Spatial-temporal data hybrid storage system and device based on smart city and storage medium

Abstract

The invention discloses a spatio-temporal data hybrid storage system and device based on a smart city and a storage medium, and relates to the technical field of data processing, the spatio-temporal data hybrid storage system based on the smart city comprises a processing module and a storage module, and the storage module comprises a file system and a database cluster. Data interaction is carried out between the file system and the database cluster, the file system comprises a local file system and a distributed file system, the database cluster comprises a relational database, a non-relational database and a search database, and data interaction is carried out between the relational database and the non-relational database. And data interaction is performed between the non-relational database and the search database. The problems that spatio-temporal data storage and management are disordered and are not easy to manage, and spatio-temporal data read-write analysis and calculation efficiency in the field of smart cities is low are solved, and the use performance and efficiency of the spatio-temporal data in the field of smart cities are improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a hybrid storage system, device and storage medium for spatiotemporal data based on a smart city. Background Art

[0002] Spatiotemporal data storage refers to the storage and management of data that contains both temporal and spatial attributes. This type of data not only has a timestamp, but also contains information such as location, shape, and distribution in geographic space, so its storage and management are relatively complex. Existing spatiotemporal data storage methods include: relational databases, spatiotemporal databases, distributed file systems, file systems that support massive storage, distributed data warehouses, in-memory databases, and distributed storage search engines.

[0003] Related spatiotemporal storage solutions are not detailed enough in the management of spatiotemporal data in practical applications, and the efficiency of reading, writing, analyzing and computing is also insufficient due to the simple architectural design, which leads to poor performance.

[0004] The above contents are only used to assist in understanding the technical solution of the present application and do not constitute an admission that the above contents are prior art. Summary of the invention

[0005] The main purpose of this application is to provide a hybrid spatiotemporal data storage system, device and storage medium based on smart cities, aiming to solve the technical problem that related spatiotemporal storage solutions are not detailed enough in practical applications for the management of spatiotemporal data, and the efficiency of reading and writing analysis and calculation is insufficient due to the simple architecture design, which leads to poor performance.

[0006] To achieve the above-mentioned purpose, the present application proposes a hybrid storage system for spatiotemporal data based on smart cities, wherein the hybrid storage system for spatiotemporal data based on smart cities includes a processing module and a storage module, the storage module includes a file system and a database cluster, data interaction between the file system and the database cluster, the file system includes a local file system and a distributed file system, the database cluster includes a relational database, a non-relational database and a search database, data interaction between the relational database and the non-relational database, data interaction between the non-relational database and the search database, the non-relational database includes a document database, an in-memory database and a graph database, and the search database includes a time series database and a full-text search engine.

[0007] In one embodiment, the relational database includes population data, legal person data, land data, housing data, urban governance data, economic management data, public service data, public safety data, metadata, user and authority management data; the relational database and the document database both include two-dimensional vector data, visual material data, three-dimensional symbol data and two-dimensional symbol data; the document data includes video file data, BIM model data, raster data, point cloud data, three-dimensional element model data, real-life three-dimensional model data, personal workspace management data and group workspace management data; the document database and the memory database both include vector tile data, raster tile data and three-dimensional model slice data; the graph database includes social media data, e-commerce data, association relationship data, network relationship data and topological relationship data; the time series database includes trajectory data, sensor device perception data and location service data; the full-text search engine includes standard address data and log management data.

[0008] In one embodiment, the spatiotemporal data in the storage module includes basic urban spatiotemporal data, urban management object data, urban operation status perception data, security management data and metadata. The basic urban spatiotemporal data includes basic geographic information data and full-space three-dimensional model data. The urban management object data includes basic management object data and industry management object data. The urban operation status perception data includes Internet of Things perception data and Internet open data.

[0009] In one embodiment, the basic geographic information data includes basic geographic elements, basic geological elements and marine geographic elements, the full-space three-dimensional model data includes new three-dimensional data, three-dimensional terrain models and three-dimensional element models, the basic management object data includes population data, legal person data, land data and house data, the industry management object data includes urban governance data, public service data, economic management data and public safety data, the Internet of Things perception data includes trajectory perception data, video surveillance data, facility perception data, environmental perception data, meteorological perception data, hydrological perception data and security perception data, and the Internet open data includes social media data, e-commerce data, online map data and location service data.

[0010] In one embodiment, the user workspace data in the storage module includes city entity data, analytical computing data and visualization data. The city entity data includes political district entities, land entities, courtyard entities, building entities, house entities, human entities, legal entities, transportation entities, water system entities, pipeline entities and city component entities. The analytical computing data includes geometric data, semantic data, network topology data, entity relationship data, attribute data and address data. The visualization data includes vector tile data, raster tile data, three-dimensional model slice data, two-dimensional symbol data, three-dimensional symbol data and visualization material data.

[0011] In one embodiment, the platform data includes the spatiotemporal data, security management data and metadata, the security management data includes user and authority management data and log management data, the metadata includes platform metadata and user workspace metadata, and the process of the processing module converting the platform data into entity data includes: Based on the target object, determining the associated information of the target object through the platform data; Based on the target object and the associated information, entity data of the target object is generated.

[0012] In one embodiment, the data analysis process of the processing module includes: Generate analysis results through data simulation based on the platform data and / or the entity data; Based on the platform data and / or the entity data and / or the analysis results, a visualization result is generated through data scenario construction.

[0013] In one embodiment, after the step of generating a visualization result through data simulation based on the platform data and / or the entity data and / or the analysis result, the step further includes: Based on the control information and according to the analysis result, generating a target analysis result; Based on the operation information and according to the visualization result, a target visualization result is generated.

[0014] In addition, to achieve the above-mentioned purpose, the present application also proposes a terminal device, which includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the computer program is configured to implement the steps of the spatiotemporal data hybrid storage system based on the smart city as described above.

[0015] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the spatiotemporal data hybrid storage system based on the smart city as described above are implemented.

[0016] The present application provides a technical solution: the spatiotemporal data hybrid storage system based on smart cities includes a processing module and a storage module, the storage module includes a file system and a database cluster, the file system and the database cluster interact with each other, the file system includes a local file system and a distributed file system, the database cluster includes a relational database, a non-relational database and a search database, the relational database and the non-relational database interact with each other, the non-relational database and the search database interact with each other, the non-relational database includes a document database, a memory database and a graph database, and the search database includes a time series database and a full-text search engine. Classification management is carried out for the spatiotemporal data in the smart city, and then through the efficient hybrid storage architecture design, efficient use performance of spatiotemporal storage is achieved.

