Vehicle data processing method, device and equipment and vehicle

Abstract

The invention discloses a vehicle data processing method, device and equipment and a vehicle, and relates to the technical field of vehicles. The vehicle data processing method comprises the following steps: acquiring human-computer interaction data in a vehicle; determining a man-machine interaction function corresponding to the man-machine interaction data; according to the man-machine interaction function, determining a target device corresponding to the man-machine interaction function in an edge computing device and a cloud service device; and transmitting the man-machine interaction data to the target device, so as to process the man-machine interaction data through the target device, determining a man-machine interaction function corresponding to the man-machine interaction data, and determining the target device corresponding to the man-machine interaction data as an edge computing device or a cloud service device through the man-machine interaction function. Different data processing devices can be selected according to different man-machine interaction functions, so that the response speed of the vehicle in man-machine interaction is increased.

Description

Technical Field

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

[0002] The essence of cockpit intelligence is to integrate the two modules of user interaction and entertainment information based on the human-computer interaction scenario in the car cockpit to provide users with an efficient, intuitive and futuristic driving experience.

[0003] The human-computer interaction technology of smart cockpits has gone through a development from the mechanical era to the electronic era, and then to the current intelligent era. With the development of human-computer interaction technology in smart cockpits, the amount of data that needs to be processed is also growing rapidly.

[0004] In the related art, the human-computer interaction technology used for vehicles usually has a delay problem in data processing due to untimely data processing, which causes the vehicle to not respond in time in the human-computer interaction. Summary of the invention

[0005] The main purpose of the embodiments of the present application is to provide a vehicle data processing method, device, equipment and vehicle, aiming to improve the response speed of the vehicle in human-computer interaction.

[0006] To achieve the above-mentioned purpose, one aspect of an embodiment of the present application proposes a vehicle data processing method, comprising the following steps: acquiring human-computer interaction data in the vehicle; determining a human-computer interaction function corresponding to the human-computer interaction data; determining a target device corresponding to the human-computer interaction function in an edge computing device and a cloud service device based on the human-computer interaction function; transmitting the human-computer interaction data to the target device so as to process the human-computer interaction data through the target device.

[0007] In some embodiments, transmitting the human-computer interaction data to the target device so as to process the human-computer interaction data through the target device includes: transmitting the human-computer interaction data to the target device; during the transmission of the human-computer interaction data, determining data compression parameters associated with the target device; and sending the data compression parameters to the target device so that the target device compresses the transmitted human-computer interaction data based on the data compression parameters.

[0008] In some embodiments, determining the data compression parameters associated with the target device includes: obtaining bandwidth information and load information of the communication network between the target device and the on-board device of the vehicle; determining the data compression ratio associated with the target device based on the bandwidth information and the load information of the communication network, wherein the data compression parameters include the data compression ratio.

[0009] In some embodiments, determining the data compression parameters associated with the target device includes: obtaining the data type of the human-computer interaction data transmitted during the transmission process; determining the data compression method associated with the target device based on the data type of the transmitted human-computer interaction data, wherein the data compression parameters include the data compression method.

[0010] In some embodiments, determining the data compression parameters associated with the target device includes: obtaining the data priority and / or importance of the human-computer interaction data transmitted during the transmission process; determining the data compression level associated with the target device based on the data priority and / or importance of the transmitted human-computer interaction data, different data compression levels have different data compression priorities; wherein the data compression parameters include the data compression level.

[0011] In some embodiments, the data compression method associated with the target device is determined based on the data type of the transmitted human-computer interaction data, including: if the data type of the human-computer interaction data is numerical data, then the data compression method is determined to be lossless compression or differential compression; if the data type of the human-computer interaction data is sensor data, then the data compression method is determined to be symbol compression.

[0012] In some embodiments, after transmitting the human-computer interaction data to the target device, it includes: monitoring the load information of the edge computing device and the cloud service device; and dynamically adjusting the data calculation amount of the edge computing device and the cloud service device according to the load information.

[0013] To achieve the above-mentioned purpose, another aspect of an embodiment of the present application proposes a vehicle data processing device, which includes: an acquisition module for acquiring human-computer interaction data in the vehicle; a first determination module for determining the human-computer interaction function corresponding to the human-computer interaction data; a second determination module for determining, according to the human-computer interaction function, a target device corresponding to the human-computer interaction function in an edge computing device and a cloud service device; and a transmission module for transmitting the human-computer interaction data to the target device so as to process the human-computer interaction data through the target device.

[0014] To achieve the above-mentioned purpose, another aspect of an embodiment of the present application provides an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the above-mentioned vehicle data processing method when executing the computer program.

[0015] To achieve the above objective, another aspect of an embodiment of the present application provides a vehicle, wherein the vehicle includes a vehicle data processing device of the above embodiment or an electronic device of the above embodiment.

