CN109584515A - Method for early warning, device and the readable storage medium storing program for executing of massif disaster - Google Patents
Method for early warning, device and the readable storage medium storing program for executing of massif disaster Download PDFInfo
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- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
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- G08B21/02—Alarms for ensuring the safety of persons
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Abstract
Method for early warning, device and the readable storage medium storing program for executing of massif disaster provided by the invention obtain the ranging data on current massif surface by being scanned ranging to massif using laser radar；The three-dimensional coordinate of current massif surface scan point is determined according to the ranging data on current massif surface, and is handled through past interference, to obtain the point cloud data of current massif body surface；Formation includes the data to be predicted including the point cloud data of history massif body surface and the point cloud data of the current massif body surface, and the data to be predicted are predicted using preset machine learning algorithm model, obtain the probability that massif disaster occurs；Early warning information is pushed according to the probability that massif disaster occurs, early warning is carried out for massif disaster based on point cloud data measured by laser radar to realize, it is for based on GPS technology, since more fully corresponding early warning accuracy rate gets a promotion data.
Description
Technical field
The present invention relates to natural calamity early warning technology more particularly to a kind of method for early warning of massif disaster, device and readable
Storage medium.
Background technique
Natural calamity causes huge economic loss to country and government, causes huge prestige to the personal safety of the people
The side of body.Massif disaster as one of the major casualty in natural calamity, caused by influence that huge, incidence is high, how using automatic
It is problem in the urgent need to address that the mode of change, which is monitored it with early warning,.
In the prior art, it is generally based on GPS technology and detection and massif disaster alarm is carried out for massif, pass through benefit
Coordinate acquisition is carried out for massif with GPS satellite, and the coordinate data of acquisition is analyzed, with determine massif current state and
Predict its disaster state being likely to occur.
But massif coordinate is acquired using GPS acquire obtain data it is not comprehensive enough, can not reflect massif
The contour feature on surface, and then state-detection and when massif disaster alarm result accuracy rate obtained are being carried out not to massif
It is high.
Summary of the invention
For it is above-mentioned refer in the prior art using GPS technology carry out massif disaster alarm early warning result obtained
The not high problem of accuracy rate, the present invention provides method for early warning, device and the readable storage medium storing program for executing of a kind of massif disaster.
The present invention provides a kind of method for early warning of massif disaster, comprising:
Ranging is scanned to massif using laser radar, obtains the ranging data on current massif surface；
The three-dimensional coordinate of current massif surface scan point is determined according to the ranging data on current massif surface, and dry through the past
Processing is disturbed, to obtain the point cloud data of current massif body surface；
Formation includes the point cloud data of history massif body surface and the point cloud data of the current massif body surface
Data to be predicted inside, and the data to be predicted are predicted using preset machine learning algorithm model, it is sent out
The probability of raw massif disaster；
Early warning information is pushed according to the probability that massif disaster occurs.
Optionally, the ranging data on the current massif surface of the basis determines that the three-dimensional of current massif surface scan point is sat
Mark, and handled through past interference, to obtain the point cloud data of current massif body surface, comprising:
The three-dimensional coordinate of current massif surface scan point is determined according to the ranging data on current massif surface, front range is worked as in acquisition
The point cloud data in body surface face；
The standard deviation of each data point in the point cloud data on the current massif surface is calculated, and according to the standard deviation pair
Each data point in the point cloud data on the current massif surface is screened；
Each group of data points after screening at the current massif body surface point cloud data.
Optionally, described that each data point in the point cloud data on the current massif surface is carried out according to the standard deviation
Screening, comprising:
For the total data point in the point cloud data on the current massif surface, calculate separately each data point distance
The vertical height of tangent line, and retain each data point of the vertical height less than or equal to the standard deviation as each number after the screening
Strong point.
Optionally, the massif disaster include landslide, avalanche, mud-stone flow disaster one of Disasters Type or a variety of；
Correspondingly, described predict the data to be predicted using preset machine learning algorithm model, sent out
The probability of raw massif disaster, comprising:
The data to be predicted are handled according to preset machine learning algorithm model, obtain the data to be predicted
The probability of each Disasters Type occurs.
