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

Abstract

The present application provides a vehicle logo replacement method, a vehicle logo replacement device, and an electronic device for a map client. The method relates to the field of computer technology and can be applied to an in-vehicle terminal or navigation software. The method comprises: obtaining a role identifier set corresponding to a user account, and determining a set of vehicle logos to be recommended based on the behavioral characteristics of the user account; determining the degree of match between each vehicle logo in the set of vehicle logos to be recommended and the role identifier set, obtaining a matching result containing multiple matching degrees; determining the vehicle logo with the highest matching degree with the role identifier set based on the matching result; and triggering the user device 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 to represent the user's location on the electronic map of the map client. Thus, implementing the embodiments of the present application can achieve personalized and automatic vehicle logo replacement.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a method for replacing a vehicle logo applied to a map client, a device for replacing a vehicle logo applied to a map client, and electronic equipment. Background Art

[0002] On the Internet, persona identifiers (e.g., icons, images, etc.) are often used to identify users. For example, in social networking apps, a persona identifier might be a user's profile picture; in navigation apps, a persona identifier might be a car logo. To make their persona more vivid, users often change their persona identifiers based on their needs. Generally, software provides users with a number of pre-defined persona identifiers for them to choose from. However, in this case, the options available to users are very limited, preventing them from having a richer user experience.

[0003] It should be noted that the information disclosed in the above background technology section 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 ordinary technicians in this field. Summary of the Invention

[0004] The purpose of this application is to provide a vehicle logo replacement method, an object replacement method, a vehicle logo replacement device, an object replacement device, a computer-readable storage medium and an electronic device applied to a map client, which can realize personalized automatic replacement of the vehicle logo.

[0005] Other features and advantages of the present application will become apparent from the following detailed description, or may be learned in part by practice of the present application.

[0006] According to one aspect of the present application, a method for replacing a vehicle logo applied to a map client is provided, comprising:

[0007] Obtain the role identification set corresponding to the user account, and determine the set of car logos to be recommended based on the behavioral characteristics of the user account;

[0008] Determine the matching degree between each vehicle logo in the set of vehicle logos to be recommended and the set of character identifiers, and obtain a matching result including multiple matching degrees;

[0009] Determine the vehicle logo with the highest matching degree with the character identification set based on the matching results;

[0010] The user device is triggered to replace the current car logo in the map client with a car logo that has the highest matching degree with the role identification set; wherein the current car logo is used to indicate the user's location in the electronic map of the map client.

[0011] According to one aspect of the present application, a method for replacing a vehicle logo applied to a map client is provided, comprising:

[0012] When a login operation for the map client is detected, the user account entered in the login operation is sent to the server;

[0013] Receive the car logo with the highest matching degree with the role identification set corresponding to the user account fed back by the server;

[0014] The current car logo in the map client is replaced with a car logo that has the highest matching degree with the role identification set corresponding to the user account; wherein the current car logo is used to indicate the user's location in the electronic map of the map client.

[0015] In an exemplary embodiment of the present application, the above method further includes:

[0016] When a user operation for triggering the start of the search function is detected, the to-be-retrieved information corresponding to the user operation is obtained;

[0017] Displaying a list of vehicle logos to be selected corresponding to the information to be retrieved; wherein the list of vehicle logos to be selected includes the target role identifier corresponding to the information to be retrieved and vehicle logos associated with a set of role identifiers, where the set of role identifiers corresponds to the user account;

[0018] When a selection operation is detected on the list of vehicle logos to be selected, the current vehicle logo in the map client is replaced 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.

[0019] According to one aspect of the present application, a vehicle logo replacement device for a map client is provided, comprising: a vehicle logo determination unit to be recommended, a matching degree calculation unit, a vehicle logo determination unit, and a vehicle logo replacement unit, wherein:

[0020] A vehicle logo determination unit to be recommended, configured to obtain a set of role identifiers corresponding to a user account and determine a set of vehicle logos to be recommended based on behavioral characteristics of the user account;

[0021] a matching degree calculation unit, configured to determine a matching degree between each vehicle logo in the set of vehicle logos to be recommended and the set of character identifiers, and obtain a matching result including multiple matching degrees;

[0022] A vehicle logo determination unit, configured to determine the vehicle logo having the highest matching degree with the character identification set based on the matching result;

[0023] The vehicle logo replacement unit is used to trigger the user device to replace the current vehicle logo in the map client with a vehicle logo that has the highest matching degree with the role identification set; wherein the current vehicle logo is used to indicate the user's location in the electronic map of the map client.

[0024] In an exemplary embodiment of the present application, the apparatus further includes:

[0025] An information acquisition unit, configured to, upon receiving a search request, acquire the information to be retrieved in the search request;

[0026] An identification query unit is used to query the target role identification corresponding to the information to be retrieved. If the target role identification belongs to the role identification set, a list of vehicle logos to be selected is generated, which includes the target role identification and vehicle logos that match the role identification set, and is fed back to the user device so that the user device displays the list of vehicle logos to be selected; wherein, the list of vehicle logos to be selected corresponds to the user account.

[0027] In an exemplary embodiment of the present application, the apparatus further includes:

[0028] a vehicle logo acquisition unit, configured to acquire, when the target character identifier does not belong to the character identifier set, at least one related vehicle logo corresponding to the target character identifier from a character identifier library; wherein the at least one related vehicle logo and the target character identifier belong to the same game project;

[0029] The vehicle logo replacement unit is further configured to feed back at least one relevant vehicle logo and target character identifier to the user equipment, so that the user equipment displays the at least one relevant vehicle logo and target character identifier.

[0030] In an exemplary embodiment of the present application, the object acquisition unit feeds back at least one relevant vehicle logo and target character identifier to the user device, so that the user device displays the at least one relevant vehicle logo and target character identifier, including:

[0031] Sorting the at least one related vehicle logo in descending order according to usage popularity to obtain a sorting result;

[0032] The target character identifier and the ranking result are fed back to the user device, so that the user device displays the target character identifier and the ranking result; wherein the display priority of the target character identifier is higher than any related vehicle logo in the ranking result.

[0033] In an exemplary embodiment of the present application, the list of vehicle logos to be selected further includes at least one popular object, and the vehicle logo replacement unit generates a list of vehicle logos to be selected that includes the target character identifier and vehicle logos that match the set of character identifiers, including:

[0034] Selecting at least one popular object within a unit time period according to a trigger time of a user operation for triggering the start of a search function; wherein the trigger time is a deadline of the unit time period;

[0035] generating a list of to-be-selected vehicle logos comprising the target character identifier, the at least one popular object, and vehicle logos matching the set of character identifiers;

[0036] The target character identifier has a higher display priority than the vehicle logos that match the character identifier set, and the vehicle logos that match the character identifier set have a higher display priority than the at least one popular object.

[0037] In an exemplary embodiment of the present application, the vehicle logo replacement unit queries the target role identifier corresponding to the information to be retrieved, including:

[0038] Determine the label corresponding to the information to be retrieved;

[0039] Selecting a target set from a set of objects corresponding to different labels according to the labels;

[0040] The object that matches the information to be retrieved is called from the target collection as the target role identifier.

[0041] In an exemplary embodiment of the present application, the vehicle logo replacement unit determines the label corresponding to the information to be retrieved, including:

[0042] Perform keyword extraction on the information to be retrieved to obtain extraction results;

[0043] If the extraction result indicates that the keyword exists in the information to be retrieved, the tag corresponding to the keyword is determined as the tag of the information to be retrieved.

[0044] In an exemplary embodiment of the present application, the vehicle logo replacement unit is further configured to display a prompt message indicating a search failure and at least one hot search word when the extraction result indicates that the keyword does not exist in the information to be retrieved;

[0045] The above device also includes:

[0046] an object set determining unit, configured to, upon receiving a user operation acting on a target hot word among at least one search hot word, determine a specific set to which the target hot word belongs from object sets corresponding to different tags;

[0047] The object calling unit is used to call an object matching the target hot word from a specific set as a role identifier for replacing the current car logo.

[0048] In an exemplary embodiment of the present application, if the number of role identifiers is greater than 2, the vehicle logo replacement unit searches for a target role identifier corresponding to the information to be retrieved, including:

[0049] Group all role identifiers to obtain multiple object sets;

[0050] Performing information queries on multiple object sets in sequence according to the information to be retrieved, and obtaining query results corresponding to the multiple object sets respectively; wherein the multiple object sets correspond to different popularity, and the multiple object sets are arranged in descending order based on popularity;

[0051] If there is a query result that hits the information to be retrieved, the object set corresponding to the query result that hits the information to be retrieved is determined, so as to call the target role identifier corresponding to the information to be retrieved from the corresponding object set.

[0052] In an exemplary embodiment of the present application, the vehicle logo replacement unit groups all role identifiers to obtain multiple object sets, including:

[0053] Calculating the popularity values ​​of all role identifiers based on the object information; wherein the object information includes at least one of the object name, object identifier, object usage count, usage duration, and online duration;

[0054] All role identifiers are grouped according to their heat values ​​to obtain multiple object sets.

[0055] In an exemplary embodiment of the present application, the to-be-recommended car logo determining unit determines a to-be-recommended car logo set corresponding to the user account based on the behavioral characteristics of the user account, including:

[0056] Training a recommendation model based on the behavioral characteristics of the user account;

[0057] The set of vehicle logos to be recommended is determined according to the trained recommendation model.

[0058] In an exemplary embodiment of the present application, the vehicle logo to be recommended determining unit trains a recommendation model based on the behavioral characteristics of the user account, including:

[0059] Obtaining a behavior feature associated with the user account; wherein the behavior feature is associated with the role identifier set, and the behavior feature includes at least one of an object purchase feature, an object collection feature, an object click feature, an object download cancellation feature, an object usage feature, and an object switching feature;

[0060] Determine label features corresponding to all objects according to the behavior features; wherein all objects include a set of role identifiers corresponding to the user account, and the label features are used to characterize object call conditions;

[0061] generating sample data according to the behavior characteristics and the label characteristics;

[0062] The recommendation model is trained using the sample data.

[0063] In an exemplary embodiment of the present application, generating sample data according to behavioral features and label features includes:

[0064] Determining adjacent behavior features corresponding to a first adjacent moment based on the moment corresponding to the behavior feature in the time series, and calculating a first difference result based on the behavior feature and the adjacent behavior features;

[0065] Determine an adjacent label feature corresponding to a second adjacent moment based on the moment corresponding to the label feature in the time series, and calculate a second difference result based on the label feature and the adjacent label feature;

[0066] Sample data is generated according to the first difference result and the second difference result.

[0067] In an exemplary embodiment of the present application, generating sample data according to the first difference result and the second difference result includes:

[0068] Convert the first difference result into an encoded feature vector;

[0069] The encoded feature vector and the second difference result are fused to obtain the sample data.

[0070] In an exemplary embodiment of the present application, the to-be-recommended vehicle logo determination unit determines a to-be-recommended vehicle logo set based on a trained recommendation model, including:

[0071] The recommendation model is used to predict the multi-classification scores of all objects. The multi-classification scores are used to represent the probability of the object being selected by the user.

