CN115798181B - Landslide early warning method, device, equipment and medium based on declination ratio - Google Patents
Landslide early warning method, device, equipment and medium based on declination ratio Download PDFInfo
- Publication number
- CN115798181B CN115798181B CN202310081282.1A CN202310081282A CN115798181B CN 115798181 B CN115798181 B CN 115798181B CN 202310081282 A CN202310081282 A CN 202310081282A CN 115798181 B CN115798181 B CN 115798181B
- Authority
- CN
- China
- Prior art keywords
- landslide
- slope
- displacement
- angle ratio
- time period
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Abstract
The invention provides a landslide early warning method, device, equipment and medium based on an off-angle ratio, and relates to the technical field of landslide early warning, wherein the landslide early warning method comprises the steps of obtaining terrain data and displacement actual measurement data of a plurality of side slopes; developing a simulation evolution test of the slope model; obtaining landslide volume and displacement simulation data of each slope model, and calculating a deflection angle ratio index of each slope model; constructing a data sample library by using the terrain data, the declination ratio index and the corresponding landslide volume of a plurality of slopes, and training and testing a neural network model by using the data sample library to obtain a landslide prediction model; the landslide prediction model predicts the landslide volume of the side slope to be predicted according to the topographic data and the deflection angle ratio index of the side slope to be predicted; and judging the landslide early warning grade formed by the slope to be predicted based on the deviation angle ratio index and the landslide volume of the slope to be predicted. The method and the device are used for solving the technical problem that the index judgment thresholds of different slopes are not unified in the prior art.
Description
Technical Field
The invention relates to the technical field of landslide early warning, in particular to a landslide early warning method, device, equipment and medium based on an off-angle ratio.
Background
In the prior art, a precise electronic instrument is generally adopted to monitor a side slope, and the change of indexes such as displacement, speed, acceleration and the like of the surface of the side slope along with time is recorded. And setting a judgment threshold for indexes such as displacement, speed and acceleration respectively, and giving out early warning when the displacement, speed and acceleration exceed the set threshold. However, since each slope has a difference, the judgment threshold values of each index of different slopes are different, and how to unify and standardize the judgment threshold values of the slopes is a technical problem to be solved in the field.
Disclosure of Invention
The invention aims to provide a landslide early warning method, device, equipment and medium based on an off-angle ratio so as to solve the problems. In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
in a first aspect, the present application provides a landslide early warning method based on a yaw angle ratio, including:
obtaining topographic data of a plurality of slopes and displacement actual measurement data of a plurality of monitoring points arranged on each slope;
constructing a slope model according to the terrain data, and carrying out a simulated evolution test of the slope model based on the displacement actual measurement data;
acquiring landslide volume formed by each slope model in the simulation evolution process and displacement simulation data generated by all monitoring points, and calculating a deflection angle ratio index of each slope model according to the displacement simulation data;
constructing a data sample library by using the terrain data, the declination ratio index and the corresponding landslide volume of a plurality of slopes, and training and testing a neural network model by using the data sample library to obtain a landslide prediction model;
obtaining the topographic data and the deviation angle ratio index of the side slope to be predicted, and predicting the landslide volume of the side slope to be predicted by a landslide prediction model according to the topographic data and the deviation angle ratio index of the side slope to be predicted;
and judging the landslide early warning grade formed by the slope to be predicted based on the deviation angle ratio index and the landslide volume of the slope to be predicted.
In a second aspect, the present application further provides a landslide early warning device based on a yaw angle ratio, including:
the acquisition module is used for: obtaining topographic data of a plurality of slopes and displacement actual measurement data of a plurality of monitoring points arranged on each slope;
simulation test module: constructing a slope model according to the terrain data, and carrying out a simulated evolution test of the slope model based on the displacement actual measurement data;
the calculation module: acquiring landslide volume formed by each slope model in the simulation evolution process and displacement simulation data generated by all monitoring points, and calculating a deflection angle ratio index of each slope model according to the displacement simulation data;
model construction module: constructing a data sample library by using the terrain data, the declination ratio index and the corresponding landslide volume of a plurality of slopes, and training and testing a neural network model by using the data sample library to obtain a landslide prediction model;
and a prediction module: obtaining the topographic data and the deviation angle ratio index of the side slope to be predicted, and predicting the landslide volume of the side slope to be predicted by a landslide prediction model according to the topographic data and the deviation angle ratio index of the side slope to be predicted;
and a judging module: and judging the landslide early warning grade formed by the slope to be predicted based on the deviation angle ratio index and the landslide volume of the slope to be predicted.
In a third aspect, the present application further provides a landslide early warning device based on a yaw ratio, including:
a memory for storing a computer program;
and the processor is used for realizing the landslide early warning method based on the declination ratio when executing the computer program.
