A lifeboat passenger safety risk visualization method and system based on digital twinning

Abstract

This invention provides a method and system for visualizing the safety risks of lifeboat occupants based on digital twins, including: collecting and verifying basic parameters related to the lifeboat release process; constructing a lifeboat gliding-free fall motion model based on theoretical mechanics, and obtaining the initial parameters for water entry at any time before water entry by solving the motion equations through numerical integration; constructing a fluid-structure coupled numerical model, and obtaining the water entry process parameters by solving the transient fluid dynamic equations through the finite volume method; constructing a 1:1 three-dimensional virtual scene with the physical entity, integrating the initial water entry parameters and the water entry process parameters, achieving motion synchronization between the virtual model and the physical entity through coordinate transformation, and achieving virtual-real linkage by combining dynamic environment rendering; calculating the absolute acceleration of each seat in the lifeboat, classifying the risk level based on the combined acceleration response as the core indicator, and realizing the visualization of the safety risks of lifeboat occupants in the high-fidelity three-dimensional virtual scene through color gradient mapping and 3D UI marking.

Description

Technical Field

[0001] This invention relates to the fields of ship safety, digital twins, and numerical simulation visualization technology, and more particularly to a method and system for visualizing the safety risks of lifeboat occupants based on digital twins. Background Technology

[0002] As a core piece of equipment for emergency evacuation from ships, the safety of lifeboat deployment is directly related to the lives of the crew. According to the SOLAS Convention, ships must conduct regular lifeboat deployment training and equipment testing. However, the lifeboat deployment process involves complex hydrodynamic processes, mechanical motion coupling, and the influence of personnel posture, making it highly susceptible to safety accidents. High-speed water entry impact and collisions between the lifeboat hull and the davit can all lead to crew injuries or fatalities.

[0003] Existing lifeboat risk analysis methods have many shortcomings: First, risk assessments are mostly based on statistical reports or two-dimensional charts, which cannot intuitively present the dynamic distribution and evolution of risks in three-dimensional space, making it difficult to accurately identify high-risk areas; Second, during training, it is impossible to predict the risk evolution trend under different sea conditions, operational errors, and other scenarios in advance, resulting in insufficient training relevance; Third, although traditional numerical simulation methods can calculate key parameters such as hydrodynamics, the results are abstract and require professional interpretation, making it difficult to transform them into intuitive risk information for crew members or managers to quickly understand.

[0004] Digital twins, Unity 3D visualization, and the finite volume method (FVM) have been gradually applied in the shipbuilding industry. However, for the visualization of risks during lifeboat descent, current methods only rely on virtual simulation or single numerical calculations, failing to form a complete technological chain encompassing "accurate numerical solutions, intuitive visualization, and virtual-real data linkage." This fails to meet the precision requirements of ship emergency safety management. Therefore, there is an urgent need for a method and system that integrates digital twins, dynamic calculations, fluid numerical simulations, and 3D visualization to visualize the safety risks of lifeboat occupants and fill the existing technological gap. Summary of the Invention

[0005] To address the aforementioned technical problems of existing lifeboat risk assessments being unintuitive, lacking integration of numerical calculations and visualization, and insufficient virtual-real interaction, this invention provides a method and system for visualizing lifeboat occupant safety risks based on digital twins. This invention primarily utilizes the Unity 3D engine to construct a lifeboat gliding-freefall motion model to calculate initial water entry parameters. It employs Star CCM+ software to construct a fluid-structure coupling numerical model based on the finite volume method to calculate water entry process parameters. Data interaction between the two ends is achieved via TCP / IP protocol. Combining the rigid body acceleration composition theorem and combined acceleration response, occupant risks are quantified and classified into five levels. Risk visualization is achieved through color gradient overlay and 3D UI markings. This results in precise control and intuitive display of risks throughout the entire lifeboat descent process, support for dynamic prediction in multiple scenarios, and improved safety of ship rescue systems.

[0006] The technical means employed in this invention are as follows:

[0007] A method for visualizing lifeboat occupant safety risks based on digital twins includes: S1. Collect and verify basic parameters related to the lifeboat release process, including ship parameters, lifeboat structural parameters, sea state parameters, and release operation parameters; S2. Construct a lifeboat gliding-free fall motion model based on theoretical mechanics, input the basic parameters and solve the motion equations through numerical integration to obtain the initial parameters of water entry at any time before water entry; S3. Construct a fluid-structure coupled numerical model, input the initial parameters of water entry into the fluid-structure coupled numerical model, and solve the transient fluid dynamic equations using the finite volume method to obtain the parameters of the water entry process; S4. Construct a high-fidelity 3D virtual scene with a 1:1 ratio to the physical entity, integrate the initial parameters and process parameters of water entry, realize the motion synchronization between the virtual model and the physical entity through coordinate transformation, and achieve virtual-real linkage by combining dynamic environment rendering. S5. Calculate the absolute acceleration of each seat in the lifeboat based on the initial water entry parameters and water entry process parameters. Use the combined acceleration response as the core indicator to classify the risk level. Visualize the safety risks of lifeboat occupants in the high-fidelity three-dimensional virtual scene through color gradient mapping and 3D UI marking.

[0008] Further, step S1 includes: S11. Collect ship parameters, including ship draft and hull height; S12. Collect lifeboat structural parameters, including hull dimensions, weight, moment of inertia and seat layout parameters, where the seat layout parameters include the position vector of each seat relative to the lifeboat's center of gravity; S13. Collect sea state parameters, including wind speed, wave height, and period; S14. Collect release operation parameters, including initial release height, slide angle and friction coefficient; S15. Verify the validity of the collected ship parameters, lifeboat structural parameters, sea state parameters, and release operation parameters to ensure that the parameter values ​​are within the physically reasonable range, remove abnormal data, and prompt the user to make corrections.

[0009] Further, step S2 includes: S21. Treating the lifeboat as a rigid body, select multiple generalized coordinates to completely describe the spatial position and attitude of the lifeboat. Define the generalized coordinates as follows:

[0010] in, Represents a generalized coordinate vector; These represent the rectangular coordinates of the lifeboat's center of gravity in the inertial coordinate system; These represent the lifeboats orbiting in an inertial coordinate system. Euler angles of the axis; S22. Based on external force analysis, the generalized force vector is derived, and the generalized force model is constructed as follows:

[0011] in, Represents a generalized force vector; Indicates the generalized force in the direction of translation. Indicates the generalized force in the orientation direction; S23. The dynamic equations are constructed using the Lagrange equations as follows:

[0012] in, Represent the Lagrange function; Represents time; the Lagrange function satisfies ,in Indicates the kinetic energy of the lifeboat. This represents the potential energy of the lifeboat; S24. Based on the dynamic equation, input the collected basic parameters, and obtain the hull velocity, acceleration, angular velocity, angular acceleration, position coordinates and attitude angles of the hull in the inertial coordinate system at each moment through numerical integration.

