CN117056581A

CN117056581A - Data processing method and device

Info

Publication number: CN117056581A
Application number: CN202210490874.4A
Authority: CN
Inventors: 孙士玺; 牟晓敏; 王怡刚
Original assignee: Beijing Wodong Tianjun Information Technology Co Ltd
Current assignee: Beijing Wodong Tianjun Information Technology Co Ltd
Priority date: 2022-05-07
Filing date: 2022-05-07
Publication date: 2023-11-14
Also published as: WO2023216675A1

Abstract

The application discloses a data processing method and device, and relates to the technical field of Internet of things. One embodiment of the method comprises the following steps: carrying out grid demarcation on the service map; for each grid, calculating the product of the number of the grid arranged in the longitudinal direction and a preset numerical value, and taking the sum of the product and the number of the grid arranged in the transverse direction as an address coding identifier of the grid; and receiving the input address code identification, the business keywords and the business range, carrying out grid positioning based on the input address code identification, searching other grids conforming to the business range by taking the positioned grids as the center, and returning and displaying the space data conforming to the business keywords in the other grids. According to the embodiment, the grid is described through the one-dimensional digital geo_id, so that the space position description rule is simplified, the space positions can be intuitively compared, the calculation efficiency of the space position distance is greatly improved, the specific space data can not retain high-precision longitude and latitude coordinates, meanwhile, the data is not lost, and the safety is enhanced.

Description

Data processing method and device

Technical Field

The present application relates to the field of internet of things, and in particular, to a data processing method and apparatus.

Background

In the Internet of things era, space big data is an extremely important data resource, and the efficiency of data storage, calculation, analysis, mining and the like is greatly improved by the efficient and simple space data warehouse-in rule, so that the establishment of an efficient calculation method of space data is very significant.

The existing spatial data calculation method includes longitude and latitude calculation, spatial judgment and classification by taking character strings as main keys, geoHash (an address code) and the like, but in the process of realizing the application, the inventor finds that at least the following problems exist in the prior art: although the spatial information can be reflected, the calculation efficiency of the relative spatial position relation of two or more is low, and even the visual expression cannot be realized, so that the convenience of the relative spatial position calculation needs to be enhanced.

Disclosure of Invention

In view of this, embodiments of the present application provide a data processing method and apparatus, which at least can solve the problem that in the prior art, although spatial information can be reflected, the computing efficiency of the relative spatial position relationship between two or more relative spatial positions is low, and even cannot be intuitively expressed, so as to enhance the convenience of computing the relative spatial positions.

To achieve the above object, according to an aspect of an embodiment of the present application, there is provided a data processing method including:

carrying out grid demarcation on the service map; wherein the service map is composed of spatial location information, each grid includes one piece of spatial location information, and each piece of spatial location information includes one or more pieces of spatial data;

for each grid, calculating the product of the number of the grid arranged in the longitudinal direction and a preset numerical value, and taking the sum of the product and the number of the grid arranged in the transverse direction as an address coding identifier of the grid;

and receiving the input address code identification, the business keywords and the business range, carrying out grid positioning based on the input address code identification, searching other grids conforming to the business range by taking the positioned grids as the center, and returning and displaying the space data conforming to the business keywords in the other grids.

Optionally, the demarcating the service map includes:

receiving a grid demarcation request for a service map; wherein, the grid defining request comprises service parameters;

inquiring the size of a preset statistical unit corresponding to the service parameter so as to define a grid of the service map by using the size of the preset statistical unit.

Optionally, the preset value is the number of intervals between longitudinally adjacent grids;

before the calculating the product of the number of the arrays of the grid in the longitudinal direction and the preset value, the method further comprises:

an 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 a first preset value is used as the interval number of the longitudinal adjacent grids.

Optionally, after the address code identification as the grid, the method further includes:

and screening the spatial data marked as the specific data, and removing longitude and latitude information in the specific data.

