Overlying strata damage monitoring method, device and system and storage medium

Abstract

The invention discloses an overlying strata damage monitoring method, device and system and a storage medium. The overlying strata damage monitoring method comprises the steps that S1, strain and temperature changes of an overlying strata structure are obtained; s2, obtaining structural damage and moisture permeation of the overlying strata structure; s3, the morphology change and microcracks of the overlying strata structure are obtained; and S4, according to the strain and temperature change of the overlying strata structure, the structural damage, the moisture permeation, the morphology change and the microcracks, obtaining the spatio-temporal evolution characteristics of overlying strata deformation by using a data fusion algorithm. By adopting the technical scheme of the invention, real-time monitoring and evaluation of the health state of the overlying strata structure are realized.

Description

Technical Field

[0001] This invention belongs to the field of coal mine disaster early warning technology, and in particular relates to a method, device, system, and storage medium for monitoring overburden damage. Background Technology

[0002] In geotechnical engineering, the stability of coal mine overburden structures is crucial to project safety. Traditional monitoring methods often suffer from limited monitoring range and insufficient real-time performance, making it difficult to accurately assess the potential risk of damage to the overburden structure. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a method, device, system, and storage medium for monitoring overburden damage.

[0004] To achieve the above objectives, the present invention adopts the following technical solution: A method for monitoring overburden failure includes: Step S1: Obtain the strain and temperature changes of the overlying rock structure; Step S2: Obtain structural damage and water permeability of the overlying rock structure; Step S3: Obtain the morphological changes and microcracks of the overlying rock structure; Step S4: Based on the strain and temperature changes, structural damage and water infiltration, morphological changes and microcracks of the overburden structure, a data fusion algorithm is used to obtain the spatiotemporal evolution characteristics of overburden deformation; specifically including: Step S41: Normalize the strain data and structural damage data to the same numerical range, and standardize the temperature data; Step S42: Construct a multi-layer neural network, using strain, temperature, structural damage, water infiltration, morphological changes, and microcracks as input layers, and output layers to obtain the comprehensive characteristics of overburden deformation; adjust the weights of the neural network using the backpropagation algorithm training method. Step S43: Output the spatiotemporal evolution characteristics of overlying deformation, including: extraction of temporal evolution characteristics and extraction of spatial evolution characteristics.

[0005] Preferably, in step S1, PPP-BOTDA distributed optical fiber sensing technology based on pulsed pre-pumped Brillouin optical time-domain analysis is used to determine the strain and temperature changes of the overburden structure at each point along the optical fiber according to the linear relationship between Brillouin frequency shift and strain or temperature.

[0006] Preferably, in step S2, electrodes are placed in the overburden structure by drilling holes, high-density electrodes are deployed on the rock surface, multi-point resistivity data are collected, and the resistivity data are analyzed by high-density resistivity tomography to generate a high-resolution rock resistivity image. Based on the high-resolution rock resistivity image, the structural damage and water infiltration of the overburden structure are obtained.

[0007] Preferably, in step S3, a high-resolution camera is selected and the photography points are arranged reasonably. Image data of the overburden surface is obtained through photogrammetry. Data analysis and feature extraction are performed on the image data of the overburden surface to obtain the morphological changes and microcracks of the overburden structure.

[0008] The present invention also provides a monitoring device for overburden damage, comprising: The first acquisition module is used to acquire the strain and temperature changes of the overburden structure; The second acquisition module is used to acquire structural damage and water permeability of the overburden structure; The third acquisition module is used to acquire the morphological changes and microcracks of the overburden structure; The analysis module is used to obtain the spatiotemporal evolution characteristics of overburden deformation based on strain and temperature changes, structural damage and water infiltration, morphological changes and microcracks in the overburden structure, using a data fusion algorithm.

[0009] This invention also provides an overburden damage monitoring system, comprising: a memory and a processor, wherein the memory stores a computer program executed by the processor, and the computer program executes an overburden damage monitoring method when run by the processor.

[0010] This invention also provides a storage medium storing a computer program that executes a method for monitoring overburden damage during runtime.

