ENTERPRISE AI ANALYSIS
Research on accurate operation system of UAV in complex Scene Based on multi-source intelligent Fusion and dynamic Management
By Chang Wei, Yu Shuai | December 26-28, 2025
With the deep integration of artificial intelligence, big data, cloud computing and intelligent management technology, UAV operation system is gradually evolving from single task execution to multi-scene, high-precision and intelligent direction. Aiming at the core bottlenecks such as insufficient positioning accuracy of UAV, low efficiency of multi-source data collaboration, and lack of dynamic optimization management of operation process in complex environment, this paper proposes an integrated solution that integrates GNSS/INS/ vision multi-source perception, factor graph intelligent optimization and cloud dynamic management. A multi-source information fusion positioning model based on improved factor graph was constructed. Through the deep coupling of IMU pre-integration factor, GNSS anti-interference factor and visual reprojection factor, the positioning robustness problem in complex electromagnetic environment and occlusion scene was solved. An "edge-cloud collaboration” big data processing framework for UAV operations was designed to integrate full-link data processing algorithms to provide reliable data support for precision operations. An "edge-cloud collaboration” intelligent management platform was built to realize dynamic task scheduling, resource optimal config- uration and global status monitoring. Through the experimental verification of three typical complex scenes, such as urban canyon inspection, agricultural plant protection operation and emergency material delivery, the positioning accuracy, operation efficiency and management intelligence level are systematically evaluated. Experimental results show that the positioning error of the system is controlled within 0.5m, the task completion efficiency is improved by 40%, and the response delay of resource scheduling is less than 100ms in a complex environment with 70% GNSS signal occlusion rate, which provides theoretical support and engineering practice examples for the large-scale application of UAV in complex scenes.
Executive Impact
This research introduces an integrated UAV operation system for complex environments, leveraging multi-source intelligent fusion and dynamic management. It significantly enhances positioning accuracy, operational efficiency, and system reliability, addressing limitations of traditional systems. The system achieves a positioning error of ≤0.5m, improves task completion by over 40%, and reduces scheduling latency to less than 90ms, making it suitable for large-scale enterprise deployments in scenarios like urban inspection and emergency delivery.
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Positioning Error (Max)
0%
Efficiency Improvement
0%
Positioning Continuity
0ms
Scheduling Latency
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Positioning System
Data Processing
Intelligent Management
Explores the multi-source intelligent fusion positioning model, including GNSS/INS/vision integration and factor graph optimization for enhanced robustness and accuracy.
0.43m
Mean Position Error (Urban Canyon)
Enterprise Process Flow
Sensor Data Acquisition (GNSS, INS, Visual)
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IMU Pre-Integration Factor
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GNSS Anti-Interference Factor
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Visual Reprojection Factor
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Factor Graph Optimization (Sliding Window)
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Optimal State Estimation Output
| Feature | Traditional GNSS/INS | Proposed Model |
|---|---|---|
| Mean Position Error (Urban Canyon) | 1.82m | 0.43m |
| Positioning Continuity | Low | 98.7% |
| Interference Handling | Poor | Robust (adaptive Kalman, factor graph) |
| Scale Uncertainty | N/A | Resolved by INS/GNSS Integration |
Details the 'edge-cloud collaboration' big data processing framework, ensuring real-time performance, high throughput, and data reliability for UAV operations.
68.5ms
Multi-source Fusion Latency
Enterprise Process Flow
Edge Layer: Data Acquisition & Preprocessing
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Feature Extraction (GNSS Signal)
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Data Transmission (Compressed, 5G)
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Cloud Layer: Distributed Storage (HDFS, MySQL)
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Model Training & Optimization (Random Forests, AHP)
Edge-Cloud Collaboration in Action: UAV Plant Protection
This case study demonstrates the effectiveness of the edge-cloud data processing framework in an agricultural plant protection scenario, highlighting real-time data handling and optimized decision-making.
Problem: Large agricultural areas require precise, real-time drone operations for plant protection. Traditional systems struggle with delayed data processing, leading to inefficient spraying and suboptimal resource allocation. High volumes of sensor data (visual, GNSS, IMU) need immediate analysis at the edge, with aggregated data sent to the cloud for deeper insights and model refinement.
Solution: The proposed edge-cloud framework was deployed. Edge devices on the UAV handled real-time data acquisition, outlier elimination, and feature extraction (e.g., plant health, pest detection). Compressed data was then efficiently transmitted via 5G to the cloud, where distributed storage and advanced analytics (AHP, random forests) further refined operational models and optimized resource use based on historical patterns.
Results: Achieved a 50% reduction in mean position error (0.78m) and 99.1% positioning continuity, crucial for precise spraying. Data processing latency for multi-source fusion was <70ms, supporting real-time adjustments. Overall task completion efficiency improved significantly, ensuring timely and effective plant protection across vast areas. The system demonstrated robust performance even in terrain-undulating regions with GNSS signal variations.
Covers the 'edge-cloud collaboration' intelligent management platform, including dynamic task scheduling, resource optimization, and global status monitoring.
95.0%
Anomaly Detection Accuracy
Enterprise Process Flow
Task Management (Decomposition, DRL Scheduling)
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Resource Management (Optimization, Allocation)
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Status Monitoring (WebGIS, Anomaly Detection)
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Data Management (Unified Analytics)
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User Management (RBAC, Security)
| Feature | Traditional Genetic Algorithm | Proposed DRL Algorithm |
|---|---|---|
| Task Completion Efficiency | Lower | Improved by ~40% |
| Resource Utilization | < 80% | > 91% |
| Scheduling Response Latency | > 200ms | < 90ms |
| Adaptability to Dynamic Changes | Limited | High (real-time adjustments) |
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Your AI Implementation Roadmap
A structured approach to integrate multi-source intelligent UAV operations into your enterprise, ensuring maximum impact and smooth adoption.
Phase 1: Discovery & Strategy
(2-4 Weeks)
Understand your current operational bottlenecks, define clear AI objectives, and develop a tailored implementation strategy leveraging multi-source fusion. This includes initial data audits and system requirement gathering.
Phase 2: Pilot Deployment & Integration
(4-8 Weeks)
Deploy a pilot UAV system with the integrated multi-source fusion model in a controlled complex environment. Integrate with existing IT infrastructure and begin initial data collection and processing via the edge-cloud framework. Focus on validating core positioning accuracy.
Phase 3: Optimization & Scaling
(8-12 Weeks)
Refine fusion algorithms and management platform settings based on pilot results. Expand to additional UAVs and diverse complex scenarios. Implement advanced dynamic scheduling and anomaly detection features. Conduct user training and roll out to broader operational teams.
Phase 4: Continuous Improvement & Expansion
(Ongoing)
Establish ongoing monitoring, performance tuning, and model retraining cycles. Explore new data sources (e.g., LiDAR) and advanced AI capabilities (scene recognition, obstacle prediction). Evaluate potential for digital twin integration and further scenario expansion.
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