A method and system for dynamic ballasting of a semi-submersible barge

Abstract

The application discloses a kind of semi-submersible barge dynamic ballast methods and systems, the method real-time acquisition ship attitude, each cabin water level and external environmental parameter, dynamically calculate stability height GM;Carry out static moment balance analysis, obtain the ballast water regulation amount satisfying preliminary balance;Tidal prediction and the difference of real-time water level are used to generate tidal prediction compensation amount;With theoretical regulation amount as optimization starting point, with current ship state, tidal prediction compensation amount and environmental disturbance as input, using model predictive control algorithm carries out online rolling optimization, under the premise of following cabin, flow, stability and attitude angle etc. Hard constraint, solve the optimal ballast water regulation sequence in future time domain;Finally, the sequence is issued to actuating mechanism to realize accurate automatic ballast, to complete from multi-source perception, intelligent decision to automatic execution closed-loop intelligent control, comprehensively improves the adaptive ability, control precision and operation safety of semi-submersible barge in loading, transportation and floating state operation whole process.

Description

Technical Field

[0001] This invention relates to the field of marine engineering and ship automatic control technology, and more specifically, to a method and system for dynamic load adjustment of a semi-submersible barge. Background Technology

[0002] Semi-submersible barges, as core platforms for the transportation and floating operations of heavy equipment at sea, are widely used in the loading, unloading, and maritime transfer of large prefabricated components, modular structures, and ultra-large equipment. Their operational safety and efficiency highly depend on the precise control of the hull's floating state, and the regulation of the ballast water system is the key means to achieve this control. However, existing ballast adjustment technology for semi-submersible barges mainly relies on manual operation and experience-based judgment, with a serious lack of automation and intelligence, resulting in the following main shortcomings: I. High reliance on manual labor: Traditional semi-submersible barge ballast water adjustment relies heavily on manual experience. The adjustment process is often based on the crew's subjective judgment of the hull's draft and trim, lacking real-time and precise automated control, resulting in delayed response. II. Lack of intelligent ballast adjustment systems: Existing semi-submersible barge ballast systems mostly only have basic "compartmental water intake and drainage" functions, failing to achieve global optimization of the entire vessel's buoyancy. Especially during component loading, it is difficult to simultaneously consider the hull's trim, trim, and overall stability, posing safety hazards. III. Ineffective handling of tidal effects: During shore loading, the relative height between the quay crane and the semi-submersible barge deck changes due to tidal fluctuations. If ballast adjustment is not timely, it can easily lead to reduced loading efficiency, or even loading interruption or collision risks. Existing systems often ignore tidal factors and lack automatic compensation mechanisms based on tidal prediction and real-time feedback. Fourth, the existing semi-submersible barge system lacks adaptive environmental response capabilities. Faced with complex sea conditions (such as the combined effects of wind, waves, and currents), it can only make limited manual corrections and struggles to achieve proactive load adjustment based on real-time sensor data and algorithm optimization, resulting in insufficient resilience. Therefore, current technology cannot yet meet the dynamic stability control requirements for loading and transporting large prefabricated components in complex marine environments. Summary of the Invention

[0003] The purpose of this invention is to provide a method and system for dynamic load adjustment of a semi-submersible barge, in order to solve the above-mentioned problems existing in the prior art.

[0004] The application is as follows: In a first aspect, the present invention provides a method for dynamic load adjustment of a semi-submersible barge, comprising the following steps: S1. Real-time data acquisition and status calculation: Acquire the buoyancy parameters of the semi-submersible barge hull, the water level of each ballast tank, and the external environmental parameters; the buoyancy parameters of the semi-submersible barge hull include at least the pitch angle, heel angle, and draft; the external environmental parameters include at least the wind speed, wave height, current velocity, and tidal level; based on the acquired data, calculate the stability height GM of the semi-submersible barge hull in real time. S2. Static Planning: Based on the semi-submersible barge's cargo volume distribution and center of gravity data, the weight and loading position information of prefabricated components, and the current water level of each ballast tank, a static moment balance analysis is performed to calculate a set of theoretical ballast water regulation amounts. S3. Dynamic Prediction: Based on the tidal forecast model, the predicted tidal level for future times is obtained, and the difference between it and the currently collected tidal level is calculated to generate the tidal prediction compensation amount. S4. Model Prediction Optimization: Using the theoretical ballast water regulation amount as the initial value, the longitudinal and transverse angles of the semi-submersible barge hull and the deck height calculated from the draft as the current state input, and the tidal prediction compensation amount and the environmental parameters as disturbance input, the ballast water regulation amount is solved online by using the model predictive control algorithm based on the system dynamic model. The optimization solution process aims to construct a cost function by tracking the desired semi-submersible barge hull state and minimizing changes in control actions, and transforms ballast tank capacity constraints, pump and valve flow constraints, stability height GM safety constraints, and attitude angle safety constraints into hard constraints. S5. Optimal Control Execution: The optimal ballast water regulation sequence obtained from step S4 is output to the actuator of the ballast water system.

