Sub-process Simulation Dataset Combination
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Solution Overview
Problem
Existing simulation systems face challenges in efficiently combining probability distribution functions of target features from heterogeneous sub-process simulation results and real-world observations, especially when a unified simulation model is not available, leading to high computational costs and complexity in decision-making processes.
Innovation Solution
The method involves obtaining execution maps for each sub-process, selecting relevant input and output features, and combining datasets based on fitness values to generate a probability distribution function that represents the entire process, utilizing massive parallelism and heuristics to prioritize and penalize datasets according to user preferences, allowing for real-time query capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a unified simulation model is used to combine sub-process results, then accuracy of probability distribution function is improved, but computational cost and complexity increase significantly
Solution Approach 1:
The patent segments the unified simulation model into multiple independent sub-process simulation models. Each sub-process is simulated separately using its own model and parameters, avoiding the need to construct and execute a single complex unified model. The results from these segmented simulations are then combined statistically to produce the overall probability distribution function, thereby reducing computational complexity while maintaining accuracy.
Solution Approach 2:
The patent introduces an intermediary statistical combination layer that bridges the gap between sub-process simulation results and the final probability distribution function. Instead of directly coupling all sub-processes in a unified model, the intermediary layer combines the output probability distributions of individual sub-processes using statistical methods (such as convolution or moment matching), thereby reducing computational complexity while preserving accuracy.
2Loss of information
If comprehensive datasets from all sub-processes are combined, then completeness of process representation is improved, but data processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing and storing simulation results from each sub-process in a structured format (execution maps) before the actual query. The results are organized by input scenarios and output features, allowing for rapid retrieval and combination during query execution. This preliminary organization of data significantly reduces processing time when generating probability distribution functions, while maintaining complete process representation.
3Adaptability or versatility
If multiple simulation models are executed to cover different sub-processes, then coverage of process scenarios is improved, but execution time and computational cost increase
Solution Approach 1:
The patent merges the results from multiple simulation models by combining their probability distribution outputs using statistical methods. Instead of sequentially executing all simulation models to answer a query, the system combines pre-executed simulation results from different sub-process models, significantly reducing execution time while maintaining comprehensive scenario coverage. The merging process uses the execution maps to efficiently integrate results from heterogeneous data sources.
Data Source
AI summary
Techniques are provided for automatic combination of sub-process simulation results with dataset selection based on a fitness under one or more specific scenarios. An exemplary method comprises obtaining an execution map for each sub-process in a sequence that stores results of a given sub-process execution. The results comprise a scenario, a distribution and a distribution fitness value. In response to a user query regarding a target feature and an initial dataset, initial dataset are combined with results selected from the execution map for a first sub-process in the sequence; each available dataset from the previous sub-processes in the sequence is combined with results selected from the execution map for the next sub-process; a probability distribution function (pdf) for the target feature is composed from a combined dataset that represents a simulation of the process and combines results of each of sub-process in the sequence; and the pdf is processed to answer the user query for the target feature.