CN116386302A - Intelligent monitoring and early warning system for side slope - Google Patents
Intelligent monitoring and early warning system for side slope Download PDFInfo
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- CN116386302A CN116386302A CN202310384638.9A CN202310384638A CN116386302A CN 116386302 A CN116386302 A CN 116386302A CN 202310384638 A CN202310384638 A CN 202310384638A CN 116386302 A CN116386302 A CN 116386302A
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Abstract
The invention discloses an intelligent slope monitoring and early warning system, which comprises a slope data acquisition subsystem, a slope detection subsystem and a slope detection subsystem, wherein the slope data acquisition subsystem is used for acquiring slope data and slope images; the data processing subsystem is used for carrying out demodulation processing on the side slope data; the slope disaster analysis subsystem is used for constructing a slope early warning model and carrying out slope disaster analysis according to the processed slope data and the slope image; the data storage library is used for storing slope data and slope images in each analysis process and corresponding analysis results; the early warning setting subsystem is used for setting an early warning threshold according to the analysis result of the slope disasters; the early warning subsystem is used for carrying out early warning prompts of different grades of yellow early warning, orange early warning and red early warning according to the early warning threshold value. The invention adopts the convolutional neural network and the prediction network to analyze the data according to the correlation between the data and the image, has higher accuracy than the traditional artificial judgment method, stores and sorts the data by a big data technology, and ensures the high efficiency of data extraction.
Description
Technical Field
The invention belongs to the field of slope disaster early warning, and particularly relates to an intelligent slope monitoring and early warning system.
Background
The damage form of the side slope mainly comprises landslide, collapse and debris flow, and the main reason for the landslide is that the side slope body is subjected to double influences from rainfall, surface water and underground water, so that the stability of the internal structure of the side slope is damaged, and the side slope is caused to slide downwards, and the current landslide mainly comprises integral, push-type and traction-type landslide; the main reasons for the collapse of the side slope include collapse caused by small soil property collapse and small coefficient of soil excavation and slope release, collapse caused by groundwater or soft soil layer and sand flowing, and insufficient wall protection or support. However, no matter what kind of side slope disaster, the damage generated by the side slope disaster is unpredictable, and the side slope disaster can even cause traffic blockage when encountering heavy rainfall, thereby bringing life and property loss to people.
The existing slope disaster early warning technology is often used for carrying out artificial judgment according to historical data and combining experience, and the method has the problems of large workload, easiness in being influenced by severe weather conditions, poor prediction accuracy and the like.
Disclosure of Invention
The invention aims to provide an intelligent slope monitoring and early warning system for solving the problems in the prior art.
In order to achieve the above purpose, the invention provides an intelligent monitoring and early warning system for a side slope, which comprises a side slope data acquisition subsystem, a data processing subsystem, a side slope disaster analysis subsystem, a data storage library, an early warning setting subsystem and an early warning subsystem;
the slope data acquisition subsystem is used for acquiring slope data and slope images;
the data processing subsystem is used for demodulating the slope data;
the slope disaster analysis subsystem is used for constructing a slope early warning model and analyzing the slope disaster according to the processed slope data and the slope image;
the data storage library is used for storing slope data and slope images in each analysis process and corresponding analysis results;
the early warning setting subsystem is used for setting an early warning threshold according to the analysis result of the slope disasters;
and the early warning subsystem is used for carrying out early warning prompts of different grades of yellow early warning, orange early warning and red early warning according to the early warning threshold value.
Optionally, the slope data acquisition subsystem comprises temperature sensors, vibration sensors, pressure sensors and image pick-up devices which are arranged on the surface and in the slope, and the slope data comprises vibration signals, stress data, strain data, moisture data, temperature data and seismic wave data on the surface and in the slope.
As a preferred embodiment of the present application, the data processing subsystem includes a phase reflectometer, and the phase reflectometer is used to perform phase demodulation on the slope data, obtain a measurement waveform, and transmit the measurement waveform to the slope disaster analysis subsystem.
Optionally, the slope disaster analysis subsystem constructs a training set and a testing set for the processed test waveforms and the slope images respectively, constructs a landslide monitoring frame by adopting a convolutional neural network, a connecting network and a prediction network, carries out similarity measurement on the frame containing image features in the slope images and the frame taking the image features as targets based on a CIoU-loss strategy, carries out consistency measurement based on a weight coefficient, and constructs the slope early warning model.
Optionally, the slope disaster analysis subsystem judges whether the image features exist on the slope image, if so, the slope image is directly input into the convolutional neural network for training, if not, the slope image is subjected to back propagation to change image information to obtain a corrected image, and the corrected image is subjected to counter attack to strengthen the convolutional neural network; and the enhanced convolutional neural network carries out target detection on the corrected image and carries out convolutional neural network training.
Optionally, the slope disaster analysis subsystem inputs the demodulated measured waveform and the slope image containing the image features into the slope early warning model, acquires the correlation between the measured waveform and the corresponding position image features through the convolutional neural network after training, acquires the feature parameters representing the occurrence of different slope disasters through the prediction network, and predicts by combining the data correlation, the feature parameters and the slope image to acquire the analysis result containing the disaster occurrence probability.
