A method and system for adjusting a steel-lead composite ballast device for a ship

Abstract

The present invention discloses a method and system for adjusting a steel-lead composite ballast device for a ship, wherein the method comprises: real-time monitoring and acquisition of the attitude information of the hull and the status information of the ballast device; receiving raw data from a sensor, and performing cleaning, format conversion, standardization and normalization operations. The present invention utilizes the high density characteristics of lead to reduce the volume of the ballast device, improve the utilization rate of the cabin space, monitor and acquire the attitude information of the hull and the status information of the ballast device in real time, and formulate an adjustment plan for the ballast device based on the analysis results of an optimization algorithm or a machine learning model, taking into account the real-time state of the ship, the marine environment and the distribution factors of the cargo, and sending the adjustment plan to the hull control module in the form of an instruction, automatically adjusting the number and position of the ballast device, realizing the automatic adjustment of the ballast device, and improving the adjustment efficiency and accuracy.

Description

Technical Field

[0001] The invention relates to the technical field of ballast device adjustment, and in particular to an adjustment method and system for a ship's steel-lead composite ballast device. Background Art

[0002] The ballast device is an important part of ship engineering. Its working principle is mainly based on the Archimedean principle. By arranging the ballast device at the bottom of the cabin, the gravity and buoyancy of the entire ship are balanced. When the ship's load increases, in order to maintain balance, the ballast device increases the buoyancy of the ship by giving a certain ballast force to the underwater part of the ship to achieve the purpose of ship balance. During the ship's navigation, the ballast device needs to be continuously adjusted according to the state of the hull to ensure that the ship is always in a stable operating state and to reduce the height of the ship's overall center of gravity as much as possible to meet the ship's stability requirements. At present, ship ballast devices are designed in accordance with CB 676-1978, and the material is gray cast iron with a density of 7.2kg / dm 3 , the weight of a single ballast device is 20kg.

[0003] With the rapid development of the shipping industry and the continuous advancement of ship technology, higher requirements are placed on the safety, stability and space utilization of ships. Due to the low density of gray cast iron, a large amount of space needs to be reserved inside the cabin for arranging ballast devices, which affects the space utilization of the ship and reduces the actual carrying capacity of the ship. In addition, the traditional ballast device adjustment method mainly relies on manual operation and experience judgment, which is not only inefficient but also difficult to accurately control. Therefore, it is necessary to propose a method and system for adjusting a ship's steel-lead composite ballast device to solve the problems in the prior art. Summary of the invention

[0004] The purpose of the present invention is to make up for the deficiencies of the prior art and to provide a method and system for adjusting a ship's steel-lead composite ballast device, which solves the problem that the existing ballast device requires a large amount of space to be reserved inside the cabin for arranging the ballast device, affecting the space utilization of the ship and reducing the actual carrying capacity of the ship. The ballast device can be automatically adjusted, thereby improving the adjustment efficiency and accuracy.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0006] In the first aspect, the present application provides a method for adjusting a steel-lead composite ballast device for a ship, the method comprising: real-time monitoring and obtaining the posture information of the hull and the status information of the ballast device; receiving the raw data from the sensor, and performing cleaning, format conversion, standardization and normalization operations; analyzing the processed data in combination with the algorithm model, using historical data to train the machine learning model, learning the relationship between the hull posture and the ballast device status, and predicting the future hull posture and ballast device status; based on the analysis results of the optimization algorithm or the machine learning model, considering the real-time status of the ship, the marine environment and the cargo distribution factors, formulating an adjustment plan for the ballast device; sending the adjustment plan to the hull control module through the communication module in the form of an instruction, and adjusting the number and position of the ballast device according to the received instruction. Wherein, the ballast device includes a stainless steel shell, a cast lead block is arranged inside the stainless steel shell, an embedded construction handle is installed on the stainless steel shell, and a casting hole and a vent are opened on the stainless steel shell.