In a fourth aspect, the present application further provides a readable storage medium, where a computer program is stored, where the computer program, when executed by a processor, implements the steps of the landslide early warning method based on the yaw ratio.
The beneficial effects of the invention are as follows:
according to the invention, the declination ratio is set by combining the form of the side slope, the form of the side slope and the displacement of the side slope are organically combined together and are jointly used as input indexes to train the neural network model, so that a landslide prediction model is obtained, the landslide volume formed by the side slope can be more accurately judged by the landslide prediction model, and then the landslide early warning grade is judged based on the declination ratio and the landslide volume obtained by prediction.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be apparent from the description, or may be learned by practice of the embodiments of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic flow chart of a landslide early warning method based on an angle deviation ratio in an embodiment of the invention;
fig. 2 is a schematic diagram of a landslide early warning device based on an angle deviation ratio according to an embodiment of the present invention;
fig. 3 is a schematic diagram II of a landslide early warning device based on an angle deviation ratio according to an embodiment of the invention;
fig. 4 is a schematic structural diagram of a landslide early warning device based on an off-angle ratio according to an embodiment of the present invention.
The marks in the figure:
01. an acquisition module; 011. a first acquisition unit; 012. a setting unit; 013. a first dividing unit; 014. a second acquisition unit; 02. a simulation test module; 021. a model construction unit; 022. a position determining unit; 023. an introduction unit; 024. a simulation unit; 03. a computing module; 031. a third acquisition unit; 032. a second dividing unit; 033. a monitoring unit; 034. a fourth acquisition unit; 035. a first calculation unit; 036. a third dividing unit; 037. a fifth acquisition unit; 038. a second calculation unit; 039. a third calculation unit; 0391. a first building unit; 0392. a second construction unit; 0393. a fourth calculation unit; 0394. a fifth calculation unit; 0310. a first judgment unit; 0311. a second judgment unit; 04. a model building module; 05. a prediction module; 06. a judging module; 800. landslide early warning equipment based on the declination ratio; 801. a processor; 802. a memory; 803. a multimedia component; 804. an I/O interface; 805. a communication component.
Description of the embodiments
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the invention, as presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures. Meanwhile, in the description of the present invention, the terms "first", "second", and the like are used only to distinguish the description, and are not to be construed as indicating or implying relative importance.
Examples
The embodiment provides a landslide early warning method based on an off-angle ratio.
Referring to fig. 1, the method is shown to include:
s1, obtaining terrain data of a plurality of slopes and displacement actual measurement data of a plurality of monitoring points arranged on each slope;
specifically, the step S1 includes the following steps:
s11, obtaining geological information of a plurality of slopes and soil information of each rock stratum of the slopes, and forming topographic data of the corresponding slopes by the geological information and the soil information;
specifically, the unmanned aerial vehicle technology is utilized to survey and record geological information, and the geological information comprises: slope of the side slope, slope direction, elevation, landform type, stratum lithology and the like;
acquiring a field soil sample through drilling, and carrying out field experiment to measure soil information of each soil layer, wherein the soil information comprises: internal friction angle, cohesion, gravity, elastic modulus, poisson ratio, shear modulus, liquidity index, plasticity index, porosity, water content and the like of the soil body.
S12, arranging a plurality of monitoring points along the top of the slope surface to the bottom of the slope surface of each slope surface;
s13, determining a monitoring period, dividing the monitoring period into a plurality of time sections, wherein the monitoring period can be 1 month or 2 months, and the time sections can be 1 day;
s14, acquiring the displacement of each monitoring point in each time section;
preferably, monitoring points on the slope surface are monitored by the geological wide-angle radar, the embodiment requires that the distance resolution of the geological wide-angle radar is less than or equal to 0.3 meter (on all azimuth angles), the azimuth angle resolution is less than or equal to 0.5 degrees (on all azimuth angles), the total scanning time is less than or equal to 1 minute, the deformation measurement precision is less than or equal to +/-0.1 millimeter, the maximum detection distance is more than or equal to 2000 meters, displacement of the monitoring points which are scanned each time is recorded, and displacement of each monitoring point on each day in a monitoring period is obtained, and the method comprises the following steps: increment of displacement in horizontal direction
And displacement increment in the vertical direction
。
S15, displacement actual measurement data of the side slope are formed by displacement of all monitoring points of the side slope in all time sections.
