CN109242133A - A kind of data processing method and system of earth's surface disaster alarm - Google Patents
A kind of data processing method and system of earth's surface disaster alarm Download PDFInfo
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Abstract
The present invention provides the data processing method and system of a kind of earth's surface disaster alarm, wherein, the data processing method of the earth's surface disaster alarm includes: to choose first eigenvector from n initial sample datas, sample data is the monitoring data obtained by sensor using feature vector selection algorithm；Using the Support vector regression algorithm including insensitive loss function, the first eigenvector and the first newly-increased sample data are learnt respectively, obtain the first prediction result and the second prediction result；According to the difference between the difference and the second prediction result and the second newly-increased sample data between the first prediction result and the first newly-increased sample data, the insensitive loss function is adjusted；Using the Support vector regression algorithm including insensitive loss function adjusted, first eigenvector is learnt, obtains target prediction result.The data processing method of earth's surface disaster alarm provided in an embodiment of the present invention, can promote the accuracy of prediction result.
Description
Technical field
The present invention relates to the data processing method of technical field of data processing more particularly to a kind of earth's surface disaster alarm and it is
System.
Background technique
Disaster alarm result can be obtained and being analyzed and processed to monitoring information detected by sensor, in reality
In the application process of border, monitoring personnel etc. can take the corresponding precautionary measures according to the disaster alarm result, to prevent disaster
Generation or reduce disaster caused by loss.
In the related art, for monitor earth's surface disaster sensor have it is a variety of (such as: temperature sensor, humidity pass
Sensor, pressure sensor, displacement sensor etc.), and it is large number of, to make earth's surface disaster monitoring data, there are non-linear, high
The characteristics of dimension, utilizes feature vector selection algorithm (Feature Vector to reduce the unnecessary sample training time
Selection, FVS) thought, carry out large data sets offline sample-size reduction, construct the feature samples based on sample set
Data, to achieve the purpose that reduce computation complexity, reduce the calculating time and export warning information in time.
In the related art, it for the feature samples data obtained after the reduction of above-mentioned FVS algorithm, is returned using support vector machines
Reduction method (Support Vector Regression, SVR) carries out hazard prediction.
But in actual application, during being reduced using FVS algorithm to monitoring data, so that feature
There are biggish errors between sample data and actual monitoring data, so that the SVR based on this feature sample data be caused to calculate
There is very big error between the hazard prediction result and actual conditions of method output, it follows that earth's surface calamity in the related technology
The prediction result accuracy of the data processing method of evil early warning is low.
Summary of the invention
The embodiment of the present invention provides the data processing method and system of a kind of earth's surface disaster alarm, pre- to solve earth's surface disaster
The low problem of the prediction result accuracy of alert data processing method.
In order to achieve the above object, the present invention is implemented as follows:
In a first aspect, the embodiment of the present invention provides a kind of data processing method of earth's surface disaster alarm, this method comprises:
Using feature vector selection algorithm, first eigenvector, the fisrt feature are chosen from n initial sample datas
Vector includes m sample data, wherein n is positive integer, and m is the integer less than n, and the sample data is to be obtained by sensor
The monitoring data taken；
Using the Support vector regression algorithm including insensitive loss function, respectively to the first eigenvector and
One newly-increased sample data is learnt, and the first prediction result and the second prediction result are obtained, wherein the first newly-increased sample number
According to the sample data to be increased newly in first predetermined period；
According to the difference and second prediction between first prediction result and the first newly-increased sample data
As a result the difference between the second newly-increased sample data adjusts the insensitive loss function, wherein the second newly-increased sample
Data are the sample data increased newly in second predetermined period, and described second predetermined period is later than described first predetermined period；
Using the Support vector regression algorithm including insensitive loss function adjusted, to the first eigenvector
Learnt, obtains target prediction result.
Second aspect, the embodiment of the present invention also provide a kind of warning data processing system, which includes:
First chooses module, for using feature vector selection algorithm, chooses fisrt feature from n initial sample datas
Vector, the first eigenvector include m sample data, wherein n is positive integer, and m is the integer less than n, the sample number
According to the monitoring data to be obtained by sensor；
First study module, for using the Support vector regression algorithm including insensitive loss function, respectively to institute
It states first eigenvector and the first newly-increased sample data is learnt, obtain the first prediction result and the second prediction result, wherein
The first newly-increased sample data is the sample data increased newly in first predetermined period；
Module is adjusted, for according to the difference between first prediction result and the first newly-increased sample data, with
And the difference between second prediction result and the second newly-increased sample data, adjust the insensitive loss function, wherein institute
Stating the second newly-increased sample data is the sample data increased newly in second predetermined period, and described second predetermined period is later than described the
One predetermined period；
Second study module, for using the Support vector regression algorithm including insensitive loss function adjusted,
The first eigenvector is learnt, obtains target prediction result.
