CN109003422A - Monitoring data processing method and landslide forecasting procedure for landslide - Google Patents
Monitoring data processing method and landslide forecasting procedure for landslide Download PDFInfo
- Publication number
- CN109003422A CN109003422A CN201810871963.7A CN201810871963A CN109003422A CN 109003422 A CN109003422 A CN 109003422A CN 201810871963 A CN201810871963 A CN 201810871963A CN 109003422 A CN109003422 A CN 109003422A
- Authority
- CN
- China
- Prior art keywords
- landslide
- monitoring data
- monitoring
- data
- components
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/02—Alarms for ensuring the safety of persons
- G08B21/10—Alarms for ensuring the safety of persons responsive to calamitous events, e.g. tornados or earthquakes
Landscapes
- Life Sciences & Earth Sciences (AREA)
- Environmental & Geological Engineering (AREA)
- General Life Sciences & Earth Sciences (AREA)
- Geology (AREA)
- Business, Economics & Management (AREA)
- Emergency Management (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Testing Or Calibration Of Command Recording Devices (AREA)
Abstract
This application discloses the monitoring data processing methods and landslide forecasting procedure for landslide, are related to disaster alarm field, by laying monitoring point in premonitoring region, obtain the multi -components monitoring data of monitoring point.Multi -components monitoring data include the variation of physics-mechanics character at monitoring point, the Characters of Geographical Environment of monitoring point and weather condition.Multi -components monitoring data are handled, the changing features of multi -components monitoring data are obtained.This feature variation is input in tree-model again, exports the landslide risk index of the monitoring point.Since the proposition of innovation is handled the multi -components monitoring data of the monitoring point in premonitoring region using tree-model, and then realize to the round-the-clock monitoring of landslide and the real-time update of data, early warning can be issued to the dangerous of landslide in time by the analysis to data.
Description
Technical field
The present invention relates to disaster alarm fields, and in particular to the monitoring data processing method and massif for landslide are sliding
Slope prediction method.
Background technique
Landslide (landslides) refers on massif slope certain a part of ground in gravity (including ground gravity itself
And the dynamic and static pressures of underground water), under the action of landslide vibration or other external force, produced along certain weak structural face (band)
Give birth to shear displacemant and effect and phenomenon integrally mobile to slope lower section.It is commonly called as " walking mountain ", " collapse mountain ", " sliding ", " solifluction "
Deng.Mountain blasting is compared with the lithosome block of Rock And Soil or plane of fracture segmentation on escarpment, through strong weathering, in gravity or massif
The geological phenomenon for popping out parent avalanche under the action of the vibration of landslide, rolling, being deposited in slope foot (or cheuch).Landslide and avalanche
All it is one of common geological disaster in Hills, not expected serious consequence can be caused, not only comes to human life's safety belt
It threatens, and there is destructiveness to property, environment, resource etc..
Today mainly by the supporting of massif cement in prevention landslide avalanche, it is sliding that this mode can effectively prevent massif
Slope, avalanche phenomenon.But cost is very high, will also result in certain destruction for local ecology.Generally it is chiefly used in traffic route
The massif supporting of section, can not spread to all easy landslide massifs.Therefore, it is necessary to be monitored to landslide and avalanche and in advance
Report.Existing mountain landslide supervision method has gravimetric method, water table measure method, liquid multi-point leveling measurement, routine big
Ground mensuration, recent photo photogrammetry, GPS monitoring, remote sensing monitoring and aerospace telemetry etc..Due to existing mountain landslide supervision side
Method consider factor it is relatively single, do not comprehensively consider occur landslide various aspects factor, so there are monitoring accuracies not
Height, the problem of real-time difference.
Summary of the invention
The application provides a kind of monitoring data processing method and landslide forecasting procedure for landslide, solves existing
There is the deficiency in technology to mountain landslide supervision.
According in a first aspect, providing a kind of monitoring data processing method for landslide in a kind of embodiment, comprising:
Obtain the multi -components monitoring data of different time at monitoring point in premonitoring region, the multi -components monitoring data packet
Include at least one of the variation of physics-mechanics character at the monitoring point, the Characters of Geographical Environment of the monitoring point and weather condition；
The feature of the multi -components monitoring data is input to tree-model by the feature for extracting the multi -components monitoring data,
And export the landslide risk index of prediction.
According to second aspect, a kind of forecast system of landslide is provided in a kind of embodiment, comprising:
Mountain landslide supervision net, for obtaining the multi -components monitoring data of different time at monitoring point in premonitoring region,
The multi -components monitoring data include the variation of physics-mechanics character at the monitoring point, the monitoring point Characters of Geographical Environment and
Weather condition；
Data processing centre for receiving the multi -components monitoring data of the Seismic network output, and extracts institute
The feature of the multi -components monitoring data is input to tree-model, and exports the ground of prediction by the feature for stating multi -components monitoring data
Shake risk index；
Landslide forecast unit, for receiving the landslide risk index of data processing centre's output,
And risk forecast in landslide is carried out to the premonitoring region according to the landslide risk index.
According to the third aspect, a kind of landslide forecasting procedure is provided in a kind of embodiment, comprising:
Monitoring point is set in premonitoring region；
Monitor the variation of physics-mechanics character, Characters of Geographical Environment and weather condition at the monitoring point, and export with it is described
The variation of physics-mechanics character, the Characters of Geographical Environment and the relevant monitoring data of the weather condition；
The multi -components monitoring data are analyzed using monitoring data processing method described in first aspect.
A kind of monitoring data processing method and landslide forecasting procedure for landslide of foundation above-described embodiment,
Since the proposition of innovation is handled the multi -components monitoring data of the monitoring point in premonitoring region using tree-model, to massif
Landslide carries out round-the-clock monitoring, and then realizes the dangerous sending early warning that landslide occurs.
Detailed description of the invention
Fig. 1 is a kind of structural schematic diagram of the landslide forecast system of embodiment；
Fig. 2 is a kind of flow chart of landslide forecasting procedure；
Fig. 3 is a kind of distribution map in premonitoring region setting monitoring point of embodiment；
Fig. 4 be a kind of embodiment in charged particle diurnal periodicity wave characteristic curve；
Fig. 5 is a kind of Mean curve figure of full range data in embodiment；
Fig. 6 is a kind of Ring-down count curve graph of full range data in embodiment；
Fig. 7 is a kind of global peak frequency curve chart of full range data in embodiment；
Fig. 8 is a kind of tree class algorithm structure schematic diagram.
Specific embodiment
Below by specific embodiment combination attached drawing, invention is further described in detail.Wherein different embodiments
Middle similar component uses associated similar element numbers.In the following embodiments, many datail descriptions be in order to
The application is better understood.However, those skilled in the art can recognize without lifting an eyebrow, part of feature
It is dispensed, or can be substituted by other elements, material, method in varied situations.In some cases, this Shen
Please it is relevant it is some operation there is no in the description show or describe, this is the core in order to avoid the application by mistake
More descriptions are flooded, and to those skilled in the art, these relevant operations, which are described in detail, not to be necessary, they
Relevant operation can be completely understood according to the general technology knowledge of description and this field in specification.
It is formed respectively in addition, feature described in this description, operation or feature can combine in any suitable way
Kind embodiment.Meanwhile each step in method description or movement can also can be aobvious and easy according to those skilled in the art institute
The mode carry out sequence exchange or adjustment seen.Therefore, the various sequences in the description and the appended drawings are intended merely to clearly describe a certain
A embodiment is not meant to be necessary sequence, and wherein some sequentially must comply with unless otherwise indicated.
