CN110347938B

CN110347938B - Geographic information processing method and device, electronic equipment and medium

Info

Publication number: CN110347938B
Application number: CN201910630767.5A
Authority: CN
Inventors: 颜飞华
Original assignee: Shenzhen Zhongying Weirong Technology Co ltd
Current assignee: Jianlian Technology (Guangdong) Co.,Ltd.
Priority date: 2019-07-12
Filing date: 2019-07-12
Publication date: 2021-09-21
Anticipated expiration: 2039-07-12
Also published as: CN110347938A

Abstract

The embodiment of the disclosure discloses a geographic information processing method and device, electronic equipment and a medium. Wherein, the method comprises the following steps: acquiring geographic information to be processed; determining preset precision geographical position information corresponding to the geographical information to be processed; and comparing the preset precision geographical position information with a preset geographical information grid to obtain the relation between the geographical information to be processed, wherein the preset geographical information grid is a preset geographical information grid based on an incidence relation.

Description

Geographic information processing method and device, electronic equipment and medium

Technical Field

The present disclosure relates to the field of information processing, and in particular, to a geographic information processing method, apparatus, electronic device, and medium.

Background

With the development of big data and artificial intelligence, especially cognitive intelligence technology, financial technology is increasingly applying new technologies to provide services to society. The core business of the traditional financial service, such as anti-fraud recognition, wind control and pre-loan and post-loan management, is completed based on the result of manual data processing, which can better cope with the condition that the business scale is small and the service area is fixed. However, with the internet of the financial service industry, a large amount of services of the financial services are transferred from off-line to on-line, and the rapid increase of the scale of the services, the limitation of the manual processing mode on the processing task of mass data becomes larger and larger, so that the method based on internet data acquisition and artificial intelligent processing replaces manual work more and more. However, due to the limitation of the current artificial intelligence algorithm, on some specific data processing tasks, such as the processing of the geographic location information, how to accurately identify the input geographic location information still has a great challenge for machine processing.

Disclosure of Invention

In view of the foregoing technical problems in the prior art, embodiments of the present disclosure provide a geographic information processing method, an apparatus, an electronic device, and a medium.

A first aspect of the embodiments of the present disclosure provides a geographic information processing method, including:

acquiring geographic information to be processed;

determining preset precision geographical position information corresponding to the geographical information to be processed;

and comparing the preset precision geographical position information with a preset geographical information grid to obtain the relation between the geographical information to be processed, wherein the preset geographical information grid is a preset geographical information grid based on an incidence relation.

In some embodiments, the determining preset-precision geographic location information corresponding to the geographic information to be processed includes:

acquiring a preset precision geographical position grid, wherein the preset precision geographical position grid comprises a corresponding relation between a geographical information keyword and preset precision geographical position information;

extracting geographic information keywords of the geographic information to be processed;

and inputting the geographic information key word into the preset precision geographic position grid, and determining preset precision geographic position information corresponding to the geographic information to be processed according to the corresponding relation between the geographic information key word and the preset precision geographic position information.

In some embodiments, the comparing the preset precision geographical location information with a preset geographical information grid to obtain a relationship between the to-be-processed geographical information includes:

acquiring the preset geographic information grid attribute information;

comparing the preset precision geographical position information with the preset geographical information grid attribute information, and determining a preset geographical information grid to which the preset precision geographical position information belongs;

and determining the relation between the geographic information to be processed according to the relation between the attribution preset geographic information grids.

In some embodiments, further comprising:

and generating a preset geographic information grid.

In some embodiments, the generating the preset geographic information grid includes:

determining a grid target area;

acquiring geographic information of a first object located in the grid target area and geographic information of a second object associated with the first object;

determining first preset precision geographical position information corresponding to the geographical information of the first object and second preset precision geographical position information corresponding to the geographical information of the second object;

determining a minimum candidate grid so that the candidate grid can cover the first preset precision geographical position information and the second preset precision geographical position information;

traversing all objects in the grid target area to obtain a plurality of candidate grids;

and determining a group of candidate grids from the candidate grids as target geographic information grids corresponding to the grid target area.

In some embodiments, the determining a set of candidate grids from the plurality of candidate grids as the target geographic information grid corresponding to the grid target region is implemented as:

and determining a group of candidate grids from the candidate grids as target geographic information grids corresponding to the grid target area according to the number of the objects in the candidate grids, the density of the objects and the size of the objects, so that the target geographic information grids cover or partially cover the grid target area.

In some embodiments, the generating the preset geographic information grid further includes:

and adjusting the target geographic information grid according to preset geographic adjustment information.

A second aspect of the embodiments of the present disclosure provides a geographic information processing apparatus including:

the acquisition module is configured to acquire geographic information to be processed;

the determining module is configured to determine preset precision geographical position information corresponding to the geographical information to be processed;

and the comparison module is configured to compare the preset precision geographical position information with a preset geographical information grid to obtain a relation between the geographical information to be processed, wherein the preset geographical information grid is a preset geographical information grid based on an association relation.

In some embodiments, the determining module comprises:

the system comprises a first obtaining submodule and a second obtaining submodule, wherein the first obtaining submodule is configured to obtain a preset precision geographical position grid, and the preset precision geographical position grid comprises a corresponding relation between a geographical information keyword and preset precision geographical position information;

the extraction sub-module is configured to extract geographic information keywords of the geographic information to be processed;

the first determining submodule is configured to input the geographic information keyword into the preset precision geographic position grid, and determine preset precision geographic position information corresponding to the geographic information to be processed according to the corresponding relation between the geographic information keyword and the preset precision geographic position information.