In the embodiment of the present invention, by using feature vector selection algorithm, the first spy is chosen from n initial sample datas
Vector is levied, the first eigenvector includes m sample data, wherein n is positive integer, and m is the integer less than n, the sample
Data are the monitoring data obtained by sensor；Using the Support vector regression algorithm including insensitive loss function, divide
It is other that the first eigenvector and the first newly-increased sample data are learnt, obtain the first prediction result and the second prediction knot
Fruit, wherein the first newly-increased sample data is the sample data increased newly in first predetermined period；According to first prediction
As a result the difference between the described first newly-increased sample data and second prediction result and the second newly-increased sample data it
Between difference, adjust the insensitive loss function, wherein the second newly-increased sample data is new in second predetermined period
The sample data of increasing, described second predetermined period are later than described first predetermined period；Using including insensitive loss adjusted
The Support vector regression algorithm of function, learns the first eigenvector, obtains target prediction result.In this way, can
To verify according to the sample data increased newly after prediction result to the prediction result, and insensitive damage is adjusted according to verification result
The value for losing function, to promote the accuracy of the prediction result of Support vector regression algorithm, so as to promote the earth's surface calamity
The accuracy of the prediction result of the data processing method of evil early warning.
Detailed description of the invention
Fig. 1 is a kind of flow chart of the data processing method of earth's surface disaster alarm provided in an embodiment of the present invention；
Fig. 2 is the schematic diagram of warning data processing provided in an embodiment of the present invention；
Fig. 3 is the fuzzy of E1 and Δ E in a kind of data processing method of earth's surface disaster alarm provided in an embodiment of the present invention
The schematic diagram of subset division；
Fig. 4 is the fuzzy subset of Δ ε in a kind of data processing method of earth's surface disaster alarm provided in an embodiment of the present invention
The schematic diagram of division；
Fig. 5 is the flow chart of the data processing method of another earth's surface disaster alarm provided in an embodiment of the present invention；
Fig. 6 is the schematic diagram at the disaster alarm interface in the embodiment of the present invention；
Fig. 7 is the schematic diagram of the numerical map in the embodiment of the present invention.
Specific embodiment
To keep the technical problem to be solved in the present invention, technical solution and advantage clearer, below in conjunction with attached drawing and tool
Body embodiment is described in detail.
The data processing method of earth's surface disaster alarm provided in an embodiment of the present invention can be applied to detect sensor
Monitoring data carry out vector machine recurrence learning, and obtain prediction result, earth's surface disaster just can determine according to the prediction result
Early warning prevents the hair of earth's surface disaster as a result, monitoring personnel can take adequate measures according to the earth's surface disaster alarm result
It is raw, above-mentioned earth's surface disaster can be the movement of the rock as earth's surface, soil property etc. and caused by disaster, such as: mountain area mud-rock flow
Disaster, building collapse disaster etc. use the multiple sensors row of acquisition respectively it is, of course, also possible to be Mine production disaster at this time
Surface displacement information, internal displacement information, rainfall information, soil pressure force information, the soil moisture content information, pore water pressure of Tu Chang
Force information, temperature information and humidity information etc., and using above-mentioned earth's surface disaster alarm data processing method to these information into
Row processing predicts whether the refuse dump has and the danger such as mud-rock flow, collapsing occurs, and obtains prediction result.
Referring to Figure 1, Fig. 1 is a kind of process of the data processing method of earth's surface disaster alarm provided in an embodiment of the present invention
Figure, as shown in Figure 1, method includes the following steps:
Step 101, using feature vector selection algorithm, choose first eigenvector from n initial sample datas, it is described
First eigenvector includes m sample data, wherein n is positive integer, and m is the integer less than n, and the sample data is to pass through
The monitoring data that sensor obtains.
Wherein, features described above vector selection algorithm can select m from n initial sample datas (x1, x2 ... ..., xn)
A sample data (xs1, xs2 ... ..., xsv), wherein 1≤v≤m, above-mentioned m sample data (xs1, xs2 ... ..., xsv) are
Feature vector FV after selection.
In this way, any vector in the m sample data can in the case where the set of eigenvectors of known sample data
With by FV linear expression, convenient for using Support vector regression algorithm to be learnt in step 102 to step 104.
In addition, the temperature that above-mentioned monitoring data can be the pressure information of pressure sensor acquisition, temperature sensor obtains
Any one or more in humidity information that information and humidity sensor obtain etc., such as: it is being applied to refuse dump disaster
In monitoring process, above-mentioned monitoring data can be the surface displacement information of the refuse dump, internal displacement information, rainfall information,
One of soil pressure force information, soil moisture content information, pore water pressure force information, temperature information and humidity information information or more
Kind information.Therefore, monitoring data have the characteristic that data volume is big and dimension is high.
