A digital twin platform system for process industries and its usage method

Abstract

This invention relates to the field of digital twin technology and discloses a digital twin platform system for process industries, comprising: a process mechanism modeling module for constructing a multi-scale first-principles model system based on the physicochemical laws of process industries; a dynamic coupling engine for real-time acquisition of process data, coupling the first-principles model system with the equipment operating state model for solution; a closed-loop optimization control interface for bidirectional communication with a distributed control system, generating optimized setpoints based on the solution results and executing control; and a visualization diagnostic module for multi-dimensional analysis of process parameter deviations, root cause tracing of faults, and early warning response. By directly embedding first-principles equations such as reaction kinetics and heat transfer into the virtual model through the process mechanism modeling module, the model possesses the physical and chemical interpretation capabilities for complex chemical reactions and energy transfer, thus maintaining high accuracy even under complex operating conditions.

Description

Technical Field

[0001] This invention relates to the field of digital twin technology, specifically to a digital twin platform system and its usage method for process industries. Background Technology

[0002] In today's industrial sector, process industries occupy an extremely important position, covering many key industries such as chemicals, petroleum, pharmaceuticals, food, and energy. The production processes of these process industries are often characterized by complexity, continuity, and a high degree of automation. They involve numerous production equipment, technological processes, and material flow links, and have extremely high standards for production efficiency, product quality, resource utilization, and safety assurance.

[0003] A search revealed Chinese patent CN118014285A, which discloses a visualized industrial internet digital twin platform. This platform includes a physical model construction module, a data acquisition module, a data processing module, a central processing module, a visualization module, an interaction and control module, and a permission management module. By establishing three-dimensional visualized digital twin models of industrial equipment and factory production processes, it simulates production, making production management more transparent. Furthermore, it utilizes VR / AR devices to achieve real-time simulation and monitoring of enterprise assets, enabling visualized management of processes and technologies. This invention provides real-time panoramic monitoring of industrial equipment and factory production processes, shortening troubleshooting time and effectively saving operating costs when malfunctions occur in industrial equipment or errors occur in the factory production process.

[0004] While the aforementioned digital twin platforms can achieve three-dimensional visualization and monitoring of industrial equipment and production scenarios, their model construction relies on the physical properties of the equipment and production image data, without considering the unique process mechanism knowledge of the process industry, such as the impact of reaction kinetics on the model. This leads to insufficient model fidelity, meaning that the virtual model cannot accurately simulate the process flow under complex working conditions. In the case of raw material fluctuations or changes in catalyst activity, it is difficult to predict the quality indicators of the product. Based on this, the present invention designs a process industry digital twin platform system and its usage method to solve the above problems. Summary of the Invention

[0005] The purpose of this invention is to provide a digital twin platform system and its usage method for the process industry, which solves the problem of insufficient model fidelity caused by the failure to consider the process mechanism knowledge unique to the process industry in the background technology.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0007] A process industry digital twin platform system, comprising:

[0008] The process mechanism modeling module is used to construct a multi-scale first-principles model system based on the physicochemical laws of process industries.

[0009] The dynamic coupling engine is used to collect process data in real time and solve the problem by coupling the first principle model system with the equipment operation status model.

[0010] The closed-loop optimization control interface is used for bidirectional communication with the distributed control system, generating optimized setpoints based on the solution results and executing control.

[0011] The visualization diagnostic module is used for multi-dimensional analysis of process parameter deviations, root cause tracing of faults, and early warning response.

[0012] The system improves the accuracy of the virtual model's prediction of product quality under fluctuating operating conditions by using the first-principles model system constructed by the process mechanism modeling module, combined with the real-time coupling solution and state update of the dynamic coupling engine, and the setpoint optimization and smooth control of the closed-loop optimization control interface.

