Data sharing method for digital twin cities and related device

Abstract

The invention discloses a data sharing method of a digital twin city and a related device, and relates to the field of digital twin, a data virtualization system is constructed, multi-source data virtualization is carried out on each data source of a digital twin city system, data of different business systems are stored in respective business systems, and the data sharing efficiency is improved. A centralized data warehouse does not need to be constructed, uniform access connection of multi-source heterogeneous data is achieved, and specifically, under the condition that a master control node in the data virtualization system receives a calling request of a calling end, the master control node is controlled to generate at least one task according to the calling request, and the task is sent to at least one working node; the method comprises the following steps: receiving a task from a working node, controlling the working node to access a data source corresponding to the task to obtain a task result, and feeding back the task result to a master control node, so that the master control node feeds back the task result to a calling end, thereby realizing joint access across different data sources, returning an access result in real time, and improving the data sharing efficiency of the digital twin city.

Description

Technical Field

[0001] The present application relates to the field of digital twin technology, and in particular to a data sharing method and related devices for a digital twin city. Background Art

[0002] ‌‌Digital twin cities‌ is a technology that converts all aspects of the physical world of a city into digital form. Through real-time data exchange and simulation analysis between cyberspace and the physical world, it can achieve comprehensive monitoring, prediction and optimization of the city's operating status.

[0003] Data sharing between different business units of digital twin cities has always been a very difficult link, which requires the integration and application of multi-source heterogeneous data. At present, multi-source data aggregation is generally used to achieve the integration of multi-source heterogeneous data. A commonly used multi-source data aggregation method uses the ETL (Extract, transform, load) method to extract data into the same spatial library. Another commonly used multi-source data aggregation method uses the SOA (Service-Oriented Architecture) model, customized development calls the WEB service of the third-party system, and finally completes the calculation based on the spatial calculation method of the own platform to provide the result output. In the digital twin city construction process of this series of methods, data aggregation has a lot of data migration work, and it takes a long time to perform ETL aggregation of data. In addition, since a large number of business systems belong to different functional departments, it is very difficult to integrate data. Summary of the invention

[0004] In view of the above problems, this application provides a data sharing method and related devices for a digital twin city to improve the data sharing efficiency of the digital twin city. The specific scheme is as follows: The first aspect of the present application provides a data sharing method for a digital twin city, comprising: Constructing a data virtualization system, wherein the data virtualization system includes a master control node and multiple working nodes, each of which is connected to various data sources of the digital twin city system; When the master control node receives a call request from the call end, the master control node is controlled to generate at least one task according to the call request, and the task is sent to at least one of the working nodes; Control the working node to access the data source corresponding to the task to obtain the task result, and feed back the task result to the master control node; Control the main control node to feed back the task result to the calling end.

[0005] In a possible implementation, the controlling the master control node to generate at least one task according to the call request and sending the task to at least one of the working nodes includes: The master control node is controlled to generate a plurality of the tasks according to the calling request, and the plurality of the tasks are sent to the plurality of the working nodes respectively, so that the plurality of the working nodes execute the plurality of the tasks in parallel.

[0006] In a possible implementation, the controlling the master control node to generate at least one task according to the call request and sending the task to at least one of the working nodes includes: Control the master control node to generate one of the tasks according to the call request, and send the task to one of the working nodes; The controlling the working node to access the data source corresponding to the task to obtain the task result includes: The working node is controlled to use a multi-threaded manner to access multiple data sources corresponding to the task to obtain the task result.

[0007] In a possible implementation, the data sharing method of the digital twin city further includes: Identify entities, attributes of entities, and relationships between entities in various data sources of the digital twin city system to obtain digital twin city knowledge; Performing knowledge fusion on the digital twin city knowledge; Performing knowledge reasoning and quality assessment on the digital twin city knowledge after knowledge fusion, and adding the digital twin city knowledge to the knowledge graph when the quality assessment result meets the preset requirements; Data cataloging is performed based on the data in the knowledge graph to obtain a digital twin element catalog.

[0008] In a possible implementation, the data sharing method of the digital twin city further includes: Extracting metadata from the digital twin city system; Actively managing, actively mining and actively analyzing the metadata in sequence to obtain metadata knowledge, wherein the active analysis includes correlation analysis, lineage analysis and hotness and coldness analysis; The metadata knowledge is added to a metadata knowledge base, and the metadata is added to a metadata dictionary.

[0009] In a possible implementation, the data sharing method of the digital twin city further includes: According to business needs, select multiple target operators in the spatial operator selection window in the data visualization orchestration window, and configure the operator parameters of each target operator, each of which corresponds to a process node; Matching a data source that meets the business requirements for each target operator respectively; Each of the process nodes is configured in the node information configuration window in the data visualization orchestration window to generate a task orchestration process corresponding to the business requirement.

[0010] In a possible implementation, the data sharing method of the digital twin city further includes: Generate the user's feature vector matrix based on user behavior and user attributes; Inputting the user's feature vector matrix into an analysis strategy model corresponding to a target scenario to obtain recommendation information corresponding to the target scenario; The recommendation information is sent to the user.

