Method and device for monitoring grain condition in bulk grain container

Abstract

The invention discloses a method and device for monitoring grain condition in a bulk grain container, and the method comprises the steps: collecting temperature and humidity data, obtaining the temperature and humidity data of the environment where grains are located in real time, transmitting the temperature and humidity data to a collection terminal, marking a time label on the received temperature and humidity data by the collection terminal to generate timing temperature and humidity data; transmitting the timing temperature and humidity data to a remote server; performing temperature and humidity data processing: enabling the remote server to perform filtering and wavelet analysis on the timing temperature and humidity data, interpolate and eliminate repeated data, and obtain continuous temperature and humidity data of each acquisition node; and performing temperature and humidity data fusion: fusing the continuous temperature and humidity data by using a k-medoids clustering algorithm, carrying out fusion once in each acquisition period, fusing the continuous temperature and humidity data of the plurality of acquisition nodes into a piece of continuous overall temperature and humidity data, and reflecting the overall state of the environment in which the grains are located by the overall temperature and humidity data. The invention further discloses a monitoring device applying the monitoring method.

Description

technical field [0001] The invention relates to the application of agricultural big data technology, in particular to a method and a monitoring device for monitoring grain conditions in a bulk grain container based on a k-medoids clustering algorithm. Background technique [0002] With the rapid improvement of grain production capacity in Northeast China, the construction of a comprehensive transportation network of highways, railways and waterways, and changes in market supply and demand, the pattern of "transporting grain from north to south" has quietly formed, and the amount of grain transported across provinces is increasing day by day. In this context, containers have become the main means of transportation for "transporting grain from north to south" because of its safety, economy, and ease of multimodal intermodal transport. However, during the transportation process, the information and intelligent monitoring methods of the container are lacking, and the grain situa...

AI Technical Summary

Problems solved by technology

However, during the transportation process, the information and intelligent monitoring methods of the container are lacking, and it is impossible to perceive the grain situation in the container in real time. The quality of the stored grain is difficult to monitor, and there are problems such as high transportation cost and low efficiency.

[0003] The bulk grain container monitoring method in the prior art mainly uses WSN (Wireless Sensor Network) technology, although it has the advantages of low system operating cost and rapid system update, but due to the limited communication capability of the collection nodes, the communication bandwidth is narrow and variable Frequent, susceptible to external factors

Due to the different monitoring data of the sensors distributed in the container at different times and different spatial locations during transportation, the difference in the monitoring results makes it impossible to use a single sensor to monitor the environmental parameters of the container to meet the system accuracy requirements. Grain situation, monitoring the quality of grain, so it is necessary to use multi-sensors to collect multi-point monitoring values ​​to comprehensively judge the environmental status of refrigerated cars, but the use of a large number of sensors is accompanied by the increase of data dimensions and data redundancy

[0004] At the same time, the existing technology uses the arithmetic mean method for calculation, but the arithmetic mean method must be based on accurate and reliable data. Some sensors are affected by environmental interference, faults, random errors, etc., resulting in low precision and unstable monitoring data. , the arithmetic mean method cannot accurately reflect the actual state in the container

Therefore, the data collected by multi-sensors can not meet the requirements of system monitoring by adopting the data processing method of the prior art.

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