Convergence management system and method for multi-source remote sensing big data

Abstract

The invention discloses a multi-source remote sensing big data convergence management system and method. The system comprises a data convergence layer, a data governance layer, a data storage layer and a data sharing layer. The system obtains remote sensing big data through a multi-source interface, normalizes the multi-source remote sensing big data, generates standard entity data and a metadata governance label set, positions target entity data based on the associated metadata governance label set, and achieves safe sharing of the target entity data. According to the method, the problems of standardization and normalization of multi-source heterogeneous data in the treatment and management process of remote sensing big data are effectively solved; gathering and integrating massive multi-source remote sensing data, and cleaning, checking, processing and carrying out quality inspection on the data to form a clean, complete and consistent data lake; a unified data management rule and platform are established, data in a lake is reasonably distributed, a data sharing channel is safe and controllable, and the data management cost of individuals and enterprises is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-source remote sensing big data management, and in particular to a multi-source remote sensing big data aggregation management system and method. Background Art

[0002] With the development of geographic information systems and remote sensing technology, the efficiency of acquiring and updating remote sensing data and its derivative products has further increased, resulting in explosive growth in data volumes across a wide range of fields, including natural resources, transportation, meteorology, oceanography, environmental protection, and emergency response. Remote sensing data inventories in various satellite data centers have reached petabytes, with increasingly prominent characteristics such as massive data volume, multi-source heterogeneity, spatiotemporal correlation, and write-once-read-many capabilities. Effectively maintaining this massive data, rapidly retrieving required data, and leveraging its amplification, overlay, and multiplication capabilities have become key challenges in remote sensing data utilization.

[0003] Currently, remote sensing data is constrained by institutional constraints and is cataloged and managed independently by large, integrated data centers owned by satellite data producers, specialized data departments within scientific research institutions, international open-source data organizations, and commercial alliances. Individual files are large, and the storage formats, organizational structures, and data content of different data sources vary significantly, making effective communication difficult. Data correlation is low, and application scenarios are relatively limited. Furthermore, due to structural differences and inherent complexity of different types of remote sensing data, the aggregation, storage, processing, and distribution of data often operate independently. Existing data aggregation and management methods are mostly targeted at a single data source, making it difficult to develop a comprehensive solution.

[0004] For example, invention application number 202211537601.7 discloses a method and management system for organizing and managing multi-source remote sensing image metadata traceability information. This application embeds a traceability model into the metadata model, designing a metadata organization model with enhanced traceability expression. This enriches metadata content, facilitates traceability tracking, metadata search, and retrieval, and meets complex traceability requirements. However, this approach also suffers from the independent business processes of data aggregation, storage, processing, and distribution, making it difficult to develop a comprehensive solution.

[0005] In summary, in the face of the continuous expansion of remote sensing data volume and data types, there is an urgent need to form a set of aggregation management solutions that can cope with massive, multi-source heterogeneous remote sensing big data, effectively carry out data governance, improve data quality, break data silos, and provide data support for data analysis and corporate decision-making. Summary of the Invention

[0006] In response to the above-mentioned problems, the purpose of the present invention is to provide a multi-source remote sensing big data aggregation management system and method, integrating the aggregation, storage, processing and distribution services of multi-source data to form a full-scale multi-source remote sensing big data aggregation management solution.

[0007] The embodiments of the present invention provide a system and method for managing the aggregation of multi-source remote sensing big data.

[0008] Aspect 1: A multi-source remote sensing big data aggregation and management system, comprising:

[0009] The data aggregation layer is used to aggregate multi-source remote sensing big data through interfaces and verify the data integrity;

[0010] The data governance layer is used to normalize multi-source remote sensing big data and generate standardized entity data and metadata governance tag sets;

[0011] The data storage layer is used to store entity data and metadata management tag sets, so that the data ID in the metadata management tag set is associated with the entity data;

[0012] The data sharing layer locates the target entity data based on the metadata governance tag set and realizes the secure sharing of the target entity data.

[0013] A second aspect: A method for managing the aggregation of multi-source remote sensing big data, comprising the following steps:

[0014] S1. Obtain remote sensing big data through multi-source interfaces and verify data integrity;

[0015] S2. Normalize multi-source remote sensing big data to generate standardized entity data and metadata governance label sets;

[0016] S3: Storing entity data and metadata governance tag sets, so that the data ID in the stored metadata governance tag set is associated with the entity data;

[0017] S4. Based on the associated metadata governance tag set, locate the target entity data and realize the secure sharing of the target entity data.

