A simulation collaborative digital simulation system and method suitable for shipping safety

Abstract

The application provides a simulation collaborative digital simulation system and method suitable for shipping safety, relates to the technical field of shipping digital simulation, and the system comprises: a collaborative control core for generating a scene starting instruction according to scene parameters, and generating a collaborative instruction according to thermal-solid coupling basic data and scene parameters based on a time sequence collaboration and scene adaptation fusion algorithm; a thermal-solid coupling simulation module for generating thermal-solid coupling basic data in response to the scene starting instruction; a personnel evacuation simulation module for generating personnel evacuation simulation data based on the thermal-solid coupling basic data; a device scheduling module for generating device scheduling data based on the thermal-solid coupling basic data and the personnel evacuation simulation data; and an emergency force module for generating emergency force scheduling data according to device state data in the device scheduling data according to a multi-level emergency response mechanism. The application realizes unified scheduling and real-time linkage of multiple modules, and solves the problems of low collaboration efficiency, large data interaction delay and poor scene adaptability.

Description

Technical Field

[0001] This invention relates to the field of shipping digital simulation technology, and more specifically, to a collaborative digital simulation system and method for shipping safety. Background Technology

[0002] Shipping safety is a crucial issue in the transportation sector, encompassing the safety of personnel and property on ships and offshore platforms, as well as the protection of marine ecosystems. With the development of digital technology, simulation technology is widely applied in scenarios such as ship design, safety assessment, emergency drills, and maritime command. Currently, various simulation tools and systems exist for shipping safety, such as personnel evacuation simulation software, structural heat conduction and collision simulation tools, and emergency equipment dispatch simulation systems. Some systems can perform simulation calculations for single or a few functional modules.

[0003] However, existing technologies lack a digital simulation system capable of efficiently coordinating multiple modules such as thermo-mechanical coupling simulation, personnel evacuation simulation, equipment scheduling, and emergency force deployment. Specifically, existing solutions generally suffer from the following shortcomings: First, the interfaces between functional modules are inconsistent and the data formats are incompatible, requiring extensive manual intervention for data conversion during collaborative simulation, resulting in low collaborative efficiency and susceptibility to errors; Second, the system lacks a unified collaborative control center, and the results of thermo-mechanical coupling analysis cannot drive personnel evacuation route planning, equipment scheduling schemes, and emergency force responses in real time, leading to temporal discrepancies and logical disconnects between modules, making it difficult to form a closed-loop linkage; Third, different shipping safety scenarios (such as fire, collision, grounding, and severe weather) have significantly different requirements for simulation parameters and linkage rules, making it difficult for existing systems to quickly adapt to various scenarios, thus limiting their practicality.

[0004] Therefore, there is an urgent need to provide a collaborative digital simulation system for shipping safety that can achieve efficient collaboration of multiple modules, scene adaptation, and real-time data linkage. Summary of the Invention

[0005] The problem solved by this invention is one or more of the aforementioned related technical problems.

[0006] To address the aforementioned problems, this invention provides a collaborative digital simulation system and method suitable for shipping safety.

[0007] In a first aspect, the present invention provides a collaborative digital simulation system for shipping safety, comprising: The collaborative control center is used to acquire scene parameters, generate initialization instructions and scene-based startup instructions based on the scene parameters, and issue the scene-based startup instructions to the thermo-mechanical coupling simulation module; and to receive the thermo-mechanical coupling basic data output by the thermo-mechanical coupling simulation module, and generate collaborative instructions based on the thermo-mechanical coupling basic data and the scene parameters according to the time-series collaborative and scene adaptation fusion algorithm; the initialization instructions are used to preset parameters, reset states, or load initial data for other modules besides the thermo-mechanical coupling module. The thermo-mechanical coupling simulation module is used to respond to the scenario-based start command, and based on the preset calculation model, perform thermo-mechanical coupling simulation calculations on the preset core load-bearing and protection structure of the ship, and generate the thermo-mechanical coupling basic data. The personnel evacuation simulation module is used to respond to the initialization command and the coordination command, and based on the structural deformation data, temperature data and visibility data in the thermo-solid coupling basic data, as well as the preset ship cabin layout and personnel distribution data, it runs the evacuation path planning and simulation by integrating the social force model and the personnel behavior model based on the intelligent agent model, and generates personnel evacuation simulation data. The equipment scheduling module is used to respond to the initialization command and the coordination command, and based on the structural damage location and temperature distribution in the thermo-structure coupling basic data and the congestion area and personnel trapped location in the personnel evacuation simulation data, it runs an equipment scheduling algorithm that integrates rule-based scheduling and genetic algorithm to generate equipment scheduling data. The emergency response module is used to respond to the initialization command and the coordination command, and generate emergency response data based on the equipment status data in the equipment scheduling data and in accordance with a preset multi-level emergency response mechanism.

[0008] Optionally, the time-series collaboration and scene adaptation fusion algorithm generates collaboration instructions based on the thermo-solid coupling basic data and the scene parameters, including: The scenario urgency coefficient is determined based on the scenario type in the scenario parameters, and the timing coordination factor is obtained based on the logistic function, the scenario urgency coefficient and the data time difference between modules. The scene adaptation weights are obtained based on the temperature field, structural stress, damaged area, and water or oil spill rate in the thermo-solid coupling basic data. The time-series collaboration factor and the scene adaptation weight are substituted into the scene linkage rule library in the scene parameters, and the collaboration instruction is generated through a preset mapping function. The coordination instructions include the timing synchronization frequency, parameter adjustment range, and module startup priority of each module, which are used to drive the personnel evacuation simulation module, the equipment scheduling module, and the emergency force module to perform dynamic parameter adjustment and linkage response.

[0009] Optionally, the thermo-mechanical coupling simulation module specifically includes: The input interface unit is used to receive material properties, geometric models, load conditions, and thermal boundary conditions. The heat source management unit is used to automatically switch and calculate the heat source parameters of fire heat source, collision friction heat source or equipment heat source according to the scene type in the scene parameters. The computational fluid dynamics simulation unit is used to run a computational fluid dynamics model to perform simulation calculations on the fluid domain based on the scenario parameters and the heat source parameters, and generate thermal boundary condition data including heat flux density, convective heat transfer coefficient and ambient temperature distribution. The physical model unit is used to construct the transient heat conduction equation, the thermal stress model based on thermoelastic theory, and the structural mechanics finite element model. In response to the scenario-based start command, the material properties, the load conditions, and the thermal boundary conditions are input into the transient heat conduction equation for processing to obtain the solid domain temperature field distribution. The solid domain temperature field distribution is then input into the thermal stress model and the structural mechanics finite element model to obtain the solid domain stress field distribution and structural deformation. The output interface unit is used to perform spatial interpolation and data extraction at different times and cross sections of the solid domain temperature field to generate temperature field distribution data; extract principal stress and shear stress tensors from the solid domain stress field, and perform equivalent stress calculation and threshold comparison based on the yield strength in the material properties to generate structural stress distribution data; analyze the structural deformation, extract the overall structural deflection and local deformation from the nodal displacement vectors to generate structural deformation data; calculate the safety margin of each structural unit based on the structural stress distribution data and the yield strength in the material properties, by using the ratio of yield strength to calculated stress, and generate failure probability data for each structural unit in combination with a preset fatigue damage model; and package the temperature field distribution data, structural stress distribution data, structural deformation data, and structural failure probability data into the thermo-structure coupling basic data.

[0010] Optionally, the personnel evacuation simulation module specifically includes: An initialization unit is used to respond to the initialization command, load the preset ship cabin layout and personnel distribution data, and complete the initial configuration of the evacuation scenario. The linkage interface unit is used to receive temperature data and visibility data in the thermo-solid coupling basic data through the synchronous call mechanism of the collaborative control center. When the temperature data exceeds a preset temperature threshold or the visibility data is lower than a preset visibility threshold, it is determined to be a dangerous area and triggers path replanning. Dynamic path planning unit, used for The algorithm calculates the initial optimal evacuation path and, based on the high-temperature area diffusion and structural deformation data in the thermo-solid coupling basic data, as well as the dangerous areas determined by the linkage interface unit, triggers a dynamic path selection mechanism to adjust the evacuation path in real time to avoid dangerous areas and congested locations, and generates planned path data. The behavior model fusion unit is used to input the ship cabin layout, personnel distribution data, and planned route data into the social force model to simulate group evacuation behavior, and simultaneously run the personnel behavior model to simulate individual decision-making behavior with different personnel attributes, generating evacuation process data including the real-time location, movement speed, direction selection, and changes in panic state of each individual. The personnel attributes include movement speed and reaction time configured according to age group and panic factor. The output processing unit is used to extract congestion points, high-risk areas, and casualty predictions from the evacuation process data, and package the extracted data into the personnel evacuation simulation data.

[0011] Optionally, the device scheduling module specifically includes: The equipment status tracking unit is used to respond to the initialization command, load and update in real time the location, availability, working status and response time of fire extinguishing equipment, life-saving equipment, communication equipment and ventilation equipment, and generate basic equipment status data. The collaborative interaction unit is used to respond to the collaborative command and generate scheduling constraint data based on the structural damage location and heat source distribution in the thermo-structure coupling basic data, and the congestion area and trapped personnel location in the personnel evacuation simulation data. The scheduling algorithm fusion unit is used to take the basic device status data and the scheduling constraint data as input, run the rule-based scheduling algorithm in basic emergency scenarios, prioritize scheduling neighboring devices to generate an initial scheduling scheme; and run the genetic algorithm in complex scenarios with the optimization objectives of shortest scheduling time, widest coverage, and lowest device loss to iteratively optimize the initial scheduling scheme and generate optimized scheduling scheme data. The output processing unit is used to generate the device scheduling data, which includes device scheduling instructions, activation time and coverage area, based on the optimized scheduling scheme data, the device number and response time in the device status basic data, and the target location in the scheduling constraint data.

[0012] Optionally, the emergency response module specifically includes: The force configuration unit is used to respond to the initialization command, load the parameter models of internal emergency forces, external rescue teams, air rescue forces and medical emergency forces, and generate basic force data. The parameter models include response time, resource capabilities and deployment methods. The response level determination unit is used to respond to the collaborative instruction, obtain equipment operation effect data and scene deterioration index from the equipment scheduling data, and automatically determine the scene level according to the comparison between the equipment operation effect data and the preset threshold and the changing trend of the scene deterioration index, and generate response level data including the current level and upgrade suggestions according to the preset multi-level emergency response mechanism. The collaborative scheduling unit is used to generate emergency force scheduling data, which includes the arrival time of rescue forces, resource allocation schemes, and rescue progress, based on the force base data, the response level data, and the equipment status data.

[0013] Optionally, the collaborative management and control center integrates a workflow engine and an event bus, and adopts a dual scheduling strategy combining priority scheduling and time-dependent scheduling. The priority scheduling strategy sets the thermo-mechanical coupling simulation module to have the highest priority, while the time-dependent scheduling strategy uses the data readiness status output by the thermo-mechanical coupling simulation module as a necessary condition for triggering the activation of the personnel evacuation simulation module, the equipment scheduling module, and the emergency response module. The workflow engine decomposes the entire simulation process into standardized workflow nodes for automated management, and the event bus is used to receive status events from each module and trigger corresponding scheduling actions in real time.

[0014] Optionally, the simulation collaborative digital simulation system for shipping safety further includes: The data sharing platform is used to respond to the initialization command to complete the creation of the data directory and cache initialization of the distributed storage nodes; and to centrally store the thermal-solid coupling basic data, the personnel evacuation simulation data, the equipment scheduling data and the emergency force scheduling data based on the distributed storage architecture, and to provide data interaction interfaces for each module to achieve real-time data synchronization. The scenario adaptation layer is used to build or customize parameter templates and linkage rules for shipping safety scenarios, generate the scenario parameters in response to user operations, and send the scenario parameters to the collaborative control center. The interface adaptation layer is used to build standardized interfaces, access and convert external data in different formats, and provide basic data in a unified format. The human-computer interaction module is used to receive ship parameters imported by the user, display the simulation results summarized by the data sharing platform, and receive control commands from the user. The human-computer interaction module is also used to link with the large visualization screen of the maritime command center to display in real time the entire integrated simulation process, including scene perception, structural analysis, risk warning, personnel evacuation, equipment scheduling and emergency force linkage.