Optionally, the data to be predicted include hillside slope shape plot against time sequence and hillside slope surface point cloud coordinate
Time series.
On the other hand, the present invention provides a kind of prior-warning devices of massif disaster, comprising:
Laser radar apparatus obtains the survey on current massif surface for being scanned ranging to massif using laser radar
Away from data；
Processing equipment, for determining that the three-dimensional of current massif surface scan point is sat according to the ranging data on current massif surface
Mark, and handled through past interference, to obtain the point cloud data of current massif body surface；Formation includes this body surface of history massif
Data to be predicted including the point cloud data of the point cloud data in face and the current massif body surface, and utilize preset machine
Learning algorithm model predicts the data to be predicted, obtains the probability that massif disaster occurs；It is also used to according to the hair
The probability of raw massif disaster pushes early warning information.
Optionally, the laser radar apparatus is mounted in the upright bar on hillside opposite of massif, and with the mountain towards massif
The mode that slope carries out is scanned ranging to the massif.
On the other hand, the present invention provides a kind of prior-warning devices of massif disaster, comprising: memory and the memory
The processor of connection, and it is stored in the computer program that can be run on the memory and on the processor, feature exists
In,
The processor executes method described in any of the above embodiments when running the computer program.
Last aspect, the present invention provides a kind of readable storage medium storing program for executing, which is characterized in that including program, when it is at end
When being run on end, so that terminal executes method described in any of the above embodiments.
Method for early warning, device and the readable storage medium storing program for executing of massif disaster provided by the invention, by utilizing laser radar pair
Massif is scanned ranging, obtains the ranging data on current massif surface；Worked as according to the determination of the ranging data on current massif surface
The three-dimensional coordinate of front range body surface Surface scan point, and handled through past interference, to obtain the point cloud data of current massif body surface；
Formation include including the point cloud data of history massif body surface and the point cloud data of the current massif body surface to
Prediction data, and the data to be predicted are predicted using preset machine learning algorithm model, it obtains and massif calamity occurs
Harmful probability；Early warning information is pushed according to the probability that massif disaster occurs, to realize based on measured by laser radar
Point cloud data carries out early warning for massif disaster, for based on GPS technology, since data are more fully corresponding pre-
Alert accuracy rate gets a promotion.
Detailed description of the invention
Through the above attached drawings, it has been shown that the specific embodiment of the disclosure will be hereinafter described in more detail.These attached drawings
It is not intended to limit the scope of this disclosure concept by any means with verbal description, but is by referring to specific embodiments
Those skilled in the art illustrate the concept of the disclosure.
Fig. 1 be the present invention is based on network architecture schematic diagram；
Fig. 2 is a kind of flow diagram of the method for early warning for massif disaster that the embodiment of the present invention one provides；
Fig. 3 is a kind of flow diagram of the method for early warning of massif disaster provided by Embodiment 2 of the present invention；
Fig. 4 is a kind of structural schematic diagram of the prior-warning device for massif disaster that the embodiment of the present invention three provides；
Fig. 5 is a kind of hardware schematic of the prior-warning device for massif disaster that the embodiment of the present invention four provides.
The drawings herein are incorporated into the specification and forms part of this specification, and shows the implementation for meeting the disclosure
Example, and together with specification for explaining the principles of this disclosure.
Specific embodiment
In order to make the object, technical scheme and advantages of the embodiment of the invention clearer, below in conjunction with the embodiment of the present invention
In attached drawing, technical scheme in the embodiment of the invention is clearly and completely described.
Natural calamity causes huge economic loss to country and government, causes huge prestige to the personal safety of the people
The side of body.Massif disaster as one of the major casualty in natural calamity, caused by influence that huge, incidence is high, how using automatic
It is problem in the urgent need to address that the mode of change, which is monitored it with early warning,.
In the prior art, it is generally based on GPS technology and detection and massif disaster alarm is carried out for massif, pass through benefit
Coordinate acquisition is carried out for massif with GPS satellite, and the coordinate data of acquisition is analyzed, with determine massif current state and
Predict its disaster state being likely to occur.