Optionally, the searching for other grids conforming to the service range with the located grid as the center returns the spatial data conforming to the service keywords in the other grids and displays the spatial data, including:

determining a distance calculation formula corresponding to the service range;

inputting the input address code identification, other address code identifications positioned around the input address code identification and the preset numerical value into the distance calculation formula to calculate the distance between other grids and the positioned grid, and further screening one or more grids conforming to the service range;

and screening one or more pieces of space data conforming to the business keywords from the one or more pieces of space data of the grids.

To achieve the above object, according to another aspect of an embodiment of the present application, there is provided a data processing apparatus including:

the demarcation module is used for demarcating the grid of the business map; wherein the service map is composed of spatial location information, each grid includes one piece of spatial location information, and each piece of spatial location information includes one or more pieces of spatial data;

the computing module is used for computing the product of the number of the grid arranged in the longitudinal direction and a preset numerical value for each grid, and taking the sum of the product and the number of the grid arranged in the transverse direction as an address coding identifier of the grid;

and the processing module is used for receiving the input address code identification, the business keywords and the business range, carrying out grid positioning based on the input address code identification, searching other grids conforming to the business range by taking the positioned grids as the center, returning the space data conforming to the business keywords in the other grids and displaying the space data.

Optionally, the demarcation module is configured to:

the computing module is further configured to:

Optionally, the device further comprises a screening module for:

Optionally, the processing module is configured to:

determining a distance calculation formula corresponding to the service range;

To achieve the above object, according to still another aspect of an embodiment of the present application, there is provided a data processing electronic device.

The electronic equipment of the embodiment of the application comprises: one or more processors; and a storage device for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement any of the data processing methods described above.

To achieve the above object, according to still another aspect of the embodiments of the present application, there is provided a computer-readable medium having stored thereon a computer program which, when executed by a processor, implements any of the above-described data processing methods.

According to the solution provided by the present application, one embodiment of the above application has the following advantages or beneficial effects: the grid is described by the one-dimensional digital geo_id, so that the space position description rule is simplified, the space positions can be intuitively compared, the calculation efficiency of the space position distance is greatly improved, the specific space data can not retain high-precision longitude and latitude coordinates, the data is not lost, and the safety of the space data is enhanced.

Further effects of the above-described non-conventional alternatives are described below in connection with the embodiments.

Drawings

The drawings are included to provide a better understanding of the application and are not to be construed as unduly limiting the application. Wherein:

FIG. 1 is a schematic flow diagram of a data processing method according to an embodiment of the present application;

FIG. 2 is a schematic illustration of a partitioned business map grid according to an embodiment of the present application;

FIG. 3 is a schematic flow chart of the implementation of the scheme;

FIG. 4 is a schematic diagram of the main modules of a data processing apparatus according to an embodiment of the present application;

FIG. 5 is an exemplary system architecture diagram in which embodiments of the present application may be applied;

fig. 6 is a schematic diagram of a computer system suitable for use in implementing an embodiment of the present application.

Detailed Description

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

It is noted that embodiments of the application and features of the embodiments may be combined with each other without conflict. The technical scheme of the application obtains, stores, uses, processes and the like the data, which all meet the relevant regulations of national laws and regulations.

The prior art and the shortcomings are described in detail herein, and the currently more commonly used spatial data calculation methods are respectively as follows:

mode one: and directly calculating a distance value between the two points through a spatial geometrical relationship by using longitude and latitude information, and further determining the spatial relationship between the two points.

Mode two: and carrying out space judgment and classification by taking the character string as a main key. For example, the spatial attribute is extended by associating data with the province names Beijing, henan, and the like.

Mode three: geoHash is a currently mainstream technology for implementing location services, and is generally used in combination with a first mode to improve computing efficiency.

In carrying out the present application, the inventors have found that at least the following problems exist in the prior art:

the method is generally used for carrying out space description by using a two-dimensional space, the data space rule is difficult to be examined, and the high-precision longitude and latitude information is not utilized for protecting the privacy of the user. The space marked by longitude and latitude can not directly reflect the space distance of the meter system, and the calculation efficiency is low.

The second mode has high standardization requirements on the primary key values, for example, the two data are related by using county, the relationship can be ensured to be correct only after unified standardization of the county names is ensured, but the precision is still limited, and the precision requirement of micro scene application cannot be met.

