A virtual teaching scene construction method and device

Abstract

This application discloses a method and apparatus for constructing virtual teaching scenarios, relating to the field of digital education technology. Its main purpose is to accurately match knowledge points to virtual teaching scenarios that conform to curriculum standards. The technical solution is as follows: A resource library for virtual teaching is constructed based on a spatial information database and a multimedia resource library; the content in the resource library is categorized in multiple dimensions according to a preset set of spatial distribution types, a set of data types, and a set of scene types, constructing a first mapping relationship and a second mapping relationship between the spatial distribution type set and the data type set and the scene type set, respectively; knowledge points required for virtual teaching are extracted based on teaching texts; the target data type and target scene type for each knowledge point are determined using the first and second mapping relationships; based on the target data type and target scene type, a virtual teaching scenario corresponding to each knowledge point is constructed in a three-dimensional virtual globe platform, and the virtual teaching scenario is visualized.

Description

Technical Field

[0001] This application relates to the field of digital education technology, and in particular to a method and apparatus for constructing virtual teaching scenarios. Background Technology

[0002] With the accelerated digital transformation of education, virtual globe platforms are being widely used in spatial subjects such as geography and history to enhance the intuitiveness and immersion of knowledge presentation. However, traditional teaching still relies on static textbooks, which struggle to dynamically demonstrate changes in spatial scale and the interaction between human activities and the natural environment. There is an urgent need to concretize abstract knowledge.

[0003] Currently, while existing virtual 3D earth platforms can provide massive spatial data storage and basic visualization functions, their data resources lack a systematic correspondence mechanism with teaching knowledge points. The massive spatial data on these platforms is mainly general geographic information and is not structured and classified according to curriculum standards. In other words, the massive spatial data provided by the platform cannot be directly matched with the knowledge points required for teaching that conform to curriculum standards. This makes it impossible to accurately match knowledge points to virtual teaching scenarios that conform to curriculum standards, resulting in the need for manual data selection during teaching and causing a serious disconnect between data resources and teaching needs. Summary of the Invention

[0004] In view of the above problems, this application provides a method and apparatus for constructing virtual teaching scenarios. The main purpose is to accurately match knowledge points to virtual teaching scenarios that conform to curriculum standards, thereby bridging the gap between data resources and teaching needs.

[0005] To solve the above-mentioned technical problems, this application proposes the following solution:

[0006] Firstly, this application provides a method for constructing a virtual teaching scenario, the method comprising:

[0007] Based on spatial information databases and multimedia resource databases, construct resource databases required for virtual teaching;

[0008] Based on the preset set of spatial distribution types, set of data types, and set of scene types, the content in the resource library is classified in multiple dimensions, and a first mapping relationship and a second mapping relationship are constructed between the set of spatial distribution types and the set of data types and the set of scene types, respectively.

[0009] Based on the teaching text, extract the knowledge points required for virtual teaching, and each knowledge point corresponds to a target spatial distribution type in the set of spatial distribution types;

[0010] Using the first mapping relationship and the second mapping relationship, the target data type and target scenario type of each knowledge point are determined respectively;

[0011] Based on the target data type and the target scene type, a virtual teaching scene corresponding to each knowledge point is constructed in a three-dimensional virtual earth platform, and the virtual teaching scene is visualized. The virtual teaching scene is used to represent the scene content corresponding to the target scene type in the three-dimensional virtual earth platform based on the expressive characteristics corresponding to the target data type.

[0012] Secondly, this application provides a virtual teaching scenario construction device, the device comprising:

[0013] The first building unit is used to construct the resource library required for virtual teaching based on the spatial information library and multimedia resource library;

[0014] The processing unit is used to classify the content in the resource library obtained by the first construction unit in multiple dimensions according to the preset spatial distribution type set, data type set and scene type set, and to construct a first mapping relationship and a second mapping relationship between the spatial distribution type set and the data type set and the scene type set respectively;

[0015] The extraction unit is used to extract the knowledge points required for virtual teaching based on the teaching text, wherein one knowledge point corresponds to a target spatial distribution type in the set of spatial distribution types;

[0016] A determining unit is used to determine the target data type and target scene type of each knowledge point obtained by the extraction unit using the first mapping relationship and the second mapping relationship obtained by the processing unit;

[0017] The second construction unit is used to construct a virtual teaching scene corresponding to each knowledge point in a three-dimensional virtual earth platform based on the target data type and the target scene type obtained by the determining unit, and to perform visualization processing on the virtual teaching scene. The virtual teaching scene is used to represent the scene content corresponding to the target scene type in the three-dimensional virtual earth platform based on the expressive characteristics corresponding to the target data type.

[0018] To achieve the above objectives, according to a third aspect of this application, a storage medium is provided, the storage medium including a stored program, wherein, when the program is executed, the device where the storage medium is located is controlled to execute the virtual teaching scene construction method of the first aspect described above.

[0019] To achieve the above objectives, according to a fourth aspect of this application, a processor is provided for running a program, wherein the program executes the virtual teaching scenario construction method of the first aspect described above.

[0020] Using the above technical solution, this application provides a method and apparatus for constructing virtual teaching scenarios. First, a resource library for virtual teaching is constructed based on a spatial information library and a multimedia resource library. Then, according to a preset set of spatial distribution types, a set of data types, and a set of scene types, the content in the resource library is categorized in multiple dimensions, and a first mapping relationship and a second mapping relationship are constructed between the set of spatial distribution types and the set of data types and the set of scene types, respectively. Next, based on the teaching text, the knowledge points required for virtual teaching are extracted, with each knowledge point corresponding to a target spatial distribution type in the set of spatial distribution types. Then, using the first and second mapping relationships, the target data type and target scene type of each knowledge point are determined, respectively. Finally, based on the target data type and target scene type, a virtual teaching scenario corresponding to each knowledge point is constructed in a three-dimensional virtual globe platform, and the virtual teaching scenario is visualized. The virtual teaching scenario is used to represent the scene content corresponding to the target scene type in the three-dimensional virtual globe platform based on the expressive characteristics corresponding to the target data type. The technical solution provided in this application establishes a resource library required for virtual teaching. Based on a preset set of spatial distribution types, data types, and scene types, it constructs a first mapping relationship between spatial distribution types and data types, and a second mapping relationship between spatial distribution types and scene types. Then, it uses the first and second mapping relationships to match the data types and scene types corresponding to knowledge points. Finally, it constructs a virtual teaching scene for each knowledge point in a 3D virtual globe platform. This achieves precise and automated matching of knowledge points with target data types and target scene types, completely solving the problem of the lack of a systematic correspondence mechanism between massive spatial data and teaching knowledge points in existing technologies. It dynamically adapts knowledge points to virtual teaching scenes that conform to curriculum standards without manual data screening, ensuring that teaching content strictly follows curriculum requirements and adapts to different teaching stages. This significantly improves teaching efficiency and scene adaptability, effectively bridging the gap between data resources and teaching needs. It enables the concrete presentation of abstract geographical knowledge and the realistic display of specific teaching scenes, providing standardized and intelligent virtual teaching support for the digital transformation of education.

