Sea Ice Shipping Risk Evaluation Using Daily Thickness Data
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Solution Overview
Problem
Current methods for evaluating shipping risks due to sea ice disasters at the Bohai Sea lack high-temporal and spatial-resolution ice condition data, making it difficult to provide a theoretical reference for navigation security in winter.
Innovation Solution
A method and device for evaluating shipping risks using historical daily-scale sea ice thickness data sets to assess risk levels, vulnerability, and exposure, determining disaster-bearing capability, and calculating shipping risks for different types of disaster-affected bodies within various return periods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If semi-quantitative evaluation method using annual freezing duration, maximum sea ice thickness and density data is used, then evaluation can be performed with available data, but the temporal-spatial resolution is insufficient and cannot provide theoretical reference for navigation security
Solution Approach 1:
The patent segments the Bohai Sea into multiple monitoring regions and divides evaluation into discrete components: disaster-inducing factors (sea ice thickness, freezing duration), disaster-affected bodies (vessels, platforms, aquaculture facilities), and risk metrics. This segmentation enables high temporal-spatial resolution evaluation by processing each segment independently with daily-scale data.
Solution Approach 2:
The patent transitions from traditional one-dimensional annual maximum data to multi-dimensional daily-scale temporal-spatial data. By incorporating time dimension (daily observations) and space dimension (multiple monitoring regions with coordinates), the system achieves comprehensive high-resolution evaluation that captures ice condition variations throughout the freezing season across different locations.
2Measurement precision
If high temporal-spatial resolution daily-scale sea ice thickness data is collected and processed, then accurate shipping risk evaluation can be achieved, but data processing complexity and computational requirements increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing historical sea ice data to establish baseline statistics, pre-identifying disaster-affected bodies in monitoring regions, and pre-calculating vulnerability metrics. This preliminary preparation enables faster real-time risk evaluation when new daily ice data arrives, reducing processing time for urgent navigation decisions.
Solution Approach 2:
The system implements self-service through automated data collection from remote sensing satellites and buoy networks, automatic processing of daily ice thickness data, and self-updating of risk evaluations. The evaluation system continuously processes incoming data without manual intervention, generating updated shipping risk assessments automatically each day during the freezing season.
3Reliability
If comprehensive disaster-affected body data (vessels, oil and gas platforms, aquaculture facilities) is integrated, then complete risk assessment is achieved, but data acquisition difficulty increases due to lack of typical disaster-affected body data
Solution Approach 1:
The patent applies universality by using a unified evaluation framework that handles multiple types of disaster-affected bodies (vessels, oil and gas platforms, aquaculture facilities) through the same risk assessment methodology. The system processes diverse objects uniformly by categorizing them into standard types with defined vulnerability characteristics, enabling comprehensive assessment without requiring separate specialized systems for each object type.
Solution Approach 2:
The patent uses monitoring regions as intermediaries to connect ice condition data with disaster-affected body data. Each monitoring region contains spatial information about vessels, platforms, and aquaculture facilities, serving as an intermediary layer that links environmental hazard data (sea ice conditions) with vulnerable targets. This intermediary structure facilitates data integration by providing a common spatial reference framework.
Data Source
AI summary
The present disclosure provides a method and a device for evaluating a shipping risk caused by a sea ice disaster, and a computing device. The method includes: obtaining a historical daily-scale sea ice thickness data set of a monitored region; evaluating a risk level of sea ice disaster-inducing factors, vulnerability of a disaster-affected body and an exposure level in accordance with the historical daily-scale sea ice thickness data set; determining a disaster-bearing capability and a risk level of the disaster-affected body in the sea ice disaster in accordance with the risk level of the sea ice disaster-inducing factors, the vulnerability of the disaster-affected body and the exposure level; and evaluating shipping risks for different types of disaster-affected bodies within different return periods in accordance with the disaster-bearing capability and the risk level of the disaster-affected body in the sea ice disaster.