CN115424415A - Landslide disaster intelligent early warning method based on GNSS real-time monitoring - Google Patents
Landslide disaster intelligent early warning method based on GNSS real-time monitoring Download PDFInfo
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Abstract
The invention relates to the technical field of slope early warning, and discloses a landslide hazard intelligent early warning method based on GNSS real-time monitoring, which comprises the following steps: preprocessing GNSS monitoring data; performing anomaly detection on all GNSS devices based on the preprocessed data; removing monitoring data of abnormal GNSS equipment; firstly, preprocessing the retained historical monitoring data of the normal GNSS equipment again, then analyzing and extracting the actual deformation trend of the time sequence of the retained historical data monitored by the normal GNSS equipment, and finally calculating the deformation difference and the deformation speed of two adjacent time intervals of the historical data monitored by the normal GNSS equipment based on the extracted actual deformation trend. The invention adopts the intelligent and real-time monitoring and early warning method for landslide disaster based on crack meter monitoring, and does not need to manually set the deformation threshold value for landslide disaster occurrence, thereby avoiding missing report or false report caused by improper threshold value setting and improving the accuracy of slope early warning.
Description
Technical Field
The invention relates to the technical field of slope early warning, in particular to a landslide hazard intelligent early warning method based on GNSS real-time monitoring.
Background
At present, landslide is one of natural disasters which easily occur in the nature, and great influence is caused on the production and the life of human beings. Therefore, an automatic online monitoring system is established for the side slope with potential landslide risk, and timely early warning information is issued through monitoring data, so that the system has important significance for reducing landslide accidents and reducing accident loss.
With the success of networking of Beidou satellites, monitoring of side slope surface displacement by using a GNSS (Global Navigation Satellite System) is one of the most common side slope displacement monitoring methods at present, GNSS equipment can acquire high-precision three-dimensional space position information all day long, has very high sampling frequency, can realize real-time continuous online monitoring of side slopes, has the advantages of low manufacturing cost, simplicity in maintenance and the like, and has been applied to large-scale engineering.
In engineering practice, GNSS equipment is installed in the surface of monitoring the side slope outdoors, because the influence of environmental factors such as temperature, humidity, trees shelter from, the GNSS monitoring value has certain volatility, wherein may be mixed with gross error data, causes the difficulty for subsequent data analysis and monitoring early warning.
Therefore, how to improve the accuracy of the GNSS monitoring and early warning is a problem to be solved urgently in the GNSS slope monitoring and early warning.
Disclosure of Invention
The invention aims to provide a landslide hazard intelligent early warning method based on GNSS real-time monitoring, and aims to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme:
a slope monitoring and early warning method based on GNSS comprises the following steps:
preprocessing GNSS monitoring data;
performing anomaly detection on all GNSS devices based on the preprocessed data;
removing monitoring data of abnormal GNSS equipment; firstly, preprocessing the retained historical monitoring data of normal GNSS equipment again, then analyzing and extracting the actual deformation trend of the time sequence of the retained historical data monitored by the normal GNSS equipment, and finally calculating the deformation difference and the deformation speed of two adjacent time intervals of the historical data monitored by the normal GNSS equipment based on the extracted actual deformation trend;
classifying 40 clustering centers of a data sequence consisting of the deformation difference and the deformation speed by adopting a K-means method, carrying out clustering analysis to obtain a clustering center coordinate of deformation data monitored by the GNSS, establishing an early warning index, and dividing the grade of landslide risk;
and acquiring new data, preprocessing the new data, judging whether the data are reserved, extracting the trend of the reserved data, calculating the deformation difference and the deformation speed of the new data according to the extracted actual deformation trend, and obtaining the corresponding risk grade by contrasting with the early warning index.
