Vehicle logo replacement method and device applied to map client and electronic equipment

Abstract

The invention provides a vehicle logo replacement method applied to a map client, a vehicle logo replacement device applied to the map client and electronic equipment. The method relates to the technical field of computers and can be applied to vehicle-mounted terminals or navigation software. Comprising the steps of obtaining a role identifier set corresponding to a user account, and determining a to-be-recommended vehicle logo set based on behavior characteristics of the user account; determining a matching degree between each vehicle logo in the to-be-recommended vehicle logo set and the role identifier set, and obtaining a matching result containing a plurality of matching degrees; determining a vehicle logo with the highest matching degree with the role identifier set according to a matching result; triggering the user equipment to replace the current vehicle logo in the map client with the vehicle logo with the highest matching degree with the role identifier set; wherein the current vehicle logo is used for representing the position of the user in the electronic map of the map client. Therefore, by implementing the embodiment of the invention, personalized automatic vehicle logo replacement can be realized.

Description

[0001] This application is a divisional application filed with the Chinese Patent Office on June 21, 2021, with application number 2021106873660, entitled "Method, Apparatus and Electronic Device for Changing Car Logos Applied to Map Clients", the entire contents of which are incorporated herein by reference. Technical Field

[0002] This application relates to the field of computer technology, and more specifically, to a method for changing car logos in a map client, a device for changing car logos in a map client, and an electronic device. Background Technology

[0003] In the online world, user identifiers (such as icons and images) are commonly used to identify users. For example, in social media, a user's avatar might be a user's profile picture; in navigation software, a car logo might be a user's identifier. To make their identifier more accurately represent them, users typically change their identifiers as needed. Generally, software provides users with a set of default user identifiers to choose from. However, in this case, the options available to users are very limited, failing to offer a richer user experience.

[0004] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this application, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this application is to provide a method for changing car logos, a method for changing objects, a device for changing car logos, a device for changing objects, a computer-readable storage medium, and an electronic device for changing car logos in a map client, which can realize personalized automatic car logo changing.

[0006] Other features and advantages of this application will become apparent from the following detailed description, or may be learned in part from practice of this application.

[0007] According to one aspect of this application, a method for changing vehicle logos in a map client is provided, comprising:

[0008] Obtain the set of role identifiers corresponding to user accounts, and determine the set of car icons to be recommended based on the behavioral characteristics of user accounts;

[0009] Determine the matching degree between each car logo in the set of car logos to be recommended and the set of role identifiers, and obtain matching results containing multiple matching degrees;

[0010] Based on the matching results, determine the car logo that has the highest matching degree with the set of role identifiers;

[0011] The user device is triggered to replace the current vehicle icon in the map client with the vehicle icon that best matches the set of role identifiers; the current vehicle icon is used to represent the user's location in the electronic map of the map client.

[0012] According to one aspect of this application, a method for changing vehicle logos in a map client is provided, comprising:

[0013] When a login operation is detected for the map client, the user account entered in the login operation is sent to the server;

[0014] Receive the car logo that best matches the user account's set of role identifiers, as reported by the server.

[0015] Replace the current vehicle icon in the map client with the vehicle icon that best matches the user's account's role identifier set; the current vehicle icon represents the user's location on the electronic map in the map client.

[0016] In one exemplary embodiment of this application, the method further includes:

[0017] When a user action is detected that triggers the search function, the search information corresponding to the user action is retrieved.

[0018] Display a list of candidate car logos corresponding to the information to be searched; the list of candidate car logos includes the target role identifier corresponding to the information to be searched and the car logos related to the set of role identifiers, where the set of role identifiers corresponds to the user account;

[0019] When a selection operation is detected on the list of candidate car icons, the current car icon in the map client is replaced with the car icon corresponding to the selection operation in response to the selection operation; wherein, the current car icon is used to represent the user's location in the electronic map of the map client.

[0020] According to one aspect of this application, a vehicle logo replacement device for a map client is provided, comprising: a vehicle logo to be recommended determination unit, a matching degree calculation unit, a vehicle logo determination unit, and a vehicle logo replacement unit, wherein:

[0021] The car logo to be recommended unit is used to obtain the set of role identifiers corresponding to user accounts and determine the set of car logos to be recommended based on the behavioral characteristics of user accounts;

[0022] The matching degree calculation unit is used to determine the matching degree between each car logo in the set of car logos to be recommended and the set of role identifiers, and to obtain a matching result containing multiple matching degrees;

[0023] The logo determination unit is used to determine the logo that has the highest matching degree with the set of role identifiers based on the matching results.

[0024] The vehicle logo replacement unit is used to trigger the user device to replace the current vehicle logo in the map client with the vehicle logo that has the highest matching degree with the role identifier set; wherein, the current vehicle logo is used to represent the user's location in the electronic map of the map client.

[0025] In one exemplary embodiment of this application, the above-described apparatus further includes:

[0026] An information acquisition unit is used to acquire the information to be retrieved in the retrieval request when a retrieval request is received;

[0027] The identifier query unit is used to query the target role identifier corresponding to the information to be retrieved. If the target role identifier belongs to the set of role identifiers, a list of candidate car logos containing the target role identifier and car logos that match the set of role identifiers is generated and fed back to the user device so that the user device can display the list of candidate car logos; wherein, the list of candidate car logos corresponds to the user account.

[0028] In one exemplary embodiment of this application, the above-described apparatus further includes:

[0029] The car logo acquisition unit is used to acquire at least one related car logo corresponding to the target character logo from the character logo library when the target character logo does not belong to the character logo set; wherein, the at least one related car logo and the target character logo belong to the same game project;

[0030] The logo replacement unit is also used to feed back at least one relevant logo and target role identifier to the user equipment, so that the user equipment can display at least one relevant logo and target role identifier.

[0031] In one exemplary embodiment of this application, the object acquisition unit feeds back at least one relevant vehicle logo and a target role identifier to the user equipment, so that the user equipment displays at least one relevant vehicle logo and a target role identifier, including:

[0032] The at least one related car logo is sorted from highest to lowest based on its popularity of use, resulting in a sorting result.

[0033] The target role identifier and the sorting result are fed back to the user equipment so that the user equipment can display the target role identifier and the sorting result; wherein, the display priority of the target role identifier is higher than any related car logo in the sorting result.

[0034] In one exemplary embodiment of this application, the candidate logo list further includes at least one popular object, and the logo changing unit generates a candidate logo list containing the target role identifier and logos matching the set of role identifiers, including:

[0035] Select at least one popular object within a unit of time based on the trigger time of the user action used to initiate the search function; wherein, the trigger time is the end time of the unit of time.

[0036] Generate a list of candidate car logos that includes the target role identifier, the at least one popular object, and car logos that match the set of role identifiers;

[0037] Among them, the display priority of the target role identifier is higher than that of the car logo that matches the set of role identifiers, and the display priority of the car logo that matches the set of role identifiers is higher than that of the at least one popular object.

[0038] In one exemplary embodiment of this application, the car logo replacement unit queries the target role identifier corresponding to the information to be retrieved, including:

[0039] Determine the tags corresponding to the information to be retrieved;

[0040] Select the target set from the set of objects corresponding to different labels;

[0041] Retrieve objects from the target set that match the information to be retrieved as target role identifiers.

[0042] In one exemplary embodiment of this application, the car logo replacement unit determines the tag corresponding to the information to be retrieved, including:

[0043] Keyword extraction is performed on the information to be retrieved to obtain the extraction results;

[0044] If the extraction results indicate that keywords exist in the information to be retrieved, then the tags corresponding to the keywords will be determined as the tags of the information to be retrieved.

[0045] In one exemplary embodiment of this application, the car logo replacement unit is further configured to display a prompt message indicating search failure and at least one hot search term when the extraction result indicates that no keyword exists in the information to be retrieved;

[0046] The above-mentioned device also includes:

[0047] The object set determination unit is used to determine the specific set to which the target hot word belongs from the object sets corresponding to different tags when a user operation is received that acts on the target hot word in at least one search hot word;

[0048] The object invocation unit is used to invoke an object that matches the target hot word from a specific set as a role identifier to replace the current car logo.

[0049] In one exemplary embodiment of this application, if the number of role identifiers is greater than 2, the car logo replacement unit queries the target role identifier corresponding to the information to be retrieved, including:

[0050] Group all role identifiers to obtain multiple object sets;

[0051] Based on the information to be retrieved, information queries are performed on multiple object sets in sequence to obtain query results corresponding to each object set; among them, multiple object sets correspond to different levels of popularity, and the multiple object sets are arranged in descending order of popularity;

[0052] If a query result matches the information to be retrieved, the set of objects corresponding to the query result matching the information to be retrieved is determined, and the target role identifier corresponding to the information to be retrieved is retrieved from the set of objects.

[0053] In one exemplary embodiment of this application, the car logo replacement unit groups all role identifiers to obtain multiple object sets, including:

[0054] The popularity score of all role identifiers is calculated based on the object information; the object information includes at least one of the following: object name, object identifier, number of times the object is used, usage duration, and online duration.

[0055] All character identifiers are grouped according to their popularity value, resulting in multiple object sets.

[0056] In one exemplary embodiment of this application, the car logo determination unit determines the set of car logos to be recommended corresponding to the user account based on the behavioral characteristics of the user account, including:

[0057] A recommendation model is trained based on the behavioral characteristics of the user accounts;

[0058] The set of car logos to be recommended is determined based on the trained recommendation model.

[0059] In one exemplary embodiment of this application, the vehicle logo determination unit trains a recommendation model based on the behavioral characteristics of the user account, including:

[0060] Obtain behavioral characteristics related to the user account; wherein, the behavioral characteristics are related to the set of role identifiers, and the behavioral characteristics include at least one of object purchase characteristics, object collection characteristics, object click characteristics, object cancel download characteristics, object usage characteristics, and object switching characteristics;

[0061] Based on the behavioral characteristics, label features corresponding to all objects are determined; wherein, all objects include a set of role identifiers corresponding to the user account, and the label features are used to characterize the object invocation situation;

[0062] Sample data is generated based on the behavioral features and the label features;

[0063] The recommendation model is trained using the sample data.

[0064] In one exemplary embodiment of this application, generating sample data based on behavioral features and tag features includes:

[0065] Based on the time corresponding to the behavioral features in the time series, determine the adjacent behavioral features corresponding to the first adjacent time, and calculate the first difference result based on the behavioral features and the adjacent behavioral features;

[0066] Based on the label features in the time series, determine the adjacent label features corresponding to the second adjacent time, and calculate the second difference result based on the label features and the adjacent label features.

[0067] Sample data is generated based on the first and second difference results.

[0068] In one exemplary embodiment of this application, generating sample data based on a first difference result and a second difference result includes:

[0069] The first difference result is converted into an encoded feature vector;

[0070] The sample data is obtained by fusing the encoded feature vector and the second difference result.

