WO2023216675A1

WO2023216675A1 - Data processing method and apparatus

Info

Publication number: WO2023216675A1
Application number: PCT/CN2023/077321
Authority: WO
Inventors: 孙士玺; 牟晓敏; 王怡刚
Original assignee: 北京沃东天骏信息技术有限公司
Priority date: 2022-05-07
Filing date: 2023-02-21
Publication date: 2023-11-16
Also published as: CN117056581A

Abstract

The present application relates to the technical field of the Internet of Things. Disclosed are a data processing method and apparatus. A specific embodiment of the method comprises: performing gridding on a service map; for each grid cell, calculating the product of an arrangement number of the grid cells in the longitudinal direction and a preset numerical value, and taking the sum of the product and an arrangement number of the grid cells in the transverse direction as an address coding identifier of the grid cells; and receiving an input address code identifier, a service keyword and a service range, performing grid cell positioning on the basis of the input address code identifier, taking a positioned grid acell s a center to search for other grid cells that are in line with the service range, and returning spatial data, which is in line with the service keyword, in the other grid cells and displaying same.

Description

Data processing methods and devices

Cross-references to related applications

This application claims priority to Chinese Patent Application No. 202210490874.4, which is titled "A Data Processing Method and Device" and was submitted on May 7, 2022. The disclosure of the above-mentioned Chinese patent application is cited in its entirety as this application. part or all of.

Technical field

The present disclosure relates to the technical field of the Internet of Things, and in particular, to a data processing method and device.

Background technique

In the Internet of Things era, spatial big data has become an extremely important data resource. Efficient and simple spatial data warehousing rules will greatly improve the efficiency of data storage, calculation, analysis, mining, etc. Therefore, establishing an efficient computing method for spatial data is Very meaningful.

Existing spatial data calculation methods include longitude and latitude calculation, spatial judgment and classification using strings as the main keys, GeoHash (an address encoding), etc. However, in the process of realizing the present disclosure, the inventor found that the existing technology has at least the following problems: Although they can reflect spatial information, the calculation efficiency of two or more relative spatial position relationships is low and cannot even be expressed intuitively. Therefore, it is necessary to enhance the convenience of relative spatial position calculation.

Contents of the invention

In view of this, embodiments of the present disclosure provide a data processing method and device.

According to an aspect of an embodiment of the present disclosure, a data processing method is provided, including:

Grid the business map; wherein, the business map is composed of spatial location information, each grid includes one spatial location information, and each spatial location information includes one or more spatial data;

For each grid, calculate the product of the vertical arrangement number of the grid and the preset value, and The sum of the product and the horizontal arrangement number of the grid is used as the address code identifier of the grid;

Receive the input address code identification, business keywords and business scope, perform grid positioning based on the input address code identification, center on the positioned grid, find other grids that match the business scope, and return the other grids that match the business key Word spatial data and displayed.

According to one or more embodiments of the present disclosure, gridding a business map includes:

Receive a grid delimitation request for a service map; wherein the grid delimitation request includes service parameters;

Query the preset statistical unit size corresponding to the business parameter, so as to use the preset statistical unit size to grid delineate the business map.

According to one or more embodiments of the present disclosure, the preset value is the number of intervals between vertically adjacent grids;

Before calculating the product of the vertical arrangement number of the grid and the preset value, it also includes:

The order of magnitude of the total number of transverse grids or the total number of longitudinal grids is determined, and the sum of the order of magnitude and the first preset value is used as the number of intervals between vertically adjacent grids.

According to one or more embodiments of the present disclosure, after the address encoding identification as a grid, it further includes:

Filter the spatial data marked as specific data and remove the latitude and longitude information in the specific data.

According to one or more embodiments of the present disclosure, taking the positioned grid as the center, searching for other grids that match the business scope, returning and displaying spatial data in other grids that match the business keywords includes:

Determine the distance calculation formula corresponding to the business scope;

Enter the input address code identifier, other address code identifiers located around the input address code identifier, and the preset value into the distance calculation formula to calculate the distance between other grids and the positioned grid. , and then filter out those that fit the stated business scope one or more grids;

From the spatial data of the one or more grids, one or more spatial data that match the business keywords are filtered out.

