Real-time simulation method and system for hoisting operation based on digital twinning

Abstract

The invention relates to the field of digital twinning, in particular to a hoisting operation real-time simulation method and system based on digital twinning. The method comprises the steps that a comprehensive and accurate data basis is provided for follow-up accurate simulation by obtaining a lifting simulation model and collecting operation data such as an operation instruction, equipment state feedback and environment monitoring, the lifting simulation model is driven based on the operation data to conduct real-time numerical simulation calculation, a simulation result can be rapidly obtained, and the simulation efficiency is improved. The real-time simulation of the hoisting operation process is realized, the simulation result is visually presented in real time, an operator can intuitively understand the hoisting operation condition, predicted hoisting operation in a future period is predicted based on the simulation result, the operation trend is mastered in advance, and the fault state of the current hoisting control system in the future period is determined according to the predicted hoisting operation. And fault early warning in the hoisting operation process is realized.

Description

Technical Field

[0001] This application relates to the field of digital twins, and in particular to a real-time simulation method and system for lifting and lowering operations based on digital twins. Background Technology

[0002] Saturation diving is a key technology for deep-sea operations, rescue, and scientific research. As a complex surface support platform, the mobile saturation diving system's core function is to safely and accurately deploy and recover diving bells. This process involves the coordinated operation of a series of precision equipment, including the bell-laying winch, cable ballast winch, umbilical winch, gantry crane, bell-grabbing mechanism, and supporting hydraulic power station.

[0003] In existing technologies, hoisting control systems typically employ a "distributed PLC control + host computer monitoring" architecture. Operators, positioned on a control panel inside the submersible control container, issue commands via touchscreen, buttons, switches, and handles. These commands are processed by the PLC and transmitted to the hydraulic station control box via industrial networks such as PROFINET, driving the corresponding solenoid valves and actuators to ultimately control the movement of various winches and gantry cranes. Simultaneously, the system collects equipment status data (such as limit positions, pressure, temperature, and fault signals) through sensors and feeds this data back to the control panel for display and alarm functions.

[0004] However, the existing mobile saturation diving hoisting control system relies heavily on post-alarms for sensor signals that have malfunctioned or reached their limit thresholds, such as overload alarms and limit position alarms. It lacks the ability to predict and warn of potential and progressive safety risks. Therefore, how to achieve fault warning during hoisting operations has become an urgent problem to be solved. Summary of the Invention

[0005] To enable fault early warning during hoisting operations, this application provides a real-time simulation method and system for hoisting operations based on digital twins.

[0006] Firstly, this application provides a real-time simulation method for hoisting and lowering operations based on digital twins, employing the following technical solution: A real-time simulation method for hoisting and lowering operations based on digital twins, comprising: Obtain the hoisting simulation model corresponding to the current hoisting control system, and collect the control data corresponding to the current hoisting control system. The control data includes: operation command signals, equipment status feedback signals, and environmental monitoring signals. Based on the control data, the hoisting simulation model is driven to perform real-time numerical simulation calculations to obtain simulation results; The simulation results are visualized in real time, and the expected hoisting and launching operations corresponding to the current hoisting and launching control system in future periods are predicted based on the simulation results. Based on the expected hoisting operation, the fault status of the current hoisting control system within the future period is determined, wherein the fault status is either faulty or non-faulty.

[0007] By adopting the above technical solution, and by acquiring the hoisting simulation model and collecting control data such as operation commands, equipment status feedback, and environmental monitoring, a comprehensive and accurate data foundation is provided for subsequent precise simulation. Based on this control data, the hoisting simulation model is driven to perform real-time numerical simulation calculations, which can quickly obtain simulation results and realize real-time simulation of the hoisting operation process. The simulation results are visualized in real time, allowing operators to intuitively understand the hoisting operation situation. At the same time, based on the simulation results, the expected hoisting operations in the future cycle can be predicted, allowing for advance understanding of operation trends. Based on the expected hoisting operations, the fault status of the current hoisting control system in the future cycle can be determined, realizing fault early warning during the hoisting operation process. This helps to take timely measures to avoid faults, ensure the safe and stable operation of the diving hoisting control system, and improve the efficiency and reliability of hoisting operations.

[0008] Before obtaining the hoisting simulation model corresponding to the current hoisting control system, the method further includes: The system acquires the mechanical equipment model, the object being lifted model, and the current marine environment data corresponding to the current lifting control system. The object being lifted model includes object parameters, which include the object's mass attribute parameters, structural geometric parameters, and hydrodynamic coefficients. The current marine environment data includes current velocity profiles and wave spectrum parameters. Establish the rigid body motion equations and transfer function models corresponding to the mechanical equipment model, and couple the rigid body motion equations and the transfer function models to construct the equipment dynamics model; Based on the mass property parameters and the hydrodynamic coefficients, a dynamic framework for the suspended object is established. Obtain the physical parameters of the connecting cable corresponding to the suspended object, and establish a discretized tension-geometric coupling model of the connecting cable; The dynamic framework of the suspended object is combined with the discretized tension-geometric coupling model of the connecting cable to construct the coupled dynamic model of the suspended object; Based on the wave spectrum parameters, the motion field of water particles is calculated using linear wave theory; Based on the velocity profile and the motion field of the water particles, the fluid load on the suspended object is calculated using the Morrison equation, and an environmental fluid load model is constructed. Based on the equipment dynamics model, the coupled dynamics model of the suspended object, and the environmental fluid load model, a hoisting simulation model is obtained.

[0009] In one possible implementation, based on the control data, the hoisting simulation model is driven to perform real-time numerical simulation calculations to obtain simulation results, including: The operation command signal is used as the input boundary condition of the equipment dynamics model, and the equipment position and speed information in the equipment status feedback signal is used as the initial state of the equipment dynamics model. The cable tension and hydraulic pressure information in the equipment status feedback signal are used as the reference benchmark for model verification. The environmental monitoring signal is used as the input to the environmental fluid load model; A numerical integration algorithm with a fixed step size is used to simultaneously solve the coupled dynamic model of the equipment, the coupled dynamic model of the suspended object, and the environmental fluid load model, and the coupling variables are exchanged within each simulation step. The coupling variables include at least the cable tension and the motion state of the equipment.

[0010] In one possible implementation, the step of predicting the expected hoisting operation corresponding to the current hoisting control system within a future period based on the simulation results includes: Starting from the current moment, the current motion state of the equipment, the motion state of the suspended object, and the environmental load state in the simulation results are taken as the initial state of the hoisting simulation model. Using the currently acquired operation command signal as input, the hoisting simulation model is driven to perform advanced simulation at a simulation step size that is faster than real-time simulation calculation; The time series data output by the advanced simulation in the future period is obtained as the expected hoisting operation; wherein, the time series data includes at least: the predicted trajectory sequence of the hoisted object, the predicted load sequence of key connection points, and the predicted stress sequence of key components of the equipment.

[0011] In one possible implementation, the step of using the currently acquired operation command signal as input to drive the hoisting simulation model to perform advanced extrapolation at a simulation step size faster than real-time simulation calculation includes: Extract the system characteristic parameter sequence for future cycles from the simulation results. The system characteristic parameter sequence includes at least the winch speed change sequence, cable tension fluctuation sequence, suspended object attitude angle change sequence, and environmental load dynamic response sequence. Based on the system characteristic parameter sequence, a time series prediction algorithm is used to fit the parameter change trend and generate a system characteristic parameter prediction curve for future periods. Retrieve a preset hoisting operation rule library, which contains the mapping relationship between system feature parameters and hoisting operation commands; Using the currently acquired operation command signal as input, combined with the system characteristic parameter prediction curve and the mapping relationship, the hoisting simulation model is driven to perform advanced extrapolation at a simulation step size faster than real-time simulation calculation.