[0018] Optionally, the multi-source remote sensing big data gathered in S1 is: through the five three-dimensional monitoring means of "sky, air, land and sea", the data of the five geographical spheres of biosphere, atmosphere, hydrosphere, lithosphere and electromagnetic sphere are integrated to form an integrated layout of "sky, air, land and sea" and a multi-sphere earth observation coverage big data system.

[0019] Optionally, in S2:

[0020] The normalization process of multi-source entity data is to convert the remote sensing data format into COG files and generate thumbnails in PNG format;

[0021] The standardization of metadata governance labels is to form a metadata governance label set consisting of system standard information, technical standard information, quality standard information and circulation standard information.

[0022] Optionally, the system standard information includes data level 1 category and data level 2 category;

[0023] Among them, the first-level data category includes: space data, aerospace data, atmospheric meteorology, satellite data, surveying and mapping data, natural resources, ocean data, geological data, social data, artificial intelligence and intelligence data;

[0024] The secondary data category is a further subdivision based on the primary classification.

[0025] Optionally, the technical standard information includes: data processing level, data coordinate latitude and longitude range, data collection time, data spatial scale, data time scale and data format.

[0026] Optionally, the quality standard information includes: quality rating, authority rating and data copyright file;

[0027] Among them, the quality rating is the data quality score given by the data quality inspection department after quality inspection, and is divided into levels according to A, B, C, and D; the authority rating is divided into two types: open source and commercial; the data copyright file specifications and the source and citation method of the data are indicated.

[0028] Optionally, the transfer standard information includes: dataset name, data owner, project to which it belongs, and entity data storage path;

[0029] The dataset name is a combination of metadata governance labels and entity data information. The combination rule is: data first-level category + data second-level category + processing level + project name + data name.

[0030] Optionally, S3 includes:

[0031] The entity data is transferred to the controlled library and product library through the temporary storage library, and the catalog information of the controlled library and product library is entered into the relational database according to the entity data ID to form a relational database table. The data part of the relational database table is imported into the metadata governance label after normalization. The relational database table stores the full information of the entity data.

[0032] Optionally, the entity data security sharing method includes object storage, API, FTP and network disk. Beneficial effects of the present invention:

[0033] 1. The present invention provides a multi-source remote sensing big data aggregation management system and method, which can effectively solve the problem of standardization and normalization of multi-source heterogeneous data in the management process of remote sensing big data. In an active or passive manner, according to continuous or specified time frequency, local or full spatial range, massive multi-source remote sensing data from domestic and foreign data centers are aggregated and integrated. Based on metadata information and prior knowledge of data, the data pouring in from various channels are cleaned, checked, processed, and quality-checked to form a clean, complete, and consistent data lake. Establish unified data management rules and platforms, reasonably distribute the data in the lake, realize safe and controllable data sharing channels, and reduce the data management costs of individuals and enterprises.

[0034] 2. The present invention provides multi-source data integration capabilities: through the three-dimensional monitoring method of "sky, air, land and sea" and the integration of data from five major geographical circles, it realizes the unified aggregation of multi-source heterogeneous remote sensing data, with a wide coverage and comprehensive data sources. It adopts AWSS3 protocol and ADSL link service to ensure the efficiency and stability of massive data transmission, reduce data loss and delay problems, strictly check the data set and supporting documents at the data aggregation layer to ensure data traceability, and improve data aggregation efficiency and integrity.

[0035] 3. The present invention uniformly converts multi-source data into COG files and generates PNG thumbnails, combines them with STAC-standard metadata governance tags (JSON format), solves the compatibility problem of heterogeneous data, and unifies data formats and metadata specifications; through the "eleven major categories + secondary categories" data classification system (such as satellite data, natural resources, etc.) and the "seven-level processing level" grading standard, it realizes refined management and rapid positioning of data and refines the classification system; by integrating system standards, technical standards, quality standards and flow standard information, it provides comprehensive support for data retrieval, analysis and application of multi-dimensional metadata tags.

