TW201633241A - Target address determining method and device - Google Patents

Abstract

The invention discloses a target address determining method and device. According to one specific embodiment, the method includes: from a terminal, receiving information indicating a candidate area and type of a target address; based on associated information in a geographical position range of the candidate area, establishing analytic data corresponding to the candidate area; based on the analytic data and the type of the target address, determining whether or not the candidate area is the target address. The associated information includes at least user positioning information in the geographical position range of the candidate area. The target address determining method and device has the advantages that the dimensionality of the information for determining the target address can be expanded and target address selection is more accurate and reliable.

Description

Method and apparatus for determining a target location

The present application relates to the field of computer technology, and in particular to the field of electrical digital data processing technologies, and in particular, to a method and apparatus for determining a target location.

Site selection refers to the process of demonstrating and making decisions about a location prior to construction or investment. The determination of the target location is closely related to the geographical location of the candidate area, the surrounding environment, the consumption level of residents, and the consumption habits. Most of the existing site selections are artificial behaviors. The target location is determined manually based on the surrounding passenger traffic and consumption levels of the collected settings. When the information obtained by the decision makers is not comprehensive or the decision makers have insufficient experience, It greatly affects the accuracy of site selection. Therefore, it is necessary to provide an accurate and reliable target location determination method.

To address the above deficiencies, the present application provides methods and apparatus for determining a target location.

In a first aspect, the present application provides a method for determining a target location. The method includes: receiving information indicating a candidate area and a target location type from the terminal; and establishing, according to the association information in the geographic location range of the candidate area, the analysis data of the corresponding candidate area, where the related information includes at least the geographical range of the candidate area. User location information; based on the analysis data and the target location type, determine whether the candidate region is the target location.

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

In a third aspect, the present application provides an apparatus for determining a target location, the apparatus comprising: a processor; and a storage, wherein the storage stores computer readable instructions when the computer readable instructions are processed When the processor is executed, the processor: receiving information indicating a candidate area and a target location type from the terminal; and establishing, according to the association information in the geographic location range of the candidate area, analysis data corresponding to the candidate area, the association The information includes at least user positioning information within a geographic location range of the candidate area; and determining, based on the analysis data and the target location type, whether the candidate area is a target address.

In a fourth aspect, the present application provides a non-volatile computer storage medium storing computer readable instructions, when the computer readable instructions are executed by a processor, the processor: receiving a candidate area and a target from a terminal Information of the location type; based on the association information in the geographic location range of the candidate area, establishing analysis data corresponding to the candidate area, the association information including at least user positioning information in a geographical location range of the candidate area; Determining whether the candidate region is a target address based on the analysis data and a target location type.

The method and apparatus for determining a target location provided by the present application, by receiving information indicating a candidate region and a target location type from a terminal, and then establishing analysis data of the corresponding candidate region based on the association information in the geographic location range of the candidate region, and finally based on Analyze the data and the type of target location, determine whether the candidate region is the target location, expand the dimension of the information used to determine the target location, and improve the accuracy and reliability of the target location selection.

The present application will be further described in detail below in conjunction with the drawings and embodiments. It is understood that the specific embodiments described herein are merely illustrative of the invention, rather than the invention. It should also be noted that, for ease of description, only parts related to the related invention are shown in the drawings.

It should be noted that the embodiments in the present application and the features in the embodiments may be combined with each other without conflict. The present application will be described in detail below with reference to the drawings in conjunction with the embodiments.

Please refer to FIG. 1, which shows a flowchart 100 of an exemplary implementation of a method for determining a target location of the present application. This embodiment is mainly illustrated by the method applied to a server having a large data processing capability. It can be understood that the server is a generalized server, and may be a server, or a server cluster or a server. The server group that shared the data. The method for determining a target location includes the following steps:

In step 110, information indicating the candidate area and the target location type is received from the terminal.

In this embodiment, the terminal can exchange data with the server. The terminal may include a mobile terminal and a fixed-point set terminal that can collect and store data. For example, the terminal can be a smart phone, a tablet, a smart watch that includes a display screen, or a terminal that records and displays user information for a location store (such as a host and display screen for recording member information).

When determining the target location, the user may select the candidate region and the type of the target location to be determined on the map displayed by the terminal. For example, the user may circle a range as a candidate region on the map displayed by the terminal. The terminal may then send information indicating the candidate area and the target location type to the server, wherein the information indicating the candidate area may include the candidate area selected by the user and the zoom ratio of the map, and the server may receive the indication candidate area and the target location. Type of information. The manner of receiving may include receiving through a wireless connection and a wired connection.

