Space-time big data platform construction method based on multivariate data service

Abstract

The invention discloses a spatio-temporal big data platform construction method based on multivariate data service, and relates to the technical field of computer application technology and surveying and mapping geographic information, the method comprises the following steps: using a pre-configured cloud monitoring system and a data visualization system to carry out statistics and visual display on spatio-temporal big data and related businesses; a cloud management system is constructed, and platform service and a user system are managed in combination with a front-end framework technology and a background service interface; constructing a service supply system, and sharing platform resources in combination with a stream encryption algorithm and a video stream pushing technology; and based on a place name address engine, carrying out space-time positioning addressing processing on the identification information by adopting a place name address matching technology. The problems that multivariate data integration capability is lacked, an information security sharing platform is lacked and geographic information service demonstration application construction is insufficient are solved.

Description

Technical Field

[0001] The present invention relates to the field of computer application technology and surveying and mapping geographic information technology, and in particular to a method for constructing a spatiotemporal big data platform based on multivariate data services. Background Art

[0002] The construction of the smart city spatiotemporal big data platform covers five parts: unifying the spatiotemporal benchmark, enriching the spatiotemporal big data, building a cloud platform, establishing a cloud support environment, and developing smart applications.

[0003] The shortcomings of current technologies related to the construction of urban spatiotemporal big data platforms include: (1) Insufficient ability to integrate multiple data. (2) Insufficient construction of information security sharing platform. (3) Insufficient construction of demonstration applications of geographic information services.

[0004] Currently, no effective solution has been proposed for the problems in the related technologies. Summary of the invention

[0005] In response to the problems in the related technology, the present invention proposes a method for constructing a spatiotemporal big data platform based on multivariate data services to overcome the above-mentioned technical problems existing in the existing related technology.

[0006] To this end, the specific technical solution adopted by the present invention is as follows: A method for constructing a spatiotemporal big data platform based on multivariate data services, the method comprising: S1. Use the pre-configured cloud monitoring system and data visualization system to perform statistics and visualization on spatiotemporal big data and related services; S2. Build a cloud management system and manage platform services and user systems by combining front-end framework technology and back-end service interfaces; S3. Build a service supply system and share platform resources by combining stream encryption algorithm and video streaming technology; S4. Based on the place name and address engine, the place name and address matching technology is used to perform spatiotemporal location addressing processing on the identification information.

[0007] Furthermore, the pre-configured cloud monitoring system and data visualization system are used to perform statistics and visualization of spatiotemporal big data and related services, including: Collect statistics on applications, users and services through the cloud monitoring system, and display the statistical results; The attributes and relationships of spatiotemporal big data are displayed through the data visualization system, and mapping tools are provided to users.

[0008] Furthermore, the cloud monitoring system collects statistics on applications, users and services, and displays the statistical results, including: Statistics on the introduction of each unit, the number of services used and their operation status; statistics on the number of times users access services in different time periods and the number of services accessed; statistics on the operation status of services in the recent period; Display the number of users, number of services, visits and related monitoring information data, and configure a quick access entrance to the spatiotemporal big data platform.

[0009] Furthermore, the properties and relationships of spatiotemporal big data are displayed through the data visualization system, and map tools are provided to users, including: Display the attributes and relationships of spatiotemporal big data, including map resource data, layer resource data, ubiquitous perception data, public thematic data, geographic entity data and multi-temporal data; Map tools include POI query, on-map measurement and coordinate positioning tools.

[0010] Furthermore, the management of platform services and user systems includes: service management, video management, security management and statistical presentation of platform status, and the platform status includes platform operation status, service access status and operation and maintenance operation status.

[0011] Furthermore, we build a service supply system and combine stream encryption algorithms and video streaming technology to share platform resources, including: Encrypt and forward geographic information services based on the RC4 stream encryption algorithm; The video stream architecture is built based on FFmpeg and Node.js, Redis technology is used to temporarily verify the video stream, and the video stream is transmitted through WebSocket technology.

[0012] Furthermore, platform resources include spatial information resources, data resources and functional services.

