A Digital Twin-Based Adaptive Speed ​​Control Method and System for New Energy Ships

Abstract

The application discloses a new energy ship adaptive speed control method and system based on digital twinning, relates to the technical field of ship automatic control, and comprises the following steps: calling the geometric parameters of the new energy ship, and performing multi-physical field power analysis; constructing a digital twin of the new energy ship; synchronizing the ship operation data set to the digital twin for simulation calculation; according to the preset sailing target, a speed control strategy is formulated, the digital twin is feedback optimized, and adaptive speed control of the new energy ship is realized. The technical problems that the existing technology cannot accurately perceive the complex operation condition of the new energy ship, cannot dynamically optimize the speed according to the real-time state of the ship and the change of the external environment, and results in low ship sailing efficiency and serious energy waste are solved, and the technical effects of realizing real-time mapping and simulation analysis of the ship operation state through the digital twin of the new energy ship and improving the ship sailing efficiency and energy utilization efficiency are achieved.

Description

Technical Field

[0001] This application relates to the field of ship automatic control technology, specifically to a method and system for adaptive speed control of new energy ships based on digital twins. Background Technology

[0002] The application of new energy sources such as lithium batteries and hydrogen energy in ship propulsion has effectively reduced carbon emissions and fuel consumption. However, the intermittent nature of energy supply and the instability of power output in new energy ships pose numerous challenges to speed control. Traditional ship speed control methods, largely based on fixed models and empirical parameters, are ill-suited to the complex and ever-changing operating conditions of new energy ships. When faced with varying sea states, weather conditions, and changes in ship load, precise speed control is often impossible, leading to low navigation efficiency, significant energy waste, and even compromised navigation safety. Furthermore, traditional control methods lack the ability to dynamically perceive and coordinate the ship's propulsion system with the external environment in real time, affecting the accuracy of ship operation monitoring and control, and failing to fully realize the navigation performance and energy efficiency of new energy ships.

[0003] Therefore, current technologies suffer from the inability to accurately perceive the complex operating conditions of new energy ships and to dynamically optimize speed based on real-time ship status and changes in the external environment, resulting in low ship navigation efficiency and serious energy waste. Summary of the Invention

[0004] This application provides a digital twin-based adaptive speed control method and system for new energy ships, which solves the technical problems in the prior art, namely, the difficulty in accurately sensing the complex operating conditions of new energy ships and the inability to dynamically optimize the speed according to the real-time status of the ship and changes in the external environment, resulting in low ship navigation efficiency and serious energy waste. It achieves the technical effect of realizing real-time mapping and simulation analysis of ship operating status through the digital twin of new energy ships, thereby improving ship navigation efficiency and energy utilization efficiency.

[0005] This application provides an adaptive speed control method for new energy vessels based on digital twins. The method includes: retrieving the geometric parameters of the new energy vessel, performing multiphysics dynamic analysis according to the geometric parameters to obtain new energy power information; introducing environmental perception parameters and constructing a digital twin of the new energy vessel in combination with the new energy power information; synchronizing the real-time collected ship operation dataset to the digital twin for simulation calculation to obtain a set of ship twin operation parameters; constraining the ship twin operation parameter set according to a preset navigation target, formulating a speed control strategy, and executing the speed control strategy to perform feedback optimization on the digital twin to achieve adaptive speed control of the new energy vessel.

[0006] In a possible implementation, the adaptive speed control method for new energy ships based on digital twins further performs the following processing: identifying the hull geometric point cloud based on the geometric parameters of the new energy ship to obtain hull surface point cloud data; performing cabin structure analysis based on the geometric parameters of the new energy ship to obtain cabin structure topology data; performing position analysis on the power equipment according to the hull surface point cloud data and the cabin structure topology data to determine the power equipment layout coordinates; performing multiphysics field coupling calculations on the new energy ship according to the power equipment layout coordinates, hull surface point cloud data, and cabin structure topology data to generate multiphysics field dynamic data; fusing the multiphysics field dynamic data to generate a comprehensive dynamic performance index set; and optimizing the power configuration of the new energy ship according to the comprehensive dynamic performance index set to determine the new energy power information.

[0007] In a possible implementation, the adaptive speed control method for new energy ships based on digital twins further performs the following processing: performing interactive dynamic calculations on the new energy ship and fluid based on the power equipment layout coordinates and the hull surface point cloud data, and drawing a ship propulsion efficiency map; calculating the operating temperature rise of the new energy ship according to the power equipment layout coordinates and the cabin structure topology data to obtain equipment thermal evacuation demand information; performing finite element analysis on the new energy ship based on the hull surface point cloud data and the cabin structure topology data to calculate the structural stress field and generate hull vibration modal parameters; and adding the ship propulsion efficiency map, the equipment thermal evacuation demand information, and the hull vibration modal parameters to the multiphysics dynamic data.

[0008] In a possible implementation, the adaptive speed control method for new energy ships based on digital twins further performs the following processes: constructing a benchmark digital twin framework based on the geometric parameters of the new energy ship combined with the new energy power information; extracting the ship's center of gravity as the origin based on the new energy ship, constructing a follower coordinate system, and performing spatial unification based on the follower coordinate system to set spatial benchmark information; extracting multiple ship operation timestamps based on the new energy power information, and setting time benchmark information according to the multiple ship operation timestamps; constructing a spatiotemporal alignment engine based on the spatial benchmark information and the time benchmark information; activating the spatiotemporal alignment engine, and dynamically mapping the environmental perception parameters and the new energy power information to the benchmark digital twin framework through the spatiotemporal alignment engine to generate an environment-power coupling field; and performing digital mirroring through the environment-power coupling field to construct the digital twin.

[0009] In a possible implementation, the adaptive speed control method for new energy ships based on digital twins further performs the following processing: constructing a multi-source operation data acquisition channel for the new energy ship, and acquiring the ship operation dataset in real time through the multi-source operation data acquisition channel; performing multi-level cleaning on the ship operation dataset to generate a standardized twin input data stream; injecting the standardized twin input data stream into the dynamic data bus of the digital twin to generate multi-physics field coupling simulation parameters; performing multi-dimensional simulation according to the multi-physics field coupling simulation parameters, and fusion of the multi-dimensional simulation results to generate the ship twin operation parameter set.

[0010] In a possible implementation, the adaptive speed control method for new energy ships based on digital twins further performs the following processing: setting data execution priorities through the dynamic data bus, allocating time slots for the standardized twin input data streams according to the data execution priorities to generate multiple data streams to be injected; injecting the multiple data streams to be injected into the dynamic data bus of the digital twin to formulate a multiphysics simulation task; and performing simulation analysis according to the multiphysics simulation task to obtain the multiphysics coupling simulation parameters.

[0011] In a possible implementation, the adaptive speed control method for new energy ships based on digital twins further performs the following processes: executing the multiphysics simulation task to perform motion simulation analysis and generate motion attitude simulation parameters; executing the multiphysics simulation task to perform energy simulation analysis and generate energy efficiency simulation parameters; executing the multiphysics simulation task to perform structural force simulation analysis and generate structural stress simulation parameters; fusing the motion attitude simulation parameters, the energy efficiency simulation parameters, and the structural stress simulation parameters to construct a simulation parameter matrix; performing a closed-loop reliability verification on the simulation parameter matrix to generate simulation parameter health values; and generating the multiphysics coupled simulation parameters when the simulation parameter health values ​​are higher than a preset health threshold.