[0015] Optionally, the data sharing platform includes: The storage management unit is used to respond to the initialization command, create a data directory on the distributed storage node, and classify, store and index the thermo-solid coupling basic data, the personnel evacuation simulation data, the equipment scheduling data and the emergency force scheduling data according to the preset simulation round, scenario type and module source, so as to generate a traceable data storage structure. The caching service unit is used to provide real-time caching for the equipment scheduling module, storing the latest versions of the thermo-solid coupling basic data and the personnel evacuation simulation data, and supporting fast reading. The data traceability unit is used to record the data change history of each module in each round of simulation, associate the full-process simulation data of different simulation rounds and different shipping safety scenarios, respond to the query requests of the human-computer interaction module, and generate comparative analysis results.

[0016] Secondly, the present invention provides a collaborative digital simulation method for shipping safety, applied to the collaborative digital simulation system for shipping safety as described in the first aspect, wherein the collaborative digital simulation method for shipping safety includes: Obtain scene parameters, generate initialization instructions based on the scene parameters to complete the basic parameter configuration and initial state preset of each module in the simulation collaborative digital simulation system applicable to shipping safety, and issue a scene-based start instruction to the thermo-solid coupling simulation module in the simulation collaborative digital simulation system applicable to shipping safety. Based on a preset calculation model, a thermo-mechanical coupling simulation calculation is performed on the preset core load-bearing and protective structure of the ship to generate the thermo-mechanical coupling basic data. Based on the time-series collaboration and scene adaptation fusion algorithm, collaborative instructions are generated according to the thermo-solid coupling basic data and the scene parameters; In response to the cooperative instruction, the following sub-steps are executed: Based on the structural deformation data, temperature data, and visibility data in the aforementioned thermo-solid coupling basic data, as well as the preset ship cabin layout and personnel distribution data, the evacuation path planning and simulation are carried out by running the social force model and the personnel behavior model based on the intelligent agent model, and personnel evacuation simulation data is generated. Based on the structural damage location and temperature distribution in the aforementioned thermo-structure coupling basic data, and the congested areas and trapped personnel locations in the aforementioned personnel evacuation simulation data, an equipment scheduling algorithm that integrates rule-based scheduling and genetic algorithms is run to generate equipment scheduling data. Based on the equipment status data in the equipment scheduling data, emergency force scheduling data is generated according to a preset multi-level emergency response mechanism. A simulation report is generated based on the thermo-solid coupling basic data, the personnel evacuation simulation data, the equipment scheduling data, and the emergency force scheduling data.

[0017] The beneficial effects of the collaborative digital simulation system for shipping safety of the present invention are: By generating unified initialization and scenario-based startup commands through a collaborative control center, differentiated startup control is applied to the thermo-mechanical coupling simulation module and other modules. Combined with time-series collaboration and scenario adaptation fusion algorithms, collaborative commands are dynamically generated based on real-time thermo-mechanical coupling basic data and scenario parameters, driving the synchronous linkage of the three major modules: personnel evacuation simulation, equipment scheduling, and emergency response. This effectively solves the problems of low efficiency in multi-module collaboration, high data interaction latency, and poor scenario adaptability in existing technologies. It achieves integrated closed-loop collaboration between thermo-mechanical coupling simulation and personnel evacuation, equipment scheduling, and emergency response, eliminating the need for manual data conversion and significantly improving the real-time performance and automation of shipping safety simulation. At the same time, each module completes parameter presets and state resets based on unified initialization commands, ensuring rapid startup and consistency of the simulation process under complex multi-scenario conditions. This provides efficient, accurate, and reliable digital support for ship design, safety training, emergency drills, and maritime command. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of the structure of a collaborative digital simulation system for shipping safety according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating a collaborative digital simulation method for shipping safety according to an embodiment of the present invention. Detailed Implementation

[0019] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Although some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the accompanying drawings and embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0020] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. Furthermore, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0021] The term "comprising" and its variations as used herein are open-ended, meaning "including but not limited to"; the term "based on" means "at least partially based on"; the term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments"; and the term "optionally" means "optional embodiments". Definitions of other terms will be given in the following description. It should be noted that the concepts of "first," "second," etc., mentioned in this invention are used only to distinguish different devices, modules, or units, and are not intended to limit the order of functions performed by these devices, modules, or units or their interdependencies.

[0022] It should be noted that the one or more modifications mentioned in this invention are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise expressly indicated in the context, they should be understood as one or more.

[0023] The names of the messages or information exchanged between the multiple devices in the embodiments of the present invention are for illustrative purposes only and are not intended to limit the scope of these messages or information.

[0024] like Figure 1 As shown in the figure, an embodiment of the present invention provides a collaborative digital simulation system for shipping safety, comprising: The collaborative control center is used to acquire scene parameters, generate initialization instructions and scene-based start instructions based on the scene parameters, and issue the scene-based start instructions to the thermo-mechanical coupling simulation module; and to receive the thermo-mechanical coupling basic data output by the thermo-mechanical coupling simulation module, and generate collaborative instructions based on the thermo-mechanical coupling basic data and the scene parameters according to the time-series collaborative and scene adaptation fusion algorithm; the initialization instructions are used to preset parameters, reset states, or load initial data for other modules besides the thermo-mechanical coupling module.

[0025] Specifically, the collaborative control center first obtains the scenario parameters configured by the user through a human-computer interaction interface or an external system interface. The scenario parameters include the shipping safety scenario type (such as fire, collision, grounding, severe weather) and its corresponding physical boundary conditions (such as ambient temperature, wind speed, collision angle, impact force, etc.). These parameters are usually selected by the user through a graphical interface or imported from preset templates, or they can be dynamically assigned values ​​through API access to real-time data sources such as weather forecasts and maritime radar.

[0026] After obtaining the scene parameters, the collaborative control center generates two types of instructions: Initialization command: Issued to all modules except the thermo-mechanical coupling simulation module (personnel evacuation simulation module, equipment scheduling module, emergency response module, and data sharing platform). This command is used to complete the initial data pre-setting of each module, such as: loading ship cabin layout data (from CAD design files) and personnel distribution data (from passenger registration system or sensor statistics) to the personnel evacuation simulation module; loading emergency equipment parameter models (from equipment ledger database) to the equipment scheduling module; loading rescue force configuration tables (from maritime emergency response plan) to the emergency response module; and creating the data directory for this round of simulation and clearing historical caches to the data sharing platform.

[0027] Scenario-specific start command: Only issued to the thermo-mechanical coupling simulation module. The command contains the calculation boundary conditions specific to the scenario, such as the heat source power curve in a fire scenario and the instantaneous impact load in a collision scenario.

[0028] After completing the calculations, the thermo-structure coupling simulation module outputs basic thermo-structure coupling data. This data is generated by the module's internal CFD-FEM fusion algorithm and includes: temperature field (temperature values ​​and time gradients at various points in space), structural stress (equivalent stresses and principal stress directions at key locations on the hull), damage area (size of bulkhead openings caused by collisions or fires), and water ingress or oil spill rate (fluid seepage or leakage rate). The temperature field and stress field are simulation results, not directly collected data; however, the input data the module relies on (such as material properties and geometric models) is obtained from the ship structural design database (such as CAD / STEP files) through an interface adaptation layer.

[0029] After receiving the basic data of thermo-solid coupling, the collaborative control center combines the previously stored scene parameters and runs the sequence collaboration and scene adaptation fusion algorithm to generate collaborative instructions.

[0030] The generated collaborative instructions are distributed to the personnel evacuation simulation module, equipment scheduling module, and emergency response module respectively, to drive them to dynamically adjust parameters and respond in unison based on the latest thermo-solid coupling basic data.

[0031] The collaborative control hub enables the decoupling and orderly driving of multimodal simulation tasks. On the one hand, initialization commands ensure that each subordinate module completes its personalized configuration before the simulation starts, avoiding operational errors caused by default parameters. On the other hand, the separation of scenario-based startup commands and collaborative commands allows the computationally intensive task of thermo-structure coupling to be executed independently and with priority, while other modules only start working after receiving collaborative commands, thus significantly reducing system resource waste. More importantly, the real-time reception of thermo-structure coupling basic data and the instant generation of collaborative commands containing timing synchronization frequency, parameter adjustment range, and priority enable personnel evacuation paths to dynamically avoid structural deformation hazard zones, equipment scheduling to adjust the deployment position of fire-fighting equipment according to the real-time temperature field, and emergency response levels to automatically upgrade based on changes in the damaged area, forming a closed loop of "perception-analysis-decision-execution".

[0032] The thermo-structure coupling simulation module is used to respond to the scenario-based start command, perform thermo-structure coupling simulation calculations on the preset ship core load-bearing and protection structure based on the preset calculation model, and generate the thermo-structure coupling basic data.

[0033] Specifically, upon receiving a scenario-based start command from the collaborative control center, the thermo-structure coupling simulation module immediately initiates simulation calculations. This command includes physical boundary conditions unique to the current shipping safety scenario, such as the heat source power variation curve over time in a fire scenario, the instantaneous impact load time history in a collision scenario, and the compression displacement boundary of reefs on the ship's bottom in a grounding scenario.

[0034] The module includes a pre-built computational model for the core load-bearing and protective structures of ships. This model simulates ship bulkheads (fuel tanks, cargo holds, engine room bulkheads), decks (main deck, lower deck), hull support structures (ribs, longitudinal girder, transverse beams), and key equipment bases. The module employs an algorithm that integrates computational fluid dynamics (CFD) and the finite element method (FEM): First, the CFD model simulates the heat transfer and flow behavior of the fluid domain (fire smoke, high-temperature airflow, incoming water flow), calculating the heat flux density, convective heat transfer coefficient, and spatial distribution of ambient temperature on the structural surface. Then, these thermal boundary conditions are applied to the finite element model of the ship structure, sequentially solving the transient heat conduction equation (obtaining the change of the solid domain temperature field over time), the thermal stress equation (thermal expansion stress caused by temperature gradients), and the structural mechanics equilibrium equation (displacement and deformation under the combined action of external forces and thermal stress). The entire calculation process automatically switches heat source models based on the scenario type (fire radiation heat, collision friction heat, equipment steady-state heat) and supports adaptive time steps to balance computational accuracy and efficiency.

[0035] After iterative solving, the module generates basic thermo-mechanical coupling data. This data is a set of quantitative results describing the state of a ship structure under thermo-mechanical coupling, specifically including: Temperature field distribution: Temperature values ​​of each node of the structure at different times, usually output in the form of spatial interpolation contour maps or time series data; Structural stress distribution: principal stress, shear stress, and von Mises equivalent stress of each unit, with key areas exceeding the material yield threshold marked; Structural deformation: nodal displacement vector, overall structural deflection (such as deck settlement), and local buckling deformation dimensions; Structural failure probability: The statistical probability of failure of each structural element given by stress ratio (calculated stress / yield strength) combined with fatigue damage model.

[0036] The aforementioned data is packaged in real time and synchronized to the collaborative management and control center and data sharing platform via a high-speed data bus.

[0037] The thermo-mechanical coupling simulation module enables high-precision and timely simulation of core ship structures under various safety scenarios. Compared to traditional tools that can only independently calculate temperature or stress fields, this module deeply integrates CFD and FEM, simultaneously capturing the impact of high temperatures from fire on material strength, the superposition effect of collision impact loads and thermal stress, and the secondary stress redistribution caused by changes in the structural buoyancy after water ingress. This results in output thermo-mechanical coupling fundamental data that more closely resembles real physical processes. This data provides a unique and reliable decision-making benchmark for subsequent personnel evacuation route planning (avoiding structural collapse or high-temperature areas), equipment scheduling (precisely locating emergency equipment near damaged points), and emergency response level assessment (determining whether to escalate rescue efforts based on the probability of structural failure). Field tests show that in complex fire-collision coupling scenarios, this module can control the synchronous calculation error of structural thermal and mechanical responses to within 5%, with a single-step calculation delay of no more than 2 seconds, significantly improving the credibility and practical value of shipping safety simulation.

[0038] The personnel evacuation simulation module is used to respond to the initialization command and the coordination command, and based on the structural deformation data, temperature data and visibility data in the thermo-solid coupling basic data, as well as the preset ship cabin layout and personnel distribution data, it runs the evacuation path planning and simulation by integrating the social force model and the personnel behavior model based on the intelligent agent model, and generates personnel evacuation simulation data.

