Emergency discharge plan method for high-pressure reaction kettle

Abstract

The invention discloses a high-pressure reaction kettle emergency discharge pre-arranged planning method, which comprises the following steps of: constructing a real-time monitoring network through a multi-physical quantity sensor, automatically generating a micro scene highly coupled with field disturbance by combining intelligent signal filtering, abnormal factor aggregation and parametric modeling, and realizing agile mapping of a historical optimal discharge strategy and adaptive parameter adjustment and optimization. The risk tolerance and the response rate are accurately evaluated by means of parallel simulation deduction; an actual discharge action is compared with simulation feedback, and if a significant deviation exists, a rapid reconstruction strategy is iterated to realize closed-loop safety management and control; according to the method, the accuracy, the self-adaptability and the safety of the release decision are improved, and the emergency prevention and control efficiency of the reaction kettle under the extreme working condition is improved.

Description

Technical Field

[0001] This invention relates to the field of industrial process safety and intelligent decision support technology, and in particular to a method for emergency relief of high-pressure reactors. Background Technology

[0002] Currently, in the field of industrial process safety and intelligent decision support technology, especially in the emergency release control of major hazardous equipment such as high-pressure reactors, a technical system dominated by the combination of rule-based contingency plans and static simulation has been formed. Typical technical approaches include: developing multi-scenario emergency plans based on historical accident experience; setting early warning thresholds for key physical quantities such as pressure and temperature using mathematical models or experience databases with fixed parameters; and verifying release strategies and optimizing response measures under common operating conditions through a pre-set physical simulation platform. These solutions typically operate in conjunction with DCS / PLC automated monitoring systems. When process parameters are abnormal, corresponding safety interlocks and release control commands are triggered to ensure the safety of the reactor and its surrounding environment.

[0003] However, with the increasing complexity of chemical processes and the continuous emergence of new materials and reaction systems, high-pressure reactors face more complex abnormal dynamics: sudden events often exhibit nonlinear, multivariable, and multi-level linkage characteristics, and their spatial distribution, boundary conditions, and disturbance factors far exceed historical paradigms in actual operation, causing actual abnormal scenarios to frequently deviate from the preset operating condition template. This deviation manifests in two ways: First, existing venting strategies are clearly insufficiently adaptable to actual "new operating conditions" or "compound anomalies"—once the parameter combination falls into the blind spot of the rule plan coverage, the automatically recommended strategy is difficult to respond accurately, resulting in response delays and safety hazards. Second, existing systems have weak strategy transfer capabilities in the face of sudden scenarios, often having to revert to manual interpretation and ad-hoc decision-making, lacking rapid adaptation and closed-loop optimization mechanisms, resulting in insufficient timeliness and robustness of venting actions. Summary of the Invention

[0004] In order to solve the above-mentioned technical problems, the present invention provides a method for emergency release of high-pressure reactors.

[0005] The technical solution of this invention is implemented as follows: a method for emergency venting of a high-pressure reactor, comprising:

[0006] S1: Real-time acquisition of physical quantities such as pressure, temperature and gas composition at multiple locations inside the high-pressure reactor to obtain environmental parameter data containing multi-point spatial distribution characteristics;

[0007] S2: Perform noise reduction and anomaly pattern recognition processing on the collected environmental parameter data to obtain highly reliable anomaly monitoring signals;

[0008] S3: Perform spatiotemporal comparative analysis of abnormal monitoring signals with historical operating data and known accident databases, and apply factor clustering algorithm to extract key abnormal factors affecting the release decision;

[0009] S4: Based on the extracted key anomaly factors, automatically generate a micro-scenario parameter set that reflects the characteristics of the current abrupt change in operating conditions, including the distribution of each monitoring point, boundary conditions, and multivariate perturbation models;

[0010] S5: For the generated micro-scene parameter set, quickly map the historical best strategy from the release strategy database to obtain the strategy candidate set that best matches the current micro-scene features;

[0011] S6: Adaptively relocate the policy candidate set and use a parameter fine-tuning mechanism to select the release policy module with the highest matching degree with the current micro-scenario disturbance logic;

[0012] S7: Input the relocated discharge strategy module into the digital twin parallel simulation engine to perform multiple rounds of rapid sensitivity simulation to obtain the risk tolerance, response rate and consequence range of each strategy in the current scenario;

[0013] S8: Based on the results of sensitivity simulation, automatically select the optimal discharge strategy and send control commands to the field execution stage in real time to complete the discharge action deployment;

[0014] S9: Monitor the actual execution process of the discharge action in real time, collect the execution feedback signal, and compare the signal with the simulation prediction function to determine whether the actual deviation exceeds the safety threshold.

[0015] S10: If a significant deviation is found between the execution feedback and the simulation prediction results, the micro-scene rapid reconstruction and policy adaptive decision-making loop will be triggered again until safety standards are met, and the entire process data will be archived for subsequent optimization and upgrade of the micro-scene model and policy library.

[0016] The emergency relief plan method for high-pressure reactors provided in this application has the following beneficial effects:

[0017] (1) By triggering micro-scene generation in real time through abnormal state, the traditional single large scene static judgment is upgraded to fine-grained and dynamic "micro-scene" judgment, which effectively identifies and responds to the small deviations and coupling mutations between the actual working conditions and the preset template, so that the discharge strategy can be flexibly reorganized for any complex working conditions, greatly enhancing the system's scene migration capability and fault tolerance capability.

[0018] (2) Integrating multi-level sensing and AI anomaly pattern recognition, the entire process from data acquisition to signal processing adopts multi-channel denoising, spatial distribution management and feature fusion, which can improve the signal-to-noise ratio of anomaly detection signals by more than 30%, significantly reduce the risk of misjudgment and missed judgment, and extract highly correlated anomaly factors through clustering-dimensionality reduction-multimodal correlation analysis, thereby greatly improving the targeting and accuracy of strategy positioning.

[0019] (3) Through parameterized micro-scenes and multi-layer feature mapping algorithms, millisecond-level retrieval of strategies based on hundreds of millions of historical data is achieved. Combined with candidate set fine-tuning and disturbance sensitivity optimization processes, the strategy matching time in dynamic environments is reduced from the traditional minutes to the seconds, greatly shortening the emergency response cycle. The parameter fine-tuning and disturbance modeling mechanisms ensure that the optimal release scheme can be generated quickly even in unknown scenarios;

[0020] (4) Introduce a digital twin parallel simulation engine to conduct multiple rounds of sensitivity simulation and forward inference based on the latest technology and physical boundary conditions, so as to achieve quantitative assessment of various strategy risks, response rates and consequences ranges. Attached Figure Description

[0021] Figure 1 This is a flowchart of an emergency relief plan method for a high-pressure reactor according to the present invention;

[0022] Figure 2 This is a sub-flowchart of an emergency relief plan method for a high-pressure reactor according to the present invention;

[0023] Figure 3 This is another sub-flowchart of the emergency relief plan method for a high-pressure reactor according to the present invention. Detailed Implementation

[0024] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0025] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0026] When used herein, the singular forms of “a,” “an,” and “the” may also include the plural forms unless the context clearly indicates otherwise. It should also be understood that the terms “comprising / including” or “having,” etc., specify the presence of the stated features, wholes, steps, operations, components, parts, or combinations thereof, but do not preclude the possibility of the presence or addition of one or more other features, wholes, steps, operations, components, parts, or combinations thereof. Meanwhile, the term “and / or” as used in this specification includes any and all combinations of the associated listed items.

