HK1209864B - Method and device for determining target location - Google Patents

Description

Method and apparatus for determining a target address

Technical Field

The present application relates to the field of computer technology, specifically to the field of electronic digital data processing technology, and more particularly to a method and device for determining a target address.

Background Art

Site selection refers to the process of evaluating and deciding on a specific location before construction or investment. The determination of a target address is closely related to the location, surrounding environment, and residents' consumption levels and habits. Existing site selection is mostly manual, with decisions based on data collected about surrounding passenger flow and consumption levels. Incomplete information or inexperience can significantly impact the accuracy of site selection. Therefore, an accurate and reliable method for determining target addresses is needed.

Summary of the Invention

To address the above-mentioned drawbacks, the present application provides a method and apparatus for determining a target address.

In a first aspect, the present application provides a method for determining a target address. The method comprises: receiving information indicating a candidate area and a target address type from a terminal; establishing analysis data corresponding to the candidate area based on association information within a geographic location range of the candidate area, wherein the association information includes at least user location information within the geographic location range of the candidate area; and determining whether the candidate area is the target address based on the analysis data and the target address type.

In a second aspect, the present application provides an apparatus for determining a target address. The apparatus includes: an indication information receiving unit configured to receive information indicating a candidate area and a target address type from a terminal; a data establishment unit configured to establish analysis data corresponding to the candidate area based on association information within a geographic location range of the candidate area, wherein the association information at least includes user location information within the geographic location range of the candidate area; and a target address determination unit configured to determine whether the candidate area is the target address based on the analysis data and the target address type.

The method and device for determining a target address provided in the present application receive information indicating a candidate area and a target address type from a terminal, then establish analysis data corresponding to the candidate area based on associated information within the geographic location range of the candidate area, and finally determine whether the candidate area is the target address based on the analysis data and the target address type. This can expand the dimension of information used to determine the target address and improve the accuracy and reliability of target address selection.

BRIEF DESCRIPTION OF THE DRAWINGS

Other features, objects and advantages of the present application will become more apparent upon reading the detailed description of non-limiting embodiments made with reference to the following drawings:

FIG1 shows a flow chart of an exemplary implementation of the method for determining a target address of the present application;

FIG2 shows a flowchart of an exemplary implementation of establishing analysis data corresponding to a candidate region in the present application;

FIG3 shows a schematic diagram of the effect of dividing a map into grids;

FIG4 illustrates a flow chart of an exemplary implementation of determining the identity of a grid intersecting a candidate region;

FIG5 a shows a schematic diagram of the effect of a grid set associated with a candidate region when the candidate region is a rectangle;

FIG5 b shows a schematic diagram of the effect of the grid set associated with the candidate region when the candidate region is an irregular shape;

FIG6 shows a flowchart of an exemplary implementation of establishing analysis data within a geographical location range corresponding to a grid set;

FIG7 shows a flow chart of an exemplary implementation of establishing analysis data for each grid;

FIG8 is a schematic diagram showing the effect of presenting a grid and analysis data within the geographical location range corresponding to the grid in association with each other; and

FIG9 shows a structural diagram of an exemplary implementation of the apparatus for determining a target address of the present application.

DETAILED DESCRIPTION

The present application will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely for the purpose of explaining the relevant invention and are not intended to limit the invention. It should also be noted that, for ease of description, only portions relevant to the relevant invention are shown in the accompanying drawings.

It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

Please refer to Figure 1, which shows a flowchart 100 of an exemplary implementation of the method for determining a target address of the present application. This embodiment mainly uses the method as an example of a server with big data processing capabilities. It is understood that the server is a server in a broad sense, which can be a single server, a server cluster, or a server group that can share data. The method for determining a target address includes the following steps:

In step 110, information indicating candidate areas and target address types is received from a terminal.

In this embodiment, the terminal can exchange data with the server. The terminal can include a mobile terminal and a fixed-point terminal that can collect and store data. For example, the terminal can be a smartphone, tablet computer, or smartwatch with a display screen, or a terminal that records and displays user information at a store in a certain geographic location (such as a host and display screen that records member information).