[0017] Through classified data management based on an efficient hybrid storage architecture, we have overcome the problems of chaotic storage and management of spatiotemporal data, as well as the low efficiency of reading, writing, analysis and calculation of spatiotemporal data in the smart city field, thereby improving the performance and efficiency of spatiotemporal data in the smart city field. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0019] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0020] Figure 1 This is the basic framework diagram for spatiotemporal data storage in this application; Figure 2 This is a storage architecture diagram of the polymorphic hybrid database of this application; Figure 3 This is the data resource framework diagram for this application; Figure 4 Logical structure diagram of data storage for this application; Figure 5 A schematic diagram of the process of converting the platform data into entity data by the processing module of this application; Figure 6 This is a schematic diagram of the structure of the terminal device of this application.

[0021] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0022] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0023] Related spatiotemporal storage solutions are not detailed enough in the management of spatiotemporal data in practical applications, and the efficiency of reading, writing, analyzing and computing is also insufficient due to the simple architectural design, which leads to poor performance.

[0024] The present application provides a solution: a hybrid spatiotemporal data storage system based on a smart city includes a processing module and a storage module, the storage module includes a file system and a database cluster, data interaction between the file system and the database cluster, the file system includes a local file system and a distributed file system, the database cluster includes a relational database, a non-relational database and a search database, data interaction between the relational database and the non-relational database, data interaction between the non-relational database and the search database, the non-relational database includes a document database, an in-memory database and a graph database, and the search database includes a time series database and a full-text search engine. It overcomes the problem that the management of spatiotemporal data in actual applications is not detailed enough in related spatiotemporal storage solutions, the efficiency of read-write analysis and calculation is also insufficient due to the simple architecture design, which leads to poor performance, and improves the performance of spatiotemporal storage.

[0025] It should be noted that the execution subject of this embodiment may be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device, a terminal device, an operating system, etc. that can realize the above functions. The following takes the terminal device as an example to illustrate this embodiment and the following embodiments.

[0026] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0027] The present application embodiment provides a hybrid storage system for spatiotemporal data based on smart city, referring to Figure 1 , Figure 1 This is the basic framework diagram for spatiotemporal data storage in this application.

[0028] In this embodiment, the spatiotemporal data hybrid storage system based on smart city includes a processing module and a storage module, the storage module includes a file system and a database cluster, data interaction occurs between the file system and the database cluster, the file system includes a local file system and a distributed file system, the database cluster includes a relational database, a non-relational database and a search database, data interaction occurs between the relational database and the non-relational database, data interaction occurs between the non-relational database and the search database, the non-relational database includes a document database, an in-memory database and a graph database, and the search database includes a time series database and a full-text search engine.

[0029] In this embodiment, a relational database refers to a database that uses a relational model to organize data. It stores data in the form of rows and columns to facilitate user understanding. This series of rows and columns of a relational database is called a table, and a group of tables constitutes a database. The relational model can be simply understood as a two-dimensional table model, and a relational database is a data organization composed of two-dimensional tables and the relationships between them. Mainstream relational databases mainly include DB2, MySQL, Microsoft SQL Server and other varieties, and the syntax, functions and characteristics of each database are also unique. It is very suitable for storing some structured data, such as urban management object data. Relational databases use relational models to organize data, which are easy to understand, simple and convenient, and easy to maintain. They are very suitable for storing structured data, such as urban management object data, metadata, etc. At the same time, most of the current relational databases have extended support for spatial data management. For example, Oracle provides a spatial data storage solution, Oracle Spatial, which mainly manages spatial data through metadata tables, spatial data fields and spatial indexes, and on this basis provides a series of spatial query and spatial analysis functions, and uses R-tree index and quadtree index technology to improve the speed of spatial query and spatial analysis, and provides SQL modes and functions to implement the storage, retrieval, update and query of Feature Collection, so it is also very suitable for storing vector data of basic geographic information.

[0030] Document databases are significantly different from traditional relational databases. Relational databases usually store data in independent tables that are defined by program developers, and a single object can be spread across several tables. For a given object in a single instance in the database, a document database stores all its information, and each stored object can be different from any other object. This simplifies mapping objects into the database and usually eliminates anything like object-relational mapping. This also makes document databases more valuable for network applications. Relational databases are highly structured, while document databases allow the creation of many different types of unstructured or arbitrary format fields. The main difference from relational databases is that it does not provide support for parameter integrity and distributed transactions, but it is not mutually exclusive with relational databases. They can exchange data with each other, thereby complementing and expanding each other. The current mainstream document databases include CouchDB, MongoDB, etc.

[0031] An in-memory database is a database that stores data in memory and operates it directly. Compared with disks, the data read and write speed of memory is several orders of magnitude higher. Storing data in memory can greatly improve application performance compared to accessing it from disk.

[0032] The rise of NoSQL databases has solved the problem of storing massive amounts of data. Graph databases are also a branch of NoSQL. Compared with other branches of NoSQL, they are very suitable for natively expressing graph-structured data. They store structured data on the network instead of in tables. Currently, the mainstream graph databases include JanusGraph, Neo4J, and OrientDB. For example, Neo4J can also be seen as a high-performance graph engine that has all the features of a mature database. It can be used to organize and manage data with complex relationships, such as the intricate relationships between urban spatiotemporal data and urban management objects, and social network data.

[0033] The full name of time series database is time series database. Time series database is mainly used to store and process data with time tags, which is also called time series data. It is very suitable for storing data with strong time changes, such as traffic data. Common time series databases include OpenTSDB, Graphite, InfluxDB, etc. The time series big data solution uses a special storage method to enable time series big data to efficiently store and quickly process massive time series big data, which is an important technology for solving massive data processing. This technology uses a special data storage method to greatly improve the processing ability of time-related data. Compared with relational databases, its storage space is halved and the query speed is greatly improved. The superior query performance of time series functions far exceeds that of relational databases. InfluxDB is very suitable for IoT analysis applications.

[0034] Full-text search technology uses various types of data such as text, sound, and images as objects, and provides information retrieval based on the content of the data rather than its external features. It is characterized by the ability to effectively manage and quickly retrieve massive amounts of data, and can be used for log management and retrieval. Currently, common full-text search engines include ElasticSearch, Solr, Katta, etc. For example, ElasticSearch is a distributed, highly scalable, and highly real-time search and data analysis engine.

[0035] Distributed file systems are very advantageous in processing very large files. They can store massive files and provide reliable and mature storage management solutions for unstructured data such as text data, video data, and image data. At the same time, they can provide basic support for various database distributed architectures. The mainstream distributed file systems now include FastDFS, TFS, MFS, HDFS, Ceph, etc. These distributed file systems are diverse and each has its own areas of expertise. For example, HDFS is very suitable for accessing large files. It uses the method of storing large files in shards and blocks to improve the performance of the system. Therefore, you should reasonably choose the corresponding product according to your business needs.

[0036] As an optional implementation method, the database system bottom layer adopts an architecture that combines an optimized file system with a distributed file system, which not only meets the efficiency of single-machine storage, but also supports the scalability of multi-node distributed storage. On this solid foundation, a diversified storage engine system including relational databases, non-relational databases, and full-text search engines is built to flexibly respond to and meet the organization, storage, and management needs of various data forms.