[0013] Furthermore, based on the place name and address engine, the space-time location addressing processing of the identification information is performed by using the place name and address matching technology, including: Receive the characters to be searched input by the user and pass the characters to be searched as request parameters to the place name and address service interface; After the characters to be searched undergo character preprocessing, TCHMM semantic analysis, vocabulary comparison, update time point screening, rule segmentation, index library comparison, and place name and address library comparison, the processing results are returned to the user.

[0014] Furthermore, TCHMM semantic analysis includes: Determine the number of hits of a certain word, and obtain the number of hits of related words through word library comparison; Calculate the transition probability from a certain participle to related participles and construct a semantic transition probability matrix.

[0015] Furthermore, the spatiotemporal location addressing includes converting the address description into spatial coordinates and converting the spatial coordinates into readable address information.

[0016] The beneficial effects of the present invention are: (1) The present invention provides a unified spatial benchmark that aggregates spatiotemporal data from all aspects of a city. Based on the application needs of various fields, the present invention establishes application-oriented data and functional services by associating and integrating geographic entities with business data. It provides services including basic geographic entity APIs, industry-specific APIs, geocoding APIs, spatial analysis APIs, etc., which enhances data sharing and analysis capabilities.

[0017] (2) The present invention provides multi-dimensional data integration and data publishing services, improves data security and sharing, and promotes the diversified integration of geographic information data.

[0018] (3) The present invention aims to solve the problem that the current H265 and H264 video surveillance video encodings are used in different video devices and have different playback and parsing conditions. By constructing a video streaming platform based on FFmpeg, the present invention realizes the unified conversion of different encoding formats and performs parsing processing on the server side. The browser side calls the processed video stream, thereby reducing the delay rate of video playback and improving the video playback quality.

[0019] (4) The present invention aims to solve the problems of semantic ambiguity in the current Chinese place name and address matching word segmenter, excessive reliance on custom keyword dictionaries for parsing accuracy, and low retrieval efficiency. By introducing a hidden Markov model that takes time domain correlation into consideration, the present invention accesses the standard address database through Chinese semantic analysis, word segmentation matching, and index traversal, and calculates and sorts the matching results to achieve high-precision and high-efficiency matching of Chinese place name and address.

[0020] (5) The present invention uses a highly versatile development language to establish a long-term development environment and a universal system architecture, thereby solving the problem of low code reuse and the need for redevelopment for different application scenarios. The system can configure functions according to scenario requirements, reducing the workload of secondary development. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0022] Figure 1is a flow chart of a method for constructing a spatiotemporal big data platform based on multivariate data services according to an embodiment of the present invention; Figure 2 It is the flow chart of the spatiotemporal big data platform; Figure 3 It is a schematic diagram of the application of the DIKW model; Figure 4 It is a flowchart for the implementation of the cloud management system; Figure 5 This is the flowchart of encrypted forwarding of geographic information services based on RC4; Figure 6 This is the background flow chart of video streaming based on FFmpeg; Figure 7 It is the logical architecture diagram of the data visualization system; Figure 8 It is the technical flow chart of the place name and address engine; Fig. 9 It is the platform construction system architecture diagram; Fig.10 It is a diagram of the statistical analysis framework; Fig.11 This is a schematic diagram of the TCHMM semantic analysis construction process. DETAILED DESCRIPTION

[0023] To further illustrate each embodiment, the present invention provides drawings, which are part of the disclosure of the present invention and are mainly used to illustrate the embodiments and can be used in conjunction with the relevant descriptions in the specification to explain the operating principles of the embodiments. With reference to these contents, ordinary technicians in the field should be able to understand other possible implementations and advantages of the present invention. The components in the figures are not drawn to scale, and similar component symbols are generally used to represent similar components.

[0024] According to an embodiment of the present invention, a method for constructing a spatiotemporal big data platform based on multivariate data services is provided. The platform integrates five functional modules: cloud monitoring system, cloud management system, service supply system, data visualization system and place name and address engine, realizing multi-dimensional integration, intelligent services and secure sharing of spatiotemporal big data. Specifically, it is a data sharing method based on RC4 algorithm technology to realize multi-dimensional data fusion. The present invention improves the accuracy of place name and address engine matching. The present invention can be applied to fields such as data resource sharing, geographical national conditions monitoring, national land space planning and application management.