[0013] Further, step S3 includes: S31. When it is determined that the lifeboat has entered the water entry stage, the initial water entry parameters are received, including the lifeboat's speed, acceleration, angular velocity, angular acceleration, position coordinates, attitude angle and sea state parameters at the time of water entry (wave height, wave period, wind speed). S32. Import a 1:1 three-dimensional geometric model of the lifeboat entity. Based on the spatial position in the received initial water entry parameters, define the computational domain range of the water entry process. Discretize using a polyhedral mesh. Refine the mesh on the hull surface and key areas around the crew. Use overlapping mesh technology to achieve dynamic nesting of the background area and the moving area. Perform multi-level volume refinement on the free liquid surface fluctuation area and the hull movement path. At the same time, strictly control the size transition between the background mesh and the overlapping mesh at the interface. S33. Solving the flow field based on the three-dimensional unsteady Reynolds-averaged Navier-Stokes equations, defining the fluid medium as an incompressible water-air two-phase flow, using the fluid volume method to capture the free liquid surface, and discretizing the convection terms in the volume fraction transport equation using a high-resolution interface capture scheme, wherein: The Reynolds-averaged Navier-Stokes equations are expressed as follows:

[0014]

[0015] in, and They represent and The average velocity component in the direction, ; Indicates the fluid dynamic viscosity coefficient; Indicates fluid density; Indicates average pressure; Represents fluid volume forces; Represents the Reynolds stress term; In the fluid volume method, it is assumed that and Let be the volume of the liquid and the volume of the mesh element, respectively, and the volume fraction of the liquid be defined as:

[0016] Among them, when At that time, the unit is a pure liquid phase. At that time, it is a pure gas phase. At this time, it is a gas-liquid two-phase mixing interface; S34. The numerical solution uses a separate flow solver, the pressure-velocity coupling uses the SIMPLE algorithm, the convection term of the momentum equation uses the second-order upwind scheme, the gradient calculation uses the Gaussian mixture-least squares method, and the Venkatakrishnan limiter is introduced to suppress numerical oscillations near the shock wave. The time-progression uses an implicit unsteady solver, a fixed time step is set, the maximum physical time is set as the stopping condition, and multiple internal iterations are performed within each time step to obtain the hull velocity, acceleration, angular velocity, angular acceleration, position coordinates and attitude angles at different times during the water entry process as parameters of the water entry process. S35. Organize the calculated water inlet process parameters into a time series and associate them with the corresponding timestamps.

[0017] Further, step S4 includes: S41. Use 3D modeling tools to construct a high-precision 3D twin model that matches the physical ship, lifeboat, and crew at a 1:1 scale. Import the constructed 3D twin model into the Unity 3D engine to complete model optimization and texture mapping. S42. Construct a dynamic environment and lighting system in the Unity 3D engine that is consistent with the physical ocean and sky. Based on the input physical parameters of the sea state, use the simulated wave undulations and wind speed of Unity 3D to drive the real environment and achieve real-time synchronization between the physical sea state and the virtual scene. S43. The Unity 3D engine integrates the initial water entry parameters and the water entry process parameters to form motion data for the entire lifeboat release process; S44. To achieve virtual model-driven operation, the generalized coordinates obtained from the solution are transformed into model parameters that can be recognized by the Unity engine through a transformation matrix.

[0018] Further, step S44 includes: S441. Map the centroid rectangular coordinates to the Unity model position using a homogeneous translation matrix. The expression for the translation matrix is:

[0019] in, Represents a homogeneous translation matrix; Represents the rectangular coordinates of the lifeboat's center of mass in the inertial coordinate system; S442, Press The rotation order involves converting Euler angles into rotation matrices, then converting them into quaternions and assigning them to the model's pose. The expression for the rotation matrix is:

[0020] in, Represents the total rotation matrix; They represent circumference respectively. A single-axis rotation matrix; Both represent Euler angles.

[0021] Further, step S5 includes: S51. Calculate the risk value based on the seat acceleration data during the water entry process. Calculate the absolute acceleration of each seat based on the rigid body acceleration composition theorem, using the following formula:

[0022] in, This represents the absolute acceleration of a single seat. This indicates the acceleration of the lifeboat's center of gravity; This represents the angular acceleration of the lifeboat. This indicates the angular velocity of the lifeboat; This represents the position vector of the seat relative to the center of gravity of the lifeboat; S52. Calculate the combined acceleration response. The calculation formula is as follows:

[0023] in, Indicates the combined acceleration response; , , These represent the absolute acceleration of the seat, respectively. Components in the three axes of the seat coordinate system; , , These represent the acceleration limits for the corresponding axes; S53. Using combined acceleration response as the core indicator for risk assessment, the risk is divided into 5 levels, among which, when At that time, the risk was classified as Level 1, which is risk-free; when At that time, the risk was classified as a minor risk level 2; when At that time, the risk was classified into three levels of moderate risk; when At that time, the risk was classified into four levels of high risk; when At that time, the risk was classified into five levels of extreme risk; S54. Map the risk level to visual rendering parameters in the 3D scene, using a color gradient mapping rule, where no risk level 1 corresponds to green, slight risk level 2 corresponds to light blue, moderate risk level 3 corresponds to yellow, high risk level 4 corresponds to orange, and extreme risk level 5 corresponds to red. S55. A dual display method is adopted, which combines color overlay of the seating area with 3D UI markers above the character's head. A long strip of 3D UI marker is created above the head of the character model in each seat. S56 provides the function of switching between first-person perspective (crew perspective), third-person perspective (global perspective), and partial close-up perspective (seat area, water entry area), and supports zooming and panning of the view. S57. By clicking on any seat area in the 3D scene with the mouse, the acceleration value of that seat and the corresponding risk level are displayed, providing a risk information query function.

[0024] This invention also provides a lifeboat occupant safety risk visualization system based on the aforementioned lifeboat occupant safety risk visualization method, comprising: a data input module, a Unity 3D motion parameter calculation module, an FVM numerical calculation module, a Unity 3D three-dimensional scene construction and rendering module, and a risk visualization interaction module. Each module constructs a precise mapping between physical entities and virtual models based on digital twin technology, collaboratively forming a technical chain of physical parameter acquisition, virtual model driving, motion parameter interaction, risk numerical calculation, and visualization display, wherein: The data input module is used to collect and verify basic parameters related to the lifeboat release process, including ship parameters, lifeboat structural parameters, sea state parameters, and release operation parameters. The Unity 3D motion parameter calculation module is integrated into the Unity 3D engine and is used to construct a lifeboat gliding-free fall motion model based on theoretical mechanics. By inputting the basic parameters and solving the motion equations through numerical integration, the initial parameters for entering the water at any time before entry can be obtained. The FVM numerical calculation module is used to construct a fluid-structure coupled numerical model using Star CCM+ software, input the initial parameters of water entry into the fluid-structure coupled numerical model, solve the transient fluid dynamic equations using the finite volume method, obtain the parameters of the water entry process, and send them back to Unity 3D. The Unity 3D three-dimensional scene construction and rendering module is used to construct a high-fidelity three-dimensional virtual scene with a 1:1 ratio to the physical entity, integrate the initial parameters and process parameters of entering the water, realize the motion synchronization between the virtual model and the physical entity through coordinate transformation, and realize the linkage between the virtual and real worlds by combining dynamic environment rendering. The risk visualization and interaction module is used to calculate the absolute acceleration of each seat in the lifeboat based on the initial water entry parameters and water entry process parameters using Unity 3D UI components. It uses the combined acceleration response as the core indicator to classify the risk level and realizes the visualization display of the safety risks of lifeboat occupants in the high-fidelity three-dimensional virtual scene through color gradient mapping and 3D UI marking.

