Design And Implementation Of Real-time Monitoring System Based On Spectral Data

With the rapid development of science and technology, the production of crops is tending to be more mechanized and large-scale. Along with the advent of the era of Internet, the data monitoring of basic environmental factors, such as temperature, humidity, carbon dioxide concentration, light intensity and so on, is becoming more and more intelligent. For the current various kinds of automatic monitoring systems, although they have realized intelligent data acquisition and real-time transmission in the conventional sense, the staff still can’t fully grasp specific growth of crops. According to the data change of the external environment factors, they can only make some indirect inferences. Thus more direct and accurate monitoring is necessary to keep eyes on the crops’growth conditions. Through analyzing the spectral data of crops and their growth soil, the staff can get accurate crop growth status, all kinds of nutrients in the soil and the content of pesticide residue, which can be used to adjust the crop breeding. However the current spectral data processing mostly stays on the level of data analysis, not forming a complete monitoring system which contains real-time acquisition, effective storage, and dynamic query and so on. Therefore, spectral data processing architecture urgently need to be more perfect.In view of the information demand of real-time, accuracy and effectiveness, this thesis designs and develops a set of information monitoring system which is based on the traditional way of intelligent monitoring. The monitoring system includes not only the basic data monitoring of environmental factors, also the spectrum data collection, storage and queries. Specific work is as follows:for the information acquisition part, the system implements real-time acquisition of the wireless sensor node data which is based on the ZigBee network protocol. For the information transmission part, it defines the resolution protocol of spectral data and basic data, also realizing data transmission between the client and the server effectively. For the information storage part, it completes the logical structure design of basic data and spectral data, based on relational model database. Especially for spectral data, the system mixes different attributes and combines with the writing Application, realizing related large data storage. For the information query part, it builds data analytical model and uses WebService interface to complete data model encapsulation. At the same time, this part accomplishes the development of mobile terminal, which is used to display the query results.The main innovation of this thesis is as follows:(1) Data transmission component (DTC):this thesis defines related resolution protocols for spectral data and basic data, which are more flexible compared with the Smart and Modbus. Based on the content of protocol, the DTC divides spectral information into different parts, ensuring the accuracy of transmitting data. Meanwhile, it uses multiple ports multi-threaded surveillance mechanism, fixing different ports to monitor different data. The DTC guarantees the parallel transmission of basic data and spectral data as well as improves the efficiency of data analysis.(2) Data storage component (DSC):the spectral data consists of a large numbers of key-value pairs, which means it would waste large space resources and reduce the query efficiency in traditional storage way. Thus for spectral data storage, this thesis adopts attribute fusion instead of the traditional concept of attribute division. The DSC stores the fusing data in normalized way, saving storage space and improving the efficiency of data insertion.(3) Data query component (DQC):the thesis designs the basic data analyzing model and spectral data analyzing model, doing peak calculation, stability evaluation, trend forecasting and so on. Especially for spectral data, the DQC implements numerical rating within the band effectively. Meanwhile according to the measurement, it determines the levels of various nutrients in soil, inferring the real-time growth of crops.