It is herein component institute serialization number itself, such as " first ", " second " etc., is only used for distinguishing described object,
Without any sequence or art-recognized meanings.And " connection ", " connection " described in the application, unless otherwise instructed, include directly and
It is indirectly connected with (connection).
The cause that mountain blasting and landslide occurs is known as very much, such as Lithology type, geological tectonic conditions, topography and geomorphology item
Part, hydrogeologic condition etc..Traditional monitoring method to landslide and mountain blasting only considers relatively single factor, does not have
There are the comprehensive physics-mechanics character data for considering geographical environment, weather condition and monitoring.In embodiments herein, first pre-
It monitors region and multiple monitoring points is set, then monitor physics-mechanics character and weather condition at monitoring point, reapply tree-model to each
Geographical environment, weather condition and the physics-mechanics character of the monitoring data of monitoring point carry out comprehensive analysis processing, predict the monitoring
The landslide risk index of point realizes monitoring and forecast to landslide.
Embodiment one:
Referring to FIG. 1, a kind of structural schematic diagram of the landslide forecast system for embodiment, the pre- syndicate in the landslide
System includes mountain landslide supervision net 10, data processing centre 20 and landslide forecast unit 30.Mountain landslide supervision net 10 is used
In obtaining the monitoring data of different time at each monitoring point in premonitoring region, and monitoring data are sent to Data processing
The heart 20, monitoring data are related to physics-mechanics character variation at monitoring point.Data processing centre 20 receives mountain landslide supervision net 10
The monitoring data of transmission, and by monitoring data, monitoring point weather condition and Characters of Geographical Environment application tree-model at
Reason obtains the landslide risk index in premonitoring region.Landslide risk index refers to the ginseng of landslide probability of happening
Value is examined, index is bigger closer to the probability that preset value landslide occurs.Landslide forecast unit 30 receives data processing
The landslide risk index that center 20 exports, and risk forecast in landslide is carried out to premonitoring region according to risk index.
Mountain landslide supervision and forecast, detailed process are carried out to premonitoring region based on above-mentioned landslide forecast system
As shown in Figure 2, comprising the following steps:
Step 201 is arranged in premonitoring region for the monitoring point to mountain landslide supervision.
As shown in figure 3, the distribution map of monitoring point is arranged in premonitoring region for a kind of embodiment, according to premonitoring region
Landform and monitoring accuracy be arranged monitoring point.Premonitoring region takes place frequently area in landslide, in the massif in the region, along road
Lay monitoring point in certain distance between stockaded village.Wherein, 301 be massif, and 302 be intermountain road, and 303 be stockaded village, and 304 are
The monitoring point of laying.It is more high more help to improve the precision of prediction of landslide that monitoring dot density is set.
Step 202 is monitored the variation of physics-mechanics character at each monitoring point, and obtains the geographical ring at monitoring point
Border feature and weather condition.
Characters of Geographical Environment include longitude and latitude where premonitoring region, height above sea level, monitoring point Lithology type, the soil body it is strong
Degree and vegetation distribution.Weather condition includes temperature, humidity, rainfall and the wind speed at monitoring point.The physics-mechanics character packet of monitoring
Include electromagnetic signal at monitoring point, acoustical signal, level of ground water and water temperature information, underground overflow band point particle signal, GPS velocity
Field data and seismic data.Be monitored to the electromagnetic radiation of monitoring point is before occurring according to landslide because of piezoelectricity, pressure
Magnetic effect and rock institute it is loaded be more than its breakdown strength generate microwave split when, the lattice of rock is destroyed, and can generate current potential
Jump gives off electromagnetic signal.Before closing on landslide generation, often there is the sound to spread out of from deep under ground, here it is ground sound.Ground
Sound is generally present in landslide and first few minutes, a few houres, several days or earlier occurs.In the larger model in landslide areas before landslide
Enclose it is interior there is a kind of universal phenomenon when abnormal temperature raising, landslide is forecast by monitoring large-scale temperature change
Important means, one is meteorological stations to observe earth's surface temperature and underground shallow layer conventional Temperature, and another kind is to utilize to be placed in depth
High precision measuring instrument in well records temperature, and also one is obtain Temperature Datum using meteorological satellite thermal infrared images.This Shen
Inventor please thinks that landslide, especially avalanche formula massif are sliding after monitoring on the spot to mountain blasting and landslide
Slope, formation mechenism first is that operating diurnal periodicity met due to the rotation of the earth, revolution etc. so that underground melt substance is over the ground
Hull shape is at impact.It is understood that the composition of the earth includes the earth's core, the earth's crust and earth mantle, there is also an asthenosphere at the top of earth mantle,
It is the place that radioactive substance is concentrated, due to being overflowed as a result, having charged particle from earth's crust surface for radioactive substance division, companion
It will lead to the variation of earth's surface physical field, chemical fields with melt substance ejection process, charged particle can pass through simultaneously from earth's crust crack
It is discharged from earth's surface.Charged particle overflows with directly having reacted in the case where earth rotation, revolution etc. operate compound diurnal periodicity
Impact of the curtain melt substance to the earth's crust.This is a kind of normal earth movements, and this normal geological activity in underground makes the earth's crust
The loose place of rupture, geologic structure is easy to cause landslide and landslide phenomenon to occur.Geological activity power can be released by it
The difference of the characteristic of the charged particle of earth's surface is put into monitor, it is possible thereby to which the characteristic variations according to charged particle are to landslide
Occurrence risk be monitored and assess.Thus it is proposed that carrying out mountain blasting by the charged particle of monitoring earth's surface spilling
With the method for mountain landslide supervision and prediction.Wherein, charged particle is the particle with charge, can be by charged particle monitoring device
It is monitored, specifically may include high energy particle, the particle of free state, heavy charged particle (such as α particle and fission fragment) and light
Charged particle (such as fast electronics and β particle) etc..Seismic data include earthquake occur series, earthquake centre and monitoring point distance,
Shake times and duration.GPS velocity field is the total looks and detail characteristic of reflecting regional crustal movement, the prediction to landslide
Play a significant role.
Step 203, the multi -components monitoring data for obtaining different time at each monitoring point in premonitoring region.
Multi -components monitoring data include Characters of Geographical Environment at monitoring point, a variety of physics at weather condition and the monitoring point
The data of measure feature variation, the data of a variety of physics-mechanics character variations are specifically using electronic measuring instrument or signal acquisition circuit
Record monitoring point at electromagnetic signal, acoustical signal, level of ground water and water temperature information, underground overflow band point particle signal, GPS velocity
The multi -components characteristic signal such as field data and seismic data.For example, the lower monitoring for overflowing charged particle is overflowed using underground over the ground
Charged particle monitoring device, specifically includes energy converter and signal acquisition circuit.Energy converter is for overflowing underground
Charged particle be changed into electric signal, signal acquisition circuit be for by the received electric signal of energy converter through amplification, identification,
Record, compares measurement, to obtain the characteristics such as counting rate and the Energy distribution of charged particle.The characteristic of charged particle is also wrapped
It includes the concentration of charged particle, mass-to-charge ratio, overflow speed and particle density etc..Electromagnetic measurement method, conductivity measurement specifically can be used
Method and optical measuring method are monitored the characteristic of the band point particle.Electromagnetic measurement method is to pass through closing coil according to charged particle
When, the principle of electrical signal of reaction can be generated in closing coil.The electrical signal of reaction generated by monitoring closing coil
It is obtained across the characteristic of closing coil charged particle, and then realizes the lower monitoring for overflowing charged particle over the ground.
Step 204 pre-processes multi -components monitoring data.