[0025] The present invention is further described with reference to the accompanying drawings and specific embodiments. Figure 1 As shown, according to the method for constructing a spatiotemporal big data platform based on multivariate data services according to an embodiment of the present invention, the method for constructing a spatiotemporal big data platform based on multivariate data services includes: S1. Use the pre-configured cloud monitoring system and data visualization system to perform statistics and visualization on spatiotemporal big data and related services; S2. Build a cloud management system and manage platform services and user systems by combining front-end framework technology and back-end service interfaces; S3. Build a service supply system and share platform resources by combining stream encryption algorithm and video streaming technology; S4. Based on the place name and address engine, the place name and address matching technology is used to perform spatiotemporal location addressing processing on the identification information.

[0026] In order to facilitate understanding of the above technical solutions of the present invention, the working principle or operation mode of the present invention in the actual process is described in detail below.

[0027] The construction of spatiotemporal big data platform mainly involves analysis planning, data collection and management, data processing and analysis, data storage and management, system development, platform deployment and operation and maintenance, etc. The specific process is as follows: Figure 2 As shown: The present invention uses the DIKW model to carry out platform planning, which is specifically implemented as follows: Figure 3 As shown, the data layer (Data) includes map base map data, thematic element data, perception data, and statistical data; the information layer (Information) includes information management, decision support, comprehensive supervision, and collaborative office; the knowledge layer (Knowledge) includes statistical analysis, information aggregation, and graded warning; the wisdom layer (Wisdom) includes indicator calculation, traffic monitoring, and expert prediction.

[0028] The present invention is mainly based on the construction of a spatiotemporal big data platform for multivariate data services. The platform can be deployed on Windows, MacOSX, Linux and other systems, and supports domestic platform deployment. Vue3 and .NET technologies are used to develop the front and back ends. The invention is divided into five parts: cloud monitoring system, cloud management system, service supply system, data visualization system and place name and address engine. The cloud monitoring system is a service information monitoring system based on cloud computing, which provides intuitive and visual data service statistics, aiming to help managers realize real-time monitoring and management of the platform operation status; the cloud management system is a background management system to ensure the stable and safe operation of the platform, manage the services released by the platform, configure user roles and resource allocation, etc.; the service supply system is a window for service resource retrieval and service release, where all kinds of spatial service resources, data resources and functional services in the platform can be queried and shared, and it is also the core of providing encrypted forwarding functions for multivariate data; the data visualization system adopts the fusion development based on leaflet and cesium (Leaflet is a popular open source JavaScript library, Cesium is a JavaScript library for creating 3D earth and geospatial applications), supports online preview of geographic information data and three-dimensional data, and provides a certain degree of reference for the construction of demonstration applications; the place name address engine technology converts place names into geographic coordinates, and allows users to search for nearby place names based on geographic coordinates. This engine is mainly used to combine place name data with geographic information systems to provide geographic information query and positioning services.

[0029] 1. Cloud monitoring system The information statistics large screen system of the spatio-temporal big data platform can help management and decision makers understand the overall status of the business at a glance by visually displaying massive data in the form of charts, images and animations, so that they can understand the operation of the business in a timely manner and make corresponding decision adjustments based on data changes. The system mainly includes application statistics, user statistics, service statistics, platform monitoring, quick entry and other functional modules, as follows: (1) Application statistics: used to display the profile of each unit using this system, the number of services used, operating conditions and other information.

[0030] (2) User statistics: used to display the number of service visits and quantity statistics of users using this system in different time periods.

[0031] (3) Service statistics: used to display the operating status of the system's services in the past five days, including visit volume and response speed, etc.

[0032] (4) Platform monitoring: used to display the number of users, number of services, number of visits and related monitoring information data of this system.

[0033] (5) Quick access: used for single sign-on to quickly access cloud management systems, service provision systems, data visualization systems, place name and address engines, etc.

[0034] 2. Cloud management system The cloud management system is the geographic information service platform operation and maintenance management system. It is a backend support system that ensures the stable and safe operation of the platform and provides management of platform services and user systems. Figure 4 As shown in the figure, the .Net interface represents the backend interface part, and the Vue3 interface represents the frontend interface part. The Vue3 interface provides the platform homepage and module entrance, and the .Net interface provides service management, video management, security management, and statistical analysis. The front-end and back-end interaction tests are performed between the Vue3 interface and the .Net interface.

