CN114511125A

CN114511125A - Space division method, device, equipment and medium

Info

Publication number: CN114511125A
Application number: CN202011150243.5A
Authority: CN
Inventors: 莫君贤; 徐灏; 黄骞; 李金�; 周银生
Original assignee: Huawei Technologies Co Ltd
Current assignee: Huawei Technologies Co Ltd
Priority date: 2020-10-23
Filing date: 2020-10-23
Publication date: 2022-05-17

Abstract

The embodiment of the application discloses a space division method, which comprises the following steps: obtaining a first space partition of a target area, wherein the first space partition divides the target area into a plurality of sub-areas; acquiring at least one user attribute data in a target area, wherein the user attribute data is used for recording the moving condition of a user in the target area; and adjusting the sub-areas divided by the first space division according to the user attribute data to obtain a second space division. The application also provides a device, a terminal and a medium, which can input user attribute data based on the first space division of the target area, adjust the first space division of the target area according to the user attribute data, and finally form a space division result superposed with the space and the crowd migration attribute. Compared with the traditional single-dimension space division mode, the method comprehensively considers the factors of the space and the crowd migration, thereby realizing a more scientific space division result.

Description

Space division method, device, equipment and medium

Technical Field

The present application relates to the field of electronics, and in particular, to a method, an apparatus, a device, and a medium for space division.

Background

When an operator optimizes network planning, the city needs to be divided into a plurality of grid units (namely micro grids) according to a certain rule, grid management is carried out, and accurate planning and optimization are realized, so that the income is improved, and the cost is reduced. The micro grid is a minimum block unit of telecommunication total service basic network planning, is also a source of user service requirements, is a basis for measuring and calculating network basic resource requirements, generally has a coverage radius of 100-500 m, has different shapes, can be used in a school, and can be used in a business (CBD) or residential quarter and the like.

In the prior art, space division is based on objective static attributes of geographic space, such as road network, rivers, buildings, population and the like, but the space division is performed based on dimensional data with unchanged space, and the situation of dynamic migration of active population cannot be adapted, so that the current space division mode cannot meet ideal requirements.

Therefore, the above problems in the prior art have yet to be solved.

Disclosure of Invention

The embodiment of the application provides a space division method, a space division device, space division equipment and a space division medium, and the space division can be adjusted according to crowd dynamic migration information.

A first aspect of an embodiment of the present application provides a space division method, including: obtaining a first spatial partition of a target area, the first spatial partition dividing the target area into a plurality of sub-areas; acquiring at least one user attribute data in the target area, wherein the user attribute data is used for recording the moving condition of a user in the target area; and adjusting the sub-areas divided by the first space division according to the user attribute data to obtain a second space division.

In this embodiment, based on the first space division of the target area, the user attribute data is input, and the first space division of the target area is adjusted according to the user attribute data, so as to finally form a space division result in which the space and the crowd migration attribute are superimposed. Compared with the traditional single-dimension space division mode, the method comprehensively considers the factors of the space and the crowd migration, thereby realizing a more scientific division result.

Optionally, the obtaining a first spatial division of the target region includes: dividing the target area to obtain a plurality of sub-areas; constructing an adjacent matrix according to the adjacent relation of the subareas, wherein the adjacent matrix is used for recording the adjacent condition of each subarea in the target area; the adjusting the sub-regions divided by the first space division according to the user attribute data to obtain a second space division, comprising: and merging at least two adjacent sub-areas in the adjacent matrix according to the user attribute data, merging a part of one sub-area into the adjacent sub-area, or splitting at least one sub-area, or deleting a part of the sub-area.

In this embodiment, the adjacent matrix relationship is constructed, and then the adjacent matrix relationship is adjusted based on the user attribute data, so that the space division result in which the space and the crowd migration attribute are superimposed is realized.

Optionally, the acquiring at least one user attribute data in the target area includes: acquiring the movement track of each user in a first sub-area and a second sub-area, wherein the first sub-area and the second sub-area are adjacent sub-areas; the merging of at least two adjacent sub-regions in the adjacent matrix according to the user attribute data includes: acquiring the total length of the movement tracks of all users in the first subarea and the second subarea; obtaining a first user trajectory length spanning the first sub-region and the second sub-region; when the proportion of the first user track length to the total length is larger than or equal to a preset value, the first sub-area and the second sub-area are combined.

In this embodiment, when the ratio of the first user track length to the total length is greater than or equal to the preset value, it is indicated that there are more user traffic between the first sub-area and the second sub-area, and the first sub-area and the second sub-area should be a continuous area according to the crowd migration situation, so that the first sub-area and the second sub-area can be combined into one sub-area, thereby implementing adjustment of the first space division.

Optionally, the acquiring at least one user attribute data in the target area includes: acquiring the residence time length of each user in a first sub-area and a second sub-area, wherein the first sub-area and the second sub-area are adjacent sub-areas; the merging of at least two adjacent sub-regions in the adjacent matrix according to the user attribute data includes: acquiring the total duration of residence time of all users in the first sub-area and the second sub-area; acquiring the total target duration of users respectively staying in the first sub-area and the second sub-area; when the proportion of the target total duration to the total duration is greater than or equal to a preset value, combining the first sub-area and the second sub-area.

In this embodiment, when the ratio of the total target duration to the total target duration is greater than or equal to the preset value, it is indicated that there is a relatively frequent user traffic between the first sub-area and the second sub-area, and the first sub-area and the second sub-area should be a continuous area according to the crowd migration condition, so that the first sub-area and the second sub-area can be combined into one sub-area, thereby implementing adjustment of the first space division.

Optionally, the dividing the target region to obtain a plurality of sub-regions includes: dividing the target area in a geographic space and a time space respectively to obtain a space-time network diagram, wherein the ordinate of the space-time network diagram is used for representing the geographic position information of the sub-area, and the abscissa of the space-time network diagram is used for representing different time periods; after the obtaining of the at least one user attribute data in the target area, the method further includes: matching at least one user attribute data in the target area to the spatio-temporal network diagram to obtain a spatio-temporal movement track of each user, wherein one point in the spatio-temporal movement track represents the geographic position of the user at the current time point; the merging of at least two adjacent sub-regions in the adjacent matrix according to the user attribute data includes: acquiring a target space-time movement track crossing a first sub-area and a second sub-area in the space-time movement track, wherein the first sub-area and the second sub-area are adjacent areas; and when the target space-time movement tracks meeting the preset conditions reach the target number, combining the first sub-area and the second area.

