CN109992636B

CN109992636B - Space-time coding method, space-time index and query method and device

Info

Publication number: CN109992636B
Application number: CN201910221266.1A
Authority: CN
Inventors: 童晓冲; 雷毅; 程承旗; 吴翔宇; 赖广陵; 李贺; 郭从洲; 张勇; 陈波
Original assignee: Information Engineering University of PLA Strategic Support Force
Current assignee: Information Engineering University of PLA Strategic Support Force
Priority date: 2019-03-22
Filing date: 2019-03-22
Publication date: 2021-06-08
Anticipated expiration: 2039-03-22
Also published as: CN109992636A

Abstract

One or more embodiments of the specification disclose a space-time coding method, a space-time index and query method and a device, which are used for realizing efficient organization, management and query of mass multi-source remote sensing data. The method comprises the following steps: dividing the longitude and latitude space of the earth according to a specified dividing mode to obtain a plurality of grids; the corresponding grid numbers in the longitude direction and the latitude direction are the same; the appointed subdivision mode comprises an appointed subdivision level; carrying out integer coding on the coordinate information corresponding to each grid to obtain a first coding value corresponding to each grid; and establishing an incidence relation between the grids and the first coding values respectively corresponding to the grids and the remote sensing image data. According to the technical scheme, the position association with the multi-source remote sensing data can be realized by utilizing global multi-scale grid integer coding, and the space-time index and query of the multi-source remote sensing data are realized.

Description

Space-time coding method, space-time index and query method and device

Technical Field

The present disclosure relates to the field of data processing technologies, and in particular, to a space-time coding method, a space-time index and query method and apparatus.

Background

The global earth observation technology, system and capability are gradually completed, and a multi-means and multi-platform stereo observation system and a novel observation system such as a PNRTC (positioning, navigation, time service, remote sensing and communication integrated space-based information service system) in development are formed. In recent years, microsatellites are in a rapid development stage, and if the pigeon constellation reaches more than 200, the earth observation data resources are greatly abundant. At present, remote sensing data has the characteristics of multiple sources, high space, time, spectral resolution, global coverage and the like, and the data volume forms the development trend of PB level and EB level. How to realize efficient organization, management and query of mass multi-source remote sensing data becomes one of the problems to be solved urgently by related business organizations and application departments.

The existing remote sensing data storage management systems or centers are mainly divided into the following two types according to data organization modes:

(1) multi-resolution pyramid image tile based approach

Typical systems that use this approach are: word Wind, Terrraverer, Bing Maps, Google Maps/Earth, heaven map, and the like. As shown in fig. 1, the method mainly uses a series of regular, seamless grid tiles with a multi-scale hierarchical structure to completely and continuously cover the earth surface space, and establishes a spatial index of the image by the position identification of the tiles. The method is mainly applied to seamless organization and visualization (image map) of remote sensing data, so that a user can quickly acquire and inquire satellite image data of a certain area and display the satellite image data, but the unified management of the remote sensing data with multi-source and multi-time equal characteristics of the same area is lacked. In addition, due to image subdivision and pyramid construction, a large number of small tile files are generated in a remote sensing data organization mode based on a global subdivision grid, and data increment of about 1/3 is caused, so that a data storage space is enlarged. If the distributed file system is adopted for storage, a large number of mapping files and log files are generated, so that single-point faults occur in some nodes, and the storage and management of the distributed file system are not facilitated.

(2) Satellite orbit strip or scene mode based on space-time recording system

Typical systems that use this approach are: NASAAEOS, European space agency data center, remote sensing market, China resource satellite application center, national satellite weather center, national satellite marine application center, etc. As shown in fig. 2, in this type of method, image data is organized according to strip or view-divided images, metadata of the image data is usually managed by a commercial database, and spatial index of the image data is established by using spatial information of the image data, which is mainly used for managing received original image data and data products. The method is simple to operate, and can meet the requirements of management and application of archived data for data organization and management with less remote sensing satellites and smaller data size. However, with the increase of the types of remote sensing data sources, the track strips among the remote sensing data production units are not corresponding to scenes with uniform segmentation standards and positions, the product data identification is lack of geoscience meaning, and the multi-source, multi-scale and multi-temporal data in the same region are lack of spatial scale and position association, so that the association of different data products in the same region is poor; the multi-source remote sensing data in the same area are often recorded in different track strips, and the time is consumed for integrating the multi-source and multi-temporal data in a specific area across departments, so that the inconvenience of remote sensing data retrieval, management and integration is brought.

From the above, the tile mode based on the multi-resolution pyramid image mainly aims to solve the problem that the real world real expression and presentation based on the image lacks the unified management of the remote sensing data with the characteristics of multiple sources, multiple scales and multiple time and the like in the same region. The position relevance among multi-source remote sensing data is poor in a satellite orbit strip or scene mode based on a space-time recording system, and satellites of different models often adopt respective data organization modes and recording modes, so that the isolated island phenomenon of data information is serious, remote sensing data in the same area is difficult to retrieve, integrate and share, and the data use efficiency is reduced. Therefore, the two modes are different in thousands of years, but the requirements of remote sensing data organization and management of a current large number of multiple sources cannot be completely met.

Disclosure of Invention

One or more embodiments of the present disclosure provide a space-time coding method, a space-time index and query method and apparatus, so as to implement efficient organization, management and query of mass multi-source remote sensing data.

To solve the above technical problem, one or more embodiments of the present specification are implemented as follows:

in one aspect, one or more embodiments of the present specification provide a space-time coding method, including:

dividing the longitude and latitude space of the earth according to a specified dividing mode to obtain a plurality of grids; the corresponding grid numbers in the longitude direction and the latitude direction are the same; the appointed subdivision mode comprises an appointed subdivision level;

carrying out integer coding on the coordinate information corresponding to each grid to obtain a first coding value corresponding to each grid;

and establishing an incidence relation between the grids and the first coding values respectively corresponding to the grids and the remote sensing image data.

In one embodiment, the subdividing the longitude and latitude space of the earth according to a specified subdividing method to obtain a plurality of grids includes:

expanding a latitude space to enable the space range of the latitude space to be consistent with that of a longitude space, and obtaining an expanded longitude and latitude space;

dividing the expanded longitude and latitude space into a plurality of grids of the appointed subdivision level according to a quadtree mode to obtain the plurality of grids; and determining the origin of coordinates of the grids.

In one embodiment, the establishing an association relationship between the grids and the first code values respectively corresponding to the grids and the remote sensing image data includes:

calculating a circumscribed rectangle of an image area corresponding to the remote sensing image data;

gridding the circumscribed rectangle by using the grid of the lowest level to obtain a first grid corresponding to the circumscribed rectangle;

judging whether the first grid meets a preset condition or not; wherein the preset condition comprises at least one of the following: the total number of the first type grids and the second type grids in the first type grids is greater than or equal to a preset upper limit value of the number of codes, and the number of the second type grids is zero;

if so, determining the first grid as a grid corresponding to the image area;

if not, circularly executing the following steps until the obtained grid meets the preset condition: aiming at a second grid with the highest priority in the second type grids, determining sub-grids of adjacent levels of the level where the second grid is located; and dividing the sub-grids into the first type grids and/or the second type grids, and updating the priority of the second type grids.

In one embodiment, the first type mesh comprises: the grid contained by the circumscribed rectangle, or the grid with the grid level greater than or equal to a preset level upper limit value;

the second type of mesh comprises: a grid intersecting the bounding rectangle and having a grid level less than the level upper limit value, or a grid including the area of influence.

