Tire automatic recommendation method, system, electronic device and medium

Abstract

The application provides a tire automatic recommendation method, system, electronic equipment and medium, and belongs to the technical field of vehicles, and the method comprises the following steps: acquiring vehicle use habit data of a target object; performing standardization processing and feature extraction on the vehicle use habit data to generate a use habit feature vector; acquiring preset tire performance parameters and preset tire recommendation rules; and generating a recommended tire list and displaying the recommended tire list based on a multidimensional matching algorithm according to the use habit feature vector in combination with the preset tire performance parameters and the preset tire recommendation rules. Through the collection of vehicle use habit data, the combination of tire performance and recommendation rules, and the automatic generation of a personalized tire recommendation scheme by using a multidimensional matching algorithm, the application can quickly and accurately recommend suitable tire products, solves the problem that traditional tire selection and purchase rely on manual experience and have low matching degrees, and improves user experience and purchase decision efficiency.

Description

Technical Field

[0001] This invention relates to the field of vehicle technology, and in particular to an automatic tire recommendation method, system, electronic device, and medium. Background Technology

[0002] As a crucial safety component of a car, tires directly impact driving safety, comfort, and fuel economy. Currently, consumers primarily rely on salesperson recommendations, online reviews, or brand preferences when choosing tires, lacking scientific and personalized recommendation methods. Summary of the Invention

[0003] The present invention aims to solve at least one of the technical problems existing in the prior art, and proposes an automatic tire recommendation method, system, electronic device and medium.

[0004] In a first aspect, embodiments of the present invention provide an automatic tire recommendation method, comprising:

[0005] Obtain vehicle usage habit data from the target group;

[0006] The vehicle usage habit data is standardized and features are extracted to generate a usage habit feature vector;

[0007] Obtain preset tire performance parameters and preset tire recommendation rules;

[0008] Based on a multi-dimensional matching algorithm, a list of recommended tires is generated and displayed by combining the user habit feature vector with the preset tire performance parameters and preset tire recommendation rules.

[0009] In some embodiments, obtaining the vehicle usage habit data of the target object includes:

[0010] Receive manually input data from the target object;

[0011] Data is obtained from the data interface of the vehicle OBD device and / or the API interface of the third-party vehicle networking platform.

[0012] Obtain the historical maintenance records of the target object;

[0013] Vehicle usage habit data is obtained based on the manually input data, interface data, and historical maintenance records.

[0014] In some embodiments, the vehicle usage data includes at least three of the following: road condition type, driving style rating, average annual mileage, and local climate characteristics.

[0015] In some embodiments, the driving road condition type includes at least two of the following: urban paved roads, highways, rural roads, unpaved roads, and winding mountain roads; the driving style score is calculated based on at least two of the following: number of rapid accelerations, frequency of emergency braking, average vehicle speed, and turning G-value.

[0016] In some embodiments, the method further includes:

[0017] Obtain different tire performance parameters and construct a tire performance database based on the tire performance parameters;

[0018] Build a recommendation rule base;

[0019] The acquisition of preset tire performance parameters and preset tire recommendation rules includes:

[0020] Preset tire performance parameters are obtained based on a tire performance database; wherein, the preset tire performance parameters include at least four of the following: abrasion index, grip rating, wet performance, noise control, and rolling resistance.

[0021] Preset tire recommendation rules are obtained based on a recommendation rule base; wherein, the preset tire recommendation rules include road condition matching rules, driving style matching rules, and climate adaptability rules.

[0022] In some embodiments, the multi-dimensional matching algorithm generates a recommended tire list based on the usage habit feature vector combined with the preset tire performance parameters and preset tire recommendation rules, including:

[0023] The tire performance vector is obtained based on the preset tire performance parameters;

[0024] Weights for different dimensions are obtained based on the preset tire recommendation rules;

[0025] Based on the multi-dimensional matching algorithm, a weighted Euclidean distance is obtained according to the usage habit feature vector, tire performance vector, and weights.

[0026] The recommended tire list is generated by sorting the tires according to the weighted Euclidean distance.

[0027] In some embodiments, the method further includes:

[0028] Obtain feedback information that displays the recommended tire list; wherein the feedback information includes the adoption status of the recommendation results and usage evaluation;

[0029] The weights of the recommendation rules are dynamically optimized based on the feedback information using a preset machine learning algorithm.

