Process industry digital twin platform system and use method

Abstract

The invention relates to the technical field of digital twinning, and discloses a process industry digital twinning platform system, which comprises a process mechanism modeling module used for constructing a multi-scale first principle model system according to physical and chemical laws of the process industry; the dynamic coupling engine is used for collecting process data in real time and carrying out coupling solution by combining the first principle model system and the equipment operation state model; the closed-loop optimization control interface is used for carrying out two-way communication with the distributed control system, generating an optimization set value based on a solving result and executing control; and the visual diagnosis module is used for carrying out multi-dimensional analysis, fault root cause tracing and early warning response on process parameter deviation. First principle equations of reaction kinetics, heat transfer theory and the like are directly embedded into the virtual model through the process mechanism modeling module, so that the model has physical and chemical interpretation capability on complex chemical reactions and energy transfer, and high precision is still kept under complex working conditions.

Description

Technical Field

[0001] The present invention relates to the field of digital twin technology, and specifically to a process industry digital twin platform system and a method of use. Background Art

[0002] In today's industrial field, 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 complex, continuous, and highly automated. They involve numerous production equipment, process flows, and material flows, and have extremely high standards for production efficiency, product quality, resource utilization, and safety assurance.

[0003] A search revealed Chinese patent number CN118014285A, which discloses a visual industrial internet digital twin platform. The 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 rights management module. By building a three-dimensional visual digital twin industrial equipment model and a factory production process model for simulated production, production management becomes more transparent. Real-time simulated monitoring of enterprise assets is achieved through VR / AR devices, enabling process management in a visual manner. This invention enables real-time, panoramic monitoring of industrial equipment and factory production processes. This shortens troubleshooting time in the event of industrial equipment failures or factory production process errors, effectively saving operating costs.

[0004] Although the above-mentioned digital twin platform can realize three-dimensional visual monitoring of industrial equipment and production scenes, its model construction relies on the physical properties of the equipment and production image data, and does not take into account the process mechanism knowledge unique to the process industry, such as the impact of reaction kinetics on the model. This will lead to insufficient model fidelity, that is, 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 product quality indicators. Based on this, the present invention designs a process industry digital twin platform system and usage method to solve the above problems. Summary of the Invention

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

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

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

[0008] Process mechanism modeling module, used to build a multi-scale first-principles model system based on the physical and chemical laws of the process industry;

[0009] A dynamic coupling engine is used to collect process data in real time and couple the first principle model system with the equipment operation status model for solution.

[0010] Closed-loop optimization control interface, used for two-way communication with the distributed control system, generating optimized setpoints based on the solution results and executing control;

[0011] Visual diagnostic module for multi-dimensional analysis of process parameter deviations, fault root cause tracing, and early warning response;

[0012] Among them, the system improves the prediction accuracy of the virtual model for product quality under fluctuating working conditions by combining the first-principles model system constructed by the process mechanism modeling module with the real-time coupling solution and state update of the dynamic coupling engine, and the set value optimization and smoothing 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 kinetics equation, which is:

[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 is constructed based on the principle of heat transfer and introduces the dynamic fouling thermal resistance correction coefficient R_f. Its heat transfer equation is:

[0018]

[0019]

[0020] Where Q is the heat transfer amount, U is the total 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 the industrial Internet of Things interface; establishes a finite element model of the equipment operation state based on the equipment geometric parameters, material properties and boundary conditions; combines the said first principle model system with the equipment operation state finite element model, and applies a differential-algebraic equation solver to solve the problem, and the solver supports an implicit integration algorithm for a rigid equation system.

[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 the model predictive control algorithm to generate optimized set values; executes the control instruction smooth transition algorithm to limit the rate of change of the set values; executes the control instruction priority scheduling algorithm to dynamically adjust the execution order of the control instructions based on the process safety level.

[0024] Preferably, the visual diagnosis module constructs a multi-dimensional phase space of process parameter deviation including time series dimension, parameter correlation dimension and equipment topology dimension; realizes fault root cause tracing and propagation path prediction based on the fault propagation path model; sets multi-level dynamic error thresholds, and triggers the corresponding early warning response mechanism when the operation deviation exceeds the threshold at each level.

