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It is particularly important to plan and design the rural landscape of the Qiang nationality, but the regional characteristics of the rural landscape of the Qiang nationality in the upper reaches of the Minjiang River must be maintained at the same time. On the basis of the regional culture formed by the regional geography and cultural environment of the Qiang ethnic group in the upper reaches of the Minjiang River, combined with the investigation of the Qiang ethnic group in the upper reaches of the Minjiang River, this study explores the formal characteristics and formation reasons of the rural landscape of the Qiang ethnic group in the upper reaches of the Minjiang River from the perspective of rural landscape planning and design. It summarizes the formal features of the Qiang rural landscape in the upper reaches of the Minjiang River in a graphical language. It analyzes the problems faced by traditional rural landscapes. It then summarizes the experience and deficiencies in practice through the research on the existing Qiang rural landscape planning and design. It analyzes the influence of the regional culture of the Qiang nationality on the planning and design of the rural landscape of the Qiang nationality and proposes a strategy for the planning and design of the rural landscape of the Qiang nationality. This study hopes that this can provide theoretical and practical reference for the planning and design methods of the Qiang rural landscape in the upper reaches of the Minjiang River and the protection, development, and dissemination of the Qiang culture in the upper reaches of the Minjiang River. Experiments show that there are 764 settlements when the radius of the river buffer is within 500 meters. [ABSTRACT FROM AUTHOR]

In the current era of increasingly complex social life, as people's demand quality is getting higher and higher, the solution of the problem often has multiple indicators to achieve the optimum. This forces the graph coloring problem (GCP) to become complicated, and it is difficult to directly obtain the optimal solution, which brings new challenges to the solution of the problem. In response to this problem, the GCP field is of great research significance. With the in-depth study of GCP, the research on multiobjective optimization (MOO) in graph coloring algorithm (GCA) is gradually carried out. Its performance advantage is of great significance for solving multicondition constraint problems. This paper aims to study the application of Genetic Algorithm (GA) in GCA. Through the analysis and research of GA, and the fusion of numerical analysis and scientific computing, it can be applied to the construction of the neighbor distinguishable uniform V-full coloring algorithm (AVDEVTCA) to solve the AVDEVTC problem. This paper describes the basic theory of MOO and graph coloring. It conducts an experimental analysis of the algorithm performance and uses the relevant theoretical formulas to explain it. The results show that the algorithm takes 5754.142 S seconds to test 21325415 images and can color a large number of results that cannot be done manually in less time. It has greatly improved in terms of time and manpower saving and greatly improved practicability and work efficiency. [ABSTRACT FROM AUTHOR]

The classification inaccuracy occurs to the evaluation calculation of the total factor production impact in the traditional digital economy development area. This paper applies the fuzzy hierarchical (Visualization in Scientific Computing, VISC) algorithm to the calculation method of the total factor production impact evaluation in the digital economy development area. The quantitative recursive method is used to evaluate the ability of the data information model, to achieve the ability of controlling the acquisition of characteristic resources, and complete the classification and summary of the index parameters of the total factor production impact of the digital economy development area. Finally, the experiment proves that this calculation method is used to develop the evaluation of the impact of total factor production digital economy development area and improve the information integration and analysis capabilities, the accuracy of the evaluation of the impact of total factor production, and the efficiency of the use of digital economy development resources. [ABSTRACT FROM AUTHOR]

On the basis of studying datasets of students' course scores, we constructed a Bayesian network and undertook probabilistic inference analysis. We selected six requisite courses in computer science as Bayesian network nodes. We determined the order of the nodes based on expert knowledge. Using 356 datasets, the K2 algorithm learned the Bayesian network structure. Then, we used maximum a posteriori probability estimation to learn the parameters. After constructing the Bayesian network, we used the message-passing algorithm to predict and infer the results. Finally, the results of dynamic knowledge inference were presented through a detailed inference process. In the absence of any evidence node information, the probability of passing other courses was calculated. A mathematics course (a basic professional course) was chosen as the evidence node to dynamically infer the probability of passing other courses. Over time, the probability of passing other courses greatly improved, and the inference results were consistent with the actual values and can thus be visualized and applied to an actual school management system. [ABSTRACT FROM AUTHOR]