Optionally, before receiving the information, the server may also obtain a data exchange request of the terminal, and verify the data exchange request, and receive the information when the verification is passed.

Next, in step 120, the analysis data corresponding to the candidate region is established based on the association information in the geographic location range of the candidate region.

In this embodiment, after receiving the information indicating the candidate area and the target location type, the server may determine the geographic location range of the candidate area, and obtain the related information in the geographical location by searching in the database saved by the server. .

The association information may be information associated with the determination of the target location within the geographic location, and at least includes user location information within the geographic location range corresponding to the candidate region. In some implementations, the associated information can include dynamic information and static information. The dynamic information may include information of users who are active in the geographic location. Optionally, the user information may include the user's age, gender, constellation, hobbies, address, occupation, assets, long-term interests, consumption frequency, and consumption type. Average consumption level information. Static information may include geographic attribute information within a geographic location, such as information on transportation sites within a geographic location, store rental information, and various types of venue information. The server may determine, according to the user positioning information, whether the user is in a geographical location corresponding to the candidate area, that is, whether the information of the user is related information in the geographic location according to the location of the user.

In some optional implementations, the server may receive the associated information in response to the associated information return request of the terminal. In other implementations, the server may obtain associated information from other data storage centers, such as the cloud. The acquisition of related information may be collected or updated in a certain period of time, such as collecting or updating once a day.

After obtaining the associated information within the geographic location of the candidate area, the server may establish an analysis data corresponding to the candidate area. Specifically, the server may perform classification, statistics, and the like on the acquired related information, thereby establishing analysis data corresponding to the candidate region. The statistical processing may include: calculating the total amount, calculating the average value, the statistical distribution law (for example, calculating the distribution function), calculating the mean square error, and the like. Correspondingly, the analysis data may include classification information, average value, total value, distribution law data, mean square error and the like, which may be data at a certain time point or data within a certain period of time. For example, if the associated information is the asset information of the user, the server may obtain an average value of the assets of the user in the geographical location based on the average of the asset information of at least some users in the geographic location corresponding to the candidate area stored in the database. Level information. For another example, if the associated information is the user's consumption type information, the server may classify the consumption type of at least some users in the geographical location (for example, the consumption is divided into the types of catering, entertainment, shopping, and living services, and the calculation is performed. The number of users of each type of consumption, and then the proportion of users of each type of consumption in the total number of users), the results of the classification statistics as the analysis data of the corresponding candidate areas.

In some alternative implementations, the analytical data can include at least human flow or human flow density. The server performs statistical analysis on the user positioning information, and can obtain the distribution statistics of the user's geographical location, thereby obtaining the human flow or the flow density data.

Thereafter, in step 130, based on the analysis data and the target location type, it is determined whether the candidate region is the target location.

In this embodiment, the server may make a decision on whether the candidate region is the target location according to the analysis data of the candidate region and the target location type in the indication information obtained in step 120. For example, the analysis data of the existing location may be established based on the information of the location of the existing target location type, and the characteristics of the analysis data of the existing location may be analyzed to determine whether the characteristics of the analysis data of the candidate region are related to the existing location. The characteristics of the data are consistent to determine whether the candidate region is the target location.

In some optional implementations, determining whether the candidate region is the target location based on the analysis data and the target location type may include: determining, according to the analysis data, whether the candidate region satisfies a predetermined condition of the target location type, to determine whether the candidate region is target location. In some implementations, the predetermined condition may be set by the user based on empirical values, such as by a user setting a human flow threshold, an average asset threshold, and the like. In other implementations, the predetermined conditions may also be determined based on the training results of the existing materials. Optionally, the location model may be used to determine whether the candidate region satisfies a predetermined condition of the target location type, and the location model may be used as a sample data from a location or merchant information of an existing target location type in the map data. Trained. Further, the location model can be established by methods such as logistic regression. Specifically, the acquired analysis data may be used as a feature, and after the feature is normalized, weights are assigned to each feature, and then the feature is input into the model to determine whether the candidate region is the target location. The model can be trained by information on other location-type locations or business information already available on the map.

It can be understood that, in the embodiment, the association information may include multiple types of association information, and the analysis data acquired by the server may also include multiple types of analysis data obtained by analyzing multiple types of association information. The more types of related information, the larger the amount of data to be processed by the server, and the larger the amount of data of the analyzed data, the wider the dimension of information collection and the higher the accuracy of the target location determination.