[0030] Secondly, embodiments of the present invention provide an automatic tire recommendation system, comprising:

[0031] The data acquisition module is used to obtain vehicle usage habit data of the target object;

[0032] The feature analysis module is used to standardize and extract features from the vehicle usage habit data to generate a usage habit feature vector.

[0033] The performance and rule acquisition module is used to acquire preset tire performance parameters and preset tire recommendation rules;

[0034] The generation and display module is used to generate and display a list of recommended tires based on the usage habit feature vector, the preset tire performance parameters, and the preset tire recommendation rules using a multi-dimensional matching algorithm.

[0035] Thirdly, embodiments of the present invention provide an electronic device, including:

[0036] One or more processors;

[0037] Memory, used to store one or more programs;

[0038] When the one or more programs are executed by the one or more processors, the one or more processors implement any of the methods described above.

[0039] Fourthly, embodiments of the present invention provide a computer-readable medium on which a computer program is stored, the computer program being executed by a processor to implement the steps of any of the methods described above.

[0040] The automatic tire recommendation method provided by this invention includes: acquiring vehicle usage habit data of a target object; standardizing and extracting features from the vehicle usage habit data to generate a usage habit feature vector; acquiring preset tire performance parameters and preset tire recommendation rules; and generating and displaying a recommended tire list based on a multi-dimensional matching algorithm, according to the usage habit feature vector combined with the preset tire performance parameters and preset tire recommendation rules. This invention automatically generates personalized tire recommendation schemes by collecting vehicle usage habit data, combining tire performance and recommendation rules, and employing a multi-dimensional matching algorithm. It can quickly and accurately recommend suitable tire products, solving the problems of traditional tire selection relying on manual experience and having low matching accuracy, thus improving user experience and purchasing decision efficiency. Attached Figure Description

[0041] Figure 1 This is a flowchart illustrating an automatic tire recommendation method provided in an embodiment of the present invention.

[0042] Figure 2 This is a structural block diagram of a lightweight application for automatic tire recommendation based on vehicle usage habits involved in an embodiment of the present invention;

[0043] Figure 3 This is a schematic diagram of the specific implementation process involved in the embodiments of the present invention;

[0044] Figure 4 This is a structural block diagram of an automatic tire recommendation system provided in an embodiment of the present invention;

[0045] Figure 5 This is a structural block diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0046] 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.

[0047] Where there is no conflict, the various embodiments of the present invention and the features thereof may be combined with each other.

[0048] As used herein, the term “and / or” includes any and all combinations of one or more related enumerated entries.

[0049] 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.

[0050] 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.

[0051] 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.

[0052] The key terms involved in this invention are defined as follows:

[0053] OBD; On-board automatic diagnostic system;

[0054] API: Application Programming Interface, used for communication and data exchange between different software systems;

[0055] H5: Mobile web page applications developed based on the HTML5 technology standard.

[0056] In related technologies, recommendation methods have the following problems: First, they rely on human experience, resulting in highly subjective recommendation results; second, they do not fully consider users' actual usage habits, such as driving conditions and driving styles; and third, the recommendation process is cumbersome, requiring users to compare multiple parameters themselves, resulting in low decision-making efficiency.

[0057] In related technologies, some e-commerce platforms offer tire recommendation functions based on vehicle model matching, but filtering based solely on vehicle model parameters fails to reflect differences in user habits. Some studies have proposed recommendation methods based on user profiles, but these are mostly concentrated in e-commerce and content recommendation fields, with limited application in tire recommendation scenarios, and a lack of lightweight and user-friendly application solutions.

[0058] To address at least one of the technical problems existing in the aforementioned related technologies, the present invention provides an automatic tire recommendation method. Figure 1 This is a flowchart illustrating an automatic tire recommendation method provided in an embodiment of the present invention.

[0059] As one embodiment of the present invention, such as Figure 1 As shown, the automatic tire recommendation method includes:

[0060] Step S1: Obtain vehicle usage habit data of the target object;

[0061] Step S2: Standardize and extract features from the vehicle usage habit data to generate a usage habit feature vector;

[0062] Step S3: Obtain preset tire performance parameters and preset tire recommendation rules;

[0063] Step S4: Based on the multi-dimensional matching algorithm, generate and display a list of recommended tires according to the usage habit feature vector, the preset tire performance parameters, and the preset tire recommendation rules.

[0064] 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 this. In this embodiment, the execution subject is a computer device as an example for explanation.