When learning using the Support vector regression algorithm monitoring data big to above-mentioned data volume, need to spend big
To cause the overlong time for exporting prediction result the effect of early warning is not achieved, it is therefore desirable to above-mentioned monitoring number in the time of amount
According to data volume reduced.
In this step, using features described above vector selection algorithm, to the above-mentioned initial sample comprising a large amount of sample data
Data are reduced, and do not change the structure of initial sample data, can reduce the support in step 102 to step 104 in this way
Vector machine regression algorithm is to operation time of the first eigenvector, so as to promote the data of the earth's surface disaster alarm
The efficiency of processing method.
Step 102, using the Support vector regression algorithm including insensitive loss function, respectively to the fisrt feature
Vector sum first increases sample data newly and is learnt, and obtains the first prediction result and the second prediction result, wherein described first is new
Increasing sample data is the sample data increased newly in first predetermined period.
Wherein, the value of above-mentioned insensitive loss function of ε can be to the prediction result of above-mentioned Support vector regression algorithm
Accuracy has an impact.When the value of ε is excessive, the efficiency and accuracy of the Support vector regression algorithm will be reduced；When the value of ε
When too small, by generate overfitting the case where, the accuracy of prediction result will also decrease.
It should be noted that above-mentioned first prediction result is to be supported in first predetermined period to first eigenvector
Prediction result obtained from vector machine regression algorithm, above-mentioned second prediction result are in second predetermined period to the first newly-increased sample
Notebook data is supported prediction result obtained from vector machine regression algorithm, and second predetermined period is later than first predetermined period.
Due to not verifying to above-mentioned first prediction result and the second prediction result, it not can confirm that above-mentioned first is pre-
The accuracy of result and the second prediction result is surveyed, such as: since the fitness in the feature vector selection algorithm in step 101 takes
Not be worthwhile and cause, the difference between the first eigenvector and initial sample data is excessive, will cause described first pre-
Surveying has error between result and the second prediction result and actual conditions.
It, can be using Support vector regression algorithm to respectively to first eigenvector and the first newly-increased sample by this step
Notebook data is learnt, and obtains above-mentioned first prediction result and above-mentioned second prediction result respectively, is step 103 and step
104 provide operating basis.
Step 103, according to difference between first prediction result and the first newly-increased sample data and described
Difference between second prediction result and the second newly-increased sample data adjusts the insensitive loss function, wherein described second
Newly-increased sample data is the sample data increased newly in second predetermined period, and described second predetermined period is later than first prediction
Period.
Wherein, in the specific application process, the monitoring data of refuse dump are constantly newly-increased, are obtained in first predetermined period
The prediction result obtained can be verified according to the newly-increased sample data obtained after first predetermined period.
In addition, if the above-mentioned prediction result obtained in first predetermined period increases newly with what is obtained after first predetermined period
Have between biggish difference and second prediction result and the second newly-increased sample data between sample data with larger
Difference, then it represents that the value of the ε in above-mentioned Support vector regression algorithm may be improper, makes to prop up by adjusting the value of ε
The accuracy for holding the prediction result of vector machine regression algorithm is higher.
Specifically, above-mentioned insensitive loss function can be adjusted using fuzzy control method.
For example, the fuzzy controller exports the increment Delta ε of ε as shown in Fig. 2, E1 and Δ E is inputted fuzzy controller, with
Adjust the value of ε.The fuzzy controller by the fuzzy variable of E1 and Δ E be divided into 7 fuzzy subsets as shown in Figure 3 NL,
NM, NS, ZE, PS, PM, PL }.Rule of thumb rule and sample calculate analysis, and E1 and Δ E respectively include as shown in Figure 3 be subordinate to
Relationship.Δ ε is divided into 5 fuzzy subsets { NB, NS, ZE, PS, PB } as shown in Figure 4 by the fuzzy controller, and according to input
E1 and Δ E and the Δ ε of output between change relationship establish fuzzy rule as shown in Table 1:
Table 1
| (Δ E, U, E1) | NL | NM | NS | ZE | PS | PM | PL |
| NL | NB | NB | NB | ZE | NS | ZE | ZE |
| NM | NB | NB | NB | PS | ZE | ZE | NS |
| NS | NB | NB | NS | PB | ZE | NS | NS |
| ZE | NB | NS | ZE | PB | ZE | NS | NB |
| PS | NS | NS | ZE | PB | NS | NB | NB |
| PM | NS | ZE | ZE | PS | NB | NB | NB |
| PL | ZE | ZE | NS | ZE | NB | NB | NB |
Wherein, the first row indicates each fuzzy subset belonging to E1, and first row indicates each fuzzy subset belonging to Δ E, U
Indicate fuzzy rule, the i.e. different fuzzy subsets that Δ ε is adapted to according to the fuzzy rule.For example, when E1 belongs to the fuzzy son of NL
Collection, Δ E belong to PL fuzzy subset, then Δ ε will be adjusted according to fuzzy rule to the direction of ZE fuzzy subset.
