Panoramic monitoring and intelligent operation and maintenance system based on digital twinning

Abstract

The invention discloses a panoramic monitoring and intelligent operation and maintenance system based on digital twinning, and mainly relates to the technical field of digital twinning. Comprises: a physical layer for interacting with a physical entity; the dynamic twinborn layer construction and operation module is used for constructing and operating a digital twinborn layer of a dynamic twinborn model and comprises a data acquisition and fusion module, a three-dimensional model construction and driving module and a model updating and optimizing module; and the intelligent application layer is used for panoramic monitoring and intelligent decision making and comprises a panoramic visual monitoring module, an intelligent operation and maintenance analysis module and a decision support and cooperative control module. The beneficial effects of the invention lie in that accurate prediction and intelligent diagnosis of the equipment state are realized by using a health degree evaluation model fusing multi-source data and a performance degradation prediction algorithm; through the self-optimization and cooperative control mechanism of the model, closed-loop optimization from sensing and analysis to decision making and execution is formed, and the initiative, accuracy and intelligent level of operation and maintenance management are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of digital twin technology, specifically a panoramic monitoring and intelligent operation and maintenance system based on digital twins. Background Technology

[0002] With the rapid development of IoT, big data, and AI technologies, the operation and maintenance management of industrial equipment, infrastructure, and large parks is moving towards digitalization and intelligence. Traditional monitoring and maintenance systems typically rely on discretely deployed sensors, independent monitoring screens (such as video surveillance), and simple threshold-based alarm mechanisms. While these systems can provide basic operational data collection and anomaly alarm functions, their limitations are becoming increasingly apparent: First, data from each subsystem is isolated, lacking effective fusion and correlation analysis, making it difficult to grasp the overall operational status from a global perspective; second, monitoring methods mainly rely on two-dimensional charts and isolated video screens, resulting in unintuitive information presentation and insufficient representation of complex equipment internal states and spatial relationships; finally, maintenance decisions largely depend on human experience, leading to delayed responses and a lack of predictive analysis capabilities for equipment performance degradation and potential failures, thus constituting a "passive response" style of maintenance.

[0003] In recent years, digital twin technology has received widespread attention as a key enabling technology for the deep integration of the physical world and cyberspace. It provides new approaches to monitoring and operation and maintenance by constructing high-fidelity virtual models of physical entities and using real-time data for driving and synchronization. However, there are still many bottlenecks in the current practice of applying digital twins to panoramic monitoring and intelligent operation and maintenance: on the one hand, existing digital twin models often focus on geometric appearance and static data display, with insufficient dynamic coupling between the model and real-time data, and the real-time performance and accuracy of virtual-real synchronization need to be improved; on the other hand, the integration of the twin model with backend intelligent analysis algorithms (such as health assessment and fault prediction) is not high, and the model update and iteration mechanism is incomplete, resulting in limited analytical decision-making capabilities based on the twin model and failing to fully realize the potential of digital twins in simulation, prediction, and optimization.

[0004] Therefore, how to build a system that integrates high-fidelity dynamic twins, deep fusion of multi-source data, panoramic visualization monitoring, and deep intelligent analysis and decision-making, and realize the transformation from "passive monitoring" to "proactive prediction and intelligent operation and maintenance", has become a technical problem that urgently needs to be solved in this field. Summary of the Invention

[0005] The purpose of this invention is to provide a panoramic monitoring and intelligent operation and maintenance system based on digital twins. Through the self-optimization and collaborative control mechanism of the model, it forms a closed-loop optimization from perception, analysis to decision-making and execution, which significantly improves the initiative, accuracy and intelligence of operation and maintenance management.

