A geographic information resource management system and method in a domestic cloud environment

Abstract

The present invention discloses a geographic information resource management system and method in a domestic cloud environment. The multimodal geographic information resource access and aggregation module provides dynamically expanded geographic information resource access capabilities, while also providing real-time computing, offline computing, and in-memory computing capabilities during the access and integration process. The semantically enhanced geographic information resource efficient organization module combines distributed key-value data structures to generate an inverted index. Simultaneously, the organization and storage methods of index data are dynamically adjusted during geographic information resource query and access operations to achieve dynamic partitioning based on heat perception. The multi-granularity geographic information resource online service module divides multimodal geographic information resources at the resource level, target level, and element level, predicts the service type, interface standard, and underlying environment requirements for geographic information resource release, and achieves rapid release of multimodal geographic information resources. The present invention can achieve effective management of multimodal geographic information resources in a domestic cloud environment.

Description

Technical Field

[0001] The present invention relates to the fields of geographic information, big data and cloud computing, and in particular to a geographic information resource management system and method in a domestic cloud environment. Background Art

[0002] The rapid development of Earth observation and mobile internet technologies has led to explosive growth in multi-source, heterogeneous geographic information resources. These multimodal information resources, characterized by spatiotemporal attributes, are not only growing exponentially in volume but also exhibit significant multimodality in type and source, encompassing a wide range of modalities, including remote sensing imagery, sensor data, and open-source text information. Cloud computing technology has developed rapidly in recent years, and its advantages in elastic resources, powerful computing power, and distributed storage have provided new opportunities for accelerating the efficient utilization of multimodal geographic information resources. Geographic information resource governance is a prerequisite for providing convenient and efficient access to massive geographic information resources.

[0003] The governance of multimodal geographic information resources in domestic cloud environments faces numerous challenges. First, the heterogeneity of information resource types (including structured, semi-structured, and unstructured data) leads to extremely complex data processing processes, and cross-platform and cross-system information resource integration presents significant technical barriers. Second, the coexistence of batch offline processing and real-time streaming computing demands places higher performance requirements on the governance of multimodal geographic information resources. More importantly, modern data governance is no longer limited to traditional information organization and storage optimization; it must also meet the service-oriented, intelligent, and automated needs of domestic cloud environments. Currently, several information resource governance methods exist, but these systems are mostly targeted at general business data and, when processing multimodal geographic information resources, face common challenges such as limited scalability, low processing efficiency, and high system complexity. In particular, given the current gaps in read / write efficiency and computational density in domestic computing environments, optimizing multimodal geographic information resources for domestic cloud environments and addressing the shortcomings of geographic information resource governance in resource integration, intelligent processing, and in-depth analysis remain pressing technical challenges.

[0004] At present, there is a lack of research on multimodal geographic information resource governance systems and methods for domestic cloud environments in related fields. That is, based on the modeling of multimodal geographic information resources and cloud service environments, an integrated governance method for connecting multimodal geographic information resources is provided. Summary of the Invention

[0005] The purpose of the present invention is to provide a geographic information resource management system and method in a domestic cloud environment.

[0006] The technical solution to realize the present invention is: a geographic information resource management system in a domestic cloud environment, including three modules: a multimodal geographic information resource access and aggregation module, a semantically enhanced geographic information resource efficient organization module, and a multi-granularity geographic information resource online service module, wherein:

[0007] The multimodal geographic information resource access and aggregation module defines an assembleable and programmable geographic information resource aggregation engine, defines aggregation rule templates to describe the geographic information resource meta-model, information resource organizational structure, and attribute mapping, provides dynamically scalable geographic information resource access capabilities, and accesses and integrates multi-source heterogeneous geographic information resources, including image data, elevation data, vector data, point cloud data, and trajectory data. At the same time, based on the differences in geographic information resources, it matches information resources with cloud environment computing resources, providing real-time computing, offline computing, and in-memory computing capabilities during the access and integration process.

[0008] The module for efficient organization of semantically enhanced geographic information resources uses the attributes, spatial information, temporal information, and quality information of geographic information resources to describe them, generate semantic vectors for geographic information resources, and generate inverted indexes based on distributed key-value data structures to support rapid retrieval and utilization of geographic information resources. During query and access operations on geographic information resources, it perceives the access popularity of geographic information resources in real time, divides geographic information resources into hot access resources, medium-frequency resources, low-frequency resources, and idle resources based on the difference in popularity, dynamically adjusts the organization and storage methods of index data, and implements dynamic partitioning based on heat perception. Three different resource partitions, namely cache, storage, and release areas, are pre-set in the cloud environment. Hot access resources are placed in the storage and cache areas, medium-frequency and low-frequency resources are placed in the storage area, and idle resources are placed in the release area.

[0009] The multi-granularity geographic information resource online service module divides multimodal geographic information resources from the resource level, target level, and element level. The resource level regards the single-linked geographic information resources as a whole and provides unified services to the outside world; the target level corresponds to an independent and complete geographic target in the geographic information resource, which is a continuous entity within a time and space range; the element level corresponds to the smallest unit of geographic information resources, including the grid in the remote sensing image and a track position in the track information; with geographic information resources of different granularities as input, it predicts the service type, interface standard and underlying environment requirements for the release of geographic information resources, and realizes the rapid release of multimodal geographic information resources.

[0010] Furthermore, the multimodal geographic information resource access and aggregation module includes an assembled aggregation engine module and a hybrid computing engine module, wherein:

[0011] The assembled aggregation engine module builds a geographic information resource meta-model containing common attribute fields and extensible custom attribute fields to achieve a structured description of heterogeneous geographic information resources; builds a unified data access framework, develops dedicated aggregation engines for different geographic information resource types, and the dedicated aggregation engine integrates a standardized geographic information resource parsing tool library at the bottom layer, and defines the mapping relationship between geographic information resource types and parsing tool libraries according to the characteristics of geographic information resources at the upper layer; the dedicated aggregation engine is registered with the geographic information resource master control engine to provide a unified entry for geographic information resource aggregation. When geographic information resource management is accessed, the geographic information resource master control engine dynamically loads and instantiates the geographic information resources required for geographic information resource access based on the description information of the geographic information resources such as resource type, resource format, spatial location, and creation time. The source aggregation engine realizes the assembly-type geographic information resource aggregation process; the geographic information resource aggregation engine is internally composed of a drive engine, an extraction and conversion engine, and a storage engine to form a chain structure. The drive sub-engine provides six geographic information resource drive modes: full access, batch submission, timestamp comparison, status mark polling, and change data capture; the extraction and conversion sub-engine instantiates corresponding extraction and conversion tools according to the type and format of different geographic information resources, checks the standardization, format compliance, and content integrity of geographic information resources, and forms standardized and complete data output; the storage sub-engine provides a hybrid storage method of relational databases, NoSQL databases, and file systems to meet the needs of multi-source geographic information resources, facilitating the underlying support for the heat-aware dynamic partitioning in the semantically enhanced efficient organization module of geographic information resources;