[0012] In one possible implementation, the simulation results are visualized in real time, including: A three-dimensional virtual operation scene is constructed, and the simulation results of the equipment dynamics model, the coupled dynamics model of the suspended object, and the environmental fluid load model are synchronously mapped in the three-dimensional virtual operation scene; Based on the equipment status feedback signal, the corresponding equipment model's motion animation is driven in real time in the three-dimensional virtual work scene, and the corresponding equipment status parameters are superimposed and displayed. Based on the output of the coupled dynamics model of the suspended object, the suspended object model is driven to perform six degrees of freedom motion in real time in the three-dimensional virtual operation scene, and the stress distribution and fluid load distribution of the key parts of the suspended object are visualized in real time in the form of dynamic cloud map or vector arrow. Based on the simulation results and the expected hoisting operation, the safe operation area, potential interference paths, and key state points of the expected hoisting operation at future moments are marked in the three-dimensional virtual operation scene using augmented reality.

[0013] In one possible implementation, determining the fault state of the current hoisting control system within the future period based on the anticipated hoisting operation includes: The expected hoisting operation is input into the hoisting simulation model, which drives the model to perform simulation and deduction for future periods, and obtains the system deduction parameters for future periods. The system deduction parameters include at least the deduction value of winch braking torque, the deduction value of cable ultimate tension, and the deduction value of the overturning risk angle of the hoisted object. Retrieve a preset fault determination threshold library, which is constructed based on the performance limit parameters of the hoisting device and the safety standards for diving operations, including the winch brake failure threshold, cable breakage tension threshold, and critical angle threshold for the overturning of the hoisted object. The system simulation parameters are compared one by one with the corresponding thresholds in the fault determination threshold library; If at least one system simulation parameter exceeds the corresponding fault determination threshold, the current hoisting and launching control system in the future cycle is determined to be in a fault state. If all system simulation parameters do not exceed the corresponding fault determination threshold, the system is determined to be in a non-fault state.

[0014] Secondly, this application provides a real-time simulation system for hoisting and lowering operations based on digital twins, employing the following technical solution: A real-time simulation system for hoisting and lowering operations based on digital twins, comprising: Real-time simulation device for hoisting and lowering operations based on digital twin; An electronic device for performing the real-time simulation method for lifting and lowering operations based on digital twins as described in any of the first aspects above.

[0015] In one possible implementation, the digital twin-based real-time simulation device for hoisting and lowering operations includes: The acquisition module is used to acquire the hoisting simulation model corresponding to the current hoisting control system and collect the control data corresponding to the current hoisting control system. The control data includes: operation command signals, equipment status feedback signals and environmental monitoring signals. The driving module is used to drive the hoisting simulation model to perform real-time numerical simulation calculations based on the control data in order to obtain simulation results. The prediction module is used to visualize the simulation results in real time and predict the expected hoisting operation of the current hoisting control system in the future period based on the simulation results. The determination module is used to determine the fault status of the current hoisting control system within the future period based on the expected hoisting operation, wherein the fault status is either faulty or non-faulty.

[0016] In one possible implementation, the electronic device includes: At least one processor; Memory; At least one application, wherein the at least one application is stored in memory and configured to be executed by at least one processor, the at least one application being configured to: perform the real-time simulation method for suspension operation based on digital twin as described in any of the first aspects above.

[0017] Fourthly, this application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium includes: storing a computer program that can be loaded by a processor and executed according to any one of the first aspects above.

[0018] In summary, this application includes the following beneficial technical effects: By acquiring a hoisting simulation model and collecting control data such as operation commands, equipment status feedback, and environmental monitoring, a comprehensive and accurate data foundation is provided for subsequent precise simulation. Based on this control data, the hoisting simulation model is driven to perform real-time numerical simulation calculations, which can quickly produce simulation results and realize real-time simulation of the hoisting operation process. The simulation results are visualized in real time, allowing operators to intuitively understand the hoisting operation situation. At the same time, based on the simulation results, the expected hoisting operations in the future cycle can be predicted, allowing for advance understanding of operation trends. Based on the expected hoisting operations, the fault status of the current hoisting control system in the future cycle can be determined, realizing fault early warning during the hoisting operation process. This helps to take timely measures to avoid faults, ensure the safe and stable operation of the underwater hoisting control system, and improve the efficiency and reliability of hoisting operations. Attached Figure Description

[0019] Figure 1 This is a flowchart illustrating a real-time simulation method for hoisting and lowering operations based on digital twins, provided in an embodiment of this application. Figure 2 This is a block diagram of a real-time simulation system for hoisting and lowering operations based on digital twins, according to an embodiment of this application. Figure 3 This is a block diagram of a real-time simulation device for hoisting and lowering operations based on digital twins, provided in an embodiment of this application. Figure 4 This is a schematic diagram of an electronic device provided in an embodiment of this application. Detailed Implementation

[0020] The following is in conjunction with the appendix Figure 1 -Appendix Figure 4 This application will be described in further detail.

[0021] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] To facilitate understanding of the technical solutions proposed in this application, several elements that will be introduced in the description of this application are first presented here. It should be understood that the following description is only for the purpose of understanding these elements and the content of the embodiments of this application, and does not necessarily cover all possible situations.

[0023] The hoisting control system provides comprehensive control, status monitoring, fault alarm, and safety protection functions for the hoisting devices (bell winch, cable ballast winch, umbilical winch, side hoisting frame, etc.) and hydraulic power station of the mobile saturation diving system. It meets the control requirements for saturation diving operation training, including the hoisting and lowering of the diving bell, cable ballast, bell and umbilical cable, gantry operation, bell grabbing mechanism operation, diving bell locking operation, and diving bell relocation operation. The system also sends the operating status and parameters of the hoisting control system to the integrated monitoring system for display and storage.

[0024] See Figure 1 This application provides a real-time simulation method for lifting and lowering operations based on digital twins. The lifting and lowering control system includes a lifting and lowering control console arranged inside a diving control container, a hydraulic station control box arranged on the lifting and lowering module, and a hydraulic station starter box. The lifting and lowering control console and the hydraulic station control box are connected via a PROFINET industrial Ethernet network and executed by electronic devices. The method includes: Step S101: Obtain the hoisting simulation model corresponding to the current hoisting control system, and collect the control data corresponding to the current hoisting control system.

[0025] The control data includes: operation command signals, equipment status feedback signals, and environmental monitoring signals. Specifically, control data is a collection of various data describing the operating status of the hoisting control system and the external environment, and is the core input driving the simulation model calculations; operation command signals are command data issued by operators through the control console to control the actions of the hoisting device, such as winch start / stop commands, speed adjustment commands, and guide device tension adjustment commands; equipment status feedback signals are data collected by various sensors of the hoisting device that reflect the real-time operating status of the equipment, such as winch drum torque signals, wire rope tension signals, and the attitude angle signal of the hoisted object; environmental monitoring signals are data collected by marine environmental monitoring equipment that reflect the environmental status of the operating sea area, such as wind speed signals, wave height signals, and ocean current speed signals.

[0026] Specifically, through a preset model call interface, the launch simulation model corresponding to the current diving launch control system is retrieved from the locally stored digital twin model library. At the same time, the electronic equipment connects to the operation console, equipment sensors and marine environment monitoring terminal of the launch control system through industrial Ethernet or wireless sensor network. It receives and summarizes operation command signals, equipment status feedback signals and environmental monitoring signals in real time at a sampling frequency of 100Hz, forming a structured control dataset and caching it in memory.

