Method for optimization of driving parameters of cultivation plant, and cultivation plant comprising a cultivation room and an adjacent facility exchanging resources
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Solution Overview
Problem
Existing greenhouse cultivation methods are energy-intensive and inefficient in optimizing the use of resources for plant growth, lacking a systematic approach to synergistically control multiple parameters affecting yield and quality.
Innovation Solution
A control unit dynamically manages an air, water, heat, light, and nutrition circuit system, using a control loop to optimize parameters based on yield and quality feedback, employing artificial intelligence and machine learning to evolve a model of parameter interactions, and adjusting settings to achieve optimal growth and resource efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional greenhouse cultivation methods are used with artificial lighting and added nutrition, then plant growth can be controlled, but energy consumption increases significantly
Solution Approach 1:
The system implements a control loop that continuously monitors plant growth and quality parameters, compares them against target values, and automatically adjusts driving parameters (lighting, nutrition, CO2 levels, humidity) to optimize growth while minimizing energy consumption. This closed-loop feedback mechanism enables dynamic optimization rather than static control.
Solution Approach 2:
The system dynamically changes multiple driving parameters simultaneously based on real-time plant response data. By adjusting lighting intensity, nutrition composition, CO2 concentration, and humidity levels in coordination, the system achieves efficient resource utilization and reduced energy consumption while maintaining controlled growth conditions.
2Productivity
If multiple driving parameters are adjusted to optimize plant growth, then yield and quality improve, but the complexity of the control system increases
Solution Approach 1:
The control unit serves multiple functions simultaneously: it monitors various plant parameters, analyzes growth and quality data, determines optimal driving parameter settings, executes parameter adjustments, and learns from results to improve future control decisions. This multi-functional integration manages complexity by consolidating control tasks into a single intelligent system rather than separate dedicated controllers for each function.
Solution Approach 2:
The system performs self-optimization by automatically analyzing the effects of parameter changes on plant growth and quality, then using this learned information to autonomously adjust parameters without requiring external intervention. The control unit learns from its own operational history and continuously improves its control strategy, reducing the need for complex external management.
3Productivity
If extensive parameter testing is conducted to find optimal growth conditions, then cultivation efficiency improves, but the time required for optimization increases
Solution Approach 1:
The system performs preliminary learning and optimization during initial cultivation cycles, building a knowledge base of parameter relationships and plant responses. This preliminary action enables faster and more accurate optimization decisions in subsequent cultivation cycles, reducing the time needed for parameter testing while improving cultivation efficiency.
Solution Approach 2:
The control system operates continuously, constantly monitoring plant parameters and making real-time adjustments to driving parameters based on current plant conditions. This continuous optimization eliminates idle testing periods and ensures that every moment contributes to improving growth outcomes, maximizing cultivation efficiency without time loss.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system minimizes energy consumption and optimizes resource use by dynamically controlling circuits, achieving enhanced yield and quality while reducing the time required for parameter testing, and adapting to changing demands.
Implementation Method 1
an air circuit (2), wherein the air circuit (2) comprises a dehumidification unit (13) configured to dehumidify air in the air circuit (2)
Data Source
AI summary
An optimization method for a cultivation plant comprises a cultivation room comprising an air circuit, a water circuit, a heat circuit, a light circuit, a nutrition circuit and a spacing circuit, and a control unit configured to control the circuits. The cultivation room includes a plant bed and a plant yield control unit configured to give input on growth and/or quality of the plants to the control unit. The control unit being configured to store driving parameters for the circuits and correlated input from the yield control unit. The cultivation plant comprises an adjacent facility frequented by carbon dioxide generating entities. The adjacent facility being arranged adjacent the cultivation room. The air circuit comprises an intake conduit configured to lead air from the adjacent facility to the air circuit.