In the embodiment, the satisfaction degree of the crowd time-varying spatial distribution function is evaluated based on the time-space grid data of partial time dimensions, and in the time-space grid network, when the target time-space movement track meeting the preset condition reaches the target number, the first sub-area and the second area are combined, so that the relevance between the adjacent sub-areas can be accurately compared on different time points and space points, and the combination between the sub-areas can be more accurately adjusted. More scientific space division is realized.

Optionally, when the target space-time movement trajectory meets a preset condition, combining the first sub-area and the second sub-area, including: predicting a second half track according to the first half track of the target space-time movement track in the first sub-area or the second sub-area to obtain a first predicted value, wherein the first predicted value is the coincidence degree of the predicted value of the second half track of the first sub-area or the second sub-area and an actual value of the second half track of the first sub-area or the second sub-area; predicting a second half-section track according to the first half-section track of the target space-time movement track to obtain a second predicted value, wherein the second predicted value is the coincidence degree of the predicted value of the second half-section track of the target space-time movement track and the actual value of the second half-section track of the target space-time movement track; and when the ratio of the second predicted value to the first predicted value meets a preset value, combining the first sub-area and the second sub-area.

In this embodiment, when the second predicted value is greater than the first predicted value, it is stated that, in the same target space-time movement trajectory, after merging, the prediction accuracy of the distribution function for the trajectory meets the requirement. The activity habit of the user crossing the first sub-area and the second sub-area corresponding to the target space-time movement track can be judged to meet the expectation of the partial function, and the crossing activity of the user between the first sub-area and the second sub-area belongs to normalized activity. Therefore, it can be judged that the first sub-area and the second sub-area should be merged into the same sub-area.

Optionally, the obtaining at least one user attribute data in the target area includes: acquiring a wiring diagram for cell switching of a user in a target area, wherein two ends of each wiring in the wiring diagram respectively represent a switched-out cell and a switched-in cell, and the switching times between the switched-in cell and the switched-in cell are greater than or equal to a preset value; the merging of a portion of one sub-region into an adjacent sub-region includes: when a first connecting line crossing two adjacent sub-areas appears in the connecting line graph, the cells at two ends of the first connecting line are merged into the same sub-area.

In the embodiment, the switching condition between each cell in the sub-regions is accurately obtained through the wiring diagram of cell switching, so that the detailed crowd migration condition is known, and fine adjustment between the sub-regions is realized. More accurate region division adjustment can be realized for the sub-regions.

Optionally, the deleting the partial area in the sub-area according to the user attribute data includes: and when the target cell in the first sub-area is not connected with other cells on the connection line graph, deleting the area where the target cell is located from the first sub-area, wherein the first sub-area is one sub-area in the target area.

In the embodiment, the switching condition between each cell in the sub-regions is accurately obtained through the wiring diagram of cell switching, so that the detailed crowd migration condition is known, and fine adjustment between the sub-regions is realized.

Optionally, after the constructing the adjacent matrix according to the adjacent relationship of the sub-regions, the method further includes: the method comprises the steps of obtaining information point POI data of a first sub-area and a second sub-area, wherein the first sub-area and the second sub-area are adjacent areas, and the POI data are used for recording geographic information of the first sub-area and the second sub-area; and when the similarity of the POI data components of the first sub-area and the second sub-area is greater than or equal to a preset value, combining the first sub-area and the second sub-area.

In this embodiment, whether sub-regions are combined or not is determined according to the similarity of POI data components between adjacent sub-regions, thereby realizing adjustment of space division.

Optionally, the dividing the target region to obtain a plurality of sub-regions includes: and dividing the target area into a plurality of sub-areas according to the road network data.

In this embodiment, the geographic space of the target area may be divided by the road network data.

A second aspect of the embodiments of the present application provides a space dividing apparatus, including:

an acquisition unit configured to acquire a first spatial division of a target region, the first spatial division dividing the target region into a plurality of sub-regions;

the acquiring unit is further configured to acquire at least one piece of user attribute data in the target area, where the user attribute data is used to record a movement situation of a user in the target area;

and the execution unit is used for adjusting the sub-areas divided by the first space division according to the user attribute data to obtain a second space division.

Optionally, the execution unit is further configured to:

dividing the target area to obtain a plurality of sub-areas;

constructing an adjacent matrix according to the adjacent relation of the sub-areas, wherein the adjacent matrix is used for recording the adjacent condition of each sub-area in the target area;

the adjusting the sub-regions divided by the first space division according to the user attribute data to obtain a second space division, comprising:

and merging at least two adjacent sub-areas in the adjacent matrix according to the user attribute data, merging a part of one sub-area into the adjacent sub-area, or splitting at least one sub-area, or deleting a part of the sub-area.

Optionally, the obtaining unit is further configured to:

acquiring the movement track of each user in a first sub-area and a second sub-area, wherein the first sub-area and the second sub-area are adjacent sub-areas;

the execution unit is further configured to:

acquiring the total length of the movement tracks of all users in the first subarea and the second subarea;

obtaining a first user trajectory length spanning the first sub-region and the second sub-region;

when the proportion of the first user track length to the total length is larger than or equal to a preset value, the first sub-area and the second sub-area are combined.

Optionally, the obtaining unit is further configured to:

acquiring the residence time length of each user in a first sub-area and a second sub-area, wherein the first sub-area and the second sub-area are adjacent sub-areas;

the execution unit is further configured to:

acquiring the total duration of residence time of all users in the first sub-area and the second sub-area;

acquiring the total target duration of users respectively staying in the first sub-area and the second sub-area;

when the proportion of the target total duration to the total duration is greater than or equal to a preset value, combining the first sub-area and the second sub-area.

Optionally, the execution unit is further configured to: :

dividing the target area in a geographic space and a time space respectively to obtain a space-time network diagram, wherein the ordinate of the space-time network diagram is used for representing the geographic position information of the sub-area, and the abscissa of the space-time network diagram is used for representing different time periods;

matching at least one user attribute data in the target area to the spatio-temporal network diagram to obtain a spatio-temporal movement track of each user, wherein one point in the spatio-temporal movement track represents the geographic position of the user at the current time point;

acquiring a target space-time movement track crossing a first sub-area and a second sub-area in the space-time movement track, wherein the first sub-area and the second sub-area are adjacent areas;

and when the target space-time movement track meets a preset condition, combining the first sub-area and the second area.

