CN114021487A - Early warning method, device and equipment for landslide collapse and readable storage medium - Google Patents
Early warning method, device and equipment for landslide collapse and readable storage medium Download PDFInfo
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Abstract
The invention provides a method, a device, equipment and a readable storage medium for early warning of slope collapse, and relates to the technical field of early warning of slope collapse. Moreover, the hill collapse early warning method provided by the invention can predict and early warn whether the potential collapse phenomenon exists in the bulge part in the hill in the area to be early warned under the condition that a large amount of historical monitoring data is not needed, and the early warning time is shortened.
Description
Technical Field
The invention relates to the technical field of early warning of slope collapse, in particular to a method, a device and equipment for early warning of slope collapse and a readable storage medium.
Background
The existing prediction method and prediction device can not predict the collapse of the hillside accurately, the measurement precision is low, the situation of false alarm is easy to occur, and the personnel are panic and the material is wasted, but in the past early warning technology, the purpose of prediction and early warning is achieved by analyzing the safety and stability of the rock block outside the hillside.
Disclosure of Invention
The invention aims to provide a method, a device and equipment for early warning of hill collapse and a readable storage medium, so as to solve the problems. In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
in a first aspect, the present application provides a method for early warning of hill collapse, including:
acquiring a first characteristic parameter, a second characteristic parameter and a third characteristic parameter, wherein the first characteristic parameter is a parameter of a first hill in an area to be early-warned, the second characteristic parameter is a variable displacement parameter of a bulge relative to the first hill, and the bulge is a bulge part protruding out of the first hill on a first hill sloping side; the third characteristic parameter is a slope top displacement parameter of the first hillside in the area to be early-warned;
obtaining a first parameter according to the first characteristic parameter, wherein the first parameter is an internal structure surface geometric parameter of the bulge;
inputting the first parameter, the second characteristic parameter and the third characteristic parameter into a trained neural network model to obtain a second parameter, wherein the second parameter comprises a fourth sub-parameter and a fifth sub-parameter, the fourth sub-parameter is a shear strength parameter of a joint surface of the bulge, and the fifth sub-parameter comprises a space geometric parameter of an inner joint surface of the first slope and a shear strength parameter of a joint surface of the first slope;
simulating a destabilization state according to the first characteristic parameter and the second parameter to obtain a stability coefficient of the bulge;
and obtaining the early warning level of the first slope collapse according to the stability coefficient of the bulge and the first characteristic parameter.
In a second aspect, the application provides an early warning device for landslide collapse, including a first obtaining module, a first constructing module, a first calculating module, a second calculating module and an analyzing module, wherein:
a first obtaining module: the system comprises a first characteristic parameter, a second characteristic parameter and a third characteristic parameter, wherein the first characteristic parameter is a parameter of a first hill in an area to be early-warned, the second characteristic parameter is a variable displacement parameter of a bulge relative to the first hill, and the bulge is a bulge part protruding out of the first hill on a first hill inclined edge; the third characteristic parameter is a slope top displacement parameter of the first hillside in the area to be early-warned;
a first building block: the first parameter is obtained according to the first characteristic parameter, and the first parameter is an internal structural surface geometric parameter of the bulge;
a first calculation module: the second parameter is obtained by inputting the first parameter, the second characteristic parameter and the third characteristic parameter into a trained neural network model, and the second parameter includes a fourth sub-parameter and a fifth sub-parameter, the fourth sub-parameter is a shear strength parameter of a joint surface of the bulge, and the fifth sub-parameter includes a space geometry parameter of an inner joint surface of the first slope and a shear strength parameter of a joint surface of the first slope;
a second calculation module: the simulation module is used for simulating a destabilization state according to the first characteristic parameter and the second parameter to obtain a stability coefficient of the bulge;
an analysis module: and the early warning level of the first slope collapse is obtained according to the stability coefficient of the bulge and the first characteristic parameter.
In a third aspect, the present application further provides an early warning device for landslide, including:
a memory for storing a computer program;
and the processor is used for realizing the steps of the early warning method for the landslide when executing the computer program.
In a fourth aspect, the present application further provides a readable storage medium, where a computer program is stored on the readable storage medium, and when the computer program is executed by a processor, the steps of the method for early warning of hill collapse are implemented.
The invention has the beneficial effects that: in the field surveying process, the geometric parameters, the mechanical parameters and other parameters of the structural surface in the hillside can not be easily obtained through simple instrument measurement, and the obtained early warning information has certain errors and is easy to have false alarm. According to the invention, the parameters which are difficult to measure can be directly obtained through the trained neural network model, so that the survey method can be simplified, the survey time is shortened, the accuracy of a prediction structure can be improved by taking the parameters which are difficult to measure as input values of instability simulation, and meanwhile, the final early warning grade is obtained by combining other factors, so that the judgment and consideration factors of the early warning grade are more perfect, and the accuracy of the early warning grade is further improved. Moreover, the hill collapse early warning method provided by the invention can predict and early warn whether the potential collapse phenomenon exists in the bulge part in the hill in the area to be early warned under the condition that a large amount of historical monitoring data is not needed, and the early warning time is shortened.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the 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 hereof 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 needed 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 invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a schematic flow chart of an early warning method for hill collapse according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an early warning device for landslide collapse according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an early warning device for hill collapse in the embodiment of the present invention.
