Decentralized Utility Metering System Reducing Data Loss
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Solution Overview
Problem
Existing remote reading systems for utility meters are prone to failures due to centralized data storage, leading to data loss and prolonged waiting periods, and frequent transmissions increase power consumption and interference issues.
Innovation Solution
A decentralized storage system where utility meters store measurements and transmit data packages via multiple networks, allowing for redundant data recreation and efficient communication, including broadcast transmissions and PUSH technology, to enhance reliability and reduce waiting periods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If measurements are transmitted at frequent intervals to improve response time, then the waiting period is reduced, but power consumption increases and transmitter interference occurs
Solution Approach 1:
The meter performs measurements and stores them locally before transmission. Multiple measurements are accumulated in memory and then transmitted together in batches, rather than transmitting each measurement immediately when taken. This preliminary storage and batch transmission approach reduces the frequency of transmissions while ensuring data is available for remote reading.
2Loss of time
If measurements are transmitted at frequent intervals to improve response time, then the waiting period is reduced, but transmitter interference with competitive systems occurs
Solution Approach 1:
The meter performs measurements and stores them locally before transmission. Multiple measurements are accumulated in memory and then transmitted together in batches, rather than transmitting each measurement immediately when taken. This preliminary storage and batch transmission approach reduces the frequency of transmissions while ensuring data is available for remote reading.
3Device complexity
If a centralized collector stores measurement data to simplify system architecture, then device complexity is reduced, but reliability decreases due to single point of failure
Solution Approach 1:
The system divides the centralized storage function into distributed storage across multiple collectors. Each collector stores measurement data from its associated meters independently. This segmentation eliminates the single point of failure in a centralized system while maintaining manageable complexity at each distributed node.
Solution Approach 2:
The system changes the architectural parameter from centralized to distributed storage. By modifying how data is stored and accessed - allowing any collector to access data from any meter through the network - the system achieves both improved reliability and maintained operational simplicity.
4Ease of operation
If a centralized collector stores measurement data to reduce architecture complexity, then ease of operation is improved, but loss of information occurs when the central component fails
Solution Approach 1:
The system divides the centralized storage function into distributed storage across multiple collectors. Each collector stores measurement data from its associated meters independently. This segmentation eliminates the single point of failure in a centralized system while maintaining manageable complexity at each distributed node.
Solution Approach 2:
The system creates redundant copies of measurement data across multiple collectors. When a meter transmits data, multiple collectors can receive and store copies. This copying ensures that if one collector fails, the data remains available at other collectors, preventing information loss while maintaining ease of data access.
Data Source
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AI summary
The present specification generally relates to the field of reading of measurements and particularly discloses a method and arrangement for providing measurements from a decentralized solution. The system is adapted to have an improved redundancy in a more robust construction that provides additional functionality and comprises a detection unit, a collection unit and a central unit.