[0021] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, the following are specific embodiments of this application. Attached Figure Description

[0022] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0023] Figure 1 A flowchart illustrating a virtual teaching scenario construction method provided in an embodiment of this application is shown.

[0024] Figure 2 This paper illustrates a flowchart of another virtual teaching scenario construction method provided in an embodiment of this application;

[0025] Figure 3 This illustration shows a block diagram of a virtual teaching scene construction device provided in an embodiment of this application;

[0026] Figure 4 This paper shows a block diagram of another virtual teaching scene construction device provided in an embodiment of this application. Detailed Implementation

[0027] Exemplary embodiments of the present application will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this application will be thorough and complete, and will fully convey the scope of the present application to those skilled in the art.

[0028] Currently, while existing virtual 3D earth platforms can provide massive spatial data storage and basic visualization functions, their data resources lack a systematic correspondence mechanism with teaching knowledge points. The massive spatial data on these platforms is mainly general geographic information and is not structured and classified according to curriculum standards. In other words, the massive spatial data provided by the platform cannot be directly matched with the knowledge points required for teaching that conform to curriculum standards. This makes it impossible to accurately match knowledge points to virtual teaching scenarios that conform to curriculum standards, resulting in the need for manual data selection during teaching and causing a serious disconnect between data resources and teaching needs.

[0029] Through research, the inventors discovered a universal classification framework that pre-defines the spatial attributes, data representation characteristics, and scene content logic of teaching content as interconnected classification sets. It also establishes dynamic mapping relationships between spatial distribution types and data types, and between spatial distribution types and scene types. Based on this dynamic mapping, virtual teaching scenes for each spatial knowledge point are constructed within a 3D virtual globe platform. This enables automated and precise matching of teaching knowledge points with data and scene types, eliminating the need for manual sifting of massive amounts of spatial data. It allows virtual teaching scenes to strictly adhere to curriculum standards and dynamically adapt to the needs of different teaching stages, completely resolving the core problem of the disconnect between data resources and teaching requirements in existing technologies.

[0030] This application provides a method for constructing virtual teaching scenarios. This method can accurately match knowledge points to virtual teaching scenarios that conform to curriculum standards, bridging the gap between data resources and teaching needs. The specific execution steps are as follows: Figure 1 As shown, it includes:

[0031] 101. Based on the spatial information database and multimedia database, construct the resource database required for virtual teaching.

[0032] In this step, the resource repository is constructed using multi-source heterogeneous data fusion technology. Multi-scale spatial data is collected from spatial information databases (including authoritative data sources such as satellite imagery resources and the National Geographic Information Public Service Platform), covering multi-resolution remote sensing images (e.g., satellite images taken by the Beijing series satellites, with a resolution of 0.5-4 meters), true 3D real-scene data (e.g., natural-level 3D real-scene data), vector boundary data (e.g., administrative division vector files), and dynamic simulation data (e.g., atmospheric circulation simulation data). Simultaneously, teaching materials are obtained from multimedia resource databases (e.g., educational video platforms and textbooks), including popular science videos on geographical knowledge (e.g., animations of the sand-dividing principle of the Dujiangyan Irrigation System), audio lectures by teachers, and graphic materials (e.g., earthquake distribution maps). After cleaning, all data is structured and stored according to a unified metadata standard, generating a resource repository index table containing fields. Key fields include: spatial scale level (cosmic, planetary, etc.), property attributes (natural morphology, man-made engineering), expressive characteristics (precise positioning, dynamic simulation, etc.), and teaching scenario category (natural resources, human activities, phenomena and causes, etc.). For example, when storing a "3D model of the solar system," its spatial scale is labeled as cosmic, its properties as natural morphology, its expressive characteristics as dynamic simulation, and its teaching scenario category as phenomenon and cause. The resource repository adopts a distributed storage architecture (such as Hadoop HDFS), supporting efficient management of petabyte-scale data and ensuring that teaching resources can be quickly accessed on demand.

[0033] 102. Based on the preset spatial distribution type set, data type set, and scene type set, classify the content in the resource library in multiple dimensions, and construct the first mapping relationship and the second mapping relationship between the spatial distribution type set and the data type set and the scene type set, respectively.

[0034] In this step, the spatial distribution type set can be divided into eight levels according to the laws of human cognition, from largest to smallest: cosmic (e.g., solar system), planetary (e.g., Earth), global (e.g., continents), regional (e.g., Southeast Asia), national (e.g., China), provincial (e.g., Guangdong Province), local (e.g., Guangzhou City), and local (e.g., Zhujiang New Town). The data type set can be divided into five categories based on its expressive characteristics: precise positioning (e.g., coordinate point annotation), realistic presentation (e.g., real-world photos), immersive experience (e.g., VR interactive models), dynamic simulation (e.g., climate simulation), and supplementary explanation (e.g., text and image analysis). The scene type set can be divided into four categories according to its teaching content: natural resources (e.g., river landforms), human activities (e.g., urban planning), phenomena and causes (e.g., earthquake causes), and value and significance (e.g., ecological value).

[0035] The resource library content is categorized in multiple dimensions. For spatial distribution, spatial attribute parsing algorithms (such as spatial range calculation based on GeoJSON) can be used to match resources to sets according to spatial scale hierarchy and property attributes. For example, "Earth rotation animation" is categorized into the "Planetary-Natural Form" subclass because its spatial range is planetary and its property is natural. For data type, data files can be automatically assigned to data type sets based on their characteristics (e.g., 3D model files with the .glb extension correspond to the immersive experience category, while GeoTIFF corresponds to the precise positioning category). For scene type, semantic analysis (NLP models recognizing the keyword "human activity") can be used to associate resources with scene types.

[0036] After classification, a mapping relationship is constructed based on the classification results. This mapping relationship includes a first mapping relationship and a second mapping relationship. The first mapping relationship is between spatial distribution types and data types. A corresponding rule base can be established based on the teaching difficulty gradient (e.g., primary school focuses on dynamic simulation, high school on precise positioning). For example, cosmic-level knowledge points are mapped to dynamic simulation and precise positioning categories; local-level man-made engineering projects are mapped to realistic presentation and immersive experience categories. The second mapping relationship is between spatial distribution types and scene types. A corresponding rule base can be established based on curriculum standards. For example, subcategories with disputed sovereignty (e.g., "Kashmir region") are mapped to natural resources, human activities, and phenomena and causes categories; subcategories without human activity association (e.g., "Himalayan Mountains") are mapped to phenomena and causes categories and value and significance categories. It should be noted that this mapping relationship is structured and stored using knowledge graph tools (e.g., Neo4j), supporting real-time querying.

[0037] 103. Extract the knowledge points required for virtual teaching based on the teaching text.

[0038] One knowledge point corresponds to one target spatial distribution type in the set of spatial distribution types.