[0007] In the second aspect, the present application provides a ship-use steel-lead composite ballast device adjustment system, characterized in that the system includes: a data acquisition module, an automatic control module, a hull control module, a communication module and a safety monitoring module. The data acquisition module includes a hull attitude sensor and a ballast device status monitoring sensor, the hull attitude sensor monitors the hull attitude information in real time; the ballast device status monitoring sensor monitors the status of the ballast device in real time. The automatic control module obtains the hull attitude information and the ballast device status information, determines the adjustment scheme of the ballast device through algorithm analysis, and sends a control instruction to the hull control system. The hull control module adjusts the number and position of the ballast device according to the instructions of the automatic control module. The communication module is responsible for realizing the communication connection between the automatic control module and the hull control module. The safety monitoring module monitors the safety status of the ship in real time, and when an abnormal situation is detected, it promptly issues an alarm or takes corresponding safety measures. Among them, the ballast device includes a stainless steel shell, a cast lead block is arranged inside the stainless steel shell, an embedded construction handle is installed on the stainless steel shell, and a casting hole and a vent are opened on the stainless steel shell.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] An embodiment of the present application provides a method and system for adjusting a ship's steel-lead composite ballast device. The steel-lead composite ballast device is cast with lead blocks inside a stainless steel shell, and the high density of lead is used to reduce the volume of the ballast device, thereby improving the utilization rate of the cabin space. The hull's posture information and the status information of the ballast device are monitored and acquired in real time. An adjustment plan for the ballast device is formulated based on the analysis results of an optimization algorithm or a machine learning model, taking into account the real-time status of the ship, the marine environment, and the distribution of cargo factors. The adjustment plan is sent to a hull control module in the form of an instruction, and the number and position of the ballast device are automatically adjusted, thereby realizing automatic adjustment of the ballast device and improving adjustment efficiency and accuracy.

[0010] Other advantages, objectives and features of the present invention will be set forth in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0012] Figure 1 A schematic diagram of a method for adjusting a ship's steel-lead composite ballast device;

[0013] Figure 2 This is a schematic diagram of the structure of an adjustment system for a marine steel-lead composite ballast device;

[0014] Figure 3 It is a schematic structural diagram of a steel-lead composite ballast device in the present invention.

[0015] Figure 3 Middle: 1. Stainless steel shell; 2. Casting lead block; 3. Embedded construction handle; 4. Casting hole; 5. Ventilation hole. DETAILED DESCRIPTION

[0016] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] Example 1

[0018] like Figure 1 As shown, the present application provides a method and system for adjusting a marine steel-lead composite ballast device, the method comprising:

[0019] Acquire a data acquisition module, wherein the data acquisition module includes a hull attitude sensor and a ballast device status monitoring sensor;

[0020] The data acquisition module is composed of two real-time data acquisition devices, including a hull attitude sensor and a ballast device status monitoring sensor. The hull attitude sensor monitors and obtains the hull's attitude information in real time, including roll, pitch, and heave parameters. The ballast device status monitoring sensor monitors the ballast device status information in real time, including position, weight, and working status, and controls the collection of various data on the hull attitude and ballast device status through the data acquisition module. The control content includes collection frequency, collection time, and collection content. The data acquisition module collects and controls various data on the hull attitude and ballast device status, providing a data basis for subsequent real-time monitoring and acquisition of the hull attitude information and the ballast device status information based on the data acquisition module.

[0021] Based on the data acquisition module, the attitude information of the hull and the status information of the ballast device are respectively monitored and acquired in real time;

[0022] The hull attitude sensors are precisely installed at key positions such as the bow, stern and midship. These sensors can capture the subtle changes in the hull's roll, pitch and heave in real time. At the same time, ballast device status monitoring sensors are installed on each ballast device to monitor the position, weight and working status of the ballast device in real time to ensure that the operation of the ballast device can be fully understood. These devices are not only highly sensitive and stable, but also able to resist various interference factors in the marine environment, ensuring the accuracy and reliability of the data, and providing a data basis for subsequent further processing of the acquired data.

[0023] Receive raw data from sensors and perform cleaning, format conversion, standardization and normalization operations;

[0024] The hull attitude sensor and the ballast device status monitoring sensor collect the hull attitude information and the ballast device status information. The data processing unit receives the raw data from the sensor and performs cleaning, format conversion, standardization and normalization operations to ensure the quality and consistency of the data. For a set of collected hull attitude information and ballast device status information data (x1, x2, ..., x n ), its mean is (μ), standard deviation is (σ), and the standardized hull attitude information and ballast device status information data (z i ) can be expressed as: For a set of collected ship posture information and ballast device status information data (x1, x2, ..., x n ), whose minimum value is (x min ), the maximum value is (x max ), normalized data (n i ) can be expressed as: Through the above processing, the quality and consistency of the hull attitude information and ballast device status information data collected by the hull attitude sensor and the ballast device status monitoring sensor can be ensured, which facilitates subsequent analysis and processing.

