Utility Management Server Data Aggregation
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Solution Overview
Problem
Existing utility monitoring systems face challenges in aggregating data from disparate subsystems, leading to inefficient and incomplete analysis, as they typically rely on local sensor data and lack automatic learning capabilities across subsystems or systems, resulting in sub-optimal performance and missed opportunities for resource conservation.
Innovation Solution
A scalable network backend with an open architecture that combines and processes data from various utility subsystems in real-time using big data technologies and machine learning approaches, including cognitive networks, to analyze potential malfunctions and provide automated corrective actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If data aggregation from disparate subsystems is performed manually by engineers, then analysis can be conducted, but the process is inefficient and may be incomplete
Solution Approach 1:
The system automatically aggregates and analyzes data from multiple subsystems without requiring manual engineer intervention. The data aggregation module autonomously collects data from diverse sources, and the analysis module automatically processes this data to identify trends and anomalies, enabling the system to serve itself rather than relying on external human resources for routine analysis tasks.
Solution Approach 2:
The patent replaces the mechanical process of manual data collection and analysis with an automated computational system. Instead of engineers manually gathering data from various subsystems and analyzing it, the system uses software modules to automatically aggregate data from multiple sources and apply analytical algorithms to identify patterns, trends, and potential issues across the utility system.
2Adaptability or versatility
If local sensor data is used for subsystem monitoring, then simple monitoring is achieved, but holistic system-wide monitoring is not possible
Solution Approach 1:
The data aggregation module is designed with universal functionality to handle multiple types of data sources and subsystems. It can aggregate data from diverse subsystems including but not limited to water treatment, power generation, and distribution systems, making the monitoring system versatile and adaptable to different utility contexts without requiring separate specialized systems for each subsystem.
Solution Approach 2:
The patent introduces a data aggregation module as an intermediary layer between individual subsystem sensors and the analysis module. This intermediary component standardizes and consolidates data from multiple disparate sources into a unified format, enabling seamless integration and holistic system-wide monitoring while managing the complexity of data aggregation through a dedicated intermediate processing layer.
3Extent of automation
If manual data analysis is performed, then engineer expertise is utilized, but automated corrective actions cannot be implemented
Solution Approach 1:
The system implements a closed-loop feedback mechanism where the analysis module continuously monitors subsystem performance and automatically triggers corrective actions when anomalies or potential issues are detected. This feedback loop enables the system to self-correct by adjusting operational parameters or alerting relevant personnel, reducing the need for manual intervention while maintaining ease of operation through automated decision-making based on real-time data.
Data Source
AI summary
A method and system of managing a utility system having a plurality of subsystems. A utility management server receives performance measurement data of a subsystem of a first utility system is received, from a first utility monitoring device (UMD). A type of the subsystem and a type of the first UMD is determined. Static rules are applied based on the type of subsystem and the type of UMD. Upon determining that the predetermined condition based on the static rules is not met, identifying a subsystem and its corresponding UMD responsible for a malfunction by retrieving contextual information from the measurements megastore server, analyzing the data from the first UMD and the contextual information, and sending a notification to the UMD having the subsystem that has been identified as being the source of the malfunction.