[0006] To achieve the above objectives, the present invention employs the following technical solution: This invention provides a panoramic monitoring and intelligent operation and maintenance system based on digital twins, comprising: The physical layer is used for data interaction with physical entities; A digital twin layer is used to build and run a digital mirror model of a physical entity. The digital twin layer is communicatively connected to the physical layer, receives real-time data, and drives the digital mirror model to run synchronously. The intelligent application layer is used for panoramic visualization monitoring and intelligent analysis and decision-making based on the state of the digital mirror model. The intelligent application layer is communicatively connected to the digital twin layer. The digital twin layer includes: The data acquisition and fusion module is used to acquire real-time operational and environmental data from multiple heterogeneous sources at the physical layer. The 3D model building and driving module is used to build a high-fidelity digital model based on the geometric, physical and behavioral rules of the physical entity, and to drive the digital model to achieve virtual-real synchronization using the data processed by the data acquisition and fusion module. The model update and optimization module is used to dynamically correct and improve the accuracy of the digital model based on changes in the physical layer's state and analysis feedback from the smart application layer. The intelligent application layer includes: The panoramic visualization monitoring module is used to integrate, render, and display digital mirror models and their associated real-time data and alarm information in multiple dimensions. The intelligent operation and maintenance analysis module is used to predict operational trends, diagnose anomalies, and assess health based on real-time and historical status data from a digital mirror model and through preset algorithm models. The decision support and collaborative control module is used to generate operation and maintenance strategy suggestions or send control commands to the physical layer based on the output results of the intelligent operation and maintenance analysis module.

[0007] Preferably, the data acquisition and fusion module performs the following steps: Step S11: Collect multi-source heterogeneous raw data, including vibration, temperature, pressure, current, video stream, and equipment logs, through sensor networks, monitoring equipment, and existing business systems deployed on physical entities; Step S12: Perform preprocessing operations on the multi-source heterogeneous raw data, including data cleaning, format standardization, timestamp alignment, and missing value handling; Step S13: Use a data fusion algorithm based on Kalman filtering or extended Kalman filtering to fuse data from different sources that describe the same object or the same state, and generate unified state description information with higher accuracy and reliability. The preprocessed and fused data are synchronously stored in the real-time database and the historical database, and provide a driving data source for the 3D model construction and driving module.

[0008] Preferably, the 3D model construction and driving module performs the following process: Step S21: Based on computer-aided design drawings, building information models or 3D scanned point cloud data of physical entities, construct a 3D geometric model that includes geometric shape, internal structure and spatial relationships. Step S22: Based on the three-dimensional geometric model, a mechanism model with physical properties and behavioral simulation capabilities is formed by adding material properties, motion constraints, physical rules and behavioral logic. Step S23: Establish a dynamic mapping relationship between the parameters of the mechanism model and the unified state description information output by the data acquisition and fusion module; Step S24: Based on the dynamic mapping relationship, the state changes of the physical entity are mapped to the corresponding parameters of the mechanism model, driving the mechanism model to perform synchronous simulation operation, thereby achieving dynamic consistency between the digital virtual body and the physical entity.

[0009] Preferably, when the intelligent operation and maintenance analysis module performs equipment health assessment, it adopts the following health index calculation model: ; in, Indicates time The overall health index of the equipment at any given time has a value range of [value range missing]. A higher value indicates a better health condition; , , Let be the weight coefficient, and satisfy... , respectively, represent the contribution weights of real-time operating parameters, performance degradation trends, and abnormal events to the overall health; Indicates the first Key operating parameters in time The normalized measured values ​​are obtained by linearly or nonlinearly mapping the parameters to a baseline range under healthy conditions, making them... ; Indicates the first The weights of key operating parameters, ; For the first The baseline reference values ​​of key operating parameters under ideal health conditions are used for further calibration; This represents a performance degradation index obtained through a trend prediction algorithm based on historical equipment operating data, and its value range is... 1 indicates no degradation, and 0 indicates complete degradation; Indicates time The severity-weighted sum of diagnosed and confirmed abnormal events that occurred within a previously preset time window, with a value range of [value missing]. 0 indicates no abnormality, and 1 indicates the most serious abnormality has occurred; The intelligent operation and maintenance analysis module calculates... The system categorizes the health status of equipment into several levels and displays these levels visually in the panoramic visualization monitoring module.

[0010] Preferably, the performance degradation index Obtained through the following steps: Extract long-term operating parameter time series of the device in a healthy state from the historical database as the training dataset; An autoregressive integral moving average model or a long short-term memory neural network is used to learn from the training dataset and establish a parameter prediction model under normal operating conditions of the equipment. The actual operating parameters within the previous historical window at the current moment are input into the parameter prediction model to obtain the predicted values ​​of each parameter at the current moment. Calculate the residuals between the actual and predicted values ​​of each key operating parameter at the current moment, and then standardize the residuals. The standardized residual vector is input into a pre-trained single-class support vector machine model to calculate the deviation score between the current state and the normal state pattern. The deviation score is passed through Type function mapping to The interval, after mapping, is the performance degradation index. The closer the value is to 0, the more severe the degradation.

