Slope dangerous rock acoustic image catastrophe feature recognition system

Abstract

The invention discloses a slope dangerous rock acoustic image catastrophe feature recognition system, which belongs to the technical field of slope monitoring, and comprises an acoustic emission signal acquisition module for collecting a first acoustic wave signal and a second acoustic wave signal generated between bed rock and surrounding rock through acoustic emission signal acquisition equipment, the first sound wave signal is a sound signal generated by cracking of the bonding surface of the bedrock and the dangerous rock, and the second sound wave signal is a sound wave signal generated when the dangerous rock slides on the bedrock; the catastrophe precursor characteristic criterion module is used for comparing the first sound wave signal with the second sound wave signal to position the arrival time of the first sound wave when the dangerous rock starts to slide, and the arrival time is used as a critical parameter and a precursor characteristic threshold value; and the video monitoring and image processing module is used for identifying the cracking deformation size of the bonding surface through an image identification technology. According to the method, the precursor characteristics of the dangerous rock catastrophe instability are accurately judged through fusion analysis of the acoustic emission signals and the video monitoring data.

Description

Technical Field

[0001] The invention belongs to the technical field of slope monitoring, and in particular relates to a system for identifying the acoustic and visual disaster characteristics of dangerous rocks on slopes. Background Art

[0002] Monitoring of dangerous rock disasters on slopes is one of the important tasks in the field of geological engineering and disaster prevention and mitigation. It mainly uses advanced monitoring technology and data analysis methods to conduct real-time monitoring, early warning and assessment of possible dangerous rock collapse, landslides and other disasters on slopes.

[0003] The monitoring and early warning technology of dangerous rocks on slopes is one of the technical difficulties in monitoring dangerous rocks on slopes. Since the deformation and destruction process of dangerous rocks on slopes is often sudden and hidden, traditional monitoring methods often cannot capture the deformation information of dangerous rocks in a timely and accurate manner. Therefore, it is necessary to develop advanced monitoring and early warning technologies to achieve real-time monitoring and early warning of dangerous rocks on slopes. Summary of the invention

[0004] In view of the problems existing in the prior art, the present invention provides a slope dangerous rock disaster feature recognition system with acoustic and visual images, which has the advantage of accurately judging the precursory features of dangerous rock disaster instability through the fusion analysis of acoustic emission signals and video monitoring data, thus solving the problems of the prior art.

[0005] The present invention is implemented as follows: a slope dangerous rock sound and image disaster feature recognition system, comprising:

[0006] An acoustic emission signal collection module collects a first acoustic wave signal and a second acoustic wave signal generated between the bedrock and the surrounding rock through an acoustic emission signal collection device, wherein the first acoustic wave signal is a sound signal generated by cracking of the bonding surface between the bedrock and the dangerous rock, and the second acoustic wave signal is a sound wave signal generated when the dangerous rock slides on the bedrock;

[0007] The disaster precursor feature judgment module locates the arrival time of the first sound wave when the dangerous rock begins to slide by comparing the first sound wave signal with the second sound wave signal, and uses this as the critical parameter and precursor feature threshold to realize the judgment of the precursor feature. When the precursor feature threshold is reached, it is the starting mechanism for the dangerous rock to start sliding.

[0008] The video monitoring and image processing module deploys industrial cameras at the front and side view positions of the slope to conduct real-time continuous video monitoring. It uses image recognition technology to identify the crack deformation size of the bonding surface and track the position change of the dangerous rock, thereby measuring the displacement of the dangerous rock sliding;

[0009] The warning information release and push module generates warning information when at least one parameter of the crack deformation size of the bonding surface and the dangerous rock sliding size reaches a preset threshold, and sends it to a preset object within a preset time.

[0010] As a preferred embodiment of the present invention, the acoustic emission signal acquisition module includes:

[0011] Signal capturing unit: collecting the first acoustic wave signal and the second acoustic wave signal generated between the bedrock and the surrounding rock through an acoustic emission signal collecting device, wherein the first acoustic wave signal and the second acoustic wave signal each have a plurality of groups;

[0012] Signal recognition unit: after capturing the first sound wave signal and the second sound wave signal, recognize and analyze characteristic parameters of the first sound wave signal and the second sound wave signal;

[0013] Signal transmission unit: transmits the characteristic parameters of the first sound wave signal and the second sound wave signal to the system center for processing and storage.

