In the current fields of quantum information processing and quantum computing, fast and accurate quantum state manipulation and preparation have been of keen interest to researchers, and their potential applications are mainly in quantum measurement, quantum information, quantum communication, and quantum sensing. In this paper, the Hilbert space of a bipartite state system is unfolded by four Bell state entanglement bases and the result is projected to the subsystem to obtain a mixed state. A quantum approximation algorithm is proposed to provide a solution to the combinatorial optimization problem, and based on the workflow of the quantum approximation optimization algorithm, an improvement is proposed to the quantum approximation optimization algorithm to solve the constrained problem using the quadratic unconstrained binary optimization method. Based on the theory of cavity magnetism, the hybrid quantum system model is constructed, and the calculation method of Hamiltonian quantity is proposed. Combined with the quantum entanglement optimal path calculation of UQAOA algorithm, the optimal value of time-microwave entanglement is obtained at r=0.234, so the compression parameter r=0.2 is used in the calculation. Based on the UQAOA algorithm for the analysis of the transmission characteristics of the generated OMA wave in air and the transmission optimization problem, the simulation obtains the reflection coefficient is slightly lower than that of the test, and the maximal error error is controlled at ±7.5dB around, and the two results are basically in agreement.

The organic combination of traditional rule of law culture and Civics education in colleges and universities is a breakthrough to improve the effectiveness of Civics education. Focusing on the Civic and political education that integrates traditional rule of law culture, the article introduces virtual reality technology and differential evolution algorithm to explore the course effect optimization method of Civic and political virtual reality teaching, and obtains the optimal content applied to the corpus through differential evolution algorithm according to the content characteristics of Civic and political education. On this basis, the evaluation index system is constructed to assess the course optimization effect of Civics virtual reality teaching. Example validation shows that the Civics corpus based on differential evolutionary algorithm and the proposed Civics virtual reality teaching method achieve better Civics course optimization effect, with an overall score of 3.833, and have the ability of practical application. Students of different genders and grades show significant differences (P<0.05) in the evaluation results of most of the first-level indicators. The application section of virtual reality technology promotes the teaching effect of traditional rule of law culture into the ideological education of colleges and universities.

The research in this paper mainly focuses on the design of the quality assessment system of Ideological and Political Education to realize the innovation of Ideological and Political Education mode. The principal component analysis algorithm is used as the core algorithm of the assessment system, and combined with the system architecture model of hierarchical design, it realizes the collection, processing, analysis and assessment of the data on the quality of Ideological and Political classes. The research results show that the assessment system based on principal component analysis algorithm in this paper has a higher accuracy rate of education quality assessment compared to the evaluation system based on a single deep learning algorithm such as RBF neural network. At the same time, the system in this paper also has a higher assessment accuracy than the evaluation system using a combination of algorithms, and shows excellent stability performance when assessing the educational quality of 150 teachers. Using this system to assess the quality of Ideological and Political Education of 8 teachers, the comprehensive ranking is more reasonable than the original ranking. The Ideological and Political education quality assessment system designed based on the principal component analysis algorithm in this paper has a far-reaching impact on the innovation and intelligent development of the Ideological and Political Education model in the digital era.

The development of electronic and electrical architectures towards domain centralization makes it difficult for traditional distributed control architectures to meet the functional needs and performance requirements of increasingly complex intelligent devices. This study utilizes a multi-model adaptive control algorithm to assist the domain controller to adjust the control parameters in real time according to the state of the device and environmental changes, and to realize the optimization of the control of the device. The wi-fi wireless networking communication technology is chosen to transmit the real-time data acquired by the sensors to the web page. The electrical and electronic architecture composed of the two combined with each other is carried to the intelligent control platform to realize the functions of sensing, positioning, planning and decision-making of the equipment platform. The study shows that: the algorithm selected in this paper can reach the target speed of the motor within 0.2s in the process of no-load and loaded operation, and the time required for balancing to the load torque is significantly reduced compared with the comparison algorithm. In this paper, the maximum throughput and CPU occupancy of the domain controller + wireless sensor architecture are lower than that of the traditional distributed architecture. And the platform constructed accordingly has no packet loss when the number of packets sent is less than 10000, and the average communication delay is between 0.65 and 1.2ms, which meets the requirements of vehicle wireless control and communication. Through the domain controller based on adaptive control algorithm to regulate the vehicle speed in real time, to ensure the safety distance between the rear vehicle and the front vehicle.

Learning path optimization aims to generate and optimize a knowledge learning sequence for learners that best meets their knowledge needs. This study focuses on the important role of online learner behavior in personalized path planning. By constructing a knowledge point difficulty model and a learning behavior prediction model based on online learning behavior, together with a user-based collaborative filtering recommendation algorithm, a personalized learning path is proposed comprehensively. The MOOC websites “College English 1” and “Xuedang Online” are selected as sample data to analyze the online learning behavior of English learners and verify the learning effect of the learning path proposed in the article through the change of students’ online time. The personalized teaching model based on the learning path is investigated in practice by taking the college English course in school A as an example. Compared with the traditional teaching mode, the optimized learning path shows a significant difference of 0.01% in the dimensions of learners’ “knowledge and skills”, “process and method” and “affective attitude”. The mean values of the optimized blended teaching mode are 4.12, 4.33 and 4.07 respectively, which are all better than the traditional teaching mode. It shows that the English learning path proposed in this paper is conducive to enhancing students’ personalized learning needs and provides a reference for promoting the effective implementation of personalized learning in the information technology environment.