[0016] The embodiments of the present application include at least the following beneficial effects:

[0017] The present application provides a vehicle data processing method, apparatus, equipment and vehicle. In an embodiment of the present application, first, human-computer interaction data in the vehicle is acquired; a human-computer interaction function corresponding to the human-computer interaction data is determined; secondly, a target device corresponding to the human-computer interaction function is determined in an edge computing device and a cloud service device according to the human-computer interaction function; finally, the human-computer interaction data is transmitted to the target device so as to process the human-computer interaction data through the target device. In this way, by determining the human-computer interaction function corresponding to the human-computer interaction data and determining that the target device corresponding to the human-computer interaction data is an edge computing device or a cloud service device through the human-computer interaction function, different data processing devices can be selected according to different human-computer interaction functions, thereby improving the response speed of the vehicle in human-computer interaction.

[0018] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0020] Figure 1 is a flow chart of a vehicle data processing method provided by some embodiments of the present application;

[0021] Figure 2 is another flow chart of a vehicle data processing method provided by some embodiments of the present application;

[0022] Figure 3 is a schematic block diagram of modules of a vehicle data processing device provided by some embodiments of the present application;

[0023] Figure 4 It is a schematic diagram of the hardware structure of an electronic device provided in some embodiments of the present application. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the reference to "embodiment" in this article means that the specific features, structures or characteristics described in conjunction with the embodiment may be included in at least one embodiment of the present application. The appearance of this phrase in various positions in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the embodiments of the present application. They are only examples of devices and methods consistent with some aspects of the embodiments of the present application as detailed in the attached claims.

[0025] It is understood that the terms "first", "second", etc. used in this application can be used to describe various concepts in this article, but unless otherwise specified, these concepts are not limited by these terms. These terms are only used to distinguish one concept from another concept. For example, without departing from the scope of the embodiment of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the words "if" and "if" as used herein can be interpreted as "at the time of" or "when" or "in response to determination".

[0026] The terms "at least one", "multiple", "each", "any", etc. used in this application, at least one includes one, two or more, multiple includes two or more, each refers to each of the corresponding multiple, and any refers to any one of the multiple.

[0027] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0028] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application may also be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present application.

[0029] In order to make the inventive concept of the present application easy to understand, before describing the embodiments of the present application in detail, the English abbreviations (terms) / related concepts involved in the embodiments of the present application are first described. The English abbreviations (terms) / related concepts involved in the embodiments of the present application are subject to the following explanations.

[0030] Human-Computer Interaction (HCI) refers to the process of information exchange between people and computers using a certain dialogue language and a certain interactive method to complete a certain task.

[0031] The essence of intelligent cockpit is to integrate the two modules of user interaction and entertainment information based on the human-computer interaction scene in the car cockpit, and provide users with an efficient, intuitive, and futuristic driving experience. The design of the intelligent cockpit is mainly to improve the user's driving experience, while also ensuring the safety and comfort of the user's driving, and ultimately realize the ultimate goal of the car as the third living space for people outside of work and family scenes.

[0032] The human-computer interaction technology of smart cockpits has gone through a development from the mechanical era to the electronic era, and then to the current intelligent era. With the development of human-computer interaction technology in smart cockpits, the amount of data that needs to be processed is also growing rapidly.

[0033] In the related technologies, the human-computer interaction technology used for vehicles usually has a delay problem in data processing due to untimely data processing, which makes the vehicle's response in human-computer interaction not timely enough. This is not only not conducive to improving the user's driving experience, but also affects the safe driving of the vehicle.

[0034] In view of this, the present application proposes a vehicle data processing method, device, equipment and vehicle. The scheme obtains human-computer interaction data in the vehicle; determines the human-computer interaction function corresponding to the human-computer interaction data; determines the target device corresponding to the human-computer interaction function in the edge computing device and the cloud service device according to the human-computer interaction function; finally, transmits the human-computer interaction data to the target device to process the human-computer interaction data through the target device. In this way, by determining the human-computer interaction function corresponding to the human-computer interaction data, and determining that the target device corresponding to it is an edge computing device or a cloud service device through the human-computer interaction function, different data processing devices can be selected according to different human-computer interaction functions, thereby improving the response speed of the vehicle in human-computer interaction.

[0035] The method provided in the embodiment of the present application may be applied to the electronic device provided in the embodiment of the present application, wherein the electronic device includes a terminal and / or a server.

[0036] The terminal may be a central processing unit (ECU) or multiple computing units distributed in the vehicle, or may be a vehicle-mounted computer, a tablet computer, a laptop computer, etc., but is not limited thereto. It should be understood that when the method in the embodiment of the present application is run in the terminal, the terminal needs to establish a communication connection with the vehicle system and be able to transmit information to each other.

[0037] The server can be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content distribution networks, as well as big data and artificial intelligence platforms.

[0038] The above is an introduction to possible implementation environments of the embodiments of the present application.