[0025] The processed data is combined with the algorithm model for analysis, and the machine learning model is trained using historical data to learn the relationship between the hull posture and the ballast device status, and predict the future hull posture and ballast device status;

[0026] The algorithm model uses a linear programming optimization algorithm to construct an objective function, and uses a machine learning algorithm to train the machine learning model on historical data. First, the objective function is constructed, which involves the cost, time or other optimization indicators of ballast device adjustment to ensure that the requirements of ship stability and safety are met. The linear regression algorithm is used for optimization calculation, and then the optimization algorithm is used to solve the optimal solution of the objective function. A large amount of historical data and real-time data are collected. The linear regression method in the optimization algorithm is used to analyze and predict the cost of ballast device adjustment. The relationship between the hull posture and the ballast device state is deeply studied using the machine learning model. Through the training of historical data, the machine learning model can capture the complex relationship and potential laws between the hull posture and the ballast device state, and can predict the future trend of changes in the hull posture and ballast device state, which provides strong support for the formulation of adjustment strategies. The ballast device adjustment plan is optimized using the optimization algorithm. In this process, an objective function is constructed, which comprehensively considers the cost, time and ship stability of the ballast device adjustment. Multiple optimization indicators, at the same time, constraints are defined. These constraints include the stability requirements of the ship, the physical limitations of the ballast device, and the constraints of the marine environment. By solving the optimal solution of the objective function, the basic plan for ballast device adjustment is obtained.

[0027] Based on the analysis results of the optimization algorithm or machine learning model, the adjustment plan of the ballast device is formulated taking into account the real-time status of the ship, the marine environment and the distribution of cargo;

[0028] During the operation of the ship, especially in the complex and changeable marine environment, the stability of the hull attitude is crucial to ensure navigation safety and cargo integrity. When formulating the ballast device adjustment plan, the real-time status of the ship is comprehensively considered, including the current attitude, speed, and heading of the ship. Through real-time monitoring and analysis of these information, the current motion state and stability of the ship can be understood, so as to formulate a more accurate ballast device adjustment plan. In addition, the marine environment is also an important factor to be considered when formulating the adjustment plan. The marine environment includes natural phenomena such as wind, waves, currents, and tides, which will affect the attitude and stability of the ship. Therefore, when formulating the adjustment plan, it is necessary to combine the real-time acquired marine environment information, analyze its potential impact on the ship's attitude, and adjust the position and weight distribution of the ballast device accordingly to reduce the impact of the marine environment on the stability of the ship. Finally, the distribution of cargo is also one of the factors to be considered when formulating the ballast device adjustment plan. The uneven distribution of cargo in the cabin may cause the ship to tilt or be unstable. Therefore, when formulating the adjustment plan, it is necessary to combine the actual distribution of cargo and balance the center of gravity of the ship by adjusting the position and weight distribution of the ballast device to ensure the stability of the ship during navigation.

[0029] When formulating an adjustment plan for the ship's steel-lead composite ballast device, it is necessary to comprehensively consider multiple factors such as the ship's real-time status, the marine environment, and the distribution of cargo. Through the analysis results based on optimization algorithms and machine learning models, a more accurate and effective ballast device adjustment plan can be formulated to improve the stability and safety of the ship and ensure that the ship can sail safely and stably in complex and changeable marine environments.

[0030] The adjustment plan is sent to the hull control module in the form of instructions through the communication module, and the number and position of the ballast device are adjusted according to the received instructions;

[0031] After formulating a detailed ballast device adjustment plan, the instructions are sent to the hull control module through the communication module. After receiving the instructions, the hull control module conducts a rigorous parsing and verification process. First, the integrity of the instructions is verified to ensure that the instructions do not miss any key information; then the accuracy of the instructions is verified to ensure that the parameters in the instructions meet the preset ranges and rules. Once the instructions are verified, the hull control module will begin to implement the ballast device adjustment strategy based on the parsed instruction content. During the adjustment process, the position and weight distribution of the ballast device are accurately adjusted by controlling the moving mechanism or adjustment device of the ballast device. It monitors the status of the ballast device and changes in the hull posture in real time, and compares them with the target status. If an abnormal situation is found or the adjustment effect is not as expected, the hull control module will immediately take countermeasures, such as adjusting the instruction parameters or optimizing the adjustment strategy.