[0065] For example, the automatic tire recommendation method of this embodiment can be applied to a lightweight application for automatic tire recommendation based on vehicle usage habits, which can be used in the automotive aftermarket. For instance, this lightweight application can be a WeChat mini-program, an Alipay mini-program, or an H5 mobile application. Figure 2 As shown, this lightweight application may include a data acquisition module, a feature analysis module, a recommendation engine module, and a user interface. These modules can interact with each other via API interfaces. The application collects user vehicle usage data, including road conditions, driving style, average annual mileage, and climate parameters. Combining this with a tire performance database and an expert recommendation rule base, it uses a multi-dimensional matching algorithm to automatically generate personalized tire recommendations. The specific steps are explained below.

[0066] In some embodiments, obtaining vehicle usage habit data of a target object includes: receiving manual input data from the target object; obtaining interface data based on the data interface of the on-board OBD device and / or the API interface of a third-party vehicle networking platform; obtaining the historical maintenance records of the target object; and obtaining vehicle usage habit data based on the manual input data, interface data, and historical maintenance records.

[0067] In some embodiments, the vehicle usage data includes at least three of the following: road condition type, driving style rating, average annual mileage, and local climate characteristics.

[0068] In some embodiments, the driving road condition type includes at least two of the following: urban paved roads, highways, rural roads, unpaved roads, and winding mountain roads; the driving style score is calculated based on at least two of the following: number of rapid accelerations, frequency of emergency braking, average vehicle speed, and turning G-value.

[0069] Specifically, the data acquisition module can obtain vehicle usage habit data from target objects such as car owners. This data includes at least three of the following: road condition type, driving style rating, average annual mileage, and regional climate characteristics. Figure 3As shown, the data acquisition module can acquire data through at least one of the following methods: manual input by the user, data interface of the on-board OBD device, API interface of a third-party vehicle networking platform, and import of historical maintenance records. The driving road condition type includes at least two of the following: urban paved roads, highways, rural roads, unpaved roads, and winding mountain roads; the driving style score is calculated based on at least two of the following: number of rapid accelerations, frequency of emergency braking, average vehicle speed, and turning G-value.

[0070] For example, the data acquisition module supports multiple data sources: manual input by the user, such as collecting basic data like vehicle information, road conditions, and average annual mileage through an interface form; OBD interface, such as reading vehicle driving data through a Bluetooth OBD device, including driving behavior data such as the number of rapid accelerations and the frequency of emergency braking; third-party platform APIs, such as accessing vehicle networking platforms (e.g., OnStar, Car-Net) to obtain historical driving data; and historical data import, such as supporting the import of mileage data from maintenance records and insurance records.

[0071] In some embodiments, the vehicle usage habit data is standardized and features are extracted to generate a usage habit feature vector.

[0072] Specifically, such as Figure 3 As shown, the feature analysis module can be used to standardize and extract features from the collected data (vehicle usage habit data) to generate user habit feature vectors. For example, it can convert driving road condition types into numerical features, quantify driving styles, and segment and encode annual mileage.

[0073] For example, in the feature analysis implementation, the feature analysis module preprocesses the raw data: for driving road condition type, the user-described road conditions, such as "mainly urban, occasionally highway", can be converted into a numerical feature vector, such as [urban: 0.8, highway: 0.2]; for driving style score, a weighted average method can be used to calculate the driving style score (0-100 points, the higher the score, the more aggressive the driving) based on data such as the number of rapid accelerations and the frequency of emergency braking; for average annual mileage, it can be segmented and coded according to mileage ranges (e.g., <10,000 km, 10,000-20,000 km, >20,000 km); for climate characteristics, data such as average annual rainfall and temperature range can be obtained based on the user's location.

[0074] For example, raw vehicle CAN bus data within a preset sampling period, such as a single trip, a single week, or a single month, is obtained, including: number of rapid accelerations, frequency of emergency braking, average vehicle speed, and turning G-value. Since the dimensions and orders of magnitude of the above four data items are different, the four data items are subjected to dimensional normalization processing. According to the requirements of the weighted average method, weight coefficients are assigned to each indicator to calculate the driving style score. The weight allocation can be based on expert experience, such as the influence of rapid acceleration and emergency braking on "intenseness" being higher than that of average vehicle speed.

