Smart Sensor Door Lock Anticipatory State Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Electronic door locks face significant power consumption issues due to frequent operation, which is not efficiently managed by existing systems, leading to potential battery drain and reduced device lifespan.
Innovation Solution
Implementing a smart home network system that uses sensory devices to determine the target state of a lock device based on detected events, such as occupancy, to only actuate the lock when necessary, thereby reducing power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the electronic door lock operates frequently to ensure security, then the security and convenience are improved, but the power consumption increases and battery life decreases
Solution Approach 1:
The system performs preliminary actions by predicting future lock state requirements based on historical data and current context (occupancy, time, location). The ML model anticipates when locking/unlocking will be needed and prepares accordingly, allowing the lock to remain in a predicted target state without frequent operational corrections, thereby reducing power consumption while maintaining security.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual lock operations versus predicted operations. The ML model learns from discrepancies between predicted and actual user behavior patterns, refining its predictions over time. This feedback loop enables increasingly accurate predictions of lock state requirements, reducing unnecessary operations and power consumption while maintaining reliable security.
2Speed
If the lock mechanism is actuated frequently to respond to events, then the responsiveness and security are improved, but the mechanical wear increases and device lifespan decreases
Solution Approach 1:
The system performs preliminary actions by predicting future lock state requirements based on historical data and current context (occupancy, time, location). The ML model anticipates when locking/unlocking will be needed and prepares accordingly, allowing the lock to remain in a predicted target state without frequent operational corrections, thereby reducing power consumption.
3Adaptability or versatility
If the system continuously monitors and adjusts lock state based on multiple inputs, then the security and convenience are improved, but the system complexity increases
Solution Approach 1:
The system introduces an intermediary ML prediction layer between the multiple input sensors and the lock actuator. This intermediary processes complex inputs (occupancy sensors, time data, location information) and translates them into simplified predictions of target lock states. The ML model acts as a mediator that handles the complexity of multi-input processing while presenting simple, actionable predictions to the lock control system, thereby managing system complexity while maintaining high adaptability.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
An electronic device associated with a lock device obtains a number of users detected within a premises, and detects a trigger event related to a lock device and premises. When the trigger event is detected, a target state of the lock device is determined based on: (1) the number of users within the premises, (2) user security profiles indicating a desired target state of the lock device when a respective user is within the premises, (3) locations of detected users; (4) user states of detected users indicating whether the respective user is asleep or active; and/or (5) a current premises mode, including an armed state and a disarmed state. A current state of the lock device is determined, and if the current state and the target state of the lock device are not the same, instructions are provided to the lock device based on the target state.