Dynamic recommendation and rule configuration method and system for vehicle-mounted scene community

Abstract

The invention provides a dynamic recommendation and rule configuration method and system for a vehicle-mounted scene community, and belongs to the technical field of vehicle-mounted software, and the method comprises the steps: obtaining a dynamic target scene rule from the vehicle-mounted scene community; acquiring a plurality of scene data, wherein each scene data is associated with at least one label; and when preference information of a target user is obtained, screening out a scene recommended to the target user from multiple pieces of scene data according to the target scene rule and the label of the scene data in combination with the preference information of the target user. According to the method, the matching degree between the recommended scene and the user demand is greatly improved through the user-defined rule and label matching, and the time for the user to obtain the effective scene is greatly shortened. According to the method, the judgment rules of the latest and hot scenes can be adjusted in real time according to community operation requirements, operation strategies in different periods are adapted, and the community activeness is improved.

Description

Technical Field

[0001] This invention relates to the field of vehicle software technology, and in particular to a method and system for dynamic recommendation and rule configuration in vehicle-mounted community scenarios. Background Technology

[0002] With the development of intelligent connected vehicles, in-vehicle scenario applications are gradually becoming an important part of enhancing user experience. Users can access, share, and use various scenarios through in-vehicle scenario communities, such as holiday scenarios, intelligent driving scenarios, and entertainment scenarios.

[0003] However, the existing in-vehicle scenario community has the following problems:

[0004] Insufficient recommendation accuracy: Existing technologies mostly use fixed recommendation rules and cannot be dynamically adjusted according to user preferences and scenario characteristics, resulting in a low degree of matching between recommended scenarios and user needs, and low efficiency for users to obtain effective scenarios.

[0005] Poor rule configuration flexibility: The rules for determining popular and latest scenarios are usually fixed in the system, and administrators cannot customize rules according to the needs of community operation, making it difficult to adapt to community operation strategies at different times.

[0006] The scene classification system is incomplete: there is a lack of unified scene classification standards and scene tags are chaotic, making it difficult for users to quickly find the scene they need, and also affecting the accuracy of recommendations.

[0007] Limited sharing mechanism: User-shared custom scenarios lack effective visibility control, and users of different car models may see inapplicable scenarios, affecting user experience and resulting in low user engagement. Summary of the Invention

[0008] This invention aims to address the problems of insufficient recommendation accuracy, inflexible rule configuration, imperfect classification system, and limited sharing mechanism in existing in-vehicle scenario communities. It proposes an improved technical solution based on dynamic recommendation and rule configuration in in-vehicle scenario communities. Through custom rule configuration, accurate classification recommendation, and flexible sharing mechanism, it realizes intelligent recommendation and personalized management of community scenarios, thereby enhancing user participation.

[0009] In a first aspect, embodiments of the present invention provide a method for dynamic recommendation and rule configuration in an in-vehicle scenario community, comprising:

[0010] Obtain dynamic target scenario rules from the in-vehicle scenario community;

[0011] Acquire data from multiple scenarios, with each scenario data associated with at least one label;

[0012] When the target user's preference information is obtained, the target scenario rules, the tags of the scenario data, and the target user's preference information are combined to filter out the scenarios recommended to the target user from multiple scenario data.

[0013] In a preferred embodiment, the step of obtaining dynamic target scene rules from the in-vehicle scene community includes:

[0014] Set target scenario rules, which include: latest scenario rules and / or popular scenario rules;

[0015] Receive a request to edit the latest scenario rule, wherein the latest scenario rule includes at least one of the following: rules published within the last X days or the latest Y rules;

[0016] Receive setting requests for X and Y;

[0017] Receive a request to edit popular scene rules, wherein the popular scene rules include: the top Z download counts;

[0018] Receive Z's configuration request.