Optionally, the execution unit is further configured to:

predicting a second half track according to the first half track of the target space-time movement track in the first sub-area or the second sub-area to obtain a first predicted value, wherein the first predicted value is the coincidence degree of the predicted value of the second half track of the first sub-area or the second sub-area and an actual value of the second half track of the first sub-area or the second sub-area;

predicting a second half-section track according to the first half-section track of the target space-time movement track to obtain a second predicted value, wherein the second predicted value is the coincidence degree of the predicted value of the second half-section track of the target space-time movement track and the actual value of the second half-section track of the target space-time movement track;

and when the ratio of the second predicted value to the first predicted value meets a preset value, combining the first sub-area and the second sub-area.

Optionally, the obtaining unit is further configured to:

acquiring a wiring diagram for cell switching of a user in a target area, wherein two ends of each wiring in the wiring diagram respectively represent a switched-out cell and a switched-in cell, and the switching times between the switched-in cell and the switched-in cell are greater than or equal to a preset value;

the execution unit is further configured to:

when a first connecting line crossing two adjacent sub-areas appears in the connecting line graph, the cells at two ends of the first connecting line are merged into the same sub-area.

Optionally, the execution unit is further configured to:

and when the target cell in the first sub-area is not connected with other cells on the connection line graph, deleting the area where the target cell is located from the first sub-area, wherein the first sub-area is one sub-area in the target area.

Optionally, the execution unit is further configured to:

the method comprises the steps of obtaining information point POI data of a first sub-area and a second sub-area, wherein the first sub-area and the second sub-area are adjacent areas, and the POI data are used for recording geographic information of the first sub-area and the second sub-area;

and when the similarity of the POI data components of the first sub-area and the second sub-area is greater than or equal to a preset value, combining the first sub-area and the second sub-area.

Optionally, the execution unit is further configured to:

and dividing the target area into a plurality of sub-areas according to the road network data.

A third aspect of embodiments of the present application provides an electronic device, including: a processor and a memory, the memory to store instructions; the processor is configured to execute the steps of the embodiments provided in the first aspect of the embodiments of the present application according to the instructions.

A fourth aspect of the embodiments of the present application provides a computer-readable storage medium, which includes instructions that, when executed on a computer, cause the computer to perform the steps of the foregoing embodiments provided in the first aspect of the embodiments of the present application.

Drawings

Fig. 1 is a schematic diagram of an embodiment of a space division method provided in an embodiment of the present application;

fig. 2 is a schematic diagram of a first space division in the space division method according to the embodiment of the present application;

fig. 3 is a schematic diagram of another embodiment of a space division method according to an embodiment of the present application;

fig. 4 is a schematic diagram of another embodiment of a space division method according to an embodiment of the present application;

fig. 5 is a schematic diagram illustrating another embodiment of a space division method according to an embodiment of the present application;

fig. 6 is a schematic diagram illustrating another embodiment of a space division method according to an embodiment of the present application;

fig. 7 is a schematic diagram of another embodiment of a space division method according to an embodiment of the present application;

fig. 8 is a schematic diagram illustrating another embodiment of a space division method according to an embodiment of the present application;

fig. 9 is a schematic diagram of another embodiment of a space division method according to an embodiment of the present application;

fig. 10a is a schematic diagram of another embodiment of a space division method according to an embodiment of the present application;

fig. 10b is a schematic diagram of another embodiment of a space division method according to an embodiment of the present application;

fig. 11 is a schematic diagram of an electronic device provided in an embodiment of the present application;

fig. 12 is a schematic diagram of a space dividing apparatus according to an embodiment of the present application.

Detailed Description

The embodiment of the application provides a space division method, a device, equipment and a medium, which can adjust space division according to crowd dynamic migration information.

The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments.

The terms "first," "second," and the like in the description and in the claims of the present application and in the above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments described herein are capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

When an operator optimizes network planning, the city needs to be divided into a plurality of grid units (namely micro grids) according to a certain rule, grid management is carried out, and accurate planning and optimization are realized, so that the income is improved, and the cost is reduced. Meanwhile, when a retailer carries out commercial site selection, the commercial value of a district is required to be considered, the commercial value comprises but is not limited to the people flow law of the district, the commercial state, the rent cost and the like, and a high-value area in a city can be identified by a space grid dividing method so as to find a high-quality site selection area in the city. These requirements all require the partitioning of the plot.

Currently, the method of dividing the area mainly includes two schemes. Firstly, according to administrative regions, natural regions, road network structures, customer distribution and the like, for example, barriers such as road networks, water flows and the like are used for dividing service-intensive regions such as urban regions, suburb counties, developed villages and towns and the like into a plurality of regions, and each region completes the convergence and convergence functions of all services in the region, so that the high-efficiency and low-cost fusion bearing of the services is realized. Dividing a city area into a plurality of grids, and calculating the people flow density of each grid; determining a core mesh from the meshes, the average pedestrian flow density of all meshes within a density range of the core mesh exceeding a predetermined density threshold; taking one core grid as a starting point, and taking all grids in the density range of the core grid as an initial grid cluster to obtain a final grid cluster; and taking each finally obtained grid cluster as a quotient circle.

The first scheme only considers objective geographic distribution, and the second scheme only considers distribution of people stream density. Only one dimension of information is considered, and more dimensions of analysis cannot be performed, so that a more scientific and accurate space division mode cannot be realized.

In order to solve the above problem, embodiments of the present application provide a space partitioning method, which can consider geographic factors and people stream migration factors at the same time, and implement more scientific and reasonable space partitioning. For the sake of understanding, the method provided by the embodiments of the present application will be described in detail below with reference to the accompanying drawings.

Referring to fig. 1, as shown in fig. 1, an embodiment of a space division method provided in the present application includes the following steps.

101. A first spatial division of a target region is obtained.

In this embodiment, the target region is divided into a plurality of sub-regions, so as to obtain a first space division. Specifically, the target area may be divided into a plurality of sub-areas by the road network data. In the geographic information system, the road network data includes administrative regions, natural regions, road network structures, customer distributions, and the like. A POI may be a house, a shop, a mailbox, a bus station, etc. Therefore, the target area is divided according to the geographic information.