The method provided by the foregoing embodiment of the present application, by receiving information indicating a candidate area and a target location type from a terminal, and then establishing association data of the candidate candidate area based on the association information in the geographic location range of the candidate area, and finally based on the analysis data and the target The location type determines whether the candidate region is the target location, which can effectively reduce the labor cost, expand the dimension of the information used to determine the target location, and improve the accuracy and reliability of the target location selection.

For the above embodiment of the present application, the scenario of the application may be: when the user needs to determine the target location, first select the candidate region and the type of the target location on the terminal, and the server receives the candidate region and the target type indication selected by the user. In the information, the related information in the geographical range of the candidate area is analyzed to obtain the analysis data for determining whether the candidate area is the target position. Finally, the server determines based on the type of the analysis data and the target location, and determines the candidate. Whether the area is the target location.

Taking the location of the selected restaurant on the map as an example, when the user sets the map zoom ratio to 1:10 ⁴ and selects a certain area of Haidian District of Beijing as the candidate area, the server can receive the user from the user's mobile phone or computer terminal. Information about the designated candidate area and the target location type (food and beverage), user location information, consumption level, user consumption type, hobbies, surrounding traffic information (such as subway station, bus station information), and rent-to-sale for the candidate area on the map. The information such as the location is analyzed and counted, and the analysis data of the candidate location is established, for example, calculating the flow rate of the candidate area, the average consumption level, analyzing the user's consumption habits, and the convenience of transportation, etc., based on the data, determining whether the candidate area is suitable for opening a restaurant. position. When the analysis data satisfies predetermined conditions, for example, when the flow rate exceeds a certain threshold or the proportion of food consumption to total consumption exceeds a certain threshold, it can be determined that the candidate area is a position suitable for opening a restaurant.

With further reference to FIG. 2, a flowchart of an exemplary implementation of an exemplary implementation of the analysis data for a corresponding candidate region, that is, a flowchart of an exemplary implementation of the method step 120, is shown. The process 200 of the method for establishing the analysis data corresponding to the candidate region includes the following steps:

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

In this embodiment, the server may divide the map into multiple levels of grids. In some implementations, the map may be divided into multiple grids with a certain length, for example, 10 km, 5 km, respectively. 2 km, 1 km, 500 m, 200 m, and 100 m are used to construct a multi-level grid on the map. The constructed grid can be rectangular. After receiving the information indicating the candidate area and the target location type, the server may determine the side length of the grid to be divided by the server based on the map scaling and the candidate area included in the indication information. For example, when the candidate area is large and the map scaling is 1:10 ⁵ , the corresponding grid can be constructed on the map with the actual distance of 1 km as the side length; and in the candidate area is smaller, the map scaling ratio is 1: At 10 ³ o'clock, the corresponding grid can be constructed on the map with a practical distance of 200 meters.

Figure 3 shows a schematic diagram of the effect of meshing on a map. As shown in Figure 3, the left and right scales are different. The maps of a certain area in the two maps are evenly divided into multiple grids of equal size. The geographic locations of each grid do not intersect each other. As the scale changes, the size and/or number of grids can also change.

Returning to Figure 2, in step 220, a set of meshes associated with the candidate regions is determined.

In this embodiment, the server may determine the set of meshes associated with the candidate regions based on the locations of the mesh and candidate regions segmented in the step 210 on the map.

In an optional implementation manner, the set of meshes related to the candidate region may be determined by: determining a mesh intersecting the candidate region, and then marking the mesh intersecting the candidate region, according to the candidate region The labels of the intersecting grids determine the set of grids associated with the candidate regions. Intersecting with the candidate region means that the mesh is completely covered or partially covered by the candidate region. The server can mark each level of the grid and then determine the grid that intersects the candidate area and record the labels of those grids. All of the meshes intersecting the candidate regions constitute the above-described mesh set, in which each mesh is numbered and the like.

Then, in step 230, the analysis data within the geographic location corresponding to the grid set is established.

In this embodiment, the server may acquire the analysis data of each grid in the foregoing grid set, and summarize the analysis data of all the grids in the grid set, and use the summarized data as the geographic corresponding to the grid set. Analytical data within the location range to establish analytical data within the geographic location corresponding to the candidate region.

With further reference to FIG. 4, a flowchart 400 of one exemplary implementation of determining an indication of a grid intersecting the candidate area is shown. The method of determining a grid indication intersecting a candidate area may include:

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

In general, the candidate area can be a continuous and closed area. In this embodiment, the server may establish a two-dimensional coordinate system on the map, acquire the boundary of the candidate region, and determine the coordinates of the boundary point.

Next, in step 420, the target mesh search range is determined based on the boundary point coordinates of the candidate region.