[0027] Please see Figures 1-3 As shown, a method for emergency venting of a high-pressure reactor includes:

[0028] S1: Real-time acquisition of physical quantities such as pressure, temperature and gas composition at multiple locations inside the high-pressure reactor to obtain environmental parameter data containing multi-point spatial distribution characteristics;

[0029] S2: Perform noise reduction and anomaly pattern recognition processing on the collected environmental parameter data to obtain highly reliable anomaly monitoring signals;

[0030] S3: Perform spatiotemporal comparative analysis of abnormal monitoring signals with historical operating data and known accident databases, and apply factor clustering algorithm to extract key abnormal factors affecting the release decision;

[0031] S4: Based on the extracted key anomaly factors, automatically generate a micro-scenario parameter set that reflects the characteristics of the current abrupt change in operating conditions, including the distribution of each monitoring point, boundary conditions, and multivariate perturbation models;

[0032] S5: For the generated micro-scene parameter set, quickly map the historical best strategy from the release strategy database to obtain the strategy candidate set that best matches the current micro-scene features;

[0033] S6: Adaptively relocate the policy candidate set and use a parameter fine-tuning mechanism to select the release policy module with the highest matching degree with the current micro-scenario disturbance logic;

[0034] S7: Input the relocated discharge strategy module into the digital twin parallel simulation engine to perform multiple rounds of rapid sensitivity simulation to obtain the risk tolerance, response rate and consequence range of each strategy in the current scenario;

[0035] S8: Based on the results of sensitivity simulation, automatically select the optimal discharge strategy and send control commands to the field execution stage in real time to complete the discharge action deployment;

[0036] S9: Monitor the actual execution process of the discharge action in real time, collect the execution feedback signal, and compare the signal with the simulation prediction function to determine whether the actual deviation exceeds the safety threshold.

[0037] S10: If a significant deviation is found between the execution feedback and the simulation prediction results, the micro-scene rapid reconstruction and policy adaptive decision-making loop will be triggered again until safety standards are met, and the entire process data will be archived for subsequent optimization and upgrade of the micro-scene model and policy library.

[0038] Step S1: Real-time acquisition of physical quantities such as pressure, temperature, and gas composition at multiple locations within the high-pressure reactor to obtain environmental parameter data containing multi-point spatial distribution characteristics. Specifically, this includes:

[0039] S1.1: Deploy professional industrial-grade sensors for pressure, temperature and gas composition at multiple key spatial nodes inside the high-pressure reactor, and determine the spatial distribution scheme according to the industrial process control scheme to form a physical quantity monitoring network covering all reaction zones, points prone to loss of control and safety boundary zones.

[0040] To control multiple key spatial nodes within the high-pressure reactor, it is necessary to select the set of spatial locations to be collected based on industrial process parameters such as reactor structural zoning, process flow, and distribution of hazardous sources. These locations include the main reaction zone, auxiliary reaction zone, outlet pipe nodes, stirring drive chamber, pressure relief port, and reactor shell safety boundary zone.

[0041] Employing professional industrial-grade sensors for pressure, temperature, and gas composition (selection parameters include: high pressure resistance ≥30MPa, corrosion resistance ≥316L, operating temperature range -40~350℃, and quantitative measurement capability of gas multi-components at the ppm level), and relying on the process node deployment strategy, different types of sensors are distributed and installed at the aforementioned target spatial nodes to ensure that the monitoring network can fully cover the reaction area, easily runaway points, and safety boundary areas.

[0042] Furthermore, a deployment optimization algorithm (a sensor deployment optimization model with the influence radius of process hazard sources as weights) is used to optimize the node distribution of each type of sensor, achieving effective redundancy and preventing single-point failures from causing sensing blind spots. This optimization model includes the following forms:

[0043]

[0044] Among them, w i This represents the hazard weight of the i-th monitoring area. Deploy node x for the sensor i To the area under its monitoring responsibility Spatial distance function;

[0045] Furthermore, on-site explosion-proof, corrosion-proof isolation, and reasonable wiring and cabling are adopted to physically install various sensors, ensuring that the sensor structure is stable, the signal interface is reliable, and that it does not interfere with the existing process flow of the reactor.

[0046] All deployed sensors and data acquisition units are assigned a one-to-one numbering connection and are registered with a unique identifier to facilitate subsequent data synchronization, fault location and maintenance.

[0047] Through the above-mentioned deployment and network formation process, the data acquisition channels of multiple key process areas and safety boundary areas are physically coupled with the industrial automation system to realize a highly reliable physical quantity monitoring network covering all reaction areas, vulnerable points and safety boundary areas;

[0048] For example, in a high-pressure polymerization reactor with an effective volume of 20 cubic meters and a design working pressure limit of 25 MPa, engineers deploy 8 pressure sensors (model: 0-30 MPa intelligent type), 6 platinum resistance temperature sensors (-40~300℃, with PT100 sensitive element), and 3 infrared gas composition sensors (detecting methane, CO2, and O2 concentrations, sensitivity 10 ppm) based on the main reaction zone volume, catalyst feeding port, and reactor cover safety relief valve location. Redundancy is determined using the risk radius method to ensure that in the event of any high temperature overpressure or local leakage in the main reaction zone, at least two different types of sensors will cover the area. Each sensor is numbered P1-P8, T1-T6, and G1-G3. The industrial control host is input and registered with "P1 (T = reaction zone center axis, R = 600 mm), T3 (L = top cover area), G2 (lower reactor relief area)" to achieve spatially unique location of the distributed network topology. After deployment, the physical connections of each monitoring point are electrically isolated and shielded to ensure interference-free signal transmission. During process operation testing, it was found that the above deployment scheme can guarantee full-space early warning; if any sensor signal is lost, its redundant nodes can automatically cover it, ensuring the continuity of data acquisition and spatial integrity.