[0027] In one possible implementation of this application embodiment, before obtaining the hoisting simulation model corresponding to the current hoisting control system, the method further includes: Acquire the mechanical equipment model, the object being lifted model, and the current marine environment data corresponding to the current lifting control system. The object being lifted model includes the object parameters, which include the object's mass attribute parameters, structural geometric parameters, and hydrodynamic coefficients. The current marine environment data includes current velocity profiles and wave spectrum parameters. Establish the rigid body motion equations and transfer function models corresponding to the mechanical equipment model, and couple the rigid body motion equations and transfer function models to construct the equipment dynamics model; A dynamic framework for the suspended object is established based on mass property parameters and hydrodynamic coefficients. Obtain the physical parameters of the connecting cable corresponding to the suspended object, and establish a discretized tension-geometric coupling model of the connecting cable; The dynamic frame of the suspended object is combined with the discretized tension-geometric coupling model of the connecting cable to construct the coupled dynamic model of the suspended object; Based on wave spectrum parameters, the motion field of water particles is calculated using linear wave theory; Based on the velocity profile and the motion field of water particles, the Morrison equation is used to calculate the fluid load on the suspended object and construct an environmental fluid load model. Based on the equipment dynamics model, the coupled dynamics model of the suspended object, and the environmental fluid load model, a hoisting simulation model is obtained.

[0028] The mechanical equipment model is a virtual 3D model constructed based on the actual structural dimensions, material properties, and motion mechanisms of the hoisting control system, encompassing core components such as the gantry, main winch, and guiding device. The hoisted object model is a virtual model obtained by digitally modeling the carrier (diving bell, cage, etc.) to be hoisted during the underwater operation, used to simulate its mechanical characteristics and motion state during the hoisting process. The hoisted object parameters are a set of parameters describing the physical and hydrodynamic characteristics of the hoisted object, forming the basis for constructing the hoisted object's dynamic model. Mass attribute parameters reflect the mass-related characteristics of the hoisted object, including total mass, center of mass coordinates, and moment of inertia. Structural geometric parameters describe the shape and structure of the hoisted object, including dimensions, surface area, volume, and component cross-sectional dimensions. Hydrodynamic coefficients characterize the strength of the interaction between the hoisted object and the water body during its movement in water, including drag coefficient, lift coefficient, and added mass coefficient. Current marine environmental data reflects the real-time environmental conditions of the sea area where the hoisting operation is conducted, serving as a key input for constructing the environmental load model. Current velocity profiles describe the distribution curves of ocean current velocity magnitude and direction at different water depths within the operational sea area. Wave spectrum parameters characterize the energy distribution characteristics of ocean waves, including wave height, wave period, and spectral peak frequency, and are used to describe the statistical properties of waves.

[0029] Through a preset model import interface, the electronic equipment retrieves mechanical equipment models that match the current hoisting control system model from the ship engineering equipment model library, and simultaneously obtains the hoisting object model (such as a diving bell or cage) from the underwater transport equipment model library. For the hoisting object model, the electronic equipment automatically parses its built-in parameter set and extracts core parameters such as the mass and center of mass of the hoisting object. In addition, the electronic equipment receives environmental monitoring data of the operating sea area in real time through the communication interface with the marine environmental monitoring platform, and filters out current velocity profiles and wave spectrum parameters from them, integrating them to form the current marine environmental data set.

[0030] Furthermore, based on the connection relationships and motion constraints of the components in the mechanical equipment model, the Newton-Euler method is used to establish rigid body motion equations for core components such as the gantry and winch, describing the relationship between the displacement, velocity, acceleration and external force of the components. At the same time, for the power transmission system and braking system of the winch, the electronic equipment establishes a transfer function model between input and output to characterize the dynamic response characteristics of the system. The external force input of the rigid body motion equation is associated with the output of the transfer function model. The coupling of the two models is achieved through parameter iteration and data interaction, thus constructing a dynamic model of the equipment that can reflect the overall motion characteristics of the mechanical equipment.

[0031] Based on the mass property parameters of the suspended object, the basic equations of rigid body dynamics of the suspended object are constructed to describe its translational and rotational characteristics. Combining the hydrodynamic coefficients of the suspended object, a force model of the water body on the suspended object is introduced, and the hydrodynamic load is incorporated as an external force term into the rigid body dynamics equations. By integrating the above equations, the electronic equipment establishes a dynamic framework of the suspended object that can reflect the mechanical equilibrium relationship when the suspended object moves in water.

[0032] The physical parameters of the connecting cable for the suspended object are retrieved from the cable product parameter library, including cable diameter, elastic modulus, density, and breaking strength. Using the finite element method (FEM), the continuous cable is divided into several rigid rod elements. By establishing geometric deformation equations and tension balance equations for each element, the correlation between cable geometry changes and tension distribution is realized, ultimately constructing a discretized tension-geometric coupling model of the connecting cable. The geometric deformation equations describe the relationship between element length, angle, and position, while the tension balance equations describe the relationship between element tension and force. The end tension output of the discretized connecting cable model is used as the external force input to the dynamic framework of the suspended object, while the motion displacement of the suspended object is used as the end position boundary condition of the discretized cable model. By establishing a parameter feedback link between the two models, dynamic coupling calculations between the motion state of the suspended object and the cable tension and geometry are achieved. Based on these combined relationships, a coupled dynamic model of the suspended object reflecting the interaction between the suspended object and the connecting cable is formed.

[0033] The pre-defined linear wave theory calculation module is invoked, and the acquired wave spectrum parameters are input into the module. Based on the analytical relationship between water particle velocity, acceleration, and wave parameters in linear wave theory, the velocity and acceleration distribution of water particles at different locations and times in the operating sea area are calculated, forming water particle motion field data that reflects the motion law of water particles in the wave field. The velocity profile data is superimposed with the water particle motion field data to obtain the comprehensive flow field velocity distribution at the location of the suspended object. The Morrison equation calculation module is invoked, and the structural geometric parameters, hydrodynamic coefficients, and comprehensive flow field velocity data of the suspended object are input into the module. The drag force, inertial force, and other fluid loads generated by ocean currents and waves on the suspended object are calculated respectively. Based on the load calculation method and the correlation between flow field data, an environmental fluid load model that can calculate the fluid load on the suspended object under different flow field conditions in real time is constructed.

[0034] Furthermore, the coupling relationship between the equipment dynamics model, the coupled dynamics model of the suspended object, and the environmental fluid load model is established: the output of the environmental fluid load model is used as the external force input of the coupled dynamics model of the suspended object, and the root tension output of the cable in the coupled dynamics model of the suspended object is used as the load input of the equipment dynamics model. By building a framework for multi-model collaborative calculation, real-time parameter interaction and data feedback between the three sub-models are realized. Based on this collaborative calculation framework, a lifting simulation model that can completely simulate the operation process of the underwater lifting control system is integrated.

[0035] Step S102: Based on the control data, drive the hoisting simulation model to perform real-time numerical simulation calculations to obtain simulation results.

[0036] Specifically, the received operation command signals can be used as boundary conditions input into the equipment dynamics submodule of the hoisting simulation model. Simultaneously, equipment state feedback signals are used to initialize and correct the model's state. Environmental monitoring signals are input into the environmental fluid submodule. Further, its built-in real-time solver is invoked, employing a fixed-step numerical integration algorithm, such as the real-time Runge-Kutta method, to synchronously solve the coupled model equations. Within each tiny simulation step, it calculates the motion of all equipment, the attitude and forces on the hoisted object, and the influence of environmental loads, and exchanges coupling data between sub-models, ultimately outputting simulation results covering all key physical quantities of the system.