[0071] In one exemplary embodiment of this application, the vehicle logo determination unit determines the set of vehicle logos to be recommended based on a trained recommendation model, including:

[0072] The recommendation model predicts multi-class ratings for all objects, and the multi-class ratings are used to represent the probability that an object is selected by the user.

[0073] All multi-class scores for all objects are simplified to binary scores; the number of scores in the multi-class scores is greater than the number of scores in the binary scores.

[0074] The set of recommended car logos for each user account is determined based on the binary classification score.

[0075] In one exemplary embodiment of this application, the sample data consists of training samples and test samples. The vehicle logo determination unit to be recommended trains a recommendation model using the sample data, including:

[0076] The recommendation model is trained using training samples;

[0077] The trained recommendation model was tested using test samples, and the model parameters were adjusted based on the test results.

[0078] In one exemplary embodiment of this application, the above-described apparatus further includes:

[0079] The environment tag acquisition unit is used to periodically acquire environment tags within a preset range centered on the user's location after the vehicle logo replacement unit triggers the user device to replace the current vehicle logo in the map client with the vehicle logo that matches the set of role identifiers.

[0080] The vehicle logo replacement unit is specifically used to trigger the user device to replace the current vehicle logo used to represent the user's location in the electronic map with the target vehicle logo when a target vehicle logo that matches the environment label exists in the matching results.

[0081] According to one aspect of this application, an electronic device is provided, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the method of any of the above by executing the executable instructions.

[0082] According to one aspect of this application, a computer-readable storage medium is provided, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the method of any one of the above.

[0083] According to one aspect of this application, a computer program product or computer program is provided, comprising computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium and executes the computer instructions, causing the computer device to perform the methods provided in the various alternative implementations described above.

[0084] The exemplary embodiments of this application may have some or all of the following beneficial effects:

[0085] In an example embodiment of this application, a method for changing car logos applied to a map client is provided. This method involves obtaining a set of role identifiers corresponding to a user account and determining a set of car logos to be recommended based on the user account's behavioral characteristics. The matching degree between each car logo in the set of car logos to be recommended and the set of role identifiers is determined, resulting in a matching result containing multiple matching degrees. Based on the matching result, the car logo with the highest matching degree to the set of role identifiers is determined. The user device is then triggered to replace the current car logo in the map client with the car logo with the highest matching degree to the set of role identifiers. Here, the current car logo represents the user's location on the electronic map of the map client. According to the above description, this application, on the one hand, can determine a personalized set of car logos to be recommended based on the behavioral characteristics corresponding to the user account, and then determine a suitable car logo for the user based on the matching degree between the set of car logos to be recommended and the set of role identifiers, thereby achieving personalized automatic car logo replacement. On the other hand, this application can avoid traffic accidents caused by users manually changing car logos while driving, achieving automated car logo replacement to improve driving safety.

[0086] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and do not limit this application. Attached Figure Description

[0087] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort.

[0088] Figure 1 A schematic diagram of an exemplary system architecture for a vehicle logo replacement method and a vehicle logo replacement device for a map client, which can be applied to embodiments of this application, is shown.

[0089] Figure 2 A schematic diagram of the structure of a computer system suitable for implementing the embodiments of this application is shown;

[0090] Figure 3 A flowchart illustrating a vehicle logo replacement method applied to a map client according to an embodiment of this application is shown schematically.

[0091] Figure 4 This illustration schematically shows a diagram of a recommendation model training process according to an embodiment of the present application;

[0092] Figure 5 This illustration schematically shows a prediction process of a recommendation model according to an embodiment of the present application;

[0093] Figure 6 This illustration schematically shows a process diagram for personalizing a character identifier according to one embodiment of the present application;

[0094] Figure 7 This illustration schematically shows a view of a candidate car logo list display interface according to one embodiment of the present application;

[0095] Figure 8 This illustration schematically depicts an object query process according to an embodiment of the present application;

[0096] Figure 9 This illustration schematically shows a role identifier search process according to one embodiment of the present application;

[0097] Figure 10 A schematic diagram illustrating a role identifier according to an embodiment of this application is shown.

[0098] Figure 11A schematic diagram of a navigation interface according to an embodiment of this application is shown.

[0099] Figure 12 A schematic diagram of a navigation interface according to an embodiment of this application is shown.

[0100] Figure 13 A schematic diagram of a navigation interface according to an embodiment of this application is shown.

[0101] Figure 14 A flowchart illustrating an object replacement method according to an embodiment of this application is shown schematically;

[0102] Figure 15 This schematic diagram illustrates a structural block diagram of an object replacement apparatus according to one embodiment of the present application;

[0103] Figure 16 A flowchart illustrating a vehicle logo replacement method applied to a map client according to an embodiment of this application is shown schematically.

[0104] Figure 17 The diagram illustrates a structural block diagram of a vehicle logo changing device applied to a map client according to one embodiment of this application. Detailed Implementation

[0105] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided to make this application more comprehensive and complete, and to fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to give a full understanding of the embodiments of this application. However, those skilled in the art will recognize that the technical solutions of this application can be practiced with one or more of the specific details omitted, or other methods, components, apparatus, steps, etc., can be employed. In other instances, well-known technical solutions are not shown or described in detail to avoid obscuring various aspects of this application.

[0106] Furthermore, the accompanying drawings are merely illustrative of this application and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0107] Figure 1 A schematic diagram of a system architecture for an exemplary application environment of a vehicle logo replacement method and a vehicle logo replacement device for a map client, which can be applied to embodiments of this application, is shown.

[0108] like Figure 1 As shown, system architecture 100 may include one or more of terminal devices 101, 102, and 103, a network 104, and a server cluster 105. Network 104 serves as the medium for providing communication links between terminal devices 101, 102, and 103 and server cluster 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables. Terminal devices 101, 102, and 103 may be various electronic devices with displays, including but not limited to desktop computers, laptops, smartphones, and tablets. It should be understood that... Figure 1 The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0109] The vehicle logo replacement method for map clients provided in this application embodiment can be executed by any server in the terminal devices 101, 102, 103 or the server cluster 105. Accordingly, the vehicle logo replacement device for map clients is generally located in the servers of the server cluster 105 or in the terminal devices 101, 102, 103. For example, in an exemplary embodiment, any server in the server cluster 105 (or terminal devices 101, 102, 103) can obtain the set of role identifiers corresponding to the user account, and determine the set of vehicle logos to be recommended based on the behavioral characteristics of the user account; determine the matching degree between each vehicle logo in the set of vehicle logos to be recommended and the set of role identifiers, and obtain a matching result containing multiple matching degrees; determine the vehicle logo with the highest matching degree with the set of role identifiers according to the matching result; trigger the user device to replace the current vehicle logo in the map client with the vehicle logo with the highest matching degree with the set of role identifiers; wherein, the current vehicle logo is used to represent the user's location in the electronic map of the map client.

[0110] Figure 2 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the present application is shown.

[0111] It should be noted that, Figure 2 The computer system 200 of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0112] like Figure 2As shown, the computer system 200 includes a central processing unit (CPU) 201, which can perform various appropriate actions and processes based on programs stored in read-only memory (ROM) 202 or programs loaded from storage section 208 into random access memory (RAM) 203. The RAM 203 also stores various programs and data required for system operation. The CPU 201, ROM 202, and RAM 203 are interconnected via a bus 204. An input / output (I / O) interface 205 is also connected to the bus 204.

[0113] The following components are connected to I / O interface 205: an input section 206 including a keyboard, mouse, etc.; an output section 207 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 208 including a hard disk, etc.; and a communication section 209 including a network interface card such as a LAN card, modem, etc. The communication section 209 performs communication processing via a network such as the Internet. Drive 210 is also connected to I / O interface 205 as needed. Removable media 211, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 210 as needed so that computer programs read from them can be installed into storage section 208 as needed.

[0114] In particular, according to embodiments of this application, the processes described below with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 209, and / or installed from removable medium 211. When the computer program is executed by central processing unit (CPU) 201, it performs the various functions defined in the methods and apparatus of this application.

[0115] Furthermore, this application also utilizes artificial intelligence (AI) technology and machine learning, specifically in predicting ratings for all objects based on a recommendation model. AI is the theory, methods, technology, and application systems that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to achieve optimal results. In other words, AI is a comprehensive technology within computer science that attempts to understand the essence of intelligence and produce a new type of intelligent machine capable of reacting in a manner similar to human intelligence. AI studies the design principles and implementation methods of various intelligent machines, enabling them to possess perception, reasoning, and decision-making capabilities.

[0116] Artificial intelligence (AI) is a comprehensive discipline encompassing a wide range of fields, including both hardware and software technologies. Fundamental AI technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interactive systems, and mechatronics. AI software technologies primarily include computer vision, speech processing, natural language processing, and machine learning / deep learning.

[0117] Machine learning (ML) is a multidisciplinary field involving probability theory, statistics, approximation theory, convex analysis, and algorithm complexity theory. It specifically studies how computers can simulate or implement human learning behavior to acquire new knowledge or skills and reorganize existing knowledge structures to continuously improve their performance. Machine learning is the core of artificial intelligence and the fundamental way to endow computers with intelligence; its applications span all areas of artificial intelligence. Machine learning and deep learning typically include techniques such as artificial neural networks, belief networks, reinforcement learning, transfer learning, inductive learning, and instructional learning.

[0118] In social media apps, a role icon can be used as a user's avatar to identify them; in navigation apps, a role icon can be used as a car icon to indicate the user's current location. Role icons can be changed according to the user's needs. Generally, users need to search for relevant keywords to obtain the corresponding role icon, and then select the desired icon to change the car icon. However, this approach suffers from low interactivity and doesn't provide a richer user experience.

[0119] Based on this, this example implementation provides a method for changing car logos in a map client. Please refer to... Figure 3 , Figure 3 A flowchart illustrating a vehicle logo replacement method applied to a map client according to an embodiment of this application is shown schematically. Figure 3 As shown, the method for changing car logos applied to a map client may include steps S310 to S340.

[0120] Step S310: Obtain the set of role identifiers corresponding to user accounts, and determine the set of car icons to be recommended based on the behavioral characteristics of user accounts.

[0121] Step S320: Determine the matching degree between each car logo in the set of car logos to be recommended and the set of role identifiers, and obtain a matching result containing multiple matching degrees.

[0122] Step S330: Determine the car logo with the highest matching degree to the set of role identifiers based on the matching results.

[0123] Step S340: Trigger the user device to replace the current vehicle icon in the map client with the vehicle icon that has the highest matching degree with the role identifier set; wherein, the current vehicle icon is used to represent the user's location in the electronic map of the map client.

[0124] It should be noted that steps S310 to S340 can be executed by either the terminal or the server. The server mentioned above can be a cloud server. A cloud server is a service platform that provides comprehensive business capabilities to various Internet users. These comprehensive services generally include public Internet infrastructure services such as computing, storage, and networking. Users of cloud services can quickly create or release any number of cloud servers without having to purchase hardware in advance.

