Implementation method of digital twin technology in intelligent management and control platform

Abstract

The application provides an implementation method of digital twin technology in a smart management and control platform, comprising: constructing a digital twin model corresponding to a physical equipment structure, state parameters and behavior logic of a plant station, and visualizing modeling through a graph element form; accessing operation data collected by various sensors and terminal devices of the plant station through an Internet of Things platform component, and high-frequency mapping the operation data to the digital twin model to realize data synchronization between a physical system and a virtual model; performing trend prediction, risk assessment and working condition optimization based on the mapping data, and generating a dynamic control strategy, which is written back to the physical equipment through a platform control system to form an intelligent closed-loop control mechanism; and effectively improving response efficiency and decision-making intelligence level of the plant station, and improving overall operation efficiency, safety and intelligence level of the smart management and control platform.

Description

Technical Field

[0001] This invention relates to the field of industrial control technology, and in particular to a method for implementing digital twin technology in an intelligent control platform. Background Technology

[0002] With the increasing complexity of equipment in industrial enterprises and the ever-growing demands for safety and economy, traditional plant operation and maintenance management methods are no longer sufficient to meet the intelligent management goals of "multi-source sensing, real-time monitoring, predictive early warning, and closed-loop control." While some enterprises have deployed DCS systems, SCADA platforms, or other information-based monitoring methods, the following problems are commonly observed: Traditional systems lack the ability to model equipment status in multiple dimensions and cannot achieve structured mapping of physical assets in the digital space. This results in the overall operation of the plant being "incomplete, inaccurate, and difficult to adjust," leading to plant operation relying on manual judgment, poor response timeliness, delayed risk warning, and low scheduling efficiency. To this end, a method for implementing digital twin technology in an intelligent management and control platform is proposed. Summary of the Invention

[0003] In view of this, the present invention provides a method for implementing digital twin technology in an intelligent management and control platform to solve or alleviate the technical problems existing in the prior art, and at least provides a beneficial option.

[0004] The technical solution of this invention is implemented as follows: a method for implementing digital twin technology in an intelligent management and control platform, comprising the following steps: S1. Construct a digital twin model of the plant's physical equipment. The digital twin model includes digital representations corresponding to the structural information, state parameters, and behavioral logic of the physical equipment. The model is constructed by calling a preset primitive library in the configuration modeling component. The primitive library includes component primitives for describing the equipment structure, state primitives for describing the operating state, and behavioral primitives for representing the control logic. The model is configured in the platform in the form of primitives at the process flow diagram level and supports multi-level nested management of equipment-level models, subsystem-level models, and plant-level models.

[0005] S2. Achieve high-frequency synchronous mapping of operational data by accessing real-time operational data collected by various sensors and terminal devices in the plant through an IoT platform component. The IoT platform component includes an edge gateway device, a communication protocol parsing module, and a data caching module, used to achieve unified access to heterogeneous data sources with multiple protocols such as Modbus, OPC UA, and MQTT. Before being mapped to the digital twin model, the real-time operational data undergoes a data preprocessing process, including timestamp standardization, missing value imputation, outlier removal, and unit conversion, to improve the accuracy and consistency of data synchronization. After the mapping is completed, the operating status of the physical entity and the digital twin model are dynamically and frequently updated.

[0006] S3. Analysis, optimization, and closed-loop control based on operational data include: using a time-series prediction model based on Long Short-Term Memory (LSTM) networks to predict the state trends of equipment load, energy consumption changes, and environmental parameters; employing a multi-factor comprehensive evaluation model for equipment risk assessment, based on runtime, temperature rise level, vibration amplitude, and historical fault records, and outputting the equipment risk level through a fuzzy logic algorithm; furthermore, optimizing key operating parameters using particle swarm optimization or genetic algorithms, with minimum energy consumption, maximum output, and minimum emissions as optimization objectives; finally, generating dynamic adjustment strategies based on the analysis results, and converting the analysis results into control commands by the decision execution module in the platform control system. These commands are then written to the PLC or DCS system by the parameter writing module, and the effectiveness of the control results is verified in real time by the feedback monitoring module, thus achieving closed-loop control of the physical system by the platform.

