Ship furniture stabilizing system self-adaptive to ship shaking

Abstract

The invention provides a ship furniture stabilizing system self-adaptive to ship shaking. Comprising a data acquisition module, an execution module and a control unit. The prediction model determines the motion trend of the ship based on the data information of the data acquisition module; the control module receives the motion trend of the ship, determines the compensation amount according to the motion trend of the ship, generates a control instruction based on the compensation amount and sends the control instruction to the execution module, and the execution module adjusts the levelness of the ship furniture according to the control instruction. By adopting a predictive compensation algorithm, the execution module is controlled to act in advance, active predictive compensation is carried out, and the accuracy of ship shake adjustment is improved. Through the modular design, installation and maintenance are easy. A prediction model in the control unit can learn motion characteristics of different ships and different sea conditions, control parameters are automatically adjusted, and the optimal stable effect is achieved. Articles are effectively prevented from slipping off, personnel are effectively prevented from losing balance due to shaking, the device is particularly suitable for high sea conditions and scenes needing fine operation, and comfort and safety are improved.

Description

Technical Field

[0001] This application relates to the field of ship design technology, and more specifically, to a ship furniture stabilization system that adapts to ship swaying. Background Technology

[0002] During navigation, ships experience complex, multi-degree-of-freedom movements such as rolling and pitching due to the effects of wind and waves. This swaying significantly impacts the comfort and safety of crew and passengers, causing items to slip from tables, food to spill, and even leading to falls due to loss of balance while moving or resting. This problem is particularly pronounced for vulnerable groups and those working in high sea states.

[0003] Currently, methods to improve ship stability mainly focus on hull design, such as installing anti-roll fins and anti-roll tanks. These designs are macroscopic solutions targeting the entire ship structure, which are costly and have limited impact on the microenvironment of interior furniture. At the furniture level, common practices include passive measures such as adding anti-slip mats, installing railings and barriers, which cannot actively adapt to and counteract swaying.

[0004] Therefore, there is an urgent need for a solution that can be integrated into the furniture itself, actively and in real time compensate for the ship's motion, and provide the occupants with a locally stable horizontal plane. Summary of the Invention

[0005] The purpose of this application is to provide an adaptive ship furniture stabilization system that can sense ship movement in real time and keep the furniture's surface level by actively controlling a linear adjuster, thereby significantly improving comfort and safety.

[0006] This application provides an adaptive ship furniture stabilization system for ship rolling, comprising: The data acquisition module includes a ship motion sensing unit and a furniture attitude sensing unit. The ship motion sensing unit is used to acquire ship attitude, ship angular velocity, and ship linear acceleration; the furniture attitude sensing unit is used to acquire furniture attitude and furniture angular velocity. The execution module includes multiple adjustment units, each located at the bottom of the furniture, for adjusting multiple heights of the furniture bottom to adjust the furniture's posture; The control unit is communicatively connected to both the execution module and the data acquisition module. The control unit includes a prediction model, which determines the ship's motion trend based on data from the data acquisition module. Based on the ship's motion trend, the prediction model determines a compensation amount, generates control commands based on the compensation amount, and sends the control commands to the execution module. The execution module adjusts the levelness of the ship's furniture according to the control commands.

[0007] In one feasible embodiment, the ship motion sensing unit determines the ship's attitude by acquiring the ship's roll angle, pitch angle, and bow angle; the ship motion sensing unit determines the ship's angular velocity by acquiring the roll angular velocity, pitch angular velocity, and bow angular velocity; and the ship motion sensing unit determines the ship's linear acceleration by acquiring the X-axis acceleration, Y-axis acceleration, and Z-axis acceleration respectively.

[0008] In one feasible approach, the furniture posture sensing unit determines the furniture posture by acquiring the furniture roll angle and the furniture pitch angle; the furniture posture sensing unit assists in judging the furniture movement trend by acquiring the furniture angular velocity.