[0125] Implementation Figure 3 The method described can determine a personalized set of recommended car logos based on the behavioral characteristics of a user's account. Then, it determines the most suitable car logo for the user based on the matching degree between this set and the user's role identifier set, thus achieving personalized automatic car logo changing. Furthermore, it can avoid traffic accidents caused by users manually changing car logos while driving, thereby improving driving safety through automated car logo changing.

[0126] The steps described above in this example implementation will now be explained in more detail.

[0127] In step S310, the set of role identifiers corresponding to the user account is obtained, and the set of car icons to be recommended is determined based on the behavioral characteristics of the user account.

[0128] Specifically, the user account can be the current user's game account (e.g., 567141453454), and the character identifier can be at least one game character identifier associated with that game account (e.g., Mario, Luigi, Bowser, Peach Princess, etc.). The character identifier is stored in a character identifier database on a cloud server. This database is also used to collect character identifiers. The game account can be obtained by the user through social media accounts, mobile phone numbers, etc. When this application is applied to map / navigation software, the user account in the map software can be bound to the current user's game account.

[0129] Specifically, both the target role identifier and the corresponding car logo in the list of candidate car logos are touch-sensitive. Furthermore, the objects displayed in the list of candidate car logos can be static or dynamic; this embodiment does not impose any limitations.

[0130] As an optional embodiment, the above method further includes: when a retrieval request is received, obtaining the information to be retrieved in the retrieval request; querying the target role identifier corresponding to the information to be retrieved; if the target role identifier belongs to the role identifier set, generating a list of candidate car logos containing the target role identifier and car logos matching the role identifier set and feeding it back to the user device so that the user device can display the list of candidate car logos; wherein, the list of candidate car logos corresponds to the user account.

[0131] Specifically, the information to be retrieved can be text information entered by the user. The user operation used to trigger the search function can be a click operation, a touch operation, a voice control operation, a gesture control operation, etc., which are not limited in this application embodiment, and the same applies to user operations for other purposes in this application.

[0132] Additionally, obtaining the information to be retrieved in the search request includes: when the user device detects a user operation on the completion control / search control, it determines that the input is complete, obtains the information to be retrieved in the input box, and generates a search request based on the information to be retrieved and sends it back to the server. Furthermore, before obtaining the information to be retrieved in the input box, the above method may also include: when the user device detects a character in the input box, it displays a suggested list. The suggested list displays hot words related to that character for the user to choose from. As characters are added or removed from the input box, the suggested list can be continuously updated so that the hot words in the suggested list always match the content in the input box. This allows the user to directly select the word they want to enter when they see it in the suggested list, without needing to continue typing characters in the input box.

[0133] Furthermore, in addition to displaying trending keywords, the suggested search list can also display the role identifier corresponding to each keyword. The method can also include: when a user action is detected acting on a target trending keyword in the suggested search list, the current car icon can be replaced with the role identifier corresponding to the target trending keyword. This simplifies the user's car icon replacement process, thereby improving replacement efficiency and enhancing the user experience.

[0134] Specifically, the server obtains the corresponding set of character identifiers based on the game account associated with the user account and packages and sends it back to the user device, including: the server obtains the corresponding set of character identifiers based on the game account associated with the user account and packages and sends the set of character identifiers back to the user device.

[0135] Furthermore, the vehicle logo can be an object that corresponds to the same tag as the character identifier, or an object belonging to the same game project as the character identifier, or an object similar to the character identifier; this application embodiment does not impose any limitations. If the vehicle logo is an object similar to the character identifier, the similarity between the corresponding vehicle logo and the character identifier (e.g., 80%) can be represented by vector similarity, which can specifically be cosine distance or Euclidean distance. The similarity can indicate the similarity between the corresponding vehicle logo identifier and the character identifier, or it can indicate the similarity between the corresponding vehicle logo name (e.g., Zelda) and the character identifier name (e.g., Princess Peach).

[0136] As an optional embodiment, determining the set of car logos to be recommended for a user account based on the user account's behavioral characteristics includes: training a recommendation model based on the user account's behavioral characteristics; and determining the set of car logos to be recommended based on the trained recommendation model.

[0137] Specifically, the recommendation model can be a statistical scoring model that combines relevant behavioral characteristics to measure users' preferences for products, games, anime, etc. Furthermore, after determining the car logo corresponding to the set of character identifiers through the recommendation model, the method can also include: pushing any character identifier from the set of character identifiers for a preset time interval; when a user action to select a character identifier is detected, replacing the current car logo representing the current location with the selected character identifier. This provides users with the option to change the object (e.g., car logo), requiring only a confirmation click, reducing the steps required for users to change car logos and thus lowering the risk during their travels.

[0138] As can be seen, implementing this optional embodiment can determine the objects that need to be recommended to the user through the recommendation model, thereby enriching the options and improving the user experience.

[0139] As an optional embodiment, training a recommendation model based on user account behavioral features includes: acquiring behavioral features related to the user account; wherein the behavioral features are related to a set of role identifiers, and the behavioral features include at least one of object purchase features, object collection features, object click features, object cancel download features, object usage features, and object switching features; determining tag features corresponding to all objects based on the behavioral features; wherein all objects include a set of role identifiers corresponding to the user account, and the tag features are used to characterize object usage; generating sample data based on the behavioral features and tag features; and training the recommendation model using the sample data.

[0140] Specifically, behavioral features can be used to represent multi-dimensional user actions, and these features can be represented by vectors. The algorithm used in the recommendation model is as follows:

[0141] ,in, , and It is a constant.

[0142] Additionally, the object purchase feature represents game characters that a user has purchased. The object favorite feature represents game characters that a user has favorited. The object click feature represents game characters that a user has clicked to view. The object cancel download feature represents the user's action of canceling a game character that was in the download process. The object usage feature represents the user's usage of each game character, which can include usage frequency. The object switching feature represents the user's action of switching from one game character to another.

[0143] In addition, label features are determined for each object based on behavioral characteristics, including: behavioral characteristics at each time point in the time series. Calculate the difference results and ; Construct binary labels for all virtual characters based on the role application information corresponding to user accounts; Determine the label features corresponding to each object based on the binary labels of all virtual characters. The binary category labels can include label 0 for "unused" and label 1 for "used". The role application status is used to characterize whether the user account has used or not used the virtual role.

[0144] As can be seen, implementing this optional embodiment can characterize users based on the determined behavioral and tag features, thereby generating sample data based on the behavioral and tag features. By training the recommendation model with the sample data, the recommendation effect of the recommendation model can be improved, and the personalized role identifiers recommended to users can better meet the user's expectations, thereby increasing the probability that the role identifiers are selected by the user.

[0145] As an optional embodiment, generating sample data based on behavioral features and label features includes: determining adjacent behavioral features corresponding to a first adjacent time based on the time corresponding to the behavioral features in the time series, and calculating a first difference result based on the behavioral features and adjacent behavioral features; determining adjacent label features corresponding to a second adjacent time based on the time corresponding to the label features in the time series, and calculating a second difference result based on the label features and adjacent label features; and generating sample data based on the first difference result and the second difference result.

[0146] Specifically, the first difference result is used to characterize the behavioral feature differences between adjacent time points, and the second difference result is used to characterize the label feature differences between adjacent time points. The first difference result may include at least one difference result (e.g., and The second difference result is similar. The first adjacent time and the second adjacent time can be the same time or different times. For example, if the current time is... Then the first adjacent time and / or the second adjacent time can be It can also be ( ); if the current time is If the current time is Then the first adjacent time and / or the second adjacent time can be It can also be And so on, without further explanation.

[0147] In addition, determining the adjacent behavioral features corresponding to the first adjacent time step based on the time step corresponding to the behavioral features in the time series includes: determining the adjacent behavioral features corresponding to the time step in the time series based on the behavioral features. Determine the first adjacent time. The corresponding adjacent behavioral features, and the corresponding time points in the time series based on these behavioral features. Determine the first adjacent time. The corresponding adjacent behavioral characteristics. Here, the first adjacent time is the adjacent time corresponding to the current time; if the current time is... Then the first adjacent time is If the current time is Then the first adjacent time is .

[0148] Furthermore, the first difference result is calculated based on the behavioral characteristics and adjacent behavioral characteristics, including: based on time... behavioral characteristics and time Adjacent behavior characteristics Calculate the first difference result , and, according to time behavioral characteristics and time Adjacent behavior characteristics Calculate the first difference result .

[0149] Furthermore, based on the time corresponding to the label features in the time series, the adjacent label features corresponding to the second adjacent time are determined, including: based on the time corresponding to the label features in the time series. Determine the second adjacent time. Corresponding adjacent label features .

[0150] Furthermore, the second difference result is calculated based on the label features and the features of adjacent labels, including: based on time... Label features Second adjacent time Corresponding adjacent label features Calculate the second difference result Among them, in hour, ;exist hour, ;exist hour, .

[0151] As can be seen, by implementing this optional embodiment, the differences between behavioral features and label features in time can be characterized through the calculation of feature results. This can generate sample data for training the recommendation model, thereby training a more accurate recommendation model to improve personalization and recommend suitable personalized role identifiers to different users, thus improving the user experience and increasing user stickiness.

[0152] As an optional embodiment, generating sample data based on the first difference result and the second difference result includes: converting the first difference result into an encoded feature vector; and fusing the encoded feature vector and the second difference result to obtain the sample data.

[0153] Specifically, the encoded feature vector can be a One-Hot encoding result. One-Hot encoding represents categorical variables as binary vectors, typically using an N-bit state register to encode N states, with each state corresponding to an independent register bit, where N is a positive integer. The sample data can contain N training samples and M test samples, where N and M are both positive integers.

[0154] In addition, before converting the first difference result into an encoded feature vector, the above method may further include: processing the first difference result... and Perform feature transformation to obtain and ;in, It can be used as a prediction sample in the sample data. = Based on this, the first difference result is converted into an encoded feature vector, including: the first difference result after feature conversion based on One-Hot encoding. The data is converted into an encoded feature vector. Based on this, the encoded feature vector and the second difference result are fused to obtain sample data, including: the encoded feature vector fused based on the user account, and the data from the first difference result. Second difference result The sample data is obtained; the fusion methods may include splicing, positional multiplication, etc.

[0155] As can be seen, by implementing this optional embodiment, the classification ability of the difference results can be improved by processing the difference results, and then the sample data for training the recommendation model can be obtained by fusing the difference results, thereby improving the recommendation effect of the recommendation model.

[0156] As an optional embodiment, the sample data consists of training samples and test samples. Training the recommendation model using the sample data includes: training the recommendation model using the training samples; testing the trained recommendation model using the test samples; and adjusting the model parameters corresponding to the trained recommendation model based on the test results.