[0014] As a preferred embodiment of the present invention, the disaster precursor characteristic criterion module includes:

[0015] An acoustic wave signal receiving unit receives the first acoustic wave signal and the second acoustic wave signal captured by the acoustic emission signal acquisition module;

[0016] Acoustic signal comparison unit: compares and analyzes the received first acoustic signal with the second acoustic signal, and determines the acoustic signal characteristics before the dangerous rock slides by comparing the acoustic signal characteristics at different stages;

[0017] Critical parameter positioning unit: Based on the results of acoustic wave signal comparison, the first acoustic wave arrival time when the dangerous rock begins to slide is located. This time point is the key critical point for judging whether the dangerous rock is about to start sliding;

[0018] Precursor characteristic threshold setting unit: after determining the critical parameters, the precursor characteristic threshold is set. When the precursor characteristic threshold is reached, it means that the dangerous rock is about to start sliding.

[0019] Precursor feature judgment execution unit: performs judgment analysis on the sound wave signal received in real time according to the set precursor feature threshold. When the sound wave signal reaches or exceeds the precursor feature threshold, the execution unit will trigger the early warning mechanism.

[0020] As a preferred embodiment of the present invention, the critical parameter positioning unit includes:

[0021] Acoustic signal screening subunit: Screen out key acoustic signals related to dangerous rock sliding from the comparison results received by the acoustic signal comparison unit;

[0022] First arrival time identification subunit: for the selected key acoustic wave signals, by analyzing the arrival time of the signal, identify the first acoustic wave arrival time directly related to the beginning of the sliding of the dangerous rock;

[0023] Time window setting subunit: based on the first arrival time, a time window is set, wherein the time window covers a period of time starting from the first arrival time, and is used to further confirm and verify the signs of dangerous rock sliding;

[0024] Critical parameter confirmation subunit: based on the characteristic analysis of the acoustic wave signal within the time window, confirm the critical parameters when the dangerous rock begins to slide;

[0025] Parameter output and transfer subunit: outputs the results of the confirmed critical parameters and transfers them to the precursor feature threshold setting unit.

[0026] As a preferred embodiment of the present invention, the video monitoring and image processing module includes the following units:

[0027] Industrial camera position monitoring unit: determines the layout position of the industrial camera by measuring and positioning technology, wherein the layout position at least includes the front view position and the side view position of the slope;

[0028] Video real-time acquisition unit: by connecting the camera and the data transmission system, the collected video data is transmitted to the monitoring center in real time;

[0029] Image recognition processing unit: through image recognition technology, it can automatically identify the cracking and deformation of the dangerous rock on the slope and the bonding surface of the bedrock, and measure its size. It can also use image tracking technology to track the position change of the dangerous rock in real time, so as to accurately measure the displacement of the sliding of the dangerous rock.

[0030] Data analysis and presentation unit: Evaluate the stability and potential risks of the slope through statistics and analysis of displacement data, and present the analysis results in the form of charts and reports.

[0031] As a preferred embodiment of the present invention, the video monitoring and image processing module further includes:

[0032] The unit for determining the position of unstable cracks in the dangerous rock of the slope can identify the cracking and deformation of the bonding surface between the dangerous rock and the bedrock through the image recognition processing unit, and can track the real-time changes of the position of the dangerous rock in combination with image tracking technology to locate the position of the cracks caused by sliding or instability of the dangerous rock.

[0033] As a preferred embodiment of the present invention, the slope dangerous rock disaster feature identification system also includes a disaster reproduction module: by integrating and analyzing the data captured by the acoustic emission signal acquisition module and the video monitoring and image processing module, the whole process of the slope dangerous rock disaster is simulated and reproduced.

[0034] As a preferred embodiment of the present invention, the disaster recovery module includes:

[0035] The acoustic signal collection unit collects and integrates the first acoustic signal generated by the cracking of the bonding surface between the bedrock and the dangerous rock and the second acoustic signal generated when the dangerous rock slides on the bedrock in the acoustic emission signal collection module, and timestamps each parameter;

[0036] The video image collection unit obtains the continuous video data of the front and side views of the slope taken in real time by the video monitoring and image processing module through the industrial camera. The video data is processed by image recognition technology to extract the cracking and deformation degree of the bonding surface and the position change information of the dangerous rock, and timestamp each parameter;

[0037] The simulation unit simulates the dynamic changes of dangerous rocks in slopes at various stages of the disaster process by matching and simulating the time series, frequency distribution, and intensity changes of acoustic wave signals and image parameters, including the initiation and expansion of cracking of the bonding surface, and the initiation, acceleration, and cessation of sliding of dangerous rocks;