[0012] In a possible implementation, the adaptive speed control method for new energy ships based on digital twins further performs the following processing: performing dynamic constraint analysis on the ship twin operating parameters according to the preset navigation target, and formulating dynamic constraint conditions and motion constraint conditions; constraining the ship twin operating parameter set according to the dynamic constraint conditions to generate a ship twin operating dynamic control interval; constraining the ship twin operating parameter set according to the motion constraint conditions to generate a ship twin operating motion control interval; performing multi-objective speed optimization on the new energy ship based on the ship twin operating dynamic control interval and the ship twin operating motion control interval to generate a multi-objective speed control sequence; and adding the multi-objective speed control sequence to the speed control strategy.

[0013] In a possible implementation, the adaptive speed control method for new energy vessels based on digital twins further performs the following processing: the speed control strategy is distributed to the vessel's actuators for real-time execution recording to obtain the actual speed parameters of the new energy vessel; a deviation criticality analysis is performed based on the vessel twin operating parameter set to construct an error-parameter mapping rule table; the actual speed parameters are synchronized to the error-parameter mapping rule table for matching to obtain an error feedback value; online calibration of the digital twin is triggered based on the error feedback value, the digital twin is updated, and simulation calculations are performed again to construct a data optimization closed loop, thereby realizing adaptive speed control for the new energy vessel.

[0014] This application also provides an adaptive speed control system for new energy ships based on digital twins. The system includes: a new energy power information acquisition module, used to retrieve the geometric parameters of the new energy ship, perform multiphysics dynamic analysis according to the geometric parameters, and obtain new energy power information; a digital twin construction module, used to introduce environmental perception parameters and construct a digital twin of the new energy ship in combination with the new energy power information; a ship twin operation parameter set acquisition module, used to synchronize the real-time collected ship operation dataset to the digital twin for simulation calculation to obtain a ship twin operation parameter set; and a speed control strategy formulation module, used to constrain the ship twin operation parameter set according to a preset navigation target, formulate a speed control strategy, execute the speed control strategy to perform feedback optimization on the digital twin, and realize adaptive speed control of the new energy ship.

[0015] This application proposes a digital twin-based adaptive speed control method and system for new energy vessels. The method retrieves the geometric parameters of the new energy vessel, performs multiphysics dynamic analysis to obtain new energy power information, introduces environmental perception parameters to construct a digital twin of the new energy vessel, synchronizes the vessel's operational dataset to the digital twin for simulation calculations, and obtains a set of twin operational parameters. Based on preset navigation goals, a speed control strategy is formulated, and feedback optimization is performed on the digital twin to achieve adaptive speed control for the new energy vessel. This solves the technical problems in existing technologies, such as the difficulty in accurately perceiving the complex operating conditions of new energy vessels and the inability to dynamically optimize speed based on real-time vessel status and external environmental changes, leading to low navigation efficiency and serious energy waste. It achieves the technical effect of real-time mapping and simulation analysis of the vessel's operating status through a digital twin of the new energy vessel, thereby improving navigation efficiency and energy utilization efficiency. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings of the embodiments of this disclosure will be briefly described below. Flowcharts are used in this application to illustrate the operations performed by the system according to the embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0017] Figure 1 A schematic diagram of the adaptive speed control method for new energy ships based on digital twins provided in this application embodiment.

[0018] Figure 2 A schematic diagram of the structure of a new energy ship adaptive speed control system based on digital twin provided in this application embodiment.

[0019] Figure labeling: New energy power information acquisition module 10, digital twin construction module 20, ship twin operation parameter set acquisition module 30, speed control strategy formulation module 40. Detailed Implementation

[0020] The above description is merely an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of this application are given below.

[0021] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description of this application will be provided in conjunction with the accompanying drawings. The described embodiments should not be considered as limitations on 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] In the following description, references to "some embodiments" describe a subset of all possible embodiments. However, it is understood that "some embodiments" can be the same or different subsets of all possible embodiments and can be combined with each other without conflict. The terms "first" and "second" are used merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only.

[0023] This application provides an adaptive speed control method for new energy ships based on digital twins, such as... Figure 1 As shown, the method includes:

[0024] Step S100: Retrieve the geometric parameters of the new energy ship, perform multiphysics dynamic analysis according to the geometric parameters, and obtain new energy power information.

[0025] Step S100 further includes step S110, identifying the hull geometric point cloud based on the geometric parameters of the new energy vessel to obtain hull surface point cloud data; step S120, performing cabin structure analysis based on the geometric parameters of the new energy vessel to obtain cabin structure topology data; step S130, performing position analysis on the power equipment based on the hull surface point cloud data and the cabin structure topology data to determine the power equipment layout coordinates; step S140, performing multiphysics field coupling calculations on the new energy vessel according to the power equipment layout coordinates, hull surface point cloud data, and cabin structure topology data to generate multiphysics field power data; step S150, fusing the multiphysics field power data to generate a comprehensive power performance index set, optimizing the power configuration of the new energy vessel according to the comprehensive power performance index set, and determining the new energy power information.

[0026] Preferably, the geometric parameters of the new energy vessel are retrieved. This involves obtaining three-dimensional spatial structural parameters such as the hull dimensions, cabin layout, and equipment installation locations of the new energy vessel through ship design drawings, 3D modeling software (such as CATIA, SolidWorks), or laser scanning. These parameters include the vessel's length, beam, depth, hull surface equations, and bulkhead position coordinates. Based on these geometric parameters, the hull is then labeled with a geometric point cloud. This involves discretizing the hull surface to convert it into a large number of three-dimensional coordinate points (point clouds), such as the waterline surface and the side surface. The process involves meshing the data, with the coordinates (x, y, z) of each point forming a point cloud to achieve digital modeling of the ship's shape and obtain point cloud data of the hull surface. Then, based on the geometric parameters of the new energy ship, the cabin structure is analyzed, and the cabin structure (such as decks, bulkheads, and partitions) is analyzed topologically to obtain cabin structure topology data. This includes determining the spatial location, connection relationship, and boundary conditions of each cabin. For example, the bulkhead coordinates are determined through geometric parameters to generate a topology map of the cabins (such as a tree structure), clarifying the spatial distribution of the power equipment room, cargo hold, bridge, etc.

[0027] Preferably, the location analysis of the power equipment is performed by combining the point cloud of the hull surface and the topology data of the compartment. This involves analyzing the installation feasibility of the power equipment (such as motors, battery packs, and fuel cell stacks), including configuring positional constraints to avoid stress concentration areas of the hull and meet the load-bearing limits of the compartment. Based on the matching degree between the equipment size and the compartment space, the optimal installation coordinates (such as X=10m, Y=2m, Z=3m) are determined, and finally the layout coordinates of the power equipment are generated to provide the precise spatial location parameters of the equipment. Then, multiphysics coupling calculations are performed on the new energy ship based on the power equipment layout coordinates, hull surface point cloud data, and cabin structure topology data. This may include: calculating the water resistance and wave-making resistance of the ship at different speeds using CFD simulation based on the hull surface point cloud; analyzing the stress and deformation of the hull and equipment under dynamic loads using finite element analysis (FEA) in the structural mechanics field; calculating the heat distribution and heat dissipation requirements of the power equipment (such as batteries and motors) during operation in the thermal field; and analyzing the electromagnetic field distribution and energy loss of the electric propulsion unit in the electromagnetic field. Specifically, the power equipment layout coordinates, hull point cloud, and cabin topology are imported into multiphysics simulation software, boundary conditions (such as speed, sea state, and ambient temperature) are set, and multiphysics dynamic data are generated by coupling various physics fields, such as hull resistance-power curves at different speeds, heat loss distribution of the power equipment, and stress cloud maps of the hull structure.