Based on the above embodiment, the method further includes:
s2, constructing a slope model according to the topographic data, and carrying out a simulation evolution test of the slope model based on the displacement actual measurement data;
specifically, the step S2 includes:
s21, constructing a slope model in modeling software according to the topographic data of the slope, preferably, drawing the slope model in modeling software NX UG 12.0, and dividing grids of each soil layer of the slope model by using software ANASYS;
s22, determining the positions of all monitoring points in the slope model, wherein the positions of the monitoring points in the slope model correspond to the positions of the monitoring points in the slope one by one;
s23, importing the slope model and the displacement actual measurement data into discrete element analysis software;
s24, simulating the evolution process of each rock stratum of the side slope in discrete element analysis software according to the imported displacement measured data;
specifically, the slope model is led into the 3DEC, the 3DEC can simulate and display the sinking and the dislocation of the rock stratum, and the formation process of rock stratum cracks, the displacement information of monitoring points on the surface of the slope model at each moment, the position information of the slope trailing edge cracks and the position information of a landslide shear outlet can be displayed in real time.
Based on the above embodiment, the method further includes:
s3, acquiring landslide volume formed in the simulation evolution process of each slope model and displacement simulation data generated by all monitoring points, and calculating a deflection angle ratio index of each slope model according to the displacement simulation data;
specifically, the step S3 includes the following steps:
s31, acquiring a time interval from the initial test evolution of the slope model to the unstability state of the slope model
；
S32, dividing the time interval into a plurality of time sections, namely determining the number of days of the time interval;
s33, monitoring the displacement of all monitoring points in each time section, and forming displacement simulation data by the displacement of all the monitoring points;
s34, acquiring the sliding surface length and the sliding surface shape formed by the slope model in a unsteady state;
s35, calculating to obtain the landslide volume based on the sliding surface length and the sliding surface shape.
S36, dividing the time interval into a plurality of equal time periods, wherein each time period comprises a plurality of time sections;
in this embodiment, the time period is 9 days;
s37, acquiring the corresponding displacement of the monitoring point in a plurality of time sections of the current time period from the displacement simulation data;
s38, calculating the corresponding deflection angle ratios of the monitoring points in a plurality of time sections of the current time period according to the plurality of displacements;
specifically, the calculation formula of the deflection angle ratio is as follows:
where i denotes the ith monitoring point, j denotes the jth day,
represents the deflection angle ratio of the ith monitoring point on the jth day, h represents the vertical distance between the slope rear edge crack point and the landslide shear outlet, d represents the horizontal distance between the slope rear edge crack point and the landslide shear outlet,
the displacement deflection angle is indicated by the angle,
indicating the slip plane overall slip angle.
S39, calculating stability judgment indexes of the monitoring points in the current time period according to the deflection angle ratios;
specifically, the method for calculating the stability judgment index comprises the following steps:
s391, constructing a first bias angle ratio third-order matrix according to a plurality of bias angle ratios of the monitoring points in the current time period;
taking the nth cycle as an example, the embodiment calculates the bias angle ratio third-order matrix of the nth cycle:
in the method, in the process of the invention,
representing the deviation angle ratio third order of the ith monitoring point in the nth time periodThe matrix is formed by a matrix of,
—
the off-angle ratio of the ith monitoring point on days 1-9 of the nth time period is shown.
S392, constructing a second bias angle ratio third-order matrix of the monitoring point in the last adjacent time period of the current time period;
constructing a third-order matrix of the bias angle ratio of the ith monitoring point in the n-1 time period according to the formula (2)
；
S393, calculating a difference value between the first offset angle ratio third-order matrix and the second offset angle ratio third-order matrix:
in the method, in the process of the invention,
representation of
Is a difference in (c).
S394, calculating according to the difference value to obtain a stability judgment index of the monitoring point in the current time period:
in the method, in the process of the invention,
and the stability judgment index of the ith monitoring point in the nth time period is represented.
S310, judging whether the stability judgment index is larger than or equal to a preset threshold value:
preferably, the preset threshold is 0.09;
if yes, calculating the mean value of the drift angle ratios of each monitoring point in the current time period according to the drift angle ratios;
the calculation formula of the deflection angle ratio mean value is as follows:
in the method, in the process of the invention,
and the mean value of the drift angle ratio of the i monitoring points in the nth time period is represented.
S311, judging whether the deviation angle ratio average value of each monitoring point meets preset conditions:
if the current time period is consistent with the current time period, taking the mean value of the drift angle ratios of all the monitoring points of the current time period as a drift angle ratio index of the slope model.
Specifically, the preset judging conditions are as follows: the average value of the drift angle ratios of the monitoring points is gradually decreased from the upper part of the side slope to the lower part of the side slope, and the average value of the drift angle ratios in the middle of the side slope belongs to a preset range [0.99,1.01].