[0039] The implementation steps of a vehicle data processing method provided by an embodiment of the present application will be described in detail below with reference to the accompanying drawings.

[0040] Please refer to Figure 1 , Figure 1 It is a flow chart of a vehicle data processing method provided by some embodiments of the present application. It should be noted that the steps shown in the flow chart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flow chart, in some cases, the steps shown or described can be executed in a different order than that here.

[0041] The method of the embodiment of the present application comprises the following steps:

[0042] Step 101: Acquire human-computer interaction data in a vehicle;

[0043] Step 102: determining a human-computer interaction function corresponding to the human-computer interaction data;

[0044] Step 103: According to the human-computer interaction function, determine a target device corresponding to the human-computer interaction function in the edge computing device and the cloud service device;

[0045] Step 104: Transmitting the human-computer interaction data to the target device so that the human-computer interaction data is processed by the target device.

[0046] Steps 101 to 104 shown in the embodiment of the present application, by determining the human-computer interaction function corresponding to the human-computer interaction data, and determining that the target device corresponding to it is an edge computing device or a cloud service device through the human-computer interaction function, can realize the selection of different data processing devices according to different human-computer interaction functions, thereby improving the response speed of the vehicle in human-computer interaction.

[0047] The specific implementation methods of the above steps are introduced below.

[0048] In step 101, human-computer interaction data in a vehicle is obtained.

[0049] The human-computer interaction data in the vehicle includes driving behavior data, vehicle status data, environmental perception data, navigation and entertainment system data, cockpit interaction data, safety and monitoring data, and Internet service data.

[0050] Among them, the human-computer interaction data involved in the smart cockpit may include: user identity recognition data, cockpit environment adjustment data, interactive interface operation data, entertainment system usage data, navigation and travel service data, vehicle status monitoring data, intelligent driving assistance data, and personalized setting data, etc.

[0051] User identity recognition data includes: facial recognition data, fingerprint recognition data, voiceprint recognition data and account login information.

[0052] Interactive interface operation data: touch operation of the central control touch screen, voice recognition and command execution records, steering wheel button and roller operation, instrument panel information viewing and switching, and HUD (Head-Up Display) head-up display settings.

[0053] Navigation and travel service data includes: navigation destination settings and route preferences, real-time traffic information reception and processing, location service usage records, and travel suggestions and planning.

[0054] Intelligent driving assistance data includes: adaptive cruise control (ACC) settings, lane keeping assist system (LKA) usage, automatic parking system operation records, and driving mode selection (such as sports mode, economy mode, etc.).

[0055] It should be understood that the acquired human-computer interaction data can usually correspond to human-computer interaction functions, such as intelligent navigation function, security monitoring function, interactive interface operation function, etc., and the complexity of these functions and the computing resources required are often quite different. For users, when using human-computer interaction functions, they hope to get feedback in time. If they wait for a long time, they will get a bad user experience. Therefore, it is necessary to select different target devices for different human-computer interaction functions to process their data, so as to speed up data processing and improve the response speed in human-computer interaction.

[0056] This application obtains human-computer interaction data in a vehicle, thereby providing a basis for determining corresponding human-computer interaction functions through these human-computer interaction data.

[0057] In step 102, a human-computer interaction function corresponding to the human-computer interaction data is determined.

[0058] The human-computer interaction functions involved in the smart cockpit include infotainment playback function, biometric function, intelligent navigation function, safety monitoring function, etc. Among them, biometric functions include sound recognition (voice recognition), facial recognition, fingerprint recognition unlocking, and eye tracking; safety monitoring functions include lane departure warning, forward collision warning, tire pressure monitoring system warning, and fatigue driving reminder. The purpose of these functions is usually to improve driving convenience, safety, and overall user experience. With the development of technology, the human-computer interaction functions in vehicles are also constantly improving, including more advanced autonomous driving technology, more natural voice and gesture recognition systems, and more intelligent personalized services, and the amount of data processing involved is also increasing significantly. Therefore, it is necessary to select appropriate data processing equipment for different human-computer interaction functions to process the data generated by them to increase the speed of their data processing and further improve the corresponding speed of human-computer interaction functions.

[0059] Determining the corresponding human-computer interaction function through human-computer interaction data can be done by collecting the interaction data of the driver and passengers from sensors, cameras, microphones, touch screens or other input devices, and performing statistical analysis on the interaction data to identify patterns, trends and anomalies in the interaction data. Machine learning algorithms, such as classification, clustering, regression analysis, etc., can then be used to identify different interaction patterns and use time series analysis to understand the dynamic changes of the interaction. Through human-computer interaction data, key features related to specific human-computer interaction functions can be extracted, such as the click position of the touch screen, the vocabulary of voice commands, the shape and direction of gestures, etc., and the relationship between these features and system responses can be further analyzed. According to the results of the analysis, the interaction data is mapped to specific interaction functions. For example, if the data shows that users frequently use voice commands to adjust the temperature of the air conditioner, it can be determined that voice control of the air conditioner is an important interaction function. If a specific area of ​​the touch screen is frequently touched, this may indicate that the menu or function corresponding to the area is frequently used by the user. In this way, the corresponding human-computer interaction function can be identified through human-computer interaction data.

