Digital integrated system of LNG receiving station

Abstract

The invention discloses a digital integrated system of an LNG receiving station. Wherein the data fusion module comprises a multi-source heterogeneous data fusion engine and is used for collecting, storing and preprocessing data of an LNG receiving station; the model building module comprises a self-adaptive digital twinning modeling framework and is used for building digital twinning models of the LNG receiving station, and the models comprise a physical model, a machine learning model and a 3D visual model; the service module comprises an intelligent scene engine and a decision support subsystem driven by a knowledge graph; and the application module comprises an application development framework of a micro-service architecture and is used for developing an application oriented to a terminal user. Thus, efficient fusion and processing of multi-source heterogeneous data are realized through fusion of a multi-layer module architecture design and an advanced technology, and the data utilization rate and the real-time performance are greatly improved. And meanwhile, the intelligent decision-making capability of the system is remarkably enhanced, and various complex operation scenes and emergency situations can be effectively handled.

Description

Technical Field

[0001] The present invention relates to the technical field of liquefied natural gas, and in particular to a digital integrated system of an LNG receiving station. Background Art

[0002] Liquefied natural gas (LNG) receiving stations are important infrastructure for regasifying LNG shipped from sea and transporting it to the natural gas pipeline network. With the adjustment of global energy structure and the increase in demand for clean energy, the position of LNG receiving stations in the energy supply chain is becoming increasingly important. In order to improve the operational efficiency and safety of LNG receiving stations, digital twin technology has been introduced into the management of LNG receiving stations. Digital twin technology achieves real-time monitoring, analysis and optimization by building an accurate digital model of the physical entity in the virtual space.

[0003] At present, the digital construction of LNG receiving stations is mainly focused on data collection and visualization. Some advanced LNG receiving stations have deployed distributed control systems (DCS) and supervisory control and data acquisition systems (SCADA) to achieve basic data collection and remote monitoring functions. However, these systems often operate independently and lack effective integration and data sharing mechanisms. At the same time, existing digital systems mainly focus on historical data analysis and simple prediction functions, making it difficult to achieve simulation and intelligent decision support for complex scenarios.

[0004] As the scale of LNG receiving stations continues to expand and operational complexity increases, the existing digital system is unable to meet the growing demand for intelligence. Summary of the invention

[0005] In view of the above problems, the present invention provides a digital integration system for an LNG receiving station, aiming to solve the problems of insufficient multi-source heterogeneous data fusion capability, lack of high-precision digital twin models, limited intelligent decision-making support capabilities, and insufficient system scalability and integration in the existing digital systems of LNG receiving stations.

[0006] According to a first aspect of the present invention, a digital integrated system for an LNG receiving station is provided, comprising:

[0007] Data fusion module, including a multi-source heterogeneous data fusion engine, used to collect, store and pre-process data from LNG receiving stations;

[0008] A model building module, including an adaptive digital twin modeling framework, for building a digital twin model of an LNG terminal, the model including a physical model, a machine learning model, and a 3D visualization model;

[0009] Service modules, including intelligent scenario engines and knowledge graph-driven decision support subsystems;

[0010] Application modules, including the application development framework of microservice architecture, are used to develop various applications for end users;

[0011] Among them, the data fusion module pre-processes the data collected from the LNG receiving station and sends it to the model construction module; the model construction module updates and optimizes the model through the pre-processed data, and sends the updated and optimized model parameters and status information to the service module; the service module performs intelligent scene generation and simulation analysis based on the model parameters and the status information, and generates support decisions, and sends the support decisions to the application module for user use.

[0012] Optional:

[0013] The multi-source heterogeneous data fusion engine includes a convolutional neural network for data cleaning and fusion.

[0014] Optionally, the step of cleaning and fusing data by the convolutional neural network includes:

[0015] Normalizing and standardizing the data;

[0016] Use convolutional neural networks to extract target features of data;

[0017] Performing time series alignment on the data according to the extracted target features;

[0018] The aligned data were fused using the weighted average method.