[0025] Furthermore, the data interaction between the FVM numerical calculation module and Unity 3D adopts the TCP / IP protocol, and the data encapsulation format is JSON, which includes interaction type, timestamp, parameter data, unit and CRC32 check value fields. The data transmission buffer size is 1024KB, and the number of retransmissions does not exceed 3.

[0026] Compared with the prior art, the present invention has the following advantages: 1. This invention uses the Unity 3D motion parameter calculation module to accurately solve the motion parameters of the pre-entry gliding-freefall phase, while the FVM numerical calculation module focuses on multi-parameter calculations under fluid coupling during the entry phase. The two work together to achieve full coverage of motion parameters throughout the entire release process. At the same time, by leveraging the 3D rendering capabilities of the Unity 3D engine, the abstract motion parameters and risk levels are transformed into a color-gradient visual scene, making the risk distribution and evolution process intuitive and perceptible. This significantly reduces the difficulty of interpreting professional data and facilitates crew and management personnel in quickly identifying high-risk areas.

[0027] 2. This invention can flexibly collect basic data under different sea conditions, operating parameters, and ship parameters through the data input module. Combined with the high-precision solution capability of the FVM numerical calculation module, it can dynamically simulate the evolution trend of lifeboat release risks under different scenarios, identify high-risk operation links and working conditions in advance, provide targeted simulation scenarios for lifesaving training, reduce safety hazards in actual training, and improve the effectiveness of training.

[0028] 3. This invention uses a 3D interactive scene built with Unity 3D to support free switching between first-person, third-person, and close-up perspectives. It also allows for precise querying of risk information via mouse clicks. Crew members can become familiar with safety operating procedures under different risk scenarios through an immersive experience. Managers can optimize safety management systems based on the risk assessment data output by the system, providing data support for the improvement of lifeboat equipment and the standardization of operating procedures, thereby comprehensively improving the overall safety of the ship's lifesaving system.

[0029] 4. The system of this invention constructs a 1:1 accurate mapping between physical entities and virtual models based on digital twin technology. It achieves motion synchronization between virtual models and physical entities through coordinate transformation and realizes real-time synchronization of environmental parameters by combining dynamic environment rendering. The FVM numerical calculation module and Unity 3D use the TCP / IP protocol for data interaction. With the help of CRC32 check, fixed buffer and retransmission mechanism, the integrity and stability of data transmission are ensured, providing reliable data guarantee for virtual-real linkage.

[0030] In summary, the technical solution of this invention solves the problems of existing technologies where risk assessment is mostly based on statistical reports or two-dimensional charts, failing to intuitively present the dynamic distribution and evolution of risks in three-dimensional space, and making it difficult to accurately identify high-risk areas; it also solves the problem of insufficient training relevance due to the inability to predict risk evolution trends under different sea conditions and operational errors during training; and it addresses the problem that traditional numerical simulation methods produce abstract calculation results that require professional interpretation and are difficult to translate into intuitive risk information for crew or management personnel to quickly understand. Therefore, the technical solution of this invention solves the problems of unintuitive risk display, disconnect between numerical calculation and visualization, and insufficient virtual-real linkage in existing technologies.

[0031] Based on the above reasons, this invention can be widely applied in fields such as ship safety, digital twins, numerical simulation visualization, and risk assessment of lifesaving equipment. Attached Figure Description

[0032] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0033] Figure 1 This is a flowchart of the method of the present invention.

[0034] Figure 2 This is a block diagram showing the system modules and their interrelationships of the present invention.

[0035] Figure 3 This is a flowchart of the FVM numerical calculation module of the present invention.

[0036] Figure 4 This is a diagram illustrating the construction effect of the computational domain and encryption region of the FVM numerical calculation module of this invention.

[0037] Figure 5 This is a flowchart illustrating the data interaction process between Star CCM+ and Unity 3D in this invention.

[0038] Figure 6 This is a block diagram of the Unity 3D digital twin three-dimensional scene of the present invention.

[0039] Figure 7 This is a visualization of the risks associated with the present invention. Detailed Implementation

[0040] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0041] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0042] This embodiment takes the freefall lifeboat landing process of a cargo ship as an example, constructs a virtual-real linkage scenario based on the concept of digital twin, and illustrates the specific application process of the system of the present invention, realizing the synchronous linkage between physical lifeboat landing and virtual simulation and risk visualization.

[0043] like Figure 1 As shown, this invention provides a method for visualizing the safety risks of lifeboat occupants based on digital twins, including: S1. Collect and verify basic parameters related to the lifeboat release process, including ship parameters, lifeboat structural parameters, sea state parameters, and release operation parameters; S2. Construct a lifeboat gliding-free fall motion model based on theoretical mechanics, input the basic parameters and solve the motion equations through numerical integration to obtain the initial parameters of water entry at any time before water entry; S3. Construct a fluid-structure coupled numerical model, input the initial parameters of water entry into the fluid-structure coupled numerical model, and solve the transient fluid dynamic equations using the finite volume method to obtain the parameters of the water entry process; S4. Construct a high-fidelity 3D virtual scene with a 1:1 ratio to the physical entity, integrate the initial parameters and process parameters of water entry, realize the motion synchronization between the virtual model and the physical entity through coordinate transformation, and achieve virtual-real linkage by combining dynamic environment rendering. S5. Calculate the absolute acceleration of each seat in the lifeboat based on the initial water entry parameters and water entry process parameters. Use the combined acceleration response as the core indicator to classify the risk level. Visualize the safety risks of lifeboat occupants in the high-fidelity three-dimensional virtual scene through color gradient mapping and 3D UI marking.

[0044] In a specific implementation, as a preferred embodiment of the present invention, step S1 includes: S11. Collect ship parameters, including ship draft and hull height; in this embodiment, the ship draft is 5.2m and the hull height is 15.5m. S12. Collect lifeboat structural parameters, including hull dimensions, weight, moment of inertia, and seat layout parameters. The seat layout parameters include the position vectors of each seat relative to the lifeboat's center of gravity. In this embodiment, the lifeboat has a length of 7.4m, a width of 2.6m, a height of 2.3m, a weight of 6200kg, and moments of inertia of [missing information]. =12000 , =15000 , =18000 The seating layout is 7 rows and 28 seats (4 seats per row, with the seat's position relative to the center of gravity vector as shown on the left side of the front row at (3.2, 1.0, 0.0) m). S13. Collect sea state parameters, including wind speed, wave height, and period; in this embodiment, the wind speed is 3.5 m / s, the wave height is 1.2 m, and the wave period is 8.0 s. S14. Collect release operation parameters, including initial release height, slide inclination angle and friction coefficient; in this embodiment, the initial release height is 12.95m, the slide inclination angle is 30° and the friction coefficient is 0.15.

[0045] S15. Verify the validity of the collected ship parameters, lifeboat structural parameters, sea state parameters, and release operation parameters to ensure that the parameter values ​​are within the physically reasonable range, remove abnormal data, and prompt the user to make corrections.

