CN-122024418-A - Underground pipeline risk early warning system and method based on muon flux abnormality

CN122024418ACN 122024418 ACN122024418 ACN 122024418ACN-122024418-A

Abstract

The invention belongs to the technical field of underground engineering safety monitoring, and provides an underground pipeline risk early warning system and method based on abnormal muon flux, wherein the technical scheme comprises a sensing layer for collecting muon flux data and environmental parameters in real time; the system comprises a sensing layer, an edge computing layer, a network transmission layer, a cloud platform, a risk assessment and classification layer, wherein the sensing layer is used for sensing data of the sensing layer, the edge computing layer is used for carrying out data preprocessing and dynamic benchmark correction on real-time acquired muon flux data and environment parameters and calculating flux abnormality indexes, the network transmission layer is used for constructing a data communication channel, adopting a layered protocol stack to carry out data transmission and remote control on the sensing layer and the edge computing layer, the cloud platform is used for extracting multidimensional features reflecting the state of an underground pipeline based on the data subjected to the dynamic benchmark correction, carrying out risk assessment and classification based on the extracted multidimensional features reflecting the state of the underground pipeline, and generating a multistage early warning response strategy according to a risk assessment classification result. The early recognition problem of risks such as pipeline leakage and cavities in the urban complex environment is solved.

Inventors

XUE YIGUO
LU LONGFEI
KONG FANMENG
GUAN ZHENXIANG
SUN WENBIN
WANG HUAIBING

Assignees

中国地质大学（北京）

Dates

Publication Date: 20260512
Application Date: 20251229

Claims (10)

1. Underground pipeline risk early warning system based on muon flux abnormality, characterized by comprising: The sensing layer is used for collecting muon flux data and environmental parameters in real time; the edge calculation layer is used for carrying out data preprocessing and dynamic reference correction on the muon flux data and the environmental parameters acquired in real time, and calculating to obtain flux abnormality indexes; The network transmission layer is used for constructing a data communication channel, and adopts a layered protocol stack to carry out data transmission and remote control on the data of the sensing layer and the edge calculation layer; The cloud platform is used for extracting multidimensional features reflecting the state of the underground pipeline based on the data corrected by the dynamic reference, performing risk assessment and classification based on the extracted multidimensional features reflecting the state of the underground pipeline, and generating a multi-stage early warning response strategy according to the risk assessment classification result.
2. The underground pipeline risk early warning system based on the muon flux anomaly as set forth in claim 1, wherein in the sensing layer, muon flux data and environmental parameters are collected in real time based on a constructed monitoring network infrastructure, wherein the construction of the monitoring network infrastructure comprises: and arranging muon detector nodes at set intervals along the underground pipeline to be monitored to ensure that the detector arrays and the pipeline trend are arranged in parallel, and configuring 3 layers of orthogonally arranged detectors and silicon photomultiplier sensor arrays for each monitoring node.
3. An underground pipeline risk early warning system based on abnormal muon flux as set forth in claim 1 wherein, in the edge calculation layer, when the preprocessed muon flux data is subjected to dynamic reference correction, a sliding time window algorithm is adopted to dynamically update a background flux reference value, and stable background flux is obtained through median filtering processing.
4. The underground pipeline risk early warning system based on abnormal muon flux as set forth in claim 1, wherein the extracting the multidimensional feature reflecting the state of the underground pipeline based on the data corrected by the dynamic reference in the cloud platform comprises: carrying out space correlation analysis on the muon flux data, and obtaining flux space gradient according to space correlation analysis results; performing time sequence feature mining on the muon flux data, and calculating flux change rate and energy spectrum features; The multi-dimensional feature vector is constructed based on the flux spatial gradient, the flux change rate, the energy spectrum feature and the flux anomaly index.
5. An underground pipeline risk early warning system based on abnormal muon flux as set forth in claim 1 wherein said spatial correlation analysis of muon flux data comprises calculating flux spatial gradients for monitoring units of adjacent 3 nodes by spatial correlation analysis Expressed as: , Wherein, the As the flux difference between node 1 and node 2, For the actual physical distance between node 1 and node 3, As the flux difference between node 2 and node 3, Is the actual physical distance between node 2 and node 3.
6. The underground pipeline risk early warning system based on muon flux anomalies as set forth in claim 1, wherein said performing risk assessment and classification in said cloud platform based on extracted multi-dimensional features reflecting underground pipeline state comprises inputting said multi-dimensional features into a trained input pre-trained XGBoost assessment model based on output medium density change rate Void volume Weighted fusion to obtain comprehensive risk score 。
7. The underground pipeline risk early warning system based on abnormal muon flux as set forth in claim 1, wherein the generating, in the cloud platform, a multi-stage early warning response strategy according to the risk assessment classification result comprises: when the risk score is slightly abnormal in the first range, automatically starting neighboring node rechecking scanning and generating a preliminary report; When the risk score is in the second range and is moderate abnormal, the mobile mu-sub detector carried by the unmanned aerial vehicle is linked for verification besides the enhanced monitoring; when the risk score is in the serious abnormality of the third range, the emergency management system is directly triggered and the pipeline automatic shutdown protocol is started, so that a complete emergency treatment closed loop is formed.
8. The underground pipeline risk early warning method based on the abnormality of the muon flux is characterized by comprising the following steps of: Collecting muon flux data and environmental parameters in real time; performing data preprocessing and dynamic reference correction on the real-time acquired muon flux data and environmental parameters, and calculating to obtain flux abnormality indexes; Constructing a data communication channel, and adopting a layered protocol stack to transmit and remotely control data of a sensing layer and data of an edge computing layer; And extracting multidimensional features reflecting the state of the underground pipeline based on the data corrected by the dynamic reference, performing risk assessment and classification based on the extracted multidimensional features reflecting the state of the underground pipeline, and generating a multi-stage early warning response strategy according to the risk assessment classification result.
9. A computer readable storage medium having stored thereon a computer program, wherein the program when executed by a processor performs the steps of a method for underground pipeline risk early warning based on muon flux anomalies as set forth in claim 8.
10. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor, when executing the program, performs the steps of a method for pre-warning of risk of an underground pipeline based on a muon flux anomaly as described in claim 8.