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

[0026] When the deviation reaches the first-level threshold, the process parameter abnormality is highlighted and the trend prediction alarm is triggered;

[0027] When the deviation reaches the second level threshold, the controller parameter self-tuning and optimization setting value are automatically corrected;

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

[0029] Preferably, the system further comprises a model self-updating module for acquiring product quality data of the online analyzer in real time; for constructing a data-driven model parameter correction mechanism, integrating the first principle model system with measured data;

[0030] The system further comprises:

[0031] Data preprocessing module, used to clean abnormal data, fill missing values ​​and standardize data;

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

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

[0034] Step S1, training the parameters of the first principle model system in the process mechanism modeling module based on historical data to establish an initial virtual model;

[0035] Step S2, updating the virtual model state in real time with the help of a dynamic coupling engine to generate a model prediction value;

[0036] Step S3, comparing the model prediction value with the measured data from the distributed control system, and triggering the diagnosis process when the deviation exceeds the threshold set by the visual diagnosis module;

[0037] Step S4, adjusting the control parameters of the distributed control system according to the optimized set values ​​generated by the model predictive control algorithm through the closed-loop optimization control interface;

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

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

[0040] Step S31, calculating the Mahalanobis distance between the model prediction value and the measured data, and determining whether it exceeds a set range;

[0041] Step S32, applying fault tree analysis to determine possible fault causes;

[0042] Step S33: Generate a report including troubleshooting suggestions and risk assessment.

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

[0044] Step S51, applying Bayesian reasoning to update the posterior distribution of the parameters of the first principle model system;

[0045] Step S52, applying sensitivity analysis to determine key model parameters;

[0046] Step S53: Implement adaptive adjustment of the model structure.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

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

[0049] 2. In the present invention, the dynamic coupling engine solves the first-principles model system and the finite-element-based equipment operating status model in real time, and uses the rigid equation implicit integration algorithm to handle working conditions with strong coupling and 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, and the quality indicator prediction value output by the first-principles model is directly used to generate the optimized set value, and the set value change rate is limited through a smooth transition algorithm to prevent drastic fluctuations when the raw materials suddenly change, thereby realizing early intervention and stable control of quality indicators.

[0050] 3. In the present invention, the visual diagnosis module constructs a phase space including three dimensions: time series, parameter association, and equipment topology. It locates the root causes such as reaction kinetic parameter drift and catalyst deactivation through the fault propagation path model, and triggers hierarchical warning and automatic response strategies under the multi-level dynamic error threshold mechanism. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

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

[0054] Figure 4 This is a flow chart of the model self-update and verification of the present invention. DETAILED DESCRIPTION

[0055] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0056] Example 1;

[0057] See also Figures 1-4 In an embodiment of the present invention, a process industry digital twin platform system includes:

[0058] Process mechanism modeling module, used to build a multi-scale first-principles model system based on the physical and chemical laws of the process industry;

[0059] Dynamic coupling engine, used to collect process data in real time and couple the first-principles model system with the equipment operation status model for solution;

[0060] Closed-loop optimization control interface, used for two-way communication with the distributed control system, generating optimized setpoints based on the solution results and executing control;

[0061] Visual diagnostic module for multi-dimensional analysis of process parameter deviations, fault root cause tracing, and early warning response;

[0062] Among them, the system improves the virtual model's prediction accuracy for product quality under fluctuating working conditions by combining the first-principles model system constructed through the process mechanism modeling module with the real-time coupling solution and state update of the dynamic coupling engine, as well as the set value optimization and smooth control of the closed-loop optimization control interface.

[0063] The process mechanism modeling module includes:

[0064] The reactor model is constructed based on the reaction kinetics equation, which is:

[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 is constructed based on the principle of heat transfer and introduces the dynamic fouling thermal resistance correction coefficient R_f. Its heat transfer equation is:

[0068]

[0069]

[0070] Where Q is the heat transfer amount, U is the total 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 Internet of Things interface; establishes a finite element model of the equipment operation status based on the equipment geometric parameters, material properties and boundary conditions; combines the first-principles model system and the equipment operation status finite element model, and applies a differential-algebraic equation solver to solve the problem. The solver supports implicit integration algorithms for rigid equations.