[0072] Simplify the multi-classification scores of all objects into binary scores; the number of scores in the multi-classification scores is greater than the number of scores in the binary scores;

[0073] Determine the set of recommended car logos corresponding to the user account based on the binary classification score.

[0074] In an exemplary embodiment of the present application, the sample data consists of training samples and test samples. The unit for determining a vehicle logo to be recommended trains a recommendation model using the sample data, including:

[0075] Train the recommendation model through training samples;

[0076] The trained recommendation model is tested through test samples, and the model parameters corresponding to the trained recommendation model are adjusted according to the test results.

[0077] In an exemplary embodiment of the present application, the apparatus further includes:

[0078] An environmental tag acquisition unit, configured to periodically acquire environmental tags within a preset range centered on the user's location based on a preset time period 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 best matches the set of character identifiers;

[0079] The vehicle logo replacement unit is specifically configured to trigger the user device to replace the current vehicle logo representing the user's location in the electronic map with the target vehicle logo when there is a target vehicle logo matching the environment tag in the matching result.

[0080] According to one aspect of the present 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 any one of the above methods by executing the executable instructions.

[0081] According to one aspect of the present application, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, any one of the above methods is implemented.

[0082] According to one aspect of the present application, a computer program product or computer program is provided, the computer program product or computer program including 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 optional implementations described above.

[0083] The exemplary embodiments of the present application may have some or all of the following beneficial effects:

[0084] In a car logo replacement method applied to a map client provided in an example embodiment of the present application, a character identification set corresponding to a user account can be obtained, and a car logo set to be recommended can be determined based on the behavioral characteristics of the user account; the matching degree of each car logo in the car logo set to be recommended and the character identification set can be determined to obtain a matching result containing multiple matching degrees; the car logo with the highest matching degree with the character identification set can be determined based on the matching result; the user device can be triggered to replace the current car logo in the map client with the car logo with the highest matching degree with the character identification set; wherein the current car logo is used to represent the user's location in the electronic map of the map client. According to the above scheme description, on the one hand, the present application can determine a personalized car logo set to be recommended based on the behavioral characteristics corresponding to the user account, and then determine a car logo suitable for the user based on the matching degree between the car logo set to be recommended and the character identification set, so as to realize personalized automatic replacement of the car logo. On the other hand, the present application can avoid traffic accidents caused by users manually changing car logos during driving, and realize automatic replacement of car logos to improve driving safety.

[0085] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] The accompanying drawings are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present application, and together with the specification, are used to explain the principles of the present application. Obviously, the drawings described below are only some embodiments of the present application, and those skilled in the art can derive other drawings based on these drawings without inventive effort.

[0087] Figure 1 A schematic diagram showing an exemplary system architecture of a vehicle logo replacement method applied to a map client and a vehicle logo replacement device applied to a map client, to which embodiments of the present application can be applied;

[0088] Figure 2 A schematic diagram showing the structure of a computer system suitable for implementing an electronic device according to an embodiment of the present application is shown;

[0089] Figure 3 A flowchart of a vehicle logo replacement method applied to a map client according to an embodiment of the present application is schematically shown;

[0090] Figure 4 The following schematically shows a recommendation model training process diagram according to an embodiment of the present application;

[0091] Figure 5 The following schematically shows a recommendation model prediction process diagram according to an embodiment of the present application;

[0092] Figure 6 The following schematically illustrates a process flow diagram for personalized replacement of a role identification according to an embodiment of the present application;

[0093] Figure 7 A schematic diagram of a display interface for a list of vehicle logos to be selected according to an embodiment of the present application is shown schematically;

[0094] Figure 8 The following schematically shows a process diagram of an object query according to an embodiment of the present application;

[0095] Figure 9 The following schematically shows a role identification search process diagram according to an embodiment of the present application;

[0096] Figure 10 Schematically shows a role identification diagram according to an embodiment of the present application;

[0097] Figure 11 The following schematically shows a navigation interface diagram according to an embodiment of the present application;

[0098] Figure 12 The following schematically shows a navigation interface diagram according to an embodiment of the present application;

[0099] Figure 13 The following schematically shows a navigation interface diagram according to an embodiment of the present application;

[0100] Figure 14 Schematically shows a flow chart of an object replacement method according to an embodiment of the present application;

[0101] Figure 15 The following schematically shows a structural block diagram of an object replacement device according to an embodiment of the present application;

[0102] Figure 16 A flowchart of a vehicle logo replacement method applied to a map client according to an embodiment of the present application is schematically shown;

[0103] Figure 17 The following schematically shows a structural block diagram of a vehicle logo replacement device applied to a map client according to an embodiment of the present application. DETAILED DESCRIPTION

[0104] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein; on the contrary, these embodiments are provided so that this application will be more comprehensive and complete and the concepts of the example embodiments will be fully conveyed to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present application. However, those skilled in the art will appreciate that the technical solutions of the present application may be practiced while omitting one or more of the specific details, or that other methods, components, devices, steps, etc. may be employed. In other cases, well-known technical solutions are not shown or described in detail to avoid obscuring various aspects of the present application.

[0105] In addition, the accompanying drawings are merely schematic illustrations of the present application and are not necessarily drawn to scale. Identical reference numerals in the figures denote identical or similar parts, and thus repetitive descriptions thereof will be omitted. Some of the blocks shown in the accompanying drawings are functional entities that do not necessarily correspond to physically or logically separate entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0106] Figure 1 A schematic diagram of the system architecture of an exemplary application environment of a vehicle logo replacement method applied to a map client and a vehicle logo replacement device applied to a map client, to which the embodiments of the present application can be applied, is shown.

[0107] like Figure 1 As shown, the system architecture 100 may include one or more terminal devices 101, 102, 103, a network 104, and a server cluster 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server cluster 105. The network 104 may include various connection types, such as wired or wireless communication links or fiber optic cables, etc. The terminal devices 101, 102, 103 may be various electronic devices with display screens, including but not limited to desktop computers, portable computers, smart phones, tablet computers, etc. It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0108] The vehicle logo replacement method applied to a map client provided in the embodiment of the present application can be executed by the terminal devices 101, 102, 103 or any server in the server cluster 105. Accordingly, the vehicle logo replacement device applied to the map client is generally set in a server of the server cluster 105 or the terminal devices 101, 102, 103. For example, in an exemplary embodiment, any server in the server cluster 105 (or the terminal devices 101, 102, 103) can obtain a role identification set corresponding to a user account, and determine a 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 role identification set, and obtain a matching result containing multiple matching degrees; determine the vehicle logo with the highest matching degree with the role identification set based on the matching result; and 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 role identification set; wherein the current vehicle logo is used to represent the user's location in the electronic map of the map client.

[0109] Figure 2 A schematic diagram of the structure of a computer system suitable for implementing an electronic device according to an embodiment of the present application is shown.

[0110] It should be noted that Figure 2 The computer system 200 of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0111] like Figure 2As shown, the computer system 200 includes a central processing unit (CPU) 201, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 202 or the program loaded from the storage unit 208 to the random access memory (RAM) 203. Various programs and data required for system operation are also stored in the RAM 203. The CPU 201, ROM 202, and RAM 203 are connected to each other via a bus 204. An input / output (I / O) interface 205 is also connected to the bus 204.

[0112] The following components are connected to the I / O interface 205: an input section 206 including a keyboard, a mouse, and the like; an output section 207 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and a speaker; a storage section 208 including a hard disk; and a communication section 209 including a network interface card such as a LAN card or a modem. The communication section 209 performs communication processing via a network such as the Internet. A drive 210 is also connected to the I / O interface 205 as needed. A removable medium 211, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 210 as needed, so that a computer program read therefrom can be installed into the storage section 208 as needed.

[0113] In particular, according to an embodiment of the present application, the process described below with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 209, and / or installed from a removable medium 211. When the computer program is executed by the central processing unit (CPU) 201, the various functions defined in the method and apparatus of the present application are executed.

[0114] In addition, this application also applies artificial intelligence technology and machine learning, specifically in predicting the scores of all objects based on the recommendation model. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results. In other words, artificial intelligence is a comprehensive technology in computer science that attempts to understand the essence of intelligence and produce a new type of intelligent machine that can respond in a similar way to human intelligence. Artificial intelligence is to study the design principles and implementation methods of various intelligent machines, so that machines have the functions of perception, reasoning and decision-making.

[0115] Artificial intelligence (AI) technology is a comprehensive discipline encompassing a wide range of fields, encompassing both hardware and software technologies. Foundational AI technologies generally include sensors, specialized AI chips, cloud computing, distributed storage, big data processing, operating / interaction systems, and mechatronics. AI software technologies primarily encompass computer vision, speech processing, natural language processing, and machine learning / deep learning.

[0116] Machine learning (ML) is a multidisciplinary field that encompasses probability theory, statistics, approximation theory, convex analysis, and algorithmic complexity theory. It specifically studies how computers can simulate or implement human learning behaviors to acquire new knowledge or skills and reorganize existing knowledge structures to continuously improve their performance. Machine learning is at the core of artificial intelligence and the fundamental way to make computers intelligent. Its applications span all areas of AI. Machine learning and deep learning typically include techniques such as artificial neural networks, belief networks, reinforcement learning, transfer learning, inductive learning, and self-learning.

[0117] In social networking apps, character logos can be used as user avatars to identify the user; in navigation apps, they can be used as vehicle logos to identify the user's current location. Character logos can be changed based on user needs, but typically require the user to search for keywords to obtain the corresponding character logo. After the search is complete, the user selects the corresponding character logo to complete the vehicle logo change. However, this approach suffers from low interactivity and fails to provide users with a richer user experience.

[0118] Based on this, this example embodiment provides a method for replacing a car logo applied to a map client. Figure 3 , Figure 3 The flowchart of the vehicle logo replacement method applied to the map client according to one embodiment of the present application is schematically shown. Figure 3 As shown, the vehicle logo replacement method applied to the map client may include: steps S310 to S340.

[0119] Step S310: Obtain a set of role identifiers corresponding to the user account, and determine a set of vehicle logos to be recommended based on the behavioral characteristics of the user account.

[0120] Step S320: determining the matching degree between each vehicle logo in the vehicle logo set to be recommended and the character identification set, and obtaining a matching result including multiple matching degrees.

[0121] Step S330: Determine the vehicle logo with the highest matching degree with the character identification set according to the matching results.

[0122] Step S340: triggering the user device to replace the current car logo in the map client with a car logo that has the highest matching degree with the role identification set; wherein the current car logo is used to indicate the user's location in the electronic map of the map client.

[0123] It should be noted that steps S310 to S340 can be executed by a terminal or a server. The aforementioned server can be a cloud server, which is a service platform that provides comprehensive business capabilities to various Internet users. 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.

[0124] Implementation Figure 3 The method shown can determine a personalized set of recommended car logos based on the behavioral characteristics of the user account. It then determines the appropriate car logo for the user based on the degree of match between the recommended car logo set and the character identification set, enabling personalized automatic logo replacement. Furthermore, this method can prevent traffic accidents caused by users manually changing the car logo while driving, achieving automated logo replacement to improve driving safety.