When determining a target address, a user may select a candidate area and the type of target address to be determined on a map displayed on the terminal. For example, the user may circle an area on the map displayed on the terminal as a candidate area. The terminal may then send information indicating the candidate area and the type of target address to a server, where the information indicating the candidate area may include the candidate area selected by the user and a zoom level of the map. The server may then receive the information indicating the candidate area and the type of target address. Receiving may be done via a wireless connection or a wired connection.

Optionally, before receiving the information, the server may also obtain a data interaction request from the terminal and authenticate the data interaction request, and only receive the above information when the authentication is successful.

Next, in step 120 , analysis data corresponding to the candidate area is created based on the associated information within the geographical location range of the candidate area.

In this embodiment, after receiving the information indicating the candidate area and the target address type, the server may determine the geographical location range of the candidate area and obtain associated information within the geographical location range by searching in a database stored in the server.

The associated information may be information associated with the determination of the target address within the geographical location range, which at least includes the user location information within the geographical location range corresponding to the candidate area. In some implementations, the associated information may include dynamic information and static information. The dynamic information may include information about users active within the geographical location range. Optionally, the user information may include the user's age, gender, zodiac sign, hobbies, address, occupation, assets, long-term interests, consumption frequency, consumption type, and average consumption level. Static information may include geographical attribute information within the geographical location range, such as information about transportation stops, store rental and sales information, and various types of venue information within the geographical location range. The server may determine whether the user is within the geographical location range corresponding to the candidate area based on the user location information, that is, it may determine whether the user's information is associated information within the geographical location range based on the user's location.

In some optional implementations, the server may receive the association information in response to a request from the terminal to report the association information. In other implementations, the server may obtain the association information from another data storage center (e.g., a cloud). The association information may be collected or updated at regular intervals, such as once a day.

After obtaining the associated information within the geographic location of the candidate area, the server can create analytical data corresponding to the candidate area. Specifically, the server can perform classification, statistical analysis, and other processing on the acquired associated information to create analytical data corresponding to the candidate area. The statistical processing can include calculating totals, averages, statistical distribution patterns (e.g., distribution functions), and mean square deviations. Accordingly, the analytical data can include classification information, averages, totals, distribution pattern data, mean square deviations, and other data, and can be data at a specific point in time or over a period of time. For example, if the associated information is user asset information, the server can calculate the average value based on the asset information of at least some users within the geographic location corresponding to the candidate area stored in the database to obtain average asset level data for users within the geographic location. For another example, if the associated information is user consumption type information, the server can perform classification and statistical analysis on the consumption types of at least some users within the geographic location (e.g., categorizing consumption into categories such as dining, entertainment, shopping, and lifestyle services, calculating the number of users in each consumption category, and then determining the proportion of users in each consumption category relative to the total number of users), and use the classification and statistical results as the analytical data corresponding to the candidate area.

In some optional implementations, the analysis data may include at least crowd flow or crowd density. The server performs statistical analysis on the user location information to obtain distribution statistics of the user's geographical location, thereby obtaining crowd flow or crowd density data.

Then, in step 130 , based on the analysis data and the target address type, it is determined whether the candidate area is the target address.

In this embodiment, the server may determine whether the candidate area is the target address based on the analysis data of the candidate area obtained in step 120 and the target address type in the indication information. For example, analysis data of an existing address may be created based on information about an existing address of the target address type, and the characteristics of the analysis data of the existing address may be analyzed to determine whether the characteristics of the analysis data of the candidate area are consistent with those of the analysis data of the existing address, thereby determining whether the candidate area is the target address.

In some optional implementations, determining whether a candidate area is a target address based on the analysis data and the target address type may include: determining whether the candidate area meets predetermined conditions for the target address type based on the analysis data, thereby determining whether the candidate area is a target address. In some implementations, the predetermined conditions may be set by the user based on empirical values, such as a user-set threshold for foot traffic or an average asset threshold. In other implementations, the predetermined conditions may also be determined based on training results from existing data. Optionally, a site selection model may be used to determine whether the candidate area meets the predetermined conditions for the target address type. The site selection model may be trained using sample data from existing locations or businesses of the target address type in map data. Furthermore, the site selection model may be established using methods such as logistic regression. Specifically, the acquired analysis data may be used as features, and after normalizing the features, a weight is assigned to each feature. The features are then input into the model to determine whether the candidate area is a target address. The model may be trained using information from other locations or businesses of the target address type already on the map.

