Data Acquisition Computer for Communication Exchange
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Solution Overview
Problem
Predicting when a data source will return to a normal operating condition in distributed communication networks is a time-consuming and error-prone task, especially with multiple data sources, hindering efficient data exchange and remedial actions.
Innovation Solution
A system comprising a data acquisition computer and a back-end application server that processes data source feedback, including a data source communication identifier, to determine when a data source expects to return to normal operation, and automates the data acquisition process to provide real-time quality of service assessments and ratings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual prediction methods are used to determine when a data source will return to normal operating condition, then flexibility and adaptability are maintained, but the process becomes time-consuming and error-prone
Solution Approach 1:
The data source system automatically monitors its own operational status and autonomously determines when it expects to return to normal operating condition, eliminating the need for external manual prediction and reducing both time and human error
Solution Approach 2:
The system implements automated feedback loops where the data source communicates its operational status and expected recovery time back to the monitoring system, enabling real-time accurate predictions without manual intervention
2Productivity
If automated data acquisition processes are implemented to monitor multiple data sources, then productivity and speed of detection are improved, but system complexity increases
Solution Approach 1:
The monitoring system employs universal protocols and standardized interfaces that allow it to monitor multiple different data sources using the same automated processes, increasing productivity without proportionally increasing complexity
Solution Approach 2:
The system introduces standardized communication intermediaries and abstraction layers between the monitoring system and diverse data sources, simplifying the complexity of automated monitoring across multiple platforms
3Reliability
If comprehensive quality of service assessments are performed across multiple parameters, then measurement precision and reliability are improved, but the complexity of data processing increases
Solution Approach 1:
The quality of service assessment is divided into separate measurable parameters (operational status, recovery time, data accuracy, data availability, data integrity) that can be independently monitored and processed, improving reliability without overwhelming complexity
Solution Approach 2:
The system transforms complex quality of service concepts into specific measurable parameters with defined thresholds and metrics, enabling reliable assessment through standardized parameter monitoring rather than complex qualitative analysis
Data Source
AI summary
According to some embodiments, a data acquisition computer may receive a first task request, including a data source communication identifier, from a back-end application. The data acquisition computer may perform a first data acquisition process and determine indications of: when the data source expects to return to a normal operating condition, a quality of service received by the data source from a service provider, and a quality of service performed by the back-end application server. The back-end application server might generate and transmit the task request, for example, a first pre-determined period of time after an event associated with the data source. The back-end application server may also detect that a second pre-determined period of time after the event has occurred and facilitate a second data acquisition process including at least one rating within a scale of ratings provided by the data source.