CN114354351A - Rock destruction process real-time monitoring and early warning system and method based on multi-source heterogeneous data - Google Patents
Rock destruction process real-time monitoring and early warning system and method based on multi-source heterogeneous data Download PDFInfo
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Abstract
The invention provides a real-time monitoring and early warning system and method for a rock destruction process based on multi-source heterogeneous data, and belongs to the technical field of experimental data informatization of a rock mechanics experimental process. The system of the invention comprises: the data acquisition unit, the data edge processing unit, the data transmission unit and the data integration analysis early warning unit; the method comprises the steps of preparing before monitoring, drawing speckle patterns and arranging sensors; data acquisition, edge processing, data preprocessing and original data storage; data transmission, namely transmitting the data to a data integration analysis early warning unit; the data integration analysis early warning unit carries out integration analysis and visual display on the data. The technical scheme of the invention can acquire and process monitoring data in real time, can effectively predict and early warn the instability damage of the rock, and has good real-time performance and accurate prediction. On the basis, the maturation result processed by the system is deduced to a large-scale rock mass, and reference is provided for prediction and early warning of mine disasters.
Description
Technical Field
The invention relates to the technical field of experimental data informatization of a rock mechanics experimental process, in particular to a real-time monitoring and early warning system and method for a rock destruction process based on multi-source heterogeneous data.
Background
Roof caving, rock burst and other problems caused by mining are the results of rock damage and fracture. The rock instability damage scale generated on the mine site is large, and the instability damage rule is difficult to directly obtain by directly researching the rock instability damage scale in consideration of the complexity of the working site environment and various uncontrollable factors. The forecasting and early warning of rock damage and fracture is a basic problem of rock mechanics, provides a theoretical basis for the forecasting and early warning of mine disasters, and is also listed as one of the unanswered hundred-year problems of rock mechanics by Zhaoyang academists. Therefore, the indoor small-scale rock can be considered as a research object, the instability and damage characteristics of the indoor small-scale rock can be researched, the instability and damage characteristics of the rock can be inverted by taking the instability and damage characteristics as a theoretical basis, the prediction and early warning can be further made on the damage and instability of the rock, and an important theoretical and experimental basis is laid for solving the problem of inducing actual engineering of the instability and damage of the rock.
In the deformation, damage and destruction processes of the rock, real-time monitoring of various means can be carried out. The dynamic strain detection can sense the slight strain on the surface of a rock body through a strain gauge attached to the rock, can measure the transverse strain and the axial strain of the rock, combines other data in the rock damage process, and can analyze the damage process of the rock according to a drawn stress-strain curve. The acoustic emission data acquisition instrument can monitor various information such as rock mass internal micro-fracture and energy in the rock failure process, and the parameters are mostly statistical parameters based on the waveform received by the sensor, and the acoustic emission data acquisition instrument often has the characteristics of diversity and correlation, and the acoustic emission parameters also contain rich information of rock stress level and fracture surface expansion. The digital speckle technology can analyze the positions of the marking points on the sample picture according to the marking points drawn on the surface of the rock sample in advance through a computer vision technology, can give local displacement and strain information of the sample according to the change of the marking points, and can further obtain a global displacement field and a global strain field, so as to obtain the information of rock fracture surface deformation. In conclusion, the whole damage and destruction process of the rock can be monitored by means of dynamic strain detection, acoustic emission monitoring, digital speckle deformation detection and the like.
However, the means such as dynamic strain detection, acoustic emission monitoring, digital speckle deformation detection and the like mainly analyze and process the acquired monitoring data after the experiment is finished, cannot analyze and process the data in the rock damage and destruction process in real time, and naturally cannot predict and early warn the rock destruction in real time. Therefore, the software system for acquiring data based on the existing testing means, researching and developing the monitoring data real-time processing and carrying out the short-term early warning on the rock damage and the fracture has important significance.
