IoT Contextual Diagnosis via Knowledge Graph Mapping
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Solution Overview
Problem
In IoT configurations, the unstructured data from sensors lacks context, making it difficult for devices to understand and utilize effectively, which hinders prompt, accurate, and cost-effective service and maintenance in technical areas like automotive and industrial settings.
Innovation Solution
A computer-implemented method that receives data from IoT devices, provides context information, maps it to a knowledge graph, and diagnoses issues using contextual engagement components and cognitive analysis techniques, enhancing problem diagnosis and maintenance by linking IoT contexts to problem diagnosis knowledge graphs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If context information is provided to IoT sensor data, then the quality and accuracy of problem diagnosis is improved, but the complexity of the system increases due to knowledge graph mapping and cognitive analysis components
Solution Approach 1:
The patent introduces a knowledge graph as an intermediary structure that bridges raw IoT sensor data and diagnostic conclusions. The knowledge graph stores contextual relationships between device components, symptoms, and failures, allowing the system to infer diagnostic information without requiring complex real-time analysis algorithms. This mediator enables accurate diagnosis while keeping the core processing logic manageable.
Solution Approach 2:
The system performs preliminary action by pre-building and storing contextual knowledge in the knowledge graph before actual diagnostic needs arise. Relationships between device components, potential failures, and diagnostic rules are established in advance, allowing the system to quickly match sensor data against pre-defined patterns rather than computing diagnoses from scratch during maintenance events.
2Productivity
If cognitive analysis techniques are used to analyze sensor data, then the speed of problem diagnosis is improved, but the use of energy and computational resources increases
Solution Approach 1:
The system applies partial action by selectively analyzing only the sensor data and knowledge graph nodes relevant to the specific diagnostic query rather than processing all available data. The cognitive analysis focuses on traversing only the necessary paths in the knowledge graph that connect observed symptoms to potential causes, avoiding exhaustive search of the entire knowledge base and reducing computational overhead.
3Adaptability or versatility
If context information is mapped to knowledge graph nodes, then the versatility and adaptability of the diagnostic system is improved, but the difficulty of detecting and measuring data relationships increases
Solution Approach 1:
The knowledge graph is segmented into distinct node types (device components, symptoms, failures, parameters) and relationship types (causes, affects, monitored_by). This segmentation allows the system to handle different kinds of data relationships through specialized processing rules for each relationship type, making the overall complex mapping task more manageable through modular handling of individual relationship categories.
Data Source
AI summary
Aspects of the present invention include a method, which includes receiving, by a processor, data from one or more communicatively coupled objects associated with a device. The method further includes providing, by the processor, context information to the received data. The method further includes mapping, by the processor, the context information associated with the received data to one or more nodes of a knowledge graph. The method further includes diagnosing, by the processor, a problem with the device, based on knowledge graph information.