In the application of moving target tracking in smart city, particle filter technology has the advantages of dealing with nonlinear and non-Gaussian problems, but when the standard particle filter uses resampling method to solve the degradation phenomenon, simply copying the particles will cause local optimization difficulties, resulting in unstable filtering accuracy. In this paper, a particle filter algorithm combined with quantum genetic algorithm (QGA) is proposed to solve the above problems. Aiming at the problem of particle exhaustion in particle filter, the algorithm adopts the method of combining evolutionary algorithm. Each particle in particle filter is regarded as a chromosome in genetic algorithm, and the fitness of each chromosome corresponds to the weight of particle. For each particle state with weight, the particle is first binary coded with qubit and quantum superposition state, and then quantum rotation gate is used for selection, crossing, mutation, and other operations, after a set number of iterations, the final particle set with accuracy and better diversity. In this paper, the filter state estimation and RMSF of N=50 and N=100 for nonlinear target tracking and the comparison of real state and state estimation trajectory in time-constant model under nonlinear target tracking are given. It can be seen that in nonlinear state, the quantum genetic and particle filter (QGPF) algorithm can achieve a higher accuracy of state estimation, and the filtering error of QGPF algorithm at each time is relatively uniform, which shows that the algorithm in this paper has better algorithm stability. Under the time-constant model, the algorithm fits the real state and realizes stable and accurate tracking.

This paper adopts an edge computing approach to conduct in-depth research and analysis on the optimization of educational information resource integration and constructs an integrated teaching resource design model concerning the integrated teaching model, human-centered mobile learning resource design, and the interdisciplinary concept of physics subject teaching method. The learning field constituted by the model is divided into two parts, the explicit field and the potential field and then designs a five-stage teaching resource based on the model. A five-stage teaching resource development path was designed based on the model. Based on the cloud service center model, we propose a hierarchical mechanism for sharing educational information resources and analyze how each hierarchical entity constructs and shares resources and the rights and responsibilities of each hierarchical entity; we explain the meaning and functions of the personalized educational resource integrated development environment provided by the cloud service center for users. The dynamic evaluation model of the value of educational information resources is summarized and proposed for the resource exchange behavior in the sharing of educational information resources, and the significance of the calculation method of resource value, parameter values, and resource value difference for resource sharing is introduced. Firstly, the mechanism for coconstruction of educational information resources at different levels is proposed, and the construction tasks of subjects at different levels in the process of coconstruction and sharing of resources are elaborated. The sharing mechanism of educational information resources covering the regularization system, evaluation mechanism, incentive mechanism, problem handling mechanism, and supporting service mechanism is proposed, and a dynamic evaluation model of the value of educational information resources is designed to improve the enthusiasm of the coconstruction and sharing subjects.