[0039] Specifically, after receiving the initialization command from the collaborative control center, the personnel evacuation simulation module first loads the preset ship cabin layout and personnel distribution data. The ship cabin layout data comes from the CAD or BIM model of the ship's design phase, describing the physical topology of each cabin, including its location, dimensions, passageway width, staircase and exit distribution. The personnel distribution data is obtained based on passenger registration information, crew schedules, or sensor statistics (such as cabin infrared counts), including the initial location, number, and attributes (age, gender, etc.) of each person. Subsequently, the module continuously waits for the triggering of collaborative commands. When a collaborative command arrives, the module obtains the following three types of dynamic environmental data in real time from the thermo-solid coupling basic data: Structural deformation data: refers to information such as nodal displacement, deck deflection, and bulkhead buckling caused by fire, high temperature, or collision impact on the hull, used to determine whether passageways are blocked or collapsed; Temperature data: temperature values ​​and gradients of structural surfaces and cabin air, used to identify high-temperature hazard areas; Visibility data: Calculated from the smoke concentration model in CFD simulation, reflecting the degree of impact of smoke on vision in a fire scenario (usually expressed as transmittance or attenuation coefficient).

[0040] The module inputs this static layout data and dynamic environmental data into a human behavior model that integrates a social force model and an agent-based model. The social force model simulates the macroscopic laws of overall crowd flow, such as the repulsive forces between people, the forces that bypass obstacles, and the driving force towards the exit. The agent-based model endows each individual with independent decision-making capabilities, dynamically adjusting their direction and speed based on their age (affecting walking speed), gender (affecting physical strength), panic factor (affecting the degree of behavioral disorder), and real-time environmental perception (such as excessively high temperature or low visibility ahead). The integration of the two approaches is as follows: the agent model determines the individual's local goals (e.g., which passage to choose), while the social force model is responsible for smoothly driving the body along the path and avoiding collisions.

[0041] Through iterative simulation, the module generates personnel evacuation simulation data, which includes the change of personnel density over time on each evacuation route, traffic flow at each exit, location and duration of congestion, specific coordinates of trapped personnel, and predicted statistics of different injury levels (minor injury, serious injury, death).

[0042] For the first time, the personnel evacuation simulation module integrates real-time thermo-mechanical coupling results, such as ship structural deformation, fire thermal radiation, and smoke visibility, into personnel behavior decisions. This means that evacuation routes are no longer statically preset but dynamically adjusted as the environment deteriorates. For example, when thermo-mechanical coupling data detects that the deck deformation of a corridor exceeds a threshold, the module immediately marks that corridor as impassable and guides personnel to detour to a structurally intact backup exit. When the local temperature rises above 60°C or visibility drops below 5 meters, the module forces personnel in that area to change direction and reduce movement speed to avoid risks. Compared to traditional evacuation software that only considers static cabin layout, this module improves the accuracy of personnel casualty prediction, while also enhancing the prediction of congestion points and shortening the response time, significantly increasing decision-making confidence in emergency command and the realism of drills in shipping safety scenarios.

[0043] The equipment scheduling module is used to respond to the initialization command and the coordination command, and based on the structural damage location and temperature distribution in the thermo-structure coupling basic data and the congestion area and personnel trapped location in the personnel evacuation simulation data, it runs an equipment scheduling algorithm that integrates rule-based scheduling and genetic algorithm to generate equipment scheduling data.

[0044] Specifically, after receiving the initialization command from the collaborative control center, the equipment scheduling module first loads the static parameter library of all shipboard emergency equipment (such as the location coordinates of each fire hydrant, the coverage radius of the foam extinguishing system, and the rated capacity and release response time of the lifeboats), completing the initialization of the equipment status list. Subsequently, the module continuously waits for collaborative commands. When a collaborative command arrives, the module extracts two types of key dynamic information from the thermo-mechanical coupling basic data: Structural damage location: refers to the coordinates and range of geometric defects such as bulkhead cracks and deck collapse caused by collision, grounding, or high temperature from fire. It is usually described by node numbers of the damaged area or in the form of a three-dimensional bounding box. Temperature distribution: Temperature cloud map data of structural surfaces and cabin spaces, used to identify high-temperature hazard areas (e.g., equipment exceeding 80°C cannot be approached by humans).

[0045] At the same time, the module obtains two other types of dynamic information from the personnel evacuation simulation data output by the personnel evacuation simulation module: Congested areas: refers to the spatial range where queues stop and the density exceeds a critical value (such as 2 people / m2) on the evacuation route, which manifests as congestion hotspots before exits or at passage intersections; Location of trapped personnel: The specific coordinates of personnel who are unable to move independently due to structural deformation, high temperature blockade, or low visibility (e.g., trapped in the cabin, at the end of the starboard main deck).

[0046] Based on the above four types of data, the module runs a device scheduling algorithm that integrates rule-based scheduling and genetic algorithms. This algorithm consists of two phases: Rule-based dispatching: Used for routine or predictable scenarios. The module has a pre-built "condition-action" rule library, such as: "If the temperature in a compartment exceeds 60°C and the structural damage is located in that compartment, then prioritize dispatching the three fire extinguishers in that compartment and adjacent compartments"; "If the trapped personnel are less than 20 meters from the lifeboat deck, then automatically dispatch the two nearest lifeboats to stand by." The rules are executed quickly, making it suitable for initial emergency response.

[0047] Genetic Algorithm Optimization: When the scenario becomes more complex (e.g., multiple fire points, multiple groups of trapped personnel, and limited equipment resources), the module automatically switches to the genetic algorithm. The algorithm aims to achieve the shortest scheduling time, widest coverage, and lowest equipment loss. It encodes equipment IDs, target locations, and departure sequences as chromosomes, and iterates through selection, crossover, and mutation to find an approximately optimal equipment allocation scheme.

[0048] Finally, the module generates equipment scheduling data, which includes specific equipment scheduling instructions (such as "fire hydrant number F-12 activated, spray direction 35°, lasting 3 minutes"), the activation time of each device (accurate to the second), and its theoretical coverage area (the area that can be protected or the rescue radius).

[0049] The equipment scheduling module enables a leap from "passive response" to "scenario-driven" operation. Traditional systems often rely on pre-set, fixed plans for equipment scheduling, failing to adapt to real-time structural damage, temperature field changes, and personnel evacuation dynamics. In this invention, the location of structural damage directly determines which type of equipment is prioritized for scheduling (e.g., leak-sealing equipment and drainage pumps are prioritized for damaged compartments), temperature distribution filters out inaccessible or dangerous equipment, congested areas guide rescue equipment around densely populated areas, and the location of trapped personnel directly drives the precise deployment of life-saving equipment. The two-level fusion of rules and genetic algorithms ensures immediate response in simple scenarios (rule triggering delay <0.5 seconds) while improving equipment utilization through global optimization in complex scenarios.

[0050] The emergency response module is used to respond to the initialization command and the coordination command, and generate emergency response data based on the equipment status data in the equipment scheduling data and in accordance with a preset multi-level emergency response mechanism.

[0051] Specifically, after receiving the initialization command from the collaborative control center, the emergency response module first loads a pre-set shipping emergency response parameter model. This model includes the response time, available resources (such as the number of portable fire extinguishers and first-aid kits), and deployment area of ​​internal ship emergency teams (e.g., engine emergency team, deck firefighting team, medical emergency team), as well as the estimated arrival time, operational capabilities (e.g., the number of casualties a helicopter can transfer at one time), and call-to-action conditions of external collaborative forces (maritime rescue vessels, rescue helicopters, pollution cleanup teams). After completing the initial parameter preset, the module enters standby mode, awaiting collaborative commands.

[0052] When a coordination command arrives, the module retrieves equipment status data from the equipment scheduling data output by the equipment scheduling module. Equipment status data is a set of parameters describing the current real-time operating status of all emergency equipment on board, specifically including: Equipment location: the compartment or coordinates where each piece of equipment (fire hydrant, lifeboat, communication terminal, etc.) is located; Availability: whether the equipment is in a normal, faulty, low-battery, under maintenance, or depleted state; Operating status: whether the equipment is currently running (e.g., a lifeboat is being deployed); Response time: the time required from receiving the scheduling command to the equipment completing its startup (e.g., fire hydrant ≤ 30s, lifeboat ≤ 5min).

[0053] The module combines this equipment status data with the force parameter model loaded during initialization, and automatically determines the required level of rescue forces and specific action plans according to a preset multi-level emergency response mechanism. The multi-level emergency response mechanism typically includes three levels: Level 1 Response (Internal Emergency): When equipment status data indicates that the ship's existing emergency equipment is sufficient to control the situation (e.g., a small fire has been extinguished by nearby fire extinguishers, and a small amount of water has been drained by bilge pumps), only the internal emergency team is activated, and no external support is requested. Level 2 Response (Regional Coordination): When equipment status data shows that the situation has deteriorated beyond the internal controllable range (e.g., multiple fire extinguishing devices have been exhausted, and the fire has spread to adjacent compartments), the module automatically triggers regional coordination, sending reinforcement requests to the nearest maritime rescue vessel and neighboring vessel emergency forces, and pre-allocating the arrival time and operational area of ​​external forces. Level 3 Response (National-level Rescue): When critical equipment failures (such as lifeboats failing to launch or main fire pumps being damaged) occur in the equipment status data, and the predicted casualties exceed the threshold (such as ≥10 people seriously injured) or the probability of structural failure exceeds 50%, the module automatically reports to the national-level maritime rescue center to coordinate the intervention of professional rescue helicopters, pollution prevention emergency vessels, and other forces.

[0054] Based on the above mechanism, the module generates emergency force dispatch data. This data includes the type of rescue force (e.g., "Maritime rescue vessel Haixun 01"), estimated arrival time (calculated based on distance and speed), resource allocation plan (e.g., "Helicopters are responsible for aerial search and rescue, and maritime vessels are responsible for firefighting and water area surveillance"), and rescue progress tracking indicators (e.g., "12 injured people have been transferred, and 8 remain").

[0055] The emergency response module enables an intelligent transformation from "manual judgment and hierarchical reporting" to "data-driven and automatic escalation." In traditional shipping emergency response, whether to request external assistance often relies on the experience and judgment of the captain or commander, which can easily lead to missed opportunities for optimal rescue due to misjudgment or delays. In this invention, the module directly quantifies the risk of loss of control in the scenario based on equipment status data from equipment scheduling data—especially characteristics such as equipment depletion and malfunction—and automatically escalates the response level strictly according to a preset multi-level mechanism. For example, when a fire in the engine room causes the carbon dioxide fire suppression system to run out of power while the temperature continues to rise, the module automatically jumps from Level 1 response to Level 2 within 1 second and calls for support from nearby maritime vessels; if structural deformation data also indicates a risk of hull fracture, a Level 3 response is further triggered to call for helicopter evacuation of the injured.

[0056] In this embodiment, the simulation collaborative digital simulation system applicable to shipping safety: It generates unified initialization commands and scenario-based start commands through a collaborative control center, and performs differentiated start control on the thermo-mechanical coupling simulation module and other modules respectively. Combined with a time-series collaborative and scenario adaptation fusion algorithm, it dynamically generates collaborative commands based on real-time thermo-mechanical coupling basic data and scenario parameters, driving the synchronous linkage of the three major modules: personnel evacuation simulation, equipment scheduling, and emergency response. This effectively solves the problems of low efficiency in multi-module collaboration, high data interaction latency, and poor scenario adaptability in existing technologies. It achieves integrated closed-loop collaboration between thermo-mechanical coupling simulation and personnel evacuation, equipment scheduling, and emergency response, eliminating the need for manual data conversion and significantly improving the real-time performance and automation of shipping safety simulation. Simultaneously, each module completes parameter presets and state resets based on unified initialization commands, ensuring rapid start-up and consistency of the simulation process under complex multi-scenario conditions, providing efficient, accurate, and reliable digital support for ship design, safety training, emergency drills, and maritime command.

[0057] Optionally, the time-series collaboration and scene adaptation fusion algorithm generates collaboration instructions based on the thermo-solid coupling basic data and the scene parameters, including: The scenario urgency coefficient is determined based on the scenario type in the scenario parameters, and the timing coordination factor is obtained based on the logistic function, the scenario urgency coefficient and the data time difference between modules. The scene adaptation weights are obtained based on the temperature field, structural stress, damaged area, and water or oil spill rate in the thermo-solid coupling basic data. The time-series collaboration factor and the scene adaptation weight are substituted into the scene linkage rule library in the scene parameters, and the collaboration instruction is generated through a preset mapping function. The coordination instructions include the timing synchronization frequency, parameter adjustment range, and module startup priority of each module, which are used to drive the personnel evacuation simulation module, the equipment scheduling module, and the emergency force module to perform dynamic parameter adjustment and linkage response.

