Rural Financial Resource Allocation Strategy Based on the Internet of Things and Fuzzy Algorithms

The effective allocation of financial resources in rural areas is important for encouraging agricultural development, poverty alleviation, and inclusive economic development. However, rural financial systems face obstacles in the reasonable and effective distribution of limited resources due to fragmented data, environmental uncertainty, and socioeconomic diversity. To address these issues, this research proposes a novel Rural Financial Resource Allocation Strategy (RFRAS) incorporating Internet of Things (IoT) and fuzzy logic algorithms. In the data collection stage, IoT-enabled devices such as soil sensors, weather monitoring, and mobile financial terminals are organized to gather real-time data on environmental conditions and financial transactions in rural regions. The collected data involve preprocessing, handling missing values, and noise filtering to guarantee high-quality input for intelligent analysis. A Fuzzy Inference System (FIS) is employed to address data ambiguity and support initial decision-making based on key parameters such as creditworthiness, urgency, and financial risk. To further improve forecast accuracy and allocation competence, an Efficient Particle Swarm Optimized Deep Q-network (EPSO-DQN) is employed to forecast future financial needs and repayment behavior. Additionally, the EPSO technique is used to fine-tune fuzzy membership functions and optimize resource distribution. The proposed FIS model efficiently optimizes rule inference time, allocation fairness, precision in classification, and membership function overlap. The simulation findings implement Python; the EPSO-DQN model achieved the highest accuracy at 96%, outperforming the existing methods. Experimental results establish that the RFRAS strategy significantly increases equality, precision, and flexibility in rural financial resource management.