Distributed satellite metadata retrieval method based on space-time characteristics

Abstract

The invention discloses a distributed satellite metadata retrieval method based on space-time characteristics. The method comprises the steps that a space retrieval range and a time retrieval range input by a user are received; recursively segmenting the target area into a plurality of equally divided retrieval grids according to longitude and latitude grids; matching each equally divided retrieval grid with the preprocessed data set to obtain a target segmented data set; intercepting a corresponding time sequence metadata subset from each target segmentation data set according to the time retrieval range; performing spatial relationship analysis and hash aggregation on each equally divided retrieval grid and the corresponding time sequence metadata subset to generate a result data set corresponding to each equally divided retrieval grid; and performing hash aggregation on all the result data sets again to generate a retrieved target metadata set. According to the method, longitude and latitude equal division and time sequence metadata storage are adopted, the target metadata set can be hit rapidly according to user input during retrieval, the time-space characteristic pertinence is high, and the retrieval efficiency is high.

Description

Technical Field

[0001] This application relates to the field of satellite data retrieval, specifically to a distributed satellite metadata retrieval method based on spatiotemporal characteristics. Background Technology

[0002] Raw satellite imagery data consists of photographs taken by remote sensors mounted on satellites. It is typically stored as binary files, with different satellite systems and sensor types using different data formats. The storage structure of raw satellite imagery data generally includes three parts: a file header, a data body, and metadata. The file header contains basic information such as file size, data type, and number of bands. The data body contains the actual image data, usually stored in band and pixel order. The metadata provides detailed descriptions of the data, such as the capture time, geographical location, and satellite attitude. This information is crucial for subsequent data analysis and preprocessing.

[0003] The retrieval and browsing of massive satellite metadata is one of the core functions of the land satellite data service platform. The main purpose of building a satellite metadata spatial retrieval service is to quickly retrieve and filter data from a dataset of hundreds of millions of metadata based on the time, space and natural attributes of satellite metadata to form the target metadata subset required by the user.

[0004] Most existing spatial retrieval services are built upon mature GIS platforms and databases, such as those based on spatial indexing technologies from ArcGIS, QGIS, and PostGIS to construct spatial databases for metadata retrieval. These databases are mostly constructed using B-tree indexes, R-tree indexes, kd-tree indexes, etc., to build tree-like spatial data storage, combined with Geohash encoding, K-nearest neighbor algorithms, etc., supporting fast retrieval of spatial targets such as points, lines, polygons, and buffers. However, these methods have the following drawbacks:

[0005] 1. Poor scalability: Centralized storage and computing architectures struggle to handle the high-concurrency retrieval demands of hundreds of millions of data points.

[0006] 2. Insufficient targeting: The system has not been optimized for the spatiotemporal characteristics of satellite metadata (such as time series and fixed latitude and longitude span), resulting in low retrieval efficiency.

[0007] 3. Waste of resources: Real-time computing leads to redundant investment of computing power and cannot support parallel access by multiple users.

[0008] Therefore, existing technologies suffer from insufficient targeting of the spatiotemporal characteristics of satellite metadata and low retrieval efficiency.

[0009] The background description is provided for the purpose of understanding the relevant technologies in this field and is not intended as an admission of prior art. Summary of the Invention

[0010] To address the aforementioned technical problems, this application provides a distributed satellite metadata retrieval method based on spatiotemporal characteristics, the specific scheme of which is as follows:

[0011] In a first aspect, embodiments of this application provide a distributed satellite metadata retrieval method based on spatiotemporal characteristics, the distributed satellite metadata retrieval method based on spatiotemporal characteristics comprising:

[0012] Receive user input for spatial and temporal search ranges;

[0013] The target area corresponding to the spatial retrieval range is recursively divided according to the latitude and longitude grid to obtain multiple equally divided retrieval grids;

[0014] Each of the equal-division retrieval grids is matched with the preprocessed dataset, and the equal-division segmented datasets that match each of the retrieval grids are determined as the target segmented datasets. The preprocessed datasets include multiple equal-division segmented datasets identified by latitude and longitude grid codes, and the metadata in each equal-division segmented dataset is stored in a preset time order.

[0015] According to the time retrieval range, extract the corresponding time series metadata subset from each of the target segmentation datasets;

[0016] Spatial relationship analysis and hash aggregation are performed on each of the equally divided retrieval grids and the corresponding time series metadata subsets to generate the result dataset corresponding to each of the equally divided retrieval grids;

[0017] All the resulting datasets are hashed again to generate the retrieved target metadata.