[0035] The system mainly includes service management, video management, security management, statistical analysis and other functional modules, as follows: Service management: including service registration and release, and service use application approval. Service registration and release supports ArcGIS Server, GeoServer, SuperMap and other map service engines. Service applications can limit the service visibility range according to administrative divisions or geographic coordinates to achieve service fence effects. Service types are mainly map services, image services, OGC (Open Geospatial Consortium) standard services, KML (Keyhole Markup Language) services, feature services, third-party services and document services.

[0036] (2) Video management: including classification management of video resources, online viewing, and configuration of user video resources.

[0037] (3) Security management: including functional modules such as user information management, department management, role management, registration management, operation log and login log management.

[0038] (4) Statistical analysis: The statistical analysis module provides statistical presentation of platform operation, service access, operation and maintenance operations, and supports the output of analysis reports to provide a reference for administrators’ daily operation and maintenance work. Fig.10 As shown in the figure, the platform operation status includes the number of users, visits, and services; the service access status includes IP traffic and response time; the operation and maintenance operation status includes access module, number of visits, and operation status. Make statistical charts of the platform operation status, service access status, and operation and maintenance operation status.

[0039] 3. Service provision system The service provision system is a window for service resource retrieval and information release. All kinds of spatial information resources, data resources and functional services in the platform can be shared here. It is also a place for users to understand the platform data services, obtain development help and related application cases. The RC4 stream encryption algorithm and FFmpeg video streaming technology used in the present invention are reflected in the service provision system, among which the encrypted forwarding flow chart of geographic information service based on RC4 is as follows: Figure 5 As shown, the process includes address storage, RC4 encryption, ciphertext matching, ciphertext storage, address decryption and parsing, token verification, database matching, etc.

[0040] FFmpeg is an open source computer program that can be used to record and convert digital audio and video and convert them into streams. This video streaming platform uses Nodejs to write processing programs, references the FFmpeg library to build a background service program and publishes an interface for Web-based calls, as follows: Build a background service program and port monitoring to publish the flow transfer interface.

[0041] A temporary verification method is built using the Redis lightweight database. A random number is generated and stored when the streaming interface is called. The random number is verified before playing the stream after streaming to prevent the streaming address from being called multiple times.

[0042] Write a video stream request processing program to use the FFmpeg program to convert the received RTSP video stream into an mpegts stream and encode H264 or H265 into mpeg1video and provide a websocket address to the outside world. H264, H265, and mpeg1video are video compression standards, and WebSocket is a network communication protocol.

[0043] The Web end uses the JSMpeg plug-in to receive the video stream pushed by ffmpeg, calls the address encrypted by the RC4 encryption algorithm, and plays it after verification.

[0044] The platform has the characteristics of low latency, low occupancy and high image quality, and is used for real-time viewing of video information. The flow chart of the video streaming platform based on FFmpeg is as follows: Figure 6 As shown, the process includes video stream address, Redis random number generation, FFmpeg format conversion, websocket service address, nodejs interface release, RC4 encryption, encryption address, Web-side JSMpeg call, platform token verification, RC4 address matching, Redis random number verification and other steps.

[0045] The platform resource center is mainly composed of functional modules such as resource center, information center, development center, download center and special application, as follows: (1) Resource Center: Mainly for users to understand the overview of the platform's existing data resources and apply for services as needed, check the review status, etc.

[0046] (2) Information Center: Displays some information documents released by system administrators.

[0047] (3) Development Center: Provides examples of map service interface calls and an introduction to the service application process.

[0048] (4) Download Center: Provides download links for technical documents, solution files, and SDK files for users to download and browse.

[0049] (5) Special Applications: Display introductions of various special applications and website links for users to visit and browse.

[0050] 4. Data visualization system Data visualization can show the attributes and relationships of data in a visual way. Compared with pure text description, visual data loading and application are more persuasive, can effectively convey data information, and increase user experience. For example, in the development of demonstration applications, users can use the visualization system to understand which data is available and load it on the map in advance, view attributes, and other operations, which can more clearly display the data attribute overview and make it easier for users to understand the purpose of related data. Specific logical architecture, such as Figure 7 shown.

