Data compression method, electronic equipment, medium and product

Abstract

The invention relates to data processing, and discloses a data compression method, electronic equipment, a medium and a product. In the method, the vehicle terminal can obtain the data type and the importance weight of the original data, and the vehicle terminal can determine the corresponding compression algorithm according to the data type and the importance weight of the original data. Moreover, the vehicle terminal can also obtain the network state parameter of the network where the vehicle terminal is located and the equipment performance parameter of the vehicle terminal, and the vehicle terminal can adjust the compression parameter according to the network state parameter and the equipment parameter. And according to the determined compression algorithm and the compression parameter, the vehicle terminal can perform compression processing on the original data. Therefore, the data size of the compressed data is smaller than that of the original data, so that the compressed data needing to be transmitted between the vehicle terminal and the cloud end improves the data transmission efficiency of the vehicle terminal and the cloud end.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a data compression method, electronic equipment, medium and product. Background Art

[0002] With the continuous development of Internet of Vehicles technology, vehicle terminals will generate a large amount of data, such as vehicle status information, vehicle location information, vehicle driving trajectory, etc. These data can provide users with a variety of services, such as navigation, safety tips, entertainment and audio and video, etc. However, if the amount of data generated by the vehicle terminal is too large, the computing resources and storage resources contained in the vehicle terminal will be less than the computing resources and storage resources required for the data generated by the vehicle terminal. At this time, the vehicle terminal needs to transmit data with the cloud to perform calculations and storage on these data through the hardware devices contained in the cloud, so that the vehicle terminal can provide corresponding services based on the processing results of these data. However, in the process of data transmission between the vehicle terminal and the cloud, due to the large amount of data to be transmitted, network congestion is prone to occur, which will reduce the efficiency of data transmission, and may affect the services provided by the vehicle terminal and affect the user experience. Summary of the invention

[0003] In order to solve the problem of low data transmission efficiency, the embodiments of the present application provide a data compression method, electronic device, medium and product, including:

[0004] In a first aspect, an embodiment of the present application provides a data compression method, which is applied to a vehicle terminal, and the method includes: obtaining first data, classifying the first data, and obtaining a data type of the first data; determining an importance weight of the first data; determining a compression algorithm based on the data type and importance weight of the first data; obtaining network status parameters of the network where the vehicle terminal is located and device performance parameters of the vehicle terminal, and determining compression parameters based on at least the network status parameters and the device performance parameters; compressing the first data according to the compression algorithm and the compression parameters to obtain compressed data.

[0005] Based on the above scheme, the vehicle terminal can obtain the first data, and determine the compression algorithm according to the data type and importance weight of the first data; the vehicle terminal can also obtain the network status parameters of the network where the vehicle terminal is located and the device performance parameters of the vehicle terminal, and determine the compression parameters at least according to the network status parameters and the device performance parameters; according to the compression algorithm and the compression parameters, the first data is compressed to obtain compressed data. It can be understood that the amount of compressed data is less than the amount of the first data that has not been compressed, which reduces the amount of data transmitted between the vehicle terminal and the cloud and improves the data transmission efficiency.

[0006] It can be understood that the first data can be the original data generated by the vehicle terminal. The importance weight of the first data can be the real-time, security, necessity, etc. of the first data. The compression algorithm can be an improved Huffman coding algorithm, a dynamic dictionary compression algorithm, a wavelet transform compression algorithm, a deep learning compression algorithm, etc. The network status parameters can be the network's bandwidth, delay, jitter, throughput, packet loss rate, error rate, load, network topology, network device parameters, protocol status, etc. The device performance parameters of the vehicle terminal can be the processor model, processor main frequency, graphics processor model, graphics processor main frequency, memory model, memory capacity, flash memory capacity, hard disk capacity, storage type, storage medium read and write speed, etc. The compression parameters can be the frequency of data transmission to the cloud, the amount of data transmitted each time, the duration of continuous data transmission, data distortion, compression speed, compression ratio, etc.

[0007] In a possible implementation of the first aspect above, the data types include: structured data, semi-structured data, and unstructured data.

[0008] In a possible implementation of the first aspect above, a compression algorithm is determined according to the data type and importance weight of the first data, including: when the first data is the structured data, the compression algorithm is determined as a compression algorithm that combines improved Huffman coding and dynamic dictionary compression; when the first data is the semi-structured data, the compression algorithm is determined as a context-adaptive compression algorithm; when the first data is unstructured data, the compression algorithm is determined as a compression algorithm that combines wavelet transform and deep learning.

[0009] In a possible implementation of the first aspect above, compression parameters are determined at least based on network status parameters and device performance parameters, including: determining compression parameters based on network status parameters, device performance parameters, importance weights and expected transmission rate of the first data.

[0010] In a possible implementation of the first aspect above, when the importance weight of the first data is greater than a first threshold, the compression parameter is determined as a first parameter, wherein the first parameter makes the data distortion degree of the first data after being compressed less than a set threshold.

[0011] In a possible implementation of the first aspect above, when the expected transmission rate of the first data is greater than a second threshold, the compression parameter is determined as a second parameter, wherein the second parameter makes the transmission rate of the first data after compression greater than or equal to the expected transmission rate.

[0012] In a possible implementation of the first aspect above, the method further includes: when the first data is sensitive data, encrypting the compressed data to obtain second data.

[0013] In a possible implementation of the first aspect above, the encryption process is a lightweight encryption process.

[0014] In a possible implementation of the first aspect above, the method also includes: evaluating the compression effect of the compression processing to determine whether the compression effect meets the compression evaluation index; if the compression effect does not meet the compression evaluation index, adjusting the compression algorithm and / or compression parameters.

[0015] In a possible implementation of the first aspect above, the compression evaluation indicators include: compression ratio, compression speed, and data distortion.

[0016] In a possible implementation of the first aspect above, information is added to the second data, and the information includes: a compression algorithm name, a data importance weight, and a data security mark.

[0017] In a second aspect, the present application provides an electronic device, comprising: a memory for storing instructions executed by one or more processors of the electronic device, and a processor, which is one of the one or more processors of the electronic device, for executing the data compression method mentioned in the first aspect of the present application or any item of the first aspect.

[0018] In a third aspect, the present application provides a readable storage medium having instructions stored thereon. When the instructions are executed on an electronic device, the electronic device executes the data compression method mentioned in the first aspect of the present application or any item of the first aspect.