According to another aspect of the embodiments of the present disclosure, a data processing apparatus is provided, including:

The delimitation module is used to delineate the grid of the business map; wherein the business map is composed of spatial location information, each grid includes one spatial location information, and each spatial location information includes one or more spatial data;

The calculation module is used to calculate, for each grid, the product of the vertical arrangement number of the grid and the preset value, and use the sum of the product and the horizontal arrangement number of the grid as the address code identification of the grid;

The processing module is used to receive the input address code identification, business keywords and business scope, perform grid positioning based on the input address code identification, take the positioned grid as the center, find other grids that match the business scope, and return other networks The spatial data that matches the business keywords in the grid are displayed.

According to yet another aspect of embodiments of the present disclosure, a data processing electronic device is provided.

The electronic device of the embodiment of the present disclosure includes: one or more processors; a storage device configured to store one or more programs. When the one or more programs are executed by the one or more processors, the One or more processors implement any of the above-mentioned data processing methods.

According to yet another aspect of the embodiments of the present disclosure, there is provided a computer-readable medium on which a computer program is stored, and when the program is executed by a processor, any one of the above-mentioned data processing methods is implemented.

Further effects of the above-mentioned non-conventional optional methods will be described below in conjunction with specific implementations.

Description of the drawings

The accompanying drawings are used for a better understanding of the present disclosure and do not constitute an improper limitation of the present disclosure. in:

Figure 1 is a main flow diagram of a data processing method according to an embodiment of the present disclosure;

Figure 2 is a schematic diagram of dividing a business map grid according to an embodiment of the present disclosure;

Figure 3 is a schematic diagram of the implementation flow of the present disclosure;

Figure 4 is a schematic diagram of the main modules of a data processing device according to an embodiment of the present disclosure;

Figure 5 is an exemplary system architecture diagram in which embodiments of the present disclosure may be applied;

FIG. 6 is a schematic structural diagram of a computer system suitable for implementing a mobile device or server according to an embodiment of the present disclosure.

Detailed ways

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

It should be noted that the embodiments and features in the embodiments of the present disclosure can be combined with each other without conflict. The acquisition, storage, use and processing of data in the technical solution of this application all comply with the relevant provisions of national laws and regulations.

The existing technologies and shortcomings are described in detail here. Currently, the more commonly used spatial data calculation methods are the following three:

Method 1: Use longitude and latitude information to directly calculate the distance value between two points through spatial geometric relationships, and then determine the spatial relationship between the two points.

Method 2: Use string as the primary key for spatial judgment and classification. For example, data association is performed through province names such as Beijing and Henan, and then spatial attributes are expanded.

Method three: GeoHash is currently a relatively mainstream technology for implementing location services. It is usually used in combination with method one to improve computing efficiency.

In the process of realizing the present disclosure, the inventor found that the existing technology has at least the following problems:

Method 1 generally uses two-dimensional space for spatial description. It is difficult to discern the spatial patterns of data, and high-precision longitude and latitude information is not used to protect user privacy. The space marked by longitude and latitude cannot directly reflect the spatial distance in meters, and the calculation efficiency is low.

Method 2 has high requirements for the standardization of primary key values. For example, to associate two pieces of data using districts and counties, the district and county names must be unified and standardized to ensure that the association is correct. However, the accuracy is still limited and cannot meet the accuracy needs of micro-scenario applications. .

Method 3 has a boundary problem. The more the previous values of the Geohash are the same, the closer the two positions are. On the contrary, the closer the two positions are, the more the previous values are not necessarily the same. For example, there are two particularly close locations [116.3967,45.0009] and [116.3967,44.9999], whose Geohash values are y84b08j2 and wxfzbxvr respectively. Although these two points are very close to each other, they are just on both sides of the dividing point 45, resulting in Geohash The values are quite different. Moreover, Geohash cannot intuitively judge the distance relationship between the two literally. It needs to be mapped into longitude and latitude, and the spatial distance is calculated based on the longitude and latitude.

The explanations for the words involved in this plan are as follows:

Geohash: An address encoding that encodes two-dimensional longitude and latitude into a one-dimensional string, representing a rectangular area. Geohash is often used to search for nearby locations. Nearby locations are first filtered out based on Geohash, and then nearby locations are calculated based on distance.