[0028] Preferably, multi-physics dynamic data is fused to extract key indicators, which may include energy efficiency indicators (energy consumption per unit distance, power system efficiency), sailing performance indicators (maximum speed, acceleration response time), and safety indicators (equipment temperature threshold, structural stress safety margin), thereby generating a comprehensive dynamic performance indicator set. Then, the power configuration of the new energy ship is optimized. Based on the comprehensive dynamic performance indicator set, the power system parameters (such as motor power, battery capacity, and propeller type) are adjusted through optimization algorithms (such as genetic algorithms and particle swarm optimization). For example, if the energy consumption index is too high, the battery layout can be optimized to improve heat dissipation efficiency, or the motor power distribution can be adjusted to reduce drag loss. Finally, the new energy power information is determined, which may include the optimal layout scheme of the power equipment, the power system parameter configuration (such as motor rated power and battery pack voltage platform), and the power output characteristic curves under typical operating conditions (such as the speed-power correspondence), providing a data foundation for adaptive speed control, thereby realizing accurate modeling and optimization of the new energy ship power system.

[0029] Furthermore, step S140 also includes step S141, performing interactive dynamic calculations on the new energy vessel and fluid based on the power equipment layout coordinates and the hull surface point cloud data, and drawing a vessel propulsion efficiency map; step S142, performing operational temperature rise calculations on the new energy vessel according to the power equipment layout coordinates and the cabin structure topology data, and obtaining equipment thermal evacuation demand information; step S143, performing finite element analysis to calculate the structural stress field of the new energy vessel based on the hull surface point cloud data and the cabin structure topology data, and generating hull vibration modal parameters; step S144, adding the vessel propulsion efficiency map, the equipment thermal evacuation demand information, and the hull vibration modal parameters to the multiphysics dynamic data.

[0030] Preferably, based on the layout coordinates of the power equipment (such as the position of the thruster and the direction of the motor axis) and the point cloud data of the hull surface, interactive dynamic calculations are performed on the new energy ship and the fluid. That is, the motion of the ship in the water is simulated by computational fluid dynamics (CFD). Specifically, the hull surface mesh (the discretized result of the point cloud), the geometric parameters of the thruster (such as the propeller diameter and the number of blades), and the fluid medium properties (seawater density and viscosity) are used as input parameters to calculate the flow field distribution around the hull, the propeller wake effect and thrust characteristics, and output the calculation results, namely the ship propulsion efficiency graph. The horizontal axis is the speed (such as 0-20 knots) and the vertical axis is the propulsion efficiency (%). Different colors or curves represent open water efficiency (the efficiency of the thruster working alone), hull efficiency (the matching efficiency of the hull and the thruster) or total propulsion efficiency (the comprehensive efficiency after considering appendage resistance and wake effects). For example, when the speed is 12 knots, the graph shows that the total efficiency of the thruster can reach 78% under a certain layout, while the efficiency drops to 72% when the layout is offset by 0.5 meters.

[0031] Preferably, the operating temperature rise of new energy ships is calculated using the layout coordinates of power equipment (such as the installation location of battery packs) and cabin structure topology data (such as the thermal insulation performance of bulkheads and the routing of ventilation ducts). That is, a heat conduction-convection model is established. Specifically, the heat source is the power loss of ship power equipment (motors, frequency converters, batteries) converted into heat (such as Joule heat during battery charging and discharging). The heat transfer path is heat dissipation through air convection, bulkhead heat conduction, and forced cooling system (such as liquid cooling pipes). Based on the temperature threshold (such as the battery safety temperature ≤55℃), the heat dissipation scheme parameters are determined, and then the thermal evacuation demand information of the equipment is calculated and output. This may include the surface temperature distribution cloud map of the equipment (such as the highest temperature point of the battery pack is located in the corner, reaching 65℃) and the air temperature field distribution in the cabin (such as the temperature in the middle of the power compartment is 12℃ higher than that at the entrance).

[0032] Preferably, the structural stress field of the new energy ship is calculated by finite element analysis based on the hull surface point cloud data and the cabin structure topology data. Specifically, the hull surface point cloud data is used to perform finite element mesh generation, that is, the hull is discretized into thousands of elements (such as tetrahedral and hexahedral elements), and the point cloud density determines the mesh accuracy. The boundary conditions are determined based on the cabin structure topology data, including the hydrostatic pressure, wave load, and vibration load of the power equipment (such as the periodic force generated by the rotation of the motor). Then, the structural dynamic equations are solved to generate the hull vibration modal parameters, including the stress concentration areas and vibration modes (natural frequencies and mode shapes, fatigue hot spots) of key parts of the hull (such as the bow and stern connection). Finally, by integrating the ship's propulsion efficiency graph, equipment thermal evacuation requirements, and hull vibration modal parameters, multi-physics dynamic data is added, including ship-related numerical data (such as propulsion efficiency of 75% at a certain speed, maximum equipment temperature of 52℃, and maximum hull stress of 150MPa), visualization data (such as efficiency graph, temperature cloud map, and stress cloud map), and related constraints (such as temperature ≤ 55℃, stress ≤ 80% of allowable material stress, and vibration frequency avoiding the 12-18Hz range).

[0033] Step S200: Introduce environmental perception parameters and construct a digital twin of the new energy ship by combining the new energy power information.

[0034] Step S200 further includes step S210, constructing a benchmark digital twin framework based on the geometric parameters of the new energy vessel and the new energy power information; step S220, extracting the center of gravity of the new energy vessel as the origin, constructing a follow-up coordinate system, and performing spatial unification based on the follow-up coordinate system to set spatial benchmark information; step S230, extracting multiple vessel operation timestamps based on the new energy power information, and setting time benchmark information according to the multiple vessel operation timestamps; step S240, constructing a spatiotemporal alignment engine based on the spatial benchmark information and the time benchmark information; step S250, activating the spatiotemporal alignment engine, and dynamically mapping the environmental perception parameters and the new energy power information to the benchmark digital twin framework through the spatiotemporal alignment engine to generate an environment-power coupling field; step S260, performing digital mirroring through the environment-power coupling field to construct the digital twin.

[0035] Preferably, environmental perception parameters are introduced, that is, real-time external environmental data is acquired through ship sensors (such as weather stations, radar, GPS), shore-based data (such as marine forecasts), or satellite remote sensing. This may include meteorological parameters (wind speed, wind direction, temperature, air pressure), sea state parameters (wave height, wave direction, tidal current speed, seawater temperature), and geographical parameters (water depth, channel boundaries, obstacle coordinates, such as reefs, and the positions of other ships). Then, using the geometric parameters of the new energy ship (three-dimensional coordinate point cloud of the hull, cabin structural dimensions, equipment layout coordinates) and new energy power information (multi-physics field coupling calculation results) as input data, parametric modeling is used to associate the geometric model with the power performance data to construct a benchmark digital twin framework, which provides input and output data such as three-dimensional models of the hull and equipment, as well as power, thermal, and stress data.

