Wetland semantic virtual geographic environment construction method based on hierarchical knowledge graph

Abstract

The invention belongs to the crossing field of computer technology, geographic information science and environmental science, and discloses a hierarchical knowledge graph-based wetland semantic virtual geographic environment construction method, which comprises the following steps of: constructing a data graph, and constructing a wetland monitoring entity, the multi-source heterogeneous wetland monitoring data set is converted into a knowledge graph with entities as nodes and semantic relations as edges; constructing a calculation graph, integrating a wetland executable calculation entity into the knowledge graph, and enabling the calculation entity to execute calculation and conversion operation on entities in the data graph based on an algorithm rule; and a scene graph is constructed to represent a knowledge graph formed by different wetland application scene entities, and the application scene entities are associated with data entities from the data graph and calculation entities from the calculation graph. According to the method, the problems that in the existing wetland virtual geographical environment, the wetland monitoring data is too much and too little due to lack of semantic information and the calculation entity is too strong and too weak due to insufficient intelligence can be solved.

Description

Technical Field

[0001] The present invention relates to the cross - field of computer technology, geographic information science and environmental science, particularly to virtual geographic environment, semantic technology and knowledge graph technology, and particularly discloses a method for constructing a wetland semantic virtual geographic environment based on a hierarchical knowledge graph. Background Art

[0002] Wetland ecosystems are extremely sensitive to environmental changes, with significant vulnerability, dynamics and complexity, which pose great challenges to the monitoring, understanding, management and protection of wetlands. There is an urgent need for new technologies and methods to deepen the understanding of wetlands and achieve intelligent monitoring and management.

[0003] Virtual Geographic Environment (VGE), as a multi - dimensional dynamic digital geographical space platform based on geographical theories and integrating technologies such as virtual reality, network communication, and geographic information system, has been applied to the research of wetland ecosystems.

[0004] However, the current system - integrated wetland virtual geographic environment generally has the problem of being "visible" but not "intelligent", and it is difficult to meet the requirements of computability, manageability and decision - making for wetland ecosystems. The main reason is the lack of semantic information in the current architecture of wetland virtual geographic environment, resulting in the following two prominent contradictions: 1. The contradiction of wetland monitoring data being "both much and little": With the development of sensor network technology, the amount of wetland monitoring data has increased explosively, achieving data integration at the physical level. However, these data are characterized by multi - source heterogeneity, different spatio - temporal scales, etc., and lack semantic associations with each other, forming "logical" data islands. This makes it difficult for machines (including large language models) and humans (even experts) to deeply understand and reason about the physically integrated data to generate valuable knowledge, and the potential advantages of massive data have not been fully utilized.

[0005] 2. The contradiction of wetland computing power being "both strong and weak": With the rapid development of geographic modeling platforms and environmental intelligence technologies, the number and types of wetland computing entities (algorithms, models, services) have increased rapidly, and the service - based architecture has become the mainstream. Although service encapsulation solves the sharing and reuse of wetland computing entities, these entities still exist in the form of "individuals", cannot directly interact with the virtual environment, require manual parameter configuration before execution, and the result feedback is lagged. The existing execution methods based on service chains or predefined rules are difficult to fully adapt to the highly dynamic changes and unpredictability of the wetland environment, resulting in it being difficult to comprehensively consider all possible situations in the rule design stage, thus limiting the flexibility and intelligent level of the system.

[0006] To solve the above problems, there is an urgent need to establish a mechanism that can realize the transformation of wetland monitoring data from physical integration to logical integration and enable computing entities to automatically adapt to the dynamic changes of the wetland environment, thereby improving the intelligent level of the wetland virtual geographical environment. The application of knowledge graphs in the virtual geographical environment is still in the stage of conceptual exploration and preliminary experiments, and the potential of its formal modeling and explicit expression has not been fully utilized.

[0007] In summary, in the face of the two contradictions of "both too much and too little" in wetland monitoring data and "both too strong and too weak" in wetland computing power in the wetland virtual geographical environment, the present invention will explore a method for constructing a wetland semantic virtual geographical environment driven by a knowledge graph, and use the knowledge graph to associate wetland monitoring data and wetland computing entities in the form of a graph structure to provide support for wetland ecosystem monitoring and management. Summary of the Invention

[0008] The present invention aims to solve the technical problems of "both too much and too little" caused by the lack of semantic information in wetland monitoring data and "both too strong and too weak" caused by insufficient intelligence of computing entities in the existing wetland virtual geographical environment, so as to construct an intelligent wetland semantic virtual geographical environment.