[0060] The embodiments provided in the present application provide a basis for determining a target device through a human-computer interaction function by determining a human-computer interaction function corresponding to human-computer interaction data.

[0061] In step 103, according to the human-computer interaction function, a target device corresponding to the human-computer interaction function is determined in the edge computing device and the cloud service device.

[0062] Edge computing devices in vehicles generally refer to hardware devices installed inside the vehicle for processing data locally. The purpose of these devices is to reduce dependence on remote data centers or cloud services and improve the system's responsiveness, reliability, and security by processing data at the source of data generation (i.e., inside the vehicle).

[0063] Edge computing devices in vehicles usually have strong data processing capabilities and can perform complex computing tasks, such as sensor data processing, machine learning inference, etc. Since data processing occurs inside the vehicle, these devices can provide low-latency responses, which is crucial for applications with high real-time requirements (such as autonomous driving); edge computing devices usually have multiple network connection capabilities, such as 4G / 5G, Wi-Fi, Bluetooth, etc., to support communication with the outside world. Exemplarily, edge computing devices in vehicles can be electronic control units (ECUs), which are responsible for controlling different vehicle functions, such as advanced driver assistance systems (ADAS), in-vehicle infotainment systems, sensor fusion units, and autonomous driving computing platforms; edge computing devices in vehicles can also be edge AI processors, which are used to perform machine learning inference at the edge, such as for facial recognition, voice recognition, etc., and this application does not limit this.

[0064] Cloud service devices are typically used to centrally process and store large amounts of data, providing powerful computing capabilities and intelligent analysis services.

[0065] It should be understood that there will also be a communication network between the edge computing device in the vehicle and the cloud service device, which is used to connect the edge device and the cloud platform to achieve two-way transmission and processing of data.

[0066] Based on the above description, it can be understood that the edge computing devices and cloud service devices in the vehicle each have their own advantages in data processing. Since edge computing devices are usually installed inside the vehicle, they are suitable for processing data that has high real-time requirements, requires fast response and is frequently processed; and since the cloud has sufficient computing resources, it is suitable for processing data that requires a large amount of computing resources, non-real-time data, data that needs to be shared, and data that needs to be analyzed across vehicles and users.

[0067] For different human-computer interaction functions, different data computing devices can be selected to reduce the delay of their data and improve the response speed of the human-computer interaction function.

[0068] According to the human-computer interaction function, the target device corresponding to the human-computer interaction function can be determined in the edge computing device and the cloud service device. The data delay requirement (real-time requirement), bandwidth limitation, computing resource requirement and security can be considered.

[0069] For example, for the real-time voice recognition function, since it has high requirements for real-time data processing and the data does not need to be analyzed across vehicles, the data generated by the voice recognition function can be processed by edge computing devices to reduce data latency and provide a quick response.

[0070] For example, for the intelligent navigation function, since this function requires a lot of calculation and analysis, and shared data is needed when necessary, the optimal route can be calculated through the cloud service device, and the traffic information can be updated in real time to improve the accuracy of the data return and the response speed of the data. Here, the edge computing device can be responsible for navigation instructions. It should be understood that for the intelligent navigation function, if all are handed over to the edge computing device, due to the large amount of data for its calculation and analysis, it is likely to exceed the computing power of the edge computing device, and the intelligent navigation function requires shared data. When these shared data are transmitted to the edge computing device through the network, delays will occur. Therefore, processing the intelligent navigation function through the edge computing device is likely to result in a long waiting time, and the human-computer interaction response time is extended, which reduces the user experience. The data generated by the intelligent navigation function is processed by the cloud service device, due to its powerful computing power and natural data sharing ability, and the data delay is mainly caused by the delay of network transmission. With the development of communication technology, this delay will be further reduced. Therefore, processing the data generated by the intelligent navigation function through the cloud service device can improve the data processing speed and update the latest traffic information in real time, improve the human-computer interaction response speed and further enhance the user's driving experience.

[0071] For example, for dynamic gesture recognition functions, edge computing devices can be used to process the data generated to achieve rapid response and interaction.

[0072] For example, for safety monitoring, the driver's status can be monitored in real time through edge computing devices, and historical data can be analyzed through cloud service devices to provide safety recommendations. This can provide highly reliable safety services and further improve the response speed of human-computer interaction.

[0073] For example, for real-time voice assistants, voice recognition and processing can be performed on edge computing devices to provide real-time voice assistant services such as navigation, music control, weather query, etc.