[0036] 4. The present invention adopts a three-level storage system of temporary storage, controlled storage and product storage to realize the orderly flow of data from the original state to the standardized product, thereby improving storage efficiency; the entity data is associated with the metadata in the relational database through a unique data ID to ensure data consistency while supporting efficient retrieval; the controlled library path is stored according to the "data first-level category / second-level category / processing level / data set name" rules to facilitate data location and management and optimize data storage and retrieval performance.

[0037] 5. The present invention supports multiple sharing methods such as object storage, API, FTP, and network disk to meet the needs of different scenarios; based on key fields in the governance tag (such as time and space range, processing level, etc.), it realizes fast and accurate retrieval of data and reduces user time cost; through quality rating (AD), permission rating (open source / commercial) and copyright files, it ensures the compliance and security of data distribution.

[0038] 6. The present invention integrates multi-source data through a unified platform, promotes cross-domain data fusion, supports the comprehensive analysis needs of industries such as natural resources, meteorology, and emergency response, and breaks down data silos; standardized management and efficient retrieval reduce data processing redundancy, provide cost-effective data support for corporate decision-making, and reduce data usage costs; supports artificial intelligence analysis (such as image recognition and semantic segmentation) and thematic product development (such as disaster monitoring), enhances the commercial value and social benefits of remote sensing data, and expands application scenarios.

[0039] 7. The present invention supports international general standards such as STAC, which facilitates docking with third-party systems. The four-layer architecture (aggregation, governance, storage, and sharing) can be independently expanded to adapt to future technology upgrades or business scale growth, thereby improving the scalability and compatibility of the technical architecture. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is a schematic diagram of the structure of a multi-source remote sensing big data aggregation management system of the present invention;

[0041] Figure 2 This is a flow chart of a method for aggregating and managing multi-source remote sensing big data according to the present invention;

[0042] Figure 3 This is a schematic diagram of the data structure of the metadata management tag of the present invention;

[0043] Figure 4 Schematic diagram of the data structure of the standard information of the system of the present invention;

[0044] Figure 5 Schematic diagram of the structure of the electronic device of the present invention. DETAILED DESCRIPTION

[0045] The following describes embodiments of the present invention in detail. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar symbols throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended only to explain the present invention and are not to be construed as limiting the present invention.

[0046] Currently, remote sensing data is restricted by institutional factors. Faced with the continuous expansion of remote sensing data volume and data types, there is a lack of aggregation and management solutions for multi-source heterogeneous remote sensing big data. Due to structural inconsistencies and the complexity of the data itself, the business processes of data aggregation, storage, processing, and distribution of different types of remote sensing data are often independent. Existing data aggregation management methods mostly target a single data source, making it difficult to form a comprehensive solution.

[0047] In order to solve the above problems, the present invention provides a multi-source remote sensing big data aggregation management system. Figure 1This is a structural diagram of the multi-source remote sensing big data aggregation management system provided by an embodiment of the present invention. The system includes: a data aggregation layer, a data governance layer, a data storage layer, and a data sharing layer.

[0048] Among them, the data aggregation layer is used to aggregate multi-source remote sensing big data through interfaces and perform data integrity verification.

[0049] The data aggregation layer includes data acquisition and data cleaning. Under the premise of ensuring certain bandwidth resources and hardware facilities, it batch-acquires target data sets from multiple data sources through interfaces or services, and verifies the completeness of the data sets and supporting documents to ensure that there is a basis for data traceability.

[0050] The data governance layer is used to normalize multi-source remote sensing big data and generate standardized entity data and metadata governance label sets.

[0051] The data governance layer is the core functional layer, which standardizes multi-source remote sensing data. Based on the key elements in the remote sensing data metadata and the prior knowledge of the corresponding professional fields of the data sets, it forms a metadata governance tag set that complies with the spatiotemporal asset catalog (STAC, SpatioTemporalAsset Catalogs) specifications and converts it into JSON format as the basis for subsequent data storage.

[0052] The data storage layer is used to store entity data and metadata management tag sets, so that the data ID in the metadata management tag set is associated with the entity data.

[0053] The data storage layer includes entity data and metadata governance tag storage. Entity data storage is based on a standardized and unified storage system within the big data domain, which properly stores original remote sensing data, derivative product data, metadata, and file thumbnails; metadata governance tag storage is to transfer the metadata governance tag set formed by the data governance layer into the relational database to ensure that the data ID in the relational data table is associated with the entity data stored in the data domain.

