Decentralized ESG Data Analyzers for Energy-Efficient Validation
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Solution Overview
Problem
Existing technologies face challenges in efficiently consolidating and validating Environmental, Social, and Governance (ESG) data due to the complexity of ESG dimensions and data privacy issues, requiring secure and energy-efficient methods for data analysis in a metaverse setting.
Innovation Solution
The implementation of ESG data analyzers in a metaverse environment that collaborate to form decentralized groups, using correlation graph generators, collaboration analyzers, and anomalous event analyzers to identify potential anomalies through dynamic runtime collaboration, while ensuring secure data communication and reducing computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional centralized ESG data consolidation methods are used, then data validation can be performed, but energy consumption increases and data privacy issues arise
Solution Approach 1:
The patent segments the centralized data consolidation process into decentralized collaboration groups of ESG data analyzers. Each analyzer processes local ESG data independently and shares only validation results and correlation information, rather than consolidating all raw data centrally. This segmentation reduces the computational burden on any single system while maintaining validation accuracy through collective analysis.
Solution Approach 2:
The patent introduces correlation graph generators as intermediaries that facilitate collaboration between ESG data analyzers. These generators create correlation graphs representing relationships between ESG dimensions and analyzers, enabling indirect information exchange and validation without requiring direct access to sensitive underlying data, thus reducing energy consumption while preserving data privacy.
2Measurement precision
If comprehensive ESG data analysis is performed across all dimensions, then detection accuracy improves, but computational complexity increases
Solution Approach 1:
The patent applies local quality by assigning specific ESG dimension expertise to different analyzers within collaboration groups. Each analyzer focuses on particular ESG dimensions it is best suited for, rather than all analyzers processing all dimensions. This specialization improves detection accuracy for specific dimensions while reducing overall computational complexity through division of labor.
Solution Approach 2:
The patent transforms the complex multi-dimensional ESG data analysis problem into a graph-based correlation structure. By representing analyzers and ESG dimensions as nodes and relationships as edges in correlation graphs, the system adds a structural dimension to the analysis, enabling more efficient processing of comprehensive ESG data without proportionally increasing computational complexity.
3Use of energy by moving object
If decentralized collaboration groups are formed, then energy efficiency improves, but coordination overhead increases
Solution Approach 1:
The patent implements periodic action through scheduled collaboration cycles among decentralized ESG data analyzers. Analyzers operate independently during local data processing phases, then periodically exchange validation results and update correlation graphs at predetermined intervals. This periodic coordination reduces continuous communication overhead while maintaining energy efficiency benefits of decentralized operation.
Solution Approach 2:
The patent enables self-service by allowing each ESG data analyzer to autonomously perform local data validation and generate correlation information without requiring constant coordination with other analyzers. Analyzers independently update their local correlation graphs and only engage in collaboration when validation results need cross-verification, reducing coordination overhead while preserving decentralized energy efficiency.
Data Source
AI summary
In some examples, energy efficient collaboration for environmental social and governance (ESG) data consolidation and validation may include identifying, based on a global correlation graph, correlated ESG dimensions for each ESG data analyzer of a plurality of ESG data analyzers with respect to a set of ESG dimensions on which an ESG data analyzer of the plurality of ESG data analyzers collects data for at least one organization avatar entity (OAE) of a plurality of OAEs. In this regard, decentralized groups of collaborating ESG data analyzers may be generated based on a collaboration potential between the plurality of ESG data analyzers. For an ESG data analyzer that is collecting data and based on an associated updated data model, a potential anomalous ESG event may be identified at a specific ESG dimension. Further, operation of an OAE associated with the ESG data analyzer that is collecting data may be controlled.