It will be appreciated that in this embodiment, the aforementioned association information may include multiple types of association information, and the analysis data obtained by the server may also include multiple types of analysis data obtained by analyzing the multiple types of association information. The more types of association information there are, the greater the amount of data the server must process, the greater the amount of analysis data obtained, the wider the dimensionality of information collected, and the higher the accuracy of target address determination.

The method provided by the above-mentioned embodiment of the present application receives information indicating the candidate area and the target address type from the terminal, and then establishes analysis data corresponding to the candidate area based on the associated information within the geographical location range of the candidate area. Finally, based on the analysis data and the target address type, it determines whether the candidate area is the target address. This can effectively reduce labor costs, expand the dimensions of the information used to determine the target address, and improve the accuracy and reliability of the target address selection.

For the above-mentioned embodiments of the present application, the application scenario may be that when the user needs to determine the target address, the user first selects the candidate area and the type of target address on the terminal. When the server receives the information indicating the candidate area and target type selected by the user, it analyzes the associated information within the geographical location range of the candidate area to obtain analysis data for determining whether the candidate area is the target address. Finally, the server makes a judgment based on the analysis data and the type of the target address to determine whether the candidate area is the target address.

For example, if a user sets the map zoom ratio to 1:10 ⁴ and selects an area in Beijing's Haidian District as a candidate area, the server can receive information about the user-specified candidate area and the target address type (restaurant) from the user's mobile phone or computer. The server then analyzes and compiles information about the user's location, consumption level, consumption type, hobbies, surrounding transportation information (e.g., subway stations and bus stops), and available rental and sale locations in the candidate area. This data is then used to generate analytical data for the candidate address. For example, this data can be used to calculate the candidate area's foot traffic, average consumption level, user consumption habits, and transportation convenience. Based on this data, the server determines whether the candidate area is suitable for a restaurant. If the analytical data meets predetermined conditions, such as foot traffic exceeding a certain threshold or the proportion of restaurant consumption in total consumption exceeding a certain threshold, the server can determine that the candidate area is a suitable address for a restaurant.

Further referring to FIG2 , which shows a flowchart 200 of an exemplary implementation of the present application for establishing analysis data corresponding to a candidate region, that is, a flowchart of an exemplary implementation of step 120 of the above method. The process 200 of the method for establishing analysis data corresponding to a candidate region includes the following steps:

In step 210 , a grid is formed on the map.

In this embodiment, the server may divide the map into multiple levels of grids according to the level. In some implementations, the map may be divided into multiple grids with a certain side length, for example, constructing multiple levels of grids on the map with side lengths of 10 kilometers, 5 kilometers, 2 kilometers, 1 kilometer, 500 meters, 200 meters, and 100 meters, respectively. The constructed grids may be rectangular in shape. After receiving information indicating the candidate area and the target address type, the server may determine the side length of the grid to be divided based on the map zoom ratio and the candidate area contained in the indication information. For example, when the candidate area is large and the map zoom ratio is 1: ^10.5 , the corresponding grid may be constructed on the map with a side length of 1 kilometer of the actual distance; and when the candidate area is small and the map zoom ratio is 1: ^10.3 , the corresponding grid may be constructed on the map with a side length of 200 meters of the actual distance.

Figure 3 shows a schematic diagram of the effect of dividing a map into grids. As shown in Figure 3, the left and right images have different scales. A given area in each image is evenly divided into multiple grids of equal size, with each grid corresponding to a non-intersecting geographic location. When the map scale changes, the size and/or number of the grids can also change accordingly.

Returning to FIG. 2 , in step 220 , a grid set related to the candidate region is determined.

In this embodiment, the server may determine a grid set related to the candidate area based on the grids divided in step 210 and the positions of the candidate areas on the map.