[0054] The data sharing layer locates target entity data based on metadata governance tag sets, enabling secure sharing of target entity data. Precise searches can be performed based on query fields within metadata governance tag sets to locate target data and enable secure data sharing across multiple media.

[0055] like Figure 2 As shown, the present invention also discloses a method for converging and managing multi-source remote sensing big data based on the above-mentioned converging and managing system, comprising the steps of:

[0056] S1. Obtain remote sensing big data through multi-source interfaces and verify data integrity.

[0057] Relying on the data aggregation layer, in view of the characteristics of remote sensing data such as multiple sources, multi-granularity, multi-modality, massiveness and complex spatiotemporal correlation, through the five three-dimensional monitoring means of "sky, air, land and sea", the integration of "sky, air, land and sea" includes: space-based, near-space, air-based, ground-based and sea-based integration; at the same time, it integrates the data of five major geographical spheres, namely the biosphere, atmosphere, hydrosphere, lithosphere and electromagnetic sphere, to form an integrated layout of "sky, air, land and sea" and a multi-sphere earth observation coverage big data system.

[0058] The temporal and spatial scope of remote sensing data is determined by geographic coordinates, satellite row and column numbers, and the efficiency of data transmission business processes is guaranteed based on the AWS S3 protocol and China Unicom ADSL link service.

[0059] S2. Normalize multi-source remote sensing big data to generate standardized entity data and a set of metadata governance tags that comply with STAC.

[0060] The normalization process of multi-source entity data is to convert the remote sensing data format into a unified COG file and generate thumbnails in PNG format.

[0061] like Figure 3 As shown, the metadata governance label standardization process is to form a metadata governance label set consisting of system standard information, technical standard information, quality standard information and flow standard information.

[0062] Specifically, such as Figure 4 As shown, the system standard information includes: data level 1 class (DataType) and data level 2 class (ImgType);

[0063] The first-level data category divides data resources into eleven categories to combine data characteristics with business applications, including: space data, aerospace data, atmospheric meteorology, satellite data, surveying and mapping data, natural resources, ocean data, geological data, social data, artificial intelligence and intelligence data (QB), etc.

[0064] The secondary data classification is further subdivided based on the primary classification.

[0065] Space data refers to the information about planets in space, which is specifically divided into celestial coordinate system, planet images, planet topography and planet thematic data.

[0066] Space data refers to the analyzable data transmitted back by aircraft during their navigation activities in outer space outside the atmosphere, which is specifically divided into communication data and navigation data.

[0067] Atmospheric meteorology refers to the basic data for analyzing and describing climate characteristics and their changing patterns, which are specifically divided into meteorological, climate, atmospheric remote sensing and atmospheric physics data.

[0068] Satellite data refers to data collected by sensors on Earth observation satellites, which are converted and identified through electromagnetic waves to obtain visible images. It is specifically divided into optical satellite, infrared satellite, radar satellite, hyperspectral satellite and video satellite data.

[0069] Surveying and mapping data refers to data containing geographic information elements collected through field measurements. It is the result of integration and analysis and is specifically divided into scene data, surveying and mapping products (DEM / DSM / DOM / DLG), magnetic data, and gravity data.

[0070] Natural resources refer to an analyzable and applicable data system formed by integrating and processing data on multiple natural elements such as land, forests, grasslands, water, etc., which is specifically divided into land resources, agricultural resources, water resources, energy resources and biological resources data.

[0071] Ocean data refers to ocean-related data sets obtained through multi-source perception and detection methods such as various monitoring means and observation systems. It is specifically divided into measured data and analytical and predictive data.

[0072] Geological data refers to data that is processed, analyzed and mined for core value based on geological science and information technology. It is specifically divided into regional geological maps, geological science research, mineral resources, water conservancy and environmental geology, physical and chemical remote sensing geology and seismic data.

[0073] Social data refers to datasets formed by collecting, organizing, storing, and developing social survey data. These datasets are categorized into demographic, economic, transportation, security, healthcare, education, catering, historical, electricity, and tourism data. Artificial intelligence refers to datasets formed by analyzing multi-scale spatial data by combining massive amounts of satellite remote sensing imagery and geographic feature data. These datasets are categorized into image, video, text, voice, machine learning, semantic, and 3D data.

[0074] Intelligence data (QB) is specifically divided into strategic QB, campaign QB and tactical QB.