[0018] According to a specific embodiment disclosed in this application, before the step of receiving metadata retrieval conditions input by the user, the distributed satellite metadata retrieval method based on spatiotemporal characteristics further includes:

[0019] The satellite payload metadata is divided into equal parts by latitude and longitude grids, clustered, and encoded to obtain multiple equally divided datasets identified by latitude and longitude grid codes;

[0020] The metadata in each of the equally divided datasets is sorted and stored according to a preset time order.

[0021] According to a specific embodiment disclosed in this application, after the step of sorting and storing the metadata in each of the equally divided datasets according to a preset time order, the distributed satellite metadata retrieval method based on spatiotemporal characteristics further includes:

[0022] Receive new metadata;

[0023] Based on the location information of the newly added metadata, determine the latitude and longitude grid to which it belongs, and store the newly added metadata in the corresponding equally divided dataset according to a preset time order.

[0024] According to a specific embodiment disclosed in this application, the step of performing spatial relationship analysis and hash aggregation on each of the equally divided retrieval grids and the corresponding time-series metadata subsets to generate a result dataset corresponding to each of the equally divided retrieval grids includes:

[0025] Determine whether the conditions for dynamic segmentation processing are met;

[0026] If not satisfied, spatial relationship analysis and hash aggregation are performed on each of the equally divided retrieval grids and the corresponding time series metadata subsets to generate the result dataset corresponding to each of the equally divided retrieval grids.

[0027] If satisfied, each of the equally divided retrieval grids and the corresponding time series metadata subsets are dynamically divided again according to the same segmentation rules. Spatial analysis and hash aggregation are performed based on the dynamically divided equally divided retrieval grids and time series metadata subsets to generate the result datasets corresponding to each of the equally divided retrieval grids.

[0028] According to a specific embodiment disclosed in this application, the segmentation granularity corresponding to the segmentation rule is dynamically adjusted according to the computing resource load.

[0029] According to one specific embodiment disclosed in this application, the spatial relationship analysis includes intersection, containment, and buffer analysis.

[0030] Secondly, embodiments of this application provide a distributed satellite metadata retrieval device based on spatiotemporal characteristics, the distributed satellite metadata retrieval device based on spatiotemporal characteristics comprising:

[0031] The retrieval input module is used to receive the spatial retrieval range and temporal retrieval range input by the user;

[0032] The recursive segmentation module is used to recursively segment the target area corresponding to the spatial retrieval range according to the latitude and longitude grid to obtain multiple equally divided retrieval grids.

[0033] The grid matching module is used to match each of the equal-division retrieval grids with the preprocessed dataset, and to determine the equal-division segmented datasets that match each of the retrieval grids as the target segmented datasets. The preprocessed datasets include multiple equal-division segmented datasets identified by latitude and longitude grid codes, and the metadata in each equal-division segmented dataset is stored in a preset time order.

[0034] The time extraction module is used to extract a corresponding time series metadata subset from each of the target segmentation datasets according to the time retrieval range;

[0035] The spatiotemporal analysis module is used to perform spatial relationship analysis and hash aggregation on each of the equally divided retrieval grids and the corresponding time series metadata subsets to generate the result dataset corresponding to each of the equally divided retrieval grids;

[0036] The result aggregation module is used to perform hash aggregation on all the result datasets again to generate the retrieved target metadata dataset.

[0037] According to a specific embodiment disclosed in this application, the distributed satellite metadata retrieval device based on spatiotemporal characteristics further includes:

[0038] The preprocessing module is used to divide, cluster, and encode the satellite payload metadata into equal parts using latitude and longitude grids, resulting in multiple equally divided datasets identified by latitude and longitude grid codes.

[0039] The time sorting module is used to sort and store the metadata in each of the equally divided datasets according to a preset time order.

[0040] Thirdly, embodiments of this application provide a computer device, the computer device including a processor and a memory, the memory storing a computer program, the computer program, when executed on the processor, implements the distributed satellite metadata retrieval method based on spatiotemporal characteristics as described in the first aspect.

[0041] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed on a processor, implements the distributed satellite metadata retrieval method based on spatiotemporal characteristics as described in the first aspect.