[0049] S1.2: Perform static and dynamic calibration operations on all deployed sensors, and use industrial calibration algorithms to perform range, linearity, and drift error correction on each sensor channel to obtain highly reliable pressure sensor calibration parameters, temperature sensor calibration parameters, and gas composition measurement calibration parameters, providing an equipment foundation for real-time monitoring of the entire process;

[0050] S1.3: An industrial automation control system based on a high-pressure reactor uses a high-frequency acquisition module to collect raw pressure, temperature, and gas composition signals from each spatial node sensor, ensuring that the data acquisition operation has timestamp accuracy and a high sampling rate.

[0051] S1.4: The raw signals from pressure acquisition, temperature acquisition, and gas composition acquisition are transmitted uniformly through standardized communication protocols (such as MODBUS, PROFIBUS, etc.). A data synchronization mechanism for the acquisition link is adopted to achieve time alignment of multi-point asynchronous signals and output a synchronized data stream signal set.

[0052] S1.5: Perform real-time data integrity verification and outlier pre-removal processing (such as using CRC check and industrial extreme value monitoring algorithm) on the data stream signal set after transmission and synchronization, so as to use the obtained basic data of environmental parameters such as pressure, temperature and gas composition spatial distribution as the raw input for subsequent signal denoising and pattern recognition.

[0053] Step S2: Perform denoising and anomaly pattern recognition processing on the collected environmental parameter data to obtain highly reliable anomaly monitoring signals. Specifically, this includes:

[0054] S2.1: Perform multi-channel primary filtering on the collected multi-point spatially distributed environmental parameter data. Use a bandpass filtering algorithm to remove high-frequency interference and low-frequency drift from the pressure signal, temperature signal and gas composition signal respectively, so as to obtain the environmental parameter signal after preliminary noise reduction, and provide a stable input for subsequent data fusion.

[0055] S2.2: Based on the environmental parameter signals after preliminary noise reduction, perform multi-channel signal synchronization and normalization operations, and use covariance normalization and timestamp resampling technology to achieve spatiotemporal alignment of pressure signals, temperature signals and gas composition signals at different measuring points, and obtain a spatially distributed normalized signal array under a unified time base as the basis for multidimensional feature extraction.

[0056] S2.3: Multi-scale feature extraction is performed on the normalized signal array. Based on the wavelet transform algorithm, feature parameters such as instantaneous pressure anomaly, temperature perturbation and gas composition change point are extracted. The multi-measurement point and multi-physical quantity signal is transformed into a high-dimensional waveform feature vector, realizing the initial decoupling of abnormal trend signal and background working condition.

[0057] S2.4: Perform feature dimensionality reduction and multivariate fusion on the high-dimensional waveform feature vector, and select the main control feature components that can reflect the sudden operating conditions of the high-pressure reactor based on the principal component analysis (PCA) algorithm. Then, generate comprehensive anomaly monitoring indicators through feature weighted fusion to further enhance the distinguishability and discriminability of abnormal signals.

[0058] S2.5: A discrimination algorithm based on anomaly detection machine learning models (such as isolated forest / autoencoder) is adopted to automatically identify comprehensive anomaly monitoring indicators, output the anomaly monitoring signal spectrum and anomaly confidence score under the current operating conditions of the high-pressure reactor, and provide high-confidence input for anomaly factor extraction.

[0059] Step S3: Perform spatiotemporal comparative analysis of abnormal monitoring signals with historical operating data and a known accident database, and apply a factor clustering algorithm to extract key abnormal factors affecting the release decision. For example... Figure 2 As shown, it specifically includes:

[0060] S3.1: Perform time series calibration and multidimensional data alignment preprocessing on the abnormal monitoring signals to ensure that the abnormal monitoring signals can be compared with historical operating condition data and known accident database under a unified spatiotemporal coordinate system, providing consistent input for subsequent feature association;

[0061] S3.2: Based on the abnormal monitoring signal as the data input, historical operating condition data and known accident database are called, and dynamic time warping (DTW) and multivariate correlation matching methods are used to construct a spatiotemporal correlation matrix to form a feature comparison mapping that conforms to the actual operating condition disturbance logic;

[0062] Based on multi-dimensional data input including high-pressure reactor abnormal monitoring signals, historical operating condition data, and known accident database, for abnormal monitoring signal sequences that have been spatiotemporally aligned, the Dynamic Time Warping (DTW) algorithm (input parameters include the time series X of the abnormal monitoring signal, the historical operating condition curve sequence Y, and the paired time window) is adopted to achieve nonlinear temporal alignment between the abnormal monitoring signal and the historical operating condition curve, and to capture the shortest dynamic evolution path between the sudden operating condition signal and the historical template.

[0063] Furthermore, by using multivariate correlation matching methods (such as Pearson correlation coefficient matrix or mutual information algorithm, with parameters being multi-channel abnormal feature subsequences and historical accident sample bottom feature pool), the full-dimensional correlation quantification between aligned signals is achieved, and the correlation score vector between each set of aligned signals and accident root cause features is obtained.

[0064] The following DTW distance formula is used to evaluate the time series similarity between the abnormal monitoring signal x and the historical operating condition sample y:

[0065]

[0066] Where x and y are time series, π represents all possible alignment paths, and d(x) i y j () represents the Euclidean distance;

[0067] Furthermore, based on the DTW alignment results, the correlation between each anomalous subsequence and historical event curves is calculated using the Pearson correlation coefficient formula:

[0068]

[0069] Where, x i With y i For the aligned sequence elements, and Each is its own mean;

[0070] For multivariate scenarios, the mutual information algorithm is used to quantify the information transmission strength between the characteristics of each monitoring point and the accident conditions. The mutual information I(X;Y) is calculated as follows:

[0071]

[0072] Based on the above dynamic alignment and correlation quantification results, a spatiotemporal multidimensional correlation matrix is ​​constructed for all abnormal monitoring signals, historical operating conditions and accident curves. The correlation matrix entries include DTW optimal distance, Pearson correlation coefficient and mutual information value.

[0073] Through standardization, all alignment correlation indicators are normalized into a feature-matching mapping matrix, realizing a structured mapping between historical accident templates, operating condition disturbance modes and abnormal states.

[0074] By establishing a comprehensive feature mapping structure, the input anomaly monitoring signals are structurally connected to the historical accident database and operating condition database, thereby achieving a highly adaptive correlation between the dynamic evolution of the anomaly monitoring signals and historical root cause scenarios.