In some embodiments, the comparison module comprises:

the second obtaining submodule is configured to obtain the preset geographic information grid attribute information;

the comparison submodule is configured to compare the preset precision geographical position information with the preset geographical information grid attribute information and determine a preset geographical information grid to which the preset precision geographical position information belongs;

and the second determining submodule is configured to determine the relationship between the geographic information to be processed according to the relationship between the attribution preset geographic information grids.

In some embodiments, further comprising:

a generating module configured to generate a preset geographic information grid.

In some embodiments, the generating module comprises:

a third determination submodule configured to determine a mesh target area;

a third acquisition submodule configured to acquire geographic information of a first object located in the mesh target area and geographic information of a second object associated with the first object;

a fourth determining sub-module configured to determine first preset-precision geographical position information corresponding to the geographical information of the first object and second preset-precision geographical position information corresponding to the geographical information of the second object;

a fifth determining submodule configured to determine a minimum candidate grid such that the candidate grid can cover the first preset-precision geographical position information and the second preset-precision geographical position information;

a traversal submodule configured to traverse all objects in the mesh target region, resulting in a plurality of candidate meshes;

a sixth determining submodule configured to determine a group of candidate grids from the plurality of candidate grids as a target geographic information grid corresponding to the grid target area.

In some embodiments, the sixth determination submodule is configured to:

In some embodiments, the generating module further comprises:

and the adjusting submodule is configured to adjust the target geographic information grid according to preset geographic adjusting information.

A third aspect of embodiments of the present disclosure provides a computer program product comprising a computer program stored on a computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, are operable to implement a method as in the preceding embodiments.

The embodiment of the disclosure obtains the relation between the geographic information to be processed by setting a preset non-fixed geographic information grid based on the incidence relation and inputting the preset precision geographic position information corresponding to the geographic information to be processed into the preset precision geographic position information. The embodiment of the disclosure can improve the accuracy of judging the geographic information relationship, and can adjust the geographic information grids according to different application environments and actual geographic information so as to further improve the effectiveness of judging the geographic position relationship.

Drawings

The features and advantages of the present disclosure will be more clearly understood by reference to the accompanying drawings, which are illustrative and not to be construed as limiting the disclosure in any way, and in which:

FIG. l is a flow chart of a method of geographic information processing, shown in accordance with some embodiments of the present disclosure;

FIG. 2 is a block diagram of an artificial intelligence processing system to which the present disclosure is applicable;

FIG. 3 is an exemplary diagram of a spectra database;

FIG. 4 is a flowchart illustration of step S104 of a geographic information processing method, shown in accordance with some embodiments of the present disclosure;

FIG. 5 is a flowchart illustration of step S106 of a geographic information processing method, shown in accordance with some embodiments of the present disclosure;

FIG. 6 is a schematic diagram of a geographic information grid;

FIG. 7 is a flow chart illustrating a method of geographic information processing according to further embodiments of the present disclosure;

fig. 8 is a flowchart illustrating a step S706 of a geographic information processing method according to some embodiments of the present disclosure;

FIG. 9 is a schematic diagram of a grid in which multiple candidate grids exist;

FIG. 10 is a schematic diagram of another grid in which multiple candidate grids exist;

FIG. 11 is a flowchart illustrating a step S706 of a geographic information processing method according to further embodiments of the present disclosure;

FIG. 12 is a schematic diagram illustrating the effect of geographical adjustment information including river flow on candidate mesh generation;

FIG. 13 is a block diagram of a geographic information processing device, shown in accordance with some embodiments of the present disclosure;

FIG. 14 is a block diagram of a determination module 1320 of a geographic information processing apparatus according to some embodiments of the present disclosure;

fig. 15 is a block diagram of a comparison module 1330 of a geographic information processing apparatus according to some embodiments of the present disclosure;

FIG. 16 is a block diagram of a geographic information processing device according to further embodiments of the present disclosure;

FIG. 17 is a block diagram illustrating the structure of the generation module 1630 of the geographic information processing apparatus according to some embodiments of the present disclosure;

FIG. 18 is a block diagram illustrating a structure of a generation module 1630 of a geographic information processing apparatus according to further embodiments of the disclosure;

FIG. 19 is a schematic diagram of an electronic device shown in accordance with some embodiments of the present disclosure;

fig. 20 is a schematic structural diagram of a general-purpose computer node suitable for implementing the geographic information processing method according to the embodiment of the present disclosure.

Detailed Description

In the following detailed description, numerous specific details of the disclosure are set forth by way of examples in order to provide a thorough understanding of the relevant disclosure. However, it will be apparent to one of ordinary skill in the art that the present disclosure may be practiced without these specific details. It should be understood that the use of the terms "system," "apparatus," "unit" and/or "module" in this disclosure is a method for distinguishing between different components, elements, portions or assemblies at different levels of sequence. However, these terms may be replaced by other expressions if they can achieve the same purpose.

It will be understood that when a device, unit or module is referred to as being "on" … … "," connected to "or" coupled to "another device, unit or module, it can be directly on, connected or coupled to or in communication with the other device, unit or module, or intervening devices, units or modules may be present, unless the context clearly dictates otherwise. For example, as used in this disclosure, the term "and/or" includes any and all combinations of one or more of the associated listed items.