[0042] Compared with the prior art, this application has the following beneficial effects:

[0043] This application receives a spatial and temporal search range from user input; recursively divides the target area into multiple equally divided search grids according to latitude and longitude; matches each equally divided search grid with a preprocessed dataset to obtain a target segmented dataset; extracts corresponding time-series metadata subsets from each target segmented dataset based on the temporal search range; performs spatial relationship analysis and hash aggregation on each equally divided search grid and its corresponding time-series metadata subset to generate a result dataset corresponding to each equally divided search grid; and performs hash aggregation again on all result datasets to generate the retrieved target metadata dataset. This application uses equally divided latitude and longitude data and time-series metadata storage, enabling rapid targeting of the target metadata dataset based on user input during retrieval, exhibiting strong spatiotemporal specificity and high retrieval efficiency. Attached Figure Description

[0044] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0045] Figure 1 One of the flowcharts for a distributed satellite metadata retrieval method based on spatiotemporal characteristics provided in this application embodiment;

[0046] Figure 2 A second schematic flowchart illustrating a distributed satellite metadata retrieval method based on spatiotemporal characteristics, provided for an embodiment of this application;

[0047] Figure 3 A schematic diagram of the preprocessing flow involved in a distributed satellite metadata retrieval method based on spatiotemporal characteristics provided in an embodiment of this application;

[0048] Figure 4 One of the time sorting flowcharts involved in a distributed satellite metadata retrieval method based on spatiotemporal characteristics provided in an embodiment of this application;

[0049] Figure 5 The second schematic diagram of the time sorting process involved in a distributed satellite metadata retrieval method based on spatiotemporal characteristics provided in this application embodiment;

[0050] Figure 6 The third flowchart illustrates a distributed satellite metadata retrieval method based on spatiotemporal characteristics, provided for an embodiment of this application.

[0051] Figure 7 A schematic diagram of the recursive segmentation process involved in a distributed satellite metadata retrieval method based on spatiotemporal characteristics provided in an embodiment of this application;

[0052] Figure 8 A schematic diagram illustrating the grid matching, time extraction, and spatiotemporal analysis processes involved in a distributed satellite metadata retrieval method based on spatiotemporal characteristics provided in this application embodiment;

[0053] Figure 9 A schematic diagram of the dynamic segmentation process involved in a distributed satellite metadata retrieval method based on spatiotemporal characteristics provided in an embodiment of this application;

[0054] Figure 10 A schematic diagram illustrating the result aggregation process of a distributed satellite metadata retrieval method based on spatiotemporal characteristics provided in this application embodiment;

[0055] Figure 11A schematic diagram of the distributed computing framework involved in a distributed satellite metadata retrieval method based on spatiotemporal characteristics provided in an embodiment of this application;

[0056] Figure 12 A schematic diagram of a distributed satellite metadata retrieval device based on spatiotemporal characteristics provided in an embodiment of this application;

[0057] Figure 13 This is an exemplary structural diagram of a computer device capable of implementing the methods according to embodiments of this application. Detailed Implementation

[0058] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments.

[0059] The components of the embodiments of this application described and illustrated in the accompanying drawings can be arranged and designed in a variety of different configurations. Therefore, the following detailed description of the embodiments of this application provided in the drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0060] In the following, the terms “comprising,” “having,” and their cognates, which may be used in various embodiments of this application, are intended only to indicate a particular feature, number, step, operation, element, component, or combination thereof, and should not be construed as excluding, firstly, the presence of one or more other features, numbers, steps, operations, elements, components, or combinations thereof, or adding the possibility of one or more features, numbers, steps, operations, elements, components, or combinations thereof.

[0061] Furthermore, the terms "first," "second," and "third" are used only to distinguish descriptions and should not be interpreted as indicating or implying relative importance.

[0062] Unless otherwise specified, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which the various embodiments of this application pertain. Terms (such as those defined in commonly used dictionaries) shall be interpreted as having the same meaning as in their contextual meaning in the relevant technical field and shall not be construed as having an idealized or overly formal meaning, unless clearly defined in the various embodiments of this application.

[0063] The following detailed description of some embodiments of this application is provided in conjunction with the accompanying drawings. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0064] See Figure 1 , Figure 1 This is one of the flowcharts illustrating a distributed satellite metadata retrieval method based on spatiotemporal characteristics provided in an embodiment of this application. Figure 2 This is a second schematic flowchart illustrating a distributed satellite metadata retrieval method based on spatiotemporal characteristics, provided as an embodiment of this application. Figure 1 , Figure 2 As shown, the distributed satellite metadata retrieval method based on spatiotemporal characteristics mainly consists of two stages: a preprocessing stage and a retrieval stage. In the preprocessing stage, the distributed satellite metadata retrieval method based on spatiotemporal characteristics includes:

[0065] Step S1: The satellite payload metadata is divided into equal parts by latitude and longitude grids, clustered, and encoded to obtain multiple equally divided datasets identified by latitude and longitude grid codes;

[0066] Step S2: Sort and store the metadata in each of the equally divided datasets according to a preset time order.