One-dimensional: A one-dimensional space consisting only of points within a line. It has only length, no width and height, and can only extend infinitely to both sides. Compared with the XY (latitude and longitude) spatial identification method, it is suitable for efficient size comparison to distinguish spatial positions.

Spatial data: refers to data used to represent the location, shape, size and distribution characteristics of spatial entities. It can be used to describe targets from the real world and has characteristics such as positioning, qualitative, time and spatial relationships. Spatial data is a kind of data that uses basic spatial data structures such as points, lines, surfaces, and entities to represent the natural world on which people rely for survival.

Referring to Figure 1, shown is a main flow chart of a data processing method provided by an embodiment of the present disclosure, which includes the following steps:

S101: Grid the business map; the business map consists of spatial location information Information composition, each grid includes a spatial location information, and each spatial location information includes one or more spatial data;

S102: For each grid, calculate the product of the vertical arrangement number of the grid and the preset value, and use the sum of the product and the horizontal arrangement number of the grid as the address code identification of the grid;

S103: Receive the input address code identification, business keywords and business scope, perform grid positioning based on the input address code identification, center on the positioned grid, search for other grids that match the business scope, and return the other grids that match the business scope. The spatial data of business keywords are displayed.

In the above embodiment, for step S101, in the distributed file system, the statistical unit size is first defined according to business needs. Different businesses require different data accuracy. Therefore, the corresponding relationship between the business parameters and the statistical unit size can be established in advance. . In actual operation, business maps can be defined according to different business requirements, such as a map of a certain urban area.

If the user needs to divide a certain business map, he needs to send a grid delineation request for the map through the device. For example, click the "Grid" button in the computer interface. The request carries business parameters, which can be queried and used with the business. The size of the statistical unit corresponding to the parameter enables grid delineation of the business map, such as grid delineation of 1km*1km (the size of the statistical unit can be adjusted according to actual business needs, generally not less than 100m*100m).

When the business map is an irregular shape, there may be no entity data corresponding to some grids, and these grids will not require subsequent calculations. Referring to Figure 2, the business map is divided into k grids horizontally and n grids vertically. This solution sets that each grid only covers one spatial location information. Since the spatial data belong to different spatial locations, some spatial location information may cover multiple spatial data, and some may only cover one spatial data.

For step S102, based on the order of magnitude of the total number of horizontal grids or the total number of vertical grids, the number M of intervals between longitudinal adjacent grids is defined to facilitate subsequent calculations. Assume that the total number of horizontal grids k=18944, the order of magnitude is 4 (in scientific notation, a number is recorded in the form of a*10^b, b is the order of magnitude), add the order of magnitude 4 to 1 (that is, the first preset value, only an example, Actual adjustable), a new order of magnitude 5 is obtained, so the number of intervals between longitudinal grids is 1*10^5, which represents the difference in geo_id of vertically adjacent grids. The same method is used to define M using the total number of longitudinal grids. .

Refer to Figure 2 for details. Calculate each grid geo_id=nM+k, where n represents the number of grids arranged in the longitudinal direction, M represents the difference between adjacent grids in the longitudinal direction, and k represents the number of grids arranged in the transverse direction. . Therefore, when subsequently calculating the number of rows of the grid based on geo_id, the downward rounding method can be used: INT (geo_id/M). The difference between the corresponding geo_id–M*INT (geo_id/M) is the location of the grid. Number of columns.

Take the above M=100000 as an example. Assume that the geo_id of a certain grid is 89442. Calculate INT (89442/100000) = 0, which means that the grid is located at row 0 and column 89442. Assume that the geo_id of another grid is 569443, calculate INT (569443/100000) = 5, which means that the grid is located at row 52 and column 69443. Correspondingly, the geo_id of other grids adjacent to this grid can also be quickly calculated. For example, the geo_id of the left grid is (569443-1), the geo_id of the right grid is (569443+1), and the geo_id of the upper grid is (569443+1). The geo_id of the grid is (569443–100000), and the geo_id of the grid below is (569443+100000).