In one embodiment, the priority order of the second type of mesh is determined according to at least one of the following rules:

the smaller the number of grid layers is, the higher the priority is;

the more the number of specified type grids in the sub-grids contained in the grid is, the higher the priority is; the specified type grids comprise grids separated from areas corresponding to the remote sensing image data;

the fewer the number of first type meshes in the sub-meshes comprised by said mesh, the higher said priority.

In another aspect, one or more embodiments of the present specification provide a spatiotemporal index and query method, including:

creating a data index table for managing source data of the remote sensing image data; the data index table comprises at least one of the following fields: the source data, the data identification, the data acquisition time code and the data association grid code set of the remote sensing image data;

creating a grid coding index table for establishing a spatial index of the remote sensing image data according to each field in the data index table; the grid code index table comprises a geosynchronous grid code and a data identification set; the earth subdivision grid codes are code values corresponding to grids obtained by subdividing longitude and latitude spaces of the earth;

and inquiring the remote sensing image data to be inquired according to the data index table and the grid coding index table.

In one embodiment, the creating a data index table for managing source data of the remote sensing image data includes:

determining the field name and the data type of each field in the data index table;

reading the source data corresponding to the field name and the data type, and importing the read source data into the data index table;

determining each field in the global image density lookup table; the global image density lookup table includes at least one of the following fields: dividing grid codes, the covering times of the remote sensing image data in each grid and the multiplying power of the upper limit value of the grid number;

and acquiring time codes according to the data, and establishing time code indexes of the remote sensing image data.

In one embodiment, the data acquisition time encoding in the data index table is determined as follows:

and carrying out integer coding of a specified level on the time information of the remote sensing image data to obtain the data acquisition time code of the remote sensing image data.

In one embodiment, the performing integer coding of a specified hierarchy on the time information of the remote sensing image data to obtain the data acquisition time coding of the remote sensing image data includes:

decomposing the time information into a plurality of integers of a specified time scale;

respectively coding the integers on each specified time scale into binary numbers of specified digits to obtain binary code values of the time information on each specified time scale;

connecting the binary code values of the time information on each specified time scale in a bit domain to obtain a second code value corresponding to the time information;

and shifting the second coding value by one bit to the left to obtain the data acquisition time coding of the remote sensing image data.

In one embodiment, the specified timescale comprises at least one of a year, month, day, hour, minute, second, millisecond, microsecond;

correspondingly, the encoding of the integer on each specified time scale into a binary number of a specified bit number includes at least one of:

encoding the integer on the time scale of the year into a binary number of 17 bits;

encoding the integer on the time scale of the month into a binary number of 4 bits;

encoding the integer on the time scale of the day into a binary number of 5 bits;

encoding the integer on the time scale of the hour into a binary number of 5 bits;

encoding the integer divided on the time scale into a binary number of 6 bits;

encoding the integer on the time scale of the second into a binary number of 6 bits;

encoding the integer on the time scale of the millisecond into a binary number of 10 bits;

the integer on this time scale of microseconds is encoded as a 10bit binary number.

In one embodiment, the method further comprises:

when a specified operation instruction for first data in the data index table is received, corresponding operation is performed on the first data; updating the time coding index in the data index table;

wherein the specified operation instruction comprises a deletion instruction and/or an insertion instruction.

In one embodiment, the creating a trellis-coded index table for establishing a spatial index of the remote sensing image data according to fields in the data index table includes:

determining a data association grid coding set corresponding to each data in the data index table according to the data index table and the global image density lookup table;

inserting the data-associated trellis-encoded set into the trellis-encoded index table;

and sorting the data association grid coding sets inserted into the grid coding index table to establish a one-dimensional spatial index of the grid coding index table.

In one embodiment, the method further comprises:

when the data in the data index table changes, judging whether the changed data volume reaches a preset data volume; wherein the change comprises inserting data and/or deleting data;

and if so, recreating the grid coding index table.

In one embodiment, the querying the remote sensing image data to be queried according to the data index table and the mesh coding index table includes:

determining a query region in the geosynthetic mesh; the query area is composed of a plurality of longitude and latitude coordinates;

determining a first gridding code corresponding to the longitude and latitude coordinate according to the longitude and latitude coordinate and the earth subdivision grid code;

searching the data association grid coding set belonging to the first sub grid in the grid coding index table aiming at the first sub grid of each grid corresponding to the first gridding code as a first query result;

determining a parent grid of each grid corresponding to the first gridding code, and determining a geocellular grid code corresponding to the parent grid as a second gridding code of the query area;

aiming at a second sub-grid of each grid corresponding to the second gridding code, searching the data association grid code set belonging to the second sub-grid in the grid code index table, and screening the data association grid code set corresponding to the second sub-grid meeting a first preset condition as a second query result; the first preset condition includes: the first gridding code comprises gridding codes belonging to the second query result;

and extracting a first data identification set corresponding to the first query result and the second query result from the grid coding index table to obtain a space query result corresponding to the remote sensing image data to be queried.

determining query parameters of the remote sensing image data to be queried, wherein the query parameters comprise the query area and the query time range; the query time range comprises a start time and an end time;

respectively carrying out time coding on the starting time and the ending time to obtain a starting time coding value and an ending time coding value;

aiming at the first data identification set, screening out a field which corresponds to the first data identification set and meets a second preset condition from the data index table, and taking the field as a space-time query result corresponding to the remote sensing image data to be queried; wherein the second preset condition comprises: and the data acquisition time code corresponding to the first data identification set is positioned between the starting time code value and the ending time code value.

In yet another aspect, one or more embodiments of the present specification provide a space-time coding apparatus, including:

the subdivision module is used for subdividing the longitude and latitude space of the earth according to a specified subdivision mode to obtain a plurality of grids; the corresponding grid numbers in the longitude direction and the latitude direction are the same; the appointed subdivision mode comprises an appointed subdivision level;

the encoding module is used for carrying out integer encoding on the coordinate information corresponding to each grid to obtain a first encoding value corresponding to each grid;

and the association module is used for establishing an association relation between the grid and the first coding value and the remote sensing image data respectively corresponding to each grid.

In yet another aspect, one or more embodiments of the present specification provide a spatiotemporal indexing and querying device, including:

the remote sensing image data creating system comprises a first creating module, a second creating module and a data processing module, wherein the first creating module is used for creating a data index table used for managing source data of remote sensing image data; the data index table comprises at least one of the following fields: the source data, the data identification, the data acquisition time code and the data association grid code set of the remote sensing image data;

the second creating module is used for creating a grid coding index table for creating a spatial index of the remote sensing image data according to each field in the data index table; the grid code index table comprises a geosynchronous grid code and a data identification set; the earth subdivision grid codes are code values corresponding to grids obtained by subdividing longitude and latitude spaces of the earth;

and the query module is used for querying the remote sensing image data to be queried according to the data index table and the grid coding index table.

By adopting the technical scheme of one or more embodiments of the specification, the latitude and longitude space of the earth is divided to obtain a plurality of grids, and the coordinate information corresponding to each grid is subjected to integer coding. And obtaining the coding values respectively corresponding to the grids, and further establishing the association relationship between the grids and the coding values respectively corresponding to the grids and the remote sensing image data. Therefore, the technical scheme can realize position association with multi-source remote sensing data by utilizing global multi-scale grid integer coding, and further realize unified description of spatial position information of the image by utilizing one-dimensional integer coding.

Furthermore, the technical scheme can establish a spatial index of the multi-source remote sensing image data based on a space-time coding method of global gridding organization, and unify the time information of the remote sensing image data by utilizing a multi-scale time period integer coding method, so that the time index is established, and therefore, the efficient organization, management and query of mass multi-source remote sensing data are realized.

Drawings

In order to more clearly illustrate one or more embodiments or technical solutions in the prior art in the present specification, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in one or more embodiments of the present specification, and other drawings can be obtained by those skilled in the art without inventive exercise.

