Meta-Structure Data Analysis for Railway Infrastructure
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Solution Overview
Problem
Current data analysis systems for railway infrastructure struggle to harmonize and compare diagnostic data from diverse devices due to differences in data sets and methods, with existing solutions like SNMP and SCADA being impractical or inadequate, especially for non-networked devices and requiring manual effort for centralization.
Innovation Solution
A data analysis system comprising a database service module with a meta-structure for storing and classifying data from various devices, and an analysis engine that determines data quality based on stored rules, allowing for automated, centralized storage and analysis without imposing requirements on target devices, enabling cross-comparison and predictive diagnostics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If independent data collection for each device type is used, then data can be collected from diverse devices, but manual effort is required to centralize data for cross comparison
Solution Approach 1:
The patent creates a universal data collection framework that can handle multiple device types through a common interface. The system defines standardized data structures and collection methods that work across diverse devices, eliminating the need for device-specific collection procedures while maintaining the ability to adapt to different data formats through configuration rather than manual intervention.
Solution Approach 2:
The patent introduces an intermediary layer (data collection service) that sits between diverse devices and the central system. This intermediary handles data normalization, formatting, and initial processing, automatically transforming device-specific data into a standardized format that can be centrally aggregated without manual effort.
2Adaptability or versatility
If SNMP protocol is used for network data collection, then standardized data collection is possible, but it imposes protocol requirements on devices that may not be implementable
Solution Approach 1:
Instead of requiring devices to implement a standardized protocol (SNMP), the patent inverts the approach by having the collection system adapt to device-specific protocols and formats. The system provides multiple adapters or interfaces that translate various device protocols into a unified internal representation, removing the burden of protocol implementation from the devices themselves.
Solution Approach 2:
The patent employs configurable parameters and metadata schemas that allow the system to adapt to different device data formats without requiring protocol standardization. By changing the interpretation parameters and data mapping configurations, the system can handle diverse device formats while maintaining a consistent internal data model.
3Extent of automation
If SCADA system is used for data collection, then centralized monitoring is achieved, but the overhead of SCADA protocol makes the system unworkable
Solution Approach 1:
The patent extracts the essential centralized monitoring functionality from the SCADA framework while removing the heavy protocol overhead. It implements a lightweight data collection and aggregation system that provides centralized monitoring capabilities without the complex SCADA protocol stack, focusing only on the core functions of data collection, storage, and basic analysis.
Solution Approach 2:
The patent uses simple, lightweight data structures and communication formats instead of complex SCADA protocols. The system employs straightforward data serialization and transmission methods that minimize overhead while achieving the desired centralized monitoring effect, sacrificing the robustness of SCADA for simplicity and efficiency in the specific context.
4Reliability
If networked devices report faults to central system, then fault data is collected, but only networked devices can transmit data leaving non-networked devices unaccounted for
Solution Approach 1:
The patent creates a universal data collection mechanism that works with both networked and non-networked devices. The system provides multiple collection pathways including network-based collection for connected devices and alternative methods (such as local agents, periodic data retrieval, or integration with device maintenance procedures) for non-networked devices, ensuring comprehensive coverage across all device types.
Data Source
AI summary
A data analysis system for analyzing data from multiple devices has a database service module including a data storage subsystem storing data from collected from different devices. The data is stored in a meta-structure using primitives to classify the data. An analysis engine analyzes the data to determine whether the data defined by the meta-structure meets certain criteria in accordance with a stored set of rules. The system is useful, for example, in the detection of faults in railway infrastructure.