CN-121994311-A - Agricultural environment monitoring method and system based on Internet of things

CN121994311ACN 121994311 ACN121994311 ACN 121994311ACN-121994311-A

Abstract

The invention discloses an agricultural environment monitoring method and system based on the Internet of things, and relates to the field of agriculture, wherein the method comprises the steps of obtaining target environment parameters of a target area, wherein the target environment parameters comprise temperature parameters, humidity parameters, illumination parameters, air quality parameters and soil moisture parameters; calculating an environmental change rate according to the target environmental parameters, wherein the environmental change rate is the ratio of the absolute value of the difference value of the environmental parameters at the current moment and the last moment to the time interval of the two moments, analyzing a historical target environmental parameter set of a target area to conduct environmental change prediction, generating a first threshold and a second threshold of the environmental change rate, and adjusting the acquisition frequency of the environmental parameters of the target area by combining the first threshold, the second threshold and the environmental change rate. The invention solves the problems of high energy consumption, data redundancy or untimely monitoring caused by fixed acquisition frequency in the prior art.

Inventors

MA HONG
LI ZENGPENG
ZHAO WENJU
CHEN ZHIHUA
YANG SHENGYI

Assignees

酒泉职业技术大学

Dates

Publication Date: 20260508
Application Date: 20260408

Claims (10)

1. The agricultural environment monitoring method based on the Internet of things is characterized by comprising the following steps of: acquiring target environmental parameters of a target area, wherein the target environmental parameters comprise temperature parameters, humidity parameters, illumination parameters, air quality parameters and soil moisture parameters; Calculating an environmental change rate according to the target environmental parameter, wherein the environmental change rate is the ratio of the absolute value of the difference value of the environmental parameter at the current moment and the environmental parameter at the last moment to the time interval between the two moments; analyzing a historical target environment parameter set of a target area to predict environment change, and generating a first threshold value and a second threshold value of the environment change rate; And adjusting the acquisition frequency of the environmental parameters of the target area by combining the first threshold value, the second threshold value and the environmental change rate, wherein an adjustment rule is that low-frequency acquisition is adopted when the environmental change rate is smaller than or equal to the first threshold value, high-frequency acquisition is adopted when the environmental change rate is larger than or equal to the second threshold value, and reference acquisition is adopted when the environmental change rate is between the first threshold value and the second threshold value.
2. The internet of things-based agricultural environment monitoring method of claim 1, wherein the first threshold and the second threshold are both positive numbers, and the first threshold is smaller than the second threshold.
3. The method for monitoring agricultural environment based on the internet of things according to claim 2, wherein the obtaining the target environment parameter of the target area comprises: acquiring the environmental parameters of the target area by a sensor at the current acquisition frequency to obtain initial environmental parameters; Acquiring adjacent domain environmental parameters of a plurality of adjacent domains, calculating an average value of the adjacent domain environmental parameters, and carrying out initial calibration on the initial environmental parameters based on the average value of the adjacent domain environmental parameters to obtain an initial calibration value; And when the difference value is larger than a preset difference value, carrying out secondary calibration on the initial calibration value to obtain a target environment parameter, and when the difference value is smaller than or equal to the preset difference value, taking the initial calibration value as the target environment parameter.
4. The internet of things-based agricultural environment monitoring method of claim 3, wherein the expression of the initial calibration is: , , Wherein the method comprises the steps of The initial calibration value is indicated as such, The initial environmental parameter is represented by a set of parameters, Representing weight parameters of 0.6-0% ≤0.8, Represents the average value of the environmental parameters of the adjacent domain, The environmental parameter value representing the ith vicinity and n represents the number of vicinities.
5. The agricultural environment monitoring method based on the internet of things according to claim 4, wherein the expression of the secondary calibration is: , Wherein the method comprises the steps of Representing the value of the secondary calibration, The initial calibration value is indicated as such, Representing weight parameters, wherein 0.4 is less than or equal to ≤0.6, Representing the predicted value of the environmental parameter.
6. The agricultural environment monitoring method based on the internet of things according to claim 5, wherein the environment parameter prediction model is obtained by training a random forest regression model as a basic model, and the training process is as follows: Acquiring a historical actual environment parameter set of a target area and a plurality of adjacent areas thereof, preprocessing the historical actual environment parameter set, and acquiring the historical actual environment parameters by a sensor group; Dividing the preprocessed historical environment parameter set into a training set, a verification set and a test set according to the ratio of 7:2:1, training a basic model, taking actual environment parameters of a target area and the adjacent area as input, taking the actual environment parameters of the target area at the next moment as output, adjusting model parameters through the verification set, verifying model accuracy through the test set, and finally obtaining an environment parameter prediction model.
7. The internet of things-based agricultural environment monitoring method of claim 6, wherein the expression for calculating the rate of change of the environment from the target environment parameter is: , Wherein the method comprises the steps of Indicating the rate of change of the environment, The environmental value representing the current time of day, The environmental value at the last time point is indicated, Representing a time interval.
8. The internet of things-based agricultural environment monitoring method of claim 7, wherein analyzing the historical target environment parameter set of the target area for environment change prediction, generating the first threshold and the second threshold for the environment change rate comprises: Acquiring a time sequence data set of a target area, wherein the time sequence data set at least comprises target environment parameter data of a plurality of continuous months, and the time stamps are orderly arranged; Performing data preprocessing on the time sequence data set; based on the preprocessed time sequence data set, predicting future environmental parameters through a preset time sequence prediction model to obtain a future environmental parameter sequence; Calculating a future environmental change rate set based on the future environmental parameter sequence, and calculating a first threshold and a second threshold of the environmental change rate according to the statistical characteristics of the future environmental change rate set; the calculation formulas of the first threshold T1 and the second threshold T2 are as follows: , Wherein the method comprises the steps of Representing the mean of the set of future environmental change rates, Represents the standard deviation of the future set of environmental change rates, And Representing a preset positive coefficient of 0.3 to less than or equal to ≤0.8,0.8≤ ≤1.5。
9. The agricultural environment monitoring method based on the internet of things according to claim 8, wherein the time series prediction model is obtained by training an LSTM time series model as a base model, and the training process is as follows: Acquiring historical target environment parameters of a target area, constructing time sequence characteristics, generating a historical target data set containing time sequence changes, and preprocessing the historical target i data set; Dividing the preprocessed historical environment parameter set into a training set, a verification set and a test set according to the ratio of 7:2:1, training the basic model, taking the historical target environment parameter as input, taking the future environment parameter sequence as output, adjusting the model parameter through the verification set, verifying the model precision through the test set, and finally obtaining the time sequence prediction model.
10. Agricultural environment monitoring system based on thing networking, characterized by, include: The acquisition module is used for acquiring target environmental parameters of a target area, wherein the target environmental parameters comprise temperature parameters, humidity parameters, illumination parameters, air quality parameters and soil moisture parameters; The calculating module calculates the environmental change rate according to the target environmental parameter, wherein the environmental change rate is the ratio of the absolute value of the difference value of the environmental parameter at the current moment and the environmental parameter at the last moment to the time interval of the two moments; The prediction module is used for analyzing a historical target environment parameter set of the target area to predict the environment change so as to generate a first threshold value and a second threshold value of the environment change rate; The adjusting module is used for adjusting the acquisition frequency of the environmental parameters of the target area by combining the first threshold value, the second threshold value and the environmental change rate, wherein an adjusting rule is that low-frequency acquisition is adopted when the environmental change rate is smaller than or equal to the first threshold value, high-frequency acquisition is adopted when the environmental change rate is larger than or equal to the second threshold value, and reference acquisition is adopted when the environmental change rate is between the first threshold value and the second threshold value.

Description

Agricultural environment monitoring method and system based on Internet of things Technical Field The invention relates to the field of agriculture, in particular to an agricultural environment monitoring method and system based on the Internet of things. Background With the continuous improvement of the agricultural informatization level, the real-time monitoring of the agricultural environment by utilizing the internet of things technology has become an important means of modern agricultural management. The agricultural environment monitoring system generally collects environmental data such as temperature, humidity, illumination, soil moisture and the like by deploying various environment sensors, and transmits the data to a remote server for storage and analysis through a wireless communication network. The existing agricultural environment monitoring method mainly adopts fixed frequency to collect environment parameters, and has obvious limitations that on one hand, when the environment parameters change smoothly, the fixed high frequency collection can cause the increase of energy consumption of a sensor and the redundancy of data, so that the monitoring cost is improved, and on the other hand, when the environment parameters change suddenly, the fixed low frequency collection cannot capture the environment changes in time, so that the crop growth is adversely affected, and the requirement of accurate monitoring cannot be met. In addition, the existing agricultural environment monitoring system is designed by adopting a single module, the cooperativity among the modules is poor, the full-flow automation of environment parameter collection, processing, prediction and adjustment cannot be realized, more manual intervention is needed, the monitoring efficiency is reduced, and the monitoring requirement of large-scale agricultural planting is difficult to adapt. Disclosure of Invention The embodiment of the invention provides an agricultural environment monitoring method based on the Internet of things, which aims to at least partially solve the technical problems. In order to achieve the above object, in a first aspect, the present invention provides an agricultural environment monitoring method based on the internet of things, including: acquiring target environmental parameters of a target area, wherein the target environmental parameters comprise temperature parameters, humidity parameters, illumination parameters, air quality parameters and soil moisture parameters; Calculating an environmental change rate according to the target environmental parameter, wherein the environmental change rate is the ratio of the absolute value of the difference value of the environmental parameter at the current moment and the environmental parameter at the last moment to the time interval between the two moments; analyzing a historical target environment parameter set of a target area to predict environment change, and generating a first threshold value and a second threshold value of the environment change rate; And adjusting the acquisition frequency of the environmental parameters of the target area by combining the first threshold value, the second threshold value and the environmental change rate, wherein an adjustment rule is that low-frequency acquisition is adopted when the environmental change rate is smaller than or equal to the first threshold value, high-frequency acquisition is adopted when the environmental change rate is larger than or equal to the second threshold value, and reference acquisition is adopted when the environmental change rate is between the first threshold value and the second threshold value. With reference to the first aspect, in one possible implementation manner, the first threshold value and the second threshold value are both positive numbers, and the first threshold value is smaller than the second threshold value. With reference to the first aspect, in a possible implementation manner, the acquiring a target environmental parameter of a target area includes: acquiring the environmental parameters of the target area by a sensor at the current acquisition frequency to obtain initial environmental parameters; Acquiring adjacent domain environmental parameters of a plurality of adjacent domains, calculating an average value of the adjacent domain environmental parameters, and carrying out initial calibration on the initial environmental parameters based on the average value of the adjacent domain environmental parameters to obtain an initial calibration value; And when the difference value is larger than a preset difference value, carrying out secondary calibration on the initial calibration value to obtain a target environment parameter, and when the difference value is smaller than or equal to the preset difference value, taking the initial calibration value as the target environment parameter. With reference to the first aspect, in a possible implementation manner, the expressio