[0005] Furthermore, in step S1, the real-time calculation of the stability height GM of the semi-submersible barge hull is specifically based on the following dynamic stability model:

[0006] Where I is the moment of inertia of the waterline. The displacement volume is KG, and the height of the ship's center of gravity is KG. The dynamic stability model dynamically calculates and updates the displacement volume based on the real-time collected buoyancy parameters of the semi-submersible barge and the water levels of each ballast tank. Moment of inertia I at the waterline and height of the center of gravity KG.

[0007] Furthermore, the static moment balance analysis described in step S2 is solved under the constraint of global moment balance of the hull; the constraint is expressed by the following equilibrium equation:

[0008] Where, ∑M load∑M is the additional torque caused by the loading of precast components. ballast The balancing torque generated by the ballast water distribution, ∑M tide This is the additional buoyancy compensation moment caused by the non-uniform changes in the tidal water level in the longitudinal or transverse direction of the semi-submersible barge hull; By solving this equation, the theoretical ballast water adjustment amount of each ballast tank under static conditions can be preliminarily calculated.

[0009] Furthermore, the additional buoyancy compensation torque ∑Mtide is quantitatively calculated by establishing a tidal gradient hydrodynamic model, specifically including: The waterline of the semi-submersible barge is divided into multiple computational grid regions in both the longitudinal and transverse directions. For each grid region j, its area A is... j and the local water level change Δη in the grid area caused by tidal changes. j Calculate the additional vertical buoyancy force on this grid region:

[0010] Where ρ w Let g be the density of water, and g be the acceleration due to gravity. Each additional vertical buoyancy ΔF j Multiply by the corresponding lever arm r from its center of action to the hull reference point j The additional torque of the grid region is obtained, and the additional buoyancy compensation torque ∑M is obtained by summing the additional torques of all grid regions. tide This is used to quantify the impact of non-uniform tides on the moment balance of the ship in static programming.

[0011] Furthermore, the model predictive control algorithm predicts system behavior based on the following discrete state-space model:

[0012]

[0013] Wherein, the state vector x k Includes the pitch angle, roll angle, and deck height of the semi-submersible barge hull, and the control input vector u. k The disturbance vector ω represents the ballast water adjustment amount for each ballast tank. k Including the tidal prediction compensation amount and the environmental data, the output vector is y. k The system includes controlled semi-submersible barge hull state parameters, which include at least the pitch angle, heel angle, deck height, and stability height GM. A is the state transition matrix, representing the system's own dynamics; B is the control input matrix, representing the influence of ballast water regulation on the system state; E is the disturbance input matrix, representing the influence of environmental and tidal compensation disturbances on the system; and C is the output matrix.

[0014] Furthermore, a multi-objective optimization function is constructed as the cost function J to coordinate control performance while satisfying multiple hard constraints; the cost function J adopts the standard quadratic form:

[0015] Among them, the first penalty output y is related to the expected future reference trajectory y. ref The tracking deviation ensures the control accuracy of the semi-submersible barge's trim angle, heel angle, deck height, and stability height GM; the second penalty control input u change Δu ensures the smoothness of ballast water regulation and reduces equipment wear. k represents the current time; N p To predict the time domain length, N c To control the length of the time domain, and N c ≤N p ;y k+j|k The output vector at time k+j is predicted based on the state and disturbance information at time k. Let be the expected reference vector for the output at time k+j; Q is the weight matrix of the output tracking error, used to weigh the tracking importance of each output component; Δu k+j|k =u k+j|k -u k+j-1|k , represents the change in ballast water regulation at time k+j predicted based on information at time k, relative to the previous time; R is the weight matrix of the control increment change Δu, used to balance the smoothness requirements of the control action.

[0016] Furthermore, the hard constraints specifically include: (1) Ballast tank capacity constraint: For the i-th ballast tank, its water volume V i Satisfy V i,min ≤V i ≤V i,max V i,min and V i,max These are the minimum and maximum allowable capacities for the cabin, respectively. (2) Pump and valve flow constraint: For the i-th ballast tank, the change in ballast water regulation per unit time |Δu i |≤Δu i,max , where Δu i,max This refers to the maximum permissible flow rate of the pump valves associated with this compartment; (3) Stability height GM safety constraint: The real-time stability height GM value must satisfy GM ≥ GM min GM min This is a preset safety lower limit value; (4) Attitude angle safety constraints: The pitch angle θ and the roll angle φ must satisfy |θ|≤θ max And |φ|≤φ max, where θ max and φ max This represents the maximum permissible attitude angle.