[0011] Preferably, the panoramic visualization monitoring module performs the following process: The system integrates the dynamic digital mirror model output by the 3D model construction and driving module, the measurement point data in the real-time database, the video monitoring stream, and the alarm and evaluation results from the intelligent operation and maintenance analysis module. It offers a variety of interactive visualization functions, including free navigation of 3D scenes, exploded view of equipment structure, dynamic mounting of data panels, historical status backtracking, and virtual inspection path planning. Employing a lightweight rendering engine based on WebGL, it enables smooth display and interaction of large-scale complex 3D scenes and massive real-time data through a web browser; Based on user roles and permissions, the system can customize the display of monitoring information at different levels and in different dimensions, and support data drill-down and analysis of key indicators.

[0012] Preferably, the model update and optimization module performs the following process: Step S31: Continuously monitor the error between the key simulation output of the digital mirror model and the actual monitoring data of the corresponding physical entity; Step S32: When the error exceeds a preset threshold, the model parameter self-correction process is triggered, and an optimization algorithm based on gradient descent or Bayesian inference is used to adjust specific parameters in the mechanism model to reduce the simulation error. Step S33: Receive new patterns or association rules that are not fully described by existing models and discovered by the intelligent operation and maintenance analysis module in anomaly diagnosis or predictive analysis; Step S34: Transform the new pattern or association rule into model constraints or supplementary rules, incrementally update and integrate the behavioral logic library of the digital mirror model, and improve the model's ability to represent and predict complex working conditions and unknown states.

[0013] Preferably, the decision support and collaborative control module performs the following process: Step S41: Receive the equipment health assessment, fault warning, and remaining service life prediction information output by the intelligent operation and maintenance analysis module; Step S42: Based on the preset operation and maintenance knowledge base and rule engine, generate targeted maintenance strategy suggestions, which include maintenance type, suggested time, required resources and operation steps; Step S43: For emergency faults or scenarios requiring immediate intervention, after authorization and confirmation, a sequence of control commands is automatically generated and sent to the corresponding actuators or control systems through the physical layer to achieve rapid response and closed-loop control; Step S44: Record the execution process and results of all decision recommendations, and feed back the execution effect to the model update and optimization module and the intelligent operation and maintenance analysis module for optimizing subsequent analysis and decision-making.

[0014] Preferably, the physical layer further includes edge computing nodes, which are deployed at the network edge close to the physical entity and are used for: The raw data collected by the sensor network is preprocessed and filtered locally to reduce the amount of data and bandwidth pressure transmitted to the cloud or central server. Run lightweight anomaly detection and diagnosis algorithms to perform real-time analysis of local data, achieving rapid anomaly identification and local alarms at the millisecond to second level; In the event of network interruption or communication failure with upper-layer systems, local security interlock control or basic operation and maintenance operations are executed according to preset policies to ensure the basic security and operation of physical entities.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. Achieved panoramic, dynamic, and virtual-real integrated monitoring capabilities: This invention constructs a dynamic digital twin model that is highly synchronized with the physical entity and integrates and maps multi-source heterogeneous data (such as sensor data, video streams, and business logs) into this unified three-dimensional scene, breaking the information silos in traditional monitoring systems. Maintenance personnel can gain a penetrating insight into the internal structure, real-time operating status, historical trajectory, and environmental correlation of equipment from a global, intuitive, and interactive three-dimensional visualization interface, greatly improving the comprehensiveness and efficiency of situational awareness. 2. Improved accuracy and foresight in status assessment and fault prediction: The intelligent operation and maintenance analysis module embedded in the system of this invention, especially the equipment health assessment model based on multi-parameter fusion and trend analysis, can comprehensively assess the health status of equipment by integrating real-time data, performance degradation trends, and abnormal events. Combined with prediction models trained on historical data (such as ARIMA or LSTM), it can identify early signs of performance degradation, realizing the transformation from "threshold-based alarms" to "prediction-based early warnings," providing a scientific basis for preventive maintenance and effectively avoiding unplanned downtime. 3. A smart closed loop of "perception-analysis-decision-optimization" is formed: The system of this invention is not a simple data display and alarm platform. Its decision support module can automatically generate operation and maintenance strategies or control instructions based on the analysis results, and can be executed after authorization. More importantly, the model update and optimization module can continuously and automatically correct and enrich the digital twin model according to the actual data and prediction deviation, so that the model becomes more and more accurate with use. This closed loop enables the system to have the ability to learn and evolve on its own, and operation and maintenance decisions are more dynamic and optimized. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the system structure according to an embodiment of the present invention; Figure 2 This is an execution flowchart of the data acquisition and fusion module according to an embodiment of the present invention; Figure 3 This is an execution flowchart of the three-dimensional model construction and driving module in an embodiment of the present invention; Figure 4 This is an execution flowchart of the model update and optimization module in an embodiment of the present invention; Figure 5 This is an execution flowchart of the decision support and collaborative control module in an embodiment of the present invention. Detailed Implementation