The terminology used in the present disclosure is for the purpose of describing particular embodiments only and is not intended to limit the scope of the present disclosure. As used in the specification and claims of this disclosure, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are inclusive in the plural, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" are intended to cover only the explicitly identified features, integers, steps, operations, elements, and/or components, but not to constitute an exclusive list of such features, integers, steps, operations, elements, and/or components.

These and other features and characteristics of the present disclosure, as well as the methods of operation and functions of the related elements of structure and the combination of parts and economies of manufacture, will be better understood by reference to the following description and drawings, which form a part of this specification. It is to be expressly understood, however, that the drawings are for the purpose of illustration and description only and are not intended as a definition of the limits of the disclosure. It will be understood that the figures are not drawn to scale.

Various block diagrams are used in this disclosure to illustrate various variations of embodiments according to the disclosure. It should be understood that the foregoing and following structures are not intended to limit the present disclosure. The protection scope of the present disclosure is subject to the claims.

Fig. 1 is a schematic flow diagram of a geographic information processing method according to some embodiments of the present disclosure, as shown in fig. 1, the geographic information processing method including the steps of:

and S102, acquiring geographic information to be processed.

And S104, determining preset precision geographical position information corresponding to the geographical information to be processed.

And S106, comparing the preset precision geographical position information with a preset geographical information grid to obtain the relation between the geographical information to be processed, wherein the preset geographical information grid is a preset geographical information grid based on an incidence relation.

As mentioned above, with the development of big data and artificial intelligence, especially cognitive intelligence technology, financial technology is increasingly applying new technologies to serve society. The core business of the traditional financial service, such as anti-fraud recognition, wind control and pre-loan and post-loan management, is completed based on the result of manual data processing, which can better cope with the condition that the business scale is small and the service area is fixed. However, with the internet of the financial service industry, a large amount of services of the financial services are transferred from off-line to on-line, and the rapid increase of the scale of the services, the limitation of the manual processing mode on the processing task of mass data becomes larger and larger, so that the method based on internet data acquisition and artificial intelligent processing replaces manual work more and more. However, due to the limitation of the current artificial intelligence algorithm, on some specific data processing tasks, such as the processing of the geographic location information, how to accurately identify the input geographic location information still has a great challenge for machine processing.

First, an artificial intelligence processing system to which the present disclosure is applicable is introduced by taking an intelligent wind control system as an example, as shown in fig. 2, a user submits a financial entry application through an internet front-end system, such as an SDK, an H5 page, an internet APP program, the financial entry application accesses a task matching server through a wired or wireless communication network, in the task matching server, the financial entry application is automatically matched to different financial service providers, and then data preprocessing is performed on the financial entry data to be stored in a spectrum database, wherein the spectrum database is used for storing a large amount of knowledge spectrum data about financial services to provide data support for subsequent wind control judgment. Further, a wind control analysis task can be generated based on the financial input data, the task obtains relational data related to the input from a map database in a map query mode, then the relational data are input to a variable calculation module to obtain an evaluation variable corresponding to the relational data, and then the evaluation variable is input to a pre-obtained anti-fraud evaluation model to complete anti-fraud identification based on the financial input application.

The graph database is provided with a plurality of storage nodes and edges connecting different storage nodes, different storage nodes correspond to entities in the real world, the relationship between the storage nodes corresponds to the relationship between the entities, and the storage nodes and the relationship further comprise different attributes to represent the types of the entities and the types of the relationship. Fig. 3 is an exemplary diagram of a spectrum database, and as shown in fig. 3, nodes "zhang" and "lie" in the spectrum database are two personal entities, each of which is connected with other entity nodes such as "company", "mobile phone number" and the like through a relationship such as "work on", "own phone", and is connected with an "entry" entity node through an affiliation, and the two personal entities "zhang" and "lie" are connected through a relationship of "recommender".

In the map database shown in fig. 3, some node data have better uniqueness and are obtained by formatted data acquisition means, such as data of identification numbers, telephone numbers and the like, while some node data related to geographical positions often exist in the form of natural language information, and due to the randomness of manual writing, the natural language information usually lacks uniqueness, such as "guan ding good building in seas" and "beijing hai ding good district" although the data contents are different, but actually correspond to the same building, so that a machine has great difficulty in processing the natural language information, and the data processing error rate is high. At present, a general solution is to input a related geographic location information text into a Geographic Information System (GIS) to obtain corresponding longitude and latitude information, and then determine whether different geographic location information is at the same location according to a fixed judgment grid, where the fixed judgment grid is set according to a predetermined size and location, and geographic locations in the same judgment grid are regarded as the same location, but the fixed grid is not suitable for whether there is an association relationship between different nodes, because it is regarded as an address of the same location in space, it is possible to establish a corresponding relationship side in a map database, but the two are not necessarily in the association relationship in reality, and thus, a simple fixed grid cannot meet the high-precision requirement of geographic location information analysis. For example, although the two geographical location information, namely, "beijing city hai lake district zhongcun north street" and "beijing city dinghao mansion" do not belong to the same region, the two geographical location information are located in the same grid, and therefore the two geographical location information are defaulted to be located at the same or adjacent positions, and actually, the two geographical location information are neither the same nor adjacent, which provides an erroneous data basis for subsequent relation determination. In addition, although the size of the fixed grid can be adjusted, in practical applications, it is difficult to determine a very accurate grid size, because if the granularity of the fixed grid is set too large, different geographic locations may not be effectively distinguished, and if the granularity of the fixed grid is set too small, a corresponding connection relationship may not be established, so that it can be seen that the fixed grid can handle the uncertainty problem of natural language information to some extent, but cannot accurately and intelligently identify the database data of the map database based on the relationship, and further cannot provide more accurate spatial region division and location relationship determination.