FIG. 1 is a diagram illustrating a tile partitioning method based on a multi-resolution pyramid image according to the prior art;

FIG. 2 is a schematic representation of a spatio-temporal recording system based satellite orbit strip data organization model according to the prior art;

FIG. 3 is a schematic flow chart diagram of a space-time coding method according to an embodiment of the present description;

FIG. 4 is a schematic diagram of a single-scale trellis encoding method according to an embodiment of the present description;

FIG. 5 is a schematic diagram of a multi-scale trellis encoding method according to an embodiment of the present description;

fig. 6 is a schematic diagram of a geosynchronous grid in a space-time coding method according to an embodiment of the present disclosure;

FIG. 7 is a diagram illustrating a correspondence between data and a mesh in a space-time coding method according to an embodiment of the present disclosure;

FIG. 8 is a diagram illustrating a spatial relationship between a mesh and an image coverage area in a spatiotemporal coding method according to an embodiment of the present disclosure;

FIG. 9 is a schematic flow chart diagram of a spatiotemporal indexing and query method in accordance with an embodiment of the present description;

FIG. 10 is a diagram illustrating a time transcoding method in a spatio-temporal indexing and querying method according to an embodiment of the present disclosure;

FIG. 11 is a diagram illustrating a method for spatio-temporal indexing and query according to an embodiment of the present disclosure to create a trellis-coded index table GICT;

FIG. 12 is a schematic block diagram of a space-time coding apparatus according to an embodiment of the present description;

FIG. 13 is a schematic block diagram of a spatiotemporal indexing and querying device in accordance with an embodiment of the present description.

Detailed Description

One or more embodiments of the present disclosure provide a space-time coding method, a space-time indexing and query method and apparatus, so as to implement efficient organization, management and query of massive multi-source remote sensing data.

In order to make those skilled in the art better understand the technical solutions in one or more embodiments of the present disclosure, the technical solutions in one or more embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in one or more embodiments of the present disclosure, and it is obvious that the described embodiments are only a part of the embodiments of the present disclosure, and not all embodiments. All other embodiments that can be derived by a person skilled in the art from one or more of the embodiments of the present disclosure without making any creative effort shall fall within the protection scope of one or more of the embodiments of the present disclosure.

Fig. 3 is a schematic flow chart of a space-time coding method according to an embodiment of the present specification, as shown in fig. 3, the method including:

s302, dividing the longitude and latitude space of the earth according to a specified dividing mode to obtain a plurality of grids; the corresponding grid numbers in the longitude direction and the latitude direction are the same; and the appointed subdivision mode comprises an appointed subdivision level.

S304, integer coding is carried out on the coordinate information corresponding to each grid, and first coding values corresponding to each grid are obtained.

S306, establishing the incidence relation between the grids and the first coding values respectively corresponding to the grids and the remote sensing image data.

The steps in the above examples are explained in detail below.

Firstly, dividing the longitude and latitude space of the earth according to a specified dividing mode.

In the global multi-scale grid coding method in this embodiment, the conventional two-dimensional coordinates (longitude and latitude coordinates) are replaced with integer codes to identify, organize and manage the multi-scale grid, so as to implement coding, efficient coding calculation and space retrieval of the multi-scale grid. First, the integer coding method of the two-dimensional space single-scale grid is described, and then the process of establishing the integer coding of the two-dimensional space multi-scale grid is described based on the method.

When the integer coding of the two-dimensional space single-scale grid is carried out, the whole two-dimensional space is divided into four branches, the two-dimensional space is divided into four identical subspaces equally, each subspace is divided into four subspaces with higher levels continuously, and recursion is carried out according to the method until the specified subspace with the highest level is obtained. For example, in the m-level index, there is 2 in total^m×2^mThe specific encoding rules for each level of the trellis are shown in fig. 4.

As can be seen from fig. 4, the code is a one-dimensional zigzag curve formed by a series of integers, so that two-dimensional grid coordinates can be mapped into a one-dimensional space. Integer coding is performed on the grid coordinates, and a corresponding relation between the grid coordinates and a coding value needs to be established. In order to fully use the storage space of the computer and meet the requirements of simple and efficient coding, a grid coordinate structure shown in table 1 is designed. Under the environment of x64, when the condition of memory splicing is not considered, the maximum integer capable of being stored is 64 bits, 31 bits are respectively allocated to X, Y two coordinates (the specific reason will be explained below), and the value range of each coordinate is 0-2147483647.

TABLE 1

Means of	X	Y
			Number of bits	31	31
Value range	0～2147483647	0～2147483647
			Storing value	Grid coordinate in X direction	Grid coordinate in Y direction

According to the characteristics of Z-shaped codes, the horizontal and vertical coordinates of the grid are converted into integer codes by using a cross bit-taking method. For example, the computation steps of the single-scale integer code with grid coordinates of (20,17) are as follows: (1) binary coded bits (10100,10001) corresponding to the grid coordinates (20, 17); (2) cross positioning to 1100010010; (3) resulting in a single-scale integer encoding of the lattice 786.

The single-scale integer coding described above cannot simultaneously express multi-scale information. Since the multi-scale integer coding is established based on single-scale integer coding, the coding value of the largest level (31 st level) in the multi-scale integer coding can be obtained by shifting the single-scale integer coding value to the left by one bit. If the X, Y coordinates of the grid are 32 bits each and then converted into the single-scale integer code value, since the value cannot be shifted left by one bit to obtain the multi-scale integer code value, in the single-scale integer code method, the X, Y coordinates of the grid are 31 bits each in order to reserve 2 bits to record the scale information.

It is obvious that the integer code values in level 31 are even numbers, and the code values of other levels are generated based on this level. Averaging every adjacent 4 integer code values in 31 th level to obtain the 30 th level integer code values which are odd numbers, and repeating the steps in the same way, as shown in fig. 5, obtaining the 31-level integer code values in total, and forming an inverted quadtree.

According to the characteristics of the multi-scale grid integer code and the characteristics of the earth space, the earth grid can be divided as follows: firstly, expanding a latitude space to enable the spatial range of the latitude space to be consistent with that of a longitude space, and obtaining an expanded longitude and latitude space; secondly, dividing the expanded longitude and latitude space into a plurality of grids of a specified subdivision level according to a quadtree mode to obtain a plurality of grids; and determining the coordinate origins of the grids.

In this embodiment, the reason why the earth is divided by adopting the longitude and latitude mode is as follows: the longitude and latitude mode has the characteristic of lowest conversion cost, and the coordinate basis of the earth subdivision is in the longitude and latitude space. The latitude and longitude coordinate system may be the widely used WGS84 coordinate system.

In addition, since the multi-scale coding is based on a rectangular grid of a quadtree, when globally dividing a global longitude range (-180 °,180 °), and a latitude range (-90 °,90 °) according to the principle that two directions of the quadtree are equal, it is necessary to expand a dimensional space (i.e., in the latitude direction) so as to be consistent with a longitude-direction span, as shown in a white grid region in fig. 4. Extended latitude and longitude area coding exists but not geographically and the part is contiguous.

In one embodiment, the origin of coordinates of the multiple grids may be determined from the latitude and longitude coordinates. The origin of the longitude and latitude coordinates is selected at the intersection point of the equator and the initial meridian, but because the cross form of the Morton code is supported by the multi-scale integer coding, the processing of the complementary code is not directly supported, or the Morton code supports the unsigned integer, the selection of the origin of the grid needs to be planned, so that all the grids are in the positive direction. This embodiment selects (-180 ° ) as the origin of coordinates for the earth-subdivided grid. According to the characteristics of multi-scale coding, coordinate translation is equivalent to integer translation, so that only +/-0 x80000000 is required on the basis of integer coding.