[0157] Specifically, the ratio of training samples to test samples in the sample data can be 8:2. Furthermore, training the recommendation model using training samples includes: inputting the training samples into the recommendation model and training the model based on the gradient descent algorithm to optimize the model parameters; wherein the model parameters include at least one of weight values ​​and bias terms. It should be noted that the recommendation model is essentially a softmax classifier, which relies on the logistic regression algorithm to map the real number domain to the effective real number space [0, 1] representing the probability distribution. The gradient descent algorithm is used to solve for the minimum value along the gradient descent direction or the maximum value along the gradient ascent direction.

[0158] In addition, the trained recommendation model is tested using test samples. This includes inputting test samples into the trained recommendation model and generating test results containing metrics for evaluating the model based on the model's output. These metrics include at least one of recall, precision, and AUC (Area Under Curve). AUC is defined as the area under the ROC curve (i.e., the receiver operating characteristic curve) and the coordinate axes, and its value ranges between 0.5 and 1. The closer the AUC is to 1.0, the higher the realism of the detection method; when the AUC equals 0.5, the realism of the detection method is the lowest.

[0159] As can be seen, implementing this optional embodiment allows for further optimization of the model through testing, thereby improving the model's predictive performance.

[0160] Please see Figure 4 , Figure 4 The illustration shows a schematic diagram of a recommendation model training process according to an embodiment of this application. Figure 4 As shown, the cloud server can obtain behavioral characteristics 410 related to the user account; wherein, the behavioral characteristics are related to the set of role identifiers, and the behavioral characteristics include at least one of the following: object purchase characteristics, object collection characteristics, object click characteristics, object cancel download characteristics, object usage characteristics, and object switching characteristics.

[0161] Based on the sequence of differential features, behavioral features 410 may include behavioral features 411 at time (t-1) and behavioral features 412 at time (t-2). Based on behavioral features 411 at time (t-1) and behavioral features 412 at time (t-2), label features 413 at time (t-1) and label features 414 at time (t) can be determined for each object.

[0162] Furthermore, based on the behavioral features 411 at time (t-1) and 412 at time (t-2), the first difference result 415 at time (t-1) can be calculated, and based on the label features 413 at time (t-1) and 414 at time (t), the second difference result 416 at time (t) can be calculated.

[0163] Furthermore, the first difference result 415 at time (t-1) can be converted into an encoded feature vector 417. The sample data can be obtained by fusing the encoded feature vector 417 and the second difference result 416 at time (t). The sample data includes the training sample 418 at time (t) and the test sample 419 at time (t).

[0164] Furthermore, the recommendation model 420 can be trained using the training sample 418 at time (t). If the output of the recommendation model does not meet expectations, the recommendation model 420 can be retrained using the training sample 418 at time (t) until the output of the recommendation model meets expectations.

[0165] Furthermore, the trained recommendation model 420 can be tested using the test sample 419 at time (t), and the model parameters corresponding to the trained recommendation model 420 can be adjusted based on the test results.

[0166] When the output of the recommendation model meets expectations, the trained recommendation model 421 can be obtained, and the recommendation model 421 has also passed the test and can be put into use.

[0167] As an optional implementation, the set of car logos to be recommended is determined based on the trained recommendation model, including: predicting multi-class ratings for all objects using the recommendation model, whereby multi-class ratings represent the probability of an object being selected by a user; simplifying the multi-class ratings of all objects into binary ratings; wherein the number of ratings in the multi-class ratings is greater than the number of ratings in the binary ratings; and determining the set of car logos to be recommended corresponding to a user account based on the binary ratings.

[0168] Specifically, the set of car logos to be recommended includes at least one car logo. Binary classification scores correspond to two types, 0 and 1. Multi-class classification scores can specifically be tri-class classification scores, corresponding to three types: 0, -1, and 1.

[0169] Additionally, the recommendation model predicts multi-class ratings for all objects. These multi-class ratings represent the probability that an object will be selected by the user. This includes predicting the next time step for all objects using the recommendation model. The multi-class scoring system includes three categories: 0, -1, and 1. All objects fall into their respective categories based on the prediction results, resulting in three sets corresponding to categories 0, -1, and 1. Furthermore, the multi-class scores can be uploaded to a cloud server for storage.

[0170] Furthermore, all multi-class classification scores for objects are simplified to binary classification scores, including: expression-based... and Multiple sets of multi-category ratings are merged into two sets, corresponding to rating 1 and rating 0 respectively. 1 indicates selecting a role identifier, and 0 indicates not selecting a role identifier. Specifically, but ; or ,but ; but or Furthermore, the binary classification score can be uploaded to a cloud server so that the cloud server can store the binary classification score.

[0171] As can be seen, implementing this optional embodiment can transform the multi-classification prediction result into a binary classification result by converting the original binary classification algorithm model into a multi-classification algorithm model, thereby improving the model's prediction accuracy.

[0172] Please see Figure 5 , Figure 5 A schematic diagram illustrating a recommendation model prediction process according to an embodiment of this application is shown. Figure 5 As shown, based on the predicted sample and the recommendation model 510, the behavioral features 512 at time (t) and the behavioral features 513 at time (t-1) corresponding to the user account can be obtained. Then, the label features 511 at time (t) can be obtained based on the behavioral features 512 at time (t) and the behavioral features 513 at time (t-1).

[0173] Furthermore, based on the behavioral features 512 at time (t) and 513 at time (t-1), the first difference result 514 at time (t) can be calculated. The first difference result 514 at time (t) can be converted into an encoded feature vector 515 and input into the recommendation model 516.

[0174] Recommendation model 516 can generate output results 517 containing three-class labels for all objects. Furthermore, recommendation model 516 can simplify the output results 517 containing three-class labels into two-class labels 518. Furthermore, recommendation model 516 can determine the set of car logos to be recommended corresponding to the user account by combining the two-class labels 518 and the label features 511 at time (t).

[0175] In step S320, the matching degree between each car logo in the set of car logos to be recommended and the set of role identifiers is determined, and a matching result containing multiple matching degrees is obtained.

[0176] Specifically, each role identifier can correspond to one or more car logos, and the car logos corresponding to each role identifier can be different or partially the same.

[0177] In step S330, the car logo with the highest matching degree with the set of role identifiers is determined based on the matching results.

[0178] In step S340, the user device is triggered to replace the current vehicle icon in the map client with the vehicle icon that has the highest matching degree with the role identifier set; wherein, the current vehicle icon is used to represent the user's location in the electronic map of the map client.

[0179] Please see Figure 6 , Figure 6 This illustration schematically depicts a process for personalizing a character identifier according to one embodiment of this application. Figure 6 As shown, it includes steps S610 to S670.

[0180] Step S610: The cloud server stores all game character identifiers and behavioral characteristics related to user accounts; wherein, the behavioral characteristics are related to the set of game character identifiers, and the behavioral characteristics include at least one of the following: object purchase characteristics, object collection characteristics, object click characteristics, object cancel download characteristics, object usage characteristics, and object switching characteristics. Specifically, the cloud server stores all game character identifiers in the following way: read and store the game character identifiers from game 1, game 2, ..., game n. Where n is a positive integer.

[0181] Step S620: The cloud server determines the label features corresponding to all objects based on the behavioral features, and generates sample data based on the behavioral features and label features; wherein, the sample data consists of training samples and test samples.

[0182] Step S630: The cloud server trains the recommendation model using training samples; the trained recommendation model is tested using test samples, and the model parameters corresponding to the trained recommendation model are adjusted based on the test results.

[0183] Step S640: The cloud server predicts multi-class ratings for all objects using a recommendation model. The multi-class ratings are used to characterize the probability that an object will be selected by the user.

[0184] Step S650: The cloud server simplifies the multi-class scores of all objects into binary scores.

[0185] Step S660: The cloud server determines the set of car logos to be recommended for each user account based on the binary classification score.

[0186] Step S670: The cloud server selects the target game character identifier according to the priority order of objects in the set of car logos to be recommended, and replaces the current car logo with the target game character identifier.

[0187] It is evident that through implementation Figure 6 The illustrated embodiment can determine a personalized set of recommended car logos based on the behavioral characteristics corresponding to a user account, and then determine a suitable car logo for the user based on the matching degree between the set of recommended car logos and the set of role identifiers, thereby achieving personalized automatic car logo changing. Furthermore, it can avoid traffic accidents caused by users manually changing car logos while driving, thus achieving automated car logo changing and improving driving safety.

[0188] Please see Figure 7 , Figure 7 The illustration shows a schematic diagram of a candidate car logo list display interface according to one embodiment of this application. Figure 7 As shown, the candidate car logo list display interface provides a search function. Users can enter the text information "XXX" in the input box 710 and trigger the search control 720 to perform a search. Specifically, the user device can send the text information "XXX" to the cloud server, allowing the cloud server to query the target role identifier corresponding to the text information. If the target role identifier exists, the corresponding option "XXX" 730 is displayed in the candidate car logo list. Furthermore, the cloud server can also provide the user device with feedback on related car logos corresponding to the target role identifier. These related car logos can be displayed in the candidate car logo list in the form of the option "XXXUKD" 740. Additionally, the cloud server can provide the user device with feedback on popular items. Popular items can be displayed in the candidate car logo list in the form of the option "YYY" 750.

[0189] As an optional embodiment, querying the target role identifier corresponding to the information to be retrieved includes: determining the tag corresponding to the information to be retrieved; selecting a target set from the object set corresponding to different tags according to the tag; and calling the object matching the information to be retrieved from the target set as the target role identifier.

[0190] Specifically, tags can be used to label the domain corresponding to the information to be retrieved, such as the domain of shooting games or racing games. The selected target set and the information to be retrieved can correspond to the same tag. There can be one or more target role identifiers. Retrieving objects from the target set that match the information to be retrieved as target role identifiers includes: querying the target set for objects whose names match the information to be retrieved as target role identifiers.

[0191] As can be seen, implementing this optional embodiment allows for filtering the set first, followed by object matching within the set, thereby improving object query efficiency.

[0192] As an optional embodiment, determining the tags corresponding to the information to be retrieved includes: extracting keywords from the information to be retrieved to obtain extraction results; if the extraction results indicate that there are keywords in the information to be retrieved, then the tags corresponding to the keywords are determined as the tags of the information to be retrieved.

[0193] Specifically, keyword extraction is performed on the information to be retrieved to obtain extraction results, including: segmenting the information to be retrieved to obtain processing results containing multiple segmented words; and extracting segmented words that match a preset thesaurus as keywords. Then, the tags corresponding to the keywords are determined as tags for the information to be retrieved, including: obtaining multiple tags corresponding to the keywords and determining these multiple tags as tags for the information to be retrieved. These multiple tags include first-level tags (e.g., cloud gaming, console gaming), second-level tags (e.g., shooting games, racing games), and third-level tags (e.g., XX battlefield), etc., which are not limited in this embodiment.