[0037] One possible implementation of this application embodiment involves driving a hoisting simulation model to perform real-time numerical simulation calculations based on manipulation data to obtain simulation results, including: The operation command signal is used as the input boundary condition of the equipment dynamics model, and the equipment position and velocity information in the equipment status feedback signal is used as the initial state of the equipment dynamics model. The cable tension and hydraulic pressure information in the equipment status feedback signal are used as the reference benchmark for model verification. Use environmental monitoring signals as input to the environmental fluid load model; A numerical integration algorithm with a fixed step size is used to simultaneously solve the coupled equipment dynamics model, the coupled dynamics model of the suspended object, and the environmental fluid load model. The coupling variables are exchanged within each simulation step. The coupling variables include at least the cable tension and the motion state of the equipment.

[0038] Input boundary conditions refer to the parameters that provide external drive or constraint for the dynamic model. They are necessary prerequisites for the model to start calculation and maintain operation. The operation command signal determines the action trend of the equipment dynamic model. The initial state refers to the system state parameters at the moment the dynamic model calculation starts. Assigning values ​​to the equipment position and velocity information ensures that the virtual model and the physical equipment are synchronized, avoiding initial deviations in the simulation.

[0039] Specifically, the collected control data is analyzed and classified, the operation command signals are filtered out and standardized according to the input format of the equipment dynamics model, and the converted signals are directly input to the model's drive interface as the input boundary conditions for model calculation. The real-time position and velocity data of key components of the equipment are extracted from the equipment status feedback signals and assigned to the initial state parameters of the equipment dynamics model to ensure that the initial state of the virtual model is consistent with the actual state of the physical equipment. The cable tension and hydraulic pressure data in the equipment status feedback signals are extracted and stored in a preset verification data buffer as a reference benchmark for subsequent model calculation result accuracy verification.

[0040] Furthermore, environmental monitoring signals are extracted from the structured control dataset. After filtering out outliers in the signals, dimensional matching and unit conversion are performed according to the parameter requirements of the environmental fluid load model. For example, wind speed and wave height data are converted into load calculation parameters that the model can recognize. The processed environmental monitoring signals are then input to the parameter input port of the environmental fluid load model via the data bus, driving the model to calculate the fluid load on the suspended object and equipment based on real-time environmental data.

[0041] Furthermore, the preset fixed-step numerical integration algorithm module is invoked, and the integration step size is set to match the frequency of control data acquisition to ensure that the model calculation speed is synchronized with physical time. The multi-model collaborative solution engine is then launched to solve the equipment dynamics model, the suspended object coupled dynamics model, and the environmental fluid load model simultaneously and in parallel. During the calculation of each integration step, the equipment motion state output by the equipment dynamics model can be transmitted to the suspended object coupled dynamics model in real time through the preset coupling variable interaction interface. At the same time, the cable tension output by the suspended object coupled dynamics model is fed back to the equipment dynamics model, realizing dynamic data interaction between multiple models. Finally, the calculation results of each model are integrated to generate a complete simulation result.

[0042] Step S103: Visualize the simulation results in real time, and predict the expected hoisting operation corresponding to the current hoisting control system in the future cycle based on the simulation results.

[0043] Among them, the expected hoisting operation is an operation plan based on simulation results to ensure the safety and efficiency of hoisting operations in the future cycle, such as winch speed adjustment strategy, guide device tension optimization strategy, and emergency operation triggering conditions.

[0044] The simulation results data stream can be transmitted to its graphics rendering engine in real time. Within a 3D virtual reality scene, the engine dynamically updates and displays the visual status of all equipment, the suspended object, and the environment, such as animations, dashboards, curves, and cloud maps. Simultaneously, it extracts the system characteristic parameter sequence within a preset time window from the simulation results. After noise reduction preprocessing, a time series prediction algorithm is used to fit the parameter change trend, generating parameter prediction curves for future periods. These curves are then combined with a preset lifting operation rule library to match the corresponding operation adjustment strategies to the predicted parameters, integrating them into the expected lifting operation. The lifting operation rule library is a knowledge base built based on underwater lifting operation safety regulations and historical operational experience, containing the mapping relationship between system characteristic parameters and optimal operation commands.

[0045] One possible implementation of this application embodiment is to visualize the simulation results in real time, including: Construct a three-dimensional virtual operation scene and synchronously map the simulation results of the equipment dynamics model, the coupled dynamics model of the suspended object, and the environmental fluid load model in the three-dimensional virtual operation scene; Based on equipment status feedback signals, the corresponding equipment model's motion animation is driven in real time in the 3D virtual work scene, and the corresponding equipment status parameters are overlaid and displayed. Based on the output of the coupled dynamics model of the suspended object, the suspended object model is driven to perform six degrees of freedom motion in real time in a three-dimensional virtual operation scene, and the stress distribution and fluid load distribution of key parts of the suspended object are visualized in real time in the form of dynamic cloud map or vector arrow. Based on simulation results and expected hoisting operations, the safe operating area, potential interference paths, and key state points of the expected hoisting operations at future moments are marked in the three-dimensional virtual operation scene using augmented reality.

[0046] The 3D virtual operation scene is a virtual 3D space constructed using computer graphics, corresponding 1:1 to the real lifting operation scene. It includes visualized elements such as lifting equipment, the object being lifted, and the marine environment, serving as the carrier for visualizing simulation results. Synchronous mapping establishes a real-time association between the calculated data of each simulation model and the corresponding elements in the 3D virtual scene, allowing the state of the virtual scene to be dynamically updated as the simulation data changes. Overlay display overlays the values ​​of equipment status parameters, in the form of visualized text or charts, onto the positions of the corresponding equipment components in the 3D virtual scene, achieving a fusion display of data and scene.

[0047] The electronic equipment calls upon its built-in 3D scene modeling engine to construct a 1:1 scale 3D virtual operation scene based on the physical structural dimensions of the hoisting control system, the shape parameters of the hoisted object, and the geographical environment data of the operating sea area. The scene includes core elements such as the hoisting device itself, the hoisted object, and the marine environment background. A mapping and association channel is established between the 3D virtual scene and each simulation model. The equipment motion data output by the equipment dynamics model, the mechanical data of the hoisted object output by the coupled dynamics model, and the environmental load data output by the environmental fluid load model are synchronously mapped to the corresponding virtual components and environmental areas in the 3D scene, respectively, to achieve real-time linkage between the virtual scene and the calculation results of the simulation model.

[0048] Furthermore, key data such as winch drum speed, braking torque, and guide device tension are extracted from the equipment status feedback signals. These data are then converted into motion-driving commands for the equipment model in a 3D virtual scene. These commands drive real-time equipment actions such as the winch drum's winding and unwinding animation and the guide device's tension adjustment animation. The real-time values ​​of the equipment status parameters are overlaid on the corresponding equipment component positions in the 3D virtual scene in the form of a floating data panel. The parameter values ​​are dynamically refreshed as the equipment status feedback signals are updated, allowing operators to intuitively grasp the equipment's operating status.

[0049] Furthermore, the six-degree-of-freedom motion data, including position and attitude angles, output from the coupled dynamics model of the suspended object are analyzed and converted into motion driving parameters for the suspended object model in a 3D virtual scene. This allows the suspended object model to be driven in real time to complete translation along the x, y, and z axes and rotation around these three axes. Simultaneously, the electronic device extracts stress data and fluid load data from key parts of the suspended object output by the model. Dynamic cloud maps are used to visualize the magnitude and trend of stress distribution on the surface of the suspended object model, while vector arrows are used to visualize the direction and magnitude of fluid loads around the suspended object. The cloud map color and arrow direction are adjusted in real time as the model output data is updated. The six-degree-of-freedom motion refers to six independent motion forms of an object in three-dimensional space, including translation along the x, y, and z axes and rotation around these axes, which can completely describe the spatial motion state of the suspended object during the hoisting process. The dynamic cloud map is a visualization graphic representing the distribution of physical quantities (such as stress) using different color gradients. The colors are dynamically updated as the physical quantity values ​​change, intuitively showing the spatial distribution patterns of the physical quantities. Vector arrows are visual graphics that use the direction of the arrow to represent the direction of action of a physical quantity (such as fluid load) and the length of the arrow to represent the magnitude of the physical quantity. They are used to display the distribution of physical quantities with directional attributes. Critical parts are areas where the load is subjected to concentrated forces and is prone to structural damage during the hoisting process, such as hoisting point connections and weak areas of the equipment casing.