[0007] The embodiments of the present invention have the following advantages due to the adoption of the above technical solutions: This invention achieves a full-dimensional mapping between the physical system and the virtual model by constructing a digital twin model corresponding to the structure, status parameters, and behavioral logic of the plant's physical equipment, enhancing the platform's visualization and understandability of equipment operation. By accessing multi-protocol industrial IoT data and performing high-frequency synchronous mapping, it solves the problems of difficult multi-source data fusion and lagging data updates in traditional systems. Combining LSTM trend prediction, fuzzy logic evaluation, and multi-objective optimization algorithms, it achieves early perception of equipment status, intelligent assessment of operational risks, and optimized control of scheduling parameters, effectively improving the plant's response efficiency and decision-making intelligence level, and enhancing the overall operational efficiency, security, and intelligence level of the intelligent management and control platform.

[0008] The above overview is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of the invention will become readily apparent from the accompanying drawings and the following detailed description. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This is a flowchart of the steps of the present invention. Detailed Implementation

[0011] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of the invention. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0012] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0013] like Figure 1 As shown in the figure, this invention provides a method for implementing digital twin technology in an intelligent management and control platform, including the following steps: Step S1: Construct a digital twin model of the plant's physical equipment In this step, a corresponding digital twin model is first established based on the physical object of the target plant. A digital twin model is a comprehensive model used in virtual space to mirror the structure, operating status and control logic of real equipment. It establishes a highly realistic "digital mapping body". In actual implementation, the configuration modeling component embedded in the platform is called to start the primitive modeling engine.

[0014] The primitive modeling engine connects to the platform's pre-built primitive library, which includes three basic types of components: (1) Equipment structure elements, used to represent the physical form and component hierarchy of specific equipment, such as pumps, valves, sensors, units, etc.; (2) Status elements are used to display the operating conditions of the equipment in real time, including status variables such as speed, flow rate, pressure, temperature, and current; (3) Behavioral primitives, which represent the action sequence, interlock logic or process state switching in the control system through logic diagrams, such as run / stop logic, switch timing, etc.

[0015] During the modeling process, users can use a drag-and-drop interface to build the model. The system supports functions such as node connection, logic configuration, and attribute binding. The platform supports one-to-one mapping between primitive models and actual plant process flow diagrams, forming a multi-level nested structure based on international modeling standards such as ISA-95, including: Equipment-level model (single equipment as nodes); subsystem-level model (pump station system, heating subsystem); plant-level model (overall system layer, with complete data flow relationships). The above steps enable the construction of digital twin models, view binding, nested hierarchy division, and logical mapping.

[0016] Step S2: Implement high-frequency synchronous mapping of runtime data After constructing the twin model, the next step is to realize high-frequency synchronous mapping between the real-time operating data of the plant's physical equipment and the model. This step relies on the Industrial Internet of Things (IIoT) platform components to complete the underlying data collection and mapping process.

[0017] On the hardware side, an edge gateway device is deployed between the data source and the platform, serving as a communication relay node. This gateway supports multi-protocol parsing and has an embedded communication protocol conversion module, making it compatible with mainstream industrial protocols such as Modbus RTU / TCP, OPCUA, and MQTT, enabling terminal devices of different brands and functions to access the platform.

[0018] Specifically, the sensors and controllers send raw data such as temperature, voltage, pressure, and displacement to the edge gateway via RS485, CAN, and Ethernet. The edge gateway has a built-in protocol adapter and data decoder to unify the data format into a platform-compatible JSON or Protobuf format. The edge gateway transmits data to the cloud or local platform, undergoing the following preprocessing steps: Timestamp standardization: Aligning data from multiple devices to a unified time base; Missing value imputation: Use moving average, nearest neighbor method or linear interpolation to imput abnormal packet loss; Outlier removal: combining Z-Score and IQR methods to identify noise; Unit conversion: unify engineering quantity units from different sources to the standard dimension system.

[0019] The platform has a data caching module and latency to ensure data integrity under high concurrency. The processed data is mapped to the digital twin model in a structured form of a bidirectional hash table and bound to the corresponding primitive attribute fields. Step S3: Analyze, optimize, and implement closed-loop control based on operational data. After achieving high-frequency synchronization between operational data and the digital twin model, the platform performs intelligent analysis and policy feedback on the mapped data, realizing a closed loop of intelligent management and control.