[0046] In a preferred embodiment of the present invention, step S2 involves creating a C# script (LifeboatDynamics.cs) in Unity and attaching it to the lifeboat 3D model object. The specific process includes: S21. Treating the lifeboat as a rigid body, select multiple generalized coordinates to completely describe the spatial position and attitude of the lifeboat. Define the generalized coordinates as follows:

[0047] in, Represents a generalized coordinate vector; These represent the rectangular coordinates of the lifeboat's center of gravity in the inertial coordinate system; These represent the lifeboats orbiting in an inertial coordinate system. Euler angles of the axis; S22. Based on external force analysis, the generalized force vector is derived, and the generalized force model is constructed as follows:

[0048] in, Represents a generalized force vector; Indicates the generalized force in the direction of translation. Represents the generalized force in the orientation direction; where force and torque are determined by gravity ( , (Lifeboat weight), slide support force (calculated based on slide inclination angle), and friction force (coefficient of friction) The force is obtained by combining the supporting force and the aerodynamic force (calculated based on wind speed and the windward area of ​​the hull); S23. The dynamic equations are constructed using the Lagrange equations as follows:

[0049] in, Represent the Lagrange function; Represents time; the Lagrange function satisfies ,in Indicates the kinetic energy of the lifeboat. This represents the potential energy of the lifeboat; S24. Based on the aforementioned dynamic equations, the collected fundamental parameters are input, and the hull velocity, acceleration, angular velocity, angular acceleration, position coordinates, and attitude angles in the inertial coordinate system at each time step are obtained through numerical integration. In this embodiment, the Runge-Kutta method is used to achieve numerical integration, outputting the values ​​at each time step. Update the value and store the result in a List. <motiondata>The collection (MotionData is a custom structure containing parameters such as timestamp, velocity, and acceleration).

[0050] In a specific implementation, as a preferred embodiment of the present invention, step S3 includes: S31. When it is determined that the lifeboat has entered the water entry stage, the initial water entry parameters are received, including the lifeboat's speed, acceleration, angular velocity, angular acceleration, position coordinates, attitude angle and sea state parameters at the time of water entry (wave height, wave period, wind speed). S32. Import a 1:1 three-dimensional geometric model of the lifeboat into the Star CCM+ software and set its parameters to be consistent with the lifeboat's structural parameters. Based on the spatial position in the received initial water entry parameters, define the computational domain range for the water entry process. Discretize using a polyhedral mesh, and refine the mesh for the hull surface and key areas around the occupants. Use overlapping mesh technology to achieve dynamic nesting of the background and motion regions. Perform multi-level volume refinement on the free surface ripple zone and the hull's motion path. At the same time, strictly control the dimensional transition at the interface between the background mesh and the overlapping mesh to ensure interpolation accuracy, effectively guaranteeing a balance between computational accuracy and efficiency. In this embodiment, the computational domain is based on the length of the lifeboat. L To establish a baseline for scaling, the background water area size is set to 11.5. L× 4 L× 8 L The overlapping grid area encloses the hull, and the size of the overlapping grid area is 1.5. L× 0.75 L× 0.8 L The base size of the overlapping mesh region is 0.25m, and the base size of the background water area is 2m. Mesh generation utilizes a cut-volume mesh generator to produce a core mesh primarily composed of hexahedrons. To accurately capture the nonlinear deformation of the free surface and the impact splashing phenomenon upon water entry, multi-level volumetric refinement was applied to the free surface undulation region, including surface refinement, local refinement in the air domain, local refinement in the water area, one layer of refinement near the free surface, two layers of refinement near the free surface, one layer of refinement far from the free surface, and two layers of refinement far from the free surface. Simultaneously, the dimensional transition between the background mesh and the overlapping mesh at the interface was strictly controlled to ensure interpolation accuracy, effectively guaranteeing a balance between computational accuracy and efficiency.

[0051] S33. Solving the flow field based on the three-dimensional unsteady Reynolds-averaged Navier-Stokes equations, defining the fluid medium as an incompressible water-air two-phase flow, using the fluid volume method to capture the free liquid surface, and discretizing the convection terms in the volume fraction transport equation using a high-resolution interface capture scheme, wherein: The Reynolds-averaged Navier-Stokes equations are expressed as follows:

[0052]

[0053] in, and They represent and The average velocity component in the direction, ; Indicates the fluid dynamic viscosity coefficient; Indicates fluid density; Indicates average pressure; Represents fluid volume forces; The Reynolds stress term is used to represent the equation. In this embodiment, the introduction of the Reynolds stress term makes the RANS equation unclosed. To close the equation, the SST proposed by Menter is adopted. The model combines The stability of the model in regions far from the wall and The model's advantage lies in capturing turbulence characteristics in the near-wall region, enabling it to efficiently and accurately predict turbulence characteristics in both the near-wall and far-field regions.

[0054] In the fluid volume method, it is assumed that and Let be the volume of the liquid and the volume of the mesh element, respectively, and the volume fraction of the liquid be defined as:

[0055] Among them, when At that time, the unit is a pure liquid phase. At that time, it is a pure gas phase. At this point, the interface is a gas-liquid two-phase mixture. In this embodiment, the flow field solution is based on the three-dimensional unsteady Reynolds-averaged Navier-Stokes equations. The fluid medium is defined as an incompressible water-gas two-phase flow. The free liquid surface is captured using the fluid volume method. The convection terms in the volume fraction transport equation are discretized using a high-resolution interface capture scheme. The turbulence model selected is SST. Model, in conjunction with the whole The wall treatment enables precise analysis of the boundary layer flow and fluid separation phenomena that occur dramatically upon the lifeboat's entry into the water. The left, top, and bottom of the background water area are designated as velocity inlets, the right side as a pressure outlet, and the front and rear as walls. The overlapping grid area moves with the lifeboat, and all six sides of the overlapping grid area are set as overlapping grids. The surface of the lifeboat is set as the wall.

[0056] S34. The dynamic fluid-multibody interaction (DFBI) module is used to couple the flow field with the rigid body motion. The numerical solution employs a separated flow solver, and the pressure-velocity coupling uses the SIMPLE algorithm. For spatial discretization, the convection term of the momentum equation uses a second-order upwind scheme, and the gradient calculation uses a Gaussian mixture-least-squares method. A Venkatakrishnan limiter is introduced to suppress numerical oscillations near the shock wave. Time progression uses an implicit unsteady-state solver with a fixed time step. Five internal iterations are performed within each time step, and the maximum physical time is set as the stopping condition. Finally, the hull velocity, acceleration, angular velocity, angular acceleration, position coordinates and attitude angles at different times during the water entry process are obtained as parameters for the water entry process. S35. Organize the calculated water inlet process parameters into a time series and associate them with the corresponding timestamps.

[0057] In a specific implementation, as a preferred embodiment of the present invention, step S4 includes: S41. Using 3D modeling tools, construct high-precision 3D twin models that match the physical ship, lifeboats, and occupants at a 1:1 scale. Import the constructed 3D twin models into the Unity 3D engine to complete model optimization and texture mapping. In this embodiment, the lifeboat model fully replicates the physical structural details such as seats and hull frames. The ship's 3D twin model replicates details such as decks, hull frame installation positions, and skid structures. The lifeboat's 3D twin model fully restores components such as the hull, seats, seat belts, and release mechanisms, and the texture mapping uses high-definition materials processed from real-life photos. The occupant virtual human 3D twin model uses a standard human body model, 1.75m tall and weighing 70kg, and is equipped with skeletal animation (supporting sitting postures, slight swaying, etc.). Export the constructed 3D models as FBX format and import them into the Unity 3D engine to complete the adaptation of the model with the Unity physics engine.