[0075] For example, the monitoring signal array collected during an anomaly warning phase of a high-pressure reactor includes 19 pressure, 10 temperature, and 3 gas composition channels, with a sampling frequency of 10Hz. The input anomaly monitoring signals are aligned using Time-of-Warping (DTW) with 88 historical typical sudden accident curves, with an alignment window spanning 300 seconds. For each set of anomaly signals and historical curves, the optimal path distance using DTW is used to filter out 10 sets of high-fit curves with a distance less than 200 (normalized Euclidean distance). For these 10 sets of curves, the temporal correlation between multi-channel pressure and gas concentration is calculated using the Pearson correlation coefficient matrix, obtaining a correlation distribution between 1.0 and -0.7. Seven sets of signals with an absolute correlation value greater than 0.8 are then filtered. The mutual information algorithm is used to evaluate the information interaction strength between each set of signals, eliminating redundant anomaly signals. The optimal output is a high-correlation mapping pair between key anomaly features and their corresponding accident templates, achieving high-precision spatiotemporal comparison between current operating condition anomalies and historical operating condition disturbances. The application effect is evident in the initial screening stage, which identifies novel reactant-related factors with over 80% superposition similarity and the highest mutual information, providing a high-confidence basis for subsequent abnormal factor clustering and strategy adaptation.

[0076] S3.3: Using the constructed spatiotemporal correlation matrix, based on the characteristics of the industrial process situation, principal component analysis (PCA) and density clustering (DBSCAN) algorithms are used to perform distribution clustering on the abnormal monitoring signals, historical operating condition fluctuation characteristics, and accident templates in the control mapping, so as to preliminarily screen the set of potential influencing factors.

[0077] Using the spatiotemporal correlation matrix obtained by dynamic time warping (DTW) and multivariate correlation matching as input, this study focuses on multidimensional feature data of abnormal monitoring signals, historical operating condition fluctuation characteristics, and accident templates.

[0078] Principal component analysis (PCA) algorithm (with hyperparameter settings such as a cumulative variance threshold of 0.98 for feature contribution) is used to calculate the covariance matrix of multivariate feature sequences in the spatiotemporal correlation matrix, extract the feature vectors of the main variance directions, and form a principal component dimensionality reduction dataset that reflects the abnormal evolution of the reactor.

[0079] Furthermore, through the formula

[0080] Z = X·W

[0081] Where X is the standardized original feature matrix, W is the loading matrix composed of the principal component feature vectors selected by the PCA model, and Z is the dimensionality-reduced principal component dataset, realizing the mapping of the main statistical characteristics of high-dimensional anomalies and historical working conditions.

[0082] Based on the PCA dimensionality reduction results, the density-based spatial clustering (DBSCAN) algorithm (with a distance threshold ε and a minimum number of samples within a cluster MinPts) is used to determine the dense regions and boundary points of sample points in the dimensionality-reduced principal component space, and automatically classify the distribution categories of feature data.

[0083] Using the DBSCAN clustering core distance formula:

[0084] d(p, q)≤ε

[0085] Determine whether two principal component sample points p and q belong to the same cluster, so as to automatically identify the distribution pattern between abnormal monitoring signals and historical operating condition characteristics and screen abnormal cluster samples.

[0086] Furthermore, based on the clustering results, center vector extraction and boundary point marking are performed on the member data within each cluster to form a preliminary screening result of the potential influencing factor set;

[0087] By combining PCA and DBSCAN analysis, the outlier or cluster information of abnormal monitoring signals in the feature distribution is dynamically compared with historical working conditions and accident templates, providing a basic dataset and grouping boundary for subsequent multidimensional working condition attribution and factor clustering.

[0088] For example, in the scenario of abnormal operating conditions analysis of a high-pressure reactor, the input data includes abnormal monitoring signals of pressure, temperature, and gas concentration from 20 distributed monitoring points, as well as 1000 historical operating conditions and 30 typical accident fluctuation data over a period of one year. The cumulative contribution variance of the PCA algorithm is set to 98%, and the first 5 principal components are extracted, compressing the original high-dimensional features to 5 dimensions. Using the DBSCAN algorithm, with a clustering distance threshold ε = 1.2 and a minimum number of samples within a cluster MinPts = 10, 4 abnormal feature clusters are identified in the principal component space, and isolated points are automatically removed. In the output clusters, the average Euclidean distance of each member is less than 0.8, while the distance between the boundary point and the center point is greater than 1.3, clearly distinguishing the clustering areas of potential abnormal operating condition influencing factors. Applying the initial screening results of this clustering can efficiently locate the abnormal distribution samples most relevant to the real-time operating conditions, providing a structured feature partitioning basis for the next step of cluster refinement and risk decision modeling, effectively improving the starting accuracy of abnormal attribution analysis and the adaptability of venting strategies.

[0089] S3.4: Implement factor clustering algorithms (such as K-means or spectral clustering) on ​​the obtained set of potential influencing factors, and further summarize and aggregate key abnormal factor characteristic variables directly related to the high-pressure reactor venting strategy from multidimensional operating condition parameters (such as pressure mutation rate, specific gas concentration threshold jump, and reactivity factor).

[0090] S3.5: For the key anomalous factor characteristic variables screened by factor clustering, evaluate their risk sensitivity and decision contribution (such as information gain and mutual information indicators), screen out the ultimate key anomalous factors that play a leading role in the current micro-scenario generation and subsequent leakage strategy formulation, and output them to the micro-scenario parameter dynamic generation module in a standard structure.

[0091] Step S4: Based on the extracted key anomaly factors, automatically generate a micro-scene parameter set reflecting the characteristics of the current abrupt change in operating conditions, including the distribution of each monitoring point, boundary conditions, and multivariate perturbation models. For example... Figure 3 As shown, it specifically includes:

[0092] S4.1: Based on the key anomaly factors input, apply anomaly factor parameterization technology to quantitatively parameterize each anomaly factor to generate a standardized set of anomaly factor parameters, providing a unified input for subsequent micro-scene parameter calculations;

[0093] For the final key anomaly factor structure output by step S3.5, the input objects include the set of different physical quantity control factors after spatiotemporal clustering of the high-pressure reactor anomaly monitoring signals;

[0094] Anomaly parameterization technology is used to quantitatively characterize each key anomaly factor (such as pressure mutation rate, extreme gas concentration, novel reactivity parameters, etc.). Based on the actual process and database preset, the original anomaly factors are mapped to standardized physical quantity parameters.

[0095] Furthermore, by using a range normalization algorithm (with the interval set to [0,1]), abnormal factors of different dimensions and sources are normalized to ensure the consistency and comparability of subsequent multi-parameter calculations.

[0096] The characteristic scale factor weighting method (parameter: historical risk contribution weight of each factor) is used to perform weighted fusion on the parameters of different abnormal factors to generate a set of weighted abnormal factor parameters that reflect the risk level and impact of different main control factors;

[0097] Furthermore, through anomaly factor category coding and multidimensional parameter indexing algorithms, all normalized and weighted factor parameters are structurally encapsulated to generate a unique and traceable set of standardized anomaly factor parameters.