[0032] Furthermore, the method further comprises:

[0033] Installing the hull attitude sensor at the key position of the hull can monitor and obtain the attitude information of the hull's roll, pitch and heave parameters in real time;

[0034] The ballast device status monitoring sensor is installed on the ballast device, and the ballast device status monitoring sensor monitors the status information of the ballast device position, weight and working status in real time.

[0035] In order to ensure that the attitude information of the hull can be obtained accurately and in real time, sensors are installed in key parts of the hull. These key parts usually include the bow, stern and midship positions, which can monitor the roll, pitch and heave attitude parameters of the hull in real time. By accurately measuring these parameters, the real-time attitude status of the ship can be fully understood, providing an important basis for the subsequent adjustment of the ballast device. In order to ensure that the working status of the ballast device can be mastered in real time, it is installed on each ballast device so that they can be monitored in all directions. The position information understands the distribution of the ballast device on the hull, so as to judge its impact on the ship's attitude. The weight information can reflect the actual load of the ballast device and provide a basis for adjusting the weight distribution of the ballast device. By real-time monitoring and analyzing these data, problems with the ballast device can be discovered in time and corresponding measures can be taken to deal with them. For example, if a ballast device is found to be offset or its weight is abnormal, we can adjust its position or add or reduce ballast in time to restore the balance and stability of the ship.

[0036] Furthermore, the method further comprises:

[0037] The data processing unit receives the raw data from the sensor, and uses standardization and normalization algorithms to clean, convert the format, standardize and normalize the raw data collected by the hull attitude sensor, ballast device status monitoring sensor, and the collected data to ensure the quality and consistency of the collected data and facilitate subsequent analysis.

[0038] The received raw data is collected in real time, and contains real-time change information of the hull attitude and ballast device status. The raw data often has noise, outliers and inconsistent formats, which will affect the accuracy and reliability of subsequent analysis. Therefore, the data processing unit will carefully check and screen the raw data to remove the noise and outliers. The data processing unit will convert the format of the cleaned data. Since different sensors may use different data formats and units, in order to facilitate subsequent analysis and comparison, we need to convert these data into the same format and unit. After completing the format conversion, the data processing unit will further standardize and normalize the data. Through standardization and normalization, it can be ensured that the data collected by different sensors have the same weight and influence in terms of numerical value, avoiding some data from being ignored or over-emphasized because of excessively large or small dimensions. This helps to improve the accuracy and reliability of subsequent analysis and can more accurately understand the changing laws of the hull attitude and ballast device status.

[0039] Furthermore, the method further comprises:

[0040] The algorithm model uses a linear programming optimization algorithm to construct an objective function, which includes multiple optimization indicators, such as the cost, time, and ship stability of ballast device adjustment. These indicators can be weighted according to actual needs. The objective function is: Among them, x i is the adjustment amount of the ith ballast device, c i is the corresponding unit adjustment cost;

[0041] Defining constraints is also an important part of the optimization algorithm. Constraints include the stability requirements of the ship, the physical limitations of the ballast device, and the constraints of the marine environment to ensure that the optimal solution is feasible and safe in actual operation. The constraints include:

[0042] Roll constraint: where a i is the influence coefficient of the i-th ballast device on the rolling;

[0043] Pitch constraint: where b i is the influence coefficient of the i-th ballast device on pitch;

[0044] Heave constraints: where d i is the influence coefficient of the ith ballast device on heave;

[0045] Total weight conservation constraint: where w i is the weight of the i-th ballast device. This constraint ensures the conservation of the total weight of the ballast device before and after adjustment;

[0046] Boundary constraints: For each ballast device, there are usually upper and lower limit constraints on the adjustment amount, such as: Among them l i and u i are the lower and upper limits of the adjustment amount of the i-th ballast device, respectively;

[0047] By solving the optimal solution of the objective function, the optimization algorithm can provide the best plan or strategy for adjusting the ballast device and provide guidance for subsequent adjustment operations.