As the key driving force to promote the development of new quality productivity, the internal logic of the integration of production and education is to provide core support for the development of new quality productivity by training high-quality workers, providing high-quality labor elements and creating an efficient innovation platform. However, at present, the integration of middle and teaching in undergraduate education faces challenges such as “school hot and enterprise cold”, school-enterprise cooperation obstacles, and imperfect mechanism. This paper analyzes the current situation of the integration of production and education in undergraduate education, constructs the corresponding mathematical model. And uses genetic algorithm to solve the optimization objectives of curriculum design and teaching resource allocation under the integration of production and education, include the incorporation of enterprise elements, such as the proportion of enterprise practice courses, enterprise mentors, joint research and development data. Based on the above, the feasibility of GA optimization algorithm is tested from three perspectives: comparison of the same kind, practical application and student satisfaction. In order to effectively enable the development of new quality productivity, it is necessary to optimize the education major setting in accordance with industrial changes, deepen the learning situation and customize practical courses, deepen the school-enterprise cooperation and development platform, strengthen collaborative innovation, and improve the incentive mechanism, so as to form an effective connection between the education chain, the talent chain, the industrial chain and the innovation chain, and jointly promote the high-quality development of undergraduate education.

Teaching and correcting athletes’ techniques by analyzing and referring to the performance of professional tournament players can improve the teaching level and quality of wushu movements. In this paper, the performance of college students in UFC tournaments is taken as the research data, and the multilayer perceptron algorithm is used to process the images and carry out the global modeling of wushu fighting action images. The network coding design is used to improve the data transmission rate of the algorithm, and the activation function is used as the nonlinear expression method of the algorithm. The Tanh_Softsign activation function is improved to counteract the noise interference of the dataset images, in order to construct the multilayer perceptual machine algorithm and develop the learning of martial arts fighting action scores. After optimizing the learning of UFC martial arts action scores by this algorithm, this algorithm shows a high correlation between the performance scores of students and the professional teachers’ scores of an elective class of martial arts in a university with P>0.05, which indicates that the algorithm in this paper can accurately assess the students’ action performance.

Based on the status quo of Sanya Digital Intelligence Tourism Economy, this paper puts forward the strategy of intelligent teaching change under the dual-leader cultivation mode of colleges and universities. Relying on clustering analysis technology to achieve the mining processing of the whole process data of the wisdom teaching platform, to promote the optimization of the process of wisdom teaching change. The catechism data of the basic course of tourism management of a smart teaching platform is collected, and z-score and PCA principal component analysis are utilized to eliminate the quantitative influence of the data. The best cluster values were determined by hierarchical cluster analysis, and the learners were divided into three cluster groups with the help of K-Means clustering algorithm. One-way ANOVA was introduced to compare the achievement data before and after smart teaching of the three groups of students to explore the effect of smart teaching. The results showed that among the paper grades, category 2 students had the greatest change in the mean value of their grades. In practical grades, the mean value of category 2 students’ practical grades was 95.63, which was 20.18 and 26.75 points higher than those of category 0 and category 1 students, respectively. p-value of 1.56951E-17 was less than 0.05, which indicated that the grades of the three categories of students showed significant differences.

This paper combines the necessary functional requirements for teaching system generated by teaching activities in the context of mobile Internet, designs the general framework of the system, users and their rights management, and constructs a set of teaching system. Subsequently, the traditional PSO algorithm is introduced, and the processing scheme of the scheduling problem is defined as particles to form an initial particle swarm, while the particle swarm position in the algorithm is updated by drawing on the crossover idea of the genetic algorithm, so as to optimize and obtain the scheduling algorithm based on DPSO. Then we test the teaching system of this paper from three levels of pressure bearing, response delay and stability performance to ensure the operating environment of the scheduling algorithm of this paper. The courses of three colleges of a university are used as experimental data to analyze the performance of the scheduling algorithm in this paper. In the comparison of course arrangement in different colleges, the adaptability of this paper’s scheduling algorithm is above 0.900, while the highest adaptability of manual scheduling is only 0.8147, which indicates that compared with manual scheduling, this paper’s scheduling algorithm is able to make a more reasonable course arrangement.

Aiming at the potential risks existing in the power market transaction under the new power system, and considering the temporal attributes of the information, this paper proposes to use dynamic Bayesian network to construct the risk monitoring and early warning model of the power market transaction. The dynamic Bayesian network is utilized to calculate the correlation between different risk factors, estimate the risk value of power market transactions, and classify the warning level. Taking the southern regional electricity market as the research object, the relationship between electricity price and transaction volume is explored based on the experimental dataset. A credit grading system is introduced to carry out transaction prediction simulation experiments, relying on the prediction data to determine the link between electricity price and transaction volume. The results show that overall power price and transaction volume show a negative correlation, but in June, when the power price is 0.4370 yuan per kWh, the transaction volume still reaches 19.65 million kWh, and the inverse relationship between the transaction volume and the price is not obvious. The use of dynamic Bayesian network to construct the power market transaction risk monitoring and early warning model can dynamically update and adjust the risk monitoring with the passage of time, making the power market transaction early warning more flexible and real-time.