The third mode has a boundary problem that the more the values in front of Geohash are the same, the closer the two positions are, whereas the closer the two positions are, the more the values in front are not necessarily the same. There are two particularly close locations [116.3967,45.0009], [116.3967,44.9999], the Geohash values of which are y84b08j2 and wxfzbxvr, respectively, which are very closely spaced but which are quite different due to the fact that they are just on either side of the demarcation point 45. And the Geohash cannot intuitively judge the distance relation between the Geohash and the Geohash literally, and the Geohash needs to be mapped into longitude and latitude, and the space distance is calculated according to the longitude and latitude.

The words related to the scheme are explained as follows:

geohash: an address code encodes two-dimensional longitude and latitude into a one-dimensional character string representing a rectangular area. Geohash is often used for searching nearby places, nearby places are screened out according to Geohash, and then the nearby places are obtained through calculation according to the distance.

One-dimensional: the space formed by the points in one line is only one dimension, which has only length, no width and no height, and can only extend towards two sides infinitely. Compared with the XY (longitude and latitude) space identification method, the method is suitable for efficiently comparing the sizes, and therefore the space positions are resolved.

Spatial data: data, which refers to information representing aspects of the location, shape, size, and distribution characteristics of a spatial entity, may be used to describe objects from the real world, with characteristics of location, nature, time, and spatial relationships. Spatial data is a basic spatial data structure such as points, lines, planes, entities, etc. to represent the natural world of data that people depend on to live.

Referring to fig. 1, a main flowchart of a data processing method provided by an embodiment of the present application is shown, including the following steps:

s101: carrying out grid demarcation on the service map; wherein the service map is composed of spatial location information, each grid includes one piece of spatial location information, and each piece of spatial location information includes one or more pieces of spatial data;

s102: for each grid, calculating the product of the number of the grid arranged in the longitudinal direction and a preset numerical value, and taking the sum of the product and the number of the grid arranged in the transverse direction as an address coding identifier of the grid;

s103: and receiving the input address code identification, the business keywords and the business range, carrying out grid positioning based on the input address code identification, searching other grids conforming to the business range by taking the positioned grids as the center, and returning and displaying the space data conforming to the business keywords in the other grids.

In the above embodiment, for step S101, in the distributed file system, the size of the statistical unit is defined according to the service requirement, and the data accuracy required by different services is also inconsistent, so that the correspondence between the service parameter and the size of the statistical unit may be established in advance. In actual operation, the service map, such as a map of a certain urban area, may be defined according to different service requirements.

If a user needs to divide a certain service map, a request for dividing the grid of the map needs to be sent through a device, for example, clicking a "dividing grid" button in a computer interface, wherein the request carries service parameters, and the grid dividing operation of the service map is realized by inquiring and using the size of a statistical unit corresponding to the service parameters, for example, the grid dividing operation of 1km by 1km is performed (the size of the statistical unit can be adjusted according to actual service requirements and is generally not less than 100m by 100 m).

When the service map is an irregular graph, there may be some grids under which no entity data corresponds, and the grids will not have a subsequent calculation requirement. Referring to fig. 2, the service map is divided into k grids horizontally and n grids vertically. The scheme sets that only one piece of space position information is covered under each grid, and because the space data belong to different space positions, a plurality of space data can be covered under some space position information, and only one piece of space data can be covered under some space position information.

For step S102, the number of intervals M between the vertical neighboring meshes is defined based on the number of the horizontal meshes or the number of the vertical meshes, which is convenient for the subsequent calculation. Assuming that the total number of transverse grids k=18944 is of the order of 4 (in the scientific counting method, a number is denoted as a×10≡b, and b is of the order of magnitude), adding 1 to the order of 4 (i.e. the first preset value, which is merely an example, is actually adjustable), gives a new order of 5, so that the number of intervals between longitudinal grids is 1×10≡5, representing the difference in geo_ids of the longitudinally adjacent grids, and the same applies to the manner in which M is defined for the total number of longitudinal grids.