[0050] Based on real-time simulation results, the safe operating boundary for hoisting operations is calculated. In the 3D virtual scene, the safe operating area is marked with a semi-transparent colored region. By analyzing the spatial relationship between the hoisted object's trajectory and the surrounding environment, potential collision and interference risks are identified, and potential interference paths are marked with red dashed lines. Furthermore, based on the simulation data for the expected future cycle of the hoisting operation, key state points for future moments are marked as highlighted points on the expected motion path of the hoisted object in the 3D virtual scene. All markings are overlaid in the 3D virtual scene using augmented reality, and are linked in real-time with the motion status of the equipment and the hoisted object. These key state points can include the point where the hoisted object enters the water and the point where it hovers at the operating depth; these key states can be set according to actual conditions.

[0051] One possible implementation of this application embodiment predicts the expected hoisting operation corresponding to the current hoisting control system within a future period based on simulation results, including: Starting from the current moment, the current motion state of the equipment, the motion state of the object being lifted, and the environmental load state in the simulation results are used as the initial state of the lifting simulation model. Using the currently acquired operation command signal as input, the hoisting simulation model is driven to perform advanced simulation at a simulation step size that is faster than the real-time simulation calculation. Obtain time-series data from the advanced simulation within the future period as the basis for the anticipated hoisting operation; the time-series data shall include at least: the predicted trajectory sequence of the hoisted object, the predicted load sequence of key connection points, and the predicted stress sequence of key components of the equipment.

[0052] The equipment motion status refers to the real-time operating status of the core equipment in the hoisting control system, including parameters such as the winch drum's rotational speed and torque, the gantry's swing angle, and the tension of the guide device. The hoisted object motion status refers to the real-time motion status of the hoisted diving equipment (such as a diving bell or cage), including parameters such as the object's position coordinates, attitude angle, lifting speed, and acceleration. The environmental load status refers to the load status exerted by the marine environment on the hoisting system and the hoisted object at the current moment, including parameters such as the magnitude and direction of wave loads, ocean current loads, and wind loads. The current moment is marked as the starting point of the advance simulation. Then, three types of core state data corresponding to the current moment are extracted from the real-time simulation results: the motion state data of the hoisting machinery, the motion state data of the hoisted object, and the environmental load state data. Furthermore, the above three types of data are standardized according to the parameter input format of the hoisting simulation model. The processed state data is assigned to the initial state parameter set of the hoisting simulation model to ensure that the starting state of the advance simulation is seamlessly connected with the current real-time simulation state.

[0053] The system extracts currently valid operation command signals from real-time acquired control data, verifies their validity, and converts them into an input command format recognizable by the hoisting simulation model. It then adjusts the model's simulation calculation step size to be smaller than that of the real-time simulation to improve the simulation speed. The converted operation command signals are then input into the hoisting simulation model, driving the model to conduct advanced simulations for future cycles based on the pre-set initial state and fast-forward simulation step size. During the simulation, the coupling relationship between the various sub-modules within the model remains unchanged to ensure the rationality of the prediction results.

[0054] After the hoisting simulation model completes the forward projection of the future cycle, time series data for the entire cycle is extracted from the model output results. Three types of core prediction sequences are selected: the hoisted object motion trajectory prediction sequence, the key connection point load prediction sequence, and the equipment key component stress prediction sequence. These sequence data are then subjected to noise reduction and validity screening to remove abnormal data points generated during the projection process. Finally, the processed time series data are integrated according to the decision-making logic of hoisting operation to form a predicted hoisting operation plan to guide future hoisting operations.

[0055] One possible implementation of this application embodiment uses the currently acquired operation command signal as input to drive the hoisting simulation model to perform advanced extrapolation at a simulation step size faster than real-time simulation calculation, including: Extract the system characteristic parameter sequence for future cycles from the simulation results. The system characteristic parameter sequence includes at least the winch speed change sequence, cable tension fluctuation sequence, suspended object attitude angle change sequence, and environmental load dynamic response sequence. Based on the system characteristic parameter sequence, a time series prediction algorithm is used to fit the parameter change trend and generate the system characteristic parameter prediction curve for future periods. Retrieve the preset hoisting operation rule library, which contains the mapping relationship between system feature parameters and hoisting operation commands; Using the currently acquired operation command signal as input, combined with the system characteristic parameter prediction curve and mapping relationship, the hoisting simulation model is driven to perform advanced extrapolation at a simulation step size faster than real-time simulation calculation.

[0056] The system's characteristic parameter sequence refers to the core parameters that characterize the operating status of the hoisting control system. Arranged chronologically, this data sequence forms the basis for analyzing system operating trends and predicting operations. The winch speed change sequence is an ordered set of data showing the change in winch drum speed over time within a future period, reflecting the winch's start-up, shutdown, and speed adjustment status. The cable tension fluctuation sequence is an ordered set of data showing the fluctuation in tension on the connecting cable over time within a future period, serving as a key basis for judging the cable's stress safety. The suspended object attitude angle change sequence is an ordered set of data showing the change in attitude parameters such as pitch and roll angles of the suspended object over time within a future period, reflecting the stability of the suspended object during the hoisting process. The environmental load dynamic response sequence is an ordered set of data showing the change in environmental loads on the hoisting system and the suspended object over time within a future period, reflecting the dynamic impact of environmental factors such as wind, waves, and currents on the system.

[0057] The time interval range of the future cycle is determined, and then all data within the interval is filtered out from the generated real-time simulation result dataset of hoisting operation according to the time sequence. According to the preset parameter classification rules, the numerical sets of four core parameters are extracted, namely winch speed, cable tension, hoisted object attitude angle, and dynamic response of environmental load. The values ​​of each type of parameter are sorted by timestamp to form corresponding change sequences, and integrated to obtain a complete system characteristic parameter sequence.

[0058] The built-in time series prediction algorithm module is invoked, and the extracted system characteristic parameter sequences, such as the winch speed change sequence and cable tension fluctuation sequence, are input into the algorithm module respectively. The data of each sequence are preprocessed by smoothing and noise reduction to eliminate abnormal data points caused by sensor acquisition errors and instantaneous environmental disturbances. The algorithm is used to perform trend fitting calculation on the preprocessed parameter sequences to explore the inherent laws of parameter changes over time. Based on the fitting results, the change curves of each type of system characteristic parameter in the future period are generated and integrated to obtain a set of system characteristic parameter prediction curves.

[0059] The pre-built hoisting operation rule library is retrieved from the locally stored expert knowledge base through a preset database call interface. This hoisting operation rule library is extracted from the safety specifications for underwater hoisting operations, historical operating experience, and fault handling cases. The electronic equipment parses the contents of the rule library to confirm the system characteristic parameter ranges such as winch speed and cable tension, as well as the mapping relationship of hoisting operation instructions such as winch start / stop and tension adjustment corresponding to each parameter range, to ensure that the rule library data is complete and can be recognized and called by the simulation model.