[0019] Optionally, the aligned data can be fused using a weighted average method using the following formula:

[0020]

[0021] Among them, D fused To fuse the aligned data, D i is the data of the ith data source, w i is the corresponding weight, and n is the number of data sources.

[0022] Optionally, the application module also includes a web management portal, a mobile application, and a VR / AR application; wherein the web management portal adopts the React framework for front-end interaction, the mobile application supports remote monitoring and operation of iOS and Android platforms, and the VR / AR application is used for immersive training and remote collaboration.

[0023] Optionally, the decision support subsystem includes a knowledge graph about the LNG receiving station and an inference engine based on the knowledge graph; wherein the knowledge graph includes equipment information, process information and safety information.

[0024] Optionally, the digital integration system transmits the real-time data flow from the data fusion module to the model building module using a distributed message queue mechanism.

[0025] Optionally, the application development framework of the microservice architecture includes:

[0026] Docker containerization module, used to containerize each microservice;

[0027] Kubernetes container orchestration module, used to manage containerized microservices;

[0028] The target API gateway is used to manage and route API requests for all microservices.

[0029] Optionally, the system further includes:

[0030] A digital twin development workbench that provides visual modeling tools, an integrated code editor, and model validation testing tools;

[0031] Real-time monitoring and predictive analysis module, used to develop multi-dimensional data visualization dashboard and integrate predictive maintenance algorithm;

[0032] Optimization Decision Support module for developing multi-objective optimization algorithms and interactive "what-if" tools;

[0033] A safety and emergency management module to include digital twin-based risk assessment models and emergency response simulators;

[0034] Collaborative development and knowledge management module, used to integrate version control system and knowledge base management system.

[0035] Optionally, the digital integration system uses SSL / TLS to encrypt communications.

[0036] The above one or more technical solutions in the embodiments of this specification have at least the following technical effects:

[0037] The embodiment of the present specification provides a digital integration system for an LNG receiving station, wherein a data fusion module includes a multi-source heterogeneous data fusion engine for collecting, storing and preprocessing data of the LNG receiving station; a model construction module includes an adaptive digital twin modeling framework for building a digital twin model of the LNG receiving station, wherein the model includes a physical model, a machine learning model and a 3D visualization model; a service module includes an intelligent scene engine and a decision support subsystem driven by a knowledge graph; an application module includes an application development framework of a microservice architecture for developing various applications for end users; wherein the data fusion module preprocesses the data collected from the LNG receiving station and sends it to the model construction module; the model construction module updates and optimizes the model through the preprocessed data, and sends the updated and optimized model parameters and status information to the service module; the service module performs intelligent scene generation and simulation analysis based on the model parameters and the status information, and generates support decisions, and sends the support decisions to the application module for user use. In this way, through the integration of multi-layer module architecture design and advanced technologies, efficient fusion and processing of multi-source heterogeneous data is achieved, which greatly improves data utilization and real-time performance. It ensures high accuracy and real-time updating of the model, providing a solid foundation for the simulation and optimization of complex working conditions. At the same time, it significantly enhances the system's intelligent decision-making capabilities and can effectively respond to various complex operating scenarios and emergencies.

[0038] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented according to the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Also, throughout the accompanying drawings, the same reference figures are used to represent the same components. In the drawings:

[0040] Figure 1 A block diagram of a digital integrated system for an LNG receiving station in an embodiment of the present invention is shown.

[0041] Figure 2 A flow chart of data cleaning and fusion in an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0042] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0043] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] It should be noted that similar reference numerals and letters denote similar items in the following drawings, and therefore, once an item is defined in one drawing, further definition and explanation thereof is not required in subsequent drawings.

[0045] In the description of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "set", "install", "connect", and "connect" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0046] At present, the digital construction of LNG receiving stations is mainly focused on data collection and visualization. Some advanced LNG receiving stations have deployed distributed control systems (DCS) and supervisory control and data acquisition systems (SCADA) to achieve basic data collection and remote monitoring functions. However, these systems often operate independently and lack effective integration and data sharing mechanisms. At the same time, the existing digital systems mainly focus on historical data analysis and simple prediction functions, making it difficult to achieve simulation and intelligent decision support for complex scenarios.