[0019] In a fourth aspect, an embodiment of the present application provides a computer program product, which includes computer instructions. When executed by an electronic device, the electronic device executes the computer program code of the data compression method mentioned in the first aspect of the present application or any item of the first aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0021] Figure 1 According to some embodiments of the present application, a schematic diagram of a scenario in which a vehicle terminal and a cloud transmit data is shown;

[0022] Figure 2 According to some embodiments of the present application, a schematic diagram of a data compression process is shown;

[0023] Figure 3According to some embodiments of the present application, another schematic diagram of a data compression process is shown;

[0024] Figure 4 According to some embodiments of the present application, a multimodal compression algorithm library is shown;

[0025] Figure 5 According to some embodiments of the present application, a schematic diagram of a process for adjusting compression parameters is shown;

[0026] Figure 6 According to some embodiments of the present application, a schematic diagram of a process of compression effect evaluation and optimization strategy update is shown;

[0027] Figure 7 According to some embodiments of the present application, a schematic structural diagram of a vehicle is shown. DETAILED DESCRIPTION

[0028] The illustrative embodiments of the present application include but are not limited to a data processing method, electronic device, medium and product. It can be understood that the data compression method mentioned in the embodiments of the present application can be applied to the scenario of data transmission between the vehicle terminal and the cloud.

[0029] The data compression method mentioned in the embodiment of the present application is introduced below in conjunction with a specific scenario in which a vehicle terminal transmits data to the cloud.

[0030] like Figure 1 As shown, an application scenario in which a vehicle terminal 101 transmits data to a cloud 102 via a wireless network is shown. Specifically, in the process of providing various services, the vehicle terminal 101 can transmit data to the cloud so that the cloud processes the data and provides corresponding services to users on the vehicle terminal side based on the data processing results. For example, these services include real-time navigation, road condition updates, remote monitoring, etc. In some specific implementations, Figure 1 In the application scenario shown, multiple vehicle terminals can simultaneously transmit data to the cloud 102 via a wireless network. As mentioned above, in the process of transmitting data from the vehicle terminal to the cloud, the large amount of data to be transmitted easily causes network congestion, resulting in low data transmission efficiency.

[0031] It can be understood that the data transmitted between the vehicle terminal and the cloud can be called Internet of Vehicles data. For example, this data may include real-time information such as vehicle status information, vehicle location information, and vehicle driving trajectory of the vehicle terminal.

[0032] In some specific implementations, such as Figure 1As shown in the figure, in the urban traffic scene during peak hours, a large number of vehicles are driving on the road, and each vehicle can collect real-time information from the vehicle terminal and transmit the collected real-time information from the vehicle terminal to the cloud. Due to the large number of vehicles and the large amount of real-time information generated by each vehicle, a large amount of data transmission needs to be generated between the vehicle terminal and the cloud in this application scenario.

[0033] It can be understood that when there is a large amount of data transmission demand between the vehicle terminal and the cloud, if the amount of data that the network infrastructure can transmit is less than the amount of data generated by the vehicle terminal in this scenario, network congestion will occur. If network congestion occurs, delays, data packet loss and other phenomena may occur in the process of the vehicle terminal 101 transmitting data to the cloud 102 through the wireless network, which will lead to reduced data transmission efficiency. The lower data transmission efficiency will affect the efficiency of the cloud to feedback the data processing results to the vehicle terminal, which may affect the use of related services in the vehicle terminal and the user's service experience. Therefore, how to improve the data transmission efficiency between the vehicle terminal and the cloud becomes a problem that needs to be solved.

[0034] It is understood that the vehicle in this application refers to a mechanical device used to transport people or goods. Generally, a vehicle can be composed of an engine, a chassis, a body, and electrical equipment. For example, cars, trucks, motorcycles, electric vehicles, special vehicles, etc. The vehicle terminal in this application refers to an electronic device installed on the vehicle, which includes but is not limited to: an on-board video server, a touch screen, a camera, a call device, a communication device, a biometric device, etc.

[0035] In order to solve the problem of low data transmission efficiency mentioned above, an embodiment of the present application provides a data compression method. In this method, in the scenario where the vehicle terminal transmits data with the cloud, the vehicle terminal can obtain the original data corresponding to the Internet of Vehicles data such as vehicle status information and vehicle location information, and determine the data type and importance weight of the original data. Then, the vehicle terminal determines the compression algorithm corresponding to the original data based on the data type and importance weight of the original data. Furthermore, the vehicle terminal can obtain the network status parameters of the network where the vehicle terminal is located and the device performance parameters of the vehicle terminal, and dynamically adjust the compression parameters corresponding to the compression algorithm based on the network status parameters and the device performance parameters. Finally, according to the determined compression algorithm and compression parameters, the vehicle terminal can compress the original data to obtain compressed data. Then, when the vehicle terminal transmits data with the cloud, the vehicle terminal can send the compressed data to the cloud to transmit the Internet of Vehicles data of the vehicle terminal to the cloud.

[0036] It can be understood that the above network status parameters and device performance parameters can reflect the resource conditions such as resource limitations of the vehicle terminal, and the above data types and importance weights can reflect the diversity, real-time, importance differences and other characteristics of the vehicle network data. In this way, when the present application determines the compression algorithm based on the data type and importance weight of the original data, as well as the network status parameters of the network where the vehicle terminal is located and the device performance parameters of the vehicle terminal, it can comprehensively consider the diversity, real-time, resource limitations, importance differences and other factors of the Internet of Vehicles data, adaptively select the compression algorithm and adjust the compression parameters to achieve efficient compression of the Internet of Vehicles data. In this way, in the process of transmitting data between the vehicle terminal and the cloud, the amount of compressed data after efficient compression is less than the amount of uncompressed original data, which improves the transmission efficiency.

[0037] As an example, for unstructured Internet of Vehicles data (e.g., images or videos), a compression algorithm combining wavelet transform and deep learning can be selected, which can remove redundant information in unstructured data and improve data transmission efficiency. For another example, for Internet of Vehicles data with high importance (e.g., vehicle location data), the vehicle terminal can adjust the data distortion of the compressed data to be less than a certain threshold so that the compressed data contains more original data. The threshold can be set by the user and is not limited here.

[0038] The data compression method mentioned in the embodiment of the present application is described in detail below. Figure 2 FIG. 1 shows a flow chart of a data compression method, which can be executed by the vehicle terminal 101 mentioned above. The flow chart of the data compression method can include:

[0039] S201: Acquire first data, classify the first data, and obtain a data type of the first data.

[0040] It is understandable that the vehicle terminal can obtain the first data generated by the vehicle terminal, and classify the first data to determine the data type of the first data. The first data can be the original data of the Internet of Vehicles data.