[0012] The hybrid computing engine provides space-time-aware hybrid computing capabilities based on the differences in the characteristics of geographic information resources. It schedules three different types of underlying computing engines for geographic information resource access: real-time computing, offline computing, and in-memory computing. It utilizes the resources of the underlying environment, including computing resources, storage resources, and network resources, to complete various underlying calculations required for geographic information resource governance. Using the MapReduce distributed framework, the upper layer splits computing tasks of different granularities based on the differences in geographic information resources.

[0013] Furthermore, in the storage sub-engine, a four-level storage architecture is established, including memory cache, cluster cache, database system, and offline file system, where:

[0014] Memory cache and cluster cache are used to store hot access resources and medium frequency resources, and use the LRU algorithm for replacement management;

[0015] The database system is used to store resources other than idle resources and achieve fast response through indexing strategies;

[0016] The offline file system is used to store backup files of idle resources to reduce storage overhead;

[0017] When accessing data, the nearest cache level in the memory cache and cluster cache is queried first, and the uncached resources are loaded from the database system into the cache by combining the proximity prefetching based on spatial locality and the directed loading mechanism based on task characteristics.

[0018] Furthermore, the semantically enhanced efficient organization module for geographic information resources includes an inverted semantic indexing module and a heat-aware dynamic partitioning module, wherein:

[0019] The inverted semantic indexing module extracts the key attributes of basic attributes, spatial information, temporal information, and quality information of each geographic information resource, including data name, spatial location, acquisition time, resolution, data format, and product level, generates a description text of the geographic information resource, and inputs it into a fixed-length semantic vector through a text encoder; the inverted indexing technology is used to discretize the semantic vector. First, the local sensitive hashing is used to convert the high-dimensional semantic vector into a set of discrete hash codes, and then the generated hash code is used as the index key to convert the corresponding resource identifier into a set of discrete hash codes. Store in the inverted index table;

[0020] The heat-aware dynamic partitioning module comprehensively considers long-term access trends and burst traffic to define the resource dynamic heat function. The resource dynamic heat function is expressed as

[0021]

[0022] in, is the attenuation factor, and its value range is , is the time interval, is the current timestamp, For the The timestamp of the visit, is the initial resource heat;

[0023] At the same time, the access pattern of geographic information resources is monitored in real time. The dynamic heat score of resources is calculated based on the access time, access number, and access frequency information in the access log. Geographic information resources are dynamically divided into four categories: hot access resources, medium-frequency resources, low-frequency resources, and idle resources. Resources with different heat levels are placed in different storage areas, including:

[0024] Hot-access resources are placed in a high-performance hot storage area within the cache. The "memory cache + cluster cache + database" storage strategy is used to ensure the fastest access speed. When a user accesses a hot-access resource through the geographic information resource service, the geographic information resource service first reads the geographic information resource from the memory cache. If the read fails, it attempts to read from the cluster cache and then the database. After the read is complete, the geographic information resource service backfills the read geographic information resource into the memory cache and cluster cache.

[0025] For medium-frequency resources, they are placed in the standard performance storage area, adopting the "cluster cache + database" storage strategy, and establishing indexes based on data ID, spatial location, and production time to balance access speed and storage cost. When users access medium-frequency resources through the geographic information resource service, the geographic information resource service first reads the geographic information resources from the cluster cache and then reads them from the database. After the reading is completed, the geographic information resource service backfills the read geographic information resources into the cluster cache.

[0026] For low-frequency resources, place them in a general-performance storage area, adopt the database storage strategy, and create indexes based on data ID, spatial location, and production time;

[0027] For idle resources, place them in an offline cold storage area, that is, store them in the database backup file, and record the data location to ensure that they can be loaded from the database backup file.

[0028] Furthermore, the semantically enhanced efficient organization module of geographic information resources supports incremental index updates. When new geographic information resources are accessed or existing geographic information resources are updated, the inverted semantic index module only rebuilds the index of the relevant part without rebuilding the entire index structure. During query, the query description text is also converted into a semantic vector, and then a hash code is generated. The candidate resource set is quickly obtained through the inverted index, and then the candidate set is accurately sorted using cosine similarity.

[0029] Furthermore, the multi-granularity geographic information resource online service module includes a geographic information resource multi-granularity division module and an intelligent guidance geographic information resource service release module, wherein:

[0030] The multi-granularity segmentation module for geographic information resources combines deep learning and geospatial analysis technologies to achieve intelligent identification of targets and elements in different types of geographic information resources. For image data and elevation data, a deep convolutional neural network is used for semantic segmentation to identify forest land, water areas, and impervious surface geographical elements in the image. A deep convolutional neural network is also used for target detection to identify bridges, roads, and airports in the image. For point cloud data, cluster analysis and shape recognition algorithms are used to extract buildings and terrain features. A multi-level geographic target system is formed at the resource level, target level, and element level, providing a foundation for intelligently guiding the release of geographic information resource services.

[0031] Intelligently guide the geographic information resource service publishing module, extract resource characteristics of different granularities, including spatial location, time range, data type and data quality, predict the service type and interface standard suitable for publishing, and automatically configure the required service environment. The service types include Web map services, processing services, analysis services and storage services. The interface standard automatically generates standardized interface definitions based on the recommended service types, including interface address, method name, interface parameters, response structure, and the underlying environment requirements include computing resources, storage resources and network resources.