The labels in the figure are: 601-a first acquisition module; 6011-first building subunit; 6012-a first calculation subunit; 6013-a second building subunit; 6014-a first acquisition subunit; 6015-a second acquisition subunit; 6016-a judgment unit; 602-a first building block; 603-a first calculation module; 6031-a second acquisition module; 60311-a third obtaining module; 60312-second building block; 60313-third calculation module; 60314-a fourth acquisition module; 60315-an extraction module; 60316-a fourth calculation module; 6032-training module; 604-a second computing module; 605-an analysis module; 6051-sixth calculation module; 6052-seventh calculation module; 6053-first analytical subunit; 6054-second analytical subunit; 800-early warning equipment for slope collapse; 801-a processor; 802-a memory; 803-multimedia components; 804 — an I/O interface; 805-communication component.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of 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 present invention, 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 derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It 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 and explained in subsequent figures. Meanwhile, in the description of the present invention, the terms "first", "second", and the like are used only for distinguishing the description, and are not to be construed as indicating or implying relative importance.
Example 1:
as shown in fig. 1, the method is shown to include step S100, step S200, step S300, step S400 and step S500.
S100, obtaining a first characteristic parameter, a second characteristic parameter and a third characteristic parameter, wherein the first characteristic parameter is a parameter of a first hill slope in an area to be pre-warned, the second characteristic parameter is a variable displacement parameter of a bulge relative to the first hill slope, and the bulge is a bulge part protruding out of the first hill slope from a first hill slope bevel edge; the third characteristic parameter is a slope top displacement parameter of the first hillside in the area to be early-warned.
It can be understood that, in this step, the first characteristic parameters are respectively obtained based on a satellite remote sensing technology, an unmanned aerial vehicle photography technology and a geological exploration technology, and the first characteristic parameters include a first sub-parameter, a second sub-parameter and a third sub-parameter of a first hill in the area to be early-warned. The first sub-parameters comprise geometrical parameters of an outer space of the first hill and geometrical parameters of an inner structural surface of the first hill, such as an outer boundary line of the first hill, a geometrical profile of the inner structural surface, a contour shape, and the like. The second sub-parameter is a physical parameter of the first hillside, including natural gravity, elastic modulus, poisson's ratio, and the like. The third sub-parameter is the mechanical parameter of the first hillside, including internal friction angle, cohesive force, normal stiffness, tangential stiffness, tensile strength, etc.
In step S100, a second characteristic parameter is obtained according to a change condition of real-time displacement of the detection point on the first hill slope, and the method for obtaining the second characteristic parameter includes step S101, step S102, step S103, step S104, step S105, and step S106, where:
and S101, obtaining a virtual geometric model of the convex part according to the first characteristic parameter.
It can be understood that, in this step, the virtual geometric model of the first hill is obtained by the "three-dimensional topography restoration technique" based on the first characteristic parameter, and then the virtual geometric model of the protruding portion protruding from the sloping side of the hill is obtained from the restored virtual geometric model of the first hill. The three-dimensional shape recovery technology can be a three-dimensional laser scanning technology, an image three-dimensional reconstruction technology and the like. A virtual geometric model of the first hillside is restored by utilizing a three-dimensional laser scanning technology, the technology is an advanced full-automatic high-precision stereo scanning technology, and the technology is mainly oriented to three-dimensional modeling and model reconstruction and restoration of high-precision reverse engineering. The technology can efficiently collect a large number of three-dimensional coordinate points, and completely collect various large, complex and irregular live-action three-dimensional data into a computer, thereby rapidly reconstructing a three-dimensional point cloud model of a target. Acquiring the three-dimensional point coordinates of the surface of the current first hillside by means of a three-dimensional laser scanning technology, acquiring first characteristic parameter data, reconstructing the original geometric form of the first hillside to the maximum extent by means of a computer replay process, and extracting a virtual geometric model of the bulge from the virtual geometric model of the first hillside.
Step S102, obtaining a space virtual parameter set of monitoring points according to a virtual geometric model of the bulge, wherein the space virtual parameter set comprises at least ten space virtual parameters, each space virtual parameter is a position parameter set of the monitoring points, and the monitoring points are transversely arranged in the virtual geometric model of the bulge at equal intervals;
it can be understood that, in this step, equidistant at least ten monitoring points of equidistant setting of bulge to the orthographic projection of first hillside bottom, then obtain the virtual parameter in space of every monitoring point position through the virtual geometric model of bulge, every virtual parameter in space includes the horizontal position parameter and the vertical position parameter of monitoring point, sets up the displacement condition that monitoring point can segmentation monitoring bulge on the bulge, accomplishes accurate early warning.
Step S103, setting monitoring points on the first hillside according to all the space virtual parameters in the space virtual parameter set and a preset proportion, wherein the preset proportion is the ratio of the virtual geometric model of the bulge to the geometric model of the bulge in the first hillside;
it can be understood that, in this step, according to the virtual parameter in space and the preset proportion, the monitoring points are arranged on the first hillside in a one-to-one correspondence manner for monitoring the displacement change condition of each part of the bulge relative to the first hillside in real time.
Step S104, acquiring a first space parameter of the monitoring point at the time T, wherein the first space parameter is three-dimensional space data of the monitoring point at the time T;
s105, acquiring a second space parameter of the monitoring point at the moment T +1, wherein the second space parameter is three-dimensional space data of the monitoring point at the moment T + 1;
and S106, judging whether the first space parameter and the second space parameter are equal, and if not, determining that the second space parameter is a second characteristic parameter.
It is understood that, in this step, two data before and after 1 second are compared, and if not, the data of T +1 second is the second characteristic parameter.
In step S100, a third characteristic parameter is obtained according to a change condition of real-time displacement of a detection point at a top of a first hill, and an obtaining method of the third characteristic parameter is similar to the method of the second characteristic parameter.
And S200, obtaining a first parameter according to the first characteristic parameter, wherein the first parameter is the geometrical parameter of the internal structure surface of the bulge.
