Intelligent ballast tank dynamic adjusting system and method

Abstract

The invention discloses an intelligent ballast tank dynamic adjusting system and method. The method comprises the steps that dynamic state information of a ship is collected in real time; the dynamic state information is input into a pre-established ballast tank adjusting model, the optimal weight and the optimal position of each ballast tank are calculated, and the ballast tank adjusting model comprises a first optimal weight model and a first optimal position model; an adjusting instruction of the ballast tank is generated through the adjusting model; in the adjusting process, the actual ballast tank state is monitored in real time and compared with the expected state, and an adjusting error is generated; and dynamically adjusting the ballast tank according to the adjustment error. The method has the advantages of real-time monitoring and response capability enhancement, dynamic adjustment strategy implementation, intelligent decision support, high control precision, adaptive learning and optimization and the like.

Description

Technical Field

[0001] The present invention belongs to the field of overall ship engineering, and in particular relates to an intelligent ballast tank dynamic adjustment system and method. Background Art

[0002] Traditional ballast tank management methods usually rely on manual monitoring and manual operation, and have the following major problems: Slow response speed. In a complex marine environment, the dynamic state of the ship changes rapidly. Traditional methods have difficulty responding to these changes in real time, causing the ship to sail in an unstable state, increasing the risk of tilting and capsizing. Error accumulation. Manual operation is easily affected by human factors and may lead to the accumulation of adjustment errors. Due to the lack of real-time data monitoring, it is difficult for the crew to accurately judge the status of the ballast tank, which in turn affects the overall stability of the ship. Data islands. Traditional systems often lack effective data integration capabilities. The status information of each ballast tank is scattered in different systems, resulting in a lack of a global perspective when making decisions, making it difficult to achieve optimized adjustments.

[0003] Many existing systems use static adjustment strategies and lack dynamic adaptability. These systems cannot adjust the ballast tanks according to the real-time ocean environment and ship status, resulting in unsatisfactory adjustment effects. Summary of the invention

[0004] In view of the defects existing in the above-mentioned prior art, the present invention provides a smart ballast tank dynamic adjustment method, comprising the following steps:

[0005] Step S101, collecting dynamic state information of the ship in real time, wherein the dynamic state information includes the center of gravity positions of multiple ballast tanks, the tilt angle of the ship, and the movement speed of the ship;

[0006] Step S103, inputting the dynamic state information into a pre-established ballast tank adjustment model to calculate the optimal weight and optimal position of each ballast tank, wherein the ballast tank adjustment model includes a first optimal weight model and a first optimal position model;

[0007] Step S105, generating an adjustment instruction for the ballast tank through the adjustment model;

[0008] Step S107: During the adjustment process, the actual state of the ballast tank is monitored in real time, and compared with the expected state to generate an adjustment error;

[0009] Step S109, dynamically adjusting the ballast tank according to the adjustment error.

[0010] In the step S101, a variety of sensors are used to collect dynamic information of the ship in real time, and the multiple sensors include acceleration sensors, tilt sensors and water level sensors.

[0011] Before step S103, the following steps are further included: the Kalman filtering algorithm is used to clean, denoise and fuse the dynamic state information.

[0012] Among them, the first optimal weight model in step S103 is calculated by the following formula:

[0013] Among them represents the modulus of the relative position vector of the ship; (X target , Y target , Z target ) represents the target centroid coordinates of the ship; (X current , Y current ,, Z current ) represents the current centroid coordinates of the ship, W i target represents the target weight of the i-th ballast tank; m tatal represents the total mass of the ship; g represents the acceleration due to gravity; θ represents the current inclination angle of the ship; g adjust represents the gravity factor for correction; k1 represents the coefficient related to the centroid adjustment; k2 represents the coefficient related to the motion speed; V represents the speed of the ship; E i represents the adjustment error, defined as E i = W i target - W i actual , W i actual represents the actual weight of the i-th ballast tank; K represents the control gain.