[0019] In a preferred embodiment, the step of acquiring multiple scene data, each scene data being associated with at least one tag, includes:

[0020] Receive scene category editing requests to obtain scene categories;

[0021] Receive tag editing requests and assign at least one tag to each scene category;

[0022] Receive a scene editing request and assign a scene category and tags to the first target scene.

[0023] In a preferred embodiment, the step of selecting recommended scenarios to the target user from multiple scenario data based on the target scenario rules, the tags of the scenario data, and the target user's preference information when the target user's preference information is obtained includes:

[0024] Based on the target scenario rules, scenarios that conform to the target scenario rules are selected from multiple scenario data.

[0025] The tags of scenarios that meet the target scenario rules are matched with the target user's preference information, and scenarios with a matching degree exceeding a preset threshold are selected as scenarios to be recommended to the target user.

[0026] In a preferred embodiment, after the step of acquiring multiple scene data, each scene data being associated with at least one tag, the method includes:

[0027] When the target user's historical behavior is obtained, the scenario that matches the target scenario rule is selected from multiple scenario data according to the target scenario rule;

[0028] Based on the target user's historical behavior, a collaborative filtering algorithm is used to identify similar users of the target user;

[0029] Scenes that match the target scene rules and are similar to scenes liked by similar users will be recommended to the target user.

[0030] In a preferred embodiment, the step of acquiring multiple scene data, each scene data being associated with at least one tag, further includes:

[0031] Receive a scene visibility editing request and set the first target scene visibility to the range of vehicle series based on the vehicle series of the target user's vehicle;

[0032] The step of selecting recommended scenarios to the target user from multiple scenario data based on the target scenario rules, the tags of the scenario data, and the target user's preference information when the target user's preference information is obtained includes:

[0033] Based on the target scenario rules, scenarios that conform to the target scenario rules are selected from multiple scenario data.

[0034] The tags of scenarios that conform to the target scenario rules are matched with the target user's preference information, and scenarios with a matching degree exceeding a preset threshold are filtered out.

[0035] By combining the vehicle series information of the target user, scenarios that do not belong to the same vehicle series as the target user are excluded from the scenarios where the matching degree exceeds a preset threshold, and the scenarios recommended to the target user are obtained.

[0036] In a preferred embodiment, it further includes:

[0037] Receive a sharing request and share the second target scenario to the community;

[0038] The visibility of the second target scene is set to be based on the vehicle series, so that only users of the same vehicle series can view the shared scene.

[0039] Secondly, embodiments of the present invention provide a dynamic recommendation and rule configuration system for in-vehicle scenario communities, configured to implement any of the methods described in the first aspect, the system comprising:

[0040] The rule configuration module is used to obtain dynamic target scenario rules from the in-vehicle scenario community;

[0041] The classification management module is used to acquire data from multiple scenarios, with each scenario associated with at least one tag;

[0042] The recommendation module is used to select recommended scenarios to the target user from multiple scenario data when the target user's preference information is obtained, based on the target scenario rules, the tags of the scenario data, and the target user's preference information.

[0043] Thirdly, embodiments of the present invention provide an electronic device comprising: one or more processors; a memory for storing one or more programs; and, when the one or more programs are executed by the one or more processors, causing the one or more processors to implement the method as described in any embodiment of the first aspect.

[0044] Fourthly, embodiments of the present invention provide a computer-readable medium storing a computer program that, when executed by a processor, implements the steps of the method as described in any embodiment of the first aspect.

[0045] Beneficial effects of this invention:

[0046] 1. Improve recommendation accuracy: By using custom rules and tag matching, the matching degree between the recommended scenarios and user needs is greatly improved, and the time for users to obtain effective scenarios is also greatly shortened.

[0047] 2. Enhance rule flexibility: Administrators can adjust the latest and most popular scenario judgment rules in real time according to the needs of community operation, adapt to the operation strategies at different times, and improve community activity.

[0048] 3. Improve the classification system: A unified scene classification and tag system enables users to quickly find the scene they need, improving scene search efficiency.