[0048] When constructing the objective function, the algorithm model uses a linear programming optimization algorithm and fully considers multiple optimization indicators, such as the cost and time of adjusting the ballast device and the stability of the ship. These indicators have their own weights in the objective function and can be flexibly adjusted according to actual needs and operating conditions. When defining the constraints, the stability requirements of the ship, the physical limitations of the ballast device and the constraints of the marine environment are fully considered. These constraints ensure that the optimal solution is not only feasible but also safe and reliable in actual operation. Roll is the lateral swaying motion caused by the external forces of wind and waves during the voyage of the ship, and pitch is the lateral swaying motion caused by the external forces of waves during the voyage of the ship. The longitudinal swaying motion caused by the action of force, heave is the up and down movement of the ship in the vertical direction, which is mainly affected by waves and the weight of the ship itself. In order to ensure the smooth navigation of the ship, it is necessary to limit the roll angle, pitch angle and heave. When adjusting the ballast device, it is necessary to ensure the conservation of the total weight before and after the ballast device is adjusted to maintain the buoyancy balance of the ship. In actual operation, the adjustment amount of each ballast device usually has certain upper and lower limit constraints. These boundary constraints ensure the rationality and feasibility of the adjustment of the ballast device. Therefore, the optimization algorithm provides strong guidance and support for subsequent adjustment operations, which helps to improve the navigation performance and safety of the ship.

[0049] Furthermore, the method further comprises:

[0050] Use machine learning algorithms to train machine learning models on historical data to learn the complex relationship between the hull attitude and the ballast device status;

[0051] By training the model, the machine learning algorithm can capture the underlying laws and patterns between the ship’s posture and the state of the ballast device, which enables the model to predict the future trend of changes in the ship’s posture and the state of the ballast device, providing strong support for the formulation of adjustment strategies;

[0052] The prediction results of the machine learning model can be combined with the results of the optimization algorithm to comprehensively evaluate the pros and cons of different adjustment plans and select the optimal adjustment strategy.

[0053] A neural network algorithm is used to train historical data. According to the needs of ship attitude prediction, relevant features are extracted from the preprocessed data, such as the ship's historical attitude data, sea conditions, and cargo status.

[0054] Determine the network parameters, set the number of neural network layers, the number of neurons in each layer, and the activation function parameters. The forward propagation formula of the neural network For each neuron in the neural network, its output can be expressed as: z = f (w x + b), where x is the input vector, w is the weight vector, b is the bias term, and f is the activation function. The gradient update formula in the back propagation algorithm. During the training process, the weights and bias values ​​of the neural network are adjusted through the back propagation algorithm. The update formula for weights and biases is usually: Where L is the loss function η is the learning rate, and are the partial derivatives of the loss function with respect to weights and biases, respectively;

[0055] Divide the processed data into a training set and a validation set (or test set), select an appropriate loss function, such as mean square error (MSE), to measure the difference between the model's predicted value and the true value, and use the training data and loss function to adjust the weights and bias values ​​of the neural network through the back propagation algorithm so that the model can accurately predict the ship's attitude;

[0056] Use the validation set to evaluate the trained model, calculate the prediction accuracy and error index, adjust the parameters and structure of the neural network according to the evaluation results, or try other optimization algorithms to improve the prediction performance of the model.

[0057] The use of machine learning algorithms, especially neural network algorithms, to train historical data on hull attitude and ballast device status can deeply explore the complex relationship between the two. This predictive ability is crucial for formulating accurate adjustment strategies and helps to improve the navigation performance and safety of ships. During the training process, it is first necessary to preprocess the historical data and extract features related to ship attitude prediction, such as the ship's historical attitude data, sea conditions, and cargo status. These features serve as the input of the neural network and provide a rich information basis for the model. Then, the parameters of the neural network are determined, including the number of layers, the number of neurons in each layer, and the activation function. The setting of these parameters directly affects the expression ability and learning speed of the model. Through the forward propagation algorithm, the output of each neuron can be calculated, and then the prediction result of the model can be obtained. In order to optimize the prediction performance of the model, the back propagation algorithm is used to adjust the neural network. Through the weights and bias values ​​of the network, by calculating the partial derivatives of the loss function with respect to the weights and biases, their update direction and step size can be obtained. During the training process, this process is continuously iterated so that the predicted value of the model gradually approaches the true value. In order to evaluate the performance of the model, the processed data is divided into a training set and a validation set (or test set). The training set is used to train the model, while the validation set is used to test the generalization ability of the model. By calculating the prediction accuracy and error index evaluation indicators, the performance of the model in different scenarios can be understood, and then the parameters and structure of the neural network can be adjusted, or other optimization algorithms can be tried to improve the prediction performance of the model. Finally, the prediction results of the machine learning model can be combined with the results of the optimization algorithm to form a complete set of adjustment strategy formulation processes. By comprehensively evaluating the pros and cons of different adjustment schemes, the optimal adjustment strategy can be selected to provide strong protection for the safe navigation of ships.

