Scalable Real-Time Simulator Using Segmented Participant Processes
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Solution Overview
Problem
Existing computer simulators face challenges in achieving maximum similarity to real systems while minimizing computing resources and are often inflexible, failing to adapt to different systems or numbers of participants.
Innovation Solution
A computer-implemented simulator that populates itself with participants based on a model, using components like Participant ID Generators, Schedule Generators, and Dynamic Adapters to efficiently manage participant behavior and signals, allowing for caching and distributed caching to maintain logical consistency and adapt to varying system sizes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a computer simulator is designed to achieve maximum similarity to the real system by simulating all participants, then the simulation accuracy is improved, but the computing resources (time and space) required increase enormously
Solution Approach 1:
The system segments the participant population into multiple groups, each simulated by a separate process. Instead of simulating all participants simultaneously in a single process, the simulation is divided into parallel processes that each handle a subset of participants, reducing the memory footprint and computational burden of any single process while maintaining overall simulation accuracy.
Solution Approach 2:
The system uses statistical sampling and aggregation techniques where not every participant's behavior is tracked in full detail at all times. Instead, representative samples are simulated and results are aggregated to produce accurate macro-level predictions without requiring exhaustive simulation of every individual participant's complete behavior轨迹.
2Measurement precision
If a computer simulator is designed to work with a specific model of a system, then the simulation precision for that model is improved, but the adaptability to other systems or different models decreases
Solution Approach 1:
The system employs a universal participant model with configurable parameters that can represent different types of participants (e.g., mobile phones, people, vehicles) depending on the simulation context. The same core simulation engine and participant behavior models can be applied to different systems (cellular networks, pedestrian flows, traffic patterns) by adjusting parameters rather than requiring separate specialized models for each system.
3Reliability
If the simulator maintains all participants of the system, then the completeness of the simulation is improved, but the computing resources required increase enormously
Solution Approach 1:
The participant population is divided into multiple groups, each handled by a separate process. This segmentation allows the system to maintain complete participant coverage across all processes while keeping the memory and computational requirements of any single process manageable.
Solution Approach 2:
The system transitions from a single-process simulation to a multi-process architecture, adding the dimension of parallel processing. This allows the simulation to scale horizontally across multiple processes rather than vertically within a single process, maintaining completeness while managing resource requirements through distributed computation.
Data Source
Figure 1
AI summary
The invention is a highly efficient simulator that is not necessarily dependent on the number of participants - it can scale out to a mass number of participants and still run on a single very low-end computer. The simulator populates itself with individuals and, in accordance with a model of the system that comprises rules that govern the behavior of the individual participants, generates and records signals related to the activities that each participant carries out in real time to create a simulation of how the actual system performs. The recorded signals are stored in a data base, which can be mined for decision making.