An accident consequence simulation calculation method based on coupling solution of mathematical physics models

Abstract

The application provides an accident consequence simulation calculation method based on mathematical physical model coupling solution, which comprises the following steps: constructing an accident chain knowledge graph and a physical trigger graph, establishing the causal relationship and physical constraints among equipment, state, event and consequence; mapping the historical records, expert rules and online observation data into graph entities and relationships to form a baseline accident scene and calculate baseline indicators; generating candidate paths by using graph reasoning and coupled multi-physical field simulation, and realizing the closed loop of graph reasoning and physical calculation through consistency checking; scoring and perturbation simulation analysis on the candidate paths, screening the robust target path; carrying out high-fidelity simulation on the target path, identifying key nodes combined with sensitivity analysis and minimum cut set, and generating disposal suggestions and action priority. The application realizes high credible prediction of accident evolution and closed loop linkage of emergency response, and has high precision, high robustness and engineering implementability.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of accident consequence simulation, in particular to an accident consequence simulation calculation method based on mathematical physical model coupling solution. BACKGROUND

[0002] With the continuous expansion of large-scale chemical, energy and storage facilities, the evolution process of accident consequences presents complex coupling, multi-stage transmission and strong nonlinearity. In actual accident situations, accidents are often accompanied by the coupling and superposition of multiple physical processes, such as the interaction of pressure relief and diffusion process, the parallel occurrence of thermal radiation and convection heat transfer, the coupling response of explosion shock wave and building structure, etc. These factors make the accident consequences have typical characteristics such as strong burst, high path diversity and difficult extrapolation prediction.

[0003] The existing accident consequence simulation has the following shortcomings: first, a single physical field simulation is mostly used, which lacks system modeling and causal reasoning of the accident chain, and it is difficult to reflect the dynamic evolution characteristics of complex accidents; second, the path extrapolation lacks a constraint mechanism coupled with physical accessibility, and the extrapolation result is easy to deviate greatly from the real accident, which is difficult to meet the needs of emergency practical application; third, there is a lack of methods for evaluating the uncertainty and robustness of candidate accident paths, resulting in a lack of scientific basis for plan selection.

[0004] In view of the above shortcomings, the present application provides an accident consequence simulation calculation method based on mathematical physical model coupling solution.

[0005] The above information disclosed in the background section is only used to enhance the understanding of the background of the present disclosure, and therefore it can include information that does not constitute prior art known to those of ordinary skill in the art. SUMMARY

[0006] The purpose of the present application is to provide an accident consequence simulation calculation method based on mathematical physical model coupling solution to solve the technical problems mentioned in the background art.

[0007] To achieve the above purpose, the present application provides the following technical solutions:

[0008] An accident consequence simulation calculation method based on mathematical physical model coupling solution, comprising the following steps:

[0009] S1, establishing an accident chain knowledge graph based on process flow and accident history information, representing the causal relationship between equipment, state, event and consequence; combining parameter threshold and conservation constraint to establish a physical trigger graph, forming accident trigger logic and physical accessibility constraint;

[0010] S2, map historical accident records, expert rules, and online observation data into accident chain knowledge graph entities and relationships, trigger path after call coupling multi-physical field solver, establish baseline accident consequence field, and calculate baseline consequence index as extrapolation control reference;

[0011] S3, use graph causal trigger relationship to traverse and reason to form candidate extrapolation path, map the path to simulation boundary conditions to solve accident consequence field, and perform consistency check through mass conservation, energy conservation, and trigger satisfaction degree to realize closed loop iteration of graph reasoning and physical calculation;

[0012] S4, comprehensive scoring of candidate path based on consistency index, consequence index and path complexity, using perturbation simulation and statistical analysis to obtain uncertainty interval and robustness index, and screening to obtain final robust target path;

[0013] S5, high-precision physical field simulation for robust target path, calculation of accident consequence index, combined with sensitivity analysis and minimum cut set solution to identify key trigger nodes in accident chain, and generation of disposal suggestion and action priority sequence;

[0014] S6, output the path, key node, consequence index and disposal suggestion to the upper system in a standardized data format, realize automatic linkage disposal through industrial protocol, and archive and version control the whole process of accident deduction, complete closed loop emergency response and post-tracing.

[0015] S1 specifically includes:

[0016] Establish an accident chain knowledge graph to represent devices, states, events, and causal relationship structure;

[0017] Establish a physical trigger graph to represent threshold conditions, conservation constraints, and trigger logic;

[0018] Establish a coupled multi-physical field solver to calculate accident consequence field according to source term parameters and boundary conditions;

[0019] Define accident scenario parameter vector, extrapolation path set, consequence index, consistency check index, convergence criterion and constraint set;

[0020] Set a fallback mechanism for abnormal recovery and linkage to the upper system.

[0021] S2 specifically includes:

[0022] Map historical records, expert rules, and real-time observations into entities, relationships, and initial trigger conditions in the accident chain knowledge graph;

[0023] Input the accident scenario parameter vector into the physical trigger graph to complete the parameterization of the trigger logic;

[0024] Call the coupled multiphysics solver to calculate the baseline accident consequence field based on parameters and triggering conditions;

[0025] Calculate and record baseline consequence metrics as a benchmark for subsequent extrapolation path comparisons;

[0026] Store the baseline state for reference in subsequent filtering and rollback.

[0027] S3 specifically includes:

[0028] Candidate extrapolation paths are generated based on causal relationships in the accident chain knowledge graph.

[0029] The candidate extrapolation path is mapped to the boundary conditions and source terms of the coupled multiphysics solver to calculate the simulated accident consequence field.

[0030] Calculate the simulated consequences index and perform physical reachability and conservation consistency checks on the candidate extrapolation paths based on the consistency check index;

[0031] The verification results are used to update the relationship weights in the accident chain knowledge graph and the triggering conditions in the physical triggering graph.

[0032] A two-way iterative link between graph reasoning and physical decomposition is formed to continuously optimize the credibility of candidate extrapolation paths.

[0033] S4 specifically includes:

[0034] Within the constraint set, a comprehensive score is calculated for candidate extrapolation paths based on consistency verification indicators and consequence indicators;

[0035] A sequential filtering method is used to retain the set of target extrapolation paths;

[0036] Perform perturbation simulations on the target extrapolation path set within the uncertainty range of the accident scenario parameter vector;

[0037] Evaluate the robustness of the target extrapolation path and the range characteristics of the consequence indicators;

[0038] If the convergence criterion is not met or the constraint set is violated, a backoff mechanism is triggered to adjust the relation weights, triggering conditions, or extrapolation paths, and the process returns to S3 for re-iteration.