After the expanded longitude and latitude space is divided according to a quadtree mode, the obtained grids of each level meet the following conditions:

the 0-level grid corresponds to the world and ranges from 360 degrees to 180 degrees;

the level 1 grid corresponds to 1/4 earths, the range is 180 degrees x 90 degrees;

the 2-level grid corresponds to 1/8 earths, and the range is 90 degrees multiplied by 90 degrees;

the 3-level grid corresponds to 1/32 earths, and the range is 45 degrees multiplied by 45 degrees;

the 4-level grid corresponds to 1/128 earths, ranging from 22.5 degrees x 22.5 degrees;

…

31 level mesh correspondence

The earth has a range of

According to the conversion of longitude and latitude, the scale of the 31 st-level earth subdivision grid on the equator is about: 1.86cm by 1.86cm (the equatorial radius of the earth is calculated as 6378140 m). The scale supports the minimum scale information of various types of remote sensing data at present, and supports grids with 31 different scales from the whole earth to 1.86 cm.

In conclusion, a geodetic mesh as shown in fig. 6 can be obtained.

And secondly, carrying out integer coding on the coordinate information corresponding to each grid to obtain a first coding value corresponding to each grid.

The detailed process of the multi-scale integer coding has already been described in detail in the above embodiments, and is not described herein again. The encoded values obtained by integer encoding the coordinate information corresponding to each grid are shown in fig. 5.

Finally, an association relationship between the grid and the first encoding value (i.e., the encoding value shown in fig. 5) corresponding to each grid and the remote sensing image data is established.

In the step, the remote sensing image data is associated by using a small amount of grids closest to the image range and the coding values thereof, so that the subsequent query and statistics work of the remote sensing image data is changed into the query and statistics of the space grids. Since scenes (different types and different levels) of the multisource remote sensing image have different scales, positions and coverage areas, and the geosynthetic mesh is a rigid multiscale (fixed multiscale) mesh frame, there is a case that the two cannot completely correspond to each other, such as the case that the remote sensing image data represented by a to F in fig. 7 respectively correspond to the geosynthetic meshes of each level.

The spatial relationship between the multi-scale earth subdivision grid and the coverage area of the remote sensing image is shown in fig. 8, and as can be seen from fig. 8, the coverage area 70 of the remote sensing image is contained by the grid 71 at the lowest level, the coverage area 70 of the remote sensing image contains the grid a, the coverage area 70 of the remote sensing image is intersected with the grid b, and the coverage area 70 of the remote sensing image is separated from the grid c, so the spatial relationship between the grid and the coverage area of the remote sensing image can include the following four types: the grid includes the image, the grid is included by the image, the grid intersects with the image, and the grid is separated from the image, which are respectively referred to as including, included, intersecting, and separating.

According to the above analysis, the technical scheme adoptsThe spatial relationship between the grid and the image coverage area is judged step by step from top to bottom to realize the gridding association of the image, and the coding result of the image needs to meet the following three limiting conditions: lower bound N of the encoding level after gridding_minUpper limit of coding level N after gridding_maxUpper limit of the number of codes after gridding S_max. Through the three limiting conditions, the user can adjust the gridding correlation result according to the specific situation of the image data to obtain the optimal result.

Therefore, the gridding association of the image can be realized by the following steps:

and A1, calculating the external rectangle of the image area corresponding to the remote sensing image data.

And A2, gridding the circumscribed rectangle by using the grid of the lowest level to obtain a first grid corresponding to the circumscribed rectangle.

That is, the utilization level is N_minThe specific way of gridding the external rectangle is the same as the method for gridding the image data in S306, and is not described herein again.

A3, judging whether the first grid meets a preset condition; wherein the preset condition comprises at least one of the following conditions: the total number of the first type grids and the second type grids in the first grid is greater than or equal to a preset upper limit value of the coding quantity (namely, the upper limit value S of the coding quantity after gridding is carried out_max) And the number of grids of the second type of grid is zero.

Wherein the first type of mesh comprises: the grid contained by the circumscribed rectangle, or the grid level is greater than or equal to the preset level upper limit value (namely the coding level upper limit N after the grid is formed)_max) The grid of (2). The second type of mesh includes: intersects with the external rectangle and the grid level is smaller than the upper limit value of the level (namely the upper limit value N of the coding level after the grid is formed_max) Or a grid containing the area of influence.

Step a4, if the first mesh meets the preset condition, determining the first mesh as a mesh corresponding to the image area.

If the first grid does not meet the preset condition, circularly executing the following steps until the finally obtained grid meets the preset condition: aiming at a second grid with the highest priority in all second-type grids, determining sub-grids of adjacent levels of the level where the second grid is located; and dividing the sub-grids into a first type grid and/or a second type grid, and updating the priority of the second type grid.

The steps are as follows: firstly, 4 sub-grids of a level adjacent to the level where the grid with the highest priority is located in the second type of grid are calculated, the 4 sub-grids are divided into a first type of grid and/or a second type of grid, and meanwhile, the priority order in the second type of grid is updated; and then judging whether the first type of grid and/or the second type of grid obtained after the 4 sub-grids are divided meets the preset condition, if so, outputting the grid corresponding to the image area, if not, re-determining the grid with the highest priority in the second type of grid, and repeating the steps aiming at the determined grid until the obtained grid meets the preset condition.

Wherein the priority order of the second type of mesh can be determined according to at least one of the following rules:

(1) the smaller the number of mesh layers, the higher the priority.

(2) The more the number of the specified type grids in the sub-grids contained in the grid is, the higher the priority is; the specified type mesh includes a mesh separate from a region corresponding to the remote sensing image data.

(3) The fewer the number of first type meshes in the sub-meshes comprised by the mesh, the higher the priority.

FIG. 9 is a schematic flow chart diagram of a spatio-temporal indexing and querying method according to an embodiment of the present description, as shown in FIG. 9, the method comprising:

s902, creating a data index table for managing source data of the remote sensing image data; the data index table comprises at least one of the following fields: the method comprises the steps of collecting source data, data identification, data acquisition time coding and data association grid coding of remote sensing image data.

S904, according to each field in the data index table, a grid coding index table for establishing a spatial index of the remote sensing image data is established; the grid code index table comprises the earth subdivision grid codes and a data identification set.

The earth subdivision grid codes are code values corresponding to grids obtained by subdividing longitude and latitude spaces of the earth.

And S906, inquiring the remote sensing image data to be inquired according to the data index table and the grid coding index table.

By adopting the technical scheme of one or more embodiments of the specification, the remote sensing image data to be queried is queried according to the created data index table and the grid coding index table by creating the data index table for managing the source data of the remote sensing image data and creating the grid coding index table for creating the spatial index of the remote sensing image data according to each field in the data index table. Therefore, the technical scheme can establish the spatial index of the multi-source remote sensing image data based on the space-time coding method of the global gridding organization, and unify the time information of the remote sensing image data by utilizing the multi-scale time period integer coding method to establish the time index, so that the efficient organization, management and query of mass multi-source remote sensing data are realized.

The steps in the above examples are explained in detail below.

First, a Data Index Table (DIT) for managing source Data of remote sensing image Data is created.

The remote sensing data is organized and managed in a file and database mode. Specifically, the remote sensing image data is stored in a data storage system in a file form, and the source data of the remote sensing image is managed by a database, so that the creation and the update of the time and space indexes of the remote sensing data are completed. The user can select the type of the data storage system (such as a distributed file storage system, a cloud storage system and the like) and the type of the database (such as a pure relational database Oracle, a non-relational database HBase and the like) according to the self requirement and the equipment condition.

The following describes how to create the data index table DIT by taking an Oracle database as an example.