In this embodiment, the target mesh may be a mesh in the map mesh that intersects the candidate region. The server may determine the search range of the target mesh according to the boundary point coordinates obtained in step 410. In some implementations, a target grid search range of a rectangle (minimum circumscribed rectangle) can be constructed with the minimum and maximum values of the horizontal and vertical coordinates of all boundary points as vertex coordinates, thereby speeding up the search.

Thereafter, in step 430, the mesh on the boundary of the candidate region within the target mesh search range is traversed to determine whether it is a fully covered or partially covered mesh of the candidate region.

Since the candidate regions are typically continuous and closed regions, it is possible to traverse only the meshes on the boundaries of the candidate regions.

In some embodiments, the server may also traverse all of the meshes within the target mesh search range to determine whether the mesh is fully covered or partially covered by the candidate regions, ie, exclude those meshes that do not intersect the candidate regions.

Thereafter, in step 440, if the mesh on the boundary of the candidate region is a mesh that is completely covered or partially covered by the candidate region, determining that the current mesh is a mesh intersecting the candidate region, and recording the mesh Marked.

When the mesh on the boundary of the candidate region is a mesh that is covered or partially covered by the candidate region, the mesh may be determined to be a mesh that intersects the candidate region.

In some implementations, when the candidate region is a regular shape, such as a rectangular region, a mesh indication intersecting the candidate region may be determined by calculating a lower left coordinate (x ₁ , y ₁ ) and an upper right coordinate (x) of the rectangular region. ₂ , y ₂ ), the interval is the side length of the grid, then the grid mark of any point (x, y) in the range of the rectangle can be obtained by the following conversion formula: (grid x , grid y)=(x/ Interval, y/interval), where x ₁ <x< x ₂ , y ₁ <y< y ₂ , can also directly use the coordinates (x, y) of any point in the grid as the two-dimensional indication of the grid.

With further reference to FIG. 5a, a schematic diagram of the effect of a set of meshes associated with candidate regions when the candidate region is rectangular is shown. As shown in FIG. 5a, the map is divided into a plurality of grids of equal size, and the candidate area selected by the user is a rectangular area 510 surrounded by a broken line. First, the coordinates of the boundary points of the rectangular region can be calculated, the search range of the target mesh is drawn, that is, the mesh of the shaded region 520 in FIG. 5a, and then the mesh on the boundary of the candidate region within the target mesh search range 520 is traversed. A grid is used to determine whether these grids are all covered or partially covered by the candidate regions. In FIG. 5a, the meshes on the boundary of the candidate region in the target mesh search range 520 are all meshes partially covered by the candidate regions, and then all the meshes in the target mesh search range are determined to be the mesh intersecting the candidate regions. grid.

With further reference to FIG. 5b, a schematic diagram of the effect of the set of meshes associated with the candidate regions when the candidate regions are irregular shapes is shown. As shown in Figure 5b, the candidate area is the area 530 surrounded by a dashed line. First, the coordinates of the boundary points of the region are calculated, the search range of the target mesh is extracted, that is, the mesh of the rectangular region 540 in FIG. 5b, and then the mesh on the boundary of the candidate region within the target mesh search range 540 is traversed. It is determined whether these grids are grids that are all covered or partially covered by the candidate regions. If the coordinates of any point in the grid are used as the indication of the grid, then in Figure 5b, the boundaries of the candidate regions within the target grid search range 540 are labeled (x ₃ , y ₃ ) and (x ₄ , y ₃ ). The grid is a grid covered by all candidate regions, labeled (x ₂ , y ₂ ), (x ₂ , y ₃ ), (x ₂ , y ₄ ), (x ₂ , y ₅ ), (x ₂ , y ₆ ), (x ₃ , y ₅ ), (x ₃ , y ₄ ), (x ₄ , y ₄ ), (x ₅ , y ₄ ), (x ₅ , y ₃ ), (x ₄ , y ₂ ) The mesh of (x ₄ , y ₂ ), (x ₃ , y ₂ ) is a mesh partially covered by the candidate region, and it is determined that the mesh in the range of the shadow region 550 within the target mesh search range is intersected with the candidate region. Grid.

The method provided in the above embodiment determines the target mesh search range, and then filters the mesh on the boundary of the candidate region in the target mesh search range to determine whether it belongs to the mesh of the candidate region that is completely covered or partially covered, if not Then remove it from the target mesh search range, and finally get the indication of the mesh that intersects the candidate region. The server may further determine a set of meshes associated with the candidate regions based on the indications of the grids to provide grid data for the analysis of the candidate regions.