[0037] For example, assume that the smart city system needs to process and store traffic flow data. This data includes real-time vehicle location information, road congestion, traffic accident records, etc. This data may come from different sensors and cameras. ‌Data collection‌: Sensors and cameras collect traffic flow data in real time and send the data to the processing module. ‌Data processing‌: The processing module cleans and preprocesses the raw data, such as removing noise data, calibrating timestamps, etc. According to the type of data, such as real-time vehicle location information, road congestion, etc., the processing module decides to store the data in the in-memory database and the time series database. ‌Data storage‌: Real-time vehicle location information is stored in the in-memory database for fast access and update. Road congestion and traffic accident records are stored in the time series database for time series analysis and query. ‌Data interaction‌: When it is necessary to perform correlation analysis on real-time vehicle location information and road congestion, the data between the in-memory database and the time series database interacts. If it is necessary to perform a full-text search on traffic accident records, such as searching for accident records with specific keywords, data interaction is performed between the time series database and the full-text search engine. ‌Data access and analysis‌: Users or applications can access data stored in different modules through a unified interface. Data analysts can perform complex query, analysis, and visualization operations on the stored data, such as generating traffic flow heat maps, predicting road congestion trends, etc.

[0038] By managing classified data based on an efficient hybrid storage architecture, we have overcome the problems of related spatiotemporal storage solutions. However, in actual applications, the management of spatiotemporal data is not detailed enough, and the efficiency of read and write analysis and calculation is insufficient due to the simple architecture design, which leads to poor performance. This improves the performance of spatiotemporal storage.

[0039] Based on any of the above embodiments, in Embodiment 2 of the present application, refer to Figure 2 , Figure 2This is a storage architecture diagram of the polymorphic hybrid database of this application. The relational database includes population data, legal person data, land data, housing data, urban governance data, economic management data, public service data, public safety data, metadata, user and authority management data, the relational database and the document database both include two-dimensional vector data, visual material data, three-dimensional symbol data and two-dimensional symbol data, the document data includes video file data, BIM model data, raster data, point cloud data, three-dimensional element model data, real-life three-dimensional model data, personal workspace management data and group workspace management data, the document database and the memory database both include vector tile data, raster tile data and three-dimensional model slice data, the graph database includes social media data, e-commerce data, association relationship data, network relationship data and topological relationship data, the time series database includes trajectory data, sensor device perception data and location service data, and the full-text search engine includes standard address data and log management data.

[0040] In this embodiment, vector data generally records the spatial position of geographic entities as much as possible by recording coordinates. Vector data is a data organization method that uses points, lines, surfaces and their combinations in geometry to represent the spatial distribution of geographic entities. It uses coordinate pairs, coordinate strings and closed coordinate strings to represent the positions of entity points, lines and surfaces and their spatial relationships. In order to better manage and use geographic data, hierarchical data objects are extracted based on points, lines and surfaces. These data objects include object classes, feature classes and feature data sets. In this model, entities are represented as objects with attributes, behaviors and relationships. These object types include simple objects, geographic elements, network elements, data elements and other more professional feature types; and references and topological integrity between objects are maintained through rules.

[0041] The raster data model generally displays geographic data in the form of images and pictures. In this model, the core of each raster image is a multidimensional raster matrix. The spatial coordinates are implicit in the rows and columns of the matrix. Each unit in the matrix is ​​called a raster pixel, and its value is called a pixel value. The point entity that records the location and attribute information is represented by a raster unit or pixel, the line entity is represented by a string of pixels connected to each other, and the surface entity is composed of a series of adjacent pixels. In addition to the raster matrix, each raster image also contains metadata associated with it. The metadata mainly records the object information, raster information, spatial reference system information, date and time information, band information, etc. of the raster image. As shown in the figure below, in the raster data, a point is represented as a single pixel; a line is represented as a collection of adjacent pixels connected in a certain direction; and a surface is represented as a collection of adjacent pixels clustered together.

[0042] The three-dimensional spatial data model is a unified data model for the integrated representation of three-dimensional spatial entities above and below the ground. It consists of two layers. The bottom layer is the spatial data expression layer, also known as the geometric layer, which provides the expression of geometric objects such as points, lines, surfaces, and bodies; the upper layer is the semantic layer, also known as the core layer, which takes into account spatial relationships, semantic relationships, and topological relationships and supports the expression of spatial objects at multiple levels of detail. The main components of the core layer and the geometric layer, such as geometric expression objects, attribute management objects, topological management objects, and LOD objects, are uniformly encapsulated as spatial objects using the design concept of union or aggregation. All objects in the three-dimensional space, such as terrain models, road models, pipeline models, building models, geological body models, etc., are uniformly expressed using this spatial object, which is the object to be studied and managed by the three-dimensional spatial database.

[0043] Spatial tile data is a way to segment and organize geospatial data according to a specific hierarchy and division method. It divides the earth's surface into a series of rectangular tiles and assigns a unique identifier to each tile. Each tile can contain spatial information such as maps, satellite images, terrain data, etc. The organization of spatial tile data can be based on different tiling schemes, such as pyramid tiles or quadtree tiles. In this way, spatial tile data can be stored and accessed at different levels. Higher-level tiles cover a larger geographical area, while lower-level tiles provide more detailed geographical information. Choose a suitable storage format according to the type of tile data, such as common vector data formats or raster data formats. For large-scale data, consider using block storage to divide each tile data into multiple blocks for storage to improve storage and retrieval efficiency. Storage architecture design: For large-scale spatial tile data, a distributed storage architecture is adopted to store data in multiple servers to improve storage capacity and processing capabilities. Common architectures include master-slave architecture, distributed file system or object storage.

[0044] BIM is a digital representation of the physical and functional characteristics of a facility. It is a shared knowledge resource about facility information, forming a reliable digital decision-making basis throughout its life cycle from concept to demolition. BIM should submit or exchange model data in the form of files. BIM data consists of four conceptual layers: core layer, sharing layer, professional field layer and resource layer.

[0045] Time series data mainly refers to data with time tags, which mainly includes trajectory data, sensor device perception data, and LBS location service data. Modeling of time series data should take into account that data is dynamically changing, which can be simply understood as entity changes in the time dimension. In model design, it can be divided into two parts: data objects and time series, and data objects are recorded in the set time series.

[0046] The urban entity object model is mainly an abstraction of urban entity object data, including administrative district entities, land entities, courtyard entities, building entities, housing entities, human entities, transportation entities, water system entities, pipeline entities, urban component entities and other urban entities. The modeling of these data should not only take into account the properties of the entity itself, that is, abstract the properties of the corresponding entity and give it an accurate description, but also take into account the geographical attributes attached to the entity and the relationship between entities. Therefore, the urban entity model can be simply understood as the urban entity objects based on the geographic data model and the relationship network between entity objects.

