Greenhouse intelligent decision-making method based on rough set theory and D-S evidence theory

Abstract

The invention discloses a greenhouse intelligent decision-making method based on a rough set theory and a D-S evidence theory, and relates to the technical field of intelligent agriculture, and the method comprises the steps: obtaining processed data through fuzzy C-means clustering processing, kernel solving and rough set attribute reduction; constructing a basic probability distribution function by using the rough set, and calculating the support degree among the influence factors of the greenhouse; and applying an improved D-S evidence theory, introducing the calculated BPA elementary probability assignment matrix, and constructing a confidence coefficient matrix to complete the combination of the greenhouse influence factors to obtain a decision result. A traditional SVM algorithm for small sample machine learning is utilized, a BPA basic probability assignment matrix is introduced, a decision result is obtained, and algorithm comparison verification is carried out on the decision result and a D-S evidence theory algorithm.

Description

technical field

[0001] The invention relates to the technical field of intelligent agriculture, in particular to a greenhouse intelligent decision-making method based on rough set theory and D-S evidence theory. Background technique

[0002] Agriculture is an important pillar industry of the country, and the improvement of intelligent decision-making technology of greenhouse influencing factors is the top priority of my country's agricultural development. The intelligent decision-making technology of greenhouse influencing factors is mainly to regulate the external conditions of crop growth. According to different crops, each influencing factor is different, and it is necessary to provide the correct influencing factors to adjust and ensure the good growth of crops.

[0003] Due to the influence of various unstable factors such as changes in the influencing factors in the greenhouse and internal hardware problems of the monitoring equipment, the original data obtained by th...

Examples

Embodiment 1

[0076] According to the specific influencing factors information of the selected greenhouses in Northwest China, the expert knowledge table of greenhouse influencing factors control is obtained, as shown in Table 1. There are a total of 12 groups of samples, each group of samples has 6 greenhouse influencing factors and passed 6 The only decision result is judged by the influencing factors. The greenhouse influencing factors are composed of six factors: temperature, humidity, light intensity, soil temperature, soil humidity, and carbon dioxide volume fraction. The decision results include 4 categories such as 1, 2, 3, and 4. , which are to open the roll film, open the roll film and start the fan, start the fan and the cooling curtain, and do not act. The data in Table 1 contain greenhouse impact factor indicators and corresponding decision-making results, but it is difficult for users to understand the information contained in the greenhouse impact factor data, so it is difficu...

Embodiment 2

[0084] According to the attribute reduction algorithm based on information entropy, formulate its domain of discourse, conditional attributes of temperature influencing factors and decision result set, calculate the kernel of temperature influencing factor conditional attributes relative to decision results, and reduce the attributes of the kernel to obtain the reduction The simplified data paved the way for the fusion of D-S evidence theory.

[0085] In the decision-making of intelligent control of greenhouse influencing factors, the influencing factors in the whole framework indicate which control method should be adopted. Therefore, for Table 1, the entire framework can be written as {L(k), k=1, 2, 3, 4}, where k is the result of 4 kinds of decisions. The basic credibility distribution function under the power set of the greenhouse influencing factors control decision recognition framework represents the support degree of the greenhouse influencing factors information for var...

Embodiment 3

[0104] The SVM algorithm for small-sample machine learning is selected for comparison with the decision-making method based on rough sets and D-S evidence theory.

technical field

[0001] The invention relates to the technical field of intelligent agriculture, in particular to a greenhouse intelligent decision-making method based on rough set theory and D-S evidence theory. Background technique

[0002] Agriculture is an important pillar industry of the country, and the improvement of intelligent decision-making technology of greenhouse influencing factors is the top priority of my country's agricultural development. The intelligent decision-making technology of greenhouse influencing factors is mainly to regulate the external conditions of crop growth. According to different crops, each influencing factor is different, and it is necessary to provide the correct influencing factors to adjust and ensure the good growth of crops.

[0003] Due to the influence of various unstable factors such as changes in the influencing factors in the greenhouse and internal hardware problems of the monitoring equipment, the original data obtained by th...

Examples

Embodiment 1

[0076] According to the specific influencing factors information of the selected greenhouses in Northwest China, the expert knowledge table of greenhouse influencing factors control is obtained, as shown in Table 1. There are a total of 12 groups of samples, each group of samples has 6 greenhouse influencing factors and passed 6 The only decision result is judged by the influencing factors. The greenhouse influencing factors are composed of six factors: temperature, humidity, light intensity, soil temperature, soil humidity, and carbon dioxide volume fraction. The decision results include 4 categories such as 1, 2, 3, and 4. , which are to open the roll film, open the roll film and start the fan, start the fan and the cooling curtain, and do not act. The data in Table 1 contain greenhouse impact factor indicators and corresponding decision-making results, but it is difficult for users to understand the information contained in the greenhouse impact factor data, so it is difficu...

Embodiment 2

[0084] According to the attribute reduction algorithm based on information entropy, formulate its domain of discourse, conditional attributes of temperature influencing factors and decision result set, calculate the kernel of temperature influencing factor conditional attributes relative to decision results, and reduce the attributes of the kernel to obtain the reduction The simplified data paved the way for the fusion of D-S evidence theory.

[0085] In the decision-making of intelligent control of greenhouse influencing factors, the influencing factors in the whole framework indicate which control method should be adopted. Therefore, for Table 1, the entire framework can be written as {L(k), k=1, 2, 3, 4}, where k is the result of 4 kinds of decisions. The basic credibility distribution function under the power set of the greenhouse influencing factors control decision recognition framework represents the support degree of the greenhouse influencing factors information for var...

Embodiment 3

[0104] The SVM algorithm for small-sample machine learning is selected for comparison with the decision-making method based on rough sets and D-S evidence theory.

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