[0009] In one feasible embodiment, the ship motion sensing unit includes a plurality of first inertial measurement units, which are distributed and installed at multiple predetermined locations on the ship; the furniture posture sensing unit includes a plurality of second inertial measurement units.

[0010] In one feasible approach, both the first inertial measuring device and the second inertial measuring device are used to acquire the object's attitude, angular velocity, and acceleration in real time.

[0011] In one possible implementation, the adjustment unit includes a plurality of adjustable legs disposed between the ship's furniture and the deck, the plurality of adjustable legs being controlled and connected to the control unit, the control unit adjusting the extension length of the plurality of adjustable legs respectively.

[0012] In one feasible embodiment, the adjustable leg includes at least a linear adjuster, the top of which is connected to the bottom of the tabletop of the marine furniture via a ball joint.

[0013] In one feasible approach, the control unit fuses and filters the data information from the data acquisition module using a Kalman filter algorithm.

[0014] In one feasible approach, the control unit uses an autoregressive model to predict the ship's roll angle in real time, both in single and multi-step steps, and then combines this with inverse kinematics to determine the compensation amount of the adjustment unit.

[0015] In one feasible approach, the furniture posture sensing unit continuously monitors the compensated and adjusted furniture posture and furniture angular velocity, and sends the furniture posture and furniture angular velocity data to the control unit to form closed-loop control.

[0016] In the technical solution of this application, a predictive compensation algorithm is adopted to control the execution module to act in advance and perform proactive predictive compensation, thereby improving the accuracy of ship roll adjustment. Through modular design, it can be flexibly adapted to various existing or newly built furniture, facilitating installation and maintenance. The predictive model in the control unit can learn the motion characteristics of different ships and different sea states, automatically adjusting control parameters to achieve optimal stability. This effectively prevents items from slipping and personnel from losing balance due to swaying, making it particularly suitable for high sea states and scenarios requiring delicate operations, thus improving comfort and safety. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of an adaptive ship furniture stabilization system for ship swaying according to an embodiment of the present invention.

[0018] Figure 2 This is a schematic diagram of the installation of the second inertial measuring element in the adaptive ship sway stabilization system of the present invention.

[0019] Figure 3 This is a schematic diagram of the adjustable outriggers in the adaptive ship sway stabilization system for ship furniture according to an embodiment of the present invention.

[0020] The reference numerals in the attached figures are explained as follows: 1. Table bottom surface; 2. Ball joint; 3. DC electric actuator; 4. Deck; 5. Cable run-through components. Detailed Implementation

[0021] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. These embodiments are for illustrative purposes only and are not intended to limit the scope of the invention.

[0022] In the description of this invention, it should be noted that the terms "center," "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0023] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0024] Furthermore, in the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0025] See Figures 1 to 3 This application provides a ship furniture stabilization system adaptable to ship rolling, comprising: The data acquisition module includes a ship motion sensing unit and a furniture attitude sensing unit. The ship motion sensing unit is used to acquire ship attitude, ship angular velocity, and ship linear acceleration; the furniture attitude sensing unit is used to acquire furniture attitude and furniture angular velocity. The execution module includes multiple adjustment units, each located at the bottom of the furniture, for adjusting multiple heights of the furniture bottom to adjust the furniture's posture; The control unit is communicatively connected to both the execution module and the data acquisition module. The control unit includes a prediction model that determines the ship's motion trend based on data from the data acquisition module. Based on the predicted motion trend, the control model generates control commands and sends them to the execution module. The execution module adjusts the levelness of the ship's furniture according to the control commands to ensure the furniture remains level and stable during navigation.

[0026] In one feasible approach, the ship motion sensing unit determines the ship's attitude by acquiring the ship's roll angle, pitch angle, and bow angle, thereby determining the ship's macroscopic motion state. The ship motion sensing unit determines the ship's angular velocity by acquiring the roll angular velocity, pitch angular velocity, and bow angular velocity, in order to predict the ship's motion trend; The ship motion sensing unit determines the ship's linear acceleration by acquiring the X-axis acceleration, Y-axis acceleration, and Z-axis acceleration respectively, in order to help determine the ship's attitude and impact motion.

