CN114925154A

CN114925154A - Map generation method, map generation device, electronic device, map generation medium, and computer program product

Info

Publication number: CN114925154A
Application number: CN202210593232.7A
Authority: CN
Inventors: 慎东辉
Original assignee: Apollo Zhilian Beijing Technology Co Ltd
Current assignee: Apollo Zhilian Beijing Technology Co Ltd
Priority date: 2022-05-27
Filing date: 2022-05-27
Publication date: 2022-08-19

Abstract

The present disclosure provides a method, an apparatus, an electronic device, a computer-readable storage medium, and a computer program product for generating a map, which relate to computer technologies, and in particular, to the fields of intelligent transportation, internet of vehicles, automatic driving, and intelligent cockpit. The implementation scheme is as follows: responding to a request of a user, and acquiring first related information aiming at the user, wherein the first related information comprises the current position of the user; acquiring one or more target interest points related to first related information based on a user image of the user, wherein the user image comprises one or more interest point types and one or more associated position information of the user; a target map is generated that includes the one or more target points of interest.

Description

Map generation method, map generation device, electronic equipment, map generation medium and computer program product

Technical Field

The present disclosure relates to computer technologies, and in particular, to the fields of intelligent transportation, car networking, automatic driving, and intelligent cockpit, and in particular, to a method and an apparatus for generating a map, an electronic device, a computer-readable storage medium, and a computer program product.

Background

When a user opens a map client, a default Point of Interest (POI) usually occupies a larger part in a map base map, and therefore has a larger influence on the intuitive feeling and the objective interaction of the user.

The map client is usually provided with different scales, and displays default interest points on the base map according to a preset priority display strategy under the condition that different rendering entities collide. For the user, such a setting requires the user to perform sliding and/or zooming operations on the base map a large number of times to find the target position and the displayed information. In addition, when a user has a specific search requirement, the user needs to input text and the like to find an accurate interest point on the base map, which has a certain cost and influence on the user experience.

The approaches described in this section are not necessarily approaches that have been previously conceived or pursued. Unless otherwise indicated, it should not be assumed that any of the approaches described in this section qualify as prior art merely by virtue of their inclusion in this section. Similarly, unless otherwise indicated, the problems mentioned in this section should not be considered as having been acknowledged in any prior art.

Disclosure of Invention

The present disclosure provides a method, an apparatus, an electronic device, a computer-readable storage medium, and a computer program product for generating a map.

According to an aspect of the present disclosure, there is provided a method of generating a map, the map including a base map, the method including: responding to a request of a user, and acquiring first related information aiming at the user, wherein the first related information comprises the current position of the user; acquiring one or more target interest points related to first related information based on a user image of the user, wherein the user image comprises one or more interest point types and one or more associated position information of the user; a target map is generated that includes the one or more target points of interest.

According to another aspect of the present disclosure, there is provided an apparatus for generating a map, the apparatus including: the device comprises a first acquisition module, a second acquisition module and a display module, wherein the first acquisition module is configured to respond to a request of a user and acquire first related information aiming at the user, and the first related information comprises a current position where the user is located; a second obtaining module configured to obtain one or more target points of interest related to first related information based on a user representation of the user, the user representation including one or more point of interest types and one or more associated location information of the user; and a generation module configured to generate a target map including the one or more target points of interest.

According to another aspect of the present disclosure, there is provided an electronic device including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the method according to the above.

According to another aspect of the present disclosure, there is provided a non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform the method according to the above.

According to another aspect of the present disclosure, a computer program product is provided, comprising a computer program, wherein the computer program, when executed by a processor, implements the method according to the above.

According to one or more embodiments of the present disclosure, interest points matching an individual user can be automatically generated for display on a map base map based on a user image of the user, which provides a personalized map generation method for the individual user and improves the user's interaction and use experience compared to the conventional way of displaying default interest points on a map base map.

It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present disclosure, nor do they limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the embodiments and, together with the description, serve to explain the exemplary implementations of the embodiments. The illustrated embodiments are for purposes of illustration only and do not limit the scope of the claims. Throughout the drawings, identical reference numbers designate similar, but not necessarily identical, elements.

FIG. 1 illustrates a schematic diagram of an exemplary system in which various methods described herein may be implemented, according to an embodiment of the present disclosure;

FIG. 2 shows a flow diagram of a method of generating a map according to an embodiment of the present disclosure;

FIG. 3 illustrates a flow chart of a method of obtaining one or more target points of interest associated with a current location in accordance with an embodiment of the present disclosure;

FIG. 4 illustrates a flow diagram of a method of obtaining one or more target points of interest related to a current location and a current time in accordance with an embodiment of the present disclosure;

FIG. 5 illustrates a flow diagram of another method of obtaining one or more target points of interest related to a current location in accordance with an embodiment of the present disclosure;

FIG. 6 illustrates a flow diagram of a method of obtaining a user representation of a user in accordance with an embodiment of the present disclosure;

FIG. 7 illustrates a block diagram of a map application underlying framework in accordance with an embodiment of the present disclosure;

fig. 8 is a block diagram illustrating a structure of an apparatus for generating a map according to an embodiment of the present disclosure; and

FIG. 9 illustrates a block diagram of an exemplary electronic device that can be used to implement embodiments of the present disclosure.

