Considering the current “Digital Finance- Commercial Bank Risks” is not clear and rarely studied for rural commercial banks. This study builds a two-way fixed effects model based on the annual data of 122 rural commercial banks from 2014-2020year in China and found that digital finance development has an “inverted U-shaped” relationship with the risk-taking of rural commercial banks. Further studies have found that market competition and risk preference for rural commercial banks have moderating and mediating effects. We have found that: (1) During the early stages, there is an increase in competition among rural commercial banks, leading them to adopt more stable operating strategies to ensure operations. which passively increases their risk-taking, thus verifying the left half of the “inverted U-shaped (2) In the future, digital finance is expected to break through an inflection point and competition will weaken. Rural commercial banks will increase their risk preferences to make up for previous profit losses. At this time, they will be better able to accurately identify risks, ultimately reducing their level of risk-taking. This verifies the right half of the “inverted U-shaped Overall, the Internal Mechanism among “Digital Finance Development, Market Competition, Risk Preferences, and Risk-Taking” has been confirmed.

With the continuous development of science and technology, navigation and positioning technology has been applied to all aspects of society. The ultra-wide band (UWB) based positioning technology has real-time indoor and outdoor accurate tracking ability and high positioning accuracy, which has a wide range of military and civilian applications. Despite that, the data will have abnormal fluctuations in the case of strong interference due to the complex and changeable indoor environment, which may affect the accuracy of positioning and even cause serious accidents. In this paper, UWB precise positioning under signal interference is studied. A two-stream 1D residual network (TS-1DRN) model learning location features from multimodal data is proposed where the main network structure is based on ResNet2D, and a precise positioning model based on the two-stream deep residual network with fusion utilization of multimodal data is applied to accurate positioning in abnormal scenarios. Considering that the anchor coordinates and distance can be used to obtain the tag coordinates in physical model, distance data are further added with anchor coordinates as the neural network training inputs into the two-stream network compared with previous studies. The effectiveness of the proposed model is verified by comparing with the classical algorithms commonly used for UWB positioning. The positioning accuracy under NLOS is improved by about 150% in the 3D space, and it also performs well in other dimensions, with the minimum positioning error reduced to 34.9952mm. Furthermore, the data in normal scenarios were also used for training and testing, and the experimental results are also significantly improved, indicating the robustness of the proposed model.