Over-the-air (OTA) upgrading method and system for whole automobile, electronic equipment and medium

Abstract

The invention provides a whole automobile OTA upgrading method and system, electronic equipment and a medium, and belongs to the technical field of vehicles. The method comprises the steps that historical upgrading behavior data are acquired; analyzing according to the historical upgrading behavior data to generate a feature portrait; newly establishing an upgrading task, and matching a target object corresponding to the upgrading task based on the feature portrait; and binding the upgrading task with the target object, and issuing the upgrading task. According to the method, the historical upgrading behavior data of the user is collected in advance, the feature portraits of the vehicle and the user in the OTA field are generated through data processing and learning, the target object is matched according to the created upgrading task, the optimal vehicle group range of the task is obtained, a personalized vehicle OTA upgrading scheme is achieved, and the user experience and the upgrading success rate are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicles, in particular to a vehicle OTA upgrading method and system, an electronic device and a medium. BACKGROUND

[0002] In the era of intelligent and connected vehicles, software has become one of the core competencies of vehicles. In order to ensure that vehicle software can be updated in time, new functions can be introduced and potential problems can be fixed, vehicle manufacturers generally use OTA (Over-The-Air) technology to realize remote upgrading. With the acceleration of the process of vehicle intelligence, OTA technology has become a standard capability of intelligent vehicles. Through OTA technology, vehicle manufacturers can perform remote diagnosis, big data analysis, quickly fix system faults and add new functions, etc. At the same time, OTA technology also promotes the transformation of the automotive industry, so that vehicles are no longer a fixed bulk consumer product, but can meet the "long tail needs" of users through continuous software upgrading and iteration, and improve user experience.

[0003] However, traditional OTA upgrading is only for specific vehicle models, often ignoring the individual differences in user needs, resulting in users receiving upgrade prompts at inappropriate times, or failing to complete the upgrade smoothly due to insufficient memory in the vehicle, and even affecting the daily driving experience. SUMMARY

[0004] The present application aims to solve at least one of the technical problems existing in the prior art, and provides a vehicle OTA upgrading method, system, electronic device and medium.

[0005] In a first aspect, the present application provides a vehicle OTA upgrading method, comprising:

[0006] acquiring historical upgrading behavior data;

[0007] analyzing the historical upgrading behavior data to generate a feature portrait;

[0008] creating a new upgrading task, and matching a target object corresponding to the upgrading task based on the feature portrait;

[0009] binding the upgrading task with the target object, and publishing the upgrading task.

[0010] In some embodiments, the acquiring historical upgrading behavior data comprises:

[0011] acquiring the remaining capacity of the vehicle memory at the time of upgrading, and the historical frequency and duration of use of the vehicle APP based on the vehicle end;

[0012] acquiring the historical pre-booking upgrading time period based on the mobile device end;

[0013] The historical upgrade behavior data is obtained by marking and cleaning the historical upgrade time period, the remaining capacity of the in-vehicle content, and the historical use frequency and duration.

[0014] In some embodiments, the analysis based on the historical upgrade behavior data generates a feature portrait, including:

[0015] The historical upgrade behavior data is feature extracted based on a machine learning algorithm to obtain behavior features and resource features;

[0016] The behavior features and resource features are analyzed to obtain a vehicle portrait or a user portrait;

[0017] The feature portrait is generated based on the vehicle portrait or the user portrait.

[0018] In some embodiments, the new upgrade task is based on the feature portrait to match the target object corresponding to the upgrade task, including:

[0019] The upgrade task is newly created based on an OTA cloud;

[0020] The task information of the upgrade task is synchronized to a big data platform;

[0021] The task information of the upgrade task is obtained based on a decision platform;

[0022] The corresponding target object is matched for each upgrade task based on the task information and the feature portrait; wherein the target object includes a target crowd or a target vehicle group.

[0023] In some embodiments, the task information includes the size of the task upgrade package, the in-vehicle APP information to be upgraded, and the task push time period.