[0038] The visualization unit displays the simulated slope rock disaster process in a visual way, including three-dimensional animation and numerical charts.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] The system of the present invention realizes multi-dimensional monitoring of the instability of dangerous rocks on slopes. Through the fusion analysis of acoustic emission signals and video monitoring data, it can accurately judge the precursor characteristics of dangerous rock disaster instability by "hearing the sound and seeing the shape". In addition, the system has the prediction and early warning capabilities of "minute scale", and the early warning information can be accurately pushed to relevant objects in a very short time, providing strong technical support for the prevention and treatment of dangerous rock disasters on slopes, significantly improving the efficiency and accuracy of monitoring and early warning, and reducing the losses and risks caused by disasters. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 The framework of the system for identifying the characteristics of dangerous rock slopes with sound and image disasters provided by the embodiment of the present invention is Figure 1 ;

[0042] Figure 2 is a block diagram of an acoustic emission signal acquisition module provided in an embodiment of the present invention;

[0043] Figure 3 is a block diagram of a disaster precursor feature criterion module provided by an embodiment of the present invention;

[0044] Figure 4 is a block diagram of a critical parameter positioning unit provided by an embodiment of the present invention;

[0045] Figure 5 The video monitoring and image processing module provided by the embodiment of the present invention is a frame Figure 1 ;

[0046] Figure 6 The video monitoring and image processing module provided by the embodiment of the present invention is a frame Figure 2 ;

[0047] Figure 7 The framework of the system for identifying the characteristics of dangerous rock slopes with sound and image disasters provided by the embodiment of the present invention is Figure 2 ;

[0048] Figure 8 is a block diagram of a disaster recovery module provided by an embodiment of the present invention;

[0049] Fig. 9 It is a schematic diagram of a system for identifying the acoustic and visual disaster characteristics of dangerous rocks on slopes provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to further understand the content, features and effects of the present invention, the following embodiments are given as examples and described in detail with reference to the accompanying drawings.

[0051] The structure of the present invention is described in detail below in conjunction with the accompanying drawings.

[0052] like Figures 1 to 9 As shown, an embodiment of the present invention provides a system for identifying the acoustic and visual characteristics of dangerous rock disasters on a slope, comprising:

[0053] An acoustic emission signal collection module collects a first acoustic wave signal and a second acoustic wave signal generated between the bedrock and the surrounding rock through an acoustic emission signal collection device, wherein the first acoustic wave signal is a sound signal generated by cracking of the bonding surface between the bedrock and the dangerous rock, and the second acoustic wave signal is a sound wave signal generated when the dangerous rock slides on the bedrock;

[0054] The disaster precursor characteristic judgment module locates the arrival time of the first sound wave when the dangerous rock begins to slide by comparing the first sound wave signal and the second sound wave signal, and uses this as the critical parameter and precursor characteristic threshold (ultimate stress state) to realize the judgment of the precursor characteristic. When the precursor characteristic threshold is reached, it is the starting mechanism for the dangerous rock to start sliding.

[0055] The video monitoring and image processing module deploys industrial cameras at the front and side view positions of the slope to conduct real-time continuous video monitoring. It uses image recognition technology to identify the crack deformation size of the bonding surface and track the position change of the dangerous rock, thereby measuring the displacement of the dangerous rock sliding;

[0056] The warning information release and push module generates warning information when at least one parameter of the crack deformation size of the bonding surface and the dangerous rock sliding size reaches a preset threshold, and sends it to a preset object within a preset time.

[0057] Through the above settings, the acoustic and visual disaster feature recognition system for dangerous rocks on slopes captures the sound characteristic parameters and response signals of cracking of the bonding surface during the separation stage of dangerous rocks and bedrock through the acoustic emission signal acquisition module. At the same time, the video monitoring and image processing module uses industrial cameras to continuously monitor the front and side view positions of the slope in real time, and uses image recognition technology to identify the crack deformation size of the bonding surface and track the change of the position of dangerous rocks. The disaster precursor feature judgment module uses the change of the acoustic wave signal to determine the starting mechanism of dangerous rock sliding. Once the precursor feature threshold is identified, the system generates warning information through the warning information release and push module, and sends the information to the preset object within one minute.

[0058] The system realizes multi-dimensional monitoring of the instability of dangerous rocks on slopes. Through the fusion analysis of acoustic emission signals and video monitoring data, it can accurately judge the precursor characteristics of dangerous rock disaster instability by "hearing the sound and seeing the shape". In addition, the system has the ability to predict and warn at the "minute scale". The warning information can be accurately pushed to relevant objects in a very short time, providing strong technical support for the prevention and treatment of dangerous rock disasters on slopes, significantly improving the efficiency and accuracy of monitoring and warning, and reducing the losses and risks caused by disasters.

