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Techniques for multi-stage analysis of measurement data with event stream processing

  • US 10,212,024 B2
  • Filed: 06/04/2018
  • Issued: 02/19/2019
  • Est. Priority Date: 06/02/2017
  • Status: Active Grant
First Claim
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1. An apparatus comprising a processor and a storage to store instructions that, when executed by the processor, cause the processor to perform operations comprising:

  • identify direct measurement device data comprising a measured value generated by a measurement device (MD), the measured value received as part of a first data stream comprising a series of measured values over an interval in a time domain, the first data stream communicated via an event stream processing (ESP) interface of the measurement device;

    identify indirect MD data comprising a reported value associated with generation by the MD, the reported value and a unique identifier of the MD received as part of a second data stream comprising a series of reported values over the interval in the time domain and the unique identifier of the MD, the series of reported values associated with generation by the MD in a staging area based on the unique identifier received in the second data stream, the staging area communicatively coupled with the MD via a neighborhood area network (NAN) and the staging area comprising a temporary storage to aggregate reported values for a group of MDs associated with a geographic area of the MD;

    generate an expected value for the reported value with a first machine learning algorithm based on a class assigned to the MD and a portion of the series of reported values received in the second data stream prior to the reported value being received;

    compare the reported value to the expected value and the reported value to the measured value to validate the reported value associated with generation by the MD in the staging area communicatively coupled to the MD via the NAN;

    transform at least one of the direct MD data and the indirect MD data into the frequency domain to generate a spectral density for the MD;

    compare the spectral density for the MD with a model spectral density that corresponds to the class assigned to the MD to verify the class assigned to the MD, wherein the model spectral density is generated by a second machine learning algorithm with data from a set of MDs identified to characterize the class assigned to the MD;

    identify a technical loss or a non-technical loss associated with the MD based on one or more of the validation of the reported value and the verification of the class associated with the MD; and

    determine one or more corrective processes associated with the MD to perform based on whether technical loss or non-technical loss associated with the MD is identified.

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