[0032] Furthermore, in the intelligent guidance geographic information resource service publishing module,

[0033] The corresponding service type prediction is achieved by using a large language model prediction method. A series of historical examples highly relevant to the current task are extracted from historical prediction tasks. The example content includes historical prediction commands, geographic information resource information, and historical prediction results. The service type prediction instructions, resource descriptions, and service type prediction examples are input into the large language model. The learning ability of the large language model is used to learn the service classification rules implicit in the examples until the correct results are generated.

[0034] A large language model is used to predict and recommend the corresponding underlying environmental requirements. A series of historical examples that are highly relevant to the current task are extracted from historical prediction tasks. The example content includes historical prediction commands, geographic information resource information, and historical prediction results. The resource prediction instructions, resource descriptions, and resource prediction examples are input into the large language model. The learning ability of the large language model is used to learn the resource allocation rules implicit in the examples until the correct results are generated.

[0035] A method for managing geographic information resources in a domestic cloud environment is provided. The method is based on the aforementioned system for managing geographic information resources in a domestic cloud environment to achieve geographic information resource management in a domestic cloud environment. Specifically, the method comprises:

[0036] The multimodal geographic information resource access and aggregation module defines an assembleable and programmable geographic information resource aggregation engine, defines aggregation rule templates, describes the geographic information resource meta-model, information resource organizational structure and attribute mapping, provides dynamically scalable geographic information resource access capabilities, and automatically accesses and integrates multi-source heterogeneous geographic information resources, including image data, elevation data, vector data, point cloud data, and trajectory data. At the same time, based on the differences in geographic information resources, it matches information resources with cloud environment computing resources and provides real-time computing, offline computing, and in-memory computing capabilities.

[0037] The semantically enhanced efficient organization module of geographic information resources uses the attributes, spatial information, temporal information, and quality information of geographic information resources to describe geographic information resources and generate descriptive text. The descriptive text is converted into a fixed-length semantic vector of geographic information resources using a text encoder, and an inverted index is generated in combination with a distributed key-value data structure to support the rapid retrieval and use of geographic information resources. Access probes are placed in geographic information resource query and access operations to perceive the access popularity of geographic information resources in real time. Geographic information resources are divided into hot access resources, medium-frequency resources, low-frequency resources, and idle resources according to the difference in popularity. The organization and storage methods of index data are dynamically adjusted to realize dynamic partitioning based on heat perception. Three different resource partitions, namely cache area, storage area, and release area, are pre-set in the cloud environment. Hot access resources are placed in the storage area and cache area, medium-frequency resources and low-frequency resources are placed in the storage area, and idle resources are placed in the release area. The underlying characteristics of distributed storage in the domestic cloud environment are used to divide each area into blocks, and logical partitioning is realized through labels.

[0038] The multi-granularity geographic information resource online service module divides multimodal geographic information resources from the resource level, target level, and element level to meet the user's attention needs for geographic information resources at different granularities. The resource level regards the single-linked geographic information resources as a whole and provides services to the outside world in a unified manner; the target level corresponds to an independent and complete geographic target in the geographic information resource, which is a continuous entity within a time and space range; the element level corresponds to the smallest unit of geographic information resources, including the grid in the remote sensing image and a track position in the track information; with geographic information resources of different granularities as input, the service type and interface standard for the release of geographic information resources are predicted, and the underlying environmental requirements of geographic information resource services are predicted based on the user's choice, and the rapid release of multimodal geographic information resources is achieved through process-based intelligent guidance.

[0039] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for managing geographic information resources in a domestic cloud environment is implemented to achieve geographic information resource management in a domestic cloud environment.

[0040] A computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the method for managing geographic information resources in a domestic cloud environment is implemented to achieve geographic information resource management in a domestic cloud environment.

[0041] Compared with existing technologies, this invention offers the following significant advantages: 1) It achieves adaptive geographic information resource access capabilities for large-scale geographic information resources. 2) It optimizes geographic information resource organization efficiency by integrating semantic relevance and resource popularity. 3) It provides intelligent, guided service publishing, offering users more targeted and adaptable geographic information resource service content. 4) Based on these advantages, this invention can effectively manage multimodal geographic information resources in domestic cloud environments in engineering applications in the fields of geographic information services and cloud computing, demonstrating strong operability and practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 This is the architecture diagram of the multimodal geographic information resource governance system in the domestic cloud environment.

[0043] Figure 2 It is a flowchart of geographic information resource access service obtaining hot access resources.

[0044] Figure 3 It is the execution flow chart of the hybrid computing engine.

[0045] Figure 4 This is an example diagram that defines the mapping relationship between geographic information resource types and parsing tool libraries.

[0046] Figure 5 This is a sample diagram of a structured description template in JSON format. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0048] The present invention provides a multimodal geographic information resource management system and method under a localized cloud environment. The structure of the system is as follows: Figure 1 A multimodal geographic information resource management system in a domestic cloud environment consists of three modules: a multimodal geographic information resource access and aggregation module, a semantically enhanced geographic information resource efficient organization module, and a multi-granularity geographic information resource online service module.

[0049] 1. Multimodal geographic information resource access and aggregation module

[0050] The multimodal geographic information resource access and aggregation module provides dynamically scalable geographic information resource access capabilities by defining an assembleable and programmable geographic information resource aggregation engine, defining aggregation rule templates, and describing the geographic information resource metamodel (including core attributes and flexibly definable extension fields), information resource organizational structure, attribute mapping, etc. Furthermore, based on the differences in geographic information resources, the module automatically matches information resources and cloud environment computing resources during processing, providing real-time computing, offline computing, and in-memory computing capabilities. The multimodal geographic information resource automatic access module, including an assembled aggregation engine module and a hybrid computing engine module, automatically accesses and integrates multi-source heterogeneous geographic information resources, including image data, elevation data, vector data, point cloud data, trajectory data, etc., to achieve standardized description and dynamic expansion of resources.