[0036] Preferably, the center of gravity of the new energy vessel is extracted as the origin O, and a moving coordinate system O-XYZ is constructed to follow the ship's motion. The X-axis points to the bow (direction of movement), the Y-axis points to the starboard side (horizontal direction perpendicular to the X-axis), and the Z-axis is vertically upward (forming a right-handed coordinate system with the X and Y axes). For example, the coordinates of a certain device in the moving coordinate system are (10, 1, -2), indicating that it is located 10 meters in front of the center of gravity, 1 meter to the starboard side, and 2 meters below. Then, coordinate transformation is performed on all geometric and dynamic data according to the moving coordinate system to achieve spatial unification. This includes converting the hull point cloud data from the global coordinate system to the moving coordinate system and mapping environmental perception data (such as the direction of water flow velocity) to the relative velocity in the moving coordinate system. For example, the speed of the water flow relative to the ship is 2 m / s along the -X direction.

[0037] Preferably, multiple ship operation timestamps are extracted from the new energy power information, including the start / stop time of the power equipment, the time of speed change, and the time of physical field data acquisition. Then, time reference information is set according to multiple ship operation timestamps, that is, a continuous time axis is established with the ship start time as t=0. All data must be marked with timestamps. For example, a certain propulsion efficiency data corresponds to t=600s, and a certain temperature data corresponds to t=605s, to ensure that the data is called in chronological order during subsequent simulations. Then, a spatiotemporal alignment engine is constructed based on spatial and temporal reference information. Specifically, a spatial transformation matrix between ship geometric parameters and environmental parameters is established through a servo coordinate system. The temporal reference information is then correlated with the ship's operating status (such as speed and power output) to form a time-state mapping table. Kalman filtering and particle filtering are introduced to calibrate the spatial coordinates and time series in real time to ensure the accuracy of the spatial reference. Interpolation methods (such as cubic spline interpolation) are used to smooth the discrete data points in the time series to ensure the continuity of the time reference. By receiving real-time acquired ship operating data (such as GPS position and attitude sensor data) and environmental data (such as weather radar data), dynamic updates of spatial coordinates and time series are triggered.

[0038] Preferably, when real-time collected environmental sensing parameters (such as wind speed, wave height, and water flow speed) or ship operating status (such as speed and turning angle) change, the spatiotemporal alignment engine is triggered to start, and the environmental sensing parameters and new energy power information are dynamically mapped to the benchmark digital twin framework. Specifically, environmental data such as wind speed and water flow are transformed from the global coordinate system (such as latitude and longitude) to the ship's servo coordinate system, and new energy power information such as battery pack temperature and motor output power are mapped to the corresponding positions of the ship's three-dimensional model. The environmental data and power data collected by different sensors are sorted and synchronized according to a unified time benchmark, and the mapping frequency is adjusted according to the speed of data change, ultimately generating a spatiotemporally unified multidimensional data matrix.

[0039] Preferably, the input layer receives the multi-dimensional data matrix output by the spatiotemporal alignment engine; the computation layer calls the multiphysics coupling model for parallel computation, for example, the fluid dynamics module calculates the external forces acting on the ship, the thermodynamics module calculates the internal temperature of the equipment, and the energy flow module calculates the power demand; the feedback layer sets up a feedback layer to allow the calculation results of each physics field to influence each other, forming a coupling effect, for example, an increase in hull resistance leads to an increase in motor load, which in turn leads to an increase in heat generation, which further affects motor efficiency; the output layer generates a visualization result of the environment-dynamic coupling field, such as the flow field distribution around the ship, equipment temperature cloud map, energy flow path, etc.; thus generating the environment-dynamic coupling field.

[0040] Preferably, the final step involves digital mirroring via an environment-dynamic coupling field. This includes combining the ship's 3D model in the baseline digital twin framework with real-time acquired hull attitude data (such as roll and pitch angles) to generate a geometric mirror synchronized with the physical ship in real time; mapping the calculation results of the environment-dynamic coupling field onto the digital model to form a mirror of the physical state; obtaining the digital mirror output result and comparing it with the actual operating data of the physical ship, continuously adjusting the parameters of the coupling field model to ensure that the errors are within acceptable ranges, such as a speed prediction error of no more than ±0.5 knots and a temperature prediction error of no more than ±2℃. Ultimately, a digital twin of the new energy ship is obtained, which can reflect the ship's operating status in real time, predict future trends, and support simulation-based decision optimization.

[0041] Step S300: The real-time collected ship operation dataset is synchronized to the digital twin for simulation calculation to obtain the ship twin operation parameter set.

[0042] Step S300 further includes step S310, constructing a multi-source operation data acquisition channel for new energy ships, and acquiring the ship operation dataset in real time through the multi-source operation data acquisition channel; step S320, performing multi-level cleaning on the ship operation dataset to generate a standardized twin input data stream; step S330, injecting the standardized twin input data stream into the dynamic data bus of the digital twin to generate multi-physics coupling simulation parameters; step S340, performing multi-dimensional simulation according to the multi-physics coupling simulation parameters, and fusion of the multi-dimensional simulation results to generate the ship twin operation parameter set.

[0043] Preferably, industrial-grade sensors (high-precision IMU inertial measurement units, ultrasonic wave height meters, etc.) are deployed to construct a sensor network as a multi-source operational data acquisition channel for new energy ships. The ship's operational dataset is acquired in real time through the multi-source operational data acquisition channel, which may include data on the ship's hull status (GPS position, attitude angle, acceleration), power system data (battery voltage / current / SOC, motor speed / torque, fuel cell output power), environmental parameters (wind speed / wind direction, wave height / period, water temperature / salinity, water flow speed), and ship control system data (speed commands, rudder angle commands, power mode switching signals, and equipment operating status). The ship operation dataset undergoes multi-level cleaning, specifically including outlier filtering (using the Laida criterion to remove abnormal data); frequency normalization (spline interpolation for low-frequency data (structural strain <1Hz) and moving average filtering for high-frequency data (motor speed >100Hz); unit standardization (converting to SI units and adding dimensional labels); null value compensation (predicting missing time periods using an LSTM network); confidence labeling (adding precision weights to each data point); and finally, generating a standardized twin input data stream.

[0044] Preferably, the standardized twin input data stream is injected into the dynamic data bus of the digital twin. Specifically, the standardized twin input data stream is transmitted to the dynamic data bus of the digital twin through a message queue (such as Kafka). The dynamic data bus classifies the data according to its data type and distributes the data to the corresponding simulation modules. For example, the collected sensor data (such as temperature and pressure) is directly mapped to the boundary conditions of the simulation model. The internal parameters of the simulation model are adjusted according to the equipment operating status (such as motor speed and pump valve opening). Interpolation processing is performed on continuously changing data (such as ship speed and wind direction) to ensure the continuity of data within the simulation time step. Then, fluid dynamic parameters are calculated based on the ship's geometry and real-time water flow data; the thermal conductivity coefficient and convective heat transfer coefficient are set according to the equipment power and ambient temperature; and parameters such as elastic modulus and Poisson's ratio are determined based on material properties and load distribution. Among these, the pressure distribution obtained from fluid calculation is used as the load for structural analysis, the temperature field generated by equipment heat dissipation is used as the input for fluid density calculation, and the heat source distribution is calculated based on power system losses, ultimately generating multiphysics coupled simulation parameters.

[0045] Preferably, multi-dimensional simulation is performed according to the multi-physics coupled simulation parameters. That is, distributed computing is used to realize the parallel execution of multi-physics simulation, and the output of the previous time step is used as the input of the next time step. The continuous simulation results are discretized into spatiotemporal grid data. Then, the multi-dimensional simulation results are fused, that is, the results of different physical fields are unified into the ship's servo coordinate system, the results of each field are converted into a unified physical unit, and key characteristic parameters (such as drag coefficient, heat transfer coefficient, vibration frequency) are extracted. Finally, a set of ship twin operating parameters is generated, which may include navigation performance parameters, equipment status parameters, environmental adaptability parameters and time series data. This can be used for speed optimization, equipment scheduling and fault early warning, and adaptive speed control can be realized through the feedback optimization mechanism of digital twin.

