Abstract

In this paper, we first use the multiple stepwise regression to fit the price function and analyze the reason of the unfinished task from many angles. Then, by using the idea of "optimal neighbor", the matching model of task and member is established to optimize the original pricing model. Then, starting from the "small world" model, the actual packaging situation was simulated, and the price correction function was defined to reduce the impact of task distribution density on the completion situation, and the pricing scheme was further optimized. Finally, the new project is priced with an optimal package pricing formula, and the implementation effect is judged from the two perspectives of task completion and simulation results.

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