[0125] The above steps of this exemplary embodiment are described in more detail below.

[0126] In step S310 , a role identification set corresponding to the user account is obtained, and a set of vehicle logos to be recommended is determined based on the behavioral characteristics of the user account.

[0127] 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 the game account (e.g., Mario, Luigi, Bowser, Princess Peach, etc.). The character identifier is stored in a character identifier database on the cloud server; the character identifier database is also used to collect character identifiers, and the game account can be obtained by the user through social account registration, mobile phone number, etc. When the present application is applied to map software / navigation software, the user account in the map software can be bound to the current user's game account.

[0128] Specifically, both the target character identifier and the vehicle identifier corresponding to the target character identifier in the vehicle identifier list can be touchable. In addition, the objects displayed in the vehicle identifier list can be static identifiers or dynamic identifiers, which is not limited in the embodiment of the present application.

[0129] As an optional embodiment, the above method also 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, and if the target role identifier belongs to the role identifier set, generating a list of vehicle logos to be selected that includes the target role identifier and vehicle logos that match the role identifier set and feeding it back to the user device, so that the user device displays the list of vehicle logos to be selected; wherein the list of vehicle logos to be selected corresponds to the user account.

[0130] Specifically, the information to be retrieved can be text information input by the user. The user operation used to trigger the start of the search function can be a click operation, a touch operation, a voice control operation, a gesture control operation, etc., which is not limited in the embodiment of the present application, and the user operation for other purposes in the present application is similar.

[0131] In addition, obtaining the information to be retrieved in the search request includes: when the user device detects a user operation acting 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 feeds it back to the server. In addition, 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 an association list, and the association list is used to display hot words related to the character for the user to select. As the characters in the input box increase / decrease, the association list can be continuously updated so that the hot words in the association list can always match the content in the input box, so that when the user sees the word they want to enter in the association list, they can directly select it without having to continue entering characters in the input box.

[0132] Furthermore, in addition to displaying hot words in the association list, the character identifier corresponding to each hot word can also be displayed. The above method can also include: when a user operation is detected that acts on a target hot word in the association list, the current car logo can be replaced with the character identifier corresponding to the target hot word. This can simplify the user's operation of changing the car logo, thereby improving replacement efficiency and enhancing the user experience.

[0133] Specifically, the server obtains the corresponding character identification set according to the game account associated with the user account and packages and feeds back the set to the user device, including: the server obtains the corresponding character identification set identifier according to the game account associated with the user account and packages and feeds back the character identification set identifier to the user device.

[0134] In addition, the car logo is an object corresponding to the same label as the character logo, or an object belonging to the same game project as the character logo, or an object similar to the character logo, which is not limited in the embodiments of the present application. If the car logo is an object similar to the character logo, the similarity between the corresponding car logo and the character logo (e.g., 80%) can be represented by vector similarity, and the vector similarity can specifically be cosine distance or Euclidean distance. Among them, the similarity can indicate the similarity between the corresponding car logo and the character logo, and can also indicate the similarity between the corresponding car logo name (e.g., Zelda) and the character logo name (e.g., Princess Peach).

[0135] As an optional embodiment, determining the set of car logos to be recommended corresponding to the user account based on the behavioral characteristics of the user account includes: training a recommendation model based on the behavioral characteristics of the user account; and determining the set of car logos to be recommended based on the trained recommendation model.

[0136] Specifically, the recommendation model can be a statistical scoring model that measures the user's preference for products, games, animations, etc. in combination with relevant behavioral characteristics. In addition, after determining the car logo corresponding to the character logo set through the recommendation model, the above method can also include: pushing any character logo in the character logo set according to a preset duration, and when a user operation for selecting a character logo is detected, replacing the current car logo used to represent the current location with the selected character logo. This can provide users with the option of replacing the object (such as the car logo), and the user only needs to click to confirm, reducing the number of steps required for the user to replace the car logo, thereby reducing the risk of the user's travel.

[0137] It can be seen that by implementing this optional embodiment, objects that need to be recommended to users can be determined through the recommendation model, thereby enriching the options and improving the user experience.

[0138] As an optional embodiment, training a recommendation model based on the behavioral characteristics of a user account includes: obtaining behavioral characteristics related to the user account; wherein the behavioral characteristics are related to a role identification set, and the behavioral characteristics include at least one of an object purchase feature, an object collection feature, an object click feature, an object download cancellation feature, an object usage feature, and an object switching feature; determining label features corresponding to all objects respectively based on the behavioral characteristics; wherein all objects include a role identification set corresponding to the user account, and the label features are used to characterize the object call situation; generating sample data based on the behavioral characteristics and the label features; and training the recommendation model through the sample data.

[0139] Specifically, behavioral features can be used to characterize multi-dimensional user behaviors, and behavioral features can be specifically represented by vectors. The algorithm used in the recommendation model is: in, W j and Wk is a constant.

[0140] In addition, the object purchase feature is used to characterize game characters that a user has purchased. The object collection feature is used to characterize game characters that a user has collected. The object click feature is used to characterize game characters that a user has clicked to view. The object download cancellation feature is used to characterize the user's behavior of canceling a game character that was in the process of downloading. The object usage feature is used to characterize the user's usage of each game character, which may include frequency of use. The object switch feature is used to characterize the user's behavior of switching from one game character to another.

[0141] In addition, the label features corresponding to all objects are determined according to the behavioral characteristics, including: calculating the difference result ΔX according to the behavioral characteristics X at each moment in the time series t and ΔX t-1 ; Construct binary classification labels for all virtual characters according to the role application status corresponding to the user account; determine the label features Y corresponding to all objects respectively according to the binary classification labels of all virtual characters; wherein, the binary classification labels may include label 0 for indicating "unused" and label 1 for indicating "used", and the role application status is used to characterize whether the user account has (used) / (not used) the virtual character.

[0142] It can be seen that the implementation of this optional embodiment can characterize the user based on the determined behavioral characteristics and label characteristics, thereby generating sample data based on the behavioral characteristics and label characteristics. By training the recommendation model with the sample data, the recommendation effect of the recommendation model can be improved, and the role identification personalized recommended for the user can be more in line with the user's expectations, thereby increasing the probability of the role identification being selected by the user.

[0143] As an optional embodiment, sample data is generated based on behavioral features and label features, including: determining adjacent behavioral features corresponding to a first adjacent moment based on the moment corresponding to the behavioral features in a time series, and calculating a first differential result based on the behavioral features and the adjacent behavioral features; determining adjacent label features corresponding to a second adjacent moment based on the moment corresponding to the label features in the time series, and calculating a second differential result based on the label features and the adjacent label features; and generating sample data based on the first differential result and the second differential result.

[0144] Specifically, the first difference result is used to characterize the difference in behavior characteristics at adjacent moments, and the second difference result is used to characterize the difference in label characteristics at adjacent moments. The first difference result may include at least one difference result (e.g., ΔX t and ΔX t-1), and the same applies to the second difference result. The first adjacent moment and the second adjacent moment can be the same moment or different moments. For example, if the current moment is t, the first adjacent moment and / or the second adjacent moment can be (t-1) or (t+1); if the current moment is t, if the current moment is (t-1), the first adjacent moment and / or the second adjacent moment can be (t-2) or t, and so on.

[0145] In addition, determining the adjacent behavior feature corresponding to the first adjacent moment based on the moment corresponding to the behavior feature in the time series includes: determining the adjacent behavior feature corresponding to the first adjacent moment (t-1) based on the moment t corresponding to the behavior feature in the time series, and determining the adjacent behavior feature corresponding to the first adjacent moment (t-2) based on the moment (t-1) corresponding to the behavior feature in the time series. The first adjacent moment is the adjacent moment corresponding to the current moment. If the current moment is t, the first adjacent moment is (t-1), and if the current moment is (t-1), the first adjacent moment is (t-2).

[0146] Then, the first difference result is calculated based on the behavior feature and the adjacent behavior feature, including: based on the behavior feature X at time t t and the adjacent behavior feature X at time (t-1) t-1 Calculate the first difference result ΔX t =X t -X t-1 , and, according to the behavioral characteristics X at time (t-1) t-1 and the adjacent behavior feature X at time (t-2) t-2 Calculate the first difference result ΔX t-1 =X t-1 -X t-2 .

[0147] Furthermore, the adjacent label feature corresponding to the second adjacent moment is determined based on the moment corresponding to the label feature in the time series, including: determining the adjacent label feature Y corresponding to the second adjacent moment (t-1) based on the moment t corresponding to the label feature in the time series t-1 .

[0148] Then, the second difference result is calculated based on the label feature and the adjacent label feature, including: based on the label feature Y at time t t and the adjacent label feature Y corresponding to the second adjacent time (t-1) t-1 Calculate the second difference result ΔY t =Y t -Y t-1 ; Among them, in Y t -Y t-1 >0, ΔY t =1; in Yt -Y t-1 =0, ΔY t =0; in Y t -Y t-1 When ΔY < 0 t =-1.

[0149] It can be seen that the implementation of this optional embodiment can characterize the time-series differences between behavioral features and label features through the calculation of feature results, and then generate sample data for training the recommendation model. In this way, a more accurate recommendation model can be trained to improve the degree of personalization and recommend personalized role identifications to different users, thereby improving the user experience and increasing user stickiness.

[0150] As an optional embodiment, generating sample data according to the first differential result and the second differential result includes: converting the first differential result into a coded feature vector; and fusing the coded feature vector and the second differential result to obtain the sample data.

[0151] Specifically, the encoded feature vector can be a one-hot encoding result. One-hot encoding represents a categorical variable as a binary vector. It typically uses an N-bit state register to encode N states, with each state corresponding to an independent register bit, where N is a positive integer. Sample data can include N training samples and M test samples, where both N and M are positive integers.

[0152] In addition, before converting the first difference result into a coding feature vector, the above method may further include: performing a calculation on ΔX in the first difference result. t-1 and ΔX t Perform feature transformation to obtain I(ΔX t-1 ) and I(ΔX t ); where I(ΔX t ) can be used as a prediction sample in the sample data alone, I(ΔX t-1 )=dX t-1 Based on this, the first difference result is converted into a coding feature vector, including: dX in the first difference result after feature conversion based on One-Hot coding t-1 Converted into a coding feature vector. Based on this, the coding feature vector and the second difference result are fused to obtain sample data, including: based on the user account fusion coding feature vector, I (ΔX t-1 ) and the second difference result I(ΔY t ) to obtain sample data; wherein, the fusion means may include splicing, positional multiplication, etc.

[0153] It can be seen that the implementation of this optional embodiment can improve the classification ability of the differential results by processing the differential results, and then obtain sample data for training the recommendation model by fusing the differential results, so as to improve the recommendation effect of the recommendation model.

[0154] As an optional embodiment, the sample data consists of training samples and test samples, and the recommendation model is trained using the sample data, including: 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.

[0155] Specifically, the ratio of training samples (train) to test samples (test) in the sample data can be 8:2. In addition, training the recommendation model through the training samples includes: inputting the training samples into the recommendation model, training the recommendation model based on the gradient descent algorithm to optimize the various model parameters in the recommendation model; wherein the model parameters include at least one of the weight value and the bias term. 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 the minimum value along the direction of gradient descent, or to solve the maximum value along the direction of gradient ascent.