[0046] Furthermore, step S330 also includes step S331, setting data execution priority through the dynamic data bus, allocating time slots for the standardized twin input data stream according to the data execution priority, and generating multiple data streams to be injected; step S332, injecting the multiple data streams to be injected into the dynamic data bus of the digital twin, and formulating a multiphysics simulation task; step S333, performing simulation analysis according to the multiphysics simulation task, and obtaining the multiphysics coupling simulation parameters.

[0047] Preferably, data execution priorities are set via a dynamic data bus. Critical data related to navigation safety (such as abnormal rudder angles and collision warnings) is set to the highest priority; data with high-frequency changes is set to the second highest priority; data with significant multi-physics coupling effects (such as speed and wind direction) is set to the medium priority; and non-critical data that can be delayed (such as equipment maintenance logs) is set to the low priority. The standardized twin input data stream is then allocated time slots according to priority, including reserving a fixed proportion of bus bandwidth for different priority data (e.g., 40% for the highest priority, 30% for the second highest priority, 20% for the medium priority, and 10% for the low priority). The time slots are dynamically adjusted based on real-time load; for example, if the volume of the highest priority data surges, the low priority time slots are temporarily occupied, and the transmission of low priority data can be interrupted by high priority data. Based on the priority and time slot allocation results, the standardized data stream is divided into multiple independent data streams to be injected, each containing information such as timestamp, data type, and priority identifier.

[0048] Preferably, multiple data streams to be injected are injected into the dynamic data bus of the digital twin, and multiphysics simulation tasks are defined. Specifically, these include: a fluid simulation task, mapped to the ship's hydrodynamics module, processing data on the interaction between the hull and the fluid (such as speed, draft, and current velocity); a structural simulation task, mapped to the hull structure module, processing stress and vibration-related data (such as acceleration and load distribution); an energy flow simulation task, mapped to the power system module, processing energy conversion and distribution data (such as battery power and motor efficiency); and a thermal management simulation task, mapped to the thermodynamics module, processing equipment heat dissipation and ambient temperature data. When specific data arrives, the corresponding physical field is triggered. The simulation task is then carried out according to the multiphysics simulation task, including fluid simulation (calculating the flow field around the hull, drag coefficient, and wave height), structural simulation (using the finite element method to analyze the stress distribution and deformation of the hull under wave load), energy flow simulation (based on the power electronics model, calculating the battery discharge curve, motor efficiency curve, and energy loss), and thermal management simulation (simulating the equipment temperature rise process and heat dissipation efficiency through the heat conduction equation). Finally, multiphysics coupled simulation parameters are obtained, such as effective power demand, hull fatigue damage rate, and remaining battery range, to facilitate the performance evaluation, fault warning, and control strategy optimization of new energy ships.

[0049] Furthermore, step S333 also includes step a, performing the multiphysics simulation task to conduct motion simulation analysis and generate motion attitude simulation parameters; step b, performing the multiphysics simulation task to conduct energy simulation analysis and generate energy efficiency simulation parameters; step c, performing the multiphysics simulation task to conduct structural force simulation analysis and generate structural stress simulation parameters; step d, fusing the motion attitude simulation parameters, the energy efficiency simulation parameters, and the structural stress simulation parameters to construct a simulation parameter matrix; and step e, performing a reliability closed-loop verification on the simulation parameter matrix to generate simulation parameter health values. When the simulation parameter health values ​​are higher than a preset health threshold, the multiphysics coupling simulation parameters are generated.

[0050] Preferably, a multiphysics simulation task is performed for motion simulation analysis. This involves using environmental parameters (wind speed and direction, water flow velocity) and ship parameters (center of gravity position, moment of inertia, hull waterline area) as input data to simulate the ship's six-degree-of-freedom motion (pitch, sway, heave, pitch, roll, bow) under environmental loads. The output motion attitude time series, including roll angle amplitude, heave displacement, and bow angular velocity, serves as the motion attitude simulation parameters. A multiphysics simulation task is also performed for energy simulation analysis. This involves using ship propulsion system parameters (battery capacity, motor efficiency curve, fuel cell polarization curve) and ship operating conditions (speed, propulsion power) to perform energy simulation analysis. Using energy demand as input data, the system quantifies the energy flow and loss path of new energy ships, and outputs battery discharge power, motor loss, propeller efficiency, energy consumption per unit voyage, and comprehensive energy conversion chain efficiency as energy efficiency simulation parameters. It also performs multiphysics simulation tasks to conduct structural force simulation analysis, using load conditions (wave bending moment, equipment vibration load) and ship structural parameters (hull plate thickness, material yield strength) as input data to evaluate the structural safety of the hull and equipment under dynamic loads, and outputs stress distribution cloud maps (such as maximum stress at the bow connection and stress in the middle of the deck) and fatigue damage parameters (such as hot spot stress range and cumulative fatigue damage).

[0051] Preferably, the motion posture simulation parameters, energy efficiency simulation parameters, and structural stress simulation parameters are fused to construct a simulation parameter matrix. Example data is shown in Table 1.

[0052] Table 1 Simulation Parameter Matrix Data Table

[0053]

[0054] Preferably, a closed-loop verification of the reliability of the simulation parameter matrix is ​​performed. Specifically, motion attitude (such as IMU measured roll angle), energy consumption (smart meter data), and stress (strain gauge measurement values) are collected by ship sensors to obtain a database of simulation results of the same type of ship under similar working conditions. Then, the absolute error between the simulation parameters and the measured values ​​is calculated and it is determined whether the simulation results are within the confidence interval. Then, the motion error, energy error, and structural error are weighted and summed to obtain the health value of the simulation parameters. The preset health threshold is usually set to 0.8 (out of 1.0). When the health value is ≥0.8, the simulation parameters are considered reliable and multiphysics coupling parameters are generated. When the health value is <0.8, the model correction process is triggered (such as adjusting the wave load calculation coefficient), and the simulation is repeated until the standard is met, thereby ensuring the simulation accuracy.

[0055] Step S400: Constrain the set of ship twin operating parameters according to the preset navigation target, formulate a speed control strategy, execute the speed control strategy to perform feedback optimization on the digital twin, and realize adaptive speed control of new energy ships.

[0056] Preferably, the preset navigation objectives may include specified arrival time, designated route, limited energy consumption, and ensuring navigation safety. Based on the preset navigation objectives, a comprehensive analysis of the ship's twin operating parameters is conducted to generate constraints. For example, if the objective emphasizes energy consumption, upper limits are set for power parameters such as maximum motor power and propulsion system efficiency; if the objective focuses on sailing time, a lower limit is specified for speed, and the range of motion attitude parameters unfavorable to high-speed sailing is restricted. Then, considering the ship's current power performance, motion state, hull structural capacity, external environmental conditions, and preset navigation objectives, a detailed speed control strategy is formulated. This may include target speed settings for different sailing stages, such as slow and steady acceleration at low speed during the start-up phase, maintaining an economical speed during the mid-journey phase, and slowing down to dock when approaching the destination; and rules for adjusting speed in real time according to environmental changes, such as reducing speed to reduce energy consumption when encountering headwinds and appropriately increasing speed to shorten sailing time when encountering tailwinds.