[0017] Furthermore, based on the current operation stage, the values ​​of each component of the weight matrix Q in the cost function are dynamically adjusted to change the optimization priority of different control objectives, wherein: During the loading phase, the weight components in the weight matrix Q corresponding to the deck height tracking error are significantly increased, and the expected reference value of the deck height is dynamically updated based on the tidal prediction compensation amount. During the transportation phase, the weight components in the weight matrix Q corresponding to the tracking errors of pitch and roll angles are significantly improved. During the floating operation phase, the weight components in the weight matrix Q corresponding to the tracking error of the stability height GM are significantly increased.

[0018] Secondly, the present invention provides a dynamic load adjustment system for a semi-submersible barge, the system comprising: The sensor network includes: attitude sensors for measuring the pitch and roll angles of the semi-submersible barge, pressure sensors or distance sensors for measuring the draft of the barge, liquid level sensors installed in each ballast tank, and a set of environmental monitoring sensors for measuring wind speed, wave height, current velocity and tidal level. The data processing and control unit, communicatively connected to the sensor network, is configured as follows: Based on the data collected by the sensor network, the hull's stability height GM is calculated in real time. Based on the cargo capacity distribution and center of gravity data of the semi-submersible barge, the weight and loading position information of the prefabricated components, and the current water level of each ballast tank, a static moment balance analysis is performed to calculate the theoretical ballast water adjustment amount. Based on the tidal forecast model and the currently collected tidal water level, a tidal prediction compensation amount is generated. Using the theoretical ballast water regulation amount as the initial value for optimization, and taking the current longitudinal angle, heel angle, deck height, tidal prediction compensation amount, and environmental parameters of the semi-submersible barge as inputs, online rolling optimization is performed based on the system dynamic model and a model predictive control algorithm to solve for the optimal ballast water regulation amount sequence. A ballast water actuator, communicatively connected to the data processing and control unit, is used to receive and execute the optimal ballast water regulation sequence. The ballast water actuator includes pumps, valves, and pipelines associated with each ballast tank.

[0019] Thirdly, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the program to implement a dynamic loading method for a semi-submersible barge.

[0020] Compared with the prior art, the embodiments of the present invention achieve the following beneficial effects: By collecting real-time data on the buoyancy of the semi-submersible barge, the water level of each compartment, and external environmental parameters, and by using model predictive control algorithms for online rolling optimization and automatic execution, the ballast water regulation is made fast, accurate, and automated, completely overcoming the shortcomings of strong reliance on manual labor and slow response. By establishing and solving the static moment balance equation, global pre-planning can be performed before loading. Combined with the dynamic optimization of the model predictive control algorithm, the global optimization problem of simultaneously taking into account pitch, roll and overall stability is effectively solved, and safety hazards are eliminated. By generating a feedforward compensation amount by subtracting the tidal forecast water level from the real-time water level, and taking into account the additional torque generated by the tidal water level gradient in the static programming, the system can proactively and in advance compensate for the impact of tidal changes on loading operations, which greatly improves loading efficiency and docking safety. By transforming key safety limits such as cabin capacity, flow rate, stability height, and attitude angle into hard constraints in the optimization of the model predictive control algorithm, and by using environmental parameters such as wind, waves, and currents as disturbance inputs, the system can intelligently resist disturbances from complex sea conditions while meeting all engineering safety boundaries, significantly enhancing the system's adaptability and risk resistance. Attached Figure Description

[0021] Figure 1 This is a flowchart illustrating a dynamic loading method for a semi-submersible barge provided in an embodiment of the present invention. Detailed Implementation

[0022] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments given herein are for illustration and explanation only and are not intended to limit the present invention.

[0023] It should be noted that many specific details are set forth in the following description in order to provide a full understanding of the present invention. However, the present invention may have other embodiments, and therefore, the scope of protection of the present invention is not limited to the specific embodiments disclosed below.

[0024] See attached document Figure 1 As shown in the figure, the specific steps of the dynamic load adjustment method for a semi-submersible barge provided in this embodiment include: S1. Real-time data acquisition and status calculation.