[0156] In addition, the trained recommendation model is tested using test samples, including: inputting the test samples into the trained recommendation model, and generating test results containing indicators for evaluating the recommendation model based on the output results of the trained recommendation model, wherein the indicators include at least one of recall rate, precision rate, and AUC (Area Under Curve). Among them, AUC is defined as the area under the ROC curve (i.e., the receiver operating characteristic curve) and the coordinate axis, and the value range of AUC is between 0.5 and 1. The closer the AUC is to 1.0, the higher the authenticity of the detection method. When AUC is equal to 0.5, the authenticity of the detection method is the lowest.

[0157] It can be seen that by implementing this optional embodiment, the model can be further optimized through testing of the model, thereby improving the prediction effect of the model.

[0158] See also Figure 4 , Figure 4 The following schematically shows a recommendation model training process diagram according to an embodiment of the present application. Figure 4 As shown, the cloud server can obtain behavioral features 410 related to the user account; wherein the behavioral features are related to the role identification set, and the behavioral features include at least one of object purchase features, object collection features, object click features, object download cancellation features, object usage features and object switching features.

[0159] Based on the sequence of differential features, behavior features 410 may include behavior features 411 at time (t-1) and behavior features 412 at time (t-2). Based on behavior features 411 at time (t-1) and behavior features 412 at time (t-2), label features 413 at time (t-1) and label features 414 at time (t) may be determined for each object.

[0160] Furthermore, based on the behavioral features 411 at time (t-1) and the behavioral features 412 at time (t-2), the first differential result 415 at time (t-1) can be calculated, and based on the label features 413 at time (t-1) and the label features 414 at time (t), the second differential result 416 at time (t) can be calculated.

[0161] Furthermore, the first differential result 415 at time (t-1) can be converted into a coded feature vector 417, and the coded feature vector 417 and the second differential result 416 at time (t) can be fused to obtain sample data, which includes a training sample 418 at time (t) and a test sample 419 at time (t).

[0162] Furthermore, the recommendation model 420 can be trained using the training samples 418 at time (t). If the output result of the recommendation model does not meet expectations, the recommendation model 420 can be retrained using the training samples 418 at time (t) until the output result of the recommendation model meets expectations.

[0163] Furthermore, the trained recommendation model 420 may be tested using the test sample 419 at time (t), and the model parameters corresponding to the trained recommendation model 420 may be adjusted according to the test results.

[0164] When the output result 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.

[0165] As an optional embodiment, a set of car logos to be recommended is determined based on a trained recommendation model, including: predicting multi-category scores of all objects through the recommendation model, where the multi-category scores are used to characterize the probability of the objects being selected by the user; simplifying the multi-category scores of all objects into binary scores; wherein the number of scores in the multi-category scores is greater than the number of scores in the binary scores; and determining the set of car logos to be recommended corresponding to the user account based on the binary scores.

[0166] Specifically, the set of vehicle logos to be recommended includes at least one vehicle logo. The binary classification score corresponds to two types, 0 and 1. The multi-classification score can be specifically a three-classification score, which corresponds to three types, 0, -1, and 1.

[0167] In addition, the recommendation model predicts multi-category scores for all objects. The multi-category scores are used to represent the probability of an object being selected by the user. This includes: The recommendation model predicts the multi-category scores of all objects at the next time (t+1). The multi-category scores include three categories: 0, -1, and 1. All objects fall into the corresponding category according to the prediction results. In other words, three sets corresponding to the three categories of 0, -1, and 1 are obtained. Furthermore, the multi-category scores can be uploaded to the cloud server so that the cloud server can store them.

[0168] In addition, the multi-classification scores of all objects are simplified to binary classification scores, including: t+1 =Y t +ΔY t+1 and X t+1 =X t +ΔX t+1 The multiple sets under the multi-classification scoring are merged into two sets, which correspond to score 1 and score 0 respectively. 1 can indicate that the role identifier is selected, and 0 can indicate that the role identifier is not selected. Specifically, ΔY t+1 =1 then Y t+1 =1;Y t =0 or ΔY t+1 =-1, then Y t+1 =0;ΔY t+1 =0, then Y t+1 =Y t =0 or Y t+1 =Y t = 1. Furthermore, the binary classification scores may be uploaded to a cloud server so that the cloud server stores the binary classification scores.

[0169] It can be seen that the implementation of this optional embodiment can convert the multi-classification prediction results into binary classification results on the basis of converting the original binary classification algorithm model into a multi-classification algorithm model, thereby improving the model prediction accuracy.

[0170] See also Figure 5 , Figure 5 The following schematically shows a recommendation model prediction process diagram according to an embodiment of the present application. Figure 5 As shown, based on the prediction sample and recommendation model 510, the behavior characteristics 512 at time (t) and the behavior characteristics 513 at time (t-1) corresponding to the user account can be obtained, and then the label characteristics 511 at time (t) can be obtained based on the behavior characteristics 512 at time (t) and the behavior characteristics 513 at time (t-1).

[0171] Furthermore, based on the behavioral features 512 at time (t) and the behavioral features 513 at time (t-1), the first differential result 514 at time (t) can be calculated, and the first differential result 514 at time (t) can be converted into an encoded feature vector 515 and input into the recommendation model 516.

[0172] Recommendation model 516 can generate output results 517 containing three-category labels for all objects. Furthermore, recommendation model 516 can simplify output results 517 containing three-category labels into two-category labels 518. Furthermore, recommendation model 516 can combine two-category labels 518 with label features 511 at time (t) to determine a set of vehicle logos to be recommended 519 for the user account.

[0173] In step S320 , the matching degree between each vehicle logo in the set of vehicle logos to be recommended and the set of character identifications is determined to obtain a matching result including multiple matching degrees.

[0174] Specifically, each character identification may correspond to one or more vehicle logos, and the vehicle logos corresponding to each character identification may be different or partially the same.

[0175] In step S330, the vehicle logo with the highest matching degree with the character identification set is determined according to the matching results.

[0176] In step S340, the user device is triggered to replace the current car logo in the map client with a car logo that has the highest matching degree with the character identification set; wherein the current car logo is used to indicate the user's location in the electronic map of the map client.

[0177] See also Figure 6 , Figure 6 The following schematically shows a process flow diagram of personalized replacement of role identification according to an embodiment of the present application. Figure 6 As shown, it includes: steps S610 to S670.

[0178] Step S610: The cloud server stores all game character identifiers and behavioral characteristics associated with the user account. The behavioral characteristics are associated with the set of game character identifiers and include at least one of an object purchase characteristic, an object collection characteristic, an object click characteristic, an object download cancellation characteristic, an object usage characteristic, and an object switching characteristic. Specifically, the cloud server may store all game character identifiers by reading and storing the game character identifiers from Game 1, Game 2, ..., Game n, where n is a positive integer.

[0179] Step S620: The cloud server determines the label features corresponding to all objects according to the behavior features, and generates sample data according to the behavior features and the label features; wherein the sample data consists of training samples and test samples.

[0180] Step S630: The cloud server trains the recommendation model using the training samples; tests the trained recommendation model using the test samples, and adjusts the model parameters corresponding to the trained recommendation model according to the test results.

[0181] Step S640: The cloud server predicts the multi-category scores of all objects through the recommendation model. The multi-category scores are used to represent the probability of the object being selected by the user.

[0182] Step S650: The cloud server simplifies the multi-category scores of all objects into binary classification scores.

[0183] Step S660: The cloud server determines the set of vehicle logos to be recommended corresponding to the user account based on the binary classification scores.

[0184] Step S670: The cloud server selects a target game character identifier according to the priority order of the objects in the set of car logos to be recommended, and replaces the current car logo with the target game character identifier.

[0185] It can be seen that through the implementation Figure 6 In the illustrated embodiment, a personalized set of recommended car logos is determined based on the behavioral characteristics of the user account. Furthermore, a suitable car logo for the user is determined based on the degree of match between the recommended car logo set and the character identification set, enabling personalized automatic car logo replacement. Furthermore, this automated replacement of car logos can be implemented to improve driving safety by preventing accidents caused by manual car logo replacement during driving.

[0186] See also Figure 7 , Figure 7 The following schematically shows a display interface diagram of a list of selected vehicle logos according to an embodiment of the present application. Figure 7 As shown, the display interface of the list of car logos to be selected can provide a search function. The user can enter the text information "XXX" in the input box 710 and trigger the search control 720 to enable the user device to perform the search function. Specifically, the user device can send the text information "XXX" to the cloud server so that the cloud server queries the target role identifier corresponding to the text information. If the target role identifier exists, the optional item "XXX" 730 corresponding to the target role identifier is displayed in the list of car logos to be selected. In addition, the cloud server can also feedback the relevant car logos corresponding to the target role identifier to the user device, and the relevant car logos can be displayed in the list of car logos to be selected in the form of the optional item "XXXUKD" 740. In addition, the cloud server can also feedback popular objects to the user device, and the popular objects can be displayed in the list of car logos to be selected in the form of the optional item "YYY" 750.

[0187] As an optional embodiment, querying the target role identifier corresponding to the information to be retrieved includes: determining the label corresponding to the information to be retrieved; selecting a target set from the object sets corresponding to different labels according to the label; and calling the object matching the information to be retrieved from the target set as the target role identifier.

[0188] Specifically, a tag can be used to mark the field corresponding to the information to be retrieved, such as the field of shooting games or the field of racing games. The selected target set and the information to be retrieved can correspond to the same tag. The number of target role identifiers can be one or more. Retrieving an object from the target set that matches the information to be retrieved as the target role identifier includes: retrieving an object from the target set whose name matches the information to be retrieved as the target role identifier.

[0189] It can be seen that by implementing this optional embodiment, the collection can be filtered first and then the objects in the collection can be matched, thereby improving the efficiency of object query.

[0190] As an optional embodiment, determining the tag corresponding to the information to be retrieved includes: performing keyword extraction on the information to be retrieved to obtain an extraction result; if the extraction result indicates that the information to be retrieved contains keywords, determining the tag corresponding to the keyword as the tag of the information to be retrieved.

[0191] Specifically, keyword extraction is performed on the information to be retrieved to obtain an extraction result, including: performing word segmentation processing on the information to be retrieved to obtain a processing result containing multiple word segments; extracting the word segments that hit the preset vocabulary as keywords. Furthermore, the label corresponding to the keyword is determined as the label of the information to be retrieved, including: obtaining multiple labels corresponding to the keyword, and determining the multiple labels as the label of the information to be retrieved. Among them, the multiple labels include primary labels (such as cloud games, console games), secondary labels (such as shooting games, racing games), tertiary labels (such as XX battlefield), etc., which are not limited in the embodiments of the present application.

[0192] It can be seen that implementing this optional embodiment can improve query efficiency and query accuracy by extracting keywords from the information to be retrieved.