[0047] As an optional implementation method, the mature large file organization and management technology of distributed / parallel systems is used to store massive large files. At the same time, the characteristics of random reading and fast reading and writing speeds of memory are fully considered, which can be used for fast access and service of vector data and map tiles. The structured geospatial metadata can be managed using a spatial database cluster. "Hotspot" data is directly stored in the memory. In view of the fact that data will be lost when the memory is powered off, it is necessary to regularly back up to hard disk files or databases, and at the same time establish memory reliability protection to achieve multi-copy management. In this way, spatiotemporal big data will be presented in multiple storage forms such as memory, database, and file system.

[0048] For example, due to the large scale and variety of spatiotemporal big data, a large and comprehensive data model or storage architecture is not feasible, so a divide-and-conquer architecture with multiple data models and multiple storage engines is adopted to achieve it. In terms of implementation ideas, it is planned to adopt a multi-model database that can support flexible data models, such as: Document, Graph, and Key-Value storage, while also ensuring high-performance query and highly scalable applications. A database cluster storage engine is used for vector data and metadata, a memory database storage engine is used for structured data with high real-time accessibility, a NoSQL database storage engine is used for loosely structured and schema-less semi-structured data, a distributed file system storage engine is used for unstructured data, and a parallel file system storage engine is used for data that requires high-performance centralized computing.

[0049] The storage of spatiotemporal data is realized by adopting a divide-and-conquer architecture of multiple data models and multiple storage engines, which makes it possible to adapt to multiple database types and multiple data models, refine the management of spatiotemporal data, and improve the management efficiency of spatiotemporal data.

[0050] Based on any of the above embodiments, in Embodiment 3 of the present application, refer to Figure 3 , Figure 3This is the data resource framework diagram of this application. The spatiotemporal data in the storage module include basic urban spatiotemporal data, urban management object data, urban operation status perception data, security management data and metadata. The basic urban spatiotemporal data include basic geographic information data and full-space three-dimensional model data. The urban management object data include basic management object data and industry management object data. The urban operation status perception data include IoT perception data and Internet open data.

[0051] In this embodiment, urban basic spatiotemporal data refers to data with time and space dimensions, which is used to express various basic object elements in the physical space of the city. It mainly includes two secondary categories, namely basic geographic information data and full-space three-dimensional model data. Among them, basic geographic information data is mainly divided into three tertiary categories, namely basic geographic elements, basic geological elements, and marine geographic elements. The full-space three-dimensional model data is mainly divided into three tertiary categories, namely new three-dimensional data, three-dimensional terrain models, and three-dimensional element models.

[0052] Urban management object data refers to business-specific data that focuses on the field of urban management. It is used to express various types of government management and industry management information that are closely related to human life in urban social space. It mainly includes two secondary categories: basic management object data and industry management object data. Among them, basic management object data is mainly divided into four third-level categories, including population data, legal person data, land data and housing data. Industry management object data is mainly divided into four third-level categories, including urban governance data, public service data, economic management data and public safety data.

[0053] Urban operation status perception data refers to the IoT perception data obtained in real time and dynamically through IoT sensor devices and the Internet data obtained through the Internet open interface. It is used to express various activity information of the city in physical space and cyberspace. It mainly includes two secondary categories: IoT perception data and Internet open data. Among them, IoT perception data is mainly divided into seven third-level categories, including trajectory perception data, video surveillance data, facility perception data, environment perception data, meteorological perception data, hydrological perception data, and security perception data. Internet open data is mainly divided into four third-level categories, including social media data, e-commerce data, online map data, and location service data.

[0054] As an optional implementation method, a single storage mode can no longer meet users' real-time and high-efficiency requirements for CIM and BIM data processing and visualization. Customization and simplification according to application requirements is an effective way to solve complex problems. How to efficiently combine distributed / parallel file systems, spatial databases, NoSQL databases and large-capacity memory to form a polymorphic storage management architecture is the key to solving the efficient storage and fast access of large-scale spatiotemporal data. However, the multi-mode storage of data has brought difficulties to unified management, and a spatiotemporal big data resource scheduling and load balancing management module is required. Although the same type of spatiotemporal data is stored in one form, it will still be converted into different forms in memory, cache, and database during processing and analysis. Therefore, in order to facilitate subsequent calculations and analysis, it is necessary to efficiently organize and manage data.

[0055] Exemplarily, the database system adopts an underlying architecture that combines a file system with a distributed file system, which supports both single-machine storage and multi-node distributed storage requirements; and on this basis, a multi-storage engine system including a relational database, a non-relational database, and a full-text search engine is constructed to flexibly respond to the challenges of organizing, storing, and managing various data forms. ‌Underlying architecture‌: The underlying architecture of the database system adopts an architecture that combines a file system with a distributed file system. This design enables the database system to handle data storage requirements in a single-machine environment and to be expanded to a multi-node distributed storage environment, thereby improving the flexibility and scalability of the system. ‌Multi-storage engine system‌: Based on the underlying architecture, the database system constructs a multi-storage engine system including a relational database, a non-relational database, and a full-text search engine. Relational databases are good at processing structured data, providing transaction support and complex query functions; non-relational databases are more suitable for processing semi-structured and unstructured data, providing higher flexibility and scalability; and full-text search engines focus on fast retrieval of text data and provide efficient search functions. This multi-storage engine system enables the database system to flexibly respond to the organization, storage, and management requirements of various data forms.

[0056] Due to such optimization, the database system not only improves storage efficiency and access speed, but also enhances the flexibility and scalability of the system.

[0057] Based on any of the above embodiments, in Embodiment 4 of the present application, refer to Figure 3 , Figure 3This is the data resource framework diagram of this application. The basic geographic information data includes basic geographic elements, basic geological elements and marine geographic elements, the full-space three-dimensional model data includes new three-dimensional data, three-dimensional terrain models and three-dimensional element models, the basic management object data includes population data, legal person data, land data and housing data, the industry management object data includes urban governance data, public service data, economic management data and public security data, the Internet of Things perception data includes trajectory perception data, video surveillance data, facility perception data, environmental perception data, meteorological perception data, hydrological perception data and security perception data, and the Internet open data includes social media data, e-commerce data, online map data and location service data.

