中国科学院西安光学精密机械研究所机构知识库

This essay proposes two methods to analyze bee populations in a given period. The first method is a quantitative analysis of the correlation between time and population, establishing a time–population model for bees. However, this method fails to provide a precise enough result. For improvement, the analysis of bee populations is augmented with more comprehensive factors (both positive and negative), creating a unified measure to calculate the total change in population percentage by assigning weights to each individual factor. During the construction of these two methods, we completed the following five steps: Find relevant data with a numerical correlation between time and population: Data containing relevant information like time and population were downloaded from credible sources. Then, the data were fitted with linear regression to reveal the relationship between the population and time. Find possible factors that affect bee populations: External and internal factors were identified through a literature review of research articles and reputable online sources. Among these, five factors were deemed the most critical and to be used in this chapter later. Assign weights to each factor through the Entropy Weight Method (EWM) and Analytic Hierarchy Process (AHP): With EWM or AHP, a different set of weights was assigned to the factors. However, in this paper, neither of these two was used alone. Instead, a unified model that learns from both methods and hence generates a better weight for each factor is proposed and explained. Analysis of beehives needed to pollinate a 20-acre area: Parameters for the model were identified, defined, and populated using relevant data. Finally, the minimum and the maximum number of beehives that satisfy the requirements were calculated and an average of the values was obtained. Testing of the model on Buhlmann 1985: With the fully calculated weights of different factors through the integrated method, the model was tested to see if the weight assignments were reasonable. To do this, the result obtained from this model is compared with data approached by Buhlmann (1985) as an evaluation of this model. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.