Carrying out pretreatment to multi -components monitoring data includes using data processings sides such as missing values, normalization or cancelling noises
Method.Being handled the missing values of multi -components monitoring data specifically can directly delete, also can be used statistics filling, unified filling and
The methods of prediction filling is filled.Wherein statistics filling is using average, median, mode, maximum value or minimum value etc.
Statistical value is filled.Unified filling is filled out using preset values such as average, median, mode, maximum value, minimum values
It fills.Predicted value filling is to predict missing values using there is no the attributes of missing values by prediction model, that is, first use prediction
Model is further again after data are filled to be worked, such as statistics, study.It is specifically chosen which kind of mode is filled needs pair
Specific monitoring data are made a concrete analysis of.Normalized data are that multi -components monitoring data is made to contract according to a certain percentage
It puts, by scaling so that data are mapped in specific space or numberical range.Cancelling noise processing is to preset a threshold value model
It encloses, rejects the monitoring data other than threshold range, mean value is done to the monitoring data in threshold range or least square is handled.
Step 205, analysis multi -components monitoring data, obtain the spy of each component monitoring data of multi -components monitoring data
Sign.
The feature of component monitoring data includes intensity of anomaly index, weather characteristics, temporal characteristics and monitoring point geographical feature
Deng.Intensity of anomaly index is by the quantization of the variation degree of component monitoring data.It includes two steps that intensity of anomaly index, which obtains,
It is the extraction of characteristic sequence first, followed by the calculating of intensity of anomaly.Intensity of anomaly index, which specifically can be, monitors number for component
According to the difference between the component monitoring data of previous moment perhaps the difference of the component monitoring data of preset time point or
The corresponding component monitoring data of difference or current slot of the time domain change curve of the component monitoring data of preset time period
Time domain change curve component monitoring data corresponding with previous time period time domain change curve between difference.The spy of extraction
Sign sequence can be crest frequency sequence, chief composition series, waveform coding sequence or fractal dimension sequence etc..Characteristic sequence extracts
Preceding method can be used the methods of Fourier transformation, Principal Component Analysis Algorithm, pattern-recognition and higuchi algorithm and monitor to component
Data are handled.Carrying out intensity of anomaly calculating to characteristic sequence can be used sliding interquartile-range IQR method, Density Clustering or manually exempts from
Epidemic disease algorithm etc..
Temporal characteristics include being existed with the difference of component monitoring data described in different time particle or component monitoring data
The difference of different time intervals.Temporal characteristics are the feature of component monitoring data in the time domain, specifically with different time particle
Describe component monitoring data affiliated moment and different time intervals to component monitoring data carry out difference processing or to it is different when
Between the component monitoring data of unit interval do difference processing, the component monitoring data for describing different unit intervals are poor
Different situation.Different time can refer to time, month (lunar calendar or solar calendar month) or certain January (lunar calendar and solar calendar month)
Upper, middle and lower ten days, a certain date of certain January (lunar calendar and solar calendar month), a certain date a certain hour.Unit interval can be with
It is 1 year, one month, one day or one hour.For example, a certain component monitoring data temporal characteristics are obtained, it can be according to different time grain
Characteristic sequence obtained from degree portrays the time of component monitoring data, or the component monitoring data that will acquire and the previous day
Or first three days, the component monitoring data of the first two 17 day period carry out the data time-domain curve of difference processing.
Monitoring point geographical feature is geographic latitude and longitude, the height above sea level, monitoring point slope where monitoring point in premonitoring region
The open degree of landform, monitoring point Lithology type, the intensity of the soil body and vegetation distribution etc. in front of slope where degree, monitoring point,
Feature can be obtained by the statistic or conditional probability for calculating each geographical feature.
Weather characteristics are the weather conditions such as temperature, humidity, rainfall and wind speed at monitoring point, and feature can also pass through meter
It calculates each ground feature progress statistic or conditional probability obtains.Such as by taking precipitation as an example, can extract by hour precipitation,
The probability of landslide occurs under the conditions ofs past n hour accumulative precipitation or n hours peak value precipitation intensities etc. in the past and is averaged
Scale.
Because of locating geographical environment difference, the multi -components monitoring data that monitoring point obtains are supervised with respect to landslide for each monitoring point
The susceptibility of survey is also different, and the methods of receptance function can be used and set to the multi -components monitoring data progress weighted value of each monitoring point
Fixed, the weight of the multi -components monitoring data of the monitoring point in the high area of susceptibility is just high, the monitoring point in the low area of susceptibility
The weight of multi-component data is with regard to low.
For example, overflowing the monitoring of charged particle to the underground of each monitoring point in premonitoring region.By what is currently obtained
The change curve that charged particle monitoring data are generated time series by acquisition time is overflowed in underground, by current time series variation
Curve is compared with the time series variation curve of the component monitoring data obtained before, obtains described characterization monitoring point
Underground overflow charged particle monitoring data relative to before monitoring data variation feature.Current time series variation
Curve is compared with the time series variation curve of the component monitoring data obtained before including the prison to preset time point
Measured data, the monitoring data of preset time period, the time data for reaching default monitoring data and the period for characterizing monitoring data are special
The time data etc. of property are compared.The time data for characterizing the cyclophysis of monitoring data include monitoring data cyclically-varying
Period, the periodically variable start time data of monitoring data, monitoring data are greater than and/or rising less than default monitoring data
Begin time and the time data of duration etc..Degree of fluctuation algorithm also can be used and analyze each component monitoring data, according to prison
Result after measured data analysis obtains the time domain change curve of the monitoring data.For example, overflowing the spy of charged particle to earth's surface
The wave characteristic including diurnal periodicity, low point time offset, high point time migration, increase and decrease of fluctuation amplitude for levying etc..Specifically according to
Change according to the time domain of monitoring data, draws time domain change curve.Charged particle is embodied in diurnal periodicity by time domain change curve
The characteristics such as fluctuation, low point time offset, high point time migration, the increase and decrease of fluctuation amplitude.As shown in figure 4, in a kind of embodiment
Wave characteristic curve of the charged particle in diurnal periodicity, the abscissa of the curve of cyclical fluctuations of diurnal periodicity is as unit of day, and curve is anti-
What is answered is cyclically-varying of the grey density characteristics in time domain of charged particle.The mass-to-charge ratio of charged particle is overflowed through monitoring earth's surface, is overflow
The characteristics such as speed and particle density are all just like periodically variable feature shown in Fig. 4 out.By curve it is found that the band electrochondria of earth's surface
The characteristic of son is 19:10 raising at sunset, and 05:00 is reduced when day rises, and increases and/or the reduced beginning and ending time and is increased to most
High point and to bottom out the time used all relatively fixed.It can be realized according to the variation of curve to premonitoring region
Earthquake liveness be monitored.Time domain change curve can be monitoring data cyclic curve, transformation period point monitoring data
Curve and/or unit interval monitoring data curve.When monitoring data cyclic curve can be monitoring data as shown in Figure 4
Domain change curve.Transformation period point curve refers to the curve at the characteristic of charged particle changed time point, for example, with band
The curve at time point when the relevant monitoring data of the characteristic of charged particle reach maximum value or minimum value is specifically supervised in desirable Fig. 4
Measured data reaches the time graph of maximum value and/or minimum value；It rises local day and/or the characteristic of the time of sunset and charged particle
The corresponding data and curves of relevant monitoring data, in specific desirable Fig. 4 monitoring data time domain change curve rise day and/or sunset
When monitoring data data and curves；Monitoring data relevant to the characteristic of charged particle start to increase and/or reduce the time of point
Curve, the time that the specific monitoring data that can use monitoring data time domain change curve in Fig. 4 start to increase and/or reduce point are bent
Line.Unit time variable quantity curve refers to the data of monitoring data variation relevant to the characteristic of charged particle in the setting unit time
Curve, that is, the changing value of the characteristic of charged particle is the time-domain curve of ordinate in the unit time set, such as takes in Fig. 4 and supervise
The wave crest or the time-domain curve of trough duration of measured data time domain change curve, or such as take monitoring data mechanical periodicity in Fig. 4
The curve graph of required time, or such as take the time required to curve monitoring data are by trough to wave crest or by wave crest to trough in Fig. 4
Curve graph.