[0049] 4. Optimize the sharing mechanism: Based on the visibility restrictions of vehicle series, ensure that users only see applicable scenarios, improve user satisfaction, and at the same time promote user sharing and increase the amount of community sharing. Attached Figure Description

[0050] Figure 1 This is a flowchart illustrating a dynamic recommendation and rule configuration method for an in-vehicle scenario community provided in an embodiment of the present invention.

[0051] Figure 2 This is a flowchart illustrating an optional specific implementation method of step S1 in an embodiment of the present invention.

[0052] Figure 3 This is a flowchart illustrating an optional specific implementation method of step S2 in an embodiment of the present invention.

[0053] Figure 4 This is a flowchart illustrating an optional specific implementation method of step S3 in an embodiment of the present invention.

[0054] Figure 5 This is a structural block diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0055] 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.

[0056] Where there is no conflict, the various embodiments of the present invention and the features thereof may be combined with each other.

[0057] As used herein, the term “and / or” includes any and all combinations of one or more related enumerated entries.

[0058] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used herein, the singular forms “a” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will also be understood that when the terms “comprising” and / or “made of” are used in this specification, the presence of the stated feature, integral, step, operation, element, and / or component is specified, but the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or groups thereof is not excluded. Terms such as “connected” or “linked” are not limited to physical or mechanical connections but can include electrical connections, whether direct or indirect.

[0059] Unless otherwise specified, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having the meaning consistent with their meaning in the context of the relevant art and the invention, and will not be interpreted as having an idealized or overly formal meaning unless expressly so defined herein.

[0060] In the technical solution of this invention, the collection, storage, use, processing, transmission, provision, and disclosure of user personal information all comply with relevant laws and regulations and do not violate public order and good morals. The use of user data in this technical solution follows relevant national laws and regulations (e.g., the "Information Security Technology - Personal Information Security Specification"). For example: appropriate measures are taken for personal information access control; restrictions are imposed on the display of personal information; the purpose of using personal information does not exceed the scope of direct or reasonable association; and explicit identity targeting is eliminated when using personal information to avoid precisely locating a specific individual.

[0061] Some abbreviations and key terms in this invention are defined as follows:

[0062] AP: Adaptive Platform, refers to a software platform that conforms to the Adaptive AUTOSAR specification, supports service-oriented architecture, and provides flexible service communication and management mechanisms.

[0063] COM: Communication module, a component in the vehicle system responsible for inter-service communication, supporting protocols such as SOME / IP, and enabling service publishing, discovery, and data transmission.

[0064] SOA: Service-Oriented Architecture, a software architecture style that achieves loosely coupled system integration by encapsulating functionality as services, supporting service reuse and flexible composition.

[0065] PRD: Product Requirement Document, a document that describes the product's functional and performance requirements, used to guide development and testing.

[0066] SOME / IP: Scalable service-oriented middleware over IP, a scalable service-oriented middleware protocol based on IP for communication of services in automotive Ethernet.

[0067] RPC: Remote Procedure Call, a communication protocol that allows clients to call functions or methods on remote servers, such as the Method call mechanism in SOA.

[0068] ECU: Electronic Control Unit, the core component of an onboard electronic control system, responsible for performing specific control functions.

[0069] TSP: Telematics Service Provider, a platform that provides services such as in-vehicle communication and data management.

[0070] VIN: Vehicle Identification Number, a unique identifier for a vehicle used to distinguish different vehicles.

[0071] Scenario: refers to a specific sequence of functions formed by the combination of in-vehicle services and capabilities, which can meet the needs of users in specific situations (such as "Spring Festival mode" or "commuting mode"), and includes elements such as triggering conditions and execution actions.

[0072] Scene Community: A platform for users to publish, share, and acquire scenes, supporting user interaction and scene management, and enhancing user participation.