[0014] Among them, the first optimal position model in step S103 is calculated by the following formula:

[0015] P i taret = CG current +(ΔX i , ΔY i , ΔZ i ), where P i target represents the target position of the i-th ballast tank; CG current represents the current position of the ship; (ΔX i , ΔY i , ΔZ i ) represents the adjustment amount of the i-th ballast tank relative to the current centroid position.

[0016] Among them, step S105 includes generating an adjustment instruction for the ballast tank based on the fuzzy control and PID control algorithms.

[0017] Among them, the adjustment instruction of the ballast tank in step S107 communicates with other devices through wired and / or wireless communication.

[0018] Among them, the method further includes adopting a multi-level control strategy for adjustment.

[0019] Among them, after step S105, the method includes implementing the adjustment instruction through an electric valve or a pump device.

[0020] The present invention also proposes an intelligent ballast tank dynamic adjustment system, including:

[0021] Multiple sensors, which are used to collect the dynamic state information of the ship in real time, and the dynamic state information includes the center of gravity positions of multiple ballast tanks, the inclination angle of the ship, and the movement speed of the ship;

[0022] An optimal model module, which is used to input the dynamic state information into a pre-established ballast tank adjustment model, and calculate the optimal weight and optimal position of each ballast tank, wherein the ballast tank adjustment model includes a first optimal weight model and a first optimal position model;

[0023] An adjustment instruction generation module, which is used to generate an adjustment instruction for the ballast tank through the adjustment model;

[0024] An adjustment error generation module, which is used to monitor the actual state of the ballast tank in real time during the adjustment process, compare it with the expected state, and generate an adjustment error;

[0025] An adjustment module, which is used to dynamically adjust the ballast tank according to the adjustment error.

[0026] Compared with the prior art, the present invention has the following advantages:

[0027] The present invention utilizes advanced sensor technology to be able to monitor the state of the ballast tank and changes in the external environment in real time. This real-time data acquisition ability enables the system to quickly respond to dynamic changes, ensuring that the ship remains stable under various navigation conditions. By adjusting the weight of the ballast tank in real time, the risk of ship inclination and capsizing is significantly reduced, and the navigation safety is improved.

[0028] The present invention adopts a dynamic adjustment algorithm to be able to automatically calculate and adjust the target weight of the ballast tank according to the real-time monitoring data. This flexible adjustment strategy is superior to static methods and can adapt to different navigation tasks and environmental conditions. Through dynamic adjustment, the system can improve the navigation efficiency of the ship, reduce fuel consumption, and achieve more economical operation.

[0029] The present invention integrates machine learning and data analysis technologies, capable of analyzing historical data, optimizing adjustment strategies, and providing intelligent decision-making support. This intelligent decision-making mechanism significantly improves the adaptability and reliability of the system. Using data-driven intelligent analysis, the system can make faster and more accurate decisions in complex environments, reducing manual intervention.

[0030] By adopting advanced control algorithms (such as PID control), the present invention can achieve higher adjustment accuracy. Compared with traditional control methods, the present invention can handle errors more effectively and dynamically adjust the weight of the ballast tank. Precise control algorithms can effectively reduce errors during the adjustment process, ensuring the ship sails in an optimal state.

[0031] The present invention can integrate various data from different ballast tanks and environments to achieve comprehensive status visualization. This data integration ability not only improves the operation convenience of users but also enhances the overall understanding of the ship s status. Through the visualization interface, the crew can intuitively understand the status of the ballast tank, facilitating timely operation adjustments.

[0032] The present invention has an adaptive learning ability and can continuously optimize its own adjustment strategy based on historical operation data. This learning mechanism ensures that the system continuously improves during long-term operation and enhances the overall performance. The present invention accumulates experience over time and can provide a better adjustment scheme under similar conditions. Description of the Drawings

[0033] By reading the following detailed description with reference to the drawings, the above and other objects, features, and advantages of the exemplary embodiments of the present disclosure will become readily understandable. In the drawings, several embodiments of the present disclosure are shown in an exemplary rather than restrictive manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0034] Figure 1 is a flowchart showing a method for dynamically adjusting an intelligent ballast tank according to an embodiment of the present invention. Detailed Embodiments

[0035] In order to make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the scope of protection of the present invention.