[0058] Specifically, after obtaining the basic data of thermo-mechanical coupling, the collaborative control center initiates the algorithm. First, it determines the scenario urgency coefficient λ based on the scenario type in the scenario parameters. Scenario types are pre-selected by the user (e.g., fire, collision, stranding, severe weather), and the system assigns a priority value to each type, for example, fire λ=0.9, collision λ=0.7, stranding λ=0.5, and severe weather λ=0.3. Simultaneously, the collaborative control center records the timestamp of the most recent data interaction between each module using an internal clock, calculating the data time difference Δt between modules (e.g., the difference between the time the thermo-mechanical coupling module outputs data and the time the personnel evacuation module last received data, in seconds). λ and Δt are then substituted into the logistic function: ; Obtain the temporal cooperability factor The value of this factor ranges from (0,1). When Δt ≤ 1 second, α(t) approaches 1, indicating that the data is updated in a timely manner and forcing all modules to execute synchronously. When Δt is large, α(t) approaches 0, indicating that the data delay is serious and the system will automatically slow down the stepping frequency of downstream modules to avoid making decisions based on outdated data.

[0059] Secondly, the algorithm extracts four key variables from the thermo-structure coupling basic data: temperature field T (which can be the highest or representative temperature in the current scene, in °C), structural stress σ (which can be the maximum equivalent stress in key parts, in MPa), and damage area S (in m²). 2 ), Water ingress or oil spill rate V (unit: m) 3 / min). Based on the pre-defined normalized weight coefficients in the scene type (satisfying... For example, temperature weighting in fire scenarios High stress weight in collision scenarios (Higher), calculate scene adaptation weights Ws represents the overall degree of deterioration in the current scenario; a higher value indicates a more urgent risk.

[0060] Finally, the temporal coordination factor α(t) and the scene adaptation weight Ws are substituted into the scene linkage rule base Rs. The rule base is a set of predefined condition-action mapping tables, for example: "If α(t) > 0.9 and Ws > threshold 1, then the synchronization frequency is set to 0.5 seconds, and the personnel evacuation module has the highest priority"; "If α(t) < 0.5, then the update frequency of the equipment scheduling module is reduced to 2 seconds." This is achieved through a preset mapping function: Generate cooperative instructions The instruction explicitly includes three components: the timing synchronization frequency of each module (e.g., requiring the personnel evacuation module to read thermo-mechanical coupling data every 0.5 seconds), the parameter adjustment range (e.g., requiring the equipment scheduling module to increase the coverage radius of fire extinguishing equipment by 20%), and the module activation priority (e.g., forcibly prioritizing personnel evacuation simulation, then equipment scheduling, and finally emergency force coordination). The generated coordination instructions are then distributed to the personnel evacuation simulation module, the equipment scheduling module, and the emergency force module, respectively.

[0061] This invention is the first to integrate data time differences between multiple modules with scene physical quantities (temperature, stress, damage, water ingress) into a quantified collaborative instruction generation mechanism. On one hand, the timing collaboration factor α(t) enables the system to automatically perceive the real-time nature of data interaction. When the thermo-mechanical coupling computing node is delayed, it actively reduces the synchronization frequency of downstream modules to avoid erroneous simulations caused by outdated data. On the other hand, the scene adaptation weight Ws allows collaborative instructions to be dynamically adjusted according to the actual development of the accident. For example, in the early stages of a fire, the temperature rises rapidly, and the path replanning frequency of the personnel evacuation module will increase accordingly. After a collision, the damage area expands rapidly, and the equipment scheduling module will prioritize higher startup priority and expand the scheduling range. Compared with the traditional fixed frame rate collaboration method, this algorithm can reduce the invalid waiting time between multiple modules and improve the simulation error caused by timing disorder, significantly improving the realism and reliability of simulations in complex shipping safety scenarios.

[0062] Optionally, the thermo-mechanical coupling simulation module specifically includes: The input interface unit is used to receive material properties, geometric models, load conditions, and thermal boundary conditions. The heat source management unit is used to automatically switch and calculate the heat source parameters of fire heat source, collision friction heat source or equipment heat source according to the scene type in the scene parameters. The computational fluid dynamics simulation unit is used to run a computational fluid dynamics model to perform simulation calculations on the fluid domain based on the scenario parameters and the heat source parameters, and generate thermal boundary condition data including heat flux density, convective heat transfer coefficient and ambient temperature distribution. The physical model unit is used to construct the transient heat conduction equation, the thermal stress model based on thermoelastic theory, and the structural mechanics finite element model. In response to the scenario-based start command, the material properties, the load conditions, and the thermal boundary conditions are input into the transient heat conduction equation for processing to obtain the solid domain temperature field distribution. The solid domain temperature field distribution is then input into the thermal stress model and the structural mechanics finite element model to obtain the solid domain stress field distribution and structural deformation. The output interface unit is used to perform spatial interpolation and data extraction at different times and cross sections of the solid domain temperature field to generate temperature field distribution data; extract principal stress and shear stress tensors from the solid domain stress field, and perform equivalent stress calculation and threshold comparison based on the yield strength in the material properties to generate structural stress distribution data; analyze the structural deformation, extract the overall structural deflection and local deformation from the nodal displacement vectors to generate structural deformation data; calculate the safety margin of each structural unit based on the structural stress distribution data and the yield strength in the material properties, by using the ratio of yield strength to calculated stress, and generate failure probability data for each structural unit in combination with a preset fatigue damage model; and package the temperature field distribution data, structural stress distribution data, structural deformation data, and structural failure probability data into the thermo-structure coupling basic data.

[0063] Specifically, the thermo-mechanical coupling simulation module consists of an input interface unit, a heat source management unit, a computational fluid dynamics simulation unit, a physical model unit, and an output interface unit working together.

[0064] First, the input interface unit acquires four types of basic data from external sources: material properties (including density, specific heat capacity, thermal conductivity, elastic modulus, coefficient of thermal expansion, and yield strength of marine steel or aluminum alloys, derived from material databases or experimental data), geometric models (CAD 3D models of the ship structure, such as STEP / IGES format, including the thickness and weld details of components such as bulkheads, decks, and ribs), load conditions (mechanical boundaries such as collision impact force, grounding pressure, and wind and wave pressure, calculated from collision velocity, angle, etc. in scene parameters), and thermal boundary conditions (initial ambient temperature, heat transfer coefficient, etc.). This data is uniformly accessed through the interface adaptation layer.

[0065] Simultaneously, the heat source management unit automatically selects the heat source model based on the scenario type (fire, collision, stranding, etc.) in the scenario parameters: in a fire scenario, it calculates the convective and radiative heat power curves generated by fuel or cargo combustion; in a collision scenario, it dynamically calculates the frictional heat generation rate based on relative velocity and contact area; and for equipment heat sources, it applies heat flux density at the rated power steady state of equipment such as main units and generators. The generated heat source parameters (such as heat flux density changes over time) are then transmitted to the computational fluid dynamics simulation unit.

[0066] The computational fluid dynamics simulation unit receives scene parameters (such as the initial diffusion direction of fire smoke and wind speed) and heat source parameters, runs a CFD model to simulate the fluid domain (compartment air, smoke, and incoming water flow), and calculates the heat flux density (heat exchange power per unit area), convective heat transfer coefficient (reflecting the heat exchange efficiency between fluid and solid), and ambient temperature distribution (temperature field of the compartment space) on the structural surface. These results are output as thermal boundary condition data to the physical model unit.

[0067] The physical model unit pre-constructs three core mathematical models: a transient heat conduction equation (describing the time-domain propagation of heat in a solid), a thermal stress model based on thermoelastic theory (determining thermal expansion and constraint stress caused by temperature changes), and a structural mechanics finite element model (calculating displacement and deformation under the combined action of external forces and thermal stress). Upon responding to a scenario-based start command, the unit substitutes the material properties and load conditions provided by the input interface unit, as well as the thermal boundary conditions generated by the CFD unit, into the transient heat conduction equation to solve for the solid domain temperature field distribution (temperature values ​​of each node of the structure as a function of time). Then, this temperature field is used as a temperature load, combined with the original mechanical boundaries, and substituted into the thermal stress model and the structural mechanics finite element model to calculate the solid domain stress field distribution (stress tensors of each element) and structural deformation (three-dimensional displacement vectors of the nodes).

[0068] In some embodiments, the transient heat conduction equation is: ; in, Density of structural materials (kg / m³) 3 ); Specific heat capacity of material (J / (kg·K)); : Structural temperature (K); Time (s); : Thermal conductivity of the material (W / (m·K)); Temperature Laplace operator (divergence of temperature gradient); Heat source intensity (W / m³).

[0069] The thermal stress model (based on thermoelastic theory) is as follows: ;in, Thermal stress (Pa) Material elastic modulus (Pa). : Coefficient of thermal expansion of the material (1 / K) Temperature change (K).

[0070] The finite element equations for structural mechanics are: ;in, : Overall structural stiffness matrix : Nodal displacement vector : Nodal load vector (including external forces, thermal stress, and equivalent nodal forces).

[0071] Finally, the output interface unit performs post-processing on the above results: spatial interpolation is performed on the temperature field of the solid domain (mapping finite element node values ​​to cloud map mesh) and extracted according to different times and sections to generate temperature field distribution data; principal stress and shear stress tensors are extracted from the stress field of the solid domain, and equivalent stress (such as von Mises stress) is calculated based on the yield strength in material properties and compared with the yield strength threshold to identify the over-limit area, generating structural stress distribution data; the overall structural deflection (such as vertical displacement in the deck span) and local deformation (such as the indentation depth of the bulkhead) are analyzed from the nodal displacement vector of structural deformation to generate structural deformation data; then, based on the structural stress distribution data and yield strength, the safety margin (yield strength / calculated stress) is calculated, and combined with fatigue damage models (such as the linear cumulative damage rule) to predict the life consumption of each structural unit under cyclic loading, outputting failure probability data (such as the failure probability of a bulkhead after 30 minutes of fire). The above four types of data are packaged into thermo-structure coupling basic data and output to the collaborative control center and data sharing platform.

[0072] The initialization of the thermo-mechanical coupling module (such as loading the material property library and geometric model) is implicitly completed by the boundary conditions in the scenario-based startup command, without the need for a separate initialization command.

[0073] Through the collaborative efforts of the five units described above, this thermo-structure coupling simulation module achieves a one-stop automated conversion from raw ship parameters to a high-confidence physical field. The input interface unit ensures unified access to multiple data sources; the heat source management unit adaptively matches the heat source model with real-world disaster types, avoiding the tediousness and errors of manual switching; the cascade solution of CFD and FEM seamlessly couples heat transfer in the fluid domain with the thermodynamic response in the solid domain. Compared to traditional decoupled calculations (calculating temperature separately and then roughly estimating stress), this module can accurately capture the transient interaction effects of heat flow and structural deformation (e.g., high-temperature softening leading to a decrease in structural stiffness, which in turn exacerbates deformation). The output interface unit extracts features from the raw field data (equivalent stress, deflection, failure probability, etc.) and directly outputs engineering-usable indicators without requiring secondary processing by the user.

[0074] Optionally, the personnel evacuation simulation module specifically includes: An initialization unit is used to respond to the initialization command, load the preset ship cabin layout and personnel distribution data, and complete the initial configuration of the evacuation scenario. The linkage interface unit is used to receive temperature data and visibility data in the thermo-solid coupling basic data through the synchronous call mechanism of the collaborative control center. When the temperature data exceeds a preset temperature threshold or the visibility data is lower than a preset visibility threshold, it is determined to be a dangerous area and triggers path replanning. Dynamic path planning unit, used for The algorithm calculates the initial optimal evacuation path and, based on the high-temperature area diffusion and structural deformation data in the thermo-solid coupling basic data, as well as the dangerous areas determined by the linkage interface unit, triggers a dynamic path selection mechanism to adjust the evacuation path in real time to avoid dangerous areas and congested locations, and generates planned path data. The behavior model fusion unit is used to input the ship cabin layout, personnel distribution data, and planned route data into the social force model to simulate group evacuation behavior, and simultaneously run the personnel behavior model to simulate individual decision-making behavior with different personnel attributes, generating evacuation process data including the real-time location, movement speed, direction selection, and changes in panic state of each individual. The personnel attributes include movement speed and reaction time configured according to age group and panic factor. The output processing unit is used to extract congestion points, high-risk areas, and casualty predictions from the evacuation process data, and package the extracted data into the personnel evacuation simulation data.

[0075] Specifically, the personnel evacuation simulation module consists of an initialization unit, a linkage interface unit, a dynamic path planning unit, a behavior model fusion unit, and an output processing unit working together.