It can be understood that, in this step, the geometric parameters of the internal structural surface of the projection are obtained through the unmanned aerial vehicle photography technology and the geological exploration technology, and include the number of the structural surfaces of the projection, the inclination and dip angle of the structural surfaces, the distance between the structural surfaces, the combination form among different groups of structural surfaces, and the like.
Step S300, inputting the first parameter, the second characteristic parameter and the third characteristic parameter into the trained neural network model to obtain a second parameter, wherein the second parameter comprises a fourth sub-parameter and a fifth sub-parameter, the fourth sub-parameter is the shearing strength parameter of the joint surface of the bulge, and the fifth sub-parameter comprises the space geometric parameter of the inner joint surface of the first hillside and the shearing strength parameter of the joint surface of the first hillside.
It can be understood that, in this step, the second parameter is obtained through the internal structure surface geometric parameters of the bulge, and these parameters can not be obtained through simple mapping technology on site, and the parameters can be rapidly and accurately obtained through the pre-trained neural network model, so that the precision of the early warning grade can be improved, and the early warning time can be shortened.
In detail, in this step, the training method of the neural network model includes step S301 and step S302, wherein:
step S301, obtaining a third parameter and a preset index parameter, wherein the third parameter comprises a displacement parameter of a reverse rock cone which is already caved from the second hillside relative to the second hillside and a displacement parameter of the top of the second hillside, the reverse rock cone is a stack body which is formed after the reverse rock cone is caved from the second hillside, and the second hillside is a virtual hillside analysis model; the preset index parameters are key parameters causing collapse of the inverted cone.
It can be understood that, in step S301, the third parameter is a parameter obtained after the instability model is completed and is a simulation of an instability state of the virtual hill analysis model, the third parameter includes a displacement change of the top of the second hill before and after the collapse of the inverted cone and a displacement change of the inverted cone relative to the second hill, the virtual hill analysis model is a virtual model constructed by using three-dimensional discrete element software according to the geometric parameters of the second hill, and the three-dimensional discrete element software may be 3DEC discrete element software or MatDEM high-performance discrete element software.
In detail, the method for acquiring the third parameter includes step S3011, step S3012, and step S3013, where:
step S3011, obtaining a fourth characteristic parameter, a fifth characteristic parameter and a preset position parameter, wherein the fourth characteristic parameter is an outer contour geometric parameter of the inverted pyramid; the fifth characteristic parameter is the outer contour geometric parameter of the second slope after the reverse stone cone is collapsed from the second slope; the preset position parameter is a space position parameter relative to the second hillside before the rock-dumping cone collapses from the second hillside.
It can be understood that, in this step, the fourth characteristic parameter is obtained through the unmanned aerial vehicle photography technology, such parameters as the spatial geometry of the inverted stone cone, the number of the structural surfaces, the inclination and inclination of the structural surfaces, the distance between the structural surfaces, and the combination form between different groups of structural surfaces. And the fifth characteristic parameter obtains parameters such as the space geometric form, the terrain contour map and the like of the second hillside through a satellite remote sensing technology, a geological exploration technology, a photographic technology and the like. The preset position parameter is a user-defined spatial position parameter.
Step S3012, a virtual hill analysis model set is constructed according to the fourth characteristic parameter, the fifth characteristic parameter and the preset position parameter, the virtual hill analysis model set comprises at least one virtual hill analysis model, each virtual hill analysis model is formed by randomly combining elements in the fourth characteristic parameter, the fifth characteristic parameter and the preset position parameter, and the virtual hill analysis model is a geometric model of a second hill before the rock-falling cone collapses from the second hill.
It can be understood that, in this step, the first geometric model of the inverted cone is restored by using the 3DEC discrete element software according to the fourth characteristic parameter, the second geometric model of the second hillside after the inverted cone collapses is restored by using the 3DEC discrete element software according to the fifth characteristic parameter, and then the first geometric model and the second geometric model are combined in different orientations by using the 3DEC discrete element software according to the preset position parameter, so as to form a plurality of virtual hillside analysis models in different forms.
Step S3013, rootAccording to preset index parameters, combining the strength reduction algorithm to simulate the instability state of all the virtual hillside analysis models in the virtual hillside analysis model set, and when the unbalance force of the strength reduction algorithm reaches 10-5And obtaining a third parameter in the next time.
It can be understood that, in this step, all the virtual hill analysis models are simulated in the instability state by the intensity reduction algorithm, and the collapse of the inverted cone is a gradual process when the unbalance force reaches 10-5The secondary virtual hillside analysis model reaches secondary balance to form a reverse stone cone, and a third parameter can be obtained at the moment.
In step S301, the preset index parameter is a parameter value set input by self-definition before performing instability state simulation on the virtual hill analysis model, and in order to reduce interference of invalid parameters, the method for determining the preset index parameter includes step S3014, step S3015, and step S3016, where:
and S3014, acquiring survey data of the hill collapse disasters in at least two actual projects. It is understood that in this step, typical examples of collapse of rocky slopes in actual projects are collected and investigated, such as: large-scale mountain collapse occurs in Yunan province and county in 1966, large-scale mountain collapse occurs in Yuannan county in Hubei province in 1980, large-scale mountain collapse occurs in Yaan county in Sichuan province in 2001, large-scale mountain collapse occurs in Jiwei mountain in Wulong county in Chongqing city in 2009, and the like.
Step S3015, obtaining a sample parameter set characterizing the landslide and collapse disasters according to the survey data, wherein the sample parameter set comprises at least two sample parameters, each sample parameter corresponds to a factor set, the factor set comprises at least one factor, and the factors are all parameters in the survey data.