[0075] Technical standard information includes: data processing level (ProcessLevel), data coordinate longitude and latitude range (bbox), data collection time (TaskT), data spatial scale (ImgGSD), data time scale (TimeScale) and data format (Format), etc.

[0076] Among them, the data processing level is based on the data generation system and business, and the geographic information data is classified and graded according to the requirements of data management and use. Multi-source remote sensing data is divided into 7 levels according to the data processing level; among them, levels 1-2 are basic products, levels 3-5 are common products, and levels 6-7 are special products.

[0077] Level 1 products are raw data that have been reconstructed and have time reference, auxiliary information (including radiation, geometric correction coefficients, etc.) and geographic coordinate parameters (such as platform ephemeris, etc., which are not used in level 0 products) without any processing, and products processed to sensor units on this basis; Level 2 products are geophysical parameter data products with the same resolution and position as level 1 products; Level 3 products are digital orthorectified image data, 16-bit single-view image data after GS fusion / pansharp, single-view image mosaicked image data on the same track, and geometrically corrected image data (single-view / same track) Level 4 products are the image data after mosaicking and quality inspection, and the image data after national standard framing and quality inspection after mosaicking; Level 5 products are the data prepared before slicing, compressed data / integrated tile jpg format data, original tile data with black edges, original tile data encapsulated as sqlite, and tile data with black edges removed and quality inspection labels; Level 6 products are product-level mother library tile data; Level 7 products are encrypted or non-encrypted shelf product tile data.

[0078] The data coordinate longitude and latitude range is the longitude and latitude coordinates of the upper left corner, upper right corner, lower right corner and lower left corner of the data obtained from the metadata or data entity of the remote sensing data, and is a list of coordinate ranges arranged in a clockwise direction.

[0079] Quality standard information includes: quality rating (QuaAssess), permission rating (Permission) and data copyright file (Copyright).

[0080] Among them, the quality rating is the data quality score given by the data quality inspection department after quality inspection, and is divided into levels according to A, B, C, and D; the authority rating is divided into two types: open source and commercial; the data copyright file specifications and the source and citation method of the data are indicated.

[0081] The standard transfer information includes: dataset name (Collections), data owner (ProjectOwn), project (Subject), and entity data storage path (StorageT).

[0082] The dataset name is a combination of metadata governance labels and dataset information. The combination rules are as follows: data first-level category + data second-level category + processing level + project name + data name.

[0083] S3: Store entity data and metadata management tag sets, and associate the data ID in the stored metadata management tag set with the entity data.

[0084] In the data storage layer, entity data is transferred to the controlled library and product library through the temporary library. Under the premise of ensuring the uniqueness of the entity data ID, the catalog information is partitioned and entered into the relational database. The cataloging of the database table of the relational database highlights the key fields commonly used in retrieval to improve retrieval efficiency; the data part of the database table is imported into the managed metadata management tag to save the full amount of data information.

[0085] The temporary storage is a collection of various types of data stored during the data management lifecycle; multi-source remote sensing data flows in through various channels, undergoes a series of data cleaning and conversion, and is then collected into the data lake.

[0086] The controlled repository is a collection of software configuration items that have passed testing or review and are considered phased products during the data management lifecycle. Governed data from the temporary repository is transferred to the controlled repository, and the data storage path is recorded as a positioning standard for subsequent data use and sharing.

[0087] The storage path rule for the controlled library is: server prefix / data first-level category / data second-level category / processing level / data set name.

[0088] The product library is a collection of data configuration items that have been finalized (identified) and meet the delivery, production, inspection and acceptance requirements during the data management lifecycle.

[0089] S4. Based on the associated metadata governance tag set, locate the target entity data and realize the secure sharing of the target entity data.

[0090] In the data sharing layer, based on unified rules and platforms, with data as the foundation and data entry as the basis, a data middle platform is built to distribute and share data to support industry applications. Data sharing methods include but are not limited to object storage, API, FTP, and network disk.

[0091] The present invention also provides an electronic device, Figure 5 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention, such as Figure 5 As shown, the electronic device may include: a processor, a communications interface, a memory, and a communication bus, wherein the processor, the communications interface, and the memory communicate with each other via the communication bus. The processor may call logic instructions in the memory, for example, to execute the following method:

[0092] S1. Obtain remote sensing big data through multi-source interfaces and verify data integrity;

[0093] S2. Normalize multi-source remote sensing big data to generate standardized entity data and metadata governance label sets;

[0094] S3: Storing entity data and metadata governance tag sets, so that the data ID in the stored metadata governance tag set is associated with the entity data;

[0095] S4. Based on the associated metadata governance tag set, locate the target entity data and realize the secure sharing of the target entity data.