[0011] The second aspect of the present application provides a computer program product, including computer-readable instructions. When the computer-readable instructions are executed on an electronic device, the electronic device implements the data sharing method of a digital twin city according to the first aspect or any implementation method of the first aspect.

[0012] A third aspect of the present application provides an electronic device, comprising at least one processor and a memory connected to the processor, wherein: The memory is used to store computer programs; The processor is used to execute the computer program so that the electronic device can implement the data sharing method of the digital twin city according to the above-mentioned first aspect or any implementation method of the first aspect.

[0013] A fourth aspect of the present application provides a computer storage medium, which carries one or more computer programs. When the one or more computer programs are executed by an electronic device, the electronic device can implement the data sharing method of a digital twin city according to the first aspect or any implementation method of the first aspect.

[0014] Through the above-mentioned technical scheme, the present application provides a data sharing method and related devices for a digital twin city, builds a data virtualization system, and performs multi-source data virtualization on various data sources of the digital twin city system. The data of different business systems are still stored in their respective business systems. There is no need to build a centralized data warehouse, so as to achieve unified access and connection of multi-source heterogeneous data. Specifically, when the master control node in the data virtualization system receives a call request from the calling end, the master control node is controlled to generate at least one task according to the call request, and sends the task to at least one working node, and controls the working node to access the data source corresponding to the task to obtain the task result, and feeds back the task result to the master control node, so that the master control node feeds back the task result to the calling end, thereby realizing joint access across different data sources and returning the access result in real time, thereby improving the data sharing efficiency of the digital twin city. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and the originals and elements are not necessarily drawn to scale.

[0016] Figure 1 Schematic diagram of the digital twin city architecture provided for this application; Figure 2 A schematic diagram of a process flow of a data sharing method for a digital twin city provided in an embodiment of the present application; Figure 3 A schematic diagram of the data virtualization system structure provided in an embodiment of the present application; Figure 4 A schematic diagram of a knowledge graph-based data cataloging system architecture provided in an embodiment of the present application; Figure 5 A diagram of the active metadata management architecture of a digital twin city provided in an embodiment of the present application; Figure 6 A schematic diagram of a data visualization arrangement window provided in an embodiment of the present application; Figure 7 An architecture diagram of on-demand consumption of data elements provided in an embodiment of the present application; Figure 8 This is a diagram of the overall architecture of the digital twin city system provided in the embodiment of the present application. DETAILED DESCRIPTION

[0017] In the following description, in order to ensure the accuracy of the citation and the fluency of reading, the key technical terms, abbreviations or abbreviations involved in the text are summarized and explained as follows: The following describes the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. The terms used in the implementation method section of the present application are only used to explain the specific embodiments of the present application, and are not intended to limit the present application.

[0018] The embodiments of the present application are described below in conjunction with the accompanying drawings. Those skilled in the art will appreciate that, with the development of technology and the emergence of new scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0019] The terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and need not be used to describe a specific order or sequential order. It should be understood that the terms used in this way can be interchangeable under appropriate circumstances, which is only to describe the distinction mode adopted by the objects of the same attributes when describing in the embodiments of the present application. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, so that the process, method, system, product or equipment comprising a series of units need not be limited to those units, but may include other units that are not clearly listed or inherent to these processes, methods, products or equipment.

[0020] Digital twin city construction is generally carried out in cities and industrial parks with a high degree of digitization. Based on the existing urban information platform, digital twin cities are upgraded and constructed, and three-dimensional modeling of various spatial elements is promoted. Business data in key areas are gradually superimposed to achieve intelligent perception and decision-making in all spatial scenarios and build a digital city foundation. Pilot applications such as major urban construction project management, emergency event analysis and deduction, traffic road condition analysis and decision-making, ecological environment governance, and natural resource management are carried out based on digital twin cities.

[0021] In order to realize the digital twin of the city, it is first necessary to virtually express and map the physical entity objects, event objects and relationship objects in the physical space and social space in the digital space. On this basis, relying on the information infrastructure, data aggregation, transmission and processing are realized to form data resources. Under the support of general service capabilities, digital twin technology is further integrated to form digital twin services that can be provided to the outside world, and the integration with upper-level application scenarios is realized through interactive services. At the same time, it is necessary to provide three-dimensional security management and full life cycle operation management to ensure the safe and efficient operation of various assets and services in the digital space.

[0022] like Figure 1 The digital twin city architecture shown includes information infrastructure, data resources, general services, twin services, interactive services, security management, and operation management.

[0023] Information infrastructure: Information infrastructure refers to digital infrastructure that provides perception, connection, storage and computing capabilities, among which perception infrastructure includes embedded sensor infrastructure, Internet of Things infrastructure and mapping infrastructure. Connection infrastructure includes advanced connection communication equipment, facilities and systems such as 5G network, Internet of Vehicles, narrowband ubiquitous perception network and all-optical network. Storage infrastructure mainly refers to multi-level data storage centers and cloud data centers, covering a variety of storage methods, including distributed file storage, distributed structured data storage, distributed columnar data storage and distributed graph data storage. Computing infrastructure includes advanced computing infrastructure such as high-performance computing, distributed computing, cloud computing and edge computing, which supports cities to establish virtual integrated computing resource pools.

[0024] Data resources: Digital resources are the sum of all types of urban data and are the basis for building a city digital twin system. From the source of data, it can be divided into spatiotemporal basic data, IoT perception data, network transmission data, business application data, and operation evaluation data. Among them, spatiotemporal basic data include vector data, image data, elevation model data, geographic entity data, place name address data, three-dimensional model data, etc. IoT perception data includes various types of perception data and status data collected and reported by IoT perception devices, such as temperature, humidity, pressure, brightness, equipment operation status, etc. Business application data includes multi-source business application data from business information systems, industry information systems, third-party social organization information systems, etc. Operation evaluation data mainly includes operation results and evaluation data in the fields of urban planning, urban management, economic development, environmental protection, meteorology, energy, transportation, etc.

[0025] General services: General services provide basic common capabilities support for urban digital twins. Data services are general support services for the use of data resources, including but not limited to data models, asset management, and data governance. Application services provide basic capabilities to ensure urban digital twin applications and services, including but not limited to engine services, component management, and user management. Computing services include but are not limited to task scheduling, resource management, and performance monitoring. Intelligent services include but are not limited to pattern recognition, statistical analysis, knowledge graphs, etc.

[0026] Twin service: Twin service refers to the characteristic services required by the digital twin of the city, including but not limited to perception interconnection, entity mapping, multi-dimensional modeling, spatiotemporal computing, simulation deduction and visualization. Perception interconnection refers to the real-time perception and interconnected control of all elements of the city, including identification resolution, intelligent perception, real-time monitoring, collaborative control, etc. Entity mapping refers to the establishment of a multi-level and multi-dimensional mapping relationship between physical entities and virtual entities, including state indicators, object management, attribute association, feature extraction, etc. Multi-dimensional modeling is the multi-dimensional digital expression of all elements, including event modeling, spatiotemporal modeling, semantic modeling, rule modeling, etc. Spatiotemporal computing refers to multi-dimensional computing based on time and space coordinates, including spatiotemporal analysis, spatiotemporal resolution, spatiotemporal query, spatiotemporal indexing, etc. Simulation prediction refers to simulation, intelligent prediction, dynamic decision-making, etc., including algorithm integration, engine development, task management, optimization evaluation, etc. Visualization is the expression of the physical city to the digital city, including virtual-real fusion, model processing, rendering services, scene editing, etc.

[0027] Interactive service: Interactive service refers to providing various types of capability exposure interfaces, realizing cross-system data intercommunication and service calls through unified and standardized interactive interfaces, and providing platform-based, lightweight data, API, message, application and other integration capabilities, so that third-party applications can flexibly combine functional components to achieve the separation of business logic and technical logic. Open forms include but are not limited to portals, third-party services, interfaces, development tools, application components, etc.

[0028] Safety management: Safety management refers to the management of data security, information system and network security, safety warning and emergency response in accordance with the city's safety management system.

[0029] Operation management: Operation management refers to the real-time monitoring and unified presentation of the operating conditions of urban infrastructure, underground space, energy system, ecological environment, road traffic, etc. based on digital models and identification systems, perception systems and various intelligent facilities. Through digital models and software and hardware systems, rapid response, decision simulation, emergency handling, and the operation, maintenance and operation of equipment and systems are achieved. Real-time monitoring and unified presentation of the operating conditions of urban elements, ecological environment, etc. are achieved.

[0030] Data sharing between different business units of digital twin cities has always been a very difficult link. To achieve the integration and application of multi-source heterogeneous data, it is necessary to set standards, aggregate data, build data warehouses, and build digital twin cities based on various spatial data in the data warehouses. The construction cost is high, the time is long, and it cannot quickly respond to the spatiotemporal analysis needs of multiple scene changes. In the process of urban intelligence construction, different government functional departments, enterprises and institutions manage the data within their business scope. Due to different management authority requirements, there has always been a great barrier to data synchronization.

[0031] In order to solve the above problems, the embodiment of the present application provides a data sharing method for a digital twin city. The data sharing method for a digital twin city in the embodiment of the present application is described in detail below with reference to the accompanying drawings.

[0032] Reference Figure 2 , Figure 2 A schematic diagram of a data sharing method for a digital twin city provided in an embodiment of the present application is shown in FIG. Figure 2 As shown, a data processing method provided in an embodiment of the present application may include steps 201 to 204, and these steps are described in detail below.

[0033] 201: Build a data virtualization system, which includes a master control node and multiple working nodes. Each working node is connected to various data sources of the digital twin city system. See also Figure 3 The data virtualization system shown in the figure includes a master node and multiple working nodes. The call end is connected to the server end to submit a call request to the master node, and the call request includes a query request and a processing request. The data source includes various relational databases such as postgis and mysql, memory database Redis, message middleware Kafka, and big data platform Hive. The working node connects to different data sources through connectors of different data sources, thereby realizing the same connection mode of different data sources and completing data interaction.

[0034] 202: When the master control node receives a call request from the call end, the master control node is controlled to generate at least one task according to the call request, and send the task to at least one working node; The master node is the overall scheduling and management node for tasks, and is used for SQL parsing, generating execution plans, and dividing tasks.

[0035] After receiving the call request from the calling end, the main control node controls the main control node to parse the SQL statement in the call request, generates a query execution plan based on the parsed content, and generates tasks by stage based on the generated execution plan, which may include tasks of one or more stages, and the number of tasks is one or more.

[0036] It should be noted that when a working node is started, it will send the running status of the working node to the master node. The running status is available and can receive tasks assigned by the master node at any time.

[0037] The master node sends the task to at least one available worker node.

[0038] 203: Control the working node to access the data source corresponding to the task to obtain the task result, and feed the task result back to the master control node; The worker nodes communicate with the master node and other worker nodes using the REST API. The REST API (Representational State Transfer Application Programming Interface) is an API built on the REST principle.

[0039] There are two modes for worker nodes to execute tasks according to the execution plan of the master node: intra-node parallelism and inter-node parallelism.

[0040] Intra-node parallelism improves intra-node parallelism by using multi-threading within the working node to access multiple data sources corresponding to the task to obtain the task results. This has lower latency and is more efficient than inter-node parallelism.

[0041] Inter-node parallelism means that the master node generates multiple tasks based on the call request and sends the multiple tasks to multiple working nodes respectively, and the tasks are executed in parallel between different working node processes.

[0042] After completing the task, the working node collects the task results to the master node.

[0043] 204: The control master node feeds back the task result to the calling end.

[0044] The present embodiment discloses a data sharing method for a digital twin city, which constructs a data virtualization system and performs multi-source data virtualization on various data sources of the digital twin city system. The data of different business systems are still stored in their respective business systems, and there is no need to build a centralized data warehouse, so as to achieve unified access and connection of multi-source heterogeneous data. Specifically, when the main control node in the data virtualization system receives a call request from a call end, the main control node is controlled to generate at least one task according to the call request, and sends the task to at least one working node, and controls the working node to access the data source corresponding to the task to obtain the task result, and feeds back the task result to the main control node, so that the main control node feeds back the task result to the call end, thereby achieving joint access across different data sources and returning the access result in real time, thereby improving the data sharing efficiency of the digital twin city.

[0045] Before calling data in the data sharing process of digital twin cities, it is necessary to build an index for digital twin city data sharing, that is, it is necessary to catalog the digital twin city data. The traditional cataloging system mainly relies on the experience of experts and the understanding of business personnel. It defines a cataloging system according to certain rules for a period of time. The data cataloging is solidified and cannot keep pace with the times.

[0046] In order to solve the above technical problems, Figure 4 The data cataloging system architecture based on the knowledge graph is shown. The data cataloging method of the digital twin city provided in this embodiment includes the following steps 401-404: 401: Identify entities, attributes of entities, and relationships between entities in various data sources of the digital twin city system to obtain digital twin city knowledge; The data in the data source includes structured data, semi-structured data, and unstructured data.

[0047] Information extraction is performed on the semi-structured and unstructured data in various data sources of the twin city system to obtain the entities, attributes of the entities, and the relationships between entities in the data. The digital twin city knowledge includes entities, attributes of entities, and the relationships between entities.

[0048] 402: Knowledge fusion of digital twin city knowledge; Structured data has clear entity information, and the relationships between entities can be clearly established. The corresponding digital twin city knowledge can be directly applied to knowledge fusion. Semi-structured data and unstructured data can be obtained through information extraction to obtain digital twin city knowledge or knowledge fusion.

[0049] Knowledge fusion includes coreference resolution and entity disambiguation.

[0050] Coreference resolution and entity disambiguation are two related but different tasks in natural language processing (NLP).

[0051] Co-reference resolution is the process of identifying different expressions in a text that refer to the same entity through an algorithm, mapping the referential entities in the text (such as names of people, places, and institutions, etc.) to a unified, unique entity identifier, so that the entities in the text can be accurately understood and processed.

[0052] Co-reference resolution includes grammatical analysis, semantic analysis, and deep learning. Syntactic analysis is to combine words in a text into sentences by analyzing grammatical rules in linguistics, and to identify and resolve entities in the sentences. Semantic analysis is to identify entities in a text through natural language processing techniques, such as part-of-speech tagging and named entity recognition, and resolve them with the help of certain algorithms. Deep learning uses a neural network model to automatically identify entities in a text and resolve them automatically by training a large corpus.

[0053] Entity disambiguation can use the results of coreference resolution to better understand the context of entities, thereby improving the accuracy of disambiguation. Entity disambiguation solves the problem of homonymous entities in text, that is, the name of the same entity may refer to multiple different real-world entities.

[0054] There are many ways to disambiguate entities, such as clustering-based entity disambiguation and entity linking-based entity disambiguation. Clustering-based entity disambiguation analyzes the contextual features of entity referents and uses clustering algorithms to group referents that point to the same entity. Entity linking-based entity disambiguation requires an external knowledge base (such as Wikipedia, DBpedia, etc.) to link entity referents to specific entity pages in the knowledge base to determine the entity they point to.

[0055] 403: Perform knowledge reasoning and quality assessment on the digital twin city knowledge after knowledge fusion, and add the digital twin city knowledge to the knowledge graph when the quality assessment results meet the preset requirements; Based on the knowledge fusion of the digital twin city, knowledge reasoning is completed and added to the knowledge graph after quality assessment and inspection.

[0056] For the newly generated knowledge, based on knowledge reasoning, the graph relationship is continuously improved to enhance the overall knowledge system of the knowledge graph.

[0057] Knowledge reasoning is the process of using the entities, relationships and attribute information in the knowledge graph structure to derive new knowledge or implicit information through reasoning algorithms. The specific process of knowledge reasoning based on the knowledge graph includes: knowledge representation, reasoning rule definition, reasoning engine construction, reasoning algorithm selection, reasoning execution, result verification and optimization.

[0058] Knowledge representation: The knowledge in the knowledge graph needs to be represented in a form suitable for reasoning, such as encoding entities, relations, and attributes as nodes and edges in the graph.

[0059] Inference rule definition: Defining inference rules is a key step in knowledge reasoning. These rules are based on logical reasoning principles such as deductive reasoning, inductive reasoning, and analogical reasoning. Rules can be simple, such as "if A is a subclass of B, then an instance of B is also an instance of A", or complex, involving a combination of multiple entities and relationships.

[0060] Reasoning engine construction: The reasoning engine is a software system that executes reasoning rules. It can be a rule-based reasoning engine, a graph algorithm-based reasoning engine, or a machine learning-based reasoning model. The reasoning engine corresponds to the reasoning algorithm.

[0061] Reasoning algorithm selection: Select the appropriate reasoning algorithm based on the characteristics of the reasoning task. Reasoning algorithms include: logic-based reasoning (using predicate logic and first-order logic for reasoning), graph-based reasoning (using graph algorithms to traverse and pattern match on knowledge graphs, such as path search and shortest path algorithms), and deep learning-based reasoning (using neural networks, such as convolutional neural networks (CNNs) for reasoning).

[0062] Reasoning execution: Input the knowledge graph and reasoning rules into the reasoning engine and execute the reasoning algorithm.

[0063] Result verification and optimization: Verify the results obtained by reasoning to ensure their accuracy and reliability. If problems are found, it may be necessary to adjust the inference rules or optimize the algorithm parameters.

[0064] Quality assessment is to test and evaluate the quality of the constructed knowledge graph to ensure the accuracy, completeness and consistency of the knowledge graph. Quality assessment includes accuracy assessment, completeness assessment and consistency assessment.

[0065] Accuracy evaluation: Compare the entities, relationships, and attributes in the knowledge graph with the information in the original data, and calculate indicators such as accuracy, recall, and F1 value to evaluate the accuracy of the knowledge graph.

[0066] Completeness assessment: Check whether the entities, relationships, and attributes in the knowledge graph cover all relevant fields and whether there are any missing entities. You can build a domain ontology template and compare it with the actual knowledge graph to assess completeness.

[0067] Consistency assessment: Check whether the entities, relationships, and attributes in the knowledge graph follow unified naming conventions and data formats, and whether there are inconsistencies and conflicts. The knowledge graph can be tested by designing consistency detection rules.

[0068] 404: Catalog the data based on the data in the knowledge graph to obtain the digital twin element catalog.

[0069] Data cataloging refers to the process of identifying, describing, classifying and indexing digital twin-related data so that users can quickly discover, understand and access the required data. It is the basis for data sharing.

[0070] Data cataloging based on knowledge graphs is to use the structured information of knowledge graphs (entities and relationships between entities) to create metadata for data cataloging and form basic retrieval information for data cataloging. Through search, browsing and query functions, users can discover data based on dimensions such as entities, attributes and relationships.

[0071] The digital twin city cataloging system established through knowledge graph technology is a cataloging system that is updated from time to time based on data changes. It extracts information, integrates knowledge, processes knowledge, and catalogs data based on the frequency of use of digital twin city data by users, effectively solving the problem of cataloging being rigid and unable to keep pace with the times.

[0072] Before the data sharing of digital twin cities, it is necessary to effectively manage the metadata in the digital twin city system. The traditional metadata system cannot drive any "action" from metadata signals. It is basically a static tool that relies on human effort to comb and record data. This means that the success of the metadata program depends on the people who implement and manage it. These fundamental flaws make it difficult for traditional metadata management to be continuously improved and updated.

[0073] This embodiment provides a data sharing method for a digital twin city, which adopts active metadata technology to timely update the metadata of the digital twin city system, realize dynamic integration of data elements, and provide intelligent retrieval of data elements.

[0074] like Figure 5 The digital twin city active metadata management architecture diagram shown in the figure, the digital twin city active metadata management method provided in this embodiment includes the following steps 501-503: 501: Extracting metadata from digital twin city systems; The collection of metadata is mainly based on real-time online collection technology, which collects spatiotemporal metadata, business metadata, spatial operation metadata, management metadata and other types of metadata in various business systems of the digital twin city system in real time.

[0075] Spatiotemporal metadata is the description content related to development technology. It is a description of data, such as data structure, model table, etc., and a description of development content; Business metadata refers to business scope, business model description, business data directory, business domain, subject domain, business classification, business label, etc. Operation metadata is a record description of data operations, metadata update records, data access records, online and offline records, browsing records, application records, etc. Management metadata is a description of data at the management level, such as the team to which it belongs, the person in charge, etc.

[0076] Based on the established metadata collection specifications, the statistical collection and storage of metadata is realized.

[0077] 502: Actively manage, actively mine and actively analyze the metadata in sequence to obtain metadata knowledge. Active analysis includes correlation analysis, lineage analysis and hot / cold analysis. Active metadata governance is the governance and optimization of metadata quality, security, standards, specifications, value, cost and other issues. Active metadata governance includes automated metadata collection, metadata standardization and metadata model building. Automated metadata collection, by integrating various data technologies, automatically collects metadata from data sources, such as databases, data lakes, etc., and uses API interfaces to achieve metadata information acquisition between systems. Metadata standardization refers to the formulation of metadata standards to ensure consistency in the naming, definition and use of metadata. Metadata model building is to build a metadata model according to business needs and define the relationship between metadata.

[0078] Active metadata mining is the process of converting massive metadata into useful information and knowledge. Metadata mining methods include descriptive data mining and predictive mining.

[0079] Active metadata analysis is a key step in establishing metadata relationships. Commonly used analysis dimensions include correlation analysis, blood relationship analysis, hot and cold analysis, etc. By combining multiple analysis methods, the real-time and effectiveness of the metadata knowledge system can be improved.

[0080] 503: Add metadata knowledge to the metadata knowledge base, and add metadata to the metadata dictionary.

[0081] Adding metadata knowledge to the metadata knowledge base and adding metadata to the metadata dictionary is a function of metadata knowledge management.

[0082] Metadata knowledge management includes: metadata knowledge base management, metadata dictionary management, metadata services and intelligent retrieval of data elements.

[0083] Metadata knowledge base management includes adding, replacing, deleting and other operations on metadata knowledge in the metadata knowledge base.

[0084] Metadata dictionary management implements metadata definition, metadata classification, dictionary construction, editing and maintenance.

[0085] Metadata services include definition of service interfaces, service data quality, and rights management.

[0086] Intelligent retrieval of data elements includes index construction and search algorithm selection. The search algorithm can be a full-text search algorithm based on knowledge graph, an intelligent recommendation algorithm, etc.

[0087] Active metadata defines the various scenarios in which metadata can support applications. It is a real-time, intelligently driven, and action-oriented technology. Based on the dynamic analysis and management of active metadata, it can manage the various changing digital twin city data elements in real time, provide real-time analysis and mining, and realize the shared retrieval of data elements based on streaming, replication, messaging, etc.

[0088] In the above-mentioned embodiments, the data sharing and other data processing requirements of the digital twin city can be realized through data processing tasks. There are many ways to create data processing tasks. One way is to use fixed tools that cannot flexibly adapt to the data processing requirements of flexible and diverse business scenarios. Another way is to use SDK (Software Development Kit, software development kit), which requires developers to customize the encapsulation of business logic according to business needs, which is not friendly to ordinary users. A data sharing method for a digital twin city provided in this embodiment adopts a visual orchestration method, which can greatly improve the generation efficiency of data processing tasks and help non-developers quickly complete the design of a set of digital twin city data processing tasks. Specifically, it includes the following steps 601-603: 601: According to business requirements, select multiple target operators in the spatial operator selection window in the data visualization orchestration window, and configure the operator parameters of each target operator. Each target operator corresponds to a process node. like Figure 6 The data visualization arrangement window example shown in the figure has a spatial operator selection window on the left. Spatial operators include: spatial relationship operators, data processing operators, GIS service publishing operators, spatial topology design operators, etc. Among them, spatial relationship operators include: point / point relationship, point / line relationship, point / surface relationship, line / line relationship, line / surface relationship, surface / surface relationship. Data processing operators include: oblique photography conversion, pipe network data, satellite imagery, BIM lightweight, model monomerization, etc.

[0089] Exemplarily, in response to the user selecting an operator in the spatial operator selection window according to business requirements, multiple target operators are selected and the target operators are dragged to the orchestration window.

[0090] Exemplarily, the business requirements are analyzed to obtain multiple business requirement keywords, multiple target operators corresponding to the business requirements are determined based on the pre-set correspondence between the business requirement keywords and the spatial operators, and the multiple target operators are automatically selected.

[0091] Operator parameters are used to set the performance of the control operator. Operator parameters include at least one of the following parameters: operator input data, operator output data, threshold, range, number of loops, branch conditions, whether to process in parallel, etc.

[0092] 602: Match a data source that meets the business requirements for each target operator; The target operator automatically adapts to data sources that may meet business requirements. If there are multiple matching data sources, users can select the relevant data source.

[0093] 603: Configure each process node in the node information configuration window in the data visualization orchestration window to generate a task orchestration process corresponding to the business requirements.

[0094] The configuration of process nodes can be completed automatically or manually, including field configuration, text size, text transparency, fill color, stroke color, stroke width, icon size, icon transparency, etc.

[0095] Figure 6 The middle part of the data visualization orchestration window is an example of a task orchestration process. By improving the node information, a complete task process can be run. The visualized task orchestration process is the most important part of data weaving. It can intuitively view the data processing flow, where the data comes from, what processing logic it goes through, and in what form it finally serves the business scenario.

[0096] The data sharing method of the digital twin city provided in the embodiment of the present application can not only passively provide data to users based on demand, but also actively recommend data to users, and realize on-demand consumption based on the data recommendation engine.

[0097] like Figure 7 As shown in the data element on-demand consumption architecture diagram, the data element on-demand consumption method provided in this embodiment is applied to the data recommendation engine, specifically including the following steps 701-703: 701: Generate a user feature vector matrix according to user behavior and user attributes; The user behaviors of different users are stored in a user behavior database in advance, and the user attributes of different users are stored in a user attribute database.

[0098] Exemplarily, user behaviors and user attributes are periodically extracted from the user behavior database and the user attribute database, and feature conversion is performed by encoding the user behaviors and user attributes to generate a feature vector matrix of the user.

[0099] 702: Input the user's feature vector matrix into the analysis strategy model corresponding to the target scenario to obtain recommendation information corresponding to the target scenario; Build corresponding analysis strategy models for each scenario in advance, such as an item-related clustering model for recommending items to users based on similar items, and a user prediction clustering model for recommending items to users based on similar users.

[0100] The related clustering model groups similar items into one category, helping users discover potential items of interest, optimizing the organization and management of items, and facilitating personalized recommendations. The related clustering model construction method includes: feature extraction, extracting user behavior features and user attribute features from entity attributes, such as text description, user type, call history, etc.; clustering algorithm processing, using a hierarchical clustering algorithm to cluster according to entity features; clustering evaluation, using an evaluation index algorithm to evaluate and score indicators; dynamic update, updating clustering results regularly or as needed to reflect changes in entity features.

[0101] The user prediction and segmentation model divides users into different groups so as to conduct precision marketing and personalized recommendations based on the characteristics of different groups, analyze user behavior patterns, and provide support for product design and business decisions. The user prediction and segmentation model construction method includes: collecting basic information, behavior data, preference settings, etc. of users; using neural network classification algorithms to predict the group to which users belong; training models with historical data to determine the best segmentation strategy; and using cross-validation methods to evaluate the accuracy and practicality of the model.

[0102] The similar user identification model finds users with similar behaviors or interests, which are used for collaborative filtering in the recommendation system, and predicts the potential interests of the target user through the behaviors of similar users. The similar user identification model construction method includes: analyzing the user's data query history, query browsing history and other behavioral data; using the cosine similarity method to calculate the similarity between users; selecting the most similar users as the neighbors of the target user for recommendation; and regularly updating user similarity to reflect changes in user behavior.

[0103] The user interest transfer model captures the changes of user interests over time, enables the recommendation system to adapt to the evolution of user interests, and improves the long-term accuracy of the recommendation system and user satisfaction. The method of constructing the user interest transfer model includes: analyzing the time series characteristics of user behavior data and identifying interest transfer patterns; constructing the transition probability matrix between user interest states; using the conditional random field model to predict the user's interest state transfer; updating the model according to the user's new behavior data to maintain the accuracy of its prediction.

[0104] 703: Send the recommendation information to the user.

[0105] This embodiment is based on the data recommendation engine to achieve on-demand consumption, greatly improve the efficiency of data utilization, reduce unnecessary data requests and transmissions, greatly improve overall work efficiency, and enhance user perception. Based on the various usage scenario characteristics of data, user behavior classification, etc., a complete recommendation algorithm model is established to accurately provide data screening objects during the user's use of data.

[0106] The above introduces a data sharing method for a digital twin city provided in an embodiment of the present application. The following will introduce the overall architecture of the digital twin city system.

[0107] See also Figure 8 As shown in the overall architecture diagram of the digital twin city system, the digital twin city system includes: data aggregation layer, data cataloging layer, data integration layer, service construction layer, and data usage layer.

[0108] At the data aggregation layer, unified access and connection of multi-source heterogeneous data is achieved based on data element virtualization.

[0109] At the data cataloging layer, a data cataloging system is established through the spatiotemporal knowledge graph to support the systematic management of the digital twin city catalog.

[0110] At the data integration layer, active metadata technology is used to achieve dynamic integration of elements and provide intelligent retrieval of data elements.

[0111] At the service construction layer, relying on data visualization orchestration and integrating the spatial operator library, the digital twin city data service can be quickly released.

[0112] At the data usage layer, the data element recommendation engine is used to realize on-demand consumption of data elements and improve the efficiency of data usage in smart city scenarios.

[0113] An embodiment of the present application also provides an electronic device, including at least one processor and a memory connected to the processor, wherein: The memory is used to store computer programs; The processor is used to execute the computer program so that the electronic device can implement any digital twin city data sharing method provided in the embodiments of the present application.

[0114] Also provided in an embodiment of the present application is a computer program product, including computer-readable instructions. When the computer-readable instructions are executed on an electronic device, the electronic device implements any digital twin city data sharing method provided in the embodiment of the present application.

[0115] A computer-readable storage medium is also provided in an embodiment of the present application. The storage medium carries one or more computer programs. When one or more computer programs are executed by an electronic device, the electronic device can implement any digital twin city data sharing method provided in the embodiment of the present application.

[0116] It should also be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the drawings of the device embodiments provided by the present application, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines.

[0117] Through the description of the above implementation mode, the technicians in the field can clearly understand that the present application can be implemented by means of software plus necessary general hardware, and of course, it can also be implemented by special hardware including special integrated circuits, special CPUs, special memories, special components, etc. In general, all functions completed by computer programs can be easily implemented by corresponding hardware, and the specific hardware structure used to implement the same function can also be various, such as analog circuits, digital circuits or special circuits. However, for the present application, software program implementation is a better implementation mode in more cases. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a readable storage medium, such as a computer floppy disk, a U disk, a mobile hard disk, a ROM, a RAM, a disk or an optical disk, etc., including a number of instructions to enable a computer device (which can be a personal computer, a training device, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0118] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware or any combination thereof. When implemented by software, all or part of the embodiments may be implemented in the form of a computer program product.

[0119] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website site, a computer, a training device, or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, training device, or data center. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a training device, a data center, etc. that includes one or more available media integrations. The available medium may be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)), etc.

Claims

1. A data sharing method for a digital twin city, characterized in that: include: Constructing a data virtualization system, wherein the data virtualization system includes a master control node and multiple working nodes, each of which is connected to various data sources of the digital twin city system; When the master control node receives a call request from the call end, the master control node is controlled to generate at least one task according to the call request, and the task is sent to at least one of the working nodes; Control the working node to access the data source corresponding to the task to obtain the task result, and feed back the task result to the master control node; Control the main control node to feed back the task result to the calling end.

2. The data sharing method of the digital twin city according to claim 1 is characterized in that: The controlling the master control node to generate at least one task according to the call request, and sending the task to at least one of the working nodes includes: The master control node is controlled to generate a plurality of the tasks according to the calling request, and the plurality of the tasks are sent to the plurality of the working nodes respectively, so that the plurality of the working nodes execute the plurality of the tasks in parallel.

3. The data sharing method of the digital twin city according to claim 1 is characterized in that: The controlling the master control node to generate at least one task according to the call request, and sending the task to at least one of the working nodes includes: Control the master control node to generate one of the tasks according to the call request, and send the task to one of the working nodes; The controlling the working node to access the data source corresponding to the task to obtain the task result includes: The working node is controlled to use a multi-threaded manner to access multiple data sources corresponding to the task to obtain the task result.

4. The data sharing method of the digital twin city according to claim 1 is characterized in that: The data sharing method of the digital twin city also includes: Identify entities, attributes of entities, and relationships between entities in various data sources of the digital twin city system to obtain digital twin city knowledge; Performing knowledge fusion on the digital twin city knowledge; Performing knowledge reasoning and quality assessment on the digital twin city knowledge after knowledge fusion, and adding the digital twin city knowledge to the knowledge graph when the quality assessment result meets the preset requirements; Data cataloging is performed based on the data in the knowledge graph to obtain a digital twin element catalog.

5. The data sharing method of the digital twin city according to claim 1 is characterized in that: The data sharing method of the digital twin city also includes: Extracting metadata from the digital twin city system; Actively managing, actively mining and actively analyzing the metadata in sequence to obtain metadata knowledge, wherein the active analysis includes correlation analysis, lineage analysis and hotness and coldness analysis; The metadata knowledge is added to a metadata knowledge base, and the metadata is added to a metadata dictionary.

6. The data sharing method of the digital twin city according to claim 1 is characterized in that: The data sharing method of the digital twin city also includes: According to business needs, select multiple target operators in the spatial operator selection window in the data visualization orchestration window, and configure the operator parameters of each target operator, each of which corresponds to a process node; Matching a data source that meets the business requirements for each target operator respectively; Each of the process nodes is configured in the node information configuration window in the data visualization orchestration window to generate a task orchestration process corresponding to the business requirement.

7. The data sharing method of the digital twin city according to claim 1 is characterized in that: The data sharing method of the digital twin city also includes: Generate the user's feature vector matrix based on user behavior and user attributes; Inputting the user's feature vector matrix into an analysis strategy model corresponding to a target scenario to obtain recommendation information corresponding to the target scenario; The recommendation information is sent to the user.

8. A computer program product, characterized in that It includes computer-readable instructions, which, when executed on an electronic device, enable the electronic device to implement the data sharing method for a digital twin city as described in any one of claims 1 to 7.

9. An electronic device, characterized in that: The method comprises at least one processor and a memory connected to the processor, wherein: The memory is used to store computer programs; The processor is used to execute the computer program so that the electronic device can implement the data sharing method of the digital twin city as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that: The storage medium carries one or more computer programs, which, when executed by an electronic device, enable the electronic device to implement the data sharing method for a digital twin city as described in any one of claims 1 to 7.

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