[0009] To solve the above technical problems, the present invention provides a method for constructing a wetland semantic virtual geographical environment based on a hierarchical knowledge graph, and its technical solution is: Construct a data graph, a computing graph, and a scenario graph. The data graph is used to integrate wetland monitoring data entities, the computing graph is used to integrate wetland executable computing entities, and the scenario graph is used to represent different wetland application scenario entities; Construct a data graph. Based on wetland monitoring data entities, convert multi-source heterogeneous wetland monitoring data sets into a knowledge graph with entities as nodes and semantic relationships as edges; Construct a computing graph, integrate wetland executable computing entities into the knowledge graph, and enable the computing entities to perform computing and transformation operations on the entities in the data graph based on algorithm rules; Construct a scenario graph, which represents a knowledge graph composed of different wetland application scenario entities, and the application scenario entities are associated with data entities from the data graph and computing entities from the computing graph.

[0010] As an optional implementation manner of the first aspect of the present application, the steps of constructing the data graph specifically include: establishing the wetland monitoring entity, formally describing wetland discrete entities, continuous entities, and monitoring information based on the extension of the existing ontology; constructing a hybrid ontology strategy, and adopting a hierarchical semantic modeling method combining a global ontology and a local ontology to implement a hybrid ontology architecture; integrating multi-source heterogeneous wetland monitoring data, and realizing heterogeneous data integration through a hybrid integration of a materialization strategy and a virtualization strategy.

[0011] As an alternative implementation of the first aspect of the present application, in the step of realizing heterogeneous data integration through the hybrid integration of the materialization strategy and the virtualization strategy, the materialization strategy includes: for data greater than a preset query analysis threshold, data less than a preset change frequency, and data less than a preset update cost, adopting the materialization strategy; completing materialization integration through an ETL process, and the ETL process includes collecting data from an input source, mapping the data from the source schema to the target schema, and saving the transformed data to a triple store; in the transformation stage, realizing the mapping of source data into RDF by constructing RML mapping rules; in the loading stage, using a materialization engine to materialize multi-source heterogeneous wetland monitoring data into RDF triples according to the RML mapping rules and persist them into a graph database.

[0012] As an alternative implementation of the first aspect of the present application, in the step of realizing heterogeneous data integration through the hybrid integration of the materialization strategy and the virtualization strategy, the virtualization strategy includes: for batch real-time monitoring data, adopting an ontology-based data access technology mode for virtualization integration; providing a unified data view through a mediation architecture to realize that there is no need to move or copy data to a unified location; translating queries on the knowledge graph into queries on data sources in real time online.

[0013] As an alternative implementation of the first aspect of the present application, the step of constructing the data graph further includes: creating a federated storage using the federated storage function of a graph database; configuring multiple local materialized or remote virtualized SPARQL endpoints; realizing the integration of multiple virtualized and materialized RDF graphs in one knowledge graph.

[0014] As an alternative implementation of the first aspect of the present application, the step of constructing the computational graph specifically includes: designing an execution mode of an executable knowledge graph, and the execution mode is divided into two parts: synchronous and asynchronous; constructing a knowledge graph interaction mediator for the API-P of computational entities based on the API-P framework, and realizing the management and monitoring of jobs through the persistence of API-P jobs; developing a knowledge graph job manager, and the job manager is responsible for the job management and monitoring of the computational entities. The job manager realizes the persistence of the jobs in the knowledge graph by sending SPARQL statements to the endpoints of the knowledge graph; designing an input mediation algorithm and an output mediation algorithm to realize the data interaction between the computational entity API-P and the knowledge graph. The input mediation algorithm converts the entities in the knowledge graph into a list of input parameters supported by the API-P, and the output mediation algorithm integrates the execution results of the API-P into the knowledge graph.