[0041] In some embodiments, the above data types may include structured data, semi-structured data, and unstructured data. For example, when the first data is (driver age: 45 years old), it can be determined that the data type of the first data is structured data. When the first data is a log file stored in a vehicle terminal, it can be determined that the data type of the first data is semi-structured data. When the first data is image data, it can be determined that the data type of the first data is unstructured data.

[0042] S202: Determine the importance weight of the first data.

[0043] It can be understood that the vehicle terminal can assign importance weights to the first data of different data types according to preset rules to express the importance of the first data. Specifically, the importance weight of the first data can characterize the degree of requirement of the first data for the real-time, security, and necessity of the data processing process. For example, in order for the vehicle terminal to obtain the vehicle's safety warning information in real time, the vehicle terminal can assign a higher importance weight to the vehicle's safety warning information to characterize the high real-time requirements of the safety warning information during the data processing process. For another example, in order to provide navigation services to users, the vehicle terminal can assign a higher importance weight to the electronic map data to characterize the high necessity requirements of the safety warning information during the data processing process. The preset rules can be set by the user according to the needs of the actual scenario, and are not limited here.

[0044] S203: Determine a compression algorithm according to the data type and importance weight of the first data.

[0045] In some embodiments, the above-mentioned compression algorithm may include an improved Huffman coding algorithm, a dynamic dictionary compression algorithm, a wavelet transform compression algorithm, a deep learning compression algorithm, etc., which are not limited here.

[0046] It is understandable that during the compression process, the vehicle terminal can adaptively determine different compression algorithms for first data of different data types and / or different importance weights. Therefore, based on the above data types and importance weights, the present application can comprehensively consider factors such as the diversity, real-time nature, resource limitations, and importance differences of Internet of Vehicles data to determine the compression algorithm, thereby improving data compression efficiency.

[0047] S204: Obtain network status parameters of the network where the vehicle terminal is located and device performance parameters of the vehicle terminal, and determine compression parameters at least based on the network status parameters and the device performance parameters.

[0048] It can be understood that the network status parameters may include the network's bandwidth, delay, jitter, throughput, packet loss rate, etc., and the network status parameters may indicate the state of network data transmission when the vehicle terminal transmits data to the cloud. The device performance parameters of the vehicle terminal may include: the vehicle terminal's processor model, processor main frequency, graphics processor model, graphics processor main frequency, memory model, memory capacity, etc., and the device performance parameters may indicate the computing resources and storage resources contained in the vehicle terminal. It can be understood that when the vehicle terminal obtains the first data and compresses the first data, the above-mentioned network status parameters and the device performance parameters of the vehicle terminal are related to the computing resources and storage resources contained in the vehicle terminal, as well as the network resources where the vehicle terminal is located.

[0049] It can be understood that in the process of data transmission between the vehicle terminal and the cloud, since the network state parameters of the network where the vehicle terminal is located and the device performance parameters of the vehicle terminal may change, the computing resources, storage resources and network resources that the vehicle terminal can use may also change in the process of data transmission between the vehicle terminal and the cloud. Therefore, the vehicle terminal can dynamically adjust the compression parameters based on the real-time network state parameters and device performance parameters to adjust the data volume of the data to be transmitted (i.e., compressed data), thereby improving the compression efficiency. For example, when the packet loss rate of the network is large, the frequency of data transmission to the cloud can be reduced to reduce the amount of data transmitted. When the processor occupancy rate of the vehicle terminal is high, the frequency of data transmission to the cloud can also be reduced to reduce the amount of data transmitted. It can be understood that the compression parameter can be the frequency of data transmission to the cloud, the amount of data transmitted each time, the duration of continuous data transmission, data distortion, compression speed, etc., which are not limited here.

[0050] Therefore, based on the above data types and importance weights, the present application can adjust the compression parameters by comprehensively considering factors such as resource limitations, importance de-weighting, and real-time performance of Internet of Vehicles data, thereby further improving data compression efficiency.

[0051] S205: compress the first data according to the determined compression algorithm and compression parameters to obtain compressed data.

[0052] It can be understood that since the compression algorithm and compression parameters are determined by comprehensively considering factors such as the data type, importance weight, real-time, resource limitations, etc. of the first data, the determined compression algorithm and compression parameters have a higher data compression efficiency for the first data, which is beneficial to reducing the amount of compressed data after the first data is compressed.

[0053] The amount of compressed data after compression processing is smaller than the amount of the first data. Therefore, during data transmission between the vehicle terminal and the cloud, transmitting compressed data reduces the possibility of network congestion during the transmission process, which improves data transmission efficiency.

[0054] In some embodiments, after the vehicle terminal compresses the first data into compressed data using the determined compression algorithm, the compressed data can be sent to the cloud for data processing. At this time, since the amount of compressed data is small, the possibility of network congestion during transmission can be reduced, thereby improving the data transmission efficiency between the vehicle terminal and the cloud.

[0055] In other embodiments, after the vehicle terminal compresses the first data into compressed data using the determined compression algorithm, the compressed data can be stored in the storage space of the vehicle terminal. At this time, since the amount of compressed data is small, the possibility of network congestion problems during transmission can be reduced, thereby improving the data transmission efficiency between the vehicle terminal and the cloud.

[0056] In this way, the data compression method provided in the embodiment of the present application can adaptively determine the compression algorithm and compression parameters for the Internet of Vehicles data generated in real time by the vehicle terminal, thereby effectively improving the data compression efficiency. Furthermore, the storage resources and transmission resources of the compressed Internet of Vehicles data are lower, which is conducive to improving the transmission efficiency of the Internet of Vehicles data.

[0057] The data compression method mentioned in the above embodiment is described in detail below in conjunction with another embodiment. Figure 3 FIG. 1 shows a flow chart of a data compression system. The method can be executed by the vehicle terminal 100 mentioned above. The data compression method may include the following process:

[0058] S301: Data preprocessing.

[0059] In some optional implementations, during the data compression process, the vehicle terminal may obtain the original data and classify the original data to obtain the data type corresponding to the original data. According to the data type of the original data and the preset rules stored in the vehicle terminal, the vehicle terminal may assign importance weights to the original data.

[0060] It is understandable that the vehicle terminal can obtain different raw data. For example, in actual scenarios, the raw data of the vehicle terminal 100 may include but is not limited to: vehicle identification data, vehicle perception data, vehicle decision data, vehicle operation data, driver personal information, roadside perception data, roadside fusion calculation data, application service data, cloud basic information data, cloud control data, network monitoring data, high-precision map data, traffic big data, third-party life service data, third-party vehicle service data, etc.