[0039] In this step, teaching texts (such as textbooks and lesson plans) can be used to extract knowledge points through Natural Language Processing (NLP) technology. A geographic text parser finely tuned using the BERT model is employed to identify key entities and spatial attributes. Specifically, textbook paragraphs are scanned to extract phrases containing spatial elements (such as "Earth's rotation causes day and night alternation"), generating structured knowledge point entries containing fields such as: knowledge point name, spatial range (such as "Earth"), natural / man-made attribute (such as "natural"), and teaching-related scenario (such as "astronomical phenomena"). Based on the spatial range and property attributes of the knowledge points, they are matched to a preset set of spatial distribution types. For example, the spatial range of "Earth's rotation" is "Earth" (planetary level), and the property attribute is "natural," matching the "planetary level - natural form" subclass; the spatial range of "Yangtze River Basin development" is "Yangtze River Basin" (regional level), and the property attribute is "man-made," matching the "regional level - man-made project" subclass. The target spatial distribution type for each knowledge point is determined, providing input for subsequent mapping.

[0040] 104. Using the first and second mapping relationships, determine the target data type and target scenario type for each knowledge point.

[0041] In this step, the first mapping relationship database is queried based on the target spatial distribution type of the knowledge point. For example, if the target spatial distribution type of the knowledge point "Earth's rotation" is "planetary level - natural form", it will automatically match to the "dynamic simulation" category based on the first mapping relationship (planetary level corresponds to dynamic simulation class and precise positioning class). Similarly, the second mapping relationship database is queried based on the target spatial distribution type of the knowledge point. For example, if the target spatial distribution type of "Earth's rotation" is "planetary level - natural form", it will match to the "phenomenon and cause" category based on the second mapping relationship (planetary level corresponds to phenomenon and cause class). The matching process is executed in real time by a rule engine (such as Drools) to obtain the corresponding target data type and target scene type.

[0042] 105. Based on the target data type and target scene type, construct virtual teaching scenes corresponding to each knowledge point in a 3D virtual globe platform, and visualize the virtual teaching scenes.

[0043] Among them, virtual teaching scenarios are used to represent the scene content corresponding to the target scene type in a three-dimensional virtual earth platform based on the expressive characteristics corresponding to the target data type.

[0044] In this step, data corresponding to the target data type (such as dynamic simulation data) is retrieved from the resource library, including vector boundaries (Earth's rotation orbit lines), multi-resolution remote sensing images (Earth satellite images), dynamic simulation models (rotation animations), and multimedia materials (audio lectures by teachers). All data is structured and integrated according to the content logic of the target scene type (e.g., for phenomenon and cause types, the logic chain of "rotation process causing day and night phenomena" needs to be displayed) to form a scene data package (containing a JSON configuration file defining the model loading order and interaction trigger points). The scene data package is loaded into a 3D virtual globe platform (such as CesiumJS), and the knowledge points are precisely located according to the geographic coordinate system (WGS84) (e.g., the Earth rotation model is placed in equatorial coordinates). The 3D virtual globe platform automatically constructs the scene framework to obtain a virtual teaching scene for each knowledge point, including terrain rendering, entity modeling, scene association, and visualization processing. Among them, terrain rendering refers to loading elevation data to generate the Earth's surface, solid modeling refers to embedding dynamic simulation models (such as rotation animation), scene association refers to adding interactive elements (such as clicking the model to trigger a day-night change demonstration), and visualization processing refers to configuring multi-dimensional interactive functions, such as scene zooming, free rotation of the viewpoint, clicking to trigger explanations, dynamic simulation demonstrations, and time-series comparison displays.

[0045] Ultimately, each generated virtual teaching scenario (such as the "Earth's rotation" scenario) will be accurately displayed in a 3D virtual Earth. That is, based on the expressive characteristics of the target data type, the scenario content of the target scenario type will be presented, achieving the teaching goal of "concretizing abstract knowledge and making specific scenarios realistic".

[0046] Based on the above Figure 1 As can be seen from the implementation method, the virtual teaching scenario construction method provided in this application establishes a resource library required for virtual teaching, and constructs a first mapping relationship between spatial distribution types and data types and a second mapping relationship between spatial distribution types and scene types based on a preset set of spatial distribution types, data types, and scene types. Then, it uses the first and second mapping relationships to match the data types and scene types corresponding to knowledge points, and finally constructs a virtual teaching scenario for each knowledge point in a three-dimensional virtual globe platform. This achieves accurate and automated matching of knowledge points with target data types and target scene types, completely solving the problem of the lack of a systematic correspondence mechanism between massive spatial data and teaching knowledge points in existing technologies. It can dynamically adapt knowledge points to virtual teaching scenarios that meet curriculum standards without manual data screening, ensuring that teaching content strictly follows curriculum requirements and adapts to different teaching stages, significantly improving teaching efficiency and scenario adaptability, effectively bridging the gap between data resources and teaching needs, enabling abstract geographical knowledge to be presented concretely and specific teaching scenarios to be realistically displayed, and providing standardized and intelligent virtual teaching support for the digital transformation of education.

[0047] Furthermore, the preferred embodiments of this application are based on the above... Figure 1 Based on this, a detailed explanation of the process of constructing virtual teaching scenarios is provided, including the specific steps as follows: Figure 2 As shown, it includes:

[0048] 201. Based on the spatial information database and multimedia database, construct the resource database required for virtual teaching.

[0049] This step combines the description of step 101 in the above method, and the same content will not be repeated here.

[0050] 202. Based on the spatial scale hierarchy and property attributes of geographic entities in the resource database, the corresponding content is classified into the spatial distribution type set.

[0051] The spatial scale levels are divided into cosmic, planetary, global, regional, national, provincial, local, and local levels according to the laws of human cognition. The nature and attributes include natural forms and man-made engineering.

[0052] In this step, the precise analysis of spatial scale hierarchy and property attributes can be automatically completed using a Geographic Information System (GIS) and an artificial intelligence analysis engine. First, the geographic entity data in the resource repository (including vector files, remote sensing image metadata, and descriptive text) is traversed. A spatial extent calculation engine (based on the GeoJSON standard) is invoked to extract the geographic boundary coordinates of each entity and calculate its spatial coverage (such as latitude and longitude range, area). For example, for the "Solar System Model" data, its spatial extent is analyzed to cover the orbit of the solar system (approximately 12 billion kilometers in diameter). The spatial scale hierarchy is divided according to human cognitive patterns from largest to smallest: cosmic level, planetary level, global zone level, regional level, national level, provincial level, local level, and local level. Each level can have its own corresponding spatial extent. By comparing the spatial extent, it is automatically classified as cosmic level. For the "Mount Everest Topographic Map," the spatial extent is limited to the Himalayas (approximately 500,000 square kilometers), and it is similarly automatically classified as regional level. For the "Beijing Municipal Administrative Boundary," the extent covers the entire Beijing area (16,000 square kilometers), and it is automatically classified as provincial level. Simultaneously, the image semantic analysis module (based on a ResNet-50 pre-trained model) classifies geographic entity images in the resource repository. If an image displays natural landforms (such as forests, rivers, and mountains), it is labeled as a natural morphology class defined in the document. If an image contains man-made structures or projects (such as bridges or urban planning maps), it is labeled as a man-made project class. For example, "real-life image of the Three Gorges Dam" is identified as a man-made engineering structure and classified as a man-made project class; "satellite image of the Amazon rainforest" is identified as a natural ecosystem and classified as a natural morphology class. The combined results of spatial scale hierarchy and property attributes (such as "cosmic level - natural morphology class" and "provincial level - man-made project class") are dynamically mapped to the document's preset spatial distribution type set subclasses to generate structured classification records, thereby classifying the corresponding content into the spatial distribution type set.