[0098] Through the above combined algorithms and processing methods, the original monitoring data of key anomaly factors are transformed into a unified and standardized set of anomaly factor parameters, providing highly compatible and quantifiable parameter inputs for subsequent micro-scene spatial distribution modeling and disturbance logic generation;

[0099] For example, in identifying abnormal operating conditions of a high-pressure reactor, the key anomaly factors input include a pressure mutation rate of 0.07 MPa / s, a CO gas concentration jump of 0.65%, an abnormal increase in the reaction heat rate of 1.06 kJ / s, and an ambient temperature rise of 2.2 °C. Applying parameterized mapping rules, the pressure mutation rate is normalized to 0.420, the CO concentration to 0.812, the reaction heat rate to 0.665, and the ambient temperature rise to 0.633. Historical data risk weighting coefficients are assigned values ​​of 0.40, 0.30, 0.20, and 0.10 respectively, and the weighted fusion formula is executed.

[0100] P fused =0.40×0.420+0.30×0.812+0.20×0.665+0.10×0.633=0.536

[0101] The master control parameters for this anomaly fusion were obtained. Then, a standard structure was generated based on the factor category and parameter index:

[0102] The parameters {PressureRate=0.420, CO=0.812, HeatRate=0.665, EnvTemp=0.633, FusedScore=0.536} provide a complete and standardized set of parameters for S4.2 spatial distribution parameter modeling. Extensive empirical data has verified that the above parameterization technique can efficiently standardize and fuse multi-source, heterogeneous original anomaly indicators, outputting a controlled and physically consistent set of parameters, ensuring the real-time performance and accuracy of dynamic modeling of micro-scenes and precise policy adaptation.

[0103] S4.2: Using the set of abnormal factor parameters as input, and combining the existing topology of pressure, temperature and gas composition monitoring point distribution, a multi-point spatial mapping algorithm is used to model the spatial distribution of the area affected by abnormal factors, and obtain the set of spatial distribution parameters under the coverage of monitoring points.

[0104] S4.3: Based on the spatially distributed parameter set, the boundary condition mapping algorithm is used to quantify the multi-source boundary conditions such as the current pressure limit, ambient temperature range, and device physical limit into a structured boundary parameter vector, thereby strengthening the constraint of the scene physical boundary conditions on the micro-scene modeling.

[0105] S4.4: Using anomaly factor parameters, spatial distribution parameters, and boundary parameters as inputs, a perturbation logic model describing the dynamic coupling relationship between parameters is established through a multivariate perturbation modeling algorithm, and multivariate perturbation model parameters that can reproduce actual sudden change conditions are obtained.

[0106] S4.5: Integrated feature embedding is performed on abnormal factor parameters, spatial distribution parameters, boundary parameters and multivariate disturbance model parameters. Micro-scene feature fusion technology is used to aggregate and generate a micro-scene parameter set that reflects the sudden change characteristics of the current working condition, laying the foundation for strategy mapping and simulation adaptation.

[0107] Step S5: For the generated micro-scene parameter set, quickly map the historical optimal strategy from the leakage strategy database to obtain the strategy candidate set that best matches the current micro-scene features. Specifically, this includes:

[0108] S5.1: Standardize the generated micro-scene parameter set, and use the spatial distribution matrix normalization and interference model encoding method to transform the working parameters, boundary conditions and multivariate disturbance logic of multiple monitoring points into standardized micro-scene feature vectors that can be used for retrieval, so as to facilitate efficient retrieval and matching in the discharge strategy database.

[0109] S5.2: Based on standardized micro-scene feature vectors, a multi-layer feature mapping algorithm (such as feature similarity retrieval based on Euclidean distance) is used to quickly locate historical working condition records with similar parameter distributions, boundary constraints and disturbance models in the discharge strategy database, and screen out the first round of strategy candidate set to lay the foundation for the next step of candidate refinement;

[0110] Using standardized micro-scene feature vectors as retrieval input, a multi-layer feature mapping algorithm (parameters: spatial distribution parameters, boundary condition parameters, multivariate perturbation model encoding) is invoked to compare the feature vectors with historical data in the high-pressure reactor venting strategy database.

[0111] Using the Euclidean distance metric formula

[0112]

[0113] The distance between the current micro-scene standardized feature vector x and each historical working condition record y in the database is calculated to quantify the similarity between the two in the high-dimensional parameter space, where n represents the dimension of the scene parameters;

[0114] Furthermore, by setting multi-level parameter hierarchical weights (spatial distribution, boundary conditions, and multivariate perturbation models are assigned weights α, β, and γ respectively), a weighted Euclidean distance algorithm is used to achieve deep similarity retrieval of composite parameter features;

[0115]

[0116] Where S1, S2, and S3 are the sets of spatial distribution, boundary conditions, and disturbance model parameters, respectively, and α+β+γ=1;

[0117] Furthermore, by using a feature vector similarity threshold filtering strategy, the candidate set is limited to include historical strategy records that are at least N times closer to the standardized micro-scene features, and an initial strategy candidate set index is output.

[0118] For the first round of candidate output, a feature hierarchical mapping retrieval mechanism is used to check the local mapping quality between spatial parameters, boundary parameters and perturbation models again, and to label the parameter hit rate information of each candidate strategy and the input micro-scene.

[0119] A multi-level similarity ranking method (such as Top-K matching and hierarchical weighted scoring) is adopted to obtain the first round of high-matching strategy candidate set after measurement and ranking, which prepares basic data for subsequent candidate refinement and boundary adaptability evaluation.

[0120] By using a multi-layer feature mapping algorithm and a weighted Euclidean distance metric, the normalized micro-scene parameters and the historical release strategy library are used to achieve efficient multi-dimensional similarity retrieval, obtain a first-round strategy candidate set covering the current working condition features, and achieve the technical effect of rapid adaptation and accurate screening of strategies for sudden scenarios.

[0121] For example, in a certain high-pressure reactor abnormal event, the feature vector obtained after standardization of the micro-scene parameter set is {0.42,0.81,0.66,0.63,0.43,0.56,0.70}. Assume that M = 5000 working condition data in the historical strategy library have been standardized and encoded, with spatial distribution weight α = 0.40, boundary condition weight β = 0.30, and disturbance model weight γ = 0.30.

[0122] In actual retrieval, the feature vector y corresponding to each historical strategy entry is... m Calculate according to the following formula:

[0123]

[0124] Loop through all y m The top N=20 strategies with the smallest distance are selected as candidates for the first round.

[0125] Among the 20 candidate strategies, we further compare whether the spatial distribution hits all parameters and whether the perturbation model logic class codes all overlap. Strategies with a hit rate of less than 60% are deleted, and finally 15 highly matching strategy indices are output.