[0058] S42. Construct a dynamic environment and lighting system consistent with the physical ocean and sky in the Unity 3D engine. Based on the input sea state physical parameters, simulate the effects of wave undulations and wind speed in Unity 3D to drive the real environment, achieving real-time synchronization between the physical sea state and the virtual scene. In this embodiment, import the Unity Ocean Render plugin, create an Ocean object in the scene, and associate the sea state parameters (wind speed controls the intensity of wave undulations, wave height controls the wave amplitude, and wave period controls the wave frequency) with the parameters collected by the data input module. Sky 3D twin model: Set Skybox to Unity's default "Clear Sky", create a Directional Light to simulate sunlight, set the light intensity to 1.2, and the shadow type to Soft Shadows; enable real-time lighting baking to bake the scene lightmap.

[0059] S43. The Unity 3D engine integrates the initial water entry parameters and the water entry process parameters to form motion data for the entire lifeboat release process. In this embodiment, the initial water entry parameters are the parameters at the moment of water entry during the gliding-free fall phase before water entry, and the water entry process parameters are the parameters at each moment during the fluid-structure coupling phase after water entry. S44. To achieve virtual model-driven operation, the generalized coordinates obtained from the solution are transformed into model parameters that can be recognized by the Unity engine through a transformation matrix.

[0060] In a specific implementation, as a preferred embodiment of the present invention, step S44 includes: S441. Motion parameter integration and coordinate transformation, motion synchronization before water entry: From List <motiondata>Motion parameters are read according to timestamps, and the centroid Cartesian coordinates are mapped to the Unity model position using a homogeneous translation matrix. The expression for the translation matrix is:

[0061] in, Represents a homogeneous translation matrix; This represents the rectangular coordinates of the lifeboat's center of mass in the inertial coordinate system. In this embodiment, the core logic used is the left multiplication of a matrix with a homogeneous coordinate vector, specifically: using the homogeneous vector of the lifeboat model's initial position. For operands, Preset reference coordinates for the Unity scene, by Obtain the transformed vector Extract the first three components and assign them to the model's transform.position to complete the dynamic position update.

[0062] S442, Lifeboat Model Rotation: Press The rotation order involves converting Euler angles into rotation matrices, then converting them into quaternions and assigning them to the model's pose. The expression for the rotation matrix is:

[0063] in, Represents the total rotation matrix; They represent circumference respectively. A single-axis rotation matrix; All represent Euler angles. In this embodiment, when using it, first press... Sequential left multiplication of a single-axis matrix yields Then, the matrix is ​​converted into a quaternion using the Unity 3D built-in function Quaternion.FromRotationMatrix(R), and assigned to the model transform.rotation to avoid gimbal lock issues and achieve precise pose synchronization.

[0064] S443. Water Entry Motion Synchronization: Receive water entry parameters from Star CCM+ and update the lifeboat's position and attitude in time-stamp order according to the translation and rotation methods described above, achieving virtual-real linkage. Further fluid effect linkage is achieved by adding a particle system to the bottom of the lifeboat to simulate water splashing effects; the particle emission rate is positively correlated with the transmitted speed parameters.

[0065] In a specific implementation, as a preferred embodiment of the present invention, step S5 includes: S51. Calculate the risk value based on the seat acceleration data during the water entry process. Calculate the absolute acceleration of each seat based on the rigid body acceleration composition theorem, using the following formula:

[0066] in, This represents the absolute acceleration of a single seat. This indicates the acceleration of the lifeboat's center of gravity; This represents the angular acceleration of the lifeboat. This indicates the angular velocity of the lifeboat; This represents the position vector of the seat relative to the center of gravity of the lifeboat; S52. Calculate the combined acceleration response. The calculation formula is as follows:

[0067] in, Indicates the combined acceleration response; , , These represent the absolute acceleration of the seat, respectively. Components in the three axes of the seat coordinate system; , , These represent the acceleration limits for the corresponding axes. In this embodiment, it is preferred that... 15 g , 7 g , g It is the acceleration due to gravity; S53. Using combined acceleration response as the core indicator for risk assessment, the risk is divided into 5 levels, among which, when At that time, the risk was classified as Level 1, which is risk-free; when At that time, the risk was classified as a minor risk level 2; when At that time, the risk was classified into three levels of moderate risk; when At that time, the risk was classified into four levels of high risk; when At that time, the risk was classified into five levels of extreme risk; S54. Map the risk level to visual rendering parameters in the 3D scene, using a color gradient mapping rule, where no risk level 1 corresponds to green, slight risk level 2 corresponds to light blue, moderate risk level 3 corresponds to yellow, high risk level 4 corresponds to orange, and extreme risk level 5 corresponds to red. S55. A dual display method is adopted, using color overlay of the seating area and 3D UI markers above the character's head. A long strip of 3D UI marker is created above the head of the character model in each seat. In this embodiment, the specific creation process is as follows: S551. Create a new World Space mode Canvas in Unity and set the Canvas's Render Mode to "World Space" to ensure that the UI size matches the scale of the 3D scene. Use this Canvas as a child object of the lifeboat's 3D model and inherit the lifeboat's motion state to avoid the UI from becoming disconnected from the scene.

[0068] S552. Create a new Image component under Canvas as the main body of the long strip UI. Ensure that the bottom of the UI is aligned with the top bone of the character's head. Add rounded corner components to the UI to avoid sharp edges. At the same time, remove the "Raycast Target" property of the Image component to prevent it from obscuring scene interaction operations.

[0069] S553. The Image component is used as a child object of the corresponding occupant model's head skeleton node. The offset is finely adjusted through the Position property of RectTransform so that the UI is located directly above the head and does not obscure the person's head. After binding, the UI will move synchronously with the head rotation and body posture changes of the person model, always maintaining a fixed relative position with the occupant.

[0070] S554. Material and Rendering Settings: A dedicated material is assigned to the elongated UI, using Unity's default UI / Default material. The "Specular" property of the material is disabled to avoid reflections affecting the visual effect. The material's Render Queue is set to "Transparent," and the Alpha value is adjusted to 0.9 (balancing recognizability and transparency) to ensure the UI does not obscure scene details below. The color control of the elongated UI is dynamically driven by Unity scripts. The core adjustment parameters and logic are as follows: The core control component is the "Color" property of the Image component. A risk level listener function is added to the script. When the seat acceleration parameters are updated, the corresponding risk level is automatically determined, and the Color.Lerp function is called to achieve a smooth color transition, avoiding visual abruptness caused by sudden color changes. This ensures that the UI color and seat risk level are synchronized in real-time and accurately, allowing for quick identification of the risk status of each occupant from different perspectives. In this embodiment, a dedicated material is created for each seat model, using the Standard material shader. A color control script is written to dynamically modify the material color according to the risk level (e.g., level 1: ...). , Level 5: A smooth color transition is achieved using the `Color.Lerp` function. A 3D UI is created. In Unity, a World Space mode Canvas is created, with the Canvas scaling factor set to 0 and Render Mode to World Space. An Image component is created under the Canvas, its size is set, and a Round Corners component is added, with the Raycast Target property disabled. The Canvas is then used as a child object of the occupant model's head skeleton, and the Rect Transform position is adjusted to (0, 0.1m, 0) to ensure the UI is positioned directly above the head. A UI color control script is written to monitor changes in seat risk level and synchronously modify the "Color" property of the Image component, with the Alpha value fixed at 0.9. The specific correspondence is as follows: Level 1 risk (green): Level 2 risk (light blue): Level 3 risk (yellow): Level 4 risk (orange): Level 5 risk (red): .