In an optional implementation, the set of grids associated with the candidate area can be determined by: determining the grids that intersect the candidate area, then identifying these grids that intersect the candidate area, and determining the set of grids associated with the candidate area based on the identification of the grids that intersect the candidate area. Intersecting the candidate area means that the grid is fully or partially covered by the candidate area. The server can identify the grids at each level and then determine the grids that intersect the candidate area, and record the identification of these grids. All grids that intersect the candidate area constitute the aforementioned grid set, in which each grid is identified by a number or other method.

Then, in step 230 , analysis data within the geographical location range corresponding to the grid set is created.

In this embodiment, the server can obtain the analysis data of each grid in the above-mentioned grid set, and summarize the analysis data of all grids in the above-mentioned grid set, and use the summarized data as the analysis data within the geographical location range corresponding to the grid set, thereby establishing the analysis data within the geographical location range corresponding to the candidate area.

4, which shows a flowchart 400 of an exemplary implementation of determining the identity of a grid intersecting the candidate region. The method of determining the identity of a grid intersecting the candidate region may include:

In step 410, the coordinates of the boundary points of the candidate region are obtained.

Generally speaking, the candidate area can be a continuous and closed area. In this embodiment, the server can establish a two-dimensional coordinate system on the map, obtain the boundary of the candidate area, and determine the coordinates of the boundary points.

Next, in step 420 , a target grid search range is determined based on the coordinates of the boundary points of the candidate region.

In this embodiment, the target grid can be a grid in the map grid that intersects with the candidate area. The server can determine the search range of the target grid based on the boundary point coordinates obtained in step 410. In some implementations, the target grid search range can be constructed as a rectangle (minimum bounding rectangle) using the minimum and maximum values of the horizontal and vertical coordinates of all boundary points as vertex coordinates, thereby accelerating the search.

Then, in step 430, the grids on the boundary of the candidate area within the target grid search range are traversed to determine whether they are grids that fully cover or partially cover the candidate area.

Since the candidate region is usually a continuous and closed region, only the grids on the boundary of the candidate region may be traversed.

In some embodiments, the server may also traverse all grids within the target grid search range to determine whether the grid is fully or partially covered by the candidate area, that is, exclude grids that do not intersect with the candidate area.

Then, in step 440 , if the grid on the boundary of the candidate area is a grid that is fully or partially covered by the candidate area, the current grid is determined to be a grid that intersects with the candidate area, and the identifier of the grid is recorded.

When a grid on the boundary of the candidate region is a grid that fully covers or partially covers the candidate region, the grid may be determined to be a grid that intersects with the candidate region.

In some implementations, when the candidate area is a regular shape, such as a rectangular area, the grid identifier intersecting with the candidate area can be determined as follows: calculate the coordinates of the lower left corner ( _x1 , _y1 ) and the upper right corner ( _x2 , _y2 ) of the rectangular area, where interval is the side length of the grid. The grid identifier of any point (x, y) within the rectangular area can be obtained by the following conversion formula: (grid x, grid y) = (x/interval, y/interval), where _x1 <x< _x2 , _y1 <y< _y2 . The coordinates (x, y) of any point in the grid can also be directly used as the two-dimensional identifier of the grid.

Further reference is made to Figure 5a, which shows a schematic diagram of the effect of the grid set associated with the candidate area when the candidate area is a rectangle. As shown in Figure 5a, the map in the figure is divided into multiple grids of equal size, and the candidate area selected by the user is the rectangular area 510 surrounded by the dotted line. First, the coordinates of the boundary points of the rectangular area can be calculated to draw the search range of the target grid, that is, the grids in the shaded area 520 in Figure 5a. After that, the grids on the boundary of the candidate area within the target grid search range 520 are traversed to determine whether these grids are grids that fully cover or partially cover the candidate area. In Figure 5a, the grids on the boundary of the candidate area within the target grid search range 520 are all grids that are partially covered by the candidate area, and it is determined that all grids within the target grid search range are grids that intersect with the candidate area.