Further, when the mesh on the boundary of the candidate region is a mesh partially covered by the candidate region, the analysis data in the geographical range corresponding to the partially covered mesh may be weighted and adjusted. Since the boundary part of the candidate area does not completely cover the grid in which it is located, the analysis data of the candidate area may be incorrect. For example, if the analysis data is human traffic, if the boundary part of the candidate area is directly covered, the grid data may not be completely covered. After being passed back to the server, the analysis data created will generate errors, which will affect the accuracy of the target position 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 the error, the area of each mesh covered by the boundary part of the candidate area may be calculated first, and the ratio of the area to the total area of the grid is obtained, and the ratio is used as a weighting factor, multiplied by the grid. The analysis data is used as the return value. In some cases, for example, when the analysis data is the average value, the density data, and the distribution data, the weighting may be omitted or the weighting factor may be 1, that is, the analysis data is directly used as the return value.

With further reference to FIG. 6, a flow diagram of an exemplary implementation of the analysis data within the geographic location range corresponding to the set of grids, ie, an exemplary implementation flow 600 of step 230, is illustrated. As shown in FIG. 6, the analysis data in the geographic location corresponding to the set of the grid may include:

In step 610, analysis data is created for each of the grids in the set of meshes;

In this embodiment, the server can associate the grid with the analysis data to establish analysis data for each grid. In some implementations, the server may determine the user's grid location based on the acquired user location information, and associate the analysis data based on the other information statistical analysis of the user with the determined grid. The analysis data may include classified information, average value, total value, distribution law data, mean square error and the like, and may be data at a certain time point or data within a certain period of time.

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

Next, in step 620, based on the analysis data of each grid, the analysis data in the geographic location range corresponding to the candidate region-related mesh collection is generated.

The server summarizes the analysis data of all the grids in the grid set related to the candidate region, and then generates the analysis data in the geographic location range corresponding to the grid set related to the candidate region.

With further reference to FIG. 7, a flow diagram of an exemplary implementation of establishing analysis data for each grid, that is, a flow 700 showing an exemplary implementation of step 610, is shown. As shown in FIG. 7, the establishing the analysis data for each grid may include:

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

After obtaining the user positioning information in the geographic location corresponding to the candidate area, the server may determine the geographic location of the user according to the user positioning information, and find or calculate a grid corresponding to the geographic location, and determine that the user corresponds to the grid. A geographic point of geographic location. By performing the above processing on the positioning information of the user with a large amount of data, multiple data points can be acquired within the geographical range corresponding to each grid.

Next, in step 720, statistical analysis of the associated information of the data points is performed to establish analytical data.

After obtaining multiple data points in the geographic location corresponding to each grid, the corresponding related information of each data point (such as a user) in the grid may be analyzed and analyzed, such as performing summation calculation, and statistical analysis will be performed. The results are used as analytical data for the grid.

In some embodiments, the analysis data may also be established for each grid by first determining a grid corresponding to the geographic location of the user based on the user location information, and then within the geographic location corresponding to the same grid. When the related information is used for statistics, the analysis data of the grid can be obtained. The following is an example of establishing human traffic data for each grid, and specifically describes the process by which the server establishes analysis data for each grid in this way. First, the server can obtain the coordinates (x, y) of the user through the terminal (for example, through the positioning log of the mobile phone), and then calculate the grid mark (grid x, grid y) corresponding to the coordinates (x, y), and count the same time. The number of users with the same grid mark in the same point or the same time period, that is, the flow data of multiple grids can be obtained.

In a further embodiment, the method for determining a target location provided by the present application may further include: transmitting, to the terminal, analysis data within a geographic location range corresponding to the set of meshes, for the terminal to use the mesh with the Analytical data is presented in association. For example, the server may send the terminal traffic data in the geographic location corresponding to the grid to the terminal, and after receiving the indication information, the terminal presents the human traffic and the grid in association with the grid to analyze the data. Intuitive display to the user. In some implementations, the manner of rendering can include rendering. Specifically, different colors can be rendered depending on the size of the human traffic. For example, a region with a large flow of people may be rendered in red, and a region with a small flow of traffic may be rendered in green, or when the flow of people within a geographic location corresponding to the grid on the local map is gradually increased, the grid is The color rendering can be from the initial green, gradually changing to yellow, and then gradually changing to red. In addition, the above time may also be an instant time. In this case, the specific data of the analysis data may be displayed on the corresponding geographic information point in real time; or the grid and the analysis data may be displayed by the dynamic effect (for example, the concentration change of a single color) to reflect the instant. Sex.

Those of ordinary skill in the art will appreciate that other presentations now known or later developed may be used for the presentation of analytical data, such as directly displaying the analytical data on a map, or in response to user grid selection operations. Analyze the data and the like on the grid associated with the candidate area. This application has no limitation on the presentation of analytical data.