[0075] In some embodiments, the method further includes: acquiring different tire performance parameters and constructing a tire performance database based on the tire performance parameters; constructing a recommendation rule base; the acquisition of preset tire performance parameters and preset tire recommendation rules includes: acquiring preset tire performance parameters based on the tire performance database; wherein the preset tire performance parameters include at least four of the following: abrasion index, grip rating, wet performance, noise control, and rolling resistance; acquiring preset tire recommendation rules based on the recommendation rule base; wherein the preset tire recommendation rules include road condition matching rules, driving style matching rules, and climate adaptability rules.

[0076] Specifically, the tire performance database stores performance parameters for different brands and models of tires, including at least four of the following: tread wear index, grip rating, wet performance, noise control, and rolling resistance. The recommendation rule base stores tire recommendation rules based on expert experience, including road condition matching rules, driving style matching rules, and climate adaptability rules. For example, road condition matching rules recommend high-tread wear tires for unpaved roads; driving style matching rules recommend high-grip tires for aggressive driving; and climate adaptability rules recommend tires with good wet performance for rainy areas.

[0077] For example, the database can be constructed as follows: A tire performance database may include the following fields: tire brand, model, specifications, tread depth index, grip rating, wet performance rating, noise level, rolling resistance coefficient, price range, etc. Data sources include, but are not limited to, tire manufacturer technical manuals, data from third-party testing organizations, and user reviews.

[0078] For example, the recommendation rule base is stored using a rule engine, and each rule includes a condition part and a recommendation weight. For example, rule 1: IF road conditions include unpaved roads AND annual mileage > 20,000 km, THEN recommend tires with a wear resistance index > 400, weight 0.8; rule 2: IF driving style score > 70, THEN recommend tires with a grip rating of AA or higher, weight 0.7.

[0079] In some embodiments, a recommended tire list is generated based on a multi-dimensional matching algorithm according to the usage habit feature vector combined with the preset tire performance parameters and preset tire recommendation rules. This includes: obtaining a tire performance vector based on the preset tire performance parameters; obtaining weights for different dimensions based on the preset tire recommendation rules; obtaining a weighted Euclidean distance based on the usage habit feature vector, tire performance vector, and weights using the multi-dimensional matching algorithm; and sorting the tires according to the weighted Euclidean distance to generate the recommended tire list.

[0080] Specifically, such as Figure 3 As shown, the recommendation engine module can generate a list of recommended tires by calculating the matching degree based on user habit feature vectors, combined with a tire performance database and a recommendation rule base, using a multi-dimensional matching algorithm. The user interface can display the recommendation results, receive user feedback, and support personalized parameter adjustments. The multi-dimensional matching algorithm uses weighted Euclidean distance to calculate the similarity between the user habit feature vector and the tire performance vector, and the weights can be dynamically adjusted based on the importance of rules in the recommendation rule base. The user interface supports visual displays of the recommendation reasons, including radar charts of matching degrees across various dimensions and performance comparison charts.

[0081] For example, the recommendation engine module uses user habit feature vectors, combined with a tire performance database and a recommendation rule base, to calculate the matching degree through a multi-dimensional matching algorithm and generate a list of recommended tires. The matching algorithm can employ methods such as weighted Euclidean distance and cosine similarity, with weights dynamically adjusted based on rule importance. The user interface displays the recommendation results, receives user feedback, and supports personalized parameter adjustments. Figure 3 As shown, the interface provides visual displays, such as matching degree radar charts and performance comparison charts, to help users intuitively understand the reasons for the recommendations.

[0082] Specifically, in the implementation of the recommendation algorithm, the recommendation engine module uses the following matching algorithm: standardizing the user feature vector (usage habit feature vector) U and the tire performance vector T; calculating the weighted Euclidean distance: Based on the rules in the recommendation rule base, dynamically adjust the weights of each dimension w. i Sort the tires by distance from smallest to largest, and take the top N tires as the recommended list.

[0083] For example, in the formula for calculating the weighted Euclidean distance, D represents the weighted Euclidean distance value between the user feature vector and the tire performance vector; i represents the dimension index of the feature vector; w i U represents the weight coefficient of the i-th dimension in the matching calculation. i T represents the component of the user feature vector, the quantified value of the user's needs in the i-th dimension; iRepresents the tire performance vector component, which is the quantified value of the actual performance of the candidate tire in the i-th dimension.