[0017] The present invention will be further illustrated below with reference to specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Furthermore, it should be understood that after reading the teachings of this invention, those skilled in the art can make various alterations or modifications to the invention, and these equivalent forms also fall within the scope defined in this application.

[0018] In this invention, terms such as "upper," "lower," "left," "right," "front," "back," "vertical," "horizontal," "side," and "bottom" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. These terms are used only to facilitate the description of the structural relationships of the various components or elements of this invention and do not specifically refer to any component or element in this invention. They should not be construed as limiting the invention.

[0019] Example: This embodiment uses an "olefin polymerization production line" of a chemical enterprise as a virtual application scenario to illustrate the specific implementation of the system of the present invention. This production line includes multiple high-risk, continuous processes such as raw material refining, catalyst preparation, polymerization reaction, monomer recovery, granulation, and pneumatic conveying. Core equipment includes a large polymerization reactor, a circulating gas compressor, a heat exchanger, a powder conveying system, and numerous high-pressure pipelines and valves. Its safe, stable, and efficient operation is crucial to ensuring product quality and production safety.

[0020] 1. System Overall Architecture Implementation: like Figure 1 As shown, the specific implementation of this system in this scenario includes a three-layer architecture: Physical layer: Vibration sensors, pressure transmitters, multi-point temperature sensors, flow meters, online component analyzers, corrosion probes, and DCS (Distributed Control System) data interfaces are deployed on key equipment and pipelines in the production line, forming a multi-source sensing network covering the entire process. These terminals are responsible for collecting equipment operating status, process parameters, and environmental safety data.

[0021] Digital Twin Layer: Deployed on the enterprise's industrial control cloud platform, it is responsible for creating and maintaining a dynamic, high-fidelity digital twin of the entire aggregated production line. It connects to the physical layer via an industrial ring network and receives real-time data streams.

[0022] Intelligent Application Layer: Deployed on the same cloud platform, it provides a web-based immersive interactive interface for production operations and security monitoring teams. This layer exchanges information with the digital twin layer through an internal high-speed data bus.

[0023] 2. Specific implementation of the data acquisition and fusion module: like Figure 2 As shown, the specific workflow of this module is as follows: Step S11: Synchronously acquire multi-source data from the physical layer; for example, acquire shaft vibration (unit: mm / s), bearing temperature (unit: ℃), and outlet pressure (unit: MPa) from the circulating gas compressor; acquire multi-point temperature, internal pressure, and stirring current from the polymerization reactor; acquire monomer concentration in the circulating gas from the online gas chromatograph; Step S12: Preprocess the raw data; for example, smooth the pressure signal to remove pulse interference, unify the data of different sampling frequencies to 1Hz time sequence by interpolation, and fill the missing data segments generated during instrument calibration with the mean of the effective data before and after. Step S13: Data fusion is performed using an extended Kalman filter (EKF); taking the estimation of critical temperature states inside the reactor as an example, there are temperature values ​​directly measured by thermocouples. Temperature values ​​calculated based on reaction thermodynamics model and feed / discharge flow rates EKF performs fusion through the following steps: State prediction: ( State vector , It is a nonlinear reaction kinetic model. For input vectors ); Calculate Kalman gain : ;in, For the prediction error covariance matrix, The observation matrix (maps the state to the measurement value). To measure the noise covariance matrix (based on the accuracy calibration of each sensor); State update (fusion): , This is the actual measurement vector; This algorithm outputs more accurate and reliable unified state estimates of temperature and pressure inside the reactor, greatly enhancing the reliability of monitoring the core state of the reaction and serving as source data to drive the digital twin model.