Optionally, the data repository adopts a micro-service cluster architecture mode to split and deploy services, adopts a big data technology and a big data algorithm to store, process and calculate historical data and real-time data, and simultaneously sorts and sorts the stored data according to data analysis time and analysis results.
Optionally, the early warning setting subsystem sets early warning weights according to risk analysis results of the slope early warning model, and sets early warning thresholds for vibration signals, moisture data and stress data in the slope data according to the early warning weights.
Optionally, the early warning subsystem carries out early warning prompt of different forms according to the early warning threshold value, including signal lamp suggestion, audio broadcast, early warning type of early warning subsystem includes rainfall early warning, landslide early warning, collapse early warning, crack early warning.
The invention has the technical effects that:
the invention adopts the sensor to collect data, adopts the phase reflectometer to process the data, adopts the convolutional neural network and the prediction network to analyze the data according to the relativity of the data and the image, has higher accuracy than the traditional manual judgment method, stores and sorts the data by a big data technology, ensures the high efficiency of data extraction, and finally carries out dynamic and hierarchical early warning setting according to the analysis result, thereby improving the system efficiency and avoiding false alarm.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application, illustrate and explain the application and are not to be construed as limiting the application. In the drawings:
FIG. 1 is a schematic diagram of a slope intelligent monitoring and early warning system in an embodiment of the invention;
FIG. 2 is a schematic diagram of monitoring types of a slope intelligent monitoring and early warning system in an embodiment of the invention;
fig. 3 is a schematic diagram of the working principle of the slope intelligent monitoring and early warning system in the embodiment of the invention.
Detailed Description
It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is illustrated in the flowcharts, in some cases the steps illustrated or described may be performed in an order other than that illustrated herein.
Example 1
1-3, the embodiment provides a slope intelligent monitoring and early warning system, which comprises a slope data acquisition subsystem, a data processing subsystem, a slope disaster analysis subsystem, a data storage library, an early warning setting subsystem and an early warning subsystem; specifically:
the slope data acquisition subsystem comprises temperature sensors, vibration sensors, pressure sensors and an imaging device which are arranged on the surface and in the slope, and the imaging device is used for shooting the slope in real time to acquire a slope image; the slope data comprise vibration signals, stress data, strain data, moisture data, temperature data and seismic wave data on the slope surface and in the slope, and the vibration signals, the stress data, the strain data, the moisture data, the temperature data and the seismic wave data are correspondingly acquired through the sensors.
The data processing subsystem comprises a phase reflectometer, phase demodulation is carried out on collected side slope data through the phase reflectometer, a measurement waveform is obtained, the measurement waveform is transmitted to the side slope disaster analysis subsystem for data analysis, and the pretreatment of the side slope data is realized;
after the data acquisition and processing process is finished, the slope disaster analysis subsystem respectively builds a training set and a testing set for the processed test waveforms and the slope images, builds a landslide monitoring frame by adopting a convolutional neural network, a connecting network and a prediction network, carries out similarity measurement on the frame containing image features in the slope images and the frame taking the image features as targets based on a CIoU-loss strategy, builds a slope early warning model based on weight coefficients, and carries out slope disaster analysis according to the processed slope data and the slope images through the slope early warning model;
in the landslide monitoring framework, images containing different slope characteristics in a training set are aggregated through a convolutional neural network, and image characteristics are obtained. The method comprises the steps of splicing image features through a connecting network, connecting a convolutional neural network with a prediction network, realizing the connecting network through a pyramid network structure, carrying out fixed-size pooling on features with any size through the pyramid structure in the process of splicing the image features, splicing the features obtained by each pooling, obtaining feature images with fixed lengths, and inputting the feature images into the prediction network for training. And finally, detecting the characteristics of the spliced image through a prediction network, and acquiring a detection boundary frame and classification of the side slope disasters.
After the model construction is completed, the slope disaster analysis subsystem inputs the demodulated measured waveform and the slope image containing the image characteristics into a slope early warning model, acquires the correlation between the measured waveform and the image characteristics of the corresponding position through a convolution neural network after training is completed, acquires characteristic parameters representing the occurrence of different slope disasters through the prediction network, and predicts by combining the data correlation, the characteristic parameters and the slope image to acquire an analysis result containing the disaster occurrence probability.
As a preferred embodiment of the present application, the data repository is used for storing the slope data and the slope image in each analysis process and the corresponding analysis results; specifically, the data storage library adopts a micro-service cluster architecture mode to split and deploy services, adopts a big data technology and a big data algorithm to store, process and calculate historical data and real-time data, and simultaneously sorts and sorts the stored data according to data analysis time and analysis results.