[0020] For time-series runtime data, the platform uses a deep learning model based on LSTM (Long Short-Term Memory) network for state trend prediction. LSTM network can capture short-term fluctuations and long-term trends in runtime data. Its structure includes forget gates and memory units, making it suitable for handling time-series variables such as equipment load, energy consumption, and temperature rise. The platform supports online training and offline inference of the model. Combined with a sliding window strategy, the prediction model parameters are periodically updated to ensure prediction accuracy.

[0021] Then, for the health status of the equipment, the platform constructs a multi-factor assessment model, integrating the following variables: Running time (T); vibration amplitude (V); motor temperature rise (ΔT); The last 3 fault intervals (F1, F2, F3); Real-time deviation index (ε).

[0022] The fuzzy logic reasoning method is used to fuzzify, derive rules and defuzzify each input item, and output the risk level (low, medium and high) as the basis for early warning and scheduling.

[0023] Subsequently, the working condition optimization strategy is implemented. In the multi-objective optimization problem, the objective function can be defined as: J=α E+β C γ Y; Where E represents energy consumption; C represents emission cost; Y represents unit output; α, β, γ are weighting coefficients; the platform supports calling the PSO particle swarm algorithm or GA genetic algorithm for scheduling parameter optimization; The control strategy commands generated based on the above analysis results are sent to the platform control system, which consists of three main modules: Decision execution module: Transforms optimization results into specific executable parameters; Parameter writing module: Writes parameters to PLC, DCS, or SCADA systems via API interface to complete instruction issuance; Feedback monitoring module: Collects execution results and uses judgment logic to confirm whether the execution effect meets the target. If there is a deviation, it sends the results back to the twin model for self-learning correction.

[0024] In this embodiment, Digital twin technology is a comprehensive information technology that integrates modeling, real-time data, simulation and control feedback, used to build digital models in virtual space that highly correspond to physical objects; LSTM is a recurrent neural network (RNN) structure specifically designed for processing time series data. It introduces "forget gate," "input gate," and "output gate" mechanisms on the basis of traditional RNN, effectively alleviating the gradient vanishing problem and capturing long-term dependencies. Fuzzy logic algorithm is a logical reasoning method that handles uncertainty and fuzziness. It is often used to transform human language rules into mathematical models. In this invention, fuzzy logic algorithm is used for intelligent assessment of equipment risk level. Particle Swarm Optimization (PSO) is a global optimization algorithm based on swarm intelligence. It simulates the foraging process of birds and performs iterative search. In the optimization stage, PSO can quickly search for the optimal combination of operating parameters to achieve the goals of minimum energy consumption, maximum output, or minimum emissions. The algorithm achieves global approximation of the objective function by updating particle positions and velocities, and is suitable for optimization tasks with multidimensional continuous variables. Genetic Algorithm (GA) is a heuristic search algorithm that simulates the process of natural selection and gene inheritance. It is good at handling complex multi-objective optimization problems. In this invention, GA can be used with or replace PSO to solve the optimal operating scheme of a plant under multiple operating conditions. Industrial Internet of Things (IIoT) technology enables data interconnection and remote collaboration between devices through the sensing layer, network layer, and platform layer. In this invention, the IIoT platform supports access via multiple protocols such as Modbus, OPCUA, and MQTT. The overall workflow of this invention begins with the digital modeling of the physical equipment of the plant. Through data-driven approaches, it achieves virtual-physical linkage, intelligent analysis, and closed-loop control, thereby comprehensively improving the level of intelligence in plant operation. First, the platform constructs a digital twin model through configuration modeling components. The model uses primitives as the smallest building unit and is divided into three categories: structural primitives, state primitives, and behavioral primitives, which correspond to the physical form, operating status, and control logic of the equipment, respectively. Users can drag and combine various graphic elements based on the actual process flow diagram of the plant through a visual interface to form a nested model with a clear hierarchical structure at the equipment level, subsystem level, and plant level. After the model is built, it is verified through logic simulation and then put into operation online. Subsequently, the platform collects operating data from various sensors and controllers through edge gateway devices deployed at the plant site. The edge gateway has embedded communication protocol parsing and caching modules, supporting unified access to multiple heterogeneous industrial protocols such as Modbus, OPCUA, and MQTT, ensuring that data from multiple sources can be aggregated in real time. After the collected data undergoes preprocessing steps such as timestamp standardization, missing value completion, outlier removal, and unit conversion, it is structured and mapped to the corresponding twin model graphic element attributes, completing high-frequency dynamic synchronization between physical equipment and digital models. Afterward, the platform initiates intelligent analysis based on the mapped data. The module works as follows: On one hand, it uses a prediction model based on Long Short-Term Memory (LSTM) networks to analyze the operating status and predict the changing trends of equipment load, energy consumption, and environmental variables. On the other hand, it constructs a multi-factor fuzzy logic model to conduct risk assessment of the equipment, identify potential hazards, and output risk levels. Simultaneously, with the goals of minimum energy consumption, maximum efficiency, and minimum emissions, it calls particle swarm optimization or genetic algorithms to optimize key parameters. Finally, the analysis results are transformed into dynamic control strategies. Control commands are generated by the decision-making module of the platform control system and sent to the PLC or DCS control system through the parameter writing module to achieve parameter adjustment of the actual equipment. The execution results are collected in real time by the feedback monitoring module and compared with the expected results. If the deviation exceeds the threshold, the self-learning correction mechanism of the twin model is triggered to update and optimize the parameters and enter the next round of closed-loop control.

