事故多发区预警数据

通过对报位信息数据的实时分析，反映船只在事故多发区中的航行情况，基于对事故多发区的船舶数量，密度，进入时间，对长期逗留船舶做出预警，以便决策者能够及时采取相应的措施，为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：通过对报位信息数据中标识符的比对，剔除不匹配的报位数据。
3.预警模型建立：利用收集的船只实时报位信息及往期报警数据，根据多元统计模型建立预警模型。预警模型通过对事故发生频率、船舶交通流量密度、航路类型等重要因素进行以事故发生频率为主的主成分分析，使用历史数据对选定的多元统计模型进行训练和参数估计。使用交叉验证的方法评估模型的性能和准确度，并进行必要的调整和改进。利用训练好的多元统计模型对新的渔船事故数据进行预测和分析，以提高预警的准确性
4.预警规则设定：使用历史数据建立一个基线模型，通过聚类分析识别出渔船事故多发区的分布规律。再根据基线模型的结果和事故密度、事故频率，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事故多发区的预测，及时生成预警信息反馈给用户，以便决策者能够及时采取相应的措施。

This dataset conducts real-time analysis on vessel position reporting data to reflect the navigation status of ships in high-risk accident zones. It issues early warnings for long-staying vessels based on the number of ships, their density, and entry time in these accident-prone areas, enabling decision-makers to take timely corresponding measures and providing data support for platforms such as marine big data service platforms.
1. Data Collection and Processing: Collect and organize real-time vessel position reporting data. Clean, correct and uniformly process the data to ensure its accuracy and consistency.
2. Feature Extraction and Selection: Compare the identifiers in the vessel position reporting data to eliminate mismatched reporting data.
3. Early Warning Model Establishment: Use the collected real-time vessel position reporting data and historical alarm data to establish an early warning model based on multivariate statistical models. The model performs principal component analysis with accident occurrence frequency as the core on key factors including accident occurrence frequency, ship traffic density and route types. Train the selected multivariate statistical model and estimate its parameters using historical data. Evaluate the model's performance and accuracy through cross-validation, and make necessary adjustments and improvements. Use the trained multivariate statistical model to predict and analyze new fishing vessel accident data, so as to improve the accuracy of early warnings.
4. Early Warning Rule Setting: Establish a baseline model using historical data, and identify the distribution rules of high-risk fishing vessel accident zones through cluster analysis. Then set the thresholds for triggering early warnings based on the results of the baseline model, as well as accident density and frequency.
5. Early Warning Output and Feedback: Conduct real-time monitoring and analysis on the real-time vessel position reporting data according to the early warning rules. When the early warning conditions are triggered, provide predictions of ship accident high-risk zones through the model, and promptly generate early warning information to feed back to users, so that decision-makers can take timely corresponding measures.

搜集汇总

数据集介绍

特点

事故多发区预警数据集包含2162条船只的实时报位信息，每日更新，用于实时分析船只航行情况，预警事故多发区，支持海洋大数据服务平台。数据包括船名、设备编号、报警时间、报警类型、经纬度、航向和航速等字段。