Edge Analytics Controllers for Local Building Automation
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Solution Overview
Problem
Conventional building automation systems rely on centralized controllers, which can be complex and inefficient, lacking local data analysis and control capabilities, and do not effectively communicate or determine hierarchies between devices.
Innovation Solution
Edge analytics controller devices equipped with processors and memory that perform secure, open-BAS operations by analyzing data from sensors, applying semantic tags, and controlling building operations, while communicating with other devices and automatically determining device hierarchies through networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If centralized controllers are used in building automation systems, then control functionality is provided, but system complexity and inefficiency increase due to lack of local data analysis capabilities
Solution Approach 1:
The patent segments the centralized control system into distributed edge analytics controllers that operate autonomously at local levels. Each edge controller processes data locally rather than funneling all data through a central system, reducing communication overhead and enabling parallel processing across multiple devices. This segmentation directly addresses the contradiction by distributing computational tasks to improve productivity while reducing central system complexity.
Solution Approach 2:
The patent implements local quality by enabling each edge analytics controller to perform data analysis and pattern recognition locally at the building or zone level rather than requiring centralized processing. This allows local optimization of control decisions based on real-time local conditions, improving响应速度 and efficiency while reducing the burden on centralized systems.
2Ease of operation
If conventional device controllers communicate with head end controllers, then control operations are executed, but communication efficiency decreases and hierarchy determination becomes manual
Solution Approach 1:
The patent implements self-service through automatic hierarchy determination mechanisms where edge analytics controllers autonomously discover and establish their positions in the control hierarchy through peer-to-peer communication protocols. This eliminates manual configuration requirements and enables dynamic adaptation to system changes, directly improving ease of operation while reducing configuration complexity.
Solution Approach 2:
The patent incorporates feedback mechanisms where edge controllers continuously exchange status information and hierarchy data through the network, enabling automatic updates and reconfiguration as system conditions change. This feedback loop maintains optimal communication efficiency and hierarchy structure without manual intervention.
3Productivity
If centralized control systems are used, then building operations are controlled, but local data analysis capabilities are lacking leading to control inefficiency
Solution Approach 1:
The patent segments data processing functions by equipping edge analytics controllers with local analytics engines that perform pattern recognition, trend analysis, and decision-making locally. This segmentation prevents information loss by ensuring data is analyzed at the source rather than requiring complete transmission to centralized systems, thereby improving control efficiency while preserving local intelligence.
Solution Approach 2:
The patent implements preliminary action by performing data analysis and pattern recognition locally at edge controllers before control decisions are made. This preliminary local processing extracts actionable insights from raw data in real-time, enabling faster local responses without waiting for centralized analysis, thus improving control efficiency while preventing information loss.
Data Source
AI summary
In some embodiments, a controller may include a circuit. The circuit may include a network interface configured to couple to a network, a plurality of input/output (I/O) ports configured to couple to a respective plurality of devices, a processor coupled to the network interface and to the plurality of I/O ports, and a memory accessible to the processor. The memory may be configured to store instructions that, when executed, cause the processor to receive data from one or more of the plurality of I/O ports, automatically apply semantic tags to the data, automatically combine the data into a single data stream to determine a pattern, and selectively adjust one or more of a plurality of building operations management tasks in response to determining the pattern.