[0024] In some embodiments, the upgrade task is bound to the target object and the upgrade task is published, including:

[0025] The upgrade task is relationally bound to the target object and stored in a big data platform;

[0026] Before publishing the upgrade task, the target object bound to the upgrade task is obtained based on the big data platform;

[0027] The vehicle group information is determined based on the target object;

[0028] The upgrade task is published to all vehicles or users in the vehicle group information.

[0029] In some embodiments, the method further includes:

[0030] Obtaining upgrade result information and current behavior data of the vehicle based on the upgrade task feedback;

[0031] Optimizing the matching relationship between the upgrade task and the target object according to the upgrade result information and the current behavior data, to obtain an optimized upgrade strategy;

[0032] Releasing the upgrade task according to the optimized upgrade strategy.

[0033] In a second aspect, an automobile whole vehicle OTA upgrade system is provided, comprising:

[0034] A behavior data collection module is configured to obtain historical upgrade behavior data.

[0035] A behavior analysis module is configured to analyze the historical upgrade behavior data and generate a feature portrait.

[0036] A task matching module is configured to newly create an upgrade task and match a target object corresponding to the upgrade task based on the feature portrait.

[0037] A task binding module is configured to bind the upgrade task to the target object and release the upgrade task.

[0038] In a third aspect, an electronic device is provided, comprising:

[0039] One or more processors;

[0040] A memory is configured to store one or more programs.

[0041] When the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any of the above.

[0042] In a fourth aspect, a computer readable medium is provided, and the computer readable medium stores a computer program, which, when executed by a processor, implements the steps in the method described in any of the above.

[0043] The automobile whole vehicle OTA upgrade method provided by the application comprises the following steps: obtaining historical upgrade behavior data; analyzing the historical upgrade behavior data and generating a feature portrait; newly creating an upgrade task and matching a target object corresponding to the upgrade task based on the feature portrait; and binding the upgrade task to the target object and releasing the upgrade task. The application collects historical upgrade behavior data of users in advance, generates a feature portrait of vehicles and users in the OTA field through data processing and learning, matches a target object according to a created upgrade task, obtains a best vehicle group range of the task, implements an individualized automobile OTA upgrade scheme, and improves user experience and upgrade success rate. Attached Figure Description

[0044] Figure 1 A flowchart illustrating an OTA (Over-The-Air) upgrade method for a complete vehicle provided in an embodiment of the present invention;

[0045] Figure 2 This is a schematic diagram of an OTA upgrade solution for a complete vehicle involved in an embodiment of the present invention;

[0046] Figure 3 This is an OTA upgrade sequence diagram involving behavioral data collection and learning in an embodiment of the present invention;

[0047] Figure 4 This is an OTA upgrade sequence diagram generated in an embodiment of the present invention involving personalized upgrade tasks;

[0048] Figure 5 This is an OTA upgrade timing diagram involving OTA task detection and upgrade in an embodiment of the present invention;

[0049] Figure 6 A structural block diagram of an OTA upgrade system for an automobile provided in an embodiment of the present invention;

[0050] Figure 7 This is a structural block diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0051] To enable those skilled in the art to better understand the technical solutions of the present invention, exemplary embodiments of the present invention are described below in conjunction with the accompanying drawings, including various details of the embodiments of the present invention to aid understanding. These should be considered merely exemplary. Therefore, those skilled in the art should recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of the present invention. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0052] Where there is no conflict, the various embodiments of the present invention and the features thereof may be combined with each other.

[0053] As used herein, the term “and / or” includes any and all combinations of one or more related enumerated entries.

[0054] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and / or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. "Coupled" or "connected" or similar terms are not restricted to physical or mechanical connections or associations, but can also include electrical connections, whether direct or indirect.

[0055] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and the present disclosure, and will not be interpreted in an overly literal or overly formal sense unless expressly so defined herein.