[0011] This invention employs three technologies: distributed fiber optic sensing, resistivity tomography, and close-range photogrammetry. Distributed fiber optic sensing utilizes fiber optic sensing, resistivity tomography measures the potential difference between electrodes, and close-range photogrammetry visually records morphological changes on the overburden surface in the form of photographs or images. The combination of these three technologies allows for the acquisition of information on overburden structural changes from different dimensions. Distributed fiber optic sensing provides information on internal strain and temperature changes, resistivity tomography presents images of internal structural and physical property changes, and close-range photogrammetry records surface morphological changes. This provides comprehensive coverage of the entire process of overburden structural changes, from minute internal deformations and physical property changes to surface morphological changes. Because optical fibers are brittle and easily broken, while electrodes are typically metal and less prone to breakage, resistivity tomography can still function even after a mine collapse where distributed fiber optic sensing is ineffective, allowing for the detection of potential subsequent collapses and providing better safety assurance. This invention utilizes distributed fiber optic sensing (DFOS), high-density resistivity tomography (HD-ET), and close-range photogrammetry (CRP) technologies to achieve real-time monitoring and assessment of the health status of coal mine overburden structures through multi-sensor data fusion analysis. It has significant application value in engineering structure monitoring and geological disaster early warning. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0013] Figure 1 This is a flowchart of the overburden damage monitoring method according to an embodiment of the present invention; Figure 2 It is a distributed fiber optic sensing (DFOS) system where optical fibers are deployed in the overburden structure; Figure 3 It is the electrode distribution for high-density resistivity tomography (HD-ET); Figure 4 These are the photography points set up using close-range photogrammetry (CRP) technology; Figure 5 It is a physical experimental model for the fusion of distributed optical fiber and high-density resistivity tomography. Figure 6 It is a schematic diagram of the cracks and separation of the overburden rock, as well as the related deformation and failure mechanisms. Detailed Implementation

[0014] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0015] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0016] Example 1: like Figure 1 As shown, an embodiment of the present invention provides a method for monitoring overburden damage, comprising: Step S1: Obtain the strain and temperature changes of the overburden structure using distributed optical fiber sensing (DFOS) technology.

[0017] Step S2: Obtain structural damage and water permeability of the overburden structure using high-density resistivity tomography (HD-ET) technology.

[0018] Step S3: Obtain the morphological changes and microcracks of the overlying rock structure using close-range photogrammetry (CRP) technology. Step S4: Based on the strain and temperature changes, structural damage and water infiltration, morphological changes and microcracks of the overburden structure, a data fusion algorithm is used to obtain the spatiotemporal evolution characteristics of overburden deformation. These characteristics include: a rock stratum failure process dominated by bending and tension; a rock stratum failure process dominated by overall shear; and a rock stratum failure process dominated by shear and sliding. Specifically, these include: Step S41, Data Preprocessing: Eliminate erroneous signals caused by the accuracy of the detection equipment or external interference, normalize the strain data and structural damage data to the same numerical range, and standardize the temperature data so that it can be used in the fusion calculation together with other physical quantity data (such as strain). Step S42: Construct and train the neural network: Construct a multi-layer neural network, using strain, temperature, structural damage, water infiltration, morphological changes, and microcracks as input layers, and output layers to obtain the comprehensive characteristics of overburden deformation; use the backpropagation algorithm to train the neural network, adjust the weights, and enable the neural network to accurately predict the type of overburden deformation based on the input data; Step S43: Output the spatiotemporal evolution characteristics of overlying deformation: including: extraction of temporal evolution characteristics and extraction of spatial evolution characteristics; wherein, Extraction of time evolution features: Analyze the time change curve of strain data to determine the growth rate, observe the change of structural damage degree over time, and statistically analyze the growth of the number and length of microcracks to reflect the time evolution of overburden failure. For example, a sudden increase in the number of microcracks may indicate the overburden failure process. Spatial evolution feature extraction: Based on relevant data from different locations, draw spatial distribution maps of overburden deformation, analyze spatial changes on the overburden surface in conjunction with morphological change data, determine areas of concentrated deformation, and infer the spatial distribution patterns of rock strata failure processes.