[0074] For example, for multimedia entertainment systems, local multimedia processing can be performed on edge devices, and content recommendations and updates can be provided on the cloud platform to ensure that users enjoy a high-quality entertainment experience.

[0075] The embodiments provided in the present application determine the target device corresponding to the human-computer interaction function in the edge computing device and the cloud service device according to the human-computer interaction function, and process the data of the human-computer interaction function through the corresponding target device, which can improve the data response speed and improve the timeliness of the human-computer interaction response.

[0076] In step 104, the human-computer interaction data is transmitted to the target device so that the human-computer interaction data is processed by the target device.

[0077] It should be understood that the target device here is a target device corresponding to the human-computer interaction function determined in the edge computing device and the cloud service device according to the human-computer interaction function, that is, a target device that can improve the human-computer interaction response speed. Among them, the target device can be an edge computing device or a cloud service device.

[0078] In some embodiments, human-computer interaction data is transmitted to a target device so as to process the human-computer interaction data through the target device, including: transmitting the human-computer interaction data to the target device; during the transmission of the human-computer interaction data, determining a data compression parameter associated with the target device; and sending the data compression parameter to the target device so that the target device compresses the transmitted human-computer interaction data based on the data compression parameter.

[0079] In the process of transmitting human-computer interaction data to edge computing devices and / or cloud service devices, corresponding data compression parameters can be determined. Compressing the transmitted human-computer interaction data using the corresponding data compression parameters can reduce the amount of data transmission and further improve the response time of human-computer interaction.

[0080] In some embodiments, determining data compression parameters associated with a target device may include obtaining bandwidth information and load information of a communication network between the target device and an on-board device of a vehicle; and determining a data compression ratio associated with the target device based on the bandwidth information and the load information of the communication network, wherein the data compression parameters include the data compression ratio.

[0081] The compression ratio refers to the ratio between the original data size and the compressed data size. It is an important indicator to measure the efficiency of data compression, and is usually used to describe the degree to which the compression algorithm can reduce the data volume. Among them, compression ratio = original data size / compressed data size. When the compression ratio is greater than 1, it means that the compressed data volume is smaller than the original data, which shows that the compression is effective; the higher the compression ratio, the better the effect of the compression algorithm is usually, because it can more effectively reduce the storage requirements or transmission bandwidth of the data; the compression ratio can also be expressed as a percentage. For example, if the original data size is 100MB and the compressed data size is 10MB, then the compression ratio is 10, or a compression rate of 90%.

[0082] Generally, the data compression ratio may be determined by bandwidth information and load information of a communication network between a target device and an onboard device of a vehicle.

[0083] For example, the compression ratio, such as the quality factor of JPEG and the bit rate of H.264, can be dynamically adjusted according to the network bandwidth and system load to find the best balance between the compression ratio and data quality.

[0084] In some embodiments, determining data compression parameters associated with a target device includes obtaining a data type of human-computer interaction data transmitted during a transmission process; determining a data compression method associated with the target device based on the data type of the transmitted human-computer interaction data, wherein the data compression parameters include a data compression method.

[0085] The data compression method can be selected according to the type of data being transmitted, thereby reducing the amount of data transmission and improving the human-computer interaction response speed.

[0086] Exemplarily, if the data type transmitted is numerical data, lossless compression or differential compression can be selected to compress the numerical data. Lossless compression algorithms include LZ77, LZ78 and Huffman coding. These algorithms can ensure the integrity of the data and are suitable for numerical data with high precision requirements. If the numerical data is continuous, a differential compression method can be used to store only the difference between adjacent data, thereby reducing the amount of data.

[0087] For example, if the data type transmitted is image data, JPEG compression or PNG compression can be selected to compress the image data. JPEG is a lossy compression algorithm. By adjusting the compression parameters, a balance can be made between the compression ratio and the image quality. For image data that requires lossless compression, the PNG format can be used. Although the compression ratio is not as high as JPEG, it can ensure that the image quality is not damaged.

[0088] For example, if the data type transmitted is video data, H.264 or H.265 can be selected to compress the video data. These compression standards can significantly reduce the amount of video data while maintaining high video quality. During the compression process, the resolution and frame rate of the video can be dynamically adjusted according to actual needs to adapt to the limitations of bandwidth and computing resources.

[0089] For example, if the data type transmitted is sensor data, symbol compression can be selected. For sensor data, symbol compression methods such as run length encoding (RLE Run Length Encoding) and Delta encoding can be used. These methods are suitable for sensor data that does not change much and can significantly reduce the amount of data. The sampling rate can also be dynamically adjusted according to the characteristics of the sensor data to reduce the amount of data.

[0090] In some embodiments, determining data compression parameters associated with a target device further includes obtaining data priority and / or importance of human-computer interaction data transmitted during the transmission process; determining a data compression level associated with the target device based on the data priority and / or importance of the transmitted human-computer interaction data, different data compression levels having different data compression priorities; wherein the data compression parameters include the data compression level.