[0051] 1. Prefabricated Convergence Engine

[0052] The assembled aggregation engine module realizes the structured description of heterogeneous geographic information resources by constructing a geographic information resource meta-model that includes common attribute fields and extensible custom attribute fields. When designing the geographic information resource meta-model, a unified analysis is conducted on the standards related to typical geographic information resources such as image data, elevation data, vector data, point cloud data, and trajectory data, and the common information in the standards is extracted as common attribute fields; combined with the specific characteristics of geographic information resources, statistics are conducted based on the fields that appear more frequently in the actual data attribute information of existing geographic information resources, and custom attribute fields are designed for each type of resource. In order to ensure the scalability of custom attribute fields, KV format is used for storage. A unified data access framework is designed, and dedicated aggregation engines are developed for different types of geographic information resources. Specifically, the dedicated aggregation engine integrates standardized geographic information resource parsing tool libraries (including GDAL, GeoTools, and OpenCV) at the bottom layer, and the upper layer defines the mapping relationship between geographic information resource types and parsing tool libraries according to the characteristics of geographic information resources, such as Figure 4 shown.

[0053] These engines are registered with the Geographic Information Resource Master Control Engine, providing a unified entry point for the aggregation of geographic information resources. During the management and integration of geographic information resources, the Geographic Information Resource Master Control Engine dynamically loads and instantiates the driver engine, extraction and conversion engine, and storage engine required for the integration of geographic information resources based on the description of the resource type, resource format, spatial location, and creation time. These engines are then sequentially connected together to form a chain-like geographic information resource aggregation engine, enabling an assembled geographic information resource aggregation process.

[0054] The geographic information resource aggregation engine has a chain structure composed of three types of sub-engines: the driving engine, the extraction and conversion engine, and the storage engine. The driving sub-engine provides six driving modes for geographic information resources, including full access, batch submission, timestamp comparison, status flag polling, and change data capture. Full access and batch submission are manually triggered to access and process the full data and batch data of the specified path. Timestamp comparison, status flag polling, and change data capture are triggered by triggers, which trigger the geographic information resource access process by monitoring the timestamp information, status flag information, resource change information, etc. of the geographic information resource. The extraction and conversion sub-engine instantiates the corresponding extraction and conversion tools according to the type and format of different geographic information resources, checks the standardization, format compliance, and content integrity of the geographic information resources, and forms a standardized and complete data output. In response to the needs of multi-source geographic information resources, the storage sub-engine provides a hybrid storage method of relational databases, NoSQL databases, and file systems, which facilitates the underlying support for heat-aware dynamic partitioning in the semantically enhanced efficient organization module of geographic information resources; a four-level storage architecture is established, including memory cache, cluster cache, database system, and offline file system. Memory cache and cluster cache are used to store hot access resources and medium-frequency resources, and the LRU algorithm is used for replacement management; the database system is used to store resources other than idle resources, and achieves fast response through indexing strategies; the offline file system is used to store backup files of idle resources to reduce storage overhead. When accessing data, the nearest cache level in the memory cache and cluster cache is queried first, and the non-cached resources are loaded from the database system into the cache in combination with the proximity prefetching based on spatial locality and the directed loading mechanism based on task characteristics. The process of obtaining hot access resources by the geographic information resource access service is as follows: Figure 2 shown.

[0055] 2. Hybrid computing engine

[0056] The hybrid computing engine module provides space-time-aware hybrid computing capabilities based on the characteristics of geographic information resources. It automatically schedules three different types of underlying computing engines for geographic information resources: real-time computing, offline computing, and memory computing. It fully utilizes the resources of the underlying environment (including computing resources, storage resources, and network resources) to complete various types of efficient and complex underlying computing required for geographic information resource management, such as data projection and data compression. The specific process is as follows: Figure 3 Specifically, the hybrid computing engine uses the MapReduce distributed framework at the bottom layer, and the upper layer is divided into computing tasks of different granularities based on the differences in geographic information resources. Expressed as

[0057]

[0058] in For the calculation engine prediction function, based on geographic information resources , underlying environment and computational constraints Predicts one or more engine types that need to be instantiated. It can be further expressed as CPU, memory Mem, disk Disk and network Network, that is,

[0059]

[0060] Computation engine constraints It can be further expressed as efficiency constraints, complexity constraints, real-time constraints and storage constraints, that is,

[0061]

[0062] 2. Semantic Enhancement Module for Efficient Organization of Geographic Information Resources

[0063] The semantically enhanced efficient organization module for geographic information resources uses geographic information resource attributes, spatial information, temporal information, and quality information to describe geographic information resources and generate descriptive text. A text encoder is used to convert this descriptive text into a fixed-length semantic vector for the geographic information resource. This is combined with a distributed key-value data structure to generate an inverted index, supporting the rapid retrieval and utilization of geographic information resources. Access probes are placed during geographic information resource query and access operations to detect the access popularity of geographic information resources in real time. Based on this popularity, geographic information resources are divided into hot, medium-frequency, low-frequency, and idle resources. The organization and storage of index data are dynamically adjusted to achieve heat-aware dynamic partitioning. Three different resource partitions are pre-configured in the cloud environment: a cache area, a storage area, and a release area. Hot resources are placed in the storage area and cache area, medium-frequency and low-frequency resources are placed in the storage area, and idle resources are placed in the release area. Leveraging the underlying characteristics of distributed storage in domestic cloud environments, each area is divided into blocks, achieving logical partitioning through labels. The semantically enhanced efficient organization module for geographic information resources consists of two main components: an inverted semantic indexing module and a heat-aware dynamic partitioning module. The inverted semantic indexing module converts multimodal geographic information resources (including attributes, space, time and data quality, etc.) into a unified semantic representation, and establishes an efficient retrieval index based on this representation. At the same time, the heat-aware dynamic partitioning module dynamically adjusts the storage strategy according to resource usage, ensuring that the multimodal geographic information resource governance system has rapid response and intelligent management capabilities in a large-scale data environment.

[0064] 1. Inverted semantic index construction

[0065] The inverted semantic indexing module uses four types of information: basic attributes, spatial information, temporal information, and quality information of geographic information resources to abstractly describe geographic information resources and generate standardized description text. For each geographic information resource (such as image data, elevation data, vector data, point cloud data, trajectory data, etc.), the module first extracts key attributes from its basic attributes, spatial information, temporal information, and quality information, including data name, spatial location, acquisition time, resolution, data format, and product level, and generates a description text of the geographic information resource, which can be expressed as:

[0066]

[0067] in, Indicates the first properties, Represents the process of generating description text. Description text generation can be achieved by pre-defining a structured description template, or by inputting resource attributes into a large language model to generate a natural language description text. On the one hand, the description text can increase the readability of geographic information resource attribute information. On the other hand, by introducing controllable redundant information, it can make up for the retrieval difficulties caused by the sparseness of geographic information resource features, laying the foundation for subsequent semantic processing. For example, a possible structured description template in JSON format is as follows Figure 5 shown.