Optionally, in the data preprocessing, the preprocessing method includes:
if the absolute value of the data deformation difference of two adjacent time points is greater than 2000mm, deleting the corresponding data, otherwise, retaining;
if the time interval between two adjacent monitoring data is less than 600s, deleting the corresponding data, otherwise, keeping the corresponding data;
and (3) data extraction, namely extracting the linear deformation trend of the monitoring data along with time in a linear fitting mode.
Optionally, when anomaly detection is performed on the equipment;
counting the time interval of two adjacent monitoring data in the equipment, and if the average time interval is about 3600s, determining that the equipment is in the first type of abnormity;
and subtracting the linear deformation trend corresponding to the time point from the monitoring data to obtain the fluctuation of the linear deformation trend corresponding to the monitoring data, and counting the standard deviation of the fluctuation of the linear deformation trend corresponding to the monitoring data, wherein if the standard deviation is overlarge, the monitoring equipment is considered to have the second type of abnormality.
Optionally, in the actual deformation trend extraction, the time series of the historical data of the normal device monitoring which is firstly reserved is used for analyzing and extracting the actual deformation trend of the time series of the historical data of the device monitoring by adopting a time series data prediction tool Prophet and setting a 95% confidence interval.
Optionally, the risk levels are no warning, blue warning, yellow warning, orange warning and red warning, respectively.
Optionally, in determining whether the new access data is reserved, the following principle is utilized:
if the absolute value of the data deformation difference of two adjacent time points is greater than 2000mm, deleting the corresponding data, otherwise, keeping the corresponding data;
and if the time interval between two adjacent monitoring data is less than 600s, deleting the corresponding data, and otherwise, keeping the corresponding data.
Optionally, when extracting a deformation trend of the retained new data, first selecting a dynamic time window with a length of L data points, and then setting a 95% confidence interval by using a time series data prediction tool Prophet to obtain an actual deformation trend of the data in the time window;
the method comprises the steps of continuously updating the time window to be a monitoring data point accessed in real time at the front part of the time window, continuously deleting the oldest historical data point at the rear part of the time window, and always keeping the time window length to be L.
The beneficial effects of the invention are:
according to the method, data monitored by abnormal GNSS can be effectively eliminated, the characteristics of deformation difference and deformation speed of historical data monitored by normal equipment are obtained through an unsupervised learning (clustering analysis) method, on the basis, an intelligent and real-time monitoring and early warning method for landslide disaster monitoring based on a crack meter is established, and the risk level of landslide disaster occurrence is evaluated in real time according to the deformation difference and the deformation speed, so that landslide disaster forecasting and early warning are realized. By adopting the intelligent and real-time monitoring and early warning method for landslide disaster based on crack meter monitoring, the deformation threshold value for landslide disaster occurrence is not required to be manually set, so that the condition that the missed report or false report caused by improper threshold value setting can be effectively prevented, and the accuracy of slope early warning is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort. In the drawings:
fig. 1 is a schematic block diagram of a slope monitoring and early warning method based on GNSS according to the present invention;
FIG. 2 is a graph illustrating how a Prophet method is used to extract a deformation trend of historical monitoring data in the early warning method of the present invention;
fig. 3 is a schematic diagram of cluster center coordinates and early warning grade division of deformation data monitored in the slope monitoring and early warning method based on GNSS provided by the present invention;
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. 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 application.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
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, it need not be further defined or explained in subsequent figures.
Example (b):
referring to fig. 1-3, the intelligent landslide hazard early warning method based on GNSS real-time monitoring provided by the present scheme includes the following steps:
s1, preprocessing monitoring data of the GNSS.
And S2, carrying out anomaly detection on all GNSS devices based on the preprocessed data.
S3, eliminating monitoring data of abnormal GNSS equipment; the method comprises the steps of firstly preprocessing the reserved historical monitoring data of normal GNSS equipment again, then analyzing and extracting the actual deformation trend of the time sequence of the reserved historical data monitored by the normal GNSS equipment, and finally calculating the deformation difference and the deformation speed of two adjacent time intervals of the historical data monitored by the normal GNSS equipment based on the extracted actual deformation trend.