After the analysis result is obtained, the early warning setting subsystem sets early warning weight according to the risk analysis result of the slope early warning model, sets an early warning threshold value for vibration signals, moisture data and stress data in the slope data according to the early warning weight, and controls early warning time of the early warning subsystem according to the early warning threshold value;
when the value in the slope data reaches the early warning threshold value, the early warning subsystem carries out early warning prompt in different forms according to the early warning threshold value, wherein the early warning subsystem comprises signal lamp prompt and audio broadcasting, the early warning types comprise rainfall early warning, landslide early warning, collapse early warning and crack early warning, the signal lamp prompt modes comprise yellow early warning, orange early warning and red early warning, and the yellow to red early warning threshold values from low values to high values are respectively represented.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a system, or as a computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing is merely a preferred embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions easily contemplated by those skilled in the art within the technical scope of the present application should be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (9)
1. The intelligent monitoring and early warning system for the side slope is characterized by comprising a side slope data acquisition subsystem, a data processing subsystem, a side slope disaster analysis subsystem, a data storage library, an early warning setting subsystem and an early warning subsystem;
the slope data acquisition subsystem is used for acquiring slope data and slope images;
the data processing subsystem is used for demodulating the slope data;
the slope disaster analysis subsystem is used for constructing a slope early warning model and analyzing the slope disaster according to the processed slope data and the slope image;
the data storage library is used for storing slope data and slope images in each analysis process and corresponding analysis results;
the early warning setting subsystem is used for setting an early warning threshold according to the analysis result of the slope disasters;
and the early warning subsystem is used for carrying out early warning prompts of different grades of yellow early warning, orange early warning and red early warning according to the early warning threshold value.
2. The intelligent monitoring and early warning system for the side slope according to claim 1, wherein the side slope data acquisition subsystem comprises temperature sensors, vibration sensors, pressure sensors and an imaging device which are arranged on the surface and in the side slope, and the side slope data comprises vibration signals, stress data, strain data, moisture data, temperature data and seismic wave data on the surface and in the side slope.
3. The intelligent monitoring and early warning system for a side slope according to claim 1, wherein the data processing subsystem comprises a phase reflectometer, the side slope data is phase-demodulated by the phase reflectometer, a measurement waveform is obtained, and the measurement waveform is transmitted to the side slope disaster analysis subsystem.
4. The intelligent monitoring and early warning system for the side slope according to claim 1, wherein the side slope disaster analysis subsystem respectively builds a training set and a testing set for the processed testing waveforms and the side slope images, builds a landslide monitoring frame by adopting a convolutional neural network, a connecting network and a prediction network, carries out similarity measurement on a frame containing image features in the side slope images and a frame taking the image features as targets based on a CIoU-loss strategy, carries out consistency measurement based on weight coefficients, and builds the side slope early warning model.
5. The intelligent monitoring and early warning system for the side slope according to claim 1, wherein the side slope disaster analysis subsystem judges whether image features exist on the side slope image, if so, the side slope image is directly input into the convolutional neural network for training, and if not, image information is changed by back propagation of the side slope image, a corrected image is obtained, and the side slope image is subjected to counter attack to strengthen the convolutional neural network; and the enhanced convolutional neural network carries out target detection on the corrected image and carries out convolutional neural network training.
6. The intelligent monitoring and early warning system for the side slope according to claim 1, wherein the side slope disaster analysis subsystem inputs the demodulated measured waveform and the side slope image containing the image characteristics into the side slope early warning model, obtains the correlation between the measured waveform and the image characteristics of the corresponding position through a convolutional neural network after training is completed, obtains characteristic parameters representing the occurrence of different side slope disasters through the prediction network, and predicts by combining the data correlation, the characteristic parameters and the side slope image to obtain an analysis result containing the disaster occurrence probability.
7. The intelligent monitoring and early warning system for the side slope according to claim 1, wherein the data storage library adopts a micro-service cluster architecture mode to split and deploy services, adopts a big data technology and a big data algorithm to store, process and calculate historical data and real-time data, and simultaneously sorts and sorts the stored data according to data analysis time and analysis results.
8. The intelligent monitoring and early warning system for the side slope according to claim 1, wherein the early warning setting subsystem sets early warning weights according to risk analysis results of the side slope early warning model, and sets early warning thresholds for vibration signals, moisture data and stress data in side slope data according to the early warning weights.
9. The intelligent slope monitoring and early warning system according to claim 1, wherein the early warning subsystem carries out early warning prompts of different forms according to the early warning threshold value, the early warning subsystem comprises a signal lamp prompt and an audio broadcast, and the early warning types of the early warning subsystem comprise rainfall early warning, landslide early warning, collapse early warning and crack early warning.
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| CN202310384638.9A CN116386302A (en) | 2023-04-12 | 2023-04-12 | Intelligent monitoring and early warning system for side slope |
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| CN202310384638.9A CN116386302A (en) | 2023-04-12 | 2023-04-12 | Intelligent monitoring and early warning system for side slope |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117144942A (en) * | 2023-08-30 | 2023-12-01 | 广东交通职业技术学院 | Slope engineering reinforcement state sensing protection monitoring method and system |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117144942A (en) * | 2023-08-30 | 2023-12-01 | 广东交通职业技术学院 | Slope engineering reinforcement state sensing protection monitoring method and system |
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