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Multi-granularity coal mine gas risk prediction method based on cloud model

A technology for coal mine gas and risk prediction, which is applied in measurement devices, complex mathematical operations, instruments, etc., can solve problems such as the lack of research on the mutual conversion relationship between quantitative analysis and qualitative evaluation, and the difficulty in effectively guiding quantitative production practice in coal mine risk assessment.

Active Publication Date: 2020-09-18
CHONGQING UNIV OF POSTS & TELECOMM
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0003] The above mining of coal mine data is generally carried out at a fixed granularity level, and there is a lack of research on the mutual conversion relationship between quantitative analysis and qualitative evaluation, which makes it difficult for qualitative coal mine risk assessment to effectively guide quantitative production practice.

Method used

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  • Multi-granularity coal mine gas risk prediction method based on cloud model
  • Multi-granularity coal mine gas risk prediction method based on cloud model
  • Multi-granularity coal mine gas risk prediction method based on cloud model

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Embodiment 1

[0058] This embodiment mainly illustrates the improved algorithm-adaptive hybrid cloud transformation algorithm (A_MCT algorithm) proposed by the present invention on the adaptive Gaussian cloud transformation algorithm (A-GCT algorithm) that is suitable for coal mine safety big data, and mainly involves steps S1~ S3, through the dataset sample set {x i |i=1,2,...,N}, the upper limit of concept ambiguity β, the threshold of data distribution skewness γ, and finally output qualitative concepts represented by various cloud models, specifically including the following steps:

[0059] Step1: Calculate the skewness of the data distribution and perform logarithmic transformation

[0060] Set a data distribution skewness threshold γ to consider the overall data distribution and count the frequency distribution of the original data p(x i ), calculate the skewness Δp of the original data distribution, if Δp>γ, then transform the frequency distribution of the original data, expressed a...

Embodiment 2

[0071] This embodiment mainly illustrates the construction of time granules and space granules in the present invention.

[0072] Step1: time grain

[0073] Formulate granular standards, that is, the minute granularity is the real-time change of the sensor monitoring value, the hour granularity is the situation fluctuation and trend within the current hour period, the day granularity is the situation distribution in each period of the current day, and the monthly granularity is the current month. The situation fluctuates, and the annual granularity is the overall concentration situation of the year;

[0074] Based on the granulation standard, the original data is divided into p time slices {T 1 , T 2 ,...,T i ,...,T p}; where ω is the window size of the time slice, and its different time slice sizes correspond to different time granularities; the real-time data of the coal mine gas concentration obtained is one node per minute, and the width of the time slice is controlled...

Embodiment 3

[0084] This embodiment proposes a calculation method of the degree of membership.

[0085] Before calculating the degree of membership, it is necessary to judge the representation cloud of each time granular layer Whether the expected value Ex of the middle time grain belongs to obtain the original data macro-concept interval according to embodiment 1, that is, in the interval formed by the expected maximum value and minimum value of multiple hybrid clouds of the original data;

[0086] If it belongs, that is, the time grain belongs to the domain of discourse interval of the corresponding concept, calculate its expected degree of membership in the concept of membership μ=exp(-(Ex'-Ex)) 2 / 2*(En') 2 , get multi-granularity representation;

[0087] Among them, Ex is the expectation of time grain, Ex' is the expectation of the macro concept to which it belongs, En' is the entropy of the macro concept to which it belongs, Ex k is the expectation of a Gaussian cloud, En k is the...

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Abstract

The invention relates to a multi-granularity coal mine gas risk prediction method based on a cloud model. The method comprises the following steps: generating a plurality of Gaussian clouds of which the ambiguity is smaller than a threshold value according to original data; converting the concept that the discourse domain boundary is represented by Gaussian cloud into semi-trapezoidal cloud, and finally generating a plurality of hybrid clouds representing the macroscopic concept of the original data; discretizing the data set according to a coal mine supervision time framework, and calling a reverse cloud generator to convert each discrete time slice into time particles with semantics; discretizing the data set according to a coal mine supervision space architecture, and calling a reversecloud generator to convert each discrete space slice into space particles with semantics; calculating a corresponding macroscopic concept and a membership degree to which each time grain belongs; constructing cloud rule reasoning according to the multi-granularity representation result of the coal mine gas concentration, and predicting the gas concentration in a short period of time; the method can fully consider the real production environment and efficiently process massive coal mine safety production data, and belongs to the technical field of data analysis.

Description

technical field [0001] The invention belongs to the field of industrial safety process control and decision-making, and in particular relates to a cloud model-based multi-granularity coal mine gas risk prediction method. Background technique [0002] Coal is widely distributed in my country, and the buried geological conditions are complex. For a long time, the coal industry has been the industry with the most serious safety accidents in my country. With the strengthening of coal mine supervision and supervision in my country, the intelligence of digital mines is also developing steadily. In the process of informatization construction, although a large amount of coal mine safety production data has been accumulated, there are few corresponding methods or models based on safety production, which cannot provide reliable decision-making information support for coal mine safety management. How to analyze and process these massive coal mine risk data resources, and discover potent...

Claims

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Application Information

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IPC IPC(8): G06F17/18G01N33/00
CPCG06F17/18G01N33/0075G01N33/0062G01N33/0068
Inventor 代劲张磊胡峰
Owner CHONGQING UNIV OF POSTS & TELECOMM