Creating a data index table DIT is an index table for constructing attribute information of remote sensing image data, standardizing attribute data of multi-Source remote sensing data once, and uniformly managing data in a Source Data Table Set (SDTS). The data index table DIT contains, in addition to the data fields in the source data table set SDTS, the following fields: the data identification ImageID is the unique identification of the remote sensing image data, the data type of the data identification ImageID is an integer, the data identification ImageID is used as a main key of a data index table DIT and is set to be self-increment, and the purpose is to uniformly identify the remote sensing image data and improve the query efficiency of the remote sensing image data identification; the data acquisition time code is a 64-bit unsigned integer, is used for recording the acquisition time of remote sensing image data, can be used for establishing a time index and aims to unify time formats and improve the query efficiency of time; and the data type of the data association grid coding set GridCodeSet is a 64-bit unsigned integer array and is used for recording the association coding set corresponding to the remote sensing image data.

Based on the field contents in the data index table DIT, in one embodiment, the data index table DIT may be created as follows:

and step B1, determining the field name and the data type of each field in the data index table DIT.

Table 2 schematically lists field names and data types of fields in the data index table DIT in an embodiment, it should be noted that, in practical applications, in addition to the fields listed in table 2, other related fields may be added to the data index table DIT as needed.

And step B2, reading the source data corresponding to the field name and the data type, and importing the read source data into the data index table DIT.

The Source data may be stored in a Source Data Table Set (SDTS), where the SDTS represents all Source data tables in the data management system, and in general, image data attribute information of different satellites is stored in different Source data tables. Thus, the source data import data index table DIT corresponding to the field name and the data type can be read from the source data table set SDTS.

In table 2, the field "image unique identifier" is a data identifier; a field of 'acquisition time code', namely a data acquisition time code; the field "associated code set" is a data associated trellis code set. The time code for acquiring the data may be obtained by converting time information of the remote sensing image data by a time code conversion method, for example, performing integer coding of a specified level, where the time code conversion method will be described in detail later, and in this embodiment, the time code level (i.e., the specified level) is 63 levels, and the time precision is 1 microsecond. The association code set may be obtained by converting the position information of the remote sensing image data by the grid association method described in detail in the above embodiments, and is not described herein again.

TABLE 2

In one embodiment, parallel operations may be used to import multiple source data into the data index table DIT to improve data import efficiency.

In one embodiment, to prevent the large amount of data in the data index table DIT from affecting data insertion and query, table maintenance and update, and database performance, the following strategies may be adopted: when the disk occupation space of the data index table DIT exceeds a preset threshold (such as 1GB), performing table partitioning operation to form a plurality of sub-tables, so that each sub-table does not exceed the preset threshold (such as 1 GB). In this embodiment, the advantage of performing table partitioning on the data index table DIT includes: firstly, the DIT is still a table logically, so that the unified management of data is realized; secondly, the sub-tables can be mapped to a plurality of disks to realize the balance of input/output port I/O, and the performance of the system is improved; and thirdly, the reliability of the data index table DIT is enhanced, for example, when a certain table partition fails, other table partitions are still available, and only the failed table partition needs to be repaired.

And step B3, determining each field in the global image density lookup table.

Wherein, the global image density lookup table comprises at least one of the following fields: and (4) subdividing grid codes, covering times of remote sensing image data in each grid and multiplying power of the upper limit value of the grid number.

A Global Image Density Look-up Table (gidlink) is used to store the distribution Density of Global images, and the like, and its purpose includes: (1) enabling a user to master the distribution situation of global images; (2) according to the global image density lookup table GIDLUT, the user can optimize the limiting condition S when carrying out gridding association on the remote sensing image data_max(i.e., the upper limit on the number of codes after trellis). The global image density lookup table GIDLUT adopts an earth subdivision grid level N_GIDLUT(if the value is 5 without special requirement, the earth surface is divided into 512 grids), and the value is not more than the limiting condition N when the remote sensing image data are in gridding association_min(i.e., the lower limit of the coding level after gridding).

The fields in the global image density lookup table GIDLUT are shown in table 3, where: code is a subdivision grid Code and represents the Nth_GIDLUTA hierarchical earth mesh code, which is initialized before data is imported into the data index table DIT and calculated according to the earth mesh code method (i.e., the coding method for integer coding the coordinate information corresponding to the mesh described in S304 in the above embodiment); count is the number of image coverage, which represents the number of remote sensing image coverage in the grid corresponding to the subdivision grid Code, and the calculation of the value is synchronously performed when data is imported into a data index table DIT and is calculated according to a GridCodeset field in the data index table DIT; alpha is the multiplying power of the upper limit value of the grid number and represents the limiting condition S of the remote sensing image data in the corresponding grid of the subdivision grid Code when the remote sensing image data are in gridding association_max(i.e., the upper limit of the number of codes after gridding) and the ratio of the image length-width ratio k, i.e., S_maxAlpha · k. The value is calculated after the data is imported into the data index table DIT, and the calculation formula is as follows:

wherein alpha (i) represents the magnification of the upper limit value of the number of image grids corresponding to the ith grid code (i); count_minAnd Count_maxRespectively representing the minimum value and the maximum value in the Count field (namely the image covering times) in the global image density lookup table GIDLUT; the value of m can be selected by the user according to the requirement, and the value range is [0,2 ]¹⁰]；α₀The value of (2) can also be selected by the user according to the requirement, and the value range is [4,2 ]¹⁰]。

TABLE 3

Serial number	Name of field	Meaning of a field	Data type	Remarks for note
					1	Code	Subdivision trellis coding	NUMERIC(20,0)	Main key
2	Count	Number of image coverage	NUMERIC(38,0)
					3	Alpha	Multiplying power of grid number upper limit value	NUMERIC(10,0)

And step B4, acquiring time codes according to the data, and establishing time code indexes of the remote sensing image data.

The following describes how to convert the time information of the remote sensing image data by the time code conversion method to obtain the time code of the acquisition of the remote sensing image data. In the time code conversion method described below, the time code hierarchy is 63 stages, which is a hierarchy that sufficiently accurately expresses time information of the remote sensing image data,

First, time information of remote sensing image data is decomposed into a plurality of integers with specified time scales.

Wherein the specified timescales may include year (a), month (B), day (C), hour (D), minute (E), second (F), millisecond (G), microsecond (H), and the like.

And secondly, respectively coding the integers on each specified time scale into binary numbers with specified digits to obtain binary code values of the time information of the remote sensing image data on each specified time scale.

The designated time scales correspond to respective designated digits respectively, and the designated digits corresponding to the designated time scales can be the same or different. Further, the high order bits of each binary code obtained by conversion can be filled with "0".

Specifically, the designated digit corresponding to the designated time scale "year" is 17 bits, the designated digit corresponding to the designated time scale "month" is 4 bits, the designated digit corresponding to the designated time scale "day" is 5 bits, the designated digit corresponding to the designated time scale "hour" is 5 bits, the designated digit corresponding to the designated time scale "minute" is 6 bits, the designated digit corresponding to the designated time scale "second" is 6 bits, the designated digit corresponding to the designated time scale "millisecond" is 10 bits, and the designated digit corresponding to the designated time scale "microsecond" is 10 bits. Based on this, the integers at each specified time scale can be encoded as binary numbers of specified number of bits respectively as follows:

coding the integer divided on the time scale into a binary number of 6 bits;

encoding an integer on the time scale of seconds into a binary number of 6 bits;

encoding an integer on the time scale of milliseconds into a binary number of 10 bits;

an integer on the timescale of microseconds is encoded as a 10-bit binary number.

And thirdly, carrying out bit domain connection on the binary code values of the time information of the remote sensing image data on each specified time scale to obtain a second code value corresponding to the time information of the remote sensing image data.