Referring specifically to fig. 2, each grid geo_id=nm+k is calculated, where n represents the number of grid arrangements in the vertical direction, M represents the difference between vertically adjacent grids, and k represents the number of grid arrangements in the horizontal direction. Therefore, when the number of rows of the grid is calculated according to the geo_id, a downward rounding mode can be adopted: INT (geo_id/M), the difference of the corresponding geo_id-M (geo_id/M) is the number of columns where the grid is located.

Taking the above m=100000 as an example, assuming that the geo_id of a certain mesh is 89442, INT (89442/100000) =0 is calculated, which means that the mesh is located in row 0 and column 89442, and assuming that the geo_id of another mesh is 569443, INT (569443/100000) =5 is calculated, which means that the mesh is located in row 52 and column 69443. Correspondingly, the geo_ids of other grids adjacent to the grid can be calculated quickly, for example, the geo_id of the left grid is (569443-1), the geo_id of the right grid is (569443+1), the geo_id of the upper grid is (569443-100000), and the geo_id of the lower grid is (569443+100000).

In practice, the above embodiment is preferable, although the M value may be defined as the k value. If m=k=18944, INT (569443/18944) =30 for the above grid with geo_id 569443, then manually calculating the geo_id of the upper grid to be (569443-18944) and the geo_id of the lower grid to be (569443+189443) is inconvenient for manual calculation, and is inconvenient for direct decision making on the human brain of the spatial relationship in most cases. And considering that the M value does not affect the division of the original service map, the above manner is preferable.

Spatial data is data based on spatial locations, e.g. spatial data in a certain spatial location comprises:

meteorological data, longitude 117.34 latitude 34.532 temperature 32 degree

Map data, longitude 117.34 latitude 34.532 park

Signaling data, longitude 117.34 latitude 34.532 5 person

If the temperature, park and number of people are separated from the space position, the data value is not great, but the position is known to be park by combining the space position, the temperature is high, and 5 people are moving. If the time dimension is increased again, more kinds of spatial data are combined with machine learning, very great data value can be generated. The spatial association of different spatial data is often high in cost, and the scheme realizes that various spatial data are added with geo_id as a spatial primary key based on the mode. Assuming that the geoid of the grid is 89442, the weather data is 89442 as the spatial primary key of the weather data, or other data identified by 89442, and the present scheme is not limited in this regard.

In addition, after the geo_id is generated, specific data such as signaling data needs to be further processed, such as removing longitude and latitude information (original id is reserved for data backtracking), so that high-precision longitude and latitude data is not exposed, and data security is improved. Such as signaling data: longitude 117.34, latitude 34.532, zhang San, 20211205, which indicates that Zhang Sanyu, month 12, 5 of 2021 occurred at this longitude and latitude, was changed after the geo_id was generated: 2958146geo_id, 1 person, 20211205, indicate 2958146 that there are 1 person in this grid, and this data can be issued for business applications.

Assume map data: 2958146geo_id, park, so that the above-mentioned signaling data combined with map data can know that there are 1 person in park, and the place where the park is located is required to be decrypted. Decryption is performed by clash of original longitude and latitude information through original id, and the original information can be managed by a specific department. Based on the above, more data security problems can be filtered, and meanwhile, the data use is not affected.

For step S103, in the service construction and excavation, the space distance calculating tool based on the geo_id construction, such as the proxel tool, the kernel density tool, etc., implements the efficient application of the space calculating method in the distributed system.

The spatial distance of any two geo_ids is calculated, and the schematic codes are 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 (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 id1,10064823AS id2)a

Where abs is a function, taking absolute value. subtstr is a c++ language function, the main function being to copy substrings, required to start from a specified location, 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. The calculation can be achieved by the calculation, the horizontal distance of two points is 6677km, the vertical distance is 71km, the Manhattan distance is 6784km, the Euclidean distance is 6677.38km, the Euclidean distance and the Manhattan distance can be rapidly calculated based on preliminary judgment of the transverse distance and the longitudinal distance, and the overall calculation amount is extremely small. The four distances are just examples, and other formulas can be adopted in practice, and the calculation is carried out according to the needs when the four distances are specifically used.