[0067] See Figure 3 , Figure 3 This diagram illustrates the preprocessing flow of a distributed satellite metadata retrieval method based on spatiotemporal characteristics, provided as an embodiment of this application. Figure 3 As shown, in specific implementation, the acquired satellite payload metadata dataset needs to be divided into equal parts by latitude and longitude grids and clustered. By encoding the latitude and longitude grids, an equally divided dataset is obtained, identified by the latitude and longitude grid codes. Figure 3 The large squares in the grid represent equally divided latitude and longitude grids. The division criteria can be customized according to user needs or specific application scenarios. For example, it can be determined based on the latitude and longitude span of the outer rectangular space of the satellite payload metadata. This is not further limited. Figure 3 The small squares in the image represent satellite payload metadata.

[0068] See Figure 4 , Figure 4 This is one of the schematic diagrams illustrating the time sorting process involved in a distributed satellite metadata retrieval method based on spatiotemporal characteristics provided in an embodiment of this application. For example... Figure 4 As shown, in specific implementation, the equally divided data set obtained in step S1 can be sorted by time to construct a time series storage of the dataset, using latitude and longitude grid encoding as the unique identifier of the sequence. The specific time sorting method can be customized according to user needs or specific application scenarios, for example, it can be according to the chronological order of time, which is not further limited.

[0069] Example: If the bounding rectangle of a satellite's metadata spans 120°-122° longitude and 30°-32° latitude, it is divided into grids with ID "120-30" and sorted by acquisition time.

[0070] After the step of sorting and storing the metadata in each of the equally divided datasets according to a preset time order, the distributed satellite metadata retrieval method based on spatiotemporal characteristics further includes:

[0071] Receive new metadata;

[0072] Based on the location information of the newly added metadata, determine the latitude and longitude grid to which it belongs, and store the newly added metadata in the corresponding equally divided dataset according to a preset time order.

[0073] Specifically, see Figure 5 , Figure 5 This is the second schematic diagram illustrating the time sorting process involved in a distributed satellite metadata retrieval method based on spatiotemporal characteristics provided in an embodiment of this application. For example... Figure 5 As shown, the newly added metadata can be identified by its latitude and longitude grid, and can be stored at the end of the time series corresponding to the ID in chronological order.

[0074] See Figure 6 , Figure 6 This is the third flowchart illustrating a distributed satellite metadata retrieval method based on spatiotemporal characteristics, provided as an embodiment of this application. Figure 6 As shown, in the retrieval phase, the distributed satellite metadata retrieval method based on spatiotemporal characteristics includes:

[0075] Step S3: Receive the spatial search range and temporal search range input by the user;

[0076] Step S4: Recursively divide the target area corresponding to the spatial retrieval range according to the latitude and longitude grid to obtain multiple equally divided retrieval grids;

[0077] Step S5: Match each of the equal-division retrieval grids with the preprocessed dataset, and determine the equal-division segmented datasets that match each of the retrieval grids as the target segmented datasets. The preprocessed datasets include multiple equal-division segmented datasets identified by latitude and longitude grid codes, and the metadata in each equal-division segmented dataset is stored in a preset time order.

[0078] Step S6: Extract the corresponding time series metadata subset from each of the target segmentation datasets according to the time retrieval range;

[0079] Step S7: Perform spatial relationship analysis and hash aggregation on each of the equally divided retrieval grids and the corresponding time series metadata subsets to generate the result dataset corresponding to each of the equally divided retrieval grids;

[0080] Step S8: Perform hash aggregation on all the result datasets again to generate the retrieved target metadata dataset.

[0081] Specifically, see Figure 7 , Figure 7 This diagram illustrates the recursive segmentation process of a distributed satellite metadata retrieval method based on spatiotemporal characteristics, provided in an embodiment of this application. First, the system receives the user's input spatial and temporal retrieval ranges, for example, retrieving satellite metadata for Inner Mongolia from 2014 to 2016. Then, based on the input spatial retrieval range, it recursively segments the data according to a latitude and longitude grid, quickly obtaining equally divided retrieval grids under the corresponding spatial conditions. Example: If the user inputs a range of longitude 115°-125° and latitude 25°-35°, it can be divided into 10 sub-grids of 1° × 1°, i.e., equally divided retrieval grids.