[0071] S56. Provides switching functions for first-person perspective (occupant perspective), third-person perspective (global perspective), and partial close-up perspective (seat area, water entry area), and supports perspective zooming and panning; in this embodiment, a perspective control script is created and keyboard shortcuts are bound (F1: first-person perspective, F2: third-person perspective, F3: partial close-up perspective); the first-person perspective is implemented by setting the camera as a sub-object of the occupant model, the third-person perspective uses a free camera (supports W, A, S, D keys for movement, and mouse wheel zoom), and the partial close-up perspective focuses on the seat area.

[0072] S57. By clicking on any seat area in the 3D scene with the mouse, the acceleration value and corresponding risk level of that seat are displayed, providing a risk information query function. In this embodiment, a view control script is created and keyboard shortcuts are bound (F1: first view, F2: third view, F3: close-up view). The first view is implemented by setting the camera as a sub-object of the occupant model, the third view uses a free camera (supporting W, A, S, D keys for movement and mouse wheel zoom), and the close-up view focuses on the seat area.

[0073] This invention also provides a lifeboat occupant safety risk visualization system based on the aforementioned lifeboat occupant safety risk visualization method. The system includes: a data input module, a Unity 3D motion parameter calculation module, an FVM numerical calculation module, a Unity 3D 3D scene construction and rendering module, and a risk visualization interaction module. Each module uses digital twin technology to construct a precise mapping between physical entities and virtual models, collaboratively forming a technical chain of physical parameter acquisition, virtual model driving, motion parameter interaction, risk numerical calculation, and visualization display. In this embodiment, during the pre-entry stage of the lifeboat from the slide to freefall, the motion parameters are obtained by the Unity 3D motion parameter calculation module based on the dynamic model and physical parameters. During the entry stage, Unity 3D transmits the initial entry parameters to the FVM numerical calculation module, which calculates the entry process parameters and sends them back to Unity 3D, achieving real-time linkage between the virtual scene and the physical conditions. Specifically: The data input module is used to collect and verify basic parameters related to the lifeboat release process, including ship parameters, lifeboat structural parameters, sea state parameters, and release operation parameters. The Unity 3D motion parameter calculation module is integrated into the Unity 3D engine and is used to construct a lifeboat gliding-free fall motion model based on theoretical mechanics. By inputting the basic parameters and solving the motion equations through numerical integration, the initial parameters for entering the water at any time before entry can be obtained. The FVM numerical calculation module is used to construct a fluid-structure coupled numerical model using Star CCM+ software. The initial parameters for water entry are input into the fluid-structure coupled numerical model, and the transient fluid dynamic equations are solved using the finite volume method to obtain the parameters for the water entry process, which are then transmitted back to Unity 3D. In this embodiment, the FVM numerical calculation module sets the parameters collected by the data input module (release operation parameters, ship parameters, lifeboat structural parameters, and sea state parameters) as initial values. It solves the motion equations through numerical integration to obtain the hull velocity, acceleration, angular velocity, angular acceleration, position coordinates, and attitude angles at any time before water entry, and sends these to the Unity 3D three-dimensional scene construction and rendering module to move the lifeboat model and calculate the risk to the lifeboat crew.

[0074] The Unity 3D three-dimensional scene construction and rendering module is used to construct a high-fidelity three-dimensional virtual scene with a 1:1 ratio to the physical entity, integrate the initial parameters and process parameters of entering the water, realize the motion synchronization between the virtual model and the physical entity through coordinate transformation, and realize the linkage between the virtual and real worlds by combining dynamic environment rendering. The risk visualization and interaction module is used to calculate the absolute acceleration of each seat in the lifeboat based on the initial water entry parameters and water entry process parameters using Unity 3D UI components. It uses the combined acceleration response as the core indicator to classify the risk level and realizes the visualization display of the safety risks of lifeboat occupants in the high-fidelity three-dimensional virtual scene through color gradient mapping and 3D UI marking.