[0051] The data visualization system mainly includes modules such as map resources, layer resources, ubiquitous perception, public topics, geographic entities, multi-temporal, and map tools, as follows: (1) Map resources: Display various map base map data for users to browse.

[0052] (2) Layer resources: Displays the thematic layer resources to which the user currently has access permissions for preview.

[0053] (3) Ubiquitous perception: Displays video resources and other perception data to which the user currently has access permissions.

[0054] (4) Public topics: Displays various public topic data, such as parking lots, bus stops, etc.

[0055] (5) Geographic entity: Displays various geographic entity data for users to browse.

[0056] (6) Multi-temporal: Displays vector and image data from multiple years for users to compare and browse.

[0057] (7) Map tools: Provide POI query, on-map measurement and coordinate positioning tools for users.

[0058] 5. Place name and address engine The place name and address engine is an important part of the smart city spatiotemporal big data platform. It is based on the place name and address database and, through the use of place name and address matching technology, it achieves the spatiotemporal positioning and addressing capability of information content with the "three domains" of time, space and attributes.

[0059] By receiving the characters to be searched input by the user, an HTTP request is sent, and the characters to be searched are passed to the place name and address service interface as request parameters. After character preprocessing, TCHMM semantic analysis, word segmentation comparison and other steps, the results are returned to the user. The specific implementation flow chart is as follows: Figure 8 The TCHMM semantic analysis construction process is as follows Fig.11 shown.

[0060] The place name and address engine specifically includes forward geocoding services, which converts address descriptions into spatial coordinates, and reverse geocoding services, which converts spatial coordinates into readable address information. Through these services, the place name and address engine can convert big data containing address or location description information into data with spatial coordinates, realize the spatialization of data, and thus provide accurate geographic location information for smart cities. Specifically, it provides forward and reverse geocoding service interfaces, and supports batch conversion of txt text input.

[0061] Platform construction system architecture diagram, such as Fig. 9 shown.

[0062] Research Methods (1) Demand analysis and user research The users of the present invention involve many roles, including business operators, managers, developers, etc. Therefore, it involves studying and analyzing user needs, understanding user behaviors and habits to determine software functions and interface design, and obtaining demand reports mainly through seminars, questionnaires and survey reports. (2) Design and architecture discussion The key technologies and model algorithms needed in the present invention are classified and discussed, simple sample effects are designed and tested, and a dedicated person is responsible for inspection and comparison, and the results are discussed and improved. The stability and reliability of the software are ensured through software testing and quality assessment.

[0063] (3) Prototype method By constructing a preliminary model of the system, running the model and continuously modifying and supplementing it according to user requirements, until a satisfactory prototype is obtained, the system is finally implemented. This method is suitable for continuous enrichment and improvement in the initial stage until a fully functional system platform is realized.

[0064] Cloud monitoring system development The cloud monitoring system mainly collects statistics of service data and adopts a large-screen cockpit-style display system. The front-end interface is developed in Vue3 mode and dynamic display effects are achieved. The system functions mainly include dynamic statistical chart display of users and their access services, user profile display of spatiotemporal big data platform, statistical data display, and single sign-on access portal integration of various subsystems.

[0065] Cloud management system development The cloud management system establishes a complete data system and information sharing mechanism through a centralized management system, and implements the management system and process of data and information through strict permission management and security mechanisms. Vue3 is used to develop the front-end interface of the management system, and the back-end service interface is developed and integrated and released through the ASP.NET Core MVC model, supporting the deployment of domestic platforms.

[0066] Service provision system development The service supply system is a window for service resource retrieval and information release. It uses Vue3 to develop the front-end interface of the management system, and develops the back-end service interface through the ASP.NET Core MVC model and integrates and releases it. In addition, the geographic information service uses RC4-based encryption forwarding, which can effectively encrypt the original service address and protect the confidentiality of the data service. The encrypted address is stored in the database and then called through a unified service interface, which greatly improves the security of data service sharing. Similarly, the video stream address is encrypted and forwarded, and then processed by the video streaming platform built based on FFmpeg for user use, which not only improves security but also improves the user experience of video services.