Below to obtain the mean value of the full range data of underground spilling charged particle, the feature song of Ring-down count and crest frequency
For line.Full range data refer to that the monitoring signals for overflowing charged particle to the underground of acquisition carry out analog-to-digital conversion, then will be after conversion
The data that digital signal amplification and sampling processing obtain.
As shown in figure 5, abscissa is time of measuring, ordinate for a kind of Mean curve figure of full range data in embodiment
For the mean value (unit: volt) of the full range data of calculating.Mean value is to take to take absolute value to full range data in the unit time to average
The curve obtained afterwards.Fig. 5 is the curve of the mean value of the full range data calculated May 30 to June 8.
As shown in fig. 6, abscissa is time of measuring for a kind of Ring-down count curve graph of full range data in embodiment, indulge
Coordinate is the number (unit: secondary/second) that measures in the unit time, Ring-down count be take in the unit time full range data upwards or to
Under pass through the number of a certain preset threshold.Fig. 6 is the curve of the Ring-down count of the full range data calculated May 30 to June 8.
As shown in fig. 7, for a kind of global peak frequency curve chart of full range data in embodiment, when abscissa is measurement
Between, ordinate is frequency (unit: hertz), and global peak frequency is first to carry out Fourier transformation to full range data, is then obtained
The time-domain curve of frequency when amplitude (taking absolute value) is maximum value.Fig. 7 is the full range data calculated May 30 to June 8
The curve of global peak frequency.
Further, characteristic sequence extraction is carried out to the multicomponent monitoring data that monitoring point obtains to remove as described above on time
Between sequential extraction procedures.Such as shown in Fig. 6, using day as time series unit, every day, the sum of Ring-down count was the time series
Element.Can be using hour as time series unit, the sum of Ring-down count is the element of the time series per hour.It can also adopt
Sequential extraction procedures are carried out with sequences such as crest frequency sequence, chief composition series, waveform coding sequence or fractal dimension sequences.To extraction
Sequence afterwards can also carry out the amount of intensity of anomaly using the methods of sliding interquartile-range IQR method, Density Clustering method or artificial immunization
Change, and then obtains the intensity of anomaly index of each component monitoring data.Also monitoring data first can be subjected to Fu before sequential extraction procedures
In the methods of leaf transformation, Principal Component Analysis Algorithm, pattern-recognition or Higuchi algorithm handled.
Step 206, the feature for obtaining multi -components monitoring data when landslide occurs, more points when occurring according to landslide
Measure the risk index of the features localization landslide of monitoring data.
Premonitoring region is provided with multiple monitoring points, position and each monitoring point of the foundation monitoring point in premonitoring region
Multi -components monitoring data feature variation, analyze premonitoring region in monitoring point multi -components monitoring data feature change
The assessment as a result, to premonitoring region progress landslide risk index is analyzed in change, then according to this landslide risk index
Carry out landslide forecast.Specifically according to the variation of the feature of multi -components monitoring data in premonitoring region, it is sliding to obtain massif
Slope risk index.When the risk index of landslide is more than presetting critical value, landslide is issued to premonitoring region
Risk forecast.According to be more than preset range number, so that it may judge the height of the risk index of the earthquake of its premonitoring measuring point.In advance
The landslide risk index surveyed is closer or is more greater than preset value, and the probability that landslide occurs in advance is higher, so that it may
It realizes and the risk of premonitoring region landslide is forecast.
Earth volume, sliding velocity and the skidding distance of landslide when landslide risk index can occur from landslide
Etc. comprehensively consider, using the risk index of generation as sample label, can be indicated in the form of scalar, multidimensional can also be used
Vector indicate, such as directly in a hour, in one day or one week in generation landslide maximum-norm earth volume,
The index of sliding velocity and skidding distance.
Step 207 is trained multi -components monitoring data based on tree class algorithm and establishes model.
As shown in figure 8, being a kind of tree class algorithm structure schematic diagram, setting class algorithm is a kind of abstract data type or implementation
The data structure of this abstract data type, for simulating the data acquisition system with tree property, it is a by n (n >=1)
Limited node forms the set with hierarchical relationship.It is to look like a projecting tree because of it that it, which is called " tree ",
Namely say it is root upward, and leaf is directed downwardly.Each node of tree class algorithm has zero or more child node, without father
The node of node is known as root node.Each non-root node has and only one father node, and other than root node, every height
Node can be divided into multiple disjoint subtrees.
Multi -components monitoring data are trained establish model include random forest (Random Forest, abbreviation RF) and
Gradient boosted tree (Gradient Boosting Regression Tree, abbreviation GBDT).Sample is temporally arranged, with
Prevent leaking data.Sample is divided into training set, verifying collection and test set.Training set is trained and is passed through according to tree-model
Verifying collection Optimal Parameters, filter out the feature of high gain, finally using the effect of test set assessment models training, choose and save most
Excellent model.
Gradient boosted tree and random deep woods belong to integrated study (Ensemble Learning), and random forest belongs to collection
At the bagging algorithm in study, gradient boosted tree belongs to the boosting algorithm in integrated study.The algorithm mistake of bagging
Journey is that first being concentrated use in Bootstraping method from original sample randomly selects n training sample, carries out k wheel altogether and extracts, obtains
To k training set (between k training set independently of each other, element can have repetition).Then it is directed to k training set, we train k
A model (this k model can depending on particular problem, such as decision tree).Finally by voting generation classification results,
It is the mean value by k model prediction result as last prediction result for regression problem.The algorithm of boosting belongs to one kind
Frame algorithm possesses serial algorithm, such as AdaBoost, GradientBoosting, LogitBoost scheduling algorithm.Training process is
It is ladder-like, it is the algorithm that Weak Classifier is promoted to strong classifier.Process is first to extract to train from original sample sample set
One basic model is adjusted the selection of training sample according to the performance of basic model, the training set selected each time
All rely on the result of last study.Weak Classifier is finally combined into strong classifier in a certain way.So repeat,
Until model number reaches the numerical value of predetermined set or loss function is less than the threshold value set.Here is by decision tree and two calculations
Method frame is combined obtained new algorithm:
Bagging+ decision tree=random forest
Gradient Boosting+ decision tree=gradient boosted tree
Common decision Tree algorithms have tri- kinds of ID3, C4.5 and CART.The model construction thought all ten of these three algorithms is classified
Seemingly, different indexs is only used.In the present embodiment use random forests algorithm the following steps are included:
1) n times, are extracted with putting back to from N number of test sample, as training set S (i), as post-class processing (CART tree)
The sample of root node, the sample not extracted is as test set, assessment errors.
2), for each node, if sample number is less than information gain on minimum sample number s or node on node
Less than least information gain m, then it is leaf node that present node, which is arranged,.Otherwise, special without k kind is chosen with putting back to from feature
Sign, assigns to left and right node for sample using the best one-dimensional characteristic of classifying quality.
3) previous step, is repeated until leaf node is all trained or be marked as to all nodes.If it is classification
Problem, the prediction output of leaf node are one kind that quantity is most in present node sample set；If it is regression problem, prediction
Output is the average value of each sample value of present node sample set.