[0015] As an alternative implementation of the first aspect of the present application, the steps of constructing the scenario graph specifically include: representing each wetland application scenario entity as a named graph, where the named graph is a triple graph and has a uniform resource identifier; using the set of semantic relationships of external information of the wetland application scenario entity to define the basic attributes of each wetland application scenario entity and the semantic connections between entities; using the set of internal structure descriptions of the wetland application scenario entity, described in quadruple form, and on the basis of the triple information including the data graph and the computational graph, specifically specifying the URI of the named graph to clarify the data entities, computational entities used by the wetland application scenario entity and their dependencies; using the named graph to implement the separation and version control of complex wetland monitoring data and computational entities.

[0016] In a second aspect, an embodiment of the present application provides a wetland semantic virtual geographical environment construction system based on a hierarchical knowledge graph, including: A data graph construction module, configured to integrate wetland monitoring data entities, including converting a multi-source heterogeneous wetland monitoring data set into a knowledge graph with entities as nodes and semantic relationships as edges based on the wetland monitoring data entities; A computational graph construction module, configured to integrate wetland executable computational entities, including integrating the wetland executable computational entities into the knowledge graph and enabling the computational entities to perform computational and transformation operations on the entities in the data graph based on algorithm rules; A scenario graph construction module, configured to represent different wetland application scenario entities; including representing a knowledge graph composed of different wetland application scenario entities, where the application scenario entities are associated with data entities from the data graph and computational entities from the computational graph.

[0017] In a third aspect, an embodiment of the present application provides an electronic device, where the electronic device includes a processor, a memory, and a program or instruction stored on the memory and executable on the processor, and when the program or instruction is executed by the processor, the steps of the method described in the first aspect are implemented.

[0018] In a fourth aspect, an embodiment of the present application provides a readable storage medium, where a program or instruction is stored on the readable storage medium, and when the program or instruction is executed by the processor, the steps of the method described in the first aspect are implemented.

[0019] Compared with the prior art, the present invention has the following beneficial effects: (1) In terms of data application, the wetland semantic virtual geographical environment can integrate multi-source heterogeneous data from both physical and logical levels with the help of ontology, eliminate the semantic gap, achieve more efficient integration, and then can be combined with machine learning technology to extract valuable information and knowledge from a large amount of wetland monitoring data, significantly improving the value density and utilization efficiency of the data.

[0020] (2) In terms of adaptive computing, the wetland semantic virtual geographical environment can realize the automatic conversion and matching of computing entity parameters and data entities based on the executable knowledge graph architecture; at runtime, it traces back the data sources and computing processes through traceability computing, and dynamically adjusts the computing process and parameter configuration according to the actual situation, so as to better adapt to the highly dynamic characteristics of the wetland ecosystem and improve the intelligence level of computing and the autonomy of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a flowchart of a method for constructing a wetland semantic virtual geographical environment based on a hierarchical knowledge graph provided by an embodiment of the present invention; Figure 2 It is a general design diagram of the wetland semantic virtual geographical environment; Figure 3 It is a schematic diagram of the semantic integration ontology structure; Figure 4 It is a schematic diagram of materialized semantic integration and virtualized semantic integration; Figure 5 It is a schematic diagram of the materialized integration process of wetland monitoring data; Figure 6 It is a schematic diagram of the structure of a system for constructing a wetland semantic virtual geographical environment based on a hierarchical knowledge graph provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.

[0023] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, rather than to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein. In addition, "and / or" in the specification and claims indicates at least one of the connected objects, and the character " / " generally represents an "or" relationship between the associated objects before and after. In the description of the present invention, "a plurality" means two or more, unless otherwise specifically defined.

[0024] Embodiment 1 Please refer to Figure 1 , which is a flowchart of a method for constructing a wetland semantic virtual geographical environment based on a hierarchical knowledge graph proposed in the first embodiment of the present invention. Figure 2shows the overall design framework of the wetland semantic virtual geographical environment constructed by the present invention. Its core is a hierarchical knowledge graph, including a data graph (G D ), a computational graph (G C ), and a scene graph (G S ).

[0025] It should be noted that the wetland semantic virtual geographical environment consists of a data layer, a computational layer, and a scene layer, corresponding to integrated data entities, computational entities, and scene entities respectively. The three layers use semantic technologies to integrate data entities, computational entities, and scene entities respectively.