[0013] Preferably, the process mechanism modeling module includes:

[0014] The reactor model is constructed based on the reaction kinetic equations, which are as follows:

[0015]

[0016] Where r is the reaction rate, k is the pre-exponential factor, E is the activation energy, R is the gas constant, T is the reaction temperature, f is the reactant concentration function, and g is the catalyst activity function;

[0017] The heat exchange model, based on the principles of heat transfer, incorporates a dynamic fouling thermal resistance correction coefficient R_f, and its heat transfer equation is:

[0018]

[0019]

[0020] Where Q is the heat exchange, U is the overall heat transfer coefficient, A is the heat transfer area, and Ta is the logarithmic mean temperature difference.

[0021] Hh is the hot-side convective heat transfer coefficient, Rw is the wall thermal resistance, Rf(t) is the dynamic fouling thermal resistance, and Hc is the cold-side convective heat transfer coefficient.

[0022] Preferably, the dynamic coupling engine collects multi-dimensional process parameters from the distributed control system in real time through an industrial IoT interface; establishes a finite element model of the equipment's operating state based on the equipment's geometric parameters, material properties, and boundary conditions; combines the first principle model system with the finite element model of the equipment's operating state, and applies a differential-algebraic equation solver to solve the equations, wherein the solver supports implicit integration algorithms for rigid equation systems.

[0023] Preferably, the closed-loop optimization control interface establishes a two-way communication channel with the distributed control system based on the industrial real-time communication protocol; applies a model predictive control algorithm to generate optimized setpoints; executes a control command smooth transition algorithm to limit the rate of change of setpoints; and executes a control command priority scheduling algorithm to dynamically adjust the execution order of control commands based on the process safety level.

[0024] Preferably, the visualization diagnostic module constructs a multi-dimensional phase space for process parameter deviations, including time series dimension, parameter correlation dimension, and equipment topology dimension; it realizes fault root cause tracing and propagation path prediction based on the fault propagation path model; and it sets multi-level dynamic error thresholds, triggering corresponding early warning response mechanisms when the operating deviation exceeds each level of threshold.

[0025] Preferably, the early warning response mechanism includes:

[0026] When the deviation reaches the first-level threshold, an alarm is triggered to highlight abnormal process parameters and predict trends.

[0027] When the deviation reaches the second-level threshold, the controller parameters are automatically adjusted and the optimized setpoints are automatically corrected.

[0028] When the deviation reaches the third-level threshold, the emergency stop interlocking system and equipment protection action sequence are triggered.

[0029] Preferably, the system further includes a model self-updating module, used to acquire product quality data from the online analyzer in real time; and to construct a data-driven model parameter correction mechanism that integrates the first principle model system with measured data.

[0030] The system further includes:

[0031] The data preprocessing module is used to perform outlier cleaning, missing value imputation, and data standardization.

[0032] The model validation module is used to perform model structure validation, parameter identification, and uncertainty analysis.

[0033] According to a second aspect of the present invention, a method for using a digital twin platform system for process industries is also provided, comprising the following steps:

[0034] Step S1: Based on historical data, train the parameters of the first-principles model system in the process mechanism modeling module to establish an initial virtual model;

[0035] Step S2: Update the virtual model state in real time using a dynamic coupling engine to generate model prediction values;

[0036] Step S3: Compare the model prediction value with the measured data from the distributed control system. When the deviation exceeds the threshold set by the visualization diagnostic module, the diagnostic process is triggered.

[0037] Step S4: Adjust the control parameters of the distributed control system according to the optimized setpoints generated by the model predictive control algorithm through the closed-loop optimization control interface.

[0038] Step S5: Based on the product quality feedback data obtained by the online analyzer, dynamically adjust the parameters of the first principle model system.

[0039] Preferably, step S3 further includes the following steps:

[0040] Step S31: Calculate the Mahalanobis distance between the model prediction value and the measured data, and determine whether it exceeds the set range;

[0041] Step S32: Apply fault tree analysis to determine possible causes of failure;

[0042] Step S33: Generate a report that includes troubleshooting recommendations and risk assessment.

[0043] Preferably, step S5 further includes the following steps:

[0044] Step S51: Apply Bayesian inference to update the posterior distribution of the parameters of the first principle model system.