[0082] See Figure 8 , Figure 8 This diagram illustrates the grid matching, time truncation, and spatiotemporal analysis processes involved in a distributed satellite metadata retrieval method based on spatiotemporal characteristics, as provided in an embodiment of this application. Figure 8 As shown, after recursively segmenting to obtain equally divided retrieval grids, each equally divided retrieval grid is matched with the equally divided dataset obtained in the preprocessing stage, and then in-grid conditional filtering calculations are performed. First, the metadata time series is truncated according to the input time retrieval range, and the truncated time series metadata subset is spatially analyzed with the equally divided retrieval grids. The spatial relationship analysis includes intersection, containment, and buffer analysis. A "buffer" refers to a range of influence or service area of ​​a geospatial target, specifically a polygon of a certain width automatically created around a point, line, or polygon entity. For example, a certain range near a river is considered a danger zone, which is a type of buffer.

[0083] It should be noted that the same segmentation system is used in both the preprocessing and retrieval stages. That is, for any map region, its corresponding equally divided segmented dataset and equally divided retrieval grid are consistent.

[0084] The steps of performing spatial relationship analysis and hash aggregation on each of the equally divided retrieval grids and the corresponding time-series metadata subsets to generate the result dataset corresponding to each of the equally divided retrieval grids include:

[0085] Determine whether the conditions for dynamic segmentation processing are met;

[0086] If not satisfied, spatial relationship analysis and hash aggregation are performed on each of the equally divided retrieval grids and the corresponding time series metadata subsets to generate the result dataset corresponding to each of the equally divided retrieval grids.

[0087] If satisfied, each of the equally divided retrieval grids and the corresponding time series metadata subsets are dynamically divided again according to the same segmentation rules. Spatial analysis and hash aggregation are performed based on the dynamically divided equally divided retrieval grids and time series metadata subsets to generate the result datasets corresponding to each of the equally divided retrieval grids.

[0088] See Figure 9 , Figure 9 This diagram illustrates the dynamic segmentation process involved in a distributed satellite metadata retrieval method based on spatiotemporal characteristics, provided as an embodiment of this application. Figure 9 As shown, when performing spatial relationship analysis on each of the equally divided retrieval grids and the corresponding time-series metadata subsets, the equally divided retrieval grids can be dynamically subdivided into smaller grids, and data spatial analysis can be performed in parallel under the subdivided grids.

[0089] Specifically, the process first determines whether the dynamic segmentation processing conditions are met. If they are met, dynamic segmentation is performed again. The dynamic segmentation processing conditions can be customized by user needs or specific application scenarios, and are not further limited here. For example, it could be triggered by receiving a corresponding user instruction; or the segmentation granularity could be dynamically adjusted according to the computing resource load, depending on whether real-time computing resources meet a threshold.

[0090] The retrieval grid and the extracted time-series metadata subset are dynamically divided again according to the same segmentation rules. Spatial analysis is then performed on the segmented datasets along with spatial conditions. The analysis results are then hashed and aggregated to obtain the final dataset for the equally divided grid. This combination of metadata latitude and longitude segmentation preprocessing and retrieval mechanism, and dynamic segmentation during computation, significantly enhances the parallelization capability of the computational process. Example: If the data volume within grid ID "120-30" is too large, it is automatically divided into four sub-grids, each processed in parallel by different computation nodes. The results are then returned after hash aggregation.

[0091] See Figure 10 , Figure 10 This diagram illustrates the result aggregation process of a distributed satellite metadata retrieval method based on spatiotemporal characteristics, provided as an embodiment of this application. Figure 10 As shown, after step 7 is completed, all the result datasets are hashed and aggregated again to generate the retrieved target metadata dataset, which corresponds to the "satellite metadata of Inner Mongolia region from 2014 to 2016" entered by the user earlier.

[0092] Specifically, the retrieval conditions constructed using the aforementioned distributed satellite metadata retrieval method based on spatiotemporal characteristics, processing each workflow node, are suitable for integration into mainstream distributed computing frameworks. This allows the spatiotemporal metadata retrieval computation to leverage the distributed computing framework to achieve significant computational scalability. See also... Figure 11 , Figure 11 This diagram illustrates the distributed computing framework involved in a distributed satellite metadata retrieval method based on spatiotemporal characteristics, provided as an embodiment of this application. Figure 11 As shown, in specific implementation, a large-scale parallel distributed computing task can be constructed to realize the computing process corresponding to the above-mentioned distributed satellite metadata retrieval method based on spatiotemporal characteristics. The massive metadata spatial retrieval function can be completed within the distributed computing framework.