[0058] In this embodiment, basic geographic information data refers to data that expresses the spatial and attribute information of natural geographic elements and human geographic elements on the surface of the earth. It is mainly composed of natural geographic information, such as landforms, water systems, vegetation, and social geographic information, such as settlements, transportation, boundaries, and other elements, and also includes geographic coordinate system grids used for geographic information positioning. In addition, basic geographic information data also includes geodetic data, digital line drawing data, digital orthophoto data, digital elevation model data, and digital raster map data. Basic geographic elements are the basic components of natural geography and social geography in basic geographic information data. Such as landforms, water systems, vegetation, settlements, transportation, boundaries, etc., these are the basic elements that constitute basic geographic information data. Basic geological elements refer to basic information related to the earth's lithosphere, crustal structure, geological structure, etc. Marine geographic elements refer to basic information related to the marine environment, seabed topography, marine resources, etc. New three-dimensional data, three-dimensional terrain models, and three-dimensional element models: These are all manifestations of basic geographic information data in three-dimensional space. New 3D data may refer to 3D geographic information data obtained using new technologies; 3D terrain models are 3D simulations of surface terrain; and 3D feature models are 3D simulations of specific geographic features on the surface, such as buildings and bridges. Basic management object data refers to information related to basic objects in urban management. For example, population data, legal person data, land data, and housing data are all basic information in urban management. Industry management object data refers to information related to management objects within a specific industry. For example, urban governance data, public service data, economic management data, and public security data are all management information for specific industries or fields. IoT perception data refers to various perception information obtained through IoT technology, such as trajectory perception data, video surveillance data, facility perception data, environmental perception data, meteorological perception data, hydrological perception data, and security perception data. These are all data obtained in real time through IoT devices. Internet open data refers to publicly available data obtained from the Internet. For example, social media data, e-commerce data, online map data, and location service data are all data that can be obtained from the Internet and used for various applications.

[0059] As an optional implementation method, the smart city spatiotemporal data classification fully understands the various types of spatiotemporal data information in the smart city, and classifies the various types of spatiotemporal data resources in the smart city according to the main data that constitute the three-dimensional space of the city in the information age. It is divided into 3 first-level categories, 6 second-level categories, 25 third-level categories and more than 150 fourth-level categories. The 3 first-level categories are urban basic spatiotemporal data, urban management object data and urban operation status perception data.

[0060] For example, ‌Basic geographic elements‌: China's provincial and municipal boundary data, including the names of provinces and cities, boundary coordinates, etc., and vector data of major rivers and lakes in the world, including river names, flow directions, lake locations, etc. ‌Basic geological elements‌: China's geological structure map, showing the distribution of different geological structural units, such as plates and faults; a rock type distribution map of a certain region, indicating the distribution range of various types of rocks, such as granite and limestone. ‌Marine geographic elements‌: Global coastline data, accurately describing the shape and location of coastlines of various countries; Pacific Ocean seabed topographic map, showing seabed mountains, trenches and other landforms. ‌New 3D data‌: Urban 3D model dataset, including 3D representation of elements such as buildings, roads, and green spaces. 3D terrain model, used to show the 3D morphology of landforms such as mountains and plains. ‌3D terrain model‌: Digital elevation model data, used to represent the ups and downs of the surface; high-precision 3D terrain model of a mountain area, used for geological disaster assessment. ‌3D element model‌: Bridge 3D model, showing the structure and appearance of the bridge in detail; Ancient and famous tree 3D model, used for cultural heritage protection and display. ‌Population data‌: Population census data of a city, including population size, age structure, gender ratio, etc. ‌Legal person data‌: Enterprise registration information database, including enterprise name, legal representative, registered capital, etc. ‌Land data‌: Land use status map of a county, showing the distribution of different land uses, such as cultivated land, forest land, and construction land. ‌Housing data‌: Housing registration information of a community, including house area, unit type, property ownership, etc. ‌Urban governance data‌: Statistics on urban garbage classification and treatment, including the amount and treatment methods of various types of garbage. ‌Public service data‌: Distribution map of public hospitals in a city and statistics on the number of beds. ‌Economic management data‌: Economic indicator data such as GDP growth rate, industrial added value, unemployment rate, etc. of a region. ‌Public safety data‌: Public safety data such as traffic accident rate and number of fire accidents in a city. ‌Trajectory perception data‌: Vehicle GPS trajectory data, recording vehicle driving routes and speeds. ‌Video surveillance data‌: Real-time video streams captured by urban surveillance cameras for security monitoring. ‌Facility perception data‌: data on the switch status and brightness adjustment of smart street lights. ‌Environmental perception data‌: data on PM2.5 concentration, temperature, humidity, etc. provided by air quality monitoring stations. ‌Meteorological perception data‌: meteorological data such as wind speed, wind direction, and rainfall provided by meteorological stations. ‌Hydrological perception data‌: water level data provided by river water level monitoring stations for flood warning. ‌Safety perception data‌: fire alarm data provided by smoke alarms, including alarm time and location, etc. ‌Social media data‌: discussion data and user comments on a hot topic on Weibo. ‌E-commerce data‌: sales, evaluation, price, and other data of products on an e-commerce platform. ‌Online map data‌: road information, point of interest data, etc. provided by Baidu Maps. ‌Location service data‌: user mobile phone positioning data, used to analyze user activity range and travel habits.

[0061] Diversified classification management of data not only improves the efficiency of data management, but also promotes data sharing and integration, and enhances data security and privacy protection.

[0062] Based on any of the above embodiments, in Embodiment 5 of the present application, refer to Figure 4 , Figure 4 This is the logical structure diagram of data storage for this application.

[0063] In this embodiment, spatiotemporal data can be simply understood as data that records the dynamic changes of spatial objects in the time dimension. The spatiotemporal database is a database that perfectly integrates the temporal database and the spatial database and can simultaneously store the temporal and spatial information of objects. The data it stores include spatial data, temporal data, and attribute data. Most of the spatiotemporal data models currently studied are processed by adding temporal information to the spatial data model, that is, adding temporal extensions to the spatial data model.

[0064] Urban management objects are the main part of a city's normal operation. This part of data can be divided into two categories: basic thematic data and industry thematic data. Basic thematic data mainly covers basic urban skeleton data such as population data, legal person data, land data, housing data and infrastructure; industry thematic data mainly includes urban operation data such as urban governance data, economic management data, public service data and public safety data. The two types of data play different roles in urban operation and serve different service objects. The former is more inclined to the basic skeleton of the city, with a low frequency of change in time, and can be used as a basic data base. The latter is more inclined to behavioral business, and the frequency of data update changes is very fast. In order to better serve the society with data, the two groups of data are processed separately, and the distributed polymorphic database model is used to store the two types of data. The object-oriented idea is used to associate the basic thematic data of urban management objects such as population, legal person, land, and housing with the basic spatiotemporal data of the city to form a basic base of urban spatiotemporal data, which is convenient for the access and use of more business data. Due to the huge amount of data and the complex and diverse data types, we draw on the design model of the basic spatiotemporal database and adopt a distributed storage method to match and associate these urban management object data with the basic geographic spatiotemporal database to achieve unified integration of basic data.