[0025] Collect buoyancy parameters of the semi-submersible barge hull and water level h of each ballast tank. i The external environmental parameters include: the semi-submersible barge hull floating parameters include at least the heel angle θ, the roll angle φ, and the draft T; the external environmental parameters include at least the wind speed V, wave height H, current velocity U, and tidal level η(t). After collecting the above data, the core safety indicator—stability height GM—is calculated in real time. This calculation is based on the restoring arm formula in ship statics and is made dynamic:

[0026] Where I is the moment of inertia of the waterline surface area about the longitudinal or transverse axis passing through its centroid. Its value is calculated in real time based on the current draft and trim and heel angles, combined with pre-stored hull line data. The current displacement volume is also obtained by interpolation from the hull lines data based on draft and attitude data. KG represents the ship's center of gravity height, a dynamic value calculated by considering the empty ship's center of gravity, the weight and center of gravity of water in each ballast tank, and the weight and center of gravity of prefabricated components already loaded. The empty ship's center of gravity and tank center of gravity are fixed, known values, while ballast water and component loads are calculated based on real-time water levels and loading plans. By continuously inputting the latest data, the model continuously updates its output, thus achieving continuous dynamic monitoring of ship stability.

[0027] S2. Static Programming.

[0028] Before loading large precast components, the control system performs a detailed static balance pre-analysis based on the known loading plan to verify the plan's feasibility and generate an initial load adjustment scheme. This step relies on three types of input information: first, the semi-submersible barge's cargo volume distribution and center of gravity data, which are inherent parameters of the vessel, including the volume of each ballast tank and the three-dimensional coordinates (x, y, x) of its geometric center in the hull coordinate system. b,i ,y b,i ,z b,i Secondly, the weight and loading location information of the prefabricated components, i.e., the mass m of each component planned to be loaded. i and its predetermined placement coordinates on the ship (x i ,y i ,z i The third step is to obtain the current water level of each ballast tank in the first step, which is used to determine the initial ballast water distribution.

[0029] The core of static programming is to establish and solve the static moment equilibrium equations for the entire ship, ensuring that the hull is in equilibrium in still water after loading is completed. These equilibrium equations are expressed as:

[0030] Where, ∑M load ∑M is the additional torque caused by the loading of precast components. ballast The balancing torque generated by the ballast water distribution, ∑M tide This is the additional buoyancy compensation moment caused by the non-uniform changes in the tidal water level in the longitudinal or transverse direction of the semi-submersible barge hull; This equation must hold true in both the longitudinal (tilting about the Y-axis) and transverse (tilting about the X-axis) directions. The calculation of each torque is explained in detail below: Additional loading torque ∑M load Caused by the loading of prefabricated components. The weight of each component i is W. i =m i • g (g is the acceleration due to gravity). When a component is offset in the longitudinal and transverse directions of the hull, its weight will generate a torque about the hull's coordinate axes.

[0031] Longitudinal pitch moment:

[0032] Lateral tilting moment:

[0033] Where, x i ,y i These represent the longitudinal and transverse horizontal distances of the center of gravity of the i-th component relative to the reference point, respectively.

[0034] Ballast water balance moment ∑M ballast : Generated by ballast water distribution, used to balance loading torque. The weight W of the water in each ballast tank i b,i =ρ w •V i ⋅g, where ρ w V is the density of seawater. i This refers to the volume of water inside the cabin.

[0035] Longitudinal pitch moment:

[0036] Lateral tilting moment:

[0037] Here, x b,i ,y b,i These are the lateral and longitudinal coordinates of the center of gravity of the water in the i-th ballast tank. In the calculation, it is usually assumed that the water level inside the tank is horizontal and that its center of gravity is located at the geometric center of the tank.

[0038] Tidal additional buoyancy compensation torque ∑M tide It does not assume uniform tidal changes, but rather considers that tides may cause water level gradients fore and aft or along the sides of the ship. Its calculations employ a discretization method. The ship's waterline is divided into N regular grid regions in both the longitudinal and transverse directions.

[0039] For each grid region j, based on its area A j And the local water level change Δη caused by tides in this area. j (This can be obtained by combining the tidal forecast model with the ship's position), calculate the additional vertical buoyancy force on this grid:

[0040] The additional buoyancy ΔF j The moment of the center of buoyancy acting on this grid about the reference point is ΔM. j =ΔF j •r j , where r j The lever arm is the lever arm in the corresponding direction (longitudinal or transverse).

[0041] Summing the additional torques over all grid regions yields the total tidal additional buoyancy compensation torque ∑M. tide .

[0042] By simultaneously solving the longitudinal and transverse moment balance equations, a set of theoretical ballast water adjustment values ​​ΔV can be obtained to ensure that the hull reaches static equilibrium after loading. i This refers to the amount of water that needs to be injected or discharged into each compartment.

[0043] S3. Dynamic prediction.