[0024] 3. Specific implementation of the 3D model construction and driving module: like Figure 3 As shown, the implementation process of this module is as follows: Step S21: Using the plant's P&ID diagram (piping and instrumentation diagram) and the 3D design model of the reactor equipment, construct a detailed 3D geometric model that includes all reactors, towers, heat exchangers, pipes, valves and instrument positions to accurately reflect the spatial layout and connection relationships. Step S22: Add chemical mechanism properties to the geometric model, such as assigning material properties and heat capacity to the reactor wall and agitator; define fluid properties (such as viscosity and density) and flow-pressure drop relationships for pipelines; define opening-flow characteristic curves for control valves, forming a mechanism model that integrates geometric, physical and process rules; Step S23: Establish data mapping. For example, establish a dynamic binding relationship between parameters such as "internal pressure", "stirring speed", and "jacket cooling water outlet temperature" of the "polymerization reactor R-101" in the mechanism model and the corresponding data streams output by the data acquisition and fusion module; Step S24: Real-time driving: The system injects the fused real-time process data and equipment status data into the corresponding parameters of the mechanism model at a frequency of 1Hz (a higher frequency is used for fast processes), driving the virtual factory and the physical factory to keep running synchronously, realizing the virtual-physical mapping and visual monitoring of the entire process.

[0025] 4. Specific implementation of health assessment in the intelligent operation and maintenance analysis module: In this embodiment, a health assessment is performed on the key dynamic equipment "circulating gas compressor C-201"; Specification of Health Index Model Parameters: Formula used: ; Key operating parameter selection and processing: Select three key parameters: The effective value of axial vibration velocity ( ), Temperature of the non-drive end bearing of the drive motor ( ), The deviation between the outlet pressure and the rated pressure ( Perform the following operations: Normalization: ,in Vibration alarm threshold; ,in For normal temperature This is the alarm threshold; , The maximum allowable pressure deviation; Weighting: Based on failure mode and impact analysis, (Vibration directly reflects the mechanical state) , ; Baseline value setting: ; Performance degradation index Calculation: For the axial vibration sequence, perform the following steps: An LSTM prediction model is trained using normal vibration data from the past 6 months. The model input is the vibration sequence of the past 20 time steps, and the output is the predicted value for the next time step. Input the actual vibration values ​​of the previous 20 time steps into the model to obtain the predicted values. ; Calculate residuals ; residual Standard deviation of historical normal residuals Comparison yields standardized deviation scores. ; Will Input a pre-trained single-class SVM model using normal data to obtain the decision function value. ; Mapping using the Sigmoid function: ,when When the number is a large negative number (significant deviation). Approaching 0; Severity of Abnormal Events The statistics show that related anomalies that occurred in the past 72 hours are counted as follows: "Vibration Level 2 Alarm" is counted as 0.5, "Low Lubricating Oil Pressure Alarm" is counted as 0.7, and "Interlock Shutdown" is counted as 1.0. Take the maximum of these values; if there are no anomalies, then... ; Overall weight: set , , It emphasizes real-time parameters while considering performance trends and sudden anomalies; Suppose at a certain moment, , , Calculated , , ,but The health level is determined to be "sub-healthy", triggering an observation prompt.

[0026] 5. Specific implementation of the panoramic visualization monitoring module: This module is developed based on the WebGL Three.js engine. After logging into the system in a browser, maintenance personnel can see a 3D virtual factory that corresponds to the physical factory at a 1:1 scale (e.g., ...). Figure 1 As shown in the intelligent application layer, users can perform operations such as floor switching, equipment viewing, and pipe transparency. Clicking on the polymerization reactor allows users to view the status of its internal agitator and dynamically display its temperature cloud map, pressure distribution, and real-time health status. Values ​​and key process curves. When a corrosion rate warning is issued for a section of high-temperature pipeline, that section will be highlighted in red in the 3D scene, and the predicted remaining wall thickness will be displayed.