[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; applies the model predictive control algorithm to generate optimized set values; executes the control instruction smooth transition algorithm to limit the rate of change of the set values; and executes the control instruction priority scheduling algorithm to dynamically adjust the execution order of control instructions based on the process safety level.

[0074] The visual diagnosis module constructs a multi-dimensional phase space of process parameter deviations, including time series dimensions, parameter correlation dimensions, and equipment topology dimensions. It implements fault root cause tracing and propagation path prediction based on the fault propagation path model. It sets multi-level dynamic error thresholds, and when the operating deviation exceeds the thresholds at each level, the corresponding early warning response mechanism is triggered.

[0075] The early warning response mechanism includes:

[0076] When the deviation reaches the first-level threshold, the process parameter abnormality is highlighted and the trend prediction alarm is triggered;

[0077] When the deviation reaches the second level threshold, the controller parameter self-tuning and optimization setting value are automatically corrected;

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

[0079] The system also includes a model self-update module for real-time acquisition of product quality data from online analyzers; a data-driven model parameter correction mechanism that integrates the first-principles model system with measured data;

[0080] The system further comprises:

[0081] Data preprocessing module, used to clean abnormal data, fill missing values ​​and standardize data;

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

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

[0084] The dynamic coupling engine uses the OPCUA industrial Ethernet protocol to collect multidimensional process parameters such as temperature, pressure, flow rate, and component concentration from the distributed control system in real time at a fixed 200ms cycle. It then maps these parameters to the boundary conditions of a finite element model constructed based on an unstructured mesh. This finite element model discretizes the equipment geometry using eight-node hexahedral elements, and material properties are dynamically updated with temperature to ensure the numerical stability of the structural-thermal-fluid three-field coupled calculations. During the solution phase, the dynamic coupling engine invokes a rigid DAE solver based on the backward difference formula and employs an adaptive step-size control strategy to simultaneously solve the first-principles model and the equipment state model.

[0085] After obtaining the solution, the closed-loop optimization control interface inputs the deviation between the predicted product quality index and the set target into the MPC controller. The MPC controller adopts a rolling horizon optimization strategy based on the state-space model. Its objective function comprehensively considers the multi-objective weights of product quality, energy consumption, and equipment wear, and generates the optimal set value sequence in the control time domain through a quadratic programming solver. To prevent actuator saturation and process disturbances, the optimized set value is further processed by a rate limiter and a priority arbiter before being sent to the DCS. The rate limiter uses a first-order inertia link to limit the rate of change of the set value, and the priority arbiter dynamically adjusts the execution order according to the safety integrity level to ensure the real-time performance of the critical safety control loop.

[0086] The visual diagnostic module continuously constructs three-dimensional phase space trajectories in the background, using a recursive quantitative analysis algorithm to extract nonlinear correlations between process parameters. Combined with a Petri net-based fault propagation model, it locates the root cause of deviation events and predicts their propagation paths. When deviations exceed dynamic thresholds, the module triggers a first-level visual alarm, a second-level adaptive correction, and a third-level interlocking protection action, achieving a hierarchical response. The model self-update module uses the product quality data output by the online analyzer as the true value benchmark and employs a filter-based parameter drift detection mechanism to perform online corrections on key parameters of the first-principles model.

[0087] Example 2;

[0088] See also Figures 1-4 In an embodiment of the present invention, a method for using a process industry digital twin platform system includes the following steps:

[0089] Step S1, training the parameters of the first principle model system in the process mechanism modeling module based on historical data to establish an initial virtual model;

[0090] Step S2, using a dynamic coupling engine to update the virtual model state in real time and generate a model prediction value;

[0091] Step S3, comparing the model prediction value with the measured data from the distributed control system. When the deviation exceeds the threshold set by the visual diagnosis module, the diagnosis process is triggered;

[0092] Step S4, adjusting the control parameters of the distributed control system according to the optimized set values ​​generated by the model predictive control algorithm through the closed-loop optimization control interface;

[0093] Step S5: Dynamically modify the parameters of the first principles model system based on the product quality feedback data obtained by the online analyzer.