[0026] The method for constructing the wetland semantic virtual geographical environment includes the following steps: Step 1: Construct a data graph. By constructing wetland monitoring entities, convert the multi-source heterogeneous wetland monitoring data set into a knowledge graph with entities as nodes and semantic relationships as edges.

[0027] In some embodiments, convert the multi-source heterogeneous data into an RDF knowledge graph by constructing wetland monitoring entities. This ontology reuses existing ontologies and improves them according to the characteristics of wetlands, describing discrete entities, continuous entities, and monitoring information. The data graph (G D ) adopts a hybrid ontology strategy. The global ontology defines the core semantics, and the local ontology describes specific domain data. The data integration adopts a hybrid method of materialization and virtualization: for high-frequency queries, low-variation, and high-update-cost data, map them into RDF storage through ETL and RML; for large-scale real-time data, implement virtualization integration based on the Ontology-Based Data Access (OBDA) mode. Finally, integrate the materialized and virtualized endpoints through federated storage to integrate multiple virtualized and materialized RDF graphs in one knowledge graph.

[0028] Specifically, the data graph (G D ) can be defined as a multi-relational graph , where represents the set of entities (Entity) related to wetlands or wetland monitoring data, represents the set of relationships (Relation) between wetland monitoring data entities, represents the set of facts (Fact) composed of entities and relationships. The fact is composed of a series of triples, and its form is , where respectively represent the head and tail entities, Represents the relationship from the head entity to the tail entity. Through explicit ontology definitions and formal semantic relationships, the knowledge graph explicitly expresses the information implicit in wetland monitoring data and organizes the entities and their mutual relationships in wetland monitoring data in an orderly manner, enabling the originally scattered information to be centrally displayed and easily understood.

[0029] In addition, the knowledge graph can also use embedding techniques to map wetland monitoring data entities and relationships into a low-dimensional vector space, improving computational efficiency while preserving the semantics of the original data and supporting downstream data-driven applications. For RDF graphs, the RDF2Vec graph embedding method can be used. RDF2Vec traverses the paths in the RDF graph and, using the idea of word embedding in natural language processing (such as Word2Vec), converts the entities and relationships in the data graph into low-dimensional vectors. Therefore, by combining the data graph with embedding techniques such as RDF2Vec, wetland monitoring data can be made not only visible but also computable.

[0030] Structurally, there are three strategies for realizing data semantic integration to construct a knowledge graph, namely single ontology, multi-ontology, and hybrid ontology. As Figure 3 shown, it shows the three strategy concepts of (A) single ontology as in Figure 3 , (B) multi-ontology as in Figure 3 , and (C) hybrid ontology as in Figure 3 in the construction of the data graph. The single ontology method uses a single global ontology, which is simple in structure but difficult to adapt to the dynamic changes of wetland data; the multi-ontology method uses multiple independent ontologies, which solves the heterogeneity problem but leads to poor data interoperability; the hybrid ontology method innovatively combines the global ontology and the local ontology, establishes a unified semantic framework through the global ontology to define core elements, and at the same time uses the local ontology to describe data in specific domains, which not only maintains system consistency but also enhances flexibility, effectively solving the key problem of semantic integration of wetland monitoring data. Therefore, based on this strategy, the global ontology can be used as the common semantic basis of the knowledge graph to define the basic concepts, attributes, relationships, and rules in multi-source heterogeneous wetland monitoring data; the local ontology can be used to describe wetland monitoring data for specific topics, domains, scenarios, and requirements, enhancing the flexibility and diversity of the knowledge graph.

[0031] Formally, as Figure 4As shown, two ways of the hybrid integration strategy adopted in data graph construction are presented, namely, including two methods: Materialization and Virtualization. The Materialization method physically integrates data into the graph database through the ETL process. It has high query efficiency but high storage and maintenance costs, especially not suitable for large-scale data (such as raster data). The Virtualization method realizes a unified data view through an intermediate architecture. The data remains stored in place. Although it saves storage space and maintenance costs, the query performance will be greatly affected because relational data needs to be dynamically converted into RDF triples in real time.