[0068] Generated description text The data is then fed into a text encoder (such as a pre-trained Transformer-based text encoder) and converted into a fixed-length semantic vector. This vector captures the semantic information in the text, placing similar resources closer together in the semantic space. To measure the similarity between resources during retrieval, cosine similarity can be used to ensure accurate ranking of candidate search results.

[0069] In order to achieve efficient retrieval, the semantic enhancement geographic information resource efficient organization module uses the inverted index technology to discretize the semantic vector. Specifically, the local sensitive hashing is used to convert the high-dimensional semantic vector into a set of discrete hash codes. For each semantic vector , using a set of hash functions Get the hash code. In this way, similar vectors have a greater probability of being mapped to the same hash code. Then use the generated hash code as the index key to map the corresponding resource identifier Stored in the inverted index table. The constructed index structure is

[0070]

[0071] The semantically enhanced efficient organization module for geographic information resources also supports incremental index updates. When new geographic information resources are added or existing ones are updated, only the relevant indexes are rebuilt, without rebuilding the entire index structure. This ensures the efficient operation of the multimodal geographic information resource management system. During queries, the query description text is also converted into a semantic vector, which is then hashed. The inverted index is used to quickly retrieve a set of candidate resources, which are then accurately sorted using cosine similarity.

[0072] 2. Heat-aware dynamic partitioning

[0073] The heat-aware dynamic partitioning module monitors the access patterns of geographic information resources in real time and intelligently adjusts the storage strategy of resources.

[0074] When defining the resource dynamic heat function, long-term access trends and burst traffic are considered comprehensively, which can be expressed as

[0075]

[0076] in, is the attenuation factor, and its value range is , is the time interval (hours), is the current timestamp, For the The timestamp of the visit, is the initial resource heat. The semantic enhancement of efficient organization module of geographic information resources can be used to obtain access logs related to geographic information resources by regularized filtering, including access time, access times, and access frequency information. The multimodal geographic information resource management system dynamically divides geographic information resources into four categories: hot, medium-frequency, low-frequency, and idle. Resources of varying popularity are placed in different storage areas. The different storage areas are partitioned using a combination of logical and physical partitioning, implemented using the storage sub-engine within the geographic information resource aggregation engine.

[0077] For hot-access resources, the multimodal geographic information resource management system places them in a high-performance hot storage area within the cache. This storage strategy utilizes a "memory cache (e.g., MemCache) + cluster cache (e.g., Redis cluster) + database (e.g., MySQL relational database)" approach to ensure the fastest access speeds. Specifically, the system simultaneously stores the resources in the storage cache, cluster cache, and database cache. When a user accesses a hot-access resource through a geographic information resource service, the service first attempts to read the resource from the memory cache. If the read fails, it then attempts to read from the cluster cache and then the database. After the read is complete, the service then backfills the retrieved resource into the memory cache and cluster cache. It's important to note that the cluster cache is shared across multiple geographic information resource services, facilitating the establishment of a unified cache for multiple services and preventing duplicate caching of the same resource across different services. For medium-frequency resources, the multimodal geographic information resource management system places them in a standard performance storage area, employing a "cluster cache + database" storage strategy. Indexes are established for frequently searched fields (including data ID, spatial location, and production time) to balance access speed and storage costs. When a user accesses a medium-frequency resource through the Geographic Information Resource Service, the Geographic Information Resource Service first attempts to read the resource from the cluster cache, and then from the database. Once the read is complete, the Geographic Information Resource Service backfills the retrieved resource into the cluster cache. For low-frequency resources, the multimodal Geographic Information Resource Management System places them in a general-performance storage area, specifically in a database (such as MySQL), and indexes frequently searched fields (including data ID, spatial location, and production time) to reduce storage overhead. For idle resources that are rarely accessed, the multimodal Geographic Information Resource Management System places them in offline cold storage, storing them in database backup files and recording the data location to ensure they can be loaded from the backup files when necessary, thereby minimizing storage costs. The core advantage of heat-aware dynamic partitioning lies in its adaptability. As user access patterns change, the heat score of a resource dynamically adjusts, and the multimodal Geographic Information Resource Management System automatically migrates the resource to the most appropriate storage area, achieving optimal storage resource allocation. For example, when a low-frequency access geographic information resource is placed in a general performance storage area and becomes a hot spot data due to an emergency, If the number of accesses to a resource increases significantly, the multimodal geographic information resource management system will quickly migrate it to a high-performance hot storage area to improve access efficiency. Once the hot event passes and the resource's access frequency and popularity decrease, the multimodal geographic information resource management system will gradually migrate it to a standard performance storage area. When the number of accesses decreases further, it will be migrated to a standard performance storage area. This dynamic adjustment mechanism fully utilizes the elastic storage characteristics of the cloud environment to achieve an optimal balance between storage efficiency and access performance.

[0078] 3. Multi-granularity geographic information resource online service module

[0079] The multi-granularity geographic information resource online service module divides multimodal geographic information resources from the resource level, target level, and element level to meet the user's attention needs for geographic information resources at different granularities. The resource level regards the single-linked geographic information resources as a whole and provides services to the outside world in a unified manner. The target level corresponds to an independent and complete geographic target in the geographic information resource, which is usually a continuous entity within a time and space range. The element level corresponds to the smallest unit of geographic information resources, such as a grid in a remote sensing image or a track position in track information. Taking geographic information resources of different granularities as input, the service types and interface standards that can be published by geographic information resources are predicted, and the underlying environmental requirements of geographic information resource services are predicted based on the user's choice. The rapid release of multimodal geographic information resources is achieved through process-based intelligent guidance. The multi-granularity geographic information resource online service module includes two main parts: the geographic information resource multi-granularity division module and the intelligent guidance geographic information resource service release module. The geographic information resource multi-granularity division module aims to divide geographic information resources according to different granularities (resource level, target level, element level) and provide users with multi-level and multi-angle online services. The intelligent guidance geographic information resource service release module uses multi-granularity input data to intelligently predict the type, interface standard and underlying environment requirements of resource services, assist users to quickly complete the release of resource services, improve the service level of geographic information resources, and meet the diverse needs of different users in different application scenarios.