[0047] As the scale of LNG receiving stations continues to expand and the complexity of operations increases, the existing digital system is unable to meet the growing demand for intelligence. The main problems are as follows:

[0048] The ability to integrate and process multi-source heterogeneous data is insufficient, making it difficult to fully and accurately reflect the real-time status of LNG receiving stations;

[0049] The lack of a high-precision digital twin model makes it impossible to simulate and optimize complex working conditions. Second, the existing system’s intelligent decision-making support capabilities are limited, making it difficult to cope with complex operating scenarios and emergencies.

[0050] The system lacks scalability and integration, making it difficult to adapt to the rapid development and technological updates of LNG receiving stations.

[0051] Based on the above situation, an embodiment of the present invention provides a digital integration system for an LNG receiving station, which realizes comprehensive perception, high-precision modeling, intelligent simulation and optimized decision-making of LNG receiving station data. By building a flexible and scalable platform architecture, the full process integration of data collection, model building, simulation analysis and decision support is realized, thereby significantly improving the operational efficiency, safety and intelligence level of the LNG receiving station.

[0052] Specifically, combined with Figure 1 As shown, the present invention provides a digital integration system for an LNG receiving station, comprising: a data fusion module, a model building module, a service module and an application module.

[0053] Among them, the data fusion module includes a multi-source heterogeneous data fusion engine, which is used to collect, store and pre-process the data of the LNG receiving station;

[0054] The model building module includes an adaptive digital twin modeling framework for building a digital twin model of the LNG receiving station, which includes an accurate physical model of each component of the LNG receiving station developed using the Modelica language, a machine learning model, and a 3D visualization model. The machine learning model refers to a data preprocessing and anomaly detection model developed using the TensorFlow framework. The 3D visualization model refers to a high-fidelity virtual reality display model developed using Unity3D.

[0055] The service module includes an intelligent scenario engine and a knowledge graph-driven decision support subsystem, which is used to provide intelligent scenario generation, simulation and decision support services.

[0056] The application module includes an application development framework with a microservice architecture, which is used to develop applications for end users.

[0057] The data flow of the system follows the basic principle of bottom-up, starting from the data fusion module, passing through the model building module, the service module, and finally reaching the application module. At the same time, there is also a reverse data flow in the system for feedback and optimization. The modules exchange data through standardized interfaces. This design ensures the modularity and scalability of the system.

[0058] The data fusion module pre-processes the data collected from the LNG receiving station and sends it to the model building module; this is an ongoing process to ensure that the model building module always has the latest data input. The data fusion module is also responsible for storing historical data for use by the model building module when conducting large-scale model training or optimization.

[0059] The model building module updates and optimizes the model through preprocessed data, and sends the updated and optimized model parameters and status information to the service module; this process may be real-time or periodic, depending on the specific application scenario and performance requirements. The service module can also send requests to the model building module to obtain specific model information or trigger model updates.

[0060] The service module generates intelligent scenarios and performs simulation analysis based on the model parameters and the state information, and generates support decisions, and sends the support decisions to the application module for user use. The application module can send specific simulation or analysis requests to the service module, and the service module returns the corresponding results. This interaction is usually real-time to support users' immediate operations and decisions.

[0061] The application module also undertakes the important task of collecting user feedback. User operations and feedback information will be transmitted from the application module back to the corresponding module. For example, user feedback on simulation results can be transmitted back to the service module to optimize the decision algorithm; feedback on model accuracy can be transmitted back to the model construction module to improve the model; feedback on data quality can be transmitted back to the data fusion module to optimize the data processing process.

[0062] In terms of the connection between modules, this embodiment adopts a variety of technical means. First, the modules are connected through predefined standardized interfaces, which specify the data format, transmission method and communication protocol, ensuring the compatibility and interoperability between different modules. For example, the system provides a RESTful API, which mainly includes the following interfaces:

[0063] / api / data: data management interface

[0064] / api / model: Model management interface

[0065] / api / simulation: simulation management interface

[0066] / api / decision: Decision support interface

[0067] / api / visualization: Visualization API

[0068] Each interface supports HTTP methods such as GET, POST, PUT, and DELETE to implement corresponding CRUD operations.