[0036] The terms used in the embodiments of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. The singular forms "a", "said", and "the" used in the embodiments of the present invention and the appended claims are also intended to include the plural forms unless the context clearly dictates otherwise. "Plurality" generally includes at least two.

[0037] It should be understood that although the terms first, second, third, etc. may be used in the embodiments of the present invention to describe..., these... should not be limited to these terms. These terms are only used to distinguish.... For example, without departing from the scope of the embodiments of the present invention, the first... may also be referred to as the second..., and similarly, the second... may also be referred to as the first....

[0038] It should be understood that the term "and / or" used herein is merely a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " herein generally indicates that the associated objects before and after are in an "or" relationship.

[0039] Depending on the context, the words "if", "when" as used herein may be interpreted as "when...", "when...", "in response to determining", or "in response to detecting". Similarly, depending on the context, the phrase "if determined" or "if detecting (stated condition or event)" may be interpreted as "when determined", "in response to determining", "when detecting (stated condition or event)", or "in response to detecting (stated condition or event)".

[0040] It should also be noted that the term "comprising", "including", or any other variant thereof is intended to cover non-exclusive inclusion, such that a commodity or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or elements inherent to such commodity or device. Without further limitation, an element defined by the statement "comprising one..." does not exclude the presence of additional identical elements in the commodity or device comprising the said element.

[0041] The optional embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0042] Embodiment 1

[0043] As Figure 1 shown, the present invention discloses an intelligent ballast tank dynamic regulation method, including the following steps:

[0044] Step S101: Real-time collect the dynamic state information of the ship, where the dynamic state information includes the center-of-gravity positions of multiple ballast tanks, the inclination angle of the ship, and the moving speed of the ship;

[0045] Step S103: Input the dynamic state information into a pre-established ballast tank adjustment model, and calculate the optimal weight and optimal position of each ballast tank, where the ballast tank adjustment model includes a first optimal weight model and a first optimal position model;

[0046] Step S105: Generate an adjustment instruction for the ballast tank through the adjustment model;

[0047] Step S107: During the adjustment process, real-time monitor the actual ballast tank state, compare it with the expected state, and generate an adjustment error;

[0048] Step S109: Dynamically adjust the ballast tank according to the adjustment error.

[0049] Embodiment 2:

[0050] An intelligent ballast tank dynamic adjustment method proposed by the present invention includes the following steps:

[0051] Step S101: Real-time collect the dynamic state information of the ship, where the dynamic state information includes the center-of-gravity positions of multiple ballast tanks, the inclination angle of the ship, and the moving speed of the ship;

[0052] Step S103: Input the dynamic state information into a pre-established ballast tank adjustment model, and calculate the optimal weight and optimal position of each ballast tank, where the ballast tank adjustment model includes a first optimal weight model and a first optimal position model;

[0053] Step S105: Generate an adjustment instruction for the ballast tank through the adjustment model;

[0054] Step S107: During the adjustment process, real-time monitor the actual ballast tank state, compare it with the expected state, and generate an adjustment error;

[0055] Step S109: Dynamically adjust the ballast tank according to the adjustment error.

[0056] Among them, in step S101, a variety of sensors are used to real-time collect the dynamic information of the ship, and the variety of sensors include an acceleration sensor, an inclination sensor, and a water level sensor.

[0057] Among them, before step S103, it further includes: using the Kalman filtering algorithm to clean, denoise, and fuse the dynamic state information.