4) 2 and 3, are repeated, t CART tree is generated and is returned for classifying.When prediction, if it is classification problem, then output is
That maximum class of prediction probability summation in all trees；If it is regression problem, then output is being averaged for the output of all trees
Value.
The feature of the multi -components monitoring data for the different time that each monitoring point obtains is input to and trains by step 208
Tree-model carry out landslide risk index prediction.
The characteristic sequence for the multi -components monitoring data that each monitoring point of synchronization obtains is input in tree-model and is obtained
Take the landslide risk index of each monitoring point.When landslide risk index is more than presetting critical value, to premonitoring
It surveys region and issues the forecast of landslide risk.Specifically whether the landslide risk index of decision tree model prediction is presetting
Range and according to be more than preset range number, so that it may judge the mountain blasting and landslide activity of its monitoring point
Just.The incidence of the more more then mountain blastings and landslide that surmount is higher, so that it may which premonitoring region massif is collapsed in realization
The risk occurred with landslide of collapsing is forecast.Prediction result according to landslide generates forecast card, and forecast card is submitted to
Related governmental departments.The output of prediction result unit time is primary.Unit time can be with hour, day or other times section.It is defeated
The data of in-tree model can be the changing features of the multi -components monitoring data according to single monitoring point, more points of multiple monitoring points
Measure the monitoring number such as the changing features of monitoring data or the changing features of multi -components monitoring data of premonitoring region whole monitoring point
According to.
The multi -components monitoring data of monitoring point are obtained by laying monitoring point in premonitoring region based on above embodiments.
Multi -components monitoring data include the variation of physics-mechanics character at monitoring point, the Characters of Geographical Environment of monitoring point and weather condition.It is right
Multi -components monitoring data are handled, and the changing features of multi -components monitoring data are obtained.This feature variation is input to tree mould again
In type, the landslide risk index of the monitoring point is exported, the forecast of landslide is carried out according to the risk index.Due to innovation
Proposition the multi -components monitoring data of the monitoring point in premonitoring region are handled using tree-model, and then realize to massif
The round-the-clock monitoring on landslide and the real-time update of data, the danger that landslide can be occurred in time by the analysis to data
Early warning is issued, so that the people of its premonitoring area peripheral edge is begun to take risk avoidance measures in advance, reduces the personnel of broad masses of the people
Injures and deaths and property loss.It is also possible to apply the invention to the forecast of mountain blasting and monitorings.
It will be understood by those skilled in the art that all or part of function of various methods can pass through in above embodiment
The mode of hardware is realized, can also be realized by way of computer program.When function all or part of in above embodiment
When being realized by way of computer program, which be can be stored in a computer readable storage medium, and storage medium can
To include: read-only memory, random access memory, disk, CD, hard disk etc., it is above-mentioned to realize which is executed by computer
Function.For example, program is stored in the memory of equipment, when executing program in memory by processor, can be realized
State all or part of function.In addition, when function all or part of in above embodiment is realized by way of computer program
When, which also can store in storage mediums such as server, another computer, disk, CD, flash disk or mobile hard disks
In, through downloading or copying and saving into the memory of local device, or version updating is carried out to the system of local device, when logical
When crossing the program in processor execution memory, all or part of function in above embodiment can be realized.
Use above specific case is illustrated the present invention, is merely used to help understand the present invention, not to limit
The system present invention.For those skilled in the art, according to the thought of the present invention, can also make several simple
It deduces, deform or replaces.
Claims (10)
1. a kind of monitoring data processing method for landslide characterized by comprising
The multi -components monitoring data of different time at monitoring point in premonitoring region are obtained, the multi -components monitoring data include institute
State at least one of the variation of physics-mechanics character at monitoring point, the Characters of Geographical Environment of the monitoring point and weather condition；
The feature of the multi -components monitoring data is input to tree-model by the feature for extracting the multi -components monitoring data, and defeated
The landslide risk index predicted out.
2. the method as described in claim 1, which is characterized in that physics-mechanics character includes at the monitoring point at the monitoring point
Electromagnetic signal, acoustical signal, level of ground water and water temperature information, underground overflow band point particle signal, GPS velocity field data and ground
Shake at least one of data.
3. the method as described in claim 1, which is characterized in that the feature of the multi -components monitoring data includes including abnormal journey
Spend at least one of features such as index, weather characteristics, temporal characteristics and monitoring point geographical feature.
4. method as claimed in claim 3, which is characterized in that
The intensity of anomaly index is by the difference between the multi -components monitoring data and the multi -components monitoring data of previous moment
The time domain of the multi -components monitoring data of the difference or preset time period of the multi -components monitoring data of different perhaps preset time point
The time domain change curve and previous time period pair of the corresponding multi -components monitoring data of the difference or current slot of change curve
Difference between the time domain change curve for the multi -components monitoring data answered；
The temporal characteristics include being existed with the difference of component monitoring data described in different time particle or component monitoring data
The difference of different time intervals；
The monitoring point geographical feature includes the geographic latitude and longitude of each monitoring point present position, height above sea level in premonitoring region
Open degree, the intensity and plant of monitoring point Lithology type, the soil body of slope front landform where degree, the monitoring point gradient, monitoring point
At least one of the geographical special type such as it is distributed；
The weather characteristics include at least one of weather conditions such as temperature, humidity, rainfall and wind speed at monitoring point.
5. the method as described in claim 1, which is characterized in that further include:
The landslide risk index according to prediction carries out the forecast of the sliding wave risk of massif to the premonitoring region；Prediction
The landslide risk index it is closer or be more greater than preset value, the probability that landslide occurs in advance is higher.
6. the method as described in claim 1, which is characterized in that the foundation of the tree-model includes:
Sample is divided into training set, verifying collection and test set；The sample is more points of monitoring point when landslide occurs
Measure the feature of monitoring data；
The training set is trained according to tree class algorithm and passes through the verifying collection Optimal Parameters；
The feature of the multi -components monitoring data of high gain is filtered out, finally using the effect of test set assessment models training
Fruit chooses tree-model described in optimal conduct.
7. a kind of forecast system of landslide characterized by comprising
Mountain landslide supervision net, it is described for obtaining the multi -components monitoring data of different time at monitoring point in premonitoring region
Multi -components monitoring data include the Characters of Geographical Environment and weather of the variation of physics-mechanics character at the monitoring point, the monitoring point
Situation；
Data processing centre for receiving the multi -components monitoring data of the Seismic network output, and extracts described more
The feature of the multi -components monitoring data is input to tree-model, and exports the earthquake wind of prediction by the feature of component monitoring data
Dangerous index；
Landslide forecast unit, the landslide risk index exported for receiving the data processing centre, and according to
Risk forecast in landslide is carried out to the premonitoring region according to the landslide risk index.
8. a kind of monitoring device for landslide forecast, characterized by comprising:
Memory, for storing program；
Processor, for the program by executing the memory storage to realize as of any of claims 1-6
Method.
9. a kind of computer readable storage medium, which is characterized in that including program, described program can be executed by processor with reality
Now such as method of any of claims 1-6.