[0027] The furniture posture sensing unit determines the furniture posture by acquiring the furniture roll angle and the furniture pitch angle. The furniture posture sensing unit also assists in judging the furniture movement trend by acquiring the furniture angular velocity.

[0028] In one feasible embodiment, the ship motion sensing unit includes a plurality of first inertial measurement units (IMUs) distributed and installed at multiple predetermined locations on the ship. The furniture attitude sensing unit includes a plurality of second inertial measurement units.

[0029] In this embodiment, the inertial measurement unit is an IMU (Inertial Measurement Unit) sensor, which is used to acquire the object's attitude, angular velocity, and acceleration in real time.

[0030] Specifically, the installation locations of the first inertial measurement unit include at least: The location on the aft bulkhead of the cabin, 4 meters from the baseline, is used as the main control reference. The forward bulkhead and centerline location of the living area are used for ship roll monitoring; The aft bulkhead of the bow cargo hold, used for monitoring ship pitch. The bow storage compartment is used for impact monitoring and can output data on the ship's roll angle, pitch angle, angular velocity, and linear acceleration.

[0031] The installation location of the second inertial measuring device includes at least: a geometric center location near the tabletop and a location with a rigid connection.

[0032] When installing the second inertial measuring device, at least the following should be included: like Figure 2 As shown, determine the center position directly below the desktop, and fix the second inertial measurement unit to the load-bearing structure at the center of gravity using a bracket. Ensure that the X, Y, and Z axes of the second inertial measurement unit are parallel or perpendicular to the edge of the table.

[0033] It should be noted that when selecting the installation locations of the first and second inertial measurement units, it is necessary to consider that the IMU sensors need to be installed discreetly and that wiring is also convenient.

[0034] In one feasible embodiment, the adjustment unit includes multiple adjustable legs disposed between the ship furniture and the deck. The multiple adjustable legs are respectively controlled and connected to the control unit. The control unit adjusts the extension length of the multiple adjustable legs to adjust the levelness of the tabletop, thereby driving the tabletop to generate corresponding compensating movements to counteract the effects of the ship's tilt on the ship furniture.

[0035] Specifically, such as Figure 3 As shown, the top and bottom of the adjustable leg are hinged to the ship furniture and the deck 4, respectively. The adjustable leg includes at least a linear adjuster, the top of which is connected to the bottom surface 1 of the tabletop of the ship furniture via a ball joint 2. The ball joint 2 allows the adjustable leg to adapt to changes in the angle of the bottom surface 1 of the tabletop when the ship furniture is being adjusted.

[0036] It should be noted that a telescopic sleeve is fitted over the outer side of the linear regulator. The telescopic sleeve serves only as an external cover and internal guiding structure, and does not participate in power transmission. It only adapts to the extension and retraction of the linear regulator. The telescopic sleeve encloses the linear regulator to protect it.

[0037] The linear regulator can be a DC electric actuator 3, or a lead screw and nut assembly controlled by a motor. The motor is connected to a control unit and controls the rotation of the lead screw and nut assembly, converting rotational motion into linear motion. This drives the linear regulator to perform telescopic motion.

[0038] This embodiment achieves dynamic, real-time adjustment of the desktop's levelness through the use of a linear regulator, effectively solving the problem of desktop tilting during ship navigation. Furthermore, the device features a compact structure, high transmission efficiency, and high control precision, exhibiting excellent adaptability and reliability, and can be widely applied in mobile carriers such as ships.

[0039] The bottom of the adjustable outrigger is equipped with a cable pass-through component 5. The power supply and control cables of the linear regulator are introduced into the cabin through the cable pass-through component 5 on the deck and connected to the control unit. The cable pass-through component 5 adopts a sealed structure to prevent water, gas and other media from entering the cabin.

[0040] In one feasible embodiment, the control unit includes a predictive model that determines the ship's motion trend based on data from the data acquisition module. The predictive model can combine the furniture's current posture with the ship's motion trend to determine the compensation angle and displacement required to keep the furniture level.

