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Coal mine disaster risk prediction method and system based on semantic recognition

A technology of risk prediction and semantic recognition, which is applied in the field of coal mine disaster risk prediction based on semantic recognition, can solve the problems of visually reflecting the degree of hidden danger influence, not establishing a correlation analysis model, and the degree of correlation between coal mine hidden dangers and accidents is not high, so as to improve the accuracy , reduce the risk of accidents, reduce the effect of the probability of occurrence

Pending Publication Date: 2022-04-22
应急管理部信息研究院
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] Second, at this stage, the hidden dangers of coal mines are not closely related to accidents
At present, coal mines carry out hidden danger investigation work, but there is no correlation analysis model established between various hidden dangers and accidents, and the logical relationship between accident risks and hidden dangers cannot be better realized. When investigating hidden dangers, it is impossible to dynamically and intuitively reflect the impact of hidden dangers. The degree of influence of the corresponding accident risk

Method used

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  • Coal mine disaster risk prediction method and system based on semantic recognition
  • Coal mine disaster risk prediction method and system based on semantic recognition
  • Coal mine disaster risk prediction method and system based on semantic recognition

Examples

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Embodiment 1

[0053] Such as figure 1 As shown, the present application provides a method for predicting coal mine disaster risk based on semantic recognition, which includes the following steps:

[0054] Step S1, constructing a coal mine feature extraction model in advance.

[0055] Such as figure 2 As shown, step S1 includes the following sub-steps:

[0056]Step S110, obtaining a hidden danger text vector training set.

[0057] Such as image 3 As shown, step S110 includes the following sub-steps:

[0058] Step S111, obtaining a training set of hidden danger texts marked manually.

[0059] Specifically, obtaining the hidden danger text training set of the coal mine includes: obtaining the hidden danger text information of the coal mine in a recent period (for example: 60 days), and performing manual classification labeling.

[0060] Among them, the categories are classified by experts based on experience and are likely to cause coal mine disasters. The categories include: detonatio...

Embodiment 2

[0122] Such as Figure 6 As shown, the present application provides a coal mine disaster risk prediction system 100 based on semantic recognition, which includes:

[0123] The training data acquisition module 10 is used to obtain historical coal mine accident data and coal mine non-accident data; according to the pre-built coal mine feature extraction model, extract the coal mine characteristic data in the historical coal mine accident data and coal mine non-accident data, as a coal mine risk prediction training set ;

[0124] Model building module 20, is used for coal mine risk prediction training set input in logistic regression model and trains, obtains coal mine risk prediction model;

[0125] Real-time data acquisition module 30, acquires coal mine hidden danger text data and measuring point real-time data;

[0126] The risk assessment data acquisition module 40 is used to extract the coal mine hidden danger text data and the coal mine feature vector in the real-time da...

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Abstract

The invention provides a coal mine disaster risk prediction method and system based on semantic recognition, and the method comprises the following steps: extracting coal mine feature data in historical coal mine accident data and coal mine non-accident data according to a pre-constructed coal mine feature extraction model, and taking the coal mine feature data as a coal mine risk prediction training set; inputting the coal mine risk prediction training set into a logistic regression model for training to obtain a coal mine risk prediction model; according to a coal mine feature extraction model, coal mine feature vectors in the coal mine hidden danger text information and the measuring point data are extracted to serve as coal mine risk evaluation data; and calling a coal mine risk prediction model, and performing risk prediction on the extracted coal mine risk evaluation data. According to the method, fusion of coal mine text data and measuring point data is fully considered, a dynamic association relationship between accident risks and hidden dangers is established, the underground hidden dangers and risks of the coal mine are tracked in real time, the coal mine safety management level and the hidden danger disposal efficiency are improved, accident occurrence key links are blocked, and coal mine disaster accident risks are reduced.

Description

technical field [0001] This application relates to the technical field of coal mine safety risk assessment, in particular to a method and system for predicting coal mine disaster risks based on semantic recognition. Background technique [0002] In recent years, with the strengthening of management in standardization construction of coal mines, the popularization and application of the “Trinity” (integrated management of coal mine risks, hidden dangers and safety standardization) system in coal mines has changed the traditional hidden danger management mechanism and process. With the improvement of the underground communication network of coal mines and the use of explosion-proof mobile terminals, safety management personnel can check the hidden danger information on the spot, and can enter the "Trinity" system through the explosion-proof mobile phone terminal, and the system will automatically transmit the hidden danger information to the safety management department. Recti...

Claims

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Application Information

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IPC IPC(8): G06F40/30G06F16/35G06Q10/04G06Q50/02
CPCG06F40/30G06F16/35G06Q10/04G06Q50/02
Inventor 王鹏付恩三田乐逍陈佳林疏礼春王刚王新会张倩汪鹏
Owner 应急管理部信息研究院
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