Cloud Digital Twin Platform for Multi-Vendor Entity Data Normalization
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Solution Overview
Problem
Commercial buildings and other entities face challenges in integrating, analyzing, and acting on vast data sets due to 'siloed' data systems, requiring proprietary software for each vendor, which hinders efficient energy management, cost reduction, and carbon footprint minimization.
Innovation Solution
A cloud-based platform with a digital twin generation system normalizes data from various entities, converting proprietary data into standardized formats for remote monitoring and control, enabling unified management across multiple entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If proprietary software is used for each vendor's data system, then data from that specific vendor can be analyzed, but system complexity increases and integration across multiple vendors becomes difficult
Solution Approach 1:
The patent implements a universal data normalization layer that can handle multiple vendor-specific data formats through a single standardized interface. The normalization engine translates various proprietary data structures into a common format, allowing one system to serve multiple vendor ecosystems without requiring separate proprietary software for each vendor.
Solution Approach 2:
The patent introduces a normalization engine as an intermediary component between data sources and analysis systems. This mediator translates and standardizes data from different vendors before it reaches the analysis layer, eliminating the need for direct integration with each vendor's proprietary system while maintaining full data analysis capability.
2Reliability
If data is siloed by individual building, machine type, and vendor, then data integrity for that specific source is maintained, but real-time integration and analysis across multiple sources becomes inefficient
Solution Approach 1:
The patent segments the data integration process into distinct layers: data collection layer, normalization layer, and analysis layer. Each layer handles specific tasks independently, allowing data to maintain its source integrity during collection while enabling efficient cross-source integration through the normalization layer without compromising data provenance.
Solution Approach 2:
The normalization engine serves as an intermediary that receives data from siloed sources, standardizes the formats while preserving source identification, and forwards unified data to the analysis layer. This mediator enables real-time integration across multiple silos while maintaining the ability to trace data back to its original source for integrity verification.
3Quantity of substance
If multiple proprietary software systems are deployed to manage different entity data, then comprehensive data coverage is achieved, but operational costs and maintenance complexity increase
Solution Approach 1:
The patent creates a universal data management platform that can ingest and process data from multiple vendors through a single standardized interface. The normalization engine provides multi-functional capability to handle various data formats, eliminating the need for multiple proprietary software systems while maintaining comprehensive data coverage across all entity types.
Solution Approach 2:
The patent merges multiple vendor-specific data management functions into a single unified normalization engine. By combining the capabilities of what would otherwise require separate proprietary software systems into one integrated component, the solution achieves comprehensive data coverage while reducing software maintenance complexity and operational costs.
Data Source
AI summary
A method of executing remote monitoring and controlling of a plurality of entities using a cloud-based platform. The method includes: connecting a first entity to a network system; providing a data system connected between the network system and a cloud server; receiving first equipment data and first sensor data from the first entity; generating tagged first equipment data and tagged first sensor data; transmitting the tagged first equipment data and the tagged first sensor data to the cloud server; normalizing the tagged first equipment data and the tagged first sensor data by converting the tagged first equipment and the tagged first sensor data into standardized first equipment data and standardized first sensor data; generating a first digital representation of the first entity based on the standardized first equipment data and the standardized first sensor data; and transmitting display data of the first digital representation to a user interface.