[0065] Urban perception data has the characteristics of large data volume, fast update frequency, strong real-time performance, strong time characteristics, and is a typical time series data. Urban perception data is mainly divided from three dimensions: time, space, and object. Related businesses basically cross-process data around these three dimensions, such as vehicle supervision business around vehicle objects, real-time traffic business around road sections or path spatial attributes, and traffic data statistics business of different periods around time attributes. Therefore, we first divide spatiotemporal data objects from these three dimensions. Specifically, we can complete the data object division and distribution scheme under different nodes according to different types of urban perception data processing requirements and by defining Hash functions on these three dimensions. At the same time, in order to provide fine-grained data sharing support, the data objects divided under each dimension can be further decomposed from other dimensions. For this purpose, we use the B-tree structure in the node to organize them. In this way, a hierarchical index structure based on the Hash B-tree can be formed to organize the spatiotemporal data objects divided under each dimension. The specific perception data records contained in the data object can be stored in the leaf nodes of the tree in the form of a linked list in chronological order; at the same time, the data can be divided from the spatial perspective or the perspective of urban perception objects, and the B-tree structure can be used to organize the divided data according to other dimensions.

[0066] Urban entity data is the data types frequently used in cities extracted from urban spatiotemporal data, mainly including administrative district entities, land entities, courtyard entities, building entities, housing entities, human entities, transportation entities, water system entities, pipeline entities, urban component entities, etc. In terms of database design, the focus is on the relationship between urban entities on the basis of inheriting the spatiotemporal data structure, providing better services for the operation of the city.

[0067] Analytical calculation data is mainly extracted from city entity data to serve urban analytical calculation. These data are logically divided into geometric data, semantic data, network topology data, entity relationship data, attribute data and address data. They can be used for basic geographical analytical calculations, attribute analytical calculations of urban management objects and semantic analysis based on addresses. Since analytical calculation data comes from a wide range of sources and the data organization scheme is unclear, the view scheme is considered to extract data from the city entity database and the source database; that is, the analytical calculation database only stores the definition of the relevant analytical calculation view and the result data generated by the analytical calculation.

[0068] Visual database refers to the display and operation of data in the database through graphical interface and data visualization technology. This database system aims to improve data understanding, simplify data operation, enhance decision support, and improve data analysis efficiency. The visual data of smart city is to display various types of city data through graphics, tables or three-dimensional models, so that people can intuitively understand the overall situation and operation status of the city.

[0069] Due to the constant changes in the database environment, the physical storage will also change during the operation of the database. The evaluation, adjustment, modification and other maintenance work of the database design is a long-term task, and it is also the continuation, improvement and development of the database design work. In order to better operate and maintain the database, it is necessary to design the database user permissions and the database log system in the design phase.

[0070] Metadata is data about data, describing the structure, content, links and indexes of data. In traditional databases, metadata is a description of each object in the database, such as the data dictionary is the definition of databases, tables, columns, views and other objects. In data warehouse systems, metadata is defined as data that describes data and its environment. It describes many objects, tables, columns, queries, business rules and data transfer within the data warehouse, which can help data warehouse administrators and data warehouse developers find the data they care about very easily. Metadata is data that describes the business data structure and establishment method of the data warehouse, and is the link between the various parts of the data warehouse; the management of metadata is the focus and core of the entire process of data warehouse development. Generally speaking, metadata in data warehouses has two uses. First, metadata can provide user-based information, such as metadata that records business description information of data items can help users use data. Second, metadata can support the system's management and maintenance of data, such as metadata about data item storage methods can support the system to access data in the most efficient way.

[0071] As an optional implementation method, the database design in the smart city logically mainly includes platform data and user workspace data, where the platform data mainly includes source data such as basic urban spatiotemporal data, urban management object data, urban operation status perception data, security management data and metadata, as well as urban entity data extracted based on the source data, analytical computing data to support analytical computing, and visualization data for visualization rendering services. User workspace data mainly includes data uploaded by users themselves and platform data that can be called under user permissions.

[0072] Exemplarily, ‌Basic spatiotemporal data of the city‌: ‌Basic geographic information data‌: obtained from surveying and mapping departments or geographic information systems, including topographic maps of the city, road networks, administrative division boundaries, building locations, etc. ‌Full-space 3D model data‌: using drone aerial photography, lidar scanning and other technologies, combined with 3D modeling software, to generate a 3D model of the city, including stereo representations of buildings, bridges, roads, green spaces, etc. ‌Urban management object data‌: ‌Basic management object data‌: including basic information of public facilities in the city, such as parks, squares, schools, hospitals, etc., such as location, scale, function, etc. ‌Industry management object data‌: management object data for specific industries, such as transportation, environmental protection, energy, etc., such as the location and status of traffic lights, data from environmental monitoring stations, location and operation status of energy facilities, etc. ‌Urban operation status perception data‌: ‌IoT perception data‌: data collected by sensors deployed throughout the city, such as traffic flow sensors, environmental monitoring sensors, smart meters, etc., to reflect the operation status of the city in real time. ‌Internet open data‌: data obtained from Internet platforms such as social media, online map services, and public transportation applications, such as user location information, travel habits, and consumption preferences. ‌Security management data‌: including video surveillance data, alarm data from intrusion detection systems, network security logs, etc., used for urban security monitoring and emergency response. ‌Metadata‌: data that describes other data, such as the source, format, quality, and timestamp of the data, used for data management and traceability. Clean the collected data to remove erroneous, duplicated, or invalid data. Convert the data format to ensure that the data can be correctly identified and processed by the storage module. Perform preliminary analysis and preprocessing on the data, such as data aggregation, time series analysis, and spatial analysis, to extract valuable information. ‌Urban basic spatiotemporal data‌: stored in a relational database for efficient spatial query and analysis. Full-space 3D model data can be stored in a dedicated 3D data warehouse or file system for fast model rendering and display. ‌Urban management object data‌: based on the structure and characteristics of the data, choose to store it in a relational database, such as basic management object data, or a non-relational database, such as semi-structured data in industry management object data. ‌City operation status perception data‌: IoT perception data is usually stored in a time series database for time series analysis and real-time monitoring. Internet open data can be stored in a distributed file system or a big data platform for large-scale data processing and analysis. ‌Security management data‌: Stored in a database with high security performance, such as an encrypted relational database or a dedicated security database to ensure data security and privacy. ‌Metadata‌: Stored in a metadata management system and associated with other data for data management and traceability. Provide a unified interface and query tool to allow users or applications to access data stored in different modules.Perform complex query, analysis, and visualization operations on stored data based on business needs, such as generating city operation status reports, predicting city development trends, and optimizing city management strategies. Update data regularly to ensure the timeliness and accuracy of data. Back up and restore data to prevent data loss or damage. Monitor data storage and usage, and promptly identify and handle data anomalies or security issues.

[0073] By setting platform data and user workspace data, data changes are made safer, and by setting data analysis and visualization data, the efficiency of data reading, writing and analysis is improved.

[0074] Based on any of the above embodiments, in Embodiment 6 of the present application, refer to Figure 5 , Figure 5 This is a flow chart of the processing module of this application converting the platform data into entity data. The platform data includes the spatiotemporal data, security management data and metadata, the security management data includes user and authority management data and log management data, the metadata includes platform metadata and user workspace metadata, and the process of the processing module converting the platform data into entity data includes steps A11~A12: Step A11, based on the target object, determine the associated information of the target object through the platform data.

