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A method and system for eliminating redundancy of agrometeorological data based on information entropy

A meteorological data, redundancy elimination technology, applied in data processing applications, instruments, calculations, etc., can solve problems such as performance differences, inability to eliminate redundant meteorological data, and low accuracy of agrometeorological disaster assessment, to improve processing speed. , improve the evaluation accuracy, reduce the effect of the search space

Inactive Publication Date: 2019-04-16
GUANGDONG KINGPOINT DATA SCI & TECH CO LTD
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In the existing technology, there are differences in the performance of data preprocessing and data mining analysis, which cannot eliminate redundant data in meteorological data, resulting in low accuracy of agricultural meteorological disaster assessment

Method used

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  • A method and system for eliminating redundancy of agrometeorological data based on information entropy
  • A method and system for eliminating redundancy of agrometeorological data based on information entropy
  • A method and system for eliminating redundancy of agrometeorological data based on information entropy

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0047] A method for eliminating redundancy of agricultural meteorological data based on information entropy, comprising:

[0048] The step of building a decision information table is used to build a decision information table and collect decision data, the decision data includes all object sets, condition attribute sets, decision attribute sets, attribute value sets and system functions, and the collection of decision data sets constitute decision information surface;

[0049] The step of calculating mutual information is used to calculate the mutual information between the conditional attribute set and the decision attribute set, calculate the information entropy of the decision attribute set, calculate the conditional entropy of the decision attribute set and the conditional attribute set, according to the information entropy of the decision attribute set , The conditional entropy of the decision attribute set and the condition attribute set can obtain the mutual information...

Embodiment 2

[0095] Such as Figure 4 Shown: An information entropy-based redundant elimination system for agricultural meteorological data, including:

[0096] 1. Input terminal, including:

[0097] The data collection module is used to collect historical meteorological data, meteorological element attribute data and agricultural disaster data, and send all data to the processor;

[0098] 2. Output terminal, including:

[0099] The data output module is used to receive the nuclear attribute data set R output by the output module of the processor.

[0100] 3. Processor, including:

[0101] The data generation module is used to receive all the collected data, and construct all the data into a decision-making information table DT=(U,C∪D,V,f) that affects agricultural meteorological disasters, where U is the set of all objects, and C is Condition attribute set, D is the decision attribute set, V is the attribute value set, f represents the system function;

[0102] A nuclear attribute da...

Embodiment 3

[0107] This embodiment provides a meteorological disaster assessment model. Based on the agricultural meteorological data redundancy elimination method in Embodiment 1, the meteorological redundant data is eliminated to obtain nuclear attribute data, and the meteorological disaster is realized by mining and analyzing the nuclear attribute data. Evaluate.

[0108] This embodiment also provides six comparative examples, and the six comparative examples and the first embodiment are all subjected to a meteorological disaster simulation evaluation experiment, and the experimental results of the comparative examples are compared with the experimental results of the first embodiment.

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Abstract

The invention relates to the field of agricultural data processing, in particular to a method for eliminating redundancy of agrometeorological data based on information entropy, comprising the steps of constructing a decision information table, collecting all object sets, a condition attribute set, a decision attribute set, an attribute value set and a system function, and constructing a decisioninformation table. Calculating the mutual information amount, and obtaining the mutual information amount according to the information entropy and the conditional entropy; Determining a related attribute, if the attribute a is a related attribute, making the kernel attribute data set R = R U {a}; Determining a kernel attribute data set step of determining a value of the kernel attribute data set Rand outputting the kernel attribute data set R if the mutual information amount between the kernel attribute data set R and the decision attribute set is equal to the mutual information amount between the conditional attribute set and the decision attribute set; Disaster assessment step, performing data mining and analyzing the core attribute data set R, according to the results, assessing agro-meteorological disaster. The invention can improve the processing speed of agrometeorological data and improve the accuracy of agrometeorological disaster assessment.

Description

technical field [0001] The invention relates to the field of agricultural data processing, in particular to a method and system for eliminating redundancy of agricultural meteorological data based on information entropy. Background technique [0002] With the increasingly close influence of the climate environment on agricultural production, how to efficiently process massive meteorological data and improve the scientificity and accuracy of agricultural meteorological disaster assessment has become a hot spot in agricultural meteorological disaster research. The role of agricultural meteorological disaster assessment is mainly through the analysis of Historical meteorological data information can intuitively classify and evaluate the damage level of crops and give early warning of possible disasters to guide farmers to carry out agricultural production reasonably. [0003] The amount of historical meteorological data is huge, and there are many attributes of each element. Th...

Claims

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Application Information

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IPC IPC(8): G06Q50/02
CPCG06Q50/02
Inventor 简宋全何佳宁赵轩秦于钦张清瑞
Owner GUANGDONG KINGPOINT DATA SCI & TECH CO LTD
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