Referring further to Figure 5b, a schematic diagram illustrating the effect of a grid set associated with an irregular candidate region is shown. As shown in Figure 5b, the candidate region is the area 530 enclosed by the dashed line. First, the coordinates of the boundary points of this area are calculated to define the target grid search range, i.e., the grids of the rectangular area 540 in Figure 5b. Next, the grids on the boundary of the candidate region within the target grid search range 540 are traversed to determine whether these grids fully or partially cover the candidate region. _{, y 2 ) , ( x 4 , y 2 ) , and ( x 3 , y 2 ) are grids partially covering the candidate area . ​}

The method provided in the above embodiment determines a target grid search range, then screens grids on the boundary of the candidate area within the target grid search range to determine whether they are grids that fully or partially cover the candidate area. If not, they are removed from the target grid search range, ultimately obtaining the identification of grids that intersect with the candidate area. The server can further determine the grid set associated with the candidate area based on these grid identifications, thereby providing grid data for establishing analysis data for the candidate area.

Furthermore, when the grid on the boundary of the candidate area is a grid that is partially covered by the candidate area, the analysis data within the geographical location range corresponding to the partially covered grid can be weighted and adjusted. Since the boundary of the candidate area does not completely cover the grid in which it is located, the analysis data of the established candidate area may have errors. For example, when the analysis data is the flow of people, if the grid data that is not completely covered by the boundary of the candidate area is directly returned to the server, the analysis data established will produce errors, thereby affecting the accuracy of the target address determination. In some implementations, the candidate area is much larger than the grid size, and this error can be ignored. When the candidate area is small, in order to avoid errors, the area of each grid partially covered by the boundary of the candidate area can be calculated first, and the proportion of this area to the total area of the grid can be obtained. This proportion is used as a weighting factor and multiplied by the analysis data of the grid as the return value. In some cases, such as when the analysis data is average value, density data, or distribution data, weighting is not required or the weighting factor can be 1, that is, the analysis data is directly used as the return value.

Further referring to FIG6 , which shows a flowchart of an exemplary implementation of establishing analysis data within the geographical location range corresponding to the grid set in the present application, that is, an exemplary implementation process 600 of step 230. As shown in FIG6 , establishing analysis data within the geographical location range corresponding to the grid set may include:

In step 610, analysis data is created for each grid in the grid set;

In this embodiment, the server can associate grids with analysis data to create analysis data for each grid. In some implementations, the server can determine the user's grid location based on the acquired user location information and associate analysis data obtained based on statistical analysis of other user information with the determined grid. The analysis data can include classification information, average values, total values, distribution data, mean square error, and other data, and can be data at a specific point in time or over a period of time.

In step 610 , the server associates the grid with the analysis data. In some implementations, each grid may be associated with multiple analysis data. The analysis data associated with different grids may be obtained by different statistical analysis methods and may also be of different types.

Next, in step 620 , based on the analysis data of each grid, analysis data within the geographical location range corresponding to the grid set related to the candidate area is generated.

The server aggregates the analysis data of all grids in the grid set related to the candidate area, and can generate analysis data within the geographical location range corresponding to the grid set related to the candidate area.

Further referring to FIG7 , which shows a flowchart of an exemplary implementation of the present application for establishing analysis data for each grid, that is, a flowchart of an exemplary implementation of step 610 700 . As shown in FIG7 , establishing analysis data for each grid may include:

In step 710 , based on the user location information, data points within the geographical location range corresponding to the grid are determined.

After obtaining the user's location information within the geographic location range corresponding to the candidate area, the server can determine the user's geographic location based on the user's location information, search or calculate the grid corresponding to the geographic location, and determine that the user is a data point within the geographic location range corresponding to the grid. By performing this processing on a large amount of user location information, multiple data points can be obtained within the geographic location range corresponding to each grid.

Next, in step 720 , statistical analysis is performed on the correlation information of the data points to create analysis data.

After obtaining multiple data points within the geographical location range corresponding to each grid, the corresponding associated information of each data point (eg, user) in the grid can be analyzed and statistically analyzed, such as by performing summation and the like, and the results of the statistical analysis are used as the analysis data of the grid.

