission data and the computing resource data, determine the uplink transmission delay model from the first terminal to the second terminal; Based on the downlink transmission data and the computing resource data, the downlink transmission delay model from the second terminal to the first terminal is determined; The target transmission delay model is determined based on the uplink transmission delay model and the downlink transmission delay model.

4. The method as described in claim 3, characterized in that, The computing resource data includes the first available computing power of the first terminal and the second available computing power of the second terminal. The uplink transmission data includes the uplink transmission rate from the first terminal to the second terminal and the first transmission delay of the uplink. Determining the uplink transmission delay model for communication between the first terminal and the second terminal based on the uplink transmission data and the computing resource data includes: Based on the uplink transmission rate, the first available computing power, and the second available computing power, determine the uplink encoding duration, uplink transmission duration, and uplink decoding duration from the first terminal to the second terminal; The uplink transmission delay model is determined based on the uplink encoding duration, the uplink transmission duration, the uplink decoding duration, and the first transmission delay.

5. The method as described in claim 3, characterized in that, The computing resource data includes the first available computing power of the first terminal and the second available computing power of the second terminal. The downlink transmission data includes the downlink transmission rate from the second terminal to the first terminal and the second transmission delay of the downlink. Determining the downlink transmission delay model for communication between the second terminal and the first terminal based on the downlink transmission data and the computing resource data includes: Based on the downlink transmission rate, the first available computing power, and the second available computing power, determine the downlink encoding duration, downlink transmission duration, and downlink decoding duration from the second terminal to the first terminal; The downlink transmission delay model is determined based on the downlink encoding duration, the downlink transmission duration, the downlink decoding duration, and the second transmission delay.

6. The method as described in claim 2, characterized in that, The method further includes: Based on the first probe data sent by the first terminal to the second terminal and the first response data returned by the second terminal, the network transmission data and the first computing load data of the second terminal are determined, wherein the first response data corresponds to the first probe data; Based on the first computational load data, the second available computing power is determined.

7. The method according to any one of claims 2-6, characterized in that, The method further includes: For each set of candidate compression parameters in the preset set of candidate compression parameters, the candidate transmission delay corresponding to the set of candidate compression parameters is determined based on the candidate probe data corresponding to the set of candidate compression parameters sent by the first terminal to the second terminal and the candidate response data returned by the second terminal. The candidate compression parameter corresponding to the minimum candidate transmission delay is determined as the optimal compression parameter.

8. The method as described in claim 7, characterized in that, The step of determining the candidate transmission delay corresponding to each set of candidate compression parameters from a set of preset candidate compression parameters includes: For each set of candidate compression parameters, the candidate transmission delay corresponding to the candidate probe data is determined based on the transmission time of the candidate probe data corresponding to the set of candidate compression parameters and the reception time of the candidate probe data corresponding to the set of candidate compression parameters.

9. The method according to any one of claims 1-8, characterized in that, The method further includes: The system receives the target compression parameters sent by the second terminal, wherein the target compression parameters are determined by the second terminal based on the computing resource data and the network transmission data.

10. The method according to any one of claims 1-9, characterized in that, The network transmission data includes uplink transmission data and downlink transmission data from the first terminal to the second terminal, and the computing resource data includes the first available computing power of the first terminal and the second available computing power of the second terminal. The first terminal and the second terminal are remotely connected.

11. A data transmission method applied to a second terminal, characterized in that, The method includes: The system receives encoded target data sent by a first terminal, wherein the encoded target data is data encoded by the first terminal according to target compression parameters, and the target compression parameters are determined based on the computing resource data of the first terminal and the second terminal, and the network transmission data between the first terminal and the second terminal. The encoded target data is decoded to obtain the target data.

12. A data transmission system, characterized in that, include: A first terminal is configured to determine target compression parameters for communication between the first terminal and a second terminal, wherein the target compression parameters are determined based on the computing resource data of the first terminal and the second terminal respectively, and the network transmission data between the first terminal and the second terminal; based on the target compression parameters, the target data to be sent is encoded, and the encoded target data is sent to the second terminal; The second terminal is remotely connected to the first terminal and is used to decode the encoded target data to obtain the target data.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the steps of the method according to any one of claims 1-11.

14. A vehicle, characterized in that, The method includes a processor and a memory for storing processor-executable instructions, wherein the processor is configured to execute the instructions to implement the method of any one of claims 1-11.

15. A computer program product, characterized in that, It includes a computer program or instructions that, when executed by a processor, implement the steps of the method according to any one of claims 1-11.

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