But massif coordinate is acquired using GPS acquire obtain data it is not comprehensive enough, can not reflect massif
The contour feature on surface, and then state-detection and when massif disaster alarm result accuracy rate obtained are being carried out not to massif
It is high.
For aforementioned the technical issues of referring to, the present invention provides a kind of method for early warning of massif disaster, device and readable
Storage medium.It should be noted that method for early warning, device and readable storage medium storing program for executing that the application provides massif disaster can be used in
In the scene for needing to carry out natural calamity prediction or early warning, especially in massif disaster.
Fig. 1 be the present invention is based on network architecture schematic diagram, as shown in Figure 1, in the network architecture that is based on of the present invention
Including at least the prediction meanss 2 of server 1 and massif disaster.The server-side 1 is concretely set up beyond the clouds or local service
Device or server cluster, for storing the early warning information of the prediction meanss sending of massif disaster and storing corresponding ranging data.
The prediction meanss 2 of massif disaster specifically may include the laser radar apparatus 3 being arranged on massif and be set to cloud or local
Processing equipment 4, which can be used for obtaining connection with server 1, and carry out data interaction.
Fig. 2 is a kind of flow diagram with method for early warning for massif disaster that the embodiment of the present invention one provides.
As shown in Fig. 2, the massif disaster includes: with method for early warning
Step 101 is scanned ranging to massif using laser radar, obtains the ranging data on current massif surface；
Step 102, the three-dimensional coordinate that current massif surface scan point is determined according to the ranging data on current massif surface, and
It is handled through past interference, to obtain the point cloud data of current massif body surface；
Step 103, formation include the point cloud data and the current massif body surface of history massif body surface
Data to be predicted including point cloud data, and the data to be predicted are carried out in advance using preset machine learning algorithm model
It surveys, obtains the probability that massif disaster occurs；
Step 104 pushes early warning information according to the probability that massif disaster occurs.
It should be noted that the executing subject of the method for early warning of massif disaster provided by the invention is concretely shown in Fig. 2
Massif disaster prior-warning device.
Unlike the method for early warning of existing massif disaster, what the application was based on is to be measured by laser radar massif
The ranging data of acquisition.
Specifically, the laser radar apparatus for being set up in the corresponding measurement position of massif will be right according to preset scanning strategy
Massif is scanned ranging, to obtain the ranging data on the current massif surface at scanning moment.
Then, the ranging data on the current massif surface according to acquisition is determined current massif by the prior-warning device of massif disaster
The three-dimensional coordinate of surface scan point, and handled through past interference, to obtain the point cloud data of current massif body surface.
Specifically, the prior-warning device of massif disaster can determine current massif according to the ranging data on current massif surface first
The three-dimensional coordinate of surface scan point obtains the point cloud data on current massif surface.
Wherein, the two-dimensional cross-section ranging data of the ranging data concretely slope surface on hillside, is driven by rotary holder
Laser radar rotation, so that each of two-dimensional scanning section art becomes new two-dimensional pan-tilt scanning section, all holders
Scanning section forms new 3 D stereo scanning.
Correspondingly, the Polar Coordinate Two-dimensional section ranging data of hillside slope surface can be turned by algorithm first with formula (1)
It is changed to rectangular co-ordinate waveform point cloud data, forms the two-dimensional Cartesian coordinate system data of hillside slope surface:
Wherein, L is polar coordinates ranging distance value, and H is the mounting height of laser radar apparatus, and x is that two-dimentional rectangular co-ordinate is horizontal
Coordinate, y are two-dimentional rectangular co-ordinate ordinate, and a is laser beam during the scanning process with the deviation angle of center line, and b is laser thunder
Up to the angle of equipment scanning cross-section and ground normal.
Then, the two-dimensional Cartesian coordinate system data of hillside slope surface are converted into three-dimensional seat by algorithm using formula (2)
Target point cloud data；
It should be noted that the conversion of three-dimensional world coordinate system relies on formula:
Wherein, y is the y-coordinate in two-dimensional Cartesian coordinate system, and H is the mounting height of laser radar apparatus, and z is three-dimensional coordinate
It is reference axis, y ' is three-dimensional system of coordinate reference axis, and a is the deviation angle of the center line of point art and laser radar apparatus scanning section
Degree, b are the angle that laser radar apparatus scans section and ground normal；Z, y in this formula (2) ' with formula (1) in x shape
At three reference axis of three-dimensional system of coordinate.