In some embodiments, analysis data can also be established for each grid in the following manner: first, based on the user's location information, determine the grid corresponding to the user's geographic location, and then perform statistics on the associated information within the geographic location corresponding to the same grid, and then obtain the analysis data for the grid. The following takes the establishment of pedestrian flow data for each grid as an example to specifically explain the process of the server establishing analysis data for each grid in this manner. First, the server can obtain the user's coordinates (x, y) through the terminal (for example, through the location log of the mobile phone), and then calculate the grid identifier (grid x, grid y) corresponding to the coordinates (x, y), and count the number of users with the same grid identifier at the same time point or in the same time period, so as to obtain pedestrian flow data for multiple grids.

In a further embodiment, the method for determining a target address provided herein may further include: sending analysis data within the geographic location corresponding to the grid set to a terminal, so that the terminal can present the grid in association with the analysis data. For example, the server may send pedestrian flow data for the grid and the geographic location corresponding to the grid to the terminal. Upon receiving the indication information, the terminal presents the pedestrian flow and the grid in association on a map to intuitively display the analysis data to the user. In some implementations, the presentation method may include rendering. Specifically, different colors may be rendered based on the level of pedestrian flow. For example, areas with high pedestrian flow may be rendered red, while areas with low pedestrian flow may be rendered green. Alternatively, as the pedestrian flow within the geographic location corresponding to a grid on a map gradually increases, the color of the grid may gradually change from an initial green to yellow, and then to red. Furthermore, the aforementioned time may be real-time, in which case the specific analysis data may be displayed in real time at the corresponding geographic information point; or the grid and analysis data may be displayed using dynamic effects (e.g., changes in the concentration of a single color) to reflect real-time performance.

Those skilled in the art will appreciate that other currently known or future developed display methods may be used to present the analysis data, such as directly suspending the analysis data on a map, or highlighting the analysis data on a grid associated with a candidate area in response to a user's grid selection operation. This application has no limitations on the presentation of the analysis data.

Further reference is made to Figure 8, which illustrates a schematic diagram of an effect of presenting a grid and analysis data within the geographic location range corresponding to the grid in association. As shown in Figure 8, the grid and the flow of people within the geographic location range corresponding to the grid are presented on the map of the terminal. For example, it can be seen that the flow of people within the geographic location range corresponding to grid 810 in the figure is relatively large, while the flow of people within the geographic location range corresponding to grid 820 is relatively small. By receiving the analysis data within the geographic location range corresponding to the grid sent by the server and presenting the grid in association with the analysis data, the terminal can intuitively display the analysis data of the grid on the map.

Please refer to FIG9 , which shows a structural diagram of an exemplary implementation of the apparatus for determining a target address of the present application.

As shown in FIG9 , the apparatus 900 for determining a target address may include an indication information receiving unit 910, a data establishing unit 920, and a target address determining unit 930. The indication information receiving unit 910 may be configured to receive information indicating a candidate area and a target address type from a terminal. The data establishing unit 920 may be configured to establish analysis data corresponding to the candidate area based on association information within the geographic location range of the candidate area obtained by the indication information receiving unit 910. The target address determining unit 930 may be configured to determine whether the candidate area is the target address based on the analysis data established by the data establishing unit 920 and the target address type obtained by the indication information receiving unit 910. The association information may include at least user location information within the geographic location range corresponding to the candidate area.

In this embodiment, a server including the device can exchange data with a user terminal. When determining a target address, a user can select a candidate area and the type of target address to be determined on a map displayed by the terminal. The terminal can then send information indicating the candidate area and the type of target address to an indication information receiving unit 910. The information indicating the candidate area can include the candidate area selected by the user and the zoom level of the map. The indication information receiving unit 910 can then receive the information indicating the candidate area and the type of target address. Receiving can be done wirelessly or via a wired connection.

In this embodiment, after the indication information receiving unit 910 receives information indicating the candidate area and the target address type, the data creation unit 920 can determine the geographic location range of the candidate area based on the indication information and retrieve the associated information within the geographic location range by searching the database. Furthermore, the data creation unit 920 can determine whether the user is within the geographic location range corresponding to the candidate area based on the user's location information. In other words, the data creation unit 920 can determine whether the user's information is associated information within the geographic location range based on the user's location.