[0067] Data visualization system development The data visualization system provides users with data browsing and application development references. It uses Leaflet and Cesium technologies, and the front-end also uses the Vue3 mode development interface to support operations such as browsing and attribute viewing of geographic information data and three-dimensional data.

[0068] Place name address engine development The place name and address engine provides users with powerful address matching services and returns the most accurate matching results based on the input address data. The first step of address matching is Chinese word segmentation, which is to separate multiple meaningful Chinese words from Chinese sentences, and build a data link layer on this basis to provide a link between the improved Chinese word segmenter and the proprietary place name and address database to achieve efficient retrieval and return geographic coordinates that meet the expected values. The present invention improves the previous Chinese semantic parsing method by introducing the TCHMM model, thereby achieving accurate understanding and efficient spatialization of Chinese address information. The services provided by the place name and address engine include forward and reverse geocoding service interfaces, and support batch conversion of txt text input.

[0069] The present invention solves the problems of lack of multi-data integration capability, lack of information security sharing platform and insufficient construction of geographic information service demonstration applications, while improving the geographic information data service capability.

[0070] The spatiotemporal big data management platform is designed and constructed using mainstream development technologies to ensure the scalability of the platform and support secondary development, so as to achieve the management of spatiotemporal big data of geographic information and support the deployment of domestic platforms. The diversified data integrated in the spatiotemporal big data platform is intuitively displayed by developing a data visualization system; the services released by the current mainstream geographic information software are re-encrypted and forwarded through the stream encryption algorithm to ensure the security of data sharing; video surveillance data is introduced through push streaming technology to achieve the safe and stable application of surveillance perception data; through statistical model analysis, a place name and address engine that provides high-precision and high-efficiency geographic information matching services is developed.

[0071] The technologies involved in the present invention include: (1) Management system development based on Vue3 Developed using Vue3 (a progressive JavaScript framework for building user interfaces), the rendering speed of the front-end interface is improved, and Proxy (an object) is used instead of the old version of defineProperty (a method in JavaScript), which is conducive to the development of a more powerful responsive system. In addition, the system program can have a smaller size, easy to deploy and modify, and the Composition API (an API style) provides a more flexible logic combination method and supports TypeScript (an open source programming language) integration and development.

[0072] (2) Building a backend service interface based on ASP.NET Core Developing backend interfaces in ASP.NET Core (an open source framework for building modern HTTP client and server applications) can generate cross-platform deployment packages that can run not only on Windows, but also on macOS and Linux. Its modular design makes development faster. In addition, ASP.NET Core MVC (part of the ASP.NET Core framework) provides a simplified XSRF (cross-site request forgery) protection method, which passes security tokens between View (view side) and Controller (controller side) through AntiForgeryToken (a mechanism used to prevent XSRF attacks) to improve the security of interaction.

[0073] (3) Encrypted forwarding of geographic information services based on RC4 The RC4 algorithm is a stream cipher algorithm with a very fast encryption speed. The algorithm encrypts or decrypts each byte of the plaintext in a byte stream, instead of processing the plaintext in groups, thereby increasing the processing speed. The algorithm is simple to implement, requiring only a key stream and XOR operation to complete the encryption and decryption operations. The data service management part of the system uses this encryption method to encrypt and store the source service address to achieve encrypted forwarding of geographic information services.

[0074] (4) Construction of video streaming platform based on FFmpeg FFmpeg is a cross-platform open source audio and video codec library that can be compiled and run in different operating system environments, including Linux, Mac OS X, Microsoft Windows, etc. It provides a complete solution for recording, converting, and streaming audio and video, including the advanced audio and video codec library libavcodec, which ensures high portability and codec quality and supports almost all types of audio and video formats. Based on this coding library, a video streaming platform is developed to improve the application capabilities of video resources.

[0075] (5) Construction of Chinese place name address engine based on TCHMM model The TCHMM model, i.e., the hidden Markov model that considers the correlation in the time domain, is an improvement on the traditional hidden Markov model. This improved model can better reflect the data correlation, especially when processing data with strong autocorrelation. Matching is performed for known standard addresses, and the missing addresses or non-standard addresses in the place name address database are predicted and calculated by referring to the existing standard addresses through the TCHMM model, thus realizing the place name address matching based on limited information and improving the data matching accuracy.