[0053] 203. Based on the expressive characteristics of different data types in the resource library, classify the corresponding content into data type sets.

[0054] The expressive characteristics include precise positioning, realistic presentation, immersive experience, dynamic simulation, and supplementary explanation.

[0055] In this step, based on the physical characteristics and semantic content of the data files in the resource library, the expressive characteristics are automatically classified through a multimodal feature extraction engine. First, the file metadata is parsed: For vector boundary data (such as Shapefile files), its coordinate precision (such as latitude and longitude to 6 decimal places) and spatial continuity are identified, and it is automatically classified into the precise positioning category explicitly listed in the document (e.g., "Beijing subway station coordinate data"); for high-resolution remote sensing imagery (such as GeoTIFF format, with a resolution better than 1 meter), its spatial coverage integrity and realism are detected, and it is classified into the realistic presentation category (e.g., "Panorama satellite image of the Forbidden City"); for interactive 3D models (such as GLB format, including rotation and scaling functions), the file structure features are analyzed (such as containing animation frame data), and it is classified into the immersive experience category (e.g., "3D architectural model of the Forbidden City"); for dynamically generated data (such as climate simulation videos in MP4 format), the frame rate (such as 30fps) and temporal variation features are extracted, and it is classified into the dynamic simulation category (e.g., "Global climate change animation"); for supplementary materials combining text and graphics (such as PDF documents containing map descriptions), text keywords are extracted (such as "principle analysis" and "background introduction"), and it is classified into the supplementary explanation category (e.g., "illustrated explanation of plate tectonics theory"). The classification process is executed in real time using a rule engine (based on the Drools rule base). When a file with the extension ".glb" is detected, the immersive experience category is automatically triggered; when a file contains the keyword "coordinates", the precise positioning category is triggered to classify the corresponding content into the data type set.

[0056] 204. Based on the scene content in the resource library, classify the corresponding content into the scene type set.

[0057] The scenarios include natural resources, human activities, phenomena and causes, and value and significance.

[0058] In this step, Natural Language Processing (NLP) and knowledge graph technologies are used to semantically analyze and classify the scene content in the resource library. This allows for the extraction of descriptive text (such as video titles and image descriptions) for each teaching material in the resource library. A pre-trained BERT model for the geography education domain (fine-tuned on the Geo-Text dataset) is then invoked for keyword recognition and semantic classification. For example, for the text describing "ecological value analysis of the Yangtze River Basin," the model identifies the keywords "ecological value" and "analysis," determining that its core content is a discussion of resource value, and automatically classifies it into the explicitly defined value significance category. For "research on the causes of urban traffic congestion," the keyword "cause" is identified, classifying it into the phenomenon and cause category. For "protection of the historical and cultural heritage of Beijing hutongs," "historical and cultural heritage" and "protection" are identified, classifying it into the human activities category. For "the phenomenon of glacial melting on the Qinghai-Tibet Plateau," "glacier" and "phenomenon" are identified, classifying it into the natural resources category. Simultaneously, by combining multi-source data verification, if the resource contains images (such as "Yellow River Estuary Wetland Map"), it is classified into the Natural Resources category using an image classification model (identifying natural elements such as wetlands and rivers); if the resource is an interactive scene (such as "Simulated City Planning Tool"), it is classified into the Human Activities category using functional description text (including "planning" and "design"). The classification process supports dynamic optimization. When new resource description text is detected (such as "Typhoon Formation Mechanism"), knowledge graph nodes are automatically expanded to ensure that the classification results are completely consistent with the "Scenario Type Set" (including Natural Resources, Human Activities, Phenomena and Causes, and Value and Significance) defined in the document. Finally, the scenario content classification results are bound to the unique identifier of the resource library to form structured scenario type set entries, so that the corresponding content is classified into the scenario type set.

[0059] 205. Based on the spatial scale characteristics, data expression requirements, and teaching difficulty requirements of each subcategory in the spatial distribution type set, establish a correspondence between each spatial distribution type and at least one data type in the data type set to obtain the first mapping relationship.

[0060] Among them, the subcategories with a spatial scale level of cosmic level correspond to data types such as precise positioning, dynamic simulation, and supplementary explanation; the subcategories with a spatial scale level of local level and belonging to the category of artificial engineering correspond to data types such as realistic presentation, immersive experience, dynamic simulation, and supplementary explanation.

[0061] In this step, the first mapping relationship is automatically constructed based on the spatial scale characteristics, data expression needs, and the teaching difficulty requirements of the target teaching stage. The rule engine (based on the Drools rule base) dynamically loads the teaching difficulty level system of the teaching text. For example, the lower grades of primary school focus on dynamic experience (difficulty coefficient 0.3), the junior high school stage focuses on process analysis (difficulty coefficient 0.6), and the senior high school stage focuses on precise positioning (difficulty coefficient 0.9). For the cosmic subcategory (such as "solar system"), its spatial scale characteristics (covering an area of ​​over 1 billion kilometers, sparse details) and data expression needs (requiring the demonstration of dynamic evolution process) are analyzed. Combined with the teaching difficulty requirements (dynamic simulation is prioritized in primary school, and precise positioning is emphasized in senior high school), it is automatically matched to the precise positioning category (used to mark planetary orbit coordinates in senior high school), the dynamic simulation category (used to demonstrate planetary motion in primary school), and the supplementary explanation category (used to provide astronomical background analysis in all stages). For subcategories of local-level projects belonging to the category of man-made engineering (such as the "Hong Kong-Zhuhai-Macau Bridge"), the spatial scale characteristics (range less than 100 square kilometers, densely detailed), data representation requirements (high realism and interactivity), and teaching difficulty requirements (such as immersive experience in junior high school and realistic presentation in senior high school) are analyzed to automatically match the data to the realistic presentation, immersive experience, dynamic simulation, or supplementary explanation categories. The mapping process can record the matching criteria in real time (such as "local-level - man-made engineering category corresponds to immersive experience category") for subsequent use.

[0062] 206. Based on the sovereignty attributes, human activity correlations, and curriculum standard requirements of the target teaching stage corresponding to each subcategory in the spatial distribution type set, establish a correspondence between each spatial distribution type and at least one scene type in the scene type set to obtain the second mapping relationship.

[0063] Among them, the subcategory of disputed sovereignty corresponds to the scenario types of natural resources, human activities, and phenomena and causes, while the subcategory of no human activity corresponds to the scenario types of phenomena and causes and value and significance.