After acquiring the associated information within the geographic location range of the candidate area, the data creation unit 920 may create analysis data corresponding to the candidate area. Specifically, the data creation unit 920 may classify and perform statistics on the acquired associated information to create analysis data corresponding to the candidate area.

In some optional implementations, the analysis data may include at least crowd flow or crowd density. The data creation unit 920 performs statistical analysis on the user location information to obtain distribution statistics of the user's geographical location, thereby obtaining crowd flow or crowd density data.

The target address determination unit 930 can determine whether the candidate area is the target address based on the analysis data of the candidate area obtained by the data establishment unit 920 and the target address type in the indication information received by the indication information receiving unit 910. For example, analysis data of an existing address can be established based on information about an address of the existing target address type, and the characteristics of the analysis data of the existing address can be analyzed to determine whether the characteristics of the analysis data of the candidate area are consistent with the characteristics of the analysis data of the existing address, thereby determining whether the candidate area is the target address.

In some optional implementations, the data creation unit 920 of the apparatus 900 may include a grid division unit, a grid set determination unit, and an analysis data creation unit. The grid division unit may be used to divide a map into grids, the grid set determination unit may be used to determine a grid set associated with a candidate area, and the analysis data creation unit may be used to create analysis data within the geographic location corresponding to the grid set.

In some optional implementations, the apparatus may further include a correlation information receiving unit 940 (not shown) configured to receive correlation information in response to a correlation information reporting request from a terminal. The correlation information may be collected or updated periodically, for example, once a day. In this case, the correlation information may be obtained and stored by the correlation receiving unit 940 to provide data support for the data generation unit 920 to generate analytical data.

It should be understood that the various units and subunits described in the apparatus for determining a target address 900 are configured to practice the exemplary embodiments disclosed herein. Therefore, the operations and features described above in conjunction with Figures 1, 2, 4, 6, and 7 are also applicable to the apparatus for determining a target address 900 and the units/subunits therein, and will not be further described here.

The units involved in the embodiments described in this application may be implemented by software or hardware. The units described may also be provided in a processor. For example, they may be described as follows: a processor includes an indication information receiving unit, a data establishment unit, and a target address determination unit. The names of these units do not, in some cases, constitute limitations on the units themselves. For example, the indication information receiving unit may also be described as a "unit for receiving indication information."

As another aspect, the present application further provides a computer-readable storage medium, which may be the computer-readable storage medium included in the apparatus described in the above embodiment; or a standalone computer-readable storage medium not incorporated into a terminal. The computer-readable storage medium stores one or more programs, which are used by one or more processors to execute the method for determining a target address described in the present application.

The above description is merely a preferred embodiment of the present application and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of the invention herein is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but also encompasses other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the inventive concept. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features having similar functions disclosed in this application.

Claims (16)