[0027] 6. Specific implementation of the model update and optimization module: like Figure 4 As shown, this module runs continuously: Step S31: Monitoring revealed that the average error between the simulated value of "pressure drop in the powder conveying system pipeline" in the digital twin model and the actual DCS pressure difference exceeded the set threshold (e.g., ±5%). Step S32: Trigger the self-calibration process and use Bayesian inference to optimize the "powder friction coefficient" in the pressure drop model. “Using actual differential pressure data as observational evidence, we update the prior distribution of μ to obtain the posterior probability distribution, and take the posterior mean as the optimized parameters to make the model simulation more in line with the actual material characteristics changes. Step S33: The intelligent operation and maintenance analysis module discovered through correlation analysis that when the cooling water inlet temperature is higher than a certain value and the reactor conversion rate reaches a certain range, the agitator current fluctuation amplitude will increase abnormally, indicating that there may be a risk of scaling or wall adhesion. This complex correlation was not fully described by the mechanism model before. Step S34: The model update module transforms the data-driven discovery pattern into a new "risk monitoring rule" and integrates it as supplementary knowledge into the behavior logic library of the reactor twin, enabling it to provide early warnings of risks under such complex working conditions in subsequent simulations.

[0028] 7. Specific implementation of the decision support and collaborative control module: like Figure 5 As shown: Step S41: Received an alert from the intelligent operation and maintenance analysis module: Health status of recirculating gas compressor C-201 It has dropped to 0.7, and the performance degradation index is... The vibration trend shows that it continues to worsen, and the remaining safe operating time is estimated to be about one week. Step S42: The maintenance knowledge base rule engine is triggered, matching and generating a maintenance strategy: "It is recommended to schedule a planned shutdown for maintenance within 48 hours, with key inspection items including: rotor dynamic balance, bearing clearance, and sealing condition. Required resources: maintenance team, spare parts (bearings, sealing sleeves), estimated time 36 hours." Step S43: The system detected that the reactor emergency relief system (SIS) triggered an overpressure interlock action, which constitutes a major safety incident. With authorization from the on-duty production supervisor, the system automatically generates and issues a safety response sequence to the physical layer DCS: 1. Emergency stop of feeding; 2. Activation of full reflux mode; 3. Adjustment of the cooling system to maximum load; 4. Push of the emergency response plan card to the central control room, achieving a rapid closed-loop response to the safety emergency. Step S44: The execution process and results of all the above warnings, suggestions and instructions are fully recorded and provided as feedback data to the model update and optimization module and the intelligent operation and maintenance analysis module for optimizing the equipment degradation prediction model and revising the safety operation rule library.

[0029] 8. Specific implementation of edge computing nodes: Deploy explosion-proof edge computing gateways in critical areas such as the reactor area and compressor room. These gateways: Real-time edge computing is performed on local high-frequency vibration and pressure sensor data to extract time-domain and frequency-domain features. Only the feature data and alarm status are uploaded to the cloud, which greatly reduces network bandwidth usage and central system load. Run lightweight, fast diagnostic algorithms, such as "if a significant increase in the second harmonic amplitude is detected in the vibration spectrum, it is immediately determined as 'misalignment tendency' and a warning signal is sent to the local operation panel"; When the network is interrupted, key protection functions are executed independently according to the preset safety logic, such as "if the pressure of the reactor exceeds 90% of the safety valve setting value and the main control is lost, the local solenoid valve will be immediately triggered to open for emergency release", providing the last digital safety barrier for the core device.

[0030] The above is a detailed description of the preferred embodiments of the present invention, but the present invention is not limited to the embodiments described. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention, and these equivalent modifications or substitutions are all included within the scope defined by the claims of this application.