[0126] This step outputs the first round of policy candidate set, which efficiently supports subsequent boundary adaptability refinement and disturbance response capability identification, and improves the scenario elasticity matching capability of the discharge policy under sudden abnormal working conditions.

[0127] S5.3: For each historical strategy in the first round of strategy candidate set, reference its associated applicable boundary condition metadata, combine it with the current micro-scene parameter set, and use the boundary condition adaptability measurement algorithm to perform multi-index evaluation, so as to eliminate historical strategies that are obviously unsuitable for the current micro-scene and achieve effective refinement of the strategy candidate set.

[0128] S5.4: For the selected strategy candidate set, the perturbation sensitivity matching model is used to compare the micro-scenario perturbation logic with the historical strategy perturbation response mode, select the strategy item with the highest perturbation adaptability, and combine the risk tolerance threshold to complete the priority ranking of the candidate set, so as to ensure that the output is a highly consistent strategy candidate subset.

[0129] S5.5: For the final determined subset of candidate strategies, the simulation verification metadata backtracking mechanism is used to extract the simulation results and field execution feedback signals to form a set of multi-dimensional performance indicators for candidate strategies, including response rate, risk tolerance range and final consequence prediction, providing a decision-making basis for subsequent adaptive relocation of strategies.

[0130] Step S6: Adaptively relocate the policy candidate set, and use a parameter fine-tuning mechanism to select the discharge policy module with the highest matching degree with the current micro-scenario perturbation logic. Specifically, this includes:

[0131] S6.1: The micro-scene parameter features input to the policy candidate set are processed by feature vector standardization. A normalization algorithm is used to eliminate the influence of different parameter dimensions to obtain a standardized feature vector that can be used for high-dimensional feature space distance calculation, providing a consistent data foundation for subsequent similarity measurement.

[0132] S6.2: Based on standardized feature vectors, a multivariate similarity evaluation mechanism (such as cosine similarity and multivariate Euclidean distance) is used to quantitatively calculate the adaptability between each leakage strategy module in the strategy candidate set and the micro-scene perturbation logic, so as to select the set of leakage strategy modules with the highest adaptability.

[0133] S6.3: For the top-ranked relief strategy modules in terms of adaptability, perform parameter space adjustable factor identification, and apply the adjustable factor sensitivity analysis algorithm to identify key decision parameters (such as valve opening threshold, relief flow rate range, and operation time window) in order to clarify the parameter fine-tuning range and fine-tuning objects;

[0134] S6.4: Based on the parameter fine-tuning range and object determined in step S6.3, implement parameter fine-tuning optimization of the target discharge strategy module under the drive of micro-scene perturbation, use the adaptive gradient optimization algorithm to iteratively adjust the decision parameters, dynamically correct the parameter configuration of the strategy module, and achieve high-precision matching with the current micro-scene perturbation logic;

[0135] S6.5: The discharge strategy module, after parameter fine-tuning and optimization, is combined with the original micro-scenario parameter characteristics to synchronously calculate the expected execution indicators in the strategy entity, including response rate, risk tolerance threshold and security redundancy, so as to form a complete adaptive strategy output, providing a highly consistent decision-making basis for subsequent simulation and field deployment.

[0136] Step S7: Input the relocated discharge strategy module into the digital twin parallel simulation engine to perform multiple rounds of rapid sensitivity simulation to obtain the risk tolerance, response rate, and consequence range of each strategy in the current scenario. Specifically, this includes:

[0137] S7.1: The venting strategy module and micro-scene parameter set output by adaptive relocation are structurally integrated to generate a complete strategy-scene input pair, which serves as the standard input object for the digital twin parallel simulation engine.

[0138] S7.2: Based on structured strategy-scenario input pairs, digital twin objects of the current physical equipment and environmental boundaries are synchronously constructed in the digital twin parallel simulation engine, and the discharge strategy logic is mapped to restore the real physical process.

[0139] S7.3: Utilize the digital twin parallel simulation engine to perform multiple rounds of parallel sensitivity simulation on the input venting strategy module, apply perturbations to the perturbation variables and boundary conditions in the micro-scene parameter set one by one, and realize the dynamic response evaluation of the venting strategy to multi-variable perturbations.

[0140] S7.4: For the large amount of system response data output during the simulation, calculate the risk tolerance index (such as the probability of pressure exceeding the limit, the critical failure range of the discharge, etc.) of each discharge strategy module in the current micro-scenario based on the sensitivity analysis algorithm.

[0141] S7.5: Based on the results of sensitivity analysis, the response rate index of each discharge strategy module is further quantified using the response time statistical method, and the key timing parameters from strategy triggering to stable operating conditions are extracted.

[0142] S7.6: Based on the results of all simulation rounds, evaluate the consequence range of each discharge strategy module in a specific micro-scenario, and output a comprehensive evaluation dataset of the risk, timeliness and security margin of the optimal strategy candidate, so as to provide a quantifiable basis for the final deployment decision of the subsequent strategy.

[0143] Step S8: Based on the sensitivity simulation results, automatically select the optimal discharge strategy and send control commands to the on-site execution stage in real time to complete the discharge action deployment. Specifically, this includes:

[0144] S8.1: Perform structured analysis on the results of sensitivity simulation to obtain decision evaluation factors such as risk tolerance, response rate and consequence range of the discharge strategy module under the current micro-scenario parameter set, as the basic input for optimal strategy selection;

[0145] S8.2: Based on the sensitivity simulation and evaluation factors, a multi-objective optimization algorithm is used to sort the candidate set of discharge strategies by fitness, calculate the distributed optimal solution and identify the performance trade-off point, and output the optimal discharge strategy module.

[0146] S8.3: The action parameters of the optimal discharge strategy module are synergistically integrated with the micro-scenario parameter set, and structured control commands are generated through a dynamic parameter mapping mechanism to meet the interface protocol and real-time response requirements of the field control unit.

[0147] S8.4: Performs standardized safety verification and fault tolerance enhancement processing on structured control commands, and generates a set of final control commands that can be directly issued, targeting the redundant safety logic and abnormal signal shielding of the high-pressure reactor control system.

[0148] S8.5: The final control command set is transmitted to the field discharge execution module in real time. The command is issued through a high-reliability industrial communication protocol, and a synchronous responsibility chain is established with the field feedback mechanism to realize the physical deployment and closed-loop start of the discharge action.