[0056] In the technical solutions of the present application, the collection, storage, use, processing, transmission, provision and disclosure of user personal information comply with relevant laws and regulations and do not violate public order and good customs. The use of user data in the technical solutions complies with relevant national laws and regulations (for example, "Information Security Technology Personal Information Security Specification" and the like). For example, appropriate measures are taken for personal information access control; restrictions are given to the display of personal information; the use purpose of personal information does not exceed the direct or reasonably related range; the use of personal information eliminates the explicit identity pointing and avoids precise positioning to a specific individual.

[0057] The definitions of the key terms involved in the present application include:

[0058] OTA (Over The Air): Over-the-air technology;

[0059] APP (Application): Mobile application;

[0060] Vehicle Group: A list of vehicle vin numbers with certain common characteristics;

[0061] User Group: A list of user IDs with certain common characteristics.

[0062] In the related art, a standardized vehicle OTA vehicle version upgrade method first creates a vehicle version, then obtains the vehicle version and the software version at all ECUs thereunder according to the vehicle model, then configures a vehicle version task, configures the upgrade task from the source vehicle version to the target vehicle version according to the vehicle model, and publishes it on the OTA platform. After the vehicle end detects the new version on the OTA platform, it is displayed on the user end and the vehicle end is upgraded successfully. The vehicle corresponding to the vehicle version number is updated. This method can simplify enterprise operation processes and costs, facilitate enterprise operation control, and also reduce user perception of unnecessary upgrade information and improve the user operation experience. However, traditional OTA upgrades only target specific vehicle models and often use a one-size-fits-all approach, ignoring the individual needs of users. This can result in users receiving upgrade prompts at inappropriate times, or failing to complete upgrades smoothly due to insufficient vehicle memory, and even affecting daily driving experience. Therefore, it is particularly important to develop a system that can dynamically adjust upgrade strategies based on user-specific situations.

[0063] To solve at least one of the technical problems existing in the related art described above, the present application provides a vehicle OTA upgrade method. Figure 1 A flowchart of a vehicle OTA upgrade method according to an embodiment of the present application is shown in

[0064] As an embodiment of the present application, as shown in Figure 1 The vehicle OTA upgrade method comprises the following steps:

[0065] Step S1: Obtain historical upgrade behavior data;

[0066] Step S2: Analyze the historical upgrade behavior data to generate a feature portrait;

[0067] Step S3: Create a new upgrade task and match the target object corresponding to the upgrade task based on the feature portrait;

[0068] Step S4: Bind the upgrade task to the target object and publish the upgrade task.

[0069] It should be noted that the execution subject in this embodiment can be an electronic device, which can be a computer device with data processing function, or other devices that can achieve the same or similar functions. This embodiment does not limit the execution subject, which is taken as a computer device in this embodiment for illustration.

[0070] The method described in this embodiment adopts a personalized vehicle OTA upgrade scheme, collects user behavior data in the upgrade process, the current resource state of the vehicle, and the user's use of the vehicle APP, and generates different upgrade schemes for each user. This method not only improves the success rate of upgrading, but also enhances the user experience and helps to reduce the risk caused by improper upgrading.

[0071] As shown in the exemplary personalized vehicle OTA upgrade scheme, Figure 2 The personalized vehicle OTA upgrade scheme mainly involves three modules: (1) User behavior data collection module: responsible for collecting user behavior data in the upgrade process, including the user's preferred reservation upgrade time period, the remaining resource space of the vehicle during upgrade, and the user's use frequency and duration of the vehicle APP, etc. This module can be deployed on the vehicle side and the mobile phone APP side. (2) Personalized upgrade intelligent module: based on the collected user behavior data, analyze the user's upgrade habits, application use preferences, and vehicle memory resource usage, and (3) create upgrade tasks in the OTA upgrade execution module, intelligently match the vehicle groups of each task, bind the tasks and vehicle groups, and then implement personalized upgrade strategies. For example, upgrade during the user's preferred off-peak period, ensure that the vehicle memory is sufficient before performing large version updates, and prioritize OTA tasks that contain frequently used APP versions by the user, etc. (3) OTA upgrade execution module: according to the personalized upgrade strategy generated by the (2) personalized upgrade intelligent module, i.e. the binding relationship between the upgrade task and the vehicle group, publish and take effect the upgrade task. This module can match each vehicle or APP side to a personalized upgrade task, improve the success rate of task upgrade and the user's vehicle experience. The following will be described in combination with specific steps.