In addition, when E1 is located in the section NL or the section PL, and Δ E indicates that the error amount of E2 is greater than the error amount of E1, then
The output valve of Δ ε is adjusted to reduce the value of ε.
Certainly, the quantity of the fuzzy subset of above-mentioned E1, Δ E and Δ ε can also be other numbers such as 3,4, herein
Without limitation.
It should be noted that offline SVR model described in Fig. 2 refers to, initial sample data is selected by feature vector
The first eigenvector obtained after algorithm is reduced is selected, input Learning machine is supported vector machine regression algorithm (Support
Vector Regression, SVR) off-line learning.
In this step, by comparing difference between first prediction result and the first newly-increased sample data, with
And the difference between second prediction result and the second newly-increased sample data, can determine whether the value of ε is suitable, ε's
The value of the ε is adjusted in the inappropriate situation of value, with promoted the Support vector regression algorithm prediction result it is accurate
Property, to reach the accuracy for promoting the prediction result of data processing method of the earth's surface disaster alarm.
Step 104, using the Support vector regression algorithm including insensitive loss function adjusted, to described first
Feature vector is learnt, and obtains target prediction result.
It should be noted that in actual application, since above-mentioned monitoring data continual may update, because
This, above-mentioned target prediction result can be using the Support vector regression algorithm for including insensitive loss function adjusted,
To the first eigenvector, and/or, the first newly-increased sample data, and/or, the second newly-increased sample data is learnt and is obtained
The name of prediction result out, above-mentioned first eigenvector, the first newly-increased sample data and the second newly-increased sample data is answered herein
Can not be identical for distinguishing its three, over time, above-mentioned first eigenvector, the first newly-increased sample data and
The role of two newly-increased sample datas can change, such as: if can be 08:01 when current, in the inspection that the 08:00 moment obtains
Measured data is that newly-increased sample data still if current time is 09:00, becomes in the detection data that the 08:00 moment obtains
Historical data.
Certainly, above-mentioned target prediction is as a result, can also be according to the sample number increased newly after described second predetermined period
According to being supported prediction result obtained from vector machine regression algorithm.
In this step, by using the Support vector regression algorithm including ε adjusted, to the first eigenvector
Learnt again with the described first newly-increased sample data, the accuracy of the target prediction result made is higher.
In the embodiment of the present invention, by using feature vector selection algorithm, the first spy is chosen from n initial sample datas
Vector is levied, the first eigenvector includes m sample data, wherein n is positive integer, and m is the integer less than n, the sample
Data are the monitoring data obtained by sensor；Using the Support vector regression algorithm including insensitive loss function, divide
It is other that the first eigenvector and the first newly-increased sample data are learnt, obtain the first prediction result and the second prediction knot
Fruit, wherein the first newly-increased sample data is the sample data increased newly in first predetermined period；According to first prediction
As a result the difference between the described first newly-increased sample data and second prediction result and the second newly-increased sample data it
Between difference, adjust the insensitive loss function, wherein the second newly-increased sample data is new in second predetermined period
The sample data of increasing, described second predetermined period are later than described first predetermined period；Using including insensitive loss adjusted
The Support vector regression algorithm of function, learns the first eigenvector, obtains target prediction result.In this way, can
To verify according to the sample data increased newly after prediction result to the prediction result, and insensitive damage is adjusted according to verification result
The value for losing function, to promote the accuracy of the prediction result of Support vector regression algorithm, so as to promote the earth's surface calamity
The accuracy of the prediction result of the data processing method of evil early warning.
Fig. 5 is referred to, Fig. 5 is the stream of the data processing method of another earth's surface disaster alarm provided in an embodiment of the present invention
Cheng Tu, as shown in figure 5, method includes the following steps:
Step 501, using feature vector selection algorithm, choose first eigenvector from n initial sample datas, it is described
First eigenvector includes m sample data, wherein n is positive integer, and m is the integer less than n, and the sample data is to pass through
The monitoring data that sensor obtains.
Step 502, using the Support vector regression algorithm including insensitive loss function, respectively to the fisrt feature
Vector sum first increases sample data newly and is learnt, and obtains the first prediction result and the second prediction result, wherein described first is new
Increasing sample data is the sample data increased newly in first predetermined period.