In order to solve the problems of the fixed network, in some embodiments, a geographic information processing method is provided, which obtains a relationship between geographic information to be processed by setting a preset non-fixed geographic information grid based on an association relationship and inputting preset precision geographic position information corresponding to the geographic information to be processed. The embodiment of the disclosure can improve the accuracy of judging the geographic information relationship, and can adjust the geographic information grids according to different application environments and actual geographic information so as to further improve the effectiveness of judging the geographic position relationship.

In some embodiments, the geographic information to be processed is geographic information to be processed and expected to obtain a relationship between the geographic information and the geographic information, such as residential address information, work address information, instant location information, and the like.

The preset accuracy refers to preset geographical position information generation accuracy. In some embodiments, the preset accuracy is higher than the preset accuracy threshold, that is, in this embodiment, in order to improve the effectiveness of determining the subsequent geographic information relationship, the geographic information to be processed may be first converted into high-accuracy geographic position information by using a high-resolution fixed geographic information grid, for example, the geographic information to be processed is converted into high-accuracy longitude and latitude information by using a GIS system, and then the subsequent geographic position relationship is determined.

In some embodiments, the association refers to entity nodes, such as an existing association between individual individuals, such as a social relationship between individual individuals, an association between individual individuals and other individual individuals when participating in a certain behavior, an association generated by different individual individuals based on a certain event, and so on.

That is, in some optional embodiments, as shown in fig. 4, the step S104 of determining the preset-precision geographical location information corresponding to the geographical information to be processed includes the following steps:

s402, acquiring a preset precision geographical position grid.

S404, extracting the geographic information key words of the geographic information to be processed.

S406, inputting the geographic information key word into the preset precision geographic position grid, and determining preset precision geographic position information corresponding to the geographic information to be processed according to the corresponding relation between the geographic information key word and the preset precision geographic position information.

The preset precision geographical position grid is a geographical position grid higher than a preset precision threshold value and comprises a corresponding relation between a geographical information keyword and preset precision geographical position information, and after the geographical information keyword is determined, high-precision geographical position information corresponding to the geographical information to be processed can be obtained according to the corresponding relation between the geographical information keyword and the preset precision geographical position information.

In some optional embodiments, as shown in fig. 5, the step S106 of comparing the preset precision geographical location information with a preset geographical information grid to obtain the relationship between the to-be-processed geographical information includes the following steps:

and S502, acquiring the preset geographic information grid attribute information.

S504, comparing the preset precision geographical position information with the preset geographical information grid attribute information, and determining the preset geographical information grid to which the preset precision geographical position information belongs.

S506, determining the relation between the geographic information to be processed according to the relation between the attribution preset geographic information grids.

Wherein the preset geographic information grid attribute information may include one or more of the following information: the shape of the mesh, such as a circle, a rectangle, a diamond, a polygon, or an irregular figure; the position of the mesh, such as mesh vertex coordinates, mesh center point coordinates; the size of the grid, such as the length, width, radius, etc. of the grid.

After the preset geographic information grid attribute information is obtained, the preset precision geographic position information can be compared with the preset geographic information grid attribute information to determine which preset geographic information grid the preset precision geographic position information belongs to, and then the relation between the geographic information to be processed is determined according to the relation between the preset geographic information grids. For example, as shown in fig. 6, fig. 6 shows 3 grids: grid 1, grid 2, and grid 3, where different entity nodes belonging to the same grid 2 can be considered to be in the same location or to have a neighboring relationship.

In some optional embodiments, before the comparing the preset-precision geographical location information with the preset geographical information grid to obtain the relationship between the geographical information to be processed, the method further includes a step of generating the preset geographical information grid, that is, as shown in fig. 7, the method includes the following steps:

s702, obtaining the geographic information to be processed.

S704, determining preset precision geographical position information corresponding to the geographical information to be processed.

And S706, generating a preset geographic information grid.

And S708, comparing the preset precision geographical position information with a preset geographical information grid to obtain the relation between the geographical information to be processed.

The preset geographic information grid is a geographic information grid based on an incidence relation, that is, the preset geographic information grid is generated based on incidence relation data.

In some optional embodiments, as shown in fig. 8, the step S706 of generating the preset geographic information grid includes the following steps:

s802, determining a grid target area.

S804, acquiring the geographic information of a first object located in the grid target area and the geographic information of a second object associated with the first object.

S806, determine first preset-precision geographical location information corresponding to the geographical information of the first object and second preset-precision geographical location information corresponding to the geographical information of the second object.

S808, determining a minimum candidate grid, so that the candidate grid can cover the first preset precision geographical position information and the second preset precision geographical position information.

S810, traversing all the objects in the grid target area to obtain a plurality of candidate grids.

S812, determining a group of candidate grids from the plurality of candidate grids as the target geographic information grid corresponding to the grid target region.

Since the geographic information grid is a relationship-based grid, it needs to be generated by means of association relationship data.

Specifically, an area range to which the geographic information grid is applied, i.e., a grid target area, such as the hai lake area of beijing, city, is first determined.