[0025] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various variations or substitutions within the technical scope disclosed in the present invention, and these should all be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for implementing digital twin technology in an intelligent management and control platform, characterized in that, Includes the following steps: S1. Construct a digital twin model of the plant's physical equipment. The digital twin model includes a digital representation corresponding to the physical equipment's structure, state parameters, and behavioral logic. The digital twin model is visualized and modeled in the form of primitives in the platform. S2. Real-time operating data collected by various sensors and terminal devices in the plant is accessed through the Internet of Things platform components, and the operating data is mapped to the digital twin model to achieve high-frequency data synchronization between the physical entity and the virtual model; S3. Perform state trend prediction, equipment risk assessment and operating condition optimization analysis on the mapped operating data, and generate dynamic adjustment strategies based on the analysis results. The platform control system writes back to the physical system to achieve closed-loop control.

2. The method for implementing digital twin technology in an intelligent management and control platform according to claim 1, characterized in that: In step S1 of constructing a digital twin model of the plant's physical equipment, the digital twin model is constructed by calling a preset primitive library in the configuration modeling component. The primitive library includes component primitives for describing the equipment structure, state primitives for describing the operating state, and behavior primitives for representing the control logic.

3. The method for implementing digital twin technology in an intelligent management and control platform according to claim 1, characterized in that: In step S1, the digital twin model is configured with process-level visualization based on the process flow diagram, and supports nested management of equipment-level models, subsystem-level models and plant-level models at different levels.

4. The method for implementing digital twin technology in an intelligent management and control platform according to claim 1, characterized in that: In step S2, the IoT platform components include an edge gateway device, a communication protocol parsing module, and a data caching module, which are used to achieve unified access to heterogeneous data sources with multiple protocols such as Modbus, OPC UA, and MQTT.

5. The method for implementing digital twin technology in an intelligent management and control platform according to claim 1, characterized in that: In step S2, the real-time running data undergoes data preprocessing, including timestamp standardization, missing value imputation, outlier removal, and unit conversion, before being mapped to the digital twin model.

6. The method for implementing digital twin technology in an intelligent management and control platform according to claim 1, characterized in that: In step S3, the state trend prediction adopts a time-series prediction model based on Long Short-Term Memory (LSTM) network, and the prediction targets include equipment load, energy consumption changes and environmental parameter fluctuations.

7. The method for implementing digital twin technology in an intelligent management and control platform according to claim 1, characterized in that: In step S3, the equipment risk assessment is based on a multi-factor comprehensive assessment model, including operating time, temperature rise level, vibration amplitude and historical fault records, and the equipment risk level is output using a fuzzy logic algorithm.

8. The method for implementing digital twin technology in an intelligent management and control platform according to claim 1, characterized in that: In step S3, the operating condition optimization uses a particle swarm optimization algorithm or a genetic algorithm to perform multi-objective optimization on key operating parameters, with minimum energy consumption, maximum output and minimum emissions as optimization objectives.

9. The method for implementing digital twin technology in an intelligent management and control platform according to claim 1, characterized in that: The platform control system includes a decision execution module, a parameter writing module, and a feedback monitoring module. The decision execution module converts the analysis results into control commands and triggers parameter updates. The parameter writing module writes the command parameters into the PLC or DCS system. The feedback monitoring module verifies the effectiveness of the control results in real time.

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