Then, the prior-warning device of massif disaster also carries out at interference the three-dimensional coordinate of current massif surface scan point
Reason, to obtain the point cloud data of current massif body surface.
Specifically, the standard deviation of each data point in the point cloud data on the current massif surface can be calculated, and according to
The standard deviation screens each data point in the point cloud data on the current massif surface；Each group of data points after screening
At the point cloud data of the current massif body surface.
Formula (3) shows a kind of calculation method of standard deviation:
Wherein, XiVertical height for data point apart from tangent line, E are the equal of the vertical height of all data points and tangent line
Value, J is standard deviation.
For the total data point in the point cloud data on the current massif surface, calculate separately each data point distance
The vertical height of tangent line, and retain each data point of the vertical height less than or equal to the standard deviation as each number after the screening
Strong point.
Then, can be formed includes the point cloud data of history massif body surface and the point of the current massif body surface
Data to be predicted including cloud data, and the data to be predicted are predicted using preset machine learning algorithm model,
Obtain the probability that massif disaster occurs.
Wherein, when the data to be predicted include hillside slope shape plot against time sequence and hillside slope surface point cloud coordinate
Between sequence.
And finally, then pushing early warning information according to the probability that massif disaster occurs.
The method for early warning of massif disaster provided by the invention is obtained by being scanned ranging to massif using laser radar
The ranging data in proper front range body surface face；The three of current massif surface scan point are determined according to the ranging data on current massif surface
Coordinate is tieed up, and is handled through past interference, to obtain the point cloud data of current massif body surface；Formation includes history massif sheet
Data to be predicted including the point cloud data of the point cloud data in body surface face and the current massif body surface, and utilize preset
Machine learning algorithm model predicts the data to be predicted, obtains the probability that massif disaster occurs；According to the generation
The probability of massif disaster pushes early warning information, thus realize based on point cloud data measured by laser radar for massif disaster into
Row early warning, for based on GPS technology, since more fully corresponding early warning accuracy rate gets a promotion data.
Fig. 3 is a kind of flow diagram with method for early warning of massif disaster provided by Embodiment 2 of the present invention.
As shown in figure 3, the massif disaster includes: with method for early warning
Step 201 is scanned ranging to massif using laser radar, obtains the ranging data on current massif surface；
Step 202, the three-dimensional coordinate that current massif surface scan point is determined according to the ranging data on current massif surface, and
It is handled through past interference, to obtain the point cloud data of current massif body surface；
Step 203, formation include the point cloud data and the current massif body surface of history massif body surface
Data to be predicted including point cloud data, and according to preset machine learning algorithm model to the data to be predicted at
Reason obtains the probability that each Disasters Type occurs for the data to be predicted；Wherein, massif disaster includes landslide, avalanche, mud-rock flow
One of Disasters Type of disaster is a variety of；
Step 204 pushes early warning information according to the probability that each Disasters Type occurs.
It should be noted that the executing subject of the method for early warning of massif disaster provided by the invention is concretely shown in Fig. 2
Massif disaster prior-warning device.
Similarly with aforementioned embodiments, the laser radar apparatus for being set up in the corresponding measurement position of massif will be according to default
Scanning strategy, ranging is scanned to massif, with obtain scanning the moment current massif surface ranging data.
Then, the ranging data on the current massif surface according to acquisition is determined current massif by the prior-warning device of massif disaster
The three-dimensional coordinate of surface scan point, and handled through past interference, to obtain the point cloud data of current massif body surface.
Specifically, the prior-warning device of massif disaster can determine current massif according to the ranging data on current massif surface first
The three-dimensional coordinate of surface scan point obtains the point cloud data on current massif surface.
Wherein, the two-dimensional cross-section ranging data of the ranging data concretely slope surface on hillside, is driven by rotary holder
Laser radar rotation, so that each of two-dimensional scanning section art becomes new two-dimensional pan-tilt scanning section, all holders
Scanning section forms new 3 D stereo scanning.