It can be understood that, in this step, the survey data in each actual project is analyzed to obtain sample parameters describing the landslide disaster phenomenon, a factor set corresponding to each sample parameter includes spatial geometry of the inverted cone, internal structure surface geometry, physical and mechanical parameter characteristics of the rock-soil body, and at least one of spatial geometry of the second landslide and environmental factors, and the environmental factors include the following factors:
a. joints, cracks and even faults in the mountain body are completely developed, and steep inclined structural surfaces which are close to and parallel to the extending direction of the slope body are gradually formed;
b. the mechanical parameters of rock-soil mass (particularly rock-soil mass at the weak fracture zone of rock mass) are reduced, and the water pressure of the internal gap of the rock-soil mass is increased;
c. unreasonable human activity: excavating hillside slope toe, unreasonable reservoir storage, excavating underground goaf in large area, destroying slope vegetation in large area and the like;
d. the rock mass and hillside collapse phenomenon induced by earthquake can cause the dangerous rock outside the slope to locally collapse under the action of strong vibration force, so as to drive the hillside slope to collapse on a small scale.
Step S3016, counting the occurrence frequency of each factor of all sample parameters in the sample parameter set to obtain a preset index parameter, where the preset index parameter is a factor occurring in all sample parameters in all sample sets.
It can be understood that, in this step, each factor in each sample parameter is compared and counted, and the factors appearing in each sample parameter are used as preset index parameters to filter out non-representative factors, thereby facilitating quick calculation and improving response rate. The preset index parameters in this step include the space geometric parameters of the internal structural surface of the inverted stone cone (the number of the structural surfaces of the inverted stone cone, the inclination and inclination angle of the structural surfaces, the distance between the structural surfaces, the combination form among different groups of structural surfaces, and the like), the shear strength of the structural surface of the inverted stone cone (the cohesive force of the rock-soil mass and the internal friction angle of the rock-soil mass), and the space geometric parameters and the shear strength of each joint surface inside the second slope. And respectively giving specific numerical values according to the types of the preset index parameters to obtain a parameter value set input by self-definition before the instability state simulation.
Step S302, training the neural network model according to the third parameter and the preset index parameter to obtain the trained neural network model, wherein the third parameter is used as the input of the neural network model, and the preset index parameter is used as the output of the neural network model.
It can be understood that, in this step, mapping relationships between the third parameters and the preset index parameters are respectively established, and samples are established, where all the samples form a sample set. The sample set is divided into a training set and a testing set according to the proportion of 4:1, namely 80% of samples are used for training, 20% of samples are used for predicting, and the neural network model is trained through the training set and the testing set, so that the prediction accuracy of the model can be improved, the neural network model has higher prediction capability and better applicability, and the neural network model in the step is a BP neural network model.
And S400, simulating the instability state according to the first characteristic parameter and the second parameter to obtain a stability coefficient of the convex part.
It can be understood that, in this step, a virtual geometric model of a first hill in an area to be early-warned is constructed by using three-dimensional discrete element software according to the first characteristic parameter, a second parameter obtained by the trained neural network model is used as a parameter value set input by self-definition, and the virtual geometric model of the first hill is simulated in a destabilization state by using the three-dimensional discrete element software in combination with an intensity reduction method, so as to obtain a stability coefficient of the convex part.
And S500, obtaining an early warning grade of the first slope collapse according to the stability coefficient of the bulge and the first characteristic parameter.
It is understood that, in the present step, step S501, step S502, step S503 and step S504 are included, wherein:
and S501, obtaining the position height, the volume and the rock density of the bulge according to the first characteristic parameters.
It will be appreciated that the height of the location of the projection and the rock density can be directly obtained from the first characteristic parameter, and that the volume of the projection that can be obtained is calculated from the first characteristic parameter by constructing a geometric model of the projection using 3DEC discrete element software.
And S502, respectively obtaining the gravitational potential energy and the square product value of the bulge according to the position height, the volume and the rock density of the bulge, wherein the square product value is the product of the density, the volume and the gravitational acceleration.
It will be appreciated that in this step the gravitational potential energy of the bulge is the volume, the rock density and the height position, which is the height distance between the bulge relative to the bottom of the hill, and the ride of the gravitational acceleration.
Step S503, obtaining a grade parameter based on the gravitational potential energy, the square product value and the stability coefficient of the bulge;
and step S504, comparing the grade parameters with a preset safety grade table to obtain the early warning grade of the bulge.
It will be appreciated that in this step, the safety rating system is specifically used to indicate the warning rating of the extraslope mass of the second rock. The first safety level and the second safety level are both safe conditions, the third safety level is safer conditions, and the later development trend of each monitored data is needed to judge, the rocky slope of the fourth safety level has certain potential safety hazard, certain reinforcing measures need to be taken, when the safety level is five, the rocky slope is in a dangerous condition, when the safety level is six, the rocky slope is likely to collapse, great safety hazard is caused, and related treatment measures need to be carried out timely. Specific safety rating scale criteria are shown in tables 1 to 4, in which only a part of the safety rating table is shown, wherein K is a stability coefficient, V is a square product value,
Is gravitational potential energy.
Table 1 safety rating table 1
As shown in Table 1, the product of the magnitudes when the bulge is likely to collapse is V ≧ 500m for high yield and gravitational potential energy
Under the condition, the safety level table 1 is compared to carry out safety level division, namely, under the condition, K is more than or equal to 1.25 and is a three-level safety level, and K is more than or equal to 1.1 and less than or equal to K<1.25 is the four-level security level, and so on.
Table 2 security level table 2
As shown in Table 2, the weight product of V ≧ 500m for high-yield transformation when the bulge is likely to collapse
Under the condition, the safety level table 2 is compared to carry out safety level division, namely, under the condition, K is more than or equal to 1.25 and is a secondary safety level, and K is more than or equal to 1.1 and is less than or equal to K<And 1.25 is the third-level safety level, and so on.