[0041] In one feasible approach, the control unit fuses and filters the data information from the data acquisition module using a Kalman filter algorithm to remove high-frequency noise from the raw data information of the data acquisition module and extract a smoother and more accurate representation of the ship's true motion state and the current absolute attitude of the furniture.

[0042] It should be noted that the control unit uses an embedded microprocessor, such as the STM32 series or TISitara series, to enable the control unit to have sufficient computing power to run complex algorithms.

[0043] In this embodiment, taking the ship's rolling state as an example, based on the Kalman filter method, a state-space model of the ship's rolling motion is constructed. Combined with a first inertial measurement unit, real-time, high-precision values ​​of the roll angle θ, roll angular velocity θ', and roll acceleration θ'' are obtained during the ship's rolling process, providing accurate state parameter support for ship attitude control and navigation stability analysis. Specifically, the following steps are included: S1. Define the state vector.

[0044] The state vector of the ship's roll motion is defined as a three-dimensional column vector, containing the roll angle θ, roll angular velocity θ', and roll angular acceleration θ''. The specific expression is as follows: .

[0045] In the formula: is the ship's roll angle, and is the ship's inclination angle about its longitudinal axis; Let be the ship's roll rate, and let be the first time derivative of the roll angle. Let be the ship's roll acceleration, and be the second time derivative of the roll angle.

[0046] S2. Construct a state-space model.

[0047] S21. The state transition equation for the ship's rolling motion is in discretized form, describing the state quantity transfer relationship between adjacent sampling times. The expression is as follows:

[0048] In the formula: Let k be the state vector at the k-th sampling time. Let k be the state vector at the (k-1)th sampling time. F is the state transition matrix, constructed based on the ship's roll physical motion model; The process noise at the k-th sampling time is Gaussian white noise, which represents the model's unmodeled error, small external disturbances, etc.

[0049] S22. The state transition matrix F is a 3×3 matrix, specifically in the form of:

[0050] In the formula: Δt: System sampling period, which is the time interval between two adjacent data samples; α = exp(−Δt / τ) is the relevant time constant of the roll angular acceleration, where τ is the base value of the time constant, which is taken as 2~5 seconds according to the characteristics of ship motion.

[0051] S23. Determine the process noise covariance matrix Q.

[0052] The process noise covariance matrix Q is a 3×3 diagonal matrix, representing the variance characteristics of the process noise in each state variable dimension. The values ​​of each diagonal element are adjusted according to the ship's roll motion characteristics, and the specific form is as follows:

[0053] In the formula: q1 represents the process noise variance in the roll angle dimension; q2 represents the process noise variance in the roll angular velocity dimension; q3 represents the process noise variance in the roll angle acceleration dimension.

[0054] It should be noted that in this embodiment, the default values ​​q1=0.01, q2=0.1, and q3=1.0 are used.

[0055] S24. Establish the mapping relationship between sensor measurements and state vectors as a measurement model, expressed as:

[0056] In the formula: z k Let be the measurement vector at the k-th sampling time; H is the measurement matrix; v k Let be the measurement noise at the k-th sampling time, and let be Gaussian white noise, representing the sensor measurement error.

[0057] S241. Determine the measurement vector z.

[0058] The measurement vector z is a two-dimensional column vector, composed of the roll angle calculated by the accelerometer and the roll angular velocity directly acquired by the gyroscope. Its specific expression is as follows:

[0059] In the formula: , is the roll angle calculated from the accelerometer measurement, where ay is the accelerometer y-axis measurement and az is the accelerometer z-axis measurement; : The ship's roll rate directly measured by a gyroscope.

[0060] S242. Determine the measurement matrix H.

[0061] The measurement matrix H is a 2×3 matrix that projects a 3D state vector onto a 2D measurement vector. Specifically, it takes the following form:

[0062] S243, Measurement noise covariance matrix R.