[0091] For example, different compression levels can be used for data of different importance. For example, critical control data requires high precision and can be compressed losslessly; while secondary monitoring data can be compressed losslessly to improve the compression ratio. A hierarchical compression strategy can also be used to compress and transmit data step by step according to the priority and importance of the data. High-priority data is transmitted first to ensure the real-time and integrity of critical data.

[0092] A real-time monitoring module can also be deployed on edge nodes to monitor data types and network conditions, and automatically select and adjust compression algorithms and parameters. For example, in a high-bandwidth environment, an algorithm with a lower compression ratio can be selected to ensure data quality; in a low-bandwidth environment, an algorithm with a high compression ratio can be selected to reduce the amount of data.

[0093] Optionally, the compression module and the data transmission module may be integrated to achieve seamless connection between data compression and transmission, thereby further reducing the time for data transmission processing.

[0094] For example, after data compression is completed on the edge node, it is immediately transmitted to the central control unit through the network to ensure the real-time nature of the data.

[0095] Important data can also be cached on edge computing devices to avoid data loss in the event of network failure.

[0096] In some implementations, after transmitting human-computer interaction data to a target device, the load information of the edge computing device and the cloud service device may be monitored, and the data computing amount of the edge computing device and the cloud service device may be dynamically adjusted according to the load information.

[0097] For example, the load of edge computing devices and cloud service devices can be continuously monitored, including CPU usage, memory usage, network bandwidth, storage space, etc. Analyze historical load data to identify load patterns and trends. Use machine learning models to predict future load conditions, and dynamically transfer computing tasks from devices with higher loads to devices with lower loads based on current load conditions. Furthermore, priorities can be set for different tasks to ensure that critical tasks are processed in a timely manner. Depending on the load conditions, tasks are migrated between edge devices and the cloud to balance the load. A monitoring system can also be established to trigger an alarm when it is detected that the device load exceeds a threshold.

[0098] Please refer to Figure 2 , Figure 2 Another flow chart of a vehicle data processing method provided for some embodiments of the present application. Figure 2 In the vehicle data processing method, the vehicle data processing method includes data compression, real-time monitoring and automatic recovery. Among them, the data compression can adopt the above-mentioned data compression method, which will not be repeated here.

[0099] Real-time monitoring, including real-time monitoring of edge devices and real-time monitoring of the system status of the cloud platform, to quickly discover and locate faults.

[0100] Automatic recovery means that the system should have an automatic recovery mechanism to ensure rapid resumption of normal operation after a failure occurs.

[0101] Optionally, a real-time monitoring system can be deployed to monitor key indicators (such as CPU, memory, network traffic, etc.) and application status. Once an anomaly is found, an alarm and fault detection are triggered immediately. Potential failure points and abnormal behaviors can also be identified by analyzing system logs.

[0102] The modular design ensures that when a fault is found, it can be quickly isolated without affecting the normal operation of other modules. Once a fault is detected, the faulty module is immediately isolated to prevent the fault from spreading.

[0103] After the fault is isolated, automatically try to restart the faulty module. The restart process should include steps such as initialization, state recovery, and function verification. You can also use a hot backup mechanism to maintain a copy of the system in real time. Once a fault occurs, quickly switch to the hot backup system to ensure system continuity.

[0104] Optionally, data backup can be performed regularly to ensure that the latest data status can be restored when a failure occurs. A data synchronization mechanism can also be used to ensure data consistency between the primary system and the backup system. During the recovery process, the latest data is synchronized to avoid data loss. During the data recovery process, the data is verified to ensure data integrity and consistency. If data anomalies are found, the data repair mechanism is started. During the recovery process, the data integrity is detected using a verification mechanism (such as hash value verification, checksum, etc.). If data anomalies are found, the damaged data is automatically repaired using redundant data or data repair algorithms.

[0105] In some embodiments, cloud computing and edge computing systems are also included. The system is divided into an edge layer and a cloud layer. The edge layer is responsible for real-time interaction and data preprocessing, and the cloud layer is responsible for deep analysis and data storage. Each functional module is independently developed and deployed to facilitate maintenance and upgrades.

[0106] Among them, edge computing devices can choose high-performance embedded devices to support real-time data processing. Edge devices can pre-process data, conduct preliminary analysis and decision-making on the data, and reduce the network transmission load; cloud platforms can choose mature cloud service platforms (such as Alibaba Cloud, Azure (Microsoft's cloud computing platform), Google Cloud) to provide computing and storage resources. The big data processing capabilities of the cloud platform can be used to analyze user behavior and system operation data, and make personalized recommendations and fault predictions. Between edge computing devices and cloud platforms, efficient network communication protocols can be selected, such as message queue telemetry transmission (MQTT) or HTTP / 2, to ensure the reliability and real-time performance of data transmission.

[0107] The above is an introduction to the vehicle data processing method embodiment and the cloud computing and edge computing system embodiment of the present application.