Optionally, in order to implement merging and splitting of sub-regions in a subsequent working process, it is necessary to record the adjacent relationship of the plurality of sub-regions in the first spatial partition. The specific implementation mode is as follows: and constructing an adjacent matrix according to the adjacent relation of the sub-areas, wherein the adjacent matrix is used for recording the adjacent condition of each sub-area in the target area. For example, as shown in fig. 2, the target area is divided into nine sub-areas, ABCDEFGHI, and the neighboring matrix of the target area is shown in table 1 below.

TABLE 1

In the adjacent matrix shown in table 1, the number 1 indicates that the two corresponding sub-regions are adjacent regions, and the number 0 indicates that the two sub-regions are not adjacent sub-regions.

102. At least one user attribute data in the target area is obtained.

In this embodiment, the user attribute data is used to record the movement situation of the user in the target area, so that the crowd migration situation in the target area can be known according to the user attribute data. Optionally, the user attribute data may specifically be one, a movement trajectory of the user; secondly, the residence time of the user in each sub-area; thirdly, the track of the user in the space-time network of the target area; or fourthly, one or more of the connection graphs of the cell switching of the user in the target area.

103. And adjusting the sub-areas divided by the first space division according to the user attribute data to obtain a second space division.

Further, the specific implementation manner of the step 103 is as follows: and merging at least two adjacent sub-areas in the adjacent matrix according to the user attribute data, or splitting at least one sub-area, or deleting partial areas in the sub-areas. In the specific working process, the second space division is realized in different ways according to different specific types of the user attribute data, and for convenience of understanding, the following detailed description is given to specific processing methods under different user attribute data types in combination with the accompanying drawings.

Firstly, the user attribute data is the moving track of the user.

In this embodiment, please refer to fig. 3 and 4, which specifically includes the following steps.

301. And acquiring the movement track of each user in the first sub-area and the second sub-area.

In this embodiment, the first sub-region and the second sub-region are adjacent sub-regions, that is, in table 1, the first sub-region and the second sub-region are any two adjacent sub-regions with a numerical value of 1. The first sub-area and the second sub-area are two-dimensional plane graphs, each user is taken as a point on the two-dimensional plane graphs, and the moving path forms the moving track of each user. Optionally, the movement trajectory data of each user includes user identification, time and location data, so that the movement trajectories of the respective users in the first sub-area and the second sub-area can be quantified by the three sets of data.

For example, as shown in fig. 4, the first sub-area 401 and the second sub-area 402 are adjacent sub-areas, and three movement tracks of a 41, b 42, and c 43 are included in the first sub-area 401 and the second sub-area 402, so that user identifiers, time, and position data corresponding to a ethylene propylene copolymer are respectively obtained, and thus the movement tracks of each user in the first sub-area 401 and the second sub-area 402 are recorded.

302. And acquiring the total length of the movement tracks of all the users in the first subarea and the second subarea.

In this embodiment, the total length of the movement tracks of all the users in the first sub-area and the second sub-area is the sum of the movement lengths of each user in the first sub-area and the second sub-area, for example, as shown in fig. 4, the sum of the lengths of three movement tracks, i.e., the total length of the movement tracks of all the users in the first sub-area 401 and the second sub-area 402 is the total length of the movement tracks of each user in the first sub-area and the second sub-area 42 and the third sub-area 43.

303. A first user trajectory length spanning the first and second sub-areas.

In this embodiment, the first sub-area and the second sub-area include a plurality of different movement tracks, wherein some tracks move only in the first sub-area or the second sub-area, and other tracks cross the first sub-area and the second sub-area. These first user trajectory lengths, which span the first and second sub-regions, constitute the migration between the two sub-regions, and these trajectories across the first and second sub-regions may affect the partitioning of the sub-regions.

304. And calculating the proportion of the first user track length to the total length.

In this embodiment, the first user track length is a user track length that spans the first sub-area and the second sub-area, and the total length is a sum of all track lengths of the first sub-area and the second sub-area. For example, as shown in fig. 4, the sum of the lengths of the three movement tracks a 41, b 42, and c 43 is the total length of all the movement tracks of the user in the first sub-area 401 and the second sub-area 402, where the track a 41 is a track crossing the first sub-area 401 and the second sub-area 402, and then the ratio is calculated by: (length of track A41/sum of lengths of three tracks A41, B42 and C43).

305. And when the proportion of the first user track length to the total length is greater than or equal to a preset value, combining the first sub-area with the second sub-area.

And secondly, the user attribute data is the residence time of the user in each sub-area.

In this embodiment, please refer to fig. 5, which specifically includes the following steps.

501. And acquiring the resident time length of each user in the first sub-area and the second sub-area.

In this embodiment, the first sub-region and the second sub-region are adjacent sub-regions. That is, in table 1, the first subregion and the second subregion are any two adjacent subregions having a numerical value of 1. The first sub-area and the second sub-area comprise a plurality of users, the users respectively reside in the first sub-area and the second sub-area for a period of time, and the residence time of the users is obtained. Specifically, the user attribute data includes user identifiers and the residence time of the user corresponding to each user identifier.

502. And acquiring the total duration of residence time of all users in the first sub-area and the second sub-area.

In this embodiment, the users in the first sub-area and the second sub-area include a first type of user who only resides in the first sub-area, a second type of user who only resides in the second sub-area, and a third type of user who resides in the first sub-area and the second sub-area at the same time, and the total duration T1 of the three types of user residences is obtained.

503. And acquiring the total target duration respectively staying in the first sub-area and the second sub-area.

In this embodiment, the total target duration of the first sub-area and the second sub-area respectively, i.e. the total duration t1 of the third type of user stay.

504. And calculating the proportion of the target total time length to the total time length.

In this embodiment, the target total duration is the total duration T1 of the third type of user stay, the total duration is the total duration T1 of the three types of user stay, and T1/T1 is calculated, that is, the ratio of the first sub-area to the second sub-area to span the two areas is known.

505. And when the proportion of the target total duration to the total duration is greater than or equal to a preset value, combining the first sub-area with the second sub-area.

And thirdly, the user attribute data is the track of the user in the spatio-temporal network of the target area.

In this embodiment, please refer to fig. 6 to 8, which specifically includes the following steps.