[0064] In this step, based on sovereignty attributes, human activity relevance, and curriculum standards requirements, a second mapping relationship is dynamically constructed using a teaching knowledge graph. Specifically, subcategories with disputed sovereignty correspond to scenario types in the categories of natural resources, human activities, and phenomena and causes; subcategories without human activity relevance correspond to scenario types in the categories of phenomena and causes and value and significance. The knowledge graph integrates the regional teaching requirements of the curriculum standards. Sovereignty dispute areas (such as "Kashmir") need to be associated with multi-dimensional scenarios, while areas without human activity (such as "Glaciers of the Qinghai-Tibet Plateau") focus on natural phenomena. For example, for a subcategory with disputed sovereignty (such as "Kashmir"), its sovereignty attribute (sovereignty dispute) and human activity relevance (frequent fishing activities) are detected. Combined with curriculum standards requirements (such as the emphasis on "interaction between human activities and the natural environment" in junior high school geography), it is automatically matched to the categories of natural resources (coral reef ecosystem), human activities (fisheries resource development scenarios), and phenomena and causes (historical causes of sovereignty disputes). For subcategories without human activity association (such as "Mount Everest"), their sovereignty (undisputed) and human activity association (no direct human activity) are confirmed. Based on curriculum standards (high school geography emphasizes "value of natural phenomena"), they are automatically matched to the phenomenon and cause category (mountain formation causes) and the value and significance category (ecological value, cultural significance). The mapping process is achieved through node association in the knowledge graph. Specifically, inputting the subcategory "sovereignty disputed" retrieves knowledge graph nodes and outputs a set of associated scenario types; inputting "no human activity association" outputs "phenomenon and cause category" and "value and significance category".

[0065] 207. Based on the knowledge system and teaching objectives of the teaching text, extract the knowledge points that include spatial attributes.

[0066] Spatial attributes include the geographical spatial range of the knowledge point, its natural / man-made properties, and the teaching-related scenarios.

[0067] In this step, a dedicated NLP engine for geography education (based on a finely tuned GeoBERT model, with training data including over 100,000 geography textbook corpora) is used to deeply analyze teaching texts (such as student textbooks and teacher lesson plans) and accurately extract knowledge points containing spatial attributes. First, the knowledge system framework of the teaching text is loaded, and structured paragraphs that align with the curriculum objectives are identified. The following operations are performed using a multimodal analysis engine: the Named Entity Recognition (NER) module (based on BiLSTM-CRF) extracts "Earth" as the core geographic entity; the GIS spatial extent calculation engine (based on GeoJSON boundary analysis) calculates the coverage area of ​​"Earth" (approximately 12,700 kilometers in diameter). Since spatial scale levels are divided from large to small according to human cognitive patterns, it is automatically mapped to the planetary level (Earth belongs to the planetary level). Through image-text joint analysis (ResNet-50+ text keyword matching), "rotation" is identified as a natural phenomenon, and its nature attribute is determined to be "natural," i.e., a natural form category. The semantic matching engine (based on knowledge graph node association) associates "day and night alternation" with astronomical phenomena (corresponding to the teaching objective of "understanding"). (Causes of Natural Phenomena). This generates structured knowledge point entries, containing the following fields: knowledge point name (Earth's rotation), spatial scope (Earth), natural / man-made property (nature), and teaching-related scenario (astronomical phenomena). For composite knowledge points (such as "Development and Ecological Impacts of the Yangtze River Basin"), they are automatically split into two sub-knowledge points: "Development of the Yangtze River Basin": spatial scope (Yangtze River Basin, approximately 1.8 million square kilometers) corresponds to the regional level, property (man-made) attribute corresponds to man-made engineering projects, and scenario (human activities); "Ecological Impacts of the Yangtze River Basin": spatial scope (Yangtze River Basin) corresponds to the regional level, property (natural) attribute corresponds to natural morphology, and scenario (natural resources). Finally, a structured knowledge point list containing complete spatial attributes is obtained, providing accurate input for step 208.

[0068] 208. Match each extracted knowledge point with a subcategory in the spatial distribution type set, and determine the unique corresponding target spatial distribution type based on the spatial scale level and property attributes of the knowledge point.

[0069] In this step, based on the spatial attributes of the knowledge point, a unique match with the set of spatial distribution types is achieved through a predefined matching rule engine. For example, for the knowledge point "Earth's rotation": the spatial range (Earth) is divided into levels from large to small according to spatial scale, the Earth's coverage area (planetary level) matches to the planetary level, and the property attribute (nature) matches to the natural form class. The combined result is planetary level - natural form class, which serves as the unique target spatial distribution type.

[0070] It should be noted that the matching process can also include a conflict resolution mechanism. Specifically, when the attributes of a knowledge point are ambiguous (such as "city park"), the geographic knowledge graph (with over 100,000 cases stored in Neo4j) is invoked to retrieve similar entities (e.g., "Beijing Olympic Park" is already classified as "local-man-made project"), ensuring uniqueness. A matching report is generated, recording the basis (e.g., "Earth corresponds to planetary level," "nature corresponds to natural form category"), and verifying whether it meets the requirements of the teaching stage (e.g., "Earth's rotation" in junior high school geography needs to be matched to the planetary level, not the cosmic level). Finally, the target spatial distribution type of each knowledge point is obtained.

[0071] 209. Using the first and second mapping relationships, determine the target data type and target scenario type for each knowledge point.

[0072] This step combines the description of step 104 in the above method, and the same content will not be repeated here.

[0073] Furthermore, using the first and second mapping relationships, the target data type and target scenario type for each knowledge point are determined respectively. The specific execution process is as follows: using the first mapping relationship, based on the target spatial distribution type corresponding to the knowledge point, at least one suitable data type is matched in the data type set as the target data type of the knowledge point; using the second mapping relationship, based on the target spatial distribution type corresponding to the knowledge point, at least one suitable scenario type is matched in the scenario type set as the target scenario type of the knowledge point; if there are personalized teaching needs for the knowledge point, other subcategories in the data type set or scenario type set can be expanded based on the matching results and added to the target data type or target scenario type.

[0074] In this step, the target spatial distribution type of the knowledge point is read, and a structured query is performed through the first mapping relationship database to match the target data type and target scenario type of the knowledge point. For example, for the knowledge point "formation of the solar system", its target spatial distribution type is "cosmic-natural form". At this time, the query rule is triggered: when the spatial scale level is "cosmic" and the teaching stage is "elementary school", the matching data type is "dynamic simulation". When the teaching stage is "high school", the matching data types are "precise positioning" and "supplementary explanation". It automatically adapts to the primary school teaching stage and outputs the target data type as dynamic simulation (used to demonstrate planetary motion), while retaining supplementary explanation as an alternative (used to provide background analysis). For the knowledge point of "Hong Kong-Zhuhai-Macau Bridge", the target spatial distribution type is "local-man-made project". The query rule is triggered as follows: when the spatial scale level is "local" and the property attribute is "man-made project", the matching data types are "realistic presentation", "immersive experience", "dynamic simulation", and "supplementary explanation". Based on the teaching stage (difficulty coefficient 0.6), immersive experience (interactive bridge model tour) is prioritized, while other types are reserved as extended options. The matching results are stored in a JSON structure (e.g., {"target_data_type": ["immersive experience", "dynamic simulation"]}), and consistency with the curriculum standards is verified in real time. For example, at the high school level, "immersive experience" is automatically excluded, and "realistic presentation" is forcibly matched.