S4, classifying 40 clustering centers of a data sequence consisting of the deformation difference and the deformation speed by adopting a K-means method, performing clustering analysis to obtain a clustering center coordinate of deformation data monitored by the GNSS, establishing an early warning index, and dividing the grade of landslide risk; the risk levels are no early warning, blue early warning, yellow early warning, orange early warning and red early warning respectively, and refer to fig. 3 specifically.
And S5, acquiring new data, preprocessing the new data, judging whether the data are reserved or not, extracting the trend of the reserved data, calculating the deformation difference and the deformation speed of the new data according to the extracted actual deformation trend, and contrasting the early warning indexes to obtain corresponding risk levels.
The data are detected in the data preprocessing, and the detection method comprises the following steps:
if the absolute value of the data deformation difference of two adjacent time points is larger than 2000mm, deleting the corresponding data;
since the smaller the time interval, the larger the error caused when calculating the deformation speed, the too small time interval is taken as a criterion.
If the time interval between two adjacent monitoring data is less than 600s, deleting the corresponding data;
and (3) data extraction, namely extracting the linear deformation trend of the monitoring data along with time in a linear fitting mode.
When anomaly detection is carried out on equipment;
because the sampling rate of the equipment is too low, the early warning of the slope is not facilitated, the time interval of two adjacent monitoring data in the equipment is counted, and if the average time interval is about 3600s, the equipment is considered to be in the first-class abnormity.
And subtracting the linear deformation trend corresponding to the time point from the monitoring data to obtain the fluctuation of the linear deformation trend corresponding to the monitoring data, and counting the standard deviation of the fluctuation of the linear deformation trend corresponding to the monitoring data, wherein if the standard deviation is overlarge, the monitoring equipment is considered to have the second type of abnormality. Referring specifically to fig. 2, the data points in the gray shaded area in the graph are normal data points, while the data points not in the gray shaded area are abnormal data points, and the middle curve is the extracted deformation trend curve.
The fluctuation of the linear deformation trend corresponding to the monitored data is increased due to the slope instability process and the abnormity of the monitoring equipment, but the slope instability process is a small-probability event in real life, and once the equipment is abnormal, a large amount of data with large fluctuation and corresponding to the linear deformation trend is generated, so that the standard deviation of the fluctuation of the linear deformation trend corresponding to the statistical monitored data is not greatly influenced by the slope instability process, and the influence of the abnormity of the monitoring equipment is larger. Therefore, whether the monitoring device is abnormal or not is judged by counting the standard deviation of the fluctuation of the monitoring data corresponding to the linear deformation trend.
In the actual deformation trend extraction, firstly, the time series of the historical data monitored by the normal equipment is reserved, a 95% confidence interval is set by adopting a time series data prediction tool Prophet, and the actual deformation trend of the time series of the historical data monitored by the equipment is analyzed and extracted.
The following principle is used for judging whether the data is reserved:
if the absolute value of the data deformation difference of two adjacent time points is greater than 2000mm, deleting the corresponding data, otherwise, retaining; and if the time interval between two adjacent monitoring data is less than 600s, deleting the corresponding data, otherwise, keeping the corresponding data.