Detailed Description

Exemplary embodiments of the present disclosure are described below with reference to the accompanying drawings, in which various details of the embodiments of the disclosure are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope of the present disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

In the present disclosure, unless otherwise specified, the use of the terms "first", "second", and the like to describe various elements is not intended to limit the positional relationship, the temporal relationship, or the importance relationship of the elements, and such terms are used only to distinguish one element from another. In some examples, a first element and a second element may refer to the same instance of the element, and in some cases, based on the context, they may also refer to different instances.

The terminology used in the description of the various described examples in this disclosure is for the purpose of describing the particular examples only and is not intended to be limiting. Unless the context clearly indicates otherwise, if the number of elements is not specifically limited, the element may be one or a plurality of. Furthermore, the term "and/or" as used in this disclosure is intended to encompass any and all possible combinations of the listed items.

Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

Fig. 1 illustrates a schematic diagram of an exemplary system 100 in which various methods and apparatus described herein may be implemented in accordance with embodiments of the present disclosure. Referring to fig. 1, the system 100 includes one or

more client devices

101, 102, 103, 104, 105, and 106, a server 120, and one or more communication networks 110 coupling the one or more client devices to the server 120.

Client devices

101, 102, 103, 104, 105, and 106 may be configured to execute one or more application programs (e.g., a mapping application).

In embodiments of the present disclosure, the server 120 may run one or more services or software applications that enable the method of generating a map to be performed.

In some embodiments, the server 120 may also provide other services or software applications that may include non-virtual environments and virtual environments. In certain embodiments, these services may be provided as web-based services or cloud services, for example, provided to users of

client devices

101, 102, 103, 104, 105, and/or 106 under a software as a service (SaaS) model.

In the configuration shown in fig. 1, server 120 may include one or more components that implement the functions performed by server 120. These components may include software components, hardware components, or a combination thereof, which may be executed by one or more processors. A user operating a

client device

101, 102, 103, 104, 105, and/or 106 may, in turn, utilize one or more client applications to interact with the server 120 to take advantage of the services provided by these components. It should be understood that a variety of different system configurations are possible, which may differ from system 100. Accordingly, fig. 1 is one example of a system for implementing the various methods described herein and is not intended to be limiting.

A user may use

client devices

101, 102, 103, 104, 105, and/or 106 for location searching and/or destination navigation. The client device may provide an interface that enables a user of the client device to interact with the client device. The client device may also output information to the user via the interface. Although fig. 1 depicts only six client devices, those skilled in the art will appreciate that any number of client devices may be supported by the present disclosure.

Client devices

101, 102, 103, 104, 105, and/or 106 may include various types of computer devices, such as portable handheld devices, general purpose computers (such as personal computers and laptops), workstation computers, wearable devices, smart screen devices, self-service terminal devices, service robots, gaming systems, thin clients, various messaging devices, sensors or other sensing devices, and so forth. These computer devices may run various types and versions of software applications and operating systems, such as MICROSOFT Windows, APPLE iOS, UNIX-like operating systems, Linux, or Linux-like operating systems (e.g., GOOGLE Chrome OS); or include various Mobile operating systems such as MICROSOFT Windows Mobile OS, iOS, Windows Phone, Android. Portable handheld devices may include cellular telephones, smart phones, tablets, Personal Digital Assistants (PDAs), and the like. Wearable devices may include head-mounted displays (such as smart glasses) and other devices. The gaming system may include a variety of handheld gaming devices, internet-enabled gaming devices, and the like. The client device is capable of executing a variety of different applications, such as various Internet-related applications, communication applications (e.g., email applications), Short Message Service (SMS) applications, and may use a variety of communication protocols.

Network 110 may be any type of network known to those skilled in the art that may support data communications using any of a variety of available protocols, including but not limited to TCP/IP, SNA, IPX, etc. Merely by way of example, one or more networks 110 may be a Local Area Network (LAN), an ethernet-based network, a token ring, a Wide Area Network (WAN), the internet, a virtual network, a Virtual Private Network (VPN), an intranet, an extranet, a Public Switched Telephone Network (PSTN), an infrared network, a wireless network (e.g., bluetooth, WIFI), and/or any combination of these and/or other networks.

The server 120 may include one or more general purpose computers, special purpose server computers (e.g., PC (personal computer) servers, UNIX servers, mid-end servers), blade servers, mainframe computers, server clusters, or any other suitable arrangement and/or combination. The server 120 may include one or more virtual machines running a virtual operating system, or other computing architecture involving virtualization (e.g., one or more flexible pools of logical storage that may be virtualized to maintain virtual storage for the server). In various embodiments, the server 120 may run one or more services or software applications that provide the functionality described below.

The computing units in server 120 may run one or more operating systems including any of the operating systems described above, as well as any commercially available server operating systems. The server 120 may also run any of a variety of additional server applications and/or middle tier applications, including HTTP servers, FTP servers, CGI servers, JAVA servers, database servers, and the like.

In some implementations, the server 120 may include one or more applications to analyze and consolidate data feeds and/or event updates received from users of the

client devices

101, 102, 103, 104, 105, and/or 106. Server 120 may also include one or more applications to display data feeds and/or real-time events via one or more display devices of

client devices

101, 102, 103, 104, 105, and/or 106.