Then, obtaining geographic information of a first object located in the grid target area and geographic information of a second object associated with the first object, for example, obtaining geographic information of a first entity node in a lake region, and then obtaining geographic information of a second entity node associated with the first entity node based on a relationship between the nodes, wherein the geographic information refers to geographic text information or geographic information with a precision lower than a preset precision, and the second entity node may be one or two or more.

Then, determining first preset precision geographical position information corresponding to the geographical information of the first object and second preset precision geographical position information corresponding to the geographical information of the second object; the first preset precision and the second preset precision are both higher than a preset precision threshold, and the preset precision geographical position information can be obtained by converting geographical text information or geographical information lower than the preset precision by means of a geographical information system.

Then, a minimum candidate grid is determined, so that the candidate grid can cover the first preset precision geographical position information and the second preset precision geographical position information, and a grid capable of reflecting the association relation of the first entity node and the attribute information of the grid are obtained.

Then, according to the method, all objects in the target area of the grid are traversed to obtain a plurality of candidate grids, namely the grids which correspond to other entity nodes and can embody the association relationship of other entity nodes and the attribute information of each grid. In this way, for the mesh target area, i.e. the haih lake area, a plurality of candidate meshes respectively representing association relations of different entity nodes can be obtained, wherein the candidate meshes may overlap or intersect, as shown in fig. 9.

And finally, determining a group of candidate grids from the candidate grids as target geographic information grids corresponding to the grid target area.

In order to accurately reflect the association relationship between the entity nodes in the grid target area, in some optional embodiments, a group of candidate grids is determined as a target geographic information grid corresponding to the grid target area according to the number of objects in the candidate grids, the density of the objects, and the size of the objects, and the target geographic information grid covers or partially covers the grid target area. Wherein the partial coverage may be considered as a high coverage, i.e. the ratio between the coverage area and the total area of the mesh target area is above a preset ratio threshold.

For example, the candidate grids may be first sorted from large to small according to the number of objects in the candidate grids, and then the candidate grid with the largest number of objects is sequentially selected as the target geographic information subgrid, so that the finally determined target geographic information subgrid group can cover or partially cover the grid target area, and the overlapping area is smallest. In some optional embodiments, to avoid the data validity problem caused by excessive overlapping areas, if the degree of overlap between the candidate grids is high, and one of the candidate grids is selected as the target geographic information sub-grid, the nodes in the target geographic information sub-grid may be temporarily deleted, and then the remaining candidate grids are sorted and selected based on the number of objects, as shown in fig. 10, the candidate grid located in the middle is excluded from subsequent sorting and selection when the candidate grid on the left side is selected as the target geographic information sub-grid due to the high degree of overlap between the candidate grids.

Certainly, since there may be an association relationship between entity nodes across regions, across cities, and across grid target regions, when the candidate grid is generated, some small amount of associated node noise outside the grid target region may occur, and at this time, the small amount of associated node noise may be deleted, and since the relative amount of associated node noise is small, the deletion operation does not affect the accuracy of the candidate grid.

By the method, the geographic information grid capable of fully reflecting the incidence relation between the nodes, reducing the spatial incidence degree and improving the non-spatial incidence degree is generated by the incidence relation data between the entity nodes. Different from the traditional fixed grid which uniformly divides the space, the shapes and the sizes of the sub-grids forming the geographic information grid are possibly different, so that the relevance of other dimensions of each entity node except the space can be fully reflected.

The association relationship between the entity nodes may be set according to the needs of practical applications, for example, the association relationship may be a normal personal social circle, or may be a group such as a social interest group and a fraudulent group using a personal individual as a unit, or may be other association relationships.

The generation of the preset geographical information grid is explained and illustrated next by a fraudulent party as an example. Firstly, determining the area range applied by the geographic information grid, namely a grid target area is a Haishen area of Beijing city; then inputting the geographic text information of a certain cheating group member into a GIS system to obtain high-precision longitude and latitude information corresponding to each member; generating a rectangular frame capable of covering the nodes with the members based on the high-precision longitude and latitude information of the cheating group members, and taking the rectangular frame as a candidate grid which is possibly part of a sea area target geographic information grid finally, as shown in a grid formed by black nodes in fig. 10, wherein in fig. 10, the black nodes represent the grid corresponding to the cheating group, and other nodes represent the candidate grids formed according to other association relations.

In some optional embodiments, step S706, namely the step of generating the preset geographic information grid, further includes a step of adjusting the target geographic information grid according to preset geographic adjustment information, namely as shown in fig. 11, step S706, namely the step of generating the preset geographic information grid, includes the following steps:

s1102, determining a grid target area.

S1104, obtaining geographic information of a first object located in the mesh target area and geographic information of a second object associated with the first object.

S1106, determining first preset-precision geographical location information corresponding to the geographical information of the first object and second preset-precision geographical location information corresponding to the geographical information of the second object.

S1108, determining a minimum candidate grid so that the candidate grid can cover the first preset-precision geographical location information and the second preset-precision geographical location information.

S1110, traversing all objects in the grid target area to obtain a plurality of candidate grids.

S1112, determining a group of candidate grids from the candidate grids as a target geographic information grid corresponding to the grid target area.

And S1114, adjusting the target geographic information grid according to preset geographic adjustment information.

In consideration of the fact that various variable influence factors may be encountered in different application scenarios, in one embodiment, the target geographic information grid may be adjusted or divided according to preset geographic adjustment information, so as to more truly represent the geographic information and improve the reliability of the geographic information. For example, when one candidate grid in the obtained target geographic information grid is a cross-river grid, the candidate grid can be split into two candidate grids respectively positioned at two sides of the river according to the geographic position information of the river.