10. a kind of landslide forecasting procedure characterized by comprising
Monitoring point is set in premonitoring region；
The variation of physics-mechanics character, Characters of Geographical Environment and weather condition at the monitoring point are monitored, and is exported and the physics
The variation of measure feature, the Characters of Geographical Environment and the relevant monitoring data of the weather condition；
The monitoring data are analyzed using monitoring data processing method as claimed in any one of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810871963.7A CN109003422A (en) | 2018-08-02 | 2018-08-02 | Monitoring data processing method and landslide forecasting procedure for landslide |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810871963.7A CN109003422A (en) | 2018-08-02 | 2018-08-02 | Monitoring data processing method and landslide forecasting procedure for landslide |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109003422A true CN109003422A (en) | 2018-12-14 |
Family
ID=64594609
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810871963.7A Pending CN109003422A (en) | 2018-08-02 | 2018-08-02 | Monitoring data processing method and landslide forecasting procedure for landslide |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109003422A (en) |
Cited By (27)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109783967A (en) * | 2019-01-25 | 2019-05-21 | 深圳大学 | A kind of Prediction of Landslide and system |
| CN109840617A (en) * | 2018-12-27 | 2019-06-04 | 东软集团股份有限公司 | Fire indication prediction technique, device, readable storage medium storing program for executing and electronic equipment |
| CN109858647A (en) * | 2018-12-21 | 2019-06-07 | 河海大学 | A kind of Regional Flooding Hazard Risk Assessment and predictor method coupling GIS and GBDT algorithm |
| CN109887240A (en) * | 2019-03-22 | 2019-06-14 | 福州大学 | A kind of landslide disaster safety monitoring and method for early warning based on artificial intelligence |
| CN110082830A (en) * | 2019-05-23 | 2019-08-02 | 深圳市新禾源投资咨询研究中心(有限合伙) | A kind of underground mineral resources detection method and system |
| CN110208879A (en) * | 2019-05-28 | 2019-09-06 | 中国科学院、水利部成都山地灾害与环境研究所 | High aititude mountain area freeze thawing band rock slope liability forecasting procedure |
| CN110210084A (en) * | 2019-05-18 | 2019-09-06 | 西南交通大学 | The method and system of mechanical change judgement landslide possibility based on Fusion in Time |
| CN110264671A (en) * | 2019-05-18 | 2019-09-20 | 西南交通大学 | A kind of prediction technique based on multi-sensor information fusion in landslide |
| CN110426458A (en) * | 2019-07-05 | 2019-11-08 | 中国矿业大学(北京) | A kind of new method and monitoring system using Elastic Wave Velocity prediction landslide |
| CN110661845A (en) * | 2019-08-19 | 2020-01-07 | 重庆特斯联智慧科技股份有限公司 | Spatial environment monitoring method and system based on Internet of things big data |
| CN110887746A (en) * | 2019-11-18 | 2020-03-17 | 宁波大学 | Lorentz force-based tangential loading method for shear test of oversized rock mass structural plane |
| CN111091911A (en) * | 2019-12-30 | 2020-05-01 | 重庆同仁至诚智慧医疗科技股份有限公司 | System and method for screening stroke risk |
| CN111190219A (en) * | 2020-01-14 | 2020-05-22 | 北京大学深圳研究生院 | Monitoring data processing method for earthquake prediction |
| CN111242206A (en) * | 2020-01-08 | 2020-06-05 | 吉林大学 | High-resolution ocean water temperature calculation method based on hierarchical clustering and random forests |
| CN111369056A (en) * | 2020-03-04 | 2020-07-03 | 四川大汇大数据服务有限公司 | Geological disaster prediction method and electronic equipment |
| CN111667125A (en) * | 2020-08-10 | 2020-09-15 | 成都嘉捷信诚信息技术有限公司 | Landslide displacement prediction method, landslide displacement prediction device and storage medium |
| CN112632203A (en) * | 2020-12-21 | 2021-04-09 | 武汉达梦数据技术有限公司 | Geological disaster monitoring point searching method and device |
| CN112820075A (en) * | 2021-01-15 | 2021-05-18 | 中国地质调查局水文地质环境地质调查中心 | Landslide monitoring and early warning system |
| CN113609207A (en) * | 2021-08-03 | 2021-11-05 | 招商局重庆交通科研设计院有限公司 | Data preprocessing method for slope deformation monitoring data |
| CN114067531A (en) * | 2021-11-15 | 2022-02-18 | 中大检测(湖南)股份有限公司 | Remote geological disaster monitoring system and monitoring method based on 5G |
| CN115240371A (en) * | 2022-09-23 | 2022-10-25 | 西北大学 | Mountain landslide real-time monitoring system based on data analysis |
| CN116227938A (en) * | 2023-04-26 | 2023-06-06 | 四川川核地质工程有限公司 | Mountain landslide intelligent early warning method and system based on Beidou satellite |
| CN116627953A (en) * | 2023-05-24 | 2023-08-22 | 首都师范大学 | Method for repairing loss of groundwater level monitoring data |
| WO2023193824A1 (en) * | 2022-05-05 | 2023-10-12 | 中咨数据有限公司 | Mountain landslide and collapse disaster classification method based on decision tree, and electronic device |
| CN117037432A (en) * | 2023-10-08 | 2023-11-10 | 四川省公路规划勘察设计研究院有限公司 | Risk evaluation geological disaster early warning method based on multi-method cooperation |
| CN117037457A (en) * | 2023-10-10 | 2023-11-10 | 青州鸿润电器科技有限公司 | Landslide monitoring and early warning method |
| CN117854240A (en) * | 2024-03-07 | 2024-04-09 | 江苏省地质勘查技术院 | Geological disaster early warning method and geological disaster early warning system |
Citations (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1183834A (en) * | 1995-04-07 | 1998-06-03 | 串田嘉男 | Method and apparatus for detection of diastrophism |
| CN1193743A (en) * | 1998-03-16 | 1998-09-23 | 中国矿业大学 | Method and apparatus for predicting disaster in gas bearing coal and rock |
| JP2006349515A (en) * | 2005-06-16 | 2006-12-28 | Mitsubishi Electric Corp | System and method for measuring displacement |
| CN102147232A (en) * | 2011-01-18 | 2011-08-10 | 中国地质调查局水文地质环境地质调查中心 | Equipment and method for monitoring multipoint displacement of landslide |
| CN102184617A (en) * | 2011-01-31 | 2011-09-14 | 重庆大学 | System and method for monitoring ground surface displacement in real time |
| CN102354431A (en) * | 2011-08-06 | 2012-02-15 | 河北省第一测绘院 | Monitoring and prewarning system and method for geological disasters |
| CN102999694A (en) * | 2012-10-30 | 2013-03-27 | 四川大学 | Debris flow risk evaluation method in area with frequent mountain disasters |
| CN103364830A (en) * | 2013-07-24 | 2013-10-23 | 北京师范大学 | Predication method of happening position of slump disaster after earthquake based on multiple factors |
| CN103558360A (en) * | 2013-11-11 | 2014-02-05 | 青岛理工大学 | Method for determining critical instable starting rainfall capacity of rainfall type landslip |
| CN103605897A (en) * | 2013-11-29 | 2014-02-26 | 民政部国家减灾中心 | Regional landslide hazard risk assessment method |
| CN203490806U (en) * | 2013-10-15 | 2014-03-19 | 兰州大学 | Debris flow pre-disaster early warning system based on wireless sensor network |
| CN104318103A (en) * | 2014-10-23 | 2015-01-28 | 中国科学院、水利部成都山地灾害与环境研究所 | Landslide hazard monitoring and early warning rainfall threshold judging method |