[0075] In this embodiment, the target objects include administrative areas, land, courtyards, buildings, houses, people, legal persons, transportation, water systems, pipelines, and urban components, etc. The associated information of the target object includes information associated with the target object.

[0076] As an optional implementation, by determining the target object, the associated information associated with the target object is screened out in the platform data.

[0077] Step A12: Generate entity data of the target object based on the target object and the associated information.

[0078] In this embodiment, entity data includes political district entities, land entities, courtyard entities, building entities, house entities, person entities, legal entity entities, transportation entities, water system entities, pipeline entities and urban component entities, etc., which aggregates data of different forms but related into one entity data.

[0079] As an optional implementation manner, according to the target physical object, various types of associated information associated with the target physical object are aggregated to generate an entity data.

[0080] Exemplarily, when entity data is generated for information about an administrator, information such as the administrator's authority, company, gender, etc. is collected, including different types of associated information. Finally, this information is aggregated to generate entity data for the administrator.

[0081] The generation of entity data not only facilitates data storage and retrieval, but also significantly reduces data redundancy and duplication, thus improving the overall efficiency of the database.

[0082] Based on any of the above embodiments, in Embodiment 7 of the present application, the data analysis process of the processing module includes steps B11-B12: Step B11, generating analysis results through data simulation based on the platform data and / or the entity data.

[0083] In this embodiment, data simulation is a method of predicting and analyzing actual system behavior by building a mathematical model, and solving practical problems through mathematical models and optimization methods.

[0084] As an optional implementation, platform data and entity data are combined to perform predictions through data simulation to generate prediction analysis results.

[0085] Step B12, generating visualization results through data scenario construction based on the platform data and / or the entity data and / or the analysis results.

[0086] In this embodiment, data scene construction is a whole process involving scene representation, data collection, analysis and modeling, as well as simulation and prediction. Data scene construction first requires the representation of the scene, that is, using a suitable data structure to describe and represent objects, scenes and relationships in a real or virtual environment. This may include using 3D models, graphics, data flows, etc. to display elements in the environment and the relationships between them.

[0087] As an optional implementation, the platform data, entity data and analysis results are combined to construct a three-dimensional scene, and the three-dimensional scene is rendered into a visual result.

[0088] For example, ‌Data Collection‌: Collect platform data related to smart cities, such as urban traffic flow, energy consumption, environmental indicators, and residents' needs; obtain urban geographic information data, such as building models, road networks, and demographics. ‌Data Preprocessing‌: Clean data to remove noise and incomplete data. Standardize data formats to ensure data consistency and availability. ‌Data Simulation‌: Use simulation models to simulate urban operations, such as traffic flow simulation and energy consumption simulation. Input platform data and entity data into the model to simulate urban operations under different scenarios. ‌Generate Analysis Results‌: Generate analysis results through simulation operations, such as traffic congestion index, energy utilization rate, and environmental quality index. Use evaluation indicators to quantitatively evaluate the effects of smart city construction. Data Scenario Construction: Build data scenarios for smart cities based on platform data, entity data, and analysis results; Use visualization tools such as Unreal Engine 5 and CityEngine to create a three-dimensional model of the city to display information such as the city's building layout, road network, and population distribution. ‌Add interactive elements‌: Add interactive elements to the data scene, such as traffic simulation, crowd simulation, weather system, etc., to make the scene more immersive and practical; drag the map with a mouse or touch screen to view information in different areas and achieve real-time observation and operation. Generate visualization results: Use visualization tools to display data scenes in the form of charts, dashboards, 3D models, etc. Create traffic flow maps, energy consumption maps, environmental quality maps, etc. to intuitively show the effects of smart city construction. ‌Optimize and test‌: Optimize the visualization results to ensure that they can run smoothly on different devices. Test the scene to check if there are any errors or problems that need to be fixed.

[0089] Due to the settings of data simulation and data visualization, it is possible to predict future changes based on various data of the smart city, which improves the foresight of data analysis. Through visualized data, we can have a more comprehensive understanding of the relevant information of the smart city, thereby improving the efficiency of data management.

[0090] Based on any of the above embodiments, in Embodiment 8 of the present application, after the step of generating a visualization result through data simulation based on the platform data and / or the entity data and / or the analysis result, the step further includes steps C11 to C12: Step C11, based on the control information and the analysis result, generate a target analysis result.

[0091] In this embodiment, the control information includes the parameters in the data analysis process that are controlled by the management personnel, so as to obtain different data analysis results.

[0092] As an optional implementation, the analysis result is modified accordingly according to the received control information to generate a target analysis result.

[0093] Step C12: generating a target visualization result based on the visualization result based on the operation information.

[0094] In this embodiment, the operation information includes click information and input command information, and the visualized scene is adjusted through the operation information.

[0095] As an optional implementation, the visualized scene information is updated accordingly according to the received operation information.

[0096] For example, ‌Collecting control information‌: Collecting control information such as current traffic light control strategies, environmental protection policies, and urban planning schemes. ‌Data integration and analysis‌: Integrate control information with urban operation data collected by sensors, such as traffic flow, air quality, and energy consumption. Use data analysis tools such as Python, R, and SQL to conduct in-depth analysis of the integrated data and evaluate the impact of control information on urban operation efficiency. ‌Generate target analysis results‌: Based on the analysis results of control information, predict the urban operation effects under different control strategies, such as traffic congestion, air quality improvement, and energy savings. According to the prediction results, formulate optimized control strategies and generate target analysis results, such as adjusting traffic light control strategies to reduce congestion and formulating stricter environmental protection policies to improve air quality. ‌Collecting operation information‌: Collecting operation information such as maintenance records of urban infrastructure and the use of public service facilities. ‌Data visualization‌: Use data visualization tools such as Tableau, Power BI, and GIS systems to intuitively display operation information in the form of charts, maps, and dashboards. Create city infrastructure maintenance status maps, public service facility usage heat maps, etc., to more intuitively understand the city's operating conditions. Generate target visualization results: Based on the visualization results of the operation information, combined with the target analysis results, generate target visualization results. This can be a prediction and simulation of the future city's operating conditions, or it can be an optimization suggestion for the current city's operating conditions. For example, you can create an interactive map to show the changes in indicators such as traffic flow, air quality, and energy consumption under different control strategies, and adjust and optimize the visualization content on the map based on the target analysis results.

[0097] Due to the regulation of data, the analysis of results and visualization scenes is more accurate, which improves the reliability of data analysis.

[0098] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the operating method of the electronic program guide of the present application. More simple transformations based on this technical concept are all within the protection scope of the present application.

[0099] The present application provides a terminal device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the spatiotemporal data hybrid storage system based on a smart city in the above-mentioned embodiment one.