In actual operation, although the M value can be defined as the k value, the above method is preferred in this solution. If M=k=18944, for the above-mentioned grid with geo_id 569443, INT (569443/18944)=30, subsequent manual calculation of the geo_id of the upper grid is (569443-18944), and the geo_id of the lower grid is (569443+189443 ), which is not friendly to manual calculations and is inconvenient for the human brain to directly judge and make decisions on spatial relationships in most cases. And considering that the M value does not affect the division of the original business map, the above method is preferred.

Spatial data is data based on spatial location. For example, spatial data in a certain spatial location includes:

Meteorological data, longitude 117.34 latitude 34.532 temperature 32 degrees

Map data, longitude 117.34 latitude 34.532 park

Signaling data, longitude 117.34 latitude 34.532 5 people

If separated from the spatial location, the data of temperature, park and number of people are of little value. However, combined with the spatial location, it can be known that the location is a park with high temperature and 5 people active. If you add the time dimension and more types of spatial data, combined with machine learning, you can generate very large data value. The spatial association of different spatial data is often costly. This solution adds geo_id as the spatial primary key to various spatial data based on the above method. Assuming that the geo_id of the grid is 89442, the spatial primary key of the meteorological data is the meteorological data of 89442, or other data identified by 89442. This plan does not limit this.

In addition, specific data such as signaling data needs further processing after generating the geo_id, such as removing the latitude and longitude information (retaining the original ID for data backtracking), so as not to expose high-precision latitude and longitude data and improve data security. For example, the signaling data: longitude 117.34, latitude 34.532, Zhang San, 20211205, means that Zhang San appeared at this longitude and latitude on December 5, 2021. After generating the geo_id, it changes to: 2958146 geo_id, 1 person, 20211205, which means 2958146. There is one person in the grid who can distribute this data for business applications.

Assume that the map data is: 2958146geo_id, park. Therefore, the above signaling data combined with the map data shows that there is a person in the park. The location of the park needs to be decrypted. Decryption uses the original ID to collide with the original longitude and latitude information, and the original information can be managed by a specific department. Based on the above, more data security issues can be filtered without affecting data usage.

For step S103, in business construction and mining, spatial distance calculation tools are constructed based on geo_id, such as nearby point tools, kernel density tools, etc., to achieve efficient application of spatial calculation methods in distributed systems.

Calculate the spatial distance between any two geo_ids. The code is as follows:

--id1 2958146

--id2 10064823

--M＝100000

--Each ID is measured in 1km length

SELECT

abs(int(SUBSTR(a.id1,-LENGTH(100000)+1)-SUBSTR(a.id2,-LENGTH(100000)+1)))AS horizontal distance,

abs(int((a.id2-a.id1)/100000))AS vertical distance,

abs(int(SUBSTR(a.id1,-LENGTH(100000)+1)-SUBSTR(a.id2,-LENGTH(100000)+1)))+abs(int((a.id2-a.id1)/ 100000))AS Manhattan distance,

power(power(abs(int(SUBSTR(a.id1,-LENGTH(100000)+1)-SUBSTR(a.id2,-LENGTH(100000)+1))),2)+power(abs(int(( a.id2-a.id1)/100000)),2),0.5)AS Euclidean distance

FROM

(SELECT 2958146 AS id1,10064823 AS id2)a

Among them, abs is a function, taking the absolute value. substr is a C++ language function. Its main function is to copy a substring, starting from a specified position and having a specified length. LENGTH is the length. POWER(number,power), where the parameter number represents the base and the parameter power represents the exponent. From this calculation, the horizontal distance between two points is 6677km, the vertical distance is 71km, the Manhattan distance is 6784km, and the Euclidean distance is 6677.38km. Based on the preliminary judgment of the horizontal distance and vertical distance, the Euclidean distance and Manhattan distance can be quickly calculated, and the overall calculation amount is All extremely small. The above four distances are just examples. In fact, there can be other formulas, and they should be calculated as needed when used.

It should be noted that the underlying logic is grid division based on the CGCS2000 projected coordinate system (meter system). geo_id itself is not in meter system, but through any two geo_ids, the distance between the two (meter system) can be quickly calculated. The conversion of geo_id itself requires the spatial dimension table of the business map.