Table 3 security level table 3
As shown in Table 3, the square-quantity product when the bulge is likely to collapse is V<At 500m under the condition of gravitational potential energy
Under the condition, the safety level table 3 is compared to carry out safety level division, namely, in the condition, K is more than or equal to 1.25 and is a secondary safety level, and K is more than or equal to 1.1 and is less than or equal to K<And 1.25 is the third-level safety level, and so on.
Table 4 security level table 4
As shown in Table 4, the square-quantity product when the bulge is likely to collapse is V<At 500m under the condition of gravitational potential energy
Under the condition, the safety level table 4 is compared to carry out safety level division, namely, in the condition, K is more than or equal to 1.25 and is a first-level safety level, and K is more than or equal to 1.1 and is less than or equal to K<And 1.25 is the second level of security, and so on.
Example 2:
as shown in fig. 2, the present embodiment provides an early warning device for a hill collapse, which includes a first obtaining module 601, a first constructing module 602, a first calculating module 603, a second calculating module 604, and an analyzing module 605, where:
the first obtaining module 601: the device comprises a projection part, a first characteristic parameter, a second characteristic parameter and a third characteristic parameter, wherein the first characteristic parameter is a parameter of a first hill slope in an area to be early-warned, the second characteristic parameter is a variable displacement parameter of the projection part relative to the first hill slope, and the projection part is a projection part protruding out of the first hill slope on the sloping edge of the first hill slope; the third characteristic parameter is a slope top displacement parameter of the first hillside in the area to be early-warned.
Preferably, the first obtaining module 601 includes a first constructing subunit 6011, a first calculating subunit 6012, a second constructing subunit 6013, a first obtaining unit, a second obtaining unit, and a determining unit 6016, wherein:
first building subunit 6011: and obtaining a virtual geometric model of the convex part according to the first characteristic parameter.
First calculation subunit 6012: the device comprises a projection part, a virtual parameter set and a virtual parameter set, wherein the virtual parameter set is used for obtaining a space virtual parameter set of monitoring points according to a virtual geometric model of the projection part, the space virtual parameter set comprises at least ten space virtual parameters, each space virtual parameter is a position parameter set of the monitoring point, and the monitoring points are transversely arranged in the virtual geometric model of the projection part at equal intervals.
Second building subunit 6013: and the monitoring points are arranged on the first hillside according to all the space virtual parameters in the space virtual parameter set and a preset proportion, and the preset proportion is a ratio of the virtual geometric model of the convex part to the geometric model of the convex part in the first hillside.
First acquisition subunit 6014: the method is used for obtaining a first space parameter of the monitoring point at the T moment, and the first space parameter is three-dimensional space data of the monitoring point at the T moment.
Second acquisition subunit 6015: and the method is used for acquiring a second space parameter of the monitoring point at the moment T +1, wherein the second space parameter is three-dimensional space data of the monitoring point at the moment T + 1.
Determination unit 6016: and the second space parameter is used for judging whether the first space parameter and the second space parameter are equal, and if not, the second space parameter is a second characteristic parameter.
The first building block 602: and the device is used for obtaining a first parameter according to the first characteristic parameter, wherein the first parameter is the geometric parameter of the internal structure surface of the bulge.
The first calculation module 603: and the second parameter comprises a fourth sub-parameter and a fifth sub-parameter, the fourth sub-parameter is the shearing strength parameter of the joint surface of the bulge, and the fifth sub-parameter comprises the space geometric parameter of the inner joint surface of the first slope and the shearing strength parameter of the joint surface of the first slope.
Preferably, the first computing module 603 includes a second acquisition module 6031 and a training module 6032, wherein:
second acquisition module 6031: the device comprises a first parameter and a second parameter, wherein the first parameter comprises a displacement parameter of a reverse rock cone which is collapsed from a first hillside relative to the first hillside and a displacement parameter of the top of the first hillside, the reverse rock cone is a stack body which is formed after the reverse rock cone is collapsed from the first hillside, and the first hillside is a virtual hillside analysis model; the preset index parameters are key parameters causing collapse of the inverted cone.
Further, the second obtaining module 6031 includes a third obtaining module 60311, a second building module 60312, and a third calculating module 60313, wherein:
the third acquisition module 60311: the device is used for acquiring a fourth characteristic parameter, a fifth characteristic parameter and a preset position parameter, wherein the fourth characteristic parameter is an outer contour geometric parameter of the inverted pyramid; the fifth characteristic parameter is the outer contour geometric parameter of the second slope after the reverse stone cone is collapsed from the second slope; the preset position parameter is a space position parameter relative to the second hillside before the rock-dumping cone collapses from the second hillside.
Second building block 60312: the virtual slope analysis model set is constructed according to the fourth characteristic parameter, the fifth characteristic parameter and the preset position parameter, the virtual slope analysis model set comprises at least one virtual slope analysis model, each virtual slope analysis model is formed by randomly combining elements in the fourth characteristic parameter, the fifth characteristic parameter and the preset position parameter, and the virtual slope analysis model is a geometric model of a second slope before the rock-falling cone collapses from the second slope.
Third calculation module 60313: and the simulation system is used for simulating the instability state of all the virtual slope analysis models in the virtual slope analysis model set by combining the strength reduction algorithm according to the preset index parameters, and obtaining a third parameter when the unbalanced force of the strength reduction algorithm reaches 10-5 times.
Further, the second obtaining module 6031 further includes a fourth obtaining module 60314, an extracting module 60315, and a fourth calculating module 60316, wherein:
the fourth acquisition module 60314: the method is used for acquiring survey data of the hill collapse disasters in at least two actual projects.