[0039] S5 specifically includes:

[0040] The coupled multiphysics solver is invoked to perform a high-fidelity coupled solution on the target extrapolation path set;

[0041] Obtain the target accident consequence scene and target consequence indicators;

[0042] Key triggering nodes in the accident chain knowledge graph are identified based on sensitivity analysis of target consequence indicators.

[0043] Extract the minimum cut set based on the graph structure centrality, which is used to represent the key accident control point;

[0044] Generate a treatment suggestion sequence and corresponding priority according to the target extrapolation path.

[0045] S6 specifically comprises:

[0046] Output the target extrapolation path set, key trigger node, minimum cut set, target accident consequence field and target consequence index to the upper system;

[0047] Output the treatment suggestion sequence and corresponding priority for linkage control and emergency response;

[0048] Archive the graph state, trigger graph parameters, solver results and consistency check evidence.

[0049] Record the extrapolation path iteration convergence process and rollback trajectory for online calibration and traceability analysis;

[0050] Form a closed loop of knowledge graph, physical trigger and numerical solution, and provide reusable deduction basis for subsequent accident warning and treatment.

[0051] The beneficial effects of the present application are:

[0052] The present application constructs an accident chain knowledge graph and a physical trigger graph, expresses the logical relationship between the device running state, process flow, accident event and consequence in a structured manner, and is coupled with a multi-physical field simulation model, so that accident deduction is no longer dependent on single path assumption, but supports multi-path, multi-stage, dynamic evolution of full link reasoning and simulation. This technology breaks through the scene staticity of traditional accident consequence simulation, realizes the change from single point prediction to multi-path dynamic extrapolation, and greatly improves the coverage and accuracy of accident prediction.

[0053] The present application introduces multi-dimensional consistency check indicators such as mass conservation, energy conservation and trigger logic satisfaction, which can strictly screen the candidate accident evolution path, eliminate the "pseudo path" that does not conform to the physical constraint, and form a "real path" set verified by physical calculation. Compared with the traditional method which depends on experience rules, this mechanism significantly reduces the deduction error and uncertainty, so that the accident path reasoning has self-correction and convergence ability, and improves the stability and reproducibility of the model.

[0054] The present application performs disturbance sampling and uncertainty propagation calculation on the accident input parameters, constructs a robustness index, selects the extrapolation path that still maintains high credibility under multiple working conditions and multiple disturbances, and enhances the anti-disturbance ability and generalization ability of the prediction result. This method effectively overcomes the problem that the path screening in the prior art is too sensitive to the initial conditions, so that the system can still maintain stable and reliable warning and deduction effect under actual complex working conditions.

[0055] The present application is based on sensitivity analysis and minimum cut set solving, automatically identifies the trigger node with the greatest impact on the consequences in the accident chain, and realizes the identification of the high-value intervention point of accident propagation. The system can automatically generate treatment action suggestions and priority sequences according to the importance and influence degree of the node, provide scientific basis and clear action path for accident emergency treatment, and significantly improve the response efficiency and prevention and control effect.

[0056] The present application organically integrates graph reasoning, physical algorithm, disturbance robustness analysis and engineering linkage, realizes the integrated closed loop of "from prediction to action", can adapt to various complex accident scenes (such as storage and transportation leakage, explosion, fire, etc.), has good engineering universality, real-time and scalability, and provides technical support for the intrinsic safety protection of major hazard sources. BRIEF DESCRIPTION OF DRAWINGS

[0057] Figure 1 It is a schematic diagram of the accident consequence simulation calculation method based on mathematical and physical model coupling solving of the present application. DETAILED DESCRIPTION

[0058] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0059] Embodiment one: as shown, the present embodiment provides an accident consequence simulation calculation method based on mathematical and physical model coupling solving, comprising the following steps: Figure 1 S1, construct an accident chain knowledge graph and a physical trigger graph, based on process flow, equipment account and accident history information, establish an accident chain knowledge graph to represent the causal relationship between equipment, state, event and consequence; and combine the monitoring parameter threshold and the conservation constraint to establish a physical trigger graph, form the accident trigger logic and the physical reachability constraint;

[0060] S2, data aggregation and baseline accident scene simulation, map historical accident records, expert rules and online observation data into accident chain knowledge graph entities and relationships, call a coupled multi-physical field solver after triggering path, establish a baseline accident consequence field, and calculate the baseline consequence index as an extrapolation reference;

[0061]

[0062] ​S3, generating candidate paths based on graph reasoning and physical algorithm, traversing and reasoning candidate extrapolation paths using graph atlas causal trigger relationship, mapping the path to the simulation boundary condition for accident consequence field solving, consistency checking through mass conservation, energy conservation and trigger satisfaction degree, eliminating paths that do not meet physical constraints, realizing closed loop iteration of graph reasoning and physical algorithm;

[0063] S4, path scoring and uncertainty analysis, comprehensive scoring of candidate paths based on consistency index, consequence index and path complexity, obtaining uncertainty interval and robustness index by disturbance simulation and statistical analysis, and screening the final robust target path;

[0064] S5, high-fidelity simulation and key node identification, high-precision physical field simulation for robust target path, calculation of accident consequence index, identification of key trigger nodes in accident chain by combining sensitivity analysis and minimum cut set solution, and generation of disposal suggestions and action priority sequence;

[0065] S6, result output, linkage and traceability, outputting the path, key node, consequence index and disposal suggestion to the upper system in a standardized data format, realizing automatic linkage disposal through industrial protocol, archiving and version control of the whole process of accident deduction, and completing closed loop emergency response and post-tracing.

[0066] S1 specifically includes the following sub-steps:

[0067] S110, establishing an accident chain knowledge graph: based on process flow, equipment account and accident history information, an accident chain knowledge graph is established to represent the causal relationship between accident-related equipment, state, event and consequence;

[0068] The accident chain knowledge graph includes the following entity types:

[0069] Device entities (such as valves, pipelines, storage tanks, cooling towers, reaction kettles, etc.);

[0070] State entities (such as pressure over-limit, temperature over-limit, leakage, ignition failure);

[0071] Event entities (such as diffusion, explosion, combustion, rupture);

[0072] Consequence entities (such as personnel injury, facility damage, chain reaction);

[0073] The relationship types include causal trigger relationship, state transition relationship and functional dependency relationship, and the relationship weights are represented by a weighted directed graph structure, with a typical entity quantity range of 50-300, a relationship type quantity of 3-6 types, and a graph level depth of 2-5 layers;

[0074] The weight initialization can be calculated by the following formula:

[0075]

[0076] wherein represents the trigger edge weight from node i to node j; represents the accident trigger edge from node i to node j in the accident chain knowledge graph, corresponding to the evolution path of a specific accident event; represents an element in the set of all triggerable candidate accident evolution edges at the same node i;

[0077] represents the attribute function value of the edge , which can be defined according to the intensity of accident triggering, the degree of exceeding the threshold, the occurrence probability, or the risk level, etc. represents the sum of all trigger edge attribute function values of node i.