[0073] Dynamic recommendations: Based on preset rules (such as latest and popular), user preferences and vehicle information, push matching scenarios to users in real time to improve recommendation accuracy.

[0074] Popularity rules: Configuration rules used to determine whether a scene is a popular scene. They are usually based on metrics such as download volume and usage frequency, and administrators can customize parameters (such as "top 20 downloads").

[0075] Latest Rules: Configuration rules used to determine whether a scenario is the latest scenario. Based on the release time, it supports administrator-defined parameters (such as "released in the last 30 days").

[0076] Scene classification system: Standardized classification of scenes (such as festivals, intelligent driving, entertainment, etc.), with each category associated with specific tags to facilitate user retrieval and system recommendations.

[0077] Tag matching: This mechanism compares scene tags with user preference tags to filter out scenes with high matching scores, thereby improving the relevance of recommendations.

[0078] Sharing mechanism: The function of users publishing custom scenarios to the community supports limiting scenario visibility based on vehicle series, ensuring that scenarios are only displayed to applicable vehicles.

[0079] Service Matrix Version: A unified version identifier for in-vehicle SOA services. Each vehicle model has its own independently managed service matrix version, which includes information on all services supported by that vehicle model.

[0080] Vehicle Series: A classification of vehicles based on brand and model characteristics (such as a specific series of models under a certain brand), used to limit the visibility of scene sharing.

[0081] Capabilities: Natural language descriptions of service interfaces, which are the basic units of scene orchestration and encapsulate specific operational logic (such as "open the car window" and "adjust the air conditioning temperature").

[0082] Services: Software modules that can independently complete some functions are the foundation of capabilities and are published and invoked through COM modules.

[0083] Figure 1 This is a flowchart illustrating a dynamic recommendation and rule configuration method for an in-vehicle scenario community provided by an embodiment of the present invention; as follows: Figure 1 As shown, the method includes:

[0084] Step S1: Obtain dynamic target scene rules from the in-vehicle scene community;

[0085] Step S2: Obtain multiple scene data, with each scene data associated with at least one label;

[0086] Step S3: When the target user's preference information is obtained, based on the target scenario rules, the tags of the scenario data, and the target user's preference information, a scenario recommended to the target user is selected from multiple scenario data.

[0087] Among them, dynamic target scene rules refer to the fact that the target scene rules are set and editable, rather than fixed. Each scene is assigned a corresponding tag, and based on the set target scene rules and user preferences, scenes that meet the conditions can be recommended to the user. This tag matching algorithm can achieve the effect of accurate scene recommendation through tag matching.

[0088] Existing technologies mostly use fixed recommendation rules and do not match them according to user preferences and scenario characteristics, resulting in low matching degree between recommended scenarios and user needs, and low efficiency for users to obtain effective scenarios. Compared with the fixed recommendation rules of existing technologies, the dynamic rules and tag matching of this invention greatly improve the recommendation accuracy and also greatly shorten the time for users to obtain effective scenarios.

[0089] In addition, existing technologies use fixed recommendation rules, which require code-level adjustments to modify, resulting in long response times for scene rule updates. This invention's dynamic target scene rules can be dynamically updated through custom configuration of target scene rules, greatly shortening the scene rule update response time.

[0090] In some embodiments, such as Figure 2 As shown, step S1, obtaining dynamic target scene rules from the vehicle scene community, includes:

[0091] Step S11: Set target scene rules, which include: latest scene rules and / or popular scene rules;

[0092] Step S12: Receive the latest scene rule editing request, wherein the latest scene rule includes at least one of the following: published in the last X days or the latest Y rules;

[0093] Step S13: Receive setting requests for X and Y, such as setting X=30 and Y=20;

[0094] Step S13: Receive a request to edit popular scene rules, wherein the popular scene rules include: the top Z download counts;

[0095] Step S15: Receive the setting request for Z, such as setting Z=10.