[0060] The currently acquired operation command signals are converted into an input format recognizable by the hoisting simulation model. At the same time, the parameter change patterns in the system characteristic parameter prediction curves and the mapping relationships in the hoisting operation rule base are integrated into the auxiliary decision input conditions of the model. The calculation step size of the simulation model is adjusted to be smaller than that of the real-time simulation to improve the extrapolation efficiency. The operation command signals and auxiliary decision conditions are simultaneously input into the hoisting simulation model, driving the model to conduct advanced simulation extrapolation of future cycles in the virtual environment at a speed faster than actual time, and fully reproduce the operating state of the hoisting system in the future cycle.

[0061] Step S104: Based on the expected hoisting operation, determine the fault status of the current hoisting control system in the future cycle.

[0062] The fault status is either faulty or not faulty.

[0063] The generated expected lifting operation is input into the lifting simulation model, driving the model to perform simulations for future cycles. This yields system simulation parameters such as winch braking torque, cable ultimate tension, and the overturning risk angle of the lifted object. A preset fault judgment threshold library is retrieved, and the system simulation parameters are compared one by one with the winch braking failure threshold, cable breakage tension threshold, and overturning critical angle threshold in the threshold library. If at least one parameter exceeds the corresponding threshold, the system is determined to be in a fault state in the future cycle, and the fault type and emergency response suggestions are output. If all parameters do not exceed the threshold, the system is determined to be in a non-fault state.

[0064] One possible implementation of this application embodiment involves determining the fault state of the current hoisting control system within a future cycle based on anticipated hoisting operations, including: The expected hoisting operation is input into the hoisting simulation model, which drives the model to perform simulations and extrapolations for future cycles, and obtains the system extrapolation parameters for the future cycles. The system extrapolation parameters include at least the extrapolated values ​​of winch braking torque, cable ultimate tension, and overturning risk angle of the hoisted object. Retrieve the preset fault judgment threshold library, which is built based on the performance limit parameters of the hoisting device and the safety standards for diving operations. It includes the winch brake failure threshold, cable breakage tension threshold, and critical angle threshold for the overturning of the hoisted object. The system simulation parameters are compared one by one with the corresponding thresholds in the fault determination threshold library; If at least one system simulation parameter exceeds the corresponding fault determination threshold, the current hoisting and launching control system in the future cycle is determined to be in a fault state. If all system simulation parameters do not exceed the corresponding fault determination threshold, the system is determined to be in a non-fault state.

[0065] Among them, the expected hoisting operation is an operation plan generated based on real-time simulation results and parameter trend predictions, which guides future hoisting operations.

[0066] The generated expected lifting operation is formatted to match the command input interface of the lifting simulation model. The converted expected lifting operation is then input into the lifting simulation model, and the initial state of the model is set to the real-time state of the current lifting control system. The future cycle simulation process of the model is then initiated. According to the preset simulation time length and calculation step size, the model is driven to simulate the dynamic changes of the system during the execution of the expected lifting operation. After the simulation is completed, the electronic equipment extracts the simulation values ​​of key parameters from the model output results, including the simulation values ​​of winch braking torque, cable ultimate tension, and overturning risk angle of the suspended object, and integrates them to form a set of system simulation parameters for the future cycle.

[0067] The system retrieves a pre-built fault determination threshold library from the locally stored safety threshold database via a preset database access interface. This fault determination threshold library is a comprehensive extraction of the equipment's factory performance limit parameters, diving operation industry safety standards, and historical fault case data. The electronic equipment performs an integrity check on the retrieved threshold library to confirm that it contains key threshold parameters such as winch brake failure threshold, cable breakage tension threshold, and critical angle threshold for the overturning of the suspended object. This ensures that each threshold parameter corresponds one-to-one with the system's deduced parameters, meeting the requirements for fault determination.

[0068] A one-to-one correspondence is established between the system simulation parameters and the threshold parameters in the fault judgment threshold library. Following the order of winch braking torque, cable ultimate tension, and overturning risk angle of the suspended object, the value of each system simulation parameter is compared with the corresponding fault judgment threshold. During the comparison process, the electronic equipment records the comparison results of each parameter in real time, marks the simulation parameters that exceed the threshold range, and forms a complete parameter comparison report to provide data support for subsequent fault status judgment.

[0069] Furthermore, the parameter comparison report is analyzed to count the number of system projection parameters exceeding the corresponding fault judgment threshold. If at least one system projection parameter in the report exceeds the corresponding fault judgment threshold, it indicates that there is a risk of component failure or safety accident in the hoisting control system when the expected hoisting operation is performed in the future cycle. In this case, the electronic equipment determines that the current hoisting control system in the future cycle is in a fault state and simultaneously records the type and magnitude of the exceeding parameter, providing a basis for subsequent emergency response. If the values ​​of all system projection parameters do not exceed the corresponding safety threshold values ​​in the fault judgment threshold library, it indicates that the operating load of each component of the hoisting control system is within the safe range when the expected hoisting operation is performed in the future cycle, and there is no risk of component failure or safety accident. In this case, the electronic equipment determines that the current hoisting control system in the future cycle is in a non-fault state and outputs the judgment result, providing a safety basis for operators to perform the expected hoisting operation.

[0070] This application provides a real-time simulation method for lifting and launching operations based on digital twins. By acquiring a lifting and launching simulation model and collecting control data such as operation commands, equipment status feedback, and environmental monitoring, a comprehensive and accurate data foundation is provided for subsequent precise simulation. Based on this control data, the lifting and launching simulation model is driven to perform real-time numerical simulation calculations, which can quickly obtain simulation results and realize real-time simulation of the lifting and launching operation process. The simulation results are visualized in real time, allowing operators to intuitively understand the lifting and launching operation situation. At the same time, based on the simulation results, the expected lifting and launching operations in the future period can be predicted, allowing for advance understanding of operation trends. Based on the expected lifting and launching operations, the fault status of the current lifting and launching control system in the future period can be determined, realizing fault early warning during the lifting and launching operation process. This helps to take timely measures to avoid faults, ensure the safe and stable operation of the underwater lifting and launching control system, and improve the efficiency and reliability of lifting and launching operations.

[0071] The above embodiments introduce a real-time simulation method for hoisting and lowering operations based on digital twins from the perspective of method flow. The following embodiments introduce a real-time simulation system for hoisting and lowering operations based on digital twins from the perspective of virtual modules or virtual units. For details, please refer to the following embodiments.

[0072] See Figure 2 A real-time simulation system 20 for hoisting and lowering operations based on digital twins, comprising: Real-time simulation device for hoisting and lowering operations based on digital twin; Electronic equipment 202.

[0073] See Figure 3 One possible implementation of this application embodiment, the real-time simulation device 201 for hoisting and lowering operations based on digital twins, may specifically include: an acquisition module 2011, a driving module 2012, a prediction module 2013, and a determination module 2014, wherein: The real-time simulation device 201 for hoisting and lowering operations based on digital twins includes: The acquisition module 2011 is used to acquire the hoisting simulation model corresponding to the current hoisting control system and collect the control data corresponding to the current hoisting control system. The control data includes: operation command signals, equipment status feedback signals and environmental monitoring signals. The drive module 2012 is used to drive the hoisting simulation model to perform real-time numerical simulation calculations based on the control data in order to obtain simulation results. The prediction module 2013 is used to visualize the simulation results in real time and predict the expected hoisting operation of the current hoisting control system in the future cycle based on the simulation results. The determination module 2014 is used to determine the fault status of the current hoisting control system in a future cycle based on the expected hoisting operation, and the fault status is either faulty or non-faulty.