[0093] This application employs a distributed satellite metadata retrieval method based on spatiotemporal characteristics. It utilizes latitude and longitude equal partitioning and time-series metadata storage, enabling rapid retrieval of datasets corresponding to spatially divided equal partitions. The combination of a metadata latitude and longitude equal partitioning preprocessing / retrieval mechanism and a dynamic partitioning mechanism during computation greatly enhances the parallelization capability of the computation process. The retrieval condition processing nodes constructed based on this method are suitable for integration into mainstream distributed computing frameworks, allowing the spatiotemporal metadata retrieval computation to achieve significant computational scalability by relying on distributed computing frameworks.

[0094] Corresponding to the above method embodiments, see [link to relevant documentation]. Figure 12 The present invention also provides a distributed satellite metadata retrieval device 1200 based on spatiotemporal characteristics, the distributed satellite metadata retrieval device 1200 based on spatiotemporal characteristics comprising:

[0095] The preprocessing module 1201 is used to perform latitude and longitude grid segmentation, clustering and encoding on the satellite payload metadata to obtain multiple equally segmented datasets identified by latitude and longitude grid codes;

[0096] The time sorting module 1202 is used to sort and store the metadata in each of the equally divided datasets according to a preset time order.

[0097] The retrieval input module 1203 is used to receive the spatial retrieval range and temporal retrieval range input by the user.

[0098] The recursive segmentation module 1204 is used to recursively segment the target area corresponding to the spatial retrieval range according to the latitude and longitude grid to obtain multiple equally divided retrieval grids.

[0099] The grid matching module 1205 is used to match each of the equally divided retrieval grids with the preprocessed dataset, and to determine the equally divided segmented datasets that match each of the retrieval grids as the target segmented datasets. The preprocessed datasets include multiple equally divided segmented datasets identified by latitude and longitude grid codes, and the metadata in each equally divided segmented dataset is stored in a preset time order.

[0100] The time extraction module 1206 is used to extract a corresponding time series metadata subset from each of the target segmentation datasets according to the time retrieval range;

[0101] The spatiotemporal analysis module 1207 is used to perform spatial relationship analysis and hash aggregation on each of the equally divided retrieval grids and the corresponding time series metadata subsets to generate the result dataset corresponding to each of the equally divided retrieval grids;

[0102] The result aggregation module 1208 is used to perform hash aggregation on all the result datasets again to generate the retrieved target metadata dataset.

[0103] Those skilled in the art will understand that the spatiotemporal characteristic-based distributed satellite metadata retrieval device according to the embodiments of this application can be combined with the features of the spatiotemporal characteristic-based distributed satellite metadata retrieval method according to the embodiments of this application, and vice versa.

[0104] In addition, a computer device is provided, including a processor and a memory, wherein the memory stores a computer program, and the computer program, when executed on the processor, implements the above-mentioned distributed satellite metadata retrieval method based on spatiotemporal characteristics.

[0105] Figure 13 A schematic diagram is shown of a method or computer device 1300 that can implement embodiments of the present application. In some embodiments, more or fewer computer devices may be included than those shown. In some embodiments, it may be implemented using a single or multiple computer devices. In some embodiments, it may be implemented using cloud-based or distributed computer devices.

[0106] like Figure 13As shown, computer device 1300 includes a processor 1301, which can perform various appropriate operations and processes based on programs and / or data stored in read-only memory (ROM) 1302 or programs and / or data loaded from storage portion 1308 into random access memory (RAM) 1303. Processor 1301 may be a multi-core processor or may contain multiple processors. In some embodiments, processor 1301 may include a general-purpose main processor and one or more special coprocessors, such as a central processing unit (CPU), graphics processing unit (GPU), neural network processor (NPU), digital signal processor (DSP), etc. Various programs and data required for the operation of computer device 1300 are also stored in RAM 1303. Processor 1301, ROM 1302, and RAM 1303 are interconnected via bus 1304. Input / output (I / O) interface 1305 is also connected to bus 1304.

[0107] The processor and memory described above are used together to execute programs stored in the memory. When the program is executed by a computer, it can implement the methods, steps, or functions described in the above embodiments.