In some embodiments, the server 120 may be a server of a distributed system, or a server incorporating a blockchain. The server 120 may also be a cloud server, or a smart cloud computing server or a smart cloud host with artificial intelligence technology. The cloud Server is a host product in a cloud computing service system, and is used for solving the defects of high management difficulty and weak service expansibility in the conventional physical host and Virtual Private Server (VPS) service.

The system 100 may also include one or more databases 130. In some embodiments, these databases may be used to store data and other information. For example, one or more of the databases 130 may be used to store information such as audio files and video files. The database 130 may reside in various locations. For example, the database used by the server 120 may be local to the server 120, or may be remote from the server 120 and may communicate with the server 120 via a network-based or dedicated connection. The database 130 may be of different types. In certain embodiments, the database used by the server 120 may be, for example, a relational database. One or more of these databases may store, update, and retrieve data to and from the databases in response to the commands.

In some embodiments, one or more of the databases 130 may also be used by applications to store application data. The databases used by the application may be different types of databases, such as key-value stores, object stores, or regular stores supported by a file system.

The system 100 of fig. 1 may be configured and operated in various ways to enable application of the various methods and apparatus described in accordance with the present disclosure.

Fig. 2 shows a flow diagram of a method 200 of generating a map according to an embodiment of the present disclosure. The method 200 may be performed at a server (e.g., server 120 shown in FIG. 1) or at a client device (e.g., any of the client devices 101-106 shown in FIG. 1). That is, the execution subject of each step of the method 200 may be the server 120 shown in fig. 1, or may be the

client devices

101, 102, 103, 104, 105, and/or 106 shown in fig. 1.

As shown in fig. 2, the method 200 includes:

step S201, responding to a request of a user, and acquiring first relevant information aiming at the user, wherein the first relevant information comprises a current position of the user;

step S202, one or more target interest points related to the first related information are obtained based on a user portrait of the user, wherein the user portrait comprises one or more interest point types and one or more associated position information of the user; and

step S203, generating a target map including the one or more target interest points.

Therefore, compared with the traditional application use scene in which the user needs to perform sliding and/or zooming operations on the map base map more times to find the target position and the displayed information when opening the map application, the method can automatically generate the interest points matched with the individual user based on the user image of the user to be displayed on the map base map when the user opens the map application, and compared with the traditional mode of displaying the default interest points on the map base map, the personalized map generation method is provided for the individual user, and the interaction and use experience of the user are improved.

In the technical solution of the present disclosure, the acquisition, storage, application, and the like of the personal information of the related user all meet the regulations of the relevant laws and regulations, and do not violate the common customs of the public order. It should be further noted that, in this embodiment, the current location and the like of the user included in the first related information of the user are obtained after being provided by the user authorization (i.e., obtaining the user's own consent) for the purpose of generating the target map including one or more target points of interest. In addition, the information/data obtained is not intended to characterize a particular type of user and thus is not reflective of the personal information of a particular type of user.

In step S201, in response to a request of a user, first relevant information for the user is obtained, where the first relevant information includes a current location where the user is located. In some embodiments, the user's request may be either explicit or implicit. As an example, where the user's request is an explicit request, when the user opens the map application, a convenient interaction control may be displayed on the application interface such that the user may activate via a gesture (e.g., a click, swipe, etc. simple gesture that does not require the user to devote more attention than, for example, a zoom operation to find a particular landmark) the process of generating a personalized map (e.g., a target map) in which the map floor map is overlaid with points of interest that match the user. As another example, in the case where the user's request is an implicit request, when the user opens a map application, the operation of opening the map application itself is considered as the user issuing an implicit request to activate the process of generating a personalized map. In some embodiments, the user's request may also be embodied as the user interacting with the map application interface and/or a control thereon via a gesture (e.g., the gesture described above) upon waking up the map application from the background, or the user waking up the map application from the background itself. In some embodiments, the current location at which the user is located may be authorized from a positioning device of the client device, such as location information (latitude and longitude coordinates) of the client device obtained through a particular positioning technology, which is typically identified on a mapping application as the location of a positioned object (e.g., the client device).

In step S202, one or more target interest points related to the first related information are obtained based on a user profile of the user, the user profile including one or more interest point types and one or more associated location information of the user. In this context, the term user representation means that each specific information of a user is abstracted into tags with which the user image is materialized, thereby providing a targeted service to the user.

In some embodiments, the one or more target points of interest represent points of interest to be displayed that match (e.g., match to some degree, such as within a certain confidence interval) the user's relevant information (e.g., first relevant information). As an example, when the related information (e.g., the first related information) of the user indicates that the user is located near a large business establishment, the points of interest to be displayed that match the related information may be a mall, an entertainment venue, a restaurant, a landmark, etc. within the business establishment. As another example, when the related information (e.g., the first related information) of the user indicates that the user is located near his/her address, the point of interest to be displayed that matches the related information may be a supermarket, a community service center, a street hospital, or the like near the address. In some embodiments, the relevant information (e.g., the first relevant information) for the user may include other information in addition to the current location where the user is located, including, but not limited to, the time-lapse location of the user (e.g., instead of authorizing location information to be obtained from a location device of the client device in real-time, location information of the client device is obtained at certain time intervals in order to save power consumption of the client), the current time, date information indicating weekday/weekend, and so forth.