Correspondingly, the Polar Coordinate Two-dimensional section ranging data of hillside slope surface can be turned by algorithm first with formula (1)
It is changed to rectangular co-ordinate waveform point cloud data, forms the two-dimensional Cartesian coordinate system data of hillside slope surface:
Wherein, L is polar coordinates ranging distance value, and H is the mounting height of laser radar apparatus, and x is that two-dimentional rectangular co-ordinate is horizontal
Coordinate, y are two-dimentional rectangular co-ordinate ordinate, and a is laser beam during the scanning process with the deviation angle of center line, and b is laser thunder
Up to the angle of equipment scanning cross-section and ground normal.
Then, the two-dimensional Cartesian coordinate system data of hillside slope surface are converted into three-dimensional seat by algorithm using formula (2)
Target point cloud data；
It should be noted that the conversion of three-dimensional world coordinate system relies on formula:
Wherein, y is the y-coordinate in two-dimensional Cartesian coordinate system, and H is the mounting height of laser radar apparatus, and z is three-dimensional coordinate
It is reference axis, y ' is three-dimensional system of coordinate reference axis, and a is the deviation angle of the center line of point art and laser radar apparatus scanning section
Degree, b are the angle that laser radar apparatus scans section and ground normal；Z, y in this formula (2) ' with formula (1) in x shape
At three reference axis of three-dimensional system of coordinate.
Then, the prior-warning device of massif disaster also carries out at interference the three-dimensional coordinate of current massif surface scan point
Reason, to obtain the point cloud data of current massif body surface.
Specifically, the standard deviation of each data point in the point cloud data on the current massif surface can be calculated, and according to
The standard deviation screens each data point in the point cloud data on the current massif surface；Each group of data points after screening
At the point cloud data of the current massif body surface.
Formula (3) shows a kind of calculation method of standard deviation:
Wherein, XiVertical height for data point apart from tangent line, E are the equal of the vertical height of all data points and tangent line
Value, J is standard deviation.
For the total data point in the point cloud data on the current massif surface, calculate separately each data point distance
The vertical height of tangent line, and retain each data point of the vertical height less than or equal to the standard deviation as each number after the screening
Strong point.
Then, can be formed includes the point cloud data of history massif body surface and the point of the current massif body surface
Data to be predicted including cloud data, and the data to be predicted are predicted using preset machine learning algorithm model,
Obtain the probability that massif disaster occurs.
Wherein, when the data to be predicted include hillside slope shape plot against time sequence and hillside slope surface point cloud coordinate
Between sequence.
Unlike aforementioned embodiments, in the present embodiment, massif disaster includes landslide, avalanche, Debris-flow Hazard
One of harmful Disasters Type is a variety of；Correspondingly, described utilize preset machine learning algorithm model to described to be predicted
Data are predicted, obtain the probability that massif disaster occurs, comprising: according to preset machine learning algorithm model to described to pre-
Measured data is handled, and the probability that massif disaster occurs for the data to be predicted is obtained.
It wherein, include the point cloud data currently measured and history point cloud data in data to be predicted, meanwhile, it will
It is presented in a manner of hillside slope shape plot against time sequence and hillside slope surface point cloud coordinate time sequence.
And machine learning algorithm model is then using a large amount of history history hillside slope surface disaster data prestored in server 1
Sample is formed by with non-disaster data to be trained, and to obtain the hyper parameter of model, and is formed and be can be used for predicting each disaster
The machine learning algorithm model of type probability.
In addition, in the present embodiment, it is also necessary to solve the problems, such as landslide, avalanche, mud-rock flow Accurate classification, to realize root
Early warning information is pushed according to the probability that massif disaster occurs.Specifically, according to the hillside surface 3D data of removal distracter, into
Row multiframe continuous action processing, by disaster motion state, surface texturisation, regional morphology, the multiple index joint judgements of displacement characteristic.
