Multi-dimensional Event Model Generation via Digital Twins
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Solution Overview
Problem
Current digital event recall systems are limited in generating accurate, multi-dimensional models of physical events, particularly in areas lacking security cameras and smart sensors, due to the absence of qualitative data profiling, suitability criteria, and frameworks for data targeting and model creation based on geographic location and timestamps.
Innovation Solution
A method for generating digital representations of physical events involves selecting an event, creating a profile, obtaining relevant data from multiple sources, and using AI and software analytics to build a multi-dimensional event model, known as an 'event twin', which includes qualitative profiling and geo-location-based data processing, stored in a distributed ledger system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If security cameras and smart sensors are deployed to enable digital event recall, then measurement precision and data availability improve, but device complexity and implementation cost increase
Solution Approach 1:
The patent creates digital copies (twin models) of physical events by collecting and processing data from multiple sources including security cameras, smart sensors, and alternative data sources. These digital twins replicate the characteristics, spatial relationships, and temporal sequences of physical events, enabling accurate event reconstruction and analysis without requiring physical presence or complex real-time monitoring infrastructure in all areas.
Solution Approach 2:
The system employs a multi-functional data processing platform that can handle diverse data types from various sources (security cameras, smart sensors, alternative data sources) and apply multiple analysis techniques (pattern recognition, anomaly detection, predictive analytics) through a single unified architecture. This reduces overall system complexity by consolidating multiple specialized systems into one versatile platform.
2Manufacturing precision
If comprehensive data collection from multiple sources is performed, then data relevance and model accuracy improve, but data processing complexity and computational resources increase
Solution Approach 1:
The patent segments the event modeling process into distinct stages: data collection from multiple sources, data preprocessing and validation, feature extraction and pattern recognition, twin model generation, and event analysis. Each stage processes specific aspects of the data independently, reducing overall processing complexity while maintaining comprehensive analysis through the coordinated execution of segmented tasks.
Solution Approach 2:
The system applies partial processing strategies by focusing computational resources on the most relevant data elements and time periods for each specific event analysis. Rather than processing all collected data uniformly, the system identifies and processes only the critical subsets necessary for accurate event reconstruction, reducing computational overhead while maintaining model accuracy.
3Speed
If real-time event modeling is implemented, then response time and analytical capability improve, but computational power requirements and system complexity increase
Solution Approach 1:
The system employs periodic processing cycles where data is collected continuously but processed in structured intervals. During normal operation, data accumulates and is processed at optimized intervals rather than requiring constant full-system computational power. This periodic approach enables real-time responsiveness while managing computational power consumption through rhythmic processing bursts rather than continuous high-intensity computation.
Data Source
AI summary
A physical event to be modeled is selected. A profile for the physical event is generated based on an event type of the physical event. Data is obtained from a plurality of data sources, wherein the obtained data comprises data relevant to the physical event that is collected by the plurality of data sources, and further wherein at least a portion of the obtained data comprises one or more of spatial and temporal references associated with the collection of the data. A digital representation of the physical event is generated based on at least a portion of the obtained data and the generated profile. The digital representation is utilized to analyze one or more other physical events associated with the modeled physical event.