Claims

1. A panoramic monitoring and intelligent operation and maintenance system based on digital twins, characterized in that, include: The physical layer is used for data interaction with physical entities; A digital twin layer is used to build and run a digital mirror model of a physical entity. The digital twin layer is communicatively connected to the physical layer, receives real-time data, and drives the digital mirror model to run synchronously. The intelligent application layer is used for panoramic visualization monitoring and intelligent analysis and decision-making based on the state of the digital mirror model. The intelligent application layer is communicatively connected to the digital twin layer. The digital twin layer includes: The data acquisition and fusion module is used to acquire real-time operational and environmental data from multiple heterogeneous sources at the physical layer. The 3D model building and driving module is used to build a high-fidelity digital model based on the geometric, physical and behavioral rules of the physical entity, and to drive the digital model to achieve virtual-real synchronization using the data processed by the data acquisition and fusion module. The model update and optimization module is used to dynamically correct and improve the accuracy of the digital model based on changes in the physical layer's state and analysis feedback from the smart application layer. The intelligent application layer includes: The panoramic visualization monitoring module is used to integrate, render, and display digital mirror models and their associated real-time data and alarm information in multiple dimensions. The intelligent operation and maintenance analysis module is used to predict operational trends, diagnose anomalies, and assess health based on real-time and historical status data from a digital mirror model and through preset algorithm models. The decision support and collaborative control module is used to generate operation and maintenance strategy suggestions or send control commands to the physical layer based on the output results of the intelligent operation and maintenance analysis module.

2. The panoramic monitoring and intelligent operation and maintenance system based on digital twins according to claim 1, characterized in that, The data acquisition and fusion module performs the following steps: Step S11: Collect multi-source heterogeneous raw data, including vibration, temperature, pressure, current, video stream, and equipment logs, through sensor networks, monitoring equipment, and existing business systems deployed on physical entities; Step S12: Perform preprocessing operations on the multi-source heterogeneous raw data, including data cleaning, format standardization, timestamp alignment, and missing value handling; Step S13: Use a data fusion algorithm based on Kalman filtering or extended Kalman filtering to fuse data from different sources that describe the same object or the same state, and generate unified state description information with higher accuracy and reliability. The preprocessed and fused data are synchronously stored in the real-time database and the historical database, and provide a driving data source for the 3D model construction and driving module.

3. The panoramic monitoring and intelligent operation and maintenance system based on digital twins according to claim 1, characterized in that, The 3D model construction and driving module performs the following process: Step S21: Based on the computer-aided design drawings, building information models or 3D scanned point cloud data of the physical entity, construct a 3D geometric model that includes the geometric shape, internal structure and spatial relationships. Step S22: Based on the three-dimensional geometric model, a mechanism model with physical properties and behavioral simulation capabilities is formed by adding material properties, motion constraints, physical rules and behavioral logic. Step S23: Establish a dynamic mapping relationship between the parameters of the mechanism model and the unified state description information output by the data acquisition and fusion module; Step S24: Based on the dynamic mapping relationship, the state changes of the physical entity are mapped to the corresponding parameters of the mechanism model, driving the mechanism model to perform synchronous simulation operation, thereby achieving dynamic consistency between the digital virtual body and the physical entity.

4. The panoramic monitoring and intelligent operation and maintenance system based on digital twins according to claim 1, characterized in that, When performing equipment health assessment, the intelligent operation and maintenance analysis module uses the following health index calculation model: ； in, Indicates time The overall health index of the equipment at any given time has a value range of [value range missing]. A higher value indicates a better health condition; , , Let be the weight coefficient, and satisfy... , respectively, represent the contribution weights of real-time operating parameters, performance degradation trends, and abnormal events to the overall health; Indicates the first Key operating parameters in time The normalized measured values ​​are obtained by linearly or nonlinearly mapping the parameters to a baseline range under healthy conditions, making them... ; Indicates the first The weights of key operating parameters, ; For the first The baseline reference values ​​of key operating parameters under ideal health conditions are used for further calibration; This represents a performance degradation index obtained through a trend prediction algorithm based on historical equipment operating data, and its value range is... 1 indicates no degradation, and 0 indicates complete degradation; Indicates time The severity-weighted sum of diagnosed and confirmed abnormal events that occurred within a previously preset time window, with a value range of [value missing]. 0 indicates no abnormality, and 1 indicates the most serious abnormality has occurred; The intelligent operation and maintenance analysis module calculates... The system categorizes the health status of equipment into several levels and displays these levels visually in the panoramic visualization monitoring module.