[0059] Specifically, the acoustic emission signal acquisition module includes:

[0060] Signal capturing unit: collecting the first acoustic wave signal and the second acoustic wave signal generated between the bedrock and the surrounding rock through an acoustic emission signal collecting device, wherein the first acoustic wave signal and the second acoustic wave signal each have a plurality of groups;

[0061] Signal recognition unit: after capturing the first sound wave signal and the second sound wave signal, recognize and analyze characteristic parameters of the first sound wave signal and the second sound wave signal, such as frequency, amplitude, waveform, etc.;

[0062] Signal transmission unit: transmits the characteristic parameters of the first sound wave signal and the second sound wave signal to the system center for processing and storage.

[0063] Exemplary:

[0064] The specific setting of the signal capture unit is as follows: acoustic emission signal acquisition equipment is installed at appropriate locations on the slope. These devices can continuously monitor the sound signals between the bedrock and the dangerous rock. When the bonding surface between the bedrock and the dangerous rock begins to crack, the first sound wave signal will be generated; and when the dangerous rock begins to slide on the bedrock, the second sound wave signal will be generated. These sound wave signals are captured and recorded by the equipment.

[0065] The specific setting of the signal recognition unit is as follows: the captured sound wave signal is then sent to the signal recognition unit for analysis. In this unit, the system will identify and analyze the characteristic parameters of these sound wave signals, such as frequency, amplitude and waveform. These parameters can reflect the essential characteristics of the sound wave signal and provide a basis for subsequent processing and judgment.

[0066] The specific setting of the signal transmission unit is as follows: the characteristic parameters of the acoustic wave signal after identification and analysis will be transmitted to the system center for storage and processing. The system center will further analyze and judge these data to evaluate the stability of the slope and the risk of sliding of dangerous rocks.

[0067] Through the coordinated work of these three units, the acoustic emission signal acquisition module can capture the acoustic wave signals generated between the bedrock and the dangerous rock in real time and accurately, and convert them into digital data for subsequent processing. This provides important input data for the subsequent disaster precursor feature judgment module, allowing the system to more accurately identify the precursor features of the dangerous rock on the slope that are about to slide, thereby triggering the early warning mechanism in time to ensure the safety of personnel.

[0068] Specifically, the disaster precursor feature judgment module includes the following units:

[0069] An acoustic wave signal receiving unit receives the first acoustic wave signal and the second acoustic wave signal captured by the acoustic emission signal acquisition module;

[0070] Acoustic signal comparison unit: compares and analyzes the received first acoustic signal with the second acoustic signal, and determines the acoustic signal characteristics before the dangerous rock slides by comparing the acoustic signal characteristics at different stages, such as frequency, amplitude, and duration;

[0071] Critical parameter positioning unit: Based on the results of acoustic wave signal comparison, the first acoustic wave arrival time when the dangerous rock begins to slide is located. This time point is the key critical point for judging whether the dangerous rock is about to start sliding;

[0072] Precursor characteristic threshold setting unit: after determining the critical parameters, the precursor characteristic threshold is set. When the precursor characteristic threshold is reached, it means that the dangerous rock is about to start sliding.

[0073] Precursor feature judgment execution unit: performs judgment analysis on the sound wave signal received in real time according to the set precursor feature threshold. When the sound wave signal reaches or exceeds the precursor feature threshold, the execution unit will trigger the early warning mechanism.

[0074] Through the collaborative work of these five units, the disaster precursor feature judgment module can accurately and reliably identify the precursor features of impending sliding of dangerous rocks on the slope, providing strong support for subsequent early warning and emergency response.

[0075] In the above setting, the specific working principle of the acoustic signal comparison unit is: to compare and analyze the extracted features, and compare the differences in the features of acoustic signals at different stages or from different sources. By comparison, the unique change pattern or trend of the acoustic signal before the dangerous rock slides can be identified. For example, if all the acoustic signal features suddenly change and last for a certain period of time, the time point when all the acoustic signal features suddenly change is the critical point when the sliding is about to begin.

[0076] The following example illustrates how these five units work together:

[0077] First, the acoustic signal receiving unit starts working, receiving data from the acoustic emission signal acquisition module installed on the slope. Assume that at a certain point in time, the acquisition module captures two important acoustic signals: the first acoustic signal is collected when the bonding surface between the bedrock and the dangerous rock begins to crack, indicating that the slope begins to become unstable; and the second acoustic signal is collected at a later time, when the slope may be affected by some external factors (such as rainfall, seismic waves, etc.).