[0075] The system reads the target spatial distribution type of the knowledge points and performs a structured query through the second mapping relation database to match the target scenario type of the knowledge points. For example, for the knowledge point "Kashmir Region," the target spatial distribution type is "Regional Level - Natural Form" (Sovereignty Attribute: Disputed). The query rule is triggered as follows: when the sovereignty attribute is "Disputed" and the human activity correlation is "High," the matching scenario types are "Natural Resources," "Human Activities," and "Phenomena and Causes." The output target scenario types are Natural Resources (Coral Reef Ecosystem), Human Activities (Fisheries Development), and Phenomena and Causes (History of Sovereignty Disputes). For the knowledge point "Mount Everest," the target spatial distribution type is "Regional Level - Natural Form" (Sovereignty Attribute: Undisputed, Human Activity Correlation: Low). The query rule is triggered as follows: when the human activity correlation is "Low," the matching scenario types are "Phenomena and Causes" and "Value and Significance." The output target scenario types are Phenomena and Causes (Mountain Formation Causes) and Value and Significance (Ecological Value, Cultural Significance).

[0076] Furthermore, if personalized requests (such as "enhanced interactivity" or "focus on ecological protection") are entered into the teaching platform, the extended logic is executed. Specifically, when a teacher's instruction (such as "add VR functionality") is detected, the target data type set is automatically expanded: Original matching result: target data type = ["immersive experience"], after expansion: target data type = ["immersive experience", "dynamic simulation"] ("dynamic simulation" is added because it supports VR interaction). The knowledge graph nodes are updated in real time (e.g., the extended type of "Hong Kong-Zhuhai-Macau Bridge" is marked as "extended"), and an extension log is generated: {"timestamp": "2025-01-15T14:30:00","reason":"Teacher requests enhanced interactivity", "extended_type": "dynamic simulation"}.

[0077] The system automatically checks whether the extended type is in the data type set to avoid invalid extensions. If the extended type is outside the set (e.g., "Augmented Reality"), a prompt is triggered to allow selection of a preset type (e.g., "Immersive Experience"). For example, for the knowledge point "Development of the Yangtze River Basin" (the target scenario type defaults to "Human Activities"), if the teacher inputs "Ecological impact needs to be emphasized," the target scenario type is expanded to include both Human Activities and Natural Resources, generating a composite scenario of "Human Activities and Ecological Impact."

[0078] 210. Based on the target data type and target scene type, construct virtual teaching scenes corresponding to each knowledge point in a three-dimensional virtual globe, and visualize the virtual teaching scenes.

[0079] This step combines the description of step 105 in the above method, and the same content will not be repeated here.

[0080] Furthermore, based on the target data type and target scene type, the specific execution process for constructing virtual teaching scenes corresponding to each knowledge point in a 3D virtual globe and visualizing these virtual teaching scenes is as follows: All data corresponding to the target data type is retrieved from the resource library, and all data is structurally integrated according to the content logic of the target scene type to form a scene data package; the scene data package is loaded onto the 3D virtual globe platform, and the spatial location corresponding to the knowledge point is accurately located according to the geographic coordinate system. A 3D teaching scene framework including terrain rendering, entity modeling, and scene association is constructed to obtain the virtual teaching scene corresponding to each knowledge point; multi-dimensional interactive functions are configured for the virtual teaching scene to visualize it. These multi-dimensional interactive functions include scene zooming, free rotation of the viewpoint, click-triggered explanation, dynamic simulation demonstration, and time-series comparison display.

[0081] The data includes vector boundary data, multi-resolution remote sensing images, realistic 3D models, dynamic simulation models, and multimedia materials.

[0082] In this step, all data corresponding to the target data type is retrieved from the resource library, i.e., all data resources. Specifically, for dynamic simulation types (such as the knowledge point of "Earth's rotation"), dynamic simulation models (Earth's rotation animation), vector boundary data (Earth's rotation orbit lines), and multimedia materials (audio lectures by teachers) are retrieved; for realistic presentation types (such as the knowledge point of "Hong Kong-Zhuhai-Macau Bridge"), high-resolution remote sensing images (real-scene satellite images of the bridge), realistic 3D models (bridge's architectural structure), and supplementary explanatory images and texts (engineering parameter analysis) are retrieved. The data is then structured and integrated according to the content logic of the target scene type. For example, for "Earth's rotation" (target scene type: phenomenon and cause), the data is organized according to the causal logic chain of "rotation process leading to day and night phenomena," placing the orbit line data before the animation model, and linking the audio lectures to the day and night change demonstration points, forming a logically coherent scene data package. For "Yangtze River Basin Development" (target scene type: human activity), the bridge's real-scene model, water level change simulation animation, and ecological protection explanation videos are integrated according to the logic of "development process leading to ecological impact," forming a logically coherent scene data package. This scenario data package is used to seamlessly integrate data content with teaching objectives.

[0083] The integrated scene data package is loaded into a 3D virtual globe platform (such as CesiumJS), achieving precise spatial positioning of knowledge points based on the WGS84 geographic coordinate system. For example, the center coordinates of the "Earth's Rotation" model are precisely set to the equator, with an error controlled within 0.5 meters. The platform performs terrain rendering, calling elevation data to generate the basic Earth surface; embeds entity models (such as Earth rotation animation) and dynamically adjusts their rotation parameters; and adds scene-related elements (such as setting click trigger points on the Earth model to associate with day and night changes). After construction, the system generates a standardized scene framework. For example, the "Earth's Rotation" scene includes terrain surfaces, a dynamic Earth model, and interactive trigger points, ensuring that knowledge points are realistically presented in the virtual globe according to teaching logic.

[0084] Meanwhile, five types of interactive functions are configured for the virtual teaching scenario to enhance the intuitiveness and participation of the teaching experience, including scene zooming, free rotation of view, click-triggered explanation, dynamic simulation demonstration, and time-series comparison display. Among them, scene zooming allows users to continuously zoom the view (from local details to a global scale) using the mouse wheel, with a smooth and fluid zooming process to avoid abrupt changes in view; free rotation of view allows users to drag the mouse to rotate freely 360 degrees, and the system automatically keeps the model center in the center of the view, facilitating observation from multiple angles; click-triggered explanation allows clicking on an entity in the scene (such as the Earth model) to instantly pop up a knowledge point analysis panel, with content dynamically matching the target scene type (such as displaying causal diagrams for phenomena and causes, and displaying development cases for human activities); dynamic simulation demonstration allows real-time rendering of key parameters (such as the Earth's rotation speed) when playing dynamic models, and highlights the change process (such as the movement trajectory of the day-night boundary); time-series comparison display provides a timeline control (such as "2000 / 2025"), and automatically overlays comparative images when switching (such as remote sensing images of the Yangtze River Basin before and after development), with differences highlighted visually. All interactive functions are customized based on the target scenario type. For example, phenomena and causes must include dynamic simulation and time-series comparison, while human activities are enhanced with click-based explanations and real-world observation, ensuring that the interactive design is highly consistent with teaching needs.