The second coded value obtained in the step is a single-scale time coded value corresponding to the time information of the remote sensing image data.

And finally, shifting the second coded value by one bit to the left to obtain a data acquisition time code of the remote sensing image data, namely a 63 rd level multi-scale time coded value.

Fig. 10 schematically shows a time-code conversion method performed on the remote sensing image data.

As shown in fig. 10, the time information of the remote sensing image data is: year (A): 2018. month (B):10, day (C):1, hour (D): 13. minute (E) 30, second (F) 29, millisecond (G): 300. microsecond (H) is 0.

The time information is subjected to binary conversion to obtain: year (A): 00000011111100010, month (B) 1010, day (C) 0001, hour (D): 01101. 011110 in minutes (E), 011101 in seconds (F), and milliseconds (G): 0100101100, microsecond (H) 0000000000.

After the binary code values are spliced, obtaining a single-scale time coding value corresponding to the time information of the remote sensing image data: 71024248425328640.

and shifting the obtained single-scale time coding value by one bit to the left to obtain a 63 rd level multi-scale time coding value: 142048496850657280.

the above describes in detail the method for creating the data index table DIT, and the data index table DIT may be updated after creation is completed. Specifically, when receiving a specified operation instruction for first data in the data index table DIT, corresponding operation is performed on the first data, and the time code index in the data index table DIT is updated. Wherein, the specified operation instruction comprises a deletion instruction and/or an insertion instruction.

For example, when data in the data index table DIT needs to be deleted, the data that needs to be deleted is deleted, an index is reconstructed once for a field "acquire time code" in the data index table DIT, and the global image density lookup table GIDLUT is updated to complete updating of the data index table DIT.

After the data index table DIT is created, a trellis-coded index table is created according to each field in the data index table DIT. The Grid Code Index Table (GCIT) is a one-dimensional Code Index Table formed by Grid coding and used for storing a spatial Index of remote sensing image data.

The grid coding index table GICT is established by converting a two-dimensional spatial index into a one-dimensional coding index (based on one-dimensional indexes such as a B tree) and basically improving the construction and query efficiency of the index. As shown in table 4, in this embodiment, the trellis-coded index table gic includes at least the following fields: CodeIndex, namely the earth subdivision grid code, is a main key of a grid code index table GICT, and the sorting of the value is combined with a B tree to establish a one-dimensional code index; the ImageIDSet is a set formed by the fields ImageID in the data index table DIT and represents a data identification set of the image covered by the grid corresponding to the fields CodeIndex, and the fields are stored in a binary form, so that the splicing and splitting of the ImageID are efficient, and the query and retrieval efficiency of the data is ensured.

TABLE 4

In one embodiment, the trellis-coded index table GICT may be created according to the steps shown in FIG. 11.

Step C1, determining a data-related trellis encoding set (i.e. the image-related trellis encoding shown in fig. 11) corresponding to each data in the data index table DIT according to the data index table DIT and the global image density lookup table gidlink.

And step C2, inserting the data association grid coding set into the grid coding index table GICT.

And step C3, sorting the data association grid code set inserted into the grid code index table GICT to establish a one-dimensional spatial index of the grid code index table.

In this embodiment, after the data associated mesh coding sets are sorted, a one-dimensional spatial index, such as a B-tree index, may be established.

Through testing, under the same environment (Oracle11g, Intel Xeon X5650@2.67GHz), assuming that 1000 pieces of data are stored in the data index table DIT, it takes 5.6 minutes and 36.9 minutes to construct the grid code index table GICT and the Oracle Spatial index respectively, so that the construction of the grid code index table GICT has high efficiency.

The above describes the method for creating the trellis-coded index table GICT in detail, and the trellis-coded index table GICT may be updated after the creation is completed. Whether the trellis-coded index table GICT is updated or not depends on whether data change occurs in the data index table DIT or not. Specifically, when data in the data index table DIT changes, whether the changed data amount reaches a preset data amount is judged; if yes, reestablishing the grid coding index table GICT; if not, the grid code index table GICT does not need to be updated. Wherein the change includes inserting data and/or deleting data.

For example, if data is inserted into the data index table DIT and the amount of the inserted data is large (exceeds a preset amount of data), the trellis-coded index table GICT is reconstructed, otherwise, only new data needs to be inserted into the trellis-coded index table GICT according to the method for constructing the trellis-coded index table GICT. If the data in the data index table DIT is deleted and the deleted data amount is large (exceeds the preset data amount), reconstructing the trellis coded index table GICT, otherwise, not operating.

The present embodiment reconstructs the trellis-coded index table GICT only when the amount of the varied data is large because the efficiency of inserting or deleting large-scale data is lower than that of reconstructing the trellis-coded index table GICT.

After the grid coding index table GICT is created, the remote sensing image data to be inquired can be inquired according to the data index table DIT and the grid coding index table GICT.

According to the embodiment, the remote sensing image and the query area have four main spatial relations: the two are not intersected or mutually contained, the two are intersected, the query area comprises the image, and the image comprises the query area. Therefore, the remote sensing image and the grid formed after the gridding of the query area have the following three relations: the two grids are separated, the area grid is a father unit of the image grid, and the area grid is a child unit of the image grid. Based on this, the remote sensing image data can be queried in any of the following ways.

The method comprises the following steps of firstly, inquiring remote sensing image data in a space inquiry mode, wherein the method comprises the following steps:

and D1, determining a query area in the earth subdivision grid, wherein the query area is composed of a plurality of longitude and latitude coordinates.

In this step, the user may input boundary vector coordinate data of a query region, which may be an arbitrary polygonal region.

And D2, determining a first gridding code corresponding to the longitude and latitude coordinates according to the longitude and latitude coordinates and the earth subdivision grid code.

And D3, aiming at the first sub-grid of each grid corresponding to the first gridding code, searching a data association grid code set belonging to the first sub-grid in the grid code index table GICT as a first query result.

The first sub-grid of the grid is the sub-grid obtained by dividing the grid at a higher level. The grid code index table GICT stores the corresponding relation between the longitude and latitude coordinates corresponding to the grids and the data associated grid code set, so that when the data associated grid code set belonging to the first sub-grid is searched, the longitude and latitude coordinates corresponding to the first sub-grid may be determined first, and the data associated grid code set corresponding to the longitude and latitude coordinates corresponding to the first sub-grid may be searched.

It should be noted that the longitude and latitude coordinates corresponding to the first sub-grid may be a coordinate range, and at this time, the data associated grid code set corresponding to the found longitude and latitude coordinates corresponding to the first sub-grid is the code set corresponding to the coordinate range.

And D4, determining the parent grid of each grid corresponding to the first gridding code, and determining the geocellular grid code corresponding to the parent grid as the second gridding code of the query area.

Specifically, the code values of the parent grids of the grids corresponding to all the codes in the first gridding code are determined, and then the repeated code values are deleted, so that the second gridding code of the query area can be obtained. Wherein, the parent grid is positioned at the level of

I.e. the minimum level for the image gridding.

And D5, aiming at the second sub-grid of each grid corresponding to the second gridding code, searching a data association grid code set belonging to the second sub-grid in the grid code index table GICT, and screening the data association grid code set corresponding to the second sub-grid meeting the first preset condition as a second query result.

Wherein, the first preset condition comprises: the gridding codes belonging to the second query result exist in the first gridding codes.

And D6, extracting a first data identification set corresponding to the first query result and the second query result from the grid code index table GICT to obtain a space query result corresponding to the remote sensing image data to be queried.

Specifically, binary code values (i.e., trellis code values) corresponding to the first query result and the second query result may be extracted from the trellis code index table GICT, and then the extracted binary code values are disassembled into ImageID (i.e., data identifier) sets, and the duplicate values are deleted to obtain the first data identifier sets corresponding to the first query result and the second query result. When the binary code value is disassembled into the ImageID set, the disassembly can be carried out in a multithreading mode so as to improve the disassembly efficiency.