[0193] As an optional embodiment, the above method also includes: if the extraction result indicates that there are no keywords in the information to be retrieved, a prompt message indicating that the search failed and at least one search hot word are displayed; when a user operation acting on a target hot word in at least one search hot word is received, the specific set to which the target hot word belongs is determined from a set of objects corresponding to different tags; and an object matching the target hot word is called from the specific set as a role identifier for replacing the current vehicle logo.

[0194] Specifically, before displaying a prompt message indicating a search failure and at least one hot search word, the above method may further include: selecting the top N virtual characters as hot objects based on the frequency of use of the virtual characters within a unit time period (e.g., 1 month), and determining the names of the hot objects as hot search words.

[0195] It can be seen that implementing this optional embodiment can provide users with other options when there are no search results, thereby improving the user's usage experience.

[0196] 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; performing information query on the multiple object sets in turn according to the information to be retrieved to obtain query results corresponding to the multiple object sets respectively; wherein the multiple object sets correspond to different popularity, and the multiple object sets are arranged in descending order based on the popularity; if there is a query result that hits the information to be retrieved, then determining the object set corresponding to the query result that hits the information to be retrieved, so as to call the target role identifier corresponding to the information to be retrieved from the corresponding object set.

[0197] Specifically, information queries are performed on multiple object sets in sequence according to the information to be retrieved, and query results corresponding to the multiple object sets are obtained, including: information queries are performed on multiple object sets in sequence based on a binary tree search algorithm, and query results corresponding to the multiple object sets are obtained; wherein, a binary tree is an ordered tree in which each node has at most two subtrees, which is used for information query.

[0198] In addition, since there are multiple object sets, there are also multiple query results. The query results correspond to the object sets one by one. After obtaining the query results corresponding to multiple object sets, you can traverse and obtain the non-empty query results as the query results that hit the information to be retrieved.

[0199] It can be seen that by implementing this optional embodiment, the query result corresponding to each set can be determined by performing object search on each set. The query result that is not empty is the required query result, which can locate the target role identifier more quickly and improve query efficiency.

[0200] See also Figure 8 , Figure 8 The following schematically shows an object query process diagram according to an embodiment of the present application. Figure 8As shown, the cloud server can perform heat grouping on all game character identifiers in the game character identifier set 821, the game character identifier set 822, ..., and the game character identifier set 823 in the game character identifier database 810 to obtain object sets 831, object sets 832, ..., and object sets 833. When receiving the information to be retrieved, the cloud server can sequentially query the object sets 831, object sets 832, ..., and object sets 833 based on the information to be retrieved, thereby obtaining query results 841, query results 842, ..., and query results 843 respectively fed back by the object sets 831, object sets 832, ..., and object sets 833, thereby obtaining a total query result 850. If a query result that hits the information to be retrieved exists in the total query result 850, the object set corresponding to the query result that hits the information to be retrieved is determined, and the target game character identifier corresponding to the information to be retrieved is called from the corresponding object set.

[0201] As an optional embodiment, all role identifiers are grouped to obtain multiple object sets, including: calculating the heat values ​​of all role identifiers based on object information; wherein the object information includes at least one of the object name, object identifier, number of object uses, usage duration, and online duration; and grouping all role identifiers according to the heat value to obtain multiple object sets.

[0202] Specifically, the popularity value of a character identifier can be used to represent the degree of popularity of the character identifier among users. Character identifiers with high popularity values ​​correspond to multiple clicks, multiple purchases, and multiple collections. When this application is applied to map software bound to a game account, the object name can be the character name, the object identifier can be the 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 duration of the character's use in the game; and the online duration can be the cumulative online duration of the character in the game. In addition, each object set can correspond to a different number of objects.

[0203] Based on this, the heat value of the role identifier is calculated according to the object information, including: the current time in the object information and the number of times the object is used in the object information Duration of use And online time The expression of To calculate the heat value of the role identifier Where k represents the kth role ID, and i represents the i-th moment.

[0204] Based on this, if there are multiple game items, each game item corresponds to multiple role identifiers, and the role identifiers are grouped according to the heat value to obtain multiple object sets, including: sorting the role identifiers of all game items according to the heat value to obtain role identifiers arranged from high to low heat values, and grouping the role identifiers arranged from high to low heat values ​​according to the upper limit set number of object sets (such as 100 groups) and the preset number of objects in the object set (such as 10) to obtain multiple object sets. Among them, for adjacent object sets, the heat value of any role identifier in the object set with an earlier arrangement order is greater than the heat value of any role identifier in the object set with a later arrangement order. Further, the above method can also include: marking each role identifier in the mapping data table according to the grouping result to characterize the grouping result corresponding to the role identifier; wherein the mapping data table is used to characterize the correspondence between the role identifier and the grouping result.

[0205] As can be seen, implementing this optional embodiment enables grouping character identifiers based on their popularity, resulting in multiple object sets ranked from most popular to least popular, which can improve retrieval efficiency. If the character identifier a user is searching for is a popular character, it is likely to be in a highly ranked object set. Based on the search order from most popular to least popular, the desired character identifier can be found more quickly, allowing the character identifier to be responded to the user more quickly, making it easier for the user to replace the vehicle logo. For popular characters, a faster response speed can be achieved.

[0206] As an optional embodiment, if the target character identifier does not belong to the character identifier set, the above method also includes: obtaining at least one related car logo corresponding to the target character identifier from the character identifier library; wherein, the at least one related car logo and the target character identifier belong to the same game project; and feeding back at least one related car logo and the target character identifier to the user device so that the user device displays the at least one related car logo and the target character identifier.

[0207] Specifically, if the target character identifier does not belong to the character identifier set, the method may further include: displaying a prompt message indicating that there is no corresponding search result (e.g., "No such character identifier"). Furthermore, after feeding back at least one relevant vehicle logo and target character identifier to the user device so that the user device displays the at least one relevant vehicle logo and target character identifier, the method may further include: the user device outputting a message prompting the user to re-enter.

[0208] In addition, at least one related car logo and the target character logo belong to the same game project. Objects in the same game project can correspond to the same game project or the same type. A game project can correspond to N objects, where N is a positive integer.

[0209] When a user searches for a character identifier corresponding to their account, it indicates their recognition and preference for the character identifier associated with their account. Displaying objects similar to the character identifier in the list of available vehicle identifiers can provide the user with more similar options, allowing the user to access a wider range of character identifiers of this type. If the character identifier the user is searching for is not the character identifier bound to the user's account, it can be determined that the user only encountered the character identifier by chance, and a tentative search can be performed. Other character identifiers under the game project to which the character identifier belongs can then be presented as related vehicle identifiers. This allows for recommendations of various objects in the game project, allowing the user to access a wider range of character identifiers.

[0210] It can be seen that the implementation of this optional embodiment can output other related objects for the user to choose when the role identification searched by the user does not correspond to his account, thereby ensuring that the user can experience the car logo change function.

[0211] As an optional embodiment, at least one relevant vehicle logo and target character identifier is fed back to the user device so that the user device displays the at least one relevant vehicle logo and target character identifier, including: sorting the at least one relevant vehicle logo in descending order according to usage popularity to obtain a sorting result; feeding back the target character identifier and the sorting result to the user device so that the user device displays the target character identifier and the sorting result; wherein the display priority of the target character identifier is higher than any relevant vehicle logo in the sorting result.

[0212] Specifically, the usage heat is used to characterize the frequency with which the relevant vehicle logo is called.

[0213] It can be seen that by implementing this optional embodiment, relevant car logos can be sorted according to usage popularity, thereby making it easier for users to find the desired character logos for selection, thereby improving the user experience.

[0214] As an optional embodiment, the list of vehicle logos to be selected also includes at least one popular object, and a list of vehicle logos to be selected is generated, which includes a target character identifier and vehicle logos that match a set of character identifiers, including: selecting at least one popular object within a unit time length (e.g., one week) according to the trigger time of the user operation used to trigger the start of the search function; wherein the trigger time is the deadline of the unit time length; generating a list of vehicle logos to be selected, which includes a target character identifier, at least one popular object, and vehicle logos that match a set of character identifiers; wherein the display priority of the target character identifier is higher than that of vehicle logos that match a set of character identifiers, and the display priority of vehicle logos that match a set of character identifiers is higher than that of at least one popular object.

[0215] Specifically, the list of vehicle logos to be selected may simultaneously display the target character identification, popular objects, and related vehicle logos, with the display priority of related vehicle logos being higher than that of popular objects.

[0216] It can be seen that the implementation of this optional embodiment can enrich the display content of the list of vehicle logos to be selected, thereby providing users with more options for selection and improving the user experience.

[0217] As an optional embodiment, after generating a list of vehicle logos to be selected that includes the target character identifier and the vehicle logo, the above method also includes: when a selection operation acting on the list of vehicle logos to be selected is detected, in response to the selection operation, triggering the user device to replace the current vehicle logo displayed in the map client for indicating the user's location in the electronic map with the vehicle logo corresponding to the selection operation.

[0218] Specifically, the current car logo may also be a character logo, which may be the character logo currently used by the user. The character logo is used to replace the navigation arrow logo in the map software to indicate the user's location.

[0219] Among them, the selection operation can be used to select multiple target character identifiers. If the user selects multiple target character identifiers, in response to the selection operation, the user device is triggered to replace the current car logo displayed in the map client for representing the user's location on the electronic map with the car logo corresponding to the selection operation, including: selecting a random object from the multiple target character identifiers, in response to the selection operation, the user device is triggered to replace the current car logo displayed in the map client for representing the user's location on the electronic map with the random object, and then replacing the random object with other random objects from the multiple target character identifiers based on a preset unit time, so as to achieve random rotation display of the multiple target character identifiers. It can be seen that when the user selects multiple objects in the list of available car logos, the car logo can be replaced with any one of the multiple objects, and the multiple objects can be rotated. This can provide the user with more diverse display content and allow the user to select multiple character identifiers that they like as car logos. In addition, no local storage space is required, which can improve the memory usage problem on the mobile terminal.

[0220] It can be seen that the implementation of this optional embodiment can replace the current car logo used by the user with other objects that the user prefers. When this application is applied to the navigation function of the map software, it can facilitate the user to change the car logo representing the user's current location at any time according to personalized needs. This can improve the user's usage experience, enhance interactivity, and increase user stickiness.

[0221] As an optional embodiment, after triggering the user device to replace the current car logo in the map client with the car logo that has the highest matching degree with the character identification set, the above method also includes: periodically obtaining environmental tags within a preset range centered on the user's location according to a preset time length; if there is a target car logo that matches the environmental tag in the matching result, the user device is triggered to replace the current car logo used to represent the user's location in the electronic map with the target car logo.

[0222] Among them, the preset duration can be a preset unit duration, such as 30s. The environmental tags within the preset range centered on the user's location are periodically obtained according to the preset duration. It can be understood that the environmental tags within the preset range centered on the user's location are obtained once every 30s. Specifically, the environmental tags within the preset range centered on the user's location can be one or more, and the environmental tags are used to characterize the environment within the preset range centered on the user's location, for example, a park, a highway, a coastal area, etc. Each car logo in the matching result corresponds to one or more tags. If the environmental tag hits a car logo in the matching result, it can be determined that there is a target car logo that matches the environmental tag in the matching result.