1. A method for determining a target address, characterized in that the method comprises: Receive information from the terminal indicating the candidate region and target address type; Based on the correlation information within the geographic location range of the candidate region, analytical data corresponding to the candidate region is established, wherein the correlation information includes at least user location information within the geographic location range of the candidate region; Based on the analyzed data and the target address type, determine whether the candidate region is a target address; The step of determining whether the candidate region is a target address based on the analyzed data and the target address type includes: Based on the analysis data, a site selection model is used to determine whether the candidate area meets the predetermined conditions of the target address type, wherein the site selection model is trained using existing target address type location information in the map data as sample data. 2. The method according to claim 1, wherein establishing the analysis data corresponding to the candidate region includes: Divide the map into grids; Determine the set of grids associated with the candidate region; Establish analytical data within the geographical location range corresponding to the grid set. 3. The method according to claim 2, wherein determining the grid set associated with the candidate region includes: The grid is identified; Identify the grids that intersect with the candidate regions; The set of grids associated with the candidate region is determined based on the identifiers of the grids that intersect with the candidate region. 4. The method according to claim 3, wherein determining the identifier of the grid intersecting with the candidate region includes: Obtain the coordinates of the boundary points of the candidate region; The target grid search range is determined based on the boundary point coordinates of the candidate region; Traverse the grids on the boundary of the candidate region within the target grid search range and determine whether they are grids that are fully or partially covered by the candidate region; If so, the current grid is determined to be the grid that intersects with the candidate region, and the identifier of the grid is recorded. 5. The method according to claim 4, wherein if the grid on the boundary of the candidate region is a grid partially covered by the candidate region, the analysis data within the geographical location range corresponding to the partially covered grid is weighted and adjusted. 6. The method according to claim 2, wherein establishing the analysis data within the geographical location range corresponding to the grid set includes: Analytical data is generated for each grid in the aforementioned grid set; Based on the analysis data of each grid, analysis data is generated within the geographical location range corresponding to the grid set associated with the candidate region. 7. The method according to claim 6, wherein establishing analysis data for each grid in the grid set comprises: Based on the user location information, determine the data points within the geographical location range corresponding to the grid; Statistical analysis is performed on the correlation information of the data points to establish the analytical data. 8. The method according to claim 2, characterized in that the method further comprises: The analysis data within the geographic location range corresponding to the grid set is sent to the terminal so that the terminal can associate the grid with the analysis data and present it. 9. The method according to claim 1, characterized in that the method further comprises: In response to a terminal's request to report association information, association information is received from the terminal. 10. The method according to any one of claims 1-9, wherein the associated information further includes at least one of the following: the user's age, gender, zodiac sign, hobbies, address, occupation, assets, consumption frequency, consumption type, and average consumption level; The analysis data includes at least one of the following: pedestrian flow and pedestrian density. 11. An apparatus for determining a target address, characterized in that the apparatus comprises: An instruction information receiving unit is used to receive information from the terminal indicating candidate regions and target address types; A data establishment unit is used to establish analysis data corresponding to the candidate region based on the association information within the geographical location range of the candidate region received by the instruction information receiving unit. The association information includes at least user location information within the geographical location range of the candidate region. The target address determination unit is configured to determine whether the candidate region is a target address based on the target address type of the analysis data and indication information receiving unit established by the data establishment unit. The target address determination unit includes a judgment unit, which is used to determine whether the candidate area meets the predetermined conditions of the target address type based on the analysis data and using a site selection model, wherein the site selection model is trained using existing target address type location information in the map data as sample data. 12. The apparatus according to claim 11, wherein the data establishment unit comprises: Grid division unit, used to divide a map into grids; A grid set determination unit is used to determine the grid set associated with the candidate region; The data analysis unit is used to create analysis data within the geographical location range corresponding to the grid set. 13. The apparatus according to claim 12, wherein the mesh set determination unit comprises: A grid identification unit is used to identify the grid. A grid identifier determination unit is used to determine the identifiers of grids that intersect with the candidate region; The relevant grid set determination unit is used to determine the grid set related to the candidate region based on the identifiers of the grids intersecting with the candidate region as determined by the grid identifiers. 14. The apparatus according to claim 13, wherein the grid identification unit comprises: Boundary point coordinate acquisition unit, used to acquire the boundary point coordinates of the candidate region; The target mesh search range determination unit is used to determine the target mesh search range based on the boundary point coordinates of the candidate region obtained by the boundary point coordinate acquisition unit. The grid filtering unit is configured to traverse the grids on the boundary of the candidate region within the target grid search range determined by the target grid search range determination unit, determine whether the grids are fully or partially covered by the candidate region, and if so, determine that the current grid is a grid intersecting with the candidate region and record the identifier of the grid. 15. The apparatus according to claim 12, wherein the analysis data establishment unit comprises: A grid analysis data creation unit is used to create analysis data for each grid in the grid set; The candidate region analysis data generation unit is used to generate analysis data within the geographical location range corresponding to the grid set related to the candidate region based on the analysis data of each grid established by the grid analysis data establishment unit. 16. The apparatus according to claim 15, wherein the grid analysis data establishment unit comprises: A data point determination unit is used to determine data points within the geographical location range corresponding to the grid based on the user location information. The association information statistical analysis unit is used to perform statistical analysis on the association information of the data points determined by the data point determination unit to establish the analysis data.