Based on the above embodiment, the method further includes:
s4, constructing a data sample base according to the terrain data, the deflection angle ratio index and the corresponding landslide volume of a plurality of slopes, and training and testing a neural network model by using the data sample base to obtain a landslide prediction model;
specifically, the data sample library is divided into a training set and a testing set according to the proportion of 8:2, the neural network model is trained and tested by utilizing the training set and the testing set to obtain the constructed landslide prediction model, wherein the topographic data and the declination ratio index of the side slope are used as input indexes, the landslide volume corresponding to the side slope is used as output indexes,
based on the above embodiment, the method further includes:
s5, obtaining the topographic data and the deviation angle ratio index of the side slope to be predicted, and predicting the position and the volume of the side slope where the side slope to be predicted is located by the landslide prediction model according to the topographic data and the deviation angle ratio index of the side slope to be predicted;
based on the above embodiment, the method further includes:
s6, judging the landslide early warning grade formed by the slope to be predicted based on the deviation angle ratio index and the landslide volume of the slope to be predicted.
In this embodiment, the early warning ranking criteria are provided as shown in tables 1-3:
TABLE 1
V represents the landslide volume, and Table 1 represents the landslide volume of 2800 m or more 3 The dividing standard of the slope early warning grade under the condition.
TABLE 2
Table 2 shows that the landslide volume is at (1000, 2800) m 3 The dividing standard of the slope early warning grade under the condition.
TABLE 3 Table 3
Table 2 shows that the landslide volume is 1000m or less 3 The dividing standard of the slope early warning grade under the condition.
In this embodiment, the displacement of the surface of the slope body is monitored, the deviation angle ratio is calculated by combining the forms of different slope types to judge the landslide formation condition of the slope, and then the early warning grade is determined, so that more accurate early warning information can be obtained by the mode.
Examples
As shown in fig. 2, this embodiment provides a landslide early warning device based on an off-angle ratio, the device includes:
acquisition module 01: obtaining topographic data of a plurality of slopes and displacement actual measurement data of a plurality of monitoring points arranged on each slope;
simulation test module 02: constructing a slope model according to the terrain data, and carrying out a simulated evolution test of the slope model based on the displacement actual measurement data;
calculation module 03: acquiring landslide volume formed by each slope model in the simulation evolution process and displacement simulation data generated by all monitoring points, and calculating a deflection angle ratio index of each slope model according to the displacement simulation data;
model building module 04: constructing a data sample library by using the terrain data, the declination ratio index and the corresponding landslide volume of a plurality of slopes, and training and testing a neural network model by using the data sample library to obtain a landslide prediction model;
prediction module 05: obtaining the topographic data and the deviation angle ratio index of the side slope to be predicted, and predicting the landslide volume of the side slope to be predicted by a landslide prediction model according to the topographic data and the deviation angle ratio index of the side slope to be predicted;
judgment module 06: and judging the landslide early warning grade formed by the slope to be predicted based on the deviation angle ratio index and the landslide volume of the slope to be predicted.
Based on the above embodiments, the obtaining module 01 includes:
the first acquisition unit 011: obtaining geological information of a plurality of slopes and soil information of each rock stratum of the slopes, and forming topographic data of the corresponding slopes by the geological information and the soil information;
setting unit 012: a plurality of monitoring points are arranged along the top of the slope surface to the bottom of the slope surface of each slope surface;
first dividing unit 013: determining a monitoring period, and dividing the monitoring period into a plurality of time sections;
the second acquisition unit 014: acquiring the displacement of each monitoring point in each time section;
the constitution unit comprises: the displacement of all monitoring points of one side slope in all time sections forms the displacement actual measurement data of the side slope.
Based on the above embodiments, the simulation test module 02 includes:
model construction unit 021: constructing a slope model in modeling software according to the topographic data of the slope;
a position determination unit 022: determining the positions of all monitoring points in a slope model, wherein the positions of the monitoring points in the slope model correspond to the positions of the monitoring points in the slope one by one;
the importing unit 023: importing the slope model and the displacement actual measurement data into discrete element analysis software;
simulation unit 024: and simulating the evolution process of each rock stratum of the slope in discrete element analysis software according to the imported displacement measured data.
Based on the above embodiments, the calculation module 03 includes:
third acquisition unit 031: acquiring a time interval from the evolution of the slope model from the initial test to the instability state of the slope model;
a second dividing unit 032: dividing the time interval into a plurality of time sections;
monitoring unit 033: monitoring the displacement of all monitoring points in each time section, and forming displacement simulation data by the displacement of all monitoring points;
fourth acquisition unit 034: acquiring the sliding surface length and the sliding surface shape formed by the slope model in a unsteady state;
first calculation unit 035: and calculating the landslide volume based on the landslide length and the landslide shape.