With further reference to FIG. 8, there is shown a schematic diagram of an effect of associating the analysis data in the geographic location range corresponding to the grid and the grid. As shown in FIG. 8, the map of the terminal presents the flow of people within the geographic location corresponding to the grid and the grid. For example, it can be seen that the traffic volume in the geographic location corresponding to the grid 810 in the figure is large, and the traffic volume in the geographic location corresponding to the grid 820 is small. The terminal displays the analysis data in the geographic location corresponding to the grid sent by the server, and presents the grid in association with the analysis data, so that the analysis data of the grid can be visually displayed on the map.

Please refer to FIG. 9, which shows a block diagram of an exemplary implementation of the apparatus for determining a target location of the present application.

As shown in FIG. 9, the apparatus 900 for determining a target location may include: an indication information receiving unit 910, a data establishing unit 920, and a target location determining unit 930. The indication information receiving unit 910 can be configured to receive information indicating the candidate area and the target location type from the terminal, and the data establishing unit 920 can be configured to establish a correspondence according to the association information in the geographic location range of the candidate area acquired by the indication information receiving unit 910. The analysis data of the candidate region, the target location determining unit 930 can be used to determine whether the candidate region is the target location based on the analysis data established by the data establishing unit 920 and the target location type acquired by the information receiving unit 910. The association information includes at least user positioning information in a geographical location corresponding to the candidate area.

In this embodiment, the server including the device can exchange data with the user terminal. When determining the target location, the user may select the candidate region and the type of the target location to be determined on the map displayed by the terminal, and the terminal may send information indicating the candidate region and the target location type to the indication information receiving unit 910, where the candidate region is indicated. The information may include a candidate area selected by the user and a zoom ratio of the map, and the information receiving unit 910 may receive information indicating the candidate area and the target location type. The manner of receiving may include receiving through a wireless connection and a wired connection.

In this embodiment, after the information receiving unit 910 receives the information indicating the candidate area and the target location type, the data establishing unit 920 may determine the geographic location range of the candidate area according to the indication information, and obtain the information by searching in the database. Linked information within the geographic location. At the same time, the data establishing unit 920 can determine whether the user is in the geographical location range corresponding to the candidate area according to the user positioning information, that is, whether the information of the user is the related information in the geographical location according to the location of the user.

After acquiring the association information within the geographic location range of the candidate region, the data establishing unit 920 may establish the analysis data corresponding to the candidate region. Specifically, the data establishing unit 920 may perform processing such as classification, statistics, and the like on the acquired related information, thereby establishing analysis data corresponding to the candidate region.

In some alternative implementations, the analytical data can include at least human flow or human flow density. The data establishing unit 920 performs statistical analysis on the user positioning information, and can obtain the distribution statistics of the user's geographical location, thereby obtaining the human flow or the flow density data.

The target location determining unit 930 can make a decision on whether the candidate region is the target location according to the analysis data of the candidate region obtained by the data establishing unit 920 and the target location type in the indication information received by the information receiving unit 910. For example, the analysis data of the existing location may be established based on the information of the location of the existing target location type, and the characteristics of the analysis data of the existing location may be analyzed to determine whether the characteristics of the analysis data of the candidate region are related to the existing location. The characteristics of the data are consistent to determine whether the candidate region is the target location.

In some optional implementations, the data establishing unit 920 of the apparatus 900 may include a mesh dividing unit, a mesh set determining unit, and an analysis data establishing unit. The mesh dividing unit may be configured to divide the mesh on the map, the mesh set determining unit may be configured to determine a mesh set related to the candidate region, and the analysis data establishing unit may be used to establish a geographic location corresponding to the mesh set. Analytical data within the scope.

In some optional implementations, the apparatus may further include: an associated information receiving unit 940 (not shown) for receiving the associated information in response to the associated information report request of the terminal. The acquisition of related information may be collected or updated in a certain period of time, such as collecting or updating once a day. At this time, the associated information may be acquired and stored by the association receiving unit 940, and the analysis data providing data support is established for the data establishing unit 920.

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

The units described in the embodiments of the present application may be implemented by software or by hardware. The described unit may also be disposed in the processor. For example, it may be described as a processor including an indication information receiving unit, a data establishing unit, and a target position determining unit. The name of these units does not constitute a limitation on the unit itself in some cases. For example, the indication information receiving unit may also be described as “a unit for receiving indication information”.