[0072] In some embodiments, the historical upgrade behavior data is obtained, including: based on the vehicle side, collecting the remaining capacity of the vehicle memory during upgrade and the historical use frequency and duration of the vehicle APP; based on the mobile device side, collecting the historical reservation upgrade time period; marking and cleaning the historical reservation upgrade time period, the remaining capacity of the vehicle memory, and the historical use frequency and duration to obtain the historical upgrade behavior data.

[0073] In some embodiments, the historical upgrade behavior data is analyzed to generate a feature portrait, including: based on a machine learning algorithm, feature extraction is performed on the historical upgrade behavior data to obtain behavior features and resource features; based on the behavior features and resource features, the vehicle portrait or user portrait is obtained; and based on the vehicle portrait or user portrait, the feature portrait is generated.

[0074] Specifically, as shown in Figure 3 The collection and learning of behavior data (behavior data reporting and learning):

[0075] (1) During the OTA upgrade process, the user behavior data collection module reports the user's behavior or vehicle terminal resource situation to the cloud big data platform. For example, the frequency or duration of the user using the vehicle machine software, the remaining capacity of the vehicle machine memory, and the time period of the vehicle terminal operating OTA upgrade, etc. For example, the user's reservation upgrade time on the car APP. It should be noted that the prerequisite for collecting this information is to obtain the prior authorization of the vehicle owner.

[0076] (2) The reported behavior data is stored in the database of the big data platform, and is marked and cleaned to generate processed data (historical upgrade behavior data) for standby.

[0077] (3) The personalized upgrade intelligent module will periodically learn from the processed behavior data (historical upgrade behavior data) through a preset algorithm to generate a feature portrait for each vehicle or each user, including upgrade time period preference, vehicle machine APP software use preference, vehicle resource capacity snapshot, etc.

[0078] By way of example, user behavior data includes but is not limited to vehicle machine software usage frequency, duration, memory remaining capacity, OTA upgrade time period, car APP reservation upgrade time, etc. The collected raw data is cleaned, converted and integrated to eliminate noise, fill in missing values, standardize data format, etc. For example, data cleaning can include deleting irrelevant data, correcting incorrect data; data conversion can include converting data to a unified format, such as timestamp conversion, categorical variable encoding. Data integration can include merging data from different sources to form processed data (historical upgrade behavior data).

[0079] By way of example, through a preset algorithm for machine learning, a feature portrait is generated for each vehicle or each user: useful features are extracted from historical upgrade behavior data, which can better represent the data and help subsequent machine learning model analysis. The preset algorithm (machine learning model) includes but is not limited to: clustering algorithm (such as K-means, hierarchical clustering, etc., used to discover the similarity of user groups), classification algorithm (such as decision tree, random forest, support vector machine, etc., used to predict user behavior), regression algorithm (such as linear regression, ridge regression, etc., used to predict numerical features), deep learning algorithm (such as neural network, used to handle complex data relationships). The selected machine learning model is trained using the labeled data set. During this process, the model can learn how to extract features from the input data and generate the corresponding feature portrait.

[0080] In some embodiments, the new upgrade task is created, the target object corresponding to the upgrade task is matched based on the feature portrait, and the method comprises: creating the upgrade task based on the OTA cloud; synchronizing the task information of the upgrade task to the big data platform; obtaining the task information of the upgrade task based on the decision platform; matching the corresponding target object for each upgrade task according to the task information and the feature portrait; wherein the target object comprises a target crowd or a target vehicle group. The task information comprises the size of the task upgrade package, the vehicle machine APP information to be upgraded, and the task push time period.