[0096] The rules for determining popular and latest scenarios are usually fixed in the system, making it difficult for administrators to customize rules according to community operation needs and adapt to different community operation strategies at different times. This invention allows administrators to customize the configuration of rules for the latest and / or popular scenarios through the interface, greatly shortening the rule update response time and significantly improving efficiency. Furthermore, it allows for real-time adjustment of the rules for determining the latest and popular scenarios based on community operation needs, adapting to different operational strategies at different times and enhancing community activity.

[0097] In some embodiments, such as Figure 3 As shown, step S2, which involves acquiring multiple scene data points and associating each scene data point with at least one tag, includes the following steps:

[0098] Step S21: Receive a scene category editing request to obtain scene categories, such as: festival, intelligent driving, entertainment, work, and family.

[0099] Step S22: Receive a tag editing request and assign at least one tag to each scene category, such as assigning the tags "Spring Festival" and "Christmas" to the scene category "Holiday", and assigning the tag "Automatic Parking" to the scene category "Intelligent Driving".

[0100] Step S23: Receive a scene editing request and assign a scene category and tag to the first target scene. For example, create a scene "Spring Festival Mode" and assign the scene category "Festival" and the tag "Festival" to the scene.

[0101] Existing technologies suffer from chaotic classifications and a lack of unified scene classification standards, resulting in disorganized scene tags. This makes it difficult for users to quickly find the scenes they need, requiring an average of 10 steps to search for a scene. It also affects the accuracy of recommendations. The classification system of this invention reduces the number of steps and improves efficiency.

[0102] In some embodiments, such as Figure 4As shown, step S3, when the target user's preference information is obtained, involves filtering out recommended scenarios to the target user from multiple scenario data based on the target scenario rules, the tags of the scenario data, and the target user's preference information. This step includes:

[0103] Step S31: Based on the target scene rules, select scenes that conform to the target scene rules from multiple scene data;

[0104] Step S32: Match the tags of the scenes that meet the target scene rules with the target user's preference information, and filter out the scenes with a matching degree exceeding a preset threshold as the scenes recommended to the target user. For example, if the preset threshold is 60%, when the matching degree between the scene tag and the user's preference exceeds 60%, the scene is retained in the recommendation list.

[0105] In some embodiments, step S2, which involves acquiring multiple scene data sets, with each scene data set associated with at least one tag, is followed by:

[0106] When the target user's historical behavior (such as downloads, favorites, and usage records) is obtained, scenarios that conform to the target scenario rules are selected from multiple scenario data according to the target scenario rules.

[0107] Based on the target user's historical behavior, a collaborative filtering algorithm is used to identify similar users of the target user;

[0108] Scenes that match the target scene rules and are similar to scenes liked by similar users will be recommended to the target user.

[0109] In the recommendation process, a collaborative filtering algorithm is used instead of a tag matching algorithm. By analyzing users' historical behavior (such as downloads, favorites, and usage records), similar users' preferred scenarios are recommended. This recommendation process can further improve the recommendation accuracy by 10% when user behavior data is abundant, making it suitable for communities with a large user base.

[0110] In some embodiments, step S2, which involves acquiring multiple scene data sets, with each scene data set associated with at least one tag, further includes:

[0111] Step S24: Receive a scene visibility editing request and set the first target scene visibility to the range of vehicle series based on the vehicle series of the target user's vehicle according to the vehicle series information of the target user's vehicle.

[0112] Step S3, when the target user's preference information is obtained, involves filtering out recommended scenarios for the target user from multiple scenario data based on the target scenario rules, the tags of the scenario data, and the target user's preference information. This step includes:

[0113] Based on the target scenario rules, scenarios that conform to the target scenario rules are selected from multiple scenario data.

[0114] The tags of scenarios that conform to the target scenario rules are matched with the target user's preference information, and scenarios with a matching degree exceeding a preset threshold are filtered out.