[0045] Step S52: Apply sensitivity analysis to determine key model parameters;

[0046] Step S53: Implement adaptive adjustments to the model structure.

[0047] Compared with the prior art, the beneficial effects achieved by the present invention are:

[0048] 1. This invention directly embeds first-principles equations such as reaction kinetics and heat transfer into a virtual model through a process mechanism modeling module, enabling the model to have the physical and chemical interpretation capabilities for complex chemical reactions and energy transfer, thereby maintaining high accuracy under operating conditions such as raw material fluctuations and catalyst activity changes.

[0049] 2. In this invention, the dynamic coupling engine solves the first-principles model system and the finite element-based equipment operation state model in real time. It uses the implicit integral algorithm of rigid equations to handle strongly coupled working conditions with large time constant differences, ensuring that the model is synchronized with the real device in both transient and steady states. The closed-loop optimization control interface is based on the model predictive control algorithm. It directly uses the quality index prediction values ​​output by the first-principles model to generate the optimization setpoints, and limits the rate of change of the setpoints through a smooth transition algorithm to prevent drastic fluctuations when raw materials change abruptly, thus achieving early intervention and stable control of quality indicators.

[0050] 3. In this invention, the visualization diagnostic module constructs a phase space that includes three dimensions: time series, parameter correlation, and equipment topology. It locates the root causes such as reaction kinetic parameter drift and catalyst deactivation through a fault propagation path model, and triggers graded early warning and automatic response strategies under a multi-level dynamic error threshold mechanism. Attached Figure Description

[0051] Figure 1 This is a diagram of the overall system architecture of the present invention;

[0052] Figure 2 This is a flowchart of the dynamic coupling engine of the present invention;

[0053] Figure 3 This is the closed-loop optimization control logic diagram of the present invention;

[0054] Figure 4 This is a flowchart of the model self-updating and verification process of the present invention. Detailed Implementation

[0055] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] Example 1;

[0057] Please see Figures 1-4 In this embodiment of the invention, a process industry digital twin platform system includes:

[0058] The process mechanism modeling module is used to construct a multi-scale first-principles model system based on the physicochemical laws of process industries.

[0059] The dynamic coupling engine is used to collect process data in real time and solve the problem by coupling the first-principles model system with the equipment operation status model.

[0060] The closed-loop optimization control interface is used for bidirectional communication with the distributed control system, generating optimized setpoints based on the solution results and executing control.

[0061] The visualization diagnostic module is used for multi-dimensional analysis of process parameter deviations, root cause tracing of faults, and early warning response.

[0062] The system utilizes a first-principles model system built through a process mechanism modeling module, combined with real-time coupling solution and state updates from a dynamic coupling engine, and setpoint optimization and smooth control from a closed-loop optimization control interface, to improve the accuracy of the virtual model's prediction of product quality under fluctuating operating conditions.

[0063] The process mechanism modeling module includes:

[0064] The reactor model is constructed based on the reaction kinetic equations, which are as follows:

[0065]

[0066] Where r is the reaction rate, k is the pre-exponential factor, E is the activation energy, R is the gas constant, T is the reaction temperature, f is the reactant concentration function, and g is the catalyst activity function;

[0067] The heat exchange model, based on the principles of heat transfer, incorporates a dynamic fouling thermal resistance correction coefficient R_f, and its heat transfer equation is:

[0068]

[0069]

[0070] Where Q is the heat exchange, U is the overall heat transfer coefficient, A is the heat transfer area, and Ta is the logarithmic mean temperature difference.

[0071] Hh is the hot-side convective heat transfer coefficient, Rw is the wall thermal resistance, Rf(t) is the dynamic fouling thermal resistance, and Hc is the cold-side convective heat transfer coefficient.