[0223] Specifically, if the number of target vehicle logos is greater than 1, the user device is triggered to replace the current vehicle logo used to represent the user's location in the electronic map with the target vehicle logo, including: randomly selecting a target vehicle logo from the target vehicle logos as a replacement object, thereby triggering the user device to replace the current vehicle logo used to represent the user's location in the electronic map with the replacement object; or randomly selecting the target vehicle logo with the highest popularity from the target vehicle logos as a replacement object, thereby triggering the user device to replace the current vehicle logo used to represent the user's location in the electronic map with the replacement object.

[0224] It can be seen that the implementation of this optional embodiment can achieve the periodic replacement of the vehicle logo and improve the degree of automation of the vehicle logo replacement, thereby improving the richness of the vehicle logo display.

[0225] See also Figure 9 , Figure 9 The following schematically shows a flowchart of a game character identification search process according to an embodiment of the present application. Figure 9 As shown, it includes: steps S910 to S970.

[0226] Step S910: When a user operation acting on the search area is detected, the user equipment obtains the to-be-retrieved information corresponding to the user operation.

[0227] Step S920: The user device requests the cloud server for the target game character corresponding to the information to be retrieved.

[0228] 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, step S940 is executed. If the target game character identifier does not exist in the game character identifier database, step S960 is executed. Specifically, the cloud server may construct the game character identifier database by obtaining game character identifier set 1, game character identifier set 2, ..., and game character identifier set n; where n is a positive integer, and game character identifier set 1, game character identifier set 2, ..., and game character identifier set n may be game character identifier sets from different game projects.

[0229] Step S940: The cloud server returns the target game character identifier.

[0230] Step S950: The user device replaces the current car logo displayed to indicate the user's location on the map with the target game character logo.

[0231] Step S960: The cloud server returns a prompt message indicating that there is no corresponding search result.

[0232] Step S970: The user device outputs prompting information for prompting the user to search again.

[0233] It can be seen that implementation Figure 9 The illustrated embodiment can provide the user with a role identification search function, thereby facilitating the user to change the current vehicle logo (eg, vehicle logo) used to identify the user's current location.

[0234] See also Figure 10 , Figure 10 The figure schematically shows a role identification diagram according to an embodiment of the present application. Figure 10 The multiple icons shown in the figure can represent different role identifications, and can also be used to replace the current car logo (such as the car logo used to identify the user's current location in the navigation software).

[0235] See also Figures 11-13 , Figures 11-13 The following schematically shows a navigation interface diagram according to an embodiment of the present application. Figure 11 As shown, the current car logo 1100 can be used to identify the user's current location. When the user inputs the information to be retrieved and obtains a list of selected car logos containing the target role identifier, an object can be selected from the list of selected car logos; wherein the target role identifier corresponds to the information to be retrieved. Figure 12As shown, the user device can replace the current car logo 1100 with an object 1200 selected by the user. The object 1200 can be a target character identifier in the list of car logos to be selected, or a similar object corresponding to the target character identifier, or a popular object. The embodiment of the present application can also determine a car logo set 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 select. Figure 13 As shown, if the user selects the recommended object, the object 1200 can be replaced with the object 1300, which can realize the personalized recommendation of the character identification and the function of "changing the car logo with one click".

[0236] See also Figure 14 , Figure 14 The flowchart of the object replacement method according to one embodiment of the present application is schematically shown. Figure 14 As shown, the object replacement method may include: steps S1400 to S1420.

[0237] Step S1400: When a login operation for the map client is detected, the user account input in the login operation is sent to the server.

[0238] Step S1410: receiving the vehicle logo with the highest matching degree with the role identification set corresponding to the user account fed back by the server.

[0239] Step S1420: replacing the current car logo in the map client with a car logo that has the highest matching degree with the role identification set corresponding to the user account; wherein the current car logo is used to indicate the user's location in the electronic map of the map client.

[0240] Specifically, the above method also includes: when a user operation for triggering the start of the search function is detected, obtaining the information to be retrieved corresponding to the user operation; displaying a list of vehicle logos to be selected corresponding to the information to be retrieved; wherein, the list of vehicle logos to be selected includes the target role identifier corresponding to the information to be retrieved and vehicle logos related to the role identifier set, and the role identifier set corresponds to the user account; when a selection operation is detected on the list of vehicle logos to be selected, in response to the selection operation, the current vehicle logo in the map client is replaced with the vehicle logo corresponding 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.

[0241] It can be seen that implementation Figure 14 The embodiment shown can respond to object searches, allowing users to obtain the desired target character identification based on the search, and can present a richer range of vehicle logos in the list of available vehicle logos to increase the options for users to choose from. Through the user's selection operation, the current vehicle logo can be replaced with the object selected by the user, thereby improving interactivity and enriching the user's experience.

[0242] See also Figure 15 , Figure 15 The following schematically shows a structural block diagram of an object replacement device according to an embodiment of the present application. Figure 15 As shown, the object replacement device 1500 includes: an information sending unit 1501 , a vehicle logo receiving unit 1502 and a vehicle logo replacing unit 1503 .

[0243] The information sending unit 1501 is used to send the user account input by the login operation to the server when a login operation for the map client is detected;

[0244] The vehicle logo receiving unit 1502 is configured to receive the vehicle logo with the highest matching degree with the role identification set corresponding to the user account fed back by the server;

[0245] The car logo replacing unit 1503 is used to replace the current car logo in the map client with a car logo that has the highest matching degree with the role identification set corresponding to the user account; wherein the current car logo is used to indicate the user's location in the electronic map of the map client.

[0246] It can be seen that implementation Figure 15 The embodiment shown can respond to object searches, allowing users to obtain the desired target character identification based on the search, and can present a richer range of vehicle logos in the list of available vehicle logos to increase the options for users to choose from. Through the user's selection operation, the current vehicle logo can be replaced with the object selected by the user, thereby improving interactivity and enriching the user's experience.

[0247] See also Figure 16 , Figure 16 The flowchart of the vehicle logo replacement method applied to the map client according to one embodiment of the present application is schematically shown. Figure 16 As shown, the vehicle logo replacement method applied to the map client includes: steps S1600 to S1624.

[0248] Step S1600: The server obtains behavioral features related to the user account; wherein the behavioral features are related to the role identification set, and the behavioral features include at least one of object purchase features, object collection features, object click features, object download cancellation features, object usage features, and object switching features.

[0249] Step S1602: The server determines the label features corresponding to all objects according to the behavior features; wherein all objects include a set of role identifiers corresponding to user accounts, and the label features are used to characterize object call situations.

[0250] Step S1604: The server determines the adjacent behavior features corresponding to the first adjacent moment based on the moment corresponding to the behavior features in the time series, and calculates a first differential result based on the behavior features and the adjacent behavior features, and determines the adjacent label features corresponding to the second adjacent moment based on the moment corresponding to the label features in the time series, and calculates a second differential result based on the label features and the adjacent label features, and then converts the first differential result into a coded feature vector, and fuses the coded feature vector and the second differential result to obtain sample data.

[0251] Step S1606: The sample data consists of training samples and test samples. The server trains the recommendation model through the training samples, tests the trained recommendation model through the test samples, and adjusts the model parameters corresponding to the trained recommendation model according to the test results.

[0252] Step S1608: The server predicts the multi-category scores of all objects through the recommendation model. The multi-category scores are used to represent the probability of the object being selected by the user, and then the multi-category scores of all objects are simplified into binary scores, where the number of scores in the multi-category scores is greater than the number of scores in the binary scores. The set of recommended car logos corresponding to the user account is then determined based on the binary scores.

[0253] Step S1610: The server obtains the role identification set corresponding to the user account, obtains a matching result including multiple matching degrees based on the matching degree between each car logo in the recommended car logo set and the role identification set, and determines the car logo corresponding to the role identification set based on the matching result.

[0254] Step S1612: When the server receives the search request, it obtains the to-be-searched information corresponding to the search request.

[0255] 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 object name, object identifier, object usage count, usage duration, and online duration; and groups the role identifiers according to the popularity value to obtain multiple object sets.

[0256] Step S1616: The server performs information query on multiple object sets in sequence according to the information to be retrieved, and obtains query results corresponding to the multiple object sets respectively; wherein the multiple object sets correspond to different popularity, and the multiple object sets are arranged in descending order based on popularity.

[0257] Step S1618: If a query result matching the information to be retrieved exists, the server determines the object set 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 object set. If the target role identifier belongs to the role identifier set, step S1620 is executed. If the target role identifier does not belong to the role identifier set, step S1622 is executed.

[0258] Step S1620: The server generates a list of to-be-selected vehicle logos containing the target character identifier and the vehicle logo; wherein the list of to-be-selected vehicle logos includes the target character identifier and the vehicle logo corresponding to the target character identifier.

[0259] Step S1622: The server obtains at least one related car logo corresponding to the target character identifier from the character identifier library, and sorts the at least one related car logo in descending order according to the usage popularity to obtain a sorting result, and then feeds back a list of to-be-selected car logos including the target character identifier and the sorting result to the user device, so that the user device displays the list of to-be-selected car logos; wherein, the display priority of the target character identifier is higher than any related car logo in the sorting result, and at least one related car logo and the target character identifier belong to the same game project.

[0260] Step S1624: When the user device detects a selection operation on the list of vehicle logos to be selected, the user device may replace the current vehicle logo displayed in the map client for indicating the user's location in the electronic map with the vehicle logo corresponding to the selection operation in response to the selection operation.

[0261] It should be noted that steps S1600 to S1624 are Figure 3 The steps shown correspond to their embodiments. For the specific implementation of steps S1600 to S1624, please refer to Figure 3 The steps and embodiments shown are not described in detail here.

[0262] It can be seen that implementation Figure 16 The method shown can match a relevant car logo to the role ID corresponding to the user account. When the user selects a role ID, both the role ID and the car logo are displayed, providing the user with a richer selection of options. The user can scroll through the role IDs and car logos to select a personalized role ID to replace the current car logo, enhancing interactivity. Furthermore, because the matched car logo is associated with the role ID of the user account, the effectiveness and personalization of the displayed car logo can be improved.

[0263] Furthermore, in this exemplary embodiment, a vehicle logo replacement device applied to a map client is also provided. Figure 17As shown, the vehicle logo replacement device 1700 applied to the map client may include: a vehicle logo determination unit 1701 to be recommended, a matching degree calculation unit 1702, a vehicle logo determination unit 1703 and a vehicle logo replacement unit 1704, wherein:

[0264] The vehicle logo determination unit 1701 is configured to obtain a set of role identifiers corresponding to a user account and determine a set of vehicle logos to be recommended based on behavioral characteristics of the user account;

[0265] A matching degree calculation unit 1702 is used to determine the matching degree between each vehicle logo in the set of vehicle logos to be recommended and the set of character identifiers, and obtain a matching result including multiple matching degrees;

[0266] A vehicle logo determining unit 1703 is configured to determine the vehicle logo having the highest matching degree with the character identification set according to the matching result;

[0267] The vehicle logo replacing unit 1704 is used to trigger the user device to replace the current vehicle logo in the map client with a vehicle logo that has the highest matching degree with the role identification set; wherein the current vehicle logo is used to indicate the user's location in the electronic map of the map client.