[0115] By combining the vehicle series information of the target user, scenarios that do not belong to the same vehicle series as the target user are excluded from the scenarios where the matching degree exceeds a preset threshold, and the scenarios recommended to the target user are obtained.

[0116] Existing technologies do not restrict users to specific car models when sharing scenarios, resulting in 40% of scenarios being inapplicable. This invention allows users to share custom scenarios to the community, and restricts the visibility of the scenario based on the car model range. Only users of the same car model can view the shared scenario. Therefore, this invention ensures that users only see applicable scenarios based on the visibility restriction of the car model, reducing the proportion of inapplicable scenarios, improving user satisfaction, and promoting user sharing, thereby increasing the amount of sharing in the community.

[0117] In some embodiments, the method further includes:

[0118] Receive a sharing request and share the second target scenario to the community;

[0119] The visibility of the second target scene is set to be based on the vehicle series, so that only users of the same vehicle series can view the shared scene.

[0120] Based on the same inventive concept, embodiments of the present invention also provide a dynamic recommendation and rule configuration system for in-vehicle scenario communities, configured to implement any of the methods described in the above embodiments, the system comprising:

[0121] The rule configuration module is used to obtain dynamic target scenario rules from the in-vehicle scenario community;

[0122] The classification management module is used to acquire data from multiple scenarios, with each scenario associated with at least one tag;

[0123] The recommendation module is used to select recommended scenarios to the target user from multiple scenario data when the target user's preference information is obtained, based on the target scenario rules, the tags of the scenario data, and the target user's preference information.

[0124] Based on the same inventive concept, embodiments of the present invention also provide an electronic device. Figure 5 This is a structural block diagram of an electronic device provided in an embodiment of the present invention. Figure 5As shown, an embodiment of the present invention provides an electronic device including: one or more processors 101, a memory 102, and one or more I / O interfaces 103. The memory 102 stores one or more programs, which, when executed by the one or more processors, cause the one or more processors to implement any of the methods described in the above embodiments; the one or more I / O interfaces 103 are connected between the processor and the memory, configured to enable information interaction between the processor and the memory.

[0125] The processor 101 is a device with data processing capabilities, including but not limited to a central processing unit (CPU); the memory 102 is a device with data storage capabilities, including but not limited to random access memory (RAM, more specifically SDRAM, DDR, etc.), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), and 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).

[0126] In some embodiments, the processor 101, memory 102, and I / O interface 103 are interconnected via bus 104, and thus connected to other components of the computing device.

[0127] In some embodiments, the one or more processors 101 include a field-programmable gate array.

[0128] Based on the same inventive concept, embodiments of the present invention also provide a computer-readable medium. This computer-readable medium stores a computer program, wherein, when executed by a processor, the program implements the steps of any of the methods described in the above embodiments. The computer-readable storage medium may be a volatile or non-volatile computer-readable storage medium.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] The computer program instructions used to perform the operations of this invention may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing state information from the computer-readable program instructions. This electronic circuitry can execute the computer-readable program instructions to implement various aspects of the invention.

[0133] The computer program product described herein can be implemented specifically through hardware, software, or a combination thereof. In one alternative embodiment, the computer program product is specifically embodied in a computer storage medium; in another alternative embodiment, the computer program product is specifically embodied in a software product, such as a software development kit (SDK), etc.

[0134] Various aspects of the present invention are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0135] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0136] Computer-readable program instructions may 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 data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0137] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction, which contains one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0138] Example embodiments have been disclosed herein, and while specific terminology has been used, it is for illustrative purposes only and should be construed as such, and is not intended to be limiting. In some instances, it will be apparent to those skilled in the art that features, characteristics, and / or elements described in conjunction with particular embodiments may be used alone, or in combination with features, characteristics, and / or elements described in conjunction with other embodiments, unless otherwise expressly indicated. Therefore, those skilled in the art will understand that various changes in form and detail may be made without departing from the scope of the invention as set forth in the appended claims.