[0057] Preferably, the pre-defined speed control strategy is sent to the ship's actual actuators, such as the propulsion control system and power distribution system, driving the ship to adjust its speed according to the strategy. Simultaneously, various sensors on the ship collect real-time operational data, including actual speed, power output, and motion attitude. This collected data is synchronized to a digital twin, compared with predicted data in the ship's twin operational parameter set, and the deviation is calculated. Based on the deviation results, the digital twin is optimized to more accurately reflect the ship's actual operating state. The optimized digital twin can more accurately predict the ship's operating state under different speed control strategies, thereby evaluating the effectiveness of the current speed control strategy in real time. It can also automatically adjust the speed control strategy according to new environmental changes and ship status, ensuring that the new energy ship always operates at the optimal speed, ultimately achieving adaptive speed control for new energy ships.

[0058] Furthermore, step S400 also includes step S410, performing dynamic constraint analysis on the ship twin operating parameters according to the preset navigation target, and formulating dynamic constraint conditions and motion constraint conditions; step S420, constraining the ship twin operating parameter set according to the dynamic constraint conditions, and generating a ship twin operating dynamic control interval; step S430, constraining the ship twin operating parameter set according to the motion constraint conditions, and generating a ship twin operating motion control interval; step S440, performing multi-objective speed optimization on the new energy ship based on the ship twin operating dynamic control interval and the ship twin operating motion control interval, and generating a multi-objective speed control sequence; step S450, adding the multi-objective speed control sequence to the speed control strategy.

[0059] Preferably, dynamic constraint analysis is performed on the ship's twin operating parameters according to the preset navigation target. This involves considering the ship's current power performance, motion state, and external environmental conditions, assessing the impact of each parameter on the achievement of the target, and formulating power constraint conditions. This involves setting limits around the ship's power system operating state, including the upper limit of battery charge, the maximum power of the motor, and the range of fuel cell output power, to prevent damage to critical equipment such as batteries and motors from exceeding limits. Motion constraint conditions are also formulated, which impose constraints on the ship's navigation attitude and structural stress, such as the maximum roll angle, the maximum pitch angle, the maximum speed, and the minimum turning radius, to ensure that the ship remains stable in complex sea conditions and prevent structural damage due to excessive motion.

[0060] Preferably, based on the dynamic constraints, the dynamic-related parameters in the ship twin operation parameter set are screened and restricted. Through calculation and analysis, the feasible range of values ​​for dynamic parameters (such as motor speed and propulsion power) under the dynamic constraints is determined, generating the ship twin operation dynamic control range. For example, the motor speed is controlled at 500-1200 rpm, and the propulsion power must not exceed 300 kW. Based on the motion constraints, the motion parameters (such as speed, heading angle, roll angle, and pitch angle) in the ship twin operation parameter set are constrained to determine the reasonable range of variation of these parameters under the premise of ensuring the safe and stable movement of the ship, generating the ship twin operation motion control range. For example, the speed needs to be maintained at 8-15 knots, and the roll angle does not exceed 10 degrees.

[0061] Preferably, a multi-objective speed optimization model is constructed based on the ship's twin operation dynamic control range and motion control range. This model comprehensively considers multiple objectives such as navigation time, energy consumption, and safety, establishing an objective function. For example, under the premise of satisfying safety and dynamic constraints, it minimizes the weighted sum of navigation time and energy consumption. Then, intelligent optimization algorithms (such as genetic algorithms and particle swarm optimization algorithms) are used to solve the multi-objective speed optimization model, searching for the optimal solution within the dynamic and motion control ranges to determine the optimal speed for different navigation stages. Based on the optimization results, multiple speed commands are generated in chronological order, forming a multi-objective speed control sequence. This sequence clarifies the speed the ship should reach at each time point, achieving a balance and optimization of multiple objectives. Finally, the multi-objective speed control sequence is added to the speed control strategy. During actual navigation, the speed control strategy dynamically adjusts and precisely controls the ship's speed according to the multi-objective speed control sequence, based on real-time collected ship operation data and environmental information, combined with the simulation results of the digital twin, ensuring that the ship can efficiently and safely complete the preset navigation objectives.

[0062] Furthermore, step S400 also includes step S460, which involves sending the speed control strategy to the ship's actuators for real-time execution recording to obtain the actual speed parameters of the new energy ship; step S470, which involves performing deviation critical analysis based on the ship's twin operating parameter set to construct an error-parameter mapping rule table; step S480, which involves synchronizing the actual speed parameters to the error-parameter mapping rule table for matching to obtain an error feedback value; and step S490, which involves triggering online calibration of the digital twin based on the error feedback value, updating the digital twin, re-simulating and calculating, constructing a data optimization closed loop, and realizing adaptive speed control of the new energy ship.

[0063] Preferably, the speed control sequence is sent to the propulsion system controller (such as a frequency converter or motor driver) via the ship network. After receiving the command, the actuator tracks the speed through PID control. The actual speed of the new energy ship is collected using a Doppler speedometer, GPS speed module, etc., and the execution timestamp, control command value and actual response value are recorded synchronously to form the actual speed parameters. Then, based on the ship's twin operating parameter set, a deviation critical analysis is performed. Specifically, different deviation thresholds are set according to the ship's load (such as cargo capacity) and the severity of the environment. For example, the speed deviation critical value is ±0.5 knots (normal operating conditions) / ±1 knot (severe sea conditions), and the energy consumption deviation critical value is ±5% (short-term) / ±10% (long-term). An error-parameter mapping rule table is constructed based on historical fault data, as shown in Table 2.

[0064] Table 2 Error-Parameter Mapping Rules

[0065] Error type Possible cause parameters Mapping relationship (error → influencing parameters) Speed ​​overshoot > 0.5 knots Deviation in water flow resistance calculation Water flow velocity parameter correction +10% Abnormally high energy consumption Motor efficiency model error Motor loss factor correction +5% Roll angle exceeded Insufficient simulation of wave loads Wave spectral density parameter correction +15%

[0066] Preferably, the actual speed parameters are compared with the digital twin's operating parameters to calculate the absolute error. The error trend is then analyzed using time series data. This is then synchronized to the error-parameter mapping rule table for matching, obtaining an error feedback value. For example, if the speed deviation is positive by 0.4 knots and continues to increase, the calculated water resistance is underestimated, and the water velocity parameter needs to be increased by 10% in the feedback value. Next, the error feedback value triggers online calibration of the digital twin, including adjusting the simulation model's input parameters (e.g., correcting the water velocity from 2 m / s to 2.1 m / s) and dynamically updating the equipment efficiency curves (e.g., adjusting the overall motor efficiency curve downwards based on higher measured energy consumption). When the error exceeds the threshold (e.g., speed error > 0.5 knots for 10 consecutive minutes), model structure optimization is triggered, the digital twin is updated, and multiphysics coupling simulation calculation is performed again to generate a new set of ship twin operating parameters. This achieves dynamic synchronization between the model and the actual operating state, thereby constructing a complete data optimization closed loop, monitoring the deviation between actual and twin data in real time, and dynamically calibrating the digital twin to more accurately reflect the ship's true state. This helps to dynamically adjust the speed control strategy, enabling new energy ships to automatically optimize speed according to environmental changes and equipment status, achieve adaptive speed control, and improve navigation efficiency and safety.

[0067] In the above text, refer to Figure 1 This paper describes in detail an adaptive speed control method for new energy ships based on digital twins according to embodiments of the present invention. Next, reference will be made to... Figure 2 This invention describes an adaptive speed control system for new energy ships based on digital twins, according to an embodiment of the present invention.