[0084] It is understandable that the user interface implementation method, such as Figure 3 As shown, the user interface can be implemented as a mobile H5 page or a mini-program. The main functions include: a data input page to guide users to input vehicle information, usage habits, etc.; a recommendation results page to display a list of recommended tires, including brand, model, price, and matching score; a details page to display matching score radar charts, performance comparison charts, user reviews, etc.; and a parameter adjustment page to allow users to manually adjust the weights (e.g., whether to prioritize tire wear resistance or comfort).

[0085] In some embodiments, the method further includes: obtaining feedback information for displaying the recommended tire list; wherein the feedback information includes the adoption status of the recommendation results and usage evaluation; and dynamically optimizing the weights of the recommendation rules based on the feedback information according to a preset machine learning algorithm.

[0086] Specifically, the recommendation engine module also includes a feedback learning mechanism, which dynamically optimizes the weights of recommendation rules based on users' adoption of recommendation results and subsequent usage evaluations, thereby improving recommendation accuracy.

[0087] For example, a feedback learning mechanism records the user's adoption of the recommendation results (whether to purchase), subsequent usage evaluations such as ratings and reviews, and dynamically optimizes the weights of recommendation rules through preset machine learning algorithms such as collaborative filtering algorithms and reinforcement learning algorithms to improve the long-term accuracy of recommendations.

[0088] This embodiment of the method performs multi-dimensional matching based on users' actual usage habits (road conditions, driving style, climate, etc.), resulting in recommendations that better match user needs and achieve personalized recommendations. This solves the problem of recommendation methods in related technologies ignoring personalized factors. It employs a data-driven recommendation algorithm combined with an expert rule base, avoiding the subjectivity of manual recommendations, achieving scientific decision-making, and improving the scientific rigor and accuracy of recommendations. Implemented as a lightweight application, users do not need to install complex software; they can quickly complete tire recommendations via their mobile phones, making it convenient and lowering the barrier to entry. Through a feedback learning mechanism, continuous optimization is achieved, constantly refining the recommendation model based on user behavior and improving long-term recommendation performance. The lightweight application has low development costs and fast deployment, making it suitable for rapid promotion and iteration.

[0089] The automatic tire recommendation method provided in this embodiment includes: acquiring vehicle usage habit data of a target object; standardizing and extracting features from the vehicle usage habit data to generate a usage habit feature vector; acquiring preset tire performance parameters and preset tire recommendation rules; and generating and displaying a recommended tire list based on a multi-dimensional matching algorithm, according to the usage habit feature vector combined with the preset tire performance parameters and preset tire recommendation rules. This embodiment automatically generates personalized tire recommendation schemes by collecting vehicle usage habit data, combining tire performance and recommendation rules, and employing a multi-dimensional matching algorithm. It can quickly and accurately recommend suitable tire products, solving the problems of traditional tire selection relying on manual experience and having low matching accuracy, thus improving user experience and purchasing decision efficiency.

[0090] Reference Figure 4 , Figure 4 This is a structural block diagram of an embodiment of the automatic tire recommendation system of the present invention. Figure 4 As shown, the automatic tire recommendation system includes:

[0091] Data acquisition module 10 is used to acquire vehicle usage habit data of the target object;

[0092] Feature analysis module 20 is used to standardize and extract features from the vehicle usage habit data to generate a usage habit feature vector;

[0093] The performance and rule acquisition module 30 is used to acquire preset tire performance parameters and preset tire recommendation rules;

[0094] The generation and display module 40 is used to generate and display a list of recommended tires based on the usage habit feature vector, the preset tire performance parameters, and the preset tire recommendation rules using a multi-dimensional matching algorithm.

[0095] The automatic tire recommendation system provided in this embodiment automatically generates personalized tire recommendation schemes by collecting vehicle usage habit data, combining tire performance and recommendation rules, and employing a multi-dimensional matching algorithm. It can quickly and accurately recommend suitable tire products, solving the problems of traditional tire selection relying on human experience and having low matching accuracy, thus improving user experience and purchasing decision efficiency. Based on actual user habits (road conditions, driving style, climate, etc.), multi-dimensional matching is performed, resulting in recommendations that better match user needs and achieve personalized recommendations, solving the problem of recommendation methods ignoring personalized factors in related technologies. The data-driven recommendation algorithm, combined with an expert rule base, avoids the subjectivity of manual recommendations, achieving scientific decision-making and improving the scientific nature and accuracy of recommendations. Implemented as a lightweight application, users do not need to install complex software; they can quickly complete tire recommendations via their mobile phones, making it convenient and lowering the barrier to entry. Through a feedback learning mechanism, continuous optimization can be achieved, constantly refining the recommendation model based on user behavior and improving long-term recommendation performance. The lightweight application has low development costs and fast deployment, making it suitable for rapid promotion and iteration.