[0032] To construct a data graph to solve the integration problem of different domain, multi-source heterogeneous wetland monitoring data in the virtual geographical environment, first analyze the wetland monitoring data, establish wetland monitoring entities, and formally describe the discrete entities, continuous entities and monitoring information in the wetland monitoring data. The wetland monitoring entity can be represented as , where , C represents the set of classes related to wetland monitoring, D represents the set of data types related to wetland monitoring data, P represents the set of attributes, and , where is an object property, and both the domain and range are objects instantiated from classes; is a data type property, the domain is an object instantiated from a class, and the range is a literal. represents the set of class relationships and data type relationships ; is the set of class relationships, and its form is ; is the set of data type relationships, and its form is .

[0033] If the virtual geographical environment is regarded as a container, the discrete entities in the wetland monitoring data are distributed in the container in the form of an object model. Based on the summary of the research on existing object models and according to the characteristics of the wetland monitoring data, the wetland monitoring data based on discrete entities is represented as a six-tuple composed of type , status , time , space , attribute , and relationship feature : In the formula, Represents discrete geographical entities existing in wetland ecosystems. GeoSPARQL provides a standard way to represent geospatial data, including discrete objects such as points, lines, polygons, and the spatial relationships between them. The discrete entities in wetland monitoring data correspond to the class in the GeoSPARQL ontology, that is . Therefore, based on the reuse of GeoSPARQL, the wetland monitoring entities construct the semantic representation of discrete entities according to the domain characteristics of wetland monitoring data.

[0034] The wetland monitoring dataset can be represented as , where S represents the set of monitored samples, and T represents the set of labels. Each sample is the smallest unit of the dataset, representing a single monitoring of a certain area or object in the wetland. Each label represents a key-value pair , corresponding to wetland attributes, corresponding to the monitoring results.

[0035] Semantic integration is a process of transforming the known dataset D into a structured data graph based on the unified ontology O through a series of mapping rules from the wetland monitoring database schema to the wetland monitoring data ontology .

[0036] The present invention adopts a hybrid integration strategy to construct a wetland monitoring data graph to balance performance, cost, and flexibility. Figure 5 The ETL process for materializing wetland monitoring data. Collect data from the input source (extract), clean and map the data from the source schema (the schema of the original data source) to the target schema (transform), and save the transformed data to the triple store (load). In the extraction stage, generally speaking, wetland remote sensing information extraction includes task types such as wetland land cover classification, geographical object detection, and ecological parameter extraction. For structured data in table format, generally no further processing is required; for wetland monitoring data published through Web APIs, the Requests library in Python can be used for processing, and the data can be extracted as a hierarchical file in XML or JSON format; for vector data, the GeoPandas library can be used to read vector data in formats such as Shapefile, GeoJSON, and GML, and convert it into the in-memory form of GeoDataFrame. In the transformation stage, mainly by constructing RML mapping rules to map the source data to RDF. In the loading stage, a materialization engine is used to materialize the multi-source heterogeneous wetland monitoring data into RDF triples according to the RML mapping rules and persist them into the graph database.

[0037] Step 2: Construct a computational graph, integrate wetland executable computational entities into the knowledge graph, and enable the computational entities to perform computational and transformation operations on the entities in the data graph based on algorithmic rules.

[0038] In some embodiments, integrate wetland executable computational entities into the knowledge graph to form a computational graph (G C ). The specific implementation steps include: First, design a synchronous / asynchronous execution mode to handle simple and complex computational tasks separately; then develop an API-P interaction mediator based on the Pygeoapi framework to achieve job persistence management; next, construct a knowledge graph job manager (KGManager) to implement job graph storage through SPARQL statements; finally, design input / output mediation algorithms. The former converts knowledge graph entities into API-P input parameters, and the latter integrates the calculation results into the knowledge graph to complete the update of the data graph. This solution enables the computational entities to have the capabilities of automatic workflow execution and adaptive adjustment, and can perform computational transformation operations on the data graph entities based on algorithmic rules.