[0058] Among them, in step S103, the first optimal weight model is calculated using the following formula:

[0059] where represents the modulus of the relative position vector of the ship; (X target , Y target , Z target ) represents the target centroid coordinates of the ship; (X current , Y current , Z curent represents the current centroid coordinates of the ship, and W i targe represents the target weight of the i-th ballast tank; m total represents the total mass of the ship; g represents the acceleration due to gravity; θ represents the current inclination angle of the ship; g adjust represents the gravity factor for correction; k1 represents the coefficient related to centroid adjustment; k2 represents the coefficient related to motion speed; V represents the speed of the ship; E i represents the regulation error, defined as E i = W i target - W i actual , where W i actual represents the actual weight of the i-th ballast tank; K represents the control gain.

[0060] The three-dimensional coordinates are usually expressed as CG = (X, Y, Z), where X, Y, and Z represent the positions on the three coordinate axes respectively. ΔCG is a vector representing the relative position between the target centroid and the actual centroid, which indicates the offset of the target centroid from the actual centroid on the three coordinate axes. The dimension of k1 is kg / m, representing the influence of the change in centroid position on the target ballast tank weight. k2 is related to the square of the speed and reflects the dynamic influence, which can be obtained through dynamic tests.

[0061] The control gain K is a coefficient obtained through experiments using the PID adjustment method, which is used to amplify or reduce the influence of the regulation error. In this control system, the magnitude of the gain directly affects the response speed and stability of the system. A larger K value will make the system respond faster to the error and reduce the regulation time; if the K value is too large, it may cause the system to be unstable; while a too small K value may result in a too slow response and inability to adjust in time.

[0062] Among them, the first optimal position model in the step S103 is calculated using the following formula:

[0063] P i target = CG current + (ΔX i , ΔY u , ΔZ i ), where Pi target represents the target position of the i-th ballast tank; CG current represents the current position of the ship; (ΔX i , ΔY i , ΔZ i ) represents the adjustment amount of the i-th ballast tank relative to the current center of gravity position.

[0064] Among them, ΔX i = k3·E i ·cos(θ)·f(T); ΔY i = k4·E i ·sin(θ)·f(T); ΔZ i = k5·E i ·f(T), where f(T) represents a function related to the adjustment frequency and represents the adjustment intensity within a specific time interval T. Among them, T represents the current time interval or adjustment period, with the unit of seconds; α represents the adjustment gain coefficient, controlling the sensitivity of the response speed, with the unit of seconds -1 . k3, k4, k5 represent the coefficients related to position adjustment and represent the influence in each direction per unit error.

[0065] When T is small (i.e., frequent adjustment), the value of f(T) is close to 1, indicating a strong adjustment effect.

[0066] When T is large (i.e., long adjustment interval), the value of f(T) is close to 0, indicating a gradually weakening adjustment effect.

[0067] Among them, k1, k2, k3, k4, k5 are determined through actual tests and experimental data. These coefficients can be determined by conducting experiments with different weights and position changes and recording the system response; or by using system identification methods (such as the least squares method) to estimate these coefficients from input-output data.

[0068] Among them, the step S105 includes generating an adjustment instruction for the ballast tank based on fuzzy control and PID control algorithms.

[0069] The ship is adjusted using the following formula:

[0070]

[0071] Among them, α and β are adjustment parameters; exp is the exponential function, used to dynamically attenuate the error influence; the sigmoid function is used to smooth the control output. K p 、K i and K dThey are the proportional, integral, and derivative control coefficients, respectively, which are obtained by calculating the critical gain and oscillation period of the system through experiments by the Ziegler-Nichols method.

[0072] ΔW i The value range of max is [-W max , W i , indicating the maximum and minimum weights that each ballast tank can adjust. E

[0073] Among them, the adjustment instructions for the ballast tank in step S107 communicate with other devices through wired and / or wireless communication.

[0074] Among them, the method further includes adopting a multi-level control strategy for adjustment.

[0075] The multi-level control strategy generally includes the following levels:

[0076] The high level (strategy level), which is responsible for formulating the overall adjustment strategy based on the long-term goals and operating environment of the ship.