[0094] Step S3 further includes the following steps:

[0095] Step S31, calculating the Mahalanobis distance between the model prediction value and the measured data, and determining whether it exceeds the set range;

[0096] Step S32, applying fault tree analysis to determine possible fault causes;

[0097] Step S33: Generate a report including troubleshooting suggestions and risk assessment.

[0098] Step S5 further includes the following steps:

[0099] Step S51, applying Bayesian reasoning to update the posterior distribution of the first principles model system parameters;

[0100] Step S52, applying sensitivity analysis to determine key model parameters;

[0101] Step S53: Implement adaptive adjustment of the model structure.

[0102] The working principle of the embodiment of the present invention is: in step S1, a parameter identification algorithm based on maximum likelihood estimation is first used to perform offline training on the first-principles model parameters in the process mechanism modeling module; the training data set is composed of steady-state and dynamic operating data of the past 12 months, and its sampling frequency is uniformly regularized 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 is started in an event-driven manner. When the DCS detects that the change rate of the key process variable exceeds the set threshold (such as the temperature change rate > 0.5°C / min), the model state update is triggered; the update process adopts a prediction-correction mechanism based on the explicit Euler method, and the prediction step size is dynamically adjusted by the local Lyapunov exponent estimator to take into account both real-time performance and numerical stability.

[0104] In step S3, the visual diagnosis module uses Mahalanobis distance as the multidimensional deviation measurement indicator, and its covariance matrix is ​​updated in real time through the sliding window exponential weighting method. The window length is optimized by the Akaike Information Criterion (AIC).

[0105] In step S4, after the closed-loop optimization control interface is solved by MPC, the loop 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 nonlinear constraints are converted into linear matrix inequalities using the piecewise linearization method to reduce the complexity of the quadratic programming solution.

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

[0107] Example 3;

[0108] See also Figures 1-4 , provides a specific example, taking a cracking unit with an annual production capacity of 300,000 tons of ethylene as an example, and deploying the system of the present invention on an industrial server between DCS cabinets. The process mechanism modeling module establishes a one-dimensional reactor model for an SRT-IV cracking furnace, with 24 furnace tubes connected in parallel and 11 meters long; the cracking reaction pre-exponential factor k = 1.47×10 14 s -1, activation energy E = 265 kJ mol -1 The heat exchange area of ​​the quench heat exchanger model is 1870m 2 , dynamic fouling thermal resistance R_f(t) initial value 1.5×10 -4 m 2 KW -1 The dynamic coupling engine collects 48 temperature points, 40 flow points, and 5 chromatograph data via OPCUA in a 200ms cycle. Using the RADAU5 implicit integration solver, a single-step CPU time of 0.12s is required. The closed-loop optimization control interface MPC control time domain is 20 minutes, the prediction time domain is 60 minutes, and the furnace outlet temperature change rate is limited to 1°C min. -1 The visual diagnostic module has three deviation thresholds: Mahalanobis distances of 6.7, 9.2, and 12.5, which trigger a highlighted alarm, parameter self-tuning, and emergency shutdown, respectively. The model self-update module receives ethylene concentrations from the chromatogram every hour. If the error for five consecutive points exceeds 1.5%, 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 solves it in conjunction with the finite element model to synchronize virtual model state updates. The closed-loop optimization control module generates optimized setpoints based on the MPC algorithm and transmits them to the DCS after rate limiting and priority arbitration to ensure smooth and safe control. The visual diagnosis module constructs multi-dimensional phase space trajectories and, combined with the fault propagation model, enables root cause tracing of deviations and graded early warning. The model self-update module uses online quality data as a benchmark, employing Bayesian reasoning and sensitivity analysis to dynamically modify key parameters and continuously optimize model prediction accuracy.

