Research And Development Of Agricultural Information Collection System Based On Crowd Sensing

Agricultural information is an important carrier of agricultural informatization strategy,and efficient acquisition of precision agricultural information is a necessary condition for agricultural informatization.With the development of the Internet of Things and mobile communication technologies,new vitality has been injected into the collection of agricultural information.Crowd Sensing based on the perception capability of intelligent terminal equipment has realized a new data collection mode.The sensor information collection method,crowd intelligence has the advantages of strong flexibility,strong scalability,and low sensing cost.In order to improve the efficiency and scalability of agricultural information collection and reduce labor costs,this paper takes agricultural information as the research object,combines crowd intelligence perception and the scene characteristics of agricultural information collection,and studies the construction method of task allocation model and the design of incentive mechanism respectively.method to develop and implement an agricultural information collection system based on crowd-sensing.The main research contents are as follows:(1)Maximum spatiotemporal coverage task assignment model construction.In the scenario of agricultural information collection,agricultural data information has the characteristics of large spatial and temporal span,long periodicity and strong timeliness,which leads to the problem of low task allocation utility.A task allocation model based on maximum spatial and temporal coverage is constructed.Modeling the task assignment problem of agricultural information collection,because the problem is NP-hard,under the premise of ensuring platform benefits,the optimization goal is to maximize the space-time coverage,and the chaos based on elite reverse learning and Cauchy mutation is adopted.The mayfly algorithm solves the task assignment problem.The simulation results show that the average performance of the task allocation mechanism based on the chaotic mayfly algorithm is about23.42% higher than other methods.(2)Design of incentive mechanism based on multi-attribute reverse auction.In view of the low willingness of participants to participate in the agricultural information collection scenario,an incentive mechanism based on multi-attribute reverse auction is designed.According to the bidding information received by the platform,the winner is selected by comprehensively considering the participants' quotation,reputation value and perceived cost and other factors.Aiming at the problems of low task completion rate for edge tasks with relatively difficult perception and large time and space span,an incentive function based on participants' perceived cost is set up,and participants can obtain additional incentive rewards after completing the task.In response to the problem of low validity of perception data,an adaptive threshold method is introduced in the process of selecting winners.When the reputation value of participants is lower than the threshold,the perception platform will not assign tasks to them,and the design is based on the perception platform and tasks.The publisher's dual quality evaluation system evaluates the data,and gives the participants reasonable rewards according to the data utility and the participants' task contribution.The simulation results show that the average performance of the incentive mechanism is improved by about 15.47% compared with other methods.(3)Design and implementation of agricultural information collection system.In view of the problems of poor expansion,high cost and low timeliness of agricultural information collection system at this stage.The task allocation model and incentive mechanism are embedded into the agricultural information collection system,the We Chat applet is used as the visualization platform,and the Spring Boot framework is used to develop the agricultural information collection system.By collecting the picture information of wheat ground rhizomes for case analysis,the experimental results show that the data pass rate reaches 84.62% and the space coverage rate reaches 72.57%,which further verifies the effectiveness and feasibility of the agricultural information collection system based on crowd intelligence.