[0078] Through the above calculation, the trigger intensity of all evolution paths of node i is standardized, so that the weight sum of each path satisfies: The normalization processing not only ensures the comparability of the relative weights between different paths, but also provides a quantitative basis for subsequent path extrapolation and simulation mapping. In the path extrapolation process, the path with higher trigger intensity will obtain higher weight, so as to be given priority in multi-path evolution reasoning, thereby improving the rationality and stability of accident evolution prediction;

[0079] The method provides a calculable and scalable quantitative basis for subsequent accident chain path extrapolation, disturbance simulation, and risk warning through the normalization processing of the trigger weight of the accident evolution path, and significantly improves the stability and accuracy of accident evolution prediction.

[0080] In a typical embodiment, the attribute function can be determined as follows: ; wherein represents the occurrence probability of the accident trigger event, which can be obtained according to historical data or real-time monitoring system; represents the threshold intensity of the trigger event, such as the degree of overpressure or the proportion of overconcentration; represents the risk level of the accident type; , , is an empirical weight or a coefficient obtained by training a statistical learning model.

[0081] S120, establishing a physical trigger graph: based on the monitoring points, sensors, and engineering parameters related to accidents, a physical trigger graph is established to represent threshold conditions, conservation constraints, and trigger logic;

[0082] The threshold conditions include but are not limited to: pressure threshold : 0.6 - 1.2 MPa; temperature threshold : 50 - 90 °C; concentration threshold : 10% - 20% LEL; leak rate : 0.05 - 1.0 kg / s.

[0083] The trigger logic is represented by a Boolean expression or a rule matrix, for example:

[0084]

[0085] where Y and C represent the pressure and concentration monitoring values of the accident scenario, respectively; and represent the corresponding trigger thresholds. Only when both pressure and concentration exceed the thresholds, the accident node is activated and enters the downstream path extrapolation link. Here, the logical AND ∧ is used, which means that when the pressure Y exceeds the trigger threshold and the concentration C exceeds the trigger threshold , it is considered that the accident trigger event occurs (Trigger=True), which realizes the accident trigger judgment based on physical quantities, and improves the accuracy and automation level of accident reasoning;

[0086] The conservation constraints include mass conservation, energy conservation, and momentum conservation, with allowed errors:

[0087]

[0088] where is the mass conservation check error, which measures the proportion of the difference between the system input and output mass to the input mass, represents the total input mass of the system, represents the total output mass of the system, represents the allowed relative error threshold. When is less than or equal to , it is considered that the simulation calculation result meets the mass conservation requirement; otherwise, the calculation parameters need to be adjusted. Through the above check, the credibility and stability of the simulation results of accident consequences can be improved.

[0089] S130, establishing a coupled multi-physical field solver: according to the characteristics of the accident scene, a coupled multi-physical field solver is established, which is used to realize the multi-field coupling calculation of fluid diffusion, heat transfer, pressure fluctuation and chemical reaction.

[0090] The solver can be implemented by finite volume method (FVM) or finite element method (FEM), and the applicable computing platforms include OpenFOAM, ANSYS Fluent or COMSOL Multiphysics, etc.

[0091] Typical computational domain grid size is 10,000-100,000 nodes, time step range is 0.01-1s, simulation time window is 0-600s.

[0092] Boundary conditions include: leakage rate, environmental meteorological conditions, building obstacle layout and wind field disturbance.

[0093] Through iterative convergence criterion Control the calculation precision, the convergence residual is set to 10 -4 Magnitude.

[0094] S140, define key variables and parameters:

[0095] Define the accident scenario parameter vector , including initial pressure, temperature, concentration, leakage rate and accident time;

[0096] Define the extrapolation path set P, the number of elements is 10-100;

[0097] Define the consequence index R, including concentration peak, thermal radiation dose, explosion shock radius and personnel injury level;

[0098] Define the consistency check index , which is used to represent the consistency of the candidate path and the physical trigger diagram. Define the constraint set , including safety boundary and trigger range; define the convergence criterion , including error upper limit and iteration upper limit; define the rollback mechanism B, which is used for path recovery in abnormal situations.

[0099] S150, establish the linkage upper system interface: realize the data interaction with the upper system through OPCUA or Modbus-TCP industrial communication protocol, the transmission frequency is 1-5Hz, and the communication delay is less than 2s;

[0100] The upper system includes DCS (Distributed Control System), ECS (Emergency Control System) or SCADA system;

[0101] The data interface supports the synchronous transmission of accident state pushing, path identification, index value and disposal suggestion, the interface supports event backtracking identification and linkage trigger signal, and ensures the real-time and traceability of subsequent emergency decision.

[0102] S2 specifically includes the following sub-steps:

[0103] S210, data aggregation and graph instantiation: unify historical accident records, expert rules and online observation data to form a parseable data set;

[0104] Historical accident records are stored in a structured database or CSV file, each record contains at least the following fields: accident occurrence timestamp, device ID, status code, leak rate, temperature rise curve, environmental meteorological parameters. The typical record size is 500-5000.

[0105] Expert rules are in the form of IF-THEN or Boolean expressions, for example:

[0106] IF (T>60°C AND Q>0.1kg / s) THEN state="thermal diffusion warning"

[0107] The above expression means: if the temperature exceeds 60°C and the leak rate exceeds 0.1kg / s, the system enters a thermal diffusion warning state; where "IF" means "if", "T>60°C" means "trigger when temperature is greater than 60°C", "AND" means logical "and", "Q>0.1kg / s" means "trigger when leak rate is greater than 0.1kg / s"; "THEN state" means "once the condition is met, the system state is marked as thermal diffusion warning".

[0108] Online observation data is accessed in the form of time series flow, with a sampling frequency of 1-5Hz, including real-time signals of pressure sensors, temperature sensors and gas concentration sensors.

[0109] The above data is converted into entities and relationships in the accident chain knowledge graph through mapping logic:

[0110] Device ID→Device Entity;

[0111] Status code / sensor alarm→State Entity;

[0112] IF-THEN rule→Causal relationship edge;

[0113] Consequence type→Consequence Entity.