[0194] As can be seen, implementing this optional embodiment can improve query efficiency and query accuracy by extracting keywords from the information to be retrieved.

[0195] As an optional embodiment, the above method further includes: if the extraction result indicates that there are no keywords in the information to be retrieved, then displaying a prompt message indicating search failure and at least one search hot word; when a user operation is received that acts on the target hot word in at least one search hot word, determining the specific set to which the target hot word belongs from the object set corresponding to different tags; and calling the object matching the target hot word from the specific set as the role identifier for replacing the current car logo.

[0196] Specifically, before displaying the prompt message indicating a search failure and at least one hot search term, the above method may further include: selecting the top N virtual characters as popular objects based on their usage frequency within a unit of time (e.g., 1 month), and determining the names of the popular objects as hot search terms.

[0197] As can be seen, implementing this optional embodiment can provide users with other options when there are no search results, thereby improving the user experience.

[0198] As an optional embodiment, if the number of role identifiers is greater than 2, querying the target role identifier corresponding to the information to be retrieved includes: grouping all role identifiers to obtain multiple object sets; sequentially querying the multiple object sets according to the information to be retrieved to obtain query results corresponding to each of the multiple object sets; wherein, the multiple object sets correspond to different popularity levels, and the multiple object sets are arranged in descending order of popularity; if there is a query result that matches the information to be retrieved, then determining the object set corresponding to the query result that matches the information to be retrieved, and calling the target role identifier corresponding to the information to be retrieved from the corresponding object set.

[0199] Specifically, information queries are performed sequentially on multiple object sets based on the information to be retrieved, and query results corresponding to each of the multiple object sets are obtained. This includes: performing information queries sequentially on multiple object sets based on a binary tree search algorithm, and obtaining query results corresponding to each of the multiple object sets; wherein, a binary tree is an ordered tree in which each node has at most two subtrees, used for information querying.

[0200] Furthermore, since there are multiple sets of objects, there are also multiple query results. Each query result corresponds to one set of objects. After obtaining the query results corresponding to each of the multiple sets of objects, the non-empty query results can be traversed and used as the query results that contain the information to be retrieved.

[0201] As can be seen, by implementing this optional embodiment, the query results corresponding to each set can be determined by performing object lookup on each set. The query results that are not empty are the required query results, which can locate the target role identifier more quickly and improve query efficiency.

[0202] Please see Figure 8 , Figure 8 A schematic diagram illustrating an object query process according to an embodiment of this application is shown. Figure 8As shown, the cloud server can group all game character identifiers in game character identifier sets 821, 822, ..., 823 in the game character identifier database 810 by popularity, resulting in object sets 831, 832, ..., 833. When receiving information to be retrieved, the cloud server can sequentially query object sets 831, 832, ..., 833 based on the information to be retrieved, thereby obtaining query results 841, 842, ..., 843 respectively from object sets 831, 832, ..., 833, resulting in a total query result 850. If a query result matching the information to be retrieved exists in the total query result 850, the object set corresponding to the query result matching the information to be retrieved is determined, and the target game character identifier corresponding to the information to be retrieved is retrieved from the corresponding object set.

[0203] As an optional embodiment, all role identifiers are grouped to obtain multiple object sets, including: calculating the popularity value of all role identifiers based on object information; wherein, object information includes at least one of object name, object identifier, number of times the object is used, usage duration, and online duration; and grouping all role identifiers based on the popularity value to obtain multiple object sets.

[0204] Specifically, the popularity score of a character identifier can be used to represent the degree to which a character identifier is popular with users. A character identifier with a high popularity score corresponds to multiple clicks, multiple purchases, and multiple favorites. When this application is applied to map software linked to a game account, the object name can be a character name, the object identifier can be a character identifier image, the object usage count can be the cumulative number of times the character has been used in the game, the usage duration can be the cumulative usage time of the character in the game, and the online duration can be the cumulative online time of the character in the game. In addition, each object set can correspond to a different number of objects.

[0205] Based on this, the popularity value of the role identifier is calculated according to the object information, including: the current time in the object information. The number of times an object is used in the object information Usage time and online duration Substitution expression To calculate the popularity value of the character identifier. ;in, Indicates the first Each role identifier Indicates the first That moment.

[0206] Based on this, if there are multiple game projects, each corresponding to multiple character identifiers, the character identifiers are grouped according to their popularity values ​​to obtain multiple object sets. This includes: sorting all the character identifiers of all game projects according to their popularity values, resulting in character identifiers arranged from highest to lowest popularity; and grouping the character identifiers arranged from highest to lowest popularity values ​​according to the upper limit of the object set (e.g., 100 groups) and the preset number of objects within the object set (e.g., 10), resulting in multiple object sets. For adjacent object sets, the popularity value of any character identifier in the object set that is sorted earlier is greater than the popularity value of any character identifier in the object set that is sorted later. Further, the above method may also include: marking each character identifier in a mapping data table according to the grouping results to represent the grouping result corresponding to that character identifier; wherein, the mapping data table is used to represent the correspondence between character identifiers and grouping results.

[0207] As can be seen, implementing this optional embodiment allows for grouping character icons based on their popularity, resulting in multiple sets of objects ranked from highest to lowest popularity, thus improving retrieval efficiency. If a user searches for a popular character icon, it is likely to be among the top-ranked objects. Based on the search order from highest to lowest popularity, the desired character icon can be found more quickly, allowing for a faster response and facilitating the user's icon replacement. For popular characters, an even faster response time can be achieved.

[0208] As an optional embodiment, if the target character identifier does not belong to the character identifier set, the above method further includes: obtaining at least one related vehicle logo corresponding to the target character identifier from the character identifier library; wherein, at least one related vehicle logo and the target character identifier belong to the same game project; and feeding back at least one related vehicle logo and the target character identifier to the user device so that the user device displays at least one related vehicle logo and the target character identifier.

[0209] Specifically, if the target role identifier does not belong to the set of role identifiers, the above method may further include: displaying a prompt message indicating that there is no corresponding search result (e.g., "No such role identifier found"). Furthermore, after feeding back at least one relevant vehicle logo and the target role identifier to the user device so that the user device displays at least one relevant vehicle logo and the target role identifier, the method may further include: the user device outputting information prompting the user to re-enter the information.

[0210] In addition, at least one related car logo and target character identifier belong to the same game project. Objects within the same game project can correspond to the same game project or the same type. One game project can correspond to N objects, where N is a positive integer.

[0211] When users search for character icons associated with their accounts, it indicates their recognition and liking of those icons. Displaying similar icons in the candidate list provides users with more similar options, allowing them to access a wider variety of character icons of that type. If a user searches for a character icon not linked to their account, it can be assumed they encountered the icon accidentally and are conducting a trial search. In this case, other character icons from the game to which the searched character belongs can be presented as related icons, thus recommending various objects within that game and exposing the user to a richer array of character icons.

[0212] As can be seen, implementing this optional embodiment can output other relevant objects for the user to choose from when the user's searched role identifier does not correspond to their account, thereby ensuring that the user can experience the car logo changing function.

[0213] As an optional embodiment, feeding back at least one relevant vehicle logo and a target role identifier to the user device so that the user device can display at least one relevant vehicle logo and a target role identifier includes: sorting at least one relevant vehicle logo in descending order of usage popularity to obtain a sorting result; feeding back the target role identifier and the sorting result to the user device so that the user device can display the target role identifier and the sorting result; wherein, the display priority of the target role identifier is higher than that of any relevant vehicle logo in the sorting result.

[0214] Specifically, popularity is used to characterize the frequency with which a particular car logo is invoked.

[0215] As can be seen, implementing this optional embodiment can sort relevant car logos according to usage popularity, thereby making it easier for users to find the desired logo and improve the user experience.

[0216] As an optional embodiment, the candidate logo list further includes at least one popular object. Generating a candidate logo list containing a target role identifier and logos matching the set of role identifiers includes: selecting at least one popular object within a unit time period (e.g., one week) based on the trigger time of the user operation used to trigger the search function; wherein the trigger time is the end time of the unit time period; generating a candidate logo list containing a target role identifier, at least one popular object, and logos matching the set of role identifiers; wherein the display priority of the target role identifier is higher than that of logos matching the set of role identifiers, and the display priority of logos matching the set of role identifiers is higher than that of at least one popular object.

[0217] Specifically, the list of candidate car logos can simultaneously display the target role's identifier, popular objects, and related car logos, with related car logos having a higher display priority than popular objects.

[0218] As can be seen, implementing this optional embodiment can enrich the content displayed in the list of candidate car logos, thereby providing users with more options for selection and improving the user experience.

[0219] As an optional embodiment, after generating a list of candidate car logos including the target role identifier and the car logo, the method further includes: when a selection operation is detected acting on the list of candidate car logos, in response to the selection operation, the user device is triggered to replace the current car logo displayed in the map client to represent the user's location in the electronic map with the car logo corresponding to the selection operation.

[0220] Specifically, the current vehicle icon can also be a role identifier, which can be the role identifier currently used by the user. This role identifier is used to replace the navigation arrow icon in the map software to indicate the user's location.

[0221] The selection operation can be used to select multiple target role identifiers. If a user selects multiple target role identifiers, in response to the selection operation, the user device replaces the current vehicle icon displayed in the map client representing the user's location on the electronic map with the vehicle icon corresponding to the selection operation. This includes: selecting a random object from the multiple target role identifiers; in response to the selection operation, the user device replaces the current vehicle icon displayed in the map client representing the user's location on the electronic map with the random object; and then, according to a preset time interval, replaces the random object with other random objects from the multiple target role identifiers, thereby achieving random rotation of the multiple target role identifiers. As can be seen, when a user selects multiple objects from the list of candidate vehicle icons, the vehicle icon can be replaced with any one of the multiple objects, and the multiple objects can be rotated for display. This provides users with more diverse display content and allows users to select multiple favorite role identifiers as vehicle icons. Furthermore, it does not occupy local storage space, which can improve the memory usage issue on mobile devices.

[0222] As can be seen, by implementing this optional embodiment, the current car logo used by the user can be replaced with other objects that the user prefers. When this application is applied to the navigation function of map software, it is convenient for users to change the car logo representing the user's current location at any time according to their personalized needs. This can improve the user experience, enhance interactivity, and increase user stickiness.

[0223] As an optional embodiment, after the user device is triggered to replace the current vehicle icon in the map client with the vehicle icon that has the highest matching degree with the set of role identifiers, the above method further includes: periodically acquiring environmental tags within a preset range centered on the user's location according to a preset time period; if there is a target vehicle icon that matches the environmental tag in the matching result, the user device is triggered to replace the current vehicle icon used to represent the user's location in the electronic map with the target vehicle icon.