| CN105260625A (en) * | 2015-11-19 | 2016-01-20 | 阿坝师范学院 | Landslide geological disaster early warning pushing method |
| CN105911582A (en) * | 2016-06-06 | 2016-08-31 | 中国地震局地壳应力研究所 | Natural-disaster infrasonic wave monitoring system |
| CN107092798A (en) * | 2017-04-28 | 2017-08-25 | 成都理工大学 | The method for estimating stability and device of predictive model of landslide |
| CN107195164A (en) * | 2017-07-03 | 2017-09-22 | 长江水利委员会长江科学院 | Mountain flood on-line monitoring identification method for early warning and its early warning system |
| CN107516401A (en) * | 2017-08-02 | 2017-12-26 | 深圳市盛路物联通讯技术有限公司 | A kind of mountain landslide supervision method and system |
| CN107633659A (en) * | 2017-10-13 | 2018-01-26 | 中电科新型智慧城市研究院有限公司 | Dangerous slopes monitoring and pre-warning system and method |
| CN107798210A (en) * | 2017-08-15 | 2018-03-13 | 中南大学 | A kind of multi-model Prediction of Displacement in Landslide method and its system |
| JP2018048815A (en) * | 2016-09-20 | 2018-03-29 | 株式会社Ｗｈｅｒｅ | Slope failure detection method |
| CN108154651A (en) * | 2018-01-29 | 2018-06-12 | 西安工程大学 | A kind of device and method for landslide disaster Monitoring and forecasting system in real-time |
-
2018
- 2018-08-02 CN CN201810871963.7A patent/CN109003422A/en active Pending
Patent Citations (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1183834A (en) * | 1995-04-07 | 1998-06-03 | 串田嘉男 | Method and apparatus for detection of diastrophism |
| CN1193743A (en) * | 1998-03-16 | 1998-09-23 | 中国矿业大学 | Method and apparatus for predicting disaster in gas bearing coal and rock |
| JP2006349515A (en) * | 2005-06-16 | 2006-12-28 | Mitsubishi Electric Corp | System and method for measuring displacement |
| CN102147232A (en) * | 2011-01-18 | 2011-08-10 | 中国地质调查局水文地质环境地质调查中心 | Equipment and method for monitoring multipoint displacement of landslide |
| CN102184617A (en) * | 2011-01-31 | 2011-09-14 | 重庆大学 | System and method for monitoring ground surface displacement in real time |
| CN102354431A (en) * | 2011-08-06 | 2012-02-15 | 河北省第一测绘院 | Monitoring and prewarning system and method for geological disasters |
| CN102999694A (en) * | 2012-10-30 | 2013-03-27 | 四川大学 | Debris flow risk evaluation method in area with frequent mountain disasters |
| CN103364830A (en) * | 2013-07-24 | 2013-10-23 | 北京师范大学 | Predication method of happening position of slump disaster after earthquake based on multiple factors |
| CN203490806U (en) * | 2013-10-15 | 2014-03-19 | 兰州大学 | Debris flow pre-disaster early warning system based on wireless sensor network |
| CN103558360A (en) * | 2013-11-11 | 2014-02-05 | 青岛理工大学 | Method for determining critical instable starting rainfall capacity of rainfall type landslip |
| CN103605897A (en) * | 2013-11-29 | 2014-02-26 | 民政部国家减灾中心 | Regional landslide hazard risk assessment method |
| CN104318103A (en) * | 2014-10-23 | 2015-01-28 | 中国科学院、水利部成都山地灾害与环境研究所 | Landslide hazard monitoring and early warning rainfall threshold judging method |
| CN105260625A (en) * | 2015-11-19 | 2016-01-20 | 阿坝师范学院 | Landslide geological disaster early warning pushing method |
| CN105911582A (en) * | 2016-06-06 | 2016-08-31 | 中国地震局地壳应力研究所 | Natural-disaster infrasonic wave monitoring system |
| JP2018048815A (en) * | 2016-09-20 | 2018-03-29 | 株式会社Ｗｈｅｒｅ | Slope failure detection method |
| CN107092798A (en) * | 2017-04-28 | 2017-08-25 | 成都理工大学 | The method for estimating stability and device of predictive model of landslide |
| CN107195164A (en) * | 2017-07-03 | 2017-09-22 | 长江水利委员会长江科学院 | Mountain flood on-line monitoring identification method for early warning and its early warning system |
| CN107516401A (en) * | 2017-08-02 | 2017-12-26 | 深圳市盛路物联通讯技术有限公司 | A kind of mountain landslide supervision method and system |
| CN107798210A (en) * | 2017-08-15 | 2018-03-13 | 中南大学 | A kind of multi-model Prediction of Displacement in Landslide method and its system |
| CN107633659A (en) * | 2017-10-13 | 2018-01-26 | 中电科新型智慧城市研究院有限公司 | Dangerous slopes monitoring and pre-warning system and method |
| CN108154651A (en) * | 2018-01-29 | 2018-06-12 | 西安工程大学 | A kind of device and method for landslide disaster Monitoring and forecasting system in real-time |
Non-Patent Citations (1)
| Title |
|---|
| 金秀如 等: "地震监测系统AETA的数据处理设计与实现", 《计算机技术与发展》 * |
Cited By (45)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109858647A (en) * | 2018-12-21 | 2019-06-07 | 河海大学 | A kind of Regional Flooding Hazard Risk Assessment and predictor method coupling GIS and GBDT algorithm |
| CN109858647B (en) * | 2018-12-21 | 2021-07-27 | 河海大学 | Regional flood disaster risk evaluation and estimation method coupled with GIS and GBDT algorithm |
| CN109840617A (en) * | 2018-12-27 | 2019-06-04 | 东软集团股份有限公司 | Fire indication prediction technique, device, readable storage medium storing program for executing and electronic equipment |
| CN109840617B (en) * | 2018-12-27 | 2021-06-25 | 东软集团股份有限公司 | Fire index prediction method and device, readable storage medium and electronic equipment |
| WO2020151226A1 (en) * | 2019-01-25 | 2020-07-30 | 深圳大学 | Landslide prediction method and device based on training model |
| CN109783967B (en) * | 2019-01-25 | 2020-02-21 | 深圳大学 | Landslide prediction method and device based on random forest model and storage medium |
| CN109783967A (en) * | 2019-01-25 | 2019-05-21 | 深圳大学 | A kind of Prediction of Landslide and system |
| CN109887240A (en) * | 2019-03-22 | 2019-06-14 | 福州大学 | A kind of landslide disaster safety monitoring and method for early warning based on artificial intelligence |
| CN110210084B (en) * | 2019-05-18 | 2022-06-28 | 西南交通大学 | Method and system for judging landslide possibility based on time fusion mechanical change |
| CN110264671B (en) * | 2019-05-18 | 2021-03-30 | 西南交通大学 | Prediction method in landslide based on multi-sensor information fusion |
| CN110210084A (en) * | 2019-05-18 | 2019-09-06 | 西南交通大学 | The method and system of mechanical change judgement landslide possibility based on Fusion in Time |
| CN110264671A (en) * | 2019-05-18 | 2019-09-20 | 西南交通大学 | A kind of prediction technique based on multi-sensor information fusion in landslide |
| CN110082830A (en) * | 2019-05-23 | 2019-08-02 | 深圳市新禾源投资咨询研究中心(有限合伙) | A kind of underground mineral resources detection method and system |
| CN110082830B (en) * | 2019-05-23 | 2021-09-07 | 深圳市新禾源投资咨询研究中心(有限合伙) | Underground mineral resource detection method and system |
| CN110208879A (en) * | 2019-05-28 | 2019-09-06 | 中国科学院、水利部成都山地灾害与环境研究所 | High aititude mountain area freeze thawing band rock slope liability forecasting procedure |
| CN110426458A (en) * | 2019-07-05 | 2019-11-08 | 中国矿业大学(北京) | A kind of new method and monitoring system using Elastic Wave Velocity prediction landslide |
| CN110661845A (en) * | 2019-08-19 | 2020-01-07 | 重庆特斯联智慧科技股份有限公司 | Spatial environment monitoring method and system based on Internet of things big data |