[0063] The measurement noise covariance matrix R is a 2×2 diagonal matrix that characterizes the measurement noise variance of the accelerometer and gyroscope, and its specific form is as follows:

[0064] In the formula: In this embodiment, the noise variance of the accelerometer is set to 0.1.

[0065] The noise variance of the gyroscope is measured to be 0.01 in this embodiment.

[0066] S3. Based on the spatial model in S2, the Kalman filter algorithm is used to realize the recursive estimation of the ship's roll state variables, including at least prediction and updating.

[0067] S31. Based on the state estimate at time k-1 and the state transition matrix, predict the prior state estimate at time k, expressed as: .

[0068] S32. Based on the estimated error covariance at time k-1, combined with the state transition matrix and the process noise covariance matrix, predict the prior error covariance at time k, expressed as: .

[0069] In the formula: Let F be the transpose of the state transition matrix F.

[0070] S33. Based on the prior error covariance, the measurement matrix, and the measurement noise covariance matrix, calculate the Kalman gain at time k to achieve the weighted allocation between the prior estimate and the measured value. The expression is as follows:

[0071] In the formula: This is the transpose of the measurement matrix H; This is the inverse operation of a matrix.

[0072] S34. Combining the Kalman gain, the prior state estimate, and the measurement residual, we obtain the posterior state estimate at time k, which is the final state quantity estimation result, expressed as:

[0073] In the formula, To measure the residual, we characterize the deviation between the prior estimate and the sensor measurement.

[0074] S34. Based on the Kalman gain, prior error covariance, and measurement matrix, update the posterior error covariance at time k to provide a basis for the filtering calculation at the next time step. The expression is: .

[0075] In the formula: I is the identity matrix with the same dimension as the covariance matrix.

[0076] S4. Adjusting Measurement Noise. To improve the robustness of the Kalman filter during severe rolling motion of the ship and avoid the decrease in estimation accuracy caused by sudden changes in sensor measurement errors, the measurement noise variance of the accelerometer is adaptively adjusted to obtain the adaptive measurement noise variance. And replace it in the original measurement noise covariance matrix R It participates in the real-time calculation of Kalman gain.

[0077] The formula for calculating the variance of adaptive measurement noise is: .

[0078] In the formula: β is an adaptive coefficient, calibrated according to the ship's navigation conditions and sensor characteristics; The three-dimensional measurement vector of the accelerometer ; g is the gravitational acceleration vector, which is a known constant; For vector 2 norm operations, The square of the deviation between the accelerometer measurement and the gravitational acceleration reflects the severity of the ship's rolling motion.

[0079] In one feasible approach, the control unit achieves single-step and multi-step real-time prediction of the ship's roll angle through an autoregressive model, and then combines inverse kinematics to solve for the compensation amount of the linear regulators used for the tabletop support. Finally, it obtains the height compensation amount ΔL of the multiple linear regulators required for ship roll compensation and the tabletop roll compensation angle θ. comp Desktop pitch compensation angle φ comp This provides precise numerical data for the execution module, enabling horizontal stability control of the desktop during ship swaying.

[0080] Specifically, in this embodiment, four linear regulators are used as an example, namely ΔL1, ΔL2, ΔL3, and ΔL4. The specific steps include: S1. Prediction of ship rolling motion based on autoregressive model.

[0081] This embodiment uses the Burg method to estimate AR model parameters, optimizes the model by minimizing forward and backward prediction errors, and then completes single-step and multi-step prediction of the ship's roll angle based on the trained AR model, providing an advance roll angle reference for subsequent compensation calculation and compensating for system control delay.

[0082] S11. Taking the ship's roll angle time series x(t) as the modeling object, and assuming the AR model order is p, the model reflection coefficient and coefficients are recursively calculated by minimizing the sum of squares of the forward prediction error ef(t) and the backward prediction error eb(t). The specific steps are as follows: S111, Define forward prediction error and backward prediction error.

[0083] Forward prediction error: Characterizes the error in predicting the current angle value based on the angle values ​​of the previous p time steps, and is expressed as: .

