Ship state prediction method
By standardizing the processing of sensor data and training the model, a ship status prediction model was established, which solved the problem that existing technologies could not accurately predict ship status in real time, thus improving safety and efficiency.
Patent Information
- Application Number
- CN202411125084.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-08-16
- Publication Date
- 2026-03-03
AI Technical Summary
Existing methods for monitoring ship status rely on experience or simple data statistics, which cannot provide real-time and accurate status predictions and pose safety hazards.
By standardizing, preprocessing, feature standardizing, and training sensor data, a model capable of identifying and classifying ship states is established, and ship states are predicted in real time. The results are then presented in a visual graphical format.
It enables real-time and accurate prediction of ship status, improving operational safety and efficiency while reducing operational risks.
Abstract
Description
Technical Field
[0001] This invention relates to the field of ship condition monitoring and analysis technology, and in particular to a method for predicting ship condition based on sensor data. Background Technology
[0002] With the development of modern shipping, ship condition monitoring and prediction have become increasingly important. Existing ship condition monitoring methods typically rely on crew experience or simple statistical data analysis, which cannot effectively cope with complex navigation environments and changing ship conditions. This approach not only fails to provide real-time, accurate condition predictions but may also lead to potential safety hazards. Therefore, proposing a method capable of accurately and in real-time predicting ship conditions has significant practical application value. Summary of the Invention
[0003] The purpose of this invention is to provide a ship state prediction method. By standardizing and analyzing sensor data, it can effectively identify and predict different operating states of a ship, thereby improving the safety and efficiency of ship operation. To achieve the above objective, this invention proposes a ship state prediction method, specifically including the following steps: Data collection: Acquiring multidimensional data about the vessel from one or more sensors, which may include, but are not limited to, frequency (Hz), intensity (dB), temperature, pressure, etc. Data preprocessing: The collected data is preprocessed, including handling missing values, removing outliers, and performing operations such as filtering and smoothing to ensure data integrity and accuracy; Feature standardization: Standardizing the preprocessed data maps data with different features to a uniform scale to facilitate subsequent analysis; Model training: Analyze the standardized data to train a model that can identify and classify ship states. This model can adapt to processing multidimensional data and output prediction results. State prediction: Input new sensor data into the trained model to predict the current state of the ship in real time and output the corresponding state category; Results visualization: The prediction results are displayed in the form of visual graphics (such as 3D scatter plots) to help users intuitively understand the status and operation of the ships; By implementing the method of this invention, the working status of a ship can be predicted in real time and accurately, reducing operational risks and improving the safety and efficiency of ship operation.
Claims
1. A method for predicting the state of a ship, characterized in that, include: (1) Acquire multidimensional data of the vessel from one or more sensors; (2) Preprocess the multidimensional data, including processing missing values, removing outliers, and filtering and smoothing the data; (3) Standardize the preprocessed data to map data with different features to a uniform scale; (4) Use the standardized data to train a model that can identify and classify ship states; (5) Input the new sensor data into the model to predict the current state of the ship in real time; (6) Present the prediction results in the form of visual graphs.