[0044] Utilizing a tidal forecasting model based on astronomical tidal data and historical records, the system provides forecasted tidal levels for the operational sea area over the next few hours or even days. Simultaneously, the system continuously acquires real-time tidal levels via sensors. By subtracting the forecasted tidal level from the currently acquired tidal level, a tidal prediction compensation is generated. The tidal prediction compensation represents the equivalent draft or deck height change that needs to be adjusted in advance to maintain a constant relative height between the deck and the quay crane. It will be input sequentially as a known disturbance into the next step of the optimization controller.

[0045] S4. Model prediction optimization.

[0046] First, a mathematical model is established to describe the dynamic relationship between hull attitude, ballast adjustment, and external disturbances. This invention uses a linear discrete-time state-space model as the prediction model, which takes the following form:

[0047]

[0048] State vector x k Key variables that can fully characterize the system dynamics are selected, typically including the heel angle θ, the roll angle φ, and the deck height H calculated from the draft.

[0049] Control input vector u k : That is, the ballast water adjustment amount ΔV of each ballast tank to be solved. i , is the optimization variable.

[0050] perturbation vector ω kIt includes known or predictable external disturbances, mainly including the tidal prediction compensation generated by S3 and the current or short-term predicted values ​​of the environmental parameters (wind speed V, wave H, current velocity U) measured in the first step.

[0051] Output vector y k This refers to the quantities that the control system directly focuses on and aims to regulate. In this invention, the controlled output includes at least the pitch angle, roll angle, deck height, and stability height (GM). k These quantities are extracted or calculated.

[0052] The coefficient matrices A, B, and E need to be determined through system identification (based on experimental data) or mechanism modeling (based on linearization of ship hydrodynamic and fluid dynamic equations). They respectively characterize the system's own dynamics, the influence of control inputs, and the influence of external disturbances.

[0053] At each sampling time k, the model predictive control algorithm performs the following operations: Using the theoretical ballast water regulation amount calculated by S2 as the initial value for this optimization solution helps to accelerate convergence.

[0054] Based on the sensor data from the first step, determine the current state vector xk and the output yk.

[0055] Inject a known future perturbation sequence ωk,ωk+1,..., which includes tidal prediction compensation.

[0056] Within a finite time domain (prediction time domain N) p (Step), solve a constrained optimization problem. Its cost function is designed as a quadratic form, aiming to balance multiple control objectives:

[0057] The first item is the tracking error term. These are expected output reference values ​​for future moments. For example, the expected trim and list are typically zero (level), the expected deck height needs to dynamically change with tidal compensation values ​​during the loading phase, and the expected GM value can be set as a safe target value (GM... ref GM min The weight matrix Q determines the relative importance of the tracking accuracy of each output component (pitch, roll, height, GM).

[0058] The second item is the penalty for controlling the increment. N c To control the time domain length, Δu k+j|k =u k+j|k -u k+j-1|k, representing the rate of change of the control variable. This is penalized to ensure the ballast water pump valve operates as smoothly as possible, avoiding frequent start-stop cycles and drastic fluctuations, thus extending equipment lifespan. The weight matrix R is used to adjust the intensity of the control smoothness requirement.

[0059] The optimization solution must satisfy a series of engineering safety and physical limits, which serve as hard constraints for the optimization problem: 1. Tank volume constraint: The water volume V of each ballast tank i i V must be satisfied i,min ≤V i ≤V i,max V i,min and V i,max These are the minimum and maximum allowable capacities for the cabin, respectively. 2. Flow constraint: The change in regulation amount |Δu| for each compartment within a single control cycle. i The flow rate must not exceed the maximum permissible flow rate Δu of its associated pump or valve. i,max ; 3. Stability Constraint: The predicted future stability height must satisfy GM ≥ GM min GM min It is the minimum safe value determined based on the sea conditions and regulations for operation.

[0060] 4. Attitude Constraints: The absolute values ​​of the predicted pitch and roll angles must not exceed the maximum allowable values, i.e., |θ|≤θ max And |φ|≤φ max , where θ max and φ max This represents the maximum permissible attitude angle.

[0061] By solving the constrained quadratic programming (QP) problem described above, the optimal control input sequence starting from the current time step is obtained. (Control time domain N) c ≤N p ).

[0062] To optimize system performance at different stages of operation, this invention implements adaptive adjustment of the control target weights. This is achieved by modifying the weight matrix Q in the cost function online, without changing the model structure or constraints. During the shore loading phase: the system will significantly increase the weights of the diagonal elements in matrix Q corresponding to the deck height output. This means the model predictive control algorithm will spare no effort to reduce the deviation between the deck height and the expected value, ensuring its precise alignment with the quay crane docking surface. Simultaneously, the expected deck height reference value y during this phase... ref It will be updated in real time and dynamically based on the tidal prediction compensation amount in the third step.