[0072] The dynamic coupling engine collects multi-dimensional process parameters from the distributed control system in real time through the industrial IoT interface; establishes a finite element model of the equipment's operating state based on the equipment's geometric parameters, material properties, and boundary conditions; combines the first-principles model system with the finite element model of the equipment's operating state, and applies a differential-algebraic equation solver to solve the equations. The solver supports implicit integration algorithms for rigid equation systems.

[0073] The closed-loop optimization control interface establishes a two-way communication channel with the distributed control system based on the industrial real-time communication protocol; it uses model predictive control algorithms to generate optimized setpoints; it executes control command smooth transition algorithms to limit the rate of change of setpoints; and it executes control command priority scheduling algorithms to dynamically adjust the execution order of control commands based on the process safety level.

[0074] The visualization diagnostic module constructs a multi-dimensional phase space for process parameter deviations, including time series, parameter correlation, and equipment topology dimensions; it realizes root cause tracing and propagation path prediction based on the fault propagation path model; and it sets multi-level dynamic error thresholds, triggering corresponding early warning response mechanisms when the operating deviation exceeds each level of threshold.

[0075] The early warning and response mechanism includes:

[0076] When the deviation reaches the first-level threshold, an alarm is triggered to highlight abnormal process parameters and predict trends.

[0077] When the deviation reaches the second-level threshold, the controller parameters are automatically adjusted and the optimized setpoints are automatically corrected.

[0078] When the deviation reaches the third-level threshold, the emergency stop interlocking system and equipment protection action sequence are triggered.

[0079] The system also includes a model self-updating module, used to acquire product quality data from the online analyzer in real time; and to build a data-driven model parameter correction mechanism that integrates first-principles model system with measured data.

[0080] The system further includes:

[0081] The data preprocessing module is used to perform outlier cleaning, missing value imputation, and data standardization.

[0082] The model validation module is used to perform model structure validation, parameter identification, and uncertainty analysis.

[0083] The working principle of this embodiment of the invention is as follows: In the actual operation of the process industry digital twin platform system described in this embodiment, the process mechanism modeling module first establishes a multi-scale first-principles model system, including microscopic reaction kinetics, mesoscopic transfer phenomena, and macroscopic energy balance, based on the geometric structure, operating conditions, and physical property database of the target device, using a reactor-heat exchanger coupled modeling strategy. The reactor model in this system adopts a generalized reaction rate expression based on the Arrhenius equation. Its pre-exponential factor k and activation energy E are analyzed offline using a combined algorithm of Latin hypercube sampling and Bayesian calibration, utilizing historical batch data for global sensitivity analysis to determine key reaction paths and their corresponding sensitive parameter subsets. The heat exchange model introduces a dynamic fouling thermal resistance Rf(t) that evolves over time, and uses an extended Kalman filter to update its state equation online, reflecting the nonlinear attenuation effect of fouling on the heat transfer coefficient during actual operation.

[0084] The dynamic coupling engine uses the OPCUA industrial Ethernet protocol to collect multi-dimensional process parameters such as temperature, pressure, flow rate, and component concentration from the distributed control system in real time at a fixed period of 200ms. These parameters are then mapped to the boundary conditions of a finite element model built on an unstructured mesh. This finite element model uses eight-node hexahedral elements to discretize the equipment geometry, and material properties are dynamically updated with temperature changes, ensuring the numerical stability of the structure-thermal-fluid three-field coupling calculation. During the solution phase, the dynamic coupling engine calls a rigid DAE solver based on backward difference formulas, employing an adaptive step-size control strategy to simultaneously solve the first-principles model and the equipment state model.

[0085] After obtaining the solution results, the closed-loop optimization control interface inputs the deviation between the predicted product quality indicators and the set targets to the MPC controller. The MPC controller adopts a rolling time-domain optimization strategy based on a state-space model. Its objective function comprehensively considers the weights of multiple objectives such as product quality, energy consumption, and equipment wear, and generates the optimal setpoint sequence in the control time domain through a quadratic programming solver. To prevent actuator saturation and process disturbances, the optimized setpoints are further processed by a rate limiter and a priority arbitrator before being sent to the DCS. The rate limiter uses a first-order inertial element to limit the rate of change of the setpoints, while the priority arbitrator dynamically adjusts the execution order according to the safety integrity level to ensure the real-time performance of critical safety control loops.