When subsequent users apply, they only need to enter geo_id, business keywords and business scope. The business scope usually specifies a certain distance inside/outside in the horizontal direction, vertical direction, surrounding, oblique direction, etc. Different distance calculation formulas are used for different business scopes. , such as the above horizontal distance, Vertical distance, Manhattan distance and Euclidean distance. Position the grid based on the input geo_id, with the grid as the center. Regardless of the above distance calculation formula, you need to enter the geo_id of the grid, the geo_id of other grids located around the grid and corresponding to the business scope, The number M of intervals between vertical adjacent grids is used to calculate the distance between this grid and other grids, and then one or more grids that fit the business scope are screened out. Find other grids that match the business keywords and business scope, return the spatial data in other grids and display them.

For example, if the user wants to calculate the number of convenience stores within 100m around a certain geo_id, the business scope is within 100m. Based on this business scope, the spatial data that is greater than 100m away from the geo_id is filtered out, and then the business keyword "convenience store" is used. ", filter out non-convenience store spatial data within 100m of the geo_id.

For example, the user wants to calculate whether there are kindergartens, primary schools, hospitals, etc. within a range of 1000m around a certain geo_id. The business keywords are "kindergarten", "primary school", "hospital", and the business scope is within a range of 1000m. The calculation can also be completed quickly. After determining the distance between the surrounding grid and the grid, filtering is performed based on business keywords to achieve spatial data information association based on geo_id.

As shown in FIG. 3 , the business map includes spatial data, specifically spatial location information, and each spatial location information includes at least one spatial data. Grid the business map and calculate the geo_id of each grid separately to obtain the spatial dimension table of the business map. This is the data preservation and standardization processing stage. In the subsequent data production stage, multivariate spatial data analysis and mining can be carried out, such as querying specific spatial data within a certain business scope near a certain grid.

The method provided by the above embodiment adds geo_id as the spatial primary key to various types of spatial data. In subsequent applications, only the geo_id can be input to calculate the spatial distance more efficiently and improve the efficiency of spatial distance calculation.

Compared with the existing technology, the method provided by the embodiments of the present disclosure has at least the following beneficial effects:

1. Simplify spatial location description rules, enhance spatial regularity recognition, and improve spatial distance calculation efficiency. Set geo_id to describe the spatial position. Geo_id is a string of numbers, so that the spatial position can be compared intuitively. Compared with the two-dimensional identification method of longitude and latitude, it speeds up the calculation of spatial distance. High-precision data mining analysis, such as adjacent point extraction, multivariate data spatial correlation and other spatial computing requirements, can be further developed on this basis.

2. Strengthen spatial data security. Based on the geo_id identifier, high-precision longitude and latitude coordinates are not retained for specific data, which improves the security of spatial data. At the same time, the data accuracy is not lost, and the original longitude and latitude information can be reversely associated through geo_id.

Referring to Figure 4, a schematic diagram of the main modules of a data processing device 400 provided by an embodiment of the present disclosure is shown, including:

The delineation module 401 is used to delimit the business map into grids; wherein the business map is composed of spatial location information, each grid includes one spatial location information, and each spatial location information includes one or more spatial data;

The calculation module 402 is used to calculate, for each grid, the product of the vertical arrangement number of the grid and the preset value, and use the sum of the product and the horizontal arrangement number of the grid as the address code identification of the grid;

The processing module 403 is used to receive the input address code identification, business keywords and business scope, perform grid positioning based on the input address code identification, center on the positioned grid, search for other grids that match the business scope, and return other grids. The spatial data that matches the business keywords in the grid are displayed.

In the implementation device of this disclosure, the demarcation module 401 is used for:

In the implementation device of the present disclosure, the preset value is the interval number of vertically adjacent grids;

The calculation module 402 is also used to:

The implementation device of the present disclosure also includes a screening module for:

In the implementation device of this disclosure, the processing module 403 is used for:

Determine the distance calculation formula corresponding to the business scope;

Enter the input address code identifier, other address code identifiers located around the input address code identifier, and the preset value into the distance calculation formula to calculate the distance between other grids and the positioned grid. , and then filter out one or more grids that meet the business scope;

In addition, the specific implementation content of the device in the embodiment of the present disclosure has been described in detail in the above method, so the repeated content will not be described here.