[0068] The adaptive speed control system for new energy ships based on digital twins according to embodiments of the present invention addresses the technical problems in existing technologies, such as the difficulty in accurately sensing the complex operating conditions of new energy ships and the inability to dynamically optimize speed based on real-time ship status and changes in the external environment, leading to low ship navigation efficiency and serious energy waste. It achieves the technical effect of improving ship navigation efficiency and energy utilization efficiency by realizing real-time mapping and simulation analysis of ship operating status through a digital twin of the new energy ship. Figure 2 As shown, the adaptive speed control system for new energy ships based on digital twins includes: a new energy power information acquisition module 10, a digital twin construction module 20, a ship twin operation parameter set acquisition module 30, and a speed control strategy formulation module 40.

[0069] The new energy power information acquisition module 10 is used to retrieve the geometric parameters of the new energy vessel, perform multiphysics dynamic analysis according to the geometric parameters, and obtain new energy power information; the digital twin construction module 20 is used to introduce environmental perception parameters and construct a digital twin of the new energy vessel in combination with the new energy power information; the vessel twin operation parameter set acquisition module 30 is used to synchronize the real-time collected vessel operation dataset to the digital twin for simulation calculation and obtain the vessel twin operation parameter set; the speed control strategy formulation module 40 is used to constrain the vessel twin operation parameter set according to the preset navigation target, formulate a speed control strategy, execute the speed control strategy to perform feedback optimization on the digital twin, and realize adaptive speed control of the new energy vessel.

[0070] The specific configuration of the new energy power information acquisition module 10 will be described in detail below. The new energy power information acquisition module 10 further includes: identifying the hull geometric point cloud based on the geometric parameters of the new energy vessel to obtain hull surface point cloud data; performing cabin structure analysis based on the geometric parameters of the new energy vessel to obtain cabin structure topology data; performing position analysis on the power equipment according to the hull surface point cloud data and the cabin structure topology data to determine the power equipment layout coordinates; performing multiphysics field coupling calculations on the new energy vessel according to the power equipment layout coordinates, the hull surface point cloud data, and the cabin structure topology data to generate multiphysics field power data; fusing the multiphysics field power data to generate a comprehensive power performance index set; and optimizing the power configuration of the new energy vessel according to the comprehensive power performance index set to determine the new energy power information.

[0071] The specific configuration of the new energy power information acquisition module 10 will be described in detail below. The new energy power information acquisition module 10 further includes: performing interactive power calculations on the new energy vessel and fluid based on the power equipment layout coordinates and the hull surface point cloud data, and drawing a vessel propulsion efficiency map; calculating the operating temperature rise of the new energy vessel according to the power equipment layout coordinates and the cabin structure topology data to obtain equipment thermal evacuation demand information; performing finite element analysis to calculate the structural stress field of the new energy vessel based on the hull surface point cloud data and the cabin structure topology data, and generating hull vibration modal parameters; and adding the vessel propulsion efficiency map, the equipment thermal evacuation demand information, and the hull vibration modal parameters to the multiphysics dynamic data.

[0072] The specific configuration of the digital twin construction module 20 will be described in detail below. The digital twin construction module 20 further includes: constructing a baseline digital twin framework based on the geometric parameters of the new energy vessel combined with the new energy power information; extracting the vessel's center of gravity as the origin based on the new energy vessel, constructing a follow-up coordinate system, and performing spatial unification according to the follow-up coordinate system to set spatial reference information; extracting multiple vessel operation timestamps based on the new energy power information, and setting time reference information according to the multiple vessel operation timestamps; constructing a spatiotemporal alignment engine based on the spatial reference information and the time reference information; activating the spatiotemporal alignment engine, and dynamically mapping the environmental perception parameters and the new energy power information to the baseline digital twin framework through the spatiotemporal alignment engine to generate an environment-power coupling field; and constructing the digital twin through digital mirroring of the environment-power coupling field.

[0073] The specific configuration of the ship twin operation parameter set acquisition module 30 will be described in detail below. The ship twin operation parameter set acquisition module 30 further includes: constructing a multi-source operation data acquisition channel for new energy ships, acquiring the ship operation dataset in real time through the multi-source operation data acquisition channel; performing multi-level cleaning on the ship operation dataset to generate a standardized twin input data stream; injecting the standardized twin input data stream into the dynamic data bus of the digital twin to generate multi-physics coupling simulation parameters; performing multi-dimensional simulation according to the multi-physics coupling simulation parameters, and fusing the multi-dimensional simulation results to generate the ship twin operation parameter set.

[0074] The specific configuration of the ship twin operating parameter set acquisition module 30 will be described in detail below. The ship twin operating parameter set acquisition module 30 further includes: setting data execution priorities through the dynamic data bus; allocating time slots to the standardized twin input data streams according to the data execution priorities to generate multiple data streams to be injected; injecting the multiple data streams to be injected into the dynamic data bus of the digital twin to formulate a multiphysics simulation task; and performing simulation analysis according to the multiphysics simulation task to obtain the multiphysics coupling simulation parameters.

[0075] The following will describe in detail the specific configuration of the ship twin operating parameter set acquisition module 30. The ship twin operating parameter set acquisition module 30 further includes: performing motion simulation analysis by executing the multiphysics simulation task to generate motion attitude simulation parameters; performing energy simulation analysis by executing the multiphysics simulation task to generate energy efficiency simulation parameters; performing structural force simulation analysis by executing the multiphysics simulation task to generate structural stress simulation parameters; fusing the motion attitude simulation parameters, the energy efficiency simulation parameters, and the structural stress simulation parameters to construct a simulation parameter matrix; performing a closed-loop reliability verification on the simulation parameter matrix to generate simulation parameter health values; and generating the multiphysics coupling simulation parameters when the simulation parameter health values ​​are higher than a preset health threshold.

[0076] The specific configuration of the speed control strategy formulation module 40 will be described in detail below. The speed control strategy formulation module 40 further includes: performing dynamic constraint analysis on the ship twin operating parameters according to the preset navigation target, and formulating dynamic constraint conditions and motion constraint conditions; constraining the ship twin operating parameter set according to the dynamic constraint conditions to generate a ship twin operating dynamic control interval; constraining the ship twin operating parameter set according to the motion constraint conditions to generate a ship twin operating motion control interval; performing multi-objective speed optimization on the new energy ship based on the ship twin operating dynamic control interval and the ship twin operating motion control interval to generate a multi-objective speed control sequence; and adding the multi-objective speed control sequence to the speed control strategy.

[0077] The specific configuration of the speed control strategy formulation module 40 will be described in detail below. The speed control strategy formulation module 40 further includes: distributing the speed control strategy to the ship's actuators for real-time execution recording to obtain the actual speed parameters of the new energy vessel; performing deviation critical analysis based on the ship's twin operating parameter set to construct an error-parameter mapping rule table; synchronizing the actual speed parameters to the error-parameter mapping rule table for matching to obtain an error feedback value; triggering online calibration of the digital twin based on the error feedback value, updating the digital twin, re-simulating and calculating, constructing a data optimization closed loop, and realizing adaptive speed control of the new energy vessel.

[0078] The adaptive speed control system for new energy ships based on digital twins provided in this invention can execute the adaptive speed control method for new energy ships based on digital twins provided in any embodiment of this invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0079] Although this application makes various references to certain modules in the system according to the embodiments of this application, any number of different modules can be used and run on user terminals and / or servers. The various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy distinction between each other and are not used to limit the scope of protection of this invention.