[0081] In some embodiments, the upgrade task is bound to the target object, and the upgrade task is published, which comprises: binding the upgrade task to the target object in a relationship, and storing it in the big data platform; before publishing the upgrade task, obtaining the target object bound to the upgrade task based on the big data platform; determining the vehicle group information according to the target object; publishing the upgrade task to all vehicles or users in the vehicle group information.

[0082] Specifically, as shown in Figure 4 the personalized upgrade task generation comprises:

[0083] (1) Before the OTA activity, the operation personnel can create many new OTA upgrade tasks on the OTA upgrade execution module, and synchronize the task information of these OTA upgrade tasks to the big data platform. The task information includes but is not limited to the size of the task upgrade package, the vehicle machine APP information to be upgraded, the task push time period, etc.

[0084] (2) The personalized upgrade intelligent module can obtain the upgrade task information to be published from the big data platform in time, and match the most accurate crowd or vehicle group to these upgrade tasks according to these task information through a specific strategy (for example, through the relationship between people and vehicles, the crowd can be converted into a vehicle group), and bind the upgrade task and the vehicle group relationship, and store it in the big data platform database.

[0085] (3) Before publishing the OTA task, the operation personnel obtain the vehicle group information of the upgrade task from the big data platform on the OTA upgrade execution module, and then publish the upgrade task, and the upgrade task starts to take effect and is visible to all vehicles or users in the vehicle group.

[0086] Exemplarily, the most accurate crowd or vehicle group is matched to the upgrade task according to the task information through a specific strategy. The specific strategy can be a set of rules or algorithms for analyzing and deciding how to match the new OTA (Over-The-Air) upgrade task to the most suitable target crowd or vehicle group.

[0087] Exemplarily, specific strategies are employed to match the upgrade tasks with the target users or vehicles. For example, based on the previously collected user behavior data and vehicle information, the upgrade preferences, vehicle usage patterns, vehicle specifications, and other characteristics of the users are analyzed. The characteristics of the upcoming upgrade tasks, such as the size of the upgrade package, the importance, the type of upgrade (e.g., security update, feature update, etc.), the expected upgrade time window, and the like, are analyzed. The strategies and algorithms that can be used to match the upgrade tasks with the vehicle fleet are employed for matching.

[0088] Exemplarily, rule-based matching algorithms are employed: a set of rules are predefined, such as "if the remaining memory space in the vehicle is greater than X and the user has not performed an upgrade in the past month, then push a new task", these rules are based on the characteristics of the users and vehicles and the attributes of the upgrade tasks. Collaborative filtering is employed: the reactions of similar user or vehicle groups to past upgrade tasks are analyzed to predict their reactions to new tasks. Cluster analysis is employed: users or vehicles are divided into different groups, and upgrade tasks are pushed to the most matching group. Decision tree / random forest is employed: a model is built to predict which users or vehicles are most likely to successfully complete an upgrade, and then the push is made according to the prediction results. Machine learning classification algorithms are employed: the characteristics of the users and vehicles are used to train a classification model to predict which entities should receive an upgrade task. It should be noted that the specific strategies ensure compliance with all relevant data protection regulations and user privacy agreements.

[0089] In this embodiment, specific strategies are employed to ensure that the upgrade tasks can be effectively and targetedly distributed to those users or vehicles who are most likely to benefit from them, while avoiding unnecessary waste of services and resources. In this way, the acceptance rate, completion rate, and user satisfaction of the upgrade tasks can be improved.

[0090] In some embodiments, the method further comprises: obtaining upgrade result information and current behavior data of the vehicle based on the feedback of the upgrade task; optimizing the matching relationship between the upgrade task and the target object according to the upgrade result information and the current behavior data to obtain an optimized upgrade strategy; and performing the upgrade task publishing according to the optimized upgrade strategy.

[0091] Specifically, as shown in FIG. 8, OTA task detection and upgrade: Figure 5

[0092] (1) After power-on, the vehicle end initiates a detection task request to the OTA cloud end, and the OTA upgrade execution module detects whether there is an upgrade task for the vehicle. If there is an upgrade task, the upgrade task configuration information is downloaded to the vehicle end. Here, the configuration information can include the recommended upgrade time period, the upgrade package download link address, and the pre-upgrade conditions, etc.