Figure 5 shows an exemplary system architecture 500 to which embodiments of the present disclosure may be applied, including terminal devices 501, 502, 503, a network 504 and a server 505 (examples only).

Terminal devices 501, 502, and 503 can be various electronic devices with display screens and support web browsing, and are installed with various communication client applications. Users can use terminal devices 501, 502, and 503 to interact with the server 505 through the network 504 to Receive or send messages, etc.

Network 504 is used to provide a medium for communication links between terminal devices 501, 502, 503 and server 505. Network 504 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.

The server 505 may be a server that provides various services. It should be noted that the methods provided by the embodiments of the present disclosure are generally executed by the server 505. Accordingly, the device is generally provided in the server 505.

It should be understood that the number of terminal devices, networks and servers in Figure 5 is only illustrative. Depending on implementation needs, there can be any number of end devices, networks, and servers.

Referring now to FIG. 6 , a schematic structural diagram of a computer system 600 suitable for implementing a terminal device according to an embodiment of the present disclosure is shown. The terminal device shown in FIG. 6 is only an example and should not impose any restrictions on the functions and scope of use of the embodiments of the present disclosure.

As shown in FIG. 6 , computer system 600 includes a central processing unit (CPU) 601 that can operate according to a program stored in a read-only memory (ROM) 602 or loaded from a storage portion 608 into a random access memory (RAM) 603 And perform various appropriate actions and processing. In the RAM 603, various programs and data required for the operation of the system 600 are also stored. CPU 601, ROM 602 and RAM 603 are connected to each other through bus 604. An input/output (I/O) interface 605 is also connected to bus 604.

The following components are connected to the I/O interface 605: an input section 606 including a keyboard, a mouse, etc.; an output section 607 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., speakers, etc.; and a storage section 608 including a hard disk, etc. ; and a communication section 609 including a network interface card such as a LAN card, a modem, etc. The communication section 609 performs communication processing via a network such as the Internet. Driver 610 is also connected to I/O interface 605 as needed. Removable media 611, such as magnetic disks, optical disks, magneto-optical disks, semiconductor memories, etc., are installed on the drive 610 as needed, so that a computer program read therefrom is installed into the storage portion 608 as needed.

In particular, according to embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product including a computer program carried on a computer-readable medium, the computer program A program contains program code for executing the method shown in the flowchart. In such embodiments, the computer program may be downloaded and installed from the network via communication portion 609, and/or installed from removable media 611. When the computer program is executed by the central processing unit (CPU) 601, the above-described functions defined in the system of the present disclosure are performed.

It should be noted that the computer-readable medium shown in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the above two. The computer-readable storage medium may be, for example, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus or device, or any combination thereof. More specific examples of computer readable storage media may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard drive, random access memory (RAM), read only memory (ROM), removable Programmed read-only memory (EPROM or flash memory), fiber optics, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above. In this disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code therein. Such propagated data signals may take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device . Program code embodied on a computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the foregoing.

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operations of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code that contains one or more logic functions that implement the specified executable instructions. It should also be noted that in some cases as replacements Implementations may allow the functions noted in the blocks to occur out of the order noted in the figures. For example, two blocks shown one after another may actually execute substantially in parallel, or they may sometimes execute in the reverse order, depending on the functionality involved. It will also be noted that each block in the block diagram or flowchart illustration, and combinations of blocks in the block diagram or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or operations, or may be implemented by special purpose hardware-based systems that perform the specified functions or operations. Achieved by a combination of specialized hardware and computer instructions.

The modules involved in the embodiments of the present disclosure can be implemented in software or hardware. The described module can also be provided in a processor. For example, it can be described as follows: a processor includes a demarcation module, a calculation module, and a processing module. The names of these modules do not constitute a limitation on the module itself under certain circumstances. For example, the processing module can also be described as a "spatial data processing module".