[0061] In some optional implementations, the vehicle terminal may classify the raw data, that is, divide the raw data into different data types. As mentioned above, the data type may be structured data, semi-structured data, and unstructured data. It is understood that structured data may be raw data containing a specific structure. And, generally, structured data may be stored in a relational database. It is understood that the specific structure of structured data may be (a:b), for example, (driver name: Zhang San). The specific structure of structured data may also be (a, b, c, d, ...), for example, the vehicle's historical driving route record (location 1, location 2, location 3, location 4, ...), etc. The specific structure of structured data may also be a specific structure in other forms, which is not specifically limited in the embodiments of the present application. Semi-structured data may be raw data containing a specific structure. And, generally, semi-structured data may be stored in a non-relational database. For example, semi-structured data may be a log file, an extensible markup language (XML) file, etc. stored in a vehicle terminal. Unstructured data may be raw data that does not contain a specific structure, such as text data, image data, video data, etc.

[0062] It can be understood that in practical applications, different scenarios have different requirements for data compression methods. For example, in the process of compressing image data, since the image data contains redundant information, the redundant information refers to the presence of a large amount of repeated or similar information in the pixels of the image data. Therefore, in the process of compressing image data, redundant information can be deleted to improve the efficiency of compression processing. For another example, in the process of compressing safety warning information, the vehicle terminal needs to compress the safety warning information in real time. Therefore, the time for the vehicle terminal to perform compression processing can be less than a certain time threshold, and the time threshold is not limited here.

[0063] It is understandable that in order to adapt to different requirements of different scenarios for data compression methods, the vehicle terminal can evaluate the importance of the original data. It is understandable that in different scenarios, the importance of the original data can represent different data attributes of the original data. The data attribute can be real-time, security, necessity, etc. Specifically, the vehicle terminal can store preset rules, and according to the preset rules, the vehicle terminal can assign importance weights to the original data of different data types.

[0064] In some optional implementations, the vehicle terminal can store preset rules through a preset rule table. The preset rule table can be as shown in Table 1. The preset rule table may include original data, data type, importance weight, and association information between the original data, data type, and importance weight. The greater the importance weight, the greater the importance of the corresponding original data. It can be understood that the preset rule table may contain other information according to actual conditions, such as application service data, data storage location, data generation time, data sharing scope, data security identification, etc., which are not limited here. The importance weight can be set according to actual conditions and is not limited here. Through the preset rule table, the vehicle terminal can assign importance weights to original data of different data types.

[0065] Table 1 Preset rules table

[0066] Raw data Data Types Importance Weight … Driver personal information Structured Data 2 … Cloud control data Unstructured Data 5 … High-precision map data Semi-structured data 7 … Vehicle perception data Unstructured Data 9 … … … … …

[0067] S302: Selecting a multi-modal compression algorithm.

[0068] It is understandable that, as mentioned above, in actual applications, different scenarios have different requirements for compression algorithms. Therefore, in the process of data compression, the vehicle terminal can select a corresponding compression algorithm from a preset algorithm library according to the data type and importance weight of the original data to compress the original data.

[0069] The following are some typical scenarios and the requirements that the compression algorithms need to meet:

[0070] Scenario 1: The original data is vehicle perception information.

[0071] Generally, the vehicle terminal can obtain data of the surrounding environment and transmit the data to the cloud. For example, the vehicle terminal 100 can use a camera to collect video data of the surrounding environment and transmit the collected video data to the cloud. It can be understood that the video data is vehicle perception information, and before the vehicle terminal transmits the video data to the cloud, the vehicle terminal can compress the video data to reduce the amount of data transmitted and increase the transmission speed.

[0072] It is understandable that video data is unstructured data. Moreover, the electronic device can obtain the importance weight of the video data. For example, the importance weight can be the real-time nature of the video data, and according to the preset rule table, the importance weight can be 9. Since the video data contains multiple image frame data, and the image frame data contains redundant information, the video data contains redundant information. It is understandable that the vehicle terminal can select a corresponding compression algorithm from a preset algorithm library, and the requirements that the compression algorithm needs to meet may include: the compression algorithm can compress the redundant information of the video data. Moreover, in the process of compressing the video data using the compression algorithm, the compression processing time can be less than a certain time threshold, and the time threshold is not limited here.

[0073] Scenario 2: The original data is user personal information.

[0074] Generally, the vehicle terminal can store user personal information, which can be driver personal information, including but not limited to the driver's name, gender, age, account information, contact list, driving record, personal photos, etc. It can be understood that if personal information is leaked, it will cause losses to the user. Therefore, the importance weight of the user's personal information can be security, and according to the preset rule table, the importance weight can be 9.

[0075] It is understandable that the vehicle terminal can select a corresponding compression algorithm from a preset algorithm library, and the requirements that the compression algorithm needs to meet may include: the compression algorithm can be combined with an encryption algorithm to compress and encrypt user personal information. Moreover, since user personal information can be structured data, semi-structured data, or unstructured data, the compression algorithm can compress multiple data types.

[0076] Scenario 3: The original data is traffic big data.

[0077] Normally, the vehicle terminal can store traffic big data. The traffic big data may include the city's traffic flow, traffic congestion index, number of stops, average vehicle delays, average vehicle travel speed, average vehicle travel time, etc. Therefore, the importance weight of the traffic big data may be necessity, and according to the preset rule table, the importance weight may be 8. Normally, since the vehicle terminal has limited computing resources, storage resources and network resources. It can be understood that the vehicle terminal can select a compression algorithm from a preset algorithm library, and the requirements that the compression algorithm needs to meet may include: in the process of the compression algorithm compressing the traffic big data, the computing resources required for the compression processing are less than the computing resources that the vehicle terminal can provide, and the storage resources required for the compression processing are less than the storage resources that the vehicle terminal can provide. In addition, since the traffic big data can be structured data, semi-structured data or unstructured data, the compression algorithm can compress multiple data types.

[0078] In some optional implementations, the vehicle terminal can determine the corresponding compression algorithm according to the different data types of the original data to improve the compression efficiency. Specifically, the vehicle terminal can use a method combining improved Huffman coding and dynamic dictionary compression to compress structured data. The vehicle terminal can also use a context-based adaptive compression algorithm to compress semi-structured data. The vehicle terminal can also use a compression method combining wavelet transform and deep learning to compress unstructured data.

[0079] In some optional implementations, the compression algorithm in the preset algorithm library can be a compression algorithm pre-designed by the user, which can directly compress the original data in different scenarios to improve the compression efficiency, reduce the amount of data to be transmitted between the vehicle terminal and the cloud, and improve the data transmission efficiency. It can be understood that Figure 4 As shown, the compression algorithms included in the preset algorithm library may be an improved Huffman coding algorithm, a dynamic dictionary compression algorithm, a wavelet transform compression algorithm, a deep learning compression algorithm, etc., without limitation here.