[0268] It can be seen that implementation Figure 17 The device shown can determine a personalized set of recommended car logos based on the behavioral characteristics of a user account. It then determines a suitable car logo for the user based on the degree of match between the recommended car logo set and a set of character identifiers, enabling personalized automatic logo replacement. Furthermore, this system can prevent traffic accidents caused by users manually changing the car logo while driving, achieving automated logo replacement and improving driving safety.

[0269] In an exemplary embodiment of the present application, the apparatus further includes:

[0270] an information acquisition unit (not shown), configured to acquire the information to be retrieved in the retrieval request upon receiving the retrieval request;

[0271] An identification query unit (not shown) is used to query the target role identification corresponding to the information to be retrieved. If the target role identification belongs to the role identification set, a list of vehicle logos to be selected is generated, which includes the target role identification and vehicle logos that match the role identification set, and is fed back to the user device so that the user device displays the list of vehicle logos to be selected; wherein, the list of vehicle logos to be selected corresponds to the user account.

[0272] In an exemplary embodiment of the present application, the apparatus further includes:

[0273] a vehicle logo acquisition unit (not shown), configured to acquire, when the target character identifier does not belong to the character identifier set, at least one related vehicle logo corresponding to the target character identifier from a character identifier library; wherein the at least one related vehicle logo and the target character identifier belong to the same game item;

[0274] The vehicle logo replacing unit 1704 is further configured to feed back at least one relevant vehicle logo and target character identifier to the user equipment, so that the user equipment displays the at least one relevant vehicle logo and target character identifier.

[0275] It can be seen that the implementation of this optional embodiment can output other related objects for the user to choose when the role identification searched by the user does not correspond to his account, thereby ensuring that the user can experience the car logo change function.

[0276] In an exemplary embodiment of the present application, the object acquisition unit feeds back at least one relevant vehicle logo and target character identifier to the user device, so that the user device displays the at least one relevant vehicle logo and target character identifier, including:

[0277] Sorting the at least one related vehicle logo in descending order according to usage popularity to obtain a sorting result;

[0278] The target character identifier and the ranking result are fed back to the user device, so that the user device displays the target character identifier and the ranking result; wherein the display priority of the target character identifier is higher than any related vehicle logo in the ranking result.

[0279] It can be seen that by implementing this optional embodiment, relevant car logos can be sorted according to usage popularity, thereby making it easier for users to find the desired character logos for selection, thereby improving the user experience.

[0280] In an exemplary embodiment of the present application, the list of vehicle logos to be selected further includes at least one popular object, and the vehicle logo replacement unit generates a list of vehicle logos to be selected that includes the target character identifier and vehicle logos that match the set of character identifiers, including:

[0281] Selecting at least one popular object within a unit time period according to a trigger time of a user operation for triggering the start of a search function; wherein the trigger time is a deadline of the unit time period;

[0282] generating a list of to-be-selected vehicle logos comprising the target character identifier, the at least one popular object, and vehicle logos matching the set of character identifiers;

[0283] The target character identifier has a higher display priority than the vehicle logos that match the character identifier set, and the vehicle logos that match the character identifier set have a higher display priority than the at least one popular object.

[0284] It can be seen that the implementation of this optional embodiment can enrich the display content of the list of vehicle logos to be selected, thereby providing users with more options for selection and improving the user experience.

[0285] In an exemplary embodiment of the present application, the vehicle logo replacement unit 1704 queries the target role identifier corresponding to the information to be retrieved, including:

[0286] Determine the label corresponding to the information to be retrieved;

[0287] Selecting a target set from a set of objects corresponding to different labels according to the labels;

[0288] The object that matches the information to be retrieved is called from the target collection as the target role identifier.

[0289] It can be seen that by implementing this optional embodiment, the collection can be filtered first and then the objects in the collection can be matched, thereby improving the efficiency of object query.

[0290] In an exemplary embodiment of the present application, the vehicle logo replacement unit determines the label corresponding to the information to be retrieved, including:

[0291] Perform keyword extraction on the information to be retrieved to obtain extraction results;

[0292] If the extraction result indicates that the keyword exists in the information to be retrieved, the tag corresponding to the keyword is determined as the tag of the information to be retrieved.

[0293] It can be seen that implementing this optional embodiment can improve query efficiency and query accuracy by extracting keywords from the information to be retrieved.

[0294] In an exemplary embodiment of the present application, the vehicle logo replacement unit is further configured to display a prompt message indicating a search failure and at least one hot search word when the extraction result indicates that the keyword does not exist in the information to be retrieved;

[0295] The above device also includes:

[0296] an object set determining unit (not shown), configured to, upon receiving a user operation acting on a target hot word among at least one search hot word, determine a specific set to which the target hot word belongs from object sets corresponding to different tags;

[0297] The object calling unit (not shown) is used to call an object matching the target hot word from a specific set as a role identifier for replacing the current car logo.

[0298] It can be seen that implementing this optional embodiment can provide users with other options when there are no search results, thereby improving the user's usage experience.

[0299] In an exemplary embodiment of the present application, if the number of role identifiers is greater than 2, the vehicle logo replacement unit 1704 searches for a target role identifier corresponding to the information to be retrieved, including:

[0300] Group all role identifiers to obtain multiple object sets;

[0301] Performing information queries on multiple object sets in sequence according to the information to be retrieved, and obtaining query results corresponding to the multiple object sets respectively; wherein the multiple object sets correspond to different popularity, and the multiple object sets are arranged in descending order based on popularity;

[0302] If there is a query result that hits the information to be retrieved, the object set corresponding to the query result that hits the information to be retrieved is determined, so as to call the target role identifier corresponding to the information to be retrieved from the corresponding object set.

[0303] It can be seen that by implementing this optional embodiment, the query result corresponding to each set can be determined by performing object search on each set. The query result that is not empty is the required query result, which can locate the target role identifier more quickly and improve query efficiency.

[0304] In an exemplary embodiment of the present application, the vehicle logo replacement unit 1704 groups all role identifiers to obtain multiple object sets, including:

[0305] Calculating the popularity values ​​of all role identifiers based on the object information; wherein the object information includes at least one of the object name, object identifier, object usage count, usage duration, and online duration;

[0306] All role identifiers are grouped according to their heat values ​​to obtain multiple object sets.

[0307] As can be seen, implementing this optional embodiment enables grouping character identifiers based on their popularity, resulting in multiple object sets ranked from most popular to least popular, which can improve retrieval efficiency. If the character identifier a user is searching for is a popular character, it is likely to be in a highly ranked object set. Based on the search order from most popular to least popular, the desired character identifier can be found more quickly, allowing the character identifier to be responded to the user more quickly, making it easier for the user to replace the vehicle logo. For popular characters, a faster response speed can be achieved.

[0308] In an exemplary embodiment of the present application, the to-be-recommended car logo determining unit 1701 determines a to-be-recommended car logo set corresponding to the user account based on the behavioral characteristics of the user account, including:

[0309] Training a recommendation model based on the behavioral characteristics of the user account;

[0310] The set of vehicle logos to be recommended is determined according to the trained recommendation model.

[0311] It can be seen that by implementing this optional embodiment, objects that need to be recommended to users can be determined through the recommendation model, thereby enriching the options and improving the user experience.

[0312] In an exemplary embodiment of the present application, the vehicle logo to be recommended determining unit 1701 trains a recommendation model based on the behavioral characteristics of the user account, including:

[0313] Obtaining a behavior feature associated with the user account; wherein the behavior feature is associated with the role identifier set, and the behavior feature includes at least one of an object purchase feature, an object collection feature, an object click feature, an object download cancellation feature, an object usage feature, and an object switching feature;

[0314] Determine label features corresponding to all objects according to the behavior features; wherein all objects include a set of role identifiers corresponding to the user account, and the label features are used to characterize object call conditions;

[0315] generating sample data according to the behavior characteristics and the label characteristics;

[0316] The recommendation model is trained using the sample data.

[0317] It can be seen that the implementation of this optional embodiment can characterize the user based on the determined behavioral characteristics and label characteristics, thereby generating sample data based on the behavioral characteristics and label characteristics. By training the recommendation model with the sample data, the recommendation effect of the recommendation model can be improved, and the role identification personalized recommended for the user can be more in line with the user's expectations, thereby increasing the probability of the role identification being selected by the user.

[0318] In an exemplary embodiment of the present application, generating sample data according to behavioral features and label features includes:

[0319] Determining adjacent behavior features corresponding to a first adjacent moment based on the moment corresponding to the behavior feature in the time series, and calculating a first difference result based on the behavior feature and the adjacent behavior features;

[0320] Determine an adjacent label feature corresponding to a second adjacent moment based on the moment corresponding to the label feature in the time series, and calculate a second difference result based on the label feature and the adjacent label feature;

[0321] Sample data is generated according to the first difference result and the second difference result.

[0322] It can be seen that the implementation of this optional embodiment can characterize the time-series differences between behavioral features and label features through the calculation of feature results, and then generate sample data for training the recommendation model. In this way, a more accurate recommendation model can be trained to improve the degree of personalization and recommend personalized role identifications to different users, thereby improving the user experience and increasing user stickiness.

[0323] In an exemplary embodiment of the present application, generating sample data according to the first difference result and the second difference result includes:

[0324] Convert the first difference result into an encoded feature vector;

[0325] The encoded feature vector and the second difference result are fused to obtain the sample data.

[0326] It can be seen that the implementation of this optional embodiment can improve the classification ability of the differential results by processing the differential results, and then obtain sample data for training the recommendation model by fusing the differential results, so as to improve the recommendation effect of the recommendation model.

[0327] In an exemplary embodiment of the present application, the to-be-recommended vehicle logo determining unit 1701 determines a to-be-recommended vehicle logo set based on a trained recommendation model, including:

[0328] The recommendation model is used to predict the multi-classification scores of all objects. The multi-classification scores are used to represent the probability of the object being selected by the user.

[0329] Simplify the multi-classification scores of all objects into binary scores; the number of scores in the multi-classification scores is greater than the number of scores in the binary scores;

[0330] Determine the set of recommended car logos corresponding to the user account based on the binary classification score.

[0331] It can be seen that the implementation of this optional embodiment can convert the multi-classification prediction results into binary classification results on the basis of converting the original binary classification algorithm model into a multi-classification algorithm model, thereby improving the model prediction accuracy.

[0332] In an exemplary embodiment of the present application, the sample data consists of training samples and test samples. The vehicle logo determination unit 1701 trains a recommendation model using the sample data, including:

[0333] Train the recommendation model through training samples;

[0334] The trained recommendation model is tested through test samples, and the model parameters corresponding to the trained recommendation model are adjusted according to the test results.

[0335] It can be seen that by implementing this optional embodiment, the model can be further optimized through testing of the model, thereby improving the prediction effect of the model.

[0336] It should be noted that, although several modules or units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiment of the application, the features and functions of two or more modules or units described above can be concretized in one module or unit. On the contrary, the features and functions of one module or unit described above can be further divided into multiple modules or units to be concretized.