[0039] Specifically, the computational graph (G c ) is represented as a knowledge graph integrating wetland executable computational entities, and each computational entity can perform computational or transformation operations on the entities in the knowledge graph based on specific algorithms or rules, thereby enriching or updating the knowledge graph. The computational graph can be regarded as a binary tuple . Where represents the data graph, including the set of concepts (classes) and the set of wetland monitoring data entities . represents the set of executable computational entities in the knowledge graph. represents the set of wetland computing services, and each wetland computing service can be represented as , represents the constraints on service input and output. represents the set of task entities in the knowledge graph. Each task is a specific execution instance of a certain service , represents the specific input and output data of the task. As the core feature of the semantic virtual geographical environment, the computational graph realizes the functional evolution of the knowledge graph through dynamic integration of wetland computational entities. This technology deeply integrates the computational process with knowledge expression, transforming the static data graph into a dynamic knowledge graph with the ability of continuous evolution. Computational entities have dual capabilities: they can not only automatically execute tasks such as data processing and simulation operations through workflows, but also adaptively adjust parameters to respond to changes in the wetland environment or virtual environment. The ultimate goal of the computational graph is to develop towards autonomous computing, which can make decisions independently under specific conditions and execute corresponding operations to achieve intelligent optimization and virtual-real collaboration of computing.

[0040] Step 3: Construct a scenario graph, which represents a knowledge graph composed of different wetland application scenario entities. The application scenario entities are associated with data entities from the data graph and computing entities from the computing graph.

[0041] In some embodiments, the knowledge graph representing different wetland application scenario entities involves a series of data entities from the data graph and computing entities from the computing graph. The construction of the scenario graph (G S ) includes: representing each wetland application scenario entity as a Named Graph, that is , where is represented as a triple graph, is the Uniform Resource Identifier (URI) of

[0042] Specifically, the scenario graph (G S ) represents a knowledge graph composed of different wetland application scenario entities, involving a series of data entities from the data graph and computing entities from the computing graph. The application graph can be represented as . Among them, represents the set of wetland application scenario entities. Each wetland application entity is represented as a Named Graph, that is , where is represented as a triple graph, is the Uniform Resource Identifier (URI) of represents the set of semantic relationship of external information of wetland application scenario entities, which contains a series of triples, defining the basic attributes of each wetland application scenario entity (such as time, location, purpose, etc.), and the semantic connections between entities. represents the set of internal structure descriptions of wetland application scenario entities, which is described in quadruple form. On the basis of including the triple information of the data graph and the computing graph Based on the triple information, the URI of the named graph is specifically specified, thereby clarifying the data entities, computing entities, and their dependencies used by the wetland application scenario entities. As the scene management unit in the virtual geographical environment, the named graph effectively manages the wetland monitoring scenario through data isolation and version control mechanisms. Each named graph stores the data entities and computing entities of a specific scenario as an independent container, supporting separate maintenance of scenarios and coexistence of multiple versions. When the scenario needs to be modified, it can be directly executed in the corresponding named graph, avoiding overall system disturbances. This mechanism is particularly suitable for hypothesis analysis, allowing different scenario plans to be constructed in independent named graphs for simulation experiments, ensuring that the data of each plan does not interfere with each other, and significantly improving the maintainability and analysis flexibility of complex wetland monitoring systems.

[0043] Embodiment 2 Please refer to Figure 6 , which shows a schematic structural diagram of a wetland semantic virtual geographical environment construction system proposed in the second embodiment of the present application. The system includes the following key modules: The data graph construction module 100 is used to integrate wetland monitoring data entities, including converting multi-source heterogeneous wetland monitoring data sets into a knowledge graph with entities as nodes and semantic relationships as edges based on the wetland monitoring data entities; The computing graph construction module 200 is used to integrate wetland executable computing entities, including integrating the wetland executable computing entities into the knowledge graph and enabling the computing entities to perform computing and conversion operations on the entities in the data graph based on algorithm rules; The scene graph construction module 300 is used to represent different wetland application scenario entities; including representing the knowledge graph composed of different wetland application scenario entities, and the application scenario entities are associated with the data entities in the data graph and the computing entities in the computing graph.

[0044] A wetland semantic virtual geographical environment construction system based on a hierarchical knowledge graph in an embodiment of the present application can be a device, or a component, integrated circuit, or chip in a terminal. The device can be a mobile electronic device or a non-mobile electronic device. Exemplarily, the mobile electronic device can be a mobile phone, a tablet computer, a laptop computer, a handheld computer, a vehicle-mounted electronic device, a wearable device, an ultra-mobile personal computer (UMPC), a netbook, or a personal digital assistant (PDA), etc., and the non-mobile electronic device can be a server, a Network Attached Storage (NAS), a personal computer (PC), etc. The embodiments of the present application do not make specific limitations.