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

Claims

1. A process industry digital twin platform system, characterized by: include: Process mechanism modeling module, used to build a multi-scale first-principles model system based on the physical and chemical laws of the process industry; A dynamic coupling engine is used to collect process data in real time and couple the first principle model system with the equipment operation status model for solution. Closed-loop optimization control interface, used for two-way communication with the distributed control system, generating optimized setpoints based on the solution results and executing control; Visual diagnostic module for multi-dimensional analysis of process parameter deviations, fault root cause tracing, and early warning response; Among them, the system improves the prediction accuracy of the virtual model for product quality under fluctuating working conditions by combining the first-principles model system constructed by the process mechanism modeling module with the real-time coupling solution and state update of the dynamic coupling engine, and the set value optimization and smoothing control of the closed-loop optimization control interface.

2. A process industry digital twin platform system according to claim 1, characterized in that: The process mechanism modeling module includes: The reactor model is constructed based on the reaction kinetics equation, which is: ; 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 is constructed based on the principle of heat transfer and introduces the dynamic fouling thermal resistance correction coefficient R_f. Its heat transfer equation is: 、 ; Where Q is the heat transfer amount, U is the total 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.

3. The process industry digital twin platform system according to claim 2, characterized in that: The dynamic coupling engine collects multi-dimensional process parameters from the distributed control system in real time through the industrial Internet of Things interface; establishes a finite element model of the equipment operation status based on the equipment geometric parameters, material properties and boundary conditions; and combines the first principle model system with the equipment operation status finite element model, and applies a differential-algebraic equation solver to solve the problem, wherein the solver supports an implicit integration algorithm for a rigid equation system.

4. 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; applies the model predictive control algorithm to generate optimized set values; executes the control instruction smooth transition algorithm to limit the rate of change of the set values; and executes the control instruction priority scheduling algorithm to dynamically adjust the execution order of the control instructions based on the process safety level.

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

6. The process industry digital twin platform system according to claim 5, characterized in that: The early warning response mechanism includes: When the deviation reaches the first-level threshold, the process parameter abnormality is highlighted and the trend prediction alarm is triggered; When the deviation reaches the second level threshold, the controller parameter self-tuning and optimization setting value are automatically corrected; When the deviation reaches the third level threshold, the emergency stop interlock system and equipment protection action sequence are triggered.

7. The process industry digital twin platform system according to claim 1, characterized in that: The system also includes a model self-updating module for acquiring product quality data of the online analyzer in real time; Used to build a data-driven model parameter correction mechanism, integrating the first principles model system with measured data; The system further comprises: Data preprocessing module, used to clean abnormal data, fill missing values ​​and standardize data; The model verification module is used to perform model structure verification, parameter identification and uncertainty analysis.

8. A method for using a process industry digital twin platform system, implemented using the process industry digital twin platform system according to any one of claims 1 to 7, characterized in that: The method comprises the following steps: Step S1, training the parameters of the first principle model system in the process mechanism modeling module based on historical data to establish an initial virtual model; Step S2, using a dynamic coupling engine to update the virtual model state in real time and generate a model prediction value; Step S3, comparing the model prediction value with the measured data from the distributed control system, and triggering the diagnosis process when the deviation exceeds the threshold set by the visual diagnosis module; Step S4, adjusting the control parameters of the distributed control system according to the optimized set values ​​generated by the model predictive control algorithm through the closed-loop optimization control interface; Step S5: dynamically modifying the parameters of the first principle model system based on the product quality feedback data obtained by the online analyzer.

9. The method for using a process industry digital twin platform system according to claim 8, characterized in that: The step S3 further comprises the following steps: Step S31, calculating the Mahalanobis distance between the model prediction value and the measured data, and determining whether it exceeds a set range; Step S32, applying fault tree analysis to determine possible fault causes; Step S33: Generate a report including troubleshooting suggestions and risk assessment.

10. The method for using a process industry digital twin platform system according to claim 8, characterized in that: The step S5 further comprises the following steps: Step S51, applying Bayesian reasoning to update the posterior distribution of the parameters of the first principle model system; Step S52, applying sensitivity analysis to determine key model parameters; Step S53: Implement adaptive adjustment of the model structure.

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