[0080] S303: Adaptive parameter adjustment.

[0081] In some optional implementations, the vehicle terminal may obtain network status parameters of the network and device performance parameters of the vehicle terminal, and dynamically adjust the compression parameters according to the network status parameters and device performance parameters. Figure 5 As shown, first, the vehicle terminal can perform network status detection to obtain the current network status parameters. Then, the vehicle terminal can perform device resource evaluation to obtain the device performance parameters of the vehicle terminal. Finally, according to the network status parameters and device performance parameters, the vehicle terminal can dynamically adjust the compression parameters.

[0082] It can be understood that, usually, the network status of the network where the vehicle terminal is located is different in different scenarios. For example, in urban areas, the network base station coverage rate is high, and the network signal of the network where the vehicle terminal is located is usually strong and stable. In remote areas, the network base stations are sparsely distributed, and the network signal of the network where the vehicle terminal is located may be weak and unstable. For another example, the faster the vehicle terminal travels, the faster the network topology of the vehicle terminal changes, the shorter the network link exists, and the more unstable the network signal of the vehicle terminal. Therefore, in the process of data transmission between the vehicle terminal and the cloud, the vehicle terminal can perform network status detection of the network where it is located and obtain the current network status parameters. Among them, the network status parameters can be the bandwidth, delay, jitter, throughput, packet loss rate, error rate, load, network topology, network equipment parameters, protocol status, etc. of the network where the vehicle terminal is located, which are not limited here.

[0083] It can be understood that, under normal circumstances, under the limitation of the vehicle terminal hardware parameters, the hardware resources that the vehicle terminal can use are less than the hardware resources required for the actual compression processing process. The hardware resources can be the computing resources, storage resources, etc. of the vehicle terminal. For example, the vehicle terminal can perform data processing on the sensor data, and the data processing can be image processing, object recognition, path planning, data storage, etc. Due to the limitation of the computing resources of the vehicle terminal, during the image processing process, the vehicle terminal may process low-resolution images to reduce the amount of data required for image processing and improve the image processing efficiency. This may result in a decrease in the accuracy of the image processing results and affect driving safety. For another example, a driving recorder can record and store video information and audio information during the driving process of the vehicle terminal. Due to the limitation of the storage resources of the vehicle terminal, when the video information and audio information stored in the storage resources reach the upper limit, the vehicle terminal cannot continue to store new video information and audio information, which may cause information loss and affect the user experience. Therefore, under normal circumstances, the vehicle terminal can perform device resource evaluation and obtain the device performance parameters of the vehicle terminal. Among them, the equipment performance parameters can be the processor model, processor main frequency, graphics processor model, graphics processor main frequency, memory model, memory capacity, flash memory capacity, hard disk capacity, storage type, storage medium read and write speed, etc. of the vehicle terminal, and there is no restriction here.

[0084] It is understood that during the data transmission between the vehicle terminal and the cloud, the vehicle terminal can dynamically adjust the compression parameters according to different network status parameters and device performance parameters. The compression parameters can be the frequency of data transmission to the cloud, the amount of data transmitted each time, the duration of continuous data transmission, data distortion, compression speed, etc., which are not limited here.

[0085] In one achievable manner, when the network status parameters indicate that the network can transmit data, the vehicle terminal can increase the frequency of compressed data transmission to the cloud, the vehicle terminal can also increase the amount of data transmitted each time, and the vehicle terminal can also increase the duration of continuous data transmission to improve data transmission efficiency. When the network status parameters indicate that the network cannot transmit data, the vehicle terminal can reduce the frequency of compressed data transmission to the cloud, the vehicle terminal can also reduce the amount of data transmitted each time, and the vehicle terminal can also reduce the duration of continuous data transmission to avoid network congestion.

[0086] In another achievable manner, the vehicle terminal can also adjust the data distortion according to the importance weight of the original data. Specifically, since the redundant information of the original data will be discarded during the compression process, this leads to differences between the compressed data and the original data, that is, data distortion. Therefore, the data distortion can measure the degree of difference between the compressed data obtained after the compression process and the original data. Specifically, the vehicle terminal can obtain the corresponding relationship between the importance weight and the data distortion. For example, if the importance weight of the original data is greater than 8, the data distortion of the compressed process can be greater than or equal to 0.9. If the importance weight of the original data is greater than 5 and less than or equal to 8, the data distortion of the compressed process can be greater than or equal to 0.85. Therefore, when the importance weight of the original data is greater than a certain threshold, the vehicle terminal can determine the corresponding data distortion, wherein the data distortion can make the data distortion of the compressed original data less than the corresponding data distortion. It can be understood that the threshold can be set by the user, and there is no restriction here.

[0087] In another feasible manner, the vehicle terminal may also adjust the compression speed according to the real-time nature of the original data, such as increasing the compression speed. Specifically, according to the real-time nature of the original data, the vehicle terminal may set the expected data transmission rate. It is understandable that in the process of transmitting data between the vehicle terminal and the cloud, in order to increase the time used for the data transmission process, the vehicle terminal may increase the compression speed of the data compression process. The higher the compression speed, the shorter the time required for the compression process, thus improving the efficiency of the compression process. Therefore, when the expected transmission rate of the original data is greater than a certain threshold, the vehicle terminal may determine the corresponding compression speed, wherein the compression speed makes the compression speed of the original data during the compression process greater than or equal to the compression speed. It is understandable that the threshold value may be set by the user and is not limited here.

[0088] It is understandable that the vehicle terminal can also obtain other parameters and dynamically adjust the compression parameters according to the parameters. The vehicle terminal can also dynamically adjust the compression parameters according to other data attributes of the original data. Among them, the parameter can dynamically describe the hardware resources such as computing resources, storage resources and network resources that the vehicle terminal can use during the compression process. The data attribute can describe the requirements of the corresponding original data in the Internet of Vehicles technology, such as real-time, security, necessity, etc.

[0089] S304: Compression execution.

[0090] It can be understood that according to the determined compression algorithm and compression parameters, the vehicle terminal can compress the original data.

[0091] It can be understood that, as mentioned above, the vehicle terminal can use a determined compression algorithm to compress the original data. Specifically, first, the vehicle terminal can obtain the data type of the original data, and assign importance weights to the original data of different data types according to preset rules. Then, the vehicle terminal can determine the compression algorithm from the preset algorithm library according to the data type and importance weight of the original data, and use the compression algorithm to compress the original data.