[0075] In a preferred embodiment of this invention, the data interaction between the FVM numerical calculation module and Unity 3D uses the TCP / IP protocol, with data encapsulation format in JSON format. This includes fields for interaction type, timestamp, parameter data, units, and CRC32 checksum. The data transmission buffer size is 1024KB, and the number of retransmissions does not exceed 3. In this embodiment, the TCP / IP server parameters are set in Star CCM+ (IP: 192.168.1.100, port: 8080, data buffer 1024KB), the preset key "Lifeboat_Simulation_2024" is input, the CRC32 checksum function is enabled, and the data cache path is set to D: / StarCCM_Cache / . On the Unity side, a Socket client is written using C# scripts, calling the Socket class under the System.Net.Sockets namespace. The server IP and port are set to be consistent with the server, the connection timeout is 5 seconds, the automatic reconnection interval is 1 second, and the number of reconnections is 3. After calculating the initial water entry parameters, Unity 3D sends a connection request to the Star CCM+ server via a client Socket, carrying authentication information (a preset key "Lifeboat_Simulation_2024"). Upon successful verification by Star CCM+, a connection is established and a confirmation command is returned. Unity3D encapsulates the processed initial water entry parameters in a preset JSON format, including "interaction type (upload / download)," "timestamp," "velocity," "acceleration," "angular velocity," "angular acceleration," "position coordinates," "attitude angle," "wave height," "wave period," and "wind speed." This data is then sent to Star CCM+ through the established TCP connection. Star CCM+ receives the data, parses the JSON, and verifies the completeness and validity of the fields. If any fields are missing or abnormal, a retransmission command is returned; otherwise, a successful reception command is returned. After Star CCM+ completes the single-timestep calculation, it encapsulates the parameters for that moment in JSON format, including fields such as "interaction type (upload / download)," "timestamp," "velocity," "acceleration," "angular velocity," "angular acceleration," "position coordinates," and "attitude angle." This data is then sent to Unity 3D via a server-side socket. Alternatively, in batch transmission mode, after completing the full water entry process calculation, all parameters can be integrated into a JSON array based on the time sequence and transmitted back to Unity 3D all at once. Once Star CCM+ has completed the calculation and transmission of all water entry process parameters, it sends a "data transmission complete" command. Unity 3D receives and parses all the data, returns a confirmation command, and then both parties actively close the socket connection, releasing communication resources.Specifically, this embodiment provides several JSON data formats as follows: (1) Unity uploads the initial water parameters in JSON format to Star CCM+ json { "Interaction Type": "Upload" "Authentication Key": "Lifeboat_Simulation_2024", "Timestamp": "2024-05-20 14:30:00.000", "speed": { "x": 4.2, "y": 0.1, "z": 0.3, Unit: m / s }, "Acceleration": { "x": 2.1, "y": 0.05, "z": 1.2, Unit: m / s² }, Angular velocity: { "x": 0.02, "y": 0.01, "z": 0.03, Unit: rad / s }, Angular acceleration: { "x": 0.005, "y": 0.002, "z": 0.008, Unit: rad / s² }, "Location coordinates": { "x": 10.0, "y": 0.0, "z": 0.5, Unit: m }, "Attitude Angle": { "x": 0.01, "y": 0.03, "z": 0.02, Unit: rad }, "wave height": 1.2, Wave height unit: "m", Wave period: 6.0 "wave period unit": "s", Wind speed: { "x": 5.0, "y": 1.5, "z": 0.2, Unit: m / s }, CRC32 checksum: "6A3F8D2C" } (2) Star CCM+ sends water process parameters back to Unity 3D in JSON format (single time step). json { "Interaction Type": "Download" "Timestamp": "2024-05-20 14:30:00.020", "speed": { "x": 4.5, "y": 0.12, "z": 0.25, Unit: m / s }, "Acceleration": { "x": 3.2, "y": 0.08, "z": 1.5, Unit: m / s² }, Angular velocity: { "x": 0.025, "y": 0.012, "z": 0.035, Unit: rad / s }, Angular acceleration: { "x": 0.006, "y": 0.003, "z": 0.009, Unit: rad / s² }, "Location coordinates": { "x": 10.2, "y": 0.1, "z": 0.3, Unit: m }, "Attitude Angle": { "x": 0.012, "y": 0.032, "z": 0.022, Unit: rad }, CRC32 checksum: "9E7B2A1D" } (3) Star CCM+ batch back water flow process parameters in JSON format (multi-time step array) json { "Interaction Type": "Download" Data Description: Includes parameters for the entire time step from t=1.0s to t=3.0s during the water ingress process. "Time step interval": 0.02, "Time step unit": "s", "Water inlet process parameter array": [ { "Timestamp": "2024-05-20 14:30:00.020", "speed": { "x": 4.5, "y": 0.12, "z": 0.25, Unit: m / s }, "Acceleration": { "x": 3.2, "y": 0.08, "z": 1.5, Unit: m / s² }, Angular velocity: { "x": 0.025, "y": 0.012, "z": 0.035, Unit: rad / s }, Angular acceleration: { "x": 0.006, "y": 0.003, "z": 0.009, Unit: rad / s² }, "Location coordinates": { "x": 10.2, "y": 0.1, "z": 0.3, Unit: m }, "Attitude Angle": { "x": 0.012, "y": 0.032, "z": 0.022, Unit: rad } }, { "Timestamp": "2024-05-20 14:30:00.040", "speed": { "x": 5.1, "y": 0.15, "z": 0.3, Unit: m / s }, "Acceleration": { "x": 4.8, "y": 0.1, "z": 1.8, Unit: m / s² }, Angular velocity: { "x": 0.03, "y": 0.015, "z": 0.04, Unit: rad / s }, Angular acceleration: { "x": 0.008, "y": 0.004, "z": 0.012, Unit: rad / s² }, "Location coordinates": { "x": 10.5, "y": 0.15, "z": 0.2, Unit: m }, "Attitude Angle": { "x": 0.015, "y": 0.035, "z": 0.025, Unit: rad } } ], CRC32 checksum: 3F8A7D2B } Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.< / motiondata> < / motiondata>

Claims

1. A lifeboat occupant safety risk visualization method based on digital twinning, characterized in that, include: S1. Collect and verify basic parameters related to the lifeboat release process, including ship parameters, lifeboat structural parameters, sea state parameters, and release operation parameters; S2. Construct a lifeboat gliding-free fall motion model based on theoretical mechanics, input the basic parameters and solve the motion equations through numerical integration to obtain the initial parameters of water entry at any time before water entry; S3. Construct a fluid-structure coupled numerical model, input the initial parameters of water entry into the fluid-structure coupled numerical model, and solve the transient fluid dynamic equations using the finite volume method to obtain the parameters of the water entry process; S4. Construct a high-fidelity 3D virtual scene with a 1:1 ratio to the physical entity, integrate the initial parameters and process parameters of water entry, realize the motion synchronization between the virtual model and the physical entity through coordinate transformation, and achieve virtual-real linkage by combining dynamic environment rendering. S5. Calculate the absolute acceleration of each seat in the lifeboat based on the initial water entry parameters and water entry process parameters. Use the combined acceleration response as the core indicator to classify the risk level. Visualize the safety risks of lifeboat occupants in the high-fidelity three-dimensional virtual scene through color gradient mapping and 3D UI marking.

2. The method for visualizing the safety risks of lifeboat occupants based on digital twins according to claim 1, characterized in that, Step S1 includes: S11. Collect ship parameters, including ship draft and hull height; S12. Collect lifeboat structural parameters, including hull dimensions, weight, moment of inertia and seat layout parameters, where the seat layout parameters include the position vector of each seat relative to the lifeboat's center of gravity; S13. Collect sea state parameters, including wind speed, wave height, and period; S14. Collect release operation parameters, including initial release height, slide angle and friction coefficient; S15. Verify the validity of the collected ship parameters, lifeboat structural parameters, sea state parameters, and release operation parameters to ensure that the parameter values ​​are within the physically reasonable range, remove abnormal data, and prompt the user to make corrections.

3. The method for visualizing the safety risks of lifeboat occupants based on digital twins according to claim 1, characterized in that, Step S2 includes: S21. Treating the lifeboat as a rigid body, select multiple generalized coordinates to completely describe the spatial position and attitude of the lifeboat. Define the generalized coordinates as follows: in, Represents a generalized coordinate vector; These represent the rectangular coordinates of the lifeboat's center of gravity in the inertial coordinate system; These represent the lifeboats orbiting in an inertial coordinate system. Euler angles of the axis; S22. Based on external force analysis, the generalized force vector is derived, and the generalized force model is constructed as follows: in, Represents a generalized force vector; Indicates the generalized force in the direction of translation. Indicates the generalized force in the orientation direction; S23. The dynamic equations are constructed using the Lagrange equations as follows: in, Represent the Lagrange function; Represents time; the Lagrange function satisfies ,in Indicates the kinetic energy of the lifeboat. This represents the potential energy of the lifeboat; S24. Based on the dynamic equation, input the collected basic parameters, and obtain the hull velocity, acceleration, angular velocity, angular acceleration, position coordinates and attitude angles of the hull in the inertial coordinate system at each moment through numerical integration.