[0149] Step S9: Real-time monitoring of the actual execution process of the discharge action, collection of execution feedback signals, and comparison of these signals with the simulation prediction function to determine whether the actual deviation exceeds the safety threshold. Specifically, this includes:

[0150] S9.1: Collect real-time execution feedback signals from actuators deployed in the high-pressure reactor venting process to obtain multi-dimensional physical execution process signals, including venting valve opening, flow monitoring, and action response time, as the basic input for subsequent feedback judgment;

[0151] S9.2: Based on the acquired multi-dimensional physical execution process signals, signal filtering and abnormal pulse elimination techniques are used to remove noise and stabilize waveforms in the data to obtain a structured set of discharge action feedback data, laying a high-confidence data foundation for simulation function comparison;

[0152] S9.3: Using a structured set of feedback data on the venting action as input, the venting simulation prediction function issued by the digital twin parallel simulation engine is called to align and map the feedback parameters such as the venting valve opening, flow rate, and response time at the same moment with the simulation prediction parameters item by item, forming a feedback-simulation parameter comparison matrix.

[0153] S9.4: Perform normalized residual calculation on the feedback-simulation parameter comparison matrix, and use dynamic threshold discrimination technology to extract the real-time deviation vector between each physical parameter in order to obtain the discrimination index of the actual execution deviation result of the discharge action;

[0154] S9.5: Based on the actual execution deviation results, and relying on the safety control threshold of the high-pressure reactor, multi-level safety judgment is performed, and alarm signals are output to indicate whether the deviation of key execution parameters of the venting action exceeds the limit, so as to realize the real-time safety judgment between the actual effect of the venting action and the simulation expectation closed loop.

[0155] Step S10: If a significant deviation is found between the execution feedback and the simulation prediction results, the rapid reconstruction of the micro-scene and the adaptive decision-making loop of the strategy are triggered again until safety is achieved, and the entire process data is archived for subsequent optimization and upgrading of the micro-scene model and strategy library. Specifically, this includes:

[0156] S10.1: Based on the real-time collected execution feedback signal and the digital twin simulation prediction result, the industrial safety threshold discrimination algorithm is used to determine the abnormal difference between the two in order to obtain the deviation trigger signal that needs to be executed for rapid reconstruction of the micro scene, and to provide an event criterion entry point for subsequent micro scene parameter updates;

[0157] S10.2: For the deviation trigger signal, call the previous micro-scene parameter set and the latest discharge action execution feedback signal, and apply the micro-scene dynamic generation algorithm to re-evaluate and fuse abnormal factor parameters, spatial distribution parameters, boundary parameters and multivariate disturbance logic in real time, thereby generating an updated micro-scene parameter set that covers the current working condition characteristics, and realizing the rapid reconstruction of the micro-scene parameter set;

[0158] S10.3: Taking the updated micro-scene parameter set as input, the policy library adaptive mapping mechanism is used to dynamically filter the historical best leakage policy candidate set, realize the relocation of the leakage policy module, optimize the matching degree based on the parameter fine-tuning algorithm, and output the optimized leakage policy module.

[0159] S10.4: Input the optimized discharge strategy module into the digital twin parallel simulation engine, and use multiple rounds of sensitivity simulation to re-evaluate the risk tolerance, response rate and consequence range under the current working conditions, so as to generate a new round of simulation evaluation results and provide a model basis for control command optimization decisions.

[0160] S10.5: Based on the results of the new round of simulation evaluation, compare the feedback signals of the previous discharge action execution, and use an adaptive discrimination mechanism to determine whether the safety compliance conditions have been met; if not, return to S10.2 to iterate the micro-scenario reconstruction and strategy optimization process again, forming an anomaly-driven decision micro-loop until the safety requirements are met.

[0161] S10.6: Perform full-process data archiving for the data flow, key factors, and strategy change process involved in the entire process of rapid reconstruction, strategy adaptation, simulation, feedback evaluation, etc., and use a knowledge-structured archiving model to perform high-level annotation on information such as abnormal factors, strategy positioning logic, and working condition evolution trajectory, so as to provide data support for the continuous optimization and upgrading of micro-scenario models and strategy libraries.

[0162] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0163] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and rules of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for emergency venting of a high-pressure reactor, characterized in that, Includes the following steps: S1: Real-time acquisition of physical quantities at multiple locations within the high-pressure reactor to obtain environmental parameter data containing multi-point spatial distribution characteristics; S2: Perform noise reduction and abnormal pattern recognition processing on the collected environmental parameter data to obtain abnormal monitoring signals; S3: Perform spatiotemporal comparative analysis of the abnormal monitoring signals with historical operating data and known accident database, and apply factor clustering algorithm to extract key abnormal factors affecting the release decision; S4: Based on the aforementioned key anomaly factors, automatically generate a micro-scenario parameter set that reflects the characteristics of the current abrupt change in operating conditions; S5: For the micro-scene parameter set, quickly map the historical best strategy from the leakage strategy database to obtain the strategy candidate set that best matches the current micro-scene features; S6: Adaptively relocate the candidate strategy set and use a parameter fine-tuning mechanism to select the release strategy module with the highest matching degree with the current micro-scene disturbance logic; S7: Input the relocated discharge strategy module into the digital twin parallel simulation engine to perform multiple rounds of rapid sensitivity simulation to obtain the risk tolerance, response rate and consequence range of each strategy in the current scenario; S8: Based on the results of sensitivity simulation, automatically select the optimal discharge strategy and send control commands to the field execution stage in real time.

2. The emergency venting plan method for a high-pressure reactor according to claim 1, characterized in that, The process following step S7 also includes: S9: Monitor the actual execution process of the discharge action in real time, collect the execution feedback signal, and compare it with the simulation prediction function to determine whether the actual deviation exceeds the safety threshold. S10: If a significant deviation is found between the execution feedback and the simulation prediction results, the micro-scene rapid reconstruction and policy adaptive decision-making loop will be triggered again until safety standards are met, and the entire process data will be archived for subsequent optimization and upgrade of the micro-scene model and policy library.

3. The emergency venting plan method for a high-pressure reactor according to claim 1, characterized in that, Step S1 specifically includes: Professional industrial-grade sensors for pressure, temperature and gas composition are deployed at multiple key spatial nodes inside the high-pressure reactor. The spatial distribution scheme is determined according to the industrial process control scheme to form a physical quantity monitoring network covering all reaction zones, points prone to loss of control and safety boundary zones. Static and dynamic calibration operations are performed on all deployed sensors. Industrial calibration algorithms are used to correct the range, linearity, and drift error of each sensor channel to obtain highly reliable calibration parameters for pressure sensors, temperature sensors, and gas composition measurement. The industrial automation control system based on the high-pressure reactor uses a high-frequency acquisition module to collect raw pressure, temperature and gas composition signals from each spatial node sensor. The original signals from pressure acquisition, temperature acquisition, and gas composition acquisition are transmitted uniformly through a standardized communication protocol. A data synchronization mechanism for the acquisition link is used to perform time alignment of multi-point asynchronous signals and output a synchronized data stream signal set. For the data stream signal set, perform real-time data integrity verification and outlier pre-removal processing, and output environmental parameter data containing multi-point spatial distribution characteristics.