It should be noted that, the underlying logic is grid division based on the CGCS2000 projection coordinate system (m-ary), the geo_id is not m-ary itself, but the distance (m-ary) between two geo_ids can be calculated quickly by any two geo_ids, and the geo_id itself converts the space dimension table of the service map.

When the subsequent user applies, only the geo_id, the service keyword and the service range are required to be input, the service range generally designates the horizontal direction, the vertical direction, the surrounding, the oblique angle direction, a certain distance inside/outside and the like, and different distance calculation formulas are used for different service ranges, such as the horizontal distance, the vertical distance, the Manhattan distance and the Euclidean distance. Based on the input geo-id positioning grid, the distance between the grid and other grids is calculated by taking the grid as the center, and no matter what distance calculation formula is, the geo-id of the grid, the geo-ids of other grids which are positioned around the grid and correspond to the service range and the interval number M of the longitudinally adjacent grids are required to be input, so that the distance between the grid and the other grids is calculated, and one or more grids which accord with the service range are further screened out. And searching other grids conforming to the service keywords and the service range, returning the space data in the other grids and displaying the space data.

For example, if the user wants to calculate the number of convenience stores within a range of 100m around a certain geo_id, the business range is within a range of 100m around, spatial data with a distance greater than 100m from the geo_id is filtered based on the business range, and then non-convenience store spatial data with a distance of 100m from the geo_id is filtered by using a business keyword of "convenience store".

For example, the user wants to calculate whether there are kindergarten, primary school, hospital and the like in the 1000m range around a certain geo_id, the service keywords are "kindergarten", "primary school", "hospital", the service range is in the 1000m range around, and after the distance between the surrounding grid and the grid is calculated quickly, screening is performed according to the service keywords, so that spatial data information association based on the geo_id is realized.

Referring to fig. 3, the service map includes spatial data, specifically spatial location information, each including at least one spatial data. And carrying out grid division on the service map, and respectively calculating the geo_id of each grid to obtain a space dimension table of the service map, wherein the space dimension table is a data preservation standardization processing stage. In a subsequent data production phase, analysis and mining of the multi-dimensional data may be performed, such as querying specific spatial data within a certain business range around a certain grid.

According to the method provided by the embodiment, various spatial data are added with the geo_id as the spatial primary key, and the spatial distance can be calculated more efficiently only by inputting the geo_id in the subsequent application process, so that the spatial distance calculation efficiency is improved.

Compared with the prior art, the method provided by the embodiment of the application has at least the following beneficial effects:

1. simplifying the space position description rule, enhancing the recognition of the space rule and improving the space distance calculation efficiency. And the geo_id is set to describe the space position, and the geo_id is a series of numbers, so that the space position can be intuitively compared, and compared with a two-dimensional identification method of longitude and latitude, the calculation speed of the space distance is increased. The high-precision data mining analysis such as space calculation requirements of adjacent point extraction, multi-metadata space association and the like can be further developed on the basis.

2. Spatial data security is enhanced. Based on the geo_id identification, high-precision longitude and latitude coordinates are not reserved for specific data, and the safety of space data is improved. Meanwhile, the data precision is kept not lost, and original longitude and latitude information can be reversely associated through the geo-id.

Referring to fig. 4, a schematic diagram of main modules of a data processing apparatus 400 according to an embodiment of the present application is shown, including:

a demarcation module 401 for demarcating the grid of the service map; wherein the service map is composed of spatial location information, each grid includes one piece of spatial location information, and each piece of spatial location information includes one or more pieces of spatial data;

a calculating module 402, configured to calculate, for each grid, a product of the number of rows of the grid in the longitudinal direction and a preset numerical value, and use a sum of the product and the number of rows of the grid in the transverse direction as an address code identifier of the grid;

the processing module 403 is configured to receive the input address code identifier, the service keyword, and the service range, perform grid positioning based on the input address code identifier, search for other grids that conform to the service range with the positioned grid as a center, and return and display spatial data that conform to the service keyword in the other grids.