[0093] ​(2) After downloading the upgrade package at the vehicle end, the upgrade is performed at the recommended upgrade time (the upgrade time needs to be confirmed after the vehicle owner agrees), and the upgrade result information and current behavior data are reported to the OTA cloud. The upgrade result information can include whether the component upgrade is successful, failure reason, etc. The current behavior data can include the time period of the upgrade, the upgrade time length, and the vehicle state data at the time of the upgrade, etc. These data (upgrade result information and current behavior data) are written into the big data platform, facilitating the re-optimization strategy of the individualized upgrade intelligent module.

[0094] (3) When the user queries the OTA task information on the automobile APP end, the OTA upgrade execution module recommends a reservation time to the user, and the task information is sent to the APP end. The user can set the recommended reservation time or set the reservation time by himself / herself. The OTA upgrade execution module reports the behavior data to the big data platform, facilitating the re-optimization strategy of the individualized upgrade intelligent module (matching the upgrade task and the strategy of the vehicle group).

[0095] In the embodiment, the following beneficial effects are achieved through the individualized automobile OTA upgrade scheme: Through the individualized upgrade strategy, the upgrade at an inconvenient time of the user is avoided, the influence of the upgrade on the normal use of the user is reduced, and the user experience is improved. The upgrade time is reasonably arranged according to the memory resource condition of the vehicle, the upgrade failure caused by insufficient memory is avoided, and the upgrade success rate is improved. The APP frequently used by the user is preferentially upgraded, the satisfaction and dependence of the user on the intelligent function of the vehicle are improved, and the user stickiness is enhanced.

[0096] The automobile whole vehicle OTA upgrade method provided in the embodiment includes: acquiring historical upgrade behavior data; analyzing the historical upgrade behavior data to generate a feature portrait; newly creating an upgrade task, matching a target object corresponding to the upgrade task based on the feature portrait; binding the upgrade task and the target object, and publishing the upgrade task. The historical upgrade behavior data of the user is collected in advance, the feature portrait of the vehicle and the user in the OTA field is generated through data processing and learning, the target object is matched according to the created upgrade task, the best vehicle group range of the task is obtained, the individualized automobile OTA upgrade scheme is realized, and the user experience and the upgrade success rate are improved.

[0097] Reference Figure 6 , Figure 6 is a structural block diagram of an embodiment of the automobile whole vehicle OTA upgrade system. As shown in Figure 6 , the automobile whole vehicle OTA upgrade system includes:

[0098] The behavior data acquisition module 10 is configured to acquire historical upgrade behavior data.

[0099] The behavior analysis module 20 is configured to analyze the historical upgrade behavior data to generate a feature portrait.

[0100] The task matching module 30 is configured to newly create an upgrade task and match a target object corresponding to the upgrade task based on the feature portrait.

[0101] The task binding module 40 is configured to bind the upgrade task to the target object and publish the upgrade task.

[0102] Specifically, the automobile whole-vehicle OTA upgrade system adopts a personalized automobile OTA upgrade scheme, collects behavior data of users in an upgrade process, a current resource state of a vehicle, and a condition of using a vehicle APP by a user, and thus generates different upgrade schemes for each user. The system not only improves a success rate of upgrade, but also enhances user experience and helps to reduce risks caused by improper upgrade.

[0103] Exemplarily, behavior data of users is collected in advance at a vehicle end and an automobile APP end, and then the data is reported to a big data platform for data processing. An intelligent decision platform generates a portrait of a vehicle and a user in an OTA field through data processing and learning, and then matches a best vehicle group range of a task according to task data of a market OTA created on the OTA platform. Finally, the vehicle is matched in the vehicle group or the person group after the task is published, and personalized OTA upgrade is performed, which improves user experience and a success rate of upgrade.