[0085] Furthermore, as a response to the above Figure 1-2 The implementation of the method embodiment shown in this application provides a virtual teaching scenario construction device. This device is used to accurately match knowledge points to virtual teaching scenarios that conform to curriculum standards, bridging the gap between data resources and teaching needs. The embodiment of this device corresponds to the aforementioned method embodiment. For ease of reading, this embodiment will not repeat the details of the aforementioned method embodiment, but it should be understood that the device in this embodiment can correspondingly implement all the content of the aforementioned method embodiment. Specifically, as shown... Figure 3 As shown, the device includes:

[0086] The first building unit 31 is used to build a resource library required for virtual teaching based on the spatial information library and the multimedia resource library;

[0087] The processing unit 32 is used to classify the contents in the resource library obtained by the first construction unit 31 in multiple dimensions according to the preset spatial distribution type set, data type set and scene type set, and to construct a first mapping relationship and a second mapping relationship between the spatial distribution type set and the data type set and the scene type set respectively;

[0088] Extraction unit 33 is used to extract knowledge points required for virtual teaching based on teaching text, wherein one knowledge point corresponds to a target spatial distribution type in the set of spatial distribution types;

[0089] The determining unit 34 is used to determine the target data type and target scene type of each knowledge point obtained by the extraction unit 33 by using the first mapping relationship and the second mapping relationship obtained by the processing unit 32;

[0090] The second construction unit 35 is used to construct a virtual teaching scene corresponding to each knowledge point in a three-dimensional virtual globe based on the target data type and the target scene type obtained by the determining unit 34, and to perform visualization processing on the virtual teaching scene. The virtual teaching scene is used to represent the scene content corresponding to the target scene type in the three-dimensional virtual globe based on the expressive characteristics corresponding to the target data type.

[0091] Furthermore, such as Figure 4 As shown, the processing unit 32 includes:

[0092] The first processing module 321 is used to classify the corresponding content into the spatial distribution type set based on the spatial scale level and property attributes of the geographic entities in the resource database. The spatial scale level is divided into cosmic level, planetary level, global zone level, regional level, national level, provincial level, local level and local level according to the laws of human cognition. The property attributes include natural morphology and man-made engineering.

[0093] The second processing module 322 is used to classify the corresponding content into the data type set based on the expression characteristics of different data types in the resource library. The expression characteristics include precise positioning, realistic presentation, immersive experience, dynamic simulation, and supplementary explanation.

[0094] The third processing module 323 is used to classify the corresponding content into the scene type set based on the scene content in the resource library. The scene content includes natural resources, human activities, phenomena and causes, and value and significance.

[0095] Furthermore, such as Figure 4 As shown, the processing unit 32 includes:

[0096] The first module 324 is used to establish a correspondence between each spatial distribution type and at least one data type in the data type set based on the spatial scale characteristics, data expression requirements, and teaching difficulty requirements of each sub-category in the spatial distribution type set, to obtain the first mapping relationship. The sub-category with a spatial scale level of the universe corresponds to the data types of the precise positioning class, the dynamic simulation class, and the supplementary explanation class. The sub-category with a spatial scale level of the local level and belonging to the artificial engineering class corresponds to the real presentation class, the immersive experience class, the dynamic simulation class, and the supplementary explanation class.

[0097] The second establishment module 325 is used to establish a correspondence between each spatial distribution type and at least one scenario type in the scenario type set based on the sovereignty attribute, human activity correlation, and curriculum standard requirements of the target teaching stage corresponding to each subcategory in the spatial distribution type set, to obtain the second mapping relationship. The subcategory with disputed sovereignty corresponds to the scenario types of the natural resource category, the human activity category, and the phenomenon and cause category, while the subcategory without human activity correlation corresponds to the scenario types of the phenomenon and cause category and the value and significance category.

[0098] Furthermore, as shown in Figure 4, the extraction unit 33 includes:

[0099] The extraction module 331 is used to extract the knowledge points containing spatial attributes based on the knowledge system and teaching objectives of the teaching text. The spatial attributes include the geographical entity spatial range, natural / man-made properties and teaching-related scenarios corresponding to the knowledge points.

[0100] The matching module 332 is used to match each of the knowledge points extracted by the extraction module with the subcategories in the spatial distribution type set, and determine the unique corresponding target spatial distribution type based on the spatial scale level and the property attributes of the knowledge points.

[0101] Furthermore, such as Figure 4 As shown, the determining unit 34 includes:

[0102] The first determining module 341 is used to use the first mapping relationship to match at least one suitable data type in the data type set according to the target spatial distribution type corresponding to the knowledge point, and use it as the target data type of the knowledge point;

[0103] The second determining module 342 is used to use the second mapping relationship to match at least one suitable scene type in the scene type set according to the target spatial distribution type corresponding to the knowledge point, and use it as the target scene type of the knowledge point.

[0104] Furthermore, such as Figure 4 As shown, the device further includes:

[0105] The third determining module 343 is used to expand other subcategories in the data type set or scenario type set to supplement the target data type or the target scenario type if there are personalized teaching needs for the knowledge point.

[0106] Furthermore, such as Figure 4 As shown, the second building unit 35 includes:

[0107] The fourth processing module 351 is used to retrieve all data corresponding to the target data type from the resource library, and to perform structured integration of all data according to the content logic of the target scene type to form a scene data package;

[0108] The construction module 352 is used to load the scene data package obtained by the fourth processing module 351 into the three-dimensional virtual earth platform, accurately locate the spatial location corresponding to the knowledge point according to the geographic coordinate system, construct a three-dimensional teaching scene framework including terrain rendering, entity modeling, and scene association, and obtain the virtual teaching scene corresponding to each knowledge point.

[0109] The configuration module 353 is used to configure multi-dimensional interactive functions for the virtual teaching scene obtained by the construction module 352 to visualize the virtual teaching scene. The multi-dimensional interactive functions include scene zooming, free rotation of view, click-triggered explanation, dynamic simulation demonstration and time sequence comparison display.

[0110] Furthermore, embodiments of this application also provide a storage medium for storing a computer program, wherein the computer program, when running, controls the device where the storage medium is located to execute the above-described... Figure 1-2 The method for constructing virtual teaching scenarios described in the article.

[0111] Furthermore, embodiments of this application also provide a processor for running a program, wherein the program executes the above-described... Figure 1-2 The method for constructing virtual teaching scenarios described in the article.

[0112] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0113] It is understood that the relevant features in the above methods and apparatus can be referenced interchangeably. Furthermore, the terms "first," "second," etc., in the above embodiments are used to distinguish between embodiments and do not represent the superiority or inferiority of any particular embodiment.

[0114] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0115] The algorithms and displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, this application is not directed to any particular programming language. It should be understood that the content of this application described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of this application.

