基于多维时空特征预测与DLB的银行网点智能引导方法
By using multidimensional spatiotemporal feature prediction and DLB methods, the problems of spatiotemporal lag and traffic imbalance in the bank branch guidance system were solved, achieving accurate recommendations and traffic balance when users arrive, thus improving the efficiency of the bank service network and the user experience.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- XIDIAN UNIV
- Filing Date
- 2026-06-15
- Publication Date
- 2026-07-17
AI Technical Summary
Existing bank branch guidance systems lack consideration for the lag in time and space conditions, making it impossible to accurately predict the queuing status and service capacity when users arrive. This results in users facing ineffective waiting and congestion problems. Furthermore, the lack of global traffic control can easily lead to the "herd effect" and traffic uncertainty risks.
Employing a method based on multidimensional spatiotemporal feature prediction and DLB, this approach uses a collaborative architecture of cloud servers and mobile terminals to acquire user intentions in real time, perform multidimensional prediction calculations, and construct a multidimensional cost function by combining traffic conditions, queuing patterns, and service capabilities. It then recommends the optimal bank branch and regulates traffic balance through a damping feedback mechanism.
It improved the timeliness and accuracy of guidance information, achieved precise matching between business needs and service capabilities, reduced users' unproductive waiting time, optimized the resource utilization of the bank's service network, and enhanced user experience and network operating efficiency.
Smart Images
Figure CN122414520A_ABST