Through tests, under the same environment (Oracle11g, Intel Xeon X5650@2.67GHz), if 1000 pieces of data are stored in the data index table DIT, when the coverage image of each provincial region is queried, the efficiency can be averagely improved by more than 10 times by adopting the space query method compared with the traditional Oracle Spatial.

In a second mode, the remote sensing image data is inquired in a space-time inquiry mode, and the method comprises the following steps:

e1, determining query parameters of the remote sensing image data to be queried, wherein the query parameters comprise a query area and a query time range; the query time range includes a start time and an end time.

And E2, respectively time-coding the starting time and the ending time to obtain a starting time code value and an ending time code value.

In this step, when the start time and the end time are respectively time-coded, the time-coding conversion method detailed in the above embodiments may be performed, and details are not repeated here.

And E3, aiming at the first data identification set, screening out fields which correspond to the first data identification set and meet second preset conditions from the data index table DIT, and using the fields as space-time query results corresponding to the remote sensing image data to be queried.

Wherein the second preset condition comprises: the data acquisition time code corresponding to the first data identification set is located between the start time code value and the end time code value.

The first data identifier set in this step is the first data identifier set extracted from the trellis coded index table GICT in step D6 and corresponding to the first query result and the second query result. Therefore, the space-time query method needs to be established on the basis of the space query method.

According to the embodiments, the technical effects of the technical scheme at least comprise the following steps: by using a global grid multi-scale integer coding method, the correlation of spatial scale and position among multi-source, multi-scale and multi-temporal data in the same region is realized, and the integration and sharing of data of cross-region, cross-department and the like are facilitated; in addition, the spatial index based on the grid coding has high construction efficiency and simple dynamic maintenance, and is suitable for parallel construction, so that the method can be suitable for the index construction of massive and high-dynamic remote sensing data; moreover, the image space query efficiency based on grid coding and indexing is high, and the method is suitable for large-scale data query; finally, the technical scheme is not only suitable for the organization and management of the panoramic image and the product, but also suitable for the multi-resolution pyramid image.

In summary, particular embodiments of the present subject matter have been described. Other embodiments are within the scope of the following claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing may be advantageous.

Based on the same idea, the space-time coding method, the space-time index and the query method provided in one or more embodiments of the present specification further provide a space-time coding apparatus, a space-time index and a query apparatus.

FIG. 12 is a schematic block diagram of a space-time coding apparatus according to an embodiment of the present specification. As shown in fig. 12, the space-time coding apparatus 1200 includes:

the subdivision module 1210 is used for subdividing the longitude and latitude space of the earth according to a specified subdivision mode to obtain a plurality of grids; the corresponding grid numbers in the longitude direction and the latitude direction are the same; the appointed subdivision mode comprises an appointed subdivision level;

the encoding module 1220 is configured to perform integer encoding on the coordinate information corresponding to each grid to obtain first encoded values corresponding to each grid;

the association module 1230 is configured to establish an association relationship between the grids and the first code values respectively corresponding to the grids and the remote sensing image data.

In one embodiment, the subdivision module 1210 includes:

the extension unit is used for extending the latitude space so as to enable the spatial range of the latitude space to be consistent with the spatial range of the longitude space, and the extended longitude and latitude space is obtained;

the dividing unit is used for dividing the expanded longitude and latitude space into a plurality of grids of a specified subdivision level according to a quadtree mode to obtain a plurality of grids; and determining the coordinate origins of the grids.

In one embodiment, the association module 1230 includes:

the computing unit is used for computing a circumscribed rectangle of an image area corresponding to the remote sensing image data;

the gridding unit is used for gridding the external rectangle by using the grid of the lowest level to obtain a first grid corresponding to the external rectangle;

the judging unit is used for judging whether the first grid meets a preset condition or not; wherein the preset condition comprises at least one of the following conditions: the total number of the first type grids and the second type grids in the first type grids is greater than or equal to a preset coding number upper limit value, and the grid number of the second type grids is zero;

the first determining unit is used for determining the first grid as a grid corresponding to the image area if the first grid meets a preset condition;

the execution unit is used for circularly executing the following steps if the first grid does not meet the preset condition until the obtained grid meets the preset condition: aiming at a second grid with the highest priority in all second-type grids, determining sub-grids of adjacent levels of the level where the second grid is located; and dividing the sub-grids into a first type grid and/or a second type grid, and updating the priority of the second type grid.

In one embodiment, the first type of mesh comprises: the grid contained by the circumscribed rectangle, or the grid with the grid level greater than or equal to the preset level upper limit value;

the second type of mesh includes: a mesh intersecting the circumscribed rectangle and having a mesh level less than the upper limit value of the level, or a mesh including the affected area.

the smaller the number of the grid layers is, the higher the priority is;

the more the number of the specified type grids in the sub-grids contained in the grid is, the higher the priority is; the specified type grids comprise grids separated from the region corresponding to the remote sensing image data;

the fewer the number of first type meshes in the sub-meshes comprised by the mesh, the higher the priority.

By adopting the device of one or more embodiments of the specification, a plurality of grids are obtained by dividing the latitude and longitude space of the earth, and integer coding is carried out on the coordinate information corresponding to each grid. And obtaining the coding values respectively corresponding to the grids, and further establishing the association relationship between the grids and the coding values respectively corresponding to the grids and the remote sensing image data. Therefore, the technical scheme can realize position association with multi-source remote sensing data by utilizing global multi-scale grid integer coding, and further realize unified description of spatial position information of the image by utilizing one-dimensional integer coding.

FIG. 13 is a schematic block diagram of a spatiotemporal indexing and querying device in accordance with an embodiment of the present description. As shown in FIG. 13, the spatio-temporal indexing and query apparatus 1300 comprises:

a first creating module 1310 for creating a data index table for managing source data of the remote sensing image data; the data index table comprises at least one of the following fields: the source data, the data identification, the data acquisition time code and the data association grid code set of the remote sensing image data;

a second creating module 1320, configured to create a trellis coded index table for creating a spatial index of the remote sensing image data according to each field in the data index table; the grid code index table comprises a geosynchronous grid code and a data identification set; the earth subdivision grid codes are code values corresponding to grids obtained by subdividing longitude and latitude spaces of the earth;

the query module 1330 is configured to query the remote sensing image data to be queried according to the data index table and the trellis code index table.

In one embodiment, the first creation module 1310 includes:

the second determining unit is used for determining the field name and the data type of each field in the data index table;

a reading unit, configured to read the source data corresponding to the field name and the data type, and import the read source data into the data index table;

the third determining unit is used for determining each field in the global image density lookup table; the global image density lookup table includes at least one of the following fields: dividing grid codes, the covering times of the remote sensing image data in each grid and the multiplying power of the upper limit value of the grid number;

and the establishing unit is used for acquiring the time code according to the data and establishing the time code index of the remote sensing image data.

In one embodiment, the first creation module 1310 determines the data acquisition time encoding in the data index table as follows:

In one embodiment, the first creation module 1310 includes:

a decomposition unit for decomposing the time information into a plurality of integers of a specified time scale;

the encoding unit is used for encoding the integers on each specified time scale into binary numbers with specified digits respectively to obtain binary code values of the time information on each specified time scale;

the connecting unit is used for performing bit domain connection on the binary code values of the time information on each specified time scale to obtain a second code value corresponding to the time information;

and the left shift unit is used for shifting the second coding value by one bit to the left to obtain the data acquisition time code of the remote sensing image data.

correspondingly, the encoding unit is further configured to perform at least one of:

encoding the integer divided on the time scale into a binary number of 6 bits;

In one embodiment, apparatus 1300 further comprises:

the execution and update module is used for executing corresponding operation on first data in the data index table when receiving a specified operation instruction on the first data; updating the time coding index in the data index table;

In one embodiment, the second creation module 1320 includes:

a fourth determining unit, configured to determine, according to the data index table and the global image density lookup table, a data-associated trellis encoding set corresponding to each data in the data index table;

an inserting unit, configured to insert the data-associated trellis encoding set into the trellis encoding index table;

and the sorting unit is used for sorting the data association grid coding set inserted into the grid coding index table so as to establish a one-dimensional spatial index of the grid coding index table.

