渔船沉船预警数据

通过对报位信息的标识数据的实时分析，反映船只当前的航行情况，当发生渔船沉船事件时，预警系统可以追踪渔船位置、研判沉船原因，并提供搜救行动的方向和目标区域，海洋大数据服务平台等提供数据支持。1.数据收集与处理：收集和整理船只的实时报位信息。对数据进行清洗、校正和统一处理，确保数据的准确性和一致性。
2.特征提取与选择：通过报位信息数据中的特殊标识，实现报位数据的特征提取。
3.预警模型建立：利用收集的船只实时报位信息，根据以是沉船为因变量，航速、航向、经纬度变化及设备报位时间间隔为自变量进行多元线性回归分析，建立线性回归预警模型。多元线性回归模型可以表示为 y = β0 + β1x1 + β2x2 + ... + βn*xn，其中y是因变量，β0是截距（起始数据点），β1至βn是自变量的系数，x1至xn是自变量。预警模型会对特征进行训练和学习，通过最小化残差平方和来估计模型的参数，以识别和预测渔船沉船的可能性。同时通过设备的航速信息对船舶随洋流移动的轨迹预测进行分析处理。
4.预警规则设定：根据基线模型的结果，基于渔船沉船几率，设定触发预警的阈值。
5.预警输出和反馈：根据预警规则船只的实时报位信息进行实时监测和分析，当触发预警条件时，通过模型预测提供船舶随洋流移动的轨迹预测，为追踪渔船位置、研判沉船原因，并提供搜救行动的方向和目标区域提供数据支持。

Real-time analysis of identification data from position reporting information reflects the current navigation status of vessels. In the event of a fishing vessel sinking accident, the early warning system can track the vessel's location, analyze the causes of the sinking, and provide direction and target areas for search and rescue operations, with data support provided by platforms such as the Marine Big Data Service Platform.
1. Data Collection and Processing: Collect and organize real-time position reporting information of vessels. Clean, correct, and standardize the data to ensure its accuracy and consistency.
2. Feature Extraction and Selection: Extract features from position reporting data via special identifiers contained in the position reporting information dataset.
3. Early Warning Model Establishment: Using the collected real-time position reporting information of vessels, perform multiple linear regression analysis with fishing vessel sinking as the dependent variable, and navigation speed, course, changes in longitude and latitude, and equipment position reporting time interval as independent variables, to establish a linear regression early warning model. The multiple linear regression model can be expressed as $y = eta_0 + eta_1x_1 + eta_2x_2 + dots + eta_nx_n$, where $y$ is the dependent variable, $eta_0$ is the intercept (starting data point), $eta_1$ to $eta_n$ are the coefficients of the independent variables, and $x_1$ to $x_n$ are the independent variables. The early warning model will train and learn on the extracted features, estimating model parameters by minimizing the sum of squared residuals to identify and predict the probability of fishing vessel sinking. Additionally, analyze and process the trajectory prediction of the vessel's movement with ocean currents using the vessel's speed data collected from the on-board equipment.
4. Early Warning Rule Setting: Set the threshold for triggering early warnings based on the fishing vessel sinking probability, according to the results of the baseline model.
5. Early Warning Output and Feedback: Conduct real-time monitoring and analysis on the real-time position reporting information of vessels in accordance with the early warning rules. When the early warning conditions are met, the model will generate trajectory predictions for the vessel's movement with ocean currents, providing data support for tracking the fishing vessel's location, analyzing the causes of the sinking, and determining the direction and target areas for search and rescue operations.

搜集汇总

数据集介绍

特点

渔船沉船预警数据集包含709条数据，每日更新，涵盖船名、设备编号、报警时间、经纬度等关键信息。该数据集通过多元线性回归模型分析渔船航行数据，实时预警沉船事件，并为搜救行动提供支持。