[0058] The steel-lead composite ballast device comprises a stainless steel shell 1, a cast lead block 2 is arranged inside the stainless steel shell 1, an embedded construction handle 3 is installed on the stainless steel shell 1, and a casting hole 4 and a vent hole 5 are opened on the stainless steel shell 1.

[0059] The stainless steel shell 1 is a square thin-walled welded structure, which provides an enclosed storage space for the internal lead block 2 and serves as the shell of the ballast device to improve the forming performance and environmental adaptability of the ballast device; the lead block 2 is cast inside the stainless steel shell to ensure that the entire ballast device meets the required weight, and the embedded construction handle 3 meets the personnel transportation needs during the construction of the ballast device. Casting holes 4 and ventilation holes 5 are opened on the stainless steel shell 1 to meet the construction needs when casting the lead block 2 inside. After the overall casting is completed, the casting holes 4 and ventilation holes 5 are sealed with non-metallic sealing materials to ensure that the ballast device is airtight.

[0060] The present invention utilizes the high density of lead (about 1.6 times that of gray cast iron) to greatly reduce the volume of the ballast device under the same weight condition and improve the utilization rate of the cabin space. The process flow includes pre-forming the stainless steel shell 1, casting the internal lead block, and finally using non-metallic sealing materials to seal the casting hole 4 and the vent hole 5. This ensures the appearance of the ballast device and ensures that there is no welding construction after the lead block is cast, effectively solving the problems of difficult molding and low melting point of lead materials, and has high engineering application value.

[0061] In addition, the ballast device of the present invention is installed inside the ballast tank of the ship, and a moving mechanism and an adjusting device dedicated to the ballast device are installed inside the ballast tank of the ship, so that each ballast device is installed on a slide rail arranged inside the ballast tank of the ship, and the ballast device is connected to the output end of the electric or hydraulic drive device, and the electric or hydraulic drive device is controlled by the hull control module to work. Starting the electric or hydraulic drive device can drive the ballast device to slide on the slide rail installed inside the ballast tank of the ship, and the movement and weight distribution of the ballast device can be accurately controlled.

[0062] Example 2

[0063] Based on the same inventive concept as the method for adjusting a marine steel-lead composite ballast device in the aforementioned embodiment, Figure 2 As shown, the present application provides a ship-use steel-lead composite ballast device adjustment system, the system comprising:

[0064] The monitoring module includes a hull attitude sensor and a ballast device status monitoring sensor. The hull attitude sensor monitors the hull attitude information in real time; the ballast device status monitoring sensor monitors the status of the ballast device in real time;

[0065] A formulation module is used to obtain the hull attitude information and ballast device status information, determine the adjustment plan of the ballast device through algorithm analysis, and send control instructions to the hull control system;

[0066] An adjustment module, used for the hull control module to adjust the number and position of the ballast device according to the instruction of the automatic control module;

[0067] The communication module is responsible for realizing the communication connection between the automatic control module and the hull control module;

[0068] The safety monitoring module monitors the safety status of the ship in real time, and issues an alarm or takes corresponding safety measures in a timely manner when an abnormal situation is detected.

[0069] The ballast device comprises a stainless steel shell, a cast lead block is arranged inside the stainless steel shell 1, an embedded construction handle is installed on the stainless steel shell, and a casting hole and a vent hole are opened on the stainless steel shell.

[0070] Furthermore, the monitoring module includes the following execution steps:

[0071] First, the hull attitude sensors are accurately installed at key positions such as the bow, stern and midship. These sensors can capture subtle changes in the hull's roll, pitch and heave in real time. At the same time, ballast device status monitoring sensors are installed on each ballast device to monitor the position, weight and working status of the ballast device in real time, ensuring that the operation of the ballast device can be fully understood.