Based on the above embodiments, the calculation module 03 further includes:
third dividing unit 036: dividing the time interval into a plurality of equal time periods, each time period comprising a plurality of time segments;
fifth acquisition unit 037: obtaining the corresponding displacement of the monitoring point in a plurality of time sections of the current time period from the displacement simulation data;
the second calculation unit 038: calculating the corresponding deflection angle ratio of the monitoring point in a plurality of time sections of the current time period according to the plurality of displacements;
third calculation unit 039: calculating stability judgment indexes of the monitoring points in the current time period according to the deflection angle ratios;
first judgment unit 0310: judging whether the stability judgment index is larger than or equal to a preset threshold value:
if yes, calculating the mean value of the drift angle ratios of each monitoring point in the current time period according to the drift angle ratios;
the second judgment unit 0311: judging whether the average value of the drift angle ratio of each monitoring point meets the preset condition or not:
if the current time period is consistent with the current time period, taking the mean value of the drift angle ratios of all the monitoring points of the current time period as a drift angle ratio index of the slope model.
Based on the above embodiments, the third calculation unit 039 includes:
first building unit 0391: constructing a first bias angle ratio third-order matrix according to a plurality of bias angle ratios of the monitoring points in the current time period;
second building unit 0392: constructing a second bias angle ratio third-order matrix of the monitoring point in the last adjacent time period of the current time period;
fourth calculation unit 0393: calculating the difference value of the first offset angle ratio third-order matrix and the second offset angle ratio third-order matrix;
a fifth calculation unit 0394: and calculating according to the difference value to obtain the stability judgment index of the monitoring point in the current time period.
It should be noted that, regarding the apparatus in the above embodiments, the specific manner in which the respective modules perform the operations has been described in detail in the embodiments regarding the method, and will not be described in detail herein.
Examples
Corresponding to the above method embodiment, a landslide early warning device based on a yaw angle ratio is further provided in this embodiment, and a landslide early warning device based on a yaw angle ratio described below and a landslide early warning method based on a yaw angle ratio described above may be referred to correspondingly with each other.
Fig. 3 is a block diagram illustrating a slip early warning device 800 based on a yaw rate in accordance with an exemplary embodiment. As shown in fig. 4, the slip early warning apparatus 800 based on the yaw ratio may include: a processor 801, a memory 802. The slip warning device 800 based on the skew angle ratio may also include one or more of a multimedia component 803, an I/O interface 804, and a communication component 805.
The processor 801 is configured to control the overall operation of the slip early warning device 800 based on the yaw ratio, so as to complete all or part of the steps in the slip early warning method based on the yaw ratio. The memory 802 is used to store various types of data to support operation at the slip early warning device 800 based on the slip ratio, which may include, for example, instructions for any application or method operating on the slip early warning device 800 based on the slip ratio, as well as application related data, such as contact data, messages, pictures, audio, video, and the like. The Memory 802 may be implemented by any type or combination of volatile or non-volatile Memory devices, such as static random access Memory (Static Random Access Memory, SRAM for short), electrically erasable programmable Read-Only Memory (Electrically Erasable Programmable Read-Only Memory, EEPROM for short), erasable programmable Read-Only Memory (Erasable Programmable Read-Only Memory, EPROM for short), programmable Read-Only Memory (Programmable Read-Only Memory, PROM for short), read-Only Memory (ROM for short), magnetic Memory, flash Memory, magnetic disk, or optical disk. The multimedia component 803 may include a screen and an audio component. Wherein the screen may be, for example, a touch screen, the audio component being for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 802 or transmitted through the communication component 805. The audio assembly further comprises at least one speaker for outputting audio signals. The I/O interface 804 provides an interface between the processor 801 and other interface modules, which may be a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 805 is configured to perform wired or wireless communication between the landslide warning device 800 based on the yaw ratio and other devices. Wireless communication, such as Wi-Fi, bluetooth, near field communication (Near FieldCommunication, NFC for short), 2G, 3G or 4G, or a combination of one or more thereof, the respective communication component 805 may thus comprise: wi-Fi module, bluetooth module, NFC module.
In an exemplary embodiment, the slip early warning device 800 based on the yaw ratio may be implemented by one or more application specific integrated circuits (Application Specific Integrated Circuit, abbreviated as ASIC), digital signal processors (DigitalSignal Processor, abbreviated as DSP), digital signal processing devices (Digital Signal Processing Device, abbreviated as DSPD), programmable logic devices (Programmable Logic Device, abbreviated as PLD), field programmable gate arrays (Field Programmable Gate Array, abbreviated as FPGA), controllers, microcontrollers, microprocessors, or other electronic components for performing the slip early warning method based on the yaw ratio described above.
In another exemplary embodiment, there is also provided a computer readable storage medium including program instructions which, when executed by a processor, implement the steps of the slip early warning method based on the yaw ratio described above. For example, the computer readable storage medium may be the memory 802 including program instructions described above, which are executable by the processor 801 of the slip warning device 800 based on the yaw ratio to perform the slip warning method based on the yaw ratio described above.