Description

Underground pipeline risk early warning system and method based on muon flux abnormality Technical Field The invention belongs to the technical field of underground engineering safety monitoring, and particularly relates to an underground pipeline risk early warning system and method based on abnormal muon flux. Background The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art. As the urban process increases, the underground pipeline scale continues to expand, and the safe operation thereof faces serious challenges. The traditional monitoring technology such as Ground Penetrating Radar (GPR), infrared thermal imaging, sound wave detection and the like has the problems of insufficient penetrating capacity, poor real-time performance, high false alarm rate and the like. Penetration depth of GPR in high conductivity stratum is usually not more than 3m and is easy to be interfered by electromagnetic interference, infrared thermal imaging can only identify near-surface abnormality and is obviously influenced by environmental temperature, and acoustic wave detection needs to be provided with a sensor in a contact mode, so that large-scale continuous monitoring is difficult to realize. These limitations make it difficult for the prior art to meet the requirements of urban underground pipeline deep risk identification. In recent years, the cosmic ray muon imaging technology has shown unique advantages in the fields of geological exploration, nuclear facility monitoring and the like due to the extremely strong penetrating capacity and non-contact characteristics. The muon can penetrate hundreds of meters of rock stratum as secondary particles of interaction of high-energy protons and the atmosphere, and the characteristics of flux attenuation and medium density are closely related to the muon are successfully applied to volcanic internal structure detection and pyramid archaeological research. However, the existing muon imaging equipment is generally huge in size, the data processing algorithm is complex, the existing muon imaging equipment is mainly oriented to the geological and archaeological fields, and a special monitoring method for urban underground pipeline risks is not formed yet. In particular, in the field of pipeline specific monitoring, there is still a lack of systematic solutions for lightweight detector designs, real-time risk identification algorithms, and multi-stage early warning mechanisms. Disclosure of Invention In order to solve at least one technical problem in the background art, the invention provides an underground pipeline risk early warning method and system based on abnormal muon flux, which develop a novel monitoring system integrating high-sensitivity detection, intelligent risk analysis and quick early warning response so as to meet urgent requirements of urban underground space safety management and effectively solve early recognition problems of pipeline leakage, cavities and other risks in urban complex environments. In order to achieve the above purpose, the present invention adopts the following technical scheme: a first aspect of the present invention provides an underground pipeline risk early warning system based on abnormal muon flux, comprising: The sensing layer is used for collecting muon flux data and environmental parameters in real time; the edge calculation layer is used for carrying out data preprocessing and dynamic reference correction on the muon flux data and the environmental parameters acquired in real time, and calculating to obtain flux abnormality indexes; The network transmission layer is used for constructing a data communication channel, and adopts a layered protocol stack to carry out data transmission and remote control on the data of the sensing layer and the edge calculation layer; The cloud platform is used for extracting multidimensional features reflecting the state of the underground pipeline based on the data corrected by the dynamic reference, performing risk assessment and classification based on the extracted multidimensional features reflecting the state of the underground pipeline, and generating a multi-stage early warning response strategy according to the risk assessment classification result. Further, in the sensing layer, the muon flux data and the environment parameters are collected in real time based on the constructed monitoring network infrastructure, wherein the construction of the monitoring network infrastructure comprises the following steps: and arranging muon detector nodes at set intervals along the underground pipeline to be monitored to ensure that the detector arrays and the pipeline trend are arranged in parallel, and configuring 3 layers of orthogonally arranged detectors and silicon photomultiplier sensor arrays for each monitoring node. Further, in the edge calculation layer, when the preprocessed muon flux data is subjected t