[0092] It is understandable that, as mentioned above, the vehicle terminal can dynamically adjust the compression parameters according to the network status parameters and the device performance parameters. The vehicle terminal can also dynamically adjust the compression parameters according to the importance weight, real-time or other data attributes of the original data.

[0093] S305: Security processing.

[0094] It is understandable that in the process of the vehicle terminal 101 transmitting data to the cloud 102 via a wireless network, data security issues may also arise. In some possible cases, the transmitted data may be tampered with by an attacker, which will cause the compressed data received by the cloud to be inaccurate. In other possible cases, the transmitted data may also be eavesdropped by an attacker, which will cause the problem of compressed data leakage. In this way, losses are caused to the user. It is understandable that in the process of data transmission between the vehicle terminal and the cloud, based on the existing data compression method, it is impossible to ensure the security of the compressed data while compressing the original data.

[0095] In some achievable ways, during the compression process, the vehicle terminal may encrypt the sensitive data. Furthermore, the vehicle terminal may use a lightweight encryption algorithm to encrypt the sensitive data, so that during the encryption process, the computing resources required by the vehicle terminal are less than a computing resource threshold, which is not limited herein.

[0096] It can be understood that, under normal circumstances, the vehicle terminal can obtain the security identification of the original data corresponding to the compressed data, and determine whether the original data is sensitive data based on the security identification of the original data. Further, if the original data is sensitive data, the vehicle terminal can encrypt the compressed data corresponding to the original data. If the original data is not sensitive data, the vehicle terminal does not encrypt the compressed data corresponding to the original data.

[0097] In some feasible ways, the vehicle terminal can classify the security identification of the original data into no damage, general damage, serious damage, and particularly serious damage. In other feasible ways, the vehicle terminal can also classify the security identification of the original data into public data, internal data, sensitive data, and highly sensitive data. The security identification of the original data can be set according to different scenarios, and there is no limitation here. Taking the security identification of the original data as no damage, general damage, serious damage, and particularly serious damage as an example, it can be understood that when the security identification of the original data is general damage, serious damage, and particularly serious damage, during the compression process, the vehicle terminal needs to encrypt the compressed data corresponding to the original data.

[0098] It can be understood that, as mentioned above, under normal circumstances, since the computing resources and storage resources of the vehicle terminal are less than the computing resources and storage resources required in actual situations, during the process of the vehicle terminal encrypting the compressed data, the vehicle terminal can use a lightweight encryption algorithm to encrypt the compressed data so that during the encryption process, the computing resources required by the vehicle terminal are less than the above-mentioned computing resource threshold.

[0099] S306: Compression effect evaluation.

[0100] It is understood that after the vehicle terminal has finished compressing the original data, the vehicle terminal can evaluate the compression effect of the compression process by calculating the compression parameters to obtain a comprehensive evaluation result corresponding to the compression process. Furthermore, according to the comprehensive evaluation result, the vehicle terminal can dynamically adjust the compression algorithm and compression parameters.

[0101] It is understood that the vehicle terminal can evaluate the compression effect of the compression process by calculating the compression parameters. The compression parameters can be compression ratio, compression speed, data distortion, etc. Among them, the compression ratio refers to the ratio of the data volume of the original data to the data volume of the compressed data. The larger the compression ratio, the smaller the data volume of the compressed data. The compression speed refers to the time required for the compression process. The larger the compression speed, the higher the compression efficiency. As mentioned above, the data distortion refers to the degree of difference between the compressed data and the original data. The vehicle terminal can represent the data quality of the compressed data by the data distortion. It is understood that the smaller the data distortion, the more original data is retained by the compressed data. In some possible cases, in order to improve the compression ratio, during the compression process, the vehicle terminal can reduce the original data contained in the compressed data, which leads to an increase in the degree of difference between the original data and the compressed data, that is, an increase in the data distortion. Therefore, the larger the compression ratio, the greater the data distortion.

[0102] In some achievable ways, such as Figure 6 As shown, the vehicle terminal can perform a comprehensive evaluation of the compression process. The comprehensive evaluation may include that the vehicle terminal can obtain the data quality of the compressed data and the time required for the compression process based on the data distortion and the compression speed. And, it can be understood that the vehicle terminal can perform a data distortion analysis on the compression process by calculating the compression ratio. Specifically, the vehicle terminal can obtain an algorithm set by the user for data distortion analysis, and the algorithm includes the compression ratio.

[0103] In some feasible ways, according to the comprehensive evaluation results, the vehicle terminal can dynamically adjust the compression algorithm and compression parameters to optimize the compression algorithm and compression parameters determined by the vehicle terminal in different scenarios. For example, the vehicle terminal needs to transmit the acquired data of the surrounding environment to the cloud in real time, and the data of the surrounding environment requires a lower data distortion. Based on this, the vehicle terminal can increase the importance weight of the data of the surrounding environment, the vehicle terminal can also lower the compression ratio, and the vehicle terminal can also increase the compression speed, so that the compression algorithm and compression parameters determined by the vehicle terminal during the process of compressing the original data can adapt to the needs of different scenarios.

[0104] S307: Compressed data output.

[0105] It is understood that after the original data is compressed, the vehicle terminal can obtain compressed data. In some achievable ways, the vehicle terminal can add necessary compressed data information to the compressed data. The compressed data information can be a data file name, data type, data size, data description, compression algorithm name, importance weight, security mark, file suffix, data tag, etc. The specific compressed data information can be determined according to actual conditions and is not limited here.

[0106] It can be understood that in the process of transmitting data between the vehicle terminal and the cloud, the network status parameters of the network where the vehicle terminal is located, the device performance parameters of the vehicle terminal, the data type of the original data, and the importance weight of the original data may be different in different scenarios. Therefore, in the data compression method provided in the embodiment of the present application, the vehicle terminal can determine different compression algorithms and compression parameters according to different network status parameters, device performance parameters, data types of the original data, and the importance weight of the original data, so as to perform different compression processing on the original data in different scenarios, so that the original data generated in real time by the vehicle terminal can be efficiently compressed. It can be understood that the amount of compressed data that has been efficiently compressed is less than the amount of the corresponding original data, which can improve the data transmission efficiency between the vehicle terminal and the cloud.

[0107] In some embodiments, as described above, the vehicle terminal can determine different compression algorithms for raw data of different data types. This ensures that the vehicle terminal can compress raw data of different data types, perform differentiated processing on different raw data, and adapt to the diversity of raw data.

[0108] In other embodiments, as described above, the vehicle terminal may determine a compression algorithm corresponding to the original data according to the data type and importance weight of the original data to improve compression efficiency.