[0078] Next, the acoustic signal comparison unit compared and analyzed the two acoustic signals. By comparing their frequency, amplitude, and duration characteristics, the unit found that the second acoustic signal had an increase in frequency, an increase in amplitude, and a longer duration. These changes indicate that the stress state inside the slope may be changing, indicating that the dangerous rock has begun to slide.

[0079] Based on this discovery, the critical parameter positioning unit starts working. It uses the results of the acoustic signal comparison to locate the first acoustic wave arrival time when the dangerous rock begins to slide. This time point is regarded as the critical point for judging whether the dangerous rock is about to start sliding. In this example, it is assumed that the first acoustic wave arrival time is 3 pm on a certain day.

[0080] Subsequently, the precursor feature threshold setting unit sets the precursor feature threshold according to the result of the critical parameter. This threshold is determined based on historical data and expert experience. When the characteristics of the acoustic wave signal (such as frequency, amplitude, etc.) reach or exceed this threshold, it means that the dangerous rock is about to start sliding.

[0081] Finally, the precursor feature judgment execution unit starts working. It receives the acoustic wave signal in real time and performs judgment analysis according to the set precursor feature threshold. Assume that at 3:15 pm, the acoustic wave signal characteristics received in real time exceed the set threshold. At this time, the execution unit will immediately trigger the early warning mechanism and send an alarm signal to the monitoring center, indicating that the dangerous rock on the slope is about to slide so that timely countermeasures can be taken.

[0082] From this example, we can see that the five units of the disaster precursor feature judgment module can work together to accurately and reliably identify the precursor features of impending sliding of dangerous rocks on the slope, providing strong support for subsequent early warning and emergency response.

[0083] Specifically, the critical parameter positioning unit includes:

[0084] Acoustic signal screening subunit: from the comparison results received by the acoustic signal comparison unit, the key acoustic signals related to dangerous rock sliding are screened out. These signals usually have specific frequency, amplitude and duration characteristics, which are important indicators for identifying dangerous rock sliding.

[0085] First arrival time identification subunit: for the selected key sound wave signals, by analyzing the arrival time of the signal, the first sound wave arrival time directly related to the beginning of sliding of the dangerous rock is identified.

[0086] Time window setting subunit: In order to more accurately locate the starting time of dangerous rock sliding, this subunit will set a time window based on the first arrival time. The time window covers a period of time starting from the first arrival time, which is used to further confirm and verify the signs of dangerous rock sliding;

[0087] Critical parameter confirmation subunit: Based on the characteristic analysis of the acoustic wave signal within the time window, the critical parameters when the dangerous rock begins to slide are confirmed. These parameters will serve as the key basis for judging whether the dangerous rock is about to start sliding.

[0088] Parameter output and transfer subunit: Output the results of the confirmed critical parameters and transfer them to the precursor feature threshold setting unit to ensure that the subsequent analysis and early warning mechanism can be carried out based on accurate critical parameters.

[0089] Through the coordinated work of the above five sub-units, the critical parameter positioning unit can accurately and reliably locate the key critical point when the dangerous rock begins to slide, providing a solid foundation for the subsequent precursor characteristic threshold setting and judgment execution.

[0090] Specifically, the video monitoring and image processing module includes the following units:

[0091] Industrial camera position monitoring unit: determines the layout position of the industrial camera by measuring and positioning technology, wherein the layout position at least includes the front view position and the side view position of the slope;

[0092] Video real-time acquisition unit: by connecting the camera and the data transmission system, the collected video data is transmitted to the monitoring center in real time;

[0093] Image recognition processing unit: Through image recognition technology, the cracking and deformation of the dangerous rock and the bonding surface of the bedrock on the slope can be automatically identified and measured. The image tracking technology can be used to track the position change of the dangerous rock in real time, so as to accurately measure the displacement of the sliding of the dangerous rock. In addition, the recognition results need to be verified and corrected as necessary to improve the accuracy and reliability of the recognition.

[0094] Data analysis and presentation unit: Evaluate the stability and potential risks of the slope through statistics and analysis of displacement data, and present the analysis results in the form of charts and reports.

[0095] The video monitoring and image processing module first uses the industrial camera position monitoring unit to accurately deploy industrial cameras at the front and side view positions of the slope to ensure full coverage of the monitoring area. Subsequently, the real-time video acquisition unit instantly transmits the video data captured by the camera to the monitoring center. The image recognition processing unit uses advanced image recognition and tracking technology to automatically identify the cracking and deformation of the bonding surface between the dangerous rock and the bedrock on the slope, accurately measure its size, and continuously track the position changes of the dangerous rock, and calculate its sliding displacement in real time. At the same time, the unit also verifies and corrects the recognition results to ensure the accuracy and reliability of the data.