[0086] The visualization diagnostic module continuously constructs a three-dimensional phase space trajectory in the background, extracts nonlinear correlation features between process parameters using a recursive quantitative analysis algorithm, and combines a Petri net-based fault propagation model to locate the root cause and predict the propagation path of deviation events. When the deviation exceeds a dynamic threshold, the module sequentially triggers a first-level visualization alarm, a second-level adaptive correction, and a third-level interlocking protection action to achieve a graded response. The model self-updating module uses the product quality data output by the online analyzer as the truth benchmark and employs a filter-based parameter drift detection mechanism to correct the key parameters of the first-principles model online.

[0087] Example 2;

[0088] Please see Figures 1-4 In this embodiment of the invention, a method for using a process industry digital twin platform system includes the following steps:

[0089] Step S1: Based on historical data, train the parameters of the first-principles model system in the process mechanism modeling module to establish an initial virtual model;

[0090] Step S2: Update the virtual model state in real time with the help of the dynamic coupling engine to generate model prediction values;

[0091] Step S3: Compare the model prediction value with the measured data from the distributed control system. When the deviation exceeds the threshold set by the visualization diagnostic module, the diagnostic process is triggered.

[0092] Step S4: Adjust the control parameters of the distributed control system based on the optimized setpoints generated by the model predictive control algorithm through the closed-loop optimization control interface.

[0093] Step S5: Based on the product quality feedback data obtained from the online analyzer, dynamically adjust the parameters of the first principles model system.

[0094] Step S3 also includes the following steps:

[0095] Step S31: Calculate the Mahalanobis distance between the model's predicted value and the measured data, and determine whether it exceeds the set range;

[0096] Step S32: Apply fault tree analysis to determine possible causes of failure;

[0097] Step S33: Generate a report that includes troubleshooting recommendations and risk assessment.

[0098] Step S5 also includes the following steps:

[0099] Step S51: Apply Bayesian inference to update the posterior distribution of the parameters of the first-principles model system;

[0100] Step S52: Apply sensitivity analysis to determine key model parameters;

[0101] Step S53: Implement adaptive adjustments to the model structure.

[0102] The working principle of this invention embodiment is as follows: In step S1, a parameter identification algorithm based on maximum likelihood estimation is first used to train the first-principles model parameters in the process mechanism modeling module offline; the training dataset consists of steady-state and dynamic operating condition data from the past 12 months, and its sampling frequency is uniformly normalized to 1Hz by an anti-aliasing filter and high-frequency noise is removed by wavelet packet decomposition.

[0103] When step S2 is running, the dynamic coupling engine starts in an event-driven manner. When the DCS detects that the rate of change of key process variables exceeds the set threshold (such as the rate of change of temperature > 0.5℃ / min), it triggers the model state update. The update process adopts a prediction-correction mechanism based on the explicit Euler method. The prediction step size is dynamically adjusted by the local Lyapunov exponent estimator to balance real-time performance and numerical stability.

[0104] In step S3, the visualization diagnostic module uses Mahalanobis distance as a multidimensional deviation metric. Its covariance matrix is ​​updated in real time using the sliding window exponential weighting method, and the window length is optimized by the Akaike Information Content Criterion (AIC).

[0105] In step S4, after the closed-loop optimization control interface is solved by MPC, the loop time delay is compensated by the Smith predictor, and the predictor parameters are identified online by the recursive least squares method. At the same time, the piecewise linearization method is used to transform the nonlinear constraints into linear matrix inequalities to reduce the complexity of quadratic programming.

