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Pipeline Safety Event Identification and Knowledge Mining Method Based on HMM Model

A technology of knowledge mining and security events, applied in character and pattern recognition, visual data mining, computer parts, etc., can solve the problems of low recognition rate of pipeline security events, inability to predict events, and inability to obtain the dynamic time sequence change rules of events. , to achieve the effect of high event recognition rate and event recognition rate improvement

Active Publication Date: 2021-09-21
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The purpose of the present invention is: the present invention provides a pipeline safety event identification and knowledge mining method based on the HMM model, which solves the problem that the existing method based on static feature analysis cannot obtain the event dynamic timing change law, resulting in a low recognition rate of pipeline safety events and the inability to analyze The problem of making predictions about ongoing events

Method used

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  • Pipeline Safety Event Identification and Knowledge Mining Method Based on HMM Model
  • Pipeline Safety Event Identification and Knowledge Mining Method Based on HMM Model
  • Pipeline Safety Event Identification and Knowledge Mining Method Based on HMM Model

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Embodiment 1

[0104] Such as Figure 1-10 As shown, the present invention realizes long-distance pipeline safety monitoring based on the distributed optical fiber acoustic wave / vibration sensing system (DVS / DAS) of linear phase demodulation, and the application system structure and its working principle are as follows: figure 1 shown. The system hardware includes three parts: detection optical cable, optical signal demodulation equipment and signal processing host; the detection optical cable usually adopts ordinary single-mode communication optical fiber, which is buried along underground pipelines, transmission cables, and urban roads, and can also be directly used along pipelines or roads. Spare fiber cores of laid communication optical cables. The internal components of the optical signal demodulation equipment include two types of optical devices and electrical devices. The ultra-narrow linewidth laser generates a continuous coherent optical signal, which is modulated into an optical ...

Embodiment 2

[0113] Based on embodiment 1, the specific steps of step S1 are as follows:

[0114] The typical event database of each spatial point is constructed based on the typical physical event samples collected at each spatial point, and the specific steps are as follows:

[0115] In this embodiment, typical events in pipeline safety monitoring include five categories: background noise, manual excavation, mechanical excavation, traffic interference and factory interference that are easily misjudged, and the event labels are set to 1, 2, 3, 4, and 5 in sequence. Based on the time signals of different types of events collected continuously at each space point, they are divided into a short-time signal unit SU according to a fixed time length. The length of the short-time signal is set according to the actual application. In this embodiment, it is set to 1s; Unit, from the initial moment of a certain type of event, continuously accumulate L short-term signal units to form a long-term sig...

Embodiment 3

[0125] Based on embodiment 1, the specific steps of step S2 are as follows:

[0126] Use the feature vector sequence to train the typical event HMM model offline to build the typical event HMM model library; based on the feature vector sequence set of typical events, train the parameters of various event HMM models offline, and build the offline typical event HMM model library for pipeline safety monitoring, offline Training includes two steps: initialization of model parameters and iterative update:

[0127] (1) Initialize model parameters

[0128] Usually an HMM model is recorded as:

[0129] λ=(π,A,B) (4)

[0130] Among them, π is the initial probability distribution vector, π=(π 1 , π 2 ,Λ,π N ),

[0131] π i =P(q t = θ i ), 1≤i≤N (5)

[0132] A is the state transition probability matrix, A=(a ij ) N×N ,

[0133] a ij =P(q t+1 = θ j |q t = θ i ),1≤i,j≤N (6)

[0134] B is the observation value probability matrix, B=(b j (o)),

[0135]

[0136]

[01...

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Abstract

The invention discloses a pipeline safety event identification and knowledge mining method based on an HMM model, which belongs to the field of pipeline safety event monitoring; the method includes step 1: extracting multi-domain features of signals collected by each spatial point, and obtaining a feature vector sequence of signals; steps 2: Input the feature vector sequence into the HMM model for offline training, and complete the establishment of the typical event HMM model library; Step 3: After obtaining the current feature vector sequence of the signal to be identified through step 1, input it into the typical event HMM model library for identification and output Event judgment type, and calculate the optimal hidden state sequence at the same time as the event state sequence evolution process information for output to complete knowledge mining; the HMM model of the present invention analyzes and identifies based on the characteristic time sequence, effectively improving the event recognition rate; at the same time, the event state sequence evolution The process implements knowledge mining for short-term forecasting.

Description

technical field [0001] The invention belongs to the field of pipeline safety event monitoring, in particular to a pipeline safety event identification and knowledge mining method based on an HMM model. Background technique [0002] The urban underground pipe network, such as water pipes, air pipes, heating pipes and oil pipes, is the blood vessel and lifeblood of the city. Its safe operation is an important guarantee for the safety of people's lives and property. Due to frequent human theft, mechanical construction and other external force damage, pipeline leakage accidents Multiple occurrences threaten the safety of pipelines, causing immeasurable economic losses and the safety of people's lives and properties. Using the Internet of Things technology to monitor the underground pipeline network in real time, timely detect and alarm potential safety hazards such as external force damage, and prevent problems before they happen are important issues that need to be solved urgen...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/26G06F30/20G06K9/62G06K9/00
CPCG06F30/20
Inventor 吴慧娟刘香荣肖垚杨明儒陈吉平饶云江
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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