Innovation and entrepreneurship is the soul of entrepreneurship, and realizing the integration of innovation and entrepreneurship education and collaborative experiment platform in colleges and universities is an important way to promote the development of entrepreneurship and facilitate higher quality employment. In this paper, the authors propose the 3D-IVF intelligent sensor overlay algorithm wisdom laboratory system structure and application mode through in-depth investigation of collaborative laboratory, build the macroenvironment and construction demand of collaborative experiment management platform of innovation and entrepreneurship education, comprehensively analyze the current situation, problems, and effectiveness of its integration of innovation and entrepreneurship education and professional education. In addition, we explore the operation mechanism of its organizational system, curriculum system, management system, and platform construction in the practice of integrating innovation and entrepreneurship education with professional education. The average coverage rate of the 3D-IVF-based 3D coverage algorithm is 92.15%, and the deployment time is 2.5857 s; the average coverage rate is improved by 0.76%, and the deployment time is reduced by 0.1712 s. Design FIR filter using MATLAB and Hilbert transform filter, generate HDL code, and implement it on FPGA. Use the designed low-pass and band-pass filters to filter the 100 KHz square wave signal sampled by the ADC. When the number of iterations of the algorithm reaches 30, the coverage rates of the four-node deployment strategies will stabilize, i.e., the forces between the nodes of the 3D-VF-based 3D coverage algorithm of this paper. The equilibrium state is reached. We use resources and real-time experimental data sharing to realize collaborative design among team members, further optimize the curriculum structure of innovation and entrepreneurship education, adopt diversified forms of curriculum implementation, and implement a diversified curriculum evaluation system, etc.

Traditional management method of student information is not only slow in operation, poor in confidentiality, and low in work efficiency, but also prone to statistical errors and data loss, which can no longer meet the needs of new situations. Probabilistic random matrix management mode can effectively coordinate the development of various businesses and strengthen their information flow through horizontal and vertical management across functional departments. On the basis of summarizing and analyzing previous literature, this study expounded the research status and significance of student information management of higher education, elaborated the development background, current status, and future challenges of probabilistic random matrix management mode, introduced the methods and principles of probabilistic matrix factorization algorithm and random matrix factorization model, discussed the service and supervision functions of student information management, analyzed the incentive and guiding functions of student information management, conducted the process analysis of student information management for higher education based on probabilistic random matrix management mode, established student identity document and student status management modules, designed student scholarship, statistics, and data management modules, constructed a student information management system for higher education based on probabilistic random matrix management mode, and finally carried out a case application and its result analysis. The study results show that the probabilistic random matrix management mode is a combination of linear and flat organizational structures and has the advantages of short information lines, fast information feedback, and high operation efficiency; it can input the matrix form of prior data and use statistical probability knowledge to derive the probability density function of posterior feature vector and predict the recommendation result through feature vector. The probabilistic random matrix management mode first calculates student’s behavior sequence through their management information and then calculates the student’s preference sequence according to their behavior sequence and label information and subsequently calculates the similarity matrixes about the students and their information and finally integrates the obtained student similarity matrix into the probabilistic matrix factorization model.

Econophysics is a new interdiscipline where physics concept and methods are applied to financial analysis. For example, the application of theoretical physics in the modeling of financial markets has aroused wide concern. In the process of random fluctuation of prices in financial markets, many nonlinear dynamical problems are hidden in set coefficients and assumptions, resulting in the invisibility of market price fluctuations and unavailability of hidden benefits in fluctuations. Based on the analysis of price fluctuation mechanism in financial markets, this paper analyzes the characteristics of price fluctuation, and constructs the dynamical model of price fluctuation by means of physics theory, thereby providing a theoretical reference for the control and prevention of transaction risks.