Research on Distance and Reputation Coefficient - Pricing Model Based on Population Grouping

This paper mainly aims at the defects of existing pricing schemes to establish a more reasonable pricing system, and excavate the information contained in big data through the sorting of big data. Firstly, this paper analyzes and screens the data. Through correlation analysis, it is found that the pricing scheme only refers to the variables reflecting the distribution density of members, such as the number and distance of members near the target task, but lacks consideration of other variables, especially the reputation of nearby members, Therefore, the reason why a large number of tasks have not been completed is that the pricing scheme of the decision-maker is relatively one-sided, the factors considered only involve the density of the distribution of members, and the lack of consideration of other factors such as members' reputation and members' scheduled task quota. Then, this paper establishes the distance pricing model and the distance and number pricing model; Based on the above two models, a distance and reputation coefficient pricing model based on the number of people grouping is established. Compared with the original scheme, this model scheme considers the reputation value of nearby members and other factors, which is more reliable, and has the advantages of low pricing and high task acceptance.