| CN110887746A (en) * | 2019-11-18 | 2020-03-17 | 宁波大学 | Lorentz force-based tangential loading method for shear test of oversized rock mass structural plane |
| CN110887746B (en) * | 2019-11-18 | 2022-05-03 | 宁波大学 | Lorentz force-based tangential loading method for shear test of oversized rock mass structural plane |
| CN111091911A (en) * | 2019-12-30 | 2020-05-01 | 重庆同仁至诚智慧医疗科技股份有限公司 | System and method for screening stroke risk |
| CN111242206A (en) * | 2020-01-08 | 2020-06-05 | 吉林大学 | High-resolution ocean water temperature calculation method based on hierarchical clustering and random forests |
| CN111242206B (en) * | 2020-01-08 | 2022-06-17 | 吉林大学 | High-resolution ocean water temperature calculation method based on hierarchical clustering and random forests |
| CN111190219B (en) * | 2020-01-14 | 2022-06-21 | 北京大学深圳研究生院 | Monitoring data processing method for earthquake prediction |
| CN111190219A (en) * | 2020-01-14 | 2020-05-22 | 北京大学深圳研究生院 | Monitoring data processing method for earthquake prediction |
| CN111369056B (en) * | 2020-03-04 | 2023-04-25 | 国能大渡河大数据服务有限公司 | Geological disaster prediction method and electronic equipment |
| CN111369056A (en) * | 2020-03-04 | 2020-07-03 | 四川大汇大数据服务有限公司 | Geological disaster prediction method and electronic equipment |
| CN111667125B (en) * | 2020-08-10 | 2020-11-20 | 成都嘉捷信诚信息技术有限公司 | Landslide displacement prediction method, landslide displacement prediction device and storage medium |
| CN111667125A (en) * | 2020-08-10 | 2020-09-15 | 成都嘉捷信诚信息技术有限公司 | Landslide displacement prediction method, landslide displacement prediction device and storage medium |
| CN112632203B (en) * | 2020-12-21 | 2021-12-14 | 武汉达梦数据技术有限公司 | Geological disaster monitoring point searching method and device |
| CN112632203A (en) * | 2020-12-21 | 2021-04-09 | 武汉达梦数据技术有限公司 | Geological disaster monitoring point searching method and device |
| CN112820075A (en) * | 2021-01-15 | 2021-05-18 | 中国地质调查局水文地质环境地质调查中心 | Landslide monitoring and early warning system |
| CN113609207A (en) * | 2021-08-03 | 2021-11-05 | 招商局重庆交通科研设计院有限公司 | Data preprocessing method for slope deformation monitoring data |
| CN113609207B (en) * | 2021-08-03 | 2023-06-23 | 招商局重庆交通科研设计院有限公司 | Data preprocessing method for slope deformation monitoring data |
| CN114067531A (en) * | 2021-11-15 | 2022-02-18 | 中大检测(湖南)股份有限公司 | Remote geological disaster monitoring system and monitoring method based on 5G |
| WO2023193824A1 (en) * | 2022-05-05 | 2023-10-12 | 中咨数据有限公司 | Mountain landslide and collapse disaster classification method based on decision tree, and electronic device |
| CN115240371A (en) * | 2022-09-23 | 2022-10-25 | 西北大学 | Mountain landslide real-time monitoring system based on data analysis |
| CN116227938A (en) * | 2023-04-26 | 2023-06-06 | 四川川核地质工程有限公司 | Mountain landslide intelligent early warning method and system based on Beidou satellite |
| CN116227938B (en) * | 2023-04-26 | 2023-06-30 | 四川川核地质工程有限公司 | Mountain landslide intelligent early warning method and system based on Beidou satellite |
| CN116627953A (en) * | 2023-05-24 | 2023-08-22 | 首都师范大学 | Method for repairing loss of groundwater level monitoring data |
| CN116627953B (en) * | 2023-05-24 | 2023-10-27 | 首都师范大学 | Method for repairing loss of groundwater level monitoring data |
| CN117037432A (en) * | 2023-10-08 | 2023-11-10 | 四川省公路规划勘察设计研究院有限公司 | Risk evaluation geological disaster early warning method based on multi-method cooperation |
| CN117037432B (en) * | 2023-10-08 | 2023-12-19 | 四川省公路规划勘察设计研究院有限公司 | Risk evaluation geological disaster early warning method based on multi-method cooperation |
| CN117037457A (en) * | 2023-10-10 | 2023-11-10 | 青州鸿润电器科技有限公司 | Landslide monitoring and early warning method |
| CN117037457B (en) * | 2023-10-10 | 2023-12-15 | 青州鸿润电器科技有限公司 | Landslide monitoring and early warning method |
| CN117854240A (en) * | 2024-03-07 | 2024-04-09 | 江苏省地质勘查技术院 | Geological disaster early warning method and geological disaster early warning system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109003422A (en) | Monitoring data processing method and landslide forecasting procedure for landslide | |
| CN109085641B (en) | Monitoring data processing method for earthquake prediction, earthquake prediction method and system | |
| Fan et al. | Earthquake‐induced chains of geologic hazards: Patterns, mechanisms, and impacts | |
| Nefeslioglu et al. | Assessment of landslide susceptibility by decision trees in the metropolitan area of Istanbul, Turkey | |
| Wernik et al. | Ionospheric irregularities and scintillation | |
| Fuchs et al. | Climatological analyses of LMA data with an open‐source lightning flash‐clustering algorithm | |
| DiGangi et al. | An overview of the 29 May 2012 Kingfisher supercell during DC3 | |
| Bui et al. | Application of support vector machines in landslide susceptibility assessment for the Hoa Binh province (Vietnam) with kernel functions analysis | |
| Delmonte et al. | Holocene dust in East Antarctica: Provenance and variability in time and space | |
| Hibert et al. | Exploration of continuous seismic recordings with a machine learning approach to document 20 yr of landslide activity in Alaska | |
| Melsom et al. | ENSO effects on Gulf of Alaska eddies | |
| CN108845348A (en) | Precursor data processing method, method for forecasting earthquake and system for earthquake prediction | |
| Diodato et al. | Storminess and environmental changes in the Mediterranean Central Area | |
| CN108873102A (en) | Monitoring data processing method and landslide forecasting procedure for landslide | |
| Ogashawara et al. | The use of optical remote sensing for mapping flooded areas | |
| CN109001792A (en) | Monitoring data processing method, method for forecasting earthquake and system for earthquake prediction | |
| Morel et al. | Regionalizing rainfall at very high resolution over La Réunion Island using a regional climate model | |
| Dickinson et al. | HydroClimATe: hydrologic and climatic analysis toolkit | |
| Jaquet et al. | Estimation of volcanic hazards using geostatistical models | |
| CN115345511B (en) | Dynamic evaluation method, evaluation system and equipment for road corridor landslide hazard | |
| Mandal et al. | Precipitation forecast verification of the Indian summer monsoon with intercomparison of three diverse regions | |
| Zhang et al. | Lake level evidence for a mid-Holocene East Asian summer monsoon maximum and the impact of an abrupt late-Holocene drought event on prehistoric cultures in north-central China | |
| CN101915942B (en) | Earthquake predicting method based on earth polarization natural electric field and application thereof | |
| James et al. | Hydrostratigraphy characterization of the Floridan aquifer system using ambient seismic noise | |
| Bevilacqua et al. | Data analysis of the unsteadily accelerating GPS and seismic records at Campi Flegrei caldera from 2000 to 2020 |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20181214 |
|
| RJ01 | Rejection of invention patent application after publication |