The preset geographic adjustment information refers to preset information which may affect the structure of the target geographic information grid, such as geographic division information of rivers, mountains, expressways, administrative region divisions, long-term geographic damage information, long-term geographic repair information, and the like.

In another embodiment, in order to save data computation amount, improve data computation efficiency, and avoid repeated work, the preset geographic adjustment information may be considered when the candidate grid is initially generated, that is, the association relationship between the entity nodes is preprocessed based on the preset geographic adjustment information when the candidate grid is generated, for example, if valid geographic adjustment information exists between two entity nodes that originally have an association relationship, the two entity nodes may be modified to have no association relationship.

That is, the step S804 of acquiring the geographic information of the first object located in the grid target area and the geographic information of the second object associated with the first object may be implemented as:

a first object located in the mesh target area is determined.

And acquiring preset geographical adjustment information, and determining a second object associated with the first object according to the preset geographical adjustment information and the association relation between the second object and the first object.

And acquiring geographic information of the first object and the second object.

Fig. 12 shows an influence of geographical adjustment information including a river on candidate mesh generation, in which in this scenario, the association between two entity nodes located at two sides of the river is first deleted, as shown by a dotted line in the figure, and then a corresponding candidate mesh is generated based on the current association.

Therefore, after the method is applied, the obtained target geographic information grid can fully realize the relevance between the entity nodes, so that effective data support is provided for judging subsequent behaviors or events of the entity nodes, and the accuracy and precision of judging the subsequent behaviors or events are improved. FIG. 9 is a schematic diagram of a partial target geographic information grid obtained according to an embodiment of the present disclosure, in which the candidate grid in FIG. 9 partially covers a grid target area, a grid 1 of the candidate grid may represent a larger enterprise campus, and includes a plurality of buildings therein, and individual individuals in different buildings in the area all belong to the same group enterprise, so that there is a strong correlation between individual individuals in the grid; a highway exists between the grid 1 and the grid 2, so that the two grids can be regarded as different grid areas although the two grids are close to each other, and large obstacles or cost can exist when people in the geographic areas corresponding to the two grids communicate; the spatial region in the middle of the grid is considered isolated, and the entity nodes in this region are, for example, socially less individuals, which may or may not be processed using other fixed grids of geographic information.

The above is a specific implementation of the geographic information processing method provided by the present disclosure.

FIG. 13 is a schematic diagram of a geographic information processing device, shown in accordance with some embodiments of the present disclosure. As shown in fig. 13, the geographic information processing apparatus 1300 includes:

an obtaining module 1310 configured to obtain geographic information to be processed.

A determining module 1320, configured to determine preset precision geographical location information corresponding to the geographical information to be processed.

A comparing module 1330 configured to compare the preset precision geographical location information with a preset geographical information grid to obtain a relationship between the to-be-processed geographical information, where the preset geographical information grid is a preset geographical information grid based on an association relationship.

In order to solve the problems of the fixed network, in some embodiments, a geographic information processing apparatus is provided, which obtains a relationship between geographic information to be processed by setting a preset non-fixed geographic information grid based on an association relationship and inputting preset precision geographic position information corresponding to the geographic information to be processed. The embodiment of the disclosure can improve the accuracy of judging the geographic information relationship, and can adjust the geographic information grids according to different application environments and actual geographic information so as to further improve the effectiveness of judging the geographic position relationship.

That is, in some alternative embodiments, as shown in fig. 14, the determining module 1320 includes:

the first obtaining submodule 1410 is configured to obtain a preset precision geographical position grid, where the preset precision geographical position grid includes a corresponding relationship between a geographical information keyword and preset precision geographical position information.

And an extraction sub-module 1420 configured to extract the geographic information keyword of the geographic information to be processed.

The first determining sub-module 1430 is configured to input the geographic information keyword into the preset-precision geographic position grid, and determine preset-precision geographic position information corresponding to the geographic information to be processed according to a corresponding relationship between the geographic information keyword and the preset-precision geographic position information.

In some alternative embodiments, as shown in fig. 15, the comparing module 1330 includes:

the second obtaining sub-module 1510 is configured to obtain the preset geographic information grid attribute information.

A comparing submodule 1520, configured to compare the preset precision geographical position information with the preset geographical information grid attribute information, and determine a preset geographical information grid to which the preset precision geographical position information belongs.

A second determining submodule 1530 configured to determine a relationship between the to-be-processed geographic information according to a relationship between home preset geographic information grids.

After the preset geographic information grid attribute information is obtained, the preset precision geographic position information can be compared with the preset geographic information grid attribute information to determine which preset geographic information grid the preset precision geographic position information belongs to, and then the relation between the geographic information to be processed is determined according to the relation between the preset geographic information grids. For example, as shown in fig. 6, different entity nodes belonging to the same grid can be considered to be in the same position or to have a neighboring relationship.

In some optional embodiments, before the comparing module 1330, the apparatus further includes a part for generating a preset geographic information grid, that is, as shown in fig. 16, the apparatus 1600 includes:

an obtaining module 1610 configured to obtain the geographic information to be processed.

A determining module 1620 configured to determine preset precision geographical location information corresponding to the geographical information to be processed.

A generating module 1630 configured to generate a preset geographic information grid.

