Remote Database System for Stochastic Instrument Diagnosis
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Solution Overview
Problem
Identifying and diagnosing issues with complex laboratory instruments is challenging, especially when stochastic phenomena are involved, as they may not be evident from analyzing data from a single instrument, and require large volumes of rich data from multiple instruments, posing logistical and security challenges.
Innovation Solution
A database system that transmits data from instruments to remote servers for compilation and analysis using an analytics program, allowing real-time or near real-time processing and correlation of rich data with metadata to identify stochastic phenomena and suggest remedies, while also monitoring consumables and workflow to prevent instrument downtime.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large volumes of rich data are collected from multiple instruments to identify stochastic phenomena, then diagnostic accuracy is improved, but data transmission and storage requirements increase
Solution Approach 1:
The patent extracts and transmits only specific instrument performance parameters and operational data from multiple instruments to a centralized server, rather than transmitting all raw data. This selective extraction maintains diagnostic accuracy for stochastic phenomenon detection while reducing overall data transmission and storage requirements.
Solution Approach 2:
The patent combines data from multiple instrument sources into a single centralized database on the server. By merging data streams and correlating information across instruments, the system achieves comprehensive diagnostic capability without each individual instrument needing to store or process the complete data set.
2Reliability
If data is transmitted from multiple remote instruments to centralized servers for analysis, then stochastic phenomenon detection is improved, but network bandwidth and security challenges increase
Solution Approach 1:
The patent introduces localized data processing intermediaries at instrument or facility levels that pre-process and filter data before transmission to centralized servers. This intermediary layer reduces network bandwidth requirements and simplifies security management by handling data aggregation and initial analysis locally, while still enabling comprehensive stochastic phenomenon detection through centralized correlation.
3Loss of time
If real-time data analysis is performed on large datasets, then instrument issue identification speed is improved, but processing resources and time requirements increase
Solution Approach 1:
The patent performs preliminary data processing, filtering, and aggregation at the instrument and local server levels before data reaches the centralized analysis system. By preparing data in advance and pre-processing it locally, the system reduces the computational burden on centralized resources while maintaining real-time diagnostic capability.
Solution Approach 2:
The patent divides data processing into multiple segments: local instrument-level data collection and filtering, facility-level aggregation and preliminary analysis, and centralized correlation and diagnostic interpretation. This segmented approach distributes computational resources across multiple levels, reducing the processing burden on any single system while enabling real-time analysis.
Data Source
AI summary
Described herein are database systems including one or more remote analytical instruments operably connected to one or more servers. The instruments can transmit rich data to the servers, and the one or more servers can compile a database of the rich data. One or more processors associated with the servers can be configured to execute a data analytics program on the database to identify a stochastic phenomenon or to process the data and present in real-time at a location of the one or more instruments comparison information about the instruments.