[0116] In addition, the memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0117] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0118] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0119] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0120] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0121] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0122] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0123] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0124] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0125] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0126] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A virtual teaching scene construction method, characterized in that, The method includes: Based on spatial information databases and multimedia resource databases, construct resource databases required for virtual teaching; Based on the preset set of spatial distribution types, set of data types, and set of scene types, the content in the resource library is classified in multiple dimensions, and a first mapping relationship and a second mapping relationship are constructed between the set of spatial distribution types and the set of data types and the set of scene types, respectively. Based on the teaching text, extract the knowledge points required for virtual teaching, and each knowledge point corresponds to a target spatial distribution type in the set of spatial distribution types; Using the first mapping relationship and the second mapping relationship, the target data type and target scenario type of each knowledge point are determined respectively; Based on the target data type and the target scene type, a virtual teaching scene corresponding to each knowledge point is constructed in a three-dimensional virtual earth platform, and the virtual teaching scene is visualized. The virtual teaching scene is used to represent the scene content corresponding to the target scene type in the three-dimensional virtual earth platform based on the expressive characteristics corresponding to the target data type. The step of classifying the content in the resource library in multiple dimensions according to a preset set of spatial distribution types, a set of data types, and a set of scene types includes: classifying the corresponding content into the set of spatial distribution types based on the spatial scale hierarchy and property attributes of geographic entities in the resource library; classifying the corresponding content into the set of data types based on the expression characteristics of different data types in the resource library; and classifying the corresponding content into the set of scene types based on the scene content in the resource library.

2. The method according to claim 1, characterized in that, The spatial scale hierarchy is divided into cosmic level, planetary level, global zone level, regional level, national level, provincial level, local level, and local level according to the laws of human cognition. The properties and attributes include natural forms and man-made engineering. The expressive characteristics include precise positioning, realistic presentation, immersive experience, dynamic simulation, and supplementary explanation. The scenarios include natural resources, human activities, phenomena and causes, and value and significance.

3. The method according to claim 2, characterized in that, Constructing a first mapping relationship and a second mapping relationship between the spatial distribution type set and the data type set and the scene type set, respectively, includes: Based on the spatial scale characteristics, data expression requirements, and teaching difficulty requirements of the target teaching stage corresponding to each subcategory in the spatial distribution type set, a correspondence is established between each spatial distribution type and at least one data type in the data type set to obtain the first mapping relationship. The subcategory with a spatial scale level of the universe level corresponds to the data types of the precise positioning class, the dynamic simulation class, and the supplementary explanation class. The subcategory with a spatial scale level of the local level and belonging to the artificial engineering class corresponds to the real presentation class, the immersive experience class, the dynamic simulation class, and the supplementary explanation class. Based on the sovereignty attributes, human activity correlations, and curriculum standard requirements of the target teaching stage corresponding to each subcategory in the spatial distribution type set, a correspondence is established between each spatial distribution type and at least one scenario type in the scenario type set, resulting in the second mapping relationship. Subcategories with disputed sovereignty correspond to scenario types of the natural resource category, the human activity category, and the phenomenon and cause category, while subcategories without human activity correlation correspond to scenario types of the phenomenon and cause category and the value and significance category.

4. The method according to claim 2, characterized in that, Based on the teaching text, extract the knowledge points required for virtual teaching, including: Based on the knowledge system and teaching objectives of the teaching text, the knowledge points containing spatial attributes are extracted. The spatial attributes include the geographical entity spatial range, natural / man-made properties, and teaching-related scenarios corresponding to the knowledge points. Each extracted knowledge point is matched with a subcategory in the set of spatial distribution types, and a unique target spatial distribution type is determined based on the spatial scale level and property attributes of the knowledge point.

5. The method according to claim 1 or 3, characterized in that, Using the first mapping relationship and the second mapping relationship, the target data type and target scenario type of each knowledge point are determined, including: Using the first mapping relationship, at least one suitable data type is matched in the data type set according to the target spatial distribution type corresponding to the knowledge point, and used as the target data type of the knowledge point; Using the second mapping relationship, at least one suitable scene type is matched in the scene type set according to the target space distribution type corresponding to the knowledge point, and used as the target scene type of the knowledge point.

6. The method according to claim 5, characterized in that, The method further includes: If there are personalized teaching needs for the knowledge points, then other subcategories in the data type set or scenario type set are expanded and added to the target data type or target scenario type.

7. The method according to any one of claims 1-4 and 6, characterized in that, Based on the target data type and the target scene type, a virtual teaching scene corresponding to each knowledge point is constructed in a three-dimensional virtual globe, and the virtual teaching scene is visualized, including: Retrieve all data corresponding to the target data type from the resource library, and integrate all data in a structured manner according to the content logic of the target scene type to form a scene data package; The scene data package is loaded into the three-dimensional virtual earth platform, and the spatial location corresponding to the knowledge point is accurately located according to the geographic coordinate system. A three-dimensional teaching scene framework including terrain rendering, entity modeling, and scene association is constructed to obtain the virtual teaching scene corresponding to each knowledge point. The virtual teaching scene is configured with multi-dimensional interactive functions to visualize the virtual teaching scene. The multi-dimensional interactive functions include scene zooming, free rotation of view, click-triggered explanation, dynamic simulation demonstration and time sequence comparison display.

8. A virtual teaching scenario construction device, characterized in that, The device includes: The first building unit is used to construct the resource library required for virtual teaching based on the spatial information library and multimedia resource library; The processing unit is used to classify the content in the resource library obtained by the first construction unit in multiple dimensions according to the preset spatial distribution type set, data type set and scene type set, and to construct a first mapping relationship and a second mapping relationship between the spatial distribution type set and the data type set and the scene type set respectively; The extraction unit is used to extract the knowledge points required for virtual teaching based on the teaching text, wherein one knowledge point corresponds to a target spatial distribution type in the set of spatial distribution types; A determining unit is used to determine the target data type and target scene type of each knowledge point obtained by the extraction unit using the first mapping relationship and the second mapping relationship obtained by the processing unit; The second construction unit is used to construct a virtual teaching scene corresponding to each knowledge point in a three-dimensional virtual earth platform based on the target data type and the target scene type obtained by the determining unit, and to perform visualization processing on the virtual teaching scene. The virtual teaching scene is used to represent the scene content corresponding to the target scene type in the three-dimensional virtual earth platform based on the expressive characteristics corresponding to the target data type. The processing unit includes: The first processing module is used to classify the corresponding content into the spatial distribution type set based on the spatial scale hierarchy and property attributes of the geographic entities in the resource database. The second processing module is used to classify the corresponding content into the data type set based on the expression characteristics of different data types in the resource library; The third processing module is used to classify the corresponding content into the scene type set based on the scene content in the resource library.

9. A storage medium, characterized in that, The storage medium includes a stored program, wherein, when the program is running, it controls the device where the storage medium is located to execute the virtual teaching scene construction method as described in any one of claims 1 to 7.

10. A processor, characterized in that, The processor is used to run a program, wherein the program executes the virtual teaching scene construction method as described in any one of claims 1 to 7.

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