Collaborative Building Data Sharing for Predictive Event Response
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Solution Overview
Problem
Existing building management systems (BMS) typically operate independently, failing to share data effectively across different buildings, which limits their ability to leverage valuable information from neighboring or distant smart buildings for predictive analytics and automated responses.
Innovation Solution
A collaborative building network is established, enabling data sharing between smart buildings through a data grading and sharing mechanism, where data is filtered and shared based on precision and sampling rate, allowing for predictive analytics and automated responses to events impacting occupants.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If building management systems operate independently without data sharing, then data privacy and security are maintained, but predictive analytics capability and response effectiveness are limited
Solution Approach 1:
The patent introduces a data broker as an intermediary component that mediates data exchange between buildings. The data broker receives data from multiple buildings, applies sharing rules, and distributes relevant data to appropriate buildings. This intermediary structure enables collaborative predictive analytics while maintaining data privacy and security through controlled access, resolving the contradiction between information sharing and data protection.
2Reliability
If all acquired data is shared with other buildings, then collaborative predictive analytics is maximized, but data transmission overhead and processing complexity increase
Solution Approach 1:
The patent implements data grading that assigns different quality levels to data based on precision and sampling rate. Buildings can selectively share and receive data at appropriate quality levels for their specific needs. This local quality approach maximizes collaborative analytics effectiveness while minimizing unnecessary data transmission and processing complexity by matching data quality to application requirements.
Solution Approach 2:
The system changes data parameters by grading data according to precision and sampling rate, then sharing only the necessary portions based on these grades. This parameter-based filtering reduces data transmission overhead and processing complexity while maintaining the essential quality needed for collaborative predictive analytics.
3Measurement precision
If data is graded and filtered before sharing, then data quality for predictive analytics is improved, but data processing time and computational resources increase
Solution Approach 1:
The patent applies data grading and filtering rules in advance, before data is shared between buildings. The data broker pre-processes incoming data, assigns quality grades, and filters according to sharing rules beforehand. This preliminary action ensures high data quality for predictive analytics while minimizing real-time processing delays during actual data exchange operations.
Data Source
AI summary
Systems and methods of operating a collaborative building network are disclosed. In one aspect, a method of operating a building management system includes acquiring, by a first computing system, data using one or more sensors of a first building of a first entity, providing, by the first computing system, a set of sharing rules for the acquired data, determining, by the first computing system by applying the set of sharing rules, whether to share the acquired data with a second computing system associated with a second building of a second entity, and providing, by the first computing system, the acquired data to the second computing system responsive to the determination.