AWBING plus algorithm for generic object proposal generation

Permission and content management model based on ASP.NET technology and three-layer network architecture

In order to strengthen the management and control of personnel flow and the timeliness of information dissemination during the COVID-19 epidemic, it is necessary to improve the reliability of authority and content management. The extensive application of authority information management can closely connect the professional system with the production activities of enterprises. At present, the traditional authority management level is complex, and the management of complex data is chaotic. This paper is based on ASP.NET. This paper proposes a data authority management method. The main function of this method is to establish a standard three-tier structure of rights management level by using the open-source string template engine technology, tag analysis, SEO search engine optimization technology and information collection technology. This method provides a general process oriented and automatic management platform for users through the hierarchical management mechanism. This technology can meet people’s rights and content requirements for creating, publishing and updating websites without understanding the technology itself. The experimental results show that the rapid management mechanism based on authority management can accurately handle and manage authority and data which can improve the accuracy and timeliness of personnel flow control under the impact of the COVID-19 epidemic.

Application of intelligent virtual reality technology in Clothing virtual wear and color saturation after COVID-19 epidemic situation

The COVID-19 epidemic has brought a huge impact to the clothing industry. Because of the inconveniences caused by countermeasures, clothing consumers can’t go to physical stores to try on, so it is urgent to develop a virtual clothing trial system. In addition, online fitting and online shopping gradually become the trend of clothing consumption. Based on virtual reality technology, this paper proposes a virtual clothing fitting system, and studies the color saturation in the process. In this paper, a method of parametric drawing of garment characteristic curve is proposed, and the shape of garment is designed by using control vertices. Based on this, this paper presents four forms of sutured parabola space and their control point solving algorithm. According to the principle of scale method, a three-dimensional coordinate transformation model of feature points is established. The model can calculate the coordinates of each characteristic value point of clothing according to the body shape information provided by customers and the empirical formula of different clothing styles, and then reverse calculate the curve control point. Furthermore, Bezier surface generation method is used to fit the control points. After the surface patches are spliced, the 3D rigid clothing model can be obtained. Experiments show that the method of personalized clothing modeling in this paper is efficient and accurate, which can be further extended to the observation system with larger degree of freedom.

Big data analysis and empirical research on the financing and investment decision of companies after COVID-19 epidemic situation based on deep learning

The novel corona virus pneumonia has brought pressure on economic development. Large and medium-sized companies will also play a key role in the recovery of growth after the outbreak. Therefore, it is particularly important to pay attention to the impact of the epidemic on large and medium-sized companies and on the investment and financing of companies. Firstly, the structure of the network model of data analysis is designed in this paper, including the design of the network level, the selection of the number of neurons in each level, the determination of the initial weight and other related parameters. According to the design of network structure, the evaluation model of investment and financing of listed companies is established. Python is used to preprocess the data and train the sample data. By comparing the data processed by two training methods, the optimal network classification model is selected. The experimental results show that the proposed method can improve the effectiveness of investment and financing decisions of listed companies.