The extraction module 60315: the method is used for obtaining a sample parameter set for characterizing the landslide collapse disaster according to survey data, wherein the sample parameter set comprises at least two sample parameters, each sample parameter corresponds to a factor set, the factor set comprises at least one factor, and the factors are all parameters in the survey data.
The fourth calculation module 60316: and the method is used for counting the occurrence times of all the factors of all the sample parameters in the sample parameter set to obtain a preset index parameter, wherein the preset index parameter is a factor occurring in all the sample parameters in all the sample sets.
The training module 6032: and the neural network model is trained according to the third parameter and the preset index parameter to obtain the trained neural network model, the third parameter is used as the input of the neural network model, and the preset index parameter is used as the output of the neural network model.
The second calculation module 604: and the stability simulation module is used for simulating the instability state according to the first characteristic parameter and the second parameter to obtain the stability coefficient of the bulge.
The analysis module 605: and the early warning level of the first slope collapse is obtained according to the stability coefficient of the bulge and the first characteristic parameter.
Preferably, the analysis module 605 comprises a sixth calculation module 6051, a seventh calculation module 6052, a first analysis subunit 6053 and a second analysis subunit 6054, wherein:
the sixth calculation module 6051: the device is used for obtaining the position height, the volume and the rock density of the bulge according to the first characteristic parameter;
seventh calculation module 6052: the device is used for respectively obtaining the gravitational potential energy and the square product value of the convex part according to the position height, the volume and the rock density of the convex part, wherein the square product value is the product of the density, the volume and the gravitational acceleration;
first analysis subunit 6053: the system comprises a projection part, a scale parameter and a control part, wherein the scale parameter is used for obtaining the scale parameter based on the gravitational potential energy, the square quantity product value and the stability coefficient of the projection part;
second analysis subunit 6054: and the early warning grade of the bulge is obtained by comparing the grade parameters with a preset safety grade table.
It should be noted that, regarding the apparatus in the above embodiment, the specific manner in which each module performs the operation has been described in detail in the embodiment related to the method, and will not be elaborated herein.
Example 3:
corresponding to the above method embodiment, the present embodiment further provides an early warning device 800 for a hill collapse, and the following described early warning device 800 for a hill collapse and the above described early warning method for a hill collapse may be referred to correspondingly. Fig. 3 is a block diagram illustrating an early warning apparatus 800 for a hill collapse according to an exemplary embodiment. As shown in fig. 3, the early warning apparatus 800 for landslide may include: a processor 801, a memory 802. The early warning device 800 for landslide can further 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 device 800 for early warning of hill collapse, so as to complete all or part of the steps in the method, the apparatus, the device and the readable storage medium for early warning of hill collapse. The memory 802 is used to store various types of data to support the operation of the early warning device 800 for the hill collapse, which may include, for example, instructions for any application or method operating on the early warning device 800 for the hill collapse, as well as application-related data, such as contact data, messages sent or received, pictures, audio, video, and the like. The Memory 802 may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as Static Random Access Memory (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk or optical disk. The multimedia components 803 may include screen and audio components. Wherein the screen may be, for example, a touch screen and the audio component is used for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may further be stored in the memory 802 or transmitted through the communication component 805. The audio assembly also includes at least one speaker for outputting audio signals. The I/O interface 804 provides an interface between the processor 801 and other interface modules, such as a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 805 is used for performing wired or wireless communication between the early warning device 800 for the hill collapse and other devices. Wireless Communication, such as Wi-Fi, bluetooth, Near Field Communication (NFC), 2G, 3G, or 4G, or a combination of one or more of them, so that the corresponding Communication component 805 may include: Wi-Fi module, bluetooth module, NFC module.
In an exemplary embodiment, the Device 800 for warning of the hill collapse may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components, and may be used to implement the method, apparatus, Device and readable storage medium for warning of the hill collapse.
In another exemplary embodiment, a computer readable storage medium including program instructions is further provided, and the program instructions, when executed by a processor, implement the steps of the method, the apparatus, the device and the readable storage medium for early warning of hill collapse described above. For example, the computer readable storage medium may be the memory 802 including the program instructions, which are executable by the processor 801 of the landslide warning device 800 to implement the landslide warning method, the apparatus, the device and the readable storage medium.
Example 4:
corresponding to the above method embodiment, this embodiment further provides a readable storage medium, and a readable storage medium described below and the above described early warning method, device, apparatus, and readable storage medium for hill collapse may be referred to correspondingly.
A readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the method, the device, the equipment and the steps of the readable storage medium for early warning of hill collapse of the embodiments of the method are realized.
The readable storage medium may be a readable storage medium that can store program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (14)
1. A method for early warning of hill collapse is characterized by comprising the following steps:
acquiring a first characteristic parameter, a second characteristic parameter and a third characteristic parameter, wherein the first characteristic parameter is a parameter of a first hill in an area to be early-warned, the second characteristic parameter is a variable displacement parameter of a bulge relative to the first hill, and the bulge is a bulge part protruding out of the first hill on a first hill sloping side; the third characteristic parameter is a slope top displacement parameter of the first hillside in the area to be early-warned;
obtaining a first parameter according to the first characteristic parameter, wherein the first parameter is an internal structure surface geometric parameter of the bulge;
inputting the first parameter, the second characteristic parameter and the third characteristic parameter into a trained neural network model to obtain a second parameter, wherein the second parameter comprises a fourth sub-parameter and a fifth sub-parameter, the fourth sub-parameter is a shear strength parameter of a joint surface of the bulge, and the fifth sub-parameter comprises a space geometric parameter of an inner joint surface of the first slope and a shear strength parameter of a joint surface of the first slope;
simulating a destabilization state according to the first characteristic parameter and the second parameter to obtain a stability coefficient of the bulge;
and obtaining the early warning level of the first slope collapse according to the stability coefficient of the bulge and the first characteristic parameter.