[0096] In addition, for technical details not described in detail in this embodiment of the automatic tire recommendation system, please refer to the automatic tire recommendation method provided in any embodiment of the present invention, which will not be repeated here.

[0097] 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 5 As 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 automatic tire recommendation methods described in the above embodiments; the one or more I / O interfaces 103 are connected between the processors and the memory, configured to enable information interaction between the processors and the memory.

[0098] 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).

[0099] 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.

[0100] In some embodiments, the one or more processors 101 include a field-programmable gate array.

[0101] This invention also provides a computer-readable medium. The computer-readable medium stores a computer program, which, when executed by a processor, implements the steps of any of the automatic tire recommendation methods described in the above embodiments. The computer-readable storage medium may be volatile or non-volatile.

[0102] This invention also provides a computer program product, including computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code. When the computer-readable code is run in the processor of an electronic device, the processor in the electronic device executes the above-described automatic tire recommendation method.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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 automatically recommending tires, characterized in that, include: Obtain vehicle usage habit data from the target group; The vehicle usage habit data is standardized and features are extracted to generate a usage habit feature vector; Obtain preset tire performance parameters and preset tire recommendation rules; Based on a multi-dimensional matching algorithm, a list of recommended tires is generated and displayed by combining the user habit feature vector with the preset tire performance parameters and preset tire recommendation rules.

2. The method according to claim 1, characterized in that, The acquisition of vehicle usage habit data of the target object includes: Receive manually input data from the target object; Data is obtained from the data interface of the vehicle OBD device and / or the API interface of the third-party vehicle networking platform. Obtain the historical maintenance records of the target object; Vehicle usage habit data is obtained based on the manually input data, interface data, and historical maintenance records.

3. The method according to claim 2, characterized in that, The vehicle usage data includes at least three of the following: road condition type, driving style rating, average annual mileage, and local climate characteristics.

4. The method according to claim 3, characterized in that, The driving road condition type includes at least two of the following: urban paved roads, highways, rural roads, unpaved roads, and winding mountain roads; the driving style score is calculated based on at least two of the following: number of rapid accelerations, frequency of rapid braking, average vehicle speed, and turning G-value.

5. The method according to claim 1, characterized in that, The method further includes: Obtain different tire performance parameters and construct a tire performance database based on the tire performance parameters; Build a recommendation rule base; The acquisition of preset tire performance parameters and preset tire recommendation rules includes: Preset tire performance parameters are obtained based on a tire performance database; wherein, the preset tire performance parameters include at least four of the following: abrasion index, grip rating, wet performance, noise control, and rolling resistance. Preset tire recommendation rules are obtained based on a recommendation rule base; wherein, the preset tire recommendation rules include road condition matching rules, driving style matching rules, and climate adaptability rules.

6. The method according to any one of claims 1 to 5, characterized in that, The multi-dimensional matching algorithm generates a recommended tire list based on the usage habit feature vector, the preset tire performance parameters, and the preset tire recommendation rules, including: The tire performance vector is obtained based on the preset tire performance parameters; Weights for different dimensions are obtained based on the preset tire recommendation rules; Based on the multi-dimensional matching algorithm, a weighted Euclidean distance is obtained according to the usage habit feature vector, tire performance vector, and weights. The recommended tire list is generated by sorting the tires according to the weighted Euclidean distance.

7. The method according to claim 6, characterized in that, The method further includes: Obtain feedback information that displays the recommended tire list; wherein the feedback information includes the adoption status of the recommendation results and usage evaluation; The weights of the recommendation rules are dynamically optimized based on the feedback information using a preset machine learning algorithm.

8. An automatic tire recommendation system, characterized in that, include: The data acquisition module is used to obtain vehicle usage habit data of the target object; The feature analysis module is used to standardize and extract features from the vehicle usage habit data to generate a usage habit feature vector. The performance and rule acquisition module is used to acquire preset tire performance parameters and preset tire recommendation rules; The generation and display module is used to generate and display a list of recommended tires based on the usage habit feature vector, the preset tire performance parameters, and the preset tire recommendation rules using a multi-dimensional matching algorithm.

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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