[0224] The preset duration can be a pre-defined unit of time, such as 30 seconds. Environmental tags within a preset range centered on the user's location are periodically retrieved based on the preset duration. This can be understood as retrieving environmental tags within the preset range centered on the user's location every 30 seconds. Specifically, there can be one or more environmental tags within the preset range centered on the user's location. These environmental tags characterize the environment within the preset range centered on the user's location, such as a park, highway, or coastline. Each car icon in the matching results corresponds to one or more tags. If an environmental tag matches a car icon in the matching results, it can be determined that a target car icon matching the environmental tag exists in the matching results.

[0225] Specifically, if the number of target car icons is greater than 1, the user device is triggered to replace the current car icon used to represent the user's location on the electronic map with the target car icon. This includes: randomly selecting a target car icon from the target car icons as the replacement object, thereby triggering the user device to replace the current car icon used to represent the user's location on the electronic map with the replacement object; or, randomly selecting the target car icon with the highest popularity from the target car icons as the replacement object, thereby triggering the user device to replace the current car icon used to represent the user's location on the electronic map with the replacement object.

[0226] As can be seen, implementing this optional embodiment can enable the periodic replacement of car logos and improve the automation of car logo replacement, thereby enhancing the richness of car logo display.

[0227] Please see Figure 9 , Figure 9 This illustration shows a schematic diagram of a game character identifier search process according to one embodiment of this application. Figure 9 As shown, it includes steps S910 to S970.

[0228] Step S910: When a user operation is detected acting on the search area, the user device obtains the search information corresponding to the user operation.

[0229] Step S920: The user device requests the target game character corresponding to the information to be retrieved from the cloud server.

[0230] Step S930: The cloud server constructs a game character database to store game character identifiers. If the target game character identifier exists in the game character identifier database, proceed to step S940. If the target game character identifier does not exist in the game character identifier database, proceed to step S960. Specifically, the cloud server constructs the game character identifier database by obtaining game character identifier set 1, game character identifier set 2, ..., game character identifier set n; where n is a positive integer, and game character identifier set 1, game character identifier set 2, ..., game character identifier set n can be game character identifier sets from different game projects.

[0231] Step S940: The cloud server returns the target game character identifier.

[0232] Step S950: The user device replaces the current car icon displayed to indicate the user's location on the map with the target game character icon.

[0233] Step S960: The cloud server returns a message indicating that there are no corresponding search results.

[0234] Step S970: The user equipment outputs a prompt message to remind the user to search again.

[0235] It is evident that implementation Figure 9 The illustrated embodiment provides users with a role identifier search function, which allows users to easily change the current vehicle icon (e.g., car logo) used to identify their current location.

[0236] Please see Figure 10 , Figure 10 A schematic diagram of a role identifier according to an embodiment of this application is shown. Figure 10 The multiple icons shown can represent different role identifiers, or they can be used to change the current car logo (e.g., the car logo used in navigation software to identify the user's current location).

[0237] Please see Figures 11-13 , Figures 11-13 A schematic diagram of a navigation interface according to an embodiment of this application is shown. Figure 11 As shown, the current car icon 1100 can be used to identify the user's current location. When the user inputs the information to be searched and obtains a list of candidate car icons containing the target role identifier, they can select an object from the list; where the target role identifier corresponds to the information to be searched. Figure 12As shown, the user equipment can replace the current car logo 1100 with the user-selected object 1200. Object 1200 can be a target role identifier in the list of candidate car logos, a similar object corresponding to the target role identifier, or a popular object. This embodiment can also determine a set of car logos for the user based on the behavioral characteristics corresponding to the user account and select the object with the highest popularity value for output, for the user to choose from. Figure 13 As shown, if the user selects a recommended object, object 1200 can be changed to object 1300, thus enabling personalized recommendations for role identifiers and the "one-click car logo change" function.

[0238] Please see Figure 14 , Figure 14 A flowchart illustrating an object replacement method according to an embodiment of this application is shown schematically. Figure 14 As shown, the object replacement method may include steps S1400 to S1420.

[0239] Step S1400: When a login operation for the map client is detected, the user account entered in the login operation is sent to the server.

[0240] Step S1410: Receive the car logo that matches the user account's role identifier set with the highest degree of matching from the server.

[0241] Step S1420: Replace the current vehicle icon in the map client with the vehicle icon that matches the user account's role identifier set the most; wherein, the current vehicle icon is used to represent the user's location in the electronic map of the map client.

[0242] Specifically, the above method further includes: when a user operation to trigger the search function is detected, obtaining the information to be retrieved corresponding to the user operation; displaying a list of candidate car icons corresponding to the information to be retrieved; wherein the list of candidate car icons includes the target role identifier corresponding to the information to be retrieved and car icons related to the set of role identifiers, the set of role identifiers corresponding to the user account; when a selection operation is detected on the list of candidate car icons, replacing the current car icon in the map client with the car icon corresponding to the selection operation in response to the selection operation; wherein the current car icon is used to represent the user's location in the electronic map of the map client.

[0243] It is evident that implementation Figure 14 The illustrated embodiment can respond to object search, enabling users to obtain the desired target role identifier based on the search, and present a richer list of car logos to increase the options for users to choose from. The current car logo can be replaced with the user's selected object through the user's selection operation, thereby improving interactivity and enriching the user experience.

[0244] Please see Figure 15 , Figure 15 A schematic block diagram of an object changing apparatus according to one embodiment of this application is shown. Figure 15 As shown, the object replacement device 1500 includes: an information sending unit 1501, a car logo receiving unit 1502, and a car logo replacement unit 1503.

[0245] The information sending unit 1501 is used to send the user account entered in the login operation to the server when a login operation for the map client is detected.

[0246] The car logo receiving unit 1502 is used to receive the car logo that has the highest matching degree with the set of role identifiers corresponding to the user account, fed back by the server.

[0247] The vehicle logo replacement unit 1503 is used to replace the current vehicle logo in the map client with the vehicle logo that has the highest matching degree with the role identifier set corresponding to the user account; wherein, the current vehicle logo is used to represent the user's location in the electronic map of the map client.

[0248] It is evident that implementation Figure 15 The illustrated embodiment can respond to object search, enabling users to obtain the desired target role identifier based on the search, and present a richer list of car logos to increase the options for users to choose from. The current car logo can be replaced with the user's selected object through the user's selection operation, thereby improving interactivity and enriching the user experience.

[0249] Please see Figure 16 , Figure 16 A flowchart illustrating a vehicle logo replacement method applied to a map client according to an embodiment of this application is shown schematically. Figure 16 As shown, the method for changing car logos applied to map clients includes steps S1600 to S1624.

[0250] Step S1600: The server obtains behavioral characteristics related to the user account; wherein, the behavioral characteristics are related to the set of role identifiers, and the behavioral characteristics include at least one of the following: object purchase characteristics, object collection characteristics, object click characteristics, object cancel download characteristics, object usage characteristics, and object switching characteristics.

[0251] Step S1602: The server determines the tag features corresponding to all objects based on the behavioral characteristics; wherein, all objects include the set of role identifiers corresponding to user accounts, and the tag features are used to characterize the object call situation.

[0252] Step S1604: The server determines the adjacent behavioral features corresponding to the first adjacent time based on the behavioral features in the time series, calculates the first difference result based on the behavioral features and adjacent behavioral features, determines the adjacent label features corresponding to the second adjacent time based on the label features in the time series, calculates the second difference result based on the label features and adjacent label features, and then converts the first difference result into an encoded feature vector, fuses the encoded feature vector and the second difference result to obtain the sample data.

[0253] Step S1606: The sample data consists of training samples and test samples. The server trains the recommendation model using the training samples and tests the trained recommendation model using the test samples. Based on the test results, the model parameters corresponding to the trained recommendation model are adjusted.

[0254] Step S1608: The server predicts the multi-class rating of all objects through the recommendation model. The multi-class rating is used to represent the probability that an object is selected by the user. Then, the multi-class rating of all objects is simplified into a binary rating. The number of ratings in the multi-class rating is greater than the number of ratings in the binary rating. Then, the set of car logos to be recommended for the user account is determined based on the binary rating.

[0255] Step S1610: The server obtains the set of role identifiers corresponding to the user account, obtains a matching result containing multiple matching degrees based on the matching degree between each car icon in the set of car icons to be recommended and the set of role identifiers, and determines the car icon corresponding to the set of role identifiers based on the matching result.

[0256] Step S1612: When the server receives a retrieval request, it obtains the retrieval information corresponding to the retrieval request.

[0257] Step S1614: The server calculates the popularity value of the role identifier based on the object information; wherein, the object information includes at least one of the following: object name, object identifier, number of times the object is used, usage duration, and online duration; the role identifiers are grouped according to the popularity value to obtain multiple object sets.

[0258] Step S1616: The server sequentially queries multiple object sets based on the information to be retrieved, and obtains the query results corresponding to each object set; wherein, the multiple object sets correspond to different popularity, and the multiple object sets are arranged in descending order of popularity.

[0259] Step S1618: If a query result matching the information to be retrieved exists, the server determines the set of objects corresponding to the query result matching the information to be retrieved, and retrieves the target role identifier corresponding to the information to be retrieved from the corresponding set of objects. If the target role identifier belongs to the role identifier set, proceed to step S1620. If the target role identifier does not belong to the role identifier set, proceed to step S1622.

[0260] Step S1620: The server generates a list of candidate car logos containing the target role identifier and the car logo; wherein, the list of candidate car logos includes the target role identifier and the car logo corresponding to the target role identifier.

[0261] Step S1622: The server retrieves at least one related car icon corresponding to the target character icon from the character icon library, and sorts the at least one related car icon in descending order of usage popularity to obtain a sorting result. Then, the server feeds back the list of candidate car icons containing the target character icon and the sorting result to the user device so that the user device can display the list of candidate car icons. Among them, the display priority of the target character icon is higher than any related car icon in the sorting result, and at least one related car icon belongs to the same game project as the target character icon.

[0262] Step S1624: When the user equipment detects a selection operation on the list of candidate car icons, the user equipment may, in response to the selection operation, replace the current car icon displayed in the map client that represents the user's location in the electronic map with the car icon corresponding to the selection operation.

[0263] It should be noted that steps S1600 to S1624 are related to... Figure 3 For the specific implementation methods of steps S1600 to S1624, please refer to the examples shown. Figure 3 The steps and their embodiments shown are not repeated here.

[0264] It is evident that implementation Figure 16 The method shown can match relevant car logos to user accounts based on their corresponding role identifiers. When a user selects a role identifier, both the role identifier and the car logo are displayed, providing the user with more options. Users can browse through the role identifiers and car logos to personalize their choice for changing their current car logo, enhancing interactivity. Furthermore, since the matched car logos are related to the user's role identifier, the effectiveness and personalization of the displayed car logos are improved.