When detected region meets following decision logic: 1) massif slope angle is steep, i.e., slope angle spends section in [10,45]；2) form is in lower steep
In delay upper steep shape；3) domatic of area top circlewise；4) slope is consistent to being inclined to strata structure face；5) horizontal displacement is big
In vertical displacement amount；6) position of centre of gravity displacement is little；7) disaster body surface has regular crack in length and breadth.It then carries out coming down pre-
It is alert.When detected region meets following decision logic: 1) completely disengaging parent；2) vertical displacement amount is greater than horizontal displacement；3)
It is big that position of centre of gravity reduces amplitude；4) disaster body surface does not have the crack in length and breadth of rule；5) its fracture development disaster body rear,
Steep cliff rear；6) disaster source has apparent cliff landforms.Then carry out avalanche early warning.It is patrolled when detected region meets following judgement
Volume: 1) it is in asymmetric dumb-bell shape that motion state, which be plane,；2) form of accumulation area is unstable；3) area is formed from ribbon to tree
Dendritic development；4) Circulation Area form stable in development process；5) accumulation area constantly expands due to the growth of sediment yield in basin
Exhibition, closes on downstream lower deformable, then carries out debris flow early-warning.
The method for early warning of massif disaster provided by the invention is obtained by being scanned ranging to massif using laser radar
The ranging data in proper front range body surface face；The three of current massif surface scan point are determined according to the ranging data on current massif surface
Coordinate is tieed up, and is handled through past interference, to obtain the point cloud data of current massif body surface；Formation includes history massif sheet
Data to be predicted including the point cloud data of the point cloud data in body surface face and the current massif body surface, and utilize preset
Machine learning algorithm model predicts the data to be predicted, obtains the probability that massif disaster occurs；According to the generation
The probability of massif disaster pushes early warning information, thus realize based on point cloud data measured by laser radar for massif disaster into
Row early warning, for based on GPS technology, since more fully corresponding early warning accuracy rate gets a promotion data.
Fig. 4 is a kind of structural schematic diagram of the prior-warning device for massif disaster that the embodiment of the present invention three provides, such as Fig. 4 institute
Show, the prior-warning device of the massif disaster includes:
Laser radar apparatus 3 obtains the survey on current massif surface for being scanned ranging to massif using laser radar
Away from data；
Processing equipment 4, for determining the three-dimensional of current massif surface scan point according to the ranging data on current massif surface
Coordinate, and handled through past interference, to obtain the point cloud data of current massif body surface；Formation includes history massif ontology
Data to be predicted including the point cloud data of the point cloud data on surface and the current massif body surface, and utilize preset machine
Device learning algorithm model predicts the data to be predicted, obtains the probability that massif disaster occurs；It is also used to according to
The probability that massif disaster occurs pushes early warning information.
Optionally, the laser radar apparatus 3 is mounted in the upright bar on hillside opposite of massif, and with the mountain towards massif
The mode that slope carries out is scanned ranging to the massif.
The prior-warning device of massif disaster provided by the invention is obtained by being scanned ranging to massif using laser radar
The ranging data in proper front range body surface face；The three of current massif surface scan point are determined according to the ranging data on current massif surface
Coordinate is tieed up, and is handled through past interference, to obtain the point cloud data of current massif body surface；Formation includes history massif sheet
Data to be predicted including the point cloud data of the point cloud data in body surface face and the current massif body surface, and utilize preset
Machine learning algorithm model predicts the data to be predicted, obtains the probability that massif disaster occurs；According to the generation
The probability of massif disaster pushes early warning information, thus realize based on point cloud data measured by laser radar for massif disaster into
Row early warning, for based on GPS technology, since more fully corresponding early warning accuracy rate gets a promotion data.
Fig. 5 is a kind of hardware schematic of the prior-warning device for massif disaster that the embodiment of the present invention four provides.Such as Fig. 5 institute
Show, the prior-warning device of the massif disaster includes: processor 42 and is stored on memory 41 and can run on processor 42
Computer program, processor 42 run the method for executing above-described embodiment when computer program.
The present invention also provides a kind of readable storage medium storing program for executing, including program, when it runs at the terminal, so that terminal executes
The method of any of the above-described embodiment.