A comparing module 1640 configured to compare the preset precision geographic position information with a preset geographic information grid to obtain a relationship between the to-be-processed geographic information, where the preset geographic information grid is a preset geographic information grid based on an association relationship.

In some optional embodiments, as shown in fig. 17, the generating module 1630 includes:

a third determining submodule 1710 configured to determine a mesh target area.

A third obtaining submodule 1720 configured to obtain geographical information of a first object located in the mesh target area and geographical information of a second object associated with the first object.

A fourth determining submodule 1730 configured to determine first preset precision geographical position information corresponding to the geographical information of the first object and second preset precision geographical position information corresponding to the geographical information of the second object.

A fifth determining submodule 1740 configured to determine a minimum candidate grid such that the candidate grid can cover the first preset precision geographical position information and the second preset precision geographical position information.

A traversal submodule 1750 configured to traverse all objects in the mesh target region, resulting in a plurality of candidate meshes.

A sixth determining sub-module 1760 configured to determine a set of candidate grids from the plurality of candidate grids as the target geographic information grid corresponding to the grid target area.

In particular, the third determination submodule 1710 determines the area range to which the geographic information grid applies, i.e. the grid target area, for example, the hai lake area of beijing.

The third obtaining sub-module 1720 obtains geographic information of a first object located in the grid target area and geographic information of a second object associated with the first object, for example, obtains geographic information of a first entity node in a lake region, and then obtains geographic information of a second entity node associated with the first entity node based on a relationship between the nodes, where the geographic information refers to geographic text information or geographic information with a precision lower than a preset precision, and the second entity node may be one or two or more.

The fourth determining sub-module 1730 determines first preset-precision geographical position information corresponding to the geographical information of the first object and second preset-precision geographical position information corresponding to the geographical information of the second object; the first preset precision and the second preset precision are both higher than a preset precision threshold, and the preset precision geographical position information can be obtained by converting geographical text information or geographical information lower than the preset precision by means of a geographical information system.

The fifth determining sub-module 1740 determines a minimum candidate grid so that the candidate grid can cover the first preset-precision geographical location information and the second preset-precision geographical location information, thereby obtaining a grid capable of reflecting the association relationship of the first entity node and the attribute information of the grid.

The traversal submodule 1750 traverses all the objects in the mesh target area according to the method described above, to obtain a plurality of candidate meshes, that is, meshes that correspond to other entity nodes and can embody the association relationship of other entity nodes, and attribute information of each mesh. In this way, for the mesh target area, i.e. the haih lake area, a plurality of candidate meshes respectively representing association relations of different entity nodes can be obtained, wherein the candidate meshes may overlap or intersect, as shown in fig. 9.

The sixth determining sub-module 1760 determines a set of candidate grids from the plurality of candidate grids as the target geographic information grids corresponding to the grid target region.

In order to accurately reflect the association relationship between the entity nodes in the target area of the grid, in some optional embodiments, the sixth determining sub-module 1760 determines a group of candidate grids as the target geographic information grid corresponding to the target area of the grid according to the number of objects in the candidate grids, the density of the objects, and the size of the objects, and simultaneously enables the target geographic information grid to cover or partially cover the target area of the grid. Wherein the partial coverage may be considered as a high coverage, i.e. the ratio between the coverage area and the total area of the mesh target area is above a preset ratio threshold.

In some optional embodiments, the generating module 1630 further includes a part for adjusting the target geographic information grid according to preset geographic adjustment information, that is, as shown in fig. 18, the generating module 1630 includes:

a third determination submodule 1810 is configured to determine a mesh target region.

A third obtaining sub-module 1820 configured to obtain geographic information of a first object located in the mesh target area and geographic information of a second object associated with the first object.

A fourth determining submodule 1830 configured to determine first preset precision geographical position information corresponding to the geographical information of the first object and second preset precision geographical position information corresponding to the geographical information of the second object.

A fifth determining submodule 1840 configured to determine a minimum candidate grid such that the candidate grid can cover the first preset precision geographical location information and the second preset precision geographical location information.

A traversal submodule 1850 configured to traverse all objects in the target region of the mesh, resulting in a plurality of candidate meshes.

A sixth determining submodule 1860 configured to determine a set of candidate grids from the plurality of candidate grids as target geographic information grids corresponding to the grid target area.

An adjusting submodule 1870 configured to adjust the target geographic information grid according to preset geographic adjustment information.

That is, the third acquisition sub-module 1720 or the third acquisition sub-module 1820 may be configured to:

a first object located in the mesh target area is determined.

And acquiring geographic information of the first object and the second object.

Referring to fig. 19, a schematic diagram of an electronic device is provided for one embodiment of the present disclosure. As shown in fig. 19, the electronic device 1900 includes:

memory 1930 and one or more processors 1910;

wherein the memory 1930 is in communication with the one or more processors 1910, and wherein the memory 1930 has stored therein instructions 1932 executable by the one or more processors 1910, the instructions 1932 being executable by the one or more processors 1910 to cause the one or more processors 1910 to perform the geographic information processing steps described above.

One embodiment of the present disclosure provides a computer-readable storage medium, in which computer-executable instructions are stored, and the computer-executable instructions are executed to perform the above-mentioned geographic information processing steps.