Optionally, described using the Support vector regression algorithm for including insensitive loss function, to the first newly-increased sample
Before the step of data are learnt, the method also includes:
In the case that the quantity for the sample data for including in the described first newly-increased sample data is greater than preset value, using spy
Vector selection algorithm is levied, second feature vector is chosen from the described first newly-increased sample data, wherein the second feature vector
In include sample data quantity be less than the described first newly-increased sample data in include sample data quantity；
It is described to use the Support vector regression algorithm including insensitive loss function, the first newly-increased sample data is carried out
The step of study, comprising:
Using the Support vector regression algorithm including insensitive loss function, the second feature vector is carried out online
Study, obtains the second prediction result.
Wherein, above-mentioned preset value, which can according to need, is configured, such as: it is greater than in the quantity of the first newly-increased sample data
In the biggish situations of any numbers such as 200,500,1000, using feature vector selection algorithm from the first newly-increased sample number
According to middle selection second feature vector, wherein the sample size in the second feature vector is less than in the first newly-increased sample data
Sample size.
In addition, as shown in Fig. 2, the quantity for the sample data for being included in the first newly-increased sample data be less than or equal to it is pre-
If can be learnt point by point to each of the described first newly-increased sample data sample data in the case where value, in this way,
It, can be with the accuracy of hoisting machine study in the case where the negligible amounts for the sample data that first newly-increased sample data is included.
In present embodiment, when the quantity of the first newly-increased sample data is too many, feature vector selection algorithm can be used
The sample size of the first newly-increased sample data is reduced, to reduce in subsequent step using Support vector regression algorithm
The first newly-increased sample data is learnt and is verified and consumes a large amount of time.
Step 503, according to difference between first prediction result and the first newly-increased sample data and described
Difference between second prediction result and the second newly-increased sample data adjusts the insensitive loss function, wherein described second
Newly-increased sample data is the sample data increased newly in second predetermined period, and described second predetermined period is later than first prediction
Period.
Optionally, above-mentioned steps 503 can with comprising the following specific steps
It obtains absolute between the sample data generated at first in first prediction result and the first newly-increased sample
Error E 1；
It obtains absolute between the sample data generated at first in second prediction result and the second newly-increased sample
Error E 2；
According to the difference DELTA E between E1 and E2, the insensitive loss function is adjusted, wherein if E1 is more than or equal to
Preset error value, and Δ E indicates that E2 is greater than E1, then reduces the insensitive loss function.
Wherein, during obtaining newly-increased sample data, it may occur however that the failures such as network signal broken string, server crash
Situation and the case where cause newly-increased sample data not obtain timely and cause newly-increased data accumulation.Above-mentioned will be caused in this way
One newly-increased sample data and the second newly-increased sample data include a large amount of sample data.
In this way, being carried out to above-mentioned comprising the first newly-increased sample data of a large amount of sample data and the second newly-increased sample data
The process that Support vector regression algorithm is learnt will devote a tremendous amount of time, to cause the warning data Processing Algorithm
Have big difference between the time that the time of the target prediction result obtained and actual conditions occur, and the effect of early warning is not achieved.
In addition, when the generation time of the sample data increased newly in first predetermined period is later than the generation of initial sample data
Between.And above-mentioned first prediction result is the prediction result that the initial sample data according to first eigenvector, that is, after reducing obtains,
And the first newly-increased sample is the sample increased newly in first predetermined period.In this way, including multiple sample numbers in the first newly-increased sample
In the case where, comparison is carried out by the sample data that will be generated at first in the first newly-increased sample and the first prediction result,
It may insure using immediate sample data is compared with first prediction result in time with the first prediction result, it can
To ensure the accuracy of verification result.
In addition, since the above-mentioned second newly-increased sample is the data increased newly in second predetermined period, and second predetermined period
It is later than first predetermined period.And second prediction result be the prediction result obtained according to the first newly-increased sample data two.In this way,
In the case that second newly-increased sample includes multiple sample datas, by the sample data that will be generated at first in the second newly-increased sample with
Second prediction result carries out comparison, it can be ensured that use and the second prediction result in time immediate sample data and
Second prediction result is compared, it can be ensured that the accuracy of verification result.
It include a large amount of sample data in the first newly-increased sample data or the second newly-increased sample data in present embodiment
In the case where, the reduction of sample size is carried out, to the first newly-increased sample data or the second newly-increased sample data to be promoted
The processing time for stating the data processing method of earth's surface disaster alarm, promote the efficiency of early warning.
Step 504, using the Support vector regression algorithm including insensitive loss function adjusted, to described first
Feature vector is learnt, and obtains target prediction result.
Step 505, according to the size relation between the target prediction result and preset threshold, determine disaster alarm knot
Fruit.