[0080] 1. Multi-granularity division of geographic information resources

[0081] The multi-granularity division module for geographic information resources structures multimodal geographic information resources at three levels: resource, target, and element. The resource level provides services to the outside world as a complete unit, such as a complete remote sensing image or a complete set of terrain data. The target level focuses on independent and complete geographic targets within the geographic information resource, such as a forest or a body of water in a remote sensing image. The element level focuses on the smallest-grained geographic features, such as a building, a road, or a point location. The multimodal geographic information resource governance system uses a hierarchical recognition algorithm to automatically extract geographic entities of different granularities from geographic information resources and establish a multi-level spatial index structure.

[0082] The multi-granularity division of geographic information resources, combined with deep learning and geospatial analysis technology, enables intelligent identification of targets and elements in different types of geographic information resources. For example, for image data and elevation data, the multimodal geographic information resource management system uses deep convolutional neural networks for semantic segmentation to identify forests, water areas, and impervious surfaces in the image, and uses deep convolutional neural networks for target detection to identify bridges, roads, and airports in the image. For point cloud data, the multimodal geographic information resource management system uses cluster analysis and shape recognition algorithms to extract three-dimensional geographic targets such as buildings and terrain features. These recognition results are defined at three levels: resource level, target level, and element level, forming a multi-level geographic target system, which provides a basis for intelligently guiding the release of geographic information resource services.

[0083] 2. Intelligently guide the release of geographic information resource services

[0084] The intelligent guidance of geographic information resource service publishing module takes geographic information resources of different granularities as input, predicts the service types and interface standards suitable for publishing, and automatically configures the required service environment to simplify the service publishing process. Based on machine learning technology, by analyzing the characteristics of geographic information resources and historical service publishing experience, it recommends the most suitable service types, including four categories: Web map services, storage services, processing services, or analysis services. At the same time, the multimodal geographic information resource governance system will automatically generate standardized interface definitions based on the recommended service types, including interface addresses, method names, interface parameters, and response structures to ensure the standardization and universality of the services. Intelligent guidance of geographic information resource service publishing predicts the computing resources, storage resources, and network resources required for the geographic information resource services to be published in the cloud environment based on the service type and resource characteristics, ensuring that the published services have good performance and reliability.

[0085] The intelligent guidance of geographic information resource service release utilizes resource characteristics of different granularities to automatically predict and recommend the most suitable service type, interface standard, and underlying environment requirements (including computing resources, storage resources, and network resources), simplifying the service release process. For each granularity of resource, key feature information (including spatial location, time range, data type, and data quality) is extracted and integrated into a unified feature vector, namely .in, They represent the values ​​corresponding to different key feature information in geographic information resources.

[0086] Using the classification model, according to the feature vector Recommend corresponding service types, including web map services, processing services, analysis services, and storage services. Specifically, the classification model is implemented using a large language model prediction method. A series of historical examples highly relevant to the current task are extracted from historical prediction tasks (example content includes historical prediction commands, geographic information resource information, and historical prediction results). The service type prediction instructions, resource descriptions, and service type prediction examples are input into the large language model. The learning ability of the large language model is used to learn the service classification rules implicit in the examples until the correct results are generated.

[0087] Based on the predicted service type and specific characteristics of the resource, the multimodal geographic information resource governance system automatically generates standardized interface definitions, including interface paths, return formats, and calling methods. Target interface definition Expressed as

[0088]

[0089] in, is the interface path, For interface methods (including Get, Post, Put), It is an interface parameter, encapsulated in KV format or JSON format. For the response structure, Indicates that the above information is encapsulated as a RESTful-style interface based on the HTTP protocol. At the same time, the multimodal geographic information resource governance system uses a prediction model based on a large language model to predict the computing resources, storage resources, and network resources required for the geographic information resource services to be released in the cloud environment, ensuring that the released services have good performance and reliability. Specifically, similar to the implementation method of the classification model, a series of historical examples that are highly relevant to the current task are extracted from historical prediction tasks (the example content includes historical prediction commands, geographic information resource information, and historical prediction results), and the resource prediction instructions, resource descriptions, and resource prediction examples are input into the large language model. The learning ability of the large language model is used to learn the resource allocation rules implicit in the examples until the correct results are generated.

[0090] The process of publishing intelligently guided geographic information resource services mainly includes the following steps:

[0091] 1) Input multi-granularity resources, the user selects the geographic information resources to be published, and the multimodal geographic information resource management system automatically reads the feature information and spatial index data corresponding to each granularity.

[0092] 2) Service demand prediction: the multimodal geographic information resource management system generates feature vectors for the input resources , and through the prediction function Recommend appropriate service types and interface standards.

[0093] 3) Release plan generation: The multimodal geographic information resource management system automatically generates a standardized interface release plan document, which includes the interface address, method name, interface parameters, and response structure. Users can directly adopt or make fine adjustments.

[0094] 4) Resource demand prediction: the multimodal geographic information resource management system generates feature vectors for the input resources. , and through the prediction function Recommend appropriate resource types and allocation quantities.

[0095] 5) Based on the resource demand prediction results, the multimodal geographic information resource governance system uses mirroring as the main packaging method for geographic information resource services, and uses containerization technology to ensure strict consistency for each service module and its operating environment.

[0096] The present invention also proposes a method for managing geographic information resources in a domestic cloud environment, and realizes geographic information resource management in a domestic cloud environment based on the geographic information resource management system in the domestic cloud environment.

[0097] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for managing geographic information resources in a domestic cloud environment is implemented to achieve geographic information resource management in a domestic cloud environment.

[0098] A computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the method for managing geographic information resources in a domestic cloud environment is implemented to achieve geographic information resource management in a domestic cloud environment.

