Parallel Processor Units for Natural Disaster Damage Calculation
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Solution Overview
Problem
Current systems require excessive computing time to calculate expected damages from natural disasters for large insurance portfolios, making them inefficient for daily use and unsuitable for assessing multiple portfolios.
Innovation Solution
A computer-based system with networked processor units and a database that assigns insured objects to risk types and geographical risk areas, using damage sensitivity functions stored locally in each processor unit to calculate partial damages and aggregate them efficiently across multiple units.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of historical and simulated natural disaster events considered is increased to improve accuracy of expected losses, then the computing time required increases to several dozen hours
Solution Approach 1:
The patent divides the portfolio of insured objects into multiple groups based on geographical location and risk characteristics. Each processor unit is assigned to handle a specific group, allowing parallel processing of damage calculations. This segmentation enables the system to process hundreds of thousands of insured objects and thousands of damage sensitivity functions simultaneously across multiple processors, reducing total computing time from several dozen hours to a practical duration for daily use while maintaining comprehensive coverage of historical and simulated disaster events.
Solution Approach 2:
The patent introduces a new dimension of parallel processing by distributing calculations across multiple processor units that operate simultaneously. Instead of sequentially processing each insured object through a single processor, the system creates a multi-dimensional processing architecture where multiple processors handle different geographical regions or risk groups in parallel, dramatically accelerating the computation of expected losses while considering extensive historical and simulated disaster data.
2Measurement precision
If the geographical resolution of insured objects is increased to improve accuracy of damage sensitivities, then the complexity of the system increases
Solution Approach 1:
The patent segments the portfolio into multiple groups based on geographical location and risk characteristics, with each processor unit handling a specific segment. This segmentation allows the system to maintain high geographical resolution for accurate damage sensitivity calculations while distributing the computational complexity across multiple processors. Each processor manages a smaller, more manageable subset of insured objects with detailed geographical information, reducing the complexity burden on any single processing unit.
Solution Approach 2:
The patent applies local quality by assigning specific damage sensitivity functions to different geographical regions and risk groups. Each processor unit is configured with the appropriate damage sensitivity functions relevant to its assigned region, allowing for highly accurate local calculations without requiring every processor to handle all possible damage scenarios. This localized approach maintains high geographical resolution where needed while reducing overall system complexity.
Data Source
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AI summary
System (1) has data structuring module (11) to link the insured objects with a risk type and a geographical risk area for a risk group. Instruction generator (12) provides various instruction data for respective processor units (31,31',31"). Control modules (311) control each unit respectively to determine a damage sensitivity function. It calculates elemental damage details for expected damage from natural catastrophe events on the objects based on their assigned instruction data and function. Damage calculation module (13) determines expected damage for the portfolio based on the details. The instruction data has details related to the portion of the insured objects and their association with a risk type and a geographical risk area. An independent claim is included for a computer-implemented method for calculating expected damage as a result of natural catastrophe events on a multiplicity of insured objects in a portfolio.