[0069] In this embodiment, for large amounts of data transmission that require asynchronous processing, such as real-time data flow from the data fusion module to the model building module, a distributed message queue mechanism is used for connection. This approach can effectively handle sudden large-scale data and ensure reliable data transmission.

[0070] The service bus is used as the connection hub between the service module and the application module. The service bus is responsible for managing and coordinating the calls of various services, realizing the decoupling and flexible combination of services. In order to support real-time data updates and user interactions, real-time communication protocols such as WebSocket are used in appropriate inter-module connections. This is especially applicable to real-time data transmission from the data fusion module to the model building module, and real-time result push from the service module to the application module.

[0071] In this embodiment, in order to optimize the data flow, an appropriate caching mechanism is set between the modules to reduce repeated data transmission and calculation. For example, commonly used model parameters are cached between the model building module and the service module, and frequently accessed analysis results are cached between the service module and the application module. During the data transmission process between modules, large volume data is compressed to reduce network bandwidth usage and improve transmission efficiency. When the model building module transmits updates to the service module, an incremental update mechanism is used to transmit only the changed parts, thereby reducing the amount of data transmission.

[0072] Through this carefully designed multi-module architecture and optimized inter-module interaction mechanism, efficient data flow and close inter-module collaboration are achieved. This not only ensures the modularity and scalability of the system, but also ensures the high performance and real-time response capability of the entire system through optimized connection and data flow mechanisms. This provides strong technical support for the digital twin application of LNG receiving stations, effectively improving the operational efficiency and intelligence level of LNG receiving stations.

[0073] In this embodiment, the system provides a RESTful API, including a data management interface, a model management interface, a simulation management interface, a decision support interface, and a visualization interface.

[0074] In one embodiment, the multi-source heterogeneous data fusion engine uses a distributed storage system (such as Hadoop HDFS) to store massive historical data, a time series database (such as InfluxDB) to store real-time sensor data, and a graph database (such as Neo4j) to store knowledge graphs.

[0075] The multi-source heterogeneous data fusion engine mainly includes a convolutional neural network for data cleaning and fusion.

[0076] Among them, combined Figure 2 As shown, the convolutional neural network includes steps 101 to 104 for data cleaning and fusion:

[0077] Step 101: normalizing and standardizing the data;

[0078] Step 102: Use a convolutional neural network to extract target features of the data;

[0079] Step 103: performing time series alignment on the data according to the extracted target features;

[0080] Step 104: Use the weighted average method to fuse the aligned data.

[0081] In one embodiment, the aligned data is fused using a weighted average method using the following formula:

[0082]

[0083] Among them, D fused To fuse the aligned data, D i is the data of the ith data source, w i is the corresponding weight, and n is the number of data sources.

[0084] In one embodiment, the key to the adaptive twin digital modeling framework lies in its adaptive algorithm, which can automatically adjust model parameters according to real-time data. Its core idea can be expressed by the following formula:

[0085] θ t+1 =θ t +α·▽L(θ t )

[0086] Among them, θ t represents the model parameters at time t, α is the learning rate, ▽L(θ t ) is the gradient of the loss function with respect to the parameters. In this way, the model can be continuously optimized to improve accuracy.

[0087] In addition, the intelligent scenario engine uses an intelligent scenario generator based on reinforcement learning to automatically generate various operational and emergency scenarios. Its high-performance simulation engine supports large-scale parallel computing, which significantly improves the simulation speed and accuracy.

[0088] Optionally, the decision support subsystem includes a knowledge graph about the LNG receiving station and a reasoning engine based on the knowledge graph; wherein the knowledge graph includes equipment information, process information and safety information of the LNG receiving station, and the reasoning engine developed based on this knowledge graph can provide intelligent suggestions for optimizing decisions.