[0106] When performing dynamic correction of model parameters in step S5, the online analyzer data and model prediction results are first fused using a Bayesian inference framework. The prior distribution is selected as a normal-inverse gamma conjugate distribution to reduce the computational burden. Then, sensitivity analysis based on Sobol sequences is used to identify the top 5% of parameters that contribute the most to the quality index under test, and structural adaptive adjustments are performed on this subset of parameters. The adjustment strategies include adding or removing reaction paths, replacing heat transfer correlations, and reconstructing boundary conditions.

[0107] Example 3;

[0108] Please see Figures 1-4 A specific embodiment is provided, taking a 300,000-ton-per-year ethylene cracking unit as an example, where the system of this invention is deployed on an industrial server in a DCS cabinet. The process mechanism modeling module establishes a one-dimensional reactor model for the SRT-IV type cracking furnace, with 24 furnace tubes connected in parallel, each 11m long; the pre-exponential factor of the cracking reaction is k=1.47×10. 14 s -1Activation energy E = 265 kJ / mol -1 The heat exchanger model has a heat exchange area of ​​1870 m². 2 The initial value of the dynamic fouling thermal resistance R_f(t) is 1.5 × 10⁻⁶. -4 m 2 KW -1 The time constant is 45 min. The dynamic coupling engine, via OPCUA, collects data from 48 temperature points, 40 flow points, and 5 chromatographs at 200 ms intervals. The RADAU5 implicit integrator solver is used, with a single-step CPU time of 0.12 s. The closed-loop optimized control interface (MPC) controls the time domain for 20 min and predicts the time domain for 60 min. The furnace outlet temperature change rate is limited to 1℃ / min. -1 The visualization diagnostic module has three levels of deviation thresholds: Mahalanobis distances of 6.7, 9.2, and 12.5 trigger a high-brightness alarm, parameter self-tuning, and emergency shutdown respectively. The model self-update module receives the chromatographic ethylene concentration every 1 hour. If the error is >1.5% for 5 consecutive points, the Bayesian update of the k, E, and C_coke coefficients takes effect after verification.

[0109] Working Principle: The system constructs a multi-scale first-principles model of reaction and heat transfer through the process mechanism modeling module. It utilizes a dynamic coupling engine to collect DCS data in real time and solve it simultaneously with the finite element model, achieving synchronous updates of the virtual model's state. The closed-loop optimization control module generates optimized setpoints based on the MPC algorithm, which are then sent to the DCS after rate limiting and priority arbitration to ensure smooth and safe control. The visualization diagnostic module constructs multi-dimensional phase space trajectories and, combined with a fault propagation model, enables root cause tracing and graded early warning of deviations. The model self-updating module uses online quality data as a benchmark, employing Bayesian inference and sensitivity analysis to dynamically correct key parameters and continuously optimize the model's prediction accuracy.

[0110] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A digital twin platform system for process industries, characterized in that, include: The process mechanism modeling module, based on the geometric structure, operating conditions and physical property database of the target device, adopts a reactor-heat exchanger coupled modeling strategy to establish a multi-scale first-principles model system that includes microscopic reaction kinetics, mesoscopic transport phenomena and macroscopic energy balance. The dynamic coupling engine is used to collect process data in real time and solve the problem by coupling the first principle model system with the equipment operation status model. The closed-loop optimization control interface is used for bidirectional communication with the distributed control system, generating optimized setpoints based on the solution results and executing control. The visualization diagnostic module is used for multi-dimensional analysis of process parameter deviations, root cause tracing of faults, and early warning response. The system improves the accuracy of the virtual model's prediction of product quality under fluctuating operating conditions by using the first-principles model system built by the process mechanism modeling module, combined with the real-time coupling solution and state update of the dynamic coupling engine, and the setpoint optimization and smooth control of the closed-loop optimization control interface. The process mechanism modeling module includes: The reactor model is constructed based on the reaction kinetic equations, which are as follows: Where r is the reaction rate, k is the pre-exponential factor, E is the activation energy, R is the gas constant, T is the reaction temperature, f is the reactant concentration function, and g is the catalyst activity function; The heat exchange model, based on the principles of heat transfer, incorporates a dynamic fouling thermal resistance correction coefficient R_f, and its heat transfer equation is: Where Q is the heat exchange, U is the overall heat transfer coefficient, A is the heat transfer area, and Ta is the logarithmic mean temperature difference. Hh is the hot-side convective heat transfer coefficient, Rw is the wall thermal resistance, Rf(t) is the dynamic fouling thermal resistance, and Hc is the cold-side convective heat transfer coefficient. The dynamic coupling engine collects multi-dimensional process parameters from the distributed control system in real time through an industrial IoT interface; establishes a finite element model of the equipment's operating state based on the equipment's geometric parameters, material properties, and boundary conditions; combines the first principle model system with the finite element model of the equipment's operating state, and applies a differential-algebraic equation solver to solve the equations, which supports implicit integration algorithms for rigid equation systems.