Examples
Corresponding to the above method embodiment, a readable storage medium is further provided in this embodiment, and a readable storage medium described below and a landslide early warning method based on a skew angle ratio described above may be referred to correspondingly.
A readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps of the landslide early warning method based on the yaw angle ratio of the above method embodiment.
The readable storage medium may be a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, and the like.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (10)
1. A landslide early warning method based on an off-angle ratio is characterized by comprising the following steps:
obtaining topographic data of a plurality of slopes and displacement actual measurement data of a plurality of monitoring points arranged on each slope;
constructing a slope model according to the terrain data, and carrying out a simulated evolution test of the slope model based on the displacement actual measurement data;
obtaining landslide volume formed by each slope model in the simulation evolution process and displacement simulation data generated by all monitoring points, and calculating a deflection angle ratio index of each slope model according to the displacement simulation data, wherein the method specifically comprises the following steps:
acquiring a time interval from the evolution of the slope model from the initial test to the instability state of the slope model;
dividing the time interval into a plurality of time sections;
obtaining the corresponding displacement of the monitoring point in a plurality of time sections of the current time period from the displacement simulation data;
calculating the corresponding deflection angle ratio of the monitoring point in a plurality of time sections of the current time period according to the plurality of displacements;
wherein i represents the ith monitoring point, j represents the jth day, τ ij Represents the ithThe deflection angle ratio of each monitoring point on the j th day, h represents the vertical distance between the slope rear edge crack point and the landslide shear outlet, d represents the horizontal distance between the slope rear edge crack point and the landslide shear outlet, alpha represents the displacement deflection angle, theta represents the overall deflection angle of the sliding surface, deltax represents the displacement increment in the horizontal direction, and Deltay represents the displacement increment in the vertical direction;
constructing a data sample library by using the terrain data, the declination ratio index and the corresponding landslide volume of a plurality of slopes, and training and testing a neural network model by using the data sample library to obtain a landslide prediction model;
obtaining the topographic data and the deviation angle ratio index of the side slope to be predicted, and predicting the landslide volume of the side slope to be predicted by a landslide prediction model according to the topographic data and the deviation angle ratio index of the side slope to be predicted;
and judging the landslide early warning grade formed by the slope to be predicted based on the deviation angle ratio index and the landslide volume of the slope to be predicted.
2. The slip early warning method based on the yaw ratio according to claim 1, wherein obtaining the slip volume formed by each slope model in the simulation evolution process and displacement simulation data generated by all monitoring points comprises:
acquiring a time interval from the evolution of the slope model from the initial test to the instability state of the slope model;
dividing the time interval into a plurality of time sections;
monitoring the displacement of all monitoring points in each time section, and forming displacement simulation data by the displacement of all monitoring points;
acquiring the sliding surface length and the sliding surface shape formed by the slope model in a unsteady state;
and calculating the landslide volume based on the landslide length and the landslide shape.
3. The slip early warning method based on the yaw rate according to claim 2, wherein calculating the yaw rate index of each slope model from the displacement simulation data comprises:
dividing the time interval into a plurality of equal time periods, each time period comprising a plurality of time segments;
obtaining the corresponding displacement of the monitoring point in a plurality of time sections of the current time period from the displacement simulation data;
calculating the corresponding deflection angle ratio of the monitoring point in a plurality of time sections of the current time period according to the plurality of displacements;
calculating stability judgment indexes of the monitoring points in the current time period according to the deflection angle ratios;
when the stability judgment index is larger than or equal to a preset threshold value, calculating the mean value of the drift angle ratios of each monitoring point in the current time period according to a plurality of drift angle ratios;
when the deviation angle ratio mean value of each monitoring point meets a preset condition, the deviation angle ratio mean value of all the monitoring points in the current time period is used as a deviation angle ratio index of the slope model.
4. The landslide warning method based on the declination ratio according to claim 3, wherein the calculating the stability judgment index of the monitoring point in the current time period according to the declination ratios comprises:
constructing a first bias angle ratio third-order matrix according to a plurality of bias angle ratios of the monitoring points in the current time period;
constructing a second bias angle ratio third-order matrix of the monitoring point in the last adjacent time period of the current time period;
calculating the difference value of the first offset angle ratio third-order matrix and the second offset angle ratio third-order matrix;
and calculating according to the difference value to obtain the stability judgment index of the monitoring point in the current time period.