[0074] In one possible implementation of this application embodiment, the real-time simulation device 201 for hoisting and lowering operations based on digital twins further includes: The model acquisition module is used to acquire the mechanical equipment model, the object being lifted model, and the current marine environment data corresponding to the current lifting and launching control system. The object being lifted model includes the object parameters, which include the mass attribute parameters, structural geometric parameters, and hydrodynamic coefficients of the object being lifted. The current marine environment data includes the current velocity profile and wave spectrum parameters. The first module is used to establish the rigid body motion equations and transfer function models corresponding to the mechanical equipment model, and to couple the rigid body motion equations and transfer function models to construct the equipment dynamics model. The second module is used to establish the dynamic framework of the suspended object based on mass property parameters and hydrodynamic coefficients. The third module is used to obtain the physical parameters of the connecting cable corresponding to the suspended object and to establish a discretized tension-geometric coupling model of the connecting cable. The first building module is used to combine the dynamic frame of the suspended object with the discretized tension-geometric coupling model of the connecting cable to construct the coupled dynamic model of the suspended object. The calculation module is used to calculate the motion field of water particles based on wave spectrum parameters and using linear wave theory. The second building module is used to calculate the fluid load on the suspended object based on the velocity profile and the motion field of water particles using the Morrison equation, and to build an environmental fluid load model. The module is used to obtain a hoisting simulation model based on the equipment dynamics model, the coupled dynamics model of the hoisted object, and the environmental fluid load model.

[0075] In one possible implementation of this application embodiment, when the driving module 2012 drives the hoisting simulation model to perform real-time numerical simulation calculations based on control data to obtain simulation results, it is specifically used for: The operation command signal is used as the input boundary condition of the equipment dynamics model, and the equipment position and velocity information in the equipment status feedback signal is used as the initial state of the equipment dynamics model. The cable tension and hydraulic pressure information in the equipment status feedback signal are used as the reference benchmark for model verification. Use environmental monitoring signals as input to the environmental fluid load model; A numerical integration algorithm with a fixed step size is used to simultaneously solve the coupled equipment dynamics model, the coupled dynamics model of the suspended object, and the environmental fluid load model. The coupling variables are exchanged within each simulation step. The coupling variables include at least the cable tension and the motion state of the equipment.

[0076] In one possible implementation of this application embodiment, when the prediction module 2013 predicts the expected hoisting operation corresponding to the current hoisting control system within a future period based on simulation results, it is specifically used for: Starting from the current moment, the current motion state of the equipment, the motion state of the object being lifted, and the environmental load state in the simulation results are used as the initial state of the lifting simulation model. Using the currently acquired operation command signal as input, the hoisting simulation model is driven to perform advanced simulation at a simulation step size that is faster than the real-time simulation calculation. Obtain time-series data from the advanced simulation within the future period as the basis for the anticipated hoisting operation; the time-series data shall include at least: the predicted trajectory sequence of the hoisted object, the predicted load sequence of key connection points, and the predicted stress sequence of key components of the equipment.

[0077] In one possible implementation of this application embodiment, when the prediction module 2013 uses the currently acquired operation command signal as input to drive the hoisting simulation model to perform advanced extrapolation at a simulation step size faster than real-time simulation calculation, it is specifically used for: Extract the system characteristic parameter sequence for future cycles from the simulation results. The system characteristic parameter sequence includes at least the winch speed change sequence, cable tension fluctuation sequence, suspended object attitude angle change sequence, and environmental load dynamic response sequence. Based on the system characteristic parameter sequence, a time series prediction algorithm is used to fit the parameter change trend and generate the system characteristic parameter prediction curve for future periods. Retrieve the preset hoisting operation rule library, which contains the mapping relationship between system feature parameters and hoisting operation commands; Using the currently acquired operation command signal as input, combined with the system characteristic parameter prediction curve and mapping relationship, the hoisting simulation model is driven to perform advanced extrapolation at a simulation step size faster than real-time simulation calculation.

[0078] In one possible implementation of this application embodiment, the prediction module 2013, when visualizing the simulation results in real time, is specifically used for: Construct a three-dimensional virtual operation scene and synchronously map the simulation results of the equipment dynamics model, the coupled dynamics model of the suspended object, and the environmental fluid load model in the three-dimensional virtual operation scene; Based on equipment status feedback signals, the corresponding equipment model's motion animation is driven in real time in the 3D virtual work scene, and the corresponding equipment status parameters are overlaid and displayed. Based on the output of the coupled dynamics model of the suspended object, the suspended object model is driven to perform six degrees of freedom motion in real time in a three-dimensional virtual operation scene, and the stress distribution and fluid load distribution of key parts of the suspended object are visualized in real time in the form of dynamic cloud map or vector arrow. Based on simulation results and expected hoisting operations, the safe operating area, potential interference paths, and key state points of the expected hoisting operations at future moments are marked in the three-dimensional virtual operation scene using augmented reality.

[0079] In one possible implementation of this application embodiment, when determining the fault status of the current hoisting control system in a future period based on the expected hoisting operation, the determining module 2014 is specifically used for: The expected hoisting operation is input into the hoisting simulation model, which drives the model to perform simulations and extrapolations for future cycles, and obtains the system extrapolation parameters for the future cycles. The system extrapolation parameters include at least the extrapolated values ​​of winch braking torque, cable ultimate tension, and overturning risk angle of the hoisted object. Retrieve the preset fault judgment threshold library, which is built based on the performance limit parameters of the hoisting device and the safety standards for diving operations. It includes the winch brake failure threshold, cable breakage tension threshold, and critical angle threshold for the overturning of the hoisted object. The system simulation parameters are compared one by one with the corresponding thresholds in the fault determination threshold library; If at least one system simulation parameter exceeds the corresponding fault determination threshold, the current hoisting and launching control system in the future cycle is determined to be in a fault state. If all system simulation parameters do not exceed the corresponding fault determination threshold, the system is determined to be in a non-fault state.

[0080] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0081] This application also describes an electronic device from the perspective of a physical apparatus, as shown in the figure. Figure 4 The illustrated electronic device 202 includes a processor 2021 and a memory 2023. The processor 2021 and the memory 2023 are connected, for example, via a bus 2022. Optionally, the electronic device 202 may also include a transceiver 2024. It should be noted that in practical applications, the transceiver 2024 is not limited to one type, and the structure of this electronic device 202 does not constitute a limitation on the embodiments of this application.

[0082] The processor 2021 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), a FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. The processor 2021 may also be a combination that implements computing functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0083] Bus 2022 may include a pathway for transmitting information between the aforementioned components. Bus 2022 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 2022 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 4 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0084] The memory 2023 may be ROM (Read Only Memory) or other types of static storage devices capable of storing static information and instructions, RAM (Random Access Memory) or other types of dynamic storage devices capable of storing information and instructions, or EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0085] The memory 2023 stores application code that executes the scheme of this application, and its execution is controlled by the processor 2021. The processor 2021 executes the application code stored in the memory 2023 to implement the content shown in the foregoing method embodiments.

[0086] Among them, electronic devices include, but are not limited to: mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and in-vehicle terminals (such as in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers, and can also be servers, etc. Figure 4 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0087] This application provides a computer-readable storage medium storing a computer program that, when run on a computer, enables the computer to execute the corresponding content in the aforementioned method embodiments.

[0088] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0089] The above are only some embodiments of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A real-time simulation method for hoisting and lowering operations based on digital twins, characterized in that, The method is applied to a diving hoisting control system, the method is executed by electronic equipment, and the method includes: Obtain the hoisting simulation model corresponding to the current hoisting control system, and collect the control data corresponding to the current hoisting control system. The control data includes: operation command signals, equipment status feedback signals, and environmental monitoring signals. Based on the control data, the hoisting simulation model is driven to perform real-time numerical simulation calculations to obtain simulation results; The simulation results are visualized in real time, and the expected hoisting and launching operations corresponding to the current hoisting and launching control system in future periods are predicted based on the simulation results. Based on the expected hoisting operation, the fault status of the current hoisting control system within the future period is determined, wherein the fault status is either faulty or non-faulty.