In step S203, a target map comprising the one or more target points of interest is generated. In some embodiments, the act of generating a target map including the one or more target points of interest may include a sub-act of adaptively adjusting a map scale. For example, following the example described above in which the relevant information (e.g., the first relevant information) of the user indicates that the user is located near his address, if the default map scale is a display range with a radius on the order of several kilometers, then continuing to display the one or more target points of interest acquired in step S202 on the original default scale may cause the target points of interest to collide, thereby making it difficult for the user to clearly view the personalized points of interest (e.g., target points of interest) pushed to him. Thus, while generating a target map including one or more target points of interest, the target points of interest to be displayed may be clearly shown (e.g., the showing information (such as names) of the target points of interest do not overlap or blank each other) on the map base map by adaptively adjusting the map scale, so that the user may discern clearly each target point of interest and its associated showing information without manually scaling the scale.

Aiming at the scenes that the traditional map application shows a map base map on the same scale by different users and displays default interest points on the map base map, and the rendering objects (such as 2D or 3D interest point outlines) or the showing information of the interest points on the same map scale are not shown due to a collision blanking showing strategy, and the like, the method automatically generates the interest points matched with the individual users according to the related information of the users, particularly based on the user portrait of the users, so as to be displayed on the map base map when the users open the map application, compared with the traditional method of displaying the default interest points on the map base map, the personalized map generating method is provided for the individual users, the users are prevented from investing more attention to drag and/or zoom the map base map to a certain extent, or target interest points are found through text input and the like (which is particularly important in scenes that the users need to concentrate on other matters, such as when a user is driving a vehicle on a highway, relevant points of interest (e.g., rest areas, gas stations, exits, etc.) can be presented to the user directly once the map application is opened without requiring the user to devote attention to dragging and/or zooming the map base map), thereby improving the user's interaction and use experience.

In an embodiment of the present application, the first related information further includes a current time.

In some embodiments, the user representation may include one or more point of interest types and one or more associated location information for the user. As an example, the user's one or more point of interest types may be categorized as hotels, restaurants, shopping, entertainment, travel, medicine, and so forth. As an example, the one or more associated location information may accordingly indicate a location in which the user's one or more point of interest types are concentrated. For example, when the user representation indicates that the types of points of interest preferred by the user include dining and shopping, the associated location information may include location information indicating locations on the user where the types of dining and shopping points of interest are concentrated (e.g., the user has gone or frequently went). Obtaining a target point of interest associated with a current location of a user based on a user representation may help to improve an adoption feedback rate of the obtained target point of interest. For example, following the above examples that the types of interest points preferred by the user include dining and shopping, when the current location information of the user indicates that the user is located in a business district, the target interest points pushed and displayed to the user may cover various types such as hotels, dining, shopping, entertainment, services, etc., and the types of interest points can be reduced to two types, namely dining and shopping by means of the user representation, so that the pushed and displayed target interest points can better meet the potential needs of the user, and the interaction and use experience of the user can be further improved. In addition, the introduction of the user representation also causes a larger number of target interest points that can be shown on the same scale and fall within the interest point types preferred by the user than the case of obtaining the target interest points only by means of the relevant information (such as the current location) of the user, thereby improving the effective information load provided for the user.

Fig. 3 illustrates a flow diagram of a method 300 of obtaining one or more target points of interest related to a current location in accordance with an embodiment of the present disclosure. The method 300 may be performed at a server (e.g., server 120 shown in FIG. 1) or at a client device (e.g., any of the client devices 101-106 shown in FIG. 1). That is, the execution subject of each step of the method 300 may be the server 120 shown in fig. 1, or may be the

client devices

101, 102, 103, 104, 105, and/or 106 shown in fig. 1.

As shown in fig. 3, the method 300 includes:

step S301, in response to that the one or more associated location information of the user at least indicates the current location, obtaining one or more interest point types related to the current location from a first database, where the first database includes an association relationship between the one or more interest point types and the one or more associated location information; and

step S302, based on the current position and the one or more interest point types, obtaining the one or more target interest points.

In step S301, in response to that the one or more associated location information of the user at least indicates the current location, one or more interest point types related to the current location are obtained from a first database, where the first database includes an association relationship between the one or more interest point types and the one or more associated location information. In particular, when the user representation indicates that the user's associated location information indicates at least the current location at which the user is located (i.e., the current location at which the user has gone or frequently traveled), one or more point of interest types related to the current location may be obtained from a first database (which may be, for example, a constructed general or special purpose database) that may include the point of interest type(s) covered by the current location at which the user has traveled indicated by the mined user representation.

In step S302, the one or more target points of interest are obtained based on the current location and the one or more point of interest types. For example, assuming that the user representation indicates that the point of interest types preferred by the user only include restaurants, when the associated location information included in the user representation indicates at least the current location where the user is located (i.e., the current location where the user has gone or frequently went), one or more point of interest types related to the current location (including restaurant types preferred by the user) may be obtained from the first database as described above, and a plurality of target points of interest belonging to more than one type of restaurants may be further obtained based on the current location and the obtained one or more point of interest types. This is particularly useful in scenarios covered under the scale of some map base maps where the user's preferred point of interest types include a small number of points of interest (e.g., actual businesses, buildings, places, etc.), which can provide the user with additional points of interest near their current location in addition to their preferred point of interest types.

In an embodiment of the application, when the first related information further includes a current time, one or more target points of interest related to the current location and the current time are obtained based on a user representation of the user, and the user representation includes one or more point of interest types, one or more associated location information, and one or more associated time information of the user, wherein, in response to the one or more associated location information of the user indicating at least the current location, the one or more point of interest types related to the current location and the current time are obtained from a first database, and the first database includes an association relationship between the one or more point of interest types, the one or more associated time information, and the one or more associated location information.

