SOAD Sensor Recommendation System for Rapid Prototyping
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Solution Overview
Problem
Existing systems lack an efficient method for selecting and recommending sensors for specific applications, as they primarily focus on analytics and insights within a single domain, failing to consider sensor selection and deployment for prototyping across diverse scenarios.
Innovation Solution
A processor-implemented method and system using service-oriented-architecture and design (SOAD) that recommends sensors by generating a knowledge graph, employing reinforcement learning and finite element analysis to map sensor concepts to application concepts, and generating a design for the recommended sensors and associated pipelines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing sensor analytics systems are used, then analytics and insights can be obtained within a single domain, but sensor selection and deployment for prototyping across diverse scenarios cannot be performed
Solution Approach 1:
The patent creates a universal sensor recommendation system that can handle multiple domains and application scenarios through a centralized knowledge graph. The system integrates domain knowledge, sensor specifications, and application requirements into a single framework that serves diverse prototyping needs, making the system multi-functional rather than domain-specific
Solution Approach 2:
The knowledge graph acts as an intermediary layer between sensor databases and application requirements. It mediates the complex matching process by structuring relationships between sensors, domains, and applications, simplifying the selection process while handling diverse scenarios without requiring direct complex queries to multiple data sources
2Reliability
If pipelines are customized for each sensor, deployment scenario, and application, then optimal performance is achieved, but a lot of time is consumed by researchers and engineers
Solution Approach 1:
The system performs preliminary actions by pre-structuring the knowledge graph with sensor specifications, domain characteristics, and application requirements before actual sensor selection is needed. This advance preparation enables rapid recommendation generation without requiring time-consuming customization for each new sensor-application pairing
Solution Approach 2:
The knowledge graph creates reusable templates and patterns for sensor-pipeline mappings across different applications. Once a suitable sensor configuration is identified for one application, the knowledge graph captures this as a reusable pattern that can be copied and adapted for similar applications, reducing redundant development time
Data Source
AI summary
Sensors are regularly used to understand physical processing by computing systems and measuring physical quantities using principles of Physics which are then calibrated to yield the value and unit of interest. Some works have tried to generalize analytics across domains. However, they do not consider the problem of selecting sensors for a given application or having sensors as a service bouquet for application developer. Embodiments herein provide a method and system for recommending sensors for an application using elements of service-oriented-architecture and design (SOAD). Herein, the system contains a catalog of services which contain sensors and associated pipelines. These pipelines are used by the application developer along with calibration and fusion models through an Integrated Development and Prototyping Environment (IDPE). The IDPE is used to create application specific artificial intelligence (AI) models which get validated/modified based on prototype environment using the IDPE, which is capable of accepting application deployment data.