First, a method for acquiring a spatiotemporal network is introduced, and a method for constructing the spatiotemporal network includes the following steps.

601. And respectively dividing the target area in a geographic space and a time space to obtain a space-time network diagram.

In this embodiment, the ordinate of the spatio-temporal network diagram is used to represent the geographical location information of the sub-regions, and the abscissa of the spatio-temporal network diagram is used to represent different time periods.

In the specific working process, firstly, grid discretization is carried out on the geographic space of the space place, for example, grid average segmentation is carried out according to a square grid with the size of 20m (or even segmentation is carried out according to the shapes of a hexagon and the like), the target area is divided in the geographic space, and then grid discretization is carried out according to the time dimension, for example, grid average segmentation is carried out according to the length of 5 minutes. Thus, a spatiotemporal network diagram as shown in fig. 7 is obtained, in fig. 7, the ordinate of each point is used to represent the geographical location information S of a sub-area, specifically including the coordinate information of the user in the target area, and the abscissa is used to represent different time points T, thereby forming a spatiotemporal grid of the target area in space and time dimensions.

602. And matching at least one user attribute data in the target area into the spatio-temporal network diagram to obtain the spatio-temporal movement locus of each user.

In this embodiment, the user attribute data is matched to the spatio-temporal network diagram to obtain coordinate points as shown in fig. 7, and these coordinate points are connected in series to form a spatio-temporal movement trajectory of the user in the first sub-region 701 and the second sub-region 701. Each user in the target area corresponds to a space-time movement track of the user, and one point in the space-time movement track represents the geographical position of the user at the current time point.

Based on the steps shown in the above steps 601 to 602, the spatio-temporal movement trajectories of all users in the target region are recorded in the obtained spatio-temporal network diagram, so as to obtain user attribute data, and based on these spatio-temporal movement trajectories, the following steps are further performed to realize the adjustment of the sub-region division.

603. And acquiring a target space-time movement track crossing the first sub-area and the second sub-area in the space-time movement track.

In this embodiment, the first sub-region and the second sub-region are adjacent regions, that is, in table 1, the first sub-region and the second sub-region are any two adjacent sub-regions with a numerical value of 1. In the first sub-area and the second sub-area, some target space-time movement tracks crossing the first sub-area and the second sub-area are obtained, and the tracks meeting preset conditions in the target space-time movement tracks are obtained. The following steps are then performed.

604. And when the target space-time movement track meets the preset condition, combining the first sub-area and the second area.

In this embodiment, the method for determining whether the target spatiotemporal movement trajectory meets the preset condition specifically includes the following steps.

1. And predicting the second half track according to the first half track of the target space-time movement track in the first sub-area or the second sub-area to obtain a first predicted value.

In this embodiment, the first predicted value is a coincidence ratio of a predicted value of the second half track of the first sub-area or the second sub-area and an actual value of the second half track of the first sub-area or the second sub-area. As shown in fig. 7, for a target spatiotemporal motion trajectory, the portion of the target spatiotemporal motion trajectory located in the first sub-region 701 is a first sub-trajectory, and the portion of the target spatiotemporal motion trajectory located in the second sub-region 702 is a second sub-trajectory. In a specific working process, the first sub-track may be taken to execute the step 1, or the second sub-track may be taken to execute the step 1. The embodiment of the present application is not limited, and for convenience of understanding, the first sub-trace is taken to perform the step 1 as an example for explanation.

It should be noted that the "first half" and the "second half" described in step 1 and step 2 below are not limited to 50%, and those skilled in the art can arbitrarily allocate the proportions of the first half and the second half as needed, for example, the first half occupies 80% of the total length, and the second half occupies 20% of the total length. The specific implementation process is as shown in fig. 8, and the first 80% of the first sub-track 81 is obtained as the first half track 811. Based on the first half track, a predicted second half track 812 of a 20% length section after the first sub track 81 is predicted by a distribution function. Then, in fig. 8, the coincidence degree between the predicted second-half track 812 and the actual second-half track 813 is compared, and if half of the coordinate points between the predicted second-half track 812 and the actual second-half track 813 coincide with each other, the coincidence degree between the predicted value and the actual value of the second-half track of the first sub-track is 50%, that is, the first predicted value is equal to 50%.

It should be noted that the distribution function is any function capable of predicting a trajectory in the prior art, and a person skilled in the art may select an appropriate algorithm according to actual needs, and the embodiment of the present application is not limited thereto.

Optionally, after the step 1 is completed, the first sub-region and the second sub-region need to be merged, and then a target spatio-temporal movement trajectory of the merged sub-region that spans the first sub-region and the second sub-region is acquired, so as to perform the following step 2. Thereby determining whether the first sub-region and the second sub-region are really suitable for merging.

2. And predicting the second half section of track according to the first half section of track of the target space-time movement track to obtain a second predicted value.

In this embodiment, the second predicted value is a coincidence ratio of a predicted value of the second-half trajectory of the target space-time trajectory and an actual value of the second-half trajectory of the target space-time trajectory. The target spatiotemporal movement trajectory is a trajectory that spans the first sub-region 801 and the second sub-region 802. As shown in fig. 8, the first 80% of the length of the target spatiotemporal movement trajectory 82 is obtained as the first half trajectory 821. Based on the first half-segment trajectory 821, a predicted second half-segment trajectory 822 of a 20% length segment after the target spatio-temporal movement trajectory 82 is predicted by a distribution function. Then, in fig. 8, the coincidence degree between the predicted second-half trajectory 822 and the actual second-half trajectory 823 is predicted, and if 60% of the coordinate points between the predicted second-half trajectory 822 and the actual second-half trajectory 823 are coincided, the coincidence degree between the predicted value and the actual value of the target space-time movement trajectory second-half trajectory is 60%, that is, the second predicted value is equal to 60%.

The distribution functions used in step 1 and step 2 are the same function.

3. And when the ratio of the second predicted value to the first predicted value is greater than a preset value, combining the first sub-area and the second sub-area.

In this embodiment, when the ratio of the second predicted value to the first predicted value is greater than a preset value, for example, when the second predicted value is greater than the first predicted value, and the ratio of the second predicted value to the first predicted value is greater than 100% at this time, or the second predicted value is equal to 90% of the first predicted value, when it is determined that the ratio of the second predicted value to the first predicted value is greater than the preset value, a specific ratio of the preset value may be set by a person skilled in the art according to actual needs, and the embodiment of the present application is not limited thereto.