Disclosure of Invention
According to the technical problem, a real-time monitoring and early warning system and method for the rock destruction process based on multi-source heterogeneous data are provided. The invention provides a rock destruction process real-time monitoring and early warning system based on multi-source heterogeneous data mainly through secondary development of a monitoring instrument, data transmission service establishment and data display interface design, and prediction and early warning of rock destruction can be realized based on the system. The invention can acquire and process monitoring data in real time and predict the destruction time, the destruction energy and the fracture mode of the rock.
The technical means adopted by the invention are as follows:
a rock destruction process real-time monitoring and early warning system based on multi-source heterogeneous data comprises: the data acquisition unit, the data edge processing unit, the data transmission unit and the data integration analysis early warning unit; wherein:
the data acquisition unit is used for acquiring mechanical response data in the rock instability destruction process in real time;
the data edge processing unit is used for receiving the mechanical response data acquired by the data acquisition unit and preprocessing and storing the data in real time;
the data transmission unit is used for transmitting the data processed by the data edge processing unit in real time and transmitting the data processing result to the data integration analysis early warning unit;
the data integration analysis early warning unit is used for receiving the monitoring data transmitted by the data transmission unit in real time, performing integration analysis on the data, performing prediction early warning on rock instability damage according to a processing result, simultaneously performing operation control on the monitoring early warning system, and performing visual display on a real-time processing result, wherein the display includes real-time data display and real-time prediction result display.
Further, the mechanical response data comprises strain data of the rock in the loading process, acoustic emission data induced by crack initiation and propagation, and digital speckle image data of the rock surface.
Furthermore, the data acquisition unit comprises a dynamic strain gauge and strain gauge sensor for acquiring monitoring data in real time, an acoustic emission data acquisition instrument and acoustic emission probe, and a digital speckle image acquisition camera; wherein:
the dynamic strain gauge and the strain gauge sensor are used for monitoring strain data of a rock local point in the loading process, acquiring the strain data in real time through a LabVIEW secondary development program, and sending the strain data to the data edge processing unit through an HTTP data transmission protocol;
the acoustic emission data acquisition instrument and the acoustic emission probe are used for monitoring elastic waves induced by crack initiation and expansion in the rock in the loading process, namely acoustic emission data, acquiring the acoustic emission data in real time through a LabVIEW secondary development program, and sending the acoustic emission data to the data edge processing unit through an HTTP data transmission protocol;
the digital speckle image acquisition camera is used for monitoring digital speckle image data of the rock surface in the loading process, and the image data is obtained by timing shooting and transmitted to the data edge processing unit through Wi-Fi.
Further, the data edge processing unit comprises a data preprocessing module and a data storage module; wherein:
the data preprocessing module is configured to perform edge data processing on the data received by the data acquisition unit, and includes: denoising and zeroing conversion are carried out on the strain data of the rock local points acquired by the dynamic strain gauge and the strain gauge sensor; extracting characteristic parameters of waveform data acquired by an acoustic emission data acquisition instrument and an acoustic emission probe to obtain an acoustic emission energy value and a ringing number; processing a digital speckle image shot by a camera to obtain strain field information;
and the data storage module is used for storing the raw data subjected to edge processing into a database.
Further, the transmission mode adopted by the data transmission module comprises a WebSocket data transmission protocol, C #. NET-FileSystemWatcher; wherein:
after the strain data and the acoustic emission data are processed by the data edge processing unit, the data are forwarded to the data integration analysis early warning unit for integration analysis through a WebSocket protocol;
and after the speckle images are shot, the speckle images are processed by a data edge processing unit, and the processing results are obtained by sensing through a C #. NET-FileSystemWatcher and displayed in a data integration analysis early warning unit.