[0080] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for adaptive speed control of a new energy ship based on digital twinning, characterized in that, The method comprises: retrieve the geometric parameters of the new energy ship, perform multi-physical field dynamic analysis according to the geometric parameters, and obtain new energy power information; introducing environmental perception parameters, combining the new energy power information to construct the digital twin of the new energy ship; synchronize the real-time collected ship operation data set to the digital twin for simulation calculation, and obtain the ship twin operation parameter set; According to the preset navigation target, the ship twin operation parameter set is constrained, the speed control strategy is formulated, the feedback optimization of the digital twin is performed by executing the speed control strategy, and the adaptive speed control of the new energy ship is realized; Wherein, retrieve the geometric parameters of the new energy ship, perform multi-physical field dynamic analysis according to the geometric parameters, and obtain new energy power information, the method comprises: Based on the geometric parameters of the new energy ship, the hull geometric point cloud is identified, and the hull surface point cloud data is obtained; Based on the geometric parameters of the new energy ship, the cabin structure analysis is carried out, and the cabin structure topology data is obtained; According to the hull surface point cloud data, the cabin structure topology data, the position analysis of the power equipment is carried out, and the power equipment layout coordinates are determined; According to the power equipment layout coordinates, the hull surface point cloud data, the cabin structure topology data, the multi-physical field coupling calculation of the new energy ship is carried out, and the multi-physical field dynamic data is generated; The multi-physical field dynamic data is fused to generate a set of comprehensive dynamic performance indicators, and the power configuration optimization of the new energy ship is carried out according to the set of comprehensive dynamic performance indicators, and the new energy power information is determined.

2. The digital-twin-based adaptive speed control method for new energy ships according to claim 1, characterized in that, According to the power equipment layout coordinates, the hull surface point cloud data, the cabin structure topology data, the multi-physical field coupling calculation of the new energy ship is carried out, and the multi-physical field dynamic data is generated, the method comprises: Based on the power equipment layout coordinates, the hull surface point cloud data, the interactive dynamic calculation of the new energy ship and the fluid is carried out, and the ship propulsion efficiency map is drawn; According to the power equipment layout coordinates, the cabin structure topology data, the running temperature rise calculation of the new energy ship is carried out, and the equipment heat dissipation demand information is obtained; According to the hull surface point cloud data, the cabin structure topology data, the finite element analysis calculation structure stress field of the new energy ship is carried out, and the hull vibration modal parameter is generated; The ship propulsion efficiency map, the equipment heat dissipation demand information, and the hull vibration modal parameter are added to the multi-physical field dynamic data.

3. The digital-twin-based adaptive speed control method for new energy ships according to claim 1, wherein, Introducing environmental perception parameters, combining the new energy power information to construct the digital twin of the new energy ship, the method comprises: Based on the geometric parameters of the new energy ship, the reference digital twin framework is constructed combining the new energy power information; Based on the new energy ship, the ship gravity center is extracted as the origin, a follow-up coordinate system is constructed, space unification is carried out according to the follow-up coordinate system, and space reference information is set; Based on the new energy power information, a plurality of ship operation time stamps are extracted, and time reference information is set according to the plurality of ship operation time stamps; According to the space reference information and the time reference information, a space-time alignment engine is constructed; activating the space-time alignment engine, dynamically mapping the environment perception parameter and the new energy power information to the benchmark digital twin framework through the space-time alignment engine, and generating an environment-power coupling field; performing digital mirroring through the environment-power coupling field to construct the digital twin.

4. The digital-twin-based adaptive speed control method for new energy ships according to claim 1, characterized in that, The method comprises the following steps: constructing a multi-source operation data acquisition channel of the new energy ship, and acquiring the ship operation data set in real time through the multi-source operation data acquisition channel; performing multi-level cleaning on the ship operation data set to generate standardized twin input data stream; injecting the standardized twin input data stream into the dynamic data bus of the digital twin to generate multi-physical field coupling simulation parameters; performing multi-dimensional simulation according to the multi-physical field coupling simulation parameters, performing simulation fusion on the multi-dimensional simulation results, and generating the ship twin operation parameter set.

5. The digital-twin-based adaptive speed control method for new energy ships according to claim 4, characterized in that, The method comprises the following steps: setting data execution priority through the dynamic data bus, time slot allocation of the standardized twin input data stream according to the data execution priority, and generating a plurality of to-be-injected data streams; injecting the plurality of to-be-injected data streams into the dynamic data bus of the digital twin, and formulating a multi-physical field simulation task; performing simulation analysis according to the multi-physical field simulation task to obtain the multi-physical field coupling simulation parameters.

6. The digital-twin-based adaptive speed control method for new energy ships according to claim 5, characterized in that, The method comprises the following steps: performing motion simulation analysis on the multi-physical field simulation task to generate motion attitude simulation parameters; performing energy simulation analysis on the multi-physical field simulation task to generate energy efficiency simulation parameters; performing structural force simulation analysis on the multi-physical field simulation task to generate structural stress simulation parameters; fusing the motion attitude simulation parameters, the energy efficiency simulation parameters, and the structural stress simulation parameters to construct a simulation parameter matrix; performing credibility closed-loop verification on the simulation parameter matrix to generate a simulation parameter health value, and generating the multi-physical field coupling simulation parameters when the simulation parameter health value is higher than a preset health threshold.

7. The digital-twin-based adaptive speed control method for new energy ships according to claim 1, characterized in that, The method comprises the following steps: performing dynamic constraint analysis on the ship twin operation parameters according to the preset navigation target to formulate power constraint conditions and motion constraint conditions; constraining the ship twin operation parameter set according to the power constraint conditions to generate a ship twin operation power control interval; constraining the ship twin operation parameter set according to the motion constraint conditions to generate a ship twin operation motion control interval; performing multi-target speed optimization on the new energy ship based on the ship twin operation power control interval and the ship twin operation motion control interval to generate a multi-target speed control sequence; adding the multi-target speed control sequence to the speed control strategy.

8. The digital-twin-based adaptive speed control method for new energy ships according to claim 1, characterized in that, The speed control strategy is fed back to optimize the digital twin, realizing adaptive speed control of the new energy ship, and the method comprises: The speed control strategy is fed back to optimize the digital twin, realizing adaptive speed control of the new energy ship, and the method comprises: The actual speed parameters of the new energy ship are obtained by feeding the speed control strategy to the ship executive mechanism for real-time execution and recording; Based on the ship twin running parameter set, deviation critical analysis is carried out to construct error-parameter mapping rule table; The actual speed parameters are synchronized to the error-parameter mapping rule table for matching to obtain error feedback value; 9. A new energy ship adaptive speed control system based on digital twinning, characterized in that, According to the error feedback value, online calibration of the digital twin is triggered, the digital twin is updated for simulation calculation again, data optimization closed loop is constructed, and adaptive speed control of the new energy ship is realized. The system is used to implement the adaptive speed control method of the new energy ship based on digital twin, and the system comprises: A new energy power information acquisition module is used to call the geometric parameters of the new energy ship, perform multi-physical field power analysis according to the geometric parameters, and obtain new energy power information; A digital twin construction module is used to introduce environmental perception parameters and construct a digital twin of the new energy ship in combination with the new energy power information; A ship twin running parameter set acquisition module is used to synchronize the real-time collected ship running data set to the digital twin for simulation calculation to obtain a ship twin running parameter set; A speed control strategy formulation module is used to constrain the ship twin running parameter set according to the preset navigation target, formulate a speed control strategy, execute the speed control strategy to optimize the digital twin, and realize adaptive speed control of the new energy ship.

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