2. The method for early warning of landslide collapse according to claim 1, wherein the training method of the neural network model comprises:
acquiring a third parameter and a preset index parameter, wherein the third parameter comprises a displacement parameter of a reverse rock cone which is already caved from a second hillside relative to the second hillside and a displacement parameter of the top of the second hillside, the reverse rock cone is a stack body which is formed after the reverse rock cone is caved from the second hillside, and the second hillside is a virtual hillside analysis model; the preset index parameters are key parameters for causing the falling rock cone to collapse;
and training a neural network model according to the third parameter and the preset index parameter to obtain the trained neural network model, wherein the third parameter is used as the input of the neural network model, and the preset index parameter is used as the output of the neural network model.
3. The method for early warning of landslide collapse according to claim 2, wherein the method for obtaining the third parameter comprises:
acquiring a fourth characteristic parameter, a fifth characteristic parameter and a preset position parameter, wherein the fourth characteristic parameter is an outer contour geometric parameter of the inverted pyramid; the fifth characteristic parameter is an outer contour geometric parameter of the second hillside after the reverse-rock cone is collapsed from the second hillside; the preset position parameter is a space position parameter of the reverse rock awl relative to the second hillside before the reverse rock awl is not collapsed from the second hillside;
constructing a virtual slope analysis model set according to the fourth characteristic parameter, the fifth characteristic parameter and the preset position parameter, wherein the virtual slope analysis model set comprises at least one virtual slope analysis model, each virtual slope analysis model is formed by randomly combining elements in the fourth characteristic parameter, the fifth characteristic parameter and the preset position parameter, and the virtual slope analysis model is a geometric model of the second slope before the reverse rock cone collapses from the second slope;
according to the preset index parameters, combining with a strength reduction algorithm to simulate the instability state of all the virtual hillside analysis models in the virtual hillside analysis model set, and when the unbalance force of the strength reduction algorithm reaches 10-5And obtaining a third parameter in the next time.
4. The method for early warning of landslide collapse according to claim 2, wherein the method for determining the preset index parameter comprises:
acquiring survey data of hill collapse disasters in at least two actual projects;
obtaining a sample parameter set for depicting a landslide collapse disaster according to the survey data, wherein the sample parameter set comprises at least two sample parameters, each sample parameter corresponds to a factor set, the factor set comprises at least one factor, and the factors are all parameters in the survey data;
and counting the occurrence frequency of each factor of all the sample parameters in the sample parameter set to obtain a preset index parameter, wherein the preset index parameter is the factor which occurs in all the sample parameters in all the sample sets.
5. The method for early warning of landslide collapse according to claim 1, wherein the method for obtaining the second characteristic parameter comprises:
obtaining a virtual geometric model of the convex part according to the first characteristic parameter;
obtaining a space virtual parameter set of monitoring points according to the virtual geometric model of the convex part, wherein the space virtual parameter set comprises at least ten space virtual parameters, each space virtual parameter is a position parameter set of the monitoring point, and the monitoring points are transversely arranged in the virtual geometric model of the convex part at equal intervals;
setting the monitoring point on the first hillside according to all the space virtual parameters in the space virtual parameter set and a preset proportion, wherein the preset proportion is a ratio of a virtual geometric model of the bulge to a geometric model of the bulge in the first hillside;
acquiring a first space parameter of the monitoring point at the time T, wherein the first space parameter is three-dimensional space data of the monitoring point at the time T;
acquiring a second space parameter of the monitoring point at the moment T +1, wherein the second space parameter is three-dimensional space data of the monitoring point at the moment T + 1;
and judging whether the first space parameter and the second space parameter are equal, if not, the second space parameter is the second characteristic parameter.
6. The method for warning of landslide according to claim 1, wherein obtaining a first warning level of landslide according to the stability factor of the bulge and the first characteristic parameter comprises:
obtaining the position height, the volume and the rock density of the bulge according to the first characteristic parameter;
respectively obtaining the gravitational potential energy and the square product value of the convex part according to the position height, the volume and the rock density of the convex part, wherein the square product value is the product of the density, the volume and the gravitational acceleration;
obtaining a grade parameter based on the gravitational potential energy, the square product value and the stability coefficient of the bulge;
and comparing the grade parameters with a preset safety grade table to obtain the early warning grade of the bulge.
7. The utility model provides an early warning device that hillside collapses which characterized in that includes:
a first obtaining module: the system comprises a first characteristic parameter, a second characteristic parameter and a third characteristic parameter, wherein the first characteristic parameter is a parameter of a first hill in an area to be early-warned, the second characteristic parameter is a variable displacement parameter of a bulge relative to the first hill, and the bulge is a bulge part protruding out of the first hill on a first hill inclined edge; the third characteristic parameter is a slope top displacement parameter of the first hillside in the area to be early-warned;
a first building block: the first parameter is obtained according to the first characteristic parameter, and the first parameter is an internal structural surface geometric parameter of the bulge;
a first calculation module: the second parameter is obtained by inputting the first parameter, the second characteristic parameter and the third characteristic parameter into a trained neural network model, and the second parameter includes a fourth sub-parameter and a fifth sub-parameter, the fourth sub-parameter is a shear strength parameter of a joint surface of the bulge, and the fifth sub-parameter includes a space geometry parameter of an inner joint surface of the first slope and a shear strength parameter of a joint surface of the first slope;
a second calculation module: the simulation module is used for simulating a destabilization state according to the first characteristic parameter and the second parameter to obtain a stability coefficient of the bulge;
an analysis module: and the early warning level of the first slope collapse is obtained according to the stability coefficient of the bulge and the first characteristic parameter.