Those of ordinary skill in the art will appreciate that: realize that all or part of the steps of above-mentioned each method embodiment can lead to
The relevant hardware of program instruction is crossed to complete.Program above-mentioned can be stored in a computer readable storage medium.The journey
When being executed, execution includes the steps that above-mentioned each method embodiment to sequence；And storage medium above-mentioned include: ROM, RAM, magnetic disk or
The various media that can store program code such as person's CD.
Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention., rather than its limitations；To the greatest extent
Pipe present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that: its according to
So be possible to modify the technical solutions described in the foregoing embodiments, or to some or all of the technical features into
Row equivalent replacement；And these are modified or replaceed, various embodiments of the present invention technology that it does not separate the essence of the corresponding technical solution
The range of scheme.
Claims (9)
1. a kind of method for early warning of massif disaster characterized by comprising
Ranging is scanned to massif using laser radar, obtains the ranging data on current massif surface；
The three-dimensional coordinate of current massif surface scan point is determined according to the ranging data on current massif surface, and through past interference at
Reason, to obtain the point cloud data of current massif body surface；
Including formation includes the point cloud data of history massif body surface and the point cloud data of the current massif body surface
Data to be predicted, and the data to be predicted are predicted using preset machine learning algorithm model, obtain and mountain occurs
The probability of body disaster；
Early warning information is pushed according to the probability that massif disaster occurs.
2. the method for early warning of massif disaster according to claim 1, which is characterized in that the current massif surface of basis
Ranging data determines the three-dimensional coordinate of current massif surface scan point, and handles through past interference, to obtain current massif ontology
The point cloud data on surface, comprising:
The three-dimensional coordinate that current massif surface scan point is determined according to the ranging data on current massif surface, obtains current massif table
The point cloud data in face；
The standard deviation of each data point in the point cloud data on the current massif surface is calculated, and according to the standard deviation to described
Each data point in the point cloud data on current massif surface is screened；
Each group of data points after screening at the current massif body surface point cloud data.
3. the method for early warning of massif disaster according to claim 2, which is characterized in that it is described according to the standard deviation to institute
Each data point stated in the point cloud data on current massif surface is screened, comprising:
For the total data point in the point cloud data on the current massif surface, calculate separately each data point apart from tangent line
Vertical height, and retain vertical height less than or equal to the standard deviation each data point as each data after the screening
Point.
4. the method for early warning of massif disaster according to claim 1, which is characterized in that the massif disaster include landslide,
Avalanche, mud-stone flow disaster one of Disasters Type or a variety of；
Correspondingly, described predict the data to be predicted using preset machine learning algorithm model, obtains and mountain occurs
The probability of body disaster, comprising:
The data to be predicted are handled according to preset machine learning algorithm model, the data to be predicted is obtained and occurs
The probability of each Disasters Type.
5. the method for early warning of massif disaster according to claim 1-4, which is characterized in that the data to be predicted
Including hillside slope shape plot against time sequence and hillside slope surface point cloud coordinate time sequence.
6. a kind of prior-warning device of massif disaster characterized by comprising
Laser radar apparatus obtains the ranging number on current massif surface for being scanned ranging to massif using laser radar
According to；
Processing equipment, for determining the three-dimensional coordinate of current massif surface scan point according to the ranging data on current massif surface,
And handled through past interference, to obtain the point cloud data of current massif body surface；Formation includes history massif body surface
Point cloud data and the current massif body surface point cloud data including data to be predicted, and utilize preset engineering
It practises algorithm model to predict the data to be predicted, obtains the probability that massif disaster occurs；It is also used to according to the generation
The probability of massif disaster pushes early warning information.
7. the prior-warning device of massif disaster according to claim 6, which is characterized in that the laser radar apparatus is mounted on
In the upright bar on the hillside opposite of massif, and ranging is scanned to the massif in such a way that the hillside towards massif carries out.
8. a kind of prior-warning device of massif disaster characterized by comprising memory, the processor being connect with the memory,
And it is stored in the computer program that can be run on the memory and on the processor, which is characterized in that
Perform claim requires the described in any item methods of 1-5 when the processor runs the computer program.
9. a kind of readable storage medium storing program for executing, which is characterized in that including program, when it runs at the terminal, so that terminal right of execution
Benefit requires the described in any item methods of 1-5.
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