[0045] The wetland semantic virtual geographical environment construction system based on a hierarchical knowledge graph in the embodiments of the present application can be a device with an operating system. The operating system can be the Android operating system, the iOS operating system, or other possible operating systems, which are not specifically limited in the embodiments of the present application.

[0046] The wetland semantic virtual geographical environment construction system based on a hierarchical knowledge graph provided in the embodiments of the present application can implement each process achieved by the wetland semantic virtual geographical environment construction method based on a hierarchical knowledge graph in the method embodiments. To avoid repetition, it will not be elaborated here.

[0047] Optionally, the embodiments of the present application also provide an electronic device, including a processor, a memory, and a program or instruction stored on the memory and executable on the processor. When the program or instruction is executed by the processor, it implements each process of the above-mentioned embodiments of the wetland semantic virtual geographical environment construction method based on a hierarchical knowledge graph and can achieve the same technical effects. To avoid repetition, it will not be elaborated here.

[0048] The embodiments of the present application also provide a readable storage medium, on which a program or instruction is stored. When the program or instruction is executed by the processor, it implements each process of the above-mentioned embodiments of the wetland semantic virtual geographical environment construction method and can achieve the same technical effects. To avoid repetition, it will not be elaborated here.

[0049] Among them, the processor is the processor in the electronic device described in the above embodiments. The readable storage medium includes computer-readable storage media, such as computer read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs, etc.

[0050] It should be noted that in this article, the terms "including", "comprising" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed, or elements inherent to such a process, method, article or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the process, method, article or device including that element. In addition, it should be pointed out that the scope of the methods and devices in the embodiments of the present application is not limited to performing functions in the order shown or discussed, and may also include performing functions in a substantially simultaneous manner or in the reverse order according to the functions involved. For example, the described methods may be performed in an order different from that described, and various steps may be added, omitted, or combined. Additionally, the features described with reference to certain examples may be combined in other examples.

[0051] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-described example methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions for causing a terminal (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of the present application.

[0052] The embodiments of the present application have been described above in conjunction with the accompanying drawings. However, the present application is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Under the inspiration of the present application, those of ordinary skill in the art can also make many forms without departing from the purpose of the present application and the scope protected by the claims, and all of them fall within the protection scope of the present application.

Claims

1. A method for constructing a wetland semantic virtual geographical environment based on a hierarchical knowledge graph, characterized in that Including: Constructing a data graph, a computational graph, and a scenario graph, where the data graph is used to integrate wetland monitoring data entities, the computational graph is used to integrate wetland executable computing entities, and the scenario graph is used to represent different wetland application scenario entities; Constructing a data graph, based on wetland monitoring data entities, transforming a multi-source heterogeneous wetland monitoring data set into a knowledge graph with entities as nodes and semantic relationships as edges; Constructing a computational graph, integrating wetland executable computing entities into the knowledge graph, and enabling the computing entities to perform computing and transformation operations on the entities in the data graph based on algorithm rules; Constructing a scenario graph, representing a knowledge graph composed of different wetland application scenario entities, where the application scenario entities are associated with data entities from the data graph and computing entities from the computational graph.

2. The method for constructing a wetland semantic virtual geographical environment based on a hierarchical knowledge graph according to claim 1, characterized in that, The steps of constructing the data graph specifically include: Establishing the wetland monitoring entities, and formally describing wetland discrete entities, continuous entities, and monitoring information based on the extension of existing ontologies; Constructing a hybrid ontology strategy, and implementing a hybrid ontology architecture by adopting a hierarchical semantic modeling method that combines a global ontology and a local ontology; Integrating multi-source heterogeneous wetland monitoring data, and realizing heterogeneous data integration through a hybrid integration of a materialization strategy and a virtualization strategy.