[0096] In addition, the logical instructions in the above-mentioned memory can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program code.

[0097] An embodiment of the present invention further provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method provided in each of the above embodiments is implemented, for example, including:

[0098] S1. Obtain remote sensing big data through multi-source interfaces and verify data integrity;

[0099] S2. Normalize multi-source remote sensing big data to generate standardized entity data and metadata governance label sets;

[0100] S3: Storing entity data and metadata governance tag sets, so that the data ID in the stored metadata governance tag set is associated with the entity data;

[0101] S4. Based on the associated metadata governance tag set, locate the target entity data and realize the secure sharing of the target entity data.

[0102] The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0103] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0104] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A multi-source remote sensing big data aggregation management system, characterized in that: include: The data aggregation layer is used to aggregate multi-source remote sensing big data through interfaces and verify the data integrity; The data governance layer is used to normalize multi-source remote sensing big data and generate standardized entity data and metadata governance tag sets; The data storage layer is used to store entity data and metadata management tag sets, so that the data ID in the metadata management tag set is associated with the entity data; The data sharing layer locates the target entity data based on the metadata governance tag set and realizes the secure sharing of the target entity data.

2. The convergence management system according to claim 1, characterized in that: A method for managing multi-source remote sensing big data for the aggregation management system, comprising the steps of: S1. Obtain remote sensing big data through multi-source interfaces and verify data integrity; S2. Normalize multi-source remote sensing big data to generate standardized entity data and metadata governance label sets; S3: Storing entity data and metadata governance tag sets, so that the data ID in the stored metadata governance tag set is associated with the entity data; S4. Based on the associated metadata governance tag set, locate the target entity data and realize the secure sharing of the target entity data.

3. The converged management system according to claim 2, characterized in that: The multi-source remote sensing big data gathered in S1 is: through the five three-dimensional monitoring methods of "sky, air, land and sea", the data of the five geographical spheres of biosphere, atmosphere, hydrosphere, lithosphere and electromagnetic sphere are integrated to form an integrated layout of "sky, air, land and sea" and a multi-sphere earth observation coverage big data system.

4. The converged management system according to claim 2, characterized in that: In S2: The normalization process of multi-source entity data is to convert the remote sensing data format into COG files and generate thumbnails in PNG format; The standardization of metadata governance labels is to form a metadata governance label set consisting of system standard information, technical standard information, quality standard information and circulation standard information.

5. The convergence management system according to claim 4, characterized in that: The system standard information includes data level one and data level two; Among them, the first-level data category includes: space data, aerospace data, atmospheric meteorology, satellite data, surveying and mapping data, natural resources, ocean data, geological data, social data, artificial intelligence and intelligence data; The secondary data category is a further subdivision based on the primary classification.

6. The converged management system according to claim 4, characterized in that: The technical standard information includes: data processing level, data coordinate latitude and longitude range, data collection time, data spatial scale, data time scale and data format.

7. The converged management system according to claim 4, characterized in that: The quality standard information includes: quality rating, authority rating and data copyright file; Among them, the quality rating is the data quality score given by the data quality inspection department after quality inspection, and is divided into levels according to A, B, C, and D; the permission rating is divided into two types: open source and commercial; the data copyright file is used to standardize and indicate the source and citation method of the data.

8. The convergence management system according to claim 4, characterized in that: The transfer standard information includes: dataset name, data owner, project, and entity data storage path; The dataset name is a combination of metadata governance labels and entity data information. The combination rule is: data first-level category + data second-level category + processing level + project name + data name.

9. The converged management system according to claim 4, characterized in that: Said S3 includes: After transferring the entity data from the temporary storage to the controlled database and product database, the data catalog information in the controlled database and product database is entered into the relational database in partitions to form the catalog table and data table of the relational database; Among them, the catalog table records the key fields used for retrieval management, and the data table imports the metadata governance tags after normalization to save the full information of the entity data.

10. The converged management system according to claim 4, characterized in that: The secure sharing methods of entity data include object storage, API, FTP and network disk.

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