[0096] This module significantly improves the automation and intelligence level of slope monitoring. Through real-time video acquisition and high-precision image recognition technology, it can quickly capture and accurately analyze the dynamic changes of dangerous rocks on the slope, providing strong data support for the assessment of slope stability. The data analysis and display unit converts complex data into intuitive charts and reports, which makes it easier for monitoring personnel to quickly understand the slope conditions and discover potential risks in a timely manner, providing a scientific basis for taking effective protective measures. This not only improves the efficiency and accuracy of slope monitoring, but also effectively reduces the risk of geological disasters.

[0097] Specifically, the video monitoring and image processing module also includes:

[0098] The unit for determining the position of unstable cracks in the dangerous rock of the slope can identify the cracking and deformation of the bonding surface between the dangerous rock and the bedrock through the image recognition processing unit, and can track the real-time changes of the position of the dangerous rock in combination with image tracking technology to locate the position of the cracks caused by sliding or instability of the dangerous rock.

[0099] The system first uses a high-definition camera to conduct 24-hour uninterrupted video monitoring of the slope. When the camera captures the bonding surface between the rock on the slope (i.e., dangerous rock) and the surrounding rock (bedrock) begins to crack and deform, the image recognition processing unit will immediately intervene. This unit uses image recognition algorithms to accurately analyze the image information in the video, identify which areas are dangerous rocks and which areas are bedrock, and determine whether the bonding surface between them has cracked and deformed. Once the cracking and deformation are identified, the slope dangerous rock instability crack position determination unit will play a further role. This unit will combine image tracking technology to track the position changes of dangerous rocks in real time. Since dangerous rocks will produce cracks along a certain path during sliding or instability, by tracking the position changes of dangerous rocks in real time, the positions of these cracks caused by sliding or instability of dangerous rocks can be accurately located.

[0100] For example, during the monitoring process, if the image recognition processing unit finds that the bonding surface between a dangerous rock and the bedrock on the slope has obvious cracks and deformation, the slope dangerous rock instability crack position determination unit will immediately start the image tracking technology to track the position changes of the dangerous rock in real time. After a period of tracking, the positions of multiple cracks generated by the dangerous rock during the sliding process are located, and these position information is fed back in a timely manner.

[0101] Deep learning algorithms can use convolutional neural networks (CNNs) and models under deep learning frameworks such as TensorFlow or PyTorch. These algorithms can automatically learn and identify subtle differences between dangerous rocks and bedrock, including features such as color, texture, and shape, by training a large amount of labeled image data. In particular, CNN can automatically extract hierarchical feature representations from raw images. In the application scenario of slope monitoring, CNN can learn the characteristic patterns of cracking and deformation of the bonding surface between dangerous rocks and bedrock, and accurately detect and identify them in real-time video streams. In addition to CNN, other image processing techniques such as edge detection, threshold segmentation, morphological operations, etc. can also be combined to further improve the accuracy and efficiency of image recognition. These technologies can help the system more accurately locate cracked and deformed areas and conduct detailed analysis and judgment on them.

[0102] Specifically, the slope dangerous rock disaster feature recognition system also includes a disaster reproduction module: by integrating and analyzing the data captured by the acoustic emission signal acquisition module and the video monitoring and image processing module, the whole process of the slope dangerous rock disaster is simulated and reproduced.

[0103] Specifically, the disaster recurrence module includes:

[0104] The acoustic signal collection unit collects and integrates the first acoustic signal generated by the cracking of the bonding surface between the bedrock and the dangerous rock and the second acoustic signal generated when the dangerous rock slides on the bedrock in the acoustic emission signal collection module, and timestamps each parameter;

[0105] The video image collection unit obtains the continuous video data of the front and side views of the slope taken in real time by the video monitoring and image processing module through the industrial camera. The video data is processed by image recognition technology to extract the cracking and deformation degree of the bonding surface and the position change information of the dangerous rock, and timestamp each parameter;

[0106] The simulation unit simulates the dynamic changes of dangerous rocks in slopes at various stages of the disaster process by matching and simulating the time series, frequency distribution, intensity change and other characteristics of acoustic wave signals and image parameters, including the initiation and expansion process of cracking of the bonding surface, and the initiation, acceleration and cessation of dangerous rock sliding;

[0107] The visualization unit displays the simulated slope rock disaster process in a visual way, including 3D animation and numerical charts. These simulation results can not only be used to analyze and evaluate the mechanism and law of slope rock disaster, but also provide scientific basis and decision support for engineering measures such as slope reinforcement and rock management.