[0089] In this embodiment, the application layer adopts the application development framework of the microservice architecture to encapsulate each functional module as an independent service. The core of this architecture is the Docker containerization module, which is used to containerize each microservice; the Kubernetes container orchestration module, which is used to manage containerized microservices; and the target API gateway, which is used to manage and route API requests for all microservices. Flexible deployment and efficient management of services are achieved.

[0090] In one embodiment, the application module also includes a web management portal, a mobile application, and a VR / AR application; wherein the web management portal uses the React framework for front-end interaction, the mobile application supports remote monitoring and operation of iOS and Android platforms, and the VR / AR application is used for immersive training and remote collaboration.

[0091] Optionally, the system further includes:

[0092] A digital twin development workbench that provides visual modeling tools, an integrated code editor, and model validation testing tools;

[0093] Real-time monitoring and predictive analysis module, used to develop multi-dimensional data visualization dashboard and integrate predictive maintenance algorithm;

[0094] Optimization Decision Support module for developing multi-objective optimization algorithms and interactive "what-if" tools;

[0095] A safety and emergency management module to include digital twin-based risk assessment models and emergency response simulators;

[0096] Collaborative development and knowledge management module, used to integrate version control system and knowledge base management system.

[0097] In addition, the digital integration system uses SSL / TLS to encrypt communications; implements role-based access control (RBAC) and uses multi-copy deployment to ensure high availability; and also implements automatic backup and disaster recovery mechanisms.

[0098] About system scalability:

[0099] Adopt microservice architecture to support independent expansion of modules;

[0100] Use Kubernetes' automatic scaling capabilities;

[0101] Provide a plug-in system to support custom function extensions;

[0102] Integrate with existing systems:

[0103] Provide standardized data interfaces (such as OPC UA, Modbus);

[0104] Support import / export of mainstream industrial control system data formats;

[0105] Provide WebSocket interface to achieve real-time data synchronization;

[0106] System testing and verification:

[0107] Unit testing: Use the Jest framework to perform unit testing on each module;

[0108] Integration testing: Use Postman to perform API integration testing;

[0109] System testing: Conduct full-process testing in a simulated environment;

[0110] Performance testing: Use JMeter for load testing and stress testing;

[0111] Training and User Guides:

[0112] Provide online documentation and video tutorials;

[0113] Organize on-site training and remote training;

[0114] Establish user communities to promote exchange of experiences;

[0115] Potential system problems and solutions:

[0116] Data quality issues: Implement automatic data cleaning and anomaly detection;

[0117] Model accuracy problem: Use online learning and transfer learning techniques;

[0118] System performance issues: Implement dynamic load balancing and resource scheduling;

[0119] Security issues: Conduct security audits and vulnerability scans regularly.

[0120] This example takes the optimization of energy efficiency of LNG receiving station as an example to illustrate:

[0121] (1) Data collection: Collect real-time data from various sensors and control systems at the LNG receiving terminal.

[0122] (2) Data preprocessing: Use a multi-source heterogeneous data fusion engine to clean and fuse data.

[0123] (3) Model construction: Build a digital twin model of the LNG receiving station based on historical data and physical models.

[0124] (4) Scenario generation: Use the intelligent scenario engine to create a variety of operational scenarios.

[0125] (5) Simulation analysis: Simulate different scenarios and analyze energy consumption.

[0126] (6) Optimization decision: Generate energy efficiency optimization suggestions based on simulation results and knowledge graph.

[0127] (7) Scheme implementation: The optimization scheme is sent to the LNG receiving station control system through the API.

[0128] (8) Effect monitoring: Real-time monitoring of optimized energy efficiency and continuous optimization of the model.

[0129] Through the above steps, this system can help LNG receiving stations significantly improve energy efficiency and reduce operating costs.