[0099] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0100] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A geographic information resource management system in a localized cloud environment, characterized by: It includes three modules: multimodal geographic information resource access and aggregation module, semantic enhanced geographic information resource efficient organization module, and multi-granularity geographic information resource online service module, among which: The multimodal geographic information resource access and aggregation module defines an assembleable and programmable geographic information resource aggregation engine, defines aggregation rule templates to describe the geographic information resource meta-model, information resource organizational structure, and attribute mapping, provides dynamically scalable geographic information resource access capabilities, and accesses and integrates multi-source heterogeneous geographic information resources, including image data, elevation data, vector data, point cloud data, and trajectory data. At the same time, based on the differences in geographic information resources, it matches information resources with cloud environment computing resources, providing real-time computing, offline computing, and in-memory computing capabilities during the access and integration process. The module for efficient organization of semantically enhanced geographic information resources uses the attributes, spatial information, temporal information, and quality information of geographic information resources to describe them, generate semantic vectors for geographic information resources, and generate inverted indexes based on distributed key-value data structures to support rapid retrieval and utilization of geographic information resources. During query and access operations on geographic information resources, it perceives the access popularity of geographic information resources in real time, divides geographic information resources into hot access resources, medium-frequency resources, low-frequency resources, and idle resources based on the difference in popularity, dynamically adjusts the organization and storage methods of index data, and implements dynamic partitioning based on heat perception. Three different resource partitions, namely cache, storage, and release areas, are pre-set in the cloud environment. Hot access resources are placed in the storage and cache areas, medium-frequency and low-frequency resources are placed in the storage area, and idle resources are placed in the release area. The multi-granularity geographic information resource online service module divides multimodal geographic information resources from the resource level, target level, and element level. The resource level regards the single-linked geographic information resources as a whole and provides unified services to the outside world; the target level corresponds to an independent and complete geographic target in the geographic information resource, which is a continuous entity within a time and space range; the element level corresponds to the smallest unit of geographic information resources, including the grid in the remote sensing image and a track position in the track information; with geographic information resources of different granularities as input, it predicts the service type, interface standard and underlying environment requirements for the release of geographic information resources, and realizes the rapid release of multimodal geographic information resources.

2. The geographic information resource management system in a localized cloud environment according to claim 1 is characterized in that: The multimodal geographic information resource access and aggregation module includes an assembled aggregation engine module and a hybrid computing engine module, wherein: The assembled aggregation engine module builds a geographic information resource meta-model containing common attribute fields and extensible custom attribute fields to achieve a structured description of heterogeneous geographic information resources; builds a unified data access framework, develops dedicated aggregation engines for different geographic information resource types, and the dedicated aggregation engine integrates a standardized geographic information resource parsing tool library at the bottom layer, and defines the mapping relationship between geographic information resource types and parsing tool libraries according to the characteristics of geographic information resources at the upper layer; the dedicated aggregation engine is registered with the geographic information resource master control engine to provide a unified entry for geographic information resource aggregation. When geographic information resource management is accessed, the geographic information resource master control engine dynamically loads and instantiates the geographic information resources required for geographic information resource access based on the description information of the geographic information resources such as resource type, resource format, spatial location, and creation time. The source aggregation engine realizes the assembly-type geographic information resource aggregation process; the geographic information resource aggregation engine is internally composed of a drive engine, an extraction and conversion engine, and a storage engine to form a chain structure. The drive sub-engine provides six geographic information resource drive modes: full access, batch submission, timestamp comparison, status mark polling, and change data capture; the extraction and conversion sub-engine instantiates corresponding extraction and conversion tools according to the type and format of different geographic information resources, checks the standardization, format compliance, and content integrity of geographic information resources, and forms standardized and complete data output; the storage sub-engine provides a hybrid storage method of relational databases, NoSQL databases, and file systems to meet the needs of multi-source geographic information resources, facilitating the underlying support for the heat-aware dynamic partitioning in the semantically enhanced efficient organization module of geographic information resources; The hybrid computing engine provides space-time-aware hybrid computing capabilities based on the differences in the characteristics of geographic information resources. It schedules three different types of underlying computing engines for geographic information resource access: real-time computing, offline computing, and in-memory computing. It utilizes the resources of the underlying environment, including computing resources, storage resources, and network resources, to complete various underlying calculations required for geographic information resource governance. Using the MapReduce distributed framework, the upper layer splits computing tasks of different granularities based on the differences in geographic information resources.

3. The geographic information resource management system in a localized cloud environment according to claim 2 is characterized in that: In the storage sub-engine, a four-level storage architecture is established, including memory cache, cluster cache, database system, and offline file system. Memory cache and cluster cache are used to store hot access resources and medium frequency resources, and use the LRU algorithm for replacement management; The database system is used to store resources other than idle resources and achieve fast response through indexing strategies; The offline file system is used to store backup files of idle resources to reduce storage overhead; When accessing data, the nearest cache level in the memory cache and cluster cache is queried first, and the uncached resources are loaded from the database system into the cache by combining the proximity prefetching based on spatial locality and the directed loading mechanism based on task characteristics.

4. The geographic information resource management system in a localized cloud environment according to claim 1 is characterized in that: The semantically enhanced efficient organization module for geographic information resources includes an inverted semantic indexing module and a heat-aware dynamic partitioning module, where: The inverted semantic indexing module extracts the key attributes of basic attributes, spatial information, temporal information, and quality information of each geographic information resource, including data name, spatial location, acquisition time, resolution, data format, and product level, generates a description text of the geographic information resource, and inputs it into a fixed-length semantic vector through a text encoder; the inverted indexing technology is used to discretize the semantic vector. First, the local sensitive hashing is used to convert the high-dimensional semantic vector into a set of discrete hash codes, and then the generated hash code is used as the index key to convert the corresponding resource identifier into a set of discrete hash codes. Store in the inverted index table; The heat-aware dynamic partitioning module comprehensively considers long-term access trends and burst traffic to define the resource dynamic heat function. The resource dynamic heat function is expressed as ； in, is the attenuation factor, and its value range is , is the time interval, is the current timestamp, For the The timestamp of the visit, is the initial resource heat; At the same time, the access pattern of geographic information resources is monitored in real time. The dynamic heat score of resources is calculated based on the access time, access number, and access frequency information in the access log. Geographic information resources are dynamically divided into four categories: hot access resources, medium-frequency resources, low-frequency resources, and idle resources. Resources with different heat levels are placed in different storage areas, including: Hot-access resources are placed in a high-performance hot storage area within the cache. The "memory cache + cluster cache + database" storage strategy ensures the fastest access speed. When a user accesses a hot-access resource through the geographic information resource service, the geographic information resource service first reads the geographic information resource from the memory cache. If the read fails, it attempts to read from the cluster cache and then the database. Once the read is complete, the geographic information resource service backfills the read geographic information resource into the memory cache and cluster cache. For medium-frequency resources, they are placed in the standard performance storage area, using a "cluster cache + database" storage strategy. Indexes are established based on data ID, spatial location, and production time to balance access speed and storage costs. When users access medium-frequency resources through the geographic information resource service, the geographic information resource service first reads the geographic information resources from the cluster cache and then reads them from the database. After the reading is completed, the geographic information resource service backfills the read geographic information resources into the cluster cache. For low-frequency resources, place them in a general-performance storage area, adopt the database storage strategy, and create indexes based on data ID, spatial location, and production time; For idle resources, place them in an offline cold storage area, that is, store them in the database backup file, and record the data location to ensure that they can be loaded from the database backup file.