[0063] During the maritime transport phase: the system will significantly increase the weights in matrix Q corresponding to the pitch and roll angle outputs. At this time, the primary goal of the model predictive control algorithm is to suppress the hull's rolling motion caused by wind, waves, and currents, maintaining smooth and comfortable navigation. The desired attitude angle reference value is usually set to zero.

[0064] During the floating operation phase at sea (such as diving, lifting, and platform operations): the system will significantly increase the weights in matrix Q corresponding to the stability height GM output. While ensuring that GM does not fall below the safety lower limit (hard constraint), the model predictive control algorithm will proactively optimize the GM value to a higher expected target value. ref To maximize the stability and safety margin during operation.

[0065] Model predictive control algorithms implement only the first control input in the optimal sequence. This yields the optimal ballast water adjustment amount that should be implemented immediately at the current sampling time. The rolling optimization process is repeated at the next sampling time.

[0066] S5. Optimal control execution.

[0067] The optimal ballast water regulation (usually expressed as the flow command or target water level for each compartment) is sent to the ballast water actuator via an industrial fieldbus or wireless network.

[0068] The actuator consists of a series of distributed hardware components: each ballast tank is equipped with a water pump (for pumping in or out water), an inlet valve, a drain valve, and a piping system connected to the seagate or overboard. The system's local controller (such as a PLC) receives instructions from the central processing unit and precisely controls the start, stop, and opening of the corresponding pumps and valves, thereby injecting or draining water from specific ballast tanks as required by the instructions.

[0069] Through this precise execution, the weight distribution of the hull is altered, thereby affecting its buoyancy (draft, trim, list) and stability (GM value), ultimately achieving automatic and precise maintenance or adjustment to the desired state.

[0070] In one alternative embodiment, a dynamic load adjustment system for a semi-submersible barge is provided. The system physically consists of three main parts connected as a cohesive whole via a communication network.

[0071] Sensor networks: Serving as the system's perception layer, they are distributed throughout the entire ship. Specifically, they include: Hull attitude measurement unit: mainly includes a high-precision inertial measurement unit (IMU), which is fixedly installed on a rigid part of the hull structure and is used to continuously measure the pitch and roll angles.

[0072] Draft measurement unit: Pressure sensors or non-contact radar / laser rangefinders are installed at typical locations such as the bow, midships, and stern of the hull to measure the draft of the hull at different locations relative to the water surface.

[0073] Chamber monitoring unit: Pressure level sensors or radar level gauges are installed at the bottom or side wall of each ballast tank to monitor the water level of each ballast tank in real time.

[0074] Environmental sensing unit: includes an ultrasonic anemometer mounted on the mast, a wave radar or accelerometer-type wave sensor for monitoring waves, an acoustic Doppler current profiler (ADCP) for measuring ocean current speed and direction, and a GNSS tide level receiver or a data link for communicating with shore-based tide level stations to obtain accurate absolute water level, together providing wind speed, wave height, current velocity and tidal water level data.

[0075] Data processing and control unit: As the system's decision-making layer, it typically consists of an industrial-grade computer, a programmable logic controller (PAC), or a high-performance embedded system. It connects to the sensor network via a data acquisition module to receive all real-time data; its software core sequentially executes stability dynamic calculations, static planning, tidal prediction compensation, and model predictive control optimization with adaptive strategies; finally, it sends out immediate instructions from the calculated optimal ballast water regulation sequence through the control output module.

[0076] Ballast water actuator: As the system's execution layer, it is the result of automating a traditional ballast system. It includes: Power equipment: Electric or hydraulically driven centrifugal pumps serving each ballast tank or group of ballast tanks.

[0077] Control valves: Electric or hydraulic butterfly / ball valves are installed on the inlet and outlet pipelines of each ballast tank to control the flow of water.

[0078] Piping network: The piping system that connects pumps, valves, ballast tanks, and seabed doors / overboard discharge outlets.

[0079] Local control cabinet: It contains a programmable logic controller (PLC) and a drive frequency converter (if applicable), which is responsible for receiving instructions from the central control unit and directly driving specific pumps and valves to complete precise start-up, shutdown and opening control.

[0080] In an alternative embodiment, an electronic device is provided that implements the dynamic load adjustment method by running a computer program. This electronic device may be part of a ship's integrated bridge system, a stand-alone industrial control computer, or a cloud server. The device includes: Storage: such as hard disk, solid-state drive or flash memory, used to persistently store the computer program code required to implement the method of the present invention, as well as the parameter database (such as ship type data, cabin capacity table, constraint threshold, tide forecast database, etc.) required for program operation.

[0081] Processor: such as a central processing unit (CPU) or a graphics processing unit (GPU), connected to the memory via a bus.