In one embodiment, apparatus 1300 further comprises:

the judging module is used for judging whether the changed data volume reaches a preset data volume or not when the data in the data index table changes; wherein the change comprises inserting data and/or deleting data;

and the third creating module is used for re-creating the grid coding index table if the changed data volume reaches the preset data volume.

In one embodiment, the query module 1330 includes:

a fifth determining unit for determining a query region in the geosynthetic mesh; the query area is composed of a plurality of longitude and latitude coordinates;

a sixth determining unit, configured to determine, according to the longitude and latitude coordinates and the geosynchronous grid code, a first grid code corresponding to the longitude and latitude coordinates;

a first searching unit, configured to search, for a first sub-grid of each grid corresponding to the first gridding code, the data-associated grid code set belonging to the first sub-grid in the grid code index table, as a first query result;

a seventh determining unit, configured to determine a parent grid of each grid corresponding to the first gridding code, and determine a geodetic grid code corresponding to the parent grid as a second gridding code of the query region;

a second searching unit, configured to search, in the trellis encoding index table, the data associated trellis encoding set belonging to the second sub-trellis for a second sub-trellis of each trellis corresponding to the second trellis encoding, and screen the data associated trellis encoding set corresponding to the second sub-trellis that meets a first preset condition as a second query result; the first preset condition includes: the first gridding code comprises gridding codes belonging to the second query result;

and the extracting unit is used for extracting a first data identification set corresponding to the first query result and the second query result from the grid coding index table to obtain a space query result corresponding to the remote sensing image data to be queried.

In one embodiment, the query module 1330 includes:

an eighth determining unit, configured to determine query parameters of the remote sensing image data to be queried, where the query parameters include the query region and a query time range; the query time range comprises a start time and an end time;

the time coding unit is used for respectively carrying out time coding on the starting time and the ending time to obtain a starting time coding value and an ending time coding value;

a screening unit, configured to screen, for the first data identifier set, a field that corresponds to the first data identifier set and satisfies a second preset condition from the data index table, and use the field as a spatio-temporal query result corresponding to the remote sensing image data to be queried; wherein the second preset condition comprises: and the data acquisition time code corresponding to the first data identification set is positioned between the starting time code value and the ending time code value.

By adopting the device of one or more embodiments of the specification, the spatial index of the multi-source remote sensing image data can be established based on the space-time coding method of the global gridding organization, and the time information of the remote sensing image data is unified by utilizing the multi-scale time period integer coding method, so that the time index is established, and therefore, the efficient organization, management and query of mass multi-source remote sensing data are realized.

It should be understood by those skilled in the art that the above-mentioned space-time coding apparatus can be used to implement the above-mentioned space-time coding method, and the above-mentioned space-time indexing and querying apparatus can be used to implement the above-mentioned space-time indexing and querying method, wherein the detailed description thereof should be similar to that of the above-mentioned method, and further description thereof is omitted here for the sake of avoiding complexity.

The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functionality of the various elements may be implemented in the same one or more software and/or hardware implementations in implementing one or more embodiments of the present description.

One skilled in the art will recognize that one or more embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, one or more embodiments of the present description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, one or more embodiments of the present description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

One or more embodiments of the present specification are described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.

Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.

It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

One or more embodiments of the present description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in 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 computer storage media including memory storage devices.

The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.

The above description is only one or more embodiments of the present disclosure, and is not intended to limit the present disclosure. Various modifications and alterations to one or more embodiments described herein will be apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of one or more embodiments of the present specification should be included in the scope of claims of one or more embodiments of the present specification.

Claims

1. A method of space-time coding, comprising:

establishing an incidence relation between the grids and the first coding values respectively corresponding to the grids and the remote sensing image data;

the method for dividing the longitude and latitude space of the earth according to the specified division mode to obtain a plurality of grids comprises the following steps:

dividing the expanded longitude and latitude space into a plurality of grids of the appointed subdivision level according to a quadtree mode to obtain the plurality of grids;

and, determining origin of coordinates of the plurality of grids; determining the origin of coordinates of the multiple grids according to the longitude and latitude coordinates; the origin of the longitude and latitude coordinates is selected at the intersection point of the equator and the initial meridian, but because the multi-scale integer coding supports the cross form of Morton codes and does not directly support the processing of complementary codes, or the Morton codes support unsigned integers, the selection of the origin of the grids needs to be planned, so that all the grids are in the positive direction; selecting (-180 degrees ) as an initial coordinate origin of the earth subdivision grid; according to the characteristics of multi-scale coding, coordinate translation is equivalent to integer translation, so that only +/-0 x80000000 is needed on the basis of integer coding;

…

31 level mesh correspondence

The earth has a range of

The establishing of the incidence relation between the grids and the first code values respectively corresponding to the grids and the remote sensing image data comprises the following steps:

if so, determining the first grid as a grid corresponding to the image area;

if not, circularly executing the following steps until the obtained grid meets the preset condition: aiming at a second grid with the highest priority in the second type grids, determining sub-grids of adjacent levels of the level where the second grid is located; dividing the sub-grids into the first type grids and/or the second type grids, and updating the priority of the second type grids;

the first type of mesh comprises: the grid contained by the circumscribed rectangle, or the grid with the grid level greater than or equal to a preset level upper limit value;

the second type of mesh comprises: and a mesh intersecting the circumscribed rectangle and having a mesh level smaller than the level upper limit value, or a mesh including the image area.

2. The method of claim 1, wherein the priority order of the second type of mesh is determined according to at least one of the following rules:

the smaller the number of grid layers is, the higher the priority is;

3. A spatio-temporal indexing and querying method, in cooperation with the spatio-temporal coding method of claim 1, comprising:

4. The method of claim 3, the creating a data index table for managing source data of the remotely sensed image data comprising:

5. The method of claim 4, the data acquisition time encoding in the data index table determined as follows:

6. The method of claim 5, wherein the encoding the time information of the remote sensing image data by an integer of a specified level to obtain the data acquisition time code of the remote sensing image data comprises:

7. The method of claim 6, the specified timescale comprising at least one of a year, month, day, hour, minute, second, millisecond, microsecond;

encoding the integer divided on the time scale into a binary number of 6 bits;

8. The method of claim 4, further comprising:

9. The method of claim 3, wherein creating a trellis-coded index table for building a spatial index of the remotely sensed image data from fields in the data index table comprises:

10. The method of claim 3, further comprising:

and if so, recreating the grid coding index table.

11. The method according to claim 3, wherein the querying the remote sensing image data to be queried according to the data index table and the trellis coded index table comprises:

12. The method according to claim 11, wherein the querying the remote sensing image data to be queried according to the data index table and the trellis coded index table comprises:

13. A space-time coding apparatus, comprising:

the association module is used for establishing an association relation between the grids and the first coding values and the remote sensing image data which respectively correspond to the grids;

…

31 level mesh correspondence

The earth has a range of

The association module is specifically configured to:

if so, determining the first grid as a grid corresponding to the image area;

14. A spatio-temporal indexing and querying device, in combination with the spatio-temporal coding device of claim 13, comprising: