Nuclear power data application integration method and integration platform

Abstract

The invention provides a nuclear power data application integration method and platform, and the method comprises the following steps: constructing a twin scene model of a nuclear power site, and generating data point locations corresponding to all monitoring point locations of an industrial Internet of Things; acquiring a monitoring data set of the industrial Internet of Things, binding the monitoring data set to a corresponding data point location, and simulating real-time operation of the twin scene model; and carrying out resource management service on the twinborn scene model in the real-time operation simulation. According to the integration method and the integration platform provided by the invention, equipment messages can be efficiently processed and forwarded, and deep analysis is carried out; the system also provides excellent opening capability, and supports rapid construction of a digital solution.

Description

Technical Field

[0001] The present invention relates to the technical field of nuclear power industrial Internet of Things, and in particular to a nuclear power data application integration method and integration platform. Background Art

[0002] Industrial Internet of Things (IIoT) refers to the technology and application of interconnecting industrial equipment, sensors, networks and software systems to achieve information exchange and automatic control between equipment. Its development background mainly includes the following aspects: Technological progress: With the rapid development of new generation information technologies such as the Internet of Things, cloud computing, big data, and artificial intelligence, the level of intelligence, interconnection and autonomy of industrial equipment has been continuously improved, providing the basis and conditions for the realization of IIoT. Industrial demand: The industrial field has an increasing demand for improving production efficiency, reducing costs, and improving product quality and safety. IIoT can help enterprises achieve more efficient and reliable production operations by realizing functions such as remote monitoring, predictive maintenance, and intelligent optimization of equipment. Data-driven: IIoT can collect and transmit a large amount of equipment data in real time. This data can be used for analysis and decision-making, helping enterprises understand equipment status, production processes, and market demand, thereby optimizing production plans and resource allocation. Industrial upgrading: With the transformation and upgrading of traditional manufacturing to intelligent manufacturing, industrial enterprises need to connect and integrate existing equipment and systems to realize the digitization and intelligence of production processes. IIoT provides enterprises with a flexible and scalable solution. In short, the development background of the industrial Internet of Things platform is based on the comprehensive effect of multiple factors such as technological progress, industrial demand, data-driven, industrial upgrading and policy support. It has brought huge development opportunities and challenges to the industrial field and is gradually changing the way and model of traditional industrial production.

[0003] In the existing industrial IoT platform, IoT devices and systems face risks in network security and privacy protection, and security technology and management need to be strengthened. IoT technology standards have not yet been fully unified, and there are still certain problems with compatibility and interoperability between different manufacturers and devices. The IoT platform involves a large amount of device data and personal information, and a reasonable privacy protection mechanism is required to avoid data abuse and leakage. There is a lack of industry or industrial application support, and relatively professional tools and personnel are needed to develop applications suitable for specific industry backgrounds, such as nuclear power. Platform tools lack flexibility and ease of use, and the cost of secondary development is high. Summary of the invention

[0004] In view of the shortcomings of the prior art mentioned above, the purpose of the present invention is to provide a nuclear power data application integration method and integration platform to solve the risks faced by IoT devices and systems in the prior art in terms of network security, privacy protection, etc.; the IoT technical standards have not yet been fully unified, and there are still certain problems with the compatibility and interoperability between different manufacturers and devices; the IoT platform involves a large amount of device data and personal information, and requires a reasonable privacy protection mechanism to avoid data abuse and leakage; there is a lack of industry or industrial application support; the platform tools lack flexibility and ease of use, and the cost of secondary development is high.

[0005] To achieve the above-mentioned purpose and other related purposes, the present invention provides a nuclear power data application integration method, comprising the following steps:

[0006] Build a twin scenario model of the nuclear power site and generate data points corresponding to each monitoring point of the industrial Internet of Things;

[0007] Obtain the monitoring data set of the Industrial Internet of Things, bind it to the corresponding data points, and simulate the real-time operation of the twin scenario model;

[0008] Provide resource management services for twin scenario models in real-time simulations.

[0009] In one embodiment of the present invention, a twin scene model of a nuclear power site is constructed, and data points corresponding to each monitoring point of the industrial Internet of Things are generated, including: three-dimensional models of buildings and equipment in the nuclear power site are designed separately, and a twin scene model is established in combination with the scene of the nuclear power site and the picture style of the equipment; data points are generated in the twin scene model according to the monitoring points of each equipment at the nuclear power site.

[0010] In one embodiment of the present invention, constructing a twin scenario model of a nuclear power site also includes: combining and configuring model components, and carrying corresponding model classification templates to establish models of each asset group; obtaining an extended secondary development service model, combining it with multiple asset group models, and generating a twin scenario model.

[0011] In one embodiment of the present invention, the model components include a data input component, a data processing component, a visualization component and an operation and maintenance management component, and the model classification template includes an equipment template, a production line template and a factory template.

[0012] In one embodiment of the present invention, the data types of the monitoring data set of the industrial Internet of Things include: time series data type, value type, string type, file data type, database type, API interface and audio and video data type.

[0013] In one embodiment of the present invention, binding to corresponding data points includes: storing the monitoring data set into an asset database corresponding to each data point according to its data type.

[0014] In one embodiment of the present invention, the asset database includes: a real-time database, a relational database and a non-relational database; the real-time database is used to store and analyze real-time time series data including sensors, Internet of Things devices, applications, Internet of Vehicles, and Industrial Internet; the relational database is used to store and manage structured data; the non-relational database is used to store and process unstructured data including text, images, audio and video.

[0015] In one embodiment of the present invention, the real-time database includes an InfluxDB time series database and a TDEngine time series database, the relational database includes a PostgreSQL relational database, and the non-relational database includes a MongoDB non-relational database.

[0016] In one embodiment of the present invention, resource management services are provided for the twin scenario model in the real-time simulation, including: establishing multiple asset analysis models, and processing various types of data in the asset database according to the asset analysis models, performing preventive maintenance, predictive maintenance and asset performance optimization on the assets, wherein the asset analysis model includes: a presentation model for static data and dynamic data, an event triggering model, a message notification model, a performance evaluation analysis model, a mechanism model, a data-driven AI model, and a business operation and maintenance service model.

[0017] The present invention also provides a nuclear power data application integration platform, including: a twin modeling unit, which constructs a twin scene model of the nuclear power site and generates data points corresponding to each monitoring point of the industrial Internet of Things; an operation simulation unit, which obtains the monitoring data set of the industrial Internet of Things, binds it to the corresponding data points, and simulates the real-time operation of the twin scene model; and a management service unit, which performs resource management services for the twin scene model in the real-time operation simulation.

[0018] Beneficial effects of the present invention: The present invention proposes a nuclear power data application integration method and integration platform. The integration method and integration platform use equipment access and management, data integration and low-code development tools. These functions ensure that all devices can be quickly accessed, and can efficiently process and forward equipment messages for in-depth analysis. In addition, the system also provides excellent open capabilities to support the rapid construction of digital solutions. Specifically, by establishing digital asset connections between virtual and physical devices, visual access to heterogeneous devices is achieved. By managing and controlling assets and processes, visualization of asset collaboration and handover is achieved. By mapping based on standard semantics, spatial association is achieved, and visualization of asset group management is achieved. By guiding operations based on asset model strategies, visualization of modeling analysis is achieved. Through reusable asset management, multi-dimensional scenarios are enabled to achieve visualization of operation and maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 Flow chart of the integration method of the present invention.

[0020] Figure 2 It is a schematic diagram of the integration method architecture in a preferred embodiment of the present invention.

[0021] Figure 3 This is a schematic diagram of twin scene model mapping in a preferred embodiment of the present invention.

[0022] Figure 4 It is a communication transmission schematic diagram of the nuclear power data application integration process in a preferred embodiment of the present invention.

[0023] Figure 5 This is a flow chart of visual page template design management in a preferred embodiment of the present invention.

[0024] Figure 6 FIG. 4 is an architecture diagram of the integrated platform of the present invention. DETAILED DESCRIPTION

[0025] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0026] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0027] In the following description, numerous details are discussed to provide a more thorough explanation of the embodiments of the present invention. However, it is obvious to those skilled in the art that the embodiments of the present invention can be implemented without these specific details. In other embodiments, well-known structures and devices are shown in the form of block diagrams rather than in detail to avoid making the embodiments of the present invention difficult to understand.

[0028] See also Figure 1 In one embodiment of the present invention, the present invention provides a nuclear power data application integration method, comprising the following steps:

[0029] Build a twin scenario model of the nuclear power site and generate data points corresponding to each monitoring point of the industrial Internet of Things;

[0030] Obtain the monitoring data set of the Industrial Internet of Things, bind it to the corresponding data points, and simulate the real-time operation of the twin scenario model;

[0031] Provide resource management services for twin scenario models in real-time simulations.

[0032] In this embodiment, the steps are as follows: constructing a twin scene model of a nuclear power site, and generating data points corresponding to each monitoring point of the industrial Internet of Things. It can be seen that in the process of nuclear power data application integration of the present invention, nuclear power data processing is performed based on the constructed twin scene model of the nuclear power site. When the twin scene model of the nuclear power site is established, a three-dimensional model of, for example, nuclear power equipment, nuclear power plant buildings, etc. can be made according to the specific usage scenario requirements. And after the three-dimensional model is established, when the nuclear power data is managed, the nuclear power data corresponds to the industrial Internet of Things monitoring data of the nuclear power site. Therefore, in the generated twin scene model, it is also necessary to further generate data points corresponding to each monitoring point of the industrial Internet of Things. Thereby, after the nuclear power data or monitoring data corresponding to each monitoring point is collected at the nuclear power site, the data points corresponding to the twin scene model can also be simulated in real time to form corresponding data information.

[0033] Through the steps: obtain the monitoring data set of the industrial Internet of Things, bind it to the corresponding data points, and simulate the real-time operation of the twin scenario model. It can be seen that after the twin scenario model and the corresponding data points are established, it is necessary to supplement the corresponding data points with the monitoring and collection data of the industrial Internet of Things, that is, the monitoring data set mentioned above. And after obtaining the monitoring data set, the data in the monitoring data set are respectively bound to the data points corresponding to each data. In order to realize the real-time operation simulation of the twin scenario model according to the monitoring data of each point. Through the above content, it is possible to integrate the twin scenario model with the monitoring data of the nuclear power site according to the corresponding data points, thereby completing the data integration processing of the upper-level application scenarios corresponding to different models.

[0034] Through the steps: Provide resource management services for the twin scenario model in the real-time operation simulation. It can be seen that when the real-time operation simulation of the twin scenario model is performed according to the monitoring data set, the resource management service can be further implemented according to its specific operation conditions. That is, it is possible to estimate various possible maintenance conditions, such as maintenance conditions, regular maintenance conditions, etc., and it is also possible to analyze and optimize various data obtained based on monitoring, such as integrating some analysis tools, such as equipment energy efficiency analysis, equipment quality analysis, etc. to achieve the goal of performance analysis.

[0035] Through the above-mentioned nuclear power data application integration method, digital asset connections between virtual and physical devices can be established, thereby realizing visual access to heterogeneous devices. Digital assets and processes can also be managed and controlled to achieve visualization during asset collaboration and handover.

[0036] In one embodiment of the present invention, a twin scene model of a nuclear power site is constructed, and data points corresponding to each monitoring point of the industrial Internet of Things are generated, including: three-dimensional models of buildings and equipment in the nuclear power site are designed separately, and a twin scene model is established in combination with the scene of the nuclear power site and the picture style of the equipment; data points are generated in the twin scene model according to the monitoring points of each equipment at the nuclear power site.

[0037] In this embodiment, when constructing the twin scene of the nuclear power site, the buildings, equipment, etc. in the nuclear power site can be made by using the three-dimensional model design. And further, it can cooperate with the scene of the nuclear power site, for example, after the three-dimensional models of the designed buildings, equipment, etc. are imported, the corresponding scene is built to construct a twin scene model. After that, the data points corresponding to the monitoring points of each monitoring device at the nuclear power site are generated in the twin scene model. To realize the storage of monitoring data of the corresponding monitoring equipment based on the data point. Specifically, the Internet of Things devices corresponding to the monitoring point include industrial equipment, sensors, networks, etc. Buildings include factories, workshops, floors, etc. that are pre-created according to the nuclear power site.

[0038] In one embodiment of the present invention, constructing a twin scenario model of a nuclear power site also includes: combining and configuring model components, and carrying corresponding model classification templates to establish each asset group model; obtaining an extended secondary development service model, combining it with multiple asset group models, and generating a twin scenario model. In this embodiment, when constructing a twin scenario model of a nuclear power site, the asset group model can be first established by importing a specific template in the form of a set component. In addition, different nuclear power application scenarios can be built through multiple asset group models, that is, multiple asset group models can be directly combined to create a twin scenario model. Of course, it is also possible to build a twin scenario model by combining an extended secondary development service model with multiple asset group models. Specifically, the extended secondary development service model can be a microservice or component developed by the user and mounted by the user.

[0039] Specifically, the model components include data input components, data processing components, visualization components, and operation and maintenance management components, and the model classification templates include equipment templates, production line templates, and plant templates. In this embodiment, different asset group models are constructed by using different model components such as data input components, data processing components, visualization components, and operation and maintenance management components, in combination with model classification templates. Moreover, the topological architecture of the plant, production line, and corresponding equipment can be displayed in the asset group model, realizing the visual access of heterogeneous equipment.

[0040] In one embodiment of the present invention, the data types of the monitoring data set of the industrial Internet of Things include: time series data type, value type, string type, file data type, database type, API interface and audio and video data type.

[0041] In this embodiment, when acquiring a data set, device access can be achieved, and edge nodes and gateway devices can be supported to access through the Internet of Things protocol MQTT and other methods, and terminal devices can be supported to directly upload data through SDK (Software Development Kit). Device management can be achieved, and multi-level device management is supported; device object model definition is supported, and different information can be defined through attributes, commands, and events, and multiple modules can be created to group device information, and the viewing and management of devices connected to the system can be supported through a topology diagram, and the device shadow (device twin) function is supported. Message communication can be achieved, and two-way message communication between applications and devices is supported, object model message communication is supported, and two-way message communication with user-defined topics is supported. Data flow can be achieved, and data forwarding function can be supported. Database data and MQTT data can be forwarded, and data forwarding to databases, MQTT, Kafka (a high-throughput distributed publish-subscribe message system) message middleware, etc. are supported, and custom data forwarding formats are supported, and data filtering and data selection operators are supported. It can realize data analysis, support time series data analysis with custom time periods and custom measurement points, perform comparative analysis on multiple measurement point values, and support aggregation processing such as mean, minimum, maximum, sum, and count on raw data. It can realize data monitoring, support custom data alarm thresholds, support setting alarm anti-shake to prevent excessive interference, and support docking with multi-channel alarm notification services for real-time alarm message push. It can also realize single instance support for 50,000 points / s device data collection, 100,000 / s data writing, and 100,000 / s forwarding, and can be expanded vertically and horizontally to support millions of message concurrency capabilities. It can realize data source management and data access, and support multiple types of data sources such as SQL Server (a relational database system), Oracle (a relational database management system), PostgreSQL (a client / server relational database management system), MySQL (a relational database management system), MongoDB (a database based on distributed file storage), InfluxDB (time series database), HTTP (Hypertext Transfer Protocol), CSV (comma separated values), and JSON (a lightweight data exchange format).

[0042] When managing data sets, you can create data sets directly from the Table (a data set consisting of rows and columns) in the data source. Supports the creation, renaming, and deletion of Groups. Supports the movement of data sets across groups. Supports the renaming and deletion of data sets. Supports quick preview of data sets. Supports full and incremental data updates of data sets. Supports setting up a scheduled schedule to update data sets. When developing data, you can use SQL (Structured Query Language) statements to associate, filter, and convert multiple data sources. Supports using programming languages ​​to write UDFs (user-defined functions) and UDAFs (custom aggregate functions), and implements complex calculation logic through custom coding. Data also supports open sharing, that is, providing Rest APIs (RESTful APIs or RESTful Web APIs, an application programming interface). By calling Rest APIs, you can use the data sets developed in the tool to achieve integration and interaction between applications and data.

[0043] In one embodiment of the present invention, binding to corresponding data points includes: storing the monitoring data set into an asset database corresponding to each data point according to its data type.

[0044] Specifically, the asset database includes: real-time database, relational database and non-relational database; the real-time database is used to store and analyze real-time time series data including sensors, IoT devices, applications, Internet of Vehicles, and Industrial Internet; the relational database is used to store and manage structured data; the non-relational database is used to store and process unstructured data including text, images, audio and video.

[0045] Preferably, the real-time database includes an InfluxDB (an open source distributed time series, event and indicator database) time series database and a TDEngine (a high-performance, distributed Internet of Things, industrial big data platform) time series database, a relational database includes a PostgreSQL (an open source relational database management system) relational database, and a non-relational database includes a MongoDB (a database based on distributed file storage) non-relational database. In this embodiment, when establishing a real-time database, a time series database such as InfluxDB or TDEngine is used, which is specifically used to store and analyze real-time data. InfluxDB is an open source time series database that is mainly used to store and analyze time series data from sources such as sensors, Internet of Things devices, and applications. TDEngine is a high-performance time series database designed for the Internet of Things, Internet of Vehicles, Industrial Internet and other fields. These time series databases have the characteristics of high efficiency, stability and easy expansion when processing large amounts of real-time data. When establishing a relational database, a relational database such as PostgreSQL is used, which is specifically used to store and manage structured data. PostgreSQL is a powerful open source relational database system that supports SQL queries and has rich data types and high scalability. It is suitable for scenarios that require processing large amounts of data, complex queries, and high security. When building a non-relational database, use MongoDB to store unstructured data, which may include text, images, audio, and video. MongoDB is an open source document database that uses JSON-like documents to store data, with high availability, high scalability, and ease of use. It is suitable for scenarios that require storage and processing of large amounts of unstructured data, such as social media, the Internet of Things, and big data analysis.

[0046] Furthermore, resource management services are provided for the twin scenario models in the real-time simulation, including: establishing multiple asset analysis models, and processing various types of data in the asset database according to the asset analysis models, and performing preventive maintenance, predictive maintenance and asset performance optimization on assets. Among them, the asset analysis model includes: static data and dynamic data presentation models, event triggering models, message notification delivery models, performance evaluation analysis models, mechanism models, data-driven AI models, and business operation and maintenance service models.

[0047] See also Figure 2In one embodiment of the present invention, the monitoring data of the industrial Internet of Things, such as edge data, is collected and uploaded to the resource performance management layer (resource performance management development module). The resource performance management layer obtains various types of data information through the asset model API (application programming interface), management configuration API, visualization API, report API, time / alarm API, asset evaluation API, MES / Workflow (manufacturing execution system / workflow) integration API and OTA (over-the-air download technology) API. When the resource performance management layer manages the asset database using the asset analysis model, it can process various types of data, including time series data, value type, string type, file type, database, API interface, audio and video and other data types. With the idea of ​​independent decoupling, the underlying microservices are componentized, and the models of static data, dynamic data presentation, event triggering, message notification, performance evaluation analysis, mechanism model, data-driven AI model, business operation and maintenance services are used to build a complete and rich microservice component, while providing the expansion needs of advanced users with maximum flexibility, allowing users to mount their own developed microservices or components. The asset management service layer provides cross-domain fixed assets, mobile assets, basic asset facilities, and personnel management for preventive maintenance / repair of assets, asset performance optimization, etc. It provides templated configuration services, comprehensive asset management services, and scalable services. Based on the microservice framework, it manages a large number of microservice components, supports service registration discovery, disaster recovery and circuit breaker protection, dynamically expands microservice instances based on data and system load support scale, and reduces capacity to achieve optimal and reasonable resource allocation. The microservice framework supports multiple allocation strategies through load balancing services and high-concurrency application scenarios to meet different application requirements. Specifically, the asset data obtained through the asset performance management layer can be equipped with digital twin templates (twin scenario models) to achieve different functions. For example, by connecting fixed assets, a manufacturing execution system is established. By connecting mobile assets, logistics management is performed. By connecting infrastructure, smart buildings are built. By connecting end users, maintenance system integration is achieved. Finally, in the asset management service layer, asset data can be used to achieve preventive maintenance, predictive maintenance, and asset performance optimization.

[0048] See also Figure 3In a preferred embodiment given, the digital twin model layer adopts configuration operation, and performs component combination configuration, model mounting, twin model design and establishment, and asset topology management establishment by simply dragging and connecting. The twin scene model template generated after the configuration operation can be quickly bound to the upper-level application (for example: connected with fixed assets to realize the manufacturing execution system, connected with mobile assets to establish logistics management, connected with infrastructure to establish a command building, connected with end users, and realize maintenance system integration) to achieve rapid development or system integration in the field. Specifically, the component model mainly includes data input components, data processing components, visualization components, and operation and maintenance management components. Static attributes, cloud input, video, positioning, time zone, edge input and other tool information can be set in the data input component. The data processing component can set OEE (equipment overall efficiency), equipment status, custom indicators, scripts, alarm codes, general alarms, alarms (high and low) and custom components and other tool information. The visualization component can set only dashboards, SaaS composer (digital twin visualization tool), reports and other tool information. The operation and maintenance management component can set tool information such as production plan and OTA (Over-the-Air Technology). When used with the model classification template, the model classification template includes equipment template, production line template and factory template. Among them, the equipment template includes static attributes, edge input, custom indicators, alarm code, dashboard only and report. The production line template includes static attributes, video, custom indicators, script, SaaS composer and report. The factory template includes static attributes, positioning, custom indicators, general alarm, dashboard only and production plan. In the formed configured asset management architecture, multiple asset groups are included, and each asset group consists of different factories, different production lines and different equipment.

[0049] See also Figure 4In one embodiment of the present invention, the APM (application performance monitoring) front desk is responsible for building APMPortal (an application performance monitoring tool) and secondary development services (for example, users mount their own developed microservices or components). Among them, in APM Portal, template management and component management are used to realize the construction of a twin scenario model corresponding to the nuclear power site. Then the model is applied to the APM background in combination with the secondary development service. The APM background includes MSF (development system) and APM Serivice (APM service). Among them, MSF includes load balancing, dynamic expansion and registration center. The interface access to APM Serivice is realized through load balancing, the expansion and contraction of APM Serivice is realized through dynamic expansion, and the registration service of APM Serivice is completed through the registration center. APM Serivice includes APM-core (application performance monitoring core) (multiple instances), Alarm, Metrics (beam indicators), AI and simulation management services (multiple instances) and DIS services (multiple instances). APM Serivice is also connected through a database. In the database, there are Postgresql DB (an open source relational database management system) read-write separation cluster, MongoDB (a database based on distributed file storage) read-write separation cluster, influxDB (an open source distributed time series, event and indicator database) cluster and MinlO (a high-performance, distributed object storage system) cluster. The transmission between relational data and APM Serivice is completed through the Postgresql DB read-write separation cluster, the task persistence and loading and the transmission between non-relational data and APM Serivice are completed through the MongoDB read-write separation cluster, the transmission between time series data and APM Serivice is completed through the influxDB cluster, and the transmission between document data and APM Serivice is completed through the MinlO cluster. APM Serivice also establishes communication with the edge data collection and control through the Rabbitmq HA cluster in the public basic service to upload data of asset equipment through MQTT. The public basic services also include Kafka HA (a high-throughput, distributed, publish / subscribe-based messaging system with high availability) cluster, Redis HA (an open source, memory-based key-value database with high availability written in ANSI C) cluster, Etcd HA (a high-availability, open source, distributed Key / value storage system with high availability) cluster and Rabbitma HA (an open source message broker software with high availability) cluster, and they are connected to APM Serivice respectively.

[0050] See also Figure 5 In one embodiment given, a complete set of visual page template design management is provided, which supports graphical tools. Users can quickly complete data preprocessing logic definition, modeling, and data analysis algorithm definition with zero code according to actual needs, allowing users to interact with machines in real time. It is based on B / S architecture (browser / server architecture, a network architecture mode), replacing the traditional IDE (integrated development environment) restrictions and allowing cross-platform use, meeting the needs of various industrial fields and business solutions. With 100% customization, users can rebuild the on-site environment and provide real-time and detailed display. And build 2D / 3D visual interfaces through visual page template design management tools. The system supports the low-latency transmission requirements of OT data (data from industrial production machinery and equipment, automated acquisition systems, including time series data and non-time series data). The latency requirement for collecting and standardizing from the data source (equipment / system) to the upper-level application is 200ms, and the latency requirement for transmitting from the application to the device / system via the platform is 100ms. Specifically, it can include the following steps: model making, scene construction, data uploading, template and asset structure creation, data binding, and screen rendering output. When making models, 3D design software is used to make models, including buildings and equipment. When building scenes, the 3D model is imported and the scene is built by confirming the scene and equipment style. When uploading data, the data of asset equipment is uploaded through MQTT (message queue telemetry transmission), and the corresponding point information is generated. When creating templates and asset structures, equipment templates and asset blocking structures such as factories, workshops, floors, and equipment are created, and their equipment is bound. When binding data, the data points of the model or the corresponding icons are bound, and the data points can include static data and dynamic data. When rendering and outputting the screen, the designed scene is interactively animated, and the system renders it through WebGL.

[0051] like Figure 6 As shown, the present invention also provides a nuclear power data application integration platform, including: a twin modeling unit, which constructs a twin scene model of the nuclear power site and generates data points corresponding to each monitoring point of the industrial Internet of Things; an operation simulation unit, which obtains the monitoring data set of the industrial Internet of Things, binds it to the corresponding data points, and simulates the real-time operation of the twin scene model; and a management service unit, which performs resource management services on the twin scene model in the real-time operation simulation.

[0052] In one embodiment of the present invention, in the nuclear power data application integration platform of the present invention, a twin scene model is quickly built in the form of component matching templates according to the buildings and equipment at the nuclear power site through a twin modeling unit, and data points corresponding to each monitoring point can be established in the twin scene model. And by using the operation simulation unit, the monitoring data set obtained by the industrial Internet of Things is input into each data point to realize the operation simulation of the twin scene model. And, through the management service unit, resource management services are implemented based on motion simulation. That is, it includes preventive maintenance, predictive maintenance and asset performance optimization management services for various equipment at the nuclear power site.

[0053] In summary, the present invention provides M2I (machine to intelligent gateway) connection through many industrial communication protocols such as Modbus (a serial communication protocol), OPC UA (a data security exchange interoperability standard used in the automation industry and other industries), Lora (a low-power LAN wireless standard), etc., and also includes heterogeneous data access such as third-party data (such as systems, files, and databases); heterogeneous data fusion is a data processing technology that integrates and uniformly processes data sources from different businesses and business units so that data of different formats and structures can be compatible and exchanged with each other. The visualization part is tooled through drag and drop, low-code development, component library, etc. The asset performance analysis part can integrate some analysis tools, such as equipment energy efficiency analysis, equipment quality analysis, etc. to achieve the goal of performance analysis. By managing multiple analysis models of assets, various types of data can be processed, including time series data, value types, string types, file types, databases, API interfaces, audio and video, and other data types. With the idea of ​​independent decoupling, the underlying microservices are componentized, and a complete and rich microservice component is constructed from the aspects of static data, dynamic data display, event triggering, message notification, performance evaluation analysis, mechanism model, data-driven AI model, business operation and maintenance services, etc., while providing the expansion needs of advanced users with maximum flexibility, allowing users to mount their own developed microservices or components. Therefore, the present invention effectively overcomes various shortcomings in the prior art and has a high industrial utilization value.

[0054] The above embodiments are merely illustrative of the principles and effects of the present invention, and are not intended to limit the present invention. Anyone familiar with the art may modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by a person of ordinary skill in the art without departing from the spirit and technical concept disclosed by the present invention shall still be covered by the claims of the present invention.

Claims

1. A method for integrating nuclear power data applications, characterized in that: The steps include: Build a twin scenario model of the nuclear power site and generate data points corresponding to each monitoring point of the industrial Internet of Things; Obtain the monitoring data set of the industrial Internet of Things, bind it to the corresponding data point, and simulate the real-time operation of the twin scenario model; Provide resource management services for the twin scenario model in real-time simulation.

2. The nuclear power data application integration method according to claim 1, characterized in that: Build a twin scenario model of the nuclear power site and generate data points corresponding to each monitoring point of the industrial Internet of Things, including: Design three-dimensional models of buildings and equipment at the nuclear power site, and establish the twin scene model in combination with the scene of the nuclear power site and the picture style of the equipment; According to the monitoring points of each equipment at the nuclear power site, the data points are generated in the twin scenario model.

3. The nuclear power data application integration method according to claim 1 or 2, characterized in that: Building a twin scenario model of a nuclear power site also includes: Combine and configure the model components and carry the corresponding model classification templates to establish each asset group model; Obtain an extended secondary development service model, combine it with multiple asset group models, and generate the twin scenario model.

4. The nuclear power data application integration method according to claim 3 is characterized by: The model components include a data input component, a data processing component, a visualization component and an operation and maintenance management component, and the model classification templates include an equipment template, a production line template and a factory template.

5. The nuclear power data application integration method according to claim 1 is characterized by: The data types for obtaining monitoring data sets of the industrial Internet of Things include: time series data type, value type, string type, file data type, database type, API interface and audio and video data type.

6. The nuclear power data application integration method according to claim 5 is characterized by: Binding to the corresponding data point includes: The monitoring data set is stored in the asset database corresponding to each data point according to its data type.

7. The nuclear power data application integration method according to claim 6 is characterized by: The asset database includes: a real-time database, a relational database and a non-relational database; The real-time database is used to store and analyze real-time time series data including sensors, Internet of Things devices, applications, Internet of Vehicles, and Industrial Internet; the relational database is used to store and manage structured data; the non-relational database is used to store and process unstructured data including text, images, audio, and video.

8. The nuclear power data application integration method according to claim 7 is characterized by: The real-time database includes an InfluxDB time series database and a TDEngine time series database, the relational database includes a PostgreSQL relational database, and the non-relational database includes a MongoDB non-relational database.

9. The nuclear power data application integration method according to claim 6, characterized in that: Provide resource management services for the twin scenario model in real-time simulation, including: Establish multiple asset analysis models, and process various types of data in the asset database according to the asset analysis models to perform preventive maintenance, predictive maintenance and asset performance optimization on assets, wherein the asset analysis models include: static data and dynamic data presentation models, event triggering models, message notification delivery models, performance evaluation analysis models, mechanism models, data-driven AI models, and business operation and maintenance service models.

10. A nuclear power data application integration platform, characterized in that: include: A twin modeling unit, which constructs a twin scenario model of the nuclear power site and generates data points corresponding to each monitoring point of the industrial Internet of Things; An operation simulation unit, wherein the operation simulation unit obtains a monitoring data set of the industrial Internet of Things, binds it to the corresponding data point, and simulates the real-time operation of the twin scenario model; as well as A management service unit, which performs resource management services on the twin scenario model in real-time simulation.

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