[0108] The implementation of the vehicle data processing device provided in the embodiments of the present application will be described in detail below with reference to the accompanying drawings.

[0109] Regarding the vehicle data processing method provided in the above embodiment, the present application embodiment also provides a vehicle data processing device for implementing the above method, such as Figure 3 As shown, Figure 3 This is a schematic block diagram of a vehicle data processing device according to an embodiment of the present application. The vehicle data processing device 300 includes:

[0110] An acquisition module 301 is used to acquire human-computer interaction data in a vehicle;

[0111] A first determination module 302, used to determine a human-computer interaction function corresponding to the human-computer interaction data;

[0112] A second determination module 303 is used to determine a target device corresponding to the human-computer interaction function in the edge computing device and the cloud service device according to the human-computer interaction function;

[0113] The transmission module 304 is used to transmit the human-computer interaction data to the target device so that the human-computer interaction data is processed by the target device.

[0114] It can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0115] like Figure 4 As shown, the embodiment of the present application further provides an electronic device 400, the electronic device 400 includes a memory 401, one or more processors 402 ( Figure 4Only one is shown in the figure) and a computer program stored in the memory 401 and executable on the processor 402. Among them: the memory 401 is used to store software programs and units, and the processor 402 executes various functional applications and data processing by running the software programs and units stored in the memory 401 to obtain the resources corresponding to the above-mentioned preset events. Optionally, the processor 402 implements the above-mentioned vehicle data processing method by running the above-mentioned computer program stored in the memory 401.

[0116] The memory 401, as a non-transitory computer-readable medium, can be used to store non-transitory software programs and non-transitory computer executable programs. In addition, the memory 401 may include a high-speed random access memory, and may also include a non-transitory memory, such as at least one disk storage device, a flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory 401 may optionally include a memory remotely arranged relative to the processor 402, and these remote memories may be connected to the processor 402 via a network.

[0117] It can be understood that the contents of the above method embodiments are all applicable to the electronic device embodiment, the functions specifically implemented by the electronic device embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0118] The embodiment of the present application also provides a vehicle, which includes the above-mentioned vehicle data processing device or the electric drive assembly of the above-mentioned electronic device. Specifically, the vehicle can be a private car, such as a sedan, SUV, MPV or pickup truck. The vehicle can also be an operating vehicle, such as a van, bus, small truck or large trailer. The vehicle can be a gasoline vehicle or a new energy vehicle. When the vehicle is a new energy vehicle, it can be a hybrid vehicle or a pure electric vehicle.

[0119] It can be understood that the contents of the above method embodiments are all applicable to the present vehicle embodiment, the functions specifically implemented by the present vehicle embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0120] An embodiment of the present application also provides a computer program product, which includes a computer program. When the computer program is executed by one or more processors, it can implement the steps of the vehicle data processing method as described above.

[0121] It can be understood that the contents of the above method embodiments are all applicable to the present computer program product, the functions specifically implemented by the present computer program product embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0122] The vehicle data processing method, device, electronic device, vehicle and computer program product provided in the embodiments of the present application can determine that the target device corresponding to the human-computer interaction function is an edge computing device or a cloud service device, and can select different data processing devices according to different human-computer interaction functions, thereby improving the response speed of the vehicle in human-computer interaction. In the process of transmitting human-computer interaction data to the edge computing device and / or the cloud service device, the corresponding data compression parameters are determined, and the transmitted human-computer interaction data is compressed by the corresponding data compression parameters, which can reduce the data transmission amount and further improve the response time of human-computer interaction.

[0123] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0124] Although specific embodiments are described herein, those of ordinary skill in the art will recognize that many other modifications or alternative embodiments are also within the scope of the present disclosure. For example, any of the functions and / or processing capabilities described in conjunction with a particular device or component may be performed by any other device or component. In addition, although various exemplary implementations and architectures have been described according to embodiments of the present disclosure, those of ordinary skill in the art will recognize that many other modifications to the exemplary implementations and architectures described herein are also within the scope of the present disclosure.

[0125] Some aspects of the present disclosure are described above with reference to the block diagram and flow chart of the system, method, system and / or computer program product according to the exemplary embodiments.It should be understood that the combination of one or more blocks in the block diagram and the flow chart and the blocks in the block diagram and the flow chart can be realized by executing computer executable program instructions respectively.Equally, according to some embodiments, some blocks in the block diagram and the flow chart may not need to be executed in the order shown, or may not need to be executed in full.In addition, additional components and / or operations beyond those components and / or operations shown in the blocks in the block diagram and the flow chart may be present in certain embodiments.

[0126] Therefore, the blocks in the block diagrams and flow charts support the combination of devices for performing the specified functions, the combination of elements or steps for performing the specified functions, and the program instruction devices for performing the specified functions. It should also be understood that each block in the block diagrams and flow charts and the combination of blocks in the block diagrams and flow charts can be implemented by a special-purpose hardware computer system or a combination of special-purpose hardware and computer instructions that performs a specific function, element or step.