4. The emergency venting plan method for a high-pressure reactor according to claim 3, characterized in that, The pressure sensor used in step S1 has a high pressure resistance rating of 30MPa and above, a corrosion resistance rating of 316L and above, a temperature sensor with an operating temperature range of -40 to 350℃, and a gas composition sensor with a detection sensitivity at the ppm level. Furthermore, each key monitoring area is covered by two or more types of sensors.

5. The emergency venting plan method for a high-pressure reactor according to claim 1, characterized in that, Step S2 specifically includes: Multi-channel primary filtering is performed on the collected multi-point spatially distributed environmental parameter data. A bandpass filtering algorithm is used to remove high-frequency interference and low-frequency drift from the pressure signal, temperature signal and gas composition signal respectively, and the environmental parameter signal after preliminary noise reduction is obtained. Based on the environmental parameter signal after preliminary noise reduction, multi-channel signal synchronization and normalization operations are performed. Covariance normalization and timestamp resampling techniques are used to obtain a spatially distributed normalized signal array under a unified time base. Multi-scale feature extraction is performed on the spatially distributed normalized signal array. Based on the wavelet transform algorithm, instantaneous pressure anomalies, temperature perturbations and gas composition change point feature parameters are extracted, and the multi-measurement point multi-physical quantity signals are transformed into high-dimensional waveform feature vectors. Feature dimensionality reduction and multivariate fusion are performed on the high-dimensional waveform feature vector. Based on the principal component analysis algorithm, the main control feature components that can reflect the sudden operating conditions of the high-pressure reactor are screened out, and comprehensive anomaly monitoring indicators are generated by feature weighted fusion. A discrimination algorithm based on an anomaly detection machine learning model is used to automatically identify the comprehensive anomaly monitoring indicators and output the anomaly monitoring signal spectrum and anomaly confidence score under the current operating conditions of the high-pressure reactor.

6. The emergency venting plan method for a high-pressure reactor according to claim 1, characterized in that, Step S3 specifically includes: Time series calibration and multidimensional data alignment preprocessing are performed on the abnormal monitoring signal to obtain the preprocessed abnormal monitoring signal; Based on the preprocessed abnormal monitoring signals, historical operating condition data and known accident databases are called, and a spatiotemporal correlation matrix is ​​constructed using dynamic time warping and multivariate correlation matching methods to form a feature comparison mapping that conforms to the actual operating condition disturbance logic. Using the established spatiotemporal correlation matrix, based on the characteristics of industrial process status, the abnormal monitoring signals in the comparison mapping are clustered with historical operating condition fluctuation characteristics and accident templates to preliminarily screen the set of potential influencing factors; A factor clustering algorithm is implemented on the set of potential influencing factors to further summarize and aggregate key abnormal factor characteristic variables directly related to the high-pressure reactor venting strategy from multi-dimensional operating parameters. The risk sensitivity and decision contribution of the key anomaly factor characteristic variables are evaluated, and the ultimate key anomaly factor that plays a leading role in the current micro-scene generation and subsequent leakage strategy formulation is screened out and output to the micro-scene parameter dynamic generation module in a standard structure.

7. The emergency venting plan method for a high-pressure reactor according to claim 1, characterized in that, The spatiotemporal comparison analysis combines dynamic time warping with Pearson correlation coefficient, and uses a combined principal component analysis and density clustering algorithm to reduce the dimensionality and cluster multidimensional anomaly features, historical operating conditions and accident curves. Finally, key anomaly factors are screened using K-means or spectral clustering algorithms.

8. The emergency venting plan method for a high-pressure reactor according to claim 1, characterized in that, Step S4 specifically includes: Based on the key anomaly factors input, the anomaly factor parameterization technique is applied to quantitatively parameterize each anomaly factor, generating a standardized set of anomaly factor parameters. Using the set of abnormal factor parameters as input, and combining the existing topology of pressure, temperature and gas composition monitoring point distribution, a multi-point spatial mapping algorithm is used to model the spatial distribution of the area affected by the abnormal factors, thereby obtaining the set of spatial distribution parameters under the coverage of the monitoring points. Based on the spatially distributed parameter set, a boundary condition mapping algorithm is used to quantify multi-source boundary conditions such as the current pressure limit, ambient temperature range, and device physical limit into a structured boundary parameter vector. Using the abnormal factor parameters, the spatial distribution parameters, and the boundary parameters as inputs, a perturbation logic model describing the dynamic coupling relationship between the parameters is established through a multivariate perturbation modeling algorithm, thereby obtaining multivariate perturbation model parameters that can reproduce actual sudden change conditions. The abnormal factor parameters, spatial distribution parameters, boundary parameters, and multivariate disturbance model parameters are integrated into a feature embedding process. Micro-scene feature fusion technology is then used to aggregate and generate a micro-scene parameter set that reflects the sudden change characteristics of the current working condition.

9. The emergency venting plan method for a high-pressure reactor according to claim 1, characterized in that, Step S5 specifically includes: The generated micro-scene parameter set is standardized, and the spatial distribution matrix normalization and interference model encoding method are used to transform the working parameters, boundary conditions and multivariate disturbance logic of multiple monitoring points into standardized micro-scene feature vectors that can be used for retrieval. Based on the standardized micro-scene feature vectors, a multi-layer feature mapping algorithm is used to quickly locate historical working condition records with similar parameter distributions, boundary constraints and disturbance models in the discharge strategy database, and to select the first round of strategy candidate sets. For each historical strategy in the first round of strategy candidate set, its associated applicable boundary condition metadata is referenced, and combined with the current micro-scene parameter set, a boundary condition adaptability measurement algorithm is used to evaluate multiple indicators, and historical strategies that are obviously unsuitable for the current micro-scene are eliminated. For the selected strategy candidate set, a perturbation sensitivity matching model is used to compare the micro-scenario perturbation logic with the historical strategy perturbation response mode, select the strategy item with the highest perturbation adaptability, and complete the priority ranking of the candidate set by combining the risk tolerance threshold. For the final subset of candidate strategies, a simulation verification metadata backtracking mechanism is used to extract the simulation results and field execution feedback signals to form a multi-dimensional performance index set for the candidate strategies.

10. The emergency venting plan method for a high-pressure reactor according to claim 1, characterized in that, The digital twin parallel simulation in step S7 adopts a multi-round perturbation variable application method, and calculates the risk tolerance index and response rate based on the system response data, and comprehensively determines the safety margin and consequence range of each discharge strategy.

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