Further, the data integration analysis early warning module is used for integration analysis and visual display of multi-source heterogeneous data; wherein:
the content of the integrated analysis comprises: preliminarily estimating mechanical parameters of the rock according to an empirical formula, and judging a body strain inflection point in the rock loading process by combining real-time monitoring data; forecasting and early warning the instability damage of the rock according to the processing result, wherein the forecasting and early warning contents comprise rock damage time, damage energy and a damage mode;
the visualized content includes: the real-time display of dynamic strain gauge data and a stress-strain curve, the real-time display of acoustic emission real-time data and a time-acoustic emission energy curve and a time-ringing number curve, and the real-time display of a speckle real-time processing result image and an original speckle image.
The invention also provides a real-time monitoring and early warning method of the real-time monitoring and early warning system for the rock destruction process based on the multi-source heterogeneous data, which comprises the following steps:
s1, preparation for monitoring: attaching a strain gauge and an acoustic emission probe on the surface of the rock, and monitoring strain data and acoustic emission data; drawing speckle patterns on the surface of the rock to obtain speckle image data;
s2, data acquisition: acquiring strain data by adopting a dynamic strain gauge, acquiring acoustic emission data by adopting an acoustic emission data acquisition instrument, and acquiring digital speckle image data by adopting a camera;
s3, edge processing: the data edge processing unit carries out edge processing on the acquired data and stores the processed original data;
s4, data transmission: the data transmission unit transmits data to the data integration analysis early warning unit to prepare for data integration analysis and visual display;
s5, integrated analysis and visual display: the data integration analysis early warning unit performs integration analysis and visual display on data, starts processing after the data are received, performs prediction early warning on rock instability damage according to results, displays the data processing results on a C # WinForm interface, and has a prediction early warning data derivation function.
Compared with the prior art, the invention has the following advantages:
the real-time monitoring and early warning system and method for the rock destruction process based on the multi-source heterogeneous data can acquire and process monitoring data in real time, can effectively predict and early warn the instability destruction of the rock, and is good in real-time performance and high in prediction accuracy. On the basis, the maturation result processed by the system is deduced to a large-scale rock mass, and reference is provided for prediction and early warning of mine disasters.
Based on the reasons, the method can be widely popularized in the fields of experimental data informatization in the rock mechanics experimental process and the like.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a structural block diagram of a real-time monitoring and early warning system for a rock destruction process based on multi-source heterogeneous data according to an embodiment of the present invention.
Fig. 2 is a flowchart of a rock destruction process real-time monitoring and early warning method based on multi-source heterogeneous data according to an embodiment of the present invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict. The present invention will be described in detail below with reference to the embodiments with reference to the attached drawings.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the invention, its application, or uses. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The relative arrangement of the components and steps, the numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless specifically stated otherwise. Meanwhile, it should be understood that the sizes of the respective portions shown in the drawings are not drawn in an actual proportional relationship for the convenience of description. Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail but are intended to be part of the specification where appropriate. Any specific values in all examples shown and discussed herein are to be construed as exemplary only and not as limiting. Thus, other examples of the exemplary embodiments may have different values. It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
In the description of the present invention, it is to be understood that the orientation or positional relationship indicated by the directional terms such as "front, rear, upper, lower, left, right", "lateral, vertical, horizontal" and "top, bottom", etc., are generally based on the orientation or positional relationship shown in the drawings, and are used for convenience of description and simplicity of description only, and in the absence of any contrary indication, these directional terms are not intended to indicate and imply that the device or element so referred to must have a particular orientation or be constructed and operated in a particular orientation, and therefore should not be considered as limiting the scope of the present invention: the terms "inner and outer" refer to the inner and outer relative to the profile of the respective component itself.
Spatially relative terms, such as "above … …," "above … …," "above … …," "above," and the like, may be used herein for ease of description to describe one device or feature's spatial relationship to another device or feature as illustrated in the figures. It will be understood that the spatially relative terms are intended to encompass different orientations of the device in use or operation in addition to the orientation depicted in the figures. For example, if a device in the figures is turned over, devices described as "above" or "on" other devices or configurations would then be oriented "below" or "under" the other devices or configurations. Thus, the exemplary term "above … …" can include both an orientation of "above … …" and "below … …". The device may be otherwise variously oriented (rotated 90 degrees or at other orientations) and the spatially relative descriptors used herein interpreted accordingly.
