Systems and methods for smart temperature control devices
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Solution Overview
Problem
Temperature control devices often generate false alarms due to their inability to differentiate between normal temperature fluctuations and abnormal conditions, leading to unnecessary notifications and reduced trust in alert systems.
Innovation Solution
A smart temperature control system that utilizes historical data to set and update operating parameters, including upper and lower time limits, to determine abnormal events, thereby reducing false alarms and ensuring that only genuine alerts are triggered.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional temperature control devices use fixed threshold alarms, then abnormal conditions can be detected, but false alarms increase due to inability to differentiate normal fluctuations from actual abnormalities
Solution Approach 1:
The system performs preliminary learning of normal temperature cycles during an initial period before alarm functionality is activated. Historical temperature data is collected and analyzed to establish baseline patterns of normal operation, including typical fluctuation ranges and cycles. This preliminary action enables the system to distinguish between normal variations and actual abnormalities when alarms are subsequently triggered.
Solution Approach 2:
The system continuously monitors current temperature data and compares it against learned historical patterns to dynamically adjust alarm thresholds. The feedback mechanism analyzes whether temperature deviations align with established normal cycles before triggering alerts, thereby reducing false alarms while maintaining sensitivity to genuine abnormalities.
2Reliability
If the system uses historical data to learn normal temperature cycles, then false alarms are reduced, but system complexity increases
Solution Approach 1:
The system performs self-learning of normal temperature cycles automatically during an initial operational period without requiring manual configuration or intervention. The processor autonomously analyzes historical temperature data, identifies patterns and fluctuations, and establishes baseline parameters for normal operation. This self-service approach simplifies deployment while maintaining high alarm accuracy.
3Measurement precision
If the system monitors temperature continuously and compares with learned patterns, then abnormal events are detected accurately, but processing time and computational resources increase
Solution Approach 1:
The system applies partial monitoring by focusing computational resources on detecting specific abnormal patterns rather than analyzing every temperature data point in detail. During normal operation, the system uses simplified comparison logic against learned thresholds, reserving more intensive pattern recognition only when potential abnormalities are detected, thereby reducing overall processing time while maintaining detection accuracy.
Data Source
AI summary
A smart temperature control system is disclosed. The smart temperature control system includes processing circuitry and a temperature control device. The temperature control device includes a container configured to store a product and temperature control hardware configured to control a temperature within the container. The processing circuitry includes one or more processors configured to monitor the temperature cycles within the container, determine a duration of at least portions of each temperature cycle over a time period, determine a selected time value based on the duration of the portions of each temperature cycle, and update a time limit variable with the selected time value.