Hierarchical Container Scanning for Risk Profiling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current container security screening methods are inadequate for ensuring continuous monitoring and prioritization of risk across various transitional events in the shipping container lifecycle, particularly distinguishing between short-term and long-term events to effectively identify and inspect potentially compromised containers.
Innovation Solution
A hierarchical scanning system utilizing non-intrusive and rapid inspection techniques with sensors on cranes and devices to collect and transmit data to a central monitoring station, creating risk profiles that differentiate between short-term and long-term events, prioritizing containers for comprehensive inspection based on deviations and statistical analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If continuous screening of containers occurs at all transitional events, then security reliability is improved, but the complexity of the screening system increases
Solution Approach 1:
The screening system is segmented into multiple types of scanners deployed at different transitional events (port of departure, port of arrival, storage yards). Each scanner type is optimized for its specific location and function, allowing continuous monitoring without requiring a single complex centralized system.
Solution Approach 2:
Containers are pre-screened at the port of departure before leaving the origin country. This preliminary screening identifies high-risk containers early in the supply chain, allowing focused monitoring resources to be allocated to these containers during subsequent transitional events.
2Measurement precision
If detailed inspection is performed at every transitional event, then measurement precision of container condition is improved, but the time required for screening increases
Solution Approach 1:
At most transitional events, only partial screening is performed using external scanners that detect anomalies without requiring full container inspection. Detailed inspection is reserved for containers flagged as high-risk, balancing detection precision with minimal disruption to cargo flow.
Solution Approach 2:
Containers undergo periodic screening at scheduled transitional events rather than continuous inspection. The frequency and depth of inspection varies by container risk profile, with low-risk containers receiving less frequent partial scans and high-risk containers receiving more frequent or detailed examinations.
3Reliability
If risk profiling is performed for all containers, then security coverage is improved, but the processing load and system complexity increase
Solution Approach 1:
Risk profiling data collection and processing is distributed across multiple local systems at different locations (port of departure, port of arrival, storage yards) rather than centralized. Each local system processes data relevant to its location and container population, reducing overall system complexity while maintaining comprehensive coverage.
Solution Approach 2:
The system implements feedback loops where risk profiles are continuously updated based on scanner detections, container history, and intelligence data. This automated feedback mechanism allows dynamic adjustment of screening intensity without manual intervention, managing processing loads efficiently while maintaining comprehensive security coverage.
Data Source
AI summary
A system and method for consolidating data collected using a hierarchical scanning system and assessing security risks regarding the shipping containers is provided. The hierarchical scanning system collects information from distributed and repeated screening throughout a container journey and enables pattern analysis over groups of containers. During the journey of a container, risk profiles are created at short term events based on information collected via non-intrusive rapid inspections. Using combined information from the risk profiles, the initial manifest, and group based statistical intelligence, a risk quotient for each container is determined based on deviations calculated at each point of the journey. Accordingly, authorities are alerted when the risk quotient indicates that a specific container is at risk.