[0114] Complete the initial instantiation of the graph, form the node set, relationship set and trigger state initial value, provide a structural basis for subsequent calculations.

[0115] S220, trigger state recognition and path activation: accident scene parameter vector Input physical trigger graph; for each trigger node, judge whether it is true. If true, the trigger state is marked as 1, otherwise as 0; where represents the i-th trigger node corresponding to the accident scene input parameter value, represents the trigger threshold value corresponding to the node;

[0116] Trigger logic automatically identifies paths that meet the conditions based on Boolean expressions and generates an activated path set ;

[0117] The number of typical activation paths ranges from 10 to 50, corresponding to various possible initial evolution directions of an accident. Activation states and activation paths are synchronously stored in a memory buffer and a database to provide input conditions for baseline simulation calculations.

[0118] S230, Baseline Accident Consequence Field Solution: Call the coupled multiphysics solver, based on... The boundary conditions are used to perform simulation calculations for the baseline accident scenario;

[0119] The computational domain grid size is 10,000–100,000 nodes, the time step ranges from 0.01–1 s, and the simulation duration is 0–600 s. Typical boundary conditions include: leakage rate Q: 0.05–1.0 kg / s; ambient wind speed... : 0–10 m / s; ambient temperature Temperature: 20–35°C; Initial pressure Y: 0.6–1.2 MPa;

[0120] The solution process outputs three types of results: temperature field, concentration field, and pressure field, used to characterize accident propagation, thermal radiation, and shock wave effects; the calculation process uses residuals. Controlling convergence, the target convergence error is set to 10. -4 .

[0121] S240. Baseline Consequence Indicator Calculation and Storage: Based on simulation output results, calculate baseline consequence indicators. Including: peak concentration:

[0122]

[0123] in This is the maximum concentration value, specifically within the entire calculation region, at the end time. The highest pollution concentration at any given time Let x represent the computational region and x represent the spatial location. Indicates the end time of the simulation or monitoring. Indicates position x and time The concentration value can be obtained quickly using the above formula, which serves as a key indicator for hazard analysis, emergency decision-making, and threshold comparison.

[0124] Thermal radiation dose:

[0125]

[0126] in It represents the cumulative thermal radiation energy, used to characterize the total amount of thermal radiation received by a certain point or spatial area during the entire process of an accident; Instantaneous thermal radiation intensity at time t and spatial position x, usually in units of watts per square meter (W / m 2 ); x represents the spatial position of the heated radiation; t is the time variable; represents the end time of the accident evolution or the end time of the calculation, and the above formula can obtain the cumulative value of the radiation energy of a point during the entire accident duration by time integrating the thermal radiation intensity of the entire accident process. The thermal radiation contribution of each time step is accumulated, so as to reflect the overall thermal load effect of the accident process on the target point.

[0127] For example, in the accident scenarios of fireball explosion, pool fire or jet fire, the thermal radiation intensity changes obviously with time. Through the above formula, the total radiation energy of the target area can be accurately quantified, which provides a reliable basis for thermal hazard classification, equipment protection design and emergency response;

[0128] Explosion shock wave radius : refers to the overpressure action range generated by the explosion shock wave in the explosion accident. Its determination method is: the spatial distribution of the shock wave pressure field is calculated, and the isobaric surface is extracted under a certain overpressure threshold (such as 0.02 MPa, 0.07 MPa, etc.), and then the isobaric surface is analyzed by spatial geometry to obtain the radius corresponding to the isobaric surface, that is, the explosion shock wave radius ;

[0129] At the same time, the personnel injury level, building damage index and other derived indexes can be calculated for risk classification; the calculation results are stored in JSON or relational database table format, and the fields include: index name, value, timestamp, and calculation version number; as a reference value for subsequent extrapolation path comparison and inversion algorithm.

[0130] S250, baseline state solidification and control mechanism: package storage of baseline activation path set , accident consequence field , consequence index , trigger state vector and atlas structure state;

[0131] The storage method can include a database (PostgreSQL), a binary model cache (HDF5) or an engineering file (JSON+BIN hybrid format); the typical storage volume is 1-5 MB / scene; these baseline data are used for subsequent extrapolation path screening, simulation comparison and rollback mechanism, and provide a stable reference system for the entire accident deduction algorithm; the storage process supports version number and check code to ensure the traceability and consistency of the simulation results.

[0132] S3 specifically includes the following sub-steps:

[0133] S310, accident chain knowledge graph reasoning generates candidate extrapolation path: taking the initial state of the accident chain knowledge graph obtained in S210-S250 as input, path reasoning is performed according to the causal trigger relationship in the graph.

[0134] A breadth-first (BFS) or depth-first (DFS) traversal strategy is adopted to deduce backward from the activated node and search for all reachable paths that meet the trigger conditions to form a candidate extrapolation path set .

[0135] Each path is composed of a node sequence and a corresponding relationship weight sequence , wherein the node sequence is used to represent the temporal and spatial logical order of accident evolution, and the relationship weight sequence is used to quantify the strength or occurrence probability of the trigger relationship between adjacent nodes. Through the above structured representation, the overall risk level of different paths can be calculated and compared to provide a data basis for the extrapolation reasoning and path optimization of the accident chain;

[0136] The number of typical candidate paths is 50-300, and the length of a single path is 3-10 nodes; when generating the path, the loop path is pruned to ensure that the path is directed acyclic graph (DAG), preventing repeated deduction and calculation redundancy.

[0137] S320, map the candidate path to the simulation boundary condition and perform solving:

[0138] For each candidate extrapolation path , according to the node attributes and trigger logic in the path, it is mapped to the boundary condition of the coupled multi-physical field solver. For example: node "tank leakage" → leakage rate boundary ;

[0139] Node "gas diffusion" → environmental wind field and concentration field source term; node "flash explosion" → transient pressure wave boundary ;

[0140] In the mapping process, the grid size (10,000-100,000 nodes), time step (0.01-1s) and solving accuracy (residual error ≤10 -4 ) consistent with S230 are maintained; by calling the solver, the corresponding accident consequence field is output for each , including temperature field T(x, t), concentration field C(x, t), and pressure field Y(x, t).

[0141] S330, calculate the simulation consequence index and perform consistency check; according to the simulation results of each candidate path , the corresponding simulation consequence index is calculated, including:

[0142] Concentration peak:

[0143]

[0144] wherein represents the maximum concentration value at the end of the accident in the calculation area under the i-th candidate path; represents the concentration field distribution of path i at position x and time ; through this index, the peak value of the spatial distribution of hazardous substances at the end of the accident can be characterized, which is used to judge the danger level and the warning range.

[0145] Thermal radiation dose:

[0146]

[0147] wherein represents the total amount of thermal radiation accumulated and received by path i at the corresponding position during the entire accident process; represents the instantaneous thermal radiation intensity of the path at time t, which reflects the cumulative effect of energy during the accident process and is the core parameter for thermal hazard assessment;

[0148] Explosion shock radius , which represents the explosion shock radius obtained by analyzing the explosion shock wave overpressure surface in the explosion accident triggered by path i, and is used to characterize the explosion damage range and protection radius.

[0149] Consistency checking is performed for each candidate path to determine whether the path reasoning and physical calculation meet the conservation and triggering conditions. The consistency checking index is defined as:

[0150]

[0151] wherein , , are weight coefficients, is the mass conservation error, is the energy conservation error, is the triggering condition matching confidence; when the path meets the physical conservation and triggering conditions, the consistency checking index value is higher, and it is preferentially entered into the accident extrapolation and risk assessment link; if it is lower, the path is eliminated or given a lower weight; and indicate that the smaller the error, the larger the value of 1 minus the error value, and the higher the consistency; the larger the error, the lower the consistency score.

[0152] ​The value can be set as 1 if the path completely meets the trigger condition (such as a pressure threshold, a concentration threshold, etc.), and can be a decimal between 0 and 1 or 0 if it partially meets or does not meet the trigger condition.

[0153] wherein The calculation reference is obtained by calculating the deviation of the input mass and the output mass of the system. Specifically, by mass statistics of the accident source term, the diffusion area and the boundary flux, the total input mass of the system and the total output mass of the system are obtained, and the calculation formula is: ;

[0154] Secondly, the energy conservation error is obtained in a similar way to the mass conservation. By statistics of the input and output of energy during the accident, the total input energy and the total output energy are obtained, including thermal radiation, shock wave energy and other dissipation forms. The calculation formula is: ;

[0155] Trigger condition matching confidence is obtained according to the accident trigger judgment logic. By comparing the key state parameters (such as pressure, concentration, etc.) of the accident with the set trigger threshold, it is determined whether the accident trigger condition is met. If the threshold is completely met, the value is 1; if it is partially met or close to the threshold, the confidence between 0 and 1 is calculated by normalization mapping; if it is not met, 0 is taken.

[0156] Consistency judgment rule: ; wherein is a set threshold (such as 0.85), and it is considered that the path passes the consistency check; otherwise, it is rejected.

[0157] S340, update the atlas weight and trigger state in reverse: for the path that passes the consistency check, increase the weight of the corresponding relationship in the path to strengthen the credibility; for the path that does not pass, reduce the relationship weight.

[0158] The weight update formula is:

[0159]

[0160] wherein represents the value of the relationship weight from node i to node j after updating; represents the weight value before updating; is the update step or learning rate, which controls the sensitivity of weight adjustment; This represents the consistency verification index calculated for the i-th candidate path. This is the consistency threshold, used to determine whether a path needs enhancement or weakening. The difference between the two represents the degree of deviation of the path's consistency relative to the threshold; the greater the deviation, the stronger the adjustment. If the value is below 0.1, the relationship is marked as "invalid" and removed from subsequent reasoning; at the same time, the corresponding triggering state in the triggering graph is adjusted synchronously to ensure that the graph structure is consistent with the physical process.

[0161] When path Consistency verification indicators When the threshold is exceeded, process all edges on the path. weight Perform synchronous updates; when consistency verification indicators If the threshold is exceeded only locally, the corresponding local edge weights are increased to achieve local reinforcement; if the threshold is not exceeded, the edge weights are not adjusted.

[0162] S350, Bidirectional Iterative Closed Loop of Graph Reasoning and Physical Decomposition: The updated graph weights and trigger states are input again into the path reasoning module to regenerate the candidate path set. It forms a closed-loop iteration with the simulation and verification process of S320–S330 until the convergence criterion Γ is met or the iteration limit is reached.

[0163] Convergence criteria include:

[0164]

[0165] in This represents the consistency check index value of the i-th candidate path in the k-th iteration; This represents the consistency check index value of the corresponding path at the (k-1)th iteration; The preset convergence threshold is preferably a very small positive number; This indicates that the maximum value among all path consistency changes is taken; k is the number of iteration steps; when the maximum change is less than this threshold, the path consistency check index is considered to have stabilized, and the iteration process terminates. This avoids redundant calculations, improves path reasoning efficiency, and ensures the stability and uniqueness of the final reasoning result; the output is the set of target paths that finally pass the consistency check. And store the corresponding accident consequence scene. and consequences indicators ;

[0166] Unconverged paths will trigger backtracking mechanism B, which will backtrack to the previous stable iteration state to ensure the stability and reliability of the deduction.

[0167] S4 specifically includes the following sub-steps:

[0168] S410 candidate path comprehensive score calculation: for the consistency passed candidate extrapolation path set obtained through S310-S350 , for each path Calculate the comprehensive score ; the score dimension includes the consistency check index , the consequence severity index and the path length , and the comprehensive score function is defined as:

[0169]

[0170] Wherein represents the comprehensive score of the i-th candidate path; represents the consistency check index of the i-th candidate path (reflecting whether the path meets the constraints of mass conservation, energy conservation, trigger condition confidence, etc.); represents the accident consequence severity index related to the i-th path (such as maximum concentration peak, shock wave radius, thermal radiation dose, etc.); represents the path length or complexity index of the i-th path (which can be represented as the number of nodes, the number of propagation steps, the path topology complexity, etc.); , is a function for quantitatively mapping the consequence severity and path length, such as normalization, logarithmic mapping or weight scaling; , , respectively represent the weight coefficients of the consistency check index, the consequence severity index and the path length index, which are used to adjust the contribution proportion of each item in the comprehensive score. By weighting and superimposing the multi-dimensional attributes of the candidate path, the quantitative scoring and sorting of the path are realized, so as to preferentially select the key accident evolution path with high credibility, high hazard and low complexity.

[0171] Wherein is a normalization function for accident severity, which can be:

[0172]

[0173] Wherein represents the initial value of the accident consequence severity index related to the i-th path; is the maximum value of all candidate path consequence severity indexes; is the minimum value of all candidate path consequence severity indexes; is the normalized consequence severity score, ranging from [0, 1].

