Enterprise AI Analysis
Design of anti-frost smoke machine system for mountain orchard based on multi-objective optimization
This paper addresses the challenge of frost damage to fruit trees in mountainous orchards by proposing a novel, eco-friendly anti-frost smoke machine system. The system uses solar energy and a biochar/vegetable oil smoke agent, with its deployment optimized through a multi-objective framework combining the Hippo Optimization Algorithm (HOA) for placement and a k-minimum spanning tree (k-MST) model with Mixed-Integer Linear Programming (MILP) for wiring. This approach, which incorporates slope correction for accurate smoke diffusion modeling, significantly reduces installation and wiring costs while maintaining effective frost protection. Compared to traditional methods, it achieves up to a 55.55% reduction in machines and 44.16% in wiring, demonstrating strong economic and environmental benefits, especially in complex terrains.
Key Findings for Enterprise Leaders
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Typical Smoke Coverage
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HOA Machine Advantage
Deep Analysis & Enterprise Applications
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The proposed system leverages solar energy for self-sufficiency and employs an eco-friendly biochar/vegetable oil-based smoke agent. This agent significantly reduces harmful gas emissions compared to diesel and plant debris, with combustion products improving soil fertility. An assisted diffusion device, featuring an axial fan and a pipe with uniformly distributed holes, enhances smoke diffusion velocity, ensuring effective frost protection across the orchard.
The core of the smoke dispersion modeling is the Gaussian plume model, adapted to account for mountainous terrain. A crucial slope factor η(α) is introduced to correct the concentration distribution, reflecting altered dispersion paths due to terrain lifting and gravitational settling. This correction ensures accurate simulation of smoke behavior, where concentration decays faster on slopes compared to flat terrain, crucial for effective deployment planning.
A sophisticated hierarchical framework combines the Hippo Optimization Algorithm (HOA) as the outer optimizer to determine the optimal number and locations of smoke machines. The inner layer utilizes a k-Minimum Spanning Tree (k-MST) model, formulated via Mixed-Integer Linear Programming (MILP), to optimize the wiring layout for selected machines, minimizing cable installation costs. This integrated approach ensures both effective coverage and cost efficiency.
Simulation results show that the multi-objective optimization method achieves significant savings, with up to 55.55% fewer smoke machines and 44.16% less wiring length compared to traditional methods, while maintaining over 85% smoke coverage. Computational Fluid Dynamics (CFD) simulations further validate the smoke concentration distribution, confirming the model's effectiveness and applicability in complex terrain conditions.
55.55%
Reduction in Anti-Frost Smoke Machines & Wiring Length Over Traditional Methods
The multi-objective optimization achieved a maximum reduction of 55.55% in the number of smoke machines and up to 44.16% in wiring length when compared to traditional, experience-based deployment strategies, significantly lowering installation costs.
Enterprise Process Flow
Establish spatial attenuation function of smoke heating effect
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Establish fog machine candidate points and concentration check points inside the orchard
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Construct multi-objective optimization objective function
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Introduce Hippo Optimization Algorithm (HOA) for outer optimization search
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Construct inner layer routing optimization model (k-MST + MILP)
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Construct multi-objective optimization framework combining HOA and MILP-KMST
| Metric | HOA Optimized | GA Baseline | HOA Advantage |
|---|---|---|---|
| Smoke Machines (units) | 17 | 24 | 29.17% Reduction |
| Wiring Length (m) | 145.68 | 227.49 | 35.96% Reduction |
| Smoke Coverage | 85.75% | 80.39% | 5.56% Increase |
Real-World Application in Mountainous Apple Orchard
Location: Ninglang County, Lijiang City, Yunnan Province, China
Challenge: A large-scale mountainous apple orchard frequently affected by late-spring frost. Traditional frost protection methods are ineffective or too costly due to complex terrain, relying primarily on environmentally problematic manual smoke generation.
Solution Applied: Implemented the proposed integrated frost protection system based on optimized anti-frost smoke machines, utilizing solar power and eco-friendly smoke agents with multi-objective optimization for placement and wiring.
Outcome: The system provided a significantly more economic and environmentally sustainable solution for frost prevention, overcoming the limitations of traditional methods in challenging mountainous terrain. It demonstrated high engineering practicality and adaptability, ensuring effective crop protection and reduced operational costs.
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Your AI Implementation Roadmap
A phased approach to integrate this cutting-edge optimization into your operations.
Phase 1: Feasibility & Initial Design
Duration: 2-3 Weeks
Conduct detailed site analysis (topography, microclimate), refine smoke diffusion models, and develop initial optimization parameters. Establish sensor network requirements for real-time data collection.
Phase 2: Prototype Development & Testing
Duration: 4-6 Weeks
Develop a scaled prototype of the anti-frost smoke machine and integrated control system. Conduct controlled field tests to validate smoke dispersion and heating effects, and refine the HOA/k-MST optimization algorithms.
Phase 3: Full-Scale Deployment & Integration
Duration: 8-12 Weeks
Manufacture and install the optimized number of smoke machines and wiring infrastructure. Integrate solar power systems and the dual-layer control system for automated operation and energy management.
Phase 4: Monitoring, Optimization & Training
Duration: Ongoing
Implement continuous monitoring of temperature, humidity, and wind. Collect performance data to further refine optimization models. Provide training to local growers for system operation and maintenance, ensuring long-term sustainability.
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