8. The device for early warning of landslide collapse according to claim 7, wherein the first computing module comprises:
a second obtaining module: the system comprises a first parameter and a second parameter, wherein the first parameter comprises a displacement parameter of a reverse rock cone which is collapsed from a first hillside relative to the first hillside and a displacement parameter of a top of the first hillside, the reverse rock cone is a stack body which is formed after the reverse rock cone is collapsed from the first hillside, and the first hillside is a virtual hillside analysis model; the preset index parameters are key parameters for causing the falling rock cone to collapse;
a training module: and the neural network model is trained according to the third parameter and the preset index parameter to obtain the trained neural network model, wherein the third parameter is used as the input of the neural network model, and the preset index parameter is used as the output of the neural network model.
9. The device for early warning of landslide collapse according to claim 8, wherein the second obtaining module comprises:
a third obtaining module: the device is used for acquiring a fourth characteristic parameter, a fifth characteristic parameter and a preset position parameter, wherein the fourth characteristic parameter is an outer contour geometric parameter of the inverted cone; the fifth characteristic parameter is an outer contour geometric parameter of the second hillside after the reverse-rock cone is collapsed from the second hillside; the preset position parameter is a space position parameter of the reverse rock awl relative to the second hillside before the reverse rock awl is not collapsed from the second hillside;
a second building block: the virtual slope analysis model set is constructed according to the fourth characteristic parameter, the fifth characteristic parameter and the preset position parameter, the virtual slope analysis model set comprises at least one virtual slope analysis model, each virtual slope analysis model is formed by randomly combining elements in the fourth characteristic parameter, the fifth characteristic parameter and the preset position parameter, and the virtual slope analysis model is a geometric model of the second slope before the reverse rock cone collapses from the second slope;
a third calculation module: the simulation system is used for simulating the instability state of all the virtual hillside analysis models in the virtual hillside analysis model set by combining the strength reduction algorithm according to the preset index parameters, and when the unbalance force of the strength reduction algorithm reaches 10-5And obtaining a third parameter in the next time.
10. The device for early warning of landslide collapse according to claim 8, wherein the second obtaining module further comprises:
a fourth obtaining module: the method comprises the steps of acquiring survey data of hill collapse disasters in at least two actual projects;
an extraction module: the method comprises the steps of obtaining a sample parameter set for characterizing the landslide disaster according to survey data, wherein the sample parameter set comprises at least two sample parameters, each sample parameter corresponds to a factor set, the factor set comprises at least one factor, and the factors are all parameters in the survey data;
a fourth calculation module: and the method is used for counting the occurrence frequency of each factor of all the sample parameters in the sample parameter set to obtain a preset index parameter, wherein the preset index parameter is the factor which occurs in all the sample parameters in all the sample sets.
11. The early warning device of a hill collapse according to claim 7, wherein the first obtaining module comprises:
a first building subunit: the virtual geometric model of the bulge is obtained according to the first characteristic parameter;
a first calculation subunit: the monitoring point acquisition module is used for acquiring a space virtual parameter set of monitoring points according to the virtual geometric model of the convex part, wherein the space virtual parameter set comprises at least ten space virtual parameters, each space virtual parameter is a position parameter set of the monitoring point, and the monitoring points are transversely arranged in the virtual geometric model of the convex part at equal intervals;
a second building subunit: the monitoring point is set on the first hillside according to all the space virtual parameters in the space virtual parameter set and a preset proportion, and the preset proportion is a ratio of a virtual geometric model of the bulge to a geometric model of the bulge in the first hillside;
a first acquisition subunit: the system comprises a monitoring point, a first spatial parameter acquisition module, a second spatial parameter acquisition module and a monitoring module, wherein the first spatial parameter acquisition module is used for acquiring a first spatial parameter of the monitoring point at the T moment, and the first spatial parameter is three-dimensional spatial data of the monitoring point at the T moment;
a second acquisition subunit: the second space parameter is used for acquiring a second space parameter of the monitoring point at the moment T +1, and the second space parameter is three-dimensional space data of the monitoring point at the moment T + 1;
a judging unit: and the second space parameter is used for judging whether the first space parameter and the second space parameter are equal, and if not, the second space parameter is the second characteristic parameter.
12. The device for early warning of landslide collapse according to claim 7, wherein the analysis module comprises:
a sixth calculation module: the position height, the volume and the rock density of the bulge are obtained according to the first characteristic parameter;
a seventh calculation module: the device is used for respectively obtaining the gravitational potential energy and the square product value of the convex part according to the position height, the volume and the rock density of the convex part, wherein the square product value is the product of the density, the volume and the gravitational acceleration;
first analytical subunit: the system comprises a control unit, a control unit and a control unit, wherein the control unit is used for obtaining a gravitational potential energy, a square quantity product value and a stability coefficient of the bulge part based on the gravitational potential energy, the square quantity product value and the stability coefficient of the bulge part to obtain a grade parameter;
a second analytical subunit: and the early warning level of the bulge is obtained by comparing the level parameters with a preset safety level table.
13. The utility model provides an early warning equipment that hillside collapses which characterized in that includes:
a memory for storing a computer program;
a processor for implementing the steps of the method for warning of a hill collapse as claimed in any one of claims 1 to 6 when executing the computer program.
14. A readable storage medium, characterized by: the readable storage medium has stored thereon a computer program which, when being executed by a processor, implements the steps of the method for warning of a hill collapse according to any one of claims 1 to 6.
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