[0072] Furthermore, the data processing unit receives the raw data from the sensor and performs cleaning, format conversion, standardization and normalization operations. The system first defines an objective function that includes multiple factors such as cost, time, and hull stability, and sets corresponding constraints. Then, through the optimization algorithm, the system can find the ballast device adjustment plan that makes the objective function optimal under the premise of satisfying the constraints. These plans not only consider the number and position of the ballast device, but also comprehensively consider the energy consumption and time cost factors in the adjustment process, thereby achieving comprehensive optimization of the ballast device adjustment cost. The machine learning model is trained using historical data to learn the relationship between the hull posture and the ballast device status. The trained model can be used to predict the future hull posture and ballast device status, and generate corresponding adjustment plans. Then, based on the analysis results of the optimization algorithm or machine learning model, an adjustment strategy for the ballast device is formulated, considering the real-time status of the ship, the marine environment, and the cargo distribution factors, and comprehensively deciding the optimal adjustment plan to generate a detailed ballast device adjustment plan, including the number, location, and sequence information of the adjustment.

[0073] Furthermore, after receiving the instruction, the hull control module performs strict analysis and verification. Once the instruction is verified, the hull control module will begin to implement the adjustment strategy of the ballast device according to the content of the instruction obtained through the analysis. During the adjustment process, the position and weight distribution of the ballast device are accurately adjusted by controlling the moving mechanism or adjustment device of the ballast device.

[0074] Furthermore, the communication module includes the following execution steps:

[0075] When the system is started or reset, the communication module first performs initialization operations to establish a communication connection between the automatic control module and the hull control module. The hull control module receives instructions from the automatic control module through the communication module. The hull control module parses the instruction content and adjusts the quantity and position of the ballast device through the control mechanism. During the adjustment of the ballast device, the hull control module can send feedback information to the automatic control module through the communication module, including adjustment results and current status. The automatic control module further optimizes and adjusts the adjustment plan based on the feedback information.

[0076] Furthermore, the security monitoring module includes the following execution steps:

[0077] By installing hull attitude sensors and ballast device status monitoring sensors at key locations, the ship's safety status information and ballast device status information can be obtained in real time; data from different sensors are integrated and consolidated to form a complete ship safety status data set; safety thresholds are set to compare and analyze real-time monitoring data. If a parameter exceeds or falls below the preset threshold, it is determined to be an abnormal situation; statistical methods are used to detect anomalies in monitoring data; machine learning classifiers are trained to classify monitoring data and identify abnormal conditions; once an abnormal situation is detected, the safety monitoring module will trigger the alarm system; and appropriate response measures are taken according to the type and severity of the abnormality.

[0078] The various variations and specific examples of the ship steel-lead composite ballast device adjustment method provided in Example 1 are also applicable to the ship steel-lead composite ballast device adjustment system provided in this example. Through the above detailed description of a ship steel-lead composite ballast device adjustment method, those skilled in the art can clearly know the implementation method of a ship steel-lead composite ballast device adjustment system in this example. Therefore, for the sake of brevity of the specification, it will not be described in detail here.