In the actual device of the present application, the demarcation module 401 is configured to:

In the real device, the preset numerical value is the interval number of the longitudinal adjacent grids;

the computing module 402 is further configured to:

The application also comprises a screening module for:

In the actual device of the present application, the processing module 403 is configured to:

determining a distance calculation formula corresponding to the service range;

In addition, the implementation of the apparatus in the embodiments of the present application has been described in detail in the above method, so that the description is not repeated here.

Fig. 5 shows an exemplary system architecture 500, including terminal devices 501, 502, 503, a network 504, and a server 505 (by way of example only), to which embodiments of the application may be applied.

The terminal devices 501, 502, 503 may be various electronic devices having a display screen and supporting web browsing, are installed with various communication client applications, and a user may interact with the server 505 through the network 504 using the terminal devices 501, 502, 503 to receive or transmit messages, etc.

The network 504 is used as a medium to provide communication links between the terminal devices 501, 502, 503 and the server 505. The 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 providing various services, and it should be noted that the method provided by the embodiment of the present application is generally performed by the server 505, and accordingly, the apparatus is generally disposed in the server 505.

It should be understood that the number of terminal devices, networks and servers in fig. 5 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

Referring now to FIG. 6, there is illustrated a schematic diagram of a computer system 600 suitable for use in implementing an embodiment of the present application. The terminal device shown in fig. 6 is only an example, and should not impose any limitation on the functions and the scope of use of the embodiment of the present application.

As shown in fig. 6, the computer system 600 includes a Central Processing Unit (CPU) 601, which can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the system 600 are also stored. The CPU 601, ROM 602, and RAM 603 are connected to each other through a 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 portion 606 including a keyboard, mouse, etc.; an output portion 607 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, a speaker, and the like; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The drive 610 is also connected to the I/O interface 605 as needed. Removable media 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed as needed on drive 610 so that a computer program read therefrom is installed as needed into storage section 608.

In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication portion 609, and/or installed from the removable medium 611. The above-described functions defined in the system of the present application are performed when the computer program is executed by a Central Processing Unit (CPU) 601.

The computer readable medium shown in the present application may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present application, however, the computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with the computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport 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 appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The modules involved in the embodiments of the present application may be implemented in software or in hardware. The described modules may also be provided in a processor, for example, as: a processor includes a demarcation module, a calculation module, and a processing module. The names of these modules do not in any way constitute a limitation of the module itself, for example, a processing module may also be described as a "spatial data processing module".

As another aspect, the present application also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be present alone without being fitted into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to include:

According to the technical scheme provided by the embodiment of the application, the grid is described by the one-dimensional digital geo_id, so that the space position description rule is simplified, the space positions can be intuitively compared, the calculation efficiency of the space position distance is greatly improved, the specific space data can not retain high-precision longitude and latitude coordinates, the data is not lost, and the safety of the space data is enhanced.

The above embodiments do not limit the scope of the present application. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives can occur depending upon design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application should be included in the scope of the present application.

Claims

1. A method of data processing, comprising:

2. The method of claim 1, wherein meshing the business map comprises:

3. The method according to claim 1 or 2, wherein the preset value is the number of intervals between longitudinally adjacent grids;

4. The method of claim 1, further comprising, after the address coded identification as a grid:

5. The method according to claim 1, wherein the searching other grids conforming to the service scope with the located grid as the center, and returning and displaying the spatial data conforming to the service keywords in the other grids includes:

determining a distance calculation formula corresponding to the service range;

6. A data processing apparatus, comprising:

7. The apparatus of claim 6, wherein the predetermined number is a number of intervals between longitudinally adjacent grids;

the computing module is further configured to:

8. The apparatus of claim 6, wherein the processing module is configured to:

determining a distance calculation formula corresponding to the service range;

9. An electronic device, comprising:

one or more processors;

storage means for storing one or more programs,

when executed by the one or more processors, causes the one or more processors to implement the method of any of claims 1-5.

10. A computer readable medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the method according to any of claims 1-5.