[0174] is a penalty term for path complexity, for example:

[0175]

[0176] wherein is the path length, is the maximum path length in the candidate path set; denotes the normalized path length score, ranging from [0, 1], through which the quantitative processing of path length can be realized, so that shorter paths obtain higher scores, which helps to prioritize the key paths with high accident propagation efficiency and strong importance; the typical parameter setting range is . After scoring all candidate paths, they are sorted from high to low according to the value to form a sorted set .

[0177] S420, target extrapolation path screening: for the sorted set , the sequential screening strategy is adopted, and the target path is retained according to the following conditions:

[0178] wherein is the score threshold (typical value 0.6-0.8); , ensuring the physical credibility of the path; is located in the high-risk quantile interval (such as above the 80% quantile);

[0179] The retained target extrapolation path set is denoted as , and the typical number range is 5-20; in the screening process, the path ID, score, and threshold hit are recorded to form a screening record table for tracing and verification.

[0180] S430, disturbance simulation and uncertainty propagation analysis: in the process of accident evolution path risk calculation, in order to fully consider the result uncertainty caused by input parameter disturbance, the disturbance simulation and uncertainty propagation analysis mechanism is introduced; for each , the accident scene parameters are disturbed to generate a set of parameter samples ; denotes the jth accident evolution extrapolation path obtained after scoring and screening.

[0181] The disturbance mode can include: Monte Carlo random sampling (typical sample number N=200-1000); Sobol global sensitivity analysis; Gaussian disturbance or interval disturbance. The disturbance range is set to ±10%-±30% of the original parameters; for each disturbance sample, the coupled multi-physical field solver is called to calculate the corresponding consequence index ; the index distribution under the disturbance sample is calculated to obtain the uncertainty interval The confidence level CL is calculated by counting the ratio of the number of disturbance samples satisfying the early warning condition to the total number of samples:

[0182]

[0183] The confidence level is used to reflect the probability of the accident path triggering major consequences under input uncertainty, thereby providing a basis for subsequent path weight correction and risk optimization, where the early warning condition is defined as the event set in which the simulated consequence indicator exceeds the regulatory threshold (such as explosion shock wave radius, maximum concentration, thermal radiation dose, etc.).

[0184] S440, Robustness Evaluation and Risk Ranking: Define Path Robustness Indicator :

[0185]

[0186] Where represents the robustness indicator of the jth path; CL is the confidence level, representing the probability of the path triggering an early warning in the disturbance simulation (see S430); represents the coefficient of variation of the consequence indicator, is the standard deviation, is the mean; is the weight coefficient, used to control the influence of confidence level and coefficient of variation in robustness calculation; the higher the CL, the easier the path triggers major consequences, indicating higher risk; The smaller the path consequence is more stable and reliable, indicating that the simulation uncertainty is small; The larger the path is both high-risk and stable, and more worthy of attention;

[0187] Sort the paths in according to the value of , preferentially retaining high-robustness high-risk paths; after screening, form the final robust target path set , typically 3-10 paths; record the disturbance range, distribution interval, confidence level, and robustness indicator of each path, and generate a robustness analysis report structure.

[0188] S450, Backtracking Mechanism and Termination Condition: If the number of paths in is less than the preset lower limit (e.g., 3 paths), or the robustness indicator (typical threshold 0.6-0.8), trigger the backtracking mechanism B. The backtracking mechanism performs the following steps:

[0189] Lower or threshold, relax the path screening criteria; adjust the disturbance range (e.g., ±30%→±15%); increase the number of iterations or expand the candidate path set.

[0190] If the convergence criterion is met:

[0191]

[0192] or the iteration upper limit is reached , the rollback process is terminated, and the final robust target path set and its uncertainty interval and robustness results are output for subsequent S510-S550 high-fidelity solving; wherein represents the robustness index of the jth path at the kth iteration; represents the robustness index of the corresponding path at the k-1th iteration; is a preset robustness convergence threshold, usually a small positive number (such as 10 -3 ); represents the maximum value of the change in the robustness index of all paths;

[0193] Preferably, when the maximum change in the robustness index of the path in the last two iterations is less than the threshold, it is considered that the calculation result converges, the iteration is terminated, the stability and convergence of the robustness evaluation process are ensured, and the analysis and calculation efficiency is improved.

[0194] S5 specifically includes the following sub-steps:

[0195] S510, high-fidelity post-accident consequence field solving: input the final robust target path set from S450 into a coupled multi-physics field solver to perform high-fidelity simulation calculation;

[0196] Simulation settings: grid size 50,000-200,000 nodes; time step 0.005-0.05s; total simulation time 0-900s; convergence residual threshold 10 -5 ; boundary conditions and source term mapping method consistent with S320, such as leakage rate Q, meteorological wind field , pressure wave , etc. are defined by path nodes;

[0197] Simulation results include: temperature field T(x, t); concentration field C(x, t); pressure field Y(x, t); flame propagation profile (if applicable); simulation results are stored in structured data with a spatial resolution of 0.1-0.5m and a temporal resolution of 0.05-0.5s for subsequent sensitivity analysis and key node identification.

[0198] S520, target consequence index calculation and extraction: based on the high-fidelity simulation results, calculate the target consequence index for each path , including but not limited to: maximum concentration peak , thermal radiation dose , explosion shock wave radius ;

[0199] Store the results as a triple data structure (path ID, , spatiotemporal location) to support multi-path comparison; perform spatial interpolation and temporal smoothing on the indicator data to eliminate the influence of grid differences on sensitivity calculation.

[0200] S530, Sensitivity analysis and critical trigger node identification: Perform sensitivity analysis on each path 's parameter vector to evaluate the degree of influence of parameter changes on .

[0201] Sensitivity calculation can use: Sobol global sensitivity analysis (recommended); local partial derivative method ; Morris method (if acceleration is required).

[0202] Calculate the first-order sensitivity index :

[0203]

[0204] Where represents the first-order Sobol sensitivity index of the input parameter ; i represents the i-th of the model input parameters; represents the set of all other parameters except ; R is the model output (i.e., accident consequence indicators such as shock wave radius, concentration peak, radiant heat dose, etc.); represents the variance operator, which is used to measure the overall uncertainty of the result; represents the conditional expectation of other parameters under the fixed ; represents the variance under the change of , which can be used to quantitatively evaluate the contribution of input parameters to the uncertainty of output results, so as to identify critical sensitive parameters and optimize the accuracy of accident scene simulation; Map the sensitivity of the nodes in the graph, and mark the trigger nodes corresponding to high sensitivity parameters as "critical nodes". The number of typical critical nodes is 3-10; when the sensitivity index

[0205] , it is determined that the node is a "high-impact node".

[0206] S540, Minimum cut set solving: Based on the accident chain knowledge graph, establish a directed weighted graph G=(V,E), where V is the set of trigger nodes, and E is the weighted causal relationship edge. ​

[0207] with the incident start node and the consequence node as objects, the minimum cut set is solved by using the max-flow min-cut theorem:

[0208]

[0209] In detail: To identify the key control nodes in the accident propagation network, the minimum cut set solving model is introduced. For the accident propagation graph G, the cut set is defined, and the cut value of the cut set is the total sum of the propagation edge weights across the cut set after cutting the node set C;

[0210] The minimum cut set can be obtained, which is the key node set that blocks the accident propagation at the minimum cut cost. This method can be used to identify the weakest link in risk propagation, and provide quantitative basis for accident prevention and control strategies and emergency decision-making;

[0211] The nodes in the minimum cut set represent the key control points that can interrupt the accident chain propagation once blocked; the solving algorithm can use the Edmonds-Karp or Push-Relabel algorithm, and the calculation time is usually less than 1 s / path; the nodes in are labeled as “key control nodes”, and cross-verified with the sensitivity analysis results to eliminate false positives or weakly affected nodes.

[0212] S550, treatment recommendation sequence generation and priority setting: according to the key trigger nodes and the minimum cut set, a treatment action recommendation sequence A is automatically generated; the treatment recommendation is one-to-one corresponding to the node type, for example:

[0213] Node “tank leakage”→ action “close the feed valve + start pressure relief”; node “gas diffusion”→ action “start spraying + dilution ventilation”; node “flame propagation”→ action “start fire extinguishing system + isolate the area”.

[0214] According to the sensitivity index and the position (level depth) of the node in the accident chain, the action priority is calculated:

[0215]

[0216] wherein, represents the sensitivity index of the node, which is used to describe the sensitivity of the node to the accident propagation consequences or uncertainty indicators; represents the depth of the node in the accident propagation topology structure, reflecting its propagation level distance from the accident initial source; and This is a weighting coefficient used to balance the relative importance of sensitivity and topological factors in priority evaluation; typical values ​​are... It is 0.7. It is 0.3;

[0217] Through the Values ​​are calculated and sorted to prioritize the identification of: key nodes with high sensitivity and significant impact on the spread and consequences of accidents; and early nodes located upstream of the accident source or at key propagation levels.

[0218] The output includes: the minimum cut set ; Corresponding action suggestion A; Priority list Suggested action location and timing (e.g., valves to close within 30 seconds, sprinklers to start within 60 seconds); results are output in JSON / database structured format, providing a direct execution interface for linkage with higher-level systems (DCS / ECS / SCADA).

[0219] S6 specifically includes the following sub-steps:

[0220] S610, Accident Simulation Results Output: Output the final robust target path set obtained in S550. Minimal cut-off set Action sequence A, priority list The aftermath of the accident and consequences indicators Package them uniformly into structured data objects;

[0221] The data structure uses JSON format or relational database tables, with typical fields including:

[0222] path_id (path ID); node_id (critical node ID); action (recommendation); priority (action priority); effect_radius (radius of influence); time_window (recommended handling time limit); confidence (path confidence); robustness (robustness index); hazard_index (risk level);

[0223] The output frequency is determined according to the emergency response time limit, with a typical frequency of 1–5Hz, to ensure that the system can meet the second-level response requirements.

[0224] The data output module is physically separated from the computing module, and high-reliability transmission is achieved through a message bus (such as MQTT, Kafka or ZeroMQ).

[0225] S620, Linkage upper system interface: The communication between the upper system (DCS / ECS / SCADA) adopts the industrial protocol OPCUA or Modbus-TCP, and the transmission delay is controlled within 2s.

[0226] The interface definition includes: receiving fields: action_id, trigger_time, execution_status; sending fields: path_id, node_id, action, priority, confidence.

[0227] The linkage strategy adopts an "event-action" mechanism. When the system output treatment suggestion meets the preset trigger condition, the corresponding linkage instruction is automatically triggered, such as automatically closing the leakage source valve, starting the spraying and ventilation system, and issuing an alarm to the personnel evacuation broadcast system. The interface supports synchronous and asynchronous communication modes, which can be dynamically switched according to the severity of the accident. The execution result of the linkage action is returned through the interface, which is used to update the system state and subsequent decision-making.

[0228] S630, Data archiving and version control: All intermediate data, simulation results and linkage action logs generated during accident deduction are archived.

[0229] The archiving format adopts the HDF5 or Parquet binary format, and the typical single-scene storage capacity is 10-50MB. A unique version number version_id and a checksum hash_code are generated each time the accident deduction is performed, which is used to ensure data integrity and traceability.

[0230] The archived content includes: all path score results and perturbation parameters; simulation parameters and boundary conditions; key nodes and minimum cut sets ; treatment action execution logs and feedback information; final output linkage records; support automatic compression and block storage to ensure archiving efficiency and retrieval performance.

[0231] S640, Iterative convergence and rollback record: Record the key intermediate state in the path screening and robustness evaluation iteration process, including the number of paths in each iteration, the score change , the robustness index change , and the key node set change.

[0232] The record format adopts a time series database (such as InfluxDB) or a text index database (such as Elasticsearch) for easy retrieval and visualization; an "iteration snapshot" is automatically generated at each iteration, including: iteration round number; pass / fail path ID; convergence error e; rollback trigger reason; calculation time consumption; if trigger rollback mechanism B, the snapshot is rolled back to the last stable convergence point, ensuring system stability and result consistency.

[0233] S650, closed-loop linkage and traceability mechanism is formed: through the result output, linkage trigger and log archiving of S610-S640, the "prediction-linkage-record-traceability" closed-loop emergency response system is realized.

[0234] The upper system can perform event review, emergency drill and model retraining based on the archived data to form a continuous improvement cycle; the present method supports online linkage and post-event traceability throughout the entire accident process, with a typical response time < 5s, and traceability records can be preserved for ≥5 years; all operations have time stamp signing and digital signature mechanism to ensure the traceability and evidence effectiveness of the accident handling process; the closed-loop linkage mechanism is designed in an integrated manner with the technical links of S110-S550, ensuring that the deduction results can be executed in real time, avoiding the problem of "staying in the calculation results".

[0235] The path extrapolation in the present embodiment represents the use of accident chain knowledge and physical models to automatically extrapolate multiple possible evolution routes from the current state of the accident, and to filter credible paths through physical and mathematical calculations, providing decision basis for emergency response and risk control.

[0236] The above formulas are dimensionless values calculated, the formulas are obtained by software simulation of a large amount of data to obtain a formula of the latest real situation, and the preset parameters and threshold values in the formula are set by the person skilled in the art according to the actual situation.

[0237] The above-described embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented by software, the above-described embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are wholly or partially generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center through a wired (for example, infrared, wireless, microwave, etc.) manner. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. containing one or more available medium collections. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state disk.

[0238] Those of ordinary skill in the art can realize that the modules and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those of ordinary skill in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0239] Those of ordinary skill in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described system, device, and module can refer to the corresponding processes in the foregoing method embodiments, which will not be described here.

[0240] In several embodiments provided in the present application, it should be understood that the disclosed system, device, and method can be implemented in other ways. For example, the above-described device embodiments are only schematic, for example, the division of the modules is only a logical function division, and actual implementation can have another division manner, for example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the shown or discussed ones can be indirect coupling or communication connection through some interfaces, devices, or modules, which can be electrical, mechanical, or other forms.

[0241] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed over multiple network modules. Part or all of the modules can be selected as needed to achieve the purpose of the embodiment.

[0242] In addition, the functional modules in each embodiment of the present application can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module.

[0243] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

[0244] Finally: the above is only the preferred embodiment of the present application, and is not used to limit the present application, and any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A post-accident consequence simulation calculation method based on coupling solution of mathematical physics models, characterized in that, Comprise the following steps: S1, based on the process and accident history information to establish the accident chain knowledge graph, representing the cause and effect between equipment, state, event and consequence; Combined with parameter threshold and conservation constraint to establish physical trigger diagram, form accident trigger logic and physical accessibility constraint; S2, the historical accident records, expert rules and online observation data are mapped to the accident chain knowledge graph entity and relationship, and the path is called back to couple the multi-physical field solver to establish the baseline accident consequence field and calculate the baseline consequence index as the extrapolation reference; S3, the graph causal trigger relationship is used to form a candidate extrapolation path by traversal reasoning, the path is mapped to the simulation boundary condition for accident consequence field solving, and the consistency is checked through mass conservation, energy conservation and trigger satisfaction degree, realizing the closed loop iteration of graph reasoning and physical calculation; S4, the candidate path is scored based on consistency index, consequence index and path complexity, and the uncertainty interval and robustness index are obtained by disturbance simulation and statistical analysis, and the final robust target path is selected; S5, high-precision physical field simulation is carried out for the robust target path, the accident consequence index is calculated, the key trigger nodes in the accident chain are identified by combining sensitivity analysis and minimum cut set solving, and disposal suggestions and action priority sequence are generated; S3 specifically includes: generating candidate extrapolation path on accident chain knowledge graph according to causal relationship reasoning; The candidate extrapolation path is mapped to the boundary condition and source term of the coupled multi-physical field solver, and the simulated accident consequence field is calculated; The simulation consequence index is calculated, and the physical accessibility and conservation consistency of the candidate extrapolation path are checked based on the consistency checking index; The relationship weight in the accident chain knowledge graph and the trigger condition in the physical trigger diagram are updated reversely; Form the bidirectional iteration link of graph reasoning and physical calculation, and continuously optimize the credibility of candidate extrapolation path.

2. The post-accident consequence simulation method based on coupled solution of mathematical physical models according to claim 1, characterized in that, Also includes S6, the path, key node, consequence index and disposal suggestion are output to the upper system in the standardized data format, the automatic linkage disposal is realized through the industrial protocol, the whole process of accident deduction is archived and version controlled, and the closed loop emergency response and post-tracing is completed.

3. The post-accident consequence simulation method based on coupled solution of mathematical physics models according to claim 1, characterized in that, S1 specifically includes: Establishing accident chain knowledge graph for representing equipment, state, event and causal relationship structure; Establishing physical trigger diagram for representing threshold condition, conservation constraint and trigger logic; Establishing coupled multi-physical field solver for calculating accident consequence field according to source item parameter and boundary condition; Define accident scene parameter vector, extrapolation path set, consequence index, consistency checking index, convergence criterion and constraint set; Set the rollback mechanism and linkage upper system for abnormal recovery.

4. The post-accident consequence simulation method based on coupled solution of mathematical physical models according to claim 1, characterized in that, S2 specifically includes: Mapping historical records, expert rules and real-time observation to entities, relationships and initial trigger conditions in the accident chain knowledge graph; Input the physical trigger diagram with accident scene parameter vector to complete the parameterization of trigger logic; Call the coupled multi-physical field solver to calculate the baseline accident consequence field according to the parameters and trigger conditions; Calculate and record the baseline consequence index as the benchmark for subsequent extrapolation path comparison; Store the baseline state for reference in subsequent screening and rollback.

5. The post-accident consequence simulation method based on coupled solution of mathematical physics models according to claim 1, characterized in that, S4 specifically includes: The candidate extrapolation paths are scored according to the consistency checking index and the consequence index in the constraint set; The sequential screening method is used to retain the target extrapolation path set; The perturbation simulation is performed on the target extrapolation path set within the uncertainty range of the accident scene parameter vector; The robustness and consequence index interval characteristics of the target extrapolation path are evaluated; If the convergence criterion is not met or the constraint set is violated, the rollback mechanism is triggered to adjust the relationship weight, trigger condition or extrapolation path, and the process returns to S3 for reiteration.

6. The post-accident consequence simulation method based on coupled solution of mathematical physics models according to claim 1, characterized in that, S5 specifically includes: The coupled multi-physics solver is called to perform high-fidelity coupled solving on the target extrapolation path set; The target accident consequence field and the target consequence index are obtained; The key trigger nodes in the accident chain knowledge graph are identified based on the sensitivity analysis of the target consequence index; The minimum cut set is extracted based on the graph structure centrality, which is used to represent the key accident control points; The treatment suggestion sequence and the corresponding priority are generated based on the target extrapolation path.

7. The post-accident consequence simulation method based on coupled solution of mathematical physics models according to claim 2, characterized in that, S6 specifically includes: The target extrapolation path set, key trigger nodes, minimum cut set, target accident consequence field and target consequence index are output to the upper system; The treatment suggestion sequence and the corresponding priority are output for linkage control and emergency response; The graph state, trigger graph parameters, solver results and consistency checking evidence are archived.

8. The post-accident consequence simulation method based on coupled solution of mathematical physical models according to claim 7, characterized in that, S6 also includes: recording the extrapolation path iteration convergence process and rollback trajectory for online calibration and traceability analysis; The closed loop of knowledge graph, physical trigger and numerical solution is formed, providing reusable deduction basis for subsequent accident warning and disposal.

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