[0077] The middle level (tactical level), which processes specific adjustment tasks and generates intermediate instructions to meet the goals of the high level.

[0078] The low level (execution level), which directly controls the actuators (such as electric valves and pumps) to implement specific adjustment instructions.

[0079] Among them, the high level (strategy level) is responsible for setting the overall goals according to the navigation tasks, cargo distribution, and environmental conditions, such as: keeping the ship within a specific inclination angle range, optimizing the navigation performance and fuel efficiency; understanding the changes in the external environment (such as sea state, wind speed, etc.) in real time, and adjusting the goal setting to cope with the changes.

[0080] Among them, the middle level (tactical level) is responsible for decomposing the goals set by the high level into a series of specific adjustment tasks, such as: calculating the required weight increase or decrease for each ballast tank; generating specific adjustment instructions according to the real-time monitoring data and expected state, and these instructions include the optimal weight and position of each ballast tank; adjusting the priority of the instructions according to the current problem to be solved (such as too large inclination angle) to ensure that the most important tasks are executed first.

[0081] Among them, the low level (execution level) is responsible for instruction implementation, converting the instructions generated by the middle level into specific operation commands, and controlling the operation of electric valves and pumps. During the implementation process, the state of the ballast tank is monitored in real time to ensure that the execution effect meets the expectations. If a deviation is found, it is immediately fed back to the middle level for adjustment.

[0082] A closed-loop control system is formed through continuous monitoring and feedback. The output of each layer will affect the decision-making of the next layer, ensuring the adaptive ability of the system.

[0083] After the step S105, the method includes implementing the adjustment instruction through an electric valve or a pump device.

[0084] Embodiment III

[0085] The present invention also proposes an intelligent ballast tank dynamic adjustment system, including:

[0086] A plurality of sensors for real-time acquisition of the dynamic state information of the ship, where the dynamic state information includes the center of gravity positions of a plurality of ballast tanks, the inclination angle of the ship, and the moving speed of the ship;

[0087] An optimal model module for inputting the dynamic state information into a pre-established ballast tank adjustment model to calculate the optimal weight and optimal position of each ballast tank, where the ballast tank adjustment model includes a first optimal weight model and a first optimal position model;

[0088] An adjustment instruction generation module for generating an adjustment instruction for the ballast tank through the adjustment model;

[0089] An adjustment error generation module for, during the adjustment process, real-time monitoring of the actual ballast tank state and comparison with the expected state to generate an adjustment error;

[0090] An adjustment module for dynamically adjusting the ballast tank according to the adjustment error.

[0091] Embodiment IV

[0092] The embodiments of the present disclosure provide a non-volatile computer storage medium storing computer-executable instructions that can execute the method steps as described in the above embodiments.

[0093] It should be noted that the above-mentioned computer-readable medium in the present disclosure can be a computer-readable signal medium, a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples of a computer-readable storage medium can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, apparatus, or device. In the present disclosure, a computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, in which computer-readable program code is carried. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium can also be any computer-readable medium other than a computer-readable storage medium, and this computer-readable signal medium can send, propagate, or transmit a program for use by or in combination with an instruction execution system, apparatus, or device. The program code contained on a computer-readable medium can be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0094] The above-mentioned computer-readable medium can be included in the above-mentioned electronic device; or it can exist separately without being assembled into the electronic device.

[0095] Computer program code for performing the operations of the present disclosure can be written in one or more programming languages or combinations thereof. The above-mentioned programming languages include object-oriented programming languages - such as Java, Smalltalk, C++, and also include conventional procedural programming languages - such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, executed as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network - including a local area network (LAN) or a wide area network (WAN) - or, it can be connected to an external computer (for example, by using an Internet service provider to connect through the Internet).

[0096] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram may represent a module, a segment of a program, or a part of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions noted in the blocks may occur in a different order than noted in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and combinations of blocks in the block diagram and / or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0097] The units involved in the embodiments described in the present disclosure can be implemented in software or in hardware. Among them, the name of the unit does not, in some cases, constitute a limitation on the unit itself.