In particular, when the first related information described above includes additional factors (e.g., the current time), the granularity of the point of interest types obtained from the first database based on the user representation can be made finer, i.e., the invalid information load carried by the target point of interest to be presented is reduced to some extent. For example, considering that the user representation indicates that the point of interest types preferred by the user include both dining and shopping and that the associated location information or associated location information included by the user representation indicates at least the current location where the user is located (i.e., the current location where the user has been or is frequently traveling), the point of interest type (e.g., shopping type) corresponding to the venue that is not open or not open at the current time may be filtered out (i.e., the point of interest(s) included by the corresponding point of interest type will not be shown on the map base) due to the additional factor that the first related information includes the current time (e.g., late at night), regardless of whether the point of interest(s) is a target point of interest (i.e., regardless of whether the type(s) to which the point of interest(s) belongs is preferred by the user).

In an embodiment of the present application, the associated location information indicates a location area, and the associated time information indicates a time period. In particular, when the associated location information indicates a location region, the location region may be cut and a plurality of smaller sub-regions may be obtained, each sub-region may be associated with one or more points of interest, whereby the plurality of smaller sub-regions may each have a different point of interest type. Likewise, when the associated time information indicates a time period, the time period may be divided and a plurality of shorter sub-time periods may be derived, each of which may be associated with one or more points of interest, whereby the plurality of shorter sub-time periods may each have a different point of interest type. In this way, the information items stored in the first database can be made more refined, thereby helping to avoid the tendency of the interest point types obtained from the first database based on the user representation.

It is noted that the first database described herein may store various elements (e.g., a point of interest type and a confidence thereof, associated location information and a confidence thereof, associated time information and a confidence thereof, etc.) and association relationships between various elements (e.g., an association relationship indicates that the respective elements together constitute a storage entry). It is also noted that the first database described herein may be a general or special purpose database built based on mining of user portrayal, which may be dynamically updated (e.g., passively updated based on user feedback, or actively updated based on big data techniques and the model/algorithm used, etc.). For example, table 1 below shows exemplary categories of elements covered by information entries stored in a first database, it being noted that table 1 below is introduced for the purpose of convenience in explaining the inventive concepts of the present application and is not intended to serve as a use in any interpretation of the scope of the present application. Rather, any suitable database and its stored items of information may be conceived and designed by those skilled in the art based thereon.

TABLE 1

Fig. 4 illustrates a flow diagram of a method 400 of obtaining one or more target points of interest related to a current location and a current time in accordance with an embodiment of the present disclosure. The method 400 may be performed at a server (e.g., server 120 shown in FIG. 1) or at a client device (e.g., any of the client devices 101-106 shown in FIG. 1). That is, the execution subject of each step of the method 400 may be the server 120 shown in fig. 1, or may be the

client devices

101, 102, 103, 104, 105, and/or 106 shown in fig. 1.

As shown in fig. 4, the method 400 includes:

step S401, obtaining target associated time information corresponding to the current time;

step S402, obtaining extended associated time information adjacent to the target associated time information; and

step S403, obtaining one or more interest point types related to the current location, the target associated time information and the extended associated time information from the first database.

In step S401, target associated time information corresponding to the current time is acquired. In some embodiments, the target associated time information may be a specific time interval (e.g., 5:50-6:10 pm, etc.) covering the current time (e.g., 6 pm), may be an information fragment describing the current time (e.g., evening, off-hours, etc.), and so on.

In step S402, extended associated time information adjacent to the target associated time information is acquired. In some embodiments, the extended associated time information adjacent to the target associated time information may be a specific time interval covering a certain time period from before the current time to after the current time, may be a specific time interval covering only a certain time period before or after the current time, and the like.

In step S403, one or more interest point types related to the current position, the target associated time information, and the extended associated time information are acquired from the first database, whereby the number of the acquired interest point types is extended to some extent according to the extension of the associated time information included in the user representation. By augmenting the number of potential point of interest types that match a user by appropriately increasing the time granularity, the redundant cost of user interaction with a mapping application is reduced and the user's use experience is enhanced.

Fig. 5 illustrates a flow diagram of another method 500 of obtaining one or more target points of interest associated with a current location in accordance with an embodiment of the present disclosure. The method 500 may be performed at a server (e.g., server 120 shown in FIG. 1) or at a client device (e.g., any of the client devices 101-106 shown in FIG. 1). That is, the execution subject of each step of the method 500 may be the server 120 shown in fig. 1, or may be the

client devices

101, 102, 103, 104, 105, and/or 106 shown in fig. 1.

In some embodiments, the user representation further includes target user attribute information for the user.

As shown in fig. 5, the method 500 includes:

step S501, in response to that the one or more associated location information of the user does not include the current location, obtaining one or more interest point types from a second database based on the current location and the target user attribute information, wherein the second database includes one or more user attribute information, one or more interest point types and an associated relationship among one or more location information; and

step S502, based on the target user attribute information, the current position and the one or more interest point types, obtaining the one or more target interest points.