5. The geographic information resource management system in a localized cloud environment according to claim 4 is characterized in that: The semantically enhanced efficient organization module of geographic information resources supports incremental index updates. When new geographic information resources are accessed or existing geographic information resources are updated, the inverted semantic index module only rebuilds the index of the relevant part without rebuilding the entire index structure. During query, the query description text is also converted into a semantic vector, and then a hash code is generated. The candidate resource set is quickly obtained through the inverted index, and then the candidate set is accurately sorted using cosine similarity.

6. The geographic information resource management system in a localized cloud environment according to claim 1 is characterized in that: The multi-granularity geographic information resource online service module includes a geographic information resource multi-granularity division module and an intelligent guidance geographic information resource service release module, in which: The multi-granularity segmentation module for geographic information resources combines deep learning and geospatial analysis technologies to achieve intelligent identification of targets and elements in different types of geographic information resources. For image data and elevation data, a deep convolutional neural network is used for semantic segmentation to identify forest land, water areas, and impervious surface geographical elements in the image. A deep convolutional neural network is also used for target detection to identify bridges, roads, and airports in the image. For point cloud data, cluster analysis and shape recognition algorithms are used to extract buildings and terrain features. A multi-level geographic target system is formed at the resource level, target level, and element level, providing a foundation for intelligently guiding the release of geographic information resource services. Intelligently guide the geographic information resource service publishing module, extract resource characteristics of different granularities, including spatial location, time range, data type and data quality, predict the service type and interface standard suitable for publishing, and automatically configure the required service environment. The service types include Web map services, processing services, analysis services and storage services. The interface standard automatically generates standardized interface definitions based on the recommended service types, including interface address, method name, interface parameters, response structure, and the underlying environment requirements include computing resources, storage resources and network resources.

7. The geographic information resource management system in a localized cloud environment according to claim 6 is characterized in that: In the intelligent guidance geographic information resource service publishing module, The corresponding service type prediction is achieved by using a large language model prediction method. A series of historical examples highly relevant to the current task are extracted from historical prediction tasks. The example content includes historical prediction commands, geographic information resource information, and historical prediction results. The service type prediction instructions, resource descriptions, and service type prediction examples are input into the large language model. The learning ability of the large language model is used to learn the service classification rules implicit in the examples until the correct results are generated. A large language model is used to predict and recommend the corresponding underlying environmental requirements. A series of historical examples that are highly relevant to the current task are extracted from historical prediction tasks. The example content includes historical prediction commands, geographic information resource information, and historical prediction results. The resource prediction instructions, resource descriptions, and resource prediction examples are input into the large language model. The learning ability of the large language model is used to learn the resource allocation rules implicit in the examples until the correct results are generated.

8. A method for managing geographic information resources in a localized cloud environment, characterized in that: Based on the geographic information resource management system in a localized cloud environment as described in any one of claims 1 to 7, geographic information resource management in a localized cloud environment is realized, specifically: The multimodal geographic information resource access and aggregation module defines an assembleable and programmable geographic information resource aggregation engine, defines aggregation rule templates, describes the geographic information resource meta-model, information resource organizational structure and attribute mapping, provides dynamically scalable geographic information resource access capabilities, and automatically accesses and integrates multi-source heterogeneous geographic information resources, including image data, elevation data, vector data, point cloud data, and trajectory data. At the same time, based on the differences in geographic information resources, it matches information resources with cloud environment computing resources and provides real-time computing, offline computing, and in-memory computing capabilities. The semantically enhanced efficient organization module of geographic information resources uses the attributes, spatial information, temporal information, and quality information of geographic information resources to describe geographic information resources and generate descriptive text. The descriptive text is converted into a fixed-length semantic vector of geographic information resources using a text encoder, and an inverted index is generated in combination with a distributed key-value data structure to support the rapid retrieval and use of geographic information resources. Access probes are placed in geographic information resource query and access operations to perceive the access popularity of geographic information resources in real time. Geographic information resources are divided into hot access resources, medium-frequency resources, low-frequency resources, and idle resources according to the difference in popularity. The organization and storage methods of index data are dynamically adjusted to realize dynamic partitioning based on heat perception. Three different resource partitions, namely cache area, storage area, and release area, are pre-set in the cloud environment. Hot access resources are placed in the storage area and cache area, medium-frequency resources and low-frequency resources are placed in the storage area, and idle resources are placed in the release area. The underlying characteristics of distributed storage in the domestic cloud environment are used to divide each area into blocks, and logical partitioning is realized through labels. The multi-granularity geographic information resource online service module divides multimodal geographic information resources from the resource level, target level, and element level to meet the user's attention needs for geographic information resources at different granularities. The resource level regards the single-linked geographic information resources as a whole and provides services to the outside world in a unified manner; the target level corresponds to an independent and complete geographic target in the geographic information resource, which is a continuous entity within a time and space range; the element level corresponds to the smallest unit of geographic information resources, including the grid in the remote sensing image and a track position in the track information; with geographic information resources of different granularities as input, the service type and interface standard for the release of geographic information resources are predicted, and the underlying environmental requirements of geographic information resource services are predicted based on the user's choice, and the rapid release of multimodal geographic information resources is achieved through process-based intelligent guidance.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for managing geographic information resources in a domestic cloud environment as described in claim 8 is implemented to achieve geographic information resource management in a domestic cloud environment.

10. A computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for managing geographic information resources in a domestic cloud environment as described in claim 8 is implemented to realize the management of geographic information resources in a domestic cloud environment.

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