[0098] The preferred embodiments of the present invention are described above to make the spirit of the present invention clearer and easier to understand, and are not intended to limit the present invention. Any modifications, substitutions, and improvements made within the spirit and principles of the present invention shall be included in the protection scope defined by the claims appended to the present invention.

Claims

1. An intelligent ballast tank dynamic adjustment method, characterized in that It includes the following steps: Step S101, collect the dynamic state information of the ship in real time, where the dynamic state information includes the center-of-gravity positions of multiple ballast tanks, the inclination angle of the ship, and the moving speed of the ship; Step S103, input the dynamic state information into a pre-established ballast tank adjustment model to calculate the optimal weight and optimal position of each ballast tank, where the ballast tank adjustment model includes a first optimal weight model and a first optimal position model; Step S105, generate an adjustment instruction for the ballast tank through the adjustment model; Step S107, during the adjustment process, monitor the actual ballast tank state in real time, compare it with the expected state, and generate an adjustment error; Step S109, dynamically adjust the ballast tank according to the adjustment error.

2. The method according to claim 1, wherein In step S101, a variety of sensors are used to collect the dynamic information of the ship in real time, and the variety of sensors include an acceleration sensor, an inclination sensor, and a water level sensor.

3. The method according to claim 1, wherein Before step S103, it also includes: using the Kalman filtering algorithm to clean, denoise, and fuse the dynamic state information.

4. The method according to claim 1, wherein In step S103, the first optimal weight model is calculated using the following formula: wherein represents the modulus of the relative position vector of the ship; (X target , Y target , Z target ) represents the target centroid coordinates of the ship; (X current , Y current , Z current ) represents the current centroid coordinates of the ship, and W i target represents the target weight of the i-th ballast tank; m total represents the total mass of the ship; g represents the acceleration due to gravity; θ represents the current inclination angle of the ship; g adjust represents the gravity factor for correction; k1 represents the coefficient related to centroid adjustment; k2 represents the coefficient related to motion speed; V represents the speed of the ship; E i represents the regulation error, defined as E i = W i target - W i actual , and W i actual represents the actual weight of the i-th ballast tank; K represents the control gain.

5. The method according to claim 1, wherein In step S103, the first optimal position model is calculated using the following formula: P i target = CG current +(ΔX i , ΔY i , ΔZ i ), where P i target represents the target position of the i-th ballast tank; CG current represents the current position of the ship; (ΔX i , ΔY i , ΔZ i ) represents the adjustment amount of the i-th ballast tank relative to the current center of gravity position.

6. The method according to claim 1, wherein Step S105 includes generating an adjustment instruction for the ballast tank based on fuzzy control and PID control algorithms.

7. The method according to claim 6, wherein In step S107, the adjustment instruction of the ballast tank communicates with other devices through wired and / or wireless communication.

8. The method according to claim 1, wherein The method also includes using a multi-level control strategy for adjustment.

9. The method according to claim 1, wherein After step S105, the method includes implementing the adjustment instruction through an electric valve or a pump device.

10. An intelligent ballast tank dynamic adjustment system, including: A plurality of sensors, which are used to collect the dynamic state information of the ship in real time, where the dynamic state information includes the center-of-gravity positions of multiple ballast tanks, the inclination angle of the ship, and the moving speed of the ship; An optimal model module, which is used to input the dynamic state information into a pre-established ballast tank adjustment model to calculate the optimal weight and optimal position of each ballast tank, where the ballast tank adjustment model includes a first optimal weight model and a first optimal position model; An adjustment instruction generation module, which is used to generate an adjustment instruction for the ballast tank through the adjustment model; An adjustment error generation module, which is used to monitor the actual ballast tank state in real time during the adjustment process, compare it with the expected state, and generate an adjustment error; An adjustment module, which is used to dynamically adjust the ballast tank according to the adjustment error.

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