It should be noted that the terms "first", "second", and the like are used to define the components, and are only used for convenience of distinguishing the corresponding components, and the terms have no special meanings unless otherwise stated, and therefore, the scope of the present invention should not be construed as being limited.
The invention provides a real-time monitoring and early warning system for a rock destruction process based on multi-source heterogeneous data, which has a specific system structure shown in figure 1 and comprises the following components: the data acquisition unit, the data edge processing unit, the data transmission unit and the data integration analysis early warning unit; wherein:
the data acquisition unit is used for acquiring mechanical response data in the rock instability destruction process in real time; the mechanical response data comprise strain data of the rock in the loading process, acoustic emission data induced by crack initiation and propagation and digital speckle image data of the rock surface.
The data edge processing unit is used for receiving the mechanical response data acquired by the data acquisition unit and preprocessing and storing the data in real time;
the data transmission unit is used for transmitting the data processed by the data edge processing unit in real time and transmitting the data processing result to the data integration analysis early warning unit;
the data integration analysis early warning unit is used for receiving the monitoring data transmitted by the data transmission unit in real time, performing integration analysis on the data, performing prediction early warning on rock instability damage according to a processing result, simultaneously performing operation control on the monitoring early warning system, and performing visual display on a real-time processing result, wherein the display includes real-time data display and real-time prediction result display.
In specific implementation, as a preferred embodiment of the present invention, the data acquisition unit includes a dynamic strain gauge and strain gauge sensor for acquiring monitoring data in real time, an acoustic emission data acquisition instrument and acoustic emission probe, and a digital speckle image acquisition camera; wherein:
the dynamic strain gauge and the strain gauge sensor can adopt a DHDAS dynamic strain gauge and are used for monitoring strain data of a rock local point in the loading process, acquiring the strain data in real time through a LabVIEW secondary development program and sending the strain data to the data edge processing unit through an HTTP data transmission protocol;
the acoustic emission data acquisition instrument and the acoustic emission probe can adopt a PCI-2 sound card and are used for monitoring elastic waves induced by crack initiation and expansion in the rock in the loading process, namely acoustic emission data, the acoustic emission data are acquired in real time through a LabVIEW secondary development program and the data are sent to the data edge processing unit through an HTTP data transmission protocol;
the digital speckle image acquisition camera can adopt an EOS 90D camera and is used for monitoring digital speckle image data of the rock surface in the loading process, and the image data are acquired by timing shooting and transmitted to the data edge processing unit through Wi-Fi.
In specific implementation, as a preferred embodiment of the present invention, the data edge processing unit includes a data preprocessing module and a data storage module; wherein:
the data preprocessing module is configured to perform edge data processing on the data received by the data acquisition unit, and includes: denoising and zeroing conversion are carried out on the strain data of the rock local points acquired by the dynamic strain gauge and the strain gauge sensor; extracting characteristic parameters of waveform data acquired by an acoustic emission data acquisition instrument and an acoustic emission probe to obtain an acoustic emission energy value and a ringing number; processing a digital speckle image shot by a camera to obtain strain field information; in this embodiment, the variable data is preprocessed by Python, and the data is first screened to eliminate obvious error data, and then the screened data is zeroed; acquiring acoustic emission characteristic parameters including acoustic emission energy, ringing count, maximum amplitude, rise time, duration, etc. by interpreting the waveform data; the digital speckle images are processed through Python, image data are firstly cut, image noise is eliminated, then a Farneback dense optical flow method is used for processing, displacement information can be obtained, and strain field information can be obtained.
And the data storage module is used for storing the raw data subjected to edge processing into a database.