[0076] First, the initialization unit responds to the initialization command issued by the collaborative control center, loading preset ship cabin layout and personnel distribution data. The ship cabin layout is derived from the CAD / BIM model during the ship design phase, describing the static topology of each cabin, including its location and dimensions, passageway width, staircases, and exit distribution. The personnel distribution data is obtained based on passenger registration information or cabin sensor statistics, including the initial position and attributes (age, gender, etc.) of each individual. The initialization unit completes the loading of the above data and scene configuration, laying the foundation for subsequent simulations.

[0077] Secondly, the linkage interface unit receives temperature data (temperature values ​​of structural surfaces and cabin air) and visibility data (the degree of visual obstruction calculated by a CFD smoke model, usually expressed as transmittance or attenuation coefficient) from the thermo-structure coupling basic data in real time through the synchronous call mechanism of the collaborative control center. This unit has built-in preset thresholds (e.g., temperature exceeding 60°C or visibility less than 5 meters). When either condition is triggered, the corresponding area is immediately identified as a danger zone, and a path replanning request is sent to the dynamic path planning unit.

[0078] The dynamic path planning unit first uses The algorithm calculates the initial optimal evacuation path from each person's starting position to the nearest safe exit based on the static ship hull layout. Subsequently, this unit monitors in real-time the diffusion of high-temperature regions (the movement of contour lines in the temperature field over time) and structural deformation data (such as deck deflection and passageway blockage caused by bulkhead buckling) in the thermo-structure coupling foundation data, and, combined with the hazardous areas identified by the linkage interface unit, triggers a dynamic path selection mechanism. This mechanism re-runs the algorithm by updating the node costs of the path network in real-time (setting the cost of nodes in high-temperature regions to infinity and marking nodes with structural deformation as impassable). The algorithm generates a new path after the avoidance, thus obtaining the planned path data.

[0079] The behavior model fusion unit simultaneously runs a social force model and an agent-based personnel behavior model. The social force model simulates the overall macroscopic flow of the crowd, including the mutual repulsion between people, the force of bypassing obstacles, and the driving force towards the exit. The agent-based model endows each individual with independent decision-making capabilities, dynamically adjusting movement strategies based on their attributes (age group determines basic movement speed, gender affects physical strength, and panic factor represents the degree of behavioral disorder) and real-time environmental perception (such as high temperature, low visibility, and congestion on the path ahead). This unit receives ship cabin layout and personnel distribution data provided by the initialization unit, as well as planned path data generated by the dynamic path planning unit. Using both as inputs, iterative calculations generate evacuation process data. This data records the real-time position, movement speed, direction selection, and changes in panic state of each individual at each time step, and is the core intermediate output of the entire module. Specifically, the agent model outputs the desired movement direction and speed for each individual, and the social force model calculates the interaction forces between individuals and between individuals and obstacles based on these, ultimately synthesizing the actual displacement.

[0080] In some embodiments, the social force model equation (personnel evacuation module) is: ;in, The resultant force acting on person i The driving force of the personnel themselves (directed towards the target exit). The interaction forces (including repulsion and attraction) between person i and person j. : The force between person i and obstacle w.

[0081] Finally, the output processing unit extracts key statistical information from the evacuation process data: congestion points (areas where personnel density exceeds a threshold and movement speed approaches zero), high-risk areas (areas with a casualty probability ≥50% based on a comprehensive assessment of temperature, visibility, structural deformation, and personnel density), and casualty predictions (categorized by minor injury, serious injury, and death). This data is packaged into personnel evacuation simulation data and output to the collaborative control center and data sharing platform for use by equipment scheduling and emergency response modules.

[0082] Through the synergy of the above five units, this personnel evacuation simulation module achieves a tight coupling between the static cabin layout and the dynamic environmental field (temperature, visibility, structural deformation), upgrading evacuation path planning from static preset to real-time adaptive. The initialization unit ensures the accurate loading of personalized attributes (age, panic factor); the linkage interface unit enables the module to instantly perceive dangerous areas caused by high temperatures and smoke during a fire and trigger replanning; the dynamic path planning unit utilizes... The algorithm, combined with real-time environmental costs, can complete global path updates within a preset time (e.g., 5 seconds), avoiding structural collapses or areas of high-temperature toxic gas. The behavioral model fusion unit integrates macro-level social forces with micro-level intelligent agent decision-making, ensuring both the overall efficiency of large-scale population evacuation and reflecting the behavioral differences of special individuals such as the elderly, the infirm, and the disabled, making the simulation results closer to reality. The congestion points, high-risk areas, and casualty predictions extracted by the output processing unit directly guide the equipment scheduling module to accurately deploy rescue equipment and provide the emergency response module with the locations of trapped personnel.

[0083] Optionally, the device scheduling module specifically includes: The equipment status tracking unit is used to respond to the initialization command, load and update in real time the location, availability, working status and response time of fire extinguishing equipment, life-saving equipment, communication equipment and ventilation equipment, and generate basic equipment status data. The collaborative interaction unit is used to respond to the collaborative command and generate scheduling constraint data based on the structural damage location and heat source distribution in the thermo-structure coupling basic data, and the congestion area and trapped personnel location in the personnel evacuation simulation data. The scheduling algorithm fusion unit is used to take the basic device status data and the scheduling constraint data as input, run the rule-based scheduling algorithm in basic emergency scenarios, prioritize scheduling neighboring devices to generate an initial scheduling scheme; and run the genetic algorithm in complex scenarios with the optimization objectives of shortest scheduling time, widest coverage, and lowest device loss to iteratively optimize the initial scheduling scheme and generate optimized scheduling scheme data. The output processing unit is used to generate the device scheduling data, which includes device scheduling instructions, activation time and coverage area, based on the optimized scheduling scheme data, the device number and response time in the device status basic data, and the target location in the scheduling constraint data.

[0084] Specifically, the equipment scheduling module consists of an equipment status tracking unit, a collaborative interaction unit, a scheduling algorithm fusion unit, and an output processing unit working together.

[0085] First, the equipment status tracking unit responds to the initialization command issued by the collaborative control center, loads all equipment parameters (including fire extinguishing equipment, life-saving equipment, communication equipment, and ventilation equipment) from the ship's emergency equipment ledger database, and continuously updates the location (cabin and precise coordinates) of each piece of equipment in real time through sensors or manual updates, as well as its availability (normal / faulty / low battery / awaiting maintenance), working status (standby / running / depleted), and response time (standard time required from receiving the command to starting). This unit packages the above information into basic equipment status data, which serves as a static resource pool for subsequent scheduling.

[0086] Secondly, the collaborative interaction unit responds to collaborative commands, extracting structural damage locations (coordinate range of the damaged area) and heat source distribution (contour lines or hotspot coordinates of high-temperature areas) from the thermo-structure coupling basic data, and extracting congested areas (passages or exits where personnel density exceeds a threshold) and trapped personnel locations (precise coordinates of individuals unable to move independently) from the personnel evacuation simulation data. Based on these dynamic constraints, the unit generates scheduling constraint data, including: areas where scheduling is prohibited (such as cabins with temperatures exceeding 80°C), priority coverage target points (the locations of trapped personnel), and congested paths that need to be detoured.

[0087] The scheduling algorithm fusion unit takes basic device status data (resource pool) and scheduling constraint data (constraints) as inputs and adopts a two-level fusion strategy: Basic emergency scenarios (e.g., single equipment type requirement, fewer than 3 constraints): directly run rule-based scheduling algorithms. The rule base pre-sets "proximity priority" principles (e.g., prioritizing scheduling fire extinguishing equipment closest to the location of structural damage) and "type matching" rules (e.g., prioritizing scheduling life-saving equipment at the location of trapped personnel) to quickly generate an initial scheduling plan.

[0088] In complex scenarios (e.g., multiple fire points, multiple trapped personnel, and limited equipment resources): Based on the initial scheduling scheme, a genetic algorithm is initiated for multi-objective optimization. The algorithm's optimization objectives are: minimum scheduling time (minimizing the total time for all equipment to reach the target location from its current position), maximum coverage (ensuring the equipment's operating radius covers as many hotspot areas as possible), and minimum equipment wear (prioritizing the use of reusable equipment to avoid premature depletion of disposable equipment). The genetic algorithm encodes the equipment... The target allocation scheme, through selection, crossover, mutation, and iterative evolution, outputs optimized scheduling scheme data, including the target location, departure order, and estimated operation time of each device.

[0089] Finally, the output processing unit extracts equipment scheduling instructions (such as "Fire hydrant F-12, target engine room B area, activate immediately") from the optimized scheduling scheme data, and combines this with the equipment number and response time in the basic equipment status data and the target location in the scheduling constraint data to generate complete equipment scheduling data. This data includes the activation time (accurate to the second) and coverage area (the area that the equipment's effective radius can protect) for each instruction.

[0090] Through the collaboration of the above four units, this equipment scheduling module, for the first time, couples the ship's dynamic environmental field (structural damage, heat source distribution, personnel congestion and entrapment) with the equipment status in real time, realizing intelligent scheduling from "static contingency plans" to "scenario-driven" scheduling. The equipment status tracking unit ensures the real-time accuracy of the resource pool, avoiding scheduling of faulty or exhausted equipment; the collaborative interaction unit transforms the real-time output of thermo-solid coupling and personnel evacuation into quantified constraints (restricted areas, priority targets, detour routes), ensuring that the scheduling plan remains synchronized with the spread of fire, structural collapse, and population movement. The scheduling algorithm fusion unit balances efficiency and optimality: the rule-based algorithm guarantees immediate response in normal scenarios (delay < 0.5 seconds), while the genetic algorithm improves resource utilization through global optimization in complex scenarios.

[0091] Optionally, the emergency response module specifically includes: The force configuration unit is used to respond to the initialization command, load the parameter models of internal emergency forces, external rescue teams, air rescue forces and medical emergency forces, and generate basic force data. The parameter models include response time, resource capabilities and deployment methods. The response level determination unit is used to respond to the collaborative instruction, obtain equipment operation effect data and scene deterioration index from the equipment scheduling data, and automatically determine the scene level according to the comparison between the equipment operation effect data and the preset threshold and the changing trend of the scene deterioration index, and generate response level data including the current level and upgrade suggestions according to the preset multi-level emergency response mechanism. The collaborative scheduling unit is used to generate emergency force scheduling data, which includes the arrival time of rescue forces, resource allocation schemes, and rescue progress, based on the force base data, the response level data, and the equipment status data.

[0092] Specifically, the emergency response module consists of a force configuration unit, a response level determination unit, and a coordination and dispatch unit working together, and does not involve an output processing unit (this part has been integrated into the output of the coordination and dispatch unit).

[0093] First, the force configuration unit responds to the initialization command issued by the collaborative control center, loading parameter models for three types of emergency forces: internal emergency forces (ship crew emergency teams, deck emergency teams, engine room emergency teams, including response time ≤ 5 minutes, number of personnel and division of labor areas), external rescue teams (maritime rescue vessels, coast guard, pollution prevention emergency teams, including sailing time from the current location to the accident area, firefighting / salvage / pollution prevention capabilities), and air rescue forces (rescue helicopters, drone teams, including takeoff reaction time and the number of casualties that can be transferred at one time). This unit also loads medical emergency forces (onboard medical room personnel and shore support medical teams, including minor / serious injury treatment capabilities). These parameter models are stored in tabular or structured data form and, after initialization, are integrated into basic force data for subsequent judgment and dispatch.

[0094] Secondly, the response level determination unit responds to the coordination command and extracts two types of key information from the equipment scheduling data output by the equipment scheduling module: Equipment operation performance data includes fire control rate of fire extinguishing equipment (e.g., area extinguished / total burned area), personnel rescue rate of life-saving equipment (number of people rescued / number of people trapped), and smoke extraction efficiency of ventilation equipment.

[0095] Scene degradation indicators include rate of change indicators such as temperature rise rate (°C / min), water inflow rate (m³ / min), or structural deformation rate (mm / min), which are calculated in real time from thermo-structure coupling basic data.

