CN-122017049-A - Multi-source tunnel monitoring data acquisition and processing method and system

CN122017049ACN 122017049 ACN122017049 ACN 122017049ACN-122017049-A

Abstract

The invention provides a multi-source tunnel monitoring data acquisition and processing method and a system, and relates to the technical field of tunnel monitoring data processing; the method comprises the steps of determining a plurality of audio feedback standard sections and a plurality of audio feedback abnormal sections based on audio resonance spectrum data of a tunnel lining, determining a plurality of degradation characteristic gathering sections and a void abnormal mapping chart of each degradation characteristic gathering section based on the plurality of audio feedback standard sections and the plurality of audio feedback abnormal sections, acquiring mechanical impedance sampling information of each cooperative mechanical impedance sampling point of each degradation characteristic gathering section, and determining a grouting repair entity cavity of the tunnel lining based on the plurality of mechanical impedance sampling points of each degradation characteristic gathering section and the plurality of cooperative mechanical impedance sampling points of each degradation characteristic gathering section.

Inventors

WU ZHONGMING
ZHOU MING
LI ZONGCHUAN
LIN WENGAO
ZHANG YONGYONG
LI SHULIN

Assignees

浙江华东测绘与工程安全技术有限公司
中国电建集团华东勘测设计研究院有限公司

Dates

Publication Date: 20260512
Application Date: 20260415

Claims (10)