In another aspect, the present application further provides a computer readable storage medium, which may be a computer readable storage medium included in the device described in the foregoing embodiment, or may exist separately and not assembled. A computer readable storage medium in the terminal. The computer readable storage medium stores one or more programs that are used by one or more processors to perform the methods described herein for determining a target location.

The above description is only a preferred embodiment of the present application and a description of the principles of the applied technology. It should be understood by those of ordinary skill in the art that the scope of the invention referred to in the present application is not limited to the specific combination of the above technical features, and should also be included without departing from the inventive concept. Other technical solutions formed by any combination of the above technical features or equivalent features thereof. For example, the above features are combined with the technical features disclosed in the present application, but are not limited to the technical features having similar functions.

100‧‧‧Methods for determining the target location

110-130‧‧‧ Target location determination steps

200‧‧‧Methods for establishing analytical data corresponding to candidate regions

210-230‧‧‧Steps for establishing analytical data for candidate regions

400‧‧‧Methods for determining the marking of a grid intersecting a candidate area

410-440‧‧‧Steps to determine the grid intersecting the candidate area

600‧‧‧Methods for establishing analytical data within the geographical range corresponding to the grid set

610-620‧‧‧Steps for establishing analytical data within the geographic location corresponding to the grid set

700‧‧‧Methods for establishing analytical data for each grid

710-720‧‧‧Steps for establishing analytical data for each grid

900‧‧‧Devices for determining the target location

910‧‧‧Instruction receiving unit

920‧‧‧Data establishment unit

930‧‧‧Target location determination unit

Other features, objects, and advantages of the present application will become more apparent from the detailed description of the accompanying drawings.

1 shows a flow diagram of an exemplary implementation of a method for determining a target location of the present application;

2 is a flow chart showing an exemplary implementation of the analysis data for establishing a corresponding candidate region in the present application;

Figure 3 shows a schematic diagram of the effect of dividing a grid on a map;

4 shows a flow diagram of an exemplary implementation of determining an indication of a grid intersecting a candidate area;

FIG. 5a is a schematic diagram showing the effect of a grid set related to a candidate area when the candidate area is a rectangle;

FIG. 5b is a schematic diagram showing the effect of a mesh set related to the candidate region when the candidate region is an irregular shape;

6 shows a flow diagram of an exemplary implementation of establishing analytical data within a geographic location range corresponding to a set of meshes;

Figure 7 shows a flow diagram of an exemplary implementation of establishing analysis data for each grid;

FIG. 8 is a schematic diagram showing the effect of associating the analysis data in the geographical range corresponding to the grid and the grid; and

Figure 9 shows a block diagram of an exemplary implementation of the apparatus for determining a target location of the present application.

100‧‧‧Determining the target location method

S110-S130‧‧‧ Target location determination steps

Claims (22)