[0109] In other embodiments, as described above, the vehicle terminal can adaptively adjust the compression speed to improve the efficiency of the compression process and ensure the real-time nature of data transmission.

[0110] In other embodiments, as described above, the vehicle terminal can obtain the network status parameters of the network and the device performance parameters of the vehicle terminal, and dynamically adjust the compression parameters according to the network status parameters and the device performance parameters. The vehicle terminal can also evaluate the compression effect of the compression process by calculating the compression parameters to obtain a comprehensive evaluation result corresponding to the compression process. Moreover, according to the comprehensive evaluation result, the vehicle terminal can dynamically adjust the compression algorithm and compression parameters to optimize the use of the vehicle terminal's computing resources, memory resources, and network resources.

[0111] In other embodiments, as mentioned above, the vehicle terminal may encrypt the compressed data to improve the security of the compressed data during data transmission.

[0112] To sum up, the data compression method provided in this application comprehensively considers the characteristics of Internet of Vehicles data and actual application needs, and can adaptively determine the compression algorithm and compression parameters corresponding to real-time vehicle network data, effectively improving data compression efficiency, reducing transmission and storage costs, and providing important support for the further development of Internet of Vehicles technology.

[0113] Next, combine Figure 7 Introduce the structure of the vehicle. Figure 7 A structural schematic diagram of a vehicle provided in an embodiment of the present application is shown.

[0114] Figure 7 00 is a possible functional framework diagram of a vehicle 00 provided in an embodiment of the present application. Figure 7 As shown, the functional framework of the vehicle 00 may include various subsystems, such as the sensor system 10, the control system 20, one or more peripheral devices 30 (one is shown as an example), the power supply 40, and the computer system 50. Optionally, the vehicle 00 may also include other functional systems, such as an engine system that provides power for the vehicle 00, etc., which are not limited in this application.

[0115] The sensor system 10 may include a number of detection devices, which can sense the information to be measured and convert the sensed information into electrical signals or other required forms of information output according to certain rules. As shown in the figure, these detection devices may include a global positioning system 11 (global positioning system, GPS), a vehicle speed sensor 12, an inertial measurement unit 13 (inertial measurement unit, IMU), etc., and this application does not limit this.

[0116] The global positioning system GPS11 is a system that uses GPS positioning satellites to perform real-time positioning and navigation on a global scale. In the present application, the global positioning system GPS11 can be used to achieve real-time positioning of the vehicle 00 and provide geographical location information of the vehicle 00. The vehicle speed sensor 12 is used to detect the driving speed of the vehicle 00. The inertial measurement unit 13 can include a combination of an accelerometer and a gyroscope, and is a device for measuring the angular velocity and acceleration of the vehicle 00. For example, during the driving process of the vehicle 00, the inertial measurement unit can measure the position and angle change of the vehicle body based on the inertial acceleration of the vehicle 00, such as measuring the acceleration and angular velocity of the vehicle 00.

[0117] The control system 20 may include a steering unit 21 , a braking unit 22 , and the like.

[0118] The steering unit 21 may represent a system for adjusting the direction of travel of the vehicle 00, which may include but is not limited to a steering wheel, or any other structural device for adjusting or controlling the direction of travel of the vehicle 00. The braking unit 22 may represent a system for slowing down the travel speed of the vehicle 00, which may also be referred to as the braking system of the vehicle 00. It may include but is not limited to a brake controller, a decelerator, or any other structural device for slowing down the vehicle 00. In practical applications, the braking unit 22 may use friction to slow down the tires of the vehicle 00, thereby slowing down the travel speed of the vehicle 00.

[0119] The peripheral device 30 may include several components, such as the communication system 31, the touch screen 32, the user interface 33, etc., shown in the figure. The communication system 31 is used to realize the network communication between the vehicle 00 and other devices other than the vehicle 00, such as the electronic device 2. In practical applications, the communication system 31 may use wireless communication technology or wired communication technology to realize the network communication between the vehicle 00 and other devices. The wired communication technology may refer to the communication between the vehicle 00 and other devices through a network cable or optical fiber. The wireless communication technology includes but is not limited to global system for mobile communications (GSM), general packet radio service (GPRS), code division multiple access (CDMA), wideband code division multiple access (WCDMA), time-division code division multiple access (TD-SCDMA), long term evolution (LTE), wireless local area networks (WLAN) (such as wireless fidelity (Wi-Fi) networks), Bluetooth (BT), global navigation satellite system (GNSS), frequency modulation (FM), near field communication (NFC) and infrared technology (IR), etc.

[0120] The touch screen 32 may be used to detect operation instructions on the touch screen 32 .

[0121] Several functions of the vehicle 00 are controlled and implemented by the computer system 50. The computer system 50 is used to execute the data compression method described above. The computer system 50 may include multiple processors such as a general-purpose processor 51, a memory 55 (also referred to as a storage device), and a gateway 56. In actual applications, the memory 55 is also inside the computer system 50, or it may be outside the computer system 50, for example, as a cache in the vehicle 00, etc., which is not limited in this application.

[0122] The general purpose processor 51 may be, for example, a graphics processing unit (GPU).

[0123] The memory 55 may include a volatile memory, such as a random access memory (RAM); the memory may also include a non-volatile memory, such as a read-only memory (ROM), a flash memory, a hard disk drive (HDD) or a solid state drive (SSD); the memory 55 may also include a combination of the above-mentioned types of memory. The memory 55 may be used to store a set of program codes or instructions corresponding to the program codes, so that the processor 51 calls the program codes or instructions stored in the memory 55 to implement the corresponding functions of the vehicle 00. This function includes but is not limited to Figure 7 Part or all of the functions in the functional framework diagram of vehicle 00 shown.

[0124] Optionally, in addition to storing program codes or instructions, the memory 55 may also store information such as road maps, driving routes, sensor data, etc. The computer system 50 may be combined with other elements in the functional framework diagram of the vehicle 00, such as sensors in the sensor system, GPS, etc., to implement relevant functions of the vehicle 00. For example, the computer system 50 may control the driving direction or driving speed of the vehicle 00 based on the data input from the sensor system 10, which is not limited in this application.

[0125] It should be noted that the above Figure 7 This is only a possible functional framework diagram of the vehicle 00. In practical applications, the vehicle 00 may include more or fewer systems or components, which is not limited in this application.

[0126] In some possible embodiments, the present application provides an electronic device, including: a memory for storing instructions executed by one or more processors of the electronic device, and a processor, which is one of the one or more processors of the electronic device, for executing the data compression method in the various embodiments described above. It can be understood that the electronic device can be the vehicle terminal mentioned above.

