Distributed Actor Simulation for Faster Accurate System Modeling
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems struggle to efficiently integrate and analyze large volumes of complex data from diverse sources for accurate simulation and decision-making, leading to inefficiencies and potential errors in predictive modeling.
Innovation Solution
A distributed simulation engine that integrates data retrieval, analysis, and planning modules to transform and analyze data, using system dynamics, discrete event, and agent-based paradigms for scalable and accurate simulations, predicting outcomes and risks through machine learning algorithms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If traditional modeling and simulation methodology such as system dynamic or discrete event simulation is used, then processing speed is improved, but manufacturing precision is deteriorated
Solution Approach 1:
The patent segments the simulation system into multiple distributed actor instances, where each actor represents an individual entity with its own state and behavior. This segmentation allows the system to process simulations in parallel while maintaining detailed individual-level accuracy, resolving the contradiction between processing speed and modeling precision by enabling both aggregate-level speed and individual-level detail simultaneously.
2Manufacturing precision
If accurate system modeling with individual actor models is used, then manufacturing precision is improved, but use of energy is deteriorated
Solution Approach 1:
The system divides the computational workload into independent actor segments that can be distributed across multiple processing nodes. Each actor model is self-contained and can be processed independently, enabling parallel computation that reduces total computational resources required while maintaining high modeling accuracy through detailed individual actor representations.
Solution Approach 2:
The patent implements a universal actor framework where a single actor class can represent multiple different entity types through configuration and parameters. This multi-functionality allows the system to maintain detailed individual models without proportionally increasing computational complexity, as the same underlying actor infrastructure handles diverse entity types efficiently.
3Adaptability or versatility
If multiple software packages are used for data analysis and decision processes, then adaptability is improved, but device complexity is deteriorated
Solution Approach 1:
The patent merges data retrieval, data analysis, and simulation functions into a single integrated distributed simulation engine. This consolidation eliminates the need for multiple separate software packages and manual data transformations, reducing system integration complexity while maintaining the adaptability to handle diverse data sources and analysis requirements through the unified engine's configurable architecture.
Solution Approach 2:
The distributed simulation engine is designed as a universal platform that can perform multiple functions including data retrieval from various sources, data analysis through configurable parameters, and simulation execution. This multi-functional design provides the adaptability of multiple specialized tools while avoiding their integration complexity, as all functions operate within a single cohesive system.
Data Source
AI summary
Fully integrated collection of relevant data, analysis of that data and generation of both analysis-driven decisions and analysis driven simulations of alternate candidate actions. This organizational operating system may be used predict the outcome of enacting candidate decisions based upon past and current data retrieved from both within the corporation and from a plurality of external sources pre-programmed into the system. Simulations using this data and predefined parameters to create models of actors are then run. Risk to value estimates of candidate decisions are also calculated.