[0108] The following components are connected to I / O interface 1305: an input section 1306 including a keyboard, mouse, touchscreen, etc.; an output section 1307 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 1308 including a hard disk, etc.; and a communication section 1309 including a network interface card such as a LAN card, modem, etc. The communication section 1309 performs communication processing via a network such as the Internet. A drive 1310 is also connected to I / O interface 1305 as needed. Removable media 1311, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., are installed on drive 1310 as needed so that computer programs read from them can be installed into storage section 1308 as needed. Figure 13 The diagram only shows a portion of the components and does not imply that the computer system 1300 only includes... Figure 13 The components shown.

[0109] The systems, devices, modules, or units described in the above embodiments can be implemented by a computer or its associated components. The computer may be, for example, a mobile terminal, smartphone, personal computer, laptop computer, in-vehicle human-machine interface device, personal digital assistant, media player, navigation device, game console, tablet computer, wearable device, smart TV, Internet of Things system, smart home, industrial computer, server, or a combination thereof.

[0110] Although not shown, in this application embodiment, a storage medium is provided, the storage medium storing a computer program configured to be executed when run to perform any of the spatiotemporal characteristic-based distributed satellite metadata retrieval methods of this application embodiment.

[0111] The storage media in embodiments of this application include articles that can store information using any method or technology, whether permanent or non-permanent, removable or non-removable. Examples of 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 technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information that can be accessed by a computing device.

[0112] The methods, programs, systems, apparatuses, etc., of the embodiments of this application can be executed or implemented in one or more networked computers, or practiced in a distributed computing environment. In the embodiments of this specification, in these distributed computing environments, tasks can be performed by remote processing devices connected via a communication network.

[0113] The specific implementation process of the provided computer equipment and computer-readable storage medium can be found in the specific implementation process of the distributed satellite metadata retrieval method based on spatiotemporal characteristics provided in the above embodiments, and will not be repeated here.

[0114] The computer device and computer-readable storage medium provided in this application employ latitude and longitude equal division and time-series metadata storage, which can quickly hit the dataset corresponding to the spatial condition equal division during retrieval. The combination of the metadata latitude and longitude equal division preprocessing / retrieval mechanism and the processing mechanism of dynamic division during computation greatly enhances the parallelization capability of the computation process. The retrieval condition processing nodes constructed based on this method are suitable for assembly into mainstream distributed computing frameworks, enabling the spatiotemporal retrieval computation of metadata to obtain great computational scalability by relying on the distributed computing framework.

[0115] Those skilled in the art will understand that the embodiments described in this specification can be provided as methods, systems, or computer program products. Therefore, those skilled in the art will realize that the functional modules / units or controllers and related method steps described in the above embodiments can be implemented in software, hardware, or a combination of both.

[0116] Unless explicitly stated otherwise, the actions or steps of the methods and procedures described in the embodiments of this application do not necessarily have to be performed in a specific order and can still achieve the desired results. In some implementations, multitasking and parallel processing are also possible or may be advantageous.

[0117] This document describes several embodiments of the present application; however, for the sake of brevity, the descriptions of the embodiments are not exhaustive, and identical or similar features or parts between the embodiments may be omitted. In this document, "one embodiment," "some embodiments," "example," "specific example," or "some examples" refers to at least one embodiment or example applicable to the present application, but not all embodiments. The above terms do not necessarily refer to the same embodiment or example. Without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of the different embodiments or examples.

[0118] The exemplary systems and methods of this application have been specifically shown and described with reference to the above embodiments, which are merely examples of the best mode for implementing the systems and methods. Those skilled in the art will understand that various changes can be made to the embodiments of the systems and methods described herein without departing from the spirit and scope of this application as defined in the appended claims when implementing the systems and / or methods.

Claims

1. A distributed satellite metadata retrieval method based on spatiotemporal characteristics, characterized in that, The distributed satellite metadata retrieval method based on spatiotemporal characteristics includes: Receive user input for spatial and temporal search ranges; The target area corresponding to the spatial retrieval range is recursively divided according to the latitude and longitude grid to obtain multiple equally divided retrieval grids; Each of the equal-division retrieval grids is matched with the preprocessed dataset, and the equal-division segmented datasets that match each of the retrieval grids are determined as the target segmented datasets. The preprocessed datasets include multiple equal-division segmented datasets identified by latitude and longitude grid codes, and the metadata in each equal-division segmented dataset is stored in a preset time order. According to the time retrieval range, extract the corresponding time series metadata subset from each of the target segmentation datasets; Spatial relationship analysis and hash aggregation are performed on each of the equally divided retrieval grids and the corresponding time series metadata subsets to generate the result dataset corresponding to each of the equally divided retrieval grids; All the resulting datasets are hashed again to generate the retrieved target metadata.