5. Landslide early warning device based on declination ratio, characterized by comprising:
the acquisition module is used for: obtaining topographic data of a plurality of slopes and displacement actual measurement data of a plurality of monitoring points arranged on each slope;
simulation test module: constructing a slope model according to the terrain data, and carrying out a simulated evolution test of the slope model based on the displacement actual measurement data;
the calculation module: obtaining landslide volume formed by each slope model in the simulation evolution process and displacement simulation data generated by all monitoring points, and calculating a deflection angle ratio index of each slope model according to the displacement simulation data, wherein the method specifically comprises the following steps:
acquiring a time interval from the evolution of the slope model from the initial test to the instability state of the slope model;
dividing the time interval into a plurality of time sections;
obtaining the corresponding displacement of the monitoring point in a plurality of time sections of the current time period from the displacement simulation data;
calculating the corresponding deflection angle ratio of the monitoring point in a plurality of time sections of the current time period according to the plurality of displacements;
wherein i represents the ith monitoring point, j represents the jth day, τ ij The deflection angle ratio of the ith monitoring point on the jth day is represented by h, the vertical distance between the slope rear edge crack point and the landslide shear outlet is represented by d, the horizontal distance between the slope rear edge crack point and the landslide shear outlet is represented by alpha, the displacement deflection angle is represented by theta, the overall deflection angle of the sliding surface is represented by delta x, the displacement increment in the horizontal direction is represented by delta y, and the displacement increment in the vertical direction is represented by delta y;
model construction module: constructing a data sample library by using the terrain data, the declination ratio index and the corresponding landslide volume of a plurality of slopes, and training and testing a neural network model by using the data sample library to obtain a landslide prediction model;
and a prediction module: obtaining the topographic data and the deviation angle ratio index of the side slope to be predicted, and predicting the landslide volume of the side slope to be predicted by a landslide prediction model according to the topographic data and the deviation angle ratio index of the side slope to be predicted;
and a judging module: and judging the landslide early warning grade formed by the slope to be predicted based on the deviation angle ratio index and the landslide volume of the slope to be predicted.
6. The slip early warning device based on the yaw ratio according to claim 5, wherein the calculation module includes:
a third acquisition unit: acquiring a time interval from the evolution of the slope model from the initial test to the instability state of the slope model;
a second dividing unit: dividing the time interval into a plurality of time sections;
monitoring unit: monitoring the displacement of all monitoring points in each time section, and forming displacement simulation data by the displacement of all monitoring points;
fourth acquisition unit: acquiring the sliding surface length and the sliding surface shape formed by the slope model in a unsteady state;
a first calculation unit: and calculating the landslide volume based on the landslide length and the landslide shape.
7. The slip early warning device based on the yaw ratio according to claim 6, wherein the calculation module further includes:
a third dividing unit: dividing the time interval into a plurality of equal time periods, each time period comprising a plurality of time segments;
fifth acquisition unit: obtaining the corresponding displacement of the monitoring point in a plurality of time sections of the current time period from the displacement simulation data;
a second calculation unit: calculating the corresponding deflection angle ratio of the monitoring point in a plurality of time sections of the current time period according to the plurality of displacements;
a third calculation unit: calculating stability judgment indexes of the monitoring points in the current time period according to the deflection angle ratios;
a first judgment unit: when the stability judgment index is greater than or equal to a preset threshold value,
calculating the mean value of the drift angle ratios of each monitoring point in the current time period according to the drift angle ratios;
a second judgment unit: and when the drift angle ratio mean value of each monitoring point meets the preset condition, taking the drift angle ratio mean value of all the monitoring points in the current time period as the drift angle ratio index of the slope model.
8. The slip early warning device based on the yaw ratio according to claim 7, wherein the third calculation unit includes:
a first construction unit: constructing a first bias angle ratio third-order matrix according to a plurality of bias angle ratios of the monitoring points in the current time period;
a second construction unit: constructing a second bias angle ratio third-order matrix of the monitoring point in the last adjacent time period of the current time period;
a fourth calculation unit: calculating the difference value of the first offset angle ratio third-order matrix and the second offset angle ratio third-order matrix;
a fifth calculation unit: and calculating according to the difference value to obtain the stability judgment index of the monitoring point in the current time period.
9. Landslide early warning device based on declination ratio, characterized by comprising:
a memory for storing a computer program;
a processor for implementing the steps of the slip early warning method based on the yaw ratio according to any one of claims 1 to 4 when executing the computer program.
