Intelligent Prediction and Rural Financial Development Based on Abnormal Detection of Sensor Data

Area of Science:

• Computer Science
• Agricultural Economics
• Financial Technology

Background:

• Wireless sensor networks (WSNs) generate vast data, facing challenges like limited resources, environmental vulnerabilities, and security threats.
• Abnormal node and data detection are critical for WSN security, impacting system reliability and services.
• China's rural revitalization and poverty alleviation strategies emphasize improving rural financial systems for small businesses and farmers.

Purpose of the Study:

• To research intelligent prediction methods for sensor data anomaly detection.
• To apply these methods to enhance the development of rural finance.
• To explore the integration of Internet technology for improved financial services in rural areas.

Main Methods:

• Investigating intelligent prediction techniques for anomaly detection in WSN data.
• Analyzing the application of sensor data anomaly detection in the context of rural financial development.
• Examining how Internet technology can bridge gaps in traditional financial institutions for rural clients.

Main Results:

• Demonstrated the potential of intelligent sensor data anomaly detection for improving rural financial services.
• Highlighted the complementary nature of abnormal node and data detection in WSN security.
• Showcased how "intelligent forecasting" can advance rural financial development theories and practices.

Conclusions:

• Intelligent prediction of sensor data anomalies offers a novel approach to bolster rural finance.
• Integrating advanced technologies like WSNs and Internet platforms can significantly improve financial inclusion and service delivery in rural economies.
• The research supports the strategic goals of rural revitalization through technological innovation in financial services.