Data Reliability Analysis for Supply Chain Configuration Consistency
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Solution Overview
Problem
In data-rich environments, real-time data reliability is compromised due to inconsistencies and inaccuracies in the data supply chain, particularly in systems like PLCs, DCS, and sensors, which can lead to unreliable data for critical decision-making, and the lack of standardization across different systems and vendors adds complexity.
Innovation Solution
A data reliability analysis system and method that scans, compares, and corrects configurations across the data supply chain, generating a normalized configuration database, identifying inconsistencies, and automatically updating configurations to ensure consistency and reliability, using a configuration scanner, analyzer, and modifier executed by a hardware processor.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data is collected from multiple sources in the data supply chain (sensors, PLCs, DCS), then the quantity and coverage of data increases, but inconsistencies and inaccuracies compromise data reliability
Solution Approach 1:
The patent segments the data supply chain into distinct components (sensors, PLCs, DCS systems) and applies configuration scanning and analysis to each segment individually. This allows identification of inconsistencies at each stage while maintaining overall data integrity across the entire chain.
Solution Approach 2:
The system implements continuous monitoring and analysis of data configurations with feedback loops that detect inconsistencies and trigger corrective actions. The configuration analyzer continuously compares expected configurations with actual configurations, providing feedback for maintaining data reliability.
2Adaptability or versatility
If different systems and vendors are integrated into the data supply chain, then system versatility and coverage improve, but lack of standardization increases complexity
Solution Approach 1:
The patent creates a universal configuration scanning and analysis framework that can operate across multiple different systems and vendors. The configuration rules and analysis mechanisms are designed to be vendor-agnostic, allowing the same toolset to manage diverse data supply chain components through standardized interfaces.
Solution Approach 2:
The system dynamically adjusts configuration parameters and scanning frequencies based on the specific characteristics of each system and vendor in the data supply chain. Configuration rules are customizable to accommodate different vendors' data structures and communication protocols while maintaining consistent analysis standards.
3Reliability
If configuration scanning and analysis is performed continuously to ensure data consistency, then data reliability improves, but computational resources and processing time increase
Solution Approach 1:
The patent implements periodic configuration scanning at optimized intervals rather than continuous scanning. The system determines appropriate scanning frequencies based on the criticality of different data sources and the expected rate of configuration changes, reducing computational overhead while maintaining data consistency.
Solution Approach 2:
The system performs preliminary configuration analysis and establishes baseline configurations in advance. By pre-defining configuration rules and expected parameter ranges, the system can quickly validate configurations without extensive real-time processing, reducing computational resource requirements during operation.
Data Source
AI summary
According to examples, data reliability analysis may include scanning a component of a data supply chain, and determining, based on the scanning, configurations of the component. Data reliability analysis may further include analyzing the configurations, and detecting, based on the analysis of the configurations, a change in at least one of the configurations. The change may be compared against a corresponding configuration rule to determine whether the change is a defect that affects consistency of data related to the component. In response to a determination that the change is the defect, a solution related to the defect may be determined based on the corresponding configuration rule. A configuration of the component may be modified, based on the solution related to the defect, to correct the defect that affects the consistency of the data.