[0127] In some possible embodiments, the present application provides a readable storage medium, on which instructions are stored. When the instructions are executed on an electronic device, the electronic device executes the data compression method in the various embodiments described above.

[0128] In some possible embodiments, the embodiments of the present application provide a computer program product, the computer program product includes computer instructions, and when executed by an electronic device, the electronic device executes the computer program code of the data compression method in the above various embodiments. The various embodiments of the mechanism disclosed in the present application can be implemented in hardware, software, firmware, or a combination of these implementation methods. The embodiments of the present application can be implemented as a computer program or program code executed on a programmable system, which includes at least one processor, a storage system (including volatile and non-volatile memory and / or storage elements), at least one input device, and at least one output device.

[0129] Program code can be applied to input instructions to perform the functions described in this application and generate output information. The output information can be applied to one or more output devices in a known manner. For the purposes of this application, a processing system includes any system having a processor such as, for example, a digital signal processor (DSP), a microcontroller, an application-specific integrated circuit (ASIC), or a microprocessor.

[0130] Program code can be implemented with high-level programming language or object-oriented programming language to communicate with the processing system. When necessary, program code can also be implemented with assembly language or machine language. In fact, the mechanism described in this application is not limited to the scope of any specific programming language. In either case, the language can be a compiled language or an interpreted language.

[0131] In some cases, the disclosed embodiments may be implemented in hardware, firmware, software, or any combination thereof. The disclosed embodiments may also be implemented as instructions carried or stored on one or more temporary or non-temporary machine-readable (e.g., computer-readable) storage media, which may be read and executed by one or more processors. For example, instructions may be distributed over a network or through other computer-readable media. Therefore, machine-readable media may include any mechanism for storing or transmitting information in a machine (e.g., computer) readable form, including, but not limited to, floppy disks, optical disks, optical disks, read-only memories (compact disc read-only memories, CD-ROMs), magneto-optical disks, ROMs, RAMs, erasable programmable read-only memories (erasable programmable read-only memories, EPROMs), electrically erasable programmable read-only memories (electrically erasable programmable read-only memories, EEPROMs), magnetic or optical cards, flash memory, or a tangible machine-readable memory for transmitting information (e.g., carrier waves, infrared signals, digital signals, etc.) using the Internet in electrical, optical, acoustic, or other forms of propagation signals. Accordingly, machine-readable media include any type of machine-readable media suitable for storing or transmitting electronic instructions or information in a form readable by a machine (eg, a computer).

[0132] In the accompanying drawings, some structural or method features may be shown in a specific arrangement and / or order. However, it should be understood that such a specific arrangement and / or order may not be required. Instead, in some embodiments, these features may be arranged in a manner and / or order different from that shown in the illustrative drawings. In addition, the inclusion of structural or method features in a particular figure does not mean that such features are required in all embodiments, and in some embodiments, these features may not be included or may be combined with other features.

[0133] It should be noted that the units / modules mentioned in the various device embodiments of the present application are all logical units / modules. Physically, a logical unit / module can be a physical unit / module, or a part of a physical unit / module, or can be implemented as a combination of multiple physical units / modules. The physical implementation method of these logical units / modules themselves is not the most important. The combination of functions implemented by these logical units / modules is the key to solving the technical problems proposed by the present application. In addition, in order to highlight the innovative part of the present application, the above-mentioned device embodiments of the present application do not introduce units / modules that are not closely related to solving the technical problems proposed by the present application, which does not mean that there are no other units / modules in the above-mentioned device embodiments.

[0134] It should be noted that in the examples and description of this patent, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "including one" do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0135] Although the present application has been illustrated and described with reference to certain preferred embodiments thereof, it will be apparent to those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the present application.

Claims

1. A data compression method, applied to a vehicle terminal, characterized in that: include: Acquire first data, classify the first data, and obtain a data type of the first data; determining an importance weight of the first data; Determining a compression algorithm according to the data type and importance weight of the first data; Acquire a network status parameter of a network where the vehicle terminal is located and a device performance parameter of the vehicle terminal, and determine a compression parameter at least based on the network status parameter and the device performance parameter; The first data is compressed according to the compression algorithm and the compression parameters to obtain compressed data.

2. The method according to claim 1, characterized in that The data types include: structured data, semi-structured data, and unstructured data.

3. The method according to claim 1, characterized in that The step of determining a compression algorithm according to the data type and importance weight of the first data includes: In the case where the first data is the structured data, the algorithm is determined to be a compression algorithm combining improved Huffman coding and dynamic dictionary compression; In a case where the first data is the semi-structured data, determining the algorithm as a context-adaptive compression algorithm; When the first data is the unstructured data, the algorithm is determined to be a compression algorithm that combines wavelet transform and deep learning.

4. The method according to claim 1, characterized in that: The step of determining the compression parameter at least according to the network status parameter and the device performance parameter comprises: The compression parameter is determined according to the network status parameter, the device performance parameter, the importance weight and the expected transmission rate of the first data.

5. The method according to claim 4, characterized in that When the importance weight of the first data is greater than a first threshold, the compression parameter is determined as a first parameter, wherein the first parameter makes the data distortion degree of the first data after being compressed smaller than a first set threshold.

6. The method according to claim 4, characterized in that In a case where the expected transmission rate of the first data is greater than a second threshold, the compression parameter is determined as a second parameter, wherein the second parameter makes the compression speed of the first data greater than or equal to a second preset threshold.

7. The method according to claim 1, characterized in that The method further comprises: In the case where the first data is sensitive data, the compressed data is encrypted to obtain second data.

8. The method according to claim 7, characterized in that The encryption process is a lightweight encryption process.

9. The method according to claim 1, characterized in that: The method further comprises: Evaluating the compression effect of the compression process to determine whether the compression effect meets a compression evaluation index; If the compression effect does not meet the compression evaluation index, the compression algorithm and / or compression parameters are adjusted.

10. The method according to claim 9, characterized in that The compression evaluation indicators include: compression ratio, compression speed, and data distortion.

11. The method according to claim 1, characterized in that The information added to the second data includes: a compression algorithm name, a data importance weight, and a data security mark.

12. An electronic device, characterized in that: comprising: a memory for storing instructions executed by one or more processors of the electronic device, and a processor, which is one of the one or more processors of the electronic device, configured to execute the data compression method according to any one of claims 1 to 11.

13. A readable storage medium, characterized in that: The readable storage medium stores instructions, and when the instructions are executed on an electronic device, the electronic device executes the data compression method according to any one of claims 1 to 12.

14. A computer program product, characterized in that The computer program product comprises computer instructions, and when executed by an electronic device, the electronic device performs the data compression method according to any one of claims 1 to 13.

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