[0104] The automobile whole-vehicle OTA upgrade system provided in the embodiment collects historical upgrade behavior data of users in advance, generates a feature portrait of a vehicle and a user in an OTA field through data processing and learning, matches a target object according to created upgrade tasks, obtains a best vehicle group range of a task, implements a personalized automobile OTA upgrade scheme, and improves user experience and a success rate of upgrade.

[0105] In addition, technical details not described in detail in the automobile whole-vehicle OTA upgrade system embodiment can be referred to the automobile whole-vehicle OTA upgrade method provided in any embodiment of the application, and will not be described here.

[0106] Based on the same inventive concept, the embodiment of the application further provides an electronic device. Figure 7 A structural block diagram of the electronic device provided in the embodiment of the application is shown in FIG. 4. Figure 7As shown, the electronic device provided by the embodiment of the present application comprises one or more processors 101, a memory 102, and one or more I / O interfaces 103. The memory 102 stores one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement the vehicle OTA upgrade method in any of the above embodiments. The one or more I / O interfaces 103 are connected between the processor and the memory and are configured to realize information interaction between the processor and the memory.

[0107] The processor 101 is a device with data processing capability, including but not limited to a central processing unit (CPU) and the like. The memory 102 is a device with data storage capability, including but not limited to a random access memory (RAM, more specifically SDRAM, DDR, etc.), a read-only memory (ROM), an electrically erasable programmable read-only memory (EEPROM), and a flash memory (FLASH). The I / O interface (read-write interface) 103 is connected between the processor 101 and the memory 102 and can realize information interaction between the processor 101 and the memory 102, including but not limited to a data bus (Bus) and the like.

[0108] In some embodiments, the processor 101, the memory 102, and the I / O interface 103 are connected to each other through a bus 104 and further connected to other components of the computing device.

[0109] In some embodiments, the one or more processors 101 include a field programmable gate array.

[0110] The embodiment of the present application further provides a computer readable medium. The computer readable medium stores a computer program, and when the program is executed by a processor, the steps in the vehicle OTA upgrade method in any of the above embodiments are implemented. The computer readable storage medium can be a volatile or non-volatile computer readable storage medium.

[0111] The embodiment of the present application further provides a computer program product comprising computer readable code or a non-volatile computer readable storage medium carrying computer readable code, and when the computer readable code is run in a processor of an electronic device, the processor in the electronic device executes the vehicle OTA upgrade method.

[0112] Those skilled in the art will understand that all or some of the steps, systems, and apparatuses disclosed above, and their functional modules / units, can be implemented as software, firmware, hardware, or suitable combinations thereof. In hardware implementations, the division between functional modules / units mentioned above does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed collaboratively by several physical components. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit (ASIC). Such software can be distributed on a computer-readable storage medium, which may include computer storage media (or non-transitory media) and communication media (or transient media).

[0113] As is known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information, such as computer-readable program instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), static random access memory (SRAM), flash memory or other memory technologies, portable compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical disc storage, magnetic cartridges, magnetic tape, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and is accessible to a computer. Furthermore, it is known to those skilled in the art that communication media typically contain computer-readable program instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and may include any information delivery medium.

[0114] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0115] Computer readable program instructions for carrying out operations of the present application can be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate array (FPGA), or programmable logic array (PLA) can execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present application.

[0116] The computer program product described herein can be embodied in a specific manner by hardware, software, or a combination thereof. In an optional embodiment, the computer program product is embodied as a computer storage medium. In another optional embodiment, the computer program product is embodied as a software product, such as a software development kit (SDK), and the like.

[0117] The computer program product described herein can be embodied in a specific manner by hardware, software, or a combination thereof. In an optional embodiment, the computer program product is embodied as a computer storage medium. In another optional embodiment, the computer program product is embodied as a software product, such as a software development kit (SDK), and the like.