Wherein, above-mentioned preset threshold may include multiple, above-mentioned disaster alarm result also may include with it is above-mentioned multiple pre-
If the one-to-one warning grade of threshold value, such as: when target prediction the result is that when the soil pressure force value of refuse dump, when the soil pressure force value
In the case where greater than the first preset threshold, 1 grade of early warning of disaster alarm result is obtained；When the soil pressure force value is greater than the second default threshold
In the case where value, 2 grades of early warning of disaster alarm is obtained as a result, the wherein value of the first preset threshold and the second preset threshold not phase
Together.
In the embodiment of the present invention, by the way that target prediction result to be compared with preset threshold, to determine the target prediction
As a result the risk of generation disaster is indicated whether, to obtain more intuitive disaster alarm as a result, more convenient for monitoring personnel
Easy knows the disaster alarm result.
Optionally, the data processing method of earth's surface disaster alarm provided in an embodiment of the present invention can be applied to refuse dump calamity
Evil monitoring, specifically, the refuse dump disaster monitoring may comprise steps of:
Obtain the geographical monitoring figure of refuse dump；
Show disaster alarm interface, wherein the disaster alarm interface includes geographical distribution window and is shown in described
Manage the warning information window in distribution window；
Wherein, the data processing method embodiment using the earth's surface disaster alarm is shown in the warning information window
In obtained target prediction as a result, the geographical distribution window shows the geographical monitoring figure of the refuse dump, the geography
The monitoring being arranged in a one-to-one correspondence with the installation site for the multiple monitoring sensors being arranged in the refuse dump is shown in monitoring figure
Point identification.
Wherein, above-mentioned geographical monitoring figure can be using monitoring camera, GPS satellite view, unmanned plane aerial view etc.
Arbitrarily to the monitoring view of refuse dump.
The step of geographical monitoring figure of above-mentioned acquisition refuse dump, can be in such a way that webpage transmits, from being stored with the geography
The server of monitoring figure obtains the implementation geography monitoring figure of refuse dump.
Wherein, above-mentioned warning information window can also be referred to as " newest warning information " window, can show in the window
Using in the data processing method embodiment of earth's surface disaster alarm as described above obtained target prediction result and/or disaster it is pre-
Alert result.
For example, above-mentioned target prediction result can be the calculating knot in newest warning information window 602 as shown in Figure 6
Fruit, above-mentioned disaster alarm result can be comprehensive pre-warning grade in newest warning information window 602 as shown in Figure 6.
In addition, the multiple monitoring sensors being arranged in above-mentioned refuse dump may be respectively used for the surface displacement of detection refuse dump
Information, internal displacement information, rainfall information, soil pressure force information, soil moisture content information, pore water pressure force information, temperature letter
Breath and humidity information etc., detected data can be used for differentiating that whether there is or not the danger such as mud-rock flow, collapsing occur for the refuse dump.
In addition, above-mentioned monitoring point identification can use the figure of different appearances according to the type of corresponding monitoring sensor
Shape or text, in order to identify.
In the case where with above-mentioned danger, just it can be incited somebody to action using the data processing method of earth's surface disaster alarm as described above
The risky target prediction of tool is predicted as the result is shown in above-mentioned warning information window, is taken timely measure convenient for monitoring personnel pre-
The generation to take precautions against calamities.
In present embodiment, by obtaining the geographical monitoring figure of refuse dump, monitoring personnel can be made to check refuse dump in time
Presence states, convenient in time discovery calamity danger.In addition, pass through while showing geographical distribution window and warning information window,
Be conducive to monitoring personnel while viewing the monitoring figure of warning information and scene, in addition, due to using the earth's surface disaster alarm
The obtained target prediction result of data processing method have the advantages that calculate that the time is short and accuracy is high, therefore, in this step
The target prediction result being shown in above-mentioned warning information window equally has the advantages that fast response time and accuracy is high.
Optionally, as shown in fig. 6, above-mentioned geographical distribution window 601 is shown in bottom, above-mentioned warning information window 602 is aobvious
It is shown on above-mentioned geographical distribution window, and also shows multiple monitoring point identifications on above-mentioned geographical distribution window 601.It is detecting
In the case where the first operation for target monitoring point identification 604, mesh corresponding with the target monitoring point identification 604 is shown
The messagewindow 603 of mark monitoring sensor；
Wherein, the messagewindow 603 of the target monitoring sensor is shown on the geographical distribution window 601, described
The messagewindow 603 of target monitoring sensor show the target monitoring sensor title, model and monitoring data and
At least one of the monitoring figure of the target monitoring sensor.
Wherein, above-mentioned monitoring data can be specific value detected by target monitoring sensor, such as: soil pressure prison
Survey pressure value detected by sensor etc..