[0082] A computer program is stored in the memory. When the user starts the system or a trigger condition is met, the processor is configured to load and execute the program. During program execution, the control processor operates according to a complete logical flow of real-time data acquisition and state calculation, static planning, dynamic prediction, model prediction optimization, and optimal control execution, thereby realizing intelligent dynamic load adjustment of the semi-submersible barge ballast water system.

[0083] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0084] Furthermore, those skilled in the art will understand that although some embodiments herein include certain features included in other embodiments but not others, combinations of features from different embodiments are intended to be within the scope of the invention and form different embodiments. Any of the claimed embodiments can be used in any combination.

Claims

1. A method for dynamic load adjustment of a semi-submersible barge, characterized in that, Includes the following steps: S1. Real-time data acquisition and status calculation: Acquire the buoyancy parameters of the semi-submersible barge hull, the water level of each ballast tank, and the external environmental parameters; the buoyancy parameters of the semi-submersible barge hull include at least the heel angle, roll angle, and draft; the external environmental parameters include at least the wind speed, wave height, current velocity, and tidal level; based on the acquired data, calculate the stability height GM of the semi-submersible barge hull in real time. S2. Static Planning: Based on the semi-submersible barge's cargo volume distribution and center of gravity data, the weight and loading position information of prefabricated components, and the current water level of each ballast tank, a static moment balance analysis is performed to calculate a set of theoretical ballast water regulation amounts. S3. Dynamic Prediction: Based on the tidal forecast model, the predicted tidal level for future times is obtained, and the difference between it and the currently collected tidal level is calculated to generate the tidal prediction compensation amount. S4. Model Prediction Optimization: Using the theoretical ballast water regulation amount as the initial value, the longitudinal and transverse angles of the semi-submersible barge hull and the deck height calculated from the draft as the current state input, and the tidal prediction compensation amount and the environmental parameters as the disturbance input, the ballast water regulation amount is solved online by rolling optimization based on the system dynamic model and using the model predictive control algorithm. The optimization solution process aims to construct a cost function by tracking the desired semi-submersible barge hull state and minimizing changes in control actions, and transforms ballast tank capacity constraints, pump and valve flow constraints, stability height GM safety constraints, and attitude angle safety constraints into hard constraints. S5. Optimal Control Execution: The optimal ballast water regulation sequence obtained from step S4 is output to the actuator of the ballast water system.

2. The method for dynamic load adjustment of a semi-submersible barge according to claim 1, characterized in that, In step S1, the real-time calculation of the stability height GM of the semi-submersible barge hull is specifically based on the following dynamic stability model: Where I is the moment of inertia of the waterline. The displacement volume is KG, and the height of the ship's center of gravity is KG. The dynamic stability model dynamically calculates and updates the displacement volume ∇, waterline moment of inertia I, and center of gravity height KG based on the real-time collected floating parameters of the semi-submersible barge hull and the water level of each ballast tank.

3. The method for dynamic load adjustment of a semi-submersible barge according to claim 1, characterized in that, The static moment balance analysis described in step S2 is solved under the constraint of global moment balance of the hull; the constraint is expressed by the following equilibrium equations: Where, ∑M load ∑M is the additional torque caused by loading precast components. ballast The balancing torque generated by the ballast water distribution, ∑M tide This is the additional buoyancy compensation moment caused by the non-uniform changes in the tidal water level in the longitudinal or transverse direction of the semi-submersible barge hull; By solving this equation, the theoretical ballast water adjustment amount of each ballast tank under static conditions can be preliminarily calculated.

4. The method for dynamic load adjustment of a semi-submersible barge according to claim 3, characterized in that, The additional buoyancy compensation torque ∑Mtide is quantitatively calculated by establishing a tidal gradient hydrodynamic model, specifically including: The waterline of the semi-submersible barge is divided into multiple computational grid regions in both the longitudinal and transverse directions. For each grid region j, its area A is... j and the local water level change Δη in the grid area caused by tidal changes. j Calculate the additional vertical buoyancy force on this grid region: Where ρ w Let g be the density of water, and g be the acceleration due to gravity. Each additional vertical buoyancy force ΔF j Multiply by the corresponding lever arm r from its center of action to the hull reference point j The additional torque of the grid region is obtained, and the additional buoyancy compensation torque ∑M is obtained by summing the additional torques of all grid regions. tide This is used to quantify the impact of non-uniform tides on the moment balance of the ship in static programming.