And when the ratio of the second predicted value to the first predicted value is greater than a preset value, the prediction accuracy of the distribution function to the track is in accordance with the requirement in the same target space-time movement track. The activity habit of the user crossing the first sub-area and the second sub-area corresponding to the target space-time movement track can be judged to meet the expectation of the partial function, and the crossing activity of the user between the first sub-area and the second sub-area belongs to normalized activity. Therefore, it can be judged that the first sub-area and the second sub-area should be merged into the same sub-area.

It should be noted that the target spatio-temporal movement trajectory referred to in the above steps 1 to 3 may be a trajectory of a user in the spatio-temporal network. Namely, a plurality of target space-time movement tracks are arranged between the first subarea and the second subarea. And (3) repeating the steps 1 to 3, calculating all target space-time movement tracks meeting preset conditions in the first sub-area and the second sub-area, and judging to combine the first sub-area and the second sub-area when the ratio of the number of the target space-time movement tracks meeting the preset conditions to all the space-time movement tracks reaches a preset value. Optionally, the target spatio-temporal movement trajectory involved in the above steps 1 to 3 may also be a trajectory in the spatio-temporal network obtained by fitting a function according to the movement data of all users in the first sub-area and the second sub-area. That is, only one target space-time movement track is arranged between the first sub-area and the second sub-area, and the steps 1 to 3 are performed on the target space-time movement track, so that whether the first sub-area and the second sub-area need to be combined can be judged.

And fourthly, the user attribute data is a connection diagram for cell switching of the user in the target area.

In this embodiment, please refer to fig. 9 and 10a, which specifically includes the following steps.

901. And acquiring a connection line graph for cell switching of a user in a target area.

In this embodiment, the Cell may be a Cell concept of a wireless cellular network, and corresponds to a Cell in english, two ends of each connection line in a connection diagram respectively represent a hand-in Cell and a hand-out Cell, and the number of times of handover between the hand-in Cell and the hand-out Cell is greater than or equal to a preset value. The specific method comprises the following steps: according to the switching signaling data, all wireless cells in the space and with the cell switching frequency being greater than a certain threshold (for example, the threshold is 10 switching times per hour) are found, and the connection relationship of the cells in the wireless cell area, that is, the connection diagram, is established. And if the switching frequency between the two cells is less than the threshold value, the connection relation is not established. Optionally, the handover signaling data may be sorted according to frequency, and a cell connection relation graph may be established according to sorting, for example, sorting the first three cells.

902. When a first connection across two adjacent sub-areas appears in the connection diagram, the cells at both ends of the first connection are merged into the same sub-area.

In this embodiment, as shown in fig. 10a, the target area is divided into four areas including a first sub-area 1001, a second sub-area 1002, a third sub-area 1003 and a fourth sub-area 1004 based on the road network information, where the first sub-area 1001 and the second sub-area 1002 are adjacent sub-areas, and a connection diagram is constructed in the above manner between each cell of the first sub-area 1001, it can be seen that two ends of one connection line are respectively connected to the first building 10011 in the first sub-area 1001 and the second building 10021 in the second sub-area 1002. It can be seen that the cell between the first building 10011 and the second building 10021 is a cell with frequent traffic switching, which indicates that there is a large amount of people moving between the two buildings, and therefore the cell in which the second building 10021 is located is included in the range of the first sub-area 1001.

Through the mode, although certain crowd migration situations exist between the first sub-area and the second sub-area, the crowd migration is not large-scale, the crowd migration only occurs in the second building of the second sub-area, and therefore the area where the second building is located is merged into the first sub-area, and therefore fine adjustment of division of the first sub-area and the second sub-area is achieved. The space division becomes more accurate and scientific.

Further, the step 902 describes the case of adding a partial region in the fine adjustment process, and in the actual working process, the partial region may be subtracted in the fine adjustment process according to the connection diagram, which specifically includes the following steps.

903. And when the target cell in the first sub-area is not connected with other cells on the wiring diagram, deleting the area in which the target cell is positioned from the first sub-area.

In this embodiment, as shown in fig. 10a, the first sub-region 1001 is a sub-region in the target region. The target cell 10012 is a cell in the first sub-region, and it can be seen that there is a connection line between cells in the first sub-region 1001, which indicates that there is a large crowd migration between the buildings in the first sub-region 1001, and the buildings should be divided into the same sub-region, however, there is no connection line between the target cell 10012 in the first sub-region 1001 and other cells, which indicates that there is no crowd migration between the target cell 10012 and other cells in the first sub-region 1001, so that the region where the target cell 10012 is located is removed from the first sub-region 1001, thereby implementing fine adjustment on the first sub-region 1001 and removing the portion that does not belong to the first sub-region 1001.

In the embodiment, the switching condition between each cell in the sub-regions is accurately obtained through the wiring diagram of cell switching, so that the detailed crowd migration condition is known, and fine adjustment between the sub-regions is realized. Compared with other modes provided by the embodiment of the application, the method and the device can realize more accurate region division adjustment on the sub-regions.

It should be noted that, the above four ways of providing the user attribute data are provided, and according to the difference between the four user attribute data, the method provided in the embodiment of the present application adopts different ways to adjust the sub-area in the target area. In a specific working process, on the basis of dividing a target area to obtain a first space division, the first space division may be further adjusted by point of information (POI) data, and for convenience of understanding, this manner is described in detail below with reference to the accompanying drawings.

Referring to fig. 10b, as shown in fig. 10b, another implementation manner of further adjusting the first space division in the embodiment of the present application includes the following steps.

11. And acquiring POI data of the information points of the first sub-area and the second sub-area.

The first sub-region and the second sub-region are adjacent regions, that is, in table 1, the first sub-region and the second sub-region are any two adjacent sub-regions with a value of 1. The POI data is used to record the geographical information of the first and second sub-areas, for example, one component in the POI data may be a house, a shop, a mailbox, a bus station or a school, etc. In this step, POI information of the first sub-area and the second sub-area is obtained respectively.

12. And comparing the similarity between the POI data of the first sub-area and the POI data of the second sub-area.

In this embodiment, the POI data of the first sub-area and the second sub-area have respective components, for example, the POI data of the first sub-area includes a school, a station, and a shop, and the POI data of the second sub-area includes a school, a post office, and a hospital.