In the present embodiment, checks the data of the monitoring sensor of each monitoring point setting at any time convenient for monitoring personnel and show
Field monitoring view, is conducive to find faulty monitoring sensor in time, to debug in time, ensures refuse dump monitoring data
The accuracy of processing method.
Fig. 7 is referred to, Fig. 7 is a kind of structure chart of warning data processing system provided in an embodiment of the present invention, the system
System 700 includes:
First chooses module 701, for using feature vector selection algorithm, chooses first from n initial sample datas
Feature vector, the first eigenvector include m sample data, wherein n is positive integer, and m is the integer less than n, the sample
Notebook data is the monitoring data obtained by sensor；
First study module 702, it is right respectively for using the Support vector regression algorithm including insensitive loss function
The first eigenvector and the first newly-increased sample data are learnt, and the first prediction result and the second prediction result are obtained,
In, the first newly-increased sample data is the sample data increased newly in first predetermined period；
Module 703 is adjusted, for according to the difference between first prediction result and the first newly-increased sample data,
And the difference between second prediction result and the second newly-increased sample data, adjust the insensitive loss function, wherein
The second newly-increased sample data is the sample data increased newly in second predetermined period, and described second predetermined period is later than described
First predetermined period；
Second study module 704, for being calculated using the Support vector regression for including insensitive loss function adjusted
Method learns the first eigenvector, obtains target prediction result.
Optionally, the system also includes:
Second chooses module, and the quantity of the sample data for including in the described first newly-increased sample data is greater than default
In the case where value, using feature vector selection algorithm, second feature vector is chosen from the described first newly-increased sample data,
In, the quantity for the sample data for including in the second feature vector is less than the sample in the described first newly-increased sample data included
The quantity of data；
First study module 702 is also used to using the Support vector regression algorithm including insensitive loss function,
On-line study is carried out to the second feature vector, obtains the second prediction result.
Optionally, the adjustment module 703 includes:
First acquisition unit, for obtaining the sample generated at first in first prediction result and the described first newly-increased sample
Absolute error E1 between notebook data；
Second acquisition unit, for obtaining the sample generated at first in second prediction result and the described second newly-increased sample
Absolute error E2 between notebook data；
Adjustment unit, for adjusting the insensitive loss function, wherein if E1 according to the difference DELTA E between E1 and E2
More than or equal to preset error value, and Δ E indicates that E2 is greater than E1, then reduces the insensitive loss function.
Optionally, the system also includes:
Determining module, for determining that disaster is pre- according to the size relation between the target prediction result and preset threshold
Alert result.
Each step in the data processing method embodiment of the earth's surface disaster alarm may be implemented in the embodiment of the present invention
Suddenly, and identical beneficial effect can be obtained, to avoid repeating, does not do extra repeat herein.
In several embodiments provided herein, it should be understood that disclosed method and system, it can be by other
Mode realize.For example, system embodiment described above is only schematical, for example, the division of the unit, only
For a kind of logical function partition, there may be another division manner in actual implementation, such as multiple units or components can combine
Or it is desirably integrated into another system, or some features can be ignored or not executed.Another point, shown or discussed phase
Coupling, direct-coupling or communication connection between mutually can be through some interfaces, the INDIRECT COUPLING or communication of device or unit
Connection can be electrical property, mechanical or other forms.
It, can also be in addition, the functional units in various embodiments of the present invention may be integrated into one processing unit
It is that the independent physics of each unit includes, can also be integrated in one unit with two or more units.Above-mentioned integrated list
Member both can take the form of hardware realization, can also realize in the form of hardware adds SFU software functional unit.
The above-mentioned integrated unit being realized in the form of SFU software functional unit can store and computer-readable deposit at one
In storage media.Above-mentioned SFU software functional unit is stored in a storage medium, including some instructions are used so that a computer
Equipment (can be personal computer, server or the network equipment etc.) executes information data described in each embodiment of the present invention
The part steps of the processing method of block.And storage medium above-mentioned includes: USB flash disk, mobile hard disk, read-only memory (Read-Only
Memory, abbreviation ROM), random access memory (Random Access Memory, abbreviation RAM), magnetic or disk etc. it is each
Kind can store the medium of program code.
The above is a preferred embodiment of the present invention, it is noted that for those skilled in the art
For, without departing from the principles of the present invention, it can also make several improvements and retouch, these improvements and modifications
It should be regarded as protection scope of the present invention.
