Network Sensor Timestamping for High-Resolution Meter Data Reconstruction
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Solution Overview
Problem
Existing methods for collecting data from consumption meters in supply networks are limited in depth and scope of information, lacking the ability to provide continuous, high-resolution data for comprehensive network analysis and monitoring.
Innovation Solution
A method involving sensors with measuring elements that generate time stampings based on a correlation model, storing these in memory, and transmitting them via wired or radio links for reconstruction and evaluation in a remote central facility, allowing for on-demand network analysis and reducing energy-intensive local computations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If raw measurement data is transmitted continuously from sensors to the head-end system, then the depth and scope of information is improved, but the energy consumption and device complexity increase
Solution Approach 1:
The patent extracts only the essential temporal information (time stampings) from the complete raw measurement data and transmits only these extracted elements to the head-end system. The actual measurement values are reconstructed locally using the correlation model, thereby reducing transmission energy while preserving information depth and scope.
Solution Approach 2:
The patent creates a simplified copy (time stampings) of the essential temporal characteristics of the measurement data, which can be used to reconstruct the complete information at the receiving end. This copying approach reduces the amount of data transmitted while maintaining the depth and scope of information available for analysis.
2Loss of information
If complete raw measurement data is transmitted to the head-end system, then the information scope is improved, but the device complexity and processing requirements increase
Solution Approach 1:
The patent extracts only the temporal characteristics (time stampings) from the complete measurement data, separating the essential timing information from the actual measurement values. This extraction reduces the complexity of data transmission and processing while maintaining the ability to reconstruct complete information at the head-end system.
Solution Approach 2:
Instead of transmitting complete data and processing it centrally, the patent inverts the approach by transmitting only time stampings and performing the reconstruction processing at the head-end system rather than at the sensor level, thereby reducing device complexity at the sensor while maintaining information scope.
3Loss of time
If measurement data is processed and evaluated locally at the sensor, then the response time is improved, but the energy consumption increases
Solution Approach 1:
The patent extracts only the temporal information (time stampings) for transmission, performing minimal local processing at the sensor. This extraction approach maintains fast response time by avoiding complex local computation while reducing energy consumption by transmitting only essential timing data rather than complete measurement datasets.
Data Source
AI summary
A method collects data in a network having a consumption meter as part of a supply network and containing a sensor. The sensor contains a measuring element which provides raw measurement data corresponding to a physical or physicochemical value or parameter. The sensor contains a communication device and a memory. For the determination of the measurement resolution of the sensor the conditions for generating time stampings using a correlation model are determined in advance. On a basis of the correlation model, time stampings of successive raw measurement data in the sensor are generated, the time stampings are stored in the memory. The time stampings are transmitted over a wired connection and/or a radio link so that on the basis of the time stampings using the correlation model the raw measurement data collected by the measuring element are reconstructed and evaluated. Whereas raw measurement data is used for on-demand network analysis.