As another aspect, the present disclosure also provides a computer-readable medium. The computer-readable medium may be included in the device described in the above embodiments; it may also exist separately without being assembled into the device. The above computer-readable medium carries one or more programs. When the above one or more programs are executed by a device, the device includes:

For each grid, calculate the product of the vertical arrangement number of the grid and the preset value, and use the sum of the product and the horizontal arrangement number of the grid as the address code identification of the grid;

According to the technical solution of the embodiment of the present disclosure, the grid is described by one-dimensional digital geo_id, which simplifies the spatial position description rules, enables intuitive comparison of spatial positions, greatly improves the calculation efficiency of spatial position distance, and specific spatial data can no longer retain high-precision latitude and longitude coordinates, At the same time, it ensures that data is not lost and strengthens the security of spatial data.

The above-mentioned specific embodiments do not constitute a limitation on the scope of the present disclosure. It will be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may occur depending on design requirements and other factors. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of this disclosure shall be included in the protection scope of this disclosure.

Claims

A data processing method including:

Grid the business map; wherein, the business map is composed of spatial location information, each grid includes one spatial location information, and each spatial location information includes one or more spatial data;

For each grid, calculate the product of the vertical arrangement number of the grid and the preset value, and use the sum of the product and the horizontal arrangement number of the grid as the address code identification of the grid;

Receive the input address code identification, business keywords and business scope, perform grid positioning based on the input address code identification, center on the positioned grid, find other grids that match the business scope, and return the other grids that match the business key Word spatial data and displayed.
The method according to claim 1, wherein the grid delineation of the business map includes:

Receive a grid delimitation request for a service map; wherein the grid delimitation request includes service parameters;

Query the preset statistical unit size corresponding to the business parameter, so as to use the preset statistical unit size to grid delineate the business map.
The method according to claim 1 or 2, wherein the preset value is the interval number of vertically adjacent grids;

Before calculating the product of the vertical arrangement number of the grid and the preset value, it also includes:

The order of magnitude of the total number of transverse grids or the total number of longitudinal grids is determined, and the sum of the order of magnitude and the first preset value is used as the number of intervals between vertically adjacent grids.
The method according to claim 1, wherein after the address encoding identification as a grid, it further includes:

Filter the spatial data marked as specific data and remove the latitude and longitude information in the specific data.
The method according to claim 1, wherein, taking the positioned grid as the center, searching for other grids that conform to the business scope, returning and displaying the spatial data in other grids that conform to the business keywords, includes:

Determine the distance calculation formula corresponding to the business scope;

Enter the input address code identifier, other address code identifiers located around the input address code identifier, and the preset value into the distance calculation formula to calculate the distance between other grids and the positioned grid. , and then filter out one or more grids that meet the business scope;

From the spatial data of the one or more grids, one or more spatial data that match the business keywords are filtered out.
A data processing device including:

The delimitation module is used to delineate the grid of the business map; wherein the business map is composed of spatial location information, each grid includes one spatial location information, and each spatial location information includes one or more spatial data;

The calculation module is used to calculate, for each grid, the product of the vertical arrangement number of the grid and the preset value, and use the sum of the product and the horizontal arrangement number of the grid as the address code identification of the grid;

The processing module is used to receive the input address code identification, business keywords and business scope, perform grid positioning based on the input address code identification, take the positioned grid as the center, find other grids that match the business scope, and return other networks The spatial data that matches the business keywords in the grid are displayed.
The device according to claim 6, wherein the preset value is the interval number of longitudinally adjacent grids;

The computing module is also used for:

The order of magnitude of the total number of transverse grids or the total number of longitudinal grids is determined, and the sum of the order of magnitude and the first preset value is used as the number of intervals between vertically adjacent grids.
The device according to claim 6, wherein the processing module is used for:

Determine the distance calculation formula corresponding to the business scope;

Enter the input address code identifier, other address code identifiers located around the input address code identifier, and the preset value into the distance calculation formula to calculate the distance between other grids and the positioned grid. , and then filter out one or more grids that meet the business scope;

From the spatial data of the one or more grids, one or more spatial data that match the business keywords are filtered out.
An electronic device including:

one or more processors;

a storage device for storing one or more programs,

When the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the method as described in any one of claims 1-5.
A computer-readable medium having a computer program stored thereon, which implements the method according to any one of claims 1-5 when executed by a processor.