[0127] The program modules, applications, etc. described herein may include one or more software components, including, for example, software objects, methods, data structures, etc. Each such software component may include computer-executable instructions that, in response to execution, cause at least a portion of the functionality described herein (e.g., one or more operations of the illustrative methods described herein) to be performed.

[0128] Software components can be encoded with any of various programming languages. An exemplary programming language can be a low-level programming language, such as an assembly language associated with a specific hardware architecture and / or an operating system platform. The software component comprising assembly language instructions may need to be converted to executable machine code by an assembler before being executed by hardware architecture and / or platform. Another exemplary programming language can be a higher-level programming language, which can be transplanted across multiple architectures. The software component comprising a higher-level programming language may need to be converted to an intermediate representation by an interpreter or a compiler before execution. Other examples of programming languages ​​include but are not limited to macro languages, shells or command languages, job control languages, scripting languages, database queries or search languages ​​or report writing languages. In one or more exemplary embodiments, the software component comprising the instruction of one of the above-mentioned programming language examples can be directly executed by an operating system or other software components, without first being converted into another form.

[0129] Software components may be stored as files or other data storage structures. Software components of similar type or related functions may be stored together, such as in a specific directory, folder, or library. Software components may be static (e.g., preset or fixed) or dynamic (e.g., created or modified at execution time).

[0130] The embodiments of the present application are described in detail above in conjunction with the accompanying drawings, but the present application is not limited to the above embodiments, and various changes can be made within the knowledge scope of ordinary technicians in the relevant technical field without departing from the purpose of the present application.

Claims

1. A vehicle data processing method, characterized in that: The following steps are involved: Acquiring human-computer interaction data in the vehicle; Determining a human-computer interaction function corresponding to the human-computer interaction data; According to the human-computer interaction function, determining a target device corresponding to the human-computer interaction function in the edge computing device and the cloud service device; The human-computer interaction data is transmitted to the target device so that the human-computer interaction data is processed by the target device.

2. The vehicle data processing method according to claim 1, characterized in that: The transmitting the human-computer interaction data to the target device so as to process the human-computer interaction data by the target device includes: transmitting the human-computer interaction data to the target device; During the transmission of the human-computer interaction data, determining a data compression parameter associated with the target device; The data compression parameter is sent to the target device, so that the target device compresses the transmitted human-computer interaction data based on the data compression parameter.

3. The vehicle data processing method according to claim 2, characterized in that: The determining of a data compression parameter associated with the target device comprises: Acquiring bandwidth information and load information of a communication network between the target device and an onboard device of the vehicle; A data compression ratio associated with the target device is determined according to the bandwidth information and the load information of the communication network, wherein the data compression parameter includes the data compression ratio.

4. The vehicle data processing method according to claim 2, characterized in that: The determining of a data compression parameter associated with the target device comprises: Acquire the data type of the human-computer interaction data transmitted during the transmission process; A data compression method associated with the target device is determined according to a data type of the transmitted human-computer interaction data, wherein the data compression parameters include the data compression method.

5. The vehicle data processing method according to claim 2, characterized in that: The determining of a data compression parameter associated with the target device comprises: obtaining data priority and / or importance of the human-computer interaction data transmitted during the transmission process; Determining a data compression level associated with the target device according to the data priority and / or importance of the transmitted human-computer interaction data, wherein different data compression levels have different data compression priorities; The data compression parameter includes the data compression level.

6. The vehicle data processing method according to claim 4, characterized in that: The determining, according to the data type of the transmitted human-computer interaction data, a data compression method associated with the target device includes: If the data type of the human-computer interaction data is numerical data, determining that the data compression method is lossless compression or differential compression; If the data type of the human-computer interaction data is sensor data, it is determined that the data compression method is symbol compression.

7. The vehicle data processing method according to claim 1, characterized in that: After transmitting the human-computer interaction data to the target device, the method further comprises: Monitoring load information of the edge computing device and the cloud service device; The data computing amount of the edge computing device and the cloud service device is dynamically adjusted according to the load information.

8. A vehicle data processing device, characterized in that: The device comprises: An acquisition module, used for acquiring human-computer interaction data in the vehicle; A first determining module, used to determine a human-computer interaction function corresponding to the human-computer interaction data; A second determination module is used to determine a target device corresponding to the human-computer interaction function in the edge computing device and the cloud service device according to the human-computer interaction function; The transmission module is used to transmit the human-computer interaction data to the target device so that the human-computer interaction data can be processed by the target device.

9. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the vehicle data processing method according to any one of claims 1 to 7 when executing the computer program.

10. A vehicle, characterized in that: The vehicle includes the vehicle data processing device according to claim 8 or the electronic device according to claim 9.

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