[0265] Furthermore, this example embodiment also provides a vehicle logo replacement device applied to a map client. (See reference) Figure 17As shown, the vehicle logo replacement device 1700 applied to a map client may include: a vehicle logo determination unit 1701, a matching degree calculation unit 1702, a vehicle logo determination unit 1703, and a vehicle logo replacement unit 1704, wherein:

[0266] The car logo determination unit 1701 is used to obtain the set of role identifiers corresponding to the user account and determine the set of car logos to be recommended based on the behavioral characteristics of the user account.

[0267] The matching degree calculation unit 1702 is used to determine the matching degree between each car logo in the set of car logos to be recommended and the set of role identifiers, and to obtain a matching result containing multiple matching degrees;

[0268] The logo determination unit 1703 is used to determine the logo with the highest matching degree to the set of role identifiers based on the matching results.

[0269] The vehicle logo replacement unit 1704 is used to trigger the user device to replace the current vehicle logo in the map client with the vehicle logo that has the highest matching degree with the role identifier set; wherein, the current vehicle logo is used to represent the user's location in the electronic map of the map client.

[0270] It is evident that implementation Figure 17 The device shown can determine a personalized set of recommended car logos based on the behavioral characteristics of a user's account. Then, it determines the most suitable car logo for the user based on the matching degree between this set and the user's role identifier set, thus achieving personalized automatic car logo changing. Furthermore, it can avoid traffic accidents caused by users manually changing car logos while driving, thereby improving driving safety through automated car logo changing.

[0271] In one exemplary embodiment of this application, the above-described apparatus further includes:

[0272] An information acquisition unit (not shown) is used to acquire the information to be retrieved in the retrieval request when a retrieval request is received.

[0273] The identifier query unit (not shown) is used to query the target role identifier corresponding to the information to be retrieved. If the target role identifier belongs to the set of role identifiers, a list of candidate car logos containing the target role identifier and car logos that match the set of role identifiers is generated and fed back to the user device so that the user device can display the list of candidate car logos; wherein, the list of candidate car logos corresponds to the user account.

[0274] In one exemplary embodiment of this application, the above-described apparatus further includes:

[0275] The car logo acquisition unit (not shown) is used to acquire at least one related car logo corresponding to the target character logo from the character logo library when the target character logo does not belong to the character logo set; wherein, the at least one related car logo and the target character logo belong to the same game project;

[0276] The logo replacement unit 1704 is also used to feed back at least one relevant logo and target role identifier to the user equipment, so that the user equipment displays at least one relevant logo and target role identifier.

[0277] As can be seen, implementing this optional embodiment can output other relevant objects for the user to choose from when the user's searched role identifier does not correspond to their account, thereby ensuring that the user can experience the car logo changing function.

[0278] In one exemplary embodiment of this application, the object acquisition unit feeds back at least one relevant vehicle logo and a target role identifier to the user equipment, so that the user equipment displays at least one relevant vehicle logo and a target role identifier, including:

[0279] The at least one related car logo is sorted from highest to lowest based on its popularity of use, resulting in a sorting result.

[0280] The target role identifier and the sorting result are fed back to the user equipment so that the user equipment can display the target role identifier and the sorting result; wherein, the display priority of the target role identifier is higher than any related car logo in the sorting result.

[0281] As can be seen, implementing this optional embodiment can sort relevant car logos according to usage popularity, thereby making it easier for users to find the desired logo and improve the user experience.

[0282] In one exemplary embodiment of this application, the candidate logo list further includes at least one popular object, and the logo changing unit generates a candidate logo list containing the target role identifier and logos matching the set of role identifiers, including:

[0283] Select at least one popular object within a unit of time based on the trigger time of the user action used to initiate the search function; wherein, the trigger time is the end time of the unit of time.

[0284] Generate a list of candidate car logos that includes the target role identifier, the at least one popular object, and car logos that match the set of role identifiers;

[0285] Among them, the display priority of the target role identifier is higher than that of the car logo that matches the set of role identifiers, and the display priority of the car logo that matches the set of role identifiers is higher than that of the at least one popular object.

[0286] As can be seen, implementing this optional embodiment can enrich the content displayed in the list of candidate car logos, thereby providing users with more options for selection and improving the user experience.

[0287] In one exemplary embodiment of this application, the car logo replacement unit 1704 queries the target role identifier corresponding to the information to be retrieved, including:

[0288] Determine the tags corresponding to the information to be retrieved;

[0289] Select the target set from the set of objects corresponding to different labels;

[0290] Retrieve objects from the target set that match the information to be retrieved as target role identifiers.

[0291] As can be seen, implementing this optional embodiment allows for filtering the set first, followed by object matching within the set, thereby improving object query efficiency.

[0292] In one exemplary embodiment of this application, the car logo replacement unit determines the tag corresponding to the information to be retrieved, including:

[0293] Keyword extraction is performed on the information to be retrieved to obtain the extraction results;

[0294] If the extraction results indicate that keywords exist in the information to be retrieved, then the tags corresponding to the keywords will be determined as the tags of the information to be retrieved.

[0295] As can be seen, implementing this optional embodiment can improve query efficiency and query accuracy by extracting keywords from the information to be retrieved.

[0296] In one exemplary embodiment of this application, the car logo replacement unit is further configured to display a prompt message indicating search failure and at least one hot search term when the extraction result indicates that no keyword exists in the information to be retrieved;

[0297] The above-mentioned device also includes:

[0298] An object set determination unit (not shown) is used to determine the specific set to which the target hot word belongs from the object sets corresponding to different tags when a user operation is received that acts on the target hot word in at least one search hot word;

[0299] The object invocation unit (not shown) is used to invoke an object that matches the target hot word from a specific set as a role identifier to replace the current car logo.

[0300] As can be seen, implementing this optional embodiment can provide users with other options when there are no search results, thereby improving the user experience.

[0301] In one exemplary embodiment of this application, if the number of role identifiers is greater than 2, the car logo replacement unit 1704 queries the target role identifier corresponding to the information to be retrieved, including:

[0302] Group all role identifiers to obtain multiple object sets;

[0303] Based on the information to be retrieved, information queries are performed on multiple object sets in sequence to obtain query results corresponding to each object set; among them, multiple object sets correspond to different levels of popularity, and the multiple object sets are arranged in descending order of popularity;

[0304] If a query result matches the information to be retrieved, the set of objects corresponding to the query result matching the information to be retrieved is determined, and the target role identifier corresponding to the information to be retrieved is retrieved from the set of objects.

[0305] As can be seen, by implementing this optional embodiment, the query results corresponding to each set can be determined by performing object lookup on each set. The query results that are not empty are the required query results, which can locate the target role identifier more quickly and improve query efficiency.

[0306] In one exemplary embodiment of this application, the car logo replacement unit 1704 groups all role identifiers to obtain multiple object sets, including:

[0307] The popularity score of all role identifiers is calculated based on the object information; the object information includes at least one of the following: object name, object identifier, number of times the object is used, usage duration, and online duration.

[0308] All character identifiers are grouped according to their popularity value, resulting in multiple object sets.

[0309] As can be seen, implementing this optional embodiment allows for grouping character icons based on their popularity, resulting in multiple sets of objects ranked from highest to lowest popularity, thus improving retrieval efficiency. If a user searches for a popular character icon, it is likely to be among the top-ranked objects. Based on the search order from highest to lowest popularity, the desired character icon can be found more quickly, allowing for a faster response and facilitating the user's icon replacement. For popular characters, an even faster response time can be achieved.

[0310] In one exemplary embodiment of this application, the car logo determination unit 1701 determines the set of car logos to be recommended corresponding to the user account based on the behavioral characteristics of the user account, including:

[0311] A recommendation model is trained based on the behavioral characteristics of the user accounts;

[0312] The set of car logos to be recommended is determined based on the trained recommendation model.

[0313] As can be seen, implementing this optional embodiment can determine the objects that need to be recommended to the user through the recommendation model, thereby enriching the options and improving the user experience.

[0314] In one exemplary embodiment of this application, the car logo determination unit 1701 trains a recommendation model based on the behavioral characteristics of the user account, including:

[0315] Obtain behavioral characteristics related to the user account; wherein, the behavioral characteristics are related to the set of role identifiers, and the behavioral characteristics include at least one of object purchase characteristics, object collection characteristics, object click characteristics, object cancel download characteristics, object usage characteristics, and object switching characteristics;

[0316] Based on the behavioral characteristics, label features corresponding to all objects are determined; wherein, all objects include a set of role identifiers corresponding to the user account, and the label features are used to characterize the object invocation situation;

[0317] Sample data is generated based on the behavioral features and the label features;

[0318] The recommendation model is trained using the sample data.

[0319] As can be seen, implementing this optional embodiment can characterize users based on the determined behavioral and tag features, thereby generating sample data based on the behavioral and tag features. By training the recommendation model with the sample data, the recommendation effect of the recommendation model can be improved, and the personalized role identifiers recommended to users can better meet the user's expectations, thereby increasing the probability that the role identifiers are selected by the user.

[0320] In one exemplary embodiment of this application, generating sample data based on behavioral features and tag features includes:

[0321] Based on the time corresponding to the behavioral features in the time series, determine the adjacent behavioral features corresponding to the first adjacent time, and calculate the first difference result based on the behavioral features and the adjacent behavioral features;

[0322] Based on the label features in the time series, determine the adjacent label features corresponding to the second adjacent time, and calculate the second difference result based on the label features and the adjacent label features.

[0323] Sample data is generated based on the first and second difference results.

[0324] As can be seen, by implementing this optional embodiment, the differences between behavioral features and label features in time can be characterized through the calculation of feature results. This can generate sample data for training the recommendation model, thereby training a more accurate recommendation model to improve personalization and recommend suitable personalized role identifiers to different users, thus improving the user experience and increasing user stickiness.

[0325] In one exemplary embodiment of this application, generating sample data based on a first difference result and a second difference result includes:

[0326] The first difference result is converted into an encoded feature vector;

[0327] The sample data is obtained by fusing the encoded feature vector and the second difference result.

[0328] As can be seen, by implementing this optional embodiment, the classification ability of the difference results can be improved by processing the difference results, and then the sample data for training the recommendation model can be obtained by fusing the difference results, thereby improving the recommendation effect of the recommendation model.

[0329] In one exemplary embodiment of this application, the car logo determination unit 1701 determines the set of car logos to be recommended based on a trained recommendation model, including:

[0330] The recommendation model predicts multi-class ratings for all objects, and the multi-class ratings are used to represent the probability that an object is selected by the user.

[0331] All multi-class scores for all objects are simplified to binary scores; the number of scores in the multi-class scores is greater than the number of scores in the binary scores.

[0332] The set of recommended car logos for each user account is determined based on the binary classification score.

[0333] As can be seen, implementing this optional embodiment can transform the multi-classification prediction result into a binary classification result by converting the original binary classification algorithm model into a multi-classification algorithm model, thereby improving the model's prediction accuracy.

[0334] In one exemplary embodiment of this application, the sample data consists of training samples and test samples. The vehicle logo determination unit 1701 to be recommended trains a recommendation model using the sample data, including:

[0335] The recommendation model is trained using training samples;

[0336] The trained recommendation model was tested using test samples, and the model parameters were adjusted based on the test results.

