Crowd sensing task allocation and user recruitment model cross validation method and system

Abstract

The invention discloses a crowd sensing task allocation and user recruitment model cross validation method and system. The method comprises the following steps: inputting a plurality of time-space sub-tasks obtained by dividing a crowd sensing task model according to regions and time by using a task allocation model to be subjected to cross validation, and inputting parameters of an agent agent determined by using a user recruitment model; importing the time-space subtasks into an artificial social platform in which a user agent model, an artificial social environment model and an agent interaction model are pre-established, and starting simulation calculation to generate an experimental sample; and performing data statistics on the specified evaluation indexes for the generated experimental samples to obtain an experimental result containing the specified evaluation indexes. According to the method, cross validation of the task allocation model and the user recruitment model can be realized, and the method has the advantages of high parallel computing capability, good user customization capability, simple and efficient development process, high practicability and wide application prospect.

Description

technical field [0001] The invention relates to urban crowd perception computing technology, in particular to a cross-verification method and system for task assignment and user recruitment model of crowd perception. Background technique [0002] In the application of crowd sensing, the initiator of the activity can issue some spatial tasks in the real world, invite users carrying mobile smart devices to move to the target location specified by the task to perform the task, and request data related to the specific location. The mobility of users provides unprecedented opportunities for data collection and transmission through mobile devices, for example: the air quality monitoring task initiated by the urban environmental management department, the responding participants need to move to the designated location of the task, and use relevant sensors to collect air quality data And upload the data to the central server through the smartphone. The life cycle of crowd-sensing a...

AI Technical Summary

Problems solved by technology

These studies have broken some of the original stereotyped assumptions to a certain extent, but when verifying the proposed crowd-sensing user recruitment and task assignment model, it is still only possible to carry out simulation experiments on real data sets based on the inductive optimization model, and there is no real Taking into account the impact of diversity among users, temporary withdrawal of users, and unqualified data quality

Due to the huge cost of conducting large-scale real experiments, and it is a repetitive work to conduct real experiments on all newly proposed crowd sensing user recruitment and task assignment models

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