[0076] The present invention is mainly based on the construction of relevant application scenarios of the smart city spatiotemporal big data platform, and realizes functions such as cloud monitoring system, cloud management system, service supply system, data visualization system and place name address engine. The spatiotemporal big data platform can greatly improve the unified management and security of various types of geographic information resource data, while improving the security sharing of multiple data and reducing maintenance costs, and realizing multiple supports for natural resource management and services.

[0077] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for constructing a spatiotemporal big data platform based on multivariate data services, characterized in that: The method for constructing a spatiotemporal big data platform based on multivariate data services includes: S1. Use the pre-configured cloud monitoring system and data visualization system to perform statistics and visualization on spatiotemporal big data and related services; S2. Build a cloud management system and manage platform services and user systems by combining front-end framework technology and back-end service interfaces; S3. Build a service supply system and share platform resources by combining stream encryption algorithm and video streaming technology; S4. Based on the place name and address engine, the place name and address matching technology is used to perform spatiotemporal location addressing processing on the identification information.

2. The method for constructing a spatiotemporal big data platform based on multivariate data services according to claim 1, characterized in that: The use of the pre-configured cloud monitoring system and data visualization system to perform statistics and visualize spatiotemporal big data and related services includes: Collect statistics on applications, users and services through the cloud monitoring system, and display the statistical results; The attributes and relationships of spatiotemporal big data are displayed through the data visualization system, and mapping tools are provided to users.

3. The method for constructing a spatiotemporal big data platform based on multivariate data services according to claim 2, characterized in that: The above-mentioned collecting statistics of applications, users and services through the cloud monitoring system and displaying the statistical results include: Statistics on the introduction of each unit, the number of services used and their operation status; statistics on the number of times users access services in different time periods and the number of services accessed; statistics on the operation status of services in the recent period; Display the number of users, number of services, visits and related monitoring information data, and configure a quick access entrance to the spatiotemporal big data platform.

4. The method for constructing a spatiotemporal big data platform based on multivariate data services according to claim 2, characterized in that: The data visualization system displays the attributes and relationships of spatiotemporal big data and provides map tools to users, including: Display the attributes and relationships of spatiotemporal big data, including map resource data, layer resource data, ubiquitous perception data, public thematic data, geographic entity data and multi-temporal data; Map tools include POI query, on-map measurement and coordinate positioning tools.

5. The method for constructing a spatiotemporal big data platform based on multivariate data services according to claim 1, characterized in that: The management of platform services and user systems includes: service management, video management, security management and statistical presentation of platform status, and the platform status includes platform operation status, service access status and operation and maintenance operation status.

6. The method for constructing a spatiotemporal big data platform based on multivariate data services according to claim 1, characterized in that: The construction of the service supply system and the sharing of platform resources by combining the stream encryption algorithm and the video streaming technology include: Encrypt and forward geographic information services based on the RC4 stream encryption algorithm; The video stream architecture is built based on FFmpeg and Node.js, Redis technology is used to temporarily verify the video stream, and the video stream is transmitted through WebSocket technology.

7. The method for constructing a spatiotemporal big data platform based on multivariate data services according to claim 6, characterized in that: The platform resources include spatial information resources, data resources and functional services.

8. The method for constructing a spatiotemporal big data platform based on multivariate data services according to claim 1, characterized in that: The process of performing spatiotemporal location addressing processing on identification information based on a place name and address engine and using a place name and address matching technology includes: Receive the characters to be searched input by the user and pass the characters to be searched as request parameters to the place name and address service interface; After the characters to be searched undergo character preprocessing, TCHMM semantic analysis, vocabulary comparison, update time point screening, rule segmentation, index library comparison, and place name and address library comparison, the processing results are returned to the user.

9. The method for constructing a spatiotemporal big data platform based on multivariate data services according to claim 8, characterized in that: The TCHMM semantic analysis includes: Determine the number of hits of a certain word, and obtain the number of hits of related words through word library comparison; Calculate the transition probability from a certain participle to related participles and construct a semantic transition probability matrix.

10. The method for constructing a spatiotemporal big data platform based on multivariate data services according to claim 9, characterized in that: The spatiotemporal positioning addressing includes converting the address description into spatial coordinates and converting the spatial coordinates into readable address information.

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