Automated Semantic Enrichment of Low-Voltage Network Measurement Data
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Solution Overview
Problem
Traditional low-voltage network automation faces challenges due to the large number of network nodes with different technical characteristics, requiring manual engineering for node identification and data processing, which is inefficient and not scalable for intelligent 'smart grids' transitions.
Innovation Solution
A method for automatically annotating measurement data records using mathematical analyses to generate semantic comments, allowing for partial configuration-free structuring of data, enabling integration of network nodes into intelligent electricity supply networks without additional engineering overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual engineering is used for node identification and data processing, then data accuracy and node identification precision are improved, but productivity and automation extent deteriorate
Solution Approach 1:
The system performs self-service by automatically analyzing measurement data patterns to identify network nodes and their characteristics without manual intervention. The automated data processing system extracts meaningful information from raw measurements, enabling the system to enrich its own data structure autonomously while maintaining identification precision.
Solution Approach 2:
Manual engineering processes are replaced with automated data processing mechanisms. The patent substitutes human analysts with computational algorithms that analyze measurement patterns, correlate data across multiple nodes, and automatically generate enriched data structures, thereby dramatically increasing productivity while preserving identification accuracy.
2Manufacturing precision
If manual engineering is used for node identification, then data structuring accuracy is improved, but extent of automation deteriorates
Solution Approach 1:
The system achieves self-service by automatically structuring unstructured measurement data through pattern recognition and correlation analysis. The automated process identifies network nodes, determines their characteristics, and organizes data into meaningful structures without requiring manual configuration or engineering intervention.
Solution Approach 2:
The patent transforms unstructured measurement data into structured information by changing the organizational parameters of the data. Through automated analysis of measurement patterns, time correlations, and spatial relationships, the system reorganizes raw data into enriched structures that preserve accuracy while enabling automated processing.
3Ease of operation
If configuration-free data structuring is implemented, then ease of operation and adaptability are improved, but measurement precision may deteriorate
Solution Approach 1:
The system performs self-service by automatically analyzing measurement data patterns to identify network nodes and their characteristics without manual intervention. The automated data processing system extracts meaningful information from raw measurements, enabling the system to enrich its own data structure autonomously while maintaining identification precision.
Solution Approach 2:
The automated data processing incorporates feedback mechanisms where measurement results are continuously analyzed and used to refine node identification and data structuring. The system learns from measurement patterns and adjusts its analysis algorithms to maintain precision while operating without manual configuration.
Data Source
AI summary
A method for enriching data in measurement data records of a low-voltage network, wherein a measurement data record contains at least one measured value and an item of structureless information relating to the network node from which the data record comes, such that meanings can be at least partially assigned to measurement data without the assistance of people, where arriving measurement data records, in particular asynchronously arriving measurement data records, from a plurality of network nodes is stored in a data memory in a time sequence for each network node, time sequences are subjected to a mathematical analysis via automatic data processing, and the result of the analysis is added to a measurement data record as at least one semantic comment.