[0096] The unit has built-in preset thresholds, such as: "fire control rate below 50%" or "temperature rise rate exceeding 5℃ / min for 3 consecutive minutes" are considered as insufficient equipment performance; "water inflow rate greater than 0.5m" is also considered as insufficient performance. 3 The scenario is considered to have deteriorated if the value of the structural deformation exceeds 2 mm / min or the deformation rate exceeds 2 mm / min. Based on a multi-level emergency response mechanism (Level 1: Internal enterprise emergency; Level 2: Regional coordination; Level 3: National-level rescue), the unit automatically determines the current scenario level: if all indicators are better than the threshold and controllable, Level 1 is maintained; if the operational performance of any equipment is below the threshold or any deterioration indicator exceeds the threshold but is still within the scalable range of internal resources, Level 2 is triggered; if critical equipment is exhausted, the probability of structural failure exceeds a preset value (e.g., 50%), or the predicted number of casualties is greater than the preset number of seriously injured (e.g., 10 people), Level 3 is triggered. The judgment result generates response level data, including the current level (1 / 2 / 3) and upgrade suggestions (e.g., "It is recommended to immediately upgrade to Level 2 and call the nearest maritime vessel").

[0097] Finally, the collaborative scheduling unit receives the basic force data, response level data, and equipment status data (equipment location, availability, and operational effectiveness) parsed from the equipment scheduling data, and executes the following scheduling logic: If the current response is at Level 1, only internal emergency forces will be mobilized, and tasks will be assigned according to the deployment methods in the force base data (e.g., the cabin emergency team is responsible for firefighting, and the deck emergency team is responsible for evacuation), and the arrival time of each force to the designated area will be estimated. If it is a Level II response, based on the internal resources, the system will automatically select the nearest and capable external rescue team (e.g., a maritime vessel that can arrive within 30 minutes) from the resource base data, generate a resource allocation plan (e.g., "Maritime vessel A is responsible for perimeter firefighting, and nearby merchant ship B is responsible for personnel transfer"), and estimate their arrival time. If it is a Level III response, further activate national-level rescue forces (such as rescue helicopters and specialized oil spill recovery vessels), and clarify the coordinated tasks of air and sea in the resource allocation plan (e.g., "helicopters should prioritize the transfer of seriously injured people to shore-based hospitals, and specialized anti-pollution vessels should deploy oil booms").

[0098] The unit also dynamically updates the rescue progress based on the operational effectiveness in the equipment status data (e.g., "60% of the fire has been controlled, and 8 injured people have been transferred"). The final emergency force dispatch data includes all of the above and is output to the collaborative management center and data sharing platform.

[0099] Through the closed-loop collaboration of the three units mentioned above, this emergency response module has achieved an intelligent upgrade from "experience-based judgment" to "data-driven, automatic grading." The force configuration unit ensures that the parameter models of various rescue resources are complete and configurable, adapting to the actual conditions of different ships and sea areas; the response level determination unit, by using quantitative comparisons of equipment operation effects and scenario deterioration indicators, can complete the emergency level determination within 2 seconds, avoiding the delays and subjective biases of traditional manual decision-making; the collaborative dispatch unit automatically matches the corresponding level of forces according to the identified level, and refines the arrival time, task allocation, and progress tracking, enabling seamless connection between internal teams, regional support, and national-level rescue.

[0100] Optionally, the collaborative management and control center integrates a workflow engine and an event bus, and adopts a dual scheduling strategy combining priority scheduling and time-dependent scheduling. The priority scheduling strategy sets the thermo-mechanical coupling simulation module to have the highest priority, while the time-dependent scheduling strategy uses the data readiness status output by the thermo-mechanical coupling simulation module as a necessary condition for triggering the activation of the personnel evacuation simulation module, the equipment scheduling module, and the emergency response module. The workflow engine decomposes the entire simulation process into standardized workflow nodes for automated management, and the event bus is used to receive status events from each module and trigger corresponding scheduling actions in real time.

[0101] Specifically, the collaborative management and control center integrates a workflow engine and an event bus, and adopts a dual strategy that combines priority scheduling and time-dependent scheduling to achieve fine control over the execution order and triggering conditions of multiple modules.

[0102] The workflow engine breaks down a complete shipping safety simulation process into several standardized workflow nodes, such as: Node 1 "Scenario Parameter Loading and Verification", Node 2 "Thermo-Structure Coupling Initialization", Node 3 "Thermo-Structure Coupling Solution Calculation", Node 4 "Thermo-Structure Coupling Data Readiness Verification", Node 5 "Personnel Evacuation Path Planning and Simulation", Node 6 "Equipment Scheduling Scheme Generation", Node 7 "Emergency Force Level Determination and Scheduling", and Node 8 "Simulation Result Summary and Output". Each node includes clearly defined input conditions, execution actions, output data, and status flags. The engine automatically advances according to a preset node topology order (directed acyclic graph), automatically triggering the next node upon completion of the previous one, without manual intervention.

[0103] The event bus, acting as the central internal message channel, monitors and receives status events reported by various functional modules (thermo-mechanical coupling, personnel evacuation, equipment scheduling, and emergency response) in real time, such as "thermo-mechanical coupling calculation completed," "personnel evacuation simulation completed," "equipment scheduling data updated," and "emergency response timed out." Once the event bus captures an event, it immediately pushes it to the workflow engine. The engine determines the next scheduling action (e.g., continue executing the next node, repeat the current node, or jump to the exception handling node) based on the status of the current workflow node and the event type.

[0104] The dual scheduling strategy is as follows: Priority scheduling strategy: The system pre-sets fixed priorities for each functional module, with the thermo-mechanical coupling simulation module having the highest priority, followed by the personnel evacuation simulation module, then the equipment scheduling module, and finally the emergency response module. This strategy is used in resource contention scenarios. For example, when CPU or memory resources on computing nodes are scarce, the collaborative management center prioritizes allocating computing resources to the thermo-mechanical coupling simulation module to ensure that its solution tasks can be completed with the highest priority. Resources are only released to other modules when the thermo-mechanical coupling module does not occupy additional resources.

[0105] Timing-dependent scheduling strategy: This strategy specifies the necessary conditions for module startup. Specifically, the "data ready" status output by the thermo-mechanical coupling simulation module is a necessary condition for triggering the startup of the personnel evacuation simulation module, equipment scheduling module, and emergency response module. The "data ready" status means that after the thermo-mechanical coupling simulation module completes a full CFD-FEM calculation, it submits valid thermo-mechanical coupling basic data (including temperature field, stress field, deformation, and failure probability) to the data sharing platform. After the collaborative management center verifies the data's completeness and the timestamp's validity, it publishes a "thermo-mechanical coupling data ready" event to the event bus. Before this, even if the personnel evacuation module, equipment scheduling module, or emergency response module has received the collaborative instruction, it must remain in a waiting state and cannot begin substantive simulation calculations. Only when the event bus pushes the event to the workflow engine, and the engine confirms the data is ready, will it switch the workflow node status of the downstream modules from "suspended" to "executable," thereby triggering their startup.

[0106] By integrating a workflow engine and an event bus, and implementing a dual scheduling strategy combining priority and timing dependencies, the collaborative management hub of this invention achieves automation, standardization, and controllability of the entire simulation process. The workflow engine breaks down the complex, multi-branch shipping safety simulation process into clearly traceable nodes, eliminating human error in orchestration and state loss issues inherent in traditional script-based serial calls. The event bus enables loosely coupled communication between modules, with module state changes driving subsequent actions in real time, avoiding resource waste and delays caused by polling checks. The priority scheduling strategy ensures that the most time-consuming and critical thermally coupled computations always receive optimal resource treatment, preventing downstream modules from preempting resources and blocking the main computation task. The timing-dependent scheduling strategy uses the "data ready" condition as a lock, ensuring that the thermally coupled data used by all downstream modules at startup is the latest and complete, completely solving the persistent problem of "making decisions based on outdated data" caused by timing discrepancies between modules in traditional systems.

[0107] Optionally, the simulation collaborative digital simulation system for shipping safety further includes: The data sharing platform is used to respond to the initialization command to complete the creation of the data directory and cache initialization of the distributed storage nodes; and to centrally store the thermal-solid coupling basic data, the personnel evacuation simulation data, the equipment scheduling data and the emergency force scheduling data based on the distributed storage architecture, and to provide data interaction interfaces for each module to achieve real-time data synchronization. The scenario adaptation layer is used to build or customize parameter templates and linkage rules for shipping safety scenarios, generate the scenario parameters in response to user operations, and send the scenario parameters to the collaborative control center. The interface adaptation layer is used to build standardized interfaces, access and convert external data in different formats, and provide basic data in a unified format. The human-computer interaction module is used to receive ship parameters imported by the user, display the simulation results summarized by the data sharing platform, and receive control commands from the user. The human-computer interaction module is also used to link with the large visualization screen of the maritime command center to display in real time the entire integrated simulation process, including scene perception, structural analysis, risk warning, personnel evacuation, equipment scheduling and emergency force linkage.

[0108] Optionally, the data sharing platform includes: The storage management unit is used to respond to the initialization command, create a data directory on the distributed storage node, and classify, store and index the thermo-solid coupling basic data, the personnel evacuation simulation data, the equipment scheduling data and the emergency force scheduling data according to the preset simulation round, scenario type and module source, so as to generate a traceable data storage structure. The caching service unit is used to provide real-time caching for the equipment scheduling module, storing the latest versions of the thermo-solid coupling basic data and the personnel evacuation simulation data, and supporting fast reading. The data traceability unit is used to record the data change history of each module in each round of simulation, associate the full-process simulation data of different simulation rounds and different shipping safety scenarios, respond to the query requests of the human-computer interaction module, and generate comparative analysis results.

[0109] Specifically, the support layer of this system consists of a data sharing platform, a scenario adaptation layer, an interface adaptation layer, and a human-computer interaction module. The data sharing platform is further subdivided into a storage management unit, a cache service unit, and a data traceability unit. These components work together to achieve centralized management, rapid exchange, and traceability of data throughout the entire process.

[0110] The scenario adaptation layer, located between the user and the collaborative control center, contains parameter templates and linkage rules for typical shipping safety scenarios such as fire, collision, grounding, and severe weather. Users can select preset scenarios or define new ones through the human-computer interaction module (e.g., inputting cabin layouts for a specific ship type, setting environmental wind speed and wave height, and defining emergency drill scripts). The scenario adaptation layer responds to these user actions by encapsulating scenario types, boundary conditions (such as heat source power curves and collision impact force time histories), and linkage rules (such as "triggering a secondary response when the damaged area > 1㎡") into scenario parameters, and then sending them to the collaborative control center as the basis for subsequent simulation initialization and algorithm execution.

[0111] The interface adaptation layer establishes a standardized set of interfaces (supporting multiple protocols such as RESTful API, OPC UA, and MQTT) for accessing external data in different formats. For example, it can import STEP / IGES format CAD geometric models from ship design systems, read CSV format equipment parameter tables from equipment ledger databases, obtain real-time sea state data in JSON format from meteorological service platforms, and collect measured temperature / stress values ​​in binary stream form from ship sensors. The interface adaptation layer uniformly converts this heterogeneous data into a standardized format recognizable by the system (such as Protocol Buffers or data objects defined by XML Schema), and then provides it to the input interface unit of the thermo-structure coupling simulation module and other modules.

[0112] After receiving the initialization command from the collaborative management and control center, the data sharing platform first completes the preparation of the distributed storage environment. Internally: The storage management unit creates a unique data directory for this simulation round on distributed storage nodes (named according to the rule "scenario type_simulation round_timestamp"), and establishes a classification index according to three dimensions: simulation round (round 1, round 2, etc.), scenario type (fire, collision, etc.), and module source (thermo-structure coupling, personnel evacuation, etc.). For example, the temperature field distribution data output by the thermo-structure coupling module is stored under the path " / round 1 / fire / thermo-structure coupling / temperature / ", and an index entry containing metadata (timestamp, data range, unit) is generated, thus forming a traceable data storage structure.

[0113] The caching service unit, built on Redis or Memcached, provides high-performance real-time caching for the equipment scheduling module. This unit stores the latest versions of basic thermo-mechanical coupling data and personnel evacuation simulation data (e.g., temperature field, stress field, and congestion area at the current time step), and sets a data expiration policy (automatically replacing the data when the next time step arrives). The equipment scheduling module can obtain this latest data within milliseconds through the fast read interface provided by the data sharing platform, without repeatedly querying the distributed storage.

[0114] The data traceability unit records the data change history of each module in each simulation round, including data generation time, version number, input parameters, and output result summary. By linking full-process simulation data from different simulation rounds and different shipping safety scenarios, it allows users to input query conditions through the human-computer interaction module (e.g., "compare the total evacuation time of personnel in the first and third rounds in a fire scenario"), automatically retrieving and generating comparative analysis results (tables, trend charts, error statistics, etc.), facilitating multi-round iterative optimization.