2. The distributed satellite metadata retrieval method based on spatiotemporal characteristics according to claim 1, characterized in that, Before the step of receiving user-inputted metadata retrieval conditions, the distributed satellite metadata retrieval method based on spatiotemporal characteristics further includes: The satellite payload metadata is divided into equal parts by latitude and longitude grids, clustered, and encoded to obtain multiple equally divided datasets identified by latitude and longitude grid codes; The metadata in each of the equally divided datasets is sorted and stored according to a preset time order.

3. The distributed satellite metadata retrieval method based on spatiotemporal characteristics according to claim 2, characterized in that, After the step of sorting and storing the metadata in each of the equally divided datasets according to a preset time order, the distributed satellite metadata retrieval method based on spatiotemporal characteristics further includes: Receive new metadata; Based on the location information of the newly added metadata, determine the latitude and longitude grid to which it belongs, and store the newly added metadata in the corresponding equally divided dataset according to a preset time order.

4. The distributed satellite metadata retrieval method based on spatiotemporal characteristics according to claim 1, characterized in that, The steps of performing spatial relationship analysis and hash aggregation on each of the equally divided retrieval grids and the corresponding time-series metadata subsets to generate the result dataset corresponding to each of the equally divided retrieval grids include: Determine whether the conditions for dynamic segmentation processing are met; If not satisfied, spatial relationship analysis and hash aggregation are performed on each of the equally divided retrieval grids and the corresponding time series metadata subsets to generate the result dataset corresponding to each of the equally divided retrieval grids. If satisfied, each of the equally divided retrieval grids and the corresponding time series metadata subsets are dynamically divided again according to the same segmentation rules. Spatial analysis and hash aggregation are performed based on the dynamically divided equally divided retrieval grids and time series metadata subsets to generate the result datasets corresponding to each of the equally divided retrieval grids.

5. The distributed satellite metadata retrieval method based on spatiotemporal characteristics according to claim 4, characterized in that, The granularity of the segmentation rule is dynamically adjusted according to the computing resource load.

6. The distributed satellite metadata retrieval method based on spatiotemporal characteristics according to claim 1, characterized in that, Its features are, The spatial relationship analysis includes intersection, containment, and buffer analysis.

7. A distributed satellite metadata retrieval device based on spatiotemporal characteristics, characterized in that, The distributed satellite metadata retrieval device based on spatiotemporal characteristics includes: The retrieval input module is used to receive the spatial retrieval range and temporal retrieval range input by the user; The recursive segmentation module is used to recursively segment the target area corresponding to the spatial retrieval range according to the latitude and longitude grid to obtain multiple equally divided retrieval grids. The grid matching module is used to match each of the equal-division retrieval grids with the preprocessed dataset, and to determine the equal-division segmented datasets that match each of the retrieval grids as the target segmented datasets. The preprocessed datasets include multiple equal-division segmented datasets identified by latitude and longitude grid codes, and the metadata in each equal-division segmented dataset is stored in a preset time order. The time extraction module is used to extract a corresponding time series metadata subset from each of the target segmentation datasets according to the time retrieval range; The spatiotemporal analysis module is used to perform spatial relationship analysis and hash aggregation on each of the equally divided retrieval grids and the corresponding time series metadata subsets to generate the result dataset corresponding to each of the equally divided retrieval grids; The result aggregation module is used to perform hash aggregation on all the result datasets again to generate the retrieved target metadata dataset.

8. The distributed satellite metadata retrieval device based on spatiotemporal characteristics according to claim 7, characterized in that, The distributed satellite metadata retrieval device based on spatiotemporal characteristics also includes: The preprocessing module is used to divide, cluster, and encode the satellite payload metadata into equal parts using latitude and longitude grids, resulting in multiple equally divided datasets identified by latitude and longitude grid codes. The time sorting module is used to sort and store the metadata in each of the equally divided datasets according to a preset time order.

9. A computer device, characterized in that, The computer device includes a processor and a memory, the memory storing a computer program, which, when executed on the processor, implements the distributed satellite metadata retrieval method based on spatiotemporal characteristics as described in any one of claims 1-6.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed on a processor, implements the distributed satellite metadata retrieval method based on spatiotemporal characteristics as described in any one of claims 1-6.

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