The scenario for which the method 500 is directed is that when a user turns on/wakes up a map application at a place where the user never arrived, the point of interest type related to the current location where the user was located cannot be obtained from the first database based on his user representation (since the associated location information included in the user representation of the user cannot indicate the current location, i.e., the user representation cannot necessarily be used for the selection and presentation of the target point of interest at the current location). Thus, the introduction of the target user attribute information and the simultaneous resorting to another new database can effectively alleviate the problem of target point of interest selection in this situation. The target user attribute may represent a category into which the user is classified by user portrait, for example, young people, sports enthusiasts, literature enthusiasts, food enthusiasts, and the like.

In step S501, in response to one or more associated location information of the user not including the current location, one or more types of points of interest are obtained from a second database based on the current location and target user attribute information. Wherein the second database comprises an association relationship among one or more user attribute information, one or more interest point types and one or more location information. The second database described herein may store various elements (e.g., user attribute information and confidence thereof, point of interest type and confidence thereof, associated location information and confidence thereof, associated time information and confidence thereof, etc.) and association relationships between various elements (e.g., association relationships indicate that the respective elements together constitute one storage entry). It is also noted that the second database described herein may be a general or special purpose database built based on mining of multi-user portraits (e.g., due to multiple user attributes), which may be dynamically updated (e.g., passively updated based on feedback of multiple users, or actively updated based on big data techniques and the model/algorithm used, etc.).

In step S502, the one or more target points of interest are obtained based on the target user attribute information, the current location, and the one or more point of interest types. Thus, when a user uses a map application at a location that has never been reached, the user may be presented with one or more interest points of the type of interest points obtained from the second database based on the current location and the target user attributes to which the user corresponds, i.e., when the user's own representation cannot be directly applied to the selection of the target interest points, potentially matching target interest points may be obtained in part by means of features depicted/carried by the representation of the target user group to which the user belongs.

In an embodiment of the present application, the target user attribute information includes one or more interest point types and one or more associated location information corresponding to a plurality of users classified under the same target user attribute, where the plurality of users include the user.

In an embodiment of the present application, the user representation of the user is obtained by mining a historical search log of the user.

FIG. 6 illustrates a flow diagram of a method 600 of obtaining a user representation of a user in accordance with an embodiment of the present disclosure. The method 600 may be performed at a server (e.g., server 120 shown in FIG. 1) or at a client device (e.g., any of the client devices 101-106 shown in FIG. 1). That is, the execution subject of each step of the method 600 may be the server 120 shown in fig. 1, or may be the

client devices

101, 102, 103, 104, 105, and/or 106 shown in fig. 1.

As shown in fig. 6, method 600 includes:

step S601, obtaining a historical retrieval log of the user, wherein the historical retrieval log comprises one or more historical behavior characteristics of the user, and each of the one or more historical behavior characteristics comprises retrieval time, a historical position of the user at the retrieval time, and a retrieval word input by the user;

step S602, for each of the one or more historical behavioral characteristics of the user included in the historical retrieval log:

searching a road network interest point set matched with the historical behavior characteristics in a road network database;

and

obtaining an expanded road network interest point set containing the retrieved road network interest point set;

and

step S603, obtaining one or more interest point types and one or more associated location information of the user based on one or more extended road network interest point sets obtained for one or more historical behavior features of the user included in the historical retrieval log of the user.

In step S602, the network database may be a conventional map network database, which is mainly divided into two parts: 1) a road network topology table, which includes, for example, point-like nodes, linear links (i.e., line segments between two nodes) and association relationships between the point-line surfaces; 2) a point of interest table, which includes, for example, parks, roads, buildings, etc., entities related to the user's point of interest.

In some embodiments, the matching operation in step S602 may be performed within a certain confidence range. In particular, it may be allowed to include tolerance for deviations due to user reasons in the user's historical behavior characteristics. For example, the user's historical behavior characteristics may include that the user has actively input the search term "location B of location a" when in fact location B is located at location C, and the object actually selected and navigated by the user is also location B at location C, and this search term may be matched with the road network data point "location B of location C" in the road network database, regardless of the deviation in the search term input by the user. It should be noted that the search term described herein may be a character manually input by the user, a voice uttered by the user, or a combination thereof.

In other embodiments, the expansion in step S602 may be such that the expanded road network interest point set includes the retrieved road network interest point set, and further the number of interest points under a certain interest point type obtained is expanded to some extent according to the expansion of the matching ratio between the user representation and the actual road network database.

In an embodiment of the present application, generating the target map comprising the one or more target points of interest further comprises: displaying the one or more target points of interest on the base map of the map.

In some embodiments, the one or more interest point types and the one or more associated location information corresponding to the plurality of users are obtained by:

obtaining historical retrieval logs of the plurality of users, wherein the historical retrieval logs of the plurality of users comprise one or more historical behavior characteristics corresponding to the plurality of users, and for each of the plurality of users, each of the one or more historical behavior characteristics comprises retrieval time, a historical position of the user at the retrieval time, and a retrieval word input by the user;

for each of the plurality of user's respective one or more historical behavioral features included in the plurality of user's historical retrieval logs:

retrieving a road network interest point set matched with the historical behavior characteristics from a road network database;

and

acquiring one or more interest point types and one or more associated position information corresponding to the plurality of users based on the expanded target road network interest point set acquired aiming at one or more historical behavior characteristics corresponding to the plurality of users included in the historical retrieval logs of the plurality of users.