2. The real-time simulation method for hoisting and lowering operations based on digital twins according to claim 1, characterized in that, Before obtaining the hoisting simulation model corresponding to the current hoisting control system, the method further includes: The system acquires the mechanical equipment model, the object being lifted model, and the current marine environment data corresponding to the current lifting control system. The object being lifted model includes object parameters, which include the object's mass attribute parameters, structural geometric parameters, and hydrodynamic coefficients. The current marine environment data includes current velocity profiles and wave spectrum parameters. Establish the rigid body motion equations and transfer function models corresponding to the mechanical equipment model, and couple the rigid body motion equations and the transfer function models to construct the equipment dynamics model; Based on the mass property parameters and the hydrodynamic coefficients, a dynamic framework for the suspended object is established. Obtain the physical parameters of the connecting cable corresponding to the suspended object, and establish a discretized tension-geometric coupling model of the connecting cable; The dynamic framework of the suspended object is combined with the discretized tension-geometric coupling model of the connecting cable to construct the coupled dynamic model of the suspended object; Based on the wave spectrum parameters, the motion field of water particles is calculated using linear wave theory; Based on the velocity profile and the motion field of the water particles, the fluid load on the suspended object is calculated using the Morrison equation, and an environmental fluid load model is constructed. Based on the equipment dynamics model, the coupled dynamics model of the suspended object, and the environmental fluid load model, a hoisting simulation model is obtained.

3. The real-time simulation method for hoisting and lowering operations based on digital twins according to claim 2, characterized in that, Based on the control data, the hoisting simulation model is driven to perform real-time numerical simulation calculations to obtain simulation results, including: The operation command signal is used as the input boundary condition of the equipment dynamics model, and the equipment position and velocity information in the equipment status feedback signal is used as the initial state of the equipment dynamics model. The cable tension and hydraulic pressure information in the equipment status feedback signal are used as the reference benchmark for model verification. The environmental monitoring signal is used as the input to the environmental fluid load model; A numerical integration algorithm with a fixed step size is used to simultaneously solve the coupled dynamic model of the equipment, the coupled dynamic model of the suspended object, and the environmental fluid load model, and the coupling variables are exchanged within each simulation step. The coupling variables include at least the cable tension and the motion state of the equipment.

4. The real-time simulation method for hoisting and lowering operations based on digital twins according to claim 2, characterized in that, The prediction of the expected hoisting and launching operations corresponding to the current hoisting and launching control system within a future period based on the simulation results includes: Starting from the current moment, the current motion state of the equipment, the motion state of the suspended object, and the environmental load state in the simulation results are taken as the initial state of the hoisting simulation model. Using the currently acquired operation command signal as input, the hoisting simulation model is driven to perform advanced simulation at a simulation step size that is faster than real-time simulation calculation; The time series data output by the advanced simulation in the future period is obtained as the expected hoisting operation; wherein, the time series data includes at least: the predicted trajectory sequence of the hoisted object, the predicted load sequence of key connection points, and the predicted stress sequence of key components of the equipment.

5. The real-time simulation method for hoisting and lowering operations based on digital twins according to claim 4, characterized in that, The step of using the currently acquired operation command signal as input to drive the hoisting simulation model to perform advanced extrapolation at a simulation step size faster than real-time simulation calculation includes: Extract the system characteristic parameter sequence for future cycles from the simulation results. The system characteristic parameter sequence includes at least the winch speed change sequence, cable tension fluctuation sequence, suspended object attitude angle change sequence, and environmental load dynamic response sequence. Based on the system characteristic parameter sequence, a time series prediction algorithm is used to fit the parameter change trend and generate a system characteristic parameter prediction curve for future periods. Retrieve a preset hoisting operation rule library, which contains the mapping relationship between system feature parameters and hoisting operation commands; Using the currently acquired operation command signal as input, combined with the system characteristic parameter prediction curve and the mapping relationship, the hoisting simulation model is driven to perform advanced extrapolation at a simulation step size faster than real-time simulation calculation.

6. The real-time simulation method for hoisting and lowering operations based on digital twins according to claim 2, characterized in that, The simulation results are visualized in real time, including: A three-dimensional virtual operation scenario is constructed, and the simulation results of the equipment dynamics model, the coupled dynamics model of the suspended object, and the environmental fluid load model are synchronously mapped in the three-dimensional virtual operation scenario; Based on the equipment status feedback signal, the corresponding equipment model's motion animation is driven in real time in the three-dimensional virtual work scene, and the corresponding equipment status parameters are superimposed and displayed. Based on the output of the coupled dynamics model of the suspended object, the suspended object model is driven to perform six degrees of freedom motion in real time in the three-dimensional virtual operation scene, and the stress distribution and fluid load distribution of the key parts of the suspended object are visualized in real time in the form of dynamic cloud map or vector arrow. Based on the simulation results and the expected hoisting operation, the safe operation area, potential interference paths, and key state points of the expected hoisting operation at future moments are marked in the three-dimensional virtual operation scene using augmented reality.

7. The real-time simulation method for hoisting and lowering operations based on digital twins according to claim 2, characterized in that, The step of determining the fault status of the current hoisting control system within the future period based on the expected hoisting operation includes: The expected hoisting operation is input into the hoisting simulation model, which drives the model to perform simulation and deduction for future periods, and obtains the system deduction parameters for future periods. The system deduction parameters include at least the deduction value of winch braking torque, the deduction value of cable ultimate tension, and the deduction value of the overturning risk angle of the hoisted object. Retrieve a preset fault judgment threshold library, which is constructed based on the performance limit parameters of the hoisting device and the safety standards for diving operations, including the winch brake failure threshold, cable breakage tension threshold, and critical angle threshold for the overturning of the hoisted object. The system simulation parameters are compared one by one with the corresponding thresholds in the fault determination threshold library; If at least one system simulation parameter exceeds the corresponding fault determination threshold, the current hoisting and launching control system in the future period is determined to be in a fault state. If all system simulation parameters do not exceed the corresponding fault determination threshold, the system is determined to be in a non-fault state.

8. A real-time simulation system for hoisting and lowering operations based on digital twins, characterized in that, include: Real-time simulation device for hoisting and lowering operations based on digital twin; An electronic device for performing the real-time simulation method for lifting and lowering operations based on digital twins as described in any one of claims 1 to 7.

9. The real-time simulation method for hoisting and lowering operations based on digital twins according to claim 8, characterized in that, The real-time simulation device for hoisting and lowering operations based on digital twins includes: The acquisition module is used to acquire the hoisting simulation model corresponding to the current hoisting control system and collect the control data corresponding to the current hoisting control system. The control data includes: operation command signals, equipment status feedback signals and environmental monitoring signals. The driving module is used to drive the hoisting simulation model to perform real-time numerical simulation calculations based on the control data in order to obtain simulation results. The prediction module is used to visualize the simulation results in real time and predict the expected hoisting operation of the current hoisting control system in the future period based on the simulation results. The determination module is used to determine the fault status of the current hoisting control system within the future period based on the expected hoisting operation, wherein the fault status is either faulty or non-faulty.

10. The real-time simulation method for hoisting and lowering operations based on digital twins according to claim 8 or 9, characterized in that, The electronic device includes: At least one processor; Memory; At least one application, wherein the at least one application is stored in memory and configured to be executed by at least one processor, said at least one application being configured to: perform the real-time simulation method for lifting operation based on digital twins as described in any one of claims 1 to 7.

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