A method for determining a target location, comprising: receiving information indicating a candidate region and a target location type from a terminal; and establishing analysis data corresponding to the candidate region based on association information in a geographic location range of the candidate region, The association information includes at least user location information within a geographic location range of the candidate region; and determining whether the candidate region is a target location based on the analysis data and the target location type. The method of claim 1, wherein the establishing the analysis data corresponding to the candidate region comprises: dividing a mesh on the map; determining a mesh set related to the candidate region; establishing the mesh set corresponding to Analytical data within the geographic location. The method of claim 2, wherein the determining a set of meshes associated with the candidate region comprises: marking the mesh; determining an indication of a mesh intersecting the candidate region; The labeling of the meshes at which the candidate regions intersect determines a set of meshes associated with the candidate regions. The method of claim 3, wherein the determining the indication of the mesh intersecting the candidate region comprises: acquiring a boundary point coordinate of the candidate region; determining a target mesh according to a boundary point coordinate of the candidate region Searching the range; traversing the mesh on the boundary of the candidate region within the target mesh search range, and determining whether it is a mesh that is covered or partially covered by the candidate region; if the mesh is covered or partially covered by the candidate region The grid is determined to be the grid intersecting the candidate area, and the label of the grid is recorded. The method of claim 4, wherein if the mesh on the boundary of the candidate region is a mesh partially covered by the candidate region, weighting the analysis data in the geographic location corresponding to the partially covered mesh . The method of claim 2, wherein the establishing the analysis data within the geographic location range corresponding to the set of meshes comprises: establishing analysis data for each of the mesh sets; The analysis data of each grid generates analysis data within a geographic location corresponding to the set of meshes associated with the candidate region. The method of claim 6, wherein the establishing the analysis data for each of the grids comprises: determining, according to the user location information, data points in a geographic location corresponding to the grid; The associated information is statistically analyzed to establish the analytical data. The method of claim 2, wherein the method further comprises: transmitting, to the terminal, analysis data within a geographic location range corresponding to the set of meshes for the terminal to associate the grid with the analysis data Presented. The method of claim 1, wherein the determining whether the candidate region is a target location based on the analysis data and the target location type comprises: determining, according to the analysis data, whether the candidate region satisfies the target A predetermined condition of the location type to determine whether the candidate region is a target location. The method of claim 9, wherein the determining whether the candidate region satisfies a predetermined condition of the target location type comprises: determining, by using an address model, whether the candidate region satisfies a predetermined condition of the target location type, wherein The location model is trained by using the location information of the existing target location type in the map data as sample data. The method of claim 1, wherein the method further comprises: receiving the associated information from the terminal in response to the associated information report request of the terminal. The method of any one of claims 1-11, wherein the associated information further comprises at least one of: a user's age, gender, constellation, hobby, address, occupation, assets, long-term interest, frequency of consumption, type of consumption, Average consumption level information; the analysis data includes at least one of the following: human flow, population density. An apparatus for determining a target location, comprising: an indication information receiving unit, configured to receive information indicating a candidate area and a target location type from the terminal; and a data establishing unit, configured to: based on the geographic location of the candidate area received by the indication information receiving unit Correlation information in the range, the analysis data corresponding to the candidate area is established, the association information includes at least user positioning information in a geographic location range of the candidate area; the target location determining unit is configured to establish a unit based on the data The established analysis data and the indication information receiving unit target location type determine whether the candidate region is the target location. The apparatus of claim 13, wherein the data establishing unit comprises: a mesh dividing unit configured to divide a mesh on a map; and a mesh set determining unit configured to determine a mesh set related to the candidate region And an analysis data establishing unit, configured to establish analysis data within a geographic location corresponding to the set of the grid. The apparatus of claim 14, wherein the grid set determining unit comprises: a grid marking unit for marking the grid; a grid label determining unit, configured to determine a intersection with the candidate area An indication of a grid; a correlation grid set determining unit, configured to determine a grid set related to the candidate area according to the identifier of the grid intersecting the candidate area determined according to the grid label. The apparatus of claim 15, wherein the grid label determining unit comprises: a boundary point coordinate acquiring unit, configured to acquire a boundary point coordinate of the candidate area; a target mesh search range determining unit, configured to a boundary point coordinate of the candidate area acquired by the boundary point coordinate acquiring unit determines a target mesh search range; a mesh screening unit configured to traverse the candidate within the target mesh search range determined by the target mesh search range determining unit A grid on the boundary of the region, determining whether it is a grid that is covered or partially covered by the candidate region, and if so, determining that the current grid is a grid intersecting the candidate region, and recording the label of the grid. The device of claim 14, wherein the analysis data establishing unit comprises: a mesh analysis data establishing unit, configured to establish analysis data for each of the mesh sets; a candidate region analysis data generating unit, And configured to generate, according to the analysis data of each grid established by the grid analysis data establishing unit, analysis data in a geographic location range corresponding to the grid set related to the candidate region. The device of claim 17, wherein the grid analysis data establishing unit comprises: a data point determining unit, configured to determine, according to the user positioning information, a data point in a geographical location corresponding to the grid; The information statistical analysis unit is configured to perform statistical analysis on the related information of the data points determined by the data point determining unit to establish the analysis data. The device of claim 13, wherein the target location determining unit comprises a determining unit, configured to determine, according to the analysis data, whether the candidate region satisfies a predetermined condition of the target location type to determine Whether the candidate area is a target location. The device of claim 19, wherein the determining unit is further configured to determine, by using an address model, whether the candidate region satisfies a predetermined condition of the target location type, wherein the location model is obtained from a map material Location information with a target location type is trained as sample material. A device for determining a target location, the device comprising: a processor; and a storage, wherein the storage stores computer readable instructions, the processor when the computer readable instructions are executed by the processor Receiving, by the terminal, the information indicating the candidate area and the target location type; and establishing, according to the association information in the geographic location range of the candidate area, the analysis data corresponding to the candidate area, where the related information includes at least the candidate area User location information within a geographic location; determining whether the candidate region is a target address based on the analysis data and a target location type. A non-volatile computer storage medium storing computer readable instructions, when the computer readable instructions are executed by a processor, the processor: receiving information indicating a candidate area and a target location type from the terminal; Correlating information in a geographic location of the candidate area, and analyzing data corresponding to the candidate area, where the related information includes at least user positioning information in a geographic location range of the candidate area; based on the analysis data and the target location Type, determining whether the candidate area is a target address.