[0084] Backward prediction error: Characterizes the error in predicting the angle values ​​of the previous p time steps based on the current and the angle values ​​of the next p-1 time steps, and is expressed as: .

[0085] In the formula: a i These are the coefficients to be estimated in the AR model; t is the sampling time of the time series. t=p,p+1,...,N−1, where N is the total number of samples in the time series.

[0086] S112. Calculate the reflection coefficient.

[0087] The reflection coefficient of the p-order model is defined as κ. p Forward prediction error of the first p−1 order model Backward prediction error The recursive calculation is expressed as follows: .

[0088] S113, Update AR model coefficients.

[0089] The coefficients of the p-th order AR model are recursively updated based on the reflection coefficient κp, thereby achieving gradual optimization of the model order. The update formula is as follows: .

[0090] In the formula: For the i-th coefficient of the p−1 order AR model; Let be the i-th coefficient of the p-th order AR model.

[0091] Through the above recursive calculation, the final coefficients ai and constant term c of the AR model at the optimal order are obtained. The final coefficients ai include φ1, φ2, ... φp, respectively, thus completing the offline training of the AR model.

[0092] S12. Real-time prediction based on AR model.

[0093] The trained AR model is applied to the real-time prediction of ship roll angles, including roll and pitch, supporting single-step prediction and multi-step recursive prediction, meeting the compensation requirements for different system delays, and the prediction objects are the ship roll angle θ and the ship pitch angle φ.

[0094] S121. Single-step prediction. Based on the measured angle values ​​at the current time and the previous p-1 time steps, predict the angle value at the next time step. The expression is: .

[0095] In the formula: k is the current sampling time; θ(k−i+1) is the measured value of the ship's roll angle at time k−i+1; φi are the coefficients obtained from training the AR model.

[0096] S122, Multi-step prediction, i.e., system delay compensation. Based on the single-step prediction result, recursive calculations are performed to predict the angle for the next n steps. Taking two-step prediction as an example, the expression is: .

[0097] In the formula: This is the predicted angle value at time k+1.

[0098] It should be noted that higher-order multi-step predictions are calculated recursively according to this rule, that is, the predicted value at subsequent time steps is obtained by weighting the predicted value at the previous time step with the historical measured value.

[0099] S2. Calculate the ship's roll compensation.

[0100] This embodiment takes a horizontal table supported by four linear regulators as an example. By modeling the tabletop kinematics and transforming the coordinates, the position constraint equations of the linear regulators are constructed. Then, the least squares method is used to solve the core compensation amount required for ship roll compensation and the individual compensation amount of each linear regulator, so as to realize the reverse motion compensation between the tabletop and the ship roll.

[0101] S21. Set the desktop as a rigid horizontal desktop, symmetrically supported by four linear adjusters. Establish a desktop coordinate system with the geometric center of the desktop as the origin, and define the position vectors of the four linear adjusters in the desktop coordinate system as follows: .

[0102] In the formula: L is the length dimension of the desktop; W represents the width dimension of the desktop; P1, P2, P3, and P4 are the three-dimensional coordinates of the four linear adjusters in the desktop coordinate system. The initial value of the z-axis is set to 0, which represents the horizontal state of the desktop.

[0103] S22. Change the desktop coordinate system to the ship's coordinate system.

[0104] To match the ship's rolling motion, the position of the linear regulator in the desktop coordinate system is transformed to the ship's coordinate system, which has its origin at the ship's center of gravity and rolls synchronously with the ship. A coordinate transformation matrix T is defined to perform the rotational transformation from the desktop coordinate system to the ship's coordinate system. The expression for the transformation matrix is: .

[0105] In the formula: θ is the AR model prediction of the ship's roll angle; φ is the AR model prediction value for the ship's pitch angle.

[0106] Based on the transformation matrix T, calculate the actual position vector P of the linear regulator in the ship's coordinate system. i The expression is: .