13. And when the similarity of the POI data components of the first sub-area and the second sub-area is greater than or equal to a preset value, combining the first sub-area and the second sub-area.

In this embodiment, when the similarity of the POI data components of the first sub-area and the second sub-area is greater than or equal to the preset value, it is described that the two sub-areas include a common part, for example, the POI data of the first sub-area includes 80 school-type POIs, 10 station-type POIs, and 10 shop-type POI data, and after subject analysis, it is determined that the area is a main component of a school type, and the user is mainly a group related to school activities. The second sub-area comprises 90 school type POIs, 5 post office type POIs and 5 hospital type POIs, and after subject analysis, the area is judged to be the school type as a main component, and users are mainly related to school activities. Therefore, it can be judged that the related groups of activities between the first sub-area and the second sub-area are the same, that is, the similarity of the POI data components of the first sub-area and the second sub-area is greater than or equal to a preset value. The first sub-area and the second sub-area should be merged into one area.

In this embodiment, the area division is adjusted by the components recorded in the POI data, so that the adjustment of the first space division is realized according to the features of the POI data.

The space division method provided by the embodiment of the application comprises the following steps: obtaining a first space partition of a target area, wherein the first space partition divides the target area into a plurality of sub-areas; acquiring at least one user attribute data in a target area, wherein the user attribute data is used for recording the moving condition of a user in the target area; and adjusting the sub-areas divided by the first space division according to the user attribute data to obtain a second space division. Compared with the traditional single-dimension space division mode, the method comprehensively considers the factors of the space and the crowd migration, thereby realizing more scientific space division.

Optionally, the space division method provided by the embodiment of the present application may also be applied to store site selection in the retail industry, which is called "site selection industry", and the key to success is "site selection, and site selection". For enterprises preparing investment, the store site with the most investment potential can be determined through site selection analysis; for the invested enterprises, the analysis results can be used to adjust the business strategy. The core of site selection is business circle analysis. The business district refers to the geographical region where retail stores or collections thereof attract customers, and more scientific division of the business district can be realized through the space division method provided by the application.

Further, the space division method provided in the embodiment of the present application may also be applied to other usage scenarios where there is a need for space division, and the embodiment of the present application is not limited thereto.

Described in terms of a hardware structure, the device management method may be implemented by one entity device, may also be implemented by multiple entity devices together, and may also be a logic function module in one entity device, which is not specifically limited in this embodiment of the present application.

For example, the device management method described above may be implemented by the electronic device in fig. 11. Fig. 11 is a schematic hardware structure diagram of an electronic device according to an embodiment of the present disclosure; the electronic device comprises at least one processor 1101, a communication line 1102, a memory 1103 and at least one communication interface 1104.

The processor 1101 may be a general processing unit (CPU), a microprocessor, an application-specific integrated circuit (server IC), or one or more ICs for controlling the execution of programs in accordance with the present invention.

The communication link 1102 may include a path for communicating information between the aforementioned components.

Communication interface 1104, which may be any transceiver or other communication network, may be used for communicating with other devices or communication networks, such as ethernet, Radio Access Network (RAN), Wireless Local Area Networks (WLAN), etc.

The memory 1103 may be a read-only memory (ROM) or other type of static storage device that can store static information and instructions, a Random Access Memory (RAM) or other type of dynamic storage device that can store information and instructions, an electrically erasable programmable read-only memory (EEPROM), a compact disk read-only memory (CD-ROM) or other optical disk storage, optical disk storage (including compact disk, laser disk, optical disk, digital versatile disk, blu-ray disk, etc.), magnetic disk storage or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer, but is not limited to these. The memory may be separate and coupled to the processor via a communication line 1102. The memory may also be integral to the processor.

The memory 1103 is used for storing computer-executable instructions for executing the present invention, and is controlled by the processor 1101. The processor 1101 is configured to execute computer-executable instructions stored in the memory 1103, thereby implementing the method for billing management provided by the following embodiments of the present application.

Optionally, the computer-executable instructions in the embodiments of the present application may also be referred to as application program codes, which are not specifically limited in the embodiments of the present application.

In particular implementations, processor 1101 may include one or more CPUs such as CPU0 and CPU1 in fig. 11 for one embodiment.

In particular implementations, an electronic device may include multiple processors, such as processor 1101 and processor 1107 in FIG. 11, for example, as an embodiment. Each of these processors may be a single-core (single-CPU) processor or a multi-core (multi-CPU) processor. A processor herein may refer to one or more devices, circuits, and/or processing cores for processing data (e.g., computer program instructions).

In particular implementations, the electronic device may also include an output device 1105 and an input device 1106, as one embodiment. The output device 1105 is in communication with the processor 1101 and may display information in a variety of ways. For example, the output device 1105 may be a Liquid Crystal Display (LCD), a Light Emitting Diode (LED) display device, a Cathode Ray Tube (CRT) display device, a projector (projector), or the like. The input device 1106 is in communication with the processor 1101 and may receive user input in a variety of ways. For example, the input device 1106 may be a mouse, keyboard, touch screen device or sensing device, etc.

The electronic device may be a general-purpose device or a special-purpose device. In particular implementations, the electronic device may be a server, a wireless terminal device, an embedded device, or a device having a similar structure as in fig. 11. The embodiment of the application does not limit the type of the electronic equipment.

In the embodiment of the present application, the electronic device may be divided into the functional units according to the method example, for example, each functional unit may be divided corresponding to each function, or two or more functions may be integrated into one processing unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit. It should be noted that the division of the unit in the embodiment of the present application is schematic, and is only a logic function division, and there may be another division manner in actual implementation.

For example, in a case where each functional unit is divided in an integrated manner, fig. 12 shows a schematic structural diagram of a space dividing apparatus provided in an embodiment of the present application.

As shown in fig. 12, the space dividing apparatus provided in the embodiment of the present application includes:

an obtaining unit 1201, configured to obtain a first spatial division of a target region, where the first spatial division divides the target region into a plurality of sub-regions;

the obtaining unit 1201 is further configured to obtain at least one piece of user attribute data in the target area, where the user attribute data is used to record a movement situation of a user in the target area;

an executing unit 1202, configured to adjust the sub-regions divided by the first space division according to the user attribute data, so as to obtain a second space division.

