This paper mainly uses the idea of pedigree clustering analysis, gray prediction and principal component analysis. The clustering analysis model, GM (1,1) model and principal component analysis model were established by using SPSS software to analyze the correlation matrices and principal component analysis. MATLAB software was used to calculate the correlation matrices. In January, The difference in price changes of major food prices in cities is calculated, and had forecasted the various food prices in June 2016. For the first issue, the main food is classified and the data are processed. After that, the SPSS software is used to classify the 27 kinds of food into four categories by using the pedigree cluster analysis model and the system clustering. The four categories are made by EXCEL. The price of food changes over time with a line chart that analyzes the characteristics of food price volatility. For the second issue, the gray prediction model is established based on the food classification of each kind of food price. First, the original data is cumulated, test and processed, so that the data have a strong regularity, and then establish a gray differential equation, and then use MATLAB software to solve the model. And then the residual test and post-check test, have C <0.35, the prediction accuracy is better. Finally, predict the price trend in June 2016 through the function. For the third issue, we analyzed the main components of 27 kinds of food types by celery, octopus, chicken (white striped chicken), duck and Chinese cabbage by using the data of principal given and analyzed by principal component analysis. It can be detected by measuring a small amount of food, this predict CPI value relatively accurate. Through the study of the characteristics of the region, select Shanghai and Shenyang, by looking for the relevant CPI and food price data, using spss software, principal component analysis, the impact of the CPI on several types of food, and then calculated by matlab algorithm weight, and then the data obtained by the analysis and comparison, different regions should be selected for different types of food for testing.

In this paper, an improved mathematical model for flashover behavior of polluted insulators is proposed based on experimental tests. In order to determine the flashover model of polluted insulators, the relationship between conductivity and salinity of solution pollution layer of the insulator is measured. Then, the leakage of current amplitude of four common insulators versus axial, thermal conductivity and arc constants temperature was determined. The experimental tests show that top leakage distance (TLd) to bottom leakage distance (BLd) ratio of insulators has a significant effect on critical voltage and current. Therefore, critical voltage and current were modeled by TLd to BLd ratio Index (M). Also, salinity of solution pollution layer of the insulators has been applied to this model by resistance pollution parameter. On the other hand, arc constants of each insulator in new model have been identified based on experimental results. Finally, a mathematical model is intended for critical voltage against salinity of solution pollution layer of different insulators. This model depends on insulator profile. There is a good agreement between the experimental tests of pollution insulators obtained in the laboratory and values calculated from the mathematical models developed in the present study.

This study aims to apply mathematical modelling methods focusing on the fishing songs of Poyang Lake for its conservation and digital reform. Through the principles of abstraction, model building and parameter estimation of mathematical modelling, we will quantitatively analyse the efficiency of cultural heritage and the degree of influence of digital reform. Specific methods include time series analysis, data mining and optimisation models. These tools will provide theoretical support and quantify the complexity of the problem by introducing corresponding mathematical models and formulas.

Introduction: Many detrimental effects on employees’ health and wellbeing might result from inadequate illumination in the workplace. Headaches and trouble focusing can result from eye strain brought on by inadequate illumination. The purpose of this study was to simulate and optimize workplace illumination in the ceramic industry. Materials and methods: A common Luxmeter ST-1300 was used to measure the illumination in seven workplaces at a height of 100 cm above the floor. DIALux evo version 7.1 software was used to simulate the illumination of workplaces. To optimize the illumination conditions, a numerical experiment design consisting of 16 scenarios was used for each of the workplaces. Four factors were considered for each scenario: luminaire height, number of luminaires, luminous flux, and light loss factor. The Design-Expert program version 13.0.5.0 was applied for developing the scenarios. Finally, by developing quadratic models for each workplace, the optimization process was implemented. Results: Every workplace had illumination levels that were measured to be between 250 and 300 lux. Instead of using compact fluorescent luminaires, LED technology was recommended to maximize the illumination conditions for the workers. Following optimization, 376 lux of illumination were visible at each workstation in every workspace. For the majority of the workspaces, the simulated illumination was expected to have a desirability degree greater than 0.9. The uniformity and illumination of the workplace were significantly impacted by the two factors of luminaire height and luminaire count. Conclusion: The primary outcomes of this optimization were the environmental, political, and socioeconomic ones, including reduced consumption power, high light flux, and environmental compatibility. Nonetheless, the optimization technique applied in this work can be applied to the design of similar situations, such as residential infrastructure.