During real-time early warning and deformation trend extraction of retained new data, a dynamic time window with the length of L data points is selected, then a time series data prediction tool Prophet is adopted, a 95% confidence interval is set, so that the actual deformation trend of the data in the time window is obtained, wherein the data are continuously updated to be monitored data points accessed in real time at the front part of the time window, the oldest historical data points are continuously deleted at the rear part of the time window, and the length of the time window is kept unchanged.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (7)
1. A landslide disaster intelligent early warning method based on GNSS real-time monitoring is characterized by comprising the following steps:
preprocessing the monitoring data of the GNSS;
performing anomaly detection on all GNSS devices based on the preprocessed data;
removing abnormal monitoring data of the GNSS equipment; firstly, preprocessing the retained historical monitoring data of the normal equipment again, then analyzing and extracting the actual deformation trend of the time sequence of the retained historical data monitored by the normal GNSS equipment, and finally calculating the deformation difference and the deformation speed of two adjacent time intervals of the historical data monitored by the normal equipment based on the extracted actual deformation trend;
classifying 40 clustering centers of a data sequence consisting of the deformation difference and the deformation speed by adopting a K-means method, carrying out clustering analysis to obtain a clustering center coordinate of deformation data monitored by the GNSS, establishing an early warning index, and dividing the grade of landslide risk;
and acquiring new data, preprocessing the new data, judging whether the data are reserved or not, extracting the trend of the reserved data, calculating the deformation difference and the deformation speed of the new data according to the extracted actual deformation trend, and contrasting the early warning indexes to obtain the corresponding risk level.
2. The intelligent landslide hazard early warning method based on GNSS real-time monitoring as claimed in claim 1, characterized in that: in the data preprocessing, the preprocessing method comprises the following steps:
if the absolute value of the data deformation difference of two adjacent time points is greater than 2000mm, deleting the corresponding data, otherwise, retaining;
if the time interval between two adjacent monitoring data is less than 600s, deleting the corresponding data, otherwise, keeping the corresponding data;
and (3) data extraction, namely extracting the linear deformation trend of the monitoring data along with time in a linear fitting mode.
3. The intelligent landslide hazard early warning method based on GNSS real-time monitoring as claimed in claim 1, wherein: when abnormality detection is performed on a device
Counting the time interval of two adjacent monitoring data in the equipment, and if the average time interval is about 3600s, determining that the equipment is in the first type of abnormity;
and subtracting the linear deformation trend corresponding to the time point from the monitoring data to obtain the fluctuation of the linear deformation trend corresponding to the monitoring data, and counting the standard deviation of the fluctuation of the linear deformation trend corresponding to the monitoring data, wherein if the standard deviation is overlarge, the monitoring equipment is considered to have the second type of abnormality.
4. The intelligent landslide hazard early warning method based on GNSS real-time monitoring as claimed in claim 1, wherein: in the actual deformation trend extraction, firstly, the time series of the historical data monitored by the normal equipment is reserved, a 95% confidence interval is set by adopting a time series data prediction tool Prophet, and the actual deformation trend of the time series of the historical data monitored by the equipment is analyzed and extracted.
5. The intelligent landslide hazard early warning method based on GNSS real-time monitoring as claimed in any one of claims 1-4, wherein: the risk grades are respectively non-early warning, blue early warning, yellow early warning, orange early warning and red early warning.
6. The intelligent landslide hazard early warning method based on GNSS real-time monitoring as claimed in claim 5, characterized in that: and when judging whether the new access data is reserved, the following principle is utilized:
if the absolute value of the data deformation difference of two adjacent time points is greater than 2000mm, deleting the corresponding data, otherwise, keeping the corresponding data;
and if the time interval between two adjacent monitoring data is less than 600s, deleting the corresponding data, otherwise, keeping the corresponding data.
7. The intelligent landslide hazard early warning method based on GNSS real-time monitoring as claimed in claim 6, wherein: when extracting the deformation trend of the retained new data, firstly selecting a dynamic time window with the length of L data points, and then setting a 95% confidence interval by adopting a time series data prediction tool Prophet to acquire the actual deformation trend of the data in the time window;
the method comprises the steps of continuously updating the time window to be a monitoring data point accessed in real time at the front part of the time window, continuously deleting the oldest historical data point at the rear part of the time window, and always keeping the time window length to be L.
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| CN115900838A (en) * | 2023-03-10 | 2023-04-04 | 江西飞尚科技有限公司 | Slope early warning method and system, computer equipment and readable storage medium |
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| CN115900838A (en) * | 2023-03-10 | 2023-04-04 | 江西飞尚科技有限公司 | Slope early warning method and system, computer equipment and readable storage medium |
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