2. The process industry digital twin platform system according to claim 1, characterized in that: The closed-loop optimization control interface establishes a two-way communication channel with the distributed control system based on the industrial real-time communication protocol; it uses a model predictive control algorithm to generate optimized setpoints; it executes a control command smooth transition algorithm to limit the rate of change of setpoints; and it executes a control command priority scheduling algorithm to dynamically adjust the execution order of control commands based on the process safety level.

3. The process industry digital twin platform system according to claim 1, characterized in that: The visualization diagnostic module constructs a multi-dimensional phase space for process parameter deviations, including time series dimension, parameter correlation dimension, and equipment topology dimension. Fault root cause tracing and propagation path prediction are achieved based on the fault propagation path model; Set multiple levels of dynamic error thresholds. When the operational deviation exceeds each level of threshold, the corresponding early warning response mechanism is triggered.

4. The process industry digital twin platform system according to claim 3, characterized in that: The early warning response mechanism includes: When the deviation reaches the first-level threshold, an alarm is triggered to highlight abnormal process parameters and predict trends. When the deviation reaches the second-level threshold, the controller parameters are automatically adjusted and the optimized setpoints are automatically corrected. When the deviation reaches the third-level threshold, the emergency stop interlocking system and equipment protection action sequence are triggered.

5. The process industry digital twin platform system according to claim 1, characterized in that: The system also includes a model self-updating module for real-time acquisition of product quality data from the online analyzer; This is used to construct a data-driven model parameter correction mechanism, which integrates the first principle model system with measured data. The system further includes: The data preprocessing module is used to perform outlier cleaning, missing value imputation, and data standardization. The model validation module is used to perform model structure validation, parameter identification, and uncertainty analysis.

6. A method of using a process industry digital twin platform system, implemented using the process industry digital twin platform system as described in any one of claims 1-5, characterized in that, The method includes the following steps: Step S1: Train the parameters of the first-principles model system in the process mechanism modeling module based on historical data to establish an initial virtual model; Step S2: Update the virtual model state in real time with the help of the dynamic coupling engine to generate model prediction values; Step S3: Compare the model prediction value with the measured data from the distributed control system. When the deviation exceeds the threshold set by the visualization diagnostic module, the diagnostic process is triggered. Step S4: Adjust the control parameters of the distributed control system according to the optimized setpoints generated by the model predictive control algorithm through the closed-loop optimization control interface. Step S5: Based on the product quality feedback data obtained by the online analyzer, dynamically adjust the parameters of the first principle model system.

7. The method of using a process industry digital twin platform system according to claim 6, characterized in that, Step S3 further includes the following steps: Step S31: Calculate the Mahalanobis distance between the model prediction value and the measured data, and determine whether it exceeds the set range; Step S32: Apply fault tree analysis to determine possible causes of failure; Step S33: Generate a report that includes troubleshooting recommendations and risk assessment.

8. The method of using a process industry digital twin platform system according to claim 6, characterized in that, Step S5 further includes the following steps: Step S51: Apply Bayesian inference to update the posterior distribution of the parameters of the first principle model system. Step S52: Apply sensitivity analysis to determine key model parameters; Step S53: Implement adaptive adjustments to the model structure.

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