4. The method for visualizing the safety risks of lifeboat occupants based on digital twins according to claim 1, characterized in that, Step S3 includes: S31. When it is determined that the lifeboat has entered the water entry stage, the initial water entry parameters are received, including the lifeboat's speed, acceleration, angular velocity, angular acceleration, position coordinates, attitude angle and sea state parameters at the time of water entry. S32. Import a 1:1 three-dimensional geometric model of the lifeboat entity. Based on the spatial position in the received initial water entry parameters, define the computational domain range of the water entry process. Discretize using a polyhedral mesh. Refine the mesh on the hull surface and key areas around the crew. Use overlapping mesh technology to achieve dynamic nesting of the background area and the moving area. Perform multi-level volume refinement on the free liquid surface fluctuation area and the hull movement path. At the same time, strictly control the size transition between the background mesh and the overlapping mesh at the interface. S33. Solving the flow field based on the three-dimensional unsteady Reynolds-averaged Navier-Stokes equations, defining the fluid medium as an incompressible water-air two-phase flow, using the fluid volume method to capture the free liquid surface, and discretizing the convection terms in the volume fraction transport equation using a high-resolution interface capture scheme, wherein: The Reynolds-averaged Navier-Stokes equations are expressed as follows: in, and They represent and The average velocity component in the direction, ; Indicates the fluid dynamic viscosity coefficient; Indicates fluid density; Indicates average pressure; Represents fluid volume forces; Represents the Reynolds stress term; In the fluid volume method, it is assumed that and Let be the volume of the liquid and the volume of the mesh element, respectively, and the volume fraction of the liquid is defined as: Among them, when At that time, the unit is a pure liquid phase. At that time, it is a pure gas phase. At this time, it is a gas-liquid two-phase mixing interface; S34. The numerical solution uses a separate flow solver, the pressure-velocity coupling uses the SIMPLE algorithm, the convection term of the momentum equation uses the second-order upwind scheme, the gradient calculation uses the Gaussian mixture-least squares method, and the Venkatakrishnan limiter is introduced to suppress numerical oscillations near the shock wave. The time-progression uses an implicit unsteady solver, a fixed time step is set, the maximum physical time is set as the stopping condition, and multiple internal iterations are performed within each time step to obtain the hull velocity, acceleration, angular velocity, angular acceleration, position coordinates and attitude angles at different times during the water entry process as parameters of the water entry process. S35. Organize the calculated water inlet process parameters into a time series and associate them with the corresponding timestamps.

5. The method for visualizing the safety risks of lifeboat occupants based on digital twins according to claim 1, characterized in that, Step S4 includes: S41. Use 3D modeling tools to construct a high-precision 3D twin model that matches the physical ship, lifeboat, and crew at a 1:1 scale. Import the constructed 3D twin model into the Unity 3D engine to complete model optimization and texture mapping. S42. Construct a dynamic environment and lighting system in the Unity 3D engine that is consistent with the physical ocean and sky. Based on the input physical parameters of the sea state, use the simulated wave undulations and wind speed of Unity 3D to drive the real environment and achieve real-time synchronization between the physical sea state and the virtual scene. S43, Unity 3D engine integrates the initial water entry parameters and the water entry process parameters to form the motion data of the entire lifeboat release process; S44. To achieve virtual model-driven operation, the generalized coordinates obtained from the solution are transformed into model parameters that can be recognized by the Unity engine through a transformation matrix.

6. The method for visualizing the safety risks of lifeboat occupants based on digital twins according to claim 1, characterized in that, Step S44 includes: S441. Map the centroid rectangular coordinates to the Unity model position using a homogeneous translation matrix. The expression for the translation matrix is: in, Represents a homogeneous translation matrix; Represents the rectangular coordinates of the lifeboat's center of mass in the inertial coordinate system; S442, Press The rotation order involves converting Euler angles into rotation matrices, then converting them into quaternions and assigning them to the model's pose. The expression for the rotation matrix is: in, Represents the total rotation matrix; They represent circumference respectively. A single-axis rotation matrix; Both represent Euler angles.

7. The method for visualizing the safety risks of lifeboat occupants based on digital twins according to claim 1, characterized in that, Step S5 includes: S51. Calculate the risk value based on the seat acceleration data during the water entry process. Calculate the absolute acceleration of each seat based on the rigid body acceleration composition theorem, using the following formula: in, Represents the absolute acceleration of a single seat; This indicates the acceleration of the lifeboat's center of gravity; This represents the angular acceleration of the lifeboat. This indicates the angular velocity of the lifeboat; This represents the position vector of the seat relative to the center of gravity of the lifeboat; S52. Calculate the combined acceleration response. The calculation formula is as follows: in, Indicates the combined acceleration response; , , These represent the absolute acceleration of the seat, respectively. Components in the three axes of the seat coordinate system; , , These represent the acceleration limits for the corresponding axes; S53. Using combined acceleration response as the core indicator for risk assessment, the risk is divided into 5 levels, among which, when At that time, the risk was classified as Level 1, which is risk-free; when At that time, the risk was classified as a minor risk level 2; when At that time, the risk was classified into three levels of moderate risk; when At that time, the risk was classified into four levels of high risk; when At that time, the risk was divided into five levels of extreme risk; S54. Map the risk level to visual rendering parameters in the 3D scene, using a color gradient mapping rule, where no risk level 1 corresponds to green, slight risk level 2 corresponds to light blue, moderate risk level 3 corresponds to yellow, high risk level 4 corresponds to orange, and extreme risk level 5 corresponds to red. S55. A dual display method is adopted, which combines color overlay of the seating area with 3D UI markers above the character's head. A long strip of 3D UI marker is created above the head of the character model in each seat. S56 provides the function of switching between first-person view, third-person view, and close-up view, and supports view zooming and panning; S57. By clicking on any seat area in the 3D scene with the mouse, the acceleration value of that seat and the corresponding risk level are displayed, providing a risk information query function.

8. A lifeboat occupant safety risk visualization system based on the lifeboat occupant safety risk visualization method according to any one of claims 1-7, characterized in that, include: The system comprises a data input module, a Unity 3D motion parameter calculation module, an FVM numerical calculation module, a Unity 3D 3D scene construction and rendering module, and a risk visualization and interaction module. Each module utilizes digital twin technology to construct a precise mapping between physical entities and virtual models, collaboratively forming a technical chain encompassing physical parameter acquisition, virtual model driving, motion parameter interaction, risk numerical calculation, and visualization. The data input module is used to collect and verify basic parameters related to the lifeboat release process, including ship parameters, lifeboat structural parameters, sea state parameters, and release operation parameters. The Unity 3D motion parameter calculation module is integrated into the Unity 3D engine and is used to construct a lifeboat gliding-free fall motion model based on theoretical mechanics. By inputting the basic parameters and solving the motion equations through numerical integration, the initial parameters for entering the water at any time before entry can be obtained. The FVM numerical calculation module is used to construct a fluid-structure coupled numerical model using Star CCM+ software, input the initial parameters of water entry into the fluid-structure coupled numerical model, solve the transient fluid dynamic equations using the finite volume method, obtain the parameters of the water entry process, and send them back to Unity 3D. The Unity 3D three-dimensional scene construction and rendering module is used to construct a high-fidelity three-dimensional virtual scene with a 1:1 ratio to the physical entity, integrate the initial parameters and process parameters of entering the water, realize the motion synchronization between the virtual model and the physical entity through coordinate transformation, and realize the linkage between the virtual and real worlds by combining dynamic environment rendering. The risk visualization and interaction module is used to calculate the absolute acceleration of each seat in the lifeboat based on the initial water entry parameters and water entry process parameters using Unity 3D UI components. It uses the combined acceleration response as the core indicator to classify the risk level and realizes the visualization display of the safety risks of lifeboat occupants in the high-fidelity three-dimensional virtual scene through color gradient mapping and 3D UI marking.

9. The lifeboat occupant safety risk visualization system according to claim 8, characterized in that, The FVM numerical calculation module interacts with Unity 3D using the TCP / IP protocol. The data encapsulation format is JSON, which includes fields for interaction type, timestamp, parameter data, unit, and CRC32 checksum. The data transmission buffer size is 1024KB, and the number of retransmissions does not exceed 3.

Images