[0079] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the same elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A method for adjusting a marine steel-lead composite ballast device, characterized in that: The method includes: Monitor and obtain the ship's attitude information and ballast device status information in real time; Receiving raw data from sensors, and performing cleaning, format conversion, standardization and normalization operations; including: a data processing unit receives raw data from sensors, and uses standardization and normalization algorithms to perform cleaning, format conversion, standardization and normalization operations on hull attitude sensors, ballast device status monitoring sensors and collected raw data; The processed data is analyzed in combination with the algorithm model. The historical data is used to train the machine learning model to learn the relationship between the hull posture and the ballast device status, and to predict the future hull posture and ballast device status. The algorithm model uses the linear programming optimization algorithm to construct the objective function, which contains multiple optimization indicators, including the cost, time, and ship stability of the ballast device adjustment. These indicators are weighted according to actual needs. The objective function , where x i is the adjustment amount of the ith ballast device, c i is the corresponding unit adjustment cost; define constraints, which include the stability requirements of the ship, the physical limitations of the ballast device, and the constraints of the marine environment to ensure that the optimal solution is feasible and safe in actual operation; wherein, the constraints include: roll constraint: Roll limit value, where is the influence coefficient of the i-th ballast device on the roll; pitch constraint: Pitch limit value, where is the influence coefficient of the i-th ballast device on pitch; heave constraint: Heave limit value, where is the influence coefficient of the i-th ballast device on heave; total weight conservation constraint: Total weight target value, where is the weight of the i-th ballast device. The total weight conservation constraint ensures the conservation of the total weight of the ballast device before and after adjustment. Boundary constraints: For each ballast device, there are also upper and lower limit constraints on the adjustment amount: ,in and are the lower and upper limits of the adjustment amount of the i-th ballast device, respectively; Based on the analysis results of the optimization algorithm or machine learning model, taking into account the real-time status of the ship, the marine environment and the distribution of cargo factors, an adjustment plan for the ballast device is formulated; including: based on the analysis results of the optimization algorithm and the machine learning model, a detailed ballast device adjustment plan is generated. These plans include the number, position, and sequence information of the adjustment, and are formatted in the form of instructions. The format of the instructions needs to comply with the standards or protocols that can be parsed by the hull control module to ensure accurate transmission and identification of information; the formatted instructions are sent to the hull control module through the communication module. The communication module is responsible for establishing a communication connection with the hull control module to ensure that the instructions can be reliably transmitted to the target location, which is achieved by wired or wireless means, depending on the ship's communication system and network environment; after receiving the instructions from the communication module, the hull control module parses the instructions and solves the problem. The analysis process includes verifying the integrity and accuracy of the instructions, identifying the specific adjustment parameters and instruction types in the instructions, and the hull control module needs to be able to correctly parse the communication protocol and instruction format to ensure accurate understanding and execution of the adjustment plan; the hull control module starts to implement the adjustment strategy of the ballast device according to the instructions obtained by the analysis, which includes controlling the number and position adjustment of the ballast device. By controlling the moving mechanism or adjustment device of the ballast device, the hull control module adjusts the position and weight distribution of the ballast device to achieve stability and safety of the hull posture; during the adjustment process, the hull control module will monitor the status of the ballast device and the changes in the hull posture in real time, obtain real-time data through sensors and monitoring equipment, and compare it with the target status. If an abnormal situation is found or the adjustment effect does not meet expectations, the hull control module will optimize the adjustment strategy in a timely manner or take other countermeasures; The adjustment plan is sent to the hull control module in the form of instructions through the communication module, and the number and position of the ballast device are adjusted according to the received instructions; The ballast device comprises a stainless steel shell, a cast lead block is arranged inside the stainless steel shell, an embedded construction handle is installed on the stainless steel shell, and a casting hole and a vent hole are opened on the stainless steel shell.

2. The method for adjusting a marine steel-lead composite ballast device according to claim 1, characterized in that: The real-time monitoring and acquisition of the ship's attitude information and the ballast device's status information includes: Installing the hull attitude sensor at the key position of the hull can monitor and obtain the attitude information of the hull's roll, pitch and heave parameters in real time; The ballast device status monitoring sensor is installed on the ballast device, and the ballast device status monitoring sensor monitors the status information of the ballast device position, weight and working status in real time.

3. The method for adjusting a marine steel-lead composite ballast device according to claim 1, characterized in that: The use of historical data to train a machine learning model to learn the relationship between the hull posture and the ballast device state includes: Use machine learning algorithms to train machine learning models on historical data to learn the complex relationship between the hull attitude and the ballast device status; By training the machine learning model, the machine learning algorithm can capture the underlying rules and patterns between the hull posture and the ballast device status, which enables the machine learning model to predict the future trend of changes in the hull posture and ballast device status, providing strong support for the formulation of adjustment strategies; The prediction results of the machine learning model are combined with the results of the optimization algorithm to comprehensively evaluate the advantages and disadvantages of different adjustment plans and select the optimal adjustment strategy.

4. The method for adjusting a marine steel-lead composite ballast device according to claim 3, characterized in that: The using of historical data to train the machine learning model to learn the relationship between the hull posture and the ballast device state also includes: A neural network algorithm is used to train historical data. According to the needs of ship attitude prediction, relevant features are extracted from the preprocessed data, including the ship's historical attitude data, sea conditions and cargo status. Determine the network parameters, set the number of neural network layers, the number of neurons in each layer, the activation function parameters, and the neural network forward propagation formula for each Neuron, whose output is represented as: , where x is the input vector, w is the weight vector, b is the bias term, and f is the activation function. The gradient update formula in the back-propagation algorithm is used to adjust the weights and bias values ​​of the neural network during training. The update formula for weights and biases is: , , where L is the loss function, η is the learning rate, and are the partial derivatives of the loss function with respect to weights and biases, respectively; Divide the processed data into training set and validation set, select an appropriate loss function to measure the difference between the model prediction value and the true value, and use the training data and loss function to adjust the weights and bias values ​​of the neural network through the back propagation algorithm; Use the validation set to evaluate the trained model, calculate the prediction accuracy and error index, and adjust the parameters and structure of the neural network based on the evaluation results.

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