In summary, the present disclosure provides a geographic information processing method, an apparatus, an electronic device and a computer-readable storage medium thereof. The embodiment of the disclosure obtains the relation between the geographic information to be processed by setting a preset non-fixed geographic information grid based on the incidence relation and inputting the preset precision geographic position information corresponding to the geographic information to be processed into the preset precision geographic position information. The embodiment of the disclosure can improve the accuracy of judging the geographic information relationship, and can adjust the geographic information grids according to different application environments and actual geographic information so as to further improve the effectiveness of judging the geographic position relationship.

It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described apparatuses and modules may refer to the corresponding descriptions in the foregoing device embodiments, and are not repeated herein.

While the subject matter described herein is provided in the general context of execution in conjunction with the execution of an operating system and application programs on a computer system, those skilled in the art will recognize that other implementations may also be performed in combination with other types of program modules. Generally, program modules include routines, programs, components, data structures, and other types of structures that perform particular tasks or implement particular abstract data types. Those skilled in the art will appreciate that the subject matter described herein may be practiced with other computer system configurations, including hand-held devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, minicomputers, mainframe computers, and the like, as well as distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

Those of ordinary skill in the art will appreciate that the various illustrative elements and method steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.

The functions, if implemented in the form of software functional units 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 disclosure may be embodied in the form of a software product, which is stored in a storage medium and includes several 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 disclosure. For example, the subject matter of this disclosure can be implemented and/or propagated via at least one general-purpose computer node 2010 as shown in fig. 20. In fig. 20, a general computer node 2010 includes: computer system/server 2012, peripherals 2014, and display 2016; the computer system/server 2012 includes a processing unit 2020, an input/output interface 2022, a network adapter 2024, and a memory 2030, which typically allow data transfer via a bus; further, the Memory 2030 typically comprises a variety of storage devices, such as a RAM (Random Access Memory) 2032, a cache 2034, and a storage system (typically comprising one or more mass non-volatile storage media) 2036; the program 2040 implementing some or all of the functions of the disclosed solution is stored in the memory 2030, typically in the form of a plurality of program modules 2042.

Such computer-readable storage media include physical volatile and nonvolatile, removable and non-removable media implemented in any manner or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. The computer-readable storage medium specifically includes, but is not limited to, a USB flash drive, a removable hard drive, a Read-Only Memory (ROM), a Random Access Memory (RAM), an erasable programmable Read-Only Memory (EPROM), an electrically erasable programmable Read-Only Memory (EEPROM), flash Memory or other solid state Memory technology, a CD-ROM, a Digital Versatile Disk (DVD), an HD-DVD, a Blue-Ray or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer.

It is to be understood that the above-described specific embodiments of the present disclosure are merely illustrative of or illustrative of the principles of the present disclosure and are not to be construed as limiting the present disclosure. Accordingly, any modification, equivalent replacement, improvement or the like made without departing from the spirit and scope of the present disclosure should be included in the protection scope of the present disclosure. Further, it is intended that the following claims cover all such variations and modifications that fall within the scope and bounds of the appended claims, or equivalents of such scope and bounds.

Claims

1. A geographic information processing method, comprising:

acquiring geographic information to be processed;

comparing the preset precision geographical position information with a preset geographical information grid to obtain the relation between the geographical information to be processed, wherein the preset geographical information grid is a preset geographical information grid based on an incidence relation, and the preset geographical information grid is generated by adopting the following steps:

determining a grid target area;

2. The method according to claim 1, wherein the determining preset-precision geographical location information corresponding to the geographical information to be processed comprises:

3. The method according to claim 1, wherein the comparing the preset precision geographical location information with a preset geographical information grid to obtain the relationship between the geographical information to be processed comprises:

acquiring the preset geographic information grid attribute information;

4. The method according to claim 1, wherein said determining a set of candidate grids from said plurality of candidate grids as a target geographic information grid corresponding to said grid target area is implemented as:

5. The method of claim 1, wherein the generating a preset geographic information grid further comprises:

6. A geographic information processing apparatus, comprising:

the comparison module is configured to compare the preset precision geographical position information with a preset geographical information grid to obtain a relation between the geographical information to be processed, wherein the preset geographical information grid is a preset geographical information grid based on an incidence relation;

the generation module is configured to generate a preset geographic information grid; the generation module comprises: a third determination submodule configured to determine a mesh target area; a third acquisition submodule configured to acquire geographic information of a first object located in the mesh target area and geographic information of a second object associated with the first object; a fourth determining sub-module configured to determine first preset-precision geographical position information corresponding to the geographical information of the first object and second preset-precision geographical position information corresponding to the geographical information of the second object; a fifth determining submodule configured to determine a minimum candidate grid such that the candidate grid can cover the first preset-precision geographical position information and the second preset-precision geographical position information; a traversal submodule configured to traverse all objects in the mesh target region, resulting in a plurality of candidate meshes; a sixth determining submodule configured to determine a group of candidate grids from the plurality of candidate grids as a target geographic information grid corresponding to the grid target area.

7. The apparatus of claim 6, wherein the determining module comprises:

8. The apparatus of claim 6, wherein the comparison module comprises:

9. The apparatus of claim 6, wherein the sixth determination submodule is configured to:

10. The apparatus of claim 6, wherein the generating module further comprises:

11. An electronic device, comprising:

a memory and one or more processors;

wherein the memory is communicatively coupled to the one or more processors and has stored therein instructions executable by the one or more processors, the electronic device being configured to implement the method of any of claims 1-5 when the instructions are executed by the one or more processors.

12. A computer-readable storage medium having stored thereon computer-executable instructions operable, when executed by a computing device, to implement the method of any of claims 1-5.