In specific implementation, as a preferred embodiment of the present invention, the data transmission module adopts a transmission mode including a WebSocket data transmission protocol, C #. NET-filesystems watch; wherein:
after the strain data and the acoustic emission data are processed by the data edge processing unit, the data are forwarded to the data integration analysis early warning unit for integration analysis through a WebSocket protocol;
and after the speckle images are shot, the speckle images are processed by a data edge processing unit, and the processing results are obtained by sensing through a C #. NET-FileSystemWatcher and displayed in a data integration analysis early warning unit.
In specific implementation, as a preferred implementation mode of the invention, the data integration analysis early warning module is used for integration analysis and visual display of multi-source heterogeneous data; wherein:
the content of the integrated analysis comprises: preliminarily estimating mechanical parameters of the rock according to an empirical formula, and judging a body strain inflection point in the rock loading process by combining real-time monitoring data; forecasting and early warning the instability damage of the rock according to the processing result, wherein the forecasting and early warning contents comprise rock damage time, damage energy and a damage mode; in this embodiment, the integrated analysis of the multi-source heterogeneous data mainly includes:
relationship between compressive strength and hardness in rockwell:
in the formula, σcCompressive strength in MPa; hlRepresenting the hardness in riches, without units;
relationship between compressive strength and wave velocity:
σc＝6.1914e0.0005c
in the formula, σcCompressive strength in MPa; c represents the wave velocity in m/s;
relationship between modulus of elasticity and hardness in richter:
E＝0.0822Hl-26.955
wherein E represents an elastic modulus in MPa; hlRepresenting the hardness in riches, without units;
relationship between modulus of elasticity and wave velocity:
E＝0.0054c+0.7765
wherein E represents an elastic modulus in MPa; c represents the wave velocity in m/s;
relationship between axial strain, transverse strain and bulk strain:
εv＝εy+2εx
in the formula, epsilonvDenotes the volume strain, ∈yDenotes axial strain,. epsilonxRepresents the transverse strain, both without units;
the method comprises the following steps of:
step 1: screening out all data sets meeting the inflection point condition:
εv(n)＝{εv(i)|(εv(i-2)-εv(i))*(εv(i)-εv(i+2))＜0}
in the formula, epsilonv(i)Representing all volume strain data, εv(n)Representing the screened data set meeting the inflection point condition;
step 2: values with inflection point failures greater than 5 were screened from the set:
εv(n′)＝{εv(n)|εv(n)＜εv(j)is less than 5}
In the formula, epsilonv(j)Represents the current epsilonv(n)Value of body strain, epsilon, monitored after the valuev(n′)Representing a data set with the screened failure times smaller than 5;
and step 3: selecting the value of the bulk strain inflection point from the set:
εv＝min(εv(n′))
in the formula, epsilonvRepresenting the finally screened body strain inflection value;
once the body strain inflection point is reached, calculating the total displacement according to the axis strain corresponding to the body strain inflection point, and determining the loading rate by combining the corresponding loading duration:
wherein v represents a loading speed in mm/s; h represents the height of the rock sample in mm; epsilonyAs a volume strain inflection point εvCorresponding axial strain, no unit; t is loading duration corresponding to the body strain inflection point, and the unit is s;
and (3) estimating the destruction time:
wherein T represents the estimated destruction time in units of s; sigmacThe uniaxial compressive strength is expressed in MPa; e represents the elastic modulus in GPa; v represents the loading speed in mm/s; l represents the height of the rock specimen in mm;
the damage energy estimation is divided into the following steps:
step 1: estimating peak point axial strain and peak point stress according to the inflection point body strain:
in the formula, epsilonyThe axial strain corresponding to the body strain inflection point is represented, and no unit exists; epsilonymaxRepresents the estimated peak point axial strain, unitless; sigmacmaxRepresents the estimated peak intensity in Mpa; e represents the modulus of elasticity, in Gpa;
step 2: estimating the failure energy according to the volume and the estimated axial strain and peak intensity of the peak point:
wherein U represents the estimated destruction energy in J; v represents the volume of the rock sample in m3；
And (3) estimating the failure mode:
based on the digital speckle strain field image data, the image of the rock sample fracture mode is predicted by using a watershed algorithm through OpenCV + Python. The processing steps are as follows:
step 1: loading digital speckle image data processed by a data preprocessing module;
step 2: a binarized image is obtained. Performing black and white segmentation on image data by setting a threshold;
and step 3: a background area image is obtained. Performing opening operation on the corrosion and expansion results obtained in the step 2, and performing expansion to obtain a background area image;
and 4, step 4: an image of the crack area is obtained. Recalculating to obtain a crack area image through distance conversion;
and 5: a fracture pattern image is obtained. And (4) processing the connected regions obtained in the step (3) and the step (4), and obtaining a final fracture mode image by using a watershed algorithm.