[0130] In summary, an embodiment of the present specification provides a digital integration system for an LNG receiving station, wherein a data fusion module includes a multi-source heterogeneous data fusion engine for collecting, storing and preprocessing data of the LNG receiving station; a model building module includes an adaptive digital twin modeling framework for building a digital twin model of the LNG receiving station, and the model includes a physical model, a machine learning model and a 3D visualization model; a service module includes an intelligent scenario engine and a decision support subsystem driven by a knowledge graph; an application module includes an application development framework of a microservice architecture for developing various applications for end users; wherein the data fusion module preprocesses the data collected from the LNG receiving station and sends it to the model building module; the model building module updates and optimizes the model through the preprocessed data, and sends the updated and optimized model parameters and status information to the service module; the service module performs intelligent scenario generation and simulation analysis based on the model parameters and the status information, and generates support decisions, and sends the support decisions to the application module for user use. In this way, through the integration of multi-layer module architecture design and advanced technologies, efficient integration and processing of multi-source heterogeneous data is achieved, greatly improving data utilization and real-time performance. It ensures high accuracy and real-time updating of the model, providing a solid foundation for the simulation and optimization of complex working conditions. At the same time, it significantly enhances the system's intelligent decision-making ability and can effectively respond to various complex operating scenarios and emergencies.

[0131] The above are only various embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A digital integrated system for an LNG receiving station, characterized in that: include: Data fusion module, including a multi-source heterogeneous data fusion engine, used to collect, store and pre-process data from LNG receiving stations; A model building module, including an adaptive digital twin modeling framework, for building a digital twin model of an LNG terminal, the model including a physical model, a machine learning model, and a 3D visualization model; Service modules, including intelligent scenario engines and knowledge graph-driven decision support subsystems; Application modules, including the application development framework of microservice architecture, are used to develop applications for end users; The data fusion module pre-processes the data collected from the LNG receiving station and sends the pre-processed data to the model building module; the model building module updates and optimizes the model through the pre-processed data, and sends the updated and optimized model parameters and status information to the service module; The service module performs intelligent scene generation and simulation analysis according to the model parameters and the status information, generates support decisions, and sends the support decisions to the application module for user use.

2. The system according to claim 1, characterized in that: The multi-source heterogeneous data fusion engine includes a convolutional neural network for data cleaning and fusion.

3. The system according to claim 2, characterized in that The steps of data cleaning and fusion of the convolutional neural network include: Normalize and standardize the collected data; Use convolutional neural networks to extract target features of data; Performing time series alignment on the data according to the extracted target features; The aligned data were fused using the weighted average method.

4. The system according to claim 3, characterized in that The aligned data are fused using the weighted average method using the following formula: Among them, D fused To fuse the aligned data, D i is the data of the ith data source, w i is the corresponding weight, and n is the number of data sources.

5. The system according to claim 1, characterized in that The application module also includes a web management portal, a mobile application, and a VR / AR application; wherein the web management portal uses the React framework for front-end interaction, the mobile application supports remote monitoring and operation of iOS and Android platforms, and the VR / AR application is used for immersive training and remote collaboration.

6. The system according to claim 1, characterized in that The decision support subsystem includes a knowledge graph about the LNG receiving station and a reasoning engine based on the knowledge graph; wherein the knowledge graph includes equipment information, process information and safety information of the LNG receiving station.

7. The system according to claim 1, characterized in that The digital integration system transmits the real-time data flow from the data fusion module to the model building module using a distributed message queue mechanism.

8. The system according to claim 1, characterized in that The application development framework of the microservice architecture includes: Docker containerization module, used to containerize each microservice; Kubernetes container orchestration module, used to manage containerized microservices; The target API gateway is used to manage and route API requests for all microservices.

9. The system according to claim 1, characterized in that The system further comprises: A digital twin development workbench that provides visual modeling tools, an integrated code editor, and model validation testing tools; Real-time monitoring and predictive analysis module, used to develop multi-dimensional data visualization dashboard and integrate predictive maintenance algorithm; Optimization Decision Support module for developing multi-objective optimization algorithms and interactive "what-if" tools; A safety and emergency management module to include digital twin-based risk assessment models and emergency response simulators; Collaborative development and knowledge management module, used to integrate version control system and knowledge base management system.

10. The system according to claim 1, characterized in that The digital integration system uses SSL / TLS to encrypt communications.

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