[0107] In the formula: i = 1, 2, 3, 4; L0 is the initial height of the linear regulator, i.e., the height when the desktop is horizontal; ΔL i This is the height compensation amount for the i-th linear regulator; The position increment for height compensation of the linear regulator.

[0108] S23. Construct the contact constraint equations for the linear regulator.

[0109] To ensure reliable contact between the linear regulator and the tabletop and base, each linear regulator must satisfy the following contact constraint: the difference between the position of the linear regulator in the ship's coordinate system and the position of the base must be perpendicular to the base's normal vector, expressed as: .

[0110] In the formula: Bi is the base position of the i-th linear regulator and the fixed position on the hull, which is known. ni is the normal vector of the base, perpendicular to the hull support surface, which is known; ⋅ represents the dot product operation of vectors.

[0111] Position P of the linear regulator in the ship's hull coordinate system iSubstituting into the contact constraint equations, expanding and rearranging, we obtain a system of linear equations concerning the core compensation amount: .

[0112] In the formula: ΔL is the overall height compensation amount of the linear regulator. θ is the tabletop roll compensation angle. comp , φ is the tabletop pitch compensation angle. comp ; x i y i z i is a known constant calculated from the actuator base position Bi, the normal vector ni, and the initial position, i=1,2,3,4.

[0113] S24. Determine the compensation amount using the least squares method.

[0114] The above linear equation system is an overdetermined system, consisting of 4 equations and 3 unknowns. This embodiment uses the Moore-Penrose pseudo-inverse to solve for the least squares solution, ensuring the optimality and stability of the solution. The specific solution steps are as follows: S241. Construct matrix A and vector b.

[0115] The system of linear equations simplifies to the form A⋅X=b, where: The coefficient matrix A is 4×3 in dimension, and it is known that: .

[0116] The unknown vector X is 3×1 in dimension and represents the core compensation quantity to be solved: .

[0117] The constant vector b is 4×1 in dimension, and it is known that: .

[0118] S242. Calculate Moore-Penrose pseudoinverse A+ Based on the transpose of coefficient matrix A T Calculate the pseudoinverse A + The expression is: .

[0119] In the formula: Let A be the transpose of matrix A; For matrix The inverse matrix.

[0120] S243. Determine the core compensation amount. (This refers to the pseudo-inverse...) Substituting into the overdetermined system of equations, we obtain the least-squares solution for the unknown vector X, which is the core numerical value for ship roll compensation: .

[0121] S244. Determine the individual height compensation amount for each linear regulator. Based on the core compensation amount, coefficient matrix A, and constant vector b, determine the height compensation amount for multiple linear regulators, expressed as: .

[0122] Each linear regulator adjusts its height according to its own ΔLi, in conjunction with the θ of the desktop. comp φ comp Angle adjustment enables tabletop level compensation during ship swaying.

[0123] In one feasible approach, the control unit converts the calculated compensation amount into a drive command, which is then sent to the linear regulator of the target furniture via wired or wireless communication. The linear regulator adjusts according to the command, thereby causing the furniture panel to produce a compensating movement that is opposite to the direction and amplitude of the ship's swaying.

[0124] It should be noted that wired communication can be via CAN bus, while wireless communication can be via ZigBee, Bluetooth, Mesh, etc.

[0125] In one feasible approach, the furniture posture sensing unit continuously monitors the compensated and adjusted furniture posture and angular velocity, and sends the furniture posture and angular velocity data to the control unit to form a closed-loop control, thereby achieving high-precision real-time dynamic stability control.

[0126] It should be noted that the control unit operates closed-loop control at a frequency of 100Hz, thereby achieving real-time and active compensation for ship sway.

[0127] In one feasible embodiment, this application also includes a human-computer interaction module, which includes a power switch, a mode selection button, and a status indicator light. The mode selection button can be used to select a working mode or a sleep mode. The human-computer interaction module is set on the furniture surface or controlled by a wireless terminal, including a mobile APP, a smart control panel, etc.