In the technical scheme of the disclosure, the processes of collecting, storing, using, processing, transmitting, providing, disclosing and the like of the personal information of the related user all accord with the regulations of related laws and regulations, and do not violate the common customs of public order.

Fig. 7 illustrates a block diagram of a map application underlying framework 700 in accordance with an embodiment of the disclosure. It should be noted that the frame 700 is introduced for the purpose of illustrating the inventive concepts of the present application and is not intended to serve as a purpose of any particular limitation as to the scope or implementation of the present application. Rather, those skilled in the art can devise and design any suitable underlying framework for mapping applications or any variations, equivalents, omissions, and the like of the framework 700 described below, while knowing the concepts of the present application, and such variations are intended to fall within the scope of the present application.

The framework 700 includes a map client program 701, a personalized base map server 702, a map-routing database 703, and a personalized map layer database 704.

Step S701, the map client 701 transmits desensitized user history behavior to the personalized base map server 702;

step S702, the personalized base map server 702 retrieves road network data from the map road network database 703 to match the desensitized user historical behavior it receives to form a user profile;

step S703, the personalized base map server 702 stores the user portrait in the personalized map layer database 704 to form a personalized map layer/interest point candidate;

step S704, the map client 701 transmits a user request to the personalized base map server 702;

step S705, the personalized layer database 704 obtains the target interest point from the personalized base map server 702 in response to receiving the user request;

in step S706, the personalized base map server 702 pushes the target interest point to the map client 701 to form a target map.

Fig. 8 shows a block diagram of an apparatus 800 for generating a map according to an embodiment of the present disclosure.

The apparatus 800 comprises: a first obtaining module 801, wherein the first obtaining module 801 is configured to obtain first relevant information for a user in response to a request of the user, and the first relevant information includes a current location where the user is located; a second retrieving module 802, the second retrieving module 802 configured to retrieve one or more target points of interest related to the first related information based on a user representation of the user, the user representation including one or more point of interest types and one or more associated location information of the user; and a generating module 803, the generating module 803 configured to generate a target map comprising the one or more target points of interest.

Therefore, compared with the traditional application use scene in which the user needs to perform sliding and/or zooming operations on the map base map more times to find the target position and the displayed information when opening the map application, the device can automatically generate the interest points matched with the individual user based on the user image of the user to be displayed on the map base map when the user opens the map application, compared with the traditional mode of displaying the default interest points on the map base map, the personalized map generation method is provided for the individual user, and the interaction and use experience of the user are improved.

In an embodiment of the application, the second obtaining module is further configured to: one or more target points of interest related to the current location are obtained based on a user representation of the user, the user representation including one or more point of interest types and one or more associated location information of the user.

In an embodiment of the present application, apparatus 800 further comprises a third obtaining module configured to obtain a user representation of the user, wherein obtaining the user representation of the user comprises: obtaining a historical retrieval log of the user, wherein the historical retrieval log comprises one or more historical behavior features of the user, and each of the one or more historical behavior features comprises a retrieval time, a historical position of the user at the retrieval time, and a retrieval word input by the user; for each of one or more historical behavioral characteristics of the user included in the historical retrieval log: retrieving a road network interest point set matched with the historical behavior characteristics from a road network database; acquiring an expanded road network interest point set containing the retrieved road network interest point set; and acquiring one or more interest point types and one or more associated position information of the user based on one or more expanded road network interest point sets acquired aiming at one or more historical behavior characteristics of the user and included in the historical retrieval log of the user.

There is also provided, in accordance with an embodiment of the present application, an electronic device, a readable storage medium, and a computer program product, which are capable of implementing any of the above-described methods.

Referring to fig. 9, a block diagram of a structure of an electronic device 900, which may be a server or a client of the present disclosure, which is an example of a hardware device that may be applied to aspects of the present disclosure, will now be described. Electronic device is intended to represent various forms of digital electronic computer devices, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic devices may also represent various forms of mobile devices, such as personal digital processors, cellular telephones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.

As shown in fig. 9, the electronic apparatus 900 includes a computing unit 901, which can perform various appropriate actions and processes in accordance with a computer program stored in a Read Only Memory (ROM)902 or a computer program loaded from a storage unit 908 into a Random Access Memory (RAM) 903. In the RAM 903, various programs and data required for the operation of the electronic device 900 can also be stored. The calculation unit 901, ROM 902, and RAM 903 are connected to each other via a bus 904. An input/output (I/O) interface 905 is also connected to bus 904.

A number of components in the electronic device 900 are connected to the I/O interface 905, including: an input unit 906, an output unit 907, a storage unit 908, and a communication unit 909. The input unit 906 may be any type of device capable of inputting information to the electronic device 900, and the input unit 906 may receive input numeric or character information and generate key signal inputs related to user settings and/or function controls of the electronic device, and may include, but is not limited to, a mouse, a keyboard, a touch screen, a track pad, a track ball, a joystick, a microphone, and/or a remote control. Output unit 907 may be any type of device capable of presenting information and may include, but is not limited to, a display, speakers, a video/audio output terminal, a vibrator, and/or a printer. Storage unit 908 may include, but is not limited to, a magnetic disk, an optical disk. The communication unit 909 allows the electronic device 900 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunications networks, and may include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers and/or chipsets, such as bluetooth (TM) devices, 802.11 devices, WiFi devices, WiMax devices, cellular communication devices, and/or the like.