[0115] The human-computer interaction module is deployed on the large visual screen of the maritime command center or the web interface of the ship control terminal, providing the following functions: Receive ship parameters (such as overall length, beam, and cabin layout files) imported by users. The simulation results aggregated by the data sharing platform are displayed in real time, including thermo-mechanical coupling cloud map (temperature gradient, stress distribution), personnel evacuation dynamics (individual trajectory, congestion hotspots), equipment scheduling Gantt chart, emergency force arrival time axis, etc. Receive user control commands (start simulation, pause, reset, adjust parameters, export report); Linked with the large visualization screen of the maritime command center, the simulation process is rendered into a 3D animation in real time, including scene perception (sea conditions around the ship, flame spread), structural analysis (hull deformation heat map), risk warning (red highlighting of areas with a failure probability >50%), personnel evacuation (crowd flow arrows), equipment scheduling (equipment icon movement path), and emergency force linkage (helicopter flight trajectory, rescue ship navigation route), realizing a fully integrated and immersive display of the entire process.

[0116] Through the synergy of the above four supporting components, this invention achieves full lifecycle management and efficient transfer of shipping safety simulation data. The scenario adaptation layer transforms abstract user requirements into machine-executable parameters and rules, enabling the system to flexibly support various shipping safety scenarios without redeveloping code. The interface adaptation layer solves the problem of data format fragmentation in the shipbuilding industry software ecosystem, allowing heterogeneous data sources such as CAD, CAE, and IoT sensors to seamlessly access the system, resulting in strong scalability. The distributed storage and classification indexing mechanism of the data sharing platform solves the management challenges under the explosive growth of simulation data, supporting the rapid writing and retrieval of petabyte-level data. The caching service unit compresses the data access latency of the equipment scheduling module for thermo-mechanical coupling and personnel evacuation data to the millisecond level, ensuring the feasibility of real-time decision-making. The data traceability unit completely records the lineage of each round of simulation, making multi-round iterative optimization possible. Users can clearly compare simulation results under different parameters or strategies, significantly improving the efficiency of solution optimization. The linkage between the human-computer interaction module and the large screen of the maritime command center transforms abstract simulation data into intuitive three-dimensional situation maps, helping commanders quickly understand the ship's status, personnel distribution, and force dynamics in emergency drills or real emergencies, shortening decision-making time.

[0117] In some embodiments, the simulation collaborative digital simulation system for shipping safety adopts a layered distributed deployment architecture, including: an application layer deployed at a maritime command center or ship control terminal; a business collaboration layer deployed on a high-performance server containing the collaborative management and control hub; a functional module layer deployed on independent computing nodes containing the thermo-solid coupling simulation module, personnel evacuation simulation module, equipment scheduling module, and emergency response module; and a support layer deployed on distributed storage nodes or gateway nodes containing the data sharing platform, interface adaptation layer, and scenario adaptation layer. The modules employ a communication mechanism combining synchronous calls and asynchronous messages; core data interaction uses RPC synchronous calls, while non-core data uses MQTT asynchronous messages.

[0118] In some specific embodiments, based on the system architecture and collaborative logic of the simulation collaborative digital simulation system applicable to shipping safety, the following uses a passenger ship collision accident as an implementation case to specifically illustrate the simulation collaboration process: The simulation focuses on a passenger ship with a total length of 150m, a beam of 20m, and five decks, carrying 300 passengers and 20 crew members. The ship is equipped with 10 lifeboats, 20 life rafts, 500 life jackets, 40 sets of fire-fighting equipment, and three emergency crew teams. External maritime support includes two maritime patrol vessels and one rescue helicopter. The ship has a steel structure, and the simulation primarily focuses on the entire process of damage and flooding following a collision on the starboard side of the passenger ship with a cargo ship, including passenger evacuation, equipment deployment, and external rescue coordination.

[0119] Select the "collision" scene through the scene adaptation layer and configure the following key parameters: Collision location: Third-level cabin area on the starboard side of the passenger ship; Collision angle: 30°; Impact force: 500 kN; Emergency response activation threshold: Hull damage area ≥ 0.5m² 2 Personnel attributes: Average moving speed of normal adults is 1.2m / s, elderly / children is 0.8m / s, and injured people are 0.5m / s; External rescue: Maritime patrol boat response time is 10 minutes, and helicopter flight speed is 150km / h.

[0120] Meanwhile, real-time sea state data (wind force 3, wave height 0.8 m) is accessed through the interface adaptation layer to complete the initial parameter loading.

[0121] After receiving the scenario parameters, the collaborative control center generates two types of instructions: Initialization commands: Issued to the data sharing platform (creates the distributed storage directory / collision_round1 / ), personnel evacuation simulation module (loads ship cabin layout and personnel distribution data), equipment scheduling module (loads the emergency equipment parameter library), and emergency force module (loads the parameter models of internal teams and external rescue forces).

[0122] Scenario-based start command: Issued to the thermo-mechanical coupling simulation module, including boundary conditions such as collision angle, impact force, and sea state.

[0123] Each module has completed its self-test and is in normal working order; the system is now ready.

[0124] The thermo-structure coupling simulation module responds to the scenario-based start command and calls the CFD-FEM fusion algorithm to perform structural dynamics and thermodynamic coupling calculations under collision scenarios. It outputs the following basic thermo-structure coupling data in real time: location and area of ​​the damaged region; structural stress distribution (equivalent stress values ​​at key locations); and water inflow rate (water inflow rate per unit time).

[0125] After 5 minutes of simulation, the calculation results showed that the damaged area of ​​the third-level compartment on the starboard side reached 1.2m². 2 (Exceeding the emergency response threshold), the inflow velocity was 0.3m. 3 The stress in the surrounding cabin structure reached 260 MPa per minute (close to the material's yield strength). This data was synchronized in real-time to the collaborative control center and data sharing platform.

[0126] The collaborative control center generates collaborative instructions. The collaborative control center extracted the damaged area S=1.2m from the thermo-structure coupling basic data. 2 The structural stress σ = 260MPa, combined with the collision scene type in the scene parameters, is used to implement a runtime sequence collaboration and scene adaptation fusion algorithm. Determine the urgency coefficient of the scene to be λ=0.7 (collision scene); The time difference Δt between modules was calculated (Δt = 0.8s in this case), and the time-series coordination factor α(t) ≈ 0.86 was obtained. Calculate the scene adaptation weight Ws (in collision scenarios, the weight focuses on the damage area and stress). Substitute into the collision scene linkage rule library (built-in "damaged area > 1.0 m) 2 After stress > 250MPa → high risk, a collaborative instruction is generated, which includes: timing synchronization frequency (0.5s), parameter adjustment range (priority of personnel evacuation is increased, number of lifeboats dispatched is increased), and module activation priority (personnel evacuation module is given priority).

[0127] The instructions were issued to the personnel evacuation simulation module, the equipment dispatch module, and the emergency response module, respectively.

[0128] Personnel evacuation simulation module execution: Based on structural deformation data (determining the starboard passage is impassable), temperature data (collision caused local frictional heating, but this time it did not reach the 60℃ threshold), and visibility data (no smoke) from the thermo-mechanical coupling basic data, combined with the preset cabin layout and personnel distribution, the social force model and intelligent agent model fusion algorithm is run: Plan the optimal evacuation route: avoid the water-intake area on the starboard side and areas with excessive structural stress, and guide personnel to evacuate to the port side compartments and deck lifeguard areas; Different movement speeds and decision preferences are set according to the attributes of the people (elderly, children, injured), and vulnerable groups are given priority to be guided to the nearest exit.

[0129] Real-time output of personnel evacuation simulation data (evacuation routes, pedestrian density at each exit, and predicted congestion points).

[0130] The equipment scheduling module executes the following: It retrieves basic thermo-mechanical coupling data (damage location, damaged area) and personnel evacuation simulation data (congested area, trapped location) from the real-time cache of the data sharing platform. The equipment status tracking unit has been loaded with the status (location, availability, response time) of all fire-fighting, life-saving, communication, and ventilation equipment. The scheduling algorithm fusion unit runs a genetic algorithm in complex scenarios to optimize and generate a scheduling plan with the goal of "shortest scheduling time, widest coverage, and lowest equipment wear": first, schedule 6 lifeboats and 15 life rafts to the evacuation point on the left deck, and at the same time activate emergency broadcasting and cabin waterproofing equipment.

[0131] The module outputs device scheduling data (device number, target location, activation time, coverage area).

[0132] Emergency response module execution: Obtain equipment operation performance data (such as the number of lifeboats deployed) and scenario deterioration indicators (water ingress velocity 0.3 m) from equipment scheduling data. 3 ( / min and continues to rise). The response level determination unit automatically judges according to the three-level emergency response mechanism: due to the damaged area > 1.0 m². 2 And the water inflow velocity exceeds 0.2 m. 3 / min, determined as a Level 2 response (regional coordination), generates response level data (current level = 2, escalation suggestion = none). The coordinated scheduling unit then: Internal resources: The three crew groups are divided into evacuation guidance, equipment operation, and casualty rescue. External forces: dispatching one maritime patrol vessel to the collision area and one rescue helicopter to conduct aerial search and rescue.

[0133] Meanwhile, based on real-time feedback of the accelerated water inflow, the collaborative control center dynamically adjusted collaborative instructions: prioritizing personnel evacuation and instructing the equipment dispatch module to add 2 lifeboats, bringing the total to 8. The emergency response module packaged the updated rescue plan (arrival time, resource allocation, progress tracking) into emergency response dispatch data.

[0134] Simulation results output and verification: The simulation concluded after 40 minutes (all personnel safely evacuated, water ingress controlled). The data sharing platform aggregated data from all modules and generated a comprehensive report. Personnel evacuation: All 320 personnel were evacuated to a safe area (deck lifeguard area + maritime patrol vessel), taking a total of 28 minutes; Equipment dispatch: The dispatch of life-saving equipment took 2.5 minutes and covered all evacuation points; Emergency response teams: Maritime patrol vessels and helicopters arrived at the scene in 18 minutes and 22 minutes respectively, completing aerial search and rescue and personnel transfer with a 100% success rate. Structural status: Flooding of the hull has been effectively controlled, and there has been no secondary damage.

[0135] Effect Verification: Compare the simulation results of this system with the results of a real-world collision emergency drill involving the same passenger ship: The time error for personnel evacuation is 1.0 min (relative error 3.6%, error ≤2% after algorithm correction); Emergency equipment dispatch response time is 0.7 s (meeting the requirement of ≤1 s); The error rate of emergency response team operations was 1.8%. The overall simulation error is 1.6%.

[0136] Compared to existing single-collision simulation systems, this invention improves collaborative efficiency by 72%, shortens the simulation cycle by 48%, and reduces costs by 78%. The simulation process fully replicates the closed loop of "thermo-structure coupling calculation → dynamic personnel evacuation → adaptive equipment scheduling → multi-level linkage of emergency forces," which can effectively support safety training, emergency drills, and optimization of maritime command plans in passenger ship collision scenarios.

[0137] like Figure 2 As shown, this embodiment of the invention provides a collaborative digital simulation method for shipping safety, applied to the collaborative digital simulation system for shipping safety as described above. The collaborative digital simulation method for shipping safety includes: Obtain scene parameters, generate initialization instructions based on the scene parameters to complete the basic parameter configuration and initial state preset of each module in the simulation collaborative digital simulation system applicable to shipping safety, and issue a scene-based start instruction to the thermo-solid coupling simulation module in the simulation collaborative digital simulation system applicable to shipping safety. Based on a preset calculation model, a thermo-mechanical coupling simulation calculation is performed on the preset core load-bearing and protective structure of the ship to generate the thermo-mechanical coupling basic data. Based on the time-series collaboration and scene adaptation fusion algorithm, collaborative instructions are generated according to the thermo-solid coupling basic data and the scene parameters; In response to the cooperative instruction, the following sub-steps are executed: Based on the structural deformation data, temperature data, and visibility data in the aforementioned thermo-solid coupling basic data, as well as the preset ship cabin layout and personnel distribution data, the evacuation path planning and simulation are carried out by running the social force model and the personnel behavior model based on the intelligent agent model, and personnel evacuation simulation data is generated. Based on the structural damage location and temperature distribution in the aforementioned thermo-structure coupling basic data, and the congested areas and trapped personnel locations in the aforementioned personnel evacuation simulation data, an equipment scheduling algorithm that integrates rule-based scheduling and genetic algorithms is run to generate equipment scheduling data. Based on the equipment status data in the equipment scheduling data, emergency force scheduling data is generated according to a preset multi-level emergency response mechanism. A simulation report is generated based on the thermo-solid coupling basic data, the personnel evacuation simulation data, the equipment scheduling data, and the emergency force scheduling data.

[0138] While the present invention has been disclosed above, its scope of protection is not limited thereto. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention, and all such changes and modifications will fall within the scope of protection of the present invention.

Claims

1. A collaborative digital simulation system for shipping safety, characterized in that, include: The collaborative control center is used to acquire scene parameters, generate initialization instructions and scene-based startup instructions based on the scene parameters, and issue the scene-based startup instructions to the thermo-mechanical coupling simulation module; and to receive the thermo-mechanical coupling basic data output by the thermo-mechanical coupling simulation module, and generate collaborative instructions based on the thermo-mechanical coupling basic data and the scene parameters according to the time-series collaborative and scene adaptation fusion algorithm; the initialization instructions are used to preset parameters, reset states, or load initial data for other modules besides the thermo-mechanical coupling module. The thermo-mechanical coupling simulation module is used to respond to the scenario-based start command, and based on the preset calculation model, perform thermo-mechanical coupling simulation calculations on the preset core load-bearing and protection structure of the ship, and generate the thermo-mechanical coupling basic data. The personnel evacuation simulation module is used to respond to the initialization command and the coordination command, and based on the structural deformation data, temperature data and visibility data in the thermo-solid coupling basic data, as well as the preset ship cabin layout and personnel distribution data, it runs the evacuation path planning and simulation by integrating the social force model and the personnel behavior model based on the intelligent agent model, and generates personnel evacuation simulation data. The equipment scheduling module is used to respond to the initialization command and the coordination command, and based on the structural damage location and temperature distribution in the thermo-structure coupling basic data and the congestion area and personnel trapped location in the personnel evacuation simulation data, it runs an equipment scheduling algorithm that integrates rule-based scheduling and genetic algorithm to generate equipment scheduling data. The emergency response module is used to respond to the initialization command and the coordination command, and generate emergency response data based on the equipment status data in the equipment scheduling data and in accordance with a preset multi-level emergency response mechanism.

2. The collaborative digital simulation system for shipping safety according to claim 1, characterized in that, The time-series collaboration and scene adaptation fusion algorithm generates collaboration instructions based on the thermo-solid coupling basic data and the scene parameters, including: The scenario urgency coefficient is determined based on the scenario type in the scenario parameters, and the timing coordination factor is obtained based on the logistic function, the scenario urgency coefficient and the data time difference between modules. The scene adaptation weights are obtained based on the temperature field, structural stress, damaged area, and water or oil spill rate in the thermo-solid coupling basic data. The time-series collaboration factor and the scene adaptation weight are substituted into the scene linkage rule library in the scene parameters, and the collaboration instruction is generated through a preset mapping function. The coordination instructions include the timing synchronization frequency, parameter adjustment range, and module startup priority of each module, which are used to drive the personnel evacuation simulation module, the equipment scheduling module, and the emergency force module to perform dynamic parameter adjustment and linkage response.

3. The collaborative digital simulation system for shipping safety according to claim 1, characterized in that, The thermo-mechanical coupling simulation module specifically includes: The input interface unit is used to receive material properties, geometric models, load conditions, and thermal boundary conditions. The heat source management unit is used to automatically switch and calculate the heat source parameters of fire heat source, collision friction heat source or equipment heat source according to the scene type in the scene parameters. The computational fluid dynamics simulation unit is used to run a computational fluid dynamics model to perform simulation calculations on the fluid domain based on the scenario parameters and the heat source parameters, and generate thermal boundary condition data including heat flux density, convective heat transfer coefficient and ambient temperature distribution. The physical model unit is used to construct the transient heat conduction equation, the thermal stress model based on thermoelastic theory, and the structural mechanics finite element model. In response to the scenario-based start command, the material properties, the load conditions, and the thermal boundary conditions are input into the transient heat conduction equation for processing to obtain the solid domain temperature field distribution. The solid domain temperature field distribution is then input into the thermal stress model and the structural mechanics finite element model to obtain the solid domain stress field distribution and structural deformation. The output interface unit is used to perform spatial interpolation and data extraction at different times and cross sections of the solid domain temperature field to generate temperature field distribution data; extract principal stress and shear stress tensors from the solid domain stress field, and perform equivalent stress calculation and threshold comparison based on the yield strength in the material properties to generate structural stress distribution data; analyze the structural deformation, extract the overall structural deflection and local deformation from the nodal displacement vectors to generate structural deformation data; calculate the safety margin of each structural unit based on the structural stress distribution data and the yield strength in the material properties, by using the ratio of yield strength to calculated stress, and generate failure probability data for each structural unit in combination with a preset fatigue damage model; and package the temperature field distribution data, structural stress distribution data, structural deformation data, and structural failure probability data into the thermo-structure coupling basic data.

4. The collaborative digital simulation system for shipping safety according to claim 1, characterized in that, The personnel evacuation simulation module specifically includes: An initialization unit is used to respond to the initialization command, load the preset ship cabin layout and personnel distribution data, and complete the initial configuration of the evacuation scenario. The linkage interface unit is used to receive temperature data and visibility data in the thermo-solid coupling basic data through the synchronous call mechanism of the collaborative control center. When the temperature data exceeds a preset temperature threshold or the visibility data is lower than a preset visibility threshold, it is determined to be a dangerous area and triggers path replanning. Dynamic path planning unit, used for The algorithm calculates the initial optimal evacuation path and, based on the high-temperature area diffusion and structural deformation data in the thermo-solid coupling basic data, as well as the dangerous areas determined by the linkage interface unit, triggers a dynamic path selection mechanism to adjust the evacuation path in real time to avoid dangerous areas and congested locations, and generates planned path data. The behavior model fusion unit is used to input the ship cabin layout, personnel distribution data, and planned route data into the social force model to simulate group evacuation behavior, and simultaneously run the personnel behavior model to simulate individual decision-making behavior with different personnel attributes, generating evacuation process data including the real-time location, movement speed, direction selection, and changes in panic state of each individual. The personnel attributes include movement speed and reaction time configured according to age group and panic factor. The output processing unit is used to extract congestion points, high-risk areas, and casualty predictions from the evacuation process data, and package the extracted data into the personnel evacuation simulation data.

5. The collaborative digital simulation system for shipping safety according to claim 1, characterized in that, The equipment scheduling module specifically includes: The equipment status tracking unit is used to respond to the initialization command, load and update in real time the location, availability, working status and response time of fire extinguishing equipment, life-saving equipment, communication equipment and ventilation equipment, and generate basic equipment status data. The collaborative interaction unit is used to respond to the collaborative command and generate scheduling constraint data based on the structural damage location and heat source distribution in the thermo-structure coupling basic data, and the congestion area and trapped personnel location in the personnel evacuation simulation data. The scheduling algorithm fusion unit is used to take the basic device status data and the scheduling constraint data as input, run the rule-based scheduling algorithm in basic emergency scenarios, prioritize scheduling neighboring devices to generate an initial scheduling scheme; and run the genetic algorithm in complex scenarios with the optimization objectives of shortest scheduling time, widest coverage, and lowest device loss to iteratively optimize the initial scheduling scheme and generate optimized scheduling scheme data. The output processing unit is used to generate the device scheduling data, which includes device scheduling instructions, activation time and coverage area, based on the optimized scheduling scheme data, the device number and response time in the device status basic data, and the target location in the scheduling constraint data.

6. The collaborative digital simulation system for shipping safety according to claim 1, characterized in that, The emergency response module specifically includes: The force configuration unit is used to respond to the initialization command, load the parameter models of internal emergency forces, external rescue teams, air rescue forces and medical emergency forces, and generate basic force data. The parameter models include response time, resource capabilities and deployment methods. The response level determination unit is used to respond to the collaborative instruction, obtain equipment operation effect data and scene deterioration index from the equipment scheduling data, and automatically determine the scene level according to the comparison between the equipment operation effect data and the preset threshold and the changing trend of the scene deterioration index, and generate response level data including the current level and upgrade suggestions according to the preset multi-level emergency response mechanism. The collaborative scheduling unit is used to generate emergency force scheduling data, which includes the arrival time of rescue forces, resource allocation schemes, and rescue progress, based on the force base data, the response level data, and the equipment status data.

7. The collaborative digital simulation system for shipping safety according to claim 1, characterized in that, The collaborative management and control hub integrates a workflow engine and an event bus, and adopts a dual scheduling strategy combining priority scheduling and time-dependent scheduling. The priority scheduling strategy sets the thermo-mechanical coupling simulation module as the highest priority, while the time-dependent scheduling strategy uses the data readiness status output by the thermo-mechanical coupling simulation module as a necessary condition for triggering the activation of the personnel evacuation simulation module, the equipment scheduling module, and the emergency response module. The workflow engine decomposes the entire simulation process into standardized workflow nodes for automated management, and the event bus receives status events from each module and triggers corresponding scheduling actions in real time.

8. The collaborative digital simulation system for shipping safety according to claim 1, characterized in that, Also includes: The data sharing platform is used to respond to the initialization command to complete the creation of the data directory and cache initialization of the distributed storage nodes; Furthermore, the system centrally stores the thermal-solid coupling basic data, the personnel evacuation simulation data, the equipment scheduling data, and the emergency force scheduling data based on a distributed storage architecture, and provides data interaction interfaces for each module to achieve real-time data synchronization. The scenario adaptation layer is used to build or customize parameter templates and linkage rules for shipping safety scenarios, generate the scenario parameters in response to user operations, and send the scenario parameters to the collaborative control center. The interface adaptation layer is used to build standardized interfaces, access and convert external data in different formats, and provide basic data in a unified format. The human-computer interaction module is used to receive ship parameters imported by the user, display the simulation results summarized by the data sharing platform, and receive control commands from the user. The human-computer interaction module is also used to link with the large visualization screen of the maritime command center to display in real time the entire integrated simulation process, including scene perception, structural analysis, risk warning, personnel evacuation, equipment scheduling and emergency force linkage.

9. The collaborative digital simulation system for shipping safety according to claim 8, characterized in that, The data sharing platform includes: The storage management unit is used to respond to the initialization command, create a data directory on the distributed storage node, and classify, store and index the thermo-solid coupling basic data, the personnel evacuation simulation data, the equipment scheduling data and the emergency force scheduling data according to the preset simulation round, scenario type and module source, so as to generate a traceable data storage structure. The caching service unit is used to provide real-time caching for the equipment scheduling module, storing the latest versions of the thermo-solid coupling basic data and the personnel evacuation simulation data, and supporting fast reading. The data traceability unit is used to record the data change history of each module in each round of simulation, associate the full-process simulation data of different simulation rounds and different shipping safety scenarios, respond to the query requests of the human-computer interaction module, and generate comparative analysis results.

10. A collaborative digital simulation method for shipping safety, characterized in that, The collaborative digital simulation system for shipping safety, as described in any one of claims 1 to 9, comprises the following methods: Obtain scene parameters, generate initialization instructions based on the scene parameters to complete the basic parameter configuration and initial state preset of each module in the simulation collaborative digital simulation system applicable to shipping safety, and issue a scene-based start instruction to the thermo-solid coupling simulation module in the simulation collaborative digital simulation system applicable to shipping safety. Based on a preset calculation model, a thermo-mechanical coupling simulation calculation is performed on the preset core load-bearing and protective structure of the ship to generate the thermo-mechanical coupling basic data. Based on the time-series collaboration and scene adaptation fusion algorithm, collaborative instructions are generated according to the thermo-solid coupling basic data and the scene parameters; In response to the cooperative instruction, the following sub-steps are executed: Based on the structural deformation data, temperature data, and visibility data in the aforementioned thermo-solid coupling basic data, as well as the preset ship cabin layout and personnel distribution data, the evacuation path planning and simulation are carried out by running the social force model and the personnel behavior model based on the intelligent agent model, and personnel evacuation simulation data is generated. Based on the structural damage location and temperature distribution in the aforementioned thermo-structure coupling basic data, and the congested areas and trapped personnel locations in the aforementioned personnel evacuation simulation data, an equipment scheduling algorithm that integrates rule-based scheduling and genetic algorithms is run to generate equipment scheduling data. Based on the equipment status data in the equipment scheduling data, emergency force scheduling data is generated according to a preset multi-level emergency response mechanism. A simulation report is generated based on the thermo-solid coupling basic data, the personnel evacuation simulation data, the equipment scheduling data, and the emergency force scheduling data.

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