[0100] Reference below Figure 6 , which shows a schematic diagram of the structure of a terminal device suitable for implementing the embodiment of the present application. The terminal device in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, personal digital assistants (PDA, Personal Digital Assistant), tablet computers (PAD, Portable Application Description), portable multimedia players (PMP, Portable Media Player), vehicle terminals (such as vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 6 The terminal device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0101] like Figure 6 As shown, the terminal device may include a processing device 1001 (such as a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM) 1004. In the random access memory 1004, various programs and data required for the operation of the terminal device are also stored. The processing device 1001, the read-only memory 1002, and the random access memory 1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the terminal device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows a terminal device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or provided instead.

[0102] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a read-only memory 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0103] The terminal device provided by the present application adopts the hybrid storage system of spatiotemporal data based on smart city in the above embodiment, which can solve the technical problem that the management of spatiotemporal data in practical applications is not detailed enough, and the calculation efficiency of read-write analysis is also insufficient due to the simple architecture design, which leads to poor performance. Compared with the prior art, the beneficial effects of the terminal device provided by the present application are the same as the beneficial effects of the hybrid storage system of spatiotemporal data based on smart city provided by the above embodiment, and the other technical features in the terminal device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0104] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0105] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0106] The present application provides a computer-readable storage medium having computer-readable program instructions (i.e., computer programs) stored thereon, and the computer-readable program instructions are used to execute the spatiotemporal data hybrid storage system based on smart cities in the above-mentioned embodiments.

[0107] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM, Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM, CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, radio frequencies (RF, Radio Frequency), etc., or any suitable combination of the above.

[0108] The computer-readable storage medium may be included in the terminal device; or may exist independently without being installed in the terminal device.

[0109] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by the terminal device, the terminal device: the spatiotemporal data hybrid storage system based on the smart city includes a processing module and a storage module, the storage module includes a file system and a database cluster, data interaction between the file system and the database cluster, the file system includes a local file system and a distributed file system, the database cluster includes a relational database, a non-relational database and a search database, data interaction between the relational database and the non-relational database, data interaction between the non-relational database and the search database, the non-relational database includes a document database, an in-memory database and a graph database, and the search database includes a time series database and a full-text search engine.

[0110] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0111] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0112] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0113] The readable storage medium provided by the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned spatiotemporal data hybrid storage system based on smart cities, and can solve the technical problem that the management of spatiotemporal data in related spatiotemporal storage solutions is not detailed enough in practical applications, and the efficiency of reading and writing analysis calculations is also insufficient due to the simple architecture design, which leads to low performance. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as the beneficial effects of the spatiotemporal data hybrid storage system based on smart cities provided by the above-mentioned embodiments, which will not be repeated here.

[0114] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A hybrid spatiotemporal data storage system based on smart city, characterized in that: The hybrid storage system for spatiotemporal data based on smart cities includes a processing module and a storage module. The storage module includes a file system and a database cluster. Data is exchanged between the file system and the database cluster. The file system includes a local file system and a distributed file system. The database cluster includes a relational database, a non-relational database and a search database. Data is exchanged between the relational database and the non-relational database. Data is exchanged between the non-relational database and the search database. The non-relational database includes a document database, an in-memory database and a graph database. The search database includes a time series database and a full-text search engine.

2. The spatiotemporal data hybrid storage system based on smart city according to claim 1, characterized in that: The relational database includes population data, legal person data, land data, housing data, urban governance data, economic management data, public service data, public safety data, metadata, user and authority management data; the relational database and the document database both include two-dimensional vector data, visual material data, three-dimensional symbol data and two-dimensional symbol data; the document data includes video file data, BIM model data, raster data, point cloud data, three-dimensional element model data, real-scene three-dimensional model data, personal workspace management data and group workspace management data; the document database and the memory database both include vector tile data, raster tile data and three-dimensional model slice data; the graph database includes social media data, e-commerce data, association relationship data, network relationship data and topological relationship data; the time series database includes trajectory data, sensor device perception data and location service data; the full-text search engine includes standard address data and log management data.

3. The spatiotemporal data hybrid storage system based on smart city according to claim 1, characterized in that: The spatiotemporal data in the storage module include basic urban spatiotemporal data, urban management object data, urban operation status perception data, security management data and metadata. The basic urban spatiotemporal data include basic geographic information data and full-space three-dimensional model data. The urban management object data include basic management object data and industry management object data. The urban operation status perception data include Internet of Things perception data and Internet open data.

4. The spatiotemporal data hybrid storage system based on smart city according to claim 3, characterized in that: The basic geographic information data includes basic geographic elements, basic geological elements and marine geographic elements, the full-space three-dimensional model data includes new three-dimensional data, three-dimensional terrain models and three-dimensional element models, the basic management object data includes population data, legal person data, land data and housing data, the industry management object data includes urban governance data, public service data, economic management data and public safety data, the Internet of Things perception data includes trajectory perception data, video surveillance data, facility perception data, environmental perception data, meteorological perception data, hydrological perception data and security perception data, and the Internet open data includes social media data, e-commerce data, online map data and location service data.

5. The spatiotemporal data hybrid storage system based on smart city according to claim 3, characterized in that: The user workspace data in the storage module includes city entity data, analytical calculation data and visualization data. The city entity data includes political district entities, land entities, courtyard entities, building entities, house entities, human entities, legal entities, transportation entities, water system entities, pipeline entities and city component entities. The analytical calculation data includes geometric data, semantic data, network topology data, entity relationship data, attribute data and address data. The visualization data includes vector tile data, raster tile data, three-dimensional model slice data, two-dimensional symbol data, three-dimensional symbol data and visualization material data.

6. The spatiotemporal data hybrid storage system based on smart city according to claim 3, characterized in that: The platform data includes the spatiotemporal data, security management data and metadata, the security management data includes user and authority management data and log management data, the metadata includes platform metadata and user workspace metadata, and the process of the processing module converting the platform data into entity data includes: Based on the target object, determining the associated information of the target object through the platform data; Based on the target object and the associated information, entity data of the target object is generated.

7. The spatiotemporal data hybrid storage system based on smart city according to claim 6, characterized in that: The data analysis process of the processing module includes: Generate analysis results through data simulation based on the platform data and / or the entity data; Based on the platform data and / or the entity data and / or the analysis results, a visualization result is generated through data scenario construction.

8. The spatiotemporal data hybrid storage system based on smart city according to claim 7, characterized in that: After the step of generating a visualization result through data simulation based on the platform data and / or the entity data and / or the analysis result, the step further includes: Based on the control information and according to the analysis result, generating a target analysis result; Based on the operation information and according to the visualization result, a target visualization result is generated.

9. A terminal device, characterized in that: The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the spatiotemporal data hybrid storage system based on a smart city as described in any one of claims 1 to 8.

10. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the spatiotemporal data hybrid storage system based on a smart city are implemented as described in any one of claims 1 to 8.

Images