5. A panoramic monitoring and intelligent operation and maintenance system based on digital twins according to claim 4, characterized in that, The performance degradation index Obtained through the following steps: Extract long-term operating parameter time series of the device in a healthy state from the historical database as the training dataset; An autoregressive integral moving average model or a long short-term memory neural network is used to learn from the training dataset and establish a parameter prediction model under normal operating conditions of the equipment. The actual operating parameters within the previous historical window at the current moment are input into the parameter prediction model to obtain the predicted values ​​of each parameter at the current moment. Calculate the residuals between the actual and predicted values ​​of each key operating parameter at the current moment, and then standardize the residuals. The standardized residual vector is input into a pre-trained single-class support vector machine model to calculate the deviation score between the current state and the normal state pattern. The deviation score is passed through Type function mapping to The interval, after mapping, is the performance degradation index. The closer the value is to 0, the more severe the degradation.

6. The panoramic monitoring and intelligent operation and maintenance system based on digital twins according to claim 1, characterized in that, The panoramic visualization monitoring module performs the following process: The system integrates the dynamic digital mirror model output by the 3D model construction and driving module, the measurement point data in the real-time database, the video monitoring stream, and the alarm and evaluation results from the intelligent operation and maintenance analysis module. It offers a variety of interactive visualization functions, including free navigation of 3D scenes, exploded view of equipment structure, dynamic mounting of data panels, historical status backtracking, and virtual inspection path planning. Employing a lightweight rendering engine based on WebGL, it enables smooth display and interaction of large-scale complex 3D scenes and massive real-time data through a web browser; Based on user roles and permissions, the system can customize the display of monitoring information at different levels and in different dimensions, and support data drill-down and analysis of key indicators.

7. A panoramic monitoring and intelligent operation and maintenance system based on digital twins according to claim 1, characterized in that, The model update and optimization module performs the following process: Step S31: Continuously monitor the error between the key simulation output of the digital mirror model and the actual monitoring data of the corresponding physical entity; Step S32: When the error exceeds a preset threshold, the model parameter self-correction process is triggered, and an optimization algorithm based on gradient descent or Bayesian inference is used to adjust specific parameters in the mechanism model to reduce the simulation error. Step S33: Receive new patterns or association rules that are not fully described by existing models and discovered by the intelligent operation and maintenance analysis module in anomaly diagnosis or predictive analysis; Step S34: Transform the new pattern or association rule into model constraints or supplementary rules, incrementally update and integrate the behavioral logic library of the digital mirror model, and improve the model's ability to represent and predict complex working conditions and unknown states.

8. A panoramic monitoring and intelligent operation and maintenance system based on digital twins according to claim 1, characterized in that, The decision support and collaborative control module performs the following process: Step S41: Receive the equipment health assessment, fault warning, and remaining service life prediction information output by the intelligent operation and maintenance analysis module; Step S42: Based on the preset operation and maintenance knowledge base and rule engine, generate targeted maintenance strategy suggestions, which include maintenance type, suggested time, required resources and operation steps; Step S43: For emergency faults or scenarios requiring immediate intervention, after authorization and confirmation, a sequence of control commands is automatically generated and sent to the corresponding actuators or control systems through the physical layer to achieve rapid response and closed-loop control; Step S44: Record the execution process and results of all decision recommendations, and feed back the execution effect to the model update and optimization module and the intelligent operation and maintenance analysis module for optimizing subsequent analysis and decision-making.

9. A panoramic monitoring and intelligent operation and maintenance system based on digital twins according to claim 1, characterized in that, The physical layer also includes edge computing nodes, which are deployed at the network edge close to the physical entity and are used for: The raw data collected by the sensor network is preprocessed and filtered locally to reduce the amount of data and bandwidth pressure transmitted to the cloud or central server. Run lightweight anomaly detection and diagnosis algorithms to perform real-time analysis of local data, achieving rapid anomaly identification and local alarms at the millisecond to second level; In the event of network interruption or communication failure with upper-layer systems, local security interlock control or basic operation and maintenance operations are executed according to preset policies to ensure the basic security and operation of physical entities.

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