[0337] In an exemplary embodiment of the present application, the apparatus further includes:

[0338] An environmental tag acquisition unit (not shown) is configured to periodically acquire environmental tags within a preset range centered on the user's location based on a preset time period 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 best matches the set of character identifiers.

[0339] The vehicle logo replacing unit 1704 is specifically configured to trigger the user device to replace the current vehicle logo indicating the user's location in the electronic map with the target vehicle logo when there is a target vehicle logo matching the environment tag in the matching result.

[0340] It can be seen that the implementation of this optional embodiment can achieve the periodic replacement of the vehicle logo and improve the degree of automation of the vehicle logo replacement, thereby improving the richness of the vehicle logo display.

[0341] Since the various functional modules of the vehicle logo replacement device applied to a map client in the example embodiment of the present application correspond to the steps of the example embodiment of the vehicle logo replacement method applied to a map client, for details not disclosed in the embodiment of the device of the present application, please refer to the embodiment of the vehicle logo replacement method applied to a map client in the above-mentioned present application.

[0342] As another aspect, the present application further provides a computer-readable medium, which may be included in the electronic device described in the above embodiments, or may exist independently without being incorporated into the electronic device. The computer-readable medium carries one or more programs, and when the one or more programs are executed by the electronic device, the electronic device implements the method described in the above embodiments.

[0343] 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. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination of the above. More specific examples of computer-readable storage media can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this application, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device, 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, which carries computer-readable program code. This propagated data signal can take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. Program code embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wireline, optical fiber cable, RF, or any suitable combination thereof.

[0344] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the above-mentioned module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of the boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0345] The units involved in the embodiments described in this application may be implemented by software or hardware, and the units described may also be set in a processor. In some cases, the names of these units do not constitute limitations on the units themselves.

[0346] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0347] It should be understood that the present application is not limited to the exact structures described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A vehicle logo replacement method applied to a map client, characterized in that: include: Obtaining a set of role identifiers associated with a user account, and determining a set of vehicle logos to be recommended based on behavioral characteristics of the user account; The user account includes a game account of the current user, and the role identifier in the role identifier set is at least one game role identifier related to the game account; Determining a matching degree between each vehicle logo in the set of vehicle logos to be recommended and the set of character identifiers, and obtaining a matching result including multiple matching degrees; Selecting the vehicle logo with the highest matching degree with the character identification set according to the matching results; The user device is triggered to replace the current car logo in the map client with a car logo that has the highest matching degree with the role identification set; wherein the current car logo is used to indicate the user's location in the electronic map of the map client.

2. The method according to claim 1, characterized in that The method further comprises: When a search request is received, obtaining the information to be searched in the search request; Query the target role identifier corresponding to the information to be retrieved. If the target role identifier belongs to the role identifier set, generate a list of vehicle logos to be selected that includes the target role identifier and vehicle logos that match the role identifier set and feed it back to the user device, so that the user device displays the list of vehicle logos to be selected; wherein, the list of vehicle logos to be selected corresponds to the user account.

3. The method according to claim 2, characterized in that If the target role identifier does not belong to the role identifier set, the method further includes: Acquire at least one related vehicle logo corresponding to the target character logo from a character logo library; wherein the at least one related vehicle logo and the target character logo belong to the same game project; The at least one relevant vehicle logo and the target character identifier are fed back to the user equipment, so that the user equipment displays the at least one relevant vehicle logo and the target character identifier.

4. The method according to claim 3, characterized in that Feeding back the at least one relevant vehicle logo and the target character identifier to a user device so that the user device displays the at least one relevant vehicle logo and the target character identifier includes: Sorting the at least one related vehicle logo in descending order according to usage popularity to obtain a sorting result; The target character identifier and the ranking result are fed back to the user device, so that the user device displays the target character identifier and the ranking result; wherein the display priority of the target character identifier is higher than any related vehicle logo in the ranking result.

5. The method according to claim 2, characterized in that The list of vehicle logos to be selected further includes at least one popular object, and generating a list of vehicle logos to be selected that includes the target character identifier and vehicle logos that match the set of character identifiers includes: Selecting at least one popular object within a unit time period according to a trigger time of a user operation for triggering the start of a search function; wherein the trigger time is a deadline of the unit time period; generating a list of to-be-selected vehicle logos comprising the target character identifier, the at least one popular object, and vehicle logos matching the set of character identifiers; The target character identifier has a higher display priority than the vehicle logos that match the character identifier set, and the vehicle logos that match the character identifier set have a higher display priority than the at least one popular object.

6. The method according to claim 2, characterized in that Querying the target role identifier corresponding to the information to be retrieved includes: Determining a tag corresponding to the information to be retrieved; Selecting a target set from a set of objects corresponding to different labels according to the labels; An object matching the information to be retrieved is called from the target set as the target role identifier.

7. The method according to claim 6, characterized in that Determining the tag corresponding to the information to be retrieved includes: Perform keyword extraction on the information to be retrieved to obtain extraction results; If the extraction result indicates that a keyword exists in the information to be retrieved, a tag corresponding to the keyword is determined as a tag of the information to be retrieved.

8. The method according to claim 7, characterized in that The method further comprises: If the extraction result indicates that the keyword does not exist in the information to be retrieved, displaying a prompt message indicating that the search failed and at least one hot search word; When a user operation acting on a target hot word among the at least one search hot word is received, determining a specific set to which the target hot word belongs from object sets corresponding to different tags; An object matching the target hot word is called from the specific set as a role identifier for replacing the current vehicle logo.

9. The method according to claim 2, characterized in that If the number of role identifiers is greater than 2, query the target role identifier corresponding to the information to be retrieved, including: Group all role identifiers to obtain multiple object sets; Performing information queries on the multiple object sets in sequence according to the information to be retrieved, and obtaining query results corresponding to the multiple object sets respectively; wherein the multiple object sets correspond to different popularity, and the multiple object sets are arranged in descending order based on popularity; If there is a query result that hits the information to be retrieved, then the object set corresponding to the query result that hits the information to be retrieved is determined, so as to call the target role identifier corresponding to the information to be retrieved from the corresponding object set.

10. The method according to claim 9, characterized in that Group all role identifiers to obtain multiple object sets, including: Calculating the popularity values ​​of all the role identifiers according to the object information; wherein the object information includes at least one of the object name, object identifier, object usage count, usage duration, and online duration; All the role identifiers are grouped according to the heat values ​​to obtain the multiple object sets.

11. The method according to claim 1, characterized in that Determining a set of vehicle logos to be recommended corresponding to the user account based on the behavioral characteristics of the user account includes: Training a recommendation model based on the behavioral characteristics of the user account; The set of vehicle logos to be recommended is determined according to the trained recommendation model.

12. The method according to claim 11, characterized in that Training a recommendation model based on the behavioral characteristics of the user account includes: Obtaining a behavior feature associated with the user account; wherein the behavior feature is associated with the role identifier set, and the behavior feature includes at least one of an object purchase feature, an object collection feature, an object click feature, an object download cancellation feature, an object usage feature, and an object switching feature; Determine label features corresponding to all objects according to the behavior features; wherein all objects include a set of role identifiers corresponding to the user account, and the label features are used to characterize object call conditions; generating sample data according to the behavior characteristics and the label characteristics; The recommendation model is trained using the sample data.

13. The method according to claim 12, characterized in that Generating sample data according to the behavior feature and the label feature includes: Determining an adjacent behavior feature corresponding to a first adjacent moment based on the moment corresponding to the behavior feature in the time series, and calculating a first difference result based on the behavior feature and the adjacent behavior feature; Determining an adjacent label feature corresponding to a second adjacent moment based on the moment corresponding to the label feature in the time series, and calculating a second difference result based on the label feature and the adjacent label feature; The sample data is generated according to the first difference result and the second difference result.

14. The method according to claim 13, wherein: Generating the sample data according to the first difference result and the second difference result includes: Converting the first difference result into an encoded feature vector; The encoded feature vector and the second difference result are fused to obtain the sample data.

15. The method according to claim 11, characterized in that Determining the set of vehicle logos to be recommended based on the trained recommendation model includes: Predicting multi-category scores for all objects using the recommendation model, where the multi-category scores are used to represent the probability of the objects being selected by the user; Simplifying the multi-category scores of all the objects into binary scores; wherein the number of scores in the multi-category scores is greater than the number of scores in the binary scores; The set of vehicle logos to be recommended is determined according to the binary classification scores.

16. The method according to claim 1, wherein After triggering 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 character identification set, the method further includes: Periodically obtain environmental tags within a preset range centered on the user's location based on a preset duration; If there is a target vehicle logo matching the environment tag in the matching result, the user device is triggered to replace the current vehicle logo representing the user's location in the electronic map with the target vehicle logo.

17. A vehicle logo replacement method applied to 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; receiving a vehicle logo having the highest degree of matching with the role identification set corresponding to the user account fed back by the server; the received vehicle logo being the vehicle logo having the highest degree of matching with the role identification set in a set of vehicle logos to be recommended determined based on the behavioral characteristics of the user account; The user account includes a game account of the current user, and the role identifier in the role identifier set is at least one game role identifier related to the game account; The current car logo in the map client is replaced with a car logo that has the highest matching degree with the role identification set corresponding to the user account; wherein the current car logo is used to indicate the user's location in the electronic map of the map client.

18. The method according to claim 17, characterized in that The method further comprises: When a user operation for triggering the start of the search function is detected, obtaining the to-be-retrieved information corresponding to the user operation; Displaying a list of vehicle logos to be selected corresponding to the information to be retrieved; wherein the list of vehicle logos to be selected includes a target role identifier corresponding to the information to be retrieved and vehicle logos related to the role identifier set, wherein the role identifier set corresponds to the user account; When a selection operation is detected on the list of vehicle logos to be selected, the current vehicle logo in the map client is replaced with the vehicle logo corresponding to the selection operation in response to the selection operation; wherein the current vehicle logo is used to indicate the user's location in the electronic map of the map client.

19. A vehicle logo replacement device applied to a map client, characterized in that: include: a vehicle logo determination unit to be recommended, configured to obtain a set of role identifiers associated with a user account and determine a set of vehicle logos to be recommended based on behavioral characteristics of the user account; The user account includes a game account of the current user, and the role identifier in the role identifier set is at least one game role identifier related to the game account; a matching degree calculation unit, configured to determine a matching degree between each vehicle logo in the set of vehicle logos to be recommended and the set of character identifiers, and obtain a matching result including a plurality of matching degrees; a vehicle logo determination unit, configured to select a vehicle logo having the highest matching degree with the set of character identifiers based on the matching result; The vehicle logo replacement unit is used to trigger the user device to replace the current vehicle logo in the map client with a vehicle logo that has the highest matching degree with the role identification set; wherein the current vehicle logo is used to indicate the user's location in the electronic map of the map client.

20. An electronic device, characterized in that: include: processor; as well as a memory for storing executable instructions of the processor; The processor is configured to perform the method according to any one of claims 1 to 17 by executing the executable instructions.

21. A computer program product, characterized in that The method comprises computer instructions, which, when executed by a processor of a computer device, cause the computer device to perform the method according to any one of claims 1 to 17.

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