Claims (8)
1. a kind of data processing method of earth's surface disaster alarm, which is characterized in that the described method includes:
Using feature vector selection algorithm, first eigenvector, the first eigenvector are chosen from n initial sample datas
Including m sample data, wherein n is positive integer, and m is the integer less than n, and the sample data is to be obtained by sensor
Monitoring data；
It is new to the first eigenvector and first respectively using the Support vector regression algorithm including insensitive loss function
Increase sample data to be learnt, obtains the first prediction result and the second prediction result, wherein the first newly-increased sample data is
The sample data increased newly in first predetermined period；
According to the difference and second prediction result between first prediction result and the first newly-increased sample data
With the difference between the second newly-increased sample data, the insensitive loss function is adjusted, wherein the second newly-increased sample data
For the sample data increased newly in second predetermined period, described second predetermined period is later than described first predetermined period；
Using the Support vector regression algorithm including insensitive loss function adjusted, the first eigenvector is carried out
Study, obtains target prediction result.
2. the method according to claim 1, wherein described using the supporting vector for including insensitive loss function
Machine regression algorithm, before the step of learning to the first newly-increased sample data, the method also includes:
The quantity for the sample data for including in the described first newly-increased sample data be greater than preset value in the case where, using feature to
Selection algorithm is measured, second feature vector is chosen from the described first newly-increased sample data, wherein wrap in the second feature vector
The quantity of the sample data contained is less than the quantity of the sample data in the described first newly-increased sample data included；
It is described to use the Support vector regression algorithm including insensitive loss function, the first newly-increased sample data is learnt
The step of, comprising:
Using the Support vector regression algorithm including insensitive loss function, the second feature vector is learned online
It practises, obtains the second prediction result.
3. the method according to claim 1, wherein described new with described first according to first prediction result
Increase the difference between the sample data generated at first in sample data, and, second prediction result is newly-increased with described second
Difference between sample data, the step of adjusting the insensitive loss function, comprising:
Obtain the absolute error between the sample data generated at first in first prediction result and the first newly-increased sample
E1；
Obtain the absolute error between the sample data generated at first in second prediction result and the second newly-increased sample
E2；
According to the difference DELTA E between E1 and E2, the insensitive loss function is adjusted, wherein preset if E1 is more than or equal to
Error amount, and Δ E indicates that E2 is greater than E1, then reduces the insensitive loss function.
4. the method according to claim 1, wherein described using including insensitive loss function adjusted
Support vector regression algorithm, after the step of learning to the first eigenvector, obtaining target prediction result, institute
State method further include:
According to the size relation between the target prediction result and preset threshold, disaster alarm result is determined.
5. a kind of warning data processing system, which is characterized in that the system comprises:
First chooses module, for using feature vector selection algorithm, chosen from the initial sample datas of n fisrt feature to
Amount, the first eigenvector includes m sample data, wherein n is positive integer, and m is the integer less than n, the sample data
For the monitoring data obtained by sensor；
First study module, for using the Support vector regression algorithm for including insensitive loss function, respectively to described the
One feature vector and the first newly-increased sample data are learnt, and obtain the first prediction result and the second prediction result, wherein described
First newly-increased sample data is the sample data increased newly in first predetermined period；
Module is adjusted, for according to the difference between first prediction result and the first newly-increased sample data, Yi Jisuo
The difference between the second prediction result and the second newly-increased sample data is stated, the insensitive loss function is adjusted, wherein described
Two newly-increased sample datas are the sample data increased newly in second predetermined period, and it is pre- that described second predetermined period is later than described first
Survey the period；
Second study module, for using the Support vector regression algorithm including insensitive loss function adjusted, to institute
It states first eigenvector to be learnt, obtains target prediction result.
6. system according to claim 5, which is characterized in that the system also includes:
Second chooses module, and the quantity of the sample data for including in the described first newly-increased sample data is greater than preset value
In the case of, using feature vector selection algorithm, second feature vector is chosen from the described first newly-increased sample data, wherein institute
The quantity for stating the sample data for including in second feature vector is less than the sample data in the described first newly-increased sample data included
Quantity；
First study module is also used to using the Support vector regression algorithm including insensitive loss function, to described the
Two feature vectors carry out on-line study, obtain the second prediction result.
7. system according to claim 5, which is characterized in that the adjustment module includes:
First acquisition unit, for obtaining the sample number generated at first in first prediction result and the described first newly-increased sample
Absolute error E1 between；
Second acquisition unit, for obtaining the sample number generated at first in second prediction result and the described second newly-increased sample
Absolute error E2 between；
Adjustment unit, for adjusting the insensitive loss function, wherein if E1 is greater than according to the difference DELTA E between E1 and E2
Or it is equal to preset error value, and Δ E indicates that E2 is greater than E1, then reduces the insensitive loss function.
8. system according to claim 5, which is characterized in that the system also includes:
Determining module, for determining disaster alarm knot according to the size relation between the target prediction result and preset threshold
Fruit.
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