The visualized content includes: the real-time display of dynamic strain gauge data and a stress-strain curve, the real-time display of acoustic emission real-time data and a time-acoustic emission energy curve and a time-ringing number curve, and the real-time display of a speckle real-time processing result image and an original speckle image.
The invention also provides a real-time monitoring and early warning method based on the real-time monitoring and early warning system for the rock destruction process based on the multi-source heterogeneous data, and a flow chart is shown in figure 2 and comprises the following steps:
s1, preparation for monitoring: attaching a strain gauge and an acoustic emission probe on the surface of the rock, and monitoring strain data and acoustic emission data; drawing speckle patterns on the surface of the rock to obtain speckle image data;
s2, data acquisition: acquiring strain data by adopting a dynamic strain gauge, acquiring acoustic emission data by adopting an acoustic emission data acquisition instrument, and acquiring digital speckle image data by adopting a camera;
s3, edge processing: the data edge processing unit carries out edge processing on the acquired data and stores the processed original data;
s4, data transmission: the data transmission unit transmits data to the data integration analysis early warning unit to prepare for data integration analysis and visual display;
s5, integrated analysis and visual display: the data integration analysis early warning unit performs integration analysis and visual display on data, starts processing after the data are received, performs prediction early warning on rock instability damage according to results, displays the data processing results on a C # WinForm interface, and has a prediction early warning data derivation function.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (7)
1. The utility model provides a rock destruction process real-time supervision early warning system based on heterogeneous data of multisource which characterized in that includes: the data acquisition unit, the data edge processing unit, the data transmission unit and the data integration analysis early warning unit; wherein:
the data acquisition unit is used for acquiring mechanical response data in the rock instability destruction process in real time;
the data edge processing unit is used for receiving the mechanical response data acquired by the data acquisition unit and preprocessing and storing the data in real time;
the data transmission unit is used for transmitting the data processed by the data edge processing unit in real time and transmitting the data processing result to the data integration analysis early warning unit;
the data integration analysis early warning unit is used for receiving the monitoring data transmitted by the data transmission unit in real time, performing integration analysis on the data, performing prediction early warning on rock instability damage according to a processing result, simultaneously performing operation control on the monitoring early warning system, and performing visual display on a real-time processing result, wherein the display includes real-time data display and real-time prediction result display.
2. The real-time monitoring and early warning system for the rock destruction process based on multi-source heterogeneous data according to claim 1, wherein the mechanical response data comprises strain data of the rock during loading, acoustic emission data induced by crack initiation and propagation, and digital speckle image data of the rock surface.