[0128] In summary, this application improves the accuracy of ship roll adjustment by employing a predictive compensation algorithm to control the execution module to act in advance and perform proactive predictive compensation. Through modular design, it can be flexibly adapted to various existing or newly built furniture, facilitating installation and maintenance. The predictive model in the control unit can learn the motion characteristics of different ships and different sea states, automatically adjusting control parameters to achieve optimal stability. This effectively prevents items from slipping and personnel from losing balance due to swaying, making it particularly suitable for high sea states and scenarios requiring delicate operations, thus improving comfort and safety.

[0129] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and substitutions can be made without departing from the technical principles of the present invention, and these improvements and substitutions should also be considered within the scope of protection of the present invention.

Claims

1. A ship furniture stabilization system that adapts to ship swaying, characterized in that, include: The data acquisition module includes a ship motion sensing unit and a furniture attitude sensing unit. The ship motion sensing unit is used to acquire ship attitude, ship angular velocity, and ship linear acceleration; the furniture attitude sensing unit is used to acquire furniture attitude and furniture angular velocity. The execution module includes multiple adjustment units, each located at the bottom of the furniture, for adjusting multiple heights of the furniture bottom to adjust the furniture's posture; The control unit is communicatively connected to both the execution module and the data acquisition module. The control unit includes a prediction model, which determines the ship's motion trend based on data from the data acquisition module. Based on the ship's motion trend, the prediction model determines a compensation amount, generates control commands based on the compensation amount, and sends the control commands to the execution module. The execution module adjusts the levelness of the ship's furniture according to the control commands.

2. The adaptive ship furniture stabilization system for ship swaying according to claim 1, characterized in that, The ship motion sensing unit determines the ship's attitude by acquiring the ship's roll angle, pitch angle, and bow angle; The ship motion sensing unit determines the ship's angular velocity by acquiring the roll angular velocity, pitch angular velocity, and bow angular velocity; The ship motion sensing unit determines the ship's linear acceleration by acquiring the X-axis acceleration, Y-axis acceleration, and Z-axis acceleration respectively.

3. The adaptive ship furniture stabilization system for ship swaying according to claim 1, characterized in that, The furniture posture sensing unit determines the furniture posture by acquiring the furniture roll angle and the furniture pitch angle. The furniture posture sensing unit helps determine the furniture's movement trend by acquiring the furniture's angular velocity.

4. The adaptive ship sway stabilization system for ship furniture according to claim 1, characterized in that, The ship motion sensing unit includes multiple first inertial measurement units, which are distributed and installed at multiple predetermined locations on the ship; the furniture posture sensing unit includes multiple second inertial measurement units.

5. The adaptive ship furniture stabilization system for ship swaying according to claim 4, characterized in that, Both the first inertial measurement unit and the second inertial measurement unit are used to acquire the object's attitude, angular velocity, and acceleration in real time.

6. The adaptive ship sway stabilization system for ship furniture according to claim 1, characterized in that, The adjustment unit includes multiple adjustable legs disposed between the ship's furniture and the deck. The multiple adjustable legs are respectively controlled and connected to the control unit, and the control unit adjusts the extension length of the multiple adjustable legs respectively.

7. The adaptive ship furniture stabilization system for ship swaying according to claim 6, characterized in that, The adjustable leg includes at least a linear adjuster, the top of which is connected to the bottom of the tabletop of the marine furniture via a ball joint.

8. The adaptive ship furniture stabilization system according to claim 1, characterized in that, The control unit uses a Kalman filter algorithm to fuse and filter the data information from the data acquisition module.

9. The adaptive ship furniture stabilization system for ship swaying according to claim 1, characterized in that, The control unit uses an autoregressive model to predict the ship's roll angle in real time, both in single and multi-step steps, and then combines this with inverse kinematics to determine the compensation amount of the adjustment unit.

10. The adaptive ship furniture stabilization system for ship swaying according to claim 1, characterized in that, The furniture posture sensing unit continuously monitors the furniture posture and angular velocity after compensation and adjustment, and sends the data information of furniture posture and angular velocity to the control unit to form closed-loop control.

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