[0118] These computer readable program instructions can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. These computer readable program instructions can also be stored in a computer readable storage medium that can include a non-transitory computer readable storage medium that can be a computer- readable storage medium having no data storage cycles that change state. The instructions can be executed by one or more processors of a computer, other programmable data processing apparatus, or other devices to produce a computer-implemented process such that the instructions which execute via the one or more processors of the computer or other programmable data processing devices create means for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0119] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0120] The flow diagrams and the block diagrams in the drawings are presented to illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to the present application. In this regard, each block in the flow diagrams and the block diagrams can represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical functions ("instructions"). In some alternative implementations, the functions noted in the blocks can occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks can sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and / or flow diagrams, and combinations thereof, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and

[0121] Example embodiments have been disclosed herein and, although specific terms are employed, they are used in a generic and descriptive sense only and not for purposes of limitation. In some instances, it will be apparent to those skilled in the art that features, characteristics or aspects described in relation to one embodiment can be applied to other embodiments, unless otherwise clearly stated. It will also be apparent to those skilled in the art that various modifications can be made to the described embodiments without departing from the scope of the invention as defined by the appended claims.

Claims

1. A vehicle whole vehicle OTA upgrade method, characterized in that, The method comprises: acquiring historical upgrade behavior data; analyzing the historical upgrade behavior data to generate a feature portrait; creating an upgrade task and matching a target object corresponding to the upgrade task based on the feature portrait; binding the upgrade task with the target object and publishing the upgrade task.

2. The method of claim 1, wherein, The acquiring of the historical upgrade behavior data comprises: acquiring the remaining capacity of the in-vehicle memory and the historical use frequency and duration of the in-vehicle APP when the upgrade is performed based on the vehicle end; acquiring the historical upgrade time period based on the mobile device end; labeling and cleaning the historical upgrade time period, the remaining capacity of the in-vehicle memory and the historical use frequency and duration to obtain the historical upgrade behavior data.

3. The method of claim 1, wherein, The analyzing of the historical upgrade behavior data to generate a feature portrait comprises: extracting features from the historical upgrade behavior data based on a machine learning algorithm to obtain behavior features and resource features; analyzing the behavior features and resource features to obtain a vehicle portrait or a user portrait; generating a feature portrait based on the vehicle portrait or the user portrait.

4. The method of claim 1, wherein, The creating of the upgrade task and the matching of the target object corresponding to the upgrade task based on the feature portrait comprise: creating the upgrade task based on the OTA cloud end; synchronizing the task information of the upgrade task to a big data platform; obtaining the task information of the upgrade task based on a decision platform; matching a corresponding target object for each upgrade task based on the task information and the feature portrait; wherein the target object comprises a target crowd or a target vehicle group.

5. The method of claim 4, wherein, The task information comprises the size of the upgrade package, the in-vehicle APP information to be upgraded and the task push time period.

6. The method of claim 1, wherein, The binding of the upgrade task with the target object and the publishing of the upgrade task comprise: binding the upgrade task with the target object in terms of relationship and storing in the big data platform; obtaining the target object bound by the upgrade task based on the big data platform before publishing the upgrade task; determining vehicle group information based on the target object; publishing the upgrade task to all vehicles or users in the vehicle group information.

7. The method of claim 6, wherein, The method further comprises: acquiring upgrade result information and current behavior data fed back by vehicles based on the upgrade task; optimizing the matching relationship between the upgrade task and the target object based on the upgrade result information and the current behavior data to obtain an optimized upgrade strategy; publishing the upgrade task based on the optimized upgrade strategy.

8. An automobile vehicle OTA upgrade system, characterized in that, The method comprises: a behavior data acquisition module configured to acquire historical upgrade behavior data; a behavior analysis module configured to analyze the historical upgrade behavior data to generate a feature portrait; a task matching module configured to create an upgrade task and match a target object corresponding to the upgrade task based on the feature portrait; a task binding module configured to bind the upgrade task with the target object and publish the upgrade task.

9. An electronic device, comprising: The method comprises: one or more processors; a memory configured to store one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 7.

10. A computer readable medium having stored thereon a computer program, characterized in that The computer program, which is executed by a processor, implements the steps of the method according to any one of claims 1 to 7.

Images