3. The method for constructing a wetland semantic virtual geographical environment based on a hierarchical knowledge graph according to claim 2, characterized in that, In the steps of realizing heterogeneous data integration through a hybrid integration of a materialization strategy and a virtualization strategy, the materialization strategy includes: For data greater than a preset query analysis threshold, data less than a preset change frequency, and data less than a preset update cost, adopting the materialization strategy; Completing materialization integration through an ETL process, where the ETL process includes collecting data from an input source, mapping the data from the source schema to the target schema, and saving the transformed data to a triple store; In the transformation stage, realizing the mapping of source data to RDF by constructing RML mapping rules; In the loading stage, using a materialization engine to materialize multi-source heterogeneous wetland monitoring data into RDF triples according to the RML mapping rules, and persisting them into a graph database.

4. The method for constructing a wetland semantic virtual geographical environment based on a hierarchical knowledge graph according to claim 2, wherein In the steps of realizing heterogeneous data integration through a hybrid integration of a materialization strategy and a virtualization strategy, the virtualization strategy includes: For batch real-time monitoring data, adopting an ontology-based data access technology mode for virtualization integration; Providing a unified data view through a mediation architecture to realize that there is no need to move or copy data to a unified location; Translating queries on the knowledge graph into queries on data sources in real time online.

5. The method for constructing a wetland semantic virtual geographical environment based on a hierarchical knowledge graph according to claim 2, characterized in that The steps of constructing the data graph further include: Using the federated storage function of a graph database to create a federated storage; Configuring multiple local materialized or remote virtualized SPARQL endpoints; Realizing the integration of multiple virtualized and materialized RDF graphs in one knowledge graph.

6. The method for constructing a wetland semantic virtual geographical environment based on a hierarchical knowledge graph according to claim 1, wherein The steps of constructing the computational graph specifically include: Designing an execution mode of an executable knowledge graph, where the execution mode is divided into two parts: synchronous and asynchronous; Constructing a knowledge graph interaction mediator for API-P oriented to computing entities based on the API-P framework, and realizing the management and monitoring of jobs through the persistence of API-P jobs. Develop a knowledge graph job manager, which is responsible for job management and monitoring of the computing entities. The job manager realizes the knowledge graph persistence of the jobs by sending SPARQL statements to the endpoints of the knowledge graph. Design an input mediation algorithm and an output mediation algorithm to realize data interaction between the computing entity API-P and the knowledge graph. The input mediation algorithm converts the entities of the knowledge graph into a list of input parameters supported by the API-P, and the output mediation algorithm integrates the execution results of the API-P into the knowledge graph.

7. A method for constructing a wetland semantic virtual geographical environment based on a hierarchical knowledge graph according to claim 1, characterized in that The steps of constructing the scenario graph specifically include: Represent each wetland application scenario entity as a named graph, which is a triple graph and has a uniform resource identifier. Use the semantic relationship set of external information of the wetland application scenario entity to define the basic attributes of each wetland application scenario entity and the semantic connections between entities. Use the internal structure description set of the wetland application scenario entity, which is described in quadruple form. Based on the triple information of the data graph and the computing graph, specifically specify the URI of the named graph to clarify the data entities, computing entities and their dependencies used by the wetland application scenario entity. Utilize the named graph to realize the separation and version control of complex wetland monitoring data and computing entities.

8. A wetland semantic virtual geographical environment construction system based on a hierarchical knowledge graph, characterized in that, Include: A data graph construction module for integrating wetland monitoring data entities, including converting multi-source heterogeneous wetland monitoring data sets into a knowledge graph with entities as nodes and semantic relationships as edges based on the wetland monitoring data entities. A computing graph construction module for integrating wetland executable computing entities, including integrating the wetland executable computing entities into the knowledge graph and enabling the computing entities to perform computing and transformation operations on the entities in the data graph based on algorithm rules. A scenario graph construction module for representing different wetland application scenario entities; including representing the knowledge graph composed of different wetland application scenario entities, and the application scenario entities are associated with the data entities in the data graph and the computing entities in the computing graph.

9. An electronic device, characterized in that, It includes a processor, a memory, and a program or instruction stored on the memory and executable on the processor. When the program or instruction is executed by the processor, the steps of a method for constructing a wetland semantic virtual geographic environment based on a hierarchical knowledge graph as described in any one of claims 1-7 are realized.

10. A readable storage medium, characterized in that, The program or instruction is stored on the readable storage medium. When the program or instruction is executed by the processor, the steps of a method for constructing a wetland semantic virtual geographic environment based on a hierarchical knowledge graph as described in any one of claims 1-7 are realized.

Images