5. The method for dynamic load adjustment of a semi-submersible barge according to claim 1, characterized in that, The model predictive control algorithm predicts system behavior based on the following discrete state-space model: Wherein, the state vector x k Includes the pitch angle, roll angle, and deck height of the semi-submersible barge hull, and the control input vector u. k The disturbance vector ω represents the ballast water adjustment amount for each ballast tank. k Including the tidal prediction compensation amount and the environmental data, the output vector is y. k The system includes controlled semi-submersible barge hull state parameters, which include at least the pitch angle, heel angle, deck height, and stability height GM. A is the state transition matrix, representing the system's own dynamics; B is the control input matrix, representing the influence of ballast water regulation on the system state; E is the disturbance input matrix, representing the influence of environmental and tidal compensation disturbances on the system; and C is the output matrix.

6. The method for dynamic load adjustment of a semi-submersible barge according to claim 5, characterized in that, A multi-objective optimization function is constructed as the cost function J to coordinate control performance while satisfying multiple hard constraints; the cost function J adopts the standard quadratic form: Among them, the first penalty output y is related to the future expected reference trajectory y. ref The tracking deviation ensures the control accuracy of the semi-submersible barge's trim angle, heel angle, deck height, and stability height GM; the second penalty control input u change Δu ensures the smoothness of ballast water regulation and reduces equipment wear. k represents the current time; N p To predict the time domain length, N c To control the length of the time domain, and N c ≤N p ;y k+j|k The output vector at time k+j is predicted based on the state and disturbance information at time k. Let be the expected reference vector for the output at time k+j; Q is the weight matrix of the output tracking error, used to weigh the tracking importance of each output component; Δu k+j|k =u k+j|k -u k+j-1|k , represents the change in ballast water regulation at time k+j predicted based on information at time k, relative to the previous time; R is the weight matrix of the control increment change Δu, used to balance the smoothness requirements of the control action.

7. The method for dynamic load adjustment of a semi-submersible barge according to claim 1, characterized in that, The hard constraints specifically include: (1) Ballast tank capacity constraint: For the i-th ballast tank, its water volume V i Satisfy V i,min ≤V i ≤V i,max V i,min and V i,max These are the minimum and maximum allowable capacities for the cabin, respectively. (2) Pump and valve flow constraints: For the i-th ballast tank, the change in ballast water regulation per unit time |Δu i |≤Δu i,max , where Δu i,max This refers to the maximum permissible flow rate of the pump valves associated with this compartment; (3) Stability height GM safety constraint: The real-time stability height GM value must satisfy GM ≥ GM min GM min This is a preset safety lower limit value; (4) Attitude angle safety constraints: The pitch angle θ and the roll angle φ must satisfy |θ|≤θ max And |φ|≤φ max , where θ max and φ max This represents the maximum permissible attitude angle.

8. The method for dynamic load adjustment of a semi-submersible barge according to claim 6, characterized in that, Based on the current operation stage, the values ​​of each component of the weight matrix Q in the cost function are dynamically adjusted to change the optimization priority of different control objectives, wherein: During the loading phase, the weight components in the weight matrix Q corresponding to the deck height tracking error are significantly increased, and the expected reference value of the deck height is dynamically updated based on the tidal prediction compensation amount. During the transportation phase, the weight components in the weight matrix Q corresponding to the tracking errors of pitch and roll angles are significantly improved. During the floating operation phase, the weight components in the weight matrix Q corresponding to the tracking error of the stability height GM are significantly increased.

9. A semi-submersible barge dynamic load adjustment system for implementing the dynamic load adjustment method as described in any one of claims 1 to 8, characterized in that, The system includes: The sensor network includes: attitude sensors for measuring the pitch and roll angles of the semi-submersible barge, pressure sensors or distance sensors for measuring the draft of the barge, liquid level sensors installed in each ballast tank, and a set of environmental monitoring sensors for measuring wind speed, wave height, current velocity and tidal level. The data processing and control unit, communicatively connected to the sensor network, is configured as follows: Based on the data collected by the sensor network, the hull's stability height GM is calculated in real time. Based on the cargo capacity distribution and center of gravity data of the semi-submersible barge, the weight and loading position information of the prefabricated components, and the current water level of each ballast tank, a static moment balance analysis is performed to calculate the theoretical ballast water adjustment amount. Based on the tidal forecast model and the currently collected tidal water level, a tidal prediction compensation amount is generated. Using the theoretical ballast water regulation amount as the initial value for optimization, and taking the current longitudinal angle, heel angle, deck height, tidal prediction compensation amount, and environmental parameters of the semi-submersible barge as inputs, online rolling optimization is performed based on the system dynamic model and a model predictive control algorithm to solve for the optimal ballast water regulation amount sequence. A ballast water actuator, communicatively connected to the data processing and control unit, is used to receive and execute the optimal ballast water regulation sequence. The ballast water actuator includes pumps, valves, and pipelines associated with each ballast tank.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method as described in any one of claims 1 to 8.

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