Enterprise AI Analysis
A study on the construction of an auxiliary decision-making system for urban landscape design based on artificial intelligence
This paper introduces an innovative AI-powered decision-making system for urban landscape design, leveraging deep learning for enhanced efficiency and quality. It details a hybrid B/S architecture, custom deep learning models for element recognition (improved YOLOv5 achieving 92.3% accuracy), and a multi-dimensional evaluation model integrating visual aesthetics, ecological benefits, and functional applicability (Pearson correlation 0.92). The system incorporates a deep reinforcement learning-based recommendation algorithm (85.7% accuracy) and an improved genetic algorithm for multi-objective optimization (25% efficiency gain in solution optimization). Implemented with Vue.js, Spring Boot, MySQL, MongoDB, and PyTorch, the system demonstrates significant practical results, reducing design time by 20% and improving public satisfaction to 94.6% in a case study. This work provides a new technological path for smart city construction by improving the scientificity and intelligence of landscape design.
Executive Impact & Key Metrics
Our analysis reveals significant improvements in core operational areas, translating directly into tangible business advantages.
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Recognition Accuracy
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Optimization Efficiency Gain
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Public Satisfaction Rate
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Design Time Reduction
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
The system adopts a hybrid B/S architecture, integrating Vue.js, Spring Boot, MySQL, MongoDB, and PyTorch for a robust and scalable solution. It leverages microservices for core functionalities like landscape design, data analysis, and decision support, ensuring high performance with average response times of 280ms and supporting 500 concurrent users.
The core of the system's intelligence lies in its improved YOLOv5 model for landscape element recognition, featuring a ResNet-152 backbone and spatial attention mechanisms. Trained on 15,000 labeled images, it achieves 92.3% accuracy, an 8.6% improvement over the original YOLOv5, at 45fps. This enables precise identification of plants, buildings, and water bodies.
The system employs a multi-dimensional evaluation model based on random forest for visual aesthetics, ecological benefits, and functional applicability, with a Pearson correlation of 0.92 with expert ratings. Scheme recommendations are powered by a DDPG (Deep Deterministic Policy Gradient) model, achieving 85.7% accuracy and 78.3% user adoption. Multi-objective optimization is handled by an improved genetic algorithm, reducing design time by 20% and improving solution quality by 25%.
Core System Performance
93.5% Landscape Element Recognition AccuracyEnterprise Process Flow
Element Identification
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Multi-dimensional Evaluation
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Recommendation Optimization
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Decision Support
| Algorithm Type | Convergence Time (s) | Optimal Solution Quality | Memory Occupancy (GB) | CPU Utilization (%) |
|---|---|---|---|---|
| Traditional GA | 256 | 0.82 | 4.5 | 85 |
| Improved GA | 166 | 0.97 | 3.8 | 72 |
| PSO | 198 | 0.88 | 4.2 | 78 |
City Waterfront Park Design Project
A real-world application demonstrating the system's impact.
- Project: City Waterfront Park
- Area: 25 hectares
- Investment: 320 million yuan
- Recognition Accuracy: 93.5%
- Project Score: 92 points (15% higher)
- Visitor Volume Increase: 35%
- Satisfaction Rate: 94.6%
Calculate Your Potential ROI
Quantify the impact of AI integration on your operational efficiency and cost savings with our interactive ROI calculator.
Annual Savings Potential
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Annual Hours Reclaimed
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Your AI Implementation Roadmap
A structured approach to integrating AI into your landscape design processes, ensuring a smooth and successful transition.
Phase 1: Discovery & Strategy
Initial consultation, needs assessment, and AI solution blueprinting tailored to your existing design workflows.
Phase 2: Customization & Integration
Development and customization of AI models, seamless integration with your current CAD/BIM systems and databases.
Phase 3: Training & Deployment
Comprehensive training for your design teams, pilot program deployment, and iterative refinement based on feedback.
Phase 4: Optimization & Scaling
Continuous monitoring, performance optimization, and scaling the AI system across all relevant design projects.
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