[0108] Working principle of the present invention:

[0109] The slope dangerous rock sound and image disaster feature recognition system also has the following functions:

[0110] Landslide early warning with integrated acoustic emission and video monitoring: Combined with the acoustic emission signal acquisition module and the video monitoring and image processing module, the acoustic emission signals and video images of the monitoring slope are collected in real time. Through the disaster precursor feature judgment module, the collected acoustic emission signals are analyzed to determine the starting mechanism for the dangerous rock to start sliding. At the same time, using image processing technology, the position change of the dangerous rock is tracked in real time through industrial cameras to measure its sliding displacement. When the warning threshold is reached, the system automatically issues a warning signal.

[0111] Monitoring and early warning of cracking and deformation of bonding surface: Using video monitoring and image processing modules, the cracking and deformation of the bonding surface are monitored in real time through image recognition technology. When the deformation reaches the preset threshold, the system combines the acoustic emission signal analysis in the disaster precursor feature judgment module to determine whether there is a landslide risk and issue an early warning.

[0112] Real-time monitoring of landslide displacement: Based on the video monitoring and image processing module, industrial cameras are deployed at the front and side view positions of the slope to track the position changes of dangerous rocks in real time and measure their sliding displacement. The system can display landslide displacement data in real time and provide historical data query and statistical analysis functions.

[0113] Correlation analysis between acoustic emission signals and landslide displacement: Combined with the data from the acoustic emission signal acquisition module and the video monitoring and image processing module, the acoustic emission signal (especially the second acoustic wave signal) and the displacement of dangerous rock sliding are correlated and analyzed. Through comparison and analysis, the inherent relationship between acoustic emission signals and landslide displacement is revealed, providing a scientific basis for landslide early warning and prevention.

[0114] Landslide risk assessment and decision support: The functions of the acoustic emission signal acquisition module, the disaster precursor feature judgment module, and the video monitoring and image processing module are comprehensively applied to build a landslide risk assessment and decision support system. The system can evaluate the risk level of landslides in real time and provide corresponding decision support based on the evaluation results, such as evacuating personnel and reinforcing slopes. At the same time, the system can also post-evaluate the landslide warning and prevention effects to provide a reference for future landslide prevention and control.

[0115] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0116] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A system for identifying the acoustic and visual characteristics of dangerous rock disasters on slopes, characterized in that: include: An acoustic emission signal collection module collects a first acoustic wave signal and a second acoustic wave signal generated between the bedrock and the surrounding rock through an acoustic emission signal collection device, wherein the first acoustic wave signal is a sound signal generated by cracking of the bonding surface between the bedrock and the dangerous rock, and the second acoustic wave signal is a sound wave signal generated when the dangerous rock slides on the bedrock; The disaster precursor feature judgment module locates the arrival time of the first sound wave when the dangerous rock begins to slide by comparing the first sound wave signal with the second sound wave signal, and uses this as the critical parameter and precursor feature threshold to realize the judgment of the precursor feature; The video monitoring and image processing module deploys industrial cameras at the front and side view positions of the slope to conduct real-time continuous video monitoring. It uses image recognition technology to identify the crack deformation size of the bonding surface and track the position change of the dangerous rock, thereby measuring the displacement of the dangerous rock sliding; The warning information release and push module generates warning information when at least one parameter of the crack deformation size of the bonding surface and the dangerous rock sliding size reaches a preset threshold, and sends it to a preset object within a preset time.

2. A system for identifying the acoustic and visual characteristics of dangerous rock disasters on slopes as claimed in claim 1, characterized in that: The acoustic emission signal acquisition module comprises: Signal capturing unit: collecting the first acoustic wave signal and the second acoustic wave signal generated between the bedrock and the surrounding rock through an acoustic emission signal collecting device, wherein the first acoustic wave signal and the second acoustic wave signal each have a plurality of groups; Signal recognition unit: after capturing the first sound wave signal and the second sound wave signal, recognize and analyze characteristic parameters of the first sound wave signal and the second sound wave signal; Signal transmission unit: transmits the characteristic parameters of the first sound wave signal and the second sound wave signal to the system center for processing and storage.