Claims

1. A method for dynamic recommendation and rule configuration in an in-vehicle scenario community, characterized in that, include: Obtain dynamic target scenario rules from the in-vehicle scenario community; Acquire data from multiple scenarios, with each scenario data associated with at least one label; When the target user's preference information is obtained, the target scenario rules, the tags of the scenario data, and the target user's preference information are combined to filter out the scenarios recommended to the target user from multiple scenario data.

2. The method according to claim 1, characterized in that, The steps for obtaining dynamic target scene rules from the in-vehicle scene community include: Set target scenario rules, which include: latest scenario rules and / or popular scenario rules; Receive a request to edit the latest scenario rule, wherein the latest scenario rule includes at least one of the following: rules published within the last X days or the latest Y rules; Receive setting requests for X and Y; Receive a request to edit popular scene rules, wherein the popular scene rules include: the top Z download counts; Receive Z's configuration request.

3. The method according to claim 1 or 2, characterized in that, The step of acquiring multiple scene data, with each scene data associated with at least one tag, includes: Receive scene category editing requests to obtain scene categories; Receive tag editing requests and assign at least one tag to each scene category; Receive a scene editing request and assign a scene category and tags to the first target scene.

4. The method according to claim 3, characterized in that, The step of selecting recommended scenarios to the target user from multiple scenario data based on the target scenario rules, the tags of the scenario data, and the target user's preference information when the target user's preference information is obtained includes: Based on the target scenario rules, scenarios that conform to the target scenario rules are selected from multiple scenario data. The tags of scenarios that meet the target scenario rules are matched with the target user's preference information, and scenarios with a matching degree exceeding a preset threshold are selected as scenarios to be recommended to the target user.

5. The method according to claim 3, characterized in that, The step of acquiring multiple scene data, each scene data being associated with at least one tag, is followed by: When the target user's historical behavior is obtained, the scenario that matches the target scenario rule is selected from multiple scenario data according to the target scenario rule; Based on the target user's historical behavior, a collaborative filtering algorithm is used to identify similar users of the target user; Scenes that match the target scene rules and are similar to scenes liked by similar users will be recommended to the target user.

6. The method according to claim 3, characterized in that, The step of acquiring multiple scene data, with each scene data associated with at least one tag, further includes: Receive a scene visibility editing request and set the first target scene visibility to the range of vehicle series based on the vehicle series of the target user's vehicle; The step of selecting recommended scenarios to the target user from multiple scenario data based on the target scenario rules, the tags of the scenario data, and the target user's preference information when the target user's preference information is obtained includes: Based on the target scenario rules, scenarios that conform to the target scenario rules are selected from multiple scenario data. The tags of scenarios that conform to the target scenario rules are matched with the target user's preference information, and scenarios with a matching degree exceeding a preset threshold are filtered out. By combining the vehicle series information of the target user, scenarios that do not belong to the same vehicle series as the target user are excluded from the scenarios where the matching degree exceeds a preset threshold, and the scenarios recommended to the target user are obtained.

7. The method according to claim 1, characterized in that, Also includes: Receive a sharing request and share the second target scenario to the community; The visibility of the second target scene is set to be based on the vehicle series, so that only users of the same vehicle series can view the shared scene.

8. A dynamic recommendation and rule configuration system for in-vehicle scenario communities, characterized in that, The system, configured to implement the method as described in any one of claims 1 to 7, comprises: The rule configuration module is used to obtain dynamic target scenario rules from the in-vehicle scenario community; The classification management module is used to acquire data from multiple scenarios, with each scenario associated with at least one tag; The recommendation module is used to select recommended scenarios to the target user from multiple scenario data based on the target scenario rules, the tags of the scenario data, and the target user's preference information when the target user's preference information is obtained.

9. An electronic device, characterized in that, include: One or more processors; Memory, used 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 as described in any one of claims 1 to 7.

10. A computer-readable medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method as described in any one of claims 1 to 7.

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