[0337] As can be seen, implementing this optional embodiment allows for further optimization of the model through testing, thereby improving the model's predictive performance.

[0338] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0339] In one exemplary embodiment of this application, the above-described apparatus further includes:

[0340] The environment tag acquisition unit (not shown) is used to periodically acquire environment tags within a preset range centered on the user's location after the vehicle logo replacement unit 1704 triggers the user device to replace the current vehicle logo in the map client with the vehicle logo that matches the set of role identifiers.

[0341] The vehicle logo replacement unit 1704 is specifically used to trigger the user device to replace the current vehicle logo used to represent the user's location in the electronic map with the target vehicle logo when there is a target vehicle logo in the matching result that matches the environment label.

[0342] As can be seen, implementing this optional embodiment can enable the periodic replacement of car logos and improve the automation of car logo replacement, thereby enhancing the richness of car logo display.

[0343] Since the functional modules of the vehicle logo replacement device applied to the map client in the example embodiments of this application correspond to the steps of the above-described vehicle logo replacement method applied to the map client, for details not disclosed in the device embodiments of this application, please refer to the above-described embodiments of the vehicle logo replacement method applied to the map client.

[0344] In another aspect, this application also provides a computer-readable medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the methods described in the above embodiments.

[0345] It should be noted that the computer-readable medium shown in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0346] 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 this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated 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 a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0347] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0348] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0349] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for changing vehicle logos in a map client, characterized in that, The method includes: Obtain a set of role identifiers associated with a user account, and determine a set of car icons to be recommended based on the behavioral characteristics of the user account; the user account includes the current user's game account, and the role identifiers in the set of role identifiers are at least one game role identifier associated with the game account; Determine the matching degree between each car logo in the set of car logos to be recommended and the set of role identifiers to obtain a matching result containing multiple matching degrees; Based on the matching results, determine the car logo that matches the set of character identifiers; When a search request is received, the information to be searched in the search request is obtained; The system queries the target role identifier corresponding to the information to be retrieved. If the target role identifier belongs to the set of role identifiers, a list of candidate car logos containing the target role identifier and car logos that match the set of role identifiers is generated, and the list of candidate car logos is fed back to the user device so that the user device can display the list of candidate car logos; wherein, the list of candidate car logos corresponds to the user account. When a selection operation is detected on the list of candidate car icons, the user device is triggered to replace the current car icon in the map client with the car icon corresponding to the selection operation in response to the selection operation; wherein, the current car icon is used to represent the user's location in the electronic map of the map client.

2. The method according to claim 1, characterized in that, If the target role identifier does not belong to the role identifier set, the method further includes: Obtain at least one related car logo corresponding to the target character logo from the character logo library; wherein the at least one related car logo and the target character logo belong to the same game project; The list of candidate car logos, which includes the target role identifier and at least one related car logo, is fed back to the user device so that the user device can display the list of candidate car logos.

3. The method according to claim 2, characterized in that, The list of candidate car logos, including the target role identifier and at least one related car logo, is fed back to the user device, including: The at least one related car logo is sorted from highest to lowest based on its popularity of use, resulting in a sorting result. The list of candidate car logos, including the target role identifier and the sorting result, is fed back to the user device so that the user device can display the target role identifier and the sorting result; wherein, the display priority of the target role identifier is higher than any related car logo in the sorting result.

4. The method according to claim 1, characterized in that, The list of candidate car logos also includes at least one popular object, generating a list of candidate car logos containing the target role identifier and car logos matching the set of role identifiers, including: The at least one popular object within a unit of time is selected based on the trigger time of the user action used to trigger the search function; wherein, the trigger time is the end time of the unit of time. Generate a list of candidate car logos that includes the target role identifier, the at least one popular object, and car logos that match the set of role identifiers; Among them, the display priority of the target role identifier is higher than that of the car logo that matches the set of role identifiers, and the display priority of the car logo that matches the set of role identifiers is higher than that of the at least one popular object.

5. The method according to claim 1, characterized in that, Querying the target role identifier corresponding to the information to be retrieved includes: Determine the tags corresponding to the information to be retrieved; Select a target set from the set of objects corresponding to different tags based on the tags; The object that matches the information to be retrieved is retrieved from the target set and used as the target role identifier.

6. The method according to claim 5, characterized in that, Determining the tags corresponding to the information to be retrieved includes: Keyword extraction is performed on the information to be retrieved to obtain the extraction results; If the extraction results indicate that there are keywords in the information to be retrieved, then the tags corresponding to the keywords are determined as the tags of the information to be retrieved.

7. The method according to claim 6, characterized in that, The method further includes: If the extraction result indicates that there are no keywords in the information to be retrieved, then a prompt message indicating search failure and at least one hot search term are displayed. When a user action is received that applies to a target hot word in the at least one search hot word, the specific set to which the target hot word belongs is determined from the set of objects corresponding to different tags; The object matching the target hot word is retrieved from the specific set as the role identifier for replacing the current car logo.

8. The method according to claim 1, characterized in that, If the number of role identifiers is greater than 2, query the target role identifiers corresponding to the information to be retrieved, including: Group all role identifiers to obtain multiple object sets; Based on the information to be retrieved, information queries are sequentially performed on the multiple object sets to obtain query results corresponding to each of the multiple object sets; wherein, the multiple object sets correspond to different levels of popularity, and the multiple object sets are arranged in descending order of popularity; If a query result that matches the information to be retrieved exists, the set of objects corresponding to the query result that matches the information to be retrieved is determined, and the target role identifier corresponding to the information to be retrieved is retrieved from the set of objects.

9. The method according to claim 8, characterized in that, Grouping all role identifiers yields multiple object collections, including: The popularity value of all role identifiers is calculated based on the object information; wherein, the object information includes at least one of the following: object name, object identifier, number of times the object is used, usage duration, and online duration; Based on the popularity value, all the character identifiers are grouped to obtain the multiple object sets.

10. The method according to claim 1, characterized in that, Based on the behavioral characteristics of the user account, a set of car logos to be recommended for the user account is determined, including: A recommendation model is trained based on the behavioral characteristics of the user accounts; The set of car logos to be recommended is determined based on the trained recommendation model.

11. The method according to claim 10, characterized in that, Training a recommendation model based on the behavioral characteristics of the user account includes: Obtain behavioral characteristics related to the user account; wherein, the behavioral characteristics are related to the set of role identifiers, and the behavioral characteristics include at least one of object purchase characteristics, object collection characteristics, object click characteristics, object cancel download characteristics, object usage characteristics, and object switching characteristics; Based on the behavioral characteristics, label features corresponding to all objects are determined; wherein, all objects include a set of role identifiers corresponding to the user account, and the label features are used to characterize the object invocation situation; Sample data is generated based on the behavioral features and the label features; The recommendation model is trained using the sample data.

12. The method according to claim 11, characterized in that, Sample data is generated based on the behavioral features and the label features, including: Based on the time corresponding to the behavioral feature in the time series, determine the adjacent behavioral features corresponding to the first adjacent time, and calculate the first difference result based on the behavioral feature and the adjacent behavioral features; Based on the time corresponding to the label feature in the time series, determine the adjacent label feature corresponding to the second adjacent time, and calculate the second difference result based on the label feature and the adjacent label feature; The sample data is generated based on the first difference result and the second difference result.

13. The method according to claim 12, characterized in that, The sample data is generated based on the first difference result and the second difference result, including: The first difference result is converted into an encoded feature vector; The sample data is obtained by fusing the encoded feature vector and the second difference result.

14. The method according to claim 10, characterized in that, The set of car logos to be recommended is determined based on the trained recommendation model, including: The recommendation model predicts multi-class ratings for all objects, whereby the multi-class ratings represent the probability that an object is selected by the user. The multi-class scores of all objects are simplified to binary scores; wherein the number of scores in the multi-class scores is greater than the number of scores in the binary scores. The set of car logos to be recommended is determined based on the binary classification score.

15. The method according to claim 1, characterized in that, After triggering the user device to replace the current vehicle icon in the map client with the vehicle icon corresponding to the selection operation, the method further includes: The system periodically acquires environmental tags within a preset range centered on the user's location, based on a preset time interval. If a target vehicle logo that matches the environment label exists in the matching results, the user device is triggered to replace the current vehicle logo used to represent the user's location on the electronic map with the target vehicle logo.

16. A method for changing vehicle logos in a map client, characterized in that, include: When a login operation for the map client is detected, the user account entered in the login operation is sent to the server; When a user action is detected that triggers the search function, the search information corresponding to the user action is obtained. If a target role identifier exists that corresponds to the information to be retrieved, and the target role identifier belongs to the set of role identifiers corresponding to the user account, a list of candidate car icons corresponding to the information to be retrieved is displayed; wherein, the list of candidate car icons includes the target role identifier and car icons that match the set of role identifiers; the car icons that match the set of role identifiers are those that match the set of role identifiers in the set of car icons to be recommended determined based on the user account's behavioral characteristics; wherein, the list of candidate car icons corresponds to the user account; When a selection operation is detected on the list of candidate car icons, the current car icon in the map client is replaced with the car icon corresponding to the selection operation in response to the selection operation; wherein, the current car icon is used to represent the user's location on the electronic map of the map client.

17. A vehicle logo replacement device applied to a map client, characterized in that, include: The car logo to be recommended determination unit is used to obtain a set of role identifiers related to user accounts and determine a set of car logos to be recommended based on the behavioral characteristics of the user accounts; The matching degree calculation unit is used to determine the matching degree between each car logo in the set of car logos to be recommended and the set of role identifiers, and to obtain a matching result containing multiple matching degrees; The car logo determination unit is used to determine the car logo that matches the set of role identifiers based on the matching results; An information acquisition unit is used to acquire the information to be retrieved in the retrieval request when a retrieval request is received; The identifier query unit is used to query the target role identifier corresponding to the information to be retrieved. If the target role identifier belongs to the role identifier set, a candidate car logo list containing the target role identifier and car logos that match the role identifier set is generated, and the candidate car logo list is fed back to the user device so that the user device can display the candidate car logo list; wherein, the candidate car logo list corresponds to the user account; The vehicle logo replacement unit is used to, when a selection operation is detected on the candidate vehicle logo list, trigger the user device to replace the current vehicle logo in the map client with the vehicle logo corresponding to the selection operation in response to the selection operation; wherein the current vehicle logo is used to represent the user's location in the electronic map of the map client.

18. An electronic device, characterized in that, include: processor; as well as Memory for storing the executable instructions of the processor; The processor is configured to execute the method of any one of claims 1-16 by executing the executable instructions.

19. A computer program product, characterized in that, Includes computer instructions that, when executed by a processor of a computer device, cause the computer device to perform the method according to any one of claims 1-16.

Images