Greenhouse Feedback Control Loop Optimization
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Solution Overview
Problem
Current greenhouses lack precise control and data collection methods for optimizing plant growth, relying on imprecise analog measurements and inefficient manual data collection, leading to prolonged experimentation and ad hoc assumptions.
Innovation Solution
A feedback control loop system utilizing a network of sensors and control devices connected to a server for real-time data collection and analysis, enabling precise control of environmental conditions such as CO2 levels, temperature, and light, using machine learning algorithms to optimize plant growth.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data collection and analog measurements are used, then device complexity is reduced, but measurement precision and productivity deteriorate
Solution Approach 1:
The patent replaces manual mechanical data collection methods with automated electronic sensor systems connected to processors. Digital sensors continuously measure environmental conditions (temperature, humidity, CO2 levels) and transmit data to a central processing system, eliminating the need for manual analog measurements while significantly improving measurement precision and data accuracy.
Solution Approach 2:
The system enables self-service through automated feedback control loops where sensors continuously monitor environmental conditions, the processor analyzes the data against target conditions, and control devices automatically adjust settings without human intervention. This automated self-regulating mechanism maintains precise environmental control while reducing operational complexity.
2Productivity
If precise control systems with multiple sensors and control devices are implemented, then productivity and measurement precision improve, but device complexity increases
Solution Approach 1:
The patent implements a universal control system where a single processor coordinates multiple control devices (lights, heaters, CO2 systems, irrigation) through a unified software platform. This multi-functional system allows one control unit to manage diverse environmental parameters, improving productivity by enabling simultaneous control of multiple variables while presenting a simplified user interface that masks the underlying complexity.
Solution Approach 2:
The system employs feedback control loops where sensor data is continuously fed back to the processor, which automatically adjusts control device settings to maintain target conditions. This closed-loop feedback mechanism enables rapid experimentation and optimization by automatically responding to environmental changes, significantly improving productivity while the automated nature of the feedback process prevents the system from becoming unmanageably complex.
3Manufacturing precision
If multiple environmental variables are controlled simultaneously, then manufacturing precision of plant growth conditions improves, but device complexity and ease of operation worsen
Solution Approach 1:
The control system operates autonomously by continuously reading sensor data, comparing current conditions against target parameters, and automatically adjusting control devices to maintain optimal growth conditions. This self-service capability allows precise control of multiple environmental variables (temperature, humidity, light, CO2) without requiring manual intervention or complex user coordination, thereby maintaining ease of operation while achieving high manufacturing precision.
Solution Approach 2:
The patent replaces manual coordination of multiple control devices with an automated digital control system. The processor manages the complexity of coordinating lights, heaters, CO2 injection, and irrigation systems through software algorithms, eliminating the need for operators to manually manage multiple variables. This substitution maintains precise control of all environmental parameters while significantly simplifying operation for the user.
Data Source
AI summary
Sensors provide environmental conditions and plant growth information while control devices regulate the conditions. A calculated data point for a growth of a plant may be generated, and an optimum input variable value for the growth may be obtained. A target value for a sensor data point of a sensor that monitors a condition affecting the growth or the calculated data point for the growth may be further acquired. As such, one or more control devices that most strongly correlate with the target value may be determined based on at least one of the optimum input variable value or a vector association array. Thus, at least one control device setting value for the one or more control devices may be ascertained based on a target path for achieving the target value. Accordingly, each control device may be commanded to implement a control device setting value.