The computing unit 901 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of the computing unit 901 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The calculation unit 901 performs the various methods and processes described above, such as the method 200-600. For example, in some embodiments, the method 200-600 may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 908. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 900 via the ROM 902 and/or the communication unit 909. When the computer program is loaded into the RAM 903 and executed by the computing unit 901, one or more of the steps of the

method

200 and 600 described above may be performed. Alternatively, in other embodiments, the computing unit 901 may be configured to perform the

method

200 and 600 by any other suitable means (e.g., by way of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.

The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server with a combined blockchain.

It should be understood that various forms of the flows shown above, reordering, adding or deleting steps, may be used. For example, the steps described in the present disclosure may be performed in parallel, sequentially or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.

Although embodiments or examples of the present disclosure have been described with reference to the accompanying drawings, it is to be understood that the above-described methods, systems and apparatus are merely exemplary embodiments or examples and that the scope of the present invention is not limited by these embodiments or examples, but only by the claims as issued and their equivalents. Various elements in the embodiments or examples may be omitted or may be replaced with equivalents thereof. Further, the steps may be performed in an order different from that described in the present disclosure. Further, various elements in the embodiments or examples may be combined in various ways. It is important that as technology evolves, many of the elements described herein may be replaced with equivalent elements that appear after the present disclosure.

Claims

1. A method of generating a map, comprising:

in response to receiving a request of a user, acquiring first related information aiming at the user, wherein the first related information comprises a current position of the user;

obtaining one or more target interest points related to the first related information based on a user representation of the user, the user representation including one or more interest point types and one or more associated location information of the user; and

generating a target map including the one or more target points of interest.

2. The method of claim 1, wherein the first relevant information further comprises a current time.

3. The method of claim 1 or 2, wherein obtaining one or more target points of interest related to the current location further comprises:

in response to that the one or more associated location information of the user at least indicates the current location, obtaining one or more interest point types related to the current location from a first database, wherein the first database comprises an association relationship between the one or more interest point types and the one or more associated location information; and

obtaining the one or more target points of interest based on the current location and the one or more point of interest types.

4. The method of claim 3, wherein when the first related information further includes a current time, obtaining one or more target points of interest related to the current location and current time based on a user representation of the user, and the user representation includes one or more point of interest types, one or more associated location information, and one or more associated time information of the user,

wherein, in response to one or more associated location information of the user indicating at least the current location, one or more point of interest types related to the current location and the current time are obtained from a first database, and the first database comprises an association relationship among the one or more point of interest types, the one or more associated time information and the one or more associated location information.

5. The method of claim 4, wherein the associated location information indicates a location area and the associated time information indicates a time period.

6. The method of claim 4 or 5, wherein obtaining one or more target points of interest related to the current location and current time further comprises:

acquiring target associated time information corresponding to the current time;

acquiring extended associated time information adjacent to the target associated time information; and

one or more interest point types related to the current position, the target associated time information and the extended associated time information are obtained from a first database.

7. The method of claim 1, wherein the user representation further includes target user attribute information of the user, and obtaining one or more target points of interest related to the current location further comprises:

in response to one or more associated location information of the user not including the current location, obtaining one or more interest point types from a second database based on the current location and target user attribute information, the second database including one or more user attribute information, one or more interest point types, and an association between one or more location information; and

and acquiring the one or more target interest points based on the target user attribute information, the current position and the one or more interest point types.

8. The method of claim 7, wherein the target user attribute information includes one or more interest point types and one or more associated location information corresponding to a plurality of users classified under the same target user attribute,

and wherein the plurality of users includes the user.

9. The method of claim 1, wherein the user representation of the user is obtained by mining a historical search log of the user.

10. The method of claim 9, wherein obtaining a user representation of the user comprises:

obtaining a historical retrieval log of the user, wherein the historical retrieval log comprises one or more historical behavior characteristics of the user, and each of the one or more historical behavior characteristics comprises a retrieval time, a historical location of the user at the retrieval time, and a retrieval word input by the user;

for each of one or more historical behavioral characteristics of the user included in the historical retrieval log:

retrieving a road network interest point set matched with the historical behavior characteristics from a road network database; and

obtaining an expanded road network interest point set containing the retrieved road network interest point set; and

obtaining one or more interest point types and one or more associated location information of the user based on one or more extended road network interest point sets obtained for one or more historical behavior features of the user included in the historical retrieval log of the user.

11. The method of claim 1, wherein generating a target map including the one or more target points of interest further comprises:

displaying the one or more target points of interest on the base map of the map.

12. An apparatus to generate a map, comprising:

a first obtaining module configured to obtain, in response to a request of a user, first related information for the user, the first related information including a current location where the user is located;

a second retrieval module configured to retrieve one or more target points of interest related to the first relevant information based on a user representation of the user, the user representation including one or more point of interest types and one or more associated location information of the user; and

a generation module configured to generate a target map including the one or more target points of interest.

13. The apparatus of claim 12, further comprising a third acquisition module configured to acquire a user representation of the user, wherein acquiring the user representation of the user comprises:

searching a road network interest point set matched with the historical behavior characteristics in a road network database; and

14. An electronic device, comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein

The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-11.

15. A non-transitory computer readable storage medium having stored thereon computer instructions for causing the computer to perform the method of any one of claims 1-11.

16. A computer program product comprising a computer program, wherein the computer program realizes the method of any one of claims 1-11 when executed by a processor.