10. A readable storage medium, characterized by: the readable storage medium has stored thereon a computer program which, when executed by a processor, implements the steps of the slip early warning method based on the yaw ratio according to any one of claims 1 to 4.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310081282.1A CN115798181B (en) | 2023-02-08 | 2023-02-08 | Landslide early warning method, device, equipment and medium based on declination ratio |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310081282.1A CN115798181B (en) | 2023-02-08 | 2023-02-08 | Landslide early warning method, device, equipment and medium based on declination ratio |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN115798181A CN115798181A (en) | 2023-03-14 |
| CN115798181B true CN115798181B (en) | 2023-04-25 |
Family
ID=85430484
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310081282.1A Active CN115798181B (en) | 2023-02-08 | 2023-02-08 | Landslide early warning method, device, equipment and medium based on declination ratio |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115798181B (en) |
Family Cites Families (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR100380861B1 (en) * | 1998-02-17 | 2003-04-18 | 도시바 엔지니어링 가부시끼가이샤 | Geographical displacement sensing unit and monitoring apparatus using the same |
| GB0316858D0 (en) * | 2003-07-18 | 2003-08-20 | Univ Nottingham | A process for radar measurements of the position and movement of indivual targets and of areas of land and ice |
| JP5198825B2 (en) * | 2007-10-12 | 2013-05-15 | 株式会社間組 | Disaster prevention monitoring system and disaster prevention monitoring method |
| CN106205060B (en) * | 2016-08-19 | 2018-03-27 | 临沂大学 | For outdoor pit Tailings Dam slope and land slide early alarming and forecasting method |
| CN109389807B (en) * | 2017-08-06 | 2023-09-26 | 中铁二院工程集团有限责任公司 | Intelligent monitoring and early warning system for creep type landslide |
| CN107798210B (en) * | 2017-08-15 | 2021-06-29 | 中南大学 | Multi-model landslide displacement prediction method and system |
| CN110082833B (en) * | 2019-04-30 | 2020-10-13 | 重庆交通大学 | Intelligent hydraulic engineering structure safety monitoring system and construction method |
| CN114093139B (en) * | 2022-01-13 | 2022-04-19 | 中国地质大学（武汉） | Multilayer slip surface landslide monitoring and early warning device |
| CN115164699A (en) * | 2022-06-29 | 2022-10-11 | 扬州邗江中科南工建设工程与信息化研究中心 | Low-damage road slope settlement monitoring system and monitoring method thereof |
-
2023
- 2023-02-08 CN CN202310081282.1A patent/CN115798181B/en active Active
Non-Patent Citations (1)
| Title |
|---|
| 李雪.模型试验下的公路边坡合位移演化分析.《河北工业科技》.2021,全文. * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN115798181A (en) | 2023-03-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Safi et al. | Flying with the wind: scale dependency of speed and direction measurements in modelling wind support in avian flight | |
| CN115116202B (en) | Landslide disaster early warning method, device, equipment and readable storage medium | |
| CN206818374U (en) | A kind of test device for simulating side slope mini pile deformation failure | |
| CN113111595B (en) | Intelligent slope early warning method and device | |
| CN103150871A (en) | Landslide forecasting method capable of utilizing underground water levels and displacement real-time monitoring | |
| CN105445756A (en) | Safety diagnosis system for structure | |
| CN114021487A (en) | Early warning method, device and equipment for landslide collapse and readable storage medium | |
| CN112883617B (en) | Tunnel lining monitoring range calculation method, device, equipment and readable storage medium | |
| Maccaferri et al. | On the propagation path of magma‐filled dikes and hydrofractures: The competition between external stress, internal pressure, and crack length | |
| CN115798181B (en) | Landslide early warning method, device, equipment and medium based on declination ratio | |
| Wang et al. | Prediction of PGA in earthquake early warning using a long short-term memory neural network | |
| Rengers et al. | Using high sample rate lidar to measure debris-flow velocity and surface geometry | |
| Woo et al. | Karst‐driven flexural isostasy in north‐Central Florida | |
| CN114117913A (en) | Dam post-dam open-air seepage monitoring system and measured value attribution analysis method | |
| Moore et al. | Monitoring building structures | |
| Camacho et al. | Gravimetric determination of an intrusive complex under the Island of Faial (Azores): some methodological improvements | |
| Aagaard et al. | Probabilistic estimates of surface coseismic slip and afterslip for Hayward fault earthquakes | |
| CN114371504B (en) | Earthquake epicenter position determination method, device, equipment and readable storage medium | |
| Bourrier et al. | Predictive capabilities of 2D and 3D block propagation models integrating block shape assessed from field experiments | |
| Fernandez-Hernández et al. | Rockfall detachment susceptibility map in El Hierro Island, Canary Islands, Spain | |
| US8666707B2 (en) | Model simulating the evolutionary dynamics of events or processes and method of generating a model simulating the evolutionary dynamics of events or processes | |
| CN112462423A (en) | Method and device for predicting thickness of biological reef | |
| CN113158315A (en) | Rock-soil body parameter three-dimensional non-stationary condition random field modeling method based on static cone penetration data | |
| CN115578845B (en) | Slope trailing edge crack early warning method, device, equipment and readable storage medium | |
| Kim et al. | Integrated tunnel monitoring system using wireless automated data collection technology |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |