Machine Learning Controller for Beehive Sensor Power Management
Solution Overview
Problem
Beehive monitoring and control equipment faces challenges in managing energy consumption efficiently, particularly in remote locations where access to a power grid is limited, leading to insufficiencies in energy supply.
Innovation Solution
The equipment incorporates a controller with a machine-learning tool that adjusts power consumption of sensors and actuators based on predefined bee requirements, such as survival rates and temperature/humidity levels, using data from reference bee equipment and bee activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If technical equipment is installed to monitor and control beehive parameters, then the ability to remotely monitor bees and maintain optimal conditions is improved, but energy consumption increases
Solution Approach 1:
The system dynamically adjusts the operation of sensors and actuators based on real-time battery status and environmental conditions. The controller modifies monitoring frequency and actuator activation thresholds to optimize the balance between maintaining reliable beehive control and conserving energy, rather than operating at fixed settings
Solution Approach 2:
The system changes operational parameters such as sampling intervals, transmission frequencies, and actuator activation thresholds based on battery charge levels and environmental conditions. When battery power is sufficient, the system operates with higher monitoring frequency; when power is low, it reduces monitoring intensity to extend battery life while maintaining essential functionality
2Reliability
If sensors and actuators operate continuously to maintain optimal temperature and humidity, then bee health and survival rates are improved, but battery life is reduced
Solution Approach 1:
The system applies partial action by activating actuators and intensive monitoring only when environmental parameters approach critical thresholds rather than operating continuously at full capacity. This allows the system to maintain bee survival rates by providing sufficient intervention during critical periods while conserving battery life during stable conditions
Solution Approach 2:
The system uses periodic monitoring and control actions with variable intervals. Instead of continuous operation, sensors take measurements at scheduled intervals and actuators are activated periodically when needed, adjusting the frequency based on battery status and environmental stability to balance bee welfare with energy conservation
3Loss of information
If data transmission frequency is increased to provide real-time monitoring, then the quality of remote information is improved, but power consumption increases
Solution Approach 1:
The system uses feedback mechanisms where the controller receives data from sensors, evaluates the current state of the beehive and battery status, and dynamically adjusts transmission frequency. When environmental parameters are stable and battery power is low, transmission is reduced; when parameters change significantly or power is abundant, transmission frequency increases to maintain data quality
Data Source
Figure 1A
Figure 1B
Figure 2
AI summary
Beehive equipment (100) for monitoring and controlling conditions at a beehive comprises sensors (110), actuators (120) and a controller (130). The controller (130) adjusts the power consumption of the sensors (110) and the actuators (120) by selectively controlling their power consumption individually, and according to bee requirements, such as pre-defined temperature and the humidity. The controller (130) comprises a computer module that is a machine-learning tool (131) and that has been trained with (i) data regarding operation parameters of reference bee equipment such as temperature and humidity, as well as (ii) data regarding bee activities.