3. The system for identifying the acoustic and visual characteristics of dangerous rock disasters on slopes according to claim 1, characterized in that: The disaster precursor characteristic criterion module includes: An acoustic wave signal receiving unit receives the first acoustic wave signal and the second acoustic wave signal captured by the acoustic emission signal acquisition module; Acoustic signal comparison unit: compares and analyzes the received first acoustic signal with the second acoustic signal, and determines the acoustic signal characteristics before the dangerous rock slides by comparing the acoustic signal characteristics at different stages; Critical parameter positioning unit: Based on the results of acoustic wave signal comparison, the first acoustic wave arrival time when the dangerous rock begins to slide is located. This time point is the key critical point for judging whether the dangerous rock is about to start sliding; Precursor characteristic threshold setting unit: after determining the critical parameters, the precursor characteristic threshold is set. When the precursor characteristic threshold is reached, it means that the dangerous rock is about to start sliding. Precursor feature judgment execution unit: performs judgment analysis on the sound wave signal received in real time according to the set precursor feature threshold. When the sound wave signal reaches or exceeds the precursor feature threshold, the execution unit will trigger the early warning mechanism.

4. A system for identifying the acoustic and visual characteristics of dangerous rock disasters on slopes as claimed in claim 3, characterized in that: The critical parameter positioning unit comprises: Acoustic signal screening subunit: Screen out key acoustic signals related to dangerous rock sliding from the comparison results received by the acoustic signal comparison unit; First arrival time identification subunit: for the selected key acoustic wave signals, by analyzing the arrival time of the signal, identify the first acoustic wave arrival time directly related to the beginning of the sliding of the dangerous rock; Time window setting subunit: based on the first arrival time, a time window is set, wherein the time window covers a period of time starting from the first arrival time, and is used to further confirm and verify the signs of dangerous rock sliding; Critical parameter confirmation subunit: based on the characteristic analysis of the acoustic wave signal within the time window, confirm the critical parameters when the dangerous rock begins to slide; Parameter output and transfer subunit: outputs the results of the confirmed critical parameters and transfers them to the precursor feature threshold setting unit.

5. The system for identifying the acoustic and visual characteristics of dangerous rock disasters on slopes according to claim 1, characterized in that: The video monitoring and image processing module includes the following units: Industrial camera position monitoring unit: determines the layout position of the industrial camera by measuring and positioning technology, wherein the layout position at least includes the front view position and the side view position of the slope; Video real-time acquisition unit: by connecting the camera and the data transmission system, the collected video data is transmitted to the monitoring center in real time; Image recognition processing unit: through image recognition technology, it can automatically identify the cracking and deformation of the dangerous rock on the slope and the bonding surface of the bedrock, and measure its size. It can also use image tracking technology to track the position change of the dangerous rock in real time, so as to accurately measure the displacement of the sliding of the dangerous rock. Data analysis and presentation unit: Evaluate the stability and potential risks of the slope through statistics and analysis of displacement data, and present the analysis results in the form of charts and reports.

6. A system for identifying the acoustic and visual characteristics of dangerous rock disasters on slopes as claimed in claim 5, characterized in that: The video monitoring and image processing module also includes: The unit for determining the position of unstable cracks in the dangerous rock of the slope can identify the cracking and deformation of the bonding surface between the dangerous rock and the bedrock through the image recognition processing unit, and can track the real-time changes of the position of the dangerous rock in combination with image tracking technology to locate the position of the cracks caused by sliding or instability of the dangerous rock.

7. The system for identifying the acoustic and visual characteristics of dangerous rock disasters on slopes according to claim 1, characterized in that: The slope dangerous rock sound and image disaster feature recognition system also includes a disaster reproduction module: by integrating and analyzing the data captured by the acoustic emission signal acquisition module and the video monitoring and image processing module, the whole process of the slope dangerous rock disaster is simulated and reproduced.

8. The system for identifying the acoustic and visual characteristics of dangerous rock disasters on slopes according to claim 7, characterized in that: The disaster recurrence module includes: The acoustic signal collection unit collects and integrates the first acoustic signal generated by the cracking of the bonding surface between the bedrock and the dangerous rock and the second acoustic signal generated when the dangerous rock slides on the bedrock in the acoustic emission signal collection module, and timestamps each parameter; The video image collection unit obtains the continuous video data of the front and side views of the slope taken in real time by the video monitoring and image processing module through the industrial camera. The video data is processed by image recognition technology to extract the cracking and deformation degree of the bonding surface and the position change information of the dangerous rock, and timestamp each parameter; The simulation unit simulates the dynamic changes of dangerous rocks in slopes at various stages of the disaster process by matching and simulating the time series, frequency distribution, and intensity changes of acoustic wave signals and image parameters, including the initiation and expansion of cracking of the bonding surface, and the initiation, acceleration, and cessation of sliding of dangerous rocks; The visualization unit displays the simulated slope rock disaster process in a visual way, including three-dimensional animation and numerical charts.

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