Optionally, the execution unit 1202 is further configured to:

dividing the target area to obtain a plurality of sub-areas;

Optionally, the obtaining unit 1201 is further configured to:

acquiring each moving track in a first sub-area and a second sub-area, wherein the first sub-area and the second sub-area are adjacent sub-areas;

the execution unit 1202 is further configured to:

Optionally, the obtaining unit 1201 is further configured to:

acquiring the resident time length of each of a first sub-area and a second sub-area, wherein the first sub-area and the second sub-area are adjacent sub-areas;

the execution unit 1202 is further configured to:

acquiring the total target duration respectively staying in the first sub-area and the second sub-area;

Optionally, the execution unit 1202 is further configured to:

Optionally, a part of the target spatiotemporal motion trajectory located in the first sub-region is a first sub-trajectory, and a part of the target spatiotemporal motion trajectory located in the second region is a second sub-trajectory; the execution unit 1202 is further configured to:

Optionally, the obtaining unit 1201 is further configured to:

the execution unit 1202 is further configured to:

Optionally, the execution unit 1202 is further configured to:

Embodiments of the present application also provide a computer-readable storage medium, which includes instructions that, when executed on a computer, cause the computer to perform the method in the foregoing embodiments.

The terms "first," "second," "third," "fourth," and the like in the description and in the claims of the present application and in the above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that the embodiments described herein may be practiced otherwise than as specifically illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

In the present application, "at least one item(s)" means one or more, "a plurality" means two or more. "at least one of the following" or similar expressions refer to any combination of these items, including any combination of the singular or plural items. For example, at least one (one) of a, b, or c, may represent: a, b, c, a-b, a-c, b-c, or a-b-c, wherein a, b, c may be single or multiple. In the present application, "A and/or B" is considered to include A alone, B alone, and A + B.

It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one type of logical module division, and other division manners may be available in actual implementation, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be obtained according to actual needs to achieve the purpose of the solution of the present embodiment.

In addition, each module unit in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a hardware form, and can also be realized in a software module unit form.

The integrated unit, if implemented as a software module unit and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present application may be substantially implemented or contributed to by the prior art, or all or part of the technical solution may be embodied in a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.

Those skilled in the art will recognize that, in one or more of the examples described above, the functions described in this invention may be implemented in hardware, software, firmware, or any combination thereof. When implemented in software, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that facilitates transfer of a computer program from one place to another. A storage media may be any available media that can be accessed by a general purpose or special purpose computer.

The above-described embodiments are intended to explain the objects, aspects and advantages of the present invention in further detail, and it should be understood that the above-described embodiments are merely exemplary embodiments of the present invention.

The above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method of spatial partitioning, comprising:

obtaining a first spatial partition of a target region, the first spatial partition dividing the target region into a plurality of sub-regions;

acquiring at least one user attribute data in the target area, wherein the user attribute data is used for recording the activity condition of a user in the target area;

and adjusting the sub-areas divided by the first space division according to the user attribute data to obtain a second space division.

2. The method of claim 1, wherein obtaining the first spatial division of the target region comprises:

dividing the target area to obtain a plurality of sub-areas;

adjusting the sub-regions divided by the first space division according to the user attribute data to obtain a second space division, including:

merging at least two adjacent sub-regions in the adjacent matrix according to the user attribute data, merging a part of one sub-region into an adjacent sub-region, or splitting at least one sub-region, or deleting a part of the sub-region.

3. The method of claim 2, wherein the obtaining at least one user attribute data in the target area comprises:

the method comprises the steps of obtaining the movement track of each user in a first sub-area and a second sub-area, wherein the first sub-area and the second sub-area are adjacent sub-areas;

said merging at least two said sub-regions that are adjacent in said adjacent matrix according to said user attribute data comprises:

acquiring the total length of all user movement tracks in the first subarea and the second subarea;

when the proportion of the first user track length to the total length is larger than or equal to a preset value, combining the first sub-area and the second sub-area.

4. The method of claim 2, wherein the obtaining at least one user attribute data in the target area comprises:

acquiring the total target duration of users respectively staying in the first subarea and the second subarea;

and when the proportion of the target total duration to the total duration is greater than or equal to a preset value, combining the first sub-area and the second sub-area.

5. The method of claim 2, wherein the partitioning the target region into a plurality of sub-regions comprises:

after the obtaining of the at least one user attribute data in the target area, the method further includes:

matching at least one user attribute data in the target area to the spatio-temporal network diagram to obtain a spatio-temporal movement track of each user, wherein one point in the spatio-temporal movement track represents the geographical position of the user at the current time point;

and when the target space-time movement track meets a preset condition, combining the first sub-area and the second sub-area.

6. The method according to claim 5, wherein the merging the first sub-area and the second sub-area when the target spatiotemporal motion trajectory meets a preset condition comprises:

predicting a second half track according to the first half track of the target space-time movement track to obtain a second predicted value, wherein the second predicted value is the contact ratio of the predicted value of the second half track of the target space-time movement track and the actual value of the second half track of the target space-time movement track;

7. The method of claim 2, wherein the obtaining at least one user attribute data in the target area comprises:

said merging a portion of one sub-region into an adjacent sub-region, comprising:

8. The method according to claim 7, wherein the deleting the partial area of the sub-area according to the user attribute data comprises:

when a target cell in a first sub-region is not connected with other cells on the wiring diagram, deleting a region where the target cell is located from the first sub-region, wherein the first sub-region is one sub-region in the target region, and the target cell is one cell in the first sub-region.

9. The method of claim 2, wherein said adjusting the sub-regions divided by the first spatial division according to the user attribute data to obtain a second spatial division comprises:

10. The method according to any one of claims 2 to 9, wherein the dividing the target region into a plurality of sub-regions comprises:

11. A space division apparatus, comprising:

the acquiring unit is further configured to acquire at least one piece of user attribute data in the target area, where the user attribute data is used to record a moving situation of a user in the target area;

and the execution unit is used for adjusting the sub-regions divided by the first space division according to the user attribute data to obtain a second space division.

12. An electronic device, comprising: a processor and a memory, and a control unit,

the memory is to store instructions;

the processor is configured to execute the method according to the instructions of any one of claims 1 to 10.

13. A computer-readable storage medium comprising instructions that, when executed on a computer, cause the computer to perform the method of any of claims 1 to 10.