3. The real-time monitoring and early warning system for the rock destruction process based on the multi-source heterogeneous data according to claim 1, wherein the data acquisition unit comprises a dynamic strain gauge and strain gauge sensor for acquiring monitoring data in real time, an acoustic emission data acquisition instrument and acoustic emission probe, and a digital speckle image acquisition camera; wherein:
the dynamic strain gauge and the strain gauge sensor are used for monitoring strain data of a rock local point in the loading process, acquiring the strain data in real time through a LabVIEW secondary development program, and sending the strain data to the data edge processing unit through an HTTP data transmission protocol;
the acoustic emission data acquisition instrument and the acoustic emission probe are used for monitoring elastic waves induced by crack initiation and expansion in the rock in the loading process, namely acoustic emission data, acquiring the acoustic emission data in real time through a LabVIEW secondary development program, and sending the acoustic emission data to the data edge processing unit through an HTTP data transmission protocol;
the digital speckle image acquisition camera is used for monitoring digital speckle image data of the rock surface in the loading process, and the image data is obtained by timing shooting and transmitted to the data edge processing unit through Wi-Fi.
4. The real-time monitoring and early warning system for the rock destruction process based on multi-source heterogeneous data is characterized in that the data edge processing unit comprises a data preprocessing module and a data storage module; wherein:
the data preprocessing module is configured to perform edge data processing on the data received by the data acquisition unit, and includes: denoising and zeroing conversion are carried out on the strain data of the rock local points acquired by the dynamic strain gauge and the strain gauge sensor; extracting characteristic parameters of waveform data acquired by an acoustic emission data acquisition instrument and an acoustic emission probe to obtain an acoustic emission energy value and a ringing number; processing a digital speckle image shot by a camera to obtain strain field information;
and the data storage module is used for storing the raw data subjected to edge processing into a database.
5. The real-time monitoring and early warning system for the rock destruction process based on multi-source heterogeneous data according to claim 1, wherein the transmission mode adopted by the data transmission module comprises a WebSocket data transmission protocol, C #. NET-FilesSystemWatcher; wherein:
after the strain data and the acoustic emission data are processed by the data edge processing unit, the data are forwarded to the data integration analysis early warning unit for integration analysis through a WebSocket protocol;
and after the speckle images are shot, the speckle images are processed by a data edge processing unit, and the processing results are obtained by sensing through a C #. NET-FileSystemWatcher and displayed in a data integration analysis early warning unit.
6. The real-time monitoring and early warning system for the rock destruction process based on the multi-source heterogeneous data is characterized in that the data integration analysis early warning module is used for integration analysis and visual display of the multi-source heterogeneous data; wherein:
the content of the integrated analysis comprises: preliminarily estimating mechanical parameters of the rock according to an empirical formula, and judging a body strain inflection point in the rock loading process by combining real-time monitoring data; forecasting and early warning the instability damage of the rock according to the processing result, wherein the forecasting and early warning contents comprise rock damage time, damage energy and a damage mode;
the visualized content includes: the real-time display of dynamic strain gauge data and a stress-strain curve, the real-time display of acoustic emission real-time data and a time-acoustic emission energy curve and a time-ringing number curve, and the real-time display of a speckle real-time processing result image and an original speckle image.
7. The real-time monitoring and early warning method of the real-time monitoring and early warning system for the rock destruction process based on the multi-source heterogeneous data is based on any one of claims 1 to 6, and is characterized by comprising the following steps:
s1, preparation for monitoring: attaching a strain gauge and an acoustic emission probe on the surface of the rock, and monitoring strain data and acoustic emission data; drawing speckle patterns on the surface of the rock to obtain speckle image data;
s2, data acquisition: acquiring strain data by adopting a dynamic strain gauge, acquiring acoustic emission data by adopting an acoustic emission data acquisition instrument, and acquiring digital speckle image data by adopting a camera;
s3, edge processing: the data edge processing unit carries out edge processing on the acquired data and stores the processed original data;
s4, data transmission: the data transmission unit transmits data to the data integration analysis early warning unit to prepare for data integration analysis and visual display;
s5, integrated analysis and visual display: the data integration analysis early warning unit performs integration analysis and visual display on data, starts processing after the data are received, performs prediction early warning on rock instability damage according to results, displays the data processing results on a C # WinForm interface, and has a prediction early warning data derivation function.
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