1. The multi-source tunnel monitoring data acquisition and processing method is characterized by comprising the following steps of: acquiring acoustic resonance spectrum data of tunnel lining; Determining a plurality of audio feedback standard segments and a plurality of audio feedback variant segments based on the audio resonance spectrum data of the tunnel lining; Determining a plurality of degradation feature concentration segments, a void anomaly map for each degradation feature concentration segment, based on the plurality of audio feedback standard segments and the plurality of audio feedback anomaly segments; Determining a plurality of mechanical impedance sampling points of each degradation characteristic gathering section based on the emptying abnormal mapping diagram of each degradation characteristic gathering section, and acquiring mechanical impedance sampling information of each mechanical impedance sampling point of each degradation characteristic gathering section; Determining a plurality of impedance characteristic gradient sharp change areas of each degradation characteristic accumulation section and a plurality of impedance characteristic information non-uniformity areas of each degradation characteristic accumulation section based on the mechanical impedance sampling information of each mechanical impedance sampling point of each degradation characteristic accumulation section; Determining a plurality of cooperative mechanical impedance sampling points of each degradation characteristic accumulation section based on the plurality of impedance characteristic gradient abrupt change areas of each degradation characteristic accumulation section and the plurality of impedance characteristic information inhomogeneous areas of each degradation characteristic accumulation section, and acquiring mechanical impedance sampling information of each cooperative mechanical impedance sampling point of each degradation characteristic accumulation section; And determining the grouting repairing entity cavity of the tunnel lining based on the mechanical impedance sampling points of each degradation characteristic gathering section and the cooperative mechanical impedance sampling points of each degradation characteristic gathering section.
2. The method of claim 1, wherein said determining a plurality of degradation feature accumulation segments, each degradation feature accumulation segment, based on said plurality of audio feedback standard segments, said plurality of audio feedback aliases comprises: Constructing an audio feedback spectrum, wherein the audio feedback spectrum comprises a plurality of nodes and a plurality of edges between the nodes, the nodes comprise a plurality of audio feedback standard segment nodes and a plurality of audio feedback variable segment nodes, each audio feedback variable segment node is respectively connected with the plurality of audio feedback standard segment nodes, the node of each audio feedback standard segment node is characterized by audio resonance spectrum data of each audio feedback standard segment, and the node of each audio feedback variable segment node is characterized by audio resonance spectrum data of each audio feedback variable segment; and processing the audio feedback map based on the graph neural network to determine a plurality of degradation characteristic gathering sections and a void anomaly map of each degradation characteristic gathering section.
3. The method of multi-source tunnel monitoring data acquisition and processing of claim 1, wherein said determining a grouting repair solid cavity of the tunnel lining based on the plurality of mechanical impedance sampling points of each degradation feature accumulation segment and the plurality of cooperative mechanical impedance sampling points of each degradation feature accumulation segment comprises: Clustering based on the mechanical impedance sampling information of each mechanical impedance sampling point of each degradation characteristic gathering section and the mechanical impedance sampling information of each cooperative mechanical impedance sampling point of each degradation characteristic gathering section to obtain a plurality of impedance response association clusters of each degradation characteristic gathering section, wherein each impedance response association cluster comprises the mechanical impedance sampling information of a plurality of mechanical impedance sampling points and the mechanical impedance sampling information of a plurality of cooperative mechanical impedance sampling points; Determining a plurality of core degradation feature aggregation segments based on the plurality of impedance response correlation clusters of each degradation feature aggregation segment and the void anomaly map of each degradation feature aggregation segment; Determining a plurality of suspected cavity anomaly regions of each core degradation feature accumulation segment based on the mechanical impedance sampling information of the plurality of mechanical impedance sampling points of each core degradation feature accumulation segment, the mechanical impedance sampling information of the plurality of cooperative mechanical impedance sampling points of each core degradation feature accumulation segment, and the audio resonance spectrum data of each core degradation feature accumulation segment, and acquiring geological radar analysis data of the plurality of suspected cavity anomaly regions of each core degradation feature accumulation segment; And determining the grouting repairing entity cavity of the tunnel lining based on geological radar analysis data of the plurality of suspected cavity abnormal areas of each core degradation characteristic gathering section.
4. The method of claim 2, wherein the input to the graphic neural network is the audio feedback profile and the output from the graphic neural network is a plurality of degradation feature accumulation segments, each degradation feature accumulation segment having a nulling anomaly map.
5. A multi-source tunnel monitoring data acquisition and processing system, comprising: The acquisition module is used for acquiring the acoustic resonance spectrum data of the tunnel lining; the audio feedback analysis module is used for determining a plurality of audio feedback standard segments and a plurality of audio feedback mutation segments based on the audio resonance frequency spectrum data of the tunnel lining; The characteristic identification module is used for determining a plurality of degradation characteristic gathering sections and a void anomaly mapping chart of each degradation characteristic gathering section based on the plurality of audio feedback standard sections and the plurality of audio feedback mutation sections; The mechanical impedance sampling module is used for determining a plurality of mechanical impedance sampling points of each degradation characteristic gathering section based on the emptying abnormal mapping diagram of each degradation characteristic gathering section and acquiring mechanical impedance sampling information of each mechanical impedance sampling point of each degradation characteristic gathering section; an impedance feature analysis module for determining a plurality of impedance feature gradient abrupt change regions of each degradation feature accumulation section and a plurality of impedance feature information inhomogeneous regions of each degradation feature accumulation section based on the mechanical impedance sampling information of each mechanical impedance sampling point of each degradation feature accumulation section; the collaborative sampling module is used for determining a plurality of collaborative mechanical impedance sampling points of each degradation characteristic gathering section based on the plurality of impedance characteristic gradient sharp change areas of each degradation characteristic gathering section and the plurality of impedance characteristic information non-homogeneous areas of each degradation characteristic gathering section, and acquiring mechanical impedance sampling information of each collaborative mechanical impedance sampling point of each degradation characteristic gathering section; and the cavity determining module is used for determining the grouting repairing entity cavity of the tunnel lining based on the mechanical impedance sampling points of each degradation characteristic gathering section and the cooperative mechanical impedance sampling points of each degradation characteristic gathering section.
6. The multi-source tunnel monitoring data acquisition and processing system of claim 5 wherein the feature identification module is further configured to: Constructing an audio feedback spectrum, wherein the audio feedback spectrum comprises a plurality of nodes and a plurality of edges between the nodes, the nodes comprise a plurality of audio feedback standard segment nodes and a plurality of audio feedback variable segment nodes, each audio feedback variable segment node is respectively connected with the plurality of audio feedback standard segment nodes, the node of each audio feedback standard segment node is characterized by audio resonance spectrum data of each audio feedback standard segment, and the node of each audio feedback variable segment node is characterized by audio resonance spectrum data of each audio feedback variable segment; and processing the audio feedback map based on the graph neural network to determine a plurality of degradation characteristic gathering sections and a void anomaly map of each degradation characteristic gathering section.
7. The multi-source tunnel monitoring data acquisition and processing system of claim 5, wherein the cavity determination module is further configured to: Clustering based on the mechanical impedance sampling information of each mechanical impedance sampling point of each degradation characteristic gathering section and the mechanical impedance sampling information of each cooperative mechanical impedance sampling point of each degradation characteristic gathering section to obtain a plurality of impedance response association clusters of each degradation characteristic gathering section, wherein each impedance response association cluster comprises the mechanical impedance sampling information of a plurality of mechanical impedance sampling points and the mechanical impedance sampling information of a plurality of cooperative mechanical impedance sampling points; Determining a plurality of core degradation feature aggregation segments based on the plurality of impedance response correlation clusters of each degradation feature aggregation segment and the void anomaly map of each degradation feature aggregation segment; Determining a plurality of suspected cavity anomaly regions of each core degradation feature accumulation segment based on the mechanical impedance sampling information of the plurality of mechanical impedance sampling points of each core degradation feature accumulation segment, the mechanical impedance sampling information of the plurality of cooperative mechanical impedance sampling points of each core degradation feature accumulation segment, and the audio resonance spectrum data of each core degradation feature accumulation segment, and acquiring geological radar analysis data of the plurality of suspected cavity anomaly regions of each core degradation feature accumulation segment; And determining the grouting repairing entity cavity of the tunnel lining based on geological radar analysis data of the plurality of suspected cavity abnormal areas of each core degradation characteristic gathering section.
8. The multi-source tunnel monitoring data acquisition and processing system of claim 6 wherein the input to the graphic neural network is the audio feedback profile and the output of the graphic neural network is a plurality of degradation feature accumulation segments, a nulling anomaly map for each degradation feature accumulation segment.
9. An electronic device comprising a processor, a memory, and a computer program, wherein the computer program is stored in the memory and configured to be executed by the processor to implement the multi-source tunnel monitoring data acquisition and processing method of any one of claims 1 to 4.
10. A computer readable storage medium having stored thereon a computer program, which when executed by a processor implements a multi-source tunnel monitoring data acquisition and processing method as claimed in any one of claims 1 to 4.

Description

Multi-source tunnel monitoring data acquisition and processing method and system Technical Field The invention relates to the technical field of tunnel monitoring data processing, in particular to a multi-source tunnel monitoring data acquisition and processing method and system. Background The tunnel lining health monitoring is a core link for guaranteeing the safe operation and maintenance of traffic infrastructure, and the accurate identification and positioning of the internal void diseases are directly related to the scientificity of the tunnel lining long-term stability assessment and maintenance decision. In the prior art, precise monitoring equipment based on physical principles such as a mechanical impedance tester and the like is widely applied to lining quality evaluation, and can effectively detect internal defects of a local area of a tunnel lining. However, this type of method still has systematic limitations in practical applications. In order to meet the monitoring requirement of mass extension of tunnels, the conventional method generally depends on a monitoring strategy of uniform distribution points or equidistant inspection, and can ensure the accuracy of single-point data, but the method has the defects of huge data acquisition quantity, high monitoring cost and long operation period in the whole tunnel domain. In addition, the prior art lacks a progressive screening and verifying mechanism, so that the rapid focusing and the accurate locking of the entity cavity caused by the void diseases in the tunnel lining are difficult to realize. And further, the repair decision lacks accurate basis, and cannot adapt to the high-efficiency, low-cost and accurate safe operation and maintenance requirements of the tunnel. Therefore, how to efficiently and accurately determine the grouting repairing entity cavity caused by the void defect of the tunnel lining is a current problem to be solved urgently. Disclosure of Invention The invention mainly solves the technical problem of how to efficiently and accurately determine the grouting repairing entity cavity caused by the void disease of the tunnel lining. According to a first aspect, the invention provides a multi-source tunnel monitoring data acquisition and processing method, which comprises the steps of acquiring acoustic frequency resonance spectrum data of a tunnel lining, determining a plurality of acoustic frequency feedback standard sections and a plurality of acoustic frequency feedback variable sections based on the acoustic frequency resonance spectrum data of the tunnel lining, determining a plurality of degradation characteristic aggregation sections and a void abnormal mapping chart of each degradation characteristic aggregation section based on the plurality of acoustic frequency feedback standard sections and the plurality of acoustic frequency feedback variable sections, determining a plurality of mechanical impedance sampling points of each degradation characteristic aggregation section based on the void abnormal mapping chart of each degradation characteristic aggregation section, acquiring mechanical impedance sampling information of each mechanical impedance sampling point of each degradation characteristic aggregation section, determining a plurality of impedance characteristic gradient sharp change areas of each degradation characteristic aggregation section based on the mechanical impedance sampling information of each mechanical impedance sampling point of each degradation characteristic aggregation section, determining a plurality of impedance characteristic gradient non-uniform areas of each degradation characteristic aggregation section based on the plurality of impedance characteristic gradient change areas of each degradation characteristic aggregation section, determining a plurality of impedance characteristic gradient focal point of each degradation characteristic aggregation section based on the degradation characteristic aggregation section, and the mechanical impedance sampling point of each degradation characteristic aggregation section and the degradation characteristic focal point of each degradation characteristic aggregation, and the mechanical focal point is determined. In one possible implementation, the determining the plurality of degradation characteristic gathering segments and the void anomaly map of each degradation characteristic gathering segment based on the plurality of audio feedback standard segments and the plurality of audio feedback variable segments includes constructing an audio feedback map including a plurality of nodes and a plurality of edges between the plurality of nodes, the plurality of nodes including a plurality of audio feedback standard segment nodes and a plurality of audio feedback variable segment nodes, each audio feedback variable segment node being respectively connected to a plurality of audio feedback standard segment nodes, the node of each audio feedback standard segme