[0019] In one embodiment of the present invention, in step S1, the DFOS system is used to detect strain and temperature changes on the surface of the overburden structure using a fiber optic Brillouin sensor, providing high spatial resolution monitoring capabilities. In step S2, HD-ET technology is used to perform resistivity scanning of the overburden structure using a resistivity tomography device, identifying potential structural damage and moisture infiltration, providing high-precision non-invasive monitoring characteristics. In step S3, CRP technology is used to monitor changes and microcracks on the overburden surface in real time using a camera and image processing software, providing visualized structural health information.

[0020] Furthermore, this embodiment of the invention employs PPP-BOTDA distributed fiber optic sensing technology based on pulsed pre-pumped Brillouin optical time-domain analysis. Based on the linear relationship between Brillouin frequency shift and strain or temperature, the strain or temperature at each point along the fiber can be determined. HD-ERT combines vertical resistivity probing and apparent resistivity profile measurement. Based on the conductivity differences of different electrolytes, artificial direct current is provided to the electrodes, and the resulting potential difference is measured to obtain information on the heterogeneity and electrical properties of the medium. CRP uses digital image processing technology to monitor the deformation characteristics of objects and capture surface features. Distributed strain sensing using PPP-BOTDA technology and optical cables can be used to study the deformation and failure evolution of strata during coal mining. HD-ERT technology is suitable for inverting the stress change characteristics of overburden rocks, while CRP technology is used to characterize the displacement field of the model surface.

[0021] Furthermore, distributed optical fiber sensing (DFOS) was used to monitor the overburden structure: four U-shaped optical fibers were laid in the overburden structure through drilling, such as... Figure 2 As shown, the DFOS system collects scattered signals from optical fibers and performs real-time analysis using signal processing algorithms to identify rock displacement and stress changes. Through real-time data analysis, monitoring results can be promptly fed back, and an early warning mechanism can be activated, thereby effectively ensuring project safety.

[0022] Furthermore, high-density resistivity tomography (HD-ET) was used to monitor overburden structures: electrodes were placed within the overburden structures by drilling holes, such as... Figure 3As shown, high-density electrodes are deployed on the rock surface to collect resistivity data at multiple points. This resistivity data is then processed and analyzed to generate a high-resolution image of the rock strata's resistivity. The imaging results are combined with real-time monitoring data to analyze the stability and changing trends of the rock strata, providing a scientific basis for engineering management.

[0023] Furthermore, close-range photogrammetry (CRP) is used to monitor overburden structures: high-resolution cameras are selected, and photography points are strategically placed to ensure comprehensive coverage of the monitoring area, such as... Figure 4 As shown, image data of the overlying rock surface was acquired using photogrammetry, and image processing software was used for data analysis and feature extraction. The image data was analyzed to detect and assess microscopic cracks, deformation, and permeability changes on the rock surface, and to provide quantitative monitoring reports.

[0024] Example: Take, for example, a physical model of an underground coal seam measuring 2.5m long, 0.3m wide, and 2m high.

[0025] The designed physical experimental model of fusion of distributed optical fiber and high-density resistivity tomography is as follows: Figure 5 As shown, the model consists of mudstone, fine sandstone, diorite, siltstone, and coal. The spatiotemporal evolution characteristics of overburden deformation were studied by comprehensively applying three measurement methods: DFOS technology enables long-distance, real-time monitoring of the overburden structure, using fiber optic sensors to accurately detect internal stress and deformation of the rock mass, effectively providing early warning of potential risks; HD-ET technology reveals the distribution and changes of different rock layers through resistivity imaging, providing high-resolution structural information to aid in in-depth analysis of rock mass stability; CRP technology acquires microstructure and crack changes on the rock mass surface through high-definition photography, providing engineering managers with detailed surface characteristic data and comprehensively assessing the rock mass health status. This integrated approach not only improves the comprehensiveness and accuracy of monitoring but also achieves real-time data acquisition and intelligent analysis, effectively preventing and managing overburden failure risks, providing reliable support for engineering safety and efficiency. The results show that the exfoliation layer exhibits three failure modes: a rock layer failure process dominated by bending and tension, a rock layer failure process dominated by overall shear, and a rock layer failure process dominated by shear and sliding, as shown in the figure. Figure 6 As shown, when brittle rock is subjected to the coupled effects of high-intensity bending tension and shear slip, the entire rock mass may collapse, forming large rock blocks. This failure mode has a significant and rapid impact on coal seam mining.

[0026] Example 2: This invention also provides a device for monitoring overburden damage, comprising: The first acquisition module is used to acquire the strain and temperature changes of the overburden structure; The second acquisition module is used to acquire structural damage and water permeability of the overburden structure; The third acquisition module is used to acquire the morphological changes and microcracks of the overburden structure; The analysis module is used to obtain the spatiotemporal evolution characteristics of overburden deformation based on strain and temperature changes, structural damage and water infiltration, morphological changes and microcracks in the overburden structure, using a data fusion algorithm.

[0027] Example 3: This invention also provides an overburden damage monitoring system, comprising: a memory and a processor, wherein the memory stores a computer program executed by the processor, and the computer program executes an overburden damage monitoring method when run by the processor.

[0028] Example 4: This invention also provides a storage medium storing a computer program that executes a method for monitoring overburden damage during runtime.

[0029] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for monitoring overburden damage, characterized in that, include: Step S1: Obtain the strain and temperature changes of the overlying rock structure; Step S2: Obtain structural damage and water permeability of the overlying rock structure; Step S3: Obtain the morphological changes and microcracks of the overlying rock structure; Step S4: Based on the strain and temperature changes, structural damage and water infiltration, morphological changes and microcracks of the overburden structure, a data fusion algorithm is used to obtain the spatiotemporal evolution characteristics of overburden deformation; specifically including: Step S41: Normalize the strain data and structural damage data to the same numerical range, and standardize the temperature data; Step S42: Construct a multi-layer neural network, using strain, temperature, structural damage, water infiltration, morphological changes, and microcracks as input layers, and output layers to obtain the comprehensive characteristics of overburden deformation; adjust the weights of the neural network using the backpropagation algorithm training method. Step S43: Output the spatiotemporal evolution characteristics of overlying deformation, including: extraction of temporal evolution characteristics and extraction of spatial evolution characteristics.

2. The overburden failure monitoring method as described in claim 1, characterized in that, In step S1, PPP-BOTDA distributed optical fiber sensing technology based on pulsed pre-pumped Brillouin optical time-domain analysis is used to determine the strain and temperature changes of the overburden structure at each point along the optical fiber according to the linear relationship between Brillouin frequency shift and strain or temperature.

3. The overburden damage monitoring method as described in claim 2, characterized in that, In step S2, electrodes are placed in the overburden structure by drilling holes, high-density electrodes are deployed on the rock surface, and multi-point resistivity data are collected. The resistivity data are analyzed by high-density resistivity tomography to generate a high-resolution rock resistivity image. Based on the high-resolution rock resistivity image, the structural damage and water infiltration of the overburden structure are obtained.

4. The overburden failure monitoring method as described in claim 3, characterized in that, In step S3, by selecting a high-resolution camera and reasonably arranging the photography points, image data of the overburden surface is obtained through photogrammetry. Data analysis and feature extraction are then performed on the image data of the overburden surface to obtain the morphological changes and microcracks of the overburden structure.

5. An overburden failure monitoring device for implementing the overburden failure monitoring method according to any one of claims 1 to 4, characterized in that, include: The first acquisition module is used to acquire the strain and temperature changes of the overburden structure; The second acquisition module is used to acquire structural damage and water permeability of the overburden structure; The third acquisition module is used to acquire the morphological changes and microcracks of the overburden structure; The analysis module is used to obtain the spatiotemporal evolution characteristics of overburden deformation based on strain and temperature changes, structural damage and water infiltration, morphological changes and microcracks in the overburden structure, using a data fusion algorithm.

6. A monitoring system for overburden failure, characterized in that, include: A memory and a processor, wherein the memory stores a computer program executed by the processor, the computer program performing the overburden damage monitoring method as described in any one of claims 1 to 4 when executed by the processor.

7. A storage medium, characterized in that, The storage medium stores a computer program, which executes the overburden damage monitoring method as described in any one of claims 1 to 4 when it runs.

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