Grain condition forecasting and early warning method and system based on SVM

Abstract

The invention relates to a grain condition forecasting and early warning method and system based on the SVM. The method includes the steps of setting multiple parameters which affect security levels of the grain condition, forming a standardized historical data sample, setting up a forecasting model based on an SVM regression model, collecting data of all the parameters, obtaining a forecasting result of the security level of the grain condition through the forecasting model, judging whether the change trend of the forecasting result of the security level of the grain condition is normal or not, if yes, sending the forecasting result of the security level of the grain condition to an upper computer, and if not, sending out an alarm signal. According to the method and the system, comprehensive analysis can be conducted on the collected grain condition according to the set forecasting model, and therefore the change trend of the security level of the grain condition can be forecasted; when the grain condition is abnormal, an alarm is given so that administrative staff can be prompted to do preparation work for improving the grain condition in advance, the timely foundation is provided for the control strategy of a grain condition monitoring and control system, and reliability of the monitoring and control system is improved.

Description

technical field [0001] The invention relates to the field of grain storage safety monitoring, in particular to an SVM-based grain situation prediction and early warning method and system. Background technique [0002] The prediction and early warning method is to comprehensively process the information and data from the sensors, find out the hidden laws and reveal them through mathematical models, so as to achieve more accurate and reliable control of the object. Existing methods and systems for grain situation forecasting usually use a single sensor (such as a temperature sensor) to complete the collection of grain situation signals. Even if multiple sensors (types) are used, they only reflect target information in isolation from different sides. In fact, the grain situation is jointly determined by factors such as temperature, humidity, moisture, insect damage, mold, etc. These factors must be considered comprehensively at the same time to obtain a conclusion that fully re...

AI Technical Summary

Problems solved by technology

[0003] The technical problem to be solved by the present invention is to provide an SVM-based grain situation